From 01da1765516dd11704b57e14458b446f270380f0 Mon Sep 17 00:00:00 2001 From: Emanuel Ferreira Date: Sun, 22 Sep 2024 15:37:41 -0300 Subject: [PATCH 01/53] feat: add drive link to google drive reader (#16156) --- .../llama_index/readers/google/drive/base.py | 6 ++++++ .../readers/llama-index-readers-google/pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py b/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py index 5f03aea773b6e..58f7632f42cf2 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py +++ b/llama-index-integrations/readers/llama-index-readers-google/llama_index/readers/google/drive/base.py @@ -190,6 +190,9 @@ def _get_credentials(self) -> Tuple[Credentials]: return creds + def _get_drive_link(self, file_id: str) -> str: + return f"https://drive.google.com/file/d/{file_id}/view" + def _get_fileids_meta( self, drive_id: Optional[str] = None, @@ -305,6 +308,7 @@ def _get_fileids_meta( item["mimeType"], item["createdTime"], item["modifiedTime"], + self._get_drive_link(item["id"]), ) ) else: @@ -331,6 +335,7 @@ def _get_fileids_meta( file["mimeType"], file["createdTime"], file["modifiedTime"], + self._get_drive_link(file["id"]), ) ) return fileids_meta @@ -589,6 +594,7 @@ def get_resource_info(self, resource_id: str, **kwargs) -> Dict: "content_type": meta[3], "author": meta[1], "created_date": meta[4], + "drive_link": meta[6], } def load_resource(self, resource_id: str, **kwargs) -> List[Document]: diff --git a/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml index 51cc904062f76..aaf3adefdeb25 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml @@ -47,7 +47,7 @@ maintainers = [ ] name = "llama-index-readers-google" readme = "README.md" -version = "0.4.0" +version = "0.4.1" [tool.poetry.dependencies] python = ">=3.10,<4.0" From 6a8a4410d2e522a470731dda93a7aa6e95071f4f Mon Sep 17 00:00:00 2001 From: fzowl <160063452+fzowl@users.noreply.github.com> Date: Sun, 22 Sep 2024 20:44:38 +0200 Subject: [PATCH 02/53] Introducing new VoyageAI models (#16150) --- docs/docs/examples/embeddings/voyageai.ipynb | 2 +- .../examples/node_postprocessor/VoyageAIRerank.ipynb | 4 ++-- .../llama_index/embeddings/voyageai/base.py | 11 +++++++++-- .../llama-index-embeddings-voyageai/pyproject.toml | 2 +- 4 files changed, 13 insertions(+), 6 deletions(-) diff --git a/docs/docs/examples/embeddings/voyageai.ipynb b/docs/docs/examples/embeddings/voyageai.ipynb index 87da51a937cc0..3281e067fa6e8 100644 --- a/docs/docs/examples/embeddings/voyageai.ipynb +++ b/docs/docs/examples/embeddings/voyageai.ipynb @@ -59,7 +59,7 @@ "source": [ "# get API key and create embeddings\n", "\n", - "model_name = \"voyage-law-2\" # Please check https://docs.voyageai.com/docs/embeddings for the available models\n", + "model_name = \"voyage-3\" # Please check https://docs.voyageai.com/docs/embeddings for the available models\n", "voyage_api_key = os.environ.get(\"VOYAGE_API_KEY\", \"your-api-key\")\n", "\n", "embed_model = VoyageEmbedding(\n", diff --git a/docs/docs/examples/node_postprocessor/VoyageAIRerank.ipynb b/docs/docs/examples/node_postprocessor/VoyageAIRerank.ipynb index 4356a092c1d8a..c8304616e6c7b 100644 --- a/docs/docs/examples/node_postprocessor/VoyageAIRerank.ipynb +++ b/docs/docs/examples/node_postprocessor/VoyageAIRerank.ipynb @@ -102,7 +102,7 @@ "\n", "api_key = os.environ[\"VOYAGE_API_KEY\"]\n", "voyageai_embeddings = VoyageEmbedding(\n", - " voyage_api_key=api_key, model_name=\"voyage-large-2\"\n", + " voyage_api_key=api_key, model_name=\"voyage-3\"\n", ")\n", "\n", "# load documents\n", @@ -130,7 +130,7 @@ "from llama_index.postprocessor.voyageai_rerank import VoyageAIRerank\n", "\n", "voyageai_rerank = VoyageAIRerank(\n", - " api_key=api_key, top_k=2, model=\"rerank-lite-1\", truncation=True\n", + " api_key=api_key, top_k=2, model=\"rerank-2\", truncation=True\n", ")" ] }, diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/llama_index/embeddings/voyageai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/llama_index/embeddings/voyageai/base.py index efcc8a677fd12..e8d2de56bcbfd 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/llama_index/embeddings/voyageai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/llama_index/embeddings/voyageai/base.py @@ -36,9 +36,16 @@ def __init__( callback_manager: Optional[CallbackManager] = None, **kwargs: Any, ): - if model_name == "voyage-01": + if model_name in [ + "voyage-01", + "voyage-lite-01", + "voyage-lite-01-instruct", + "voyage-02", + "voyage-2", + "voyage-lite-02-instruct", + ]: logger.warning( - "voyage-01 is not the latest model by Voyage AI. Please note that `model_name` " + f"{model_name} is not the latest model by Voyage AI. Please note that `model_name` " "will be a required argument in the future. We recommend setting it explicitly. Please see " "https://docs.voyageai.com/docs/embeddings for the latest models offered by Voyage AI." ) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml index 778d4337d4d26..2e1457f99b1f8 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-voyageai/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-embeddings-voyageai" readme = "README.md" -version = "0.2.1" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From fe37352ccf4396a0031136b6fcc73f9e677b220a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Martins?= <11438285+jl-martins@users.noreply.github.com> Date: Sun, 22 Sep 2024 19:45:42 +0100 Subject: [PATCH 03/53] Add `required_exts` option to SharePoint reader (#16152) --- .../readers/microsoft_sharepoint/base.py | 6 ++- .../pyproject.toml | 2 +- .../test_readers_microsoft_sharepoint.py | 44 +++++++++++++++++++ 3 files changed, 50 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py index f2b4e4c16fc74..742d3587c102b 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/llama_index/readers/microsoft_sharepoint/base.py @@ -5,7 +5,6 @@ from pathlib import Path import tempfile from typing import Any, Dict, List, Union, Optional -from typing import Any, Dict, List, Optional import requests from llama_index.core.readers import SimpleDirectoryReader, FileSystemReaderMixin @@ -37,6 +36,7 @@ class SharePointReader(BasePydanticReader, ResourcesReaderMixin, FileSystemReade sharepoint_folder_id (Optional[str]): The ID of the SharePoint folder to download from. Overrides sharepoint_folder_path. drive_name (Optional[str]): The name of the drive to download from. drive_id (Optional[str]): The ID of the drive to download from. Overrides drive_name. + required_exts (Optional[List[str]]): List of required extensions. Default is None. file_extractor (Optional[Dict[str, BaseReader]]): A mapping of file extension to a BaseReader class that specifies how to convert that file to text. See `SimpleDirectoryReader` for more details. attach_permission_metadata (bool): If True, the reader will attach permission metadata to the documents. Set to False if your vector store @@ -50,6 +50,7 @@ class SharePointReader(BasePydanticReader, ResourcesReaderMixin, FileSystemReade sharepoint_site_id: Optional[str] = None sharepoint_folder_path: Optional[str] = None sharepoint_folder_id: Optional[str] = None + required_exts: Optional[List[str]] = None file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = Field( default=None, exclude=True ) @@ -70,6 +71,7 @@ def __init__( sharepoint_site_name: Optional[str] = None, sharepoint_folder_path: Optional[str] = None, sharepoint_folder_id: Optional[str] = None, + required_exts: Optional[List[str]] = None, file_extractor: Optional[Dict[str, Union[str, BaseReader]]] = None, drive_name: Optional[str] = None, drive_id: Optional[str] = None, @@ -82,6 +84,7 @@ def __init__( sharepoint_site_name=sharepoint_site_name, sharepoint_folder_path=sharepoint_folder_path, sharepoint_folder_id=sharepoint_folder_id, + required_exts=required_exts, file_extractor=file_extractor, drive_name=drive_name, drive_id=drive_id, @@ -530,6 +533,7 @@ def get_metadata(filename: str) -> Any: simple_loader = SimpleDirectoryReader( download_dir, + required_exts=self.required_exts, file_extractor=self.file_extractor, file_metadata=get_metadata, recursive=recursive, diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml index 2565ce2196900..ca1efc0ad0c11 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml @@ -29,7 +29,7 @@ license = "MIT" maintainers = ["arun-soliton"] name = "llama-index-readers-microsoft-sharepoint" readme = "README.md" -version = "0.3.1" +version = "0.3.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/tests/test_readers_microsoft_sharepoint.py b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/tests/test_readers_microsoft_sharepoint.py index 2465cdbfbcfc5..cf36acf1a8717 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/tests/test_readers_microsoft_sharepoint.py +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/tests/test_readers_microsoft_sharepoint.py @@ -204,3 +204,47 @@ def test_load_documents_with_metadata(sharepoint_reader): assert documents[1].metadata["file_name"] == "file2.txt" assert documents[0].text == "File 1 content" assert documents[1].text == "File 2 content" + + +def test_required_exts(): + sharepoint_reader = SharePointReader( + client_id="dummy_client_id", + client_secret="dummy_client_secret", + tenant_id="dummy_tenant_id", + sharepoint_site_name="dummy_site_name", + sharepoint_folder_path="dummy_folder_path", + drive_name="dummy_drive_name", + required_exts=[".md"], + ) + + with tempfile.TemporaryDirectory() as tmpdirname: + readme_file_path = os.path.join(tmpdirname, "readme.md") + audio_file_path = os.path.join(tmpdirname, "audio.aac") + with open(readme_file_path, "w") as f: + f.write("Readme content") + with open(audio_file_path, "wb") as f: + f.write(bytearray([0xFF, 0xF1, 0x50, 0x80, 0x00, 0x7F, 0xFC, 0x00])) + + file_metadata = { + readme_file_path: { + "file_id": "readme_file_id", + "file_name": "readme.md", + "url": "http://dummyurl/readme.md", + "file_path": readme_file_path, + }, + audio_file_path: { + "file_id": "audio_file_id", + "file_name": "audio.aac", + "url": "http://dummyurl/audio.aac", + "file_path": audio_file_path, + }, + } + + documents = sharepoint_reader._load_documents_with_metadata( + file_metadata, tmpdirname, recursive=False + ) + + assert documents is not None + assert len(documents) == 1 + assert documents[0].metadata["file_name"] == "readme.md" + assert documents[0].text == "Readme content" From ac4e7e44d27f8c3c785428df5c20304da8a56e66 Mon Sep 17 00:00:00 2001 From: Petros Mitseas Date: Sun, 22 Sep 2024 21:51:06 +0300 Subject: [PATCH 04/53] User-defined schema in MilvusVectorStore (#16151) --- .../llama_index/vector_stores/milvus/base.py | 73 +++++++++++++------ .../pyproject.toml | 2 +- 2 files changed, 52 insertions(+), 23 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py index 6efcb97a5d01e..f25dbfc6ef502 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py @@ -9,7 +9,6 @@ from copy import deepcopy from enum import Enum - from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.indices.query.embedding_utils import get_top_k_mmr_embeddings from llama_index.core.schema import BaseNode, TextNode @@ -152,7 +151,9 @@ class MilvusVectorStore(BasePydanticVectorStore): These weights are used to adjust the importance of the dense and sparse components of the embeddings in the hybrid retrieval process. Defaults to an empty dictionary, implying that the ranker will operate with its predefined default settings. - index_managemen (IndexManagement): Specifies the index management strategy to use. Defaults to "create_if_not_exists". + index_management (IndexManagement): Specifies the index management strategy to use. Defaults to "create_if_not_exists". + scalar_field_names (list): The names of the extra scalar fields to be included in the collection schema. + scalar_field_types (list): The types of the extra scalar fields. Raises: ImportError: Unable to import `pymilvus`. @@ -203,6 +204,8 @@ class MilvusVectorStore(BasePydanticVectorStore): hybrid_ranker: str hybrid_ranker_params: dict = {} index_management: IndexManagement = IndexManagement.CREATE_IF_NOT_EXISTS + scalar_field_names: Optional[List[str]] + scalar_field_types: Optional[List[DataType]] _milvusclient: MilvusClient = PrivateAttr() _collection: Any = PrivateAttr() @@ -229,6 +232,8 @@ def __init__( hybrid_ranker: str = "RRFRanker", hybrid_ranker_params: dict = {}, index_management: IndexManagement = IndexManagement.CREATE_IF_NOT_EXISTS, + scalar_field_names: Optional[List[str]] = None, + scalar_field_types: Optional[List[DataType]] = None, **kwargs: Any, ) -> None: """Init params.""" @@ -250,6 +255,8 @@ def __init__( hybrid_ranker=hybrid_ranker, hybrid_ranker_params=hybrid_ranker_params, index_management=index_management, + scalar_field_names=scalar_field_names, + scalar_field_types=scalar_field_types, ) # Select the similarity metric @@ -277,14 +284,10 @@ def __init__( if dim is None: raise ValueError("Dim argument required for collection creation.") if self.enable_sparse is False: + schema = self._create_schema() self._milvusclient.create_collection( collection_name=collection_name, - dimension=dim, - primary_field_name=MILVUS_ID_FIELD, - vector_field_name=embedding_field, - id_type="string", - metric_type=self.similarity_metric, - max_length=65_535, + schema=schema, consistency_level=consistency_level, ) else: @@ -826,20 +829,7 @@ def _create_hybrid_index(self, collection_name: str) -> None: """ # Check if the collection exists, if not, create it if collection_name not in self._milvusclient.list_collections(): - schema = MilvusClient.create_schema( - auto_id=False, enable_dynamic_field=True - ) - schema.add_field( - field_name="id", - datatype=DataType.VARCHAR, - max_length=65535, - is_primary=True, - ) - schema.add_field( - field_name=self.embedding_field, - datatype=DataType.FLOAT_VECTOR, - dim=self.dim, - ) + schema = self._create_schema() schema.add_field( field_name=self.sparse_embedding_field, datatype=DataType.SPARSE_FLOAT_VECTOR, @@ -882,3 +872,42 @@ def _create_hybrid_index(self, collection_name: str) -> None: self._collection.create_index(self.embedding_field, dense_index) self._collection.load() + + def _create_schema(self): + """ + Creates the collection schema. The default fields include the id, embedding and doc_id. + + Returns: The schema of the collection + """ + schema = MilvusClient.create_schema(auto_id=False, enable_dynamic_field=True) + schema.add_field( + field_name="id", + datatype=DataType.VARCHAR, + max_length=65_535, + is_primary=True, + ) + schema.add_field( + field_name=self.embedding_field, + datatype=DataType.FLOAT_VECTOR, + dim=self.dim, + ) + schema.add_field( + field_name=self.doc_id_field, + datatype=DataType.VARCHAR, + max_length=65_535, + ) + if self.scalar_field_names is not None and self.scalar_field_types is not None: + if len(self.scalar_field_names) != len(self.scalar_field_types): + raise ValueError( + "scalar_field_names and scalar_field_types must have same length." + ) + + for field_name, field_type in zip( + self.scalar_field_names, self.scalar_field_types + ): + max_length = 65_535 if field_type == DataType.VARCHAR else None + schema.add_field( + field_name=field_name, datatype=field_type, max_length=max_length + ) + + return schema diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml index f5b2d12769908..ad7cc1d3a9d7f 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-milvus" readme = "README.md" -version = "0.2.3" +version = "0.2.4" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 7d9bd0f2a5472dc38f959da18d7fa570198af660 Mon Sep 17 00:00:00 2001 From: Jerry Liu Date: Sun, 22 Sep 2024 16:20:36 -0700 Subject: [PATCH 05/53] safe format prompt variables in strings with JSON (#15734) --- .../llama_index/core/llms/structured_llm.py | 42 ++------------ .../llama_index/core/prompts/base.py | 6 +- .../llama_index/core/prompts/utils.py | 31 ++++++++-- .../tests/llms/test_structured_llm.py | 56 ------------------- llama-index-core/tests/prompts/test_base.py | 18 ++++++ 5 files changed, 52 insertions(+), 101 deletions(-) delete mode 100644 llama-index-core/tests/llms/test_structured_llm.py diff --git a/llama-index-core/llama_index/core/llms/structured_llm.py b/llama-index-core/llama_index/core/llms/structured_llm.py index 11bb7c755f332..451ef96f0b8ad 100644 --- a/llama-index-core/llama_index/core/llms/structured_llm.py +++ b/llama-index-core/llama_index/core/llms/structured_llm.py @@ -39,40 +39,6 @@ OutputKeys, QueryComponent, ) -import re - - -def _escape_braces(text: str) -> str: - """ - Escape braces in text. - Only captures template variables, skips already escaped braces. - """ - - def replace(match: re.Match[str]) -> str: - if match.group(0).startswith("{{") and match.group(0).endswith("}}"): - return match.group(0) # Already escaped, return as is - return "{{" + match.group(1) + "}}" - - pattern = r"(? Sequence[ChatMessage]: - """Escape JSON in messages.""" - new_messages = [] - for message in messages: - if isinstance(message.content, str): - escaped_msg = _escape_braces(message.content) - new_messages.append( - ChatMessage( - role=message.role, - content=escaped_msg, - additional_kwargs=message.additional_kwargs, - ) - ) - else: - new_messages.append(message) - return new_messages class StructuredLLM(LLM): @@ -104,7 +70,7 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: # make this work with our FunctionCallingProgram, even though # the messages don't technically have any variables (they are already formatted) - chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) + chat_prompt = ChatPromptTemplate(message_templates=messages) output = self.llm.structured_predict( output_cls=self.output_cls, prompt=chat_prompt, llm_kwargs=kwargs @@ -120,7 +86,7 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: def stream_chat( self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponseGen: - chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) + chat_prompt = ChatPromptTemplate(message_templates=messages) stream_output = self.llm.stream_structured_predict( output_cls=self.output_cls, prompt=chat_prompt, llm_kwargs=kwargs @@ -158,7 +124,7 @@ async def achat( # make this work with our FunctionCallingProgram, even though # the messages don't technically have any variables (they are already formatted) - chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) + chat_prompt = ChatPromptTemplate(message_templates=messages) output = await self.llm.astructured_predict( output_cls=self.output_cls, prompt=chat_prompt, llm_kwargs=kwargs @@ -179,7 +145,7 @@ async def astream_chat( """Async stream chat endpoint for LLM.""" async def gen() -> ChatResponseAsyncGen: - chat_prompt = ChatPromptTemplate(message_templates=_escape_json(messages)) + chat_prompt = ChatPromptTemplate(message_templates=messages) stream_output = await self.llm.astream_structured_predict( output_cls=self.output_cls, prompt=chat_prompt, llm_kwargs=kwargs diff --git a/llama-index-core/llama_index/core/prompts/base.py b/llama-index-core/llama_index/core/prompts/base.py index bf62357a69a76..641bde7464a26 100644 --- a/llama-index-core/llama_index/core/prompts/base.py +++ b/llama-index-core/llama_index/core/prompts/base.py @@ -48,7 +48,7 @@ prompt_to_messages, ) from llama_index.core.prompts.prompt_type import PromptType -from llama_index.core.prompts.utils import get_template_vars +from llama_index.core.prompts.utils import get_template_vars, format_string from llama_index.core.types import BaseOutputParser @@ -205,7 +205,7 @@ def format( } mapped_all_kwargs = self._map_all_vars(all_kwargs) - prompt = self.template.format(**mapped_all_kwargs) + prompt = format_string(self.template, **mapped_all_kwargs) if self.output_parser is not None: prompt = self.output_parser.format(prompt) @@ -313,7 +313,7 @@ def format_messages( content_template = message_template.content or "" # if there's mappings specified, make sure those are used - content = content_template.format(**relevant_kwargs) + content = format_string(content_template, **relevant_kwargs) message: ChatMessage = message_template.model_copy() message.content = content diff --git a/llama-index-core/llama_index/core/prompts/utils.py b/llama-index-core/llama_index/core/prompts/utils.py index 956424f7c3d6a..824cfed1521eb 100644 --- a/llama-index-core/llama_index/core/prompts/utils.py +++ b/llama-index-core/llama_index/core/prompts/utils.py @@ -1,15 +1,38 @@ -from string import Formatter -from typing import List +from typing import Dict, List, Optional +import re from llama_index.core.base.llms.base import BaseLLM +class SafeFormatter: + """Safe string formatter that does not raise KeyError if key is missing.""" + + def __init__(self, format_dict: Optional[Dict[str, str]] = None): + self.format_dict = format_dict or {} + + def format(self, format_string: str) -> str: + return re.sub(r"\{([^{}]+)\}", self._replace_match, format_string) + + def parse(self, format_string: str) -> List[str]: + return re.findall(r"\{([^{}]+)\}", format_string) + + def _replace_match(self, match: re.Match) -> str: + key = match.group(1) + return str(self.format_dict.get(key, match.group(0))) + + +def format_string(string_to_format: str, **kwargs: str) -> str: + """Format a string with kwargs.""" + formatter = SafeFormatter(format_dict=kwargs) + return formatter.format(string_to_format) + + def get_template_vars(template_str: str) -> List[str]: """Get template variables from a template string.""" variables = [] - formatter = Formatter() + formatter = SafeFormatter() - for _, variable_name, _, _ in formatter.parse(template_str): + for variable_name in formatter.parse(template_str): if variable_name: variables.append(variable_name) diff --git a/llama-index-core/tests/llms/test_structured_llm.py b/llama-index-core/tests/llms/test_structured_llm.py deleted file mode 100644 index 2216058125659..0000000000000 --- a/llama-index-core/tests/llms/test_structured_llm.py +++ /dev/null @@ -1,56 +0,0 @@ -from llama_index.core.llms.structured_llm import _escape_json -from llama_index.core.base.llms.types import ChatMessage - - -def test_escape_json() -> None: - """Test escape JSON. - - If there's curly brackets, escape it. - - """ - # create dumb test case - test_case_1 = _escape_json([ChatMessage(role="user", content="test message")]) - assert test_case_1 == [ChatMessage(role="user", content="test message")] - - # create test case with two brackets - test_case_2 = _escape_json( - [ChatMessage(role="user", content="test {message} {test}")] - ) - assert test_case_2 == [ - ChatMessage(role="user", content="test {{message}} {{test}}") - ] - - # create test case with a bracket that's already escaped - shouldn't change! - test_case_3 = _escape_json( - [ChatMessage(role="user", content="test {{message}} {test}")] - ) - print(test_case_3[0].content) - assert test_case_3 == [ - ChatMessage(role="user", content="test {{message}} {{test}}") - ] - - # test with additional kwargs - test_case_4 = _escape_json( - [ - ChatMessage( - role="user", - content="test {{message}} {test}", - additional_kwargs={"test": "test"}, - ) - ] - ) - assert test_case_4 == [ - ChatMessage( - role="user", - content="test {{message}} {{test}}", - additional_kwargs={"test": "test"}, - ) - ] - - # shouldn't escape already escaped brackets with 4 brackets - test_case_5 = _escape_json( - [ChatMessage(role="user", content="test {{{{message}}}} {test}")] - ) - assert test_case_5 == [ - ChatMessage(role="user", content="test {{{{message}}}} {{test}}") - ] diff --git a/llama-index-core/tests/prompts/test_base.py b/llama-index-core/tests/prompts/test_base.py index d647e22d261c4..ab93b4b47154a 100644 --- a/llama-index-core/tests/prompts/test_base.py +++ b/llama-index-core/tests/prompts/test_base.py @@ -274,3 +274,21 @@ def _format_prompt_key1(**kwargs: Any) -> str: "user: hello tmp1-tmp2 tmp2\n" "assistant: " ) + + +def test_template_with_json() -> None: + """Test partial format.""" + prompt_txt = 'hello {text} {foo} {"bar": "baz"}' + prompt = PromptTemplate(prompt_txt) + + assert prompt.format(foo="foo2", text="world") == 'hello world foo2 {"bar": "baz"}' + + assert prompt.format_messages(foo="foo2", text="world") == [ + ChatMessage(content='hello world foo2 {"bar": "baz"}', role=MessageRole.USER) + ] + + test_case_2 = PromptTemplate("test {message} {test}") + assert test_case_2.format(message="message") == "test message {test}" + + test_case_3 = PromptTemplate("test {{message}} {{test}}") + assert test_case_3.format(message="message", test="test") == "test {message} {test}" From 66b1e86823d27fed0aca8f04035ab03bbb5b9c2c Mon Sep 17 00:00:00 2001 From: Logan Date: Sun, 22 Sep 2024 19:56:09 -0600 Subject: [PATCH 06/53] account for tools in prompt helper (#16157) --- .../core/indices/common/struct_store/base.py | 4 +- .../core/indices/common_tree/base.py | 1 + .../llama_index/core/indices/prompt_helper.py | 42 +++++++++++++++++-- .../llama_index/core/indices/tree/inserter.py | 4 ++ .../indices/tree/select_leaf_retriever.py | 4 ++ .../core/program/function_program.py | 10 ++--- .../core/response_synthesizers/accumulate.py | 4 +- .../compact_and_accumulate.py | 8 +++- .../compact_and_refine.py | 2 +- .../core/response_synthesizers/refine.py | 12 ++++-- .../response_synthesizers/simple_summarize.py | 2 + .../response_synthesizers/tree_summarize.py | 4 +- .../core/utilities/token_counting.py | 30 +++++++++---- .../indices/response/test_tree_summarize.py | 7 +++- llama-index-core/tests/tools/test_types.py | 4 +- 15 files changed, 106 insertions(+), 32 deletions(-) diff --git a/llama-index-core/llama_index/core/indices/common/struct_store/base.py b/llama-index-core/llama_index/core/indices/common/struct_store/base.py index 6577ab0b5ca95..9eae129a24526 100644 --- a/llama-index-core/llama_index/core/indices/common/struct_store/base.py +++ b/llama-index-core/llama_index/core/indices/common/struct_store/base.py @@ -98,7 +98,9 @@ def build_table_context_from_documents( text_splitter = ( self._text_splitter - or self._prompt_helper.get_text_splitter_given_prompt(prompt_with_schema) + or self._prompt_helper.get_text_splitter_given_prompt( + prompt_with_schema, llm=self._llm + ) ) # we use the ResponseBuilder to iteratively go through all texts response_builder = get_response_synthesizer( diff --git a/llama-index-core/llama_index/core/indices/common_tree/base.py b/llama-index-core/llama_index/core/indices/common_tree/base.py index f8613b798b66b..258f4996a64e4 100644 --- a/llama-index-core/llama_index/core/indices/common_tree/base.py +++ b/llama-index-core/llama_index/core/indices/common_tree/base.py @@ -99,6 +99,7 @@ def _prepare_node_and_text_chunks( node.get_content(metadata_mode=MetadataMode.LLM) for node in cur_nodes_chunk ], + llm=self._llm, ) text_chunk = "\n".join(truncated_chunks) indices.append(i) diff --git a/llama-index-core/llama_index/core/indices/prompt_helper.py b/llama-index-core/llama_index/core/indices/prompt_helper.py index 1c560f10f3f53..eb7b649898426 100644 --- a/llama-index-core/llama_index/core/indices/prompt_helper.py +++ b/llama-index-core/llama_index/core/indices/prompt_helper.py @@ -11,12 +11,16 @@ import logging from copy import deepcopy from string import Formatter -from typing import Callable, List, Optional, Sequence +from typing import TYPE_CHECKING, Callable, List, Optional, Sequence + +if TYPE_CHECKING: + from llama_index.core.tools import BaseTool from llama_index.core.base.llms.types import ChatMessage, LLMMetadata from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS from llama_index.core.llms.llm import LLM +from llama_index.core.llms.structured_llm import StructuredLLM from llama_index.core.node_parser.text.token import TokenTextSplitter from llama_index.core.node_parser.text.utils import truncate_text from llama_index.core.prompts import ( @@ -152,12 +156,24 @@ def _get_available_context_size(self, num_prompt_tokens: int) -> int: ) return context_size_tokens + def _get_tools_from_llm( + self, llm: Optional[LLM] = None, tools: Optional[List["BaseTool"]] = None + ) -> List["BaseTool"]: + from llama_index.core.program.function_program import get_function_tool + + tools = tools or [] + if isinstance(llm, StructuredLLM): + tools.append(get_function_tool(llm.output_cls)) + + return tools + def _get_available_chunk_size( self, prompt: BasePromptTemplate, num_chunks: int = 1, padding: int = 5, llm: Optional[LLM] = None, + tools: Optional[List["BaseTool"]] = None, ) -> int: """Get available chunk size. @@ -169,6 +185,8 @@ def _get_available_chunk_size( - By default, we use padding of 5 (to save space for formatting needs). - Available chunk size is further clamped to chunk_size_limit if specified. """ + tools = self._get_tools_from_llm(llm=llm, tools=tools) + if isinstance(prompt, SelectorPromptTemplate): prompt = prompt.select(llm=llm) @@ -214,6 +232,20 @@ def _get_available_chunk_size( prompt_str = get_empty_prompt_txt(prompt) num_prompt_tokens = self._token_counter.get_string_tokens(prompt_str) + num_prompt_tokens += self._token_counter.estimate_tokens_in_tools( + [x.metadata.to_openai_tool() for x in tools] + ) + + # structured llms cannot have system prompts currently -- check the underlying llm + if isinstance(llm, StructuredLLM): + num_prompt_tokens += self._token_counter.get_string_tokens( + llm.llm.system_prompt or "" + ) + elif llm is not None: + num_prompt_tokens += self._token_counter.get_string_tokens( + llm.system_prompt or "" + ) + available_context_size = self._get_available_context_size(num_prompt_tokens) result = available_context_size // num_chunks - padding if self.chunk_size_limit is not None: @@ -226,12 +258,13 @@ def get_text_splitter_given_prompt( num_chunks: int = 1, padding: int = DEFAULT_PADDING, llm: Optional[LLM] = None, + tools: Optional[List["BaseTool"]] = None, ) -> TokenTextSplitter: """Get text splitter configured to maximally pack available context window, taking into account of given prompt, and desired number of chunks. """ chunk_size = self._get_available_chunk_size( - prompt, num_chunks, padding=padding, llm=llm + prompt, num_chunks, padding=padding, llm=llm, tools=tools ) if chunk_size <= 0: raise ValueError(f"Chunk size {chunk_size} is not positive.") @@ -249,6 +282,7 @@ def truncate( text_chunks: Sequence[str], padding: int = DEFAULT_PADDING, llm: Optional[LLM] = None, + tools: Optional[List["BaseTool"]] = None, ) -> List[str]: """Truncate text chunks to fit available context window.""" text_splitter = self.get_text_splitter_given_prompt( @@ -256,6 +290,7 @@ def truncate( num_chunks=len(text_chunks), padding=padding, llm=llm, + tools=tools, ) return [truncate_text(chunk, text_splitter) for chunk in text_chunks] @@ -265,6 +300,7 @@ def repack( text_chunks: Sequence[str], padding: int = DEFAULT_PADDING, llm: Optional[LLM] = None, + tools: Optional[List["BaseTool"]] = None, ) -> List[str]: """Repack text chunks to fit available context window. @@ -273,7 +309,7 @@ def repack( """ text_splitter = self.get_text_splitter_given_prompt( - prompt, padding=padding, llm=llm + prompt, padding=padding, llm=llm, tools=tools ) combined_str = "\n\n".join([c.strip() for c in text_chunks if c.strip()]) return text_splitter.split_text(combined_str) diff --git a/llama-index-core/llama_index/core/indices/tree/inserter.py b/llama-index-core/llama_index/core/indices/tree/inserter.py index a147965f24a6d..fb8c1ec6d0d96 100644 --- a/llama-index-core/llama_index/core/indices/tree/inserter.py +++ b/llama-index-core/llama_index/core/indices/tree/inserter.py @@ -77,6 +77,7 @@ def _insert_under_parent_and_consolidate( text_chunks=[ node.get_content(metadata_mode=MetadataMode.LLM) for node in half1 ], + llm=self._llm, ) text_chunk1 = "\n".join(truncated_chunks) @@ -89,6 +90,7 @@ def _insert_under_parent_and_consolidate( text_chunks=[ node.get_content(metadata_mode=MetadataMode.LLM) for node in half2 ], + llm=self._llm, ) text_chunk2 = "\n".join(truncated_chunks) summary2 = self._llm.predict(self.summary_prompt, context_str=text_chunk2) @@ -129,6 +131,7 @@ def _insert_node( text_splitter = self._prompt_helper.get_text_splitter_given_prompt( prompt=self.insert_prompt, num_chunks=len(cur_graph_node_list), + llm=self._llm, ) numbered_text = get_numbered_text_from_nodes( cur_graph_node_list, text_splitter=text_splitter @@ -163,6 +166,7 @@ def _insert_node( node.get_content(metadata_mode=MetadataMode.LLM) for node in cur_graph_node_list ], + llm=self._llm, ) text_chunk = "\n".join(truncated_chunks) new_summary = self._llm.predict(self.summary_prompt, context_str=text_chunk) diff --git a/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py b/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py index 98ad03e4b42ed..aca4bf4aba702 100644 --- a/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py +++ b/llama-index-core/llama_index/core/indices/tree/select_leaf_retriever.py @@ -182,6 +182,7 @@ def _query_level( text_splitter = self._prompt_helper.get_text_splitter_given_prompt( prompt=query_template, num_chunks=len(cur_node_list), + llm=self._llm, ) numbered_node_text = get_numbered_text_from_nodes( cur_node_list, text_splitter=text_splitter @@ -201,6 +202,7 @@ def _query_level( text_splitter = self._prompt_helper.get_text_splitter_given_prompt( prompt=query_template_multiple, num_chunks=len(cur_node_list), + llm=self._llm, ) numbered_node_text = get_numbered_text_from_nodes( cur_node_list, text_splitter=text_splitter @@ -296,6 +298,7 @@ def _select_nodes( text_splitter = self._prompt_helper.get_text_splitter_given_prompt( prompt=query_template, num_chunks=len(cur_node_list), + llm=self._llm, ) numbered_node_text = get_numbered_text_from_nodes( cur_node_list, text_splitter=text_splitter @@ -315,6 +318,7 @@ def _select_nodes( text_splitter = self._prompt_helper.get_text_splitter_given_prompt( prompt=query_template_multiple, num_chunks=len(cur_node_list), + llm=self._llm, ) numbered_node_text = get_numbered_text_from_nodes( cur_node_list, text_splitter=text_splitter diff --git a/llama-index-core/llama_index/core/program/function_program.py b/llama-index-core/llama_index/core/program/function_program.py index df008ce3b7b34..0756e270b1f1e 100644 --- a/llama-index-core/llama_index/core/program/function_program.py +++ b/llama-index-core/llama_index/core/program/function_program.py @@ -49,7 +49,7 @@ def _parse_tool_outputs( return outputs[0] -def _get_function_tool(output_cls: Type[Model]) -> FunctionTool: +def get_function_tool(output_cls: Type[Model]) -> FunctionTool: """Get function tool.""" schema = output_cls.model_json_schema() schema_description = schema.get("description", None) @@ -194,7 +194,7 @@ def __call__( **kwargs: Any, ) -> BaseModel: llm_kwargs = llm_kwargs or {} - tool = _get_function_tool(self._output_cls) + tool = get_function_tool(self._output_cls) messages = self._prompt.format_messages(llm=self._llm, **kwargs) messages = self._llm._extend_messages(messages) @@ -218,7 +218,7 @@ async def acall( **kwargs: Any, ) -> BaseModel: llm_kwargs = llm_kwargs or {} - tool = _get_function_tool(self._output_cls) + tool = get_function_tool(self._output_cls) agent_response = await self._llm.apredict_and_call( [tool], @@ -294,7 +294,7 @@ def stream_call( raise ValueError("stream_call is only supported for LLMs.") llm_kwargs = llm_kwargs or {} - tool = _get_function_tool(self._output_cls) + tool = get_function_tool(self._output_cls) messages = self._prompt.format_messages(llm=self._llm, **kwargs) messages = self._llm._extend_messages(messages) @@ -333,7 +333,7 @@ async def gen() -> AsyncGenerator[Union[Model, List[Model]], None]: if not isinstance(self._llm, FunctionCallingLLM): raise ValueError("stream_call is only supported for LLMs.") - tool = _get_function_tool(self._output_cls) + tool = get_function_tool(self._output_cls) messages = self._prompt.format_messages(llm=self._llm, **kwargs) messages = self._llm._extend_messages(messages) diff --git a/llama-index-core/llama_index/core/response_synthesizers/accumulate.py b/llama-index-core/llama_index/core/response_synthesizers/accumulate.py index 6fbdea50453cd..a249b66b6f316 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/accumulate.py +++ b/llama-index-core/llama_index/core/response_synthesizers/accumulate.py @@ -117,7 +117,9 @@ def _give_responses( """Give responses given a query and a corresponding text chunk.""" text_qa_template = self._text_qa_template.partial_format(query_str=query_str) - text_chunks = self._prompt_helper.repack(text_qa_template, [text_chunk]) + text_chunks = self._prompt_helper.repack( + text_qa_template, [text_chunk], llm=self._llm + ) predictor: Callable if self._output_cls is None: diff --git a/llama-index-core/llama_index/core/response_synthesizers/compact_and_accumulate.py b/llama-index-core/llama_index/core/response_synthesizers/compact_and_accumulate.py index 3d1401c1e5ab0..dbcfb0f7b1bdd 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/compact_and_accumulate.py +++ b/llama-index-core/llama_index/core/response_synthesizers/compact_and_accumulate.py @@ -20,7 +20,9 @@ async def aget_response( text_qa_template = self._text_qa_template.partial_format(query_str=query_str) with temp_set_attrs(self._prompt_helper): - new_texts = self._prompt_helper.repack(text_qa_template, text_chunks) + new_texts = self._prompt_helper.repack( + text_qa_template, text_chunks, llm=self._llm + ) return await super().aget_response( query_str=query_str, @@ -41,7 +43,9 @@ def get_response( text_qa_template = self._text_qa_template.partial_format(query_str=query_str) with temp_set_attrs(self._prompt_helper): - new_texts = self._prompt_helper.repack(text_qa_template, text_chunks) + new_texts = self._prompt_helper.repack( + text_qa_template, text_chunks, llm=self._llm + ) return super().get_response( query_str=query_str, diff --git a/llama-index-core/llama_index/core/response_synthesizers/compact_and_refine.py b/llama-index-core/llama_index/core/response_synthesizers/compact_and_refine.py index a7da37f8fc210..6cdeba26df634 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/compact_and_refine.py +++ b/llama-index-core/llama_index/core/response_synthesizers/compact_and_refine.py @@ -54,4 +54,4 @@ def _make_compact_text_chunks( refine_template = self._refine_template.partial_format(query_str=query_str) max_prompt = get_biggest_prompt([text_qa_template, refine_template]) - return self._prompt_helper.repack(max_prompt, text_chunks) + return self._prompt_helper.repack(max_prompt, text_chunks, llm=self._llm) diff --git a/llama-index-core/llama_index/core/response_synthesizers/refine.py b/llama-index-core/llama_index/core/response_synthesizers/refine.py index 1f17178a707b0..c18490332df46 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/refine.py +++ b/llama-index-core/llama_index/core/response_synthesizers/refine.py @@ -220,7 +220,9 @@ def _give_response_single( ) -> RESPONSE_TEXT_TYPE: """Give response given a query and a corresponding text chunk.""" text_qa_template = self._text_qa_template.partial_format(query_str=query_str) - text_chunks = self._prompt_helper.repack(text_qa_template, [text_chunk]) + text_chunks = self._prompt_helper.repack( + text_qa_template, [text_chunk], llm=self._llm + ) response: Optional[RESPONSE_TEXT_TYPE] = None program = self._program_factory(text_qa_template) @@ -301,7 +303,7 @@ def _refine_response_single( # obtain text chunks to add to the refine template text_chunks = self._prompt_helper.repack( - refine_template, text_chunks=[text_chunk] + refine_template, text_chunks=[text_chunk], llm=self._llm ) program = self._program_factory(refine_template) @@ -410,7 +412,7 @@ async def _arefine_response_single( # obtain text chunks to add to the refine template text_chunks = self._prompt_helper.repack( - refine_template, text_chunks=[text_chunk] + refine_template, text_chunks=[text_chunk], llm=self._llm ) program = self._program_factory(refine_template) @@ -467,7 +469,9 @@ async def _agive_response_single( ) -> RESPONSE_TEXT_TYPE: """Give response given a query and a corresponding text chunk.""" text_qa_template = self._text_qa_template.partial_format(query_str=query_str) - text_chunks = self._prompt_helper.repack(text_qa_template, [text_chunk]) + text_chunks = self._prompt_helper.repack( + text_qa_template, [text_chunk], llm=self._llm + ) response: Optional[RESPONSE_TEXT_TYPE] = None program = self._program_factory(text_qa_template) diff --git a/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py b/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py index 82a5495778ea2..b6c68351077bd 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py +++ b/llama-index-core/llama_index/core/response_synthesizers/simple_summarize.py @@ -49,6 +49,7 @@ async def aget_response( truncated_chunks = self._prompt_helper.truncate( prompt=text_qa_template, text_chunks=[single_text_chunk], + llm=self._llm, ) response: RESPONSE_TEXT_TYPE @@ -83,6 +84,7 @@ def get_response( truncated_chunks = self._prompt_helper.truncate( prompt=text_qa_template, text_chunks=[single_text_chunk], + llm=self._llm, ) response: RESPONSE_TEXT_TYPE diff --git a/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py b/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py index 003a006f1bd94..bdd1e0b55f8fa 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py +++ b/llama-index-core/llama_index/core/response_synthesizers/tree_summarize.py @@ -68,7 +68,7 @@ async def aget_response( summary_template = self._summary_template.partial_format(query_str=query_str) # repack text_chunks so that each chunk fills the context window text_chunks = self._prompt_helper.repack( - summary_template, text_chunks=text_chunks + summary_template, text_chunks=text_chunks, llm=self._llm ) if self._verbose: @@ -144,7 +144,7 @@ def get_response( summary_template = self._summary_template.partial_format(query_str=query_str) # repack text_chunks so that each chunk fills the context window text_chunks = self._prompt_helper.repack( - summary_template, text_chunks=text_chunks + summary_template, text_chunks=text_chunks, llm=self._llm ) if self._verbose: diff --git a/llama-index-core/llama_index/core/utilities/token_counting.py b/llama-index-core/llama_index/core/utilities/token_counting.py index b6ddc9892f200..270648782f0b3 100644 --- a/llama-index-core/llama_index/core/utilities/token_counting.py +++ b/llama-index-core/llama_index/core/utilities/token_counting.py @@ -48,6 +48,7 @@ def estimate_tokens_in_messages(self, messages: List[ChatMessage]) -> int: additional_kwargs = {**message.additional_kwargs} + # backward compatibility if "function_call" in additional_kwargs: function_call = additional_kwargs.pop("function_call") if function_call.get("name", None) is not None: @@ -58,25 +59,36 @@ def estimate_tokens_in_messages(self, messages: List[ChatMessage]) -> int: tokens += 3 # Additional tokens for function call + if "tool_calls" in additional_kwargs: + for tool_call in additional_kwargs["tool_calls"]: + if ( + hasattr(tool_call, "function") + and tool_call.function is not None + ): + tokens += self.get_string_tokens(tool_call.function.name) + tokens += self.get_string_tokens(tool_call.function.arguments) + + tokens += 3 # Additional tokens for tool call + tokens += 3 # Add three per message - if message.role == MessageRole.FUNCTION: + if message.role == MessageRole.FUNCTION or message.role == MessageRole.TOOL: tokens -= 2 # Subtract 2 if role is "function" return tokens - def estimate_tokens_in_functions(self, functions: List[Dict[str, Any]]) -> int: - """Estimate token count for the functions. + def estimate_tokens_in_tools(self, tools: List[Dict[str, Any]]) -> int: + """Estimate token count for the tools. - We take here a list of functions created using the `to_openai_spec` function (or similar). + We take here a list of tools created using the `to_openai_tool()` function (or similar). Args: - function (list[Dict[str, Any]]): The functions to estimate the token count for. + tools (list[Dict[str, Any]]): The tools to estimate the token count for. Returns: int: The estimated token count. """ - prompt_definition = str(functions) - tokens = self.get_string_tokens(prompt_definition) - tokens += 9 # Additional tokens for function definition - return tokens + if not tools: + return 0 + + return self.get_string_tokens(str(tools)) diff --git a/llama-index-core/tests/indices/response/test_tree_summarize.py b/llama-index-core/tests/indices/response/test_tree_summarize.py index d6b70540db833..c8d8864c70e00 100644 --- a/llama-index-core/tests/indices/response/test_tree_summarize.py +++ b/llama-index-core/tests/indices/response/test_tree_summarize.py @@ -1,6 +1,6 @@ """Test tree summarize.""" -from typing import Any, List, Sequence +from typing import Any, List, Sequence, Optional from unittest.mock import Mock, patch import pytest @@ -15,7 +15,10 @@ @pytest.fixture() def mock_prompt_helper(patch_llm_predictor, patch_token_text_splitter): def mock_repack( - prompt_template: PromptTemplate, text_chunks: Sequence[str] + prompt_template: PromptTemplate, + text_chunks: Sequence[str], + llm: Optional[Any] = None, + tools: Optional[Any] = None, ) -> List[str]: merged_chunks = [] for chunks in zip(*[iter(text_chunks)] * 2): diff --git a/llama-index-core/tests/tools/test_types.py b/llama-index-core/tests/tools/test_types.py index f38831b5a092a..154768e1dbf13 100644 --- a/llama-index-core/tests/tools/test_types.py +++ b/llama-index-core/tests/tools/test_types.py @@ -1,7 +1,7 @@ import pytest from llama_index.core.bridge.pydantic import BaseModel -from llama_index.core.program.function_program import _get_function_tool +from llama_index.core.program.function_program import get_function_tool from llama_index.core.tools.types import ToolMetadata @@ -26,7 +26,7 @@ def test_toolmetadata_openai_tool_description_max_length() -> None: def test_nested_tool_schema() -> None: - tool = _get_function_tool(Outer) + tool = get_function_tool(Outer) schema = tool.metadata.get_parameters_dict() assert "$defs" in schema From caadc62575c91b1aa1817a423b29365d46253e08 Mon Sep 17 00:00:00 2001 From: Logan Date: Sun, 22 Sep 2024 21:28:03 -0600 Subject: [PATCH 07/53] v0.11.12 (#16159) --- CHANGELOG.md | 20 +++++++++++++++++++ docs/docs/CHANGELOG.md | 20 +++++++++++++++++++ llama-index-core/llama_index/core/__init__.py | 2 +- llama-index-core/pyproject.toml | 2 +- poetry.lock | 20 +++++++++---------- pyproject.toml | 4 ++-- 6 files changed, 54 insertions(+), 14 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index adcad45eff100..36b89f2335c86 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,25 @@ # ChangeLog +## [2024-09-22] + +### `llama-index-core` [0.11.12] + +- Correct Pydantic warning(s) issed for llm base class (#16141) +- globally safe format prompt variables in strings with JSON (#15734) +- account for tools in prompt helper and response synthesizers (#16157) + +### `llama-index-readers-google` [0.4.1] + +- feat: add drive link to google drive reader metadata (#16156) + +### `llama-index-readers-microsoft-sharepoint` [0.3.2] + +- Add required_exts option to SharePoint reader (#16152) + +### `llama-index-vector-stores-milvus` [0.2.4] + +- Support user-defined schema in MilvusVectorStore (#16151) + ## [2024-09-20] ### `llama-index-core` [0.11.11] diff --git a/docs/docs/CHANGELOG.md b/docs/docs/CHANGELOG.md index adcad45eff100..36b89f2335c86 100644 --- a/docs/docs/CHANGELOG.md +++ b/docs/docs/CHANGELOG.md @@ -1,5 +1,25 @@ # ChangeLog +## [2024-09-22] + +### `llama-index-core` [0.11.12] + +- Correct Pydantic warning(s) issed for llm base class (#16141) +- globally safe format prompt variables in strings with JSON (#15734) +- account for tools in prompt helper and response synthesizers (#16157) + +### `llama-index-readers-google` [0.4.1] + +- feat: add drive link to google drive reader metadata (#16156) + +### `llama-index-readers-microsoft-sharepoint` [0.3.2] + +- Add required_exts option to SharePoint reader (#16152) + +### `llama-index-vector-stores-milvus` [0.2.4] + +- Support user-defined schema in MilvusVectorStore (#16151) + ## [2024-09-20] ### `llama-index-core` [0.11.11] diff --git a/llama-index-core/llama_index/core/__init__.py b/llama-index-core/llama_index/core/__init__.py index ac34d2737bacd..c1cc7bd38d4df 100644 --- a/llama-index-core/llama_index/core/__init__.py +++ b/llama-index-core/llama_index/core/__init__.py @@ -1,6 +1,6 @@ """Init file of LlamaIndex.""" -__version__ = "0.11.11" +__version__ = "0.11.12" import logging from logging import NullHandler diff --git a/llama-index-core/pyproject.toml b/llama-index-core/pyproject.toml index 4863f0add355d..c35c5ba4522ae 100644 --- a/llama-index-core/pyproject.toml +++ b/llama-index-core/pyproject.toml @@ -46,7 +46,7 @@ name = "llama-index-core" packages = [{include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.11.11" +version = "0.11.12" [tool.poetry.dependencies] SQLAlchemy = {extras = ["asyncio"], version = ">=1.4.49"} diff --git a/poetry.lock b/poetry.lock index f1fc8e084c6d0..af8545717eb69 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1686,13 +1686,13 @@ llama-index-llms-openai = ">=0.2.0,<0.3.0" [[package]] name = "llama-index-core" -version = "0.11.11" +version = "0.11.12" description = "Interface between LLMs and your data" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "llama_index_core-0.11.11-py3-none-any.whl", hash = "sha256:fec6c6ae46a34287777598ae3da92bb1917a769e9134fb1f02dac600ef0d1afc"}, - {file = "llama_index_core-0.11.11.tar.gz", hash = "sha256:5a620a4dcb1866f5248b1c465c2693ae6a6ee1eb3e4e50c554575ded69f4590a"}, + {file = "llama_index_core-0.11.12-py3-none-any.whl", hash = "sha256:7dc7ead649bac8f09e61c6c8bf93d257f68a7315223552421be4f0ffc3a8054d"}, + {file = "llama_index_core-0.11.12.tar.gz", hash = "sha256:ce2dd037ff889d9ea6b25872228cc9de614c10445d19377f6ae5c66b93a50c61"}, ] [package.dependencies] @@ -2205,13 +2205,13 @@ pygments = ">2.12.0" [[package]] name = "mkdocs-material" -version = "9.5.35" +version = "9.5.36" description = "Documentation that simply works" optional = false python-versions = ">=3.8" files = [ - {file = "mkdocs_material-9.5.35-py3-none-any.whl", hash = "sha256:44e069d87732d29f4a2533ae0748fa0e67e270043270c71f04d0fba11a357b24"}, - {file = "mkdocs_material-9.5.35.tar.gz", hash = "sha256:0d233d7db067ac896bf22ee7950eebf2b1eaf26c155bb27382bf4174021cc117"}, + {file = "mkdocs_material-9.5.36-py3-none-any.whl", hash = "sha256:36734c1fd9404bea74236242ba3359b267fc930c7233b9fd086b0898825d0ac9"}, + {file = "mkdocs_material-9.5.36.tar.gz", hash = "sha256:140456f761320f72b399effc073fa3f8aac744c77b0970797c201cae2f6c967f"}, ] [package.dependencies] @@ -3238,13 +3238,13 @@ testutils = ["gitpython (>3)"] [[package]] name = "pymdown-extensions" -version = "10.9" +version = "10.10.1" description = "Extension pack for Python Markdown." optional = false python-versions = ">=3.8" files = [ - {file = "pymdown_extensions-10.9-py3-none-any.whl", hash = "sha256:d323f7e90d83c86113ee78f3fe62fc9dee5f56b54d912660703ea1816fed5626"}, - {file = "pymdown_extensions-10.9.tar.gz", hash = "sha256:6ff740bcd99ec4172a938970d42b96128bdc9d4b9bcad72494f29921dc69b753"}, + {file = "pymdown_extensions-10.10.1-py3-none-any.whl", hash = "sha256:6c74ea6c2e2285186a241417480fc2d3cc52941b3ec2dced4014c84dc78c5493"}, + {file = "pymdown_extensions-10.10.1.tar.gz", hash = "sha256:ad277ee4739ced051c3b6328d22ce782358a3bec39bc6ca52815ccbf44f7acdc"}, ] [package.dependencies] @@ -4667,4 +4667,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "12190ed7da73dff29412dceea2e59c0d3b5f12e852d5b55bde8982f670c28bed" +content-hash = "312441a44e35d392f2a88342cfbe2e4d20f940e2964cc38848ec05f7d725026a" diff --git a/pyproject.toml b/pyproject.toml index 50e1366d2798a..703d6c535e583 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,7 +45,7 @@ name = "llama-index" packages = [{from = "_llama-index", include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.11.11" +version = "0.11.12" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" @@ -58,7 +58,7 @@ llama-index-agent-openai = "^0.3.4" llama-index-readers-file = "^0.2.0" llama-index-readers-llama-parse = ">=0.3.0" llama-index-indices-managed-llama-cloud = ">=0.3.0" -llama-index-core = "^0.11.10" +llama-index-core = "^0.11.11" llama-index-multi-modal-llms-openai = "^0.2.0" llama-index-cli = "^0.3.1" nltk = ">3.8.1" # avoids a CVE, temp until next release, should be in llama-index-core From 8b799aded2e76b385bdde7d94bcd666ceae42cc0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jannik=20Maierh=C3=B6fer?= <48529566+jannikmaierhoefer@users.noreply.github.com> Date: Mon, 23 Sep 2024 23:07:51 +0200 Subject: [PATCH 08/53] [docs] add cookbook about tracing LlamaIndex applications with Langfuse and PostHog (#16168) --- .../LangfuseCallbackHandler.ipynb | 31 +- .../LangfuseMistralPostHog.ipynb | 403 ++++++++++++++++++ .../observability/img/dashboard-posthog-1.png | Bin 0 -> 254716 bytes .../observability/img/dashboard-posthog-2.png | Bin 0 -> 342936 bytes ...integration-posthog-llamaindex-mistral.png | Bin 0 -> 173129 bytes .../docs/module_guides/observability/index.md | 5 +- 6 files changed, 423 insertions(+), 16 deletions(-) create mode 100644 docs/docs/examples/observability/LangfuseMistralPostHog.ipynb create mode 100644 docs/docs/examples/observability/img/dashboard-posthog-1.png create mode 100644 docs/docs/examples/observability/img/dashboard-posthog-2.png create mode 100644 docs/docs/examples/observability/img/integration-posthog-llamaindex-mistral.png diff --git a/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb b/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb index fcf331708f49a..eb14d5206f67d 100644 --- a/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb +++ b/docs/docs/examples/observability/LangfuseCallbackHandler.ipynb @@ -6,17 +6,17 @@ "id": "d6509c3a", "metadata": {}, "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "id": "c0d8b66c", - "metadata": {}, - "source": [ + "\"Open\n", + "\n", "# Langfuse Callback Handler\n", "\n", - "[Langfuse](https://langfuse.com/docs) is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications.\n", + "This cookbook shows you how to use the Langfuse callback handler to monitor LlamaIndex applications.\n", + "\n", + "## What is Langfuse?\n", + "\n", + "[Langfuse](https://langfuse.com/docs) is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Langfuse offers a simple integration for automatic capture of [traces](https://langfuse.com/docs/tracing) and metrics generated in LlamaIndex applications. \n", + "\n", + "## How does it work?\n", "\n", "The `LangfuseCallbackHandler` is integrated with Langfuse and empowers you to seamlessly track and monitor performance, traces, and metrics of your LlamaIndex application. Detailed traces of the LlamaIndex context augmentation and the LLM querying processes are captured and can be inspected directly in the Langfuse UI." ] @@ -80,12 +80,11 @@ "source": [ "import os\n", "\n", - "# Langfuse\n", + "# Get keys for your project from the project settings page https://cloud.langfuse.com\n", "os.environ[\"LANGFUSE_SECRET_KEY\"] = \"sk-lf-...\"\n", "os.environ[\"LANGFUSE_PUBLIC_KEY\"] = \"pk-lf-...\"\n", - "os.environ[\n", - " \"LANGFUSE_HOST\"\n", - "] = \"https://cloud.langfuse.com\" # 🇪🇺 EU region, 🇺🇸 US region: \"https://us.cloud.langfuse.com\"\n", + "os.environ[\"LANGFUSE_HOST\"] = \"https://cloud.langfuse.com\" # 🇪🇺 EU region\n", + "# os.environ[\"LANGFUSE_HOST\"] = \"https://us.cloud.langfuse.com\" # 🇺🇸 US region\n", "\n", "# OpenAI\n", "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" @@ -261,7 +260,11 @@ "source": [ "## 📚 More details\n", "\n", - "Check out the full [Langfuse documentation](https://langfuse.com/docs) for more details on Langfuse's tracing and analytics capabilities and how to make most of this integration." + "Check out the full [Langfuse documentation](https://langfuse.com/docs) for more details on Langfuse's tracing and analytics capabilities and how to make most of this integration.\n", + "\n", + "## Feedback\n", + "\n", + "If you have any feedback or requests, please create a GitHub [Issue](https://github.com/orgs/langfuse/discussions) or share your idea with the community on [Discord](https://discord.langfuse.com/)." ] } ], diff --git a/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb b/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb new file mode 100644 index 0000000000000..88043524f17a6 --- /dev/null +++ b/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open\n", + "\n", + "# Analyze and Debug LlamaIndex Applications with PostHog and Langfuse\n", + "\n", + "In this cookbook, we show you how to build a RAG application with [LlamaIndex](https://www.llamaindex.ai/), observe the steps with [Langfuse](https://langfuse.com/), and analyze the data in [PostHog](https://posthog.com/).\n", + "\n", + "## What is Langfuse?\n", + "[Langfuse](https://langfuse.com/) is an open-source LLM engineering platform designed to help engineers understand and optimize user interactions with their language model applications. It provides tools for tracking, debugging, and improving LLM performance in real-world use cases. Langfuse is available both as a managed [cloud solution](https://cloud.langfuse.com/) and for [local or self-hosted](https://langfuse.com/docs/deployment/feature-overview) deployments.\n", + "\n", + "## What is PostHog?\n", + "[PostHog](https://posthog.com/) is a popular choice for product analytics. Combining Langfuse's LLM analytics with PostHog's product analytics makes it easy to:\n", + "\n", + "- **Analyze User Engagement**: Determine how often users interact with specific LLM features and understand their overall activity patterns.\n", + "- **Correlate Feedback with Behavior**: See how user feedback captured in Langfuse correlates with user behavior in PostHog.\n", + "- **Monitor LLM Performance**: Track and analyze metrics such as model cost, latency, and user feedback to optimize LLM performance.\n", + "\n", + "## What is LlamaIndex?\n", + "LlamaIndex [(GitHub)](https://github.com/run-llama/llama_index) is a data framework designed to connect LLMs with external data sources. It helps structure, index, and query data effectively. This makes it easier for developers to build advanced LLM applications.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to Build a Simple RAG Application with LlamaIndex and Mistral\n", + "\n", + "In this tutorial, we demonstrate how to create a chat application that provides answers to questions about hedgehog care. LlamaIndex is used to vectorize a [hedgehog care guide](https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf) with the [Mistral 8x22B model](https://docs.mistral.ai/getting-started/models/). All model generations are then traced using Langfuse's [LlamaIndex integration](https://langfuse.com/docs/integrations/llama-index/get-started).\n", + "\n", + "Finally, the [PostHog integration](https://langfuse.com/docs/analytics/posthog) allows you to view detailed analytics about your hedgehog application directly in PostHog.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Set up LlamaIndex and Mistral\n", + "\n", + "First, we set our Mistral API key as an environment variable. If you haven't already, [sign up for a Mistral acccount](https://console.mistral.ai/). Then [subscribe](https://console.mistral.ai/billing/) to a free trial or billing plan, after which you'll be able to [generate an API key](https://console.mistral.ai/api-keys/) (💡 You can use any other model supported by LlamaIndex; we just use Mistral in this cookbook).\n", + "\n", + "Then, we use LlamaIndex to initialize both a Mistral language model and an embedding model. We then set these models in the LlamaIndex `Settings` object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index llama-index-llms-mistralai llama-index-embeddings-mistralai nest_asyncio --upgrade" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the Mistral API key\n", + "import os\n", + "\n", + "os.environ[\"MISTRAL_API_KEY\"] = \"***Your-Mistral-API-Key***\"\n", + "\n", + "# Ensures that sync and async code can be used together without issues\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()\n", + "\n", + "# Import and set up llama index\n", + "from llama_index.llms.mistralai import MistralAI\n", + "from llama_index.embeddings.mistralai import MistralAIEmbedding\n", + "from llama_index.core import Settings\n", + "\n", + "# Define your LLM and embedding model\n", + "llm = MistralAI(model=\"open-mixtral-8x22b\", temperature=0.1)\n", + "embed_model = MistralAIEmbedding(model_name=\"mistral-embed\")\n", + "\n", + "# Set the LLM and embedding model in the Settings object\n", + "Settings.llm = llm\n", + "Settings.embed_model = embed_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Initialize Langfuse\n", + "\n", + "Next, we initialize the Langfuse client. [Sign up](https://cloud.langfuse.com/auth/sign-up) for Langfuse if you haven't already. Copy your [API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys) from your project settings and add them to your environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install langfuse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# get keys for your project from https://cloud.langfuse.com\n", + "LANGFUSE_SECRET_KEY = \"sk-lf-...\"\n", + "LANGFUSE_PUBLIC_KEY = \"pk-lf-...\"\n", + "LANGFUSE_HOST = \"https://cloud.langfuse.com\" # 🇪🇺 EU region\n", + "# LANGFUSE_HOST=\"https://us.cloud.langfuse.com\" # 🇺🇸 US region" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langfuse import Langfuse\n", + "\n", + "langfuse = Langfuse()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we register Langfuse's `LlamaIndexCallbackHandler` in the LlamaIndex `Settings.callback_manager` at the root of the app.\n", + "\n", + "Find out more about the Langfuse's LlamaIndex integration [here](https://langfuse.com/docs/integrations/llama-index/get-started)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import Settings\n", + "from llama_index.core.callbacks import CallbackManager\n", + "from langfuse.llama_index import LlamaIndexCallbackHandler\n", + "\n", + "langfuse_callback_handler = LlamaIndexCallbackHandler()\n", + "Settings.callback_manager = CallbackManager([langfuse_callback_handler])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3: Download data\n", + "\n", + "We download the file we want to use for RAG. In this example, we use a [hedgehog care guide](https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf) pdf file to enable the language model to answer questions about caring for hedgehogs 🦔." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-09-20 13:16:39-- https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf\n", + "Resolving www.pro-igel.de (www.pro-igel.de)... 152.53.23.200\n", + "Connecting to www.pro-igel.de (www.pro-igel.de)|152.53.23.200|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1160174 (1.1M) [application/pdf]\n", + "Saving to: ‘./hedgehog.pdf’\n", + "\n", + "./hedgehog.pdf 100%[===================>] 1.11M 2.03MB/s in 0.5s \n", + "\n", + "2024-09-20 13:16:40 (2.03 MB/s) - ‘./hedgehog.pdf’ saved [1160174/1160174]\n", + "\n" + ] + } + ], + "source": [ + "!wget 'https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf' -O './hedgehog.pdf'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the pdf using the LlamaIndex [`SimpleDirectoryReader`](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "hedgehog_docs = SimpleDirectoryReader(\n", + " input_files=[\"./hedgehog.pdf\"]\n", + ").load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: Build RAG on the hedgehog doc\n", + "\n", + "Next, we create vector embeddings of the hedgehog document using [`VectorStoreIndex`](https://docs.llamaindex.ai/en/stable/module_guides/indexing/vector_store_index/) and then convert it into a [queryable engine](https://docs.llamaindex.ai/en/stable/module_guides/deploying/query_engine/) to retrieve information based on queries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex\n", + "\n", + "hedgehog_index = VectorStoreIndex.from_documents(hedgehog_docs)\n", + "hedgehog_query_engine = hedgehog_index.as_query_engine(similarity_top_k=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, to put everything together, we query the engine and print a response:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hedgehogs that require help are those that are sick, injured, and helpless, such as orphaned hoglets. These hedgehogs in need may be temporarily taken into human care and must be released into the wild as soon as they can survive there independently.\n" + ] + } + ], + "source": [ + "response = hedgehog_query_engine.query(\"Which hedgehogs require help?\")\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All steps of the LLM chain are now tracked in Langfuse.\n", + "\n", + "Example trace in Langfuse: https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/367db23d-5b03-446b-bc73-36e289596c00" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Example trace in the Langfuse UI](img/integration-posthog-llamaindex-mistral.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5: (Optional) Implement user feedback to see how your application is performing\n", + "\n", + "To monitor the quality of your hedgehog chat application, you can use [Langfuse Scores](https://langfuse.com/docs/scores/overview) to store user feedback (e.g. thumps up/down or comments). These scores can then be analysed in PostHog.\n", + "\n", + "Scores are used to evaluate single observations or entire traces. You can create them via the annotation workflow in the Langfuse UI, run model-based evaluation or ingest via the SDK as we do it in this example.\n", + "\n", + "To get the context of the current observation, we use the [`observe()` decorator](https://langfuse.com/docs/sdk/python/decorators) and apply it to the hedgehog_helper() function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on the provided context, it is not recommended to keep wild hedgehogs as pets. The Federal Nature Conservation Act protects hedgehogs as a native mammal species, making it illegal to chase, catch, injure, kill, or take their nesting and refuge places. Exceptions apply only to sick, injured, and helpless hedgehogs, which may be temporarily taken into human care and released into the wild as soon as they can survive independently. It is important to respect the natural habitats and behaviors of wild animals, including hedgehogs.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langfuse.decorators import langfuse_context, observe\n", + "\n", + "\n", + "# Langfuse observe() decorator to automatically create a trace for the top-level function and spans for any nested functions.\n", + "@observe()\n", + "def hedgehog_helper(user_message):\n", + " response = hedgehog_query_engine.query(user_message)\n", + " trace_id = langfuse_context.get_current_trace_id()\n", + "\n", + " print(response)\n", + "\n", + " return trace_id\n", + "\n", + "\n", + "trace_id = hedgehog_helper(\"Can I keep the hedgehog as a pet?\")\n", + "\n", + "# Score the trace, e.g. to add user feedback using the trace_id\n", + "langfuse.score(\n", + " trace_id=trace_id,\n", + " name=\"user-explicit-feedback\",\n", + " value=0.9,\n", + " data_type=\"NUMERIC\", # optional, inferred if not provided\n", + " comment=\"Good to know!\", # optional\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6: See your data in PostHog\n", + "\n", + "Finally, we connect PostHog to our Langfuse account. Below is a summary of the steps to take (or see the [docs](https://posthog.com/docs/ai-engineering/langfuse-posthog) for full details):\n", + "\n", + "1. [Sign up](https://us.posthog.com/) for your free PostHog account if you haven't already\n", + "2. Copy both your project API key and host from your [project settings](https://us.posthog.com/project/settings/project-details).\n", + "3. In your [Langfuse dashboard](https://cloud.langfuse.com/), click on **Settings** and scroll down to the **Integrations** section to find the PostHog integration.\n", + "4. Click **Configure** and paste in your PostHog host and project API key (you can find these in your [PostHog project settings](https://us.posthog.com/settings/project)).\n", + "5. Click **Enabled** and then **Save**.\n", + "\n", + " Langfuse will then begin exporting your data to PostHog once a day.\n", + "\n", + "**Using the Langfuse dashboard template:**\n", + "\n", + "Once you've installed the integration, [dashboard templates](https://posthog.com/docs/ai-engineering/langfuse-posthog#using-the-langfuse-dashboard-template) help you quickly set up relevant insights.\n", + "\n", + "For our hedgehog chat application, we are using the template dashboard shown below. This enables you to analyze model cost, user feedback, and latency in PostHog.\n", + "\n", + "To create your own dashboard from a template:\n", + "\n", + "1. Go to the [dashboard](https://us.posthog.com/dashboard) tab in PostHog.\n", + "2. Click the **New dashboard** button in the top right.\n", + "3. Select **LLM metrics – Langfuse** from the list of templates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Posthog Dashboard showing user feedback and number of traces](img/dashboard-posthog-1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Posthog Dashboard showing latency and costs](img/dashboard-posthog-2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feedback\n", + "\n", + "If you have any feedback or requests, please create a GitHub [Issue](https://github.com/orgs/langfuse/discussions) or share your idea with the community on [Discord](https://discord.langfuse.com/)." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/examples/observability/img/dashboard-posthog-1.png b/docs/docs/examples/observability/img/dashboard-posthog-1.png new file mode 100644 index 0000000000000000000000000000000000000000..945d210d097cbb724e1f9823da01ddd851e8440a GIT binary patch literal 254716 zcmeFZcRXBO*EdciqeKW1L1gsaGNTTo_s;0i!YI+Amx-Q4i{5)5y(MY{At8(|x=08H z(HSBL@;k|O-_P|t*ZutY`|Ew*^Wih*oU`}YXYIAu`tG&9OO%e5@(mI?5%V_ z2s}I@B0RinkBP1TX96Xzqks>5PlU1@{#-wAFz|)gO~u3$509Gd_bE7HzndmPC%Tm{~(tZPbe&Dmxm8_e_?u=Q8m6o*i(G|O{AynZFvPhce zMPs0A&z?mI&}-0>s=-g>-d!cUu@e$b)B>W=&Jjyg_E2`|P_EuxQmO61@gbOidnc)AbGHJUp>7&U?d{Z{rX4}MCTAsOx|^&eM8@m@9#T3 zD7}IPvqfF4@9Jwlwdxf9B{LEO(Q(dPCUmntKKAkbbv|&PwEKVjfav`F+Z5c1)I@;> z62ESagg|tj5cg1yk^hg|fL7r&b(Wp{$6Y4d;$x1;0&%yo`4(5Mt-^fQ|JfsH_w3Fw zli$x{7jL&ht4=*>?j2!JWvxuv#H;iWE1K~C{`0XC^Ht8ic1uP*HguV!J@BWYQP)47 z`^bKKXXkCu#hEjTz(ojDnM@*9{2$${iFq!_Y{i6T@!UTWbo`&su%g+E6%;DfX^RQ1 z7`g1&K9+|=3l0!C77BE=aN#s3H^thMs$7;Bk*VQ#e$6KpPt^q zA`|}my8wT?uPuMynsHrx%hxL-(GhA$Lr@Zu^WdLvB-Zw-rDguIAN0V6G)4Bg5a=I| ziV>vS5ZXT+m2PRC=-Q7Bec&6aRkpHy<)5Rbja|?pa5?|e__v4p&=UCj`*%xQ z{bLm2?(MVl^Hs6zXvjX#l>2k&yO1x}{`o=;UmE8blBKvRJJ zUu1cYj$n8)CbY$M5ur5SSp@l~G;)?Wd%G?#gBC)aNYB;kAp2+Ew~Xp697z8mH5z+y zAyAfs5NP{9yZAo4u<&VNVb@C>nENT36P~8bh@JnqEk@%A0{ZmzM&o;$6F>_8%MD6B zG=3LnO}$;6^8YavG*jht1k%6G8+yCy=_kc*UCA@XjSO*G)y|ftxEuU)MO(Ut_FuPT5@C5>wYIv~ zr!uU*`l}OnYNQO#82R;e;{?rDZq%tqVBp0}bojvNm?q!91SDpyvo71iY)jF2?$3B&Uxmf^U^cmnQVe5V#0}E+22X3a(M17^UU8Zv93niO8qDfMP|?=LbFbIkBeA zV0NlXGJyPj2{yrKo7k6~o!ytxM@PV+G#?YHQPvv)xQ{}rKNtlQm7q0|>AchJDCL|w ze5&EtEyeh!PxAGX-=k%9NQHD=qZllo*l1iz`O_reKG6eiu)_)NU2oT~9}}LduYRA7 z!Rt%^{Rmay5t9L!ZXGrG-yRHmRo2|~@#FKOiBA#3uMDw}+Zk32FMc<`xX&#v#>1y+ z%8MWce?cUc4^cD(=zo8aNd)>c5CRE-es^Sp9;5jK%D5hp3jN(8>@TdPB8x1bhy_&p z_t*ifbalZTopNXlI_vC)2cQ84t0@dLJd zc5!jfYv~G&kHsfI8o+eU7Qw;VDSa0o|4Y2s>Zfl$&CdFr9c_&7+1uLM`uZAu2?&>w z^yq~-<#=f=;&8ZR4sAmtBT-S&mX;O@k7^x91b>ECXGZ4c8Z?k-Brp$6-;Sy8- z0uJ`1@NSNTFU-xM>fmdK8TlrEOMQfUYfE}$c-Xwn$Hl|LV-sVudRO!@FF$`#hO|aP zlS2PJQYgn%N<>gQXKzP`bMs=k6aZOgrE!h@AK@ z!&-m~gXmSY1OO2Z(rj~P%yFh#HcKR~>(>%_~+dK7>YjtP3 zz|71{Fl*RnCk$5UPLig#ZWyGHz9i!b*ndgP`k@(}a4uS#aGsEy zwB#rv###Tag`O%RO>a~#P5*jLip-8o{SQZ3{2#&tOj|a0oCiJX6U~q8pg|6ZuU`AG zFcZ^cB$NBGvCq43)@-?vkFRoEVVsxsQoq=fSqJ4v-fcEUVnV(KjRaoqerY4J)ZYVe z83UNM=U{5DHt6E}OmcGaqt%6GLDmt;^ZsOx>Zm;SgSj~q8&O|BaL?!ce5t?z%B1DT z(mkeu3#p^S!w2c4KP)pj?kEqi6UG1F#?O01Pe+JZk`6j2&>lKHMfo%~Hcpn~gKnVK zXkbni%OgWWIvuUPdti=lBe@d1owK&q)=xY2!%qjYM#o}9FL7e^`}a{X(+^Bdw*=y& z!O@p+4R8Y#R#BlAgyGmj!w_%zJ~jF8if|<>q0CaN=lpBH@4~AM zeR>ivEl@dfPQ1y?@#G&Im)nHBegdW&mUi`8dr3cIi3OT7eZ1qfE+;U5|GkihkwL_HMO;s!Io`4 zHQezg7O8#d0+xn=2L;Tk8?+bC_v$VguogP>LW5gxF+I(Pquvh}@oIErF|hWH*oI!U zS8eW?56kEArN(t%q%6V~Jz7=`y~hHtb=fCrMihL;4K$x$n&i*BZ=8N09AAH|n5CLI zd}y&kFhbY&-aOAdL2i$P{<^Q~+f}PB{4}^*MgY5!vpVQRL^*}-VIGff&~ zuI}#0wW+vwQ8;L185o9Eayb-3cEXPa*W^*UV6lX)nL52&Uj;@BE_v3udgWip59(Eq z4#3-uGYR0nyY$M#?0R~7DR`?sXTl_vk|~0HejD@ZIVq7>SqV9q_ZUt^jIOLllUlxZ z!-qT^wtwA=_kQEY08+>b3xPHnf!EvbG?`!_5--47^$xx7NQ6-uPal2Yl&s|p!YG0p z{8FEfTw$uGJ2hr0kJud$`iX|_qxQ>b$vbyzV`0jjeNqZ+7TFJ9+7-XAH50|vih>Qc{+F% z{(<>6Gxu5bPUyc*Wulp#C$EkgPd~|5C z5a_kA+g9t@7lWskjK6U5tJM8`;lcKHcGgvPjRM@#pB?oM?1($<-`3C)eExVMI=o!} zJYV!jHRokxx3#U0GKng^agt;A<)zd>_FaWE4u4hy=kUdA_AlTs?;LCOJ`q^l+1dI0 z#7{pVVmvYli4HJ+zu@YvY6q?Ngye9AZftC1k=sC}{lUYX3W{diFjHGLD1yZavG9 z5~?;gD{XbmuM)AdbiZFU%P!qq*$(Ya&k;7!L+B*60|AK%n~5a$sT5<7BV3*Lf;tUQ zLnhJiQp8Q3awBk=swNhqI)#P&VvbL=33!~+#~KG`M^;_b1|AV9@!SEVOMRA zhp&~

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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "import requests\n", + "from io import BytesIO\n", + "import matplotlib.pyplot as plt\n", + "\n", + "headers = {\n", + " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3\"\n", + "}\n", + "img_response = requests.get(image_urls[0], headers=headers)\n", + "\n", + "print(image_urls[0])\n", + "\n", + "img = Image.open(BytesIO(img_response.content))\n", + "plt.imshow(img)" + ] + }, + { + "cell_type": "markdown", + "id": "ec9e2750", + "metadata": {}, + "source": [ + "### Second Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c694eb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://cdn.statcdn.com/Infographic/images/normal/30322.jpeg\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img_response = requests.get(image_urls[1], headers=headers)\n", + "\n", + "print(image_urls[1])\n", + "\n", + "img = Image.open(BytesIO(img_response.content))\n", + "plt.imshow(img)" + ] + }, + { + "cell_type": "markdown", + "id": "fbd9c116", + "metadata": {}, + "source": [ + "### Complete a prompt with a bunch of images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c96ab53e", + "metadata": {}, + "outputs": [], + "source": [ + "complete_response = mistralai_mm_llm.complete(\n", + " prompt=\"Describe the images as an alternative text in a few words\",\n", + " image_documents=image_documents,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3eba4477", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The image consists of two distinct parts. The first part is a photograph of the Eiffel Tower in Paris, France, covered in snow. The Eiffel Tower stands prominently in the center, surrounded by snow-covered trees and a pathway. The scene is serene and picturesque, capturing the beauty of the iconic landmark in a winter setting.\n", + "\n", + "The second part is an infographic titled \"France's Social Divide.\" It compares socio-economic indicators between disadvantaged areas and the whole of France. The indicators include the percentage of people part of the working class, unemployment rate, percentage of 16-25-year-olds not in school and unemployed, median monthly income, poverty rate, and households living in overcrowded housing. The data shows significant disparities between disadvantaged areas and the rest of France, with disadvantaged areas having higher unemployment rates, lower median monthly income, higher poverty rates, and more households living in overcrowded housing.\n", + "\n", + "Overall, the image juxtaposes the beauty of the Eiffel Tower in winter with a detailed analysis of socio-economic inequalities in France." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(f\"{complete_response}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "26ff28b6", + "metadata": {}, + "source": [ + "### Steam Complete a prompt with a bunch of images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eab28aa6", + "metadata": {}, + "outputs": [], + "source": [ + "stream_complete_response = mistralai_mm_llm.stream_complete(\n", + " prompt=\"give me more context for this images in a few words\",\n", + " image_documents=image_documents,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae4fd47e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The image consists of two main parts: a photograph and an infographic. \n", + "\n", + "1. **Photograph**:\n", + " - The photograph depicts the Eiffel Tower in Paris, France, covered in snow. The scene is serene with snow-covered trees and a pathway leading towards the Eiffel Tower. A traditional street lamp is visible in the foreground, adding to the picturesque winter setting.\n", + "\n", + "2. **Infographic**:\n", + " - The infographic is titled \"France's Social Divide\" and compares socio-economic indicators between disadvantaged areas and the whole of France.\n", + " - **Indicators and Data**:\n", + " - **% who are part of working-class**: 33.5% in disadvantaged areas vs. 14.5% in the whole of France.\n", + " - **Unemployment rate**: 18.1% in disadvantaged areas vs. 7.3% in the whole of France.\n", + " - **% of 16-25 y/o not in school & unemployed**: 25.2% in disadvantaged areas vs. 12.9% in the whole of France.\n", + " - **Median monthly income**: €1,168 in disadvantaged areas vs. €1,822 in the whole of France.\n", + " - **Poverty rate**: 43.3% in disadvantaged areas vs. 15.5% in the whole of France.\n", + " - **Households living in overcrow" + ] + } + ], + "source": [ + "for r in stream_complete_response:\n", + " print(r.delta, end=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "3c86c542", + "metadata": {}, + "source": [ + "### Async Complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38e75a68", + "metadata": {}, + "outputs": [], + "source": [ + "response_acomplete = await mistralai_mm_llm.acomplete(\n", + " prompt=\"Describe the images as an alternative text in a few words\",\n", + " image_documents=image_documents,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f43cffb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The image consists of two distinct parts. The first part is a photograph of the Eiffel Tower in Paris, France, covered in snow. The Eiffel Tower stands prominently in the center, surrounded by snow-covered trees and a pathway. The scene is serene and picturesque, capturing the beauty of a winter day in Paris.\n", + "\n", + "The second part is an infographic titled \"France's Social Divide.\" It compares socio-economic indicators between disadvantaged areas and the whole of France. The indicators include the percentage of people part of the working class, unemployment rate, percentage of 16-25-year-olds not in school and unemployed, median monthly income, poverty rate, and households living in overcrowded housing. The data shows significant disparities between disadvantaged areas and the rest of France. For instance, disadvantaged areas have a higher percentage of people part of the working class (33.5% vs. 14.5%), a higher unemployment rate (18.1% vs. 7.3%), and a higher percentage of young people not in school and unemployed (25.2% vs. 12.9%). The median monthly income is lower in disadvantaged areas (€1,168 vs. €1,822), and the poverty rate is higher (43.3% vs. 15.5%). Additionally, 22.0% of households in disadvantaged areas live in overcrowded housing compared to 8" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(f\"{response_acomplete}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "9c8cfeec", + "metadata": {}, + "source": [ + "### Async Steam Complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d4c2850", + "metadata": {}, + "outputs": [], + "source": [ + "response_astream_complete = await mistralai_mm_llm.astream_complete(\n", + " prompt=\"Describe the images as an alternative text in a few words\",\n", + " image_documents=image_documents,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90b35a1d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The image consists of two distinct parts. The first part is a photograph of the Eiffel Tower in Paris, France, covered in snow. The tower stands tall in the background, surrounded by snow-covered trees and a pathway leading towards it. The scene is serene and picturesque, capturing the beauty of winter in Paris.\n", + "\n", + "The second part is an infographic titled \"France's Social Divide.\" This section compares socio-economic indicators between disadvantaged areas and the whole of France. The indicators include the percentage of people who are part of the working class, unemployment rates, the percentage of 16-25-year-olds not in school and unemployed, median monthly income, poverty rates, and the percentage of households living in overcrowded housing. The data shows significant disparities between disadvantaged areas and the rest of France. For example, disadvantaged areas have a higher percentage of people in the working class, higher unemployment rates, and a higher percentage of young people not in school or unemployed. Median monthly income is lower, poverty rates are higher, and a greater percentage of households live in overcrowded housing in these areas.\n", + "\n", + "Overall, the image juxtaposes the iconic beauty of Paris with a detailed analysis of social and economic inequalities within the country." + ] + } + ], + "source": [ + "async for delta in response_astream_complete:\n", + " print(delta.delta, end=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "0096fb75", + "metadata": {}, + "source": [ + "## Complete with Two images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a73aac2", + "metadata": {}, + "outputs": [], + "source": [ + "image_urls = [\n", + " \"https://tripfixers.com/wp-content/uploads/2019/11/eiffel-tower-with-snow.jpeg\",\n", + " \"https://assets.visitorscoverage.com/production/wp-content/uploads/2024/04/AdobeStock_626542468-min-1024x683.jpeg\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "ded6ce0a", + "metadata": {}, + "source": [ + "### Lets Inspect the images.\n", + "\n", + "### First Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9fa70b7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://tripfixers.com/wp-content/uploads/2019/11/eiffel-tower-with-snow.jpeg\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img_response = requests.get(image_urls[0], headers=headers)\n", + "\n", + "print(image_urls[0])\n", + "\n", + "img = Image.open(BytesIO(img_response.content))\n", + "plt.imshow(img)" + ] + }, + { + "cell_type": "markdown", + "id": "c1a0377f", + "metadata": {}, + "source": [ + "### Second Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d296a2e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://assets.visitorscoverage.com/production/wp-content/uploads/2024/04/AdobeStock_626542468-min-1024x683.jpeg\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img_response = requests.get(image_urls[1], headers=headers)\n", + "\n", + "print(image_urls[1])\n", + "\n", + "img = Image.open(BytesIO(img_response.content))\n", + "plt.imshow(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d46dc29", + "metadata": {}, + "outputs": [], + "source": [ + "image_documents_compare = load_image_urls(image_urls)\n", + "\n", + "response_multi = mistralai_mm_llm.complete(\n", + " prompt=\"What are the differences between two images?\",\n", + " image_documents=image_documents_compare,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf9560f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The first image shows the Eiffel Tower in Paris, France, covered in snow with a snowy landscape around it, while the second image shows a tennis court with a large crowd of people watching a tennis match, and the Eiffel Tower is visible in the background. The first image has a wintery atmosphere with snow-covered trees and a lamppost, while the second image has a sunny atmosphere with a clear blue sky and a large stadium filled with spectators." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(f\"{response_multi}\"))" + ] + }, + { + "cell_type": "markdown", + "id": "c8738293", + "metadata": {}, + "source": [ + "## Load Images from local files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "add2c5a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-09-24 23:56:24-- https://www.boredpanda.com/blog/wp-content/uploads/2022/11/interesting-receipts-102-6364c8d181c6a__700.jpg\n", + "Resolving www.boredpanda.com (www.boredpanda.com)... 52.222.144.111, 52.222.144.94, 52.222.144.47, ...\n", + "Connecting to www.boredpanda.com (www.boredpanda.com)|52.222.144.111|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 112631 (110K) [image/jpeg]\n", + "Saving to: ‘receipt.jpg’\n", + "\n", + "receipt.jpg 100%[===================>] 109.99K 411KB/s in 0.3s \n", + "\n", + "2024-09-24 23:56:25 (411 KB/s) - ‘receipt.jpg’ saved [112631/112631]\n", + "\n" + ] + } + ], + "source": [ + "!wget 'https://www.boredpanda.com/blog/wp-content/uploads/2022/11/interesting-receipts-102-6364c8d181c6a__700.jpg' -O 'receipt.jpg'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e305cac2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "\n", + "img = Image.open(\"./receipt.jpg\")\n", + "plt.imshow(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e91ec1b", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "# put your local directore here\n", + "image_documents = SimpleDirectoryReader(\n", + " input_files=[\"./receipt.jpg\"]\n", + ").load_data()\n", + "\n", + "response = mistralai_mm_llm.complete(\n", + " prompt=\"Transcribe the text in the image\",\n", + " image_documents=image_documents,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "073b08b9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Dine-in\n", + "\n", + "Cashier: Raul\n", + "02-Apr-2022 5:01:56P\n", + "\n", + "1 EMPANADA - BEEF $3.00\n", + "1 EMPANADA - CHEESE $3.00\n", + "1 EMPANADA - CHICKEN $3.00\n", + "1 Tallarin Huancaína Lomo Saltado $19.99\n", + "1 1/2 Pisco Sour $15.00\n", + "\n", + "Subtotal $43.99\n", + "Local Taxes 5.5% $2.42\n", + "\n", + "Total $46.41\n", + "\n", + "IMMIGRANTS MAKE AMERICA GREAT THEY\n", + "ALSO COOKED YOUR FOOD AND SERVED\n", + "YOU TODAY\n", + "GOD BLESS YOU\n", + "\n", + "Online: https://clover.com/r\n", + "/D0BQZ3R656MDC\n", + "\n", + "Order D0BQZ3R656MDC\n", + "\n", + "Clover Privacy Policy\n", + "https://clover.com/privacy" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(Markdown(f\"{response}\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llamacloud", + "language": "python", + "name": "llamacloud" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/.gitignore b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/BUILD b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/Makefile b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/README.md b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/README.md new file mode 100644 index 0000000000000..854f469fd2c0e --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/README.md @@ -0,0 +1 @@ +# LlamaIndex Multi-Modal-Llms Integration: Mistral diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/BUILD b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/__init__.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/__init__.py new file mode 100644 index 0000000000000..223018f7bfde2 --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/__init__.py @@ -0,0 +1,3 @@ +from llama_index.multi_modal_llms.mistralai.base import MistralAIMultiModal + +__all__ = ["MistralAIMultiModal"] diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/base.py new file mode 100644 index 0000000000000..34b06d595fa5a --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/base.py @@ -0,0 +1,320 @@ +from typing import Any, Callable, Dict, List, Optional, Sequence + +import httpx +from llama_index.core.base.llms.types import ( + CompletionResponse, + CompletionResponseAsyncGen, + CompletionResponseGen, + MessageRole, +) +from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.callbacks import CallbackManager +from llama_index.core.constants import ( + DEFAULT_CONTEXT_WINDOW, + DEFAULT_NUM_OUTPUTS, + DEFAULT_TEMPERATURE, +) +from llama_index.core.base.llms.generic_utils import ( + messages_to_prompt as generic_messages_to_prompt, +) +from llama_index.core.multi_modal_llms import ( + MultiModalLLM, + MultiModalLLMMetadata, +) +from llama_index.core.schema import ImageNode +from llama_index.multi_modal_llms.mistralai.utils import ( + MISTRALAI_MULTI_MODAL_MODELS, + generate_mistral_multi_modal_chat_message, + resolve_mistral_credentials, +) + +from mistralai import Mistral + + +class MistralAIMultiModal(MultiModalLLM): + model: str = Field(description="The Multi-Modal model to use from Mistral.") + temperature: float = Field(description="The temperature to use for sampling.") + max_tokens: Optional[int] = Field( + description=" The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt", + gt=0, + ) + context_window: Optional[int] = Field( + description="The maximum number of context tokens for the model.", + gt=0, + ) + max_retries: int = Field( + default=3, + description="Maximum number of retries.", + ge=0, + ) + timeout: float = Field( + default=60.0, + description="The timeout, in seconds, for API requests.", + ge=0, + ) + api_key: str = Field(default=None, description="The Mistral API key.", exclude=True) + api_base: str = Field(default=None, description="The base URL for Mistral API.") + api_version: str = Field(description="The API version for Mistral API.") + additional_kwargs: Dict[str, Any] = Field( + default_factory=dict, description="Additional kwargs for the Mistral API." + ) + default_headers: Optional[Dict[str, str]] = Field( + default=None, description="The default headers for API requests." + ) + + _messages_to_prompt: Callable = PrivateAttr() + _completion_to_prompt: Callable = PrivateAttr() + _client: Mistral = PrivateAttr() + _http_client: Optional[httpx.Client] = PrivateAttr() + + def __init__( + self, + model: str = "pixtral-12b-2409", + temperature: float = DEFAULT_TEMPERATURE, + max_tokens: Optional[int] = 300, + additional_kwargs: Optional[Dict[str, Any]] = None, + context_window: Optional[int] = DEFAULT_CONTEXT_WINDOW, + max_retries: int = 3, + timeout: float = 60.0, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + api_version: Optional[str] = None, + messages_to_prompt: Optional[Callable] = None, + completion_to_prompt: Optional[Callable] = None, + callback_manager: Optional[CallbackManager] = None, + default_headers: Optional[Dict[str, str]] = None, + http_client: Optional[httpx.Client] = None, + **kwargs: Any, + ) -> None: + api_key, api_base, api_version = resolve_mistral_credentials( + api_key=api_key, + api_base=api_base, + api_version=api_version, + ) + + super().__init__( + model=model, + temperature=temperature, + max_tokens=max_tokens, + additional_kwargs=additional_kwargs or {}, + context_window=context_window, + max_retries=max_retries, + timeout=timeout, + api_key=api_key, + api_base=api_base, + api_version=api_version, + callback_manager=callback_manager, + default_headers=default_headers, + **kwargs, + ) + self._messages_to_prompt = messages_to_prompt or generic_messages_to_prompt + self._completion_to_prompt = completion_to_prompt or (lambda x: x) + self._http_client = http_client + self._client = self._get_clients(**kwargs) + + def _get_clients(self, **kwargs: Any) -> Mistral: + return Mistral(**self._get_credential_kwargs()) + + @classmethod + def class_name(cls) -> str: + return "mistral_multi_modal_llm" + + @property + def metadata(self) -> MultiModalLLMMetadata: + """Multi Modal LLM metadata.""" + return MultiModalLLMMetadata( + num_output=self.max_tokens or DEFAULT_NUM_OUTPUTS, + model_name=self.model, + ) + + def _get_credential_kwargs(self, **kwargs: Any) -> Dict[str, Any]: + return { + "api_key": self.api_key, + **kwargs, + } + + def _get_multi_modal_chat_messages( + self, + prompt: str, + role: str, + image_documents: Sequence[ImageNode], + **kwargs: Any, + ) -> List[Dict]: + return generate_mistral_multi_modal_chat_message( + prompt=prompt, + role=role, + image_documents=image_documents, + ) + + # Model Params for Mistral Multi Modal model. + def _get_model_kwargs(self, **kwargs: Any) -> Dict[str, Any]: + if self.model not in MISTRALAI_MULTI_MODAL_MODELS: + raise ValueError( + f"Invalid model {self.model}. " + f"Available models are: {list(MISTRALAI_MULTI_MODAL_MODELS.keys())}" + ) + base_kwargs = {"model": self.model, "temperature": self.temperature, **kwargs} + if self.max_tokens is not None: + base_kwargs["max_tokens"] = self.max_tokens + return {**base_kwargs, **self.additional_kwargs} + + def _get_response_token_counts(self, raw_response: Any) -> dict: + """Get the token usage reported by the response.""" + if not isinstance(raw_response, dict): + return {} + + usage = raw_response.get("usage", {}) + # NOTE: other model providers that use the mistral client may not report usage + if usage is None: + return {} + + return { + "prompt_tokens": usage.get("prompt_tokens", 0), + "completion_tokens": usage.get("completion_tokens", 0), + "total_tokens": usage.get("total_tokens", 0), + } + + def _complete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponse: + all_kwargs = self._get_model_kwargs(**kwargs) + message_dict = self._get_multi_modal_chat_messages( + prompt=prompt, role=MessageRole.USER.value, image_documents=image_documents + ) + + response = self._client.chat.complete( + messages=message_dict, + stream=False, + **all_kwargs, + ) + + return CompletionResponse( + text=response.choices[0].message.content, + raw=response, + additional_kwargs=self._get_response_token_counts(response), + ) + + def _stream_complete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponseGen: + all_kwargs = self._get_model_kwargs(**kwargs) + message_dict = self._get_multi_modal_chat_messages( + prompt=prompt, role=MessageRole.USER.value, image_documents=image_documents + ) + + response = self._client.chat.stream(messages=message_dict, **all_kwargs) + + def gen() -> CompletionResponseGen: + content = "" + for chunk in response: + delta = chunk.data.choices[0].delta + role = delta.role or MessageRole.ASSISTANT.value + + content_delta = delta.content or "" + if content_delta is None: + pass + # continue + else: + content += content_delta + + yield CompletionResponse( + delta=content_delta, + text=content, + raw=response, + additional_kwargs=self._get_response_token_counts(response), + ) + + return gen() + + def complete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponse: + return self._complete(prompt, image_documents, **kwargs) + + def stream_complete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponseGen: + return self._stream_complete(prompt, image_documents, **kwargs) + + def chat( + self, + **kwargs: Any, + ) -> Any: + raise NotImplementedError("This function is not yet implemented.") + + def stream_chat( + self, + **kwargs: Any, + ) -> Any: + raise NotImplementedError("This function is not yet implemented.") + + # ===== Async Endpoints ===== + + async def _acomplete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponse: + all_kwargs = self._get_model_kwargs(**kwargs) + message_dict = self._get_multi_modal_chat_messages( + prompt=prompt, role=MessageRole.USER.value, image_documents=image_documents + ) + + response = await self._client.chat.complete_async( + messages=message_dict, + stream=False, + **all_kwargs, + ) + + return CompletionResponse( + text=response.choices[0].message.content, + raw=response, + additional_kwargs=self._get_response_token_counts(response), + ) + + async def acomplete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponse: + return await self._acomplete(prompt, image_documents, **kwargs) + + async def _astream_complete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponseAsyncGen: + all_kwargs = self._get_model_kwargs(**kwargs) + message_dict = self._get_multi_modal_chat_messages( + prompt=prompt, role=MessageRole.USER.value, image_documents=image_documents + ) + + response = await self._client.chat.stream_async( + messages=message_dict, **all_kwargs + ) + + async def gen() -> CompletionResponseAsyncGen: + content = "" + async for chunk in response: + delta = chunk.data.choices[0].delta + role = delta.role or MessageRole.ASSISTANT.value + + content_delta = delta.content + if content_delta is None: + pass + # continue + else: + content += content_delta + yield CompletionResponse( + delta=content_delta, + text=content, + raw=response, + additional_kwargs=self._get_response_token_counts(response), + ) + + return gen() + + async def astream_complete( + self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any + ) -> CompletionResponseAsyncGen: + return await self._astream_complete(prompt, image_documents, **kwargs) + + async def achat(self, **kwargs: Any) -> Any: + raise NotImplementedError("This function is not yet implemented.") + + async def astream_chat(self, **kwargs: Any) -> Any: + raise NotImplementedError("This function is not yet implemented.") diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/utils.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/utils.py new file mode 100644 index 0000000000000..4855c2b978e7e --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/utils.py @@ -0,0 +1,139 @@ +import base64 +import logging +from typing import Any, Dict, List, Optional, Sequence, Tuple +import filetype + +from llama_index.core.base.llms.generic_utils import get_from_param_or_env +from llama_index.core.multi_modal_llms.generic_utils import encode_image +from llama_index.core.schema import ImageDocument + +DEFAULT_MISTRALAI_API_TYPE = "mistral_ai" +DEFAULT_MISTRALAI_API_BASE = "https://api.mistral.ai/" +DEFAULT_MISTRALAI_API_VERSION = "" + + +MISTRALAI_MULTI_MODAL_MODELS = { + "pixtral-12b-2409": 128000, +} + + +MISSING_API_KEY_ERROR_MESSAGE = """No API key found for Mistral. +Please set either the MISTRAL_API_KEY environment variable \ +API keys can be found or created at \ +https://console.mistral.ai/api-keys/ +""" + +logger = logging.getLogger(__name__) + + +def infer_image_mimetype_from_base64(base64_string) -> str: + # Decode the base64 string + decoded_data = base64.b64decode(base64_string) + + # Use filetype to guess the MIME type + kind = filetype.guess(decoded_data) + + # Return the MIME type if detected, otherwise return None + return kind.mime if kind is not None else None + + +def infer_image_mimetype_from_file_path(image_file_path: str) -> str: + # Get the file extension + file_extension = image_file_path.split(".")[-1].lower() + + # Map file extensions to mimetypes + # Pixtral support the base64 source type for images, and the image/jpeg, image/png, image/gif, and image/webp media types. + # https://docs.mistral.ai/capabilities/vision/ + if file_extension == "jpg" or file_extension == "jpeg": + return "image/jpeg" + elif file_extension == "png": + return "image/png" + elif file_extension == "gif": + return "image/gif" + elif file_extension == "webp": + return "image/webp" + # Add more mappings for other image types if needed + + # If the file extension is not recognized + return "image/jpeg" + + +def generate_mistral_multi_modal_chat_message( + prompt: str, + role: str, + image_documents: Optional[Sequence[ImageDocument]] = None, +) -> List[Dict[str, Any]]: + # if image_documents is empty, return text only chat message + if image_documents is None: + return [{"role": role, "content": prompt}] + + # if image_documents is not empty, return text with images chat message + completion_content = [] + for image_document in image_documents: + image_content: Dict[str, Any] = {} + if image_document.image_path and image_document.image_path != "": + mimetype = infer_image_mimetype_from_file_path(image_document.image_path) + base64_image = encode_image(image_document.image_path) + image_content = { + "type": "image_url", + "image_url": f"data:{mimetype};base64,{base64_image}", + } + elif ( + "file_path" in image_document.metadata + and image_document.metadata["file_path"] != "" + ): + mimetype = infer_image_mimetype_from_file_path( + image_document.metadata["file_path"] + ) + base64_image = encode_image(image_document.metadata["file_path"]) + image_content = { + "type": "image_url", + "image_url": f"data:{mimetype};base64,{base64_image}", + } + elif image_document.image_url and image_document.image_url != "": + mimetype = infer_image_mimetype_from_file_path(image_document.image_url) + image_content = { + "type": "image_url", + "image_url": f"{image_document.image_url}", + } + elif image_document.image != "": + base64_image = image_document.image + mimetype = infer_image_mimetype_from_base64(base64_image) + image_content = { + "type": "image_url", + "image_url": f"data:{mimetype};base64,{base64_image}", + } + completion_content.append(image_content) + + completion_content.append({"type": "text", "text": prompt}) + + return [{"role": role, "content": completion_content}] + + +def resolve_mistral_credentials( + api_key: Optional[str] = None, + api_base: Optional[str] = None, + api_version: Optional[str] = None, +) -> Tuple[Optional[str], str, str]: + """ + "Resolve Mistral credentials. + + The order of precedence is: + 1. param + 2. env + 3. mistral module + 4. default + """ + # resolve from param or env + api_key = get_from_param_or_env("api_key", api_key, "MISTRAL_API_KEY", "") + api_base = get_from_param_or_env("api_base", api_base, "MISTRAL_API_BASE", "") + api_version = get_from_param_or_env( + "api_version", api_version, "MISTRAL_API_VERSION", "" + ) + + # resolve from Mistral module or default + final_api_key = api_key or "" + final_api_base = api_base or DEFAULT_MISTRALAI_API_BASE + final_api_version = api_version or DEFAULT_MISTRALAI_API_VERSION + + return final_api_key, str(final_api_base), final_api_version diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml new file mode 100644 index 0000000000000..93d73047ea840 --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml @@ -0,0 +1,64 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +contains_example = false +import_path = "llama_index.multi_modal_llms.mistral" + +[tool.llamahub.class_authors] +MistralMultiModal = "llama-index" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.9" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index multi-modal-llms mistral integration" +exclude = ["**/BUILD"] +license = "MIT" +name = "llama-index-multi-modal-llms-mistral" +readme = "README.md" +version = "0.1" + +[tool.poetry.dependencies] +python = ">=3.9,<4.0" +mistralai = ">=1.1.0" +llama-index-core = "^0.11.0" +filetype = "^1.2.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/BUILD b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/__init__.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/test_multi-modal-llms_mistral.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/test_multi-modal-llms_mistral.py new file mode 100644 index 0000000000000..156d862b08f68 --- /dev/null +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/test_multi-modal-llms_mistral.py @@ -0,0 +1,12 @@ +from llama_index.core.multi_modal_llms.base import MultiModalLLM +from llama_index.multi_modal_llms.mistralai import MistralAIMultiModal + + +def test_embedding_class(): + names_of_base_classes = [b.__name__ for b in MistralAIMultiModal.__mro__] + assert MultiModalLLM.__name__ in names_of_base_classes + + +def test_init(): + m = MistralAIMultiModal(max_tokens=400) + assert m.max_tokens == 400 From 0bbca43e13fc1379b04bf28d54cc08eab55347cb Mon Sep 17 00:00:00 2001 From: Aayush Kataria Date: Tue, 24 Sep 2024 23:44:09 -0700 Subject: [PATCH 22/53] Azure Cosmos DB Filtered Vector Search (#16175) * Filtered vector search changes * Filtered vector search changes --- .../vector_stores/azurecosmosmongo/base.py | 195 +++++++++++++----- .../pyproject.toml | 4 +- .../vector_stores/azurecosmosnosql/base.py | 41 +++- .../pyproject.toml | 4 +- 4 files changed, 186 insertions(+), 58 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py index 668111ad31b85..e5344e3b1a7d0 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/llama_index/vector_stores/azurecosmosmongo/base.py @@ -7,6 +7,7 @@ import logging import os from typing import Any, Dict, List, Optional, cast +from datetime import date import pymongo from llama_index.core.bridge.pydantic import PrivateAttr @@ -109,7 +110,8 @@ def __init__( "if not directly passing in client." ) self._mongodb_client = pymongo.MongoClient( - os.environ["AZURE_COSMOSDB_MONGODB_URI"] + os.environ["AZURE_COSMOSDB_MONGODB_URI"], + appname="LlamaIndex-CDBMongoVCore-VectorStore-Python", ) self._collection = self._mongodb_client[db_name][collection_name] @@ -126,29 +128,85 @@ def __init__( def _create_vector_search_index(self) -> None: db = self._mongodb_client[self._db_name] - db.command( - { - "createIndexes": self._collection_name, - "indexes": [ - { - "name": self._index_name, - "key": {self._embedding_key: "cosmosSearch"}, - "cosmosSearchOptions": { - "kind": self._cosmos_search_kwargs.get( - "kind", "vector-ivf" - ), - "numLists": self._cosmos_search_kwargs.get("numLists", 1), - "similarity": self._cosmos_search_kwargs.get( - "similarity", "COS" - ), - "dimensions": self._cosmos_search_kwargs.get( - "dimensions", 1536 - ), - }, - } - ], - } - ) + + create_index_commands = {} + kind = self._cosmos_search_kwargs.get("kind", "vector-hnsw") + + if kind == "vector-ivf": + create_index_commands = self._get_vector_index_ivf(kind) + elif kind == "vector-hnsw": + create_index_commands = self._get_vector_index_hnsw(kind) + db.command(create_index_commands) + + def _get_vector_index_ivf( + self, + kind: str, + ) -> Dict[str, Any]: + return { + "createIndexes": self._collection_name, + "indexes": [ + { + "name": self._index_name, + "key": {self._embedding_key: "cosmosSearch"}, + "cosmosSearchOptions": { + "kind": kind, + "numLists": self._cosmos_search_kwargs.get("numLists", 1), + "similarity": self._cosmos_search_kwargs.get( + "similarity", "COS" + ), + "dimensions": self._cosmos_search_kwargs.get( + "dimensions", 1536 + ), + }, + } + ], + } + + def _get_vector_index_hnsw( + self, + kind: str, + ) -> Dict[str, Any]: + return { + "createIndexes": self._collection_name, + "indexes": [ + { + "name": self._index_name, + "key": {self._embedding_key: "cosmosSearch"}, + "cosmosSearchOptions": { + "kind": kind, + "m": self._cosmos_search_kwargs.get("m", 2), + "efConstruction": self._cosmos_search_kwargs.get( + "efConstruction", 64 + ), + "similarity": self._cosmos_search_kwargs.get( + "similarity", "COS" + ), + "dimensions": self._cosmos_search_kwargs.get( + "dimensions", 1536 + ), + }, + } + ], + } + + def create_filter_index( + self, + property_to_filter: str, + index_name: str, + ) -> dict[str, Any]: + db = self._mongodb_client[self._db_name] + command = { + "createIndexes": self._collection.name, + "indexes": [ + { + "key": {property_to_filter: 1}, + "name": index_name, + } + ], + } + + create_index_responses: dict[str, Any] = db.command(command) + return create_index_responses def add( self, @@ -176,6 +234,7 @@ def add( self._embedding_key: node.get_embedding(), self._text_key: node.get_content(metadata_mode=MetadataMode.NONE) or "", self._metadata_key: metadata, + "timeStamp": date.today(), } data_to_insert.append(entry) ids.append(node.node_id) @@ -204,29 +263,17 @@ def client(self) -> Any: """Return MongoDB client.""" return self._mongodb_client - def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult: - params: Dict[str, Any] = { - "vector": query.query_embedding, - "path": self._embedding_key, - "k": query.similarity_top_k, - } - - if query.filters is not None: - raise ValueError( - "Metadata filters not implemented for azure cosmosdb mongodb yet." + def _query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: + pipeline: List[dict[str, Any]] = [] + kind = self._cosmos_search_kwargs.get("kind", "vector-hnsw") + if kind == "vector-ivf": + pipeline = self._get_pipeline_vector_ivf( + query, kwargs.get("pre_filter", {}) + ) + elif kind == "vector-hnsw": + pipeline = self._get_pipeline_vector_hnsw( + query, kwargs.get("ef_search", 40), kwargs.get("pre_filter", {}) ) - - query_field = {"$search": {"cosmosSearch": params, "returnStoredSource": True}} - - pipeline = [ - query_field, - { - "$project": { - "similarityScore": {"$meta": "searchScore"}, - "document": "$$ROOT", - } - }, - ] logger.debug("Running query pipeline: %s", pipeline) cursor = self._collection.aggregate(pipeline) # type: ignore @@ -266,6 +313,60 @@ def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult: logger.debug("Result of query: %s", result) return result + def _get_pipeline_vector_ivf( + self, query: VectorStoreQuery, pre_filter: Optional[Dict] + ) -> List[dict[str, Any]]: + params = { + "vector": query.query_embedding, + "path": self._embedding_key, + "k": query.similarity_top_k, + } + if pre_filter: + params["filter"] = pre_filter + + pipeline: List[dict[str, Any]] = [ + { + "$search": { + "cosmosSearch": params, + "returnStoredSource": True, + } + }, + { + "$project": { + "similarityScore": {"$meta": "searchScore"}, + "document": "$$ROOT", + } + }, + ] + return pipeline + + def _get_pipeline_vector_hnsw( + self, query: VectorStoreQuery, ef_search: int, pre_filter: Optional[Dict] + ) -> List[dict[str, Any]]: + params = { + "vector": query.query_embedding, + "path": self._embedding_key, + "k": query.similarity_top_k, + "efSearch": ef_search, + } + if pre_filter: + params["filter"] = pre_filter + + pipeline: List[dict[str, Any]] = [ + { + "$search": { + "cosmosSearch": params, + } + }, + { + "$project": { + "similarityScore": {"$meta": "searchScore"}, + "document": "$$ROOT", + } + }, + ] + return pipeline + def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: """Query index for top k most similar nodes. @@ -275,4 +376,4 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul Returns: A VectorStoreQueryResult containing the results of the query. """ - return self._query(query) + return self._query(query, **kwargs) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml index aaa40949fb4bc..81fc7893114d1 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosmongo/pyproject.toml @@ -21,13 +21,13 @@ ignore_missing_imports = true python_version = "3.8" [tool.poetry] -authors = ["Your Name "] +authors = ["Aayush Kataria aayushkataria3011@gmail.com"] description = "llama-index vector_stores azurecosmosmongo integration" exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-azurecosmosmongo" readme = "README.md" -version = "0.2.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py index 7bb209946c723..ec4ffeccd7930 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/llama_index/vector_stores/azurecosmosnosql/base.py @@ -5,6 +5,7 @@ """ import logging from typing import Any, Optional, Dict, cast, List +from datetime import date from azure.cosmos import CosmosClient from llama_index.core.bridge.pydantic import PrivateAttr @@ -182,6 +183,7 @@ def add( self._embedding_key: node.get_embedding(), self._text_key: node.get_content(metadata_mode=MetadataMode.NONE) or "", self._metadata_key: metadata, + "timeStamp": date.today(), } data_to_insert.append(entry) ids.append(node.node_id) @@ -206,23 +208,48 @@ def client(self) -> Any: """Return CosmosDB client.""" return self._cosmos_client - def _query(self, query: VectorStoreQuery) -> VectorStoreQueryResult: + def _query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult: params: Dict[str, Any] = { "vector": query.query_embedding, "path": self._embedding_key, "k": query.similarity_top_k, } + pre_filter = kwargs.get("pre_filter", {}) + + query = "SELECT " + + # If limit_offset_clause is not specified, add TOP clause + if pre_filter is None or pre_filter.get("limit_offset_clause") is None: + query += "TOP @limit " + + query += ( + "c.id, c.{}, c.text, c.metadata, " + "VectorDistance(c.@embeddingKey, @embeddings) AS SimilarityScore FROM c" + ) + + # Add where_clause if specified + if pre_filter is not None and pre_filter.get("where_clause") is not None: + query += " {}".format(pre_filter["where_clause"]) + + query += " ORDER BY VectorDistance(c.@embeddingKey, @embeddings)" + + # Add limit_offset_clause if specified + if pre_filter is not None and pre_filter.get("limit_offset_clause") is not None: + query += " {}".format(pre_filter["limit_offset_clause"]) + parameters = [ + {"name": "@limit", "value": params["k"]}, + {"name": "@embeddingKey", "value": self._embedding_key}, + {"name": "@embeddings", "value": params["vector"]}, + ] + top_k_nodes = [] top_k_ids = [] top_k_scores = [] for item in self._container.query_items( - query="SELECT TOP @k c.id, c.embedding, c.text, c.metadata, VectorDistance(c.embedding,@embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.embedding,@embedding)", - parameters=[ - {"name": "@k", "value": params["k"]}, - {"name": "@embedding", "value": params["vector"]}, - ], + query=query, + parameters=parameters, enable_cross_partition_query=True, ): node = metadata_dict_to_node(item[self._metadata_key]) @@ -248,4 +275,4 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul Returns: A VectorStoreQueryResult containing the results of the query. """ - return self._query(query) + return self._query(query, **kwargs) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml index 2d830de9de68b..c3b50143b6aff 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azurecosmosnosql/pyproject.toml @@ -21,13 +21,13 @@ ignore_missing_imports = true python_version = "3.8" [tool.poetry] -authors = ["Your Name "] +authors = ["Aayush Kataria aayushkataria3011@gmail.com"] description = "llama-index vector_stores azurecosmosnosql integration" exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-azurecosmosnosql" readme = "README.md" -version = "1.0.0" +version = "1.1.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 0d08269bbcbb4aaaa98195195eebae979aa5d0fc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jannik=20Maierh=C3=B6fer?= <48529566+jannikmaierhoefer@users.noreply.github.com> Date: Wed, 25 Sep 2024 15:55:12 +0200 Subject: [PATCH 23/53] Cookbook langfuse posthog (#16207) * [docs] add cookbook about tracing LlamaIndex applications with Langfuse and PostHog * linting * [docs] update image path --------- Co-authored-by: Logan Markewich --- .../examples/observability/LangfuseMistralPostHog.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb b/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb index 88043524f17a6..fcfccec407dee 100644 --- a/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb +++ b/docs/docs/examples/observability/LangfuseMistralPostHog.ipynb @@ -267,7 +267,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![Example trace in the Langfuse UI](img/integration-posthog-llamaindex-mistral.png)" + "![Example trace in the Langfuse UI](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/trace-posthog-llamaindex-miostral.png)" ] }, { @@ -366,14 +366,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "![Posthog Dashboard showing user feedback and number of traces](img/dashboard-posthog-1.png)" + "![Posthog Dashboard showing user feedback and number of traces](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/dashboard-posthog-1.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "![Posthog Dashboard showing latency and costs](img/dashboard-posthog-2.png)" + "![Posthog Dashboard showing latency and costs](https://langfuse.com/images/cookbook/example-posthog-llamaindex-mistral/dashboard-posthog-2.png)" ] }, { From faa96cefb93b07c22d1a512e575b4a884a565cd4 Mon Sep 17 00:00:00 2001 From: Jay Vala <24193355+jdvala@users.noreply.github.com> Date: Wed, 25 Sep 2024 15:57:39 +0200 Subject: [PATCH 24/53] DOCS: Remove extra dot from the import line in the docstring (#16186) --- .../llama-index-agent-openai/llama_index/agent/openai/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama-index-integrations/agent/llama-index-agent-openai/llama_index/agent/openai/base.py b/llama-index-integrations/agent/llama-index-agent-openai/llama_index/agent/openai/base.py index 2bc3a048eef5f..f617169cff2b6 100644 --- a/llama-index-integrations/agent/llama-index-agent-openai/llama_index/agent/openai/base.py +++ b/llama-index-integrations/agent/llama-index-agent-openai/llama_index/agent/openai/base.py @@ -40,7 +40,7 @@ class OpenAIAgent(AgentRunner): For the legacy implementation see: ```python - from llama_index..agent.legacy.openai.base import OpenAIAgent + from llama_index.agent.legacy.openai.base import OpenAIAgent ``` """ From f9f2d2eed35e9e32e62fe9e027ac90aa42b32969 Mon Sep 17 00:00:00 2001 From: Logan Date: Wed, 25 Sep 2024 11:42:01 -0600 Subject: [PATCH 25/53] Fix vertex init function (#16216) --- .../llama-index-llms-vertex/llama_index/llms/vertex/base.py | 4 +++- .../llms/llama-index-llms-vertex/pyproject.toml | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py index 55ee5200b26c9..be02ac3e52608 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py +++ b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py @@ -92,6 +92,7 @@ class Vertex(FunctionCallingLLM): _is_chat_model: bool = PrivateAttr() _client: Any = PrivateAttr() _chat_client: Any = PrivateAttr() + _safety_settings: Dict[str, Any] = PrivateAttr() def __init__( self, @@ -115,7 +116,7 @@ def __init__( ) -> None: init_vertexai(project=project, location=location, credentials=credentials) - self._safety_settings = safety_settings or {} + safety_settings = safety_settings or {} additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) @@ -135,6 +136,7 @@ def __init__( output_parser=output_parser, ) + self._safety_settings = safety_settings self._is_gemini = False self._is_chat_model = False if model in CHAT_MODELS: diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml index 5085a61397ac1..0468d2e1c1e4c 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-vertex" readme = "README.md" -version = "0.3.5" +version = "0.3.6" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 3bc1695be17b8b747ccc1597c5ebd8529446f63a Mon Sep 17 00:00:00 2001 From: Mike Norusis <809060+mnorusis@users.noreply.github.com> Date: Wed, 25 Sep 2024 14:26:23 -0400 Subject: [PATCH 26/53] fix NoneType object error when passing in provided client (#16174) --- .../llama_index/graph_stores/neptune/neptune.py | 2 +- .../llama-index-graph-stores-neptune/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py index 9666a87c968dd..4d1b86b1fcb98 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/neptune.py @@ -65,7 +65,7 @@ def create_neptune_database_client( try: client = None if provided_client is not None: - client = client + client = provided_client else: if credentials_profile_name is not None: session = boto3.Session(profile_name=credentials_profile_name) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml index 6801daf6e4a37..627669a34c60b 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml @@ -30,7 +30,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-neptune" readme = "README.md" -version = "0.2.1" +version = "0.2.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From d9856950b062fcdd65cacd5bf476736ceee2b859 Mon Sep 17 00:00:00 2001 From: polarbear567 <269739606@qq.com> Date: Thu, 26 Sep 2024 02:27:03 +0800 Subject: [PATCH 27/53] fix incorrect parameter (#16178) --- .../llama_index/packs/self_rag/base.py | 2 +- llama-index-packs/llama-index-packs-self-rag/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/llama-index-packs/llama-index-packs-self-rag/llama_index/packs/self_rag/base.py b/llama-index-packs/llama-index-packs-self-rag/llama_index/packs/self_rag/base.py index 43b7034afd1c1..1e0bba78a97d9 100644 --- a/llama-index-packs/llama-index-packs-self-rag/llama_index/packs/self_rag/base.py +++ b/llama-index-packs/llama-index-packs-self-rag/llama_index/packs/self_rag/base.py @@ -226,7 +226,7 @@ def _run_critic(self, paragraphs: List[str]) -> CriticOutput: # Add the paragraph as source node with its relevance score source_nodes.append( NodeWithScore( - node=TextNode(text=paragraph, id_=p_idx), + node=TextNode(text=paragraph, id_=str(p_idx)), score=isRel_score, ) ) diff --git a/llama-index-packs/llama-index-packs-self-rag/pyproject.toml b/llama-index-packs/llama-index-packs-self-rag/pyproject.toml index f70ed70284f0b..ed5d45387721e 100644 --- a/llama-index-packs/llama-index-packs-self-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-self-rag/pyproject.toml @@ -29,7 +29,7 @@ license = "MIT" maintainers = ["mmaatouk"] name = "llama-index-packs-self-rag" readme = "README.md" -version = "0.2.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From fd9198001979ecf14ffc525ee4012cbbd87884ff Mon Sep 17 00:00:00 2001 From: Logan Date: Wed, 25 Sep 2024 16:34:49 -0600 Subject: [PATCH 28/53] Workflows + Human In The Loop Support (#16220) --- docs/docs/module_guides/workflow/index.md | 53 +++++++++++++++++++ .../llama_index/core/workflow/events.py | 14 +++++ .../llama_index/core/workflow/workflow.py | 41 +++++++++++--- .../tests/workflow/test_workflow.py | 42 ++++++++++++++- 4 files changed, 141 insertions(+), 9 deletions(-) diff --git a/docs/docs/module_guides/workflow/index.md b/docs/docs/module_guides/workflow/index.md index 66364e3a7d666..e461993528fa5 100644 --- a/docs/docs/module_guides/workflow/index.md +++ b/docs/docs/module_guides/workflow/index.md @@ -352,6 +352,59 @@ class RetryOnFridayPolicy: return None ``` +## Human-in-the-loop + +Since workflows are so flexible, there are many possible ways to implement human-in-the-loop patterns. + +The easiest way to implement a human-in-the-loop is to use the `InputRequiredEvent` and `HumanResponseEvent` events during event streaming. + +```python +class HumanInTheLoopWorkflow(Workflow): + @step + async def step1(self, ev: StartEvent) -> InputRequiredEvent: + return InputRequiredEvent(prefix="Enter a number: ") + + @step + async def step2(self, ev: HumanResponseEvent) -> StopEvent: + return StopEvent(result=ev.response) + + +# workflow should work with streaming +workflow = HumanInTheLoopWorkflow() + +handler = workflow.run() +async for event in handler.stream_events(): + if isinstance(event, InputRequiredEvent): + # here, we can handle human input however you want + # this means using input(), websockets, accessing async state, etc. + # here, we just use input() + response = input(event.prefix) + handler.ctx.send_event(HumanResponseEvent(response=response)) + +final_result = await handler +``` + +Here, the workflow will wait until the `HumanResponseEvent` is emitted. + +Also note that you can break out of the loop, and resume it later. This is useful if you want to pause the workflow to wait for a human response, but continue the workflow later. + +```python +handler = workflow.run() +async for event in handler.stream_events(): + if isinstance(event, InputRequiredEvent): + break + +# now we handle the human response +response = input(event.prefix) +handler.ctx.send_event(HumanResponseEvent(response=response)) + +# now we resume the workflow streaming +async for event in handler.stream_events(): + continue + +final_result = await handler +``` + ## Stepwise Execution Workflows have built-in utilities for stepwise execution, allowing you to control execution and debug state as things progress. diff --git a/llama-index-core/llama_index/core/workflow/events.py b/llama-index-core/llama_index/core/workflow/events.py index 14981c05c3942..c431e913d0300 100644 --- a/llama-index-core/llama_index/core/workflow/events.py +++ b/llama-index-core/llama_index/core/workflow/events.py @@ -132,4 +132,18 @@ def __init__(self, result: Any = None) -> None: super().__init__(result=result) +class InputRequiredEvent(Event): + """InputRequiredEvent is sent when an input is required for a step.""" + + prefix: str = Field( + description="The prefix and description of the input that is required." + ) + + +class HumanResponseEvent(Event): + """HumanResponseEvent is sent when a human response is required for a step.""" + + response: str = Field(description="The response from the human.") + + EventType = Type[Event] diff --git a/llama-index-core/llama_index/core/workflow/workflow.py b/llama-index-core/llama_index/core/workflow/workflow.py index e3b76dc890dee..688f2b313c2ed 100644 --- a/llama-index-core/llama_index/core/workflow/workflow.py +++ b/llama-index-core/llama_index/core/workflow/workflow.py @@ -8,7 +8,7 @@ from .decorators import StepConfig, step from .context import Context -from .events import Event, StartEvent, StopEvent +from .events import InputRequiredEvent, HumanResponseEvent, Event, StartEvent, StopEvent from .errors import * from .service import ServiceManager from .utils import ( @@ -248,6 +248,8 @@ async def _task( warnings.warn( f"Step function {name} returned {type(new_ev).__name__} instead of an Event instance." ) + elif isinstance(new_ev, InputRequiredEvent): + ctx.write_event_to_stream(new_ev) else: ctx.send_event(new_ev) @@ -283,7 +285,11 @@ def run( ) -> WorkflowHandler: """Runs the workflow until completion.""" # Validate the workflow if needed - self._validate() + uses_hitl = self._validate() + if uses_hitl and stepwise: + raise WorkflowRuntimeError( + "Human-in-the-loop is not supported with stepwise execution" + ) # Start the machinery in a new Context or use the provided one ctx = self._start(ctx=ctx, stepwise=stepwise) @@ -399,10 +405,13 @@ async def _done(self, ctx: Context, ev: StopEvent) -> None: # Signal we want to stop the workflow raise WorkflowDone - def _validate(self) -> None: - """Validate the workflow to ensure it's well-formed.""" + def _validate(self) -> bool: + """Validate the workflow to ensure it's well-formed. + + Returns True if the workflow uses human-in-the-loop, False otherwise. + """ if self._disable_validation: - return + return False produced_events: Set[type] = {StartEvent} consumed_events: Set[type] = set() @@ -425,16 +434,26 @@ def _validate(self) -> None: requested_services.update(step_config.requested_services) - # Check if all consumed events are produced + # Check if all consumed events are produced (except specific built-in events) unconsumed_events = consumed_events - produced_events + unconsumed_events = { + x + for x in unconsumed_events + if not issubclass(x, (InputRequiredEvent, HumanResponseEvent)) + } if unconsumed_events: names = ", ".join(ev.__name__ for ev in unconsumed_events) raise WorkflowValidationError( f"The following events are consumed but never produced: {names}" ) - # Check if there are any unused produced events (except StopEvent) - unused_events = produced_events - consumed_events - {StopEvent} + # Check if there are any unused produced events (except specific built-in events) + unused_events = produced_events - consumed_events + unused_events = { + x + for x in unused_events + if not issubclass(x, (InputRequiredEvent, HumanResponseEvent)) + } if unused_events: names = ", ".join(ev.__name__ for ev in unused_events) raise WorkflowValidationError( @@ -451,3 +470,9 @@ def _validate(self) -> None: if missing: msg = f"The following services are not available: {', '.join(str(m) for m in missing)}" raise WorkflowValidationError(msg) + + # Check if the workflow uses human-in-the-loop + return ( + InputRequiredEvent in produced_events + or HumanResponseEvent in consumed_events + ) diff --git a/llama-index-core/tests/workflow/test_workflow.py b/llama-index-core/tests/workflow/test_workflow.py index e4763ec5c3fb8..62d0851d0e547 100644 --- a/llama-index-core/tests/workflow/test_workflow.py +++ b/llama-index-core/tests/workflow/test_workflow.py @@ -5,7 +5,13 @@ import pytest from llama_index.core.workflow.decorators import step -from llama_index.core.workflow.events import StartEvent, StopEvent +from llama_index.core.workflow.handler import WorkflowHandler +from llama_index.core.workflow.events import ( + InputRequiredEvent, + HumanResponseEvent, + StartEvent, + StopEvent, +) from llama_index.core.workflow.workflow import ( Context, Workflow, @@ -444,3 +450,37 @@ async def step(self, ctx: Context, ev: StartEvent) -> StopEvent: result = await r assert result == "Done" assert await r.ctx.get("number") == 2 + + +@pytest.mark.asyncio() +async def test_human_in_the_loop(): + class HumanInTheLoopWorkflow(Workflow): + @step + async def step1(self, ev: StartEvent) -> InputRequiredEvent: + return InputRequiredEvent(prefix="Enter a number: ") + + @step + async def step2(self, ev: HumanResponseEvent) -> StopEvent: + return StopEvent(result=ev.response) + + workflow = HumanInTheLoopWorkflow(timeout=1) + + # workflow should raise a timeout error because hitl only works with streaming + with pytest.raises(WorkflowTimeoutError): + await workflow.run() + + # workflow should not work with stepwise + with pytest.raises(WorkflowRuntimeError): + handler = workflow.run(stepwise=True) + + # workflow should work with streaming + workflow = HumanInTheLoopWorkflow() + + handler: WorkflowHandler = workflow.run() + async for event in handler.stream_events(): + if isinstance(event, InputRequiredEvent): + assert event.prefix == "Enter a number: " + handler.ctx.send_event(HumanResponseEvent(response="42")) + + final_result = await handler + assert final_result == "42" From 8235129192dd1874e816c88f836eb0935687e2a7 Mon Sep 17 00:00:00 2001 From: Andrei Fajardo <92402603+nerdai@users.noreply.github.com> Date: Wed, 25 Sep 2024 19:21:22 -0400 Subject: [PATCH 29/53] [Feature Request] Support max concurrent `workflow_instance.run()` executions. (#16215) * passing test * add test for unbounded workflow * dont await semaphore release --- .../llama_index/core/workflow/workflow.py | 12 +++ .../tests/workflow/test_workflow.py | 85 +++++++++++++++++++ 2 files changed, 97 insertions(+) diff --git a/llama-index-core/llama_index/core/workflow/workflow.py b/llama-index-core/llama_index/core/workflow/workflow.py index 688f2b313c2ed..0e413360651ff 100644 --- a/llama-index-core/llama_index/core/workflow/workflow.py +++ b/llama-index-core/llama_index/core/workflow/workflow.py @@ -48,6 +48,7 @@ def __init__( disable_validation: bool = False, verbose: bool = False, service_manager: Optional[ServiceManager] = None, + num_concurrent_runs: Optional[int] = None, ) -> None: """Create an instance of the workflow. @@ -60,11 +61,17 @@ def __init__( verbose: whether or not the workflow should print additional informative messages during execution. service_manager: The instance of the `ServiceManager` used to make nested workflows available to this workflow instance. The default value is the best choice unless you're customizing the workflow runtime. + num_concurrent_runs: maximum number of .run() executions occurring simultaneously. If set to `None`, there + is no limit to this number. """ # Configuration self._timeout = timeout self._verbose = verbose self._disable_validation = disable_validation + self._num_concurrent_runs = num_concurrent_runs + self._sem = ( + asyncio.Semaphore(num_concurrent_runs) if num_concurrent_runs else None + ) # Broker machinery self._contexts: Set[Context] = set() self._stepwise_context: Optional[Context] = None @@ -297,6 +304,8 @@ def run( result = WorkflowHandler(ctx=ctx) async def _run_workflow() -> None: + if self._sem: + await self._sem.acquire() try: # Send the first event ctx.send_event(StartEvent(**kwargs)) @@ -335,6 +344,9 @@ async def _run_workflow() -> None: result.set_result(ctx._retval) except Exception as e: result.set_exception(e) + finally: + if self._sem: + self._sem.release() asyncio.create_task(_run_workflow()) return result diff --git a/llama-index-core/tests/workflow/test_workflow.py b/llama-index-core/tests/workflow/test_workflow.py index 62d0851d0e547..9822b093a3b33 100644 --- a/llama-index-core/tests/workflow/test_workflow.py +++ b/llama-index-core/tests/workflow/test_workflow.py @@ -1,6 +1,7 @@ import asyncio import time from unittest import mock +from typing import Type import pytest @@ -484,3 +485,87 @@ async def step2(self, ev: HumanResponseEvent) -> StopEvent: final_result = await handler assert final_result == "42" + + +class DummyWorkflowForConcurrentRunsTest(Workflow): + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + self._lock = asyncio.Lock() + self.num_active_runs = 0 + + @step + async def step_one(self, ev: StartEvent) -> StopEvent: + run_num = ev.get("run_num") + async with self._lock: + self.num_active_runs += 1 + await asyncio.sleep(0.1) + return StopEvent(result=f"Run {run_num}: Done") + + @step + async def _done(self, ctx: Context, ev: StopEvent) -> None: + async with self._lock: + self.num_active_runs -= 1 + await super()._done(ctx, ev) + + async def get_active_runs(self): + async with self._lock: + return self.num_active_runs + + +class NumConcurrentRunsException(Exception): + pass + + +@pytest.mark.parametrize( + ( + "workflow", + "desired_max_concurrent_runs", + "expected_exception", + ), + [ + ( + DummyWorkflowForConcurrentRunsTest(num_concurrent_runs=1), + 1, + type(None), + ), + # This workflow is not protected, and so NumConcurrentRunsException is raised + ( + DummyWorkflowForConcurrentRunsTest(), + 1, + NumConcurrentRunsException, + ), + ], +) +async def test_workflow_run_num_concurrent( + workflow: DummyWorkflowForConcurrentRunsTest, + desired_max_concurrent_runs: int, + expected_exception: Type, +): + async def _poll_workflow( + wf: DummyWorkflowForConcurrentRunsTest, desired_max_concurrent_runs: int + ) -> None: + """Check that number of concurrent runs is less than desired max amount.""" + for _ in range(100): + num_active_runs = await wf.get_active_runs() + if num_active_runs > desired_max_concurrent_runs: + raise NumConcurrentRunsException + await asyncio.sleep(0.01) + + poll_task = asyncio.create_task( + _poll_workflow( + wf=workflow, desired_max_concurrent_runs=desired_max_concurrent_runs + ), + ) + + tasks = [] + for ix in range(1, 5): + tasks.append(workflow.run(run_num=ix)) + + results = await asyncio.gather(*tasks) + + if not poll_task.done(): + await poll_task + e = poll_task.exception() + + assert type(e) == expected_exception + assert results == [f"Run {ix}: Done" for ix in range(1, 5)] From 6afd75eea7116299af9bdb8db9004db32421841a Mon Sep 17 00:00:00 2001 From: Anoop Sharma Date: Thu, 26 Sep 2024 19:16:14 +0530 Subject: [PATCH 30/53] Removing incorrect(404) links (#16229) * Removed incorrect links * Removed all 404 links --- docs/mkdocs.yml | 8 -------- 1 file changed, 8 deletions(-) diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 1faf2856b6b4c..ed11b17adcaf3 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -122,7 +122,6 @@ nav: - ./examples/agent/react_agent_with_query_engine.ipynb - ./examples/agent/return_direct_agent.ipynb - ./examples/agent/structured_planner.ipynb - - ./examples/agents/nvidia_agent.ipynb - Chat Engines: - ./examples/chat_engine/chat_engine_best.ipynb - ./examples/chat_engine/chat_engine_condense_plus_context.ipynb @@ -226,7 +225,6 @@ nav: - ./examples/embeddings/gemini.ipynb - ./examples/embeddings/gigachat.ipynb - ./examples/embeddings/google_palm.ipynb - - ./examples/embeddings/gradient.ipynb - ./examples/embeddings/huggingface.ipynb - ./examples/embeddings/ibm_watsonx.ipynb - ./examples/embeddings/ipex_llm.ipynb @@ -280,9 +278,6 @@ nav: - ./examples/finetuning/embeddings/finetune_corpus_embedding.ipynb - ./examples/finetuning/embeddings/finetune_embedding.ipynb - ./examples/finetuning/embeddings/finetune_embedding_adapter.ipynb - - ./examples/finetuning/gradient/gradient_fine_tuning.ipynb - - ./examples/finetuning/gradient/gradient_structured.ipynb - - ./examples/finetuning/gradient/gradient_text2sql.ipynb - ./examples/finetuning/llm_judge/correctness/finetune_llm_judge_single_grading_correctness.ipynb - ./examples/finetuning/llm_judge/pairwise/finetune_llm_judge.ipynb - ./examples/finetuning/mistralai_fine_tuning.ipynb @@ -319,8 +314,6 @@ nav: - ./examples/llm/fireworks_cookbook.ipynb - ./examples/llm/friendli.ipynb - ./examples/llm/gemini.ipynb - - ./examples/llm/gradient_base_model.ipynb - - ./examples/llm/gradient_model_adapter.ipynb - ./examples/llm/groq.ipynb - ./examples/llm/huggingface.ipynb - ./examples/llm/ibm_watsonx.ipynb @@ -340,7 +333,6 @@ nav: - ./examples/llm/maritalk.ipynb - ./examples/llm/mistral_rs.ipynb - ./examples/llm/mistralai.ipynb - - ./examples/llm/mlx.ipynb - ./examples/llm/modelscope.ipynb - ./examples/llm/monsterapi.ipynb - ./examples/llm/mymagic.ipynb From 05213ce9cacccc6b9509e4261c63faa66dbbba19 Mon Sep 17 00:00:00 2001 From: Andrey Date: Thu, 26 Sep 2024 15:51:12 +0200 Subject: [PATCH 31/53] Add pagination support for Jira Reader (#16226) * Add pagination support for Jira * Version up --- .../llama_index/readers/jira/base.py | 8 ++++++-- .../readers/llama-index-readers-jira/pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py b/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py index a74a6579fead7..ab8b78558ab7e 100644 --- a/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py +++ b/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py @@ -75,8 +75,12 @@ def __init__( server=f"https://{BasicAuth['server_url']}", ) - def load_data(self, query: str) -> List[Document]: - relevant_issues = self.jira.search_issues(query) + def load_data( + self, query: str, start_at: int = 0, max_results: int = 50 + ) -> List[Document]: + relevant_issues = self.jira.search_issues( + query, startAt=start_at, maxResults=max_results + ) issues = [] diff --git a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml index 61b3e6680e90f..4482ae0732133 100644 --- a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml @@ -29,7 +29,7 @@ license = "MIT" maintainers = ["bearguy"] name = "llama-index-readers-jira" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 2574aa4ad41a67dedec3b6b03735ada7e4e75619 Mon Sep 17 00:00:00 2001 From: Logan Date: Thu, 26 Sep 2024 12:24:02 -0600 Subject: [PATCH 32/53] safe prompt helper string formatting (#16219) --- .../llama_index/core/indices/prompt_helper.py | 29 ++-------- .../tests/indices/test_prompt_helper.py | 53 ++++++++++++++++++- 2 files changed, 57 insertions(+), 25 deletions(-) diff --git a/llama-index-core/llama_index/core/indices/prompt_helper.py b/llama-index-core/llama_index/core/indices/prompt_helper.py index eb7b649898426..af9be20e9cc81 100644 --- a/llama-index-core/llama_index/core/indices/prompt_helper.py +++ b/llama-index-core/llama_index/core/indices/prompt_helper.py @@ -10,7 +10,6 @@ import logging from copy import deepcopy -from string import Formatter from typing import TYPE_CHECKING, Callable, List, Optional, Sequence if TYPE_CHECKING: @@ -29,6 +28,7 @@ SelectorPromptTemplate, ) from llama_index.core.prompts.prompt_utils import get_empty_prompt_txt +from llama_index.core.prompts.utils import format_string from llama_index.core.schema import BaseComponent from llama_index.core.utilities.token_counting import TokenCounter @@ -198,29 +198,10 @@ def _get_available_chunk_size( for message in messages: partial_message = deepcopy(message) - # get string variables (if any) - template_vars = [ - var - for _, var, _, _ in Formatter().parse(str(message)) - if var is not None - ] - - # figure out which variables are partially formatted - # if a variable is not formatted, it will be replaced with - # the template variable itself - used_vars = { - template_var: f"{{{template_var}}}" - for template_var in template_vars - } - for var_name, val in prompt.kwargs.items(): - if var_name in template_vars: - used_vars[var_name] = val - - # format partial message - if partial_message.content is not None: - partial_message.content = partial_message.content.format( - **used_vars - ) + prompt_kwargs = prompt.kwargs or {} + partial_message.content = format_string( + partial_message.content or "", **prompt_kwargs + ) # add to list of partial messages partial_messages.append(partial_message) diff --git a/llama-index-core/tests/indices/test_prompt_helper.py b/llama-index-core/tests/indices/test_prompt_helper.py index e8ce57b832198..5fa5573a9c727 100644 --- a/llama-index-core/tests/indices/test_prompt_helper.py +++ b/llama-index-core/tests/indices/test_prompt_helper.py @@ -5,8 +5,9 @@ import pytest from llama_index.core.indices.prompt_helper import PromptHelper from llama_index.core.indices.tree.utils import get_numbered_text_from_nodes +from llama_index.core.llms import ChatMessage from llama_index.core.node_parser.text.utils import truncate_text -from llama_index.core.prompts.base import PromptTemplate +from llama_index.core.prompts.base import ChatPromptTemplate, PromptTemplate from llama_index.core.prompts.prompt_utils import ( get_biggest_prompt, get_empty_prompt_txt, @@ -194,3 +195,53 @@ def test_get_biggest_prompt() -> None: biggest_prompt = get_biggest_prompt([prompt1, prompt2, prompt3]) assert biggest_prompt == prompt2 + + +def test_json_in_prompt() -> None: + """Test that a JSON object in the prompt doesn't cause an error.""" + # test with normal prompt + prompt = PromptTemplate( + 'This is the prompt {text} but it also has {"json": "in it"}' + ) + prompt.partial_format(text="hello_world") + prompt_helper = PromptHelper() + + texts = prompt_helper.repack(prompt, ["hello_world"]) + assert len(texts) == 1 + + # test with chat messages + prompt = ChatPromptTemplate.from_messages( + [ + ChatMessage( + role="system", + content="You are a helpful assistant that knows about {topic}. Here's some JSON: {'foo': 'bar'}", + ), + ChatMessage( + role="user", + content="What is the capital of the moon? Here's some JSON: {'foo': 'bar'}", + ), + ] + ) + prompt.partial_format(topic="the moon") + prompt_helper = PromptHelper() + + texts = prompt_helper.repack(prompt, ["hello_world"]) + assert len(texts) == 1 + + # test with more complex JSON + prompt = ChatPromptTemplate.from_messages( + [ + ChatMessage( + role="system", + content=( + "You are a helpful assistant that knows about {topic}\n" + "Here's some JSON: {'foo': 'bar'}\n" + "here's some other stuff: {key: val for val in d.items()}\n" + ), + ), + ChatMessage( + role="user", + content="What is the capital of the moon? Here's some JSON: {'foo': 'bar'}", + ), + ] + ) From b306c7a0860fb540187356bc2d11939b503de99d Mon Sep 17 00:00:00 2001 From: Massimiliano Pippi Date: Thu, 26 Sep 2024 20:30:36 +0200 Subject: [PATCH 33/53] docs: rewrite the examples summary (#16230) --- .../workflow/corrective_rag_pack.ipynb | 2 +- docs/docs/module_guides/workflow/index.md | 42 ++++++++++++++----- 2 files changed, 33 insertions(+), 11 deletions(-) diff --git a/docs/docs/examples/workflow/corrective_rag_pack.ipynb b/docs/docs/examples/workflow/corrective_rag_pack.ipynb index 73809573d51c5..45910b0284613 100644 --- a/docs/docs/examples/workflow/corrective_rag_pack.ipynb +++ b/docs/docs/examples/workflow/corrective_rag_pack.ipynb @@ -11,7 +11,7 @@ "A brief understanding of the paper:\n", "\n", "\n", - "Corrective Retrieval Augmented Generation (CRAG) is a method designed to enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through a an evaluator and large-scale web searches, ensuring more accurate and reliable information is used in generation.\n", + "Corrective Retrieval Augmented Generation (CRAG) is a method designed to enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through an evaluator and large-scale web searches, ensuring more accurate and reliable information is used in generation.\n", "\n", "We use `GPT-4` as a relevancy evaluator and `Tavily AI` for web searches. So, we recommend getting `OPENAI_API_KEY` and `tavily_ai_api_key` before proceeding further." ] diff --git a/docs/docs/module_guides/workflow/index.md b/docs/docs/module_guides/workflow/index.md index e461993528fa5..31c0a0665fb9f 100644 --- a/docs/docs/module_guides/workflow/index.md +++ b/docs/docs/module_guides/workflow/index.md @@ -176,6 +176,7 @@ await w.run(topic="Pirates") draw_most_recent_execution(w, filename="joke_flow_recent.html") ``` +
    ## Working with Global Context/State Optionally, you can choose to use global context between steps. For example, maybe multiple steps access the original `query` input from the user. You can store this in global context so that every step has access. @@ -492,22 +493,43 @@ You can deploy a workflow as a multi-agent service with [llama_deploy](../../mod ## Examples -You can find many useful examples of using workflows in the notebooks below: +To help you become more familiar with the workflow concept and its features, LlamaIndex documentation offers example +notebooks that you can run for hands-on learning: + +- [Common Workflow Patterns](../../examples/workflow/workflows_cookbook.ipynb) walks you through common usage patterns +like looping and state management using simple workflows. It's usually a great place to start. +- [RAG + Reranking](../../examples/workflow/rag.ipynb) shows how to implement a real-world use case with a fairly +simple workflow that performs both ingestion and querying. +- [Citation Query Engine](../../examples/workflow/citation_query_engine.ipynb) similar to RAG + Reranking, the +notebook focuses on how to implement intermediate steps in between retrieval and generation. A good example of how to +use the [`Context`](#working-with-global-context-state) object in a workflow. +- [Corrective RAG](../../examples/workflow/corrective_rag_pack.ipynb) adds some more complexity on top of a RAG +workflow, showcasing how to query a web search engine after an evaluation step. +- [Utilizing Concurrency](../../examples/workflow/parallel_execution.ipynb) explains how to manage the parallel +execution of steps in a workflow, something that's important to know as your workflows grow in complexity. + +RAG applications are easy to understand and offer a great opportunity to learn the basics of workflows. However, more complex agentic scenarios involving tool calling, memory, and routing are where workflows excel. + +The examples below highlight some of these use-cases. + +- [ReAct Agent](../../examples/workflow/react_agent.ipynb) is obviously the perfect example to show how to implement +tools in a workflow. +- [Function Calling Agent](../../examples/workflow/function_calling_agent.ipynb) is a great example of how to use the +LlamaIndex framework primitives in a workflow, keeping it small and tidy even in complex scenarios like function +calling. +- [Human In The Loop: Story Crafting](../../examples/workflow/human_in_the_loop_story_crafting.ipynb) is a powerful +example showing how workflow runs can be interactive and stateful. In this case, to collect input from a human. +- [Reliable Structured Generation](../../examples/workflow/reflection.ipynb) shows how to implement loops in a +workflow, in this case to improve structured output through reflection. + +Last but not least, a few more advanced use cases that demonstrate how workflows can be extremely handy if you need +to quickly implement prototypes, for example from literature: - [Advanced Text-to-SQL](../../examples/workflow/advanced_text_to_sql.ipynb) -- [Citation Query Engine](../../examples/workflow/citation_query_engine.ipynb) -- [Common Workflow Patterns](../../examples/workflow/workflows_cookbook.ipynb) -- [Corrective RAG](../../examples/workflow/corrective_rag_pack.ipynb) -- [Function Calling Agent](../../examples/workflow/function_calling_agent.ipynb) -- [Human In The Loop: Story Crafting](../../examples/workflow/human_in_the_loop_story_crafting.ipynb) - [JSON Query Engine](../../examples/workflow/JSONalyze_query_engine.ipynb) - [Long RAG](../../examples/workflow/long_rag_pack.ipynb) - [Multi-Step Query Engine](../../examples/workflow/multi_step_query_engine.ipynb) - [Multi-Strategy Workflow](../../examples/workflow/multi_strategy_workflow.ipynb) -- [RAG + Reranking](../../examples/workflow/rag.ipynb) -- [ReAct Agent](../../examples/workflow/react_agent.ipynb) -- [Reliable Structured Generation](../../examples/workflow/reflection.ipynb) - [Router Query Engine](../../examples/workflow/router_query_engine.ipynb) - [Self Discover Workflow](../../examples/workflow/self_discover_workflow.ipynb) - [Sub-Question Query Engine](../../examples/workflow/sub_question_query_engine.ipynb) -- [Utilizing Concurrency](../../examples/workflow/parallel_execution.ipynb) From 67b85cb45ede5068e2d54095ec2644761970dd8c Mon Sep 17 00:00:00 2001 From: Rashmi Pawar <168514198+raspawar@users.noreply.github.com> Date: Fri, 27 Sep 2024 00:02:27 +0530 Subject: [PATCH 34/53] Tool Calling: Skip allow parallel calls argument (#16225) --- docs/docs/examples/agent/nvidia_agent.ipynb | 3 +-- .../llama_index/llms/nvidia/base.py | 18 ------------------ .../llama-index-llms-nvidia/pyproject.toml | 2 +- 3 files changed, 2 insertions(+), 21 deletions(-) diff --git a/docs/docs/examples/agent/nvidia_agent.ipynb b/docs/docs/examples/agent/nvidia_agent.ipynb index d7711bc4538c1..10ee0b6239e6d 100644 --- a/docs/docs/examples/agent/nvidia_agent.ipynb +++ b/docs/docs/examples/agent/nvidia_agent.ipynb @@ -378,8 +378,7 @@ "outputs": [], "source": [ "response = agent.chat(\n", - " \"Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls.\",\n", - " allow_parallel_tool_calls=True,\n", + " \"Tell me both the risk factors and tailwinds for Uber? Do two parallel tool calls.\"\n", ")\n", "print(str(response))" ] diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py index f00f69f765670..7adf26f1cb3cc 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/llama_index/llms/nvidia/base.py @@ -37,12 +37,6 @@ ] -def force_single_tool_call(response: ChatResponse) -> None: - tool_calls = response.message.additional_kwargs.get("tool_calls", []) - if len(tool_calls) > 1: - response.message.additional_kwargs["tool_calls"] = [tool_calls[0]] - - class Model(BaseModel): id: str base_model: Optional[str] @@ -255,18 +249,6 @@ def _prepare_chat_with_tools( **kwargs, } - def _validate_chat_with_tools_response( - self, - response: ChatResponse, - tools: List["BaseTool"], - allow_parallel_tool_calls: bool = False, - **kwargs: Any, - ) -> ChatResponse: - """Validate the response from chat_with_tools.""" - if not allow_parallel_tool_calls: - force_single_tool_call(response) - return response - def get_tool_calls_from_response( self, response: "ChatResponse", diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml index 44f2ab87598a8..8fa06c44ad306 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-nvidia/pyproject.toml @@ -30,7 +30,7 @@ license = "MIT" name = "llama-index-llms-nvidia" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.4" +version = "0.2.5" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 93aeec61d4bba93812f214f76caf4be483a15b38 Mon Sep 17 00:00:00 2001 From: Michael Onjack Date: Thu, 26 Sep 2024 15:43:11 -0400 Subject: [PATCH 35/53] Ensures ChatMemoryBuffer's chat history never begins with a TOOL message (#16214) --- .../llama_index/core/memory/chat_memory_buffer.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/llama-index-core/llama_index/core/memory/chat_memory_buffer.py b/llama-index-core/llama_index/core/memory/chat_memory_buffer.py index 57bcca4944701..830e8c98a010a 100644 --- a/llama-index-core/llama_index/core/memory/chat_memory_buffer.py +++ b/llama-index-core/llama_index/core/memory/chat_memory_buffer.py @@ -123,15 +123,16 @@ def get( while token_count > self.token_limit and message_count > 1: message_count -= 1 - if chat_history[-message_count].role == MessageRole.TOOL: - # all tool messages should be preceded by an assistant message - # if we remove a tool message, we need to remove the assistant message too - message_count -= 1 - - if chat_history[-message_count].role == MessageRole.ASSISTANT: + while chat_history[-message_count].role in ( + MessageRole.TOOL, + MessageRole.ASSISTANT, + ): # we cannot have an assistant message at the start of the chat history # if after removal of the first, we have an assistant message, # we need to remove the assistant message too + # + # all tool messages should be preceded by an assistant message + # if we remove a tool message, we need to remove the assistant message too message_count -= 1 cur_messages = chat_history[-message_count:] From eb423bcb14b9cef05cbe6838bbbe0cd2ba2a4e44 Mon Sep 17 00:00:00 2001 From: Logan Date: Thu, 26 Sep 2024 14:45:49 -0600 Subject: [PATCH 36/53] fix deeplake pants (#16236) --- .../llama-index-vector-stores-deeplake/BUILD | 1 + .../llama_index/vector_stores/deeplake/base.py | 13 +------------ .../pyproject.toml | 3 ++- .../llama-index-vector-stores-deeplake/tests/BUILD | 1 + .../tests/test_vector_stores_deeplake.py | 1 + .../tests/BUILD | 1 + .../tests/BUILD | 1 + 7 files changed, 8 insertions(+), 13 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/BUILD index 0896ca890d8bf..ae454e6bf8e30 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/BUILD +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/BUILD @@ -1,3 +1,4 @@ poetry_requirements( name="poetry", + module_mapping={"pyjwt": ["jwt"]}, ) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py index 9256bfc95ea42..f9323884222dd 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py @@ -22,12 +22,7 @@ node_to_metadata_dict, ) -try: - from deeplake.core.vectorstore.deeplake_vectorstore import VectorStore - - DEEPLAKE_INSTALLED = True -except ImportError: - DEEPLAKE_INSTALLED = False +from deeplake.core.vectorstore.deeplake_vectorstore import VectorStore logger = logging.getLogger(__name__) @@ -112,12 +107,6 @@ def __init__( num_workers=ingestion_num_workers, ) - if not DEEPLAKE_INSTALLED: - raise ImportError( - "Could not import deeplake python package. " - "Please install it with `pip install deeplake`." - ) - self._vectorstore = VectorStore( path=dataset_path, ingestion_batch_size=ingestion_batch_size, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml index 1694d686deb51..98cfa889c8a4c 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-deeplake" readme = "README.md" -version = "0.2.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.9,<4.0" deeplake = ">=3.9.12" llama-index-core = "^0.11.0" +pyjwt = "*" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/BUILD b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/BUILD index 619cac15ff840..e3cd392ad8843 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/BUILD +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/BUILD @@ -1,3 +1,4 @@ python_tests( interpreter_constraints=["==3.9.*", "==3.10.*"], + dependencies=["llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake:poetry#pyjwt"], ) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/test_vector_stores_deeplake.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/test_vector_stores_deeplake.py index cb54a56a482ab..0d5823b56f508 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/test_vector_stores_deeplake.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/tests/test_vector_stores_deeplake.py @@ -1,4 +1,5 @@ import pytest +import jwt # noqa from llama_index.core import Document from llama_index.core.vector_stores.types import ( diff --git a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/tests/BUILD b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/tests/BUILD index 619cac15ff840..e3cd392ad8843 100644 --- a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/tests/BUILD +++ b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/tests/BUILD @@ -1,3 +1,4 @@ python_tests( interpreter_constraints=["==3.9.*", "==3.10.*"], + dependencies=["llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake:poetry#pyjwt"], ) diff --git a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/tests/BUILD b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/tests/BUILD index 619cac15ff840..6b07382dcda0e 100644 --- a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/tests/BUILD +++ b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/tests/BUILD @@ -1,3 +1,4 @@ python_tests( interpreter_constraints=["==3.9.*", "==3.10.*"], + dependencies=["llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake:poetry#pyjwt"] ) From 9af8fa3ef5b3860cbb0878cd116ef23679ef25ca Mon Sep 17 00:00:00 2001 From: Guodong Date: Fri, 27 Sep 2024 04:46:47 +0800 Subject: [PATCH 37/53] fix ollama chat missing keep_alive (#16182) --- .../llama_index/llms/ollama/base.py | 10 ++++++++++ .../llms/llama-index-llms-ollama/pyproject.toml | 2 +- 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/llms/llama-index-llms-ollama/llama_index/llms/ollama/base.py b/llama-index-integrations/llms/llama-index-llms-ollama/llama_index/llms/ollama/base.py index b9a6cee87cd93..c980bfe83e534 100644 --- a/llama-index-integrations/llms/llama-index-llms-ollama/llama_index/llms/ollama/base.py +++ b/llama-index-integrations/llms/llama-index-llms-ollama/llama_index/llms/ollama/base.py @@ -99,6 +99,10 @@ class Ollama(FunctionCallingLLM): default=True, description="Whether the model is a function calling model.", ) + keep_alive: Optional[Union[float, str]] = Field( + default="5m", + description="controls how long the model will stay loaded into memory following the request(default: 5m)", + ) _client: Optional[Client] = PrivateAttr() _async_client: Optional[AsyncClient] = PrivateAttr() @@ -116,6 +120,7 @@ def __init__( client: Optional[Client] = None, async_client: Optional[AsyncClient] = None, is_function_calling_model: bool = True, + keep_alive: Optional[Union[float, str]] = None, **kwargs: Any, ) -> None: super().__init__( @@ -128,6 +133,7 @@ def __init__( json_mode=json_mode, additional_kwargs=additional_kwargs, is_function_calling_model=is_function_calling_model, + keep_alive=keep_alive, **kwargs, ) @@ -279,6 +285,7 @@ def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: format="json" if self.json_mode else "", tools=tools, options=self._model_kwargs, + keep_alive=self.keep_alive, ) tool_calls = response["message"].get("tool_calls", []) @@ -311,6 +318,7 @@ def gen() -> ChatResponseGen: format="json" if self.json_mode else "", tools=tools, options=self._model_kwargs, + keep_alive=self.keep_alive, ) response_txt = "" @@ -354,6 +362,7 @@ async def gen() -> ChatResponseAsyncGen: format="json" if self.json_mode else "", tools=tools, options=self._model_kwargs, + keep_alive=self.keep_alive, ) response_txt = "" @@ -396,6 +405,7 @@ async def achat( format="json" if self.json_mode else "", tools=tools, options=self._model_kwargs, + keep_alive=self.keep_alive, ) tool_calls = response["message"].get("tool_calls", []) diff --git a/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml index 43973bfa61d9d..17182735835a5 100644 --- a/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-ollama/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-ollama" readme = "README.md" -version = "0.3.2" +version = "0.3.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 944945e46fd45ade77aeaf679302401886dd5081 Mon Sep 17 00:00:00 2001 From: YunhaoLi <62140756+yunhaoli24@users.noreply.github.com> Date: Fri, 27 Sep 2024 05:34:43 +0800 Subject: [PATCH 38/53] Enhance `insert` Method in `BaseIndex` to Support Customizable Transformations (#16206) --- llama-index-core/llama_index/core/indices/base.py | 1 + 1 file changed, 1 insertion(+) diff --git a/llama-index-core/llama_index/core/indices/base.py b/llama-index-core/llama_index/core/indices/base.py index 3916e640b478c..3564139b1d051 100644 --- a/llama-index-core/llama_index/core/indices/base.py +++ b/llama-index-core/llama_index/core/indices/base.py @@ -210,6 +210,7 @@ def insert(self, document: Document, **insert_kwargs: Any) -> None: [document], self._transformations, show_progress=self._show_progress, + **insert_kwargs, ) self.insert_nodes(nodes, **insert_kwargs) From 9d1a33f83af2cface15b5bab013221f118f41d35 Mon Sep 17 00:00:00 2001 From: Logan Date: Thu, 26 Sep 2024 17:09:22 -0600 Subject: [PATCH 39/53] v0.11.14 (#16238) --- CHANGELOG.md | 42 +++ docs/docs/CHANGELOG.md | 42 +++ .../api_reference/multi_modal_llms/mistral.md | 4 + docs/mkdocs.yml | 3 + llama-index-core/llama_index/core/__init__.py | 2 +- llama-index-core/pyproject.toml | 2 +- poetry.lock | 272 +++++++++--------- pyproject.toml | 4 +- 8 files changed, 231 insertions(+), 140 deletions(-) create mode 100644 docs/docs/api_reference/multi_modal_llms/mistral.md diff --git a/CHANGELOG.md b/CHANGELOG.md index 040b3f53da906..62733e923f25f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,47 @@ # ChangeLog +## [2024-09-26] + +### `llama-index-core` [0.11.14] + +- Enhance insert Method in BaseIndex to Support Customizable Transformations (#16206) +- Ensure ChatMemoryBuffer's chat history never begins with a TOOL message (#16214) +- safe prompt helper string formatting (#16219) +- [Feature Request] Support max concurrent workflow_instance.run() executions (#16215) +- Workflows + Human In The Loop Dedicated Support (#16220) + +### `llama-index-graph-stores-neptune` [0.2.2] + +- fix NoneType object error when passing in provided client (#16174) + +### `llama-index-llms-ollama` [0.3.3] + +- fix ollama chat missing `keep_alive` (#16182) + +### `llama-index-llms-vertex` [0.3.6] + +- Fix vertex init function (#16216) + +### `llama-index-multi-modal-llms-mistral` [0.1.0] + +- Add support for Mistral Multi modal LLM (#16191) + +### `llama-index-readers-jira` [0.3.0] + +- Add pagination support for Jira Reader (#16226) + +### `llama-index-vector-stores-azurecosmosmongo` [0.2.1] + +- Azure Cosmos DB Filtered Vector Search (#16175) + +### `llama-index-vector-stores-azurecosmosnosql` [1.1.0] + +- Azure Cosmos DB Filtered Vector Search (#16175) + +### `llama-index-vector-stores-deeplake` [0.2.1] + +- Add missing JWT dependency (#16236) + ## [2024-09-24] ### `llama-index-core` [0.11.13] diff --git a/docs/docs/CHANGELOG.md b/docs/docs/CHANGELOG.md index 040b3f53da906..62733e923f25f 100644 --- a/docs/docs/CHANGELOG.md +++ b/docs/docs/CHANGELOG.md @@ -1,5 +1,47 @@ # ChangeLog +## [2024-09-26] + +### `llama-index-core` [0.11.14] + +- Enhance insert Method in BaseIndex to Support Customizable Transformations (#16206) +- Ensure ChatMemoryBuffer's chat history never begins with a TOOL message (#16214) +- safe prompt helper string formatting (#16219) +- [Feature Request] Support max concurrent workflow_instance.run() executions (#16215) +- Workflows + Human In The Loop Dedicated Support (#16220) + +### `llama-index-graph-stores-neptune` [0.2.2] + +- fix NoneType object error when passing in provided client (#16174) + +### `llama-index-llms-ollama` [0.3.3] + +- fix ollama chat missing `keep_alive` (#16182) + +### `llama-index-llms-vertex` [0.3.6] + +- Fix vertex init function (#16216) + +### `llama-index-multi-modal-llms-mistral` [0.1.0] + +- Add support for Mistral Multi modal LLM (#16191) + +### `llama-index-readers-jira` [0.3.0] + +- Add pagination support for Jira Reader (#16226) + +### `llama-index-vector-stores-azurecosmosmongo` [0.2.1] + +- Azure Cosmos DB Filtered Vector Search (#16175) + +### `llama-index-vector-stores-azurecosmosnosql` [1.1.0] + +- Azure Cosmos DB Filtered Vector Search (#16175) + +### `llama-index-vector-stores-deeplake` [0.2.1] + +- Add missing JWT dependency (#16236) + ## [2024-09-24] ### `llama-index-core` [0.11.13] diff --git a/docs/docs/api_reference/multi_modal_llms/mistral.md b/docs/docs/api_reference/multi_modal_llms/mistral.md new file mode 100644 index 0000000000000..1d134418b0995 --- /dev/null +++ b/docs/docs/api_reference/multi_modal_llms/mistral.md @@ -0,0 +1,4 @@ +::: llama_index.multi_modal_llms.mistral + options: + members: + - MistralMultiModal diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index ed11b17adcaf3..30f83848ae562 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -416,6 +416,7 @@ nav: - ./examples/multi_modal/image_to_image_retrieval.ipynb - ./examples/multi_modal/llava_demo.ipynb - ./examples/multi_modal/llava_multi_modal_tesla_10q.ipynb + - ./examples/multi_modal/mistral_multi_modal.ipynb - ./examples/multi_modal/mm_agent.ipynb - ./examples/multi_modal/multi_modal_pydantic.ipynb - ./examples/multi_modal/multi_modal_rag_nomic.ipynb @@ -1107,6 +1108,7 @@ nav: - ./api_reference/multi_modal_llms/dashscope.md - ./api_reference/multi_modal_llms/gemini.md - ./api_reference/multi_modal_llms/index.md + - ./api_reference/multi_modal_llms/mistral.md - ./api_reference/multi_modal_llms/ollama.md - ./api_reference/multi_modal_llms/openai.md - ./api_reference/multi_modal_llms/replicate.md @@ -2194,6 +2196,7 @@ plugins: - ../llama-index-integrations/readers/llama-index-readers-quip - ../llama-index-integrations/sparse_embeddings/llama-index-sparse-embeddings-fastembed - ../llama-index-integrations/node_parser/llama-index-node-parser-topic + - ../llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai - redirects: redirect_maps: ./api/llama_index.vector_stores.MongoDBAtlasVectorSearch.html: api_reference/storage/vector_store/mongodb.md diff --git a/llama-index-core/llama_index/core/__init__.py b/llama-index-core/llama_index/core/__init__.py index 279c9e7493348..1a56579329117 100644 --- a/llama-index-core/llama_index/core/__init__.py +++ b/llama-index-core/llama_index/core/__init__.py @@ -1,6 +1,6 @@ """Init file of LlamaIndex.""" -__version__ = "0.11.13.post1" +__version__ = "0.11.14" import logging from logging import NullHandler diff --git 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llama-index-readers-llama-parse = ">=0.3.0" llama-index-indices-managed-llama-cloud = ">=0.3.0" -llama-index-core = "^0.11.13" +llama-index-core = "^0.11.14" llama-index-multi-modal-llms-openai = "^0.2.0" llama-index-cli = "^0.3.1" nltk = ">3.8.1" # avoids a CVE, temp until next release, should be in llama-index-core From 6780fb7731fe86af8c1006bef79d29763e4b07c6 Mon Sep 17 00:00:00 2001 From: Laurie Voss Date: Thu, 26 Sep 2024 17:21:26 -0700 Subject: [PATCH 40/53] Mistral multimodal import path (#16240) --- docs/docs/api_reference/multi_modal_llms/mistralai.md | 4 ++++ docs/mkdocs.yml | 2 +- .../llama-index-multi-modal-llms-mistralai/pyproject.toml | 2 +- 3 files changed, 6 insertions(+), 2 deletions(-) create mode 100644 docs/docs/api_reference/multi_modal_llms/mistralai.md diff --git a/docs/docs/api_reference/multi_modal_llms/mistralai.md b/docs/docs/api_reference/multi_modal_llms/mistralai.md new file mode 100644 index 0000000000000..83af57e5e397d --- /dev/null +++ b/docs/docs/api_reference/multi_modal_llms/mistralai.md @@ -0,0 +1,4 @@ +::: llama_index.multi_modal_llms.mistralai + options: + members: + - MistralMultiModal diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 30f83848ae562..f6b6b823e8968 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -1108,7 +1108,7 @@ nav: - ./api_reference/multi_modal_llms/dashscope.md - ./api_reference/multi_modal_llms/gemini.md - ./api_reference/multi_modal_llms/index.md - - ./api_reference/multi_modal_llms/mistral.md + - ./api_reference/multi_modal_llms/mistralai.md - ./api_reference/multi_modal_llms/ollama.md - ./api_reference/multi_modal_llms/openai.md - ./api_reference/multi_modal_llms/replicate.md diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml index 93d73047ea840..839cefda380ab 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml @@ -9,7 +9,7 @@ skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" [tool.llamahub] contains_example = false -import_path = "llama_index.multi_modal_llms.mistral" +import_path = "llama_index.multi_modal_llms.mistralai" [tool.llamahub.class_authors] MistralMultiModal = "llama-index" From 4559632cf25e123764dfbb52dbd508af9566f70e Mon Sep 17 00:00:00 2001 From: Laurie Voss Date: Thu, 26 Sep 2024 17:35:28 -0700 Subject: [PATCH 41/53] Ljv/fix mistral docs (#16241) * Mistral multimodal import path * Forgot to delete a file --- docs/docs/api_reference/multi_modal_llms/mistral.md | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 docs/docs/api_reference/multi_modal_llms/mistral.md diff --git a/docs/docs/api_reference/multi_modal_llms/mistral.md b/docs/docs/api_reference/multi_modal_llms/mistral.md deleted file mode 100644 index 1d134418b0995..0000000000000 --- a/docs/docs/api_reference/multi_modal_llms/mistral.md +++ /dev/null @@ -1,4 +0,0 @@ -::: llama_index.multi_modal_llms.mistral - options: - members: - - MistralMultiModal From 746bdce4094b86b9c49c061debf1bd2350755dfe Mon Sep 17 00:00:00 2001 From: GICodeWarrior Date: Thu, 26 Sep 2024 20:11:09 -0700 Subject: [PATCH 42/53] Fix incorrect variable name in RetrieverTool (#16242) --- llama-index-core/llama_index/core/tools/retriever_tool.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama-index-core/llama_index/core/tools/retriever_tool.py b/llama-index-core/llama_index/core/tools/retriever_tool.py index 45aa51f3ab988..509b935cdc2a1 100644 --- a/llama-index-core/llama_index/core/tools/retriever_tool.py +++ b/llama-index-core/llama_index/core/tools/retriever_tool.py @@ -87,7 +87,7 @@ def call(self, *args: Any, **kwargs: Any) -> ToolOutput: return ToolOutput( content=content, tool_name=self.metadata.name, - raw_input={"input": input}, + raw_input={"input": query_str}, raw_output=docs, ) @@ -112,7 +112,7 @@ async def acall(self, *args: Any, **kwargs: Any) -> ToolOutput: return ToolOutput( content=content, tool_name=self.metadata.name, - raw_input={"input": input}, + raw_input={"input": query_str}, raw_output=docs, ) From d5f82da9a276375bfab139e9319fec74cccdc601 Mon Sep 17 00:00:00 2001 From: bechbd Date: Thu, 26 Sep 2024 19:12:20 -0800 Subject: [PATCH 43/53] #16198 - Added features to the TextToCypher Retriever (#16221) --- .../sub_retrievers/text_to_cypher.py | 81 +++++++++++++++++-- 1 file changed, 73 insertions(+), 8 deletions(-) diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py index 6b52b9fac6128..56a46b7bb6ae4 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py @@ -11,6 +11,28 @@ "Generated Cypher query:\n{query}\n\n" "Cypher Response:\n{response}" ) +DEFAULT_SUMMARY_TEMPLATE = PromptTemplate( + """You are an assistant that helps to form nice and human understandable answers. + The information part contains the provided information you must use to construct an answer. + The provided information is authoritative, never doubt it or try to use your internal knowledge to correct it. + If the provided information is empty, say that you don't know the answer. + Make the answer sound as a response to the question. Do not mention that you based the result on the given information. + Here is an example: + + Question: How many miles is the flight between the ANC and SEA airports? + Information: + [{"r.dist": 1440}] + Helpful Answer: + It is 1440 miles to fly between the ANC and SEA airports. + + Follow this example when generating answers. + Question: + {question} + Information: + {context} + Helpful Answer:""" +) + class TextToCypherRetriever(BasePGRetriever): """A Text-to-Cypher retriever that uses a language model to generate Cypher queries. @@ -31,6 +53,13 @@ class TextToCypherRetriever(BasePGRetriever): A callable function to validate the generated Cypher query. Defaults to None. allowed_query_fields (Optional[List[str]], optional): The fields to allow in the query output. Defaults to ["text", "label", "type"]. + include_raw_response_as_metadata (Optional[bool], optional): + If True this will add the query and raw response data to the metadata property. Defaults to False. + summarize_response (Optional[bool], optional): + If True this will run the response through the provided LLM to create a more human readable + response, If False this uses the provided or default response_template. Defaults to False. + summarization_template (Optional[str], optional): + The template to use for summarizing the response. Defaults to None. """ def __init__( @@ -41,6 +70,9 @@ def __init__( response_template: Optional[str] = None, cypher_validator: Optional[Callable] = None, allowed_output_fields: Optional[List[str]] = None, + include_raw_response_as_metadata: Optional[bool] = False, + summarize_response: Optional[bool] = False, + summarization_template: Optional[Union[PromptTemplate, str]] = None, **kwargs: Any, ) -> None: if not graph_store.supports_structured_queries: @@ -53,12 +85,19 @@ def __init__( if isinstance(text_to_cypher_template, str): text_to_cypher_template = PromptTemplate(text_to_cypher_template) + if isinstance(summarization_template, str): + summarization_template = PromptTemplate(summarization_template) + self.response_template = response_template or DEFAULT_RESPONSE_TEMPLATE self.text_to_cypher_template = ( text_to_cypher_template or graph_store.text_to_cypher_template ) self.cypher_validator = cypher_validator self.allowed_output_fields = allowed_output_fields + self.include_raw_response_as_metadata = include_raw_response_as_metadata + self.summarize_response = summarize_response + self.summarization_template = summarization_template or DEFAULT_SUMMARY_TEMPLATE + super().__init__( graph_store=graph_store, include_text=False, include_properties=False ) @@ -109,15 +148,28 @@ def retrieve_from_graph(self, query_bundle: QueryBundle) -> List[NodeWithScore]: cleaned_query_output = self._clean_query_output(query_output) - node_text = self.response_template.format( - query=parsed_cypher_query, - response=str(cleaned_query_output), - ) + if self.summarize_response: + summarized_response = self.llm.predict( + self.summarization_template, + context=str(cleaned_query_output), + question=parsed_cypher_query, + ) + node_text = summarized_response + else: + node_text = self.response_template.format( + query=parsed_cypher_query, + response=str(cleaned_query_output), + ) return [ NodeWithScore( node=TextNode( text=node_text, + metadata=( + {"query": parsed_cypher_query, "response": cleaned_query_output} + if self.include_raw_response_as_metadata + else {} + ), ), score=1.0, ) @@ -141,15 +193,28 @@ async def aretrieve_from_graph( cleaned_query_output = self._clean_query_output(query_output) - node_text = self.response_template.format( - query=parsed_cypher_query, - response=str(cleaned_query_output), - ) + if self.summarize_response: + summarized_response = await self.llm.apredict( + self.summarization_template, + context=str(cleaned_query_output), + question=parsed_cypher_query, + ) + node_text = summarized_response + else: + node_text = self.response_template.format( + query=parsed_cypher_query, + response=str(cleaned_query_output), + ) return [ NodeWithScore( node=TextNode( text=node_text, + metadata=( + {"query": parsed_cypher_query, "response": cleaned_query_output} + if self.include_raw_response_as_metadata + else {} + ), ), score=1.0, ) From a719cee8ba43bb3b6c2e3b00d2458a22a7583e88 Mon Sep 17 00:00:00 2001 From: jbtelice Date: Fri, 27 Sep 2024 05:35:01 +0200 Subject: [PATCH 44/53] Fix milvus collection creation with index_config (#16165) --- .../llama_index/vector_stores/milvus/base.py | 83 ++++++++++++++++--- .../pyproject.toml | 2 +- .../tests/test_vector_stores_milvus.py | 23 +++++ 3 files changed, 96 insertions(+), 12 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py index f25dbfc6ef502..6108fb9ec56a9 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/llama_index/vector_stores/milvus/base.py @@ -284,12 +284,64 @@ def __init__( if dim is None: raise ValueError("Dim argument required for collection creation.") if self.enable_sparse is False: - schema = self._create_schema() - self._milvusclient.create_collection( - collection_name=collection_name, - schema=schema, - consistency_level=consistency_level, - ) + # Check if custom index should be created + if ( + index_config is not None + and self.index_management is not IndexManagement.NO_VALIDATION + ): + try: + # Prepare index + index_params = self.client.prepare_index_params() + index_type = index_config["index_type"] + index_params.add_index( + field_name=embedding_field, + index_type=index_type, + metric_type=self.similarity_metric, + ) + + # Create a schema according to LlamaIndex Schema. + schema = self._create_schema() + schema.verify() + + # Using private method exposed by pymilvus client, in order to avoid creating indexes twice + # Reason: create_collection in pymilvus only checks schema and ignores index_config setup + # https://github.com/milvus-io/pymilvus/issues/2265 + self.client._create_collection_with_schema( + collection_name=collection_name, + schema=schema, + index_params=index_params, + dimemsion=dim, + primary_field=MILVUS_ID_FIELD, + vector_field=embedding_field, + id_type="string", + max_length=65_535, + consistency_level=consistency_level, + ) + self._collection = Collection( + collection_name, using=self._milvusclient._using + ) + except Exception as e: + logger.error("Error creating collection with index_config") + raise NotImplementedError( + "Error creating collection with index_config" + ) from e + else: + self._milvusclient.create_collection( + collection_name=collection_name, + dimension=dim, + primary_field_name=MILVUS_ID_FIELD, + vector_field_name=embedding_field, + id_type="string", + metric_type=self.similarity_metric, + max_length=65_535, + consistency_level=consistency_level, + ) + self._collection = Collection( + collection_name, using=self._milvusclient._using + ) + + # Check if we have to create an index here to avoid duplicity of indexes + self._create_index_if_required() else: try: _ = DataType.SPARSE_FLOAT_VECTOR @@ -302,9 +354,6 @@ def __init__( ) from e self._create_hybrid_index(collection_name) - self._collection = Collection(collection_name, using=self._milvusclient._using) - self._create_index_if_required() - # Set properties if collection_properties: if self._milvusclient.get_load_state(collection_name) == LoadState.Loaded: @@ -370,7 +419,6 @@ def add(self, nodes: List[BaseNode], **add_kwargs: Any) -> List[str]: self._collection.insert(insert_batch) if add_kwargs.get("force_flush", False): self._collection.flush() - self._create_index_if_required() logger.debug( f"Successfully inserted embeddings into: {self.collection_name} " f"Num Inserted: {len(insert_list)}" @@ -829,7 +877,20 @@ def _create_hybrid_index(self, collection_name: str) -> None: """ # Check if the collection exists, if not, create it if collection_name not in self._milvusclient.list_collections(): - schema = self._create_schema() + schema = MilvusClient.create_schema( + auto_id=False, enable_dynamic_field=True + ) + schema.add_field( + field_name="id", + datatype=DataType.VARCHAR, + max_length=65535, + is_primary=True, + ) + schema.add_field( + field_name=self.embedding_field, + datatype=DataType.FLOAT_VECTOR, + dim=self.dim, + ) schema.add_field( field_name=self.sparse_embedding_field, datatype=DataType.SPARSE_FLOAT_VECTOR, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml index ad7cc1d3a9d7f..b5681883e9b06 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-milvus" readme = "README.md" -version = "0.2.4" +version = "0.2.5" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/tests/test_vector_stores_milvus.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/tests/test_vector_stores_milvus.py index a77998ffb4784..138b838d4e998 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/tests/test_vector_stores_milvus.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-milvus/tests/test_vector_stores_milvus.py @@ -1,9 +1,11 @@ +from llama_index.core.schema import TextNode from llama_index.core.vector_stores.types import ( BasePydanticVectorStore, FilterCondition, FilterOperator, MetadataFilters, MetadataFilter, + VectorStoreQuery, ) from llama_index.vector_stores.milvus import MilvusVectorStore from llama_index.vector_stores.milvus.base import _to_milvus_filter @@ -177,3 +179,24 @@ def test_to_milvus_filter_with_multiple_filters(): ) expr = _to_milvus_filter(filters) assert expr == "(a < 1 or a > 10)" + + +def test_milvus_vector_store(): + vector_store = MilvusVectorStore( + dim=1536, + collection_name="test_collection", + embedding_field="embedding", + id_field="id", + similarity_metric="COSINE", + overwrite=True, + ) + + node = TextNode(text="Hello world", embedding=[0.5] * 1536) + + vector_store.add([node]) + + result = vector_store.query( + VectorStoreQuery(query_embedding=[0.5] * 1536, similarity_top_k=1) + ) + assert len(result.nodes) == 1 + assert result.nodes[0].text == "Hello world" From b99ef2d4cf4763b888e0d60e5894fbce83a9ecd0 Mon Sep 17 00:00:00 2001 From: Ravi Theja Date: Fri, 27 Sep 2024 21:07:49 +0530 Subject: [PATCH 45/53] Update BM25 retreiver to use metadata (#16267) --- .../llama_index/retrievers/bm25/base.py | 10 ++++++++-- .../llama-index-retrievers-bm25/pyproject.toml | 2 +- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-bm25/llama_index/retrievers/bm25/base.py b/llama-index-integrations/retrievers/llama-index-retrievers-bm25/llama_index/retrievers/bm25/base.py index 1e04d70c4dd96..d204bebb975ea 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-bm25/llama_index/retrievers/bm25/base.py +++ b/llama-index-integrations/retrievers/llama-index-retrievers-bm25/llama_index/retrievers/bm25/base.py @@ -8,7 +8,13 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K from llama_index.core.indices.vector_store.base import VectorStoreIndex -from llama_index.core.schema import BaseNode, IndexNode, NodeWithScore, QueryBundle +from llama_index.core.schema import ( + BaseNode, + IndexNode, + NodeWithScore, + QueryBundle, + MetadataMode, +) from llama_index.core.storage.docstore.types import BaseDocumentStore from llama_index.core.vector_stores.utils import ( node_to_metadata_dict, @@ -75,7 +81,7 @@ def __init__( self.corpus = [node_to_metadata_dict(node) for node in nodes] corpus_tokens = bm25s.tokenize( - [node.get_content() for node in nodes], + [node.get_content(metadata_mode=MetadataMode.EMBED) for node in nodes], stopwords=language, stemmer=self.stemmer, show_progress=verbose, diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml index 80b8155095fca..dbd71ae457bec 100644 --- a/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml +++ b/llama-index-integrations/retrievers/llama-index-retrievers-bm25/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-retrievers-bm25" readme = "README.md" -version = "0.3.0" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From e46fafa295d3ecd47f3ed3a3ce9ec183450c9c0e Mon Sep 17 00:00:00 2001 From: Ravi Theja Date: Fri, 27 Sep 2024 23:58:51 +0530 Subject: [PATCH 46/53] Add support for prompt caching for Anthropic LLM (#16270) * Add support for anthropic prompt caching * Add support for anthropic prompt caching --- .../llama_index/llms/anthropic/utils.py | 17 ++++++++++++++++- .../llama-index-llms-anthropic/pyproject.toml | 4 ++-- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py index 38b12f2e53a04..98dd20f25183d 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py @@ -5,6 +5,10 @@ from anthropic.types import MessageParam, TextBlockParam, ImageBlockParam from anthropic.types.tool_result_block_param import ToolResultBlockParam from anthropic.types.tool_use_block_param import ToolUseBlockParam +from anthropic.types.beta.prompt_caching import ( + PromptCachingBetaTextBlockParam, + PromptCachingBetaCacheControlEphemeralParam, +) HUMAN_PREFIX = "\n\nHuman:" ASSISTANT_PREFIX = "\n\nAssistant:" @@ -97,7 +101,18 @@ def messages_to_anthropic_messages( else: content.append(TextBlockParam(text=item, type="text")) elif message.content: - content.append(TextBlockParam(text=message.content, type="text")) + content_ = ( + PromptCachingBetaTextBlockParam( + text=message.content, + type="text", + cache_control=PromptCachingBetaCacheControlEphemeralParam( + type="ephemeral" + ), + ) + if "cache_control" in message.additional_kwargs + else [TextBlockParam(text=message.content, type="text")] + ) + content.append(content_) tool_calls = message.additional_kwargs.get("tool_calls", []) for tool_call in tool_calls: diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml index cf083e27b1c87..36155632e69e9 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-anthropic" readme = "README.md" -version = "0.3.1" +version = "0.3.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -anthropic = {extras = ["vertex"], version = ">=0.26.2, <0.29.0"} +anthropic = {extras = ["vertex"], version = ">=0.34.2"} llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] From 5b1fe2596dfe9c60f665e0dda9c2a71969844d54 Mon Sep 17 00:00:00 2001 From: Ravi Theja Date: Sat, 28 Sep 2024 01:42:43 +0530 Subject: [PATCH 47/53] Add Anthropic Prompt caching notebook (#16272) * Add support for anthropic prompt caching * Add support for anthropic prompt caching * Update prompt caching logic * Add notebook * remove print statements * Update notebook * Add google colab link --- .../llm/anthropic_prompt_caching.ipynb | 310 ++++++++++++++++++ .../llama_index/llms/anthropic/utils.py | 11 +- .../llama-index-llms-anthropic/pyproject.toml | 2 +- 3 files changed, 321 insertions(+), 2 deletions(-) create mode 100644 docs/docs/examples/llm/anthropic_prompt_caching.ipynb diff --git a/docs/docs/examples/llm/anthropic_prompt_caching.ipynb b/docs/docs/examples/llm/anthropic_prompt_caching.ipynb new file mode 100644 index 0000000000000..429f36042aa5d --- /dev/null +++ b/docs/docs/examples/llm/anthropic_prompt_caching.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open\n", + "\n", + "# Anthropic Prompt Caching\n", + "\n", + "In this Notebook, we will demonstrate the usage of [Anthropic Prompt Caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching) with LlamaIndex abstractions.\n", + "\n", + "Prompt Caching is enabled by marking `cache_control` in the messages request.\n", + "\n", + "\n", + "## How Prompt Caching works\n", + "\n", + "When you send a request with Prompt Caching enabled:\n", + "\n", + "1. The system checks if the prompt prefix is already cached from a recent query.\n", + "2. If found, it uses the cached version, reducing processing time and costs.\n", + "3. Otherwise, it processes the full prompt and caches the prefix for future use.\n", + "\n", + "\n", + "**Note:** \n", + "\n", + "A. Prompt caching works with `Claude 3.5 Sonnet`, `Claude 3 Haiku` and `Claude 3 Opus` models.\n", + "\n", + "B. The minimum cacheable prompt length is:\n", + "\n", + " 1. 1024 tokens for Claude 3.5 Sonnet and Claude 3 Opus\n", + " 2. 2048 tokens for Claude 3 Haiku\n", + "\n", + "C. Shorter prompts cannot be cached, even if marked with `cache_control`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup API Keys" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\n", + " \"ANTHROPIC_API_KEY\"\n", + "] = \"sk-...\" # replace with your Anthropic API key" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup LLM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.anthropic import Anthropic\n", + "\n", + "llm = Anthropic(model=\"claude-3-5-sonnet-20240620\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download Data\n", + "\n", + "In this demonstration, we will use the text from the `Paul Graham Essay`. We will cache the text and run some queries based on it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-09-28 01:22:14-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 2606:50c0:8000::154, 2606:50c0:8001::154, 2606:50c0:8002::154, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|2606:50c0:8000::154|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 75042 (73K) [text/plain]\n", + "Saving to: ‘./paul_graham_essay.txt’\n", + "\n", + "./paul_graham_essay 100%[===================>] 73.28K --.-KB/s in 0.01s \n", + "\n", + "2024-09-28 01:22:14 (5.73 MB/s) - ‘./paul_graham_essay.txt’ saved [75042/75042]\n", + "\n" + ] + } + ], + "source": [ + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O './paul_graham_essay.txt'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\n", + " input_files=[\"./paul_graham_essay.txt\"],\n", + ").load_data()\n", + "\n", + "document_text = documents[0].text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prompt Caching\n", + "\n", + "Enabling Prompt Cache:\n", + "\n", + "1.\tInclude `\"cache_control\": {\"type\": \"ephemeral\"}` for the text prompt you want to cache.\n", + "2.\tAdd `extra_headers={\"anthropic-beta\": \"prompt-caching-2024-07-31\"}` in the request." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can verify if the text is cached by checking the following parameters:\n", + "\n", + "`cache_creation_input_tokens:` Number of tokens written to the cache when creating a new entry.\n", + "\n", + "`cache_read_input_tokens:` Number of tokens retrieved from the cache for this request.\n", + "\n", + "`input_tokens:` Number of input tokens which were not read from or used to create a cache." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.llms import ChatMessage\n", + "\n", + "messages = [\n", + " ChatMessage(role=\"system\", content=\"You are helpful AI Assitant.\"),\n", + " ChatMessage(\n", + " role=\"user\",\n", + " content=[\n", + " {\n", + " \"text\": f\"{document_text}\",\n", + " \"type\": \"text\",\n", + " \"cache_control\": {\"type\": \"ephemeral\"},\n", + " },\n", + " {\"text\": \"Why did Paul Graham start YC?\", \"type\": \"text\"},\n", + " ],\n", + " ),\n", + "]\n", + "\n", + "resp = llm.chat(\n", + " messages, extra_headers={\"anthropic-beta\": \"prompt-caching-2024-07-31\"}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's examine the raw response." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'msg_01KCcFZnbAGjxSKJm7LnXajp',\n", + " 'content': [TextBlock(text=\"Based on the essay, it seems Paul Graham started Y Combinator for a few key reasons:\\n\\n1. He had been thinking about ways to improve venture capital and startup funding, like making smaller investments in younger, more technical founders.\\n\\n2. He wanted to try angel investing but hadn't gotten around to it yet, despite intending to for years after Yahoo acquired his company Viaweb.\\n\\n3. He missed working with his former Viaweb co-founders Robert Morris and Trevor Blackwell and wanted to find a project they could collaborate on.\\n\\n4. His girlfriend (later wife) Jessica Livingston was looking for a new job after interviewing at a VC firm, and Graham had been telling her ideas for how to improve VC.\\n\\n5. When giving a talk to Harvard students about startups, he realized there was demand for seed funding and advice from experienced founders.\\n\\n6. They wanted to create an investment firm that would actually implement Graham's ideas about how to better fund and support early-stage startups.\\n\\n7. They were somewhat naïve about how to be angel investors, which allowed them to take novel approaches like the batch model of funding multiple startups at once.\\n\\nSo it was a convergence of Graham's ideas about improving startup funding, his desire to angel invest and work with his former co-founders again, and the opportunity presented by Jessica looking for a new job. Their lack of experience in traditional VC allowed them to take an innovative approach.\", type='text')],\n", + " 'model': 'claude-3-5-sonnet-20240620',\n", + " 'role': 'assistant',\n", + " 'stop_reason': 'end_turn',\n", + " 'stop_sequence': None,\n", + " 'type': 'message',\n", + " 'usage': Usage(input_tokens=12, output_tokens=313, cache_creation_input_tokens=17470, cache_read_input_tokens=0)}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resp.raw" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, `17470` tokens have been cached, as indicated by `cache_creation_input_tokens`.\n", + "\n", + "Now, let’s run another query on the same document. It should retrieve the document text from the cache, which will be reflected in `cache_read_input_tokens`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "messages = [\n", + " ChatMessage(role=\"system\", content=\"You are helpful AI Assitant.\"),\n", + " ChatMessage(\n", + " role=\"user\",\n", + " content=[\n", + " {\n", + " \"text\": f\"{document_text}\",\n", + " \"type\": \"text\",\n", + " \"cache_control\": {\"type\": \"ephemeral\"},\n", + " },\n", + " {\"text\": \"What did Paul Graham do growing up?\", \"type\": \"text\"},\n", + " ],\n", + " ),\n", + "]\n", + "\n", + "resp = llm.chat(\n", + " messages, extra_headers={\"anthropic-beta\": \"prompt-caching-2024-07-31\"}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': 'msg_01CpwhtuvJ8UR64xSbpxoutZ',\n", + " 'content': [TextBlock(text='Based on the essay, here are some key things Paul Graham did growing up:\\n\\n1. As a teenager, he focused mainly on writing and programming outside of school. He tried writing short stories but says they were \"awful\".\\n\\n2. In 9th grade (age 13-14), he started programming on an IBM 1401 computer at his school district\\'s data processing center. He used an early version of Fortran.\\n\\n3. He convinced his father to buy a TRS-80 microcomputer around 1980 when he was in high school. He wrote simple games, a program to predict model rocket flight, and a word processor his father used.\\n\\n4. He planned to study philosophy in college, thinking it was more powerful than other fields. \\n\\n5. In college, he got interested in artificial intelligence after reading a novel featuring an intelligent computer and seeing a documentary about an AI program called SHRDLU.\\n\\n6. He taught himself Lisp programming language in college since there were no AI classes offered.\\n\\n7. For his undergraduate thesis, he reverse-engineered the SHRDLU AI program.\\n\\n8. He graduated college with a degree in \"Artificial Intelligence\" (in quotes on the diploma).\\n\\n9. He applied to grad schools for AI and ended up going to Harvard for graduate studies.\\n\\nSo in summary, his main interests and activities growing up centered around writing, programming, and eventually artificial intelligence as he entered college and graduate school.', type='text')],\n", + " 'model': 'claude-3-5-sonnet-20240620',\n", + " 'role': 'assistant',\n", + " 'stop_reason': 'end_turn',\n", + " 'stop_sequence': None,\n", + " 'type': 'message',\n", + " 'usage': Usage(input_tokens=12, output_tokens=313, cache_creation_input_tokens=0, cache_read_input_tokens=17470)}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "resp.raw" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the response was generated using cached text, as indicated by `cache_read_input_tokens`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "llamacloud", + "language": "python", + "name": "llamacloud" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py index 98dd20f25183d..3870234abece2 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py @@ -96,6 +96,16 @@ def messages_to_anthropic_messages( if item and isinstance(item, dict) and item.get("type", None): if item["type"] == "image": content.append(ImageBlockParam(**item)) + elif "cache_control" in item and item["type"] == "text": + content.append( + PromptCachingBetaTextBlockParam( + text=item["text"], + type="text", + cache_control=PromptCachingBetaCacheControlEphemeralParam( + type="ephemeral" + ), + ) + ) else: content.append(TextBlockParam(**item)) else: @@ -134,7 +144,6 @@ def messages_to_anthropic_messages( content=content, # TODO: type detect for multimodal ) anthropic_messages.append(anth_message) - return __merge_common_role_msgs(anthropic_messages), system_prompt.strip() diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml index 36155632e69e9..292bda32a6705 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-anthropic" readme = "README.md" -version = "0.3.2" +version = "0.3.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 3635f7e015b8cafb9942cfdefa0676c4b33c2079 Mon Sep 17 00:00:00 2001 From: Zac Wellmer <9603276+zacwellmer@users.noreply.github.com> Date: Fri, 27 Sep 2024 13:36:45 -0700 Subject: [PATCH 48/53] nudge-ft package and add an example for expanding your dataset (#16269) --- .../finetune_corpus_embedding.ipynb | 183 ++++++++++++-- .../llama_index/experimental/nudge/base.py | 230 ++++++++---------- llama-index-experimental/pyproject.toml | 2 +- 3 files changed, 260 insertions(+), 155 deletions(-) diff --git a/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb b/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb index 277d5bc9a8aa1..06379eec42d8c 100644 --- a/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb +++ b/docs/docs/examples/finetuning/embeddings/finetune_corpus_embedding.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index-experimental llama-index-embeddings-huggingface torch datasets" + "%pip install llama-index-experimental llama-index-embeddings-huggingface nudge-ft torch datasets" ] }, { @@ -30,7 +30,24 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:datasets:PyTorch version 2.5.0a0+872d972e41.nv24.8 available.\n", + "PyTorch version 2.5.0a0+872d972e41.nv24.8 available.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from llama_index.finetuning import EmbeddingQAFinetuneDataset\n", "from datasets import load_dataset\n", @@ -114,7 +131,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: BAAI/bge-small-en-v1.5\n", + "Load pretrained SentenceTransformer: BAAI/bge-small-en-v1.5\n", + "INFO:sentence_transformers.SentenceTransformer:2 prompts are loaded, with the keys: ['query', 'text']\n", + "2 prompts are loaded, with the keys: ['query', 'text']\n" + ] + } + ], "source": [ "from llama_index.core.embeddings import resolve_embed_model\n", "\n", @@ -189,7 +217,7 @@ "source": [ "### Using your own Datasets\n", "\n", - "As you can see, you can run this notebook on any dataset, as long as you have queries and a mapping to relevant documents! \n", + "As you can see, you can run this notebook on any dataset, as long as you have queries and a mapping to relevant documents! If you have documents but are missing a training set of queries checkout the our tools for generating a synthetic dataset ([1](https://docs.llamaindex.ai/en/stable/api_reference/evaluation/dataset_generation/)).\n", "\n", "If you wanted, you could also write your own dataset, or even use llama-index to create your own.\n", "\n", @@ -258,7 +286,7 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Optional\n", + "from typing import Optional, Dict\n", "\n", "import torch\n", "import numpy as np\n", @@ -270,13 +298,11 @@ "\n", "\n", "def build_retriever(\n", - " dataset: EmbeddingQAFinetuneDataset,\n", + " corpus: Dict[str, str],\n", " embed_model: BaseEmbedding | str,\n", " corpus_embeddings: Optional[torch.Tensor] = None,\n", " k: int = 10,\n", ") -> BaseRetriever:\n", - " corpus = dataset.corpus\n", - "\n", " nodes = []\n", " for i, (id_, text) in enumerate(corpus.items()):\n", " if corpus_embeddings is not None:\n", @@ -302,7 +328,6 @@ "):\n", " queries = dataset.queries\n", " relevant_docs = dataset.relevant_docs\n", - "\n", " ndcg_scores = []\n", " for query_id, query in tqdm(queries.items()):\n", " retrieved_nodes = retriever.retrieve(query)\n", @@ -360,14 +385,12 @@ " train_dataset=train_dataset,\n", " val_dataset=val_dataset,\n", " embed_model=base_embed_model,\n", - " epochs=10000,\n", - " train_batch_size=len(train_dataset.queries),\n", - " val_batch_size=len(val_dataset.queries),\n", + " use_nudge_n=True,\n", ")\n", "nudge.finetune()\n", "nudge_corpus_embeddings = nudge.get_finetuned_corpus_embeddings()\n", "nudge_retriever = build_retriever(\n", - " train_dataset, base_embed_model, nudge_corpus_embeddings, k=k\n", + " train_dataset.corpus, base_embed_model, nudge_corpus_embeddings, k=k\n", ")\n", "nudge_ndcg_test = ndcg_at_k(test_dataset, nudge_retriever, k)" ] @@ -376,8 +399,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Get the adapter finetuning results\n", - "We use a smaller batchsize than NUDGE above due to the adapter finetune baseline having a significantly slower training process. We also note that even with a batchsize the size of the dataset and 10k epochs the adapter finetuned model performs similarly to the hyperparams currently used." + "## Get the adapter finetuning results" ] }, { @@ -410,7 +432,9 @@ "embedding_adapter_model = (\n", " embedding_adapater_finetune_engine.get_finetuned_model()\n", ")\n", - "ft_retriever = build_retriever(train_dataset, embedding_adapter_model, k=k)\n", + "ft_retriever = build_retriever(\n", + " train_dataset.corpus, embedding_adapter_model, k=k\n", + ")\n", "ft_ndcg_test = ndcg_at_k(test_dataset, ft_retriever, k)" ] }, @@ -429,7 +453,7 @@ "source": [ "%%capture\n", "\n", - "base_retriever = build_retriever(train_dataset, base_embed_model, k=k)\n", + "base_retriever = build_retriever(train_dataset.corpus, base_embed_model, k=k)\n", "bge_ndcg_test = ndcg_at_k(test_dataset, base_retriever, k)" ] }, @@ -451,14 +475,133 @@ "text": [ "bge test - ndcg@10: 0.71\n", "adaptor finetune test - ndcg@10: 0.72\n", - "NUDGE test - ndcg@10: 0.83\n" + "NUDGE-N test - ndcg@10: 0.87\n" ] } ], "source": [ "print(f\"bge test - ndcg@10: {bge_ndcg_test:.2f}\")\n", "print(f\"adaptor finetune test - ndcg@10: {ft_ndcg_test:.2f}\")\n", - "print(f\"NUDGE test - ndcg@10: {nudge_ndcg_test:.2f}\")" + "print(f\"NUDGE-N test - ndcg@10: {nudge_ndcg_test:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Inserting records into the dataset\n", + "It's common to have your dataset expand over time. We will now insert and finetune the nfcorpus into the scifact example we've been working with. Usually you'd have to retrain on your entire dataset to avoid catastrophic forgetting. With NUDGE, you can easily expand your dataset iteratively by focusing only on the newest batch of data, without worrying about catastrophic forgetting. This only works when the new data being inserted does not conflict (e.g. new queries for old corpus or new corpus changes k-NN to old queries) with the existing dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "new_train_dataset, new_val_dataset, new_test_dataset = load_hf_dataset(\n", + " \"nfcorpus\"\n", + ")\n", + "\n", + "# prepend \"nfcorpus-\" to the keys so they don't conflict with the scifact ids\n", + "new_train_dataset.queries = {\n", + " f\"nfcorpus-{k}\": v for k, v in new_train_dataset.queries.items()\n", + "}\n", + "new_train_dataset.relevant_docs = {\n", + " f\"nfcorpus-{k}\": [f\"nfcorpus-{doc_id}\" for doc_id in v]\n", + " for k, v in new_train_dataset.relevant_docs.items()\n", + "}\n", + "new_train_dataset.corpus = {\n", + " f\"nfcorpus-{k}\": v for k, v in new_train_dataset.corpus.items()\n", + "}\n", + "\n", + "new_val_dataset.queries = {\n", + " f\"nfcorpus-{k}\": v for k, v in new_val_dataset.queries.items()\n", + "}\n", + "new_val_dataset.relevant_docs = {\n", + " f\"nfcorpus-{k}\": [f\"nfcorpus-{doc_id}\" for doc_id in v]\n", + " for k, v in new_val_dataset.relevant_docs.items()\n", + "}\n", + "new_val_dataset.corpus = {\n", + " f\"nfcorpus-{k}\": v for k, v in new_val_dataset.corpus.items()\n", + "}\n", + "\n", + "new_test_dataset.queries = {\n", + " f\"nfcorpus-{k}\": v for k, v in new_test_dataset.queries.items()\n", + "}\n", + "new_test_dataset.relevant_docs = {\n", + " f\"nfcorpus-{k}\": [f\"nfcorpus-{doc_id}\" for doc_id in v]\n", + " for k, v in new_test_dataset.relevant_docs.items()\n", + "}\n", + "new_test_dataset.corpus = {\n", + " f\"nfcorpus-{k}\": v for k, v in new_test_dataset.corpus.items()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finetune the new records" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "nudge.insert_data_and_finetune(\n", + " new_train_dataset_batch=new_train_dataset,\n", + " new_val_dataset_batch=new_val_dataset,\n", + ")\n", + "# get our corpus embeddings with the newly inserted and tuned records\n", + "nudge_corpus_embeddings = nudge.get_finetuned_corpus_embeddings()\n", + "# aggregate the corpus\n", + "aggregated_corpus = {**train_dataset.corpus, **new_train_dataset.corpus}\n", + "# build nudge retriever\n", + "nudge_retriever = build_retriever(\n", + " aggregated_corpus, base_embed_model, nudge_corpus_embeddings, k=k\n", + ")\n", + "# get test results on nfcorpus\n", + "nudge_ndcg_nfcorpus_test = ndcg_at_k(new_test_dataset, nudge_retriever, k)\n", + "# get test results on scifact\n", + "nudge_ndcg_scifact_test = ndcg_at_k(test_dataset, nudge_retriever, k)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Display the insertion results\n", + "Check the results on our newly inserted nfcorpus records and verify that our scifact benchmark did not regress." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NUDGE-N (aggregated) test on nfcorpus - ndcg@10: 0.44\n", + "NUDGE-N (aggregated) test on scifact - ndcg@10: 0.85\n" + ] + } + ], + "source": [ + "print(\n", + " f\"NUDGE-N (aggregated) test on nfcorpus - ndcg@10: {nudge_ndcg_nfcorpus_test:.2f}\"\n", + ")\n", + "print(\n", + " f\"NUDGE-N (aggregated) test on scifact - ndcg@10: {nudge_ndcg_scifact_test:.2f}\"\n", + ")" ] } ], @@ -481,5 +624,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/llama-index-experimental/llama_index/experimental/nudge/base.py b/llama-index-experimental/llama_index/experimental/nudge/base.py index ae8aeae88d8c3..6621be59461e5 100644 --- a/llama-index-experimental/llama_index/experimental/nudge/base.py +++ b/llama-index-experimental/llama_index/experimental/nudge/base.py @@ -1,20 +1,15 @@ import logging -from typing import Any, Optional +from typing import Dict, Optional -from tqdm import tqdm, trange - -from llama_index.core.utils import print_text from llama_index.core.base.embeddings.base import BaseEmbedding from llama_index.core.utils import infer_torch_device from llama_index.finetuning.embeddings.common import EmbeddingQAFinetuneDataset logger = logging.getLogger(__name__) -IMPORT_ERROR_MSG = "PyTorch is not installed. Please install it with 'pip install torch' to use this functionality." - - -def multiclass_nll_loss(output, targets): - return (-1 * output * targets).sum(axis=-1).mean() +NUDGE_IMPORT_ERROR_MSG = "NUDGE is not installed. Please install it with 'pip install nudge-ft' to use this functionality." +NUMPY_IMPORT_ERROR_MSG = "Numpy is not installed. Please install it with 'pip install numpy' to use this functionality." +PYTORCH_IMPORT_ERROR_MSG = "Pytorch is not installed. Please install it with 'pip install torch' to use this functionality." class Nudge: @@ -24,174 +19,141 @@ class Nudge: Args: train_dataset (EmbeddingQAFinetuneDataset): Dataset to finetune on. embed_model (BaseEmbedding): Embedding model. - val_dataset (Optional[EmbeddingQAFinetuneDataset]): Validation dataset. Defaults to None. - train_batch_size (Optional[int]): Train batch size. Defaults to 10. - val_batch_size (Optional[int]): Validation batch size. Defaults to 10. - epochs (Optional[int]): Number of epochs. Defaults to 1. - dim (Optional[int]): Dimension of embedding. Defaults to None. + val_dataset (EmbeddingQAFinetuneDataset): Validation dataset. + use_nudge_n (bool): Whether to use NUDGE-N or NUDGE-M. Defaults to True. device (Optional[str]): Device to use. Defaults to None. - model_output_path (str): Path to save model output. Defaults to "model_output". - model_checkpoint_path (Optional[str]): Path to save model checkpoints. - Defaults to None (don't save checkpoints). - verbose (bool): Whether to show progress bar. Defaults to False. - bias (bool): Whether to use bias. Defaults to False. """ def __init__( self, - train_dataset: EmbeddingQAFinetuneDataset, embed_model: BaseEmbedding, - val_dataset: Optional[EmbeddingQAFinetuneDataset] = None, - train_batch_size: int = 10, - val_batch_size: int = 10, - epochs: int = 1, + train_dataset: EmbeddingQAFinetuneDataset, + val_dataset: EmbeddingQAFinetuneDataset, + use_nudge_n: bool = True, device: Optional[str] = None, - verbose: bool = False, ) -> None: """Init params.""" + try: + from nudge import NUDGEN, NUDGEM + except ImportError: + raise ImportError(NUDGE_IMPORT_ERROR_MSG) try: import torch except ImportError: - raise ImportError(IMPORT_ERROR_MSG) - - self.train_dataset = train_dataset - self.val_dataset = val_dataset - self.embed_model = embed_model - self.corpus_embeddings = self._get_corpus_embeddings(train_dataset) - - # load in data, run embedding model, define data loader - - self.train_batch_size = train_batch_size - self.val_batch_size = val_batch_size - self.train_loader = self._get_data_loader(train_dataset, train_batch_size) - self.val_loader = ( - self._get_data_loader(val_dataset, val_batch_size) - if val_dataset is not None - else None - ) + raise ImportError(PYTORCH_IMPORT_ERROR_MSG) if device is None: device = infer_torch_device() logger.info(f"Use pytorch device: {device}") self._target_device = torch.device(device) - self._epochs = epochs + self.embed_model = embed_model + self.corpus = train_dataset.corpus + self.corpus_embeddings = self._get_corpus_embeddings(self.corpus) + self.train_dataset = self._format_dataset(train_dataset, self.corpus) + self.val_dataset = self._format_dataset(val_dataset, self.corpus) + + self.nudge = ( + NUDGEN(device=self._target_device) + if use_nudge_n + else NUDGEM(device=self._target_device) + ) - self._verbose = verbose + def _format_dataset( + self, dataset: EmbeddingQAFinetuneDataset, corpus: Dict[str, str] + ): + """ + Convert the dataset into NUDGE format. - def _get_data_loader( - self, dataset: EmbeddingQAFinetuneDataset, batch_size: int - ) -> Any: - """Get data loader.""" + Args: + dataset (EmbeddingQAFinetuneDataset): Dataset to convert. + """ try: - import torch - from torch.utils.data import DataLoader + import numpy as np except ImportError: - raise ImportError(IMPORT_ERROR_MSG) + raise ImportError(NUMPY_IMPORT_ERROR_MSG) - examples: Any = [] + q_embs = [] + q_ans_indx = [] + corpus_keys = list(corpus.keys()) for query_id, query in dataset.queries.items(): - query_embedding = torch.tensor(self.embed_model.get_query_embedding(query)) - relevant_docs = dataset.relevant_docs[query_id] - relevant_docs = torch.tensor( - [1 if doc in relevant_docs else 0 for doc in dataset.corpus] - ) + query_embedding = self.embed_model.get_query_embedding(query) + q_embs.append(query_embedding) - examples.append((query_embedding, relevant_docs)) + relevant_docs = dataset.relevant_docs[query_id] + relevant_doc_indices = [corpus_keys.index(doc) for doc in relevant_docs] + q_ans_indx.append(relevant_doc_indices) - return DataLoader(examples, batch_size=batch_size) + return {"q_embs": np.array(q_embs), "q_ans_indx": q_ans_indx} - def _get_corpus_embeddings(self, dataset: EmbeddingQAFinetuneDataset): + def _get_corpus_embeddings(self, corpus: Dict[str, str]): """Get corpus embeddings.""" try: - import torch + import numpy as np except ImportError: - raise ImportError(IMPORT_ERROR_MSG) + raise ImportError(NUMPY_IMPORT_ERROR_MSG) text_embeddings = [ - self.embed_model.get_text_embedding(text) - for text in dataset.corpus.values() + self.embed_model.get_text_embedding(text) for text in corpus.values() ] - return torch.tensor(text_embeddings, requires_grad=False) + return np.array(text_embeddings) + + def finetune(self): + self.corpus_embeddings = self.nudge.finetune_embeddings( + embeddings=self.corpus_embeddings, + train_set=self.train_dataset, + val_set=self.val_dataset, + nontrain_embeddings=None, + val_batch_size=256, + gamma=None, + ) - def _evaluate_acc(self, model, loader): - """Evaluate model.""" + def insert_data_and_finetune( + self, + new_train_dataset_batch: EmbeddingQAFinetuneDataset, + new_val_dataset_batch: Optional[EmbeddingQAFinetuneDataset] = None, + ): + """ + Insert data and finetune. This should only be done if the new data you are inserting does not conflict with the already existing data. It's important to not finetune multiple times as this can cause the embeddings to lose semantic meaning since they will become further from the original embeddings. + """ try: - import torch + import numpy as np except ImportError: - raise ImportError(IMPORT_ERROR_MSG) - - model.eval() - total_acc = 0 - total_records = 0 - with torch.no_grad(): - for query_embeddings_t, relevant_docs_t in loader: - query_embeddings_t = query_embeddings_t.to(self._target_device) - relevant_docs_t = relevant_docs_t.to(self._target_device) + raise ImportError(NUMPY_IMPORT_ERROR_MSG) - preds = model(query_embeddings_t) - out = preds.max(1).indices.view(-1, 1) - truths = torch.gather(relevant_docs_t, 1, out) + new_corpus_batch = new_train_dataset_batch.corpus + # if any of the new ids are already in the existing corpus, raise an error + if any(id in self.corpus for id in new_corpus_batch): + raise ValueError( + f"ID {id} already exists in the existing corpus. New IDs must be unique." + ) - total_acc += truths.sum().item() - total_records += truths.shape[0] - return total_acc / total_records + # get the embeddings for the new corpus + new_corpus_initial_embeddings_batch = self._get_corpus_embeddings( + new_corpus_batch + ) - def finetune(self): - try: - import torch - from torch import nn - from torch.nn import functional as F - except ImportError: - raise ImportError(IMPORT_ERROR_MSG) + existing_corpus_embeddings = self.corpus_embeddings - # initialize the weights of a linear model with the normalized corpus embeddings - w_init = F.normalize(self.corpus_embeddings) - model = nn.Linear(w_init.shape[1], w_init.shape[0], bias=False) - model.weight.data = w_init - model.to(self._target_device) + new_train_dataset = self._format_dataset( + new_train_dataset_batch, new_corpus_batch + ) + new_val_dataset = self._format_dataset(new_val_dataset_batch, new_corpus_batch) + + new_corpus_embeddings_batch = self.nudge.finetune_embeddings( + embeddings=new_corpus_initial_embeddings_batch, + train_set=new_train_dataset, + val_set=new_val_dataset, + # runs faster by filtering the embeddings which will not have any queries + nontrain_embeddings=existing_corpus_embeddings, + val_batch_size=256, + gamma=None, + ) - # train the model - optimizer = torch.optim.Adam( - model.parameters(), lr=1e-5, betas=(0.9, 0.999), eps=1e-8 + self.corpus_embeddings = np.concatenate( + [existing_corpus_embeddings, new_corpus_embeddings_batch] ) - best_val_acc = self._evaluate_acc(model, self.val_loader) - - for epoch in trange(self._epochs, desc="Epoch"): - model.train() - for query_embeddings_t, relevant_docs_t in tqdm( - self.train_loader, desc=f"Epoch {epoch+1}/{self._epochs}", leave=False - ): - query_embeddings_t = query_embeddings_t.to(self._target_device) - relevant_docs_t = relevant_docs_t.to(self._target_device) - - loss = multiclass_nll_loss(model(query_embeddings_t), relevant_docs_t) - - loss.backward() - optimizer.step() - optimizer.zero_grad() - - # normalize the weights - with torch.no_grad(): - model.weight.data = F.normalize(model.weight.data) - - if self._verbose: - print_text( - f"> [Epoch {epoch}] Current loss: {loss}\n", color="blue" - ) - if self.val_loader is not None: - val_acc = self._evaluate_acc(model, self.val_loader) - if self._verbose: - print_text( - f"> [Epoch {epoch}] validation acc: {val_acc} best validation acc: {best_val_acc} \n", - color="blue", - ) - if val_acc > best_val_acc: - best_val_acc = val_acc - self.corpus_embeddings = model.weight.data.clone() - else: - self.corpus_embeddings = model.weight.data.clone() def get_finetuned_corpus_embeddings(self): return self.corpus_embeddings diff --git a/llama-index-experimental/pyproject.toml b/llama-index-experimental/pyproject.toml index ecae2d596dc96..f75c15370860e 100644 --- a/llama-index-experimental/pyproject.toml +++ b/llama-index-experimental/pyproject.toml @@ -25,7 +25,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-experimental" readme = "README.md" -version = "0.3.3" +version = "0.4.0" [tool.poetry.dependencies] python = ">=3.10,<4.0" From 86bfe121d1ac27e678873d9165b8ce0a19eb06f8 Mon Sep 17 00:00:00 2001 From: Rakesh Mehta <46493063+rakeshmehta0308@users.noreply.github.com> Date: Fri, 27 Sep 2024 23:49:28 +0100 Subject: [PATCH 49/53] Add Zyte Web Reader (#16197) --- .../data_connectors/WebPageDemo.ipynb | 128 +++++++++++++- .../llama_index/readers/web/__init__.py | 4 + .../llama_index/readers/web/zyte_web/BUILD | 5 + .../readers/web/zyte_web/README.md | 63 +++++++ .../readers/web/zyte_web/__init__.py | 0 .../llama_index/readers/web/zyte_web/base.py | 157 ++++++++++++++++++ .../readers/web/zyte_web/requirements.txt | 2 + .../llama-index-readers-web/pyproject.toml | 2 +- 8 files changed, 357 insertions(+), 4 deletions(-) create mode 100644 llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/BUILD create mode 100644 llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/README.md create mode 100644 llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/__init__.py create mode 100644 llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/base.py create mode 100644 llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/requirements.txt diff --git a/docs/docs/examples/data_connectors/WebPageDemo.ipynb b/docs/docs/examples/data_connectors/WebPageDemo.ipynb index de4a82f12d931..3330cdea468cf 100644 --- a/docs/docs/examples/data_connectors/WebPageDemo.ipynb +++ b/docs/docs/examples/data_connectors/WebPageDemo.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "id": "5747e926", "metadata": {}, @@ -599,13 +598,136 @@ " scrape_format=\"markdown\", # The scrape result format, either `markdown`(default) or `text`\n", ")" ] + }, + { + "cell_type": "markdown", + "id": "f81ccdb7", + "metadata": {}, + "source": [ + "# Using ZyteWebReader" + ] + }, + { + "cell_type": "markdown", + "id": "aee6d871", + "metadata": {}, + "source": [ + "ZyteWebReader allows a user to access the content of webpage in different modes (\"article\", \"html-text\", \"html\"). \n", + "It enables user to change setting such as browser rendering and JS as the content of many sites would require setting these options to access relevant content. All supported options can be found here: https://docs.zyte.com/zyte-api/usage/reference.html\n", + "\n", + "To install dependencies:\n", + "```shell\n", + "pip install zyte-api\n", + "```\n", + "\n", + "To get access to your ZYTE API key please visit: https://www.zyte.com/zyte-api/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f49f22bf", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.readers.web import ZyteWebReader\n", + "\n", + "# Required to run it in notebook\n", + "# import nest_asyncio\n", + "# nest_asyncio.apply()\n", + "\n", + "zyte_dw_params = {\n", + " \"browserHtml\": True, # Enable browser rendering\n", + " \"javascript\": True, # Enable JavaScript\n", + "}\n", + "\n", + "# Initiate ZyteWebReader with your Zyte API key\n", + "zyte_reader = ZyteWebReader(\n", + " api_key=\"Your Zyte API Key\",\n", + " download_kwargs=zyte_dw_params,\n", + ")\n", + "\n", + "# Load documents from URLs as markdown\n", + "documents = zyte_reader.load_data(\n", + " urls=[\"https://www.zyte.com/blog/system-integrators-extract-big-data/\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74b5d21f-7f53-4412-8f11-bbc84d85a1b5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7150" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(documents[0].text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "006254a3-5af8-4a0d-8bf0-b16b9e3dce5c", + "metadata": {}, + "outputs": [], + "source": [ + "zyte_reader = ZyteWebReader(\n", + " api_key=\"Your API Key\",\n", + " mode=\"html-text\",\n", + " download_kwargs=zyte_dw_params,\n", + ")\n", + "\n", + "# Load documents from URLs as markdown\n", + "documents = zyte_reader.load_data(\n", + " urls=[\"https://www.zyte.com/blog/system-integrators-extract-big-data/\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bfb8e5d-7690-4a55-9052-365cbf2c9ce8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19554" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(documents[0].text)" + ] + }, + { + "cell_type": "markdown", + "id": "f642faae-198e-4fad-9742-c590991c8810", + "metadata": {}, + "source": [ + "In default mode (\"article\") only the article text is extracted while in the \"html-text\" full text is extracted from the webpage, there the length of the text is significantly longer. " + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "forked-llama", "language": "python", - "name": "python3" + "name": "forked-llama" }, "language_info": { "codemirror_mode": { diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/__init__.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/__init__.py index caa5ceae7ab9d..95793e16e1ac8 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/__init__.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/__init__.py @@ -44,6 +44,9 @@ from llama_index.readers.web.whole_site.base import ( WholeSiteReader, ) +from llama_index.readers.web.zyte_web.base import ( + ZyteWebReader, +) __all__ = [ @@ -64,4 +67,5 @@ "TrafilaturaWebReader", "UnstructuredURLLoader", "WholeSiteReader", + "ZyteWebReader", ] diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/BUILD b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/BUILD new file mode 100644 index 0000000000000..0a271ceaa061d --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/BUILD @@ -0,0 +1,5 @@ +python_requirements( + name="reqs", +) + +python_sources() diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/README.md b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/README.md new file mode 100644 index 0000000000000..26677ae3ba59e --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/README.md @@ -0,0 +1,63 @@ +# ZyteWebReader + +## Instructions for ZyteWebReader + +### Setup and Installation + +`pip install llama-index-readers-web` + +1. **Install zyte-api Package**: Ensure the `zyte-api` package is installed to use the ZyteWebReader. Install it via pip with the following command: + + ```bash + pip install zyte-api + ``` + + Additionally if you are planning on using "html-text" mode, you'll also need to install `html2text` + + ```bash + pip install html2text + ``` + +2. **API Key**: Secure an API key from [Zyte](https://www.zyte.com/zyte-api/) to access the Zyte services. + +### Using ZyteWebReader + +- **Initialization**: Initialize the ZyteWebReader by providing the API key, the desired mode of operation (`article`, `html-text`, or `html`), and any optional parameters for the Zyte API. + + ```python + from llama_index.readers.web.zyte_web.base import ZyteWebReader + + zyte_reader = ZyteWebReader( + api_key="your_api_key_here", + mode="article", # or "html" or "html-text" + n_conn=5, # number of concurrent connections + download_kwargs={"additional": "parameters"}, + ) + ``` + +- **Loading Data**: To load data, use the `load_data` method with the URLs you wish to process. + +```python +documents = zyte_reader.load_data(urls=["http://example.com"]) +``` + +### Example Usage + +Here is an example demonstrating how to initialize the ZyteWebReader, load document from a URL. + +```python +# Initialize the ZyteWebReader with your API key and desired mode +zyte_reader = ZyteWebReader( + api_key="your_api_key_here", # Replace with your actual API key + mode="article", # Choose between "article", "html-text", and "html" + download_kwargs={ + "additional": "parameters" + }, # Optional additional parameters +) + +# Load documents from Paul Graham's essay URL +documents = zyte_reader.load_data(urls=["http://www.paulgraham.com/"]) + +# Display the document +print(documents) +``` diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/__init__.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/base.py new file mode 100644 index 0000000000000..cf11bc5925bdc --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/base.py @@ -0,0 +1,157 @@ +"""Zyte Web Reader.""" +import asyncio +import logging +from base64 import b64decode +from typing import Any, Dict, List, Literal, Optional +from pydantic import Field + +from llama_index.core.readers.base import BasePydanticReader +from llama_index.core.schema import Document + +logger = logging.getLogger(__name__) + + +class ZyteWebReader(BasePydanticReader): + """Load text from URLs using `Zyte api`. + + Args: + api_key: Zyte API key. + mode: Determines how the text is extracted for the page content. + It can take one of the following values: 'html', 'html-text', 'article' + n_conn: It is the maximum number of concurrent requests to use. + **download_kwargs: Any additional download arguments to pass for download. + See: https://docs.zyte.com/zyte-api/usage/reference.html + + Example: + .. code-block:: python + + from llama_index.readers.web import ZyteWebReader + + reader = ZyteWebReader( + api_key="ZYTE_API_KEY", + ) + docs = reader.load_data( + urls=["", ""], + ) + + Zyte-API reference: + https://www.zyte.com/zyte-api/ + + """ + + client_async: Optional[object] = Field(None) + api_key: str + mode: str + n_conn: int + download_kwargs: Optional[dict] + + def __init__( + self, + api_key: str, + mode: Literal["article", "html", "html-text"] = "article", + n_conn: int = 15, + download_kwargs: Optional[Dict[str, Any]] = None, + ) -> None: + """Initialize with file path.""" + super().__init__( + api_key=api_key, mode=mode, n_conn=n_conn, download_kwargs=download_kwargs + ) + try: + from zyte_api import AsyncZyteAPI + from zyte_api.utils import USER_AGENT as PYTHON_ZYTE_API_USER_AGENT + + except ImportError: + raise ImportError( + "zyte-api package not found, please install it with " + "`pip install zyte-api`" + ) + if mode not in ("article", "html", "html-text"): + raise ValueError( + f"Unrecognized mode '{mode}'. Expected one of " + f"'article', 'html', 'html-text'." + ) + + user_agent = f"llama-index-zyte-api/{PYTHON_ZYTE_API_USER_AGENT}" + self.client_async = AsyncZyteAPI( + api_key=api_key, user_agent=user_agent, n_conn=n_conn + ) + + @classmethod + def class_name(cls) -> str: + return "ZyteWebReader" + + def _zyte_html_option(self) -> str: + if "browserHtml" in self.download_kwargs: + return "browserHtml" + return "httpResponseBody" + + def _get_article(self, page: Dict) -> str: + return page["article"]["headline"] + "\n\n" + page["article"]["articleBody"] + + def _zyte_request_params(self, url: str) -> dict: + request_params: Dict[str, Any] = {"url": url} + if self.mode == "article": + request_params.update({"article": True}) + + if self.mode in ("html", "html-text"): + request_params.update({self._zyte_html_option(): True}) + + if self.download_kwargs: + request_params.update(self.download_kwargs) + return request_params + + async def fetch_items(self, urls) -> List: + results = [] + queries = [self._zyte_request_params(url) for url in urls] + async with self.client_async.session() as session: + for i, future in enumerate(session.iter(queries)): + try: + result = await future + results.append(result) + except Exception as e: + url = queries[i]["url"] + if self.continue_on_failure: + logger.warning( + f"Error {e} while fetching url {url}, " + f"skipping because continue_on_failure is True" + ) + continue + else: + logger.exception( + f"Error fetching {url} and aborting, use " + f"continue_on_failure=True to continue loading " + f"urls after encountering an error." + ) + raise + return results + + def _get_content(self, response: Dict) -> str: + if self.mode == "html-text": + try: + from html2text import html2text + + except ImportError: + raise ImportError( + "html2text package not found, please install it with " + "`pip install html2text`" + ) + if self.mode in ("html", "html-text"): + content = response[self._zyte_html_option()] + + if self._zyte_html_option() == "httpResponseBody": + content = b64decode(content).decode() + + if self.mode == "html-text": + content = html2text(content) + elif self.mode == "article": + content = self._get_article(response) + return content + + def load_data(self, urls) -> List[Document]: + docs = [] + responses = asyncio.run(self.fetch_items(urls)) + for response in responses: + content = self._get_content(response) + doc = Document(text=content, metadata={"url": response["url"]}) + docs.append(doc) + return docs diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/requirements.txt b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/requirements.txt new file mode 100644 index 0000000000000..11c0e8943ad5f --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/zyte_web/requirements.txt @@ -0,0 +1,2 @@ +zyte-api +html2text diff --git a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml index eb38ea0f97036..c410a02d9d0f5 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml @@ -45,7 +45,7 @@ license = "MIT" maintainers = ["HawkClaws", "Hironsan", "NA", "an-bluecat", "bborn", "jasonwcfan", "kravetsmic", "pandazki", "ruze00", "selamanse", "thejessezhang"] name = "llama-index-readers-web" readme = "README.md" -version = "0.2.2" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From feabea0bf3e17d8b91b31de5cac3130d5ab52c30 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sat, 28 Sep 2024 04:38:51 +0500 Subject: [PATCH 50/53] Update JiraReader (#16187) --- .../llama-index-readers-jira/llama_index/readers/jira/base.py | 1 + .../readers/llama-index-readers-jira/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py b/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py index ab8b78558ab7e..4ff6741d88690 100644 --- a/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py +++ b/llama-index-integrations/readers/llama-index-readers-jira/llama_index/readers/jira/base.py @@ -113,6 +113,7 @@ def load_data( issues.append( Document( text=f"{issue.fields.summary} \n {issue.fields.description}", + doc_id=issue.id, extra_info={ "id": issue.id, "title": issue.fields.summary, diff --git a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml index 4482ae0732133..10c7bbe11d851 100644 --- a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml @@ -29,7 +29,7 @@ license = "MIT" maintainers = ["bearguy"] name = "llama-index-readers-jira" readme = "README.md" -version = "0.3.0" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 648d5d4d84479992457d161ffef29c5881fda7ed Mon Sep 17 00:00:00 2001 From: Logan Date: Sat, 28 Sep 2024 23:55:44 -0600 Subject: [PATCH 51/53] Fix anthropic messages (#16279) --- .../llama_index/llms/anthropic/utils.py | 2 +- .../llms/llama-index-llms-anthropic/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py index 3870234abece2..f38293060371e 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py @@ -120,7 +120,7 @@ def messages_to_anthropic_messages( ), ) if "cache_control" in message.additional_kwargs - else [TextBlockParam(text=message.content, type="text")] + else TextBlockParam(text=message.content, type="text") ) content.append(content_) diff --git a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml index 292bda32a6705..8c1871fec9eaa 100644 --- a/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-anthropic" readme = "README.md" -version = "0.3.3" +version = "0.3.4" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 70a585adddeb913f5a994897be9af63070d6ba16 Mon Sep 17 00:00:00 2001 From: Ravi Theja Date: Sun, 29 Sep 2024 19:26:35 +0530 Subject: [PATCH 52/53] Fix mistralai multimodal llm package name (#16289) --- .../llama-index-multi-modal-llms-mistralai/pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml index 839cefda380ab..b0c4223f947cc 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml @@ -25,9 +25,9 @@ authors = ["Your Name "] description = "llama-index multi-modal-llms mistral integration" exclude = ["**/BUILD"] license = "MIT" -name = "llama-index-multi-modal-llms-mistral" +name = "llama-index-multi-modal-llms-mistralai" readme = "README.md" -version = "0.1" +version = "0.2" [tool.poetry.dependencies] python = ">=3.9,<4.0" From a620a2661faabb49ba2f257bff7ae2ac04d0c12b Mon Sep 17 00:00:00 2001 From: Guodong Date: Mon, 30 Sep 2024 11:30:17 +0800 Subject: [PATCH 53/53] rm extra print statements & replace create tmpfile function (#16291) --- .../llama_index/readers/minio/minio_client/base.py | 4 +--- .../readers/llama-index-readers-minio/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py b/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py index c49be47cd19d8..0f6b91b0318a5 100644 --- a/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py +++ b/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py @@ -98,7 +98,7 @@ def load_data(self) -> List[Document]: with tempfile.TemporaryDirectory() as temp_dir: if self.key: suffix = Path(self.key).suffix - filepath = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}" + _, filepath = tempfile.mkstemp(dir=temp_dir, suffix=suffix) minio_client.fget_object( bucket_name=self.bucket, object_name=self.key, file_path=filepath ) @@ -108,7 +108,6 @@ def load_data(self) -> List[Document]: ) for i, obj in enumerate(objects): file_name = obj.object_name.split("/")[-1] - print(file_name) if self.num_files_limit is not None and i > self.num_files_limit: break @@ -124,7 +123,6 @@ def load_data(self) -> List[Document]: continue filepath = f"{temp_dir}/{file_name}" - print(filepath) minio_client.fget_object(self.bucket, obj.object_name, filepath) loader = SimpleDirectoryReader( diff --git a/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml index e578c84a84338..13cb375fdfe94 100644 --- a/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-minio/pyproject.toml @@ -28,7 +28,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-readers-minio" readme = "README.md" -version = "0.2.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0"

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z**^BC^)PohdzUP&p7tM3Rnj7{TM95c4YxtGu1YsfdLZ#Xl)2(|BWYvy=fV`7QcmM6 zm$wD9JBgH)mI@uUqS&RQ+yVUJ&OAS;BT&H3n@66u*fm z^h$=l_n!uRc=ys??wt2qZhFT};^)w5Q;FYTtaj~=fkEdtazuDs&pX-%XDVA zNFm$6v!-_K^{0HxI^^5*>VCZX5hwlg(AxL;%J;t~e)=n=WB;QJ+4yP4=H@1zjlRwH zy)d9=z3`PQuOH2}GpP$Gty$k&BM*!Yt3uyq&Zn=xe)_uVr(M|>M3@;}$-MJOOl*Ix zyrBIIy?rkqKLRD|TPMC3_SDq3pA`3tH{O66={zr!LvK$P17+RPfST;%NBP}l@85CT zw{6P%(_20&d*_mx=eOkSjYhSncf6qelf16^j^xb`1 zKKy<4DB4J`E&`aLr+)hT>gn%@3#;ECa$}p)-?wSfSN61?E!ZFR!?6z(3;*9;F!}UX z_v!3=NagT}3Cjvs-oCH2Z%gc--M(uc-zJEXQ6R+3LIFz*D;eF}&r)`dZZ!a); z7cOW&2dMn)+Q;{E-IVPcCVtu*_WAEy)F6W5Eo~SNCcNAec5|R)<^LE6vvOgZyw#3?XvoJSIzWOulLVq-#*Pk|d>Q zv*Tv-1IG!B=Jfty@Ip!FA5skdzJ1XBLyWKMyb*8#%mX!w)e9ZRd)TD@^HoD;I Date: Mon, 23 Sep 2024 18:44:51 -0400 Subject: [PATCH 09/53] [Refactor] upgrading box sdk to >= 1.5.0 (#16169) --- .../readers/box/BoxAPI/box_ai_extract_beta.py | 107 ------------------ .../llama_index/readers/box/BoxAPI/box_api.py | 39 ++++--- .../llama-index-readers-box/pyproject.toml | 4 +- .../llama-index-tools-box/pyproject.toml | 4 +- .../tests/test_tools_box_ai_extract.py | 5 +- .../tests/test_tools_box_ai_prompt.py | 4 +- 6 files changed, 26 insertions(+), 137 deletions(-) delete mode 100644 llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_ai_extract_beta.py diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_ai_extract_beta.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_ai_extract_beta.py deleted file mode 100644 index 019b6f9bbcb6f..0000000000000 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_ai_extract_beta.py +++ /dev/null @@ -1,107 +0,0 @@ -from enum import Enum - -from typing import Optional - -from box_sdk_gen.internal.base_object import BaseObject - -from typing import List - -from typing import Dict - -from box_sdk_gen.serialization.json.serializer import serialize - -from box_sdk_gen.serialization.json.serializer import deserialize - -from box_sdk_gen.schemas.ai_response import AiResponse - -from box_sdk_gen.networking.auth import Authentication - -from box_sdk_gen.networking.network import NetworkSession - -from box_sdk_gen.internal.utils import prepare_params - -from box_sdk_gen.networking.fetch import FetchOptions - -from box_sdk_gen.networking.fetch import FetchResponse - -from box_sdk_gen.networking.fetch import fetch - - -class CreateAiExtractItemsTypeField(str, Enum): - FILE = "file" - - -class CreateAiExtractItems(BaseObject): - _discriminator = "type", {"file"} - - def __init__( - self, - id: str, - *, - type: CreateAiExtractItemsTypeField = CreateAiExtractItemsTypeField.FILE.value, - content: Optional[str] = None, - **kwargs, - ): - """ - :param id: The id of the item. - :type id: str - :param type: The type of the item., defaults to CreateAiAskItemsTypeField.FILE.value - :type type: CreateAiAskItemsTypeField, optional - :param content: The content of the item, often the text representation., defaults to None - :type content: Optional[str], optional - """ - super().__init__(**kwargs) - self.id = id - self.type = type - self.content = content - - -class AiExtractManager: - def __init__( - self, - *, - auth: Optional[Authentication] = None, - network_session: NetworkSession = None, - ): - if network_session is None: - network_session = NetworkSession() - self.auth = auth - self.network_session = network_session - - def create_ai_extract( - self, - prompt: str, - items: List[CreateAiExtractItems], - *, - extra_headers: Optional[Dict[str, Optional[str]]] = None, - ) -> AiResponse: - """ - Sends an AI request to supported LLMs and returns an answer specifically focused on the user's data structure given the provided context. - :param prompt: The prompt provided by the client to be answered by the LLM. The prompt's length is limited to 10000 characters. - :type prompt: str - :param items: The items to be processed by the LLM, often files. - - **Note**: Box AI handles documents with text representations up to 1MB in size, or a maximum of 25 files, whichever comes first. - If the file size exceeds 1MB, the first 1MB of text representation will be processed. - If you set `mode` parameter to `single_item_qa`, the `items` array can have one element only. - :type items: List[CreateAiAskItems] - :param extra_headers: Extra headers that will be included in the HTTP request., defaults to None - :type extra_headers: Optional[Dict[str, Optional[str]]], optional - """ - if extra_headers is None: - extra_headers = {} - request_body: Dict = {"prompt": prompt, "items": items} - headers_map: Dict[str, str] = prepare_params({**extra_headers}) - response: FetchResponse = fetch( - f"{self.network_session.base_urls.base_url}/2.0/ai/extract", - FetchOptions( - method="POST", - headers=headers_map, - data=serialize(request_body), - content_type="application/json", - response_format="json", - auth=self.auth, - network_session=self.network_session, - ), - ) - return deserialize(response.data, AiResponse) diff --git a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py index 68c3f441d4767..a864c19e21da0 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py +++ b/llama-index-integrations/readers/llama-index-readers-box/llama_index/readers/box/BoxAPI/box_api.py @@ -1,27 +1,27 @@ +import logging import os import shutil -from typing import List, Optional, Dict -import logging +from typing import Dict, List, Optional + import requests from box_sdk_gen import ( + AiItemBase, BoxAPIError, BoxClient, - File, - ByteStream, BoxSDKError, -) -from box_sdk_gen.managers.search import ( - SearchForContentScope, + ByteStream, + CreateAiAskMode, + File, SearchForContentContentTypes, + SearchForContentScope, SearchForContentType, SearchResults, ) -from box_sdk_gen.managers.ai import CreateAiAskMode, CreateAiAskItems -from llama_index.readers.box.BoxAPI.box_ai_extract_beta import ( - AiExtractManager, - CreateAiExtractItems, -) +# from llama_index.readers.box.BoxAPI.box_ai_extract_beta import ( +# AiExtractManager, +# CreateAiExtractItems, +# ) logger = logging.getLogger(__name__) @@ -201,7 +201,7 @@ def get_ai_response_from_box_files( if individual_document_prompt: mode = CreateAiAskMode.SINGLE_ITEM_QA for file in box_files: - ask_item = CreateAiAskItems(file.id) + ask_item = AiItemBase(file.id) logger.info(f"Getting AI prompt for file: {file.id} {file.name}") # get the AI prompt for the file @@ -220,7 +220,7 @@ def get_ai_response_from_box_files( else: mode = CreateAiAskMode.MULTIPLE_ITEM_QA - file_ids = [CreateAiAskItems(file.id) for file in box_files] + file_ids = [AiItemBase(file.id) for file in box_files] # get the AI prompt for the file ai_response = box_client.ai.create_ai_ask( @@ -353,17 +353,13 @@ def get_files_ai_extract_data( Raises: BoxAPIError: If an error occurs while interacting with the Box AI API. """ - ai_extract_manager = AiExtractManager( - auth=box_client.auth, network_session=box_client.network_session - ) - for file in box_files: - ask_item = CreateAiExtractItems(file.id) + ask_item = AiItemBase(file.id) logger.info(f"Getting AI extracted data for file: {file.id} {file.name}") # get the AI extracted data for the file try: - ai_response = ai_extract_manager.create_ai_extract( + ai_response = box_client.ai.create_ai_extract( prompt=ai_prompt, items=[ask_item] ) except BoxAPIError as e: @@ -484,3 +480,6 @@ def search_files_by_metadata( # return only files from the entries return [file for file in search_results.entries if file.type == "file"] + + # return only files from the entries + return [file for file in search_results.entries if file.type == "file"] diff --git a/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml index a71aaa78a7362..12ea10aa0a280 100644 --- a/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-box/pyproject.toml @@ -37,11 +37,11 @@ maintainers = [ name = "llama-index-readers-box" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.2" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -box-sdk-gen = ">=1.0.0,<1.5.0" +box-sdk-gen = ">=1.5.0" llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml index bca3f8c6c89bd..d93cb831c6e4d 100644 --- a/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-box/pyproject.toml @@ -34,11 +34,11 @@ license = "MIT" name = "llama-index-tools-box" packages = [{include = "llama_index/"}] readme = "README.md" -version = "0.2.1" +version = "0.2.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -box-sdk-gen = "^1.1.0" +box-sdk-gen = ">=1.5.0" llama-index-readers-box = "^0.2.0" llama-index-agent-openai = "^0.3.0" llama-index-core = "^0.11.0" diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py index dab8aadd64852..21af532186f2e 100644 --- a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_extract.py @@ -1,9 +1,8 @@ -import pytest import openai +import pytest from box_sdk_gen import BoxClient - -from llama_index.tools.box import BoxAIExtractToolSpec from llama_index.agent.openai import OpenAIAgent +from llama_index.tools.box import BoxAIExtractToolSpec from tests.conftest import get_testing_data diff --git a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py index 67d8cac469320..3e433d3d345bd 100644 --- a/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py +++ b/llama-index-integrations/tools/llama-index-tools-box/tests/test_tools_box_ai_prompt.py @@ -1,9 +1,7 @@ import pytest from box_sdk_gen import BoxClient - -from llama_index.tools.box import BoxAIPromptToolSpec from llama_index.agent.openai import OpenAIAgent - +from llama_index.tools.box import BoxAIPromptToolSpec from tests.conftest import get_testing_data From b1abd5deed1dd8f2cf16fb93dcd4e7af1fa50ac2 Mon Sep 17 00:00:00 2001 From: bechbd Date: Mon, 23 Sep 2024 14:52:56 -0800 Subject: [PATCH 10/53] Fixed issue #16089 where Neptune was adding additional labels (#16137) --- .../property_graph_neptune.ipynb | 11 +++++++--- .../neptune/analytics_property_graph.py | 20 +++++++++---------- .../neptune/base_property_graph.py | 1 + .../neptune/database_property_graph.py | 20 +++++++++---------- .../pyproject.toml | 2 +- 5 files changed, 30 insertions(+), 24 deletions(-) diff --git a/docs/docs/examples/property_graph/property_graph_neptune.ipynb b/docs/docs/examples/property_graph/property_graph_neptune.ipynb index 0a7122e1a8bad..f620fbb925a04 100644 --- a/docs/docs/examples/property_graph/property_graph_neptune.ipynb +++ b/docs/docs/examples/property_graph/property_graph_neptune.ipynb @@ -15,7 +15,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install boto3\n", + "%pip install boto3 nest_asyncio\n", "%pip install llama-index-llms-bedrock\n", "%pip install llama-index-graph-stores-neptune\n", "%pip install llama-index-embeddings-bedrock" @@ -74,8 +74,8 @@ "metadata": {}, "outputs": [], "source": [ - "llm = Bedrock(model=\"anthropic.claude-v2\")\n", - "embed_model = BedrockEmbedding(model=\"amazon.titan-embed-text-v1\")\n", + "llm = Bedrock(model=\"anthropic.claude-3-sonnet-20240229-v1:0\")\n", + "embed_model = BedrockEmbedding(model=\"amazon.titan-embed-text-v2:0\")\n", "\n", "Settings.llm = llm\n", "Settings.embed_model = embed_model\n", @@ -99,6 +99,11 @@ "metadata": {}, "outputs": [], "source": [ + "import nest_asyncio\n", + "\n", + "# Add this so that it works with the event loop in Jupyter Notebooks\n", + "nest_asyncio.apply()\n", + "\n", "documents = SimpleDirectoryReader(\n", " \"../../../../examples/paul_graham_essay/data\"\n", ").load_data()" diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py index 075695bd3187d..74d0c98a70a9d 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/analytics_property_graph.py @@ -6,7 +6,11 @@ remove_empty_values, create_neptune_analytics_client, ) -from .base_property_graph import NeptuneBasePropertyGraph, BASE_ENTITY_LABEL +from .base_property_graph import ( + NeptuneBasePropertyGraph, + BASE_ENTITY_LABEL, + BASE_NODE_LABEL, +) from llama_index.core.vector_stores.types import VectorStoreQuery from llama_index.core.graph_stores.types import LabelledNode, EntityNode, ChunkNode @@ -159,19 +163,15 @@ def upsert_nodes(self, nodes: List[LabelledNode]) -> None: if entity_dicts: for d in entity_dicts: self.structured_query( - """ + f""" WITH $data AS row - MERGE (e:`""" - + BASE_ENTITY_LABEL - + """` {id: row.id}) + MERGE (e:`{BASE_NODE_LABEL}` {{id: row.id}}) SET e += removeKeyFromMap(row.properties, '') - SET e.name = row.name - SET e:`""" - + str(d["name"]) - + """` + SET e.name = row.name, e:`{BASE_ENTITY_LABEL}` + SET e:`{d['label']}` WITH e, row WHERE removeKeyFromMap(row.properties, '').triplet_source_id IS NOT NULL - MERGE (c:Chunk {id: removeKeyFromMap(row.properties, '').triplet_source_id}) + MERGE (c:Chunk {{id: removeKeyFromMap(row.properties, '').triplet_source_id}}) MERGE (e)<-[:MENTIONS]-(c) WITH e, row.embedding as em CALL neptune.algo.vectors.upsert(e, em) diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py index 6e119ac641590..55e045e31711e 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/base_property_graph.py @@ -16,6 +16,7 @@ logger = logging.getLogger(__name__) BASE_ENTITY_LABEL = "__Entity__" +BASE_NODE_LABEL = "__Node__" class NeptuneBasePropertyGraph(PropertyGraphStore): diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py index e83ee6a060bd6..ac0fb03c17d26 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/llama_index/graph_stores/neptune/database_property_graph.py @@ -3,7 +3,11 @@ from typing import Any, Dict, Tuple, List, Optional from .neptune import NeptuneQueryException, create_neptune_database_client -from .base_property_graph import NeptuneBasePropertyGraph, BASE_ENTITY_LABEL +from .base_property_graph import ( + NeptuneBasePropertyGraph, + BASE_ENTITY_LABEL, + BASE_NODE_LABEL, +) from llama_index.core.vector_stores.types import VectorStoreQuery from llama_index.core.graph_stores.types import LabelledNode, EntityNode, ChunkNode @@ -123,19 +127,15 @@ def upsert_nodes(self, nodes: List[LabelledNode]) -> None: if entity_dicts: for d in entity_dicts: self.structured_query( - """ + f""" WITH $data AS row - MERGE (e:`""" - + BASE_ENTITY_LABEL - + """` {id: row.id}) + MERGE (e:`{BASE_NODE_LABEL}` {{id: row.id}}) SET e += removeKeyFromMap(row.properties, '') - SET e.name = row.name - SET e:`""" - + str(d["name"]) - + """` + SET e.name = row.name, e:`{BASE_ENTITY_LABEL}` + SET e:`{d['label']}` WITH e, row WHERE removeKeyFromMap(row.properties, '').triplet_source_id IS NOT NULL - MERGE (c:Chunk {id: removeKeyFromMap(row.properties, '').triplet_source_id}) + MERGE (c:Chunk {{id: removeKeyFromMap(row.properties, '').triplet_source_id}}) MERGE (e)<-[:MENTIONS]-(c) RETURN count(*) as count """, diff --git a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml index 1afd9f7320594..6801daf6e4a37 100644 --- a/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml +++ b/llama-index-integrations/graph_stores/llama-index-graph-stores-neptune/pyproject.toml @@ -30,7 +30,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-graph-stores-neptune" readme = "README.md" -version = "0.2.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From df79186317cabd80f75e93bdd35aee6f1edbed7b Mon Sep 17 00:00:00 2001 From: Ryan Nguyen <96593302+xpbowler@users.noreply.github.com> Date: Mon, 23 Sep 2024 18:53:26 -0400 Subject: [PATCH 11/53] Async support for redis, dynamodb (#16139) --- .../storage/chat_store/dynamodb/base.py | 80 ++++++++++ .../pyproject.toml | 5 +- .../storage/chat_store/redis/base.py | 150 ++++++++++++++++-- .../pyproject.toml | 2 +- 4 files changed, 225 insertions(+), 12 deletions(-) diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/llama_index/storage/chat_store/dynamodb/base.py b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/llama_index/storage/chat_store/dynamodb/base.py index 880334a88cbc4..ae6922ad24662 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/llama_index/storage/chat_store/dynamodb/base.py +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/llama_index/storage/chat_store/dynamodb/base.py @@ -95,6 +95,8 @@ class DynamoDBChatStore(BaseChatStore): _client: ServiceResource = PrivateAttr() _table: Any = PrivateAttr() + _aclient: ServiceResource = PrivateAttr() + _atable: Any = PrivateAttr() def __init__( self, @@ -163,6 +165,23 @@ def __init__( self._client = session.resource("dynamodb", config=config, **resource_kwargs) self._table = self._client.Table(table_name) + async def init_async_table(self): + """Initialize asynchronous table.""" + if self._atable is None: + try: + import aioboto3 + + async_session = aioboto3.Session(**self.session_kwargs) + except ImportError: + raise ImportError( + "aioboto3 package not found, install with 'pip install aioboto3'" + ) + + async with async_session.resource( + "dynamodb", config=self.botocore_config, **self.resource_kwargs + ) as dynamodb: + self._atable = await dynamodb.Table(self.table_name) + @classmethod def class_name(self) -> str: return "DynamoDBChatStore" @@ -182,6 +201,12 @@ def set_messages(self, key: str, messages: List[ChatMessage]) -> None: Item={self.primary_key: key, "History": _messages_to_dict(messages)} ) + async def aset_messages(self, key: str, messages: List[ChatMessage]) -> None: + self.init_async_table() + await self._atable.put_item( + Item={self.primary_key: key, "History": _messages_to_dict(messages)} + ) + def get_messages(self, key: str) -> List[ChatMessage]: """Retrieve all messages for the given key. @@ -200,6 +225,17 @@ def get_messages(self, key: str) -> List[ChatMessage]: return [_dict_to_message(message) for message in message_history] + async def aget_messages(self, key: str) -> List[ChatMessage]: + self.init_async_table() + response = await self._atable.get_item(Key={self.primary_key: key}) + + if response and "Item" in response: + message_history = response["Item"]["History"] + else: + message_history = [] + + return [_dict_to_message(message) for message in message_history] + def add_message(self, key: str, message: ChatMessage) -> None: """Add a message to the end of the chat history for the given key. Creates a new row if the key does not exist. @@ -216,6 +252,15 @@ def add_message(self, key: str, message: ChatMessage) -> None: self._table.put_item(Item={self.primary_key: key, "History": current_messages}) + async def async_add_message(self, key: str, message: ChatMessage) -> None: + self.init_async_table() + current_messages = _messages_to_dict(await self.aget_messages(key)) + current_messages.append(_message_to_dict(message)) + + await self._atable.put_item( + Item={self.primary_key: key, "History": current_messages} + ) + def delete_messages(self, key: str) -> Optional[List[ChatMessage]]: """Deletes the entire chat history for the given key (i.e. the row). @@ -230,6 +275,12 @@ def delete_messages(self, key: str) -> Optional[List[ChatMessage]]: self._table.delete_item(Key={self.primary_key: key}) return messages_to_delete + async def adelete_messages(self, key: str) -> Optional[List[ChatMessage]]: + self.init_async_table() + messages_to_delete = await self.aget_messages(key) + await self._atable.delete_item(Key={self.primary_key: key}) + return messages_to_delete + def delete_message(self, key: str, idx: int) -> Optional[ChatMessage]: """Deletes the message at the given index for the given key. @@ -253,6 +304,20 @@ def delete_message(self, key: str, idx: int) -> Optional[ChatMessage]: ) return None + async def adelete_message(self, key: str, idx: int) -> Optional[ChatMessage]: + self.init_async_table() + current_messages = await self.aget_messages(key) + try: + message_to_delete = current_messages[idx] + del current_messages[idx] + await self.aset_messages(key, current_messages) + return message_to_delete + except IndexError: + logger.error( + IndexError(f"No message exists at index, {idx}, for key {key}") + ) + return None + def delete_last_message(self, key: str) -> Optional[ChatMessage]: """Deletes the last message in the chat history for the given key. @@ -265,6 +330,9 @@ def delete_last_message(self, key: str) -> Optional[ChatMessage]: """ return self.delete_message(key, -1) + async def adelete_last_message(self, key: str) -> Optional[ChatMessage]: + return self.adelete_message(key, -1) + def get_keys(self) -> List[str]: """Retrieve all keys in the table. @@ -280,3 +348,15 @@ def get_keys(self) -> List[str]: ) keys.extend([item[self.primary_key] for item in response["Items"]]) return keys + + async def aget_keys(self) -> List[str]: + self.init_async_table() + response = await self._atable.scan(ProjectionExpression=self.primary_key) + keys = [item[self.primary_key] for item in response["Items"]] + while "LastEvaluatedKey" in response: + response = await self._atable.scan( + ProjectionExpression=self.primary_key, + ExclusiveStartKey=response["LastEvaluatedKey"], + ) + keys.extend([item[self.primary_key] for item in response["Items"]]) + return keys diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/pyproject.toml b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/pyproject.toml index 5b3daaf02b26d..e42086194b26d 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/pyproject.toml +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-dynamodb/pyproject.toml @@ -27,12 +27,13 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-chat-store-dynamodb" readme = "README.md" -version = "0.1.0" +version = "0.2.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" llama-index-core = "^0.11.0" -boto3 = "^1.35.13" +boto3 = "^1.34.120" +aioboto3 = "^13.1.1" boto3-stubs = {extras = ["dynamodb"], version = "^1.35.14"} [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py index 86450d4e6cbdf..ccfc96a130cd0 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py @@ -1,12 +1,19 @@ import json import logging import sys -from typing import Any, List, Optional +import asyncio +from typing import Any, List, Optional, Union, Tuple from urllib.parse import urlparse import redis from redis import Redis from redis.cluster import RedisCluster +from redis.sentinel import Sentinel + +import redis.asyncio +from redis.asyncio import Redis as AsyncRedis +from redis.asyncio.cluster import RedisCluster as AsyncRedisCluster +from redis.asyncio.sentinel import Sentinel as AsyncSentinel from llama_index.core.bridge.pydantic import Field from llama_index.core.llms import ChatMessage @@ -20,25 +27,29 @@ def _message_to_dict(message: ChatMessage) -> dict: # Convert the json object in Redis to a ChatMessage def _dict_to_message(d: dict) -> ChatMessage: - return ChatMessage.parse_obj(d) + return ChatMessage.model_validate(d) class RedisChatStore(BaseChatStore): """Redis chat store.""" redis_client: Any = Field(description="Redis client.") + aredis_client: Any = Field(default=None, description="Async Redis client.") ttl: Optional[int] = Field(default=None, description="Time to live in seconds.") def __init__( self, redis_url: str = "redis://localhost:6379", - redis_client: Optional[Any] = None, + redis_client: Optional[Redis] = None, + aredis_client: Optional[AsyncRedis] = None, ttl: Optional[int] = None, **kwargs: Any, ) -> None: """Initialize.""" - redis_client = redis_client or self._get_client(redis_url, **kwargs) - super().__init__(redis_client=redis_client, ttl=ttl) + super().__init__(ttl=ttl) + + self.redis_client = redis_client or self._get_client(redis_url, **kwargs) + self.aredis_client = aredis_client or self._aget_client(redis_url, **kwargs) @classmethod def class_name(cls) -> str: @@ -54,6 +65,14 @@ def set_messages(self, key: str, messages: List[ChatMessage]) -> None: if self.ttl: self.redis_client.expire(key, self.ttl) + async def aset_messages(self, key: str, messages: List[ChatMessage]) -> None: + await self.aredis_client.delete(key) + for message in messages: + await self.async_add_message(key, message) + + if self.ttl: + await self.aredis_client.expire(key, self.ttl) + def get_messages(self, key: str) -> List[ChatMessage]: """Get messages for a key.""" items = self.redis_client.lrange(key, 0, -1) @@ -63,6 +82,15 @@ def get_messages(self, key: str) -> List[ChatMessage]: items_json = [json.loads(m.decode("utf-8")) for m in items] return [_dict_to_message(d) for d in items_json] + async def aget_messages(self, key: str) -> List[ChatMessage]: + """Get messages for a key.""" + items = await self.aredis_client.lrange(key, 0, -1) + if len(items) == 0: + return [] + + items_json = [json.loads(m.decode("utf-8")) for m in items] + return [_dict_to_message(d) for d in items_json] + def add_message( self, key: str, message: ChatMessage, idx: Optional[int] = None ) -> None: @@ -76,11 +104,29 @@ def add_message( if self.ttl: self.redis_client.expire(key, self.ttl) + async def async_add_message( + self, key: str, message: ChatMessage, idx: Optional[int] = None + ) -> None: + """Add a message for a key.""" + if idx is None: + item = json.dumps(_message_to_dict(message)) + await self.aredis_client.rpush(key, item) + else: + await self._ainsert_element_at_index(key, idx, message) + + if self.ttl: + await self.aredis_client.expire(key, self.ttl) + def delete_messages(self, key: str) -> Optional[List[ChatMessage]]: """Delete messages for a key.""" self.redis_client.delete(key) return None + async def adelete_messages(self, key: str) -> Optional[List[ChatMessage]]: + """Delete messages for a key.""" + await self.aredis_client.delete(key) + return None + def delete_message(self, key: str, idx: int) -> Optional[ChatMessage]: """Delete specific message for a key.""" current_list = self.redis_client.lrange(key, 0, -1) @@ -93,6 +139,18 @@ def delete_message(self, key: str, idx: int) -> Optional[ChatMessage]: else: return None + async def adelete_message(self, key: str, idx: int) -> Optional[ChatMessage]: + """Delete specific message for a key.""" + current_list = await self.aredis_client.lrange(key, 0, -1) + if 0 <= idx < len(current_list): + removed_item = current_list.pop(idx) + + await self.aredis_client.delete(key) + await self.aredis_client.lpush(key, *current_list) + return removed_item + else: + return None + def delete_last_message(self, key: str) -> Optional[ChatMessage]: """Delete last message for a key.""" return self.redis_client.rpop(key) @@ -114,17 +172,35 @@ def _insert_element_at_index( self.set_messages(key, current_list) return self.get_messages(key) + async def _ainsert_element_at_index( + self, key: str, index: int, message: ChatMessage + ) -> List[ChatMessage]: + # Step 1: Retrieve the current list + current_list = await self.aget_messages(key) + # Step 2: Insert the new element at the desired index in the local list + current_list.insert(index, message) + + # Step 3: Push the modified local list back to Redis + await self.aredis_client.delete(key) # Remove the existing list + await self.aset_messages(key, current_list) + return await self.aget_messages(key) + def _redis_cluster_client(self, redis_url: str, **kwargs: Any) -> "Redis": return RedisCluster.from_url(redis_url, **kwargs) # type: ignore - def _check_for_cluster(self, redis_client: "Redis") -> bool: + def _aredis_cluster_client(self, redis_url: str, **kwargs: Any) -> "AsyncRedis": + return AsyncRedisCluster.from_url(redis_url, **kwargs) + + def _check_for_cluster(self, redis_client: Union["Redis", "AsyncRedis"]) -> bool: try: cluster_info = redis_client.info("cluster") return cluster_info["cluster_enabled"] == 1 except redis.exceptions.RedisError: return False - def _redis_sentinel_client(self, redis_url: str, **kwargs: Any) -> "Redis": + def _redis_sentinel_parser( + self, redis_url: str, **kwargs + ) -> Tuple[str, List[Tuple[str, int]]]: """ Helper method to parse an (un-official) redis+sentinel url and create a Sentinel connection to fetch the final redis client @@ -161,9 +237,18 @@ def _redis_sentinel_client(self, redis_url: str, **kwargs: Any) -> "Redis": if arg.startswith("ssl") or arg == "client_name": sentinel_args[arg] = kwargs[arg] + return sentinel_args, sentinel_list, service_name, kwargs + + def _redis_sentinel_client(self, redis_url: str, **kwargs: Any) -> "Redis": + ( + sentinel_args, + sentinel_list, + service_name, + kwargs, + ) = self._redis_sentinel_parser(redis_url, **kwargs) # sentinel user/pass is part of sentinel_kwargs, user/pass for redis server # connection as direct parameter in kwargs - sentinel_client = redis.sentinel.Sentinel( + sentinel_client = Sentinel( sentinel_list, sentinel_kwargs=sentinel_args, **kwargs ) @@ -179,7 +264,34 @@ def _redis_sentinel_client(self, redis_url: str, **kwargs: Any) -> "Redis": msg="Redis sentinel connection configured with password but Sentinel \ answered NO PASSWORD NEEDED - Please check Sentinel configuration" ) - sentinel_client = redis.sentinel.Sentinel(sentinel_list, **kwargs) + sentinel_client = Sentinel(sentinel_list, **kwargs) + else: + raise + + return sentinel_client.master_for(service_name) + + def _aredis_sentinel_client(self, redis_url: str, **kwargs: Any) -> "AsyncRedis": + ( + sentinel_args, + sentinel_list, + service_name, + kwargs, + ) = self._redis_sentinel_parser(redis_url, **kwargs) + sentinel_client = AsyncSentinel( + sentinel_list, sentinel_kwargs=sentinel_args, **kwargs + ) + + try: + asyncio.run(sentinel_client.execute_command("ping")) + except redis.exceptions.AuthenticationError: + exception_info = sys.exc_info() + exception = exception_info[1] or None + if exception is not None and "no password is set" in exception.args[0]: + logging.warning( + msg="Redis sentinel connection configured with password but Sentinel \ + answered NO PASSWORD NEEDED - Please check Sentinel configuration" + ) + sentinel_client = AsyncSentinel(sentinel_list, **kwargs) else: raise @@ -241,3 +353,23 @@ def _get_client(self, redis_url: str, **kwargs: Any) -> "Redis": redis_client.close() redis_client = self._redis_cluster_client(redis_url, **kwargs) return redis_client + + def _aget_client(self, redis_url: str, **kwargs: Any) -> "AsyncRedis": + redis_client: "AsyncRedis" + # check if normal redis:// or redis+sentinel:// url + if redis_url.startswith("redis+sentinel"): + redis_client = self._aredis_sentinel_client(redis_url, **kwargs) + elif redis_url.startswith( + "rediss+sentinel" + ): # sentinel with TLS support enables + kwargs["ssl"] = True + if "ssl_cert_reqs" not in kwargs: + kwargs["ssl_cert_reqs"] = "none" + redis_client = self._aredis_sentinel_client(redis_url, **kwargs) + else: + # connect to redis server from url, reconnect with cluster client if needed + redis_client = redis.asyncio.from_url(redis_url, **kwargs) + if self._check_for_cluster(redis_client): + redis_client.close() + redis_client = self._aredis_cluster_client(redis_url, **kwargs) + return redis_client diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml index 50429344bbb7f..37fdfeef8eb8d 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-chat-store-redis" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From f075aec49c6fbd231380cf315e354617ac2bc69c Mon Sep 17 00:00:00 2001 From: Asi Greenholts <88270351+TupleType@users.noreply.github.com> Date: Tue, 24 Sep 2024 01:54:05 +0300 Subject: [PATCH 12/53] [Vertex AI] Pass safety_settings to send_message methods to fix settings not being sent to API (#16153) --- .../llama_index/llms/vertex/base.py | 5 +++-- .../llama_index/llms/vertex/utils.py | 9 +++++++-- .../llms/llama-index-llms-vertex/pyproject.toml | 2 +- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py index 1f34b0da1801b..55ee5200b26c9 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py +++ b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py @@ -115,7 +115,7 @@ def __init__( ) -> None: init_vertexai(project=project, location=location, credentials=credentials) - safety_settings = safety_settings or {} + self._safety_settings = safety_settings or {} additional_kwargs = additional_kwargs or {} callback_manager = callback_manager or CallbackManager([]) @@ -158,7 +158,7 @@ def __init__( self._client = TextGenerationModel.from_pretrained(model) elif is_gemini_model(model): - self._client = create_gemini_client(model, safety_settings) + self._client = create_gemini_client(model, self._safety_settings) self._chat_client = self._client self._is_gemini = True self._is_chat_model = True @@ -185,6 +185,7 @@ def _model_kwargs(self) -> Dict[str, Any]: base_kwargs = { "temperature": self.temperature, "max_output_tokens": self.max_tokens, + "safety_settings": self._safety_settings, } return { **base_kwargs, diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/utils.py b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/utils.py index 8e85fe5bad5c0..f7766f5fe4631 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/utils.py +++ b/llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/utils.py @@ -93,7 +93,10 @@ def _completion_with_retry(**kwargs: Any) -> Any: generation_config = kwargs if kwargs else {} return generation.send_message( - prompt, stream=stream, tools=tools, generation_config=generation_config + prompt, + stream=stream, + tools=tools, + generation_config=generation_config, ) elif chat: generation = client.start_chat(**params) @@ -132,7 +135,9 @@ async def _completion_with_retry(**kwargs: Any) -> Any: tools = to_gemini_tools(tools) if tools else [] generation_config = kwargs if kwargs else {} return await generation.send_message_async( - prompt, tools=tools, generation_config=generation_config + prompt, + tools=tools, + generation_config=generation_config, ) elif chat: generation = client.start_chat(**params) diff --git a/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml index 1b9de3579ca7f..5085a61397ac1 100644 --- a/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-vertex/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-llms-vertex" readme = "README.md" -version = "0.3.4" +version = "0.3.5" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From ce43f766890234d066c339c915ebaa66ff292cc9 Mon Sep 17 00:00:00 2001 From: Eric Hare Date: Mon, 23 Sep 2024 16:00:41 -0700 Subject: [PATCH 13/53] feat: Depend on AstraPy 1.5 and above for AstraDBVectorStore (#16164) --- .../docs/examples/vector_stores/AstraDBIndexDemo.ipynb | 10 +++++++--- .../llama_index/vector_stores/astra_db/base.py | 9 +++++++-- .../llama-index-vector-stores-astra-db/pyproject.toml | 4 ++-- .../tests/test_astra_db.py | 2 +- 4 files changed, 17 insertions(+), 8 deletions(-) diff --git a/docs/docs/examples/vector_stores/AstraDBIndexDemo.ipynb b/docs/docs/examples/vector_stores/AstraDBIndexDemo.ipynb index 6e6b994268347..f50acee94c1d9 100644 --- a/docs/docs/examples/vector_stores/AstraDBIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/AstraDBIndexDemo.ipynb @@ -26,7 +26,8 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index-vector-stores-astra-db" + "%pip install llama-index-vector-stores-astra-db\n", + "%pip install llama-index-embeddings-openai" ] }, { @@ -36,7 +37,7 @@ "outputs": [], "source": [ "!pip install llama-index\n", - "!pip install \"astrapy>=0.6.0\"" + "!pip install \"astrapy>=1.0\"" ] }, { @@ -86,6 +87,7 @@ " SimpleDirectoryReader,\n", " StorageContext,\n", ")\n", + "from llama_index.embeddings.openai import OpenAIEmbedding\n", "from llama_index.vector_stores.astra_db import AstraDBVectorStore" ] }, @@ -164,10 +166,12 @@ "metadata": {}, "outputs": [], "source": [ + "embed_model = OpenAIEmbedding(model_name=\"text-embedding-3-small\")\n", + "\n", "storage_context = StorageContext.from_defaults(vector_store=astra_db_store)\n", "\n", "index = VectorStoreIndex.from_documents(\n", - " documents, storage_context=storage_context\n", + " documents, storage_context=storage_context, embed_model=embed_model\n", ")" ] }, diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/llama_index/vector_stores/astra_db/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/llama_index/vector_stores/astra_db/base.py index 080e26b11033f..db28fe915e5e0 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/llama_index/vector_stores/astra_db/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/llama_index/vector_stores/astra_db/base.py @@ -62,7 +62,8 @@ class AstraDBVectorStore(BasePydanticVectorStore): token (str): The Astra DB Application Token to use. api_endpoint (str): The Astra DB JSON API endpoint for your database. embedding_dimension (int): length of the embedding vectors in use. - namespace (Optional[str]): The namespace to use. If not provided, 'default_keyspace' + keyspace (Optional[str]): The keyspace to use. If not provided, 'default_keyspace' + namespace (Optional[str]): [DEPRECATED] The keyspace to use. If not provided, 'default_keyspace' Examples: `pip install llama-index-vector-stores-astra` @@ -95,6 +96,7 @@ def __init__( token: str, api_endpoint: str, embedding_dimension: int, + keyspace: Optional[str] = None, namespace: Optional[str] = None, ttl_seconds: Optional[int] = None, ) -> None: @@ -115,6 +117,9 @@ def __init__( _logger.debug("Creating the Astra DB client and database instances") + # Choose the keyspace param + keyspace_param = keyspace or namespace + # Build the Database object self._database = DataAPIClient( caller_name=getattr(llama_index, "__name__", "llama_index"), @@ -122,7 +127,7 @@ def __init__( ).get_database( api_endpoint, token=token, - namespace=namespace, + keyspace=keyspace_param, ) from astrapy.exceptions import DataAPIException diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml index bf0b7b79149ae..dfbae3ad0a9af 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/pyproject.toml @@ -27,11 +27,11 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-astra-db" readme = "README.md" -version = "0.2.0" +version = "0.3.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -astrapy = "^1.3" +astrapy = "^1.5" llama-index-core = "^0.11.0" [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/tests/test_astra_db.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/tests/test_astra_db.py index a886c6e5bb54b..aa7120453e6bb 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/tests/test_astra_db.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-astra-db/tests/test_astra_db.py @@ -19,7 +19,7 @@ def astra_db_store() -> Iterable[AstraDBVectorStore]: token=ASTRA_DB_APPLICATION_TOKEN, api_endpoint=ASTRA_DB_API_ENDPOINT, collection_name="test_collection", - namespace=ASTRA_DB_KEYSPACE, + keyspace=ASTRA_DB_KEYSPACE, embedding_dimension=2, ) store._collection.delete_many({}) From 7160c0c69a9e4f45c580b7027e32cee040dc3f0c Mon Sep 17 00:00:00 2001 From: Prashanth Rao <35005448+prrao87@users.noreply.github.com> Date: Mon, 23 Sep 2024 16:56:28 -0700 Subject: [PATCH 14/53] Revert unintended consequence of string node representation update (#16100) --- .../core/indices/property_graph/sub_retrievers/base.py | 7 ++++++- .../core/indices/property_graph/sub_retrievers/custom.py | 8 +++++++- .../property_graph/sub_retrievers/cypher_template.py | 4 +++- .../indices/property_graph/sub_retrievers/llm_synonym.py | 8 +++++++- .../property_graph/sub_retrievers/text_to_cypher.py | 4 +++- .../core/indices/property_graph/sub_retrievers/vector.py | 8 +++++++- 6 files changed, 33 insertions(+), 6 deletions(-) diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/base.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/base.py index 12272f6cfca0b..7fd5a1b4f1784 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/base.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/base.py @@ -38,11 +38,13 @@ def __init__( graph_store: PropertyGraphStore, include_text: bool = True, include_text_preamble: Optional[str] = DEFAULT_PREAMBLE, + include_properties: bool = False, **kwargs: Any, ) -> None: self._graph_store = graph_store self.include_text = include_text self._include_text_preamble = include_text_preamble + self.include_properties = include_properties super().__init__(callback_manager=kwargs.get("callback_manager", None)) def _get_nodes_with_score( @@ -57,7 +59,10 @@ def _get_nodes_with_score( node_id=source_id ) - text = f"{triplet[0]!s} -> {triplet[1]!s} -> {triplet[2]!s}" + if self.include_properties: + text = f"{triplet[0]!s} -> {triplet[1]!s} -> {triplet[2]!s}" + else: + text = f"{triplet[0].id} -> {triplet[1].id} -> {triplet[2].id}" results.append( NodeWithScore( node=TextNode( diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/custom.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/custom.py index 74152ffe760a4..6e6222c0f5fbb 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/custom.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/custom.py @@ -32,9 +32,15 @@ def __init__( self, graph_store: PropertyGraphStore, include_text: bool = False, + include_properties: bool = False, **kwargs: Any, ) -> None: - super().__init__(graph_store=graph_store, include_text=include_text, **kwargs) + super().__init__( + graph_store=graph_store, + include_text=include_text, + include_properties=include_properties, + **kwargs, + ) self.init(**kwargs) @property diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py index 2f575164079db..bc09a84e53335 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/cypher_template.py @@ -41,7 +41,9 @@ def __init__( self.output_cls = output_cls self.cypher_query = cypher_query - super().__init__(graph_store=graph_store, include_text=False) + super().__init__( + graph_store=graph_store, include_text=False, include_properties=False + ) def retrieve_from_graph(self, query_bundle: QueryBundle) -> List[NodeWithScore]: question = query_bundle.query_str diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/llm_synonym.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/llm_synonym.py index a464663d74f22..17938c1ed77a5 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/llm_synonym.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/llm_synonym.py @@ -53,6 +53,7 @@ def __init__( self, graph_store: PropertyGraphStore, include_text: bool = True, + include_properties: bool = False, synonym_prompt: Union[ BasePromptTemplate, str ] = DEFAULT_SYNONYM_EXPAND_TEMPLATE, @@ -69,7 +70,12 @@ def __init__( self._output_parsing_fn = output_parsing_fn self._max_keywords = max_keywords self._path_depth = path_depth - super().__init__(graph_store=graph_store, include_text=include_text, **kwargs) + super().__init__( + graph_store=graph_store, + include_text=include_text, + include_properties=include_properties, + **kwargs, + ) def _parse_llm_output(self, output: str) -> List[str]: if self._output_parsing_fn: diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py index afdd02bda55c9..6b52b9fac6128 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/text_to_cypher.py @@ -59,7 +59,9 @@ def __init__( ) self.cypher_validator = cypher_validator self.allowed_output_fields = allowed_output_fields - super().__init__(graph_store=graph_store, include_text=False) + super().__init__( + graph_store=graph_store, include_text=False, include_properties=False + ) def _parse_generated_cypher(self, cypher_query: str) -> str: if self.cypher_validator is not None: diff --git a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/vector.py b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/vector.py index 5ba6d98831aec..ac06729111d83 100644 --- a/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/vector.py +++ b/llama-index-core/llama_index/core/indices/property_graph/sub_retrievers/vector.py @@ -43,6 +43,7 @@ def __init__( self, graph_store: PropertyGraphStore, include_text: bool = True, + include_properties: bool = False, embed_model: Optional[BaseEmbedding] = None, vector_store: Optional[BasePydanticVectorStore] = None, similarity_top_k: int = 4, @@ -59,7 +60,12 @@ def __init__( self._similarity_score = similarity_score self._filters = filters - super().__init__(graph_store=graph_store, include_text=include_text, **kwargs) + super().__init__( + graph_store=graph_store, + include_text=include_text, + include_properties=include_properties, + **kwargs, + ) def _get_vector_store_query(self, query_bundle: QueryBundle) -> VectorStoreQuery: if query_bundle.embedding is None: From be2db5aff5f87fe349cc537b295dfcefe077f863 Mon Sep 17 00:00:00 2001 From: Logan Date: Mon, 23 Sep 2024 20:13:18 -0600 Subject: [PATCH 15/53] markdown element node parser reliability (#16172) --- .../core/instrumentation/events/llm.py | 6 ++-- .../node_parser/relational/base_element.py | 31 ++++++++++++++----- .../core/response_synthesizers/refine.py | 11 +++++-- 3 files changed, 34 insertions(+), 14 deletions(-) diff --git a/llama-index-core/llama_index/core/instrumentation/events/llm.py b/llama-index-core/llama_index/core/instrumentation/events/llm.py index 3ed824f525fd4..68521dc23d117 100644 --- a/llama-index-core/llama_index/core/instrumentation/events/llm.py +++ b/llama-index-core/llama_index/core/instrumentation/events/llm.py @@ -1,5 +1,5 @@ from typing import Any, List, Optional -from llama_index.core.bridge.pydantic import BaseModel, SerializeAsAny, ConfigDict +from llama_index.core.bridge.pydantic import SerializeAsAny, ConfigDict from llama_index.core.base.llms.types import ( ChatMessage, ChatResponse, @@ -69,7 +69,7 @@ class LLMStructuredPredictEndEvent(BaseEvent): output (BaseModel): Predicted output class. """ - output: SerializeAsAny[BaseModel] + output: SerializeAsAny[Any] @classmethod def class_name(cls) -> str: @@ -84,7 +84,7 @@ class LLMStructuredPredictInProgressEvent(BaseEvent): output (BaseModel): Predicted output class. """ - output: SerializeAsAny[BaseModel] + output: SerializeAsAny[Any] @classmethod def class_name(cls) -> str: diff --git a/llama-index-core/llama_index/core/node_parser/relational/base_element.py b/llama-index-core/llama_index/core/node_parser/relational/base_element.py index bae8e5b0679a0..0739c641cb194 100644 --- a/llama-index-core/llama_index/core/node_parser/relational/base_element.py +++ b/llama-index-core/llama_index/core/node_parser/relational/base_element.py @@ -14,7 +14,13 @@ from llama_index.core.callbacks.base import CallbackManager from llama_index.core.llms.llm import LLM from llama_index.core.node_parser.interface import NodeParser -from llama_index.core.schema import BaseNode, Document, IndexNode, TextNode +from llama_index.core.schema import ( + BaseNode, + Document, + IndexNode, + MetadataMode, + TextNode, +) from llama_index.core.utils import get_tqdm_iterable DEFAULT_SUMMARY_QUERY_STR = """\ @@ -191,7 +197,10 @@ async def _get_table_output(table_context: str, summary_query_str: str) -> Any: query_engine = index.as_query_engine(llm=llm, output_cls=TableOutput) try: response = await query_engine.aquery(summary_query_str) - return cast(PydanticResponse, response).response + if isinstance(response, PydanticResponse): + return response.response + else: + raise ValueError(f"Expected PydanticResponse, got {type(response)}") except (ValidationError, ValueError): # There was a pydantic validation error, so we will run with text completion # fill in the summary and leave other fields blank @@ -325,7 +334,7 @@ def get_nodes_from_elements( node_parser = self.nested_node_parser or SentenceSplitter() - nodes = [] + nodes: List[BaseNode] = [] cur_text_el_buffer: List[str] = [] for element in elements: if element.type == "table" or element.type == "table_text": @@ -376,15 +385,17 @@ def get_nodes_from_elements( # attempt to find start_char_idx for table # raw table string regardless if perfect or not is stored in element.element - start_char_idx: Optional[int] = None - end_char_idx: Optional[int] = None if ref_doc_text: start_char_idx = ref_doc_text.find(str(element.element)) if start_char_idx >= 0: end_char_idx = start_char_idx + len(str(element.element)) else: - start_char_idx = None - end_char_idx = None + start_char_idx = None # type: ignore + end_char_idx = None # type: ignore + else: + start_char_idx = None # type: ignore + end_char_idx = None # type: ignore + # shared index_id and node_id node_id = str(uuid.uuid4()) index_node = IndexNode( @@ -440,7 +451,11 @@ def get_nodes_from_elements( node.excluded_llm_metadata_keys = ( node_inherited.excluded_llm_metadata_keys ) - return [node for node in nodes if len(node.get_content()) > 0] + return [ + node + for node in nodes + if len(node.get_content(metadata_mode=MetadataMode.NONE)) > 0 + ] def __call__(self, nodes: Sequence[BaseNode], **kwargs: Any) -> List[BaseNode]: nodes = self.get_nodes_from_documents(nodes, **kwargs) # type: ignore diff --git a/llama-index-core/llama_index/core/response_synthesizers/refine.py b/llama-index-core/llama_index/core/response_synthesizers/refine.py index c18490332df46..2630794a4dcc3 100644 --- a/llama-index-core/llama_index/core/response_synthesizers/refine.py +++ b/llama-index-core/llama_index/core/response_synthesizers/refine.py @@ -79,7 +79,8 @@ def __call__(self, *args: Any, **kwds: Any) -> StructuredRefineResponse: self._prompt, **kwds, ) - answer = answer.model_dump_json() + if isinstance(answer, BaseModel): + answer = answer.model_dump_json() else: answer = self._llm.predict( self._prompt, @@ -94,7 +95,8 @@ async def acall(self, *args: Any, **kwds: Any) -> StructuredRefineResponse: self._prompt, **kwds, ) - answer = answer.model_dump_json() + if isinstance(answer, BaseModel): + answer = answer.model_dump_json() else: answer = await self._llm.apredict( self._prompt, @@ -185,7 +187,10 @@ def get_response( prev_response = response if isinstance(response, str): if self._output_cls is not None: - response = self._output_cls.model_validate_json(response) + try: + response = self._output_cls.model_validate_json(response) + except ValidationError: + pass else: response = response or "Empty Response" else: From b56bafc4a91a60810872d9616a79432ce42c0845 Mon Sep 17 00:00:00 2001 From: Anoop Sharma Date: Tue, 24 Sep 2024 11:16:11 +0530 Subject: [PATCH 16/53] Ollama Multimodal request timeout (#16179) * Adding default value to timeout * Upgraded package version --- .../llama_index/multi_modal_llms/ollama/base.py | 1 + .../llama-index-multi-modal-llms-ollama/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py index d61a990a8d726..8598f0bf1d860 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py @@ -67,6 +67,7 @@ class OllamaMultiModal(MultiModalLLM): gt=0, ) request_timeout: Optional[float] = Field( + default=60.0, description="The timeout for making http request to Ollama API server", ) additional_kwargs: Dict[str, Any] = Field( diff --git a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml index 6068703b75611..fcb26f84c19fb 100644 --- a/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml +++ b/llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-multi-modal-llms-ollama" readme = "README.md" -version = "0.3.2" +version = "0.3.3" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 2d6ade41d222715f58cbab512883a04af48008a1 Mon Sep 17 00:00:00 2001 From: Pietari Salo Date: Tue, 24 Sep 2024 18:07:51 +0300 Subject: [PATCH 17/53] add typing annotations (#16189) --- .../llama_index/vector_stores/timescalevector/base.py | 8 ++++---- .../pyproject.toml | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/llama_index/vector_stores/timescalevector/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/llama_index/vector_stores/timescalevector/base.py index 2f6c90bc3cabc..d5e12286b8353 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/llama_index/vector_stores/timescalevector/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/llama_index/vector_stores/timescalevector/base.py @@ -1,7 +1,7 @@ import enum import uuid from datetime import timedelta -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, ClassVar from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.constants import DEFAULT_EMBEDDING_DIM @@ -48,8 +48,8 @@ class TimescaleVectorStore(BasePydanticVectorStore): ``` """ - stores_text = True - flat_metadata = False + stores_text: bool = True + flat_metadata: bool = False service_url: str table_name: str @@ -269,7 +269,7 @@ def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None: filter: Dict[str, str] = {"doc_id": ref_doc_id} self._sync_client.delete_by_metadata(filter) - DEFAULT_INDEX_TYPE = IndexType.TIMESCALE_VECTOR + DEFAULT_INDEX_TYPE: ClassVar = IndexType.TIMESCALE_VECTOR def create_index( self, index_type: IndexType = DEFAULT_INDEX_TYPE, **kwargs: Any diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml index 172ca7d51b202..b8b521fec8dc6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-timescalevector/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-timescalevector" readme = "README.md" -version = "0.2.0" +version = "0.2.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From acaa260f7b5ad4190bd004e5db85290985da7121 Mon Sep 17 00:00:00 2001 From: Logan Date: Tue, 24 Sep 2024 10:22:10 -0600 Subject: [PATCH 18/53] v0.11.13 (#16190) --- CHANGELOG.md | 31 +++ docs/docs/CHANGELOG.md | 31 +++ docs/mkdocs.yml | 1 + llama-index-core/llama_index/core/__init__.py | 2 +- .../llama_index/core/workflow/workflow.py | 4 +- llama-index-core/pyproject.toml | 2 +- .../storage/chat_store/redis/base.py | 56 ++--- .../pyproject.toml | 2 +- poetry.lock | 212 +++++++++--------- pyproject.toml | 4 +- 10 files changed, 206 insertions(+), 139 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 36b89f2335c86..040b3f53da906 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,36 @@ # ChangeLog +## [2024-09-24] + +### `llama-index-core` [0.11.13] + +- add option for string node representation during retireval in property graphs (#16100) +- improve markdown element node parser and structured prediction reliability (#16172) + +### `llama-index-graph-stores-neptune` [0.2.1] + +- Fixed issue where Neptune was adding additional labels (#16137) + +### `llama-index-llms-vertext` [0.3.5] + +- Pass safety_settings to send_message methods to fix settings not being sent to API (#16153) + +### `llama-index-readers-box` [0.2.3] + +- upgrading box sdk to >= 1.5.0 #16169 + +### `llama-index-storage-chat-store-dynamodb` [0.2.0] + +- Async support for dynamodb (#16139) + +### `llama-index-storage-chat-store-redis` [0.3.1] + +- Async support for redis (#16139) + +### `llama-index-vector-stores-astra-db` [0.3.0] + +- Depend on AstraPy 1.5 and above for AstraDBVectorStore (#16164) + ## [2024-09-22] ### `llama-index-core` [0.11.12] diff --git a/docs/docs/CHANGELOG.md b/docs/docs/CHANGELOG.md index 36b89f2335c86..040b3f53da906 100644 --- a/docs/docs/CHANGELOG.md +++ b/docs/docs/CHANGELOG.md @@ -1,5 +1,36 @@ # ChangeLog +## [2024-09-24] + +### `llama-index-core` [0.11.13] + +- add option for string node representation during retireval in property graphs (#16100) +- improve markdown element node parser and structured prediction reliability (#16172) + +### `llama-index-graph-stores-neptune` [0.2.1] + +- Fixed issue where Neptune was adding additional labels (#16137) + +### `llama-index-llms-vertext` [0.3.5] + +- Pass safety_settings to send_message methods to fix settings not being sent to API (#16153) + +### `llama-index-readers-box` [0.2.3] + +- upgrading box sdk to >= 1.5.0 #16169 + +### `llama-index-storage-chat-store-dynamodb` [0.2.0] + +- Async support for dynamodb (#16139) + +### `llama-index-storage-chat-store-redis` [0.3.1] + +- Async support for redis (#16139) + +### `llama-index-vector-stores-astra-db` [0.3.0] + +- Depend on AstraPy 1.5 and above for AstraDBVectorStore (#16164) + ## [2024-09-22] ### `llama-index-core` [0.11.12] diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 0072efce3ef77..1faf2856b6b4c 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -468,6 +468,7 @@ nav: - ./examples/observability/AimCallback.ipynb - ./examples/observability/HoneyHiveLlamaIndexTracer.ipynb - ./examples/observability/LangfuseCallbackHandler.ipynb + - ./examples/observability/LangfuseMistralPostHog.ipynb - ./examples/observability/LlamaDebugHandler.ipynb - ./examples/observability/MLflow.ipynb - ./examples/observability/OpenInferenceCallback.ipynb diff --git a/llama-index-core/llama_index/core/__init__.py b/llama-index-core/llama_index/core/__init__.py index c1cc7bd38d4df..0860b59517796 100644 --- a/llama-index-core/llama_index/core/__init__.py +++ b/llama-index-core/llama_index/core/__init__.py @@ -1,6 +1,6 @@ """Init file of LlamaIndex.""" -__version__ = "0.11.12" +__version__ = "0.11.13" import logging from logging import NullHandler diff --git a/llama-index-core/llama_index/core/workflow/workflow.py b/llama-index-core/llama_index/core/workflow/workflow.py index 84b2fab434f12..e3b76dc890dee 100644 --- a/llama-index-core/llama_index/core/workflow/workflow.py +++ b/llama-index-core/llama_index/core/workflow/workflow.py @@ -314,7 +314,9 @@ async def _run_workflow() -> None: # Cancel any pending tasks for t in unfinished: t.cancel() - await asyncio.sleep(0) + + # wait for cancelled tasks to cleanup + await asyncio.gather(*unfinished, return_exceptions=True) if exception_raised: ctx.write_event_to_stream(StopEvent()) diff --git a/llama-index-core/pyproject.toml b/llama-index-core/pyproject.toml index c35c5ba4522ae..3f56e5c5dc9c1 100644 --- a/llama-index-core/pyproject.toml +++ b/llama-index-core/pyproject.toml @@ -46,7 +46,7 @@ name = "llama-index-core" packages = [{include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.11.12" +version = "0.11.13" [tool.poetry.dependencies] SQLAlchemy = {extras = ["asyncio"], version = ">=1.4.49"} diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py index ccfc96a130cd0..d97bbf8244dbd 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/llama_index/storage/chat_store/redis/base.py @@ -15,7 +15,7 @@ from redis.asyncio.cluster import RedisCluster as AsyncRedisCluster from redis.asyncio.sentinel import Sentinel as AsyncSentinel -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.llms import ChatMessage from llama_index.core.storage.chat_store.base import BaseChatStore @@ -33,10 +33,12 @@ def _dict_to_message(d: dict) -> ChatMessage: class RedisChatStore(BaseChatStore): """Redis chat store.""" - redis_client: Any = Field(description="Redis client.") - aredis_client: Any = Field(default=None, description="Async Redis client.") + redis_url: str = Field(default="redis://localhost:6379", description="Redis URL.") ttl: Optional[int] = Field(default=None, description="Time to live in seconds.") + _redis_client: Optional[Redis] = PrivateAttr() + _aredis_client: Optional[AsyncRedis] = PrivateAttr() + def __init__( self, redis_url: str = "redis://localhost:6379", @@ -48,8 +50,8 @@ def __init__( """Initialize.""" super().__init__(ttl=ttl) - self.redis_client = redis_client or self._get_client(redis_url, **kwargs) - self.aredis_client = aredis_client or self._aget_client(redis_url, **kwargs) + self._redis_client = redis_client or self._get_client(redis_url, **kwargs) + self._aredis_client = aredis_client or self._aget_client(redis_url, **kwargs) @classmethod def class_name(cls) -> str: @@ -58,24 +60,24 @@ def class_name(cls) -> str: def set_messages(self, key: str, messages: List[ChatMessage]) -> None: """Set messages for a key.""" - self.redis_client.delete(key) + self._redis_client.delete(key) for message in messages: self.add_message(key, message) if self.ttl: - self.redis_client.expire(key, self.ttl) + self._redis_client.expire(key, self.ttl) async def aset_messages(self, key: str, messages: List[ChatMessage]) -> None: - await self.aredis_client.delete(key) + await self._aredis_client.delete(key) for message in messages: await self.async_add_message(key, message) if self.ttl: - await self.aredis_client.expire(key, self.ttl) + await self._aredis_client.expire(key, self.ttl) def get_messages(self, key: str) -> List[ChatMessage]: """Get messages for a key.""" - items = self.redis_client.lrange(key, 0, -1) + items = self._redis_client.lrange(key, 0, -1) if len(items) == 0: return [] @@ -84,7 +86,7 @@ def get_messages(self, key: str) -> List[ChatMessage]: async def aget_messages(self, key: str) -> List[ChatMessage]: """Get messages for a key.""" - items = await self.aredis_client.lrange(key, 0, -1) + items = await self._aredis_client.lrange(key, 0, -1) if len(items) == 0: return [] @@ -97,12 +99,12 @@ def add_message( """Add a message for a key.""" if idx is None: item = json.dumps(_message_to_dict(message)) - self.redis_client.rpush(key, item) + self._redis_client.rpush(key, item) else: self._insert_element_at_index(key, idx, message) if self.ttl: - self.redis_client.expire(key, self.ttl) + self._redis_client.expire(key, self.ttl) async def async_add_message( self, key: str, message: ChatMessage, idx: Optional[int] = None @@ -110,54 +112,54 @@ async def async_add_message( """Add a message for a key.""" if idx is None: item = json.dumps(_message_to_dict(message)) - await self.aredis_client.rpush(key, item) + await self._aredis_client.rpush(key, item) else: await self._ainsert_element_at_index(key, idx, message) if self.ttl: - await self.aredis_client.expire(key, self.ttl) + await self._aredis_client.expire(key, self.ttl) def delete_messages(self, key: str) -> Optional[List[ChatMessage]]: """Delete messages for a key.""" - self.redis_client.delete(key) + self._redis_client.delete(key) return None async def adelete_messages(self, key: str) -> Optional[List[ChatMessage]]: """Delete messages for a key.""" - await self.aredis_client.delete(key) + await self._aredis_client.delete(key) return None def delete_message(self, key: str, idx: int) -> Optional[ChatMessage]: """Delete specific message for a key.""" - current_list = self.redis_client.lrange(key, 0, -1) + current_list = self._redis_client.lrange(key, 0, -1) if 0 <= idx < len(current_list): removed_item = current_list.pop(idx) - self.redis_client.delete(key) - self.redis_client.lpush(key, *current_list) + self._redis_client.delete(key) + self._redis_client.lpush(key, *current_list) return removed_item else: return None async def adelete_message(self, key: str, idx: int) -> Optional[ChatMessage]: """Delete specific message for a key.""" - current_list = await self.aredis_client.lrange(key, 0, -1) + current_list = await self._aredis_client.lrange(key, 0, -1) if 0 <= idx < len(current_list): removed_item = current_list.pop(idx) - await self.aredis_client.delete(key) - await self.aredis_client.lpush(key, *current_list) + await self._aredis_client.delete(key) + await self._aredis_client.lpush(key, *current_list) return removed_item else: return None def delete_last_message(self, key: str) -> Optional[ChatMessage]: """Delete last message for a key.""" - return self.redis_client.rpop(key) + return self._redis_client.rpop(key) def get_keys(self) -> List[str]: """Get all keys.""" - return [key.decode("utf-8") for key in self.redis_client.keys("*")] + return [key.decode("utf-8") for key in self._redis_client.keys("*")] def _insert_element_at_index( self, key: str, index: int, message: ChatMessage @@ -168,7 +170,7 @@ def _insert_element_at_index( current_list.insert(index, message) # Step 3: Push the modified local list back to Redis - self.redis_client.delete(key) # Remove the existing list + self._redis_client.delete(key) # Remove the existing list self.set_messages(key, current_list) return self.get_messages(key) @@ -181,7 +183,7 @@ async def _ainsert_element_at_index( current_list.insert(index, message) # Step 3: Push the modified local list back to Redis - await self.aredis_client.delete(key) # Remove the existing list + await self._aredis_client.delete(key) # Remove the existing list await self.aset_messages(key, current_list) return await self.aget_messages(key) diff --git a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml index 37fdfeef8eb8d..3db097b7bbbbc 100644 --- a/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml +++ b/llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-redis/pyproject.toml @@ -27,7 +27,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-storage-chat-store-redis" readme = "README.md" -version = "0.3.0" +version = "0.3.1" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" diff --git a/poetry.lock b/poetry.lock index af8545717eb69..954b241210cc6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1639,13 +1639,13 @@ files = [ [[package]] name = "llama-cloud" -version = "0.0.17" +version = "0.1.0" description = "" optional = false python-versions = "<4,>=3.8" files = [ - {file = "llama_cloud-0.0.17-py3-none-any.whl", hash = "sha256:da898dcc98de84f29886f979b1ccae1e96d9f73d1b0e07146a51d315b161e45c"}, - {file = "llama_cloud-0.0.17.tar.gz", hash = "sha256:7fd6857bbbb91937535572ccb48daa38189f55cdd7411185d8083dab29ba1299"}, + {file = "llama_cloud-0.1.0-py3-none-any.whl", hash = "sha256:e315046d856780d996886864cd315d5f216cd398b3bd3f005fcae393c66f5494"}, + {file = "llama_cloud-0.1.0.tar.gz", hash = "sha256:8b31e1fb87f48c397c1a6415816cf37afcb4abe9fe7a5eb4101a2dc6d5c6ebbc"}, ] [package.dependencies] @@ -1686,13 +1686,13 @@ llama-index-llms-openai = ">=0.2.0,<0.3.0" [[package]] name = "llama-index-core" -version = "0.11.12" +version = "0.11.13" description = "Interface between LLMs and your data" optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "llama_index_core-0.11.12-py3-none-any.whl", hash = "sha256:7dc7ead649bac8f09e61c6c8bf93d257f68a7315223552421be4f0ffc3a8054d"}, - {file = "llama_index_core-0.11.12.tar.gz", hash = "sha256:ce2dd037ff889d9ea6b25872228cc9de614c10445d19377f6ae5c66b93a50c61"}, + {file = "llama_index_core-0.11.13-py3-none-any.whl", hash = "sha256:543bdda5926a417224966fc2c976e401b35e6dacdd74e28e1309cfee8e3240aa"}, + {file = "llama_index_core-0.11.13.tar.gz", hash = "sha256:77cc5605c38a0839312762f8c4d7edda2f99bbfbc3cb745ef0c9c382b3ed3293"}, ] [package.dependencies] @@ -2656,13 +2656,13 @@ files = [ [[package]] name = "openai" -version = "1.47.0" +version = "1.47.1" description = "The official Python library for the openai API" optional = false python-versions = ">=3.7.1" files = [ - {file = "openai-1.47.0-py3-none-any.whl", hash = "sha256:9ccc8737dfa791f7bd903db4758c176b8544a8cd89d3a3d2add3cea02a34c3a0"}, - {file = "openai-1.47.0.tar.gz", hash = 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-content-hash = "312441a44e35d392f2a88342cfbe2e4d20f940e2964cc38848ec05f7d725026a" +content-hash = "629810f65f656d6b02bee18000f4eb3bda3bba33ef995929b32215a9781a0b72" diff --git a/pyproject.toml b/pyproject.toml index 703d6c535e583..7c604f863081e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,7 +45,7 @@ name = "llama-index" packages = [{from = "_llama-index", include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.11.12" +version = "0.11.13" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" @@ -58,7 +58,7 @@ llama-index-agent-openai = "^0.3.4" llama-index-readers-file = "^0.2.0" llama-index-readers-llama-parse = ">=0.3.0" llama-index-indices-managed-llama-cloud = ">=0.3.0" -llama-index-core = "^0.11.11" +llama-index-core = "^0.11.13" llama-index-multi-modal-llms-openai = "^0.2.0" llama-index-cli = "^0.3.1" nltk = ">3.8.1" # avoids a CVE, temp until next release, should be in llama-index-core From 7c6d42a22222fc3b9252ba1f1f5e4739685de1d4 Mon Sep 17 00:00:00 2001 From: Logan Date: Tue, 24 Sep 2024 11:06:25 -0600 Subject: [PATCH 19/53] allow non-flat metadata in mongodb (#16193) --- .../llama_index/vector_stores/mongodb/base.py | 2 +- .../llama-index-vector-stores-mongodb/pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py index 99ed76f2d9543..129a6532bf289 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py @@ -89,7 +89,7 @@ class MongoDBAtlasVectorSearch(BasePydanticVectorStore): """ stores_text: bool = True - flat_metadata: bool = True + flat_metadata: bool = False _mongodb_client: Any = PrivateAttr() _collection: Any = PrivateAttr() diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml index 9b53447221fdb..e39fbeb38e56b 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml @@ -29,7 +29,7 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-vector-stores-mongodb" readme = "README.md" -version = "0.3.1" +version = "0.3.2" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" From 5b4034cb807d4876b157460f809fa45b32d553e7 Mon Sep 17 00:00:00 2001 From: Logan Date: Tue, 24 Sep 2024 12:52:06 -0600 Subject: [PATCH 20/53] allow passing in httpx clients to llama-cloud (#16192) --- llama-index-core/llama_index/core/__init__.py | 2 +- .../llama_index/core/ingestion/api_utils.py | 11 +++++++++-- llama-index-core/pyproject.toml | 2 +- .../llama_index/indices/managed/llama_cloud/base.py | 13 +++++++++++-- .../pyproject.toml | 8 ++++++-- 5 files changed, 28 insertions(+), 8 deletions(-) diff --git a/llama-index-core/llama_index/core/__init__.py b/llama-index-core/llama_index/core/__init__.py index 0860b59517796..279c9e7493348 100644 --- a/llama-index-core/llama_index/core/__init__.py +++ b/llama-index-core/llama_index/core/__init__.py @@ -1,6 +1,6 @@ """Init file of LlamaIndex.""" -__version__ = "0.11.13" +__version__ = "0.11.13.post1" import logging from logging import NullHandler diff --git a/llama-index-core/llama_index/core/ingestion/api_utils.py b/llama-index-core/llama_index/core/ingestion/api_utils.py index 972b6d232d8a4..9e6340d11e57f 100644 --- a/llama-index-core/llama_index/core/ingestion/api_utils.py +++ b/llama-index-core/llama_index/core/ingestion/api_utils.py @@ -1,4 +1,5 @@ import os +import httpx from typing import Optional, TYPE_CHECKING from llama_index.core.constants import ( @@ -15,6 +16,7 @@ def get_client( base_url: Optional[str] = None, app_url: Optional[str] = None, timeout: int = 60, + httpx_client: Optional[httpx.Client] = None, ) -> "LlamaCloud": """Get the sync platform API client.""" from llama_cloud.client import LlamaCloud @@ -23,7 +25,9 @@ def get_client( app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", DEFAULT_APP_URL) api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) - return LlamaCloud(base_url=base_url, token=api_key, timeout=timeout) + return LlamaCloud( + base_url=base_url, token=api_key, timeout=timeout, httpx_client=httpx_client + ) def get_aclient( @@ -31,6 +35,7 @@ def get_aclient( base_url: Optional[str] = None, app_url: Optional[str] = None, timeout: int = 60, + httpx_client: Optional[httpx.AsyncClient] = None, ) -> "AsyncLlamaCloud": """Get the async platform API client.""" from llama_cloud.client import AsyncLlamaCloud @@ -39,4 +44,6 @@ def get_aclient( app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", DEFAULT_APP_URL) api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) - return AsyncLlamaCloud(base_url=base_url, token=api_key, timeout=timeout) + return AsyncLlamaCloud( + base_url=base_url, token=api_key, timeout=timeout, httpx_client=httpx_client + ) diff --git a/llama-index-core/pyproject.toml b/llama-index-core/pyproject.toml index 3f56e5c5dc9c1..1fe03aa2eac80 100644 --- a/llama-index-core/pyproject.toml +++ b/llama-index-core/pyproject.toml @@ -46,7 +46,7 @@ name = "llama-index-core" packages = [{include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.11.13" +version = "0.11.13.post1" [tool.poetry.dependencies] SQLAlchemy = {extras = ["asyncio"], version = ">=1.4.49"} diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py index a032de031c4d4..b67fe17b787dd 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py @@ -5,6 +5,7 @@ """ +import httpx import os import time from typing import Any, List, Optional, Sequence, Type @@ -66,6 +67,8 @@ def __init__( app_url: Optional[str] = None, show_progress: bool = False, callback_manager: Optional[CallbackManager] = None, + httpx_client: Optional[httpx.Client] = None, + async_httpx_client: Optional[httpx.AsyncClient] = None, **kwargs: Any, ) -> None: """Initialize the Platform Index.""" @@ -78,8 +81,12 @@ def __init__( # TODO: How to handle uploading nodes without running transforms on them? raise ValueError("LlamaCloudIndex does not support nodes on initialization") - self._client = get_client(api_key, base_url, app_url, timeout) - self._aclient = get_aclient(api_key, base_url, app_url, timeout) + self._httpx_client = httpx_client + self._async_httpx_client = async_httpx_client + self._client = get_client(api_key, base_url, app_url, timeout, httpx_client) + self._aclient = get_aclient( + api_key, base_url, app_url, timeout, async_httpx_client + ) self._api_key = api_key self._base_url = base_url @@ -316,6 +323,8 @@ def as_retriever(self, **kwargs: Any) -> BaseRetriever: timeout=self._timeout, organization_id=self.organization_id, dense_similarity_top_k=dense_similarity_top_k, + httpx_client=self._httpx_client, + async_httpx_client=self._async_httpx_client, **kwargs, ) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml index c68dbeb2ead23..91df56a18b8a8 100644 --- a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml @@ -19,8 +19,12 @@ LlamaCloudRetriever = "llama-index" [tool.mypy] disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit exclude = ["_static", "build", "examples", "notebooks", "venv"] +explicit_package_bases = true ignore_missing_imports = true +namespace_packages = true +plugins = "pydantic.mypy" python_version = "3.8" [tool.poetry] @@ -30,12 +34,12 @@ exclude = ["**/BUILD"] license = "MIT" name = "llama-index-indices-managed-llama-cloud" readme = "README.md" -version = "0.3.1" +version = "0.4.0" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" llama-cloud = ">=0.0.11" -llama-index-core = "^0.11.0" +llama-index-core = "^0.11.13.post1" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" From 9edc6f73b80f78dc4b79c0eda502a97817fabf75 Mon Sep 17 00:00:00 2001 From: Ravi Theja Date: Wed, 25 Sep 2024 00:22:49 +0530 Subject: [PATCH 21/53] Add support for Mistral Multi modal LLM (#16191) * Add support for Mistral Multi modal LLM * fix mistralai package * Update * Update --- .../multi_modal/mistral_multi_modal.ipynb | 727 ++++++++++++++++++ .../.gitignore | 153 ++++ .../BUILD | 3 + .../Makefile | 17 + .../README.md | 1 + .../multi_modal_llms/mistralai/BUILD | 1 + .../multi_modal_llms/mistralai/__init__.py | 3 + .../multi_modal_llms/mistralai/base.py | 320 ++++++++ .../multi_modal_llms/mistralai/utils.py | 139 ++++ .../pyproject.toml | 64 ++ .../tests/BUILD | 1 + .../tests/__init__.py | 0 .../tests/test_multi-modal-llms_mistral.py | 12 + 13 files changed, 1441 insertions(+) create mode 100644 docs/docs/examples/multi_modal/mistral_multi_modal.ipynb create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/.gitignore create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/BUILD create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/Makefile create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/README.md create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/BUILD create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/__init__.py create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/base.py create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/llama_index/multi_modal_llms/mistralai/utils.py create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/pyproject.toml create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/BUILD create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/__init__.py create mode 100644 llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-mistralai/tests/test_multi-modal-llms_mistral.py diff --git a/docs/docs/examples/multi_modal/mistral_multi_modal.ipynb b/docs/docs/examples/multi_modal/mistral_multi_modal.ipynb new file mode 100644 index 0000000000000..7997df63b12d3 --- /dev/null +++ b/docs/docs/examples/multi_modal/mistral_multi_modal.ipynb @@ -0,0 +1,727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "368686b4-f487-4dd4-aeff-37823976529d", + "metadata": {}, + "source": [ + "\"Open\n", + "\n", + "# Multi-Modal LLM using Mistral Pixtral-12B model for image reasoning\n", + "\n", + "In this notebook, we show how to use MistralAI MultiModal LLM class/abstraction for image understanding/reasoning.\n", + "\n", + "We demonstrate following functions that are supported for MistralAI Pixtral Multimodal LLM:\n", + "* `complete` (both sync and async): for a single prompt and list of images\n", + "* `stream complete` (both sync and async): for steaming output of complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "396d319e", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-multi-modal-llms-mistralai\n", + "%pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5455d8c6", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from IPython.display import Markdown, display\n", + "\n", + "os.environ[\n", + " \"MISTRAL_API_KEY\"\n", + "] = \"\" # Your MistralAI API token here" + ] + }, + { + "cell_type": "markdown", + "id": "3d0d083e", + "metadata": {}, + "source": [ + "## Initialize `MistralAIMultiModal`" + ] + }, + { + "cell_type": "markdown", + "id": "c627c8a1", + "metadata": {}, + "source": [ + "## " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8725b6d2", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.multi_modal_llms.mistralai import MistralAIMultiModal\n", + "\n", + "mistralai_mm_llm = MistralAIMultiModal(\n", + " model=\"pixtral-12b-2409\", max_new_tokens=300\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6f3c9843", + "metadata": {}, + "source": [ + "## Load Images from URLs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d531446a", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.multi_modal_llms.generic_utils import load_image_urls\n", + "\n", + "\n", + "image_urls = [\n", + " \"https://tripfixers.com/wp-content/uploads/2019/11/eiffel-tower-with-snow.jpeg\",\n", + " \"https://cdn.statcdn.com/Infographic/images/normal/30322.jpeg\",\n", + "]\n", + "\n", + "image_documents = load_image_urls(image_urls)" + ] + }, + { + "cell_type": "markdown", + "id": "0b8f8e6b", + "metadata": {}, + "source": [ + "### First Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05d94bcb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://tripfixers.com/wp-content/uploads/2019/11/eiffel-tower-with-snow.jpeg\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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