From 67e4643e3249df2f20abc7179a603127b079ac91 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Mon, 9 Sep 2024 15:37:29 +0545 Subject: [PATCH 01/20] added the core chatbot components; --- chatbot-core/database.py | 78 +++++++++++++++ chatbot-core/doc_loaders.py | 56 +++++++++++ chatbot-core/embedding.py | 134 ++++++++++++++++++++++++++ chatbot-core/llm.py | 186 ++++++++++++++++++++++++++++++++++++ chatbot-core/utils.py | 18 ++++ 5 files changed, 472 insertions(+) create mode 100644 chatbot-core/database.py create mode 100644 chatbot-core/doc_loaders.py create mode 100644 chatbot-core/embedding.py create mode 100644 chatbot-core/llm.py create mode 100644 chatbot-core/utils.py diff --git a/chatbot-core/database.py b/chatbot-core/database.py new file mode 100644 index 0000000..1abd867 --- /dev/null +++ b/chatbot-core/database.py @@ -0,0 +1,78 @@ +from typing import List +from dataclasses import dataclass, field +from qdrant_client import QdrantClient +from qdrant_client.http.models import Distance, VectorParams +from qdrant_client.http.exceptions import UnexpectedResponse +from uuid import uuid4 + +@dataclass +class QdrantDatabase: + """ Qdrant Vector Database """ + host: str="localhost" + port: int=6333 + db_client: QdrantClient=field(init=False) + collection_name: str=field(init=False) + + def initialize_db(self, collection_name: str): + """ Initialize database client """ + self.db_client = QdrantClient(host=self.host, port=self.port) + self.collection_name = collection_name + + def _collection_exists(self, collection_name: str) -> bool: + """ Check if the collection in db already exists """ + try: + self.db_client.get_collection(collection_name=collection_name) + return True + except UnexpectedResponse: + return False + + def set_collection(self, vector_size: int=384): + """ Create the database collection """ + if not self._collection_exists(self.collection_name): + self.db_client.create_collection( + collection_name=self.collection_name, + vectors_config=VectorParams( + size=vector_size, + distance=Distance.COSINE + ) + ) + else: + print(f"Collection {self.collection_name} already exists. Using the existing one.") + + def store_data(self, data: List) -> None: + """ Stores data in vector db """ + point_vectors = [ + { + "id": str(uuid4()), + "vector": v_representation, + "payload": metadata + } + for v_representation, metadata in data + ] + + response = self.db_client.upsert( + collection_name=self.collection_name, + points=point_vectors + ) + return response + + def data_search( + self, + collection_names: list, + query_vector: list, + top_n_retrieval: int=5, + score_threshold: float=0.7 + ): + """ Search data in the database """ + results = [ + self.db_client.search( + collection_name=collection_name, + query_vector=query_vector, + top=top_n_retrieval, + score_threshold=score_threshold + + ) + for collection_name in collection_names + ] + # Note the results shall contain score key; sort the results using score key and get top 5 among them. + return results \ No newline at end of file diff --git a/chatbot-core/doc_loaders.py b/chatbot-core/doc_loaders.py new file mode 100644 index 0000000..eab90e1 --- /dev/null +++ b/chatbot-core/doc_loaders.py @@ -0,0 +1,56 @@ +from typing import List +from dataclasses import dataclass +from langchain.schema import Document +from langchain_community.document_loaders import WebBaseLoader +from langchain.text_splitter import RecursiveCharacterTextSplitter + +@dataclass(kw_only=True) +class DocumentLoader: + """ + Base Class for Document Loaders + """ + chunk_size: int=200 + chunk_overlap: int=20 + + def _get_split_documents(self, documents: List[Document]): + """ + Splits documents into multiple chunks + """ + splitter = RecursiveCharacterTextSplitter( + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap, + length_function=len + ) + + return splitter.split_documents(documents) + +@dataclass +class LoaderFromText(DocumentLoader): + """ + Document loader for plain texts + """ + texts: str + + def create_document_chunks(self): + """ + Creates multiple documents from the input texts + """ + documents = [Document(page_content=self.texts)] + doc_chunks = self._get_split_documents(documents=documents) + return doc_chunks + +@dataclass +class LoaderFromWeb(DocumentLoader): + """ + Document loader for the web url + """ + url: str + + def create_document_chunks(self): + """ + Creates multiple documents from the input url + """ + loader = WebBaseLoader(web_path=self.url) + docs = loader.load() + doc_chunks = self._get_split_documents(documents=docs) + return doc_chunks diff --git a/chatbot-core/embedding.py b/chatbot-core/embedding.py new file mode 100644 index 0000000..de3b92c --- /dev/null +++ b/chatbot-core/embedding.py @@ -0,0 +1,134 @@ +from dataclasses import dataclass, field +from typing import List + +import numpy as np +from torch import Tensor + +from sentence_transformers import SentenceTransformer + +from langchain_core.embeddings import Embeddings +from langchain_community.embeddings import OllamaEmbeddings +from langchain_openai import OpenAIEmbeddings + +from dotenv import load_dotenv + +load_dotenv() + +# Note to check if the vector dimensions are same (impt for injecting in vector db) + +@dataclass +class SentenceTransformerEmbeddingModel(Embeddings): + """ + Embedding model using Sentence Transformers + """ + model: str="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" + embedding_model: SentenceTransformer = field(init=False) + + def __post_init__(self): + """ + Post initialization + """ + self.st_embedding_model = SentenceTransformer(self.model) + + def embed_documents(self, texts: list) -> np.ndarray: + """ + Generate embeddings for a list of documents + """ + return self.st_embedding_model.encode(texts) + + def embed_query(self, text: str) -> np.ndarray: + """ + Generate embedding for a piece of text + """ + return self.st_embedding_model.encode(text) + + def check_similarity(self, embeddings_1: np.ndarray, embeddings_2: np.ndarray) -> Tensor: + """ + Computes the cosine similarity between two embeddings + """ + return self.st_embedding_model.similarity(embeddings_1, embeddings_2) + + def get_model(self): + """ Returns the model """ + return self.st_embedding_model + +# TODO: check with Ollama as it always expect llama2 model +@dataclass +class OllamaEmbeddingModel(Embeddings): + """ + Embedding model using Ollama (locally deployed) + """ + model: str="all-minilm:latest" + base_url: str="http://localhost:11434" + model: str="nomic-embed-text:latest" + + def __post_init__(self): + """ + Post initialization + """ + self.ollama_embed_model = OllamaEmbeddings(model=self.model, base_url=self.base_url) + + def embed_documents(self, texts: list[str]) -> list[list[float]]: + """ + Generate embeddings for a list of documents. + """ + return self.ollama_embed_model.embed_documents(texts) + + def embed_query(self, text: str) -> list[float]: + """ + Generate embedding for a piece of text + """ + return self.ollama_embed_model.embed_query(text=text, model=self.model) + + def get_model(self): + """ Returns the model """ + return self.ollama_embed_model + +@dataclass +class OpenAIEmbeddingModel(Embeddings): + """ + Embedding Model using OpenAI + """ + model: str="text-embedding-3-small" + + def __post_init__(self): + """ + Post initialization + """ + self.openai_embed_model = OpenAIEmbeddings(model=self.model) + + def embed_documents(self, texts: List[str]): + """ + Generate embeddings for a list of documents. + """ + return self.openai_embed_model.embed_documents(texts=texts) + + def embed_query(self, text: str): + """ + Generate embedding for a piece of text + """ + return self.openai_embed_model.embed_query(text=text) + + def get_model(self): + """ Returns the model """ + return self.openai_embed_model + +if __name__ == "__main__": + embedmodel = SentenceTransformerEmbeddingModel() + #embedmodel.initialize_model() + result = embedmodel.embed_query(["This is a test"]) + print(type(result)) + #print(result) + similarities = embedmodel.check_similarity(result, result) + print(type(similarities)) + print(similarities) + + # em = OllamaEmbeddings(base_url="http://localhost:11434") + # resp = em.embed_query("This is a test") + # print(type(resp)) + # print(len(resp)) + + em = OpenAIEmbeddingModel() + resp = em.embed_query("This is a test.") + print(resp) + print(len(resp)) diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py new file mode 100644 index 0000000..c7516d1 --- /dev/null +++ b/chatbot-core/llm.py @@ -0,0 +1,186 @@ +from dataclasses import dataclass, field +from typing import Any +from dotenv import load_dotenv + +from langchain_openai import ChatOpenAI +from langchain_qdrant import QdrantVectorStore +from qdrant_client import QdrantClient +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder + +from langchain.memory import ConversationBufferWindowMemory +from langchain.schema import HumanMessage, AIMessage +from langchain.chains.history_aware_retriever import create_history_aware_retriever +from langchain.chains.combine_documents import create_stuff_documents_chain +from langchain.chains.retrieval import create_retrieval_chain + +from embedding import SentenceTransformerEmbeddingModel +from utils import EmbeddingModelType + +load_dotenv() + +@dataclass +class LLMBase: + """ LLM Base containing common methods """ + + qdrant_host: str="localhost" + qdrant_port: int=6333 + qdrant_client: QdrantClient=field(init=False) + llm_model: Any=field(init=False) + memory: Any=field(init=False) + + def __post_init__(self, mem_key: str="chat_history", conversation_max_window: int=3): + self.llm_model = None + self.qdrant_client = None + self.memory = None + + self.qdrant_client = QdrantClient(host=self.qdrant_host, port=self.qdrant_port) + self.memory = ConversationBufferWindowMemory( + k=conversation_max_window, + memory_key=mem_key, + return_messages=True + ) + + def _system_prompt_for_retrieval(self): + """ System prompt for information retrieval """ + return ( + "Given a chat history and the latest user question " + "which might reference context in the chat history, " + "formulate a standalone question which can be understood " + "without the chat history. Do NOT answer the question, " + "just reformulate it if needed and otherwise return it as is." + ) + + def _system_prompt_for_response(self): + """ + System prompt for response generation + """ + system_prompt = """ + You are an assistant for question-answering tasks.\n, + Use the following retrieved context to answer the question.\n, + If you don't get the answer from the provided context, say that 'I don't know. How can I help with the office related queries ?' + \n\n, + Context: {context} + """ + return system_prompt + + def get_selected_embedding_model(self, embedding_model: EmbeddingModelType): + """ + Get the Embedding Model based on input selection + """ + if embedding_model == EmbeddingModelType.SENTENCE_TRANSFORMES: + embedding_model = SentenceTransformerEmbeddingModel() + return embedding_model + elif embedding_model == EmbeddingModelType.OLLAMA: + pass + elif embedding_model == EmbeddingModelType.OPENAI: + pass + raise ValueError("Embedding Model is not selected. Chose the correct one.") + + +@dataclass +class OpenAIHandler(LLMBase): + """ OpenAI LLM handler for RAG """ + + model: str="gpt-3.5-turbo-1106" + temperature: float=0.2 + + def __post_init__(self): + super().__post_init__() + self.llm_model = ChatOpenAI( + model=self.model, + temperature=self.temperature + ) + + def get_prompt_template_for_retrieval(self): + """ Get the prompt template """ + system_prompt = self._system_prompt_for_retrieval() + context_prompt_template = ChatPromptTemplate.from_messages([ + ("system", system_prompt), + MessagesPlaceholder(variable_name="chat_history"), + ("human", "{input}") + ]) + return context_prompt_template + + def get_prompt_template_for_response(self): + """ Get the prompt template for response generation """ + system_prompt = self._system_prompt_for_response() + llm_response_prompt = ChatPromptTemplate.from_messages( + [ + ("system", system_prompt), + MessagesPlaceholder("chat_history"), + ("human", "{input}") + ] + ) + return llm_response_prompt + + def get_db_retriever( + self, + collection_name: str, + embedding_model: Any, + top_k_items: int=5, + score_threshold: float=0.5 + ): + """ Get the database retriever """ + db_retriever = QdrantVectorStore( + client=self.qdrant_client, + collection_name=collection_name, + embedding=embedding_model + ) + retriever = db_retriever.as_retriever( + search_type="similarity_score_threshold", + search_kwargs={ + "k": top_k_items, + "score_threshold": score_threshold + } + ) + return retriever + + def create_chain(self, db_collection_name: str): + """ Creates a llm chain """ + if not self.llm_model: + raise Exception("The LLM model is not loaded.") + + embedding_model = self.get_selected_embedding_model(EmbeddingModelType.SENTENCE_TRANSFORMES) + context_prompt_template = self.get_prompt_template_for_retrieval() + response_prompt_template = self.get_prompt_template_for_response() + + retriever = self.get_db_retriever(collection_name=db_collection_name, embedding_model=embedding_model) + + history_aware_retriever = create_history_aware_retriever( + self.llm_model, retriever, context_prompt_template + ) + + chat_response_chain = create_stuff_documents_chain( + self.llm_model, response_prompt_template + ) + + rag_chain = create_retrieval_chain( + history_aware_retriever, chat_response_chain + ) + return rag_chain + + def execute_chain(self, query: str, db_collection_name: str): + """ + Executes the chain + """ + rag_chain = self.create_chain(db_collection_name=db_collection_name) + + response = rag_chain.invoke({ + "input": query, + "chat_history": self.get_message_history()['chat_history'] + }) + self.memory.chat_memory.add_message(HumanMessage(content=query)) + self.memory.chat_memory.add_message(AIMessage(content=response['answer'])) + return response + + def get_message_history(self): + """ + Returns the historical conversational data + """ + return self.memory.load_memory_variables({}) + + +if __name__ == "__main__": + handler = OpenAIHandler() + resp = handler.execute_chain("What do you know about DEEP ?") + print(resp['answer']) diff --git a/chatbot-core/utils.py b/chatbot-core/utils.py new file mode 100644 index 0000000..9820bc8 --- /dev/null +++ b/chatbot-core/utils.py @@ -0,0 +1,18 @@ +import re +from enum import Enum +from typing import List + +class EmbeddingModelType(Enum): + """ Embedding Model Types """ + SENTENCE_TRANSFORMES=1 + OLLAMA=2 + OPENAI=3 + +def preprocess_text(texts: List[str]) -> List[str]: + """ + Preprocessing of the texts + """ + pattern = r'\s+' + + results = [re.sub(pattern, '', text) for text in texts] + return results From e738141531f6b62bc03d87bc3bd45bad0e406bcf Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Mon, 9 Sep 2024 15:37:52 +0545 Subject: [PATCH 02/20] updated the chatbot packages; --- poetry.lock | 3181 +++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 10 +- 2 files changed, 3164 insertions(+), 27 deletions(-) diff --git a/poetry.lock b/poetry.lock 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v3.0" readme = "README.md" [tool.poetry.dependencies] -python = "^3.12" +python = ">=3.10,<4.0" django = "^5.1" django-environ = "^0.11.2" psycopg2-binary = "^2.9.9" flake8 = "^7.1.1" +qdrant-client = "^1.11.1" +langchain = "^0.2.15" +langchain-community = "^0.2.14" +langchain-openai = "^0.1.23" +sentence-transformers = "3.0.1" +ollama = "^0.3.2" +python-dotenv = "1.0.1" +langchain-qdrant = "^0.1.3" [build-system] From 7228afa42a0085b5fd7444dfe0ced276559a4d84 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Mon, 9 Sep 2024 15:54:54 +0545 Subject: [PATCH 03/20] updates in db module; other clean-ups; --- chatbot-core/database.py | 8 +++----- chatbot-core/embedding.py | 20 -------------------- chatbot-core/llm.py | 5 ----- 3 files changed, 3 insertions(+), 30 deletions(-) diff --git a/chatbot-core/database.py b/chatbot-core/database.py index 1abd867..c6c270d 100644 --- a/chatbot-core/database.py +++ b/chatbot-core/database.py @@ -8,15 +8,14 @@ @dataclass class QdrantDatabase: """ Qdrant Vector Database """ + collection_name: str host: str="localhost" port: int=6333 db_client: QdrantClient=field(init=False) - collection_name: str=field(init=False) - def initialize_db(self, collection_name: str): + def __post_init__(self, collection_name: str): """ Initialize database client """ self.db_client = QdrantClient(host=self.host, port=self.port) - self.collection_name = collection_name def _collection_exists(self, collection_name: str) -> bool: """ Check if the collection in db already exists """ @@ -70,9 +69,8 @@ def data_search( query_vector=query_vector, top=top_n_retrieval, score_threshold=score_threshold - ) for collection_name in collection_names ] # Note the results shall contain score key; sort the results using score key and get top 5 among them. - return results \ No newline at end of file + return results diff --git a/chatbot-core/embedding.py b/chatbot-core/embedding.py index de3b92c..1e0b920 100644 --- a/chatbot-core/embedding.py +++ b/chatbot-core/embedding.py @@ -112,23 +112,3 @@ def embed_query(self, text: str): def get_model(self): """ Returns the model """ return self.openai_embed_model - -if __name__ == "__main__": - embedmodel = SentenceTransformerEmbeddingModel() - #embedmodel.initialize_model() - result = embedmodel.embed_query(["This is a test"]) - print(type(result)) - #print(result) - similarities = embedmodel.check_similarity(result, result) - print(type(similarities)) - print(similarities) - - # em = OllamaEmbeddings(base_url="http://localhost:11434") - # resp = em.embed_query("This is a test") - # print(type(resp)) - # print(len(resp)) - - em = OpenAIEmbeddingModel() - resp = em.embed_query("This is a test.") - print(resp) - print(len(resp)) diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py index c7516d1..e54a51b 100644 --- a/chatbot-core/llm.py +++ b/chatbot-core/llm.py @@ -179,8 +179,3 @@ def get_message_history(self): """ return self.memory.load_memory_variables({}) - -if __name__ == "__main__": - handler = OpenAIHandler() - resp = handler.execute_chain("What do you know about DEEP ?") - print(resp['answer']) From f039127c15f1f2e0ee6dc5e13829ffdf92f8ef5f Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Tue, 10 Sep 2024 14:33:44 +0545 Subject: [PATCH 04/20] some fixes and refactor code; --- chatbot-core/database.py | 7 ++- chatbot-core/embedding.py | 8 +--- chatbot-core/llm.py | 89 +++++++++++++++++++++++++-------------- chatbot-core/utils.py | 5 +++ 4 files changed, 69 insertions(+), 40 deletions(-) diff --git a/chatbot-core/database.py b/chatbot-core/database.py index c6c270d..6849247 100644 --- a/chatbot-core/database.py +++ b/chatbot-core/database.py @@ -1,3 +1,4 @@ +import logging from typing import List from dataclasses import dataclass, field from qdrant_client import QdrantClient @@ -5,6 +6,8 @@ from qdrant_client.http.exceptions import UnexpectedResponse from uuid import uuid4 +logger = logging.getLogger(__name__) + @dataclass class QdrantDatabase: """ Qdrant Vector Database """ @@ -13,7 +16,7 @@ class QdrantDatabase: port: int=6333 db_client: QdrantClient=field(init=False) - def __post_init__(self, collection_name: str): + def __post_init__(self): """ Initialize database client """ self.db_client = QdrantClient(host=self.host, port=self.port) @@ -36,7 +39,7 @@ def set_collection(self, vector_size: int=384): ) ) else: - print(f"Collection {self.collection_name} already exists. Using the existing one.") + logger.info(f"Collection {self.collection_name} already exists. Using the existing one.") def store_data(self, data: List) -> None: """ Stores data in vector db """ diff --git a/chatbot-core/embedding.py b/chatbot-core/embedding.py index 1e0b920..6b44a7d 100644 --- a/chatbot-core/embedding.py +++ b/chatbot-core/embedding.py @@ -10,10 +10,6 @@ from langchain_community.embeddings import OllamaEmbeddings from langchain_openai import OpenAIEmbeddings -from dotenv import load_dotenv - -load_dotenv() - # Note to check if the vector dimensions are same (impt for injecting in vector db) @dataclass @@ -52,7 +48,6 @@ def get_model(self): """ Returns the model """ return self.st_embedding_model -# TODO: check with Ollama as it always expect llama2 model @dataclass class OllamaEmbeddingModel(Embeddings): """ @@ -60,7 +55,6 @@ class OllamaEmbeddingModel(Embeddings): """ model: str="all-minilm:latest" base_url: str="http://localhost:11434" - model: str="nomic-embed-text:latest" def __post_init__(self): """ @@ -78,7 +72,7 @@ def embed_query(self, text: str) -> list[float]: """ Generate embedding for a piece of text """ - return self.ollama_embed_model.embed_query(text=text, model=self.model) + return self.ollama_embed_model.embed_query(text=text) def get_model(self): """ Returns the model """ diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py index e54a51b..650af72 100644 --- a/chatbot-core/llm.py +++ b/chatbot-core/llm.py @@ -1,6 +1,6 @@ from dataclasses import dataclass, field from typing import Any -from dotenv import load_dotenv +import logging from langchain_openai import ChatOpenAI from langchain_qdrant import QdrantVectorStore @@ -13,17 +13,27 @@ from langchain.chains.combine_documents import create_stuff_documents_chain from langchain.chains.retrieval import create_retrieval_chain -from embedding import SentenceTransformerEmbeddingModel +from embedding import SentenceTransformerEmbeddingModel, OllamaEmbeddingModel, OpenAIEmbeddingModel from utils import EmbeddingModelType -load_dotenv() +from langchain_community.llms.ollama import Ollama + +from main.settings import ( + EMBEDDING_MODEL_CHOICE, + OLLAMA_BASE_URL, + QDRANT_DB_HOST, + QDRANT_DB_PORT, + QDRANT_DB_COLLECTION_NAME +) + +logger = logging.getLogger(__name__) @dataclass class LLMBase: """ LLM Base containing common methods """ - qdrant_host: str="localhost" - qdrant_port: int=6333 + qdrant_host: str=QDRANT_DB_HOST + qdrant_port: int=QDRANT_DB_PORT qdrant_client: QdrantClient=field(init=False) llm_model: Any=field(init=False) memory: Any=field(init=False) @@ -63,34 +73,18 @@ def _system_prompt_for_response(self): """ return system_prompt - def get_selected_embedding_model(self, embedding_model: EmbeddingModelType): + def get_selected_embedding_model(self, embedding_model_type: int=1): """ Get the Embedding Model based on input selection """ - if embedding_model == EmbeddingModelType.SENTENCE_TRANSFORMES: - embedding_model = SentenceTransformerEmbeddingModel() - return embedding_model - elif embedding_model == EmbeddingModelType.OLLAMA: - pass - elif embedding_model == EmbeddingModelType.OPENAI: - pass + if embedding_model_type == EmbeddingModelType.SENTENCE_TRANSFORMES.value: + return SentenceTransformerEmbeddingModel() + elif embedding_model_type == EmbeddingModelType.OLLAMA.value: + return OllamaEmbeddingModel(base_url=OLLAMA_BASE_URL) + elif embedding_model_type == EmbeddingModelType.OPENAI.value: + return OpenAIEmbeddingModel() raise ValueError("Embedding Model is not selected. Chose the correct one.") - -@dataclass -class OpenAIHandler(LLMBase): - """ OpenAI LLM handler for RAG """ - - model: str="gpt-3.5-turbo-1106" - temperature: float=0.2 - - def __post_init__(self): - super().__post_init__() - self.llm_model = ChatOpenAI( - model=self.model, - temperature=self.temperature - ) - def get_prompt_template_for_retrieval(self): """ Get the prompt template """ system_prompt = self._system_prompt_for_retrieval() @@ -140,7 +134,7 @@ def create_chain(self, db_collection_name: str): if not self.llm_model: raise Exception("The LLM model is not loaded.") - embedding_model = self.get_selected_embedding_model(EmbeddingModelType.SENTENCE_TRANSFORMES) + embedding_model = self.get_selected_embedding_model(EMBEDDING_MODEL_CHOICE) context_prompt_template = self.get_prompt_template_for_retrieval() response_prompt_template = self.get_prompt_template_for_response() @@ -159,7 +153,7 @@ def create_chain(self, db_collection_name: str): ) return rag_chain - def execute_chain(self, query: str, db_collection_name: str): + def execute_chain(self, query: str, db_collection_name: str=QDRANT_DB_COLLECTION_NAME): """ Executes the chain """ @@ -171,7 +165,7 @@ def execute_chain(self, query: str, db_collection_name: str): }) self.memory.chat_memory.add_message(HumanMessage(content=query)) self.memory.chat_memory.add_message(AIMessage(content=response['answer'])) - return response + return response['answer'] if "answer" in response else "" def get_message_history(self): """ @@ -179,3 +173,36 @@ def get_message_history(self): """ return self.memory.load_memory_variables({}) + +@dataclass +class OpenAIHandler(LLMBase): + """ LLM handler using OpenAI for RAG """ + + model: str="gpt-3.5-turbo-1106" + temperature: float=0.2 + + def __post_init__(self): + super().__post_init__() + + self.llm_model = ChatOpenAI( + model=self.model, + temperature=self.temperature + ) + +@dataclass +class OllamaHandler(LLMBase): + """ LLM Handler using Ollama for RAG """ + model: str="mistral:latest" + base_url: str="http://localhost:11434" + temperature: float=0.2 + + def __post_init__(self): + super().__post_init__() + try: + self.llm_model = Ollama( + model=self.model, + base_url=self.base_url, + temperature=self.temperature + ) + except Exception as exc: + logger.error("Ollama LLM model is not successfully loaded. %s", str(exc), exc_info=True) diff --git a/chatbot-core/utils.py b/chatbot-core/utils.py index 9820bc8..56945fb 100644 --- a/chatbot-core/utils.py +++ b/chatbot-core/utils.py @@ -8,6 +8,11 @@ class EmbeddingModelType(Enum): OLLAMA=2 OPENAI=3 +class LLMType(Enum): + """ LLM Types """ + OLLAMA=1 + OPENAI=2 + def preprocess_text(texts: List[str]) -> List[str]: """ Preprocessing of the texts From 96de06582ef5b29088a184a44c0891d6281f8fb3 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Tue, 10 Sep 2024 14:34:32 +0545 Subject: [PATCH 05/20] env vars updated; --- docker-compose.yml | 7 +++++-- env.sample | 5 +++++ main/settings.py | 21 +++++++++++++++++++++ 3 files changed, 31 insertions(+), 2 deletions(-) diff --git a/docker-compose.yml b/docker-compose.yml index c473353..8fad25f 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -21,7 +21,8 @@ services: - qdrant_data:/qdrant/storage restart: always environment: - - QDRANT_HOST=0.0.0.0 + QDRANT_DB_HOST: 0.0.0.0 + QDRANT_DB_PORT: 6333 web: build: . @@ -36,7 +37,9 @@ services: DATABASE_PASSWORD: ${DATABASE_PASSWORD:-postgres} DATABASE_PORT: ${DATABASE_PORT:-5432} DATABASE_HOST: ${DATABASE_HOST:-db} - command: bash -c 'python manage.py runserver 0.0.0.0:8001' + QDRANT_DB_HOST: ${QDRANT_DB_HOST:-0.0.0.0} + QDRANT_DB_PORT: ${QDRANT_DB_PORT:-6333} + command: bash -c 'python manage.py runserver 0.0.0.0:8100' volumes: - .:/code ports: diff --git a/env.sample b/env.sample index f2b28e4..b4ff998 100644 --- a/env.sample +++ b/env.sample @@ -1,2 +1,7 @@ # additional env-variable in main/settings.py DJANGO_SECRET_KEY="" +DJANGO_SECRET_KEY +QDRANT_DB_COLLECTION_NAME +EMBEDDING_MODEL +LLM +OLLAMA_BASE_URL diff --git a/main/settings.py b/main/settings.py index 6db134f..a29f366 100644 --- a/main/settings.py +++ b/main/settings.py @@ -42,6 +42,16 @@ SENTRY_TRACES_SAMPLE_RATE=(float, 0.2), SENTRY_PROFILE_SAMPLE_RATE=(float, 0.2), # App Domain + # Vector Database + QDRANT_DB_HOST=str, + QDRANT_DB_PORT=int, + QDRANT_DB_COLLECTION_NAME=str, + # Embedding Model + EMBEDDING_MODEL=int, + LLM=int, + # Ollama url + OLLAMA_BASE_URL=str + ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ @@ -54,6 +64,17 @@ ALLOWED_HOSTS = env("DJANGO_ALLOWED_HOST") +# Embedding Model selection +EMBEDDING_MODEL_CHOICE = env("EMBEDDING_MODEL") +# LLM selection +LLM_CHOICE = env("LLM") +# Qdrant database +QDRANT_DB_HOST = env("QDRANT_DB_HOST") +QDRANT_DB_PORT = env("QDRANT_DB_PORT") +QDRANT_DB_COLLECTION_NAME = env("QDRANT_DB_COLLECTION_NAME") +# Ollama base url +OLLAMA_BASE_URL = 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readme = "README.md" [tool.poetry.dependencies] -python = ">=3.10,<4.0" +python = "^3.10" django = "^5.1" django-environ = "^0.11.2" psycopg2-binary = "^2.9.9" @@ -18,7 +18,6 @@ langchain-community = "^0.2.14" langchain-openai = "^0.1.23" sentence-transformers = "3.0.1" ollama = "^0.3.2" -python-dotenv = "1.0.1" langchain-qdrant = "^0.1.3" From ee73e09d2279de87be0d868a59056e0ecb14fe10 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Tue, 10 Sep 2024 15:49:22 +0545 Subject: [PATCH 07/20] fixes on formattings; --- chatbot-core/database.py | 55 ++++++-------- chatbot-core/doc_loaders.py | 19 +++-- chatbot-core/embedding.py | 30 ++++---- chatbot-core/llm.py | 143 ++++++++++++++---------------------- chatbot-core/utils.py | 23 +++--- main/settings.py | 3 +- 6 files changed, 121 insertions(+), 152 deletions(-) diff --git a/chatbot-core/database.py b/chatbot-core/database.py index 6849247..7025121 100644 --- a/chatbot-core/database.py +++ b/chatbot-core/database.py @@ -1,77 +1,64 @@ import logging -from typing import List from dataclasses import dataclass, field +from typing import List +from uuid import uuid4 + from qdrant_client import QdrantClient -from qdrant_client.http.models import Distance, VectorParams from qdrant_client.http.exceptions import UnexpectedResponse -from uuid import uuid4 +from qdrant_client.http.models import Distance, VectorParams logger = logging.getLogger(__name__) + @dataclass class QdrantDatabase: - """ Qdrant Vector Database """ + """Qdrant Vector Database""" + collection_name: str - host: str="localhost" - port: int=6333 - db_client: QdrantClient=field(init=False) + host: str = "localhost" + port: int = 6333 + db_client: QdrantClient = field(init=False) def __post_init__(self): - """ Initialize database client """ + """Initialize database client""" self.db_client = QdrantClient(host=self.host, port=self.port) def _collection_exists(self, collection_name: str) -> bool: - """ Check if the collection in db already exists """ + """Check if the collection in db already exists""" try: self.db_client.get_collection(collection_name=collection_name) return True except UnexpectedResponse: return False - def set_collection(self, vector_size: int=384): - """ Create the database collection """ + def set_collection(self, vector_size: int = 384): + """Create the database collection""" if not self._collection_exists(self.collection_name): self.db_client.create_collection( - collection_name=self.collection_name, - vectors_config=VectorParams( - size=vector_size, - distance=Distance.COSINE - ) + collection_name=self.collection_name, vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE) ) else: logger.info(f"Collection {self.collection_name} already exists. Using the existing one.") def store_data(self, data: List) -> None: - """ Stores data in vector db """ + """Stores data in vector db""" point_vectors = [ - { - "id": str(uuid4()), - "vector": v_representation, - "payload": metadata - } - for v_representation, metadata in data + {"id": str(uuid4()), "vector": v_representation, "payload": metadata} for v_representation, metadata in data ] - response = self.db_client.upsert( - collection_name=self.collection_name, - points=point_vectors - ) + response = self.db_client.upsert(collection_name=self.collection_name, points=point_vectors) return response def data_search( - self, - collection_names: list, - query_vector: list, - top_n_retrieval: int=5, - score_threshold: float=0.7 + self, collection_names: list, query_vector: list, top_n_retrieval: int = 5, score_threshold: float = 0.7 ): - """ Search data in the database """ + """Search data in the database""" results = [ self.db_client.search( collection_name=collection_name, query_vector=query_vector, top=top_n_retrieval, - score_threshold=score_threshold + score_threshold=score_threshold, ) for collection_name in collection_names ] diff --git a/chatbot-core/doc_loaders.py b/chatbot-core/doc_loaders.py index eab90e1..1b41534 100644 --- a/chatbot-core/doc_loaders.py +++ b/chatbot-core/doc_loaders.py @@ -1,34 +1,37 @@ -from typing import List from dataclasses import dataclass +from typing import List + from langchain.schema import Document -from langchain_community.document_loaders import WebBaseLoader from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.document_loaders import WebBaseLoader + @dataclass(kw_only=True) class DocumentLoader: """ Base Class for Document Loaders """ - chunk_size: int=200 - chunk_overlap: int=20 + + chunk_size: int = 200 + chunk_overlap: int = 20 def _get_split_documents(self, documents: List[Document]): """ Splits documents into multiple chunks """ splitter = RecursiveCharacterTextSplitter( - chunk_size=self.chunk_size, - chunk_overlap=self.chunk_overlap, - length_function=len + chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap, length_function=len ) return splitter.split_documents(documents) + @dataclass class LoaderFromText(DocumentLoader): """ Document loader for plain texts """ + texts: str def create_document_chunks(self): @@ -39,11 +42,13 @@ def create_document_chunks(self): doc_chunks = self._get_split_documents(documents=documents) return doc_chunks + @dataclass class LoaderFromWeb(DocumentLoader): """ Document loader for the web url """ + url: str def create_document_chunks(self): diff --git a/chatbot-core/embedding.py b/chatbot-core/embedding.py index 6b44a7d..f8363b3 100644 --- a/chatbot-core/embedding.py +++ b/chatbot-core/embedding.py @@ -2,22 +2,22 @@ from typing import List import numpy as np -from torch import Tensor - -from sentence_transformers import SentenceTransformer - -from langchain_core.embeddings import Embeddings from langchain_community.embeddings import OllamaEmbeddings +from langchain_core.embeddings import Embeddings from langchain_openai import OpenAIEmbeddings +from sentence_transformers import SentenceTransformer +from torch import Tensor # Note to check if the vector dimensions are same (impt for injecting in vector db) + @dataclass class SentenceTransformerEmbeddingModel(Embeddings): """ Embedding model using Sentence Transformers """ - model: str="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" + + model: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" embedding_model: SentenceTransformer = field(init=False) def __post_init__(self): @@ -43,18 +43,20 @@ def check_similarity(self, embeddings_1: np.ndarray, embeddings_2: np.ndarray) - Computes the cosine similarity between two embeddings """ return self.st_embedding_model.similarity(embeddings_1, embeddings_2) - + def get_model(self): - """ Returns the model """ + """Returns the model""" return self.st_embedding_model + @dataclass class OllamaEmbeddingModel(Embeddings): """ Embedding model using Ollama (locally deployed) """ - model: str="all-minilm:latest" - base_url: str="http://localhost:11434" + + model: str = "all-minilm:latest" + base_url: str = "http://localhost:11434" def __post_init__(self): """ @@ -75,15 +77,17 @@ def embed_query(self, text: str) -> list[float]: return self.ollama_embed_model.embed_query(text=text) def get_model(self): - """ Returns the model """ + """Returns the model""" return self.ollama_embed_model + @dataclass class OpenAIEmbeddingModel(Embeddings): """ Embedding Model using OpenAI """ - model: str="text-embedding-3-small" + + model: str = "text-embedding-3-small" def __post_init__(self): """ @@ -104,5 +108,5 @@ def embed_query(self, text: str): return self.openai_embed_model.embed_query(text=text) def get_model(self): - """ Returns the model """ + """Returns the model""" return self.openai_embed_model diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py index 650af72..d89dc2b 100644 --- a/chatbot-core/llm.py +++ b/chatbot-core/llm.py @@ -1,57 +1,55 @@ +import logging from dataclasses import dataclass, field from typing import Any -import logging +from embedding import ( + OllamaEmbeddingModel, + OpenAIEmbeddingModel, + SentenceTransformerEmbeddingModel, +) +from langchain.chains.combine_documents import create_stuff_documents_chain +from langchain.chains.history_aware_retriever import create_history_aware_retriever +from langchain.chains.retrieval import create_retrieval_chain +from langchain.memory import ConversationBufferWindowMemory +from langchain.schema import AIMessage, HumanMessage +from langchain_community.llms.ollama import Ollama +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from langchain_openai import ChatOpenAI from langchain_qdrant import QdrantVectorStore from qdrant_client import QdrantClient -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder - -from langchain.memory import ConversationBufferWindowMemory -from langchain.schema import HumanMessage, AIMessage -from langchain.chains.history_aware_retriever import create_history_aware_retriever -from langchain.chains.combine_documents import create_stuff_documents_chain -from langchain.chains.retrieval import create_retrieval_chain - -from embedding import SentenceTransformerEmbeddingModel, OllamaEmbeddingModel, OpenAIEmbeddingModel from utils import EmbeddingModelType -from langchain_community.llms.ollama import Ollama - from main.settings import ( EMBEDDING_MODEL_CHOICE, OLLAMA_BASE_URL, + QDRANT_DB_COLLECTION_NAME, QDRANT_DB_HOST, QDRANT_DB_PORT, - QDRANT_DB_COLLECTION_NAME ) logger = logging.getLogger(__name__) + @dataclass class LLMBase: - """ LLM Base containing common methods """ + """LLM Base containing common methods""" - qdrant_host: str=QDRANT_DB_HOST - qdrant_port: int=QDRANT_DB_PORT - qdrant_client: QdrantClient=field(init=False) - llm_model: Any=field(init=False) - memory: Any=field(init=False) + qdrant_host: str = QDRANT_DB_HOST + qdrant_port: int = QDRANT_DB_PORT + qdrant_client: QdrantClient = field(init=False) + llm_model: Any = field(init=False) + memory: Any = field(init=False) - def __post_init__(self, mem_key: str="chat_history", conversation_max_window: int=3): + def __post_init__(self, mem_key: str = "chat_history", conversation_max_window: int = 3): self.llm_model = None self.qdrant_client = None self.memory = None self.qdrant_client = QdrantClient(host=self.qdrant_host, port=self.qdrant_port) - self.memory = ConversationBufferWindowMemory( - k=conversation_max_window, - memory_key=mem_key, - return_messages=True - ) + self.memory = ConversationBufferWindowMemory(k=conversation_max_window, memory_key=mem_key, return_messages=True) def _system_prompt_for_retrieval(self): - """ System prompt for information retrieval """ + """System prompt for information retrieval""" return ( "Given a chat history and the latest user question " "which might reference context in the chat history, " @@ -67,13 +65,14 @@ def _system_prompt_for_response(self): system_prompt = """ You are an assistant for question-answering tasks.\n, Use the following retrieved context to answer the question.\n, - If you don't get the answer from the provided context, say that 'I don't know. How can I help with the office related queries ?' + If you don't get the answer from the provided context, \n, + say that 'I don't know. How can I help with the office related queries ?' \n\n, Context: {context} """ return system_prompt - def get_selected_embedding_model(self, embedding_model_type: int=1): + def get_selected_embedding_model(self, embedding_model_type: int = 1): """ Get the Embedding Model based on input selection """ @@ -86,51 +85,35 @@ def get_selected_embedding_model(self, embedding_model_type: int=1): raise ValueError("Embedding Model is not selected. Chose the correct one.") def get_prompt_template_for_retrieval(self): - """ Get the prompt template """ + """Get the prompt template""" system_prompt = self._system_prompt_for_retrieval() - context_prompt_template = ChatPromptTemplate.from_messages([ - ("system", system_prompt), - MessagesPlaceholder(variable_name="chat_history"), - ("human", "{input}") - ]) + context_prompt_template = ChatPromptTemplate.from_messages( + [("system", system_prompt), MessagesPlaceholder(variable_name="chat_history"), ("human", "{input}")] + ) return context_prompt_template def get_prompt_template_for_response(self): - """ Get the prompt template for response generation """ + """Get the prompt template for response generation""" system_prompt = self._system_prompt_for_response() llm_response_prompt = ChatPromptTemplate.from_messages( - [ - ("system", system_prompt), - MessagesPlaceholder("chat_history"), - ("human", "{input}") - ] + [("system", system_prompt), MessagesPlaceholder("chat_history"), ("human", "{input}")] ) return llm_response_prompt def get_db_retriever( - self, - collection_name: str, - embedding_model: Any, - top_k_items: int=5, - score_threshold: float=0.5 + self, collection_name: str, embedding_model: Any, top_k_items: int = 5, score_threshold: float = 0.5 ): - """ Get the database retriever """ + """Get the database retriever""" db_retriever = QdrantVectorStore( - client=self.qdrant_client, - collection_name=collection_name, - embedding=embedding_model + client=self.qdrant_client, collection_name=collection_name, embedding=embedding_model ) retriever = db_retriever.as_retriever( - search_type="similarity_score_threshold", - search_kwargs={ - "k": top_k_items, - "score_threshold": score_threshold - } + search_type="similarity_score_threshold", search_kwargs={"k": top_k_items, "score_threshold": score_threshold} ) return retriever def create_chain(self, db_collection_name: str): - """ Creates a llm chain """ + """Creates a llm chain""" if not self.llm_model: raise Exception("The LLM model is not loaded.") @@ -140,32 +123,23 @@ def create_chain(self, db_collection_name: str): retriever = self.get_db_retriever(collection_name=db_collection_name, embedding_model=embedding_model) - history_aware_retriever = create_history_aware_retriever( - self.llm_model, retriever, context_prompt_template - ) + history_aware_retriever = create_history_aware_retriever(self.llm_model, retriever, context_prompt_template) - chat_response_chain = create_stuff_documents_chain( - self.llm_model, response_prompt_template - ) + chat_response_chain = create_stuff_documents_chain(self.llm_model, response_prompt_template) - rag_chain = create_retrieval_chain( - history_aware_retriever, chat_response_chain - ) + rag_chain = create_retrieval_chain(history_aware_retriever, chat_response_chain) return rag_chain - def execute_chain(self, query: str, db_collection_name: str=QDRANT_DB_COLLECTION_NAME): + def execute_chain(self, query: str, db_collection_name: str = QDRANT_DB_COLLECTION_NAME): """ Executes the chain """ rag_chain = self.create_chain(db_collection_name=db_collection_name) - response = rag_chain.invoke({ - "input": query, - "chat_history": self.get_message_history()['chat_history'] - }) + response = rag_chain.invoke({"input": query, "chat_history": self.get_message_history()["chat_history"]}) self.memory.chat_memory.add_message(HumanMessage(content=query)) - self.memory.chat_memory.add_message(AIMessage(content=response['answer'])) - return response['answer'] if "answer" in response else "" + self.memory.chat_memory.add_message(AIMessage(content=response["answer"])) + return response["answer"] if "answer" in response else "" def get_message_history(self): """ @@ -176,33 +150,28 @@ def get_message_history(self): @dataclass class OpenAIHandler(LLMBase): - """ LLM handler using OpenAI for RAG """ + """LLM handler using OpenAI for RAG""" - model: str="gpt-3.5-turbo-1106" - temperature: float=0.2 + model: str = "gpt-3.5-turbo-1106" + temperature: float = 0.2 def __post_init__(self): super().__post_init__() - self.llm_model = ChatOpenAI( - model=self.model, - temperature=self.temperature - ) + self.llm_model = ChatOpenAI(model=self.model, temperature=self.temperature) + @dataclass class OllamaHandler(LLMBase): - """ LLM Handler using Ollama for RAG """ - model: str="mistral:latest" - base_url: str="http://localhost:11434" - temperature: float=0.2 + """LLM Handler using Ollama for RAG""" + + model: str = "mistral:latest" + base_url: str = "http://localhost:11434" + temperature: float = 0.2 def __post_init__(self): super().__post_init__() try: - self.llm_model = Ollama( - model=self.model, - base_url=self.base_url, - temperature=self.temperature - ) + self.llm_model = Ollama(model=self.model, base_url=self.base_url, temperature=self.temperature) except Exception as exc: logger.error("Ollama LLM model is not successfully loaded. %s", str(exc), exc_info=True) diff --git a/chatbot-core/utils.py b/chatbot-core/utils.py index 56945fb..2ac4700 100644 --- a/chatbot-core/utils.py +++ b/chatbot-core/utils.py @@ -2,22 +2,27 @@ from enum import Enum from typing import List + class EmbeddingModelType(Enum): - """ Embedding Model Types """ - SENTENCE_TRANSFORMES=1 - OLLAMA=2 - OPENAI=3 + """Embedding Model Types""" + + SENTENCE_TRANSFORMES = 1 + OLLAMA = 2 + OPENAI = 3 + class LLMType(Enum): - """ LLM Types """ - OLLAMA=1 - OPENAI=2 + """LLM Types""" + + OLLAMA = 1 + OPENAI = 2 + def preprocess_text(texts: List[str]) -> List[str]: """ Preprocessing of the texts """ - pattern = r'\s+' + pattern = r"\s+" - results = [re.sub(pattern, '', text) for text in texts] + results = [re.sub(pattern, "", text) for text in texts] return results diff --git a/main/settings.py b/main/settings.py index a29f366..10fc312 100644 --- a/main/settings.py +++ b/main/settings.py @@ -50,8 +50,7 @@ EMBEDDING_MODEL=int, LLM=int, # Ollama url - OLLAMA_BASE_URL=str - + OLLAMA_BASE_URL=str, ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ From 4d70e10404aed318e52f778b3355fc5e93abc95c Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 12:03:17 +0545 Subject: [PATCH 08/20] removed the cache; --- Dockerfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Dockerfile b/Dockerfile index f14189c..3765823 100644 --- a/Dockerfile +++ b/Dockerfile @@ -21,6 +21,7 @@ RUN apt-get update -y \ && poetry config virtualenvs.create false \ && poetry install --no-root \ # Clean-up + && rm -rf /root/.cache/pypoetry \ && pip uninstall -y poetry virtualenv-clone virtualenv \ && apt-get remove -y gcc libc-dev libproj-dev \ && apt-get autoremove -y \ From a89b7c74f8b8b39bc1089e7be737c6db0c4ac9cf Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 12:07:16 +0545 Subject: [PATCH 09/20] some params fixes and added the embedding wrapper + updated in llm module; --- chatbot-core/custom_embeddings.py | 60 ++++++++++++++++ chatbot-core/database.py | 7 +- chatbot-core/doc_loaders.py | 4 +- chatbot-core/embedding.py | 112 ------------------------------ chatbot-core/llm.py | 74 +++++++++----------- chatbot-core/utils.py | 3 +- 6 files changed, 100 insertions(+), 160 deletions(-) create mode 100644 chatbot-core/custom_embeddings.py delete mode 100644 chatbot-core/embedding.py diff --git a/chatbot-core/custom_embeddings.py b/chatbot-core/custom_embeddings.py new file mode 100644 index 0000000..709cae1 --- /dev/null +++ b/chatbot-core/custom_embeddings.py @@ -0,0 +1,60 @@ +from dataclasses import dataclass +from typing import List, Optional +import requests +from langchain.embeddings.base import Embeddings +from utils import EmbeddingModelType + +@dataclass +class CustomEmbeddingsWrapper(Embeddings): + """ + This is an Embeddings model wrapper to send request to the actual + embedding models + """ + + url: str + model_name: str + model_type: EmbeddingModelType + base_url: Optional[str] + + def __post_init__(self): + if self.model_type in [ + EmbeddingModelType.SENTENCE_TRANSFORMES, + EmbeddingModelType.OPENAI + ]: + if not (self.url and self.model_name): + raise Exception("Url or model name or both are not provided.") + elif self.model_type == EmbeddingModelType.OLLAMA: + if not(self.url and self.model_name and self.base_url): + raise Exception("Url or base_url or both are not provided.") + + def embed_query(self, text: str, timeout: int=30) -> List[float]: + """ + Sends the request to Embedding module to + embed the query to the vector representation + """ + payload = { + "type_model": self.model_type, + "name_model": self.model_name, + "texts": text + } + try: + response = requests.post(url=self.url, json=payload, timeout=timeout) + except requests.Timeout as e: + raise Exception(e) + return response.json() + + def embed_documents(self, texts: List[str], timeout: int=30) -> List[List[float]]: + """ + Sends the request to Embedding module to + embed multiple queries to the vector representation + """ + payload = { + "type_model": self.model_type, + "name_model": self.model_name, + "texts": texts + } + try: + response = requests.post(url=self.url, json=payload, timeout=timeout) + except requests.Timeout as e: + raise Exception(e) + return response.json() diff --git a/chatbot-core/database.py b/chatbot-core/database.py index 7025121..d0dc677 100644 --- a/chatbot-core/database.py +++ b/chatbot-core/database.py @@ -1,6 +1,5 @@ import logging from dataclasses import dataclass, field -from typing import List from uuid import uuid4 from qdrant_client import QdrantClient @@ -15,8 +14,8 @@ class QdrantDatabase: """Qdrant Vector Database""" collection_name: str - host: str = "localhost" - port: int = 6333 + host: str + port: int db_client: QdrantClient = field(init=False) def __post_init__(self): @@ -40,7 +39,7 @@ def set_collection(self, vector_size: int = 384): else: logger.info(f"Collection {self.collection_name} already exists. Using the existing one.") - def store_data(self, data: List) -> None: + def store_data(self, data: list) -> None: """Stores data in vector db""" point_vectors = [ {"id": str(uuid4()), "vector": v_representation, "payload": metadata} for v_representation, metadata in data diff --git a/chatbot-core/doc_loaders.py b/chatbot-core/doc_loaders.py index 1b41534..c840d22 100644 --- a/chatbot-core/doc_loaders.py +++ b/chatbot-core/doc_loaders.py @@ -32,13 +32,13 @@ class LoaderFromText(DocumentLoader): Document loader for plain texts """ - texts: str + text: str def create_document_chunks(self): """ Creates multiple documents from the input texts """ - documents = [Document(page_content=self.texts)] + documents = [Document(page_content=self.text)] doc_chunks = self._get_split_documents(documents=documents) return doc_chunks diff --git a/chatbot-core/embedding.py b/chatbot-core/embedding.py deleted file mode 100644 index f8363b3..0000000 --- a/chatbot-core/embedding.py +++ /dev/null @@ -1,112 +0,0 @@ -from dataclasses import dataclass, field -from typing import List - -import numpy as np -from langchain_community.embeddings import OllamaEmbeddings -from langchain_core.embeddings import Embeddings -from langchain_openai import OpenAIEmbeddings -from sentence_transformers import SentenceTransformer -from torch import Tensor - -# Note to check if the vector dimensions are same (impt for injecting in vector db) - - -@dataclass -class SentenceTransformerEmbeddingModel(Embeddings): - """ - Embedding model using Sentence Transformers - """ - - model: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2" - embedding_model: SentenceTransformer = field(init=False) - - def __post_init__(self): - """ - Post initialization - """ - self.st_embedding_model = SentenceTransformer(self.model) - - def embed_documents(self, texts: list) -> np.ndarray: - """ - Generate embeddings for a list of documents - """ - return self.st_embedding_model.encode(texts) - - def embed_query(self, text: str) -> np.ndarray: - """ - Generate embedding for a piece of text - """ - return self.st_embedding_model.encode(text) - - def check_similarity(self, embeddings_1: np.ndarray, embeddings_2: np.ndarray) -> Tensor: - """ - Computes the cosine similarity between two embeddings - """ - return self.st_embedding_model.similarity(embeddings_1, embeddings_2) - - def get_model(self): - """Returns the model""" - return self.st_embedding_model - - -@dataclass -class OllamaEmbeddingModel(Embeddings): - """ - Embedding model using Ollama (locally deployed) - """ - - model: str = "all-minilm:latest" - base_url: str = "http://localhost:11434" - - def __post_init__(self): - """ - Post initialization - """ - self.ollama_embed_model = OllamaEmbeddings(model=self.model, base_url=self.base_url) - - def embed_documents(self, texts: list[str]) -> list[list[float]]: - """ - Generate embeddings for a list of documents. - """ - return self.ollama_embed_model.embed_documents(texts) - - def embed_query(self, text: str) -> list[float]: - """ - Generate embedding for a piece of text - """ - return self.ollama_embed_model.embed_query(text=text) - - def get_model(self): - """Returns the model""" - return self.ollama_embed_model - - -@dataclass -class OpenAIEmbeddingModel(Embeddings): - """ - Embedding Model using OpenAI - """ - - model: str = "text-embedding-3-small" - - def __post_init__(self): - """ - Post initialization - """ - self.openai_embed_model = OpenAIEmbeddings(model=self.model) - - def embed_documents(self, texts: List[str]): - """ - Generate embeddings for a list of documents. - """ - return self.openai_embed_model.embed_documents(texts=texts) - - def embed_query(self, text: str): - """ - Generate embedding for a piece of text - """ - return self.openai_embed_model.embed_query(text=text) - - def get_model(self): - """Returns the model""" - return self.openai_embed_model diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py index d89dc2b..d7502fa 100644 --- a/chatbot-core/llm.py +++ b/chatbot-core/llm.py @@ -1,12 +1,8 @@ import logging from dataclasses import dataclass, field -from typing import Any +from typing import Any, Optional -from embedding import ( - OllamaEmbeddingModel, - OpenAIEmbeddingModel, - SentenceTransformerEmbeddingModel, -) +from custom_embeddings import CustomEmbeddingsWrapper from langchain.chains.combine_documents import create_stuff_documents_chain from langchain.chains.history_aware_retriever import create_history_aware_retriever from langchain.chains.retrieval import create_retrieval_chain @@ -17,11 +13,14 @@ from langchain_openai import ChatOpenAI from langchain_qdrant import QdrantVectorStore from qdrant_client import QdrantClient -from utils import EmbeddingModelType from main.settings import ( - EMBEDDING_MODEL_CHOICE, - OLLAMA_BASE_URL, + EMBEDDING_MODEL_NAME, + EMBEDDING_MODEL_TYPE, + EMBEDDING_MODEL_URL, + LLM_MODEL_NAME, + LLM_OLLAMA_BASE_URL, + OLLAMA_EMBEDDING_MODEL_BASE_URL, QDRANT_DB_COLLECTION_NAME, QDRANT_DB_HOST, QDRANT_DB_PORT, @@ -34,20 +33,30 @@ class LLMBase: """LLM Base containing common methods""" - qdrant_host: str = QDRANT_DB_HOST - qdrant_port: int = QDRANT_DB_PORT qdrant_client: QdrantClient = field(init=False) llm_model: Any = field(init=False) memory: Any = field(init=False) + embedding_model: CustomEmbeddingsWrapper = field(init=False) + rag_chain: Optional[Any] = None def __post_init__(self, mem_key: str = "chat_history", conversation_max_window: int = 3): self.llm_model = None self.qdrant_client = None self.memory = None - self.qdrant_client = QdrantClient(host=self.qdrant_host, port=self.qdrant_port) + try: + self.qdrant_client = QdrantClient(host=QDRANT_DB_HOST, port=QDRANT_DB_PORT) + except Exception as e: + raise Exception(f"Qdrant client is not properly setup. {str(e)}") self.memory = ConversationBufferWindowMemory(k=conversation_max_window, memory_key=mem_key, return_messages=True) + self.embedding_model = CustomEmbeddingsWrapper( + url=EMBEDDING_MODEL_URL, + model_name=EMBEDDING_MODEL_NAME, + model_type=EMBEDDING_MODEL_TYPE, + base_url=OLLAMA_EMBEDDING_MODEL_BASE_URL, + ) + def _system_prompt_for_retrieval(self): """System prompt for information retrieval""" return ( @@ -72,18 +81,6 @@ def _system_prompt_for_response(self): """ return system_prompt - def get_selected_embedding_model(self, embedding_model_type: int = 1): - """ - Get the Embedding Model based on input selection - """ - if embedding_model_type == EmbeddingModelType.SENTENCE_TRANSFORMES.value: - return SentenceTransformerEmbeddingModel() - elif embedding_model_type == EmbeddingModelType.OLLAMA.value: - return OllamaEmbeddingModel(base_url=OLLAMA_BASE_URL) - elif embedding_model_type == EmbeddingModelType.OPENAI.value: - return OpenAIEmbeddingModel() - raise ValueError("Embedding Model is not selected. Chose the correct one.") - def get_prompt_template_for_retrieval(self): """Get the prompt template""" system_prompt = self._system_prompt_for_retrieval() @@ -100,12 +97,10 @@ def get_prompt_template_for_response(self): ) return llm_response_prompt - def get_db_retriever( - self, collection_name: str, embedding_model: Any, top_k_items: int = 5, score_threshold: float = 0.5 - ): + def get_db_retriever(self, collection_name: str, top_k_items: int = 5, score_threshold: float = 0.5): """Get the database retriever""" db_retriever = QdrantVectorStore( - client=self.qdrant_client, collection_name=collection_name, embedding=embedding_model + client=self.qdrant_client, collection_name=collection_name, embedding=self.embedding_model ) retriever = db_retriever.as_retriever( search_type="similarity_score_threshold", search_kwargs={"k": top_k_items, "score_threshold": score_threshold} @@ -117,11 +112,10 @@ def create_chain(self, db_collection_name: str): if not self.llm_model: raise Exception("The LLM model is not loaded.") - embedding_model = self.get_selected_embedding_model(EMBEDDING_MODEL_CHOICE) context_prompt_template = self.get_prompt_template_for_retrieval() response_prompt_template = self.get_prompt_template_for_response() - retriever = self.get_db_retriever(collection_name=db_collection_name, embedding_model=embedding_model) + retriever = self.get_db_retriever(collection_name=db_collection_name) history_aware_retriever = create_history_aware_retriever(self.llm_model, retriever, context_prompt_template) @@ -134,9 +128,10 @@ def execute_chain(self, query: str, db_collection_name: str = QDRANT_DB_COLLECTI """ Executes the chain """ - rag_chain = self.create_chain(db_collection_name=db_collection_name) + if not self.rag_chain: + self.rag_chain = self.create_chain(db_collection_name=db_collection_name) - response = rag_chain.invoke({"input": query, "chat_history": self.get_message_history()["chat_history"]}) + response = self.rag_chain.invoke({"input": query, "chat_history": self.get_message_history()["chat_history"]}) self.memory.chat_memory.add_message(HumanMessage(content=query)) self.memory.chat_memory.add_message(AIMessage(content=response["answer"])) return response["answer"] if "answer" in response else "" @@ -152,26 +147,25 @@ def get_message_history(self): class OpenAIHandler(LLMBase): """LLM handler using OpenAI for RAG""" - model: str = "gpt-3.5-turbo-1106" temperature: float = 0.2 def __post_init__(self): super().__post_init__() - - self.llm_model = ChatOpenAI(model=self.model, temperature=self.temperature) + try: + self.llm_model = ChatOpenAI(model=LLM_MODEL_NAME, temperature=self.temperature) + except Exception as e: + raise Exception(f"OpenAI LLM model is not successfully loaded. {str(e)}") @dataclass class OllamaHandler(LLMBase): """LLM Handler using Ollama for RAG""" - model: str = "mistral:latest" - base_url: str = "http://localhost:11434" temperature: float = 0.2 def __post_init__(self): super().__post_init__() try: - self.llm_model = Ollama(model=self.model, base_url=self.base_url, temperature=self.temperature) - except Exception as exc: - logger.error("Ollama LLM model is not successfully loaded. %s", str(exc), exc_info=True) + self.llm_model = Ollama(model=LLM_MODEL_NAME, base_url=LLM_OLLAMA_BASE_URL, temperature=self.temperature) + except Exception as e: + raise Exception(f"Ollama LLM model is not successfully loaded. {str(e)}") diff --git a/chatbot-core/utils.py b/chatbot-core/utils.py index 2ac4700..09b3960 100644 --- a/chatbot-core/utils.py +++ b/chatbot-core/utils.py @@ -1,6 +1,5 @@ import re from enum import Enum -from typing import List class EmbeddingModelType(Enum): @@ -18,7 +17,7 @@ class LLMType(Enum): OPENAI = 2 -def preprocess_text(texts: List[str]) -> List[str]: +def preprocess_text(texts: list[str]) -> list[str]: """ Preprocessing of the texts """ From 2f6b9d12206e04abf362197dccb6481601e2fcf6 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 12:08:05 +0545 Subject: [PATCH 10/20] removed library and poetry updated; --- poetry.lock | 1170 +++--------------------------------------------- pyproject.toml | 2 - 2 files changed, 72 insertions(+), 1100 deletions(-) diff --git a/poetry.lock b/poetry.lock index cbb2c3b..1596b3a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -430,23 +430,6 @@ files = [ [package.extras] test = ["pytest (>=6)"] -[[package]] -name = "filelock" -version = "3.16.0" -description = "A platform independent file lock." -category = "main" -optional = false -python-versions = ">=3.8" -files = [ - {file = "filelock-3.16.0-py3-none-any.whl", hash = "sha256:f6ed4c963184f4c84dd5557ce8fece759a3724b37b80c6c4f20a2f63a4dc6609"}, - {file = "filelock-3.16.0.tar.gz", hash = "sha256:81de9eb8453c769b63369f87f11131a7ab04e367f8d97ad39dc230daa07e3bec"}, -] - -[package.extras] -docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"] -testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.1.1)", 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multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "1b9ee7dbb39c9fce49078b1aca6c43c8d5d9255140083dd37599f50aacfb0c3a" +content-hash = "002c8c8ecb7c079d62604377f65e557aac8134476578e0032a7cb820ca28ad0b" diff --git a/pyproject.toml b/pyproject.toml index 0c44a8b..1fc85d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,11 +16,9 @@ qdrant-client = "^1.11.1" langchain = "^0.2.15" langchain-community = "^0.2.14" langchain-openai = "^0.1.23" -sentence-transformers = "3.0.1" ollama = "^0.3.2" langchain-qdrant = "^0.1.3" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" From 33143c0834438c137d94124c0fa8c70faa05fe41 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 12:09:27 +0545 Subject: [PATCH 11/20] env vars updated; --- env.sample | 16 +++++++++++----- main/settings.py | 24 +++++++++++++++--------- 2 files changed, 26 insertions(+), 14 deletions(-) diff --git a/env.sample b/env.sample index b4ff998..384ca6a 100644 --- a/env.sample +++ b/env.sample @@ -1,7 +1,13 @@ # additional env-variable in main/settings.py DJANGO_SECRET_KEY="" -DJANGO_SECRET_KEY -QDRANT_DB_COLLECTION_NAME -EMBEDDING_MODEL -LLM -OLLAMA_BASE_URL +OPENAI_API_KEY= +QDRANT_DB_COLLECTION_NAME= +# Embedding Model +EMBEDDING_MODEL_URL= +EMBEDDING_MODEL_NAME= +EMBEDDING_MODEL_TYPE= +OLLAMA_EMBEDDING_MODEL_BASE_URL= +# LLM Type +LLM_TYPE= +LLM_MODEL_NAME= +LLM_OLLAMA_BASE_URL= diff --git a/main/settings.py b/main/settings.py index 10fc312..285b47c 100644 --- a/main/settings.py +++ b/main/settings.py @@ -47,10 +47,14 @@ QDRANT_DB_PORT=int, QDRANT_DB_COLLECTION_NAME=str, # Embedding Model - EMBEDDING_MODEL=int, - LLM=int, - # Ollama url - OLLAMA_BASE_URL=str, + EMBEDDING_MODEL_URL=str, + EMBEDDING_MODEL_NAME=str, + EMBEDDING_MODEL_TYPE=int, + OLLAMA_EMBEDDING_MODEL_BASE_URL=str, + # LLM Type + LLM_TYPE=int, + LLM_MODEL_NAME=str, + LLM_OLLAMA_BASE_URL=str, ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ @@ -64,16 +68,18 @@ ALLOWED_HOSTS = env("DJANGO_ALLOWED_HOST") # Embedding Model selection -EMBEDDING_MODEL_CHOICE = env("EMBEDDING_MODEL") +EMBEDDING_MODEL_TYPE = env("EMBEDDING_MODEL_TYPE") +EMBEDDING_MODEL_URL = env("EMBEDDING_MODEL_URL") +EMBEDDING_MODEL_NAME = env("EMBDDING_MODEL_NAME") +OLLAMA_EMBEDDING_MODEL_BASE_URL = env("OLLAMA_EMBEDDING_MODEL_BASE_URL") # LLM selection -LLM_CHOICE = env("LLM") +LLM_TYPE = env("LLM_TYPE") +LLM_MODEL_NAME = env("LLM_MODEL_NAME") +LLM_OLLAMA_BASE_URL = env("LLM_OLLAMA_BASE_URL") # Qdrant database QDRANT_DB_HOST = env("QDRANT_DB_HOST") QDRANT_DB_PORT = env("QDRANT_DB_PORT") QDRANT_DB_COLLECTION_NAME = env("QDRANT_DB_COLLECTION_NAME") -# Ollama base url -OLLAMA_BASE_URL = env("OLLAMA_BASE_URL") - # Application definition From 81bccee1afb6aba3d7cb358246b0d06a5d3d0817 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 12:24:54 +0545 Subject: [PATCH 12/20] updated the docker compose file; --- docker-compose.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docker-compose.yml b/docker-compose.yml index 8fad25f..cac3409 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -16,13 +16,13 @@ services: qdrant: image: qdrant/qdrant:v1.11.3 ports: - - 127.0.0.1:6333:6333 + - 6333:6333 volumes: - qdrant_data:/qdrant/storage restart: always environment: - QDRANT_DB_HOST: 0.0.0.0 - QDRANT_DB_PORT: 6333 + QDRANT__SERVICE__HOST: 0.0.0.0 + QDRANT__SERVICE__HTTP_PORT: 6333 web: build: . @@ -39,7 +39,7 @@ services: DATABASE_HOST: ${DATABASE_HOST:-db} QDRANT_DB_HOST: ${QDRANT_DB_HOST:-0.0.0.0} QDRANT_DB_PORT: ${QDRANT_DB_PORT:-6333} - command: bash -c 'python manage.py runserver 0.0.0.0:8100' + command: bash -c 'python manage.py runserver 0.0.0.0:8001' volumes: - .:/code ports: From ee1383a2813139036842e9b99ec5c08f1492a2b5 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 15:17:07 +0545 Subject: [PATCH 13/20] updated the env vars; --- chatbot-core/llm.py | 28 +++++++++------------------- docker-compose.yml | 8 ++++++++ main/settings.py | 5 +++++ 3 files changed, 22 insertions(+), 19 deletions(-) diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py index d7502fa..77b75e3 100644 --- a/chatbot-core/llm.py +++ b/chatbot-core/llm.py @@ -14,17 +14,7 @@ from langchain_qdrant import QdrantVectorStore from qdrant_client import QdrantClient -from main.settings import ( - EMBEDDING_MODEL_NAME, - EMBEDDING_MODEL_TYPE, - EMBEDDING_MODEL_URL, - LLM_MODEL_NAME, - LLM_OLLAMA_BASE_URL, - OLLAMA_EMBEDDING_MODEL_BASE_URL, - QDRANT_DB_COLLECTION_NAME, - QDRANT_DB_HOST, - QDRANT_DB_PORT, -) +from django.conf import settings logger = logging.getLogger(__name__) @@ -45,16 +35,16 @@ def __post_init__(self, mem_key: str = "chat_history", conversation_max_window: self.memory = None try: - self.qdrant_client = QdrantClient(host=QDRANT_DB_HOST, port=QDRANT_DB_PORT) + self.qdrant_client = QdrantClient(host=settings.QDRANT_DB_HOST, port=settings.QDRANT_DB_PORT) except Exception as e: raise Exception(f"Qdrant client is not properly setup. {str(e)}") self.memory = ConversationBufferWindowMemory(k=conversation_max_window, memory_key=mem_key, return_messages=True) self.embedding_model = CustomEmbeddingsWrapper( - url=EMBEDDING_MODEL_URL, - model_name=EMBEDDING_MODEL_NAME, - model_type=EMBEDDING_MODEL_TYPE, - base_url=OLLAMA_EMBEDDING_MODEL_BASE_URL, + url=settings.EMBEDDING_MODEL_URL, + model_name=settings.EMBEDDING_MODEL_NAME, + model_type=settings.EMBEDDING_MODEL_TYPE, + base_url=settings.OLLAMA_EMBEDDING_MODEL_BASE_URL, ) def _system_prompt_for_retrieval(self): @@ -124,7 +114,7 @@ def create_chain(self, db_collection_name: str): rag_chain = create_retrieval_chain(history_aware_retriever, chat_response_chain) return rag_chain - def execute_chain(self, query: str, db_collection_name: str = QDRANT_DB_COLLECTION_NAME): + def execute_chain(self, query: str, db_collection_name: str = settings.QDRANT_DB_COLLECTION_NAME): """ Executes the chain """ @@ -152,7 +142,7 @@ class OpenAIHandler(LLMBase): def __post_init__(self): super().__post_init__() try: - self.llm_model = ChatOpenAI(model=LLM_MODEL_NAME, temperature=self.temperature) + self.llm_model = ChatOpenAI(model=settings.LLM_MODEL_NAME, temperature=self.temperature) except Exception as e: raise Exception(f"OpenAI LLM model is not successfully loaded. {str(e)}") @@ -166,6 +156,6 @@ class OllamaHandler(LLMBase): def __post_init__(self): super().__post_init__() try: - self.llm_model = Ollama(model=LLM_MODEL_NAME, base_url=LLM_OLLAMA_BASE_URL, temperature=self.temperature) + self.llm_model = Ollama(model=settings.LLM_MODEL_NAME, base_url=settings.LLM_OLLAMA_BASE_URL, temperature=self.temperature) except Exception as e: raise Exception(f"Ollama LLM model is not successfully loaded. {str(e)}") diff --git a/docker-compose.yml b/docker-compose.yml index cac3409..fdb11e1 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -39,6 +39,14 @@ services: DATABASE_HOST: ${DATABASE_HOST:-db} QDRANT_DB_HOST: ${QDRANT_DB_HOST:-0.0.0.0} QDRANT_DB_PORT: ${QDRANT_DB_PORT:-6333} + QDRANT_DB_COLLECTION_NAME: ${QDRANT_DB_COLLECTION_NAME:-test} + EMBEDDING_MODEL_URL: ${EMBEDDING_MODEL_URL:-localhost} + EMBEDDING_MODEL_NAME: ${EMBEDDING_MODEL_NAME:-embedding_model} + EMBEDDING_MODEL_TYPE: ${EMBEDDING_MODEL_TYPE:-1} + LLM_TYPE: ${LLM_TYPE:-1} + LLM_MODEL_NAME: ${LLM_MODEL_NAME:-"mistral:latest"} + LLM_OLLAMA_BASE_URL: ${LLM_OLLAMA_BASE_URL:-localhost} + OPENAI_API_KEY: ${OPENAI_API_KEY:-test_key} command: bash -c 'python manage.py runserver 0.0.0.0:8001' volumes: - .:/code diff --git a/main/settings.py b/main/settings.py index 285b47c..f215706 100644 --- a/main/settings.py +++ b/main/settings.py @@ -55,6 +55,8 @@ LLM_TYPE=int, LLM_MODEL_NAME=str, LLM_OLLAMA_BASE_URL=str, + # OpenAI API key + OPENAI_API_KEY=str, ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ @@ -80,6 +82,9 @@ QDRANT_DB_HOST = env("QDRANT_DB_HOST") QDRANT_DB_PORT = env("QDRANT_DB_PORT") QDRANT_DB_COLLECTION_NAME = env("QDRANT_DB_COLLECTION_NAME") +# OPENAI API KEY +OPENAI_API_KEY = env("OPENAI_API_KEY") + # Application definition From 59d64760d181c18b20966647353102812ffb3889 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 15:17:27 +0545 Subject: [PATCH 14/20] updated the poetry lock; --- poetry.lock | 205 ++++++++++++++++++++++++++++------------------------ 1 file changed, 110 insertions(+), 95 deletions(-) diff --git a/poetry.lock b/poetry.lock index 1596b3a..6084b88 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1078,14 +1078,14 @@ langchain-core = ">=0.2.38,<0.3.0" [[package]] name = "langsmith" -version = "0.1.117" +version = "0.1.118" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." category = "main" optional = false python-versions = 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-EMBEDDING_MODEL_NAME = env("EMBDDING_MODEL_NAME") +EMBEDDING_MODEL_NAME = env("EMBEDDING_MODEL_NAME") OLLAMA_EMBEDDING_MODEL_BASE_URL = env("OLLAMA_EMBEDDING_MODEL_BASE_URL") # LLM selection LLM_TYPE = env("LLM_TYPE") From f81580c5fc7fd307bb04f05d8a18352e87028438 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 15:23:20 +0545 Subject: [PATCH 16/20] fixes;; --- chatbot-core/custom_embeddings.py | 29 ++++++++++------------------- chatbot-core/llm.py | 7 ++++--- 2 files changed, 14 insertions(+), 22 deletions(-) diff --git a/chatbot-core/custom_embeddings.py b/chatbot-core/custom_embeddings.py index 709cae1..ac1b875 100644 --- a/chatbot-core/custom_embeddings.py +++ b/chatbot-core/custom_embeddings.py @@ -1,9 +1,11 @@ from dataclasses import dataclass from typing import List, Optional + import requests from langchain.embeddings.base import Embeddings from utils import EmbeddingModelType + @dataclass class CustomEmbeddingsWrapper(Embeddings): """ @@ -17,42 +19,31 @@ class CustomEmbeddingsWrapper(Embeddings): base_url: Optional[str] def __post_init__(self): - if self.model_type in [ - EmbeddingModelType.SENTENCE_TRANSFORMES, - EmbeddingModelType.OPENAI - ]: + if self.model_type in [EmbeddingModelType.SENTENCE_TRANSFORMES, EmbeddingModelType.OPENAI]: if not (self.url and self.model_name): raise Exception("Url or model name or both are not provided.") elif self.model_type == EmbeddingModelType.OLLAMA: - if not(self.url and self.model_name and self.base_url): + if not (self.url and self.model_name and self.base_url): raise Exception("Url or base_url or both are not provided.") - def embed_query(self, text: str, timeout: int=30) -> List[float]: + def embed_query(self, text: str, timeout: int = 30) -> List[float]: """ - Sends the request to Embedding module to + Sends the request to Embedding module to embed the query to the vector representation """ - payload = { - "type_model": self.model_type, - "name_model": self.model_name, - "texts": text - } + payload = {"type_model": self.model_type, "name_model": self.model_name, "texts": text} try: response = requests.post(url=self.url, json=payload, timeout=timeout) except requests.Timeout as e: raise Exception(e) return response.json() - def embed_documents(self, texts: List[str], timeout: int=30) -> List[List[float]]: + def embed_documents(self, texts: List[str], timeout: int = 30) -> List[List[float]]: """ - Sends the request to Embedding module to + Sends the request to Embedding module to embed multiple queries to the vector representation """ - payload = { - "type_model": self.model_type, - "name_model": self.model_name, - "texts": texts - } + payload = {"type_model": self.model_type, "name_model": self.model_name, "texts": texts} try: response = requests.post(url=self.url, json=payload, timeout=timeout) except requests.Timeout as e: diff --git a/chatbot-core/llm.py b/chatbot-core/llm.py index 77b75e3..3e3c3ba 100644 --- a/chatbot-core/llm.py +++ b/chatbot-core/llm.py @@ -3,6 +3,7 @@ from typing import Any, Optional from custom_embeddings import CustomEmbeddingsWrapper +from django.conf import settings from langchain.chains.combine_documents import create_stuff_documents_chain from langchain.chains.history_aware_retriever import create_history_aware_retriever from langchain.chains.retrieval import create_retrieval_chain @@ -14,8 +15,6 @@ from langchain_qdrant import QdrantVectorStore from qdrant_client import QdrantClient -from django.conf import settings - logger = logging.getLogger(__name__) @@ -156,6 +155,8 @@ class OllamaHandler(LLMBase): def __post_init__(self): super().__post_init__() try: - self.llm_model = Ollama(model=settings.LLM_MODEL_NAME, base_url=settings.LLM_OLLAMA_BASE_URL, temperature=self.temperature) + self.llm_model = Ollama( + model=settings.LLM_MODEL_NAME, base_url=settings.LLM_OLLAMA_BASE_URL, temperature=self.temperature + ) except Exception as e: raise Exception(f"Ollama LLM model is not successfully loaded. {str(e)}") From 7d3f5e764d910970237e44886c63e83e28b6ca1d Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Thu, 12 Sep 2024 15:31:17 +0545 Subject: [PATCH 17/20] added the env var; --- docker-compose.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/docker-compose.yml b/docker-compose.yml index fdb11e1..4dc676f 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -43,6 +43,7 @@ services: EMBEDDING_MODEL_URL: ${EMBEDDING_MODEL_URL:-localhost} EMBEDDING_MODEL_NAME: ${EMBEDDING_MODEL_NAME:-embedding_model} EMBEDDING_MODEL_TYPE: ${EMBEDDING_MODEL_TYPE:-1} + OLLAMA_EMBEDDING_MODEL_BASE_URL: ${OLLAMA_EMBEDDING_MODEL_BASE_URL:-model} LLM_TYPE: ${LLM_TYPE:-1} LLM_MODEL_NAME: ${LLM_MODEL_NAME:-"mistral:latest"} LLM_OLLAMA_BASE_URL: ${LLM_OLLAMA_BASE_URL:-localhost} From 6396610b4c4b81fc7f91bf21032f85f8f62b848c Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Fri, 13 Sep 2024 14:30:23 +0545 Subject: [PATCH 18/20] added the env var; --- chatbot-core/database.py | 6 +++--- docker-compose.yml | 3 +++ env.sample | 1 + main/settings.py | 17 ++++++++++++++--- 4 files changed, 21 insertions(+), 6 deletions(-) diff --git a/chatbot-core/database.py b/chatbot-core/database.py index d0dc677..1befc8b 100644 --- a/chatbot-core/database.py +++ b/chatbot-core/database.py @@ -1,7 +1,7 @@ import logging from dataclasses import dataclass, field from uuid import uuid4 - +from django.conf import settings from qdrant_client import QdrantClient from qdrant_client.http.exceptions import UnexpectedResponse from qdrant_client.http.models import Distance, VectorParams @@ -30,11 +30,11 @@ def _collection_exists(self, collection_name: str) -> bool: except UnexpectedResponse: return False - def set_collection(self, vector_size: int = 384): + def set_collection(self): """Create the database collection""" if not self._collection_exists(self.collection_name): self.db_client.create_collection( - collection_name=self.collection_name, vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE) + collection_name=self.collection_name, vectors_config=VectorParams(size=settings.EMBEDDING_MODEL_VECTOR_SIZE, distance=Distance.COSINE) ) else: logger.info(f"Collection {self.collection_name} already exists. Using the existing one.") diff --git a/docker-compose.yml b/docker-compose.yml index 4dc676f..78e0678 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -21,6 +21,8 @@ services: - qdrant_data:/qdrant/storage restart: always environment: + # https://qdrant.tech/documentation/guides/configuration/#environment-variables + # https://github.com/qdrant/qdrant/blob/master/config/config.yaml QDRANT__SERVICE__HOST: 0.0.0.0 QDRANT__SERVICE__HTTP_PORT: 6333 @@ -42,6 +44,7 @@ services: QDRANT_DB_COLLECTION_NAME: ${QDRANT_DB_COLLECTION_NAME:-test} EMBEDDING_MODEL_URL: ${EMBEDDING_MODEL_URL:-localhost} EMBEDDING_MODEL_NAME: ${EMBEDDING_MODEL_NAME:-embedding_model} + EMBEDDING_MODEL_VECTOR_SIZE: ${EMBEDDING_MODEL_VECTOR_SIZE:-384} EMBEDDING_MODEL_TYPE: ${EMBEDDING_MODEL_TYPE:-1} OLLAMA_EMBEDDING_MODEL_BASE_URL: ${OLLAMA_EMBEDDING_MODEL_BASE_URL:-model} LLM_TYPE: ${LLM_TYPE:-1} diff --git a/env.sample b/env.sample index 384ca6a..ac02783 100644 --- a/env.sample +++ b/env.sample @@ -5,6 +5,7 @@ QDRANT_DB_COLLECTION_NAME= # Embedding Model EMBEDDING_MODEL_URL= EMBEDDING_MODEL_NAME= +EMBEDDING_MODEL_VECTOR_SIZE= EMBEDDING_MODEL_TYPE= OLLAMA_EMBEDDING_MODEL_BASE_URL= # LLM Type diff --git a/main/settings.py b/main/settings.py index 16a6181..c255d27 100644 --- a/main/settings.py +++ b/main/settings.py @@ -49,14 +49,15 @@ # Embedding Model EMBEDDING_MODEL_URL=str, EMBEDDING_MODEL_NAME=str, + EMBEDDING_MODEL_VECTOR_SIZE=int, EMBEDDING_MODEL_TYPE=int, - OLLAMA_EMBEDDING_MODEL_BASE_URL=str, + OLLAMA_EMBEDDING_MODEL_BASE_URL=(str, None), # LLM Type LLM_TYPE=int, LLM_MODEL_NAME=str, - LLM_OLLAMA_BASE_URL=str, + LLM_OLLAMA_BASE_URL=(str, None), # OpenAI API key - OPENAI_API_KEY=str, + OPENAI_API_KEY=(str, None) ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ @@ -73,6 +74,7 @@ EMBEDDING_MODEL_TYPE = env("EMBEDDING_MODEL_TYPE") EMBEDDING_MODEL_URL = env("EMBEDDING_MODEL_URL") EMBEDDING_MODEL_NAME = env("EMBEDDING_MODEL_NAME") +EMBEDDING_MODEL_VECTOR_SIZE = env("EMBEDDING_MODEL_VECTOR_SIZE") OLLAMA_EMBEDDING_MODEL_BASE_URL = env("OLLAMA_EMBEDDING_MODEL_BASE_URL") # LLM selection LLM_TYPE = env("LLM_TYPE") @@ -85,6 +87,15 @@ # OPENAI API KEY OPENAI_API_KEY = env("OPENAI_API_KEY") +if EMBEDDING_MODEL_TYPE == 2 and not OLLAMA_EMBEDDING_MODEL_BASE_URL: + raise ValueError("Ollama base url is not set.") + +if (EMBEDDING_MODEL_TYPE == 3 or LLM_TYPE == 1) and not OPENAI_API_KEY: + raise ValueError("OpenAI API key is not set.") + +if LLM_TYPE == 2 and not LLM_OLLAMA_BASE_URL: + raise ValueError("Ollama base url is not set.") + # Application definition From 561234b4f9e1639ee30c3c2d52c15fec2fd0ddb7 Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Fri, 13 Sep 2024 14:35:21 +0545 Subject: [PATCH 19/20] fixed formattings; --- chatbot-core/database.py | 4 +++- main/settings.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/chatbot-core/database.py b/chatbot-core/database.py index 1befc8b..cd77e3c 100644 --- a/chatbot-core/database.py +++ b/chatbot-core/database.py @@ -1,6 +1,7 @@ import logging from dataclasses import dataclass, field from uuid import uuid4 + from django.conf import settings from qdrant_client import QdrantClient from qdrant_client.http.exceptions import UnexpectedResponse @@ -34,7 +35,8 @@ def set_collection(self): """Create the database collection""" if not self._collection_exists(self.collection_name): self.db_client.create_collection( - collection_name=self.collection_name, vectors_config=VectorParams(size=settings.EMBEDDING_MODEL_VECTOR_SIZE, distance=Distance.COSINE) + collection_name=self.collection_name, + vectors_config=VectorParams(size=settings.EMBEDDING_MODEL_VECTOR_SIZE, distance=Distance.COSINE), ) else: logger.info(f"Collection {self.collection_name} already exists. Using the existing one.") diff --git a/main/settings.py b/main/settings.py index c255d27..7355c28 100644 --- a/main/settings.py +++ b/main/settings.py @@ -57,7 +57,7 @@ LLM_MODEL_NAME=str, LLM_OLLAMA_BASE_URL=(str, None), # OpenAI API key - OPENAI_API_KEY=(str, None) + OPENAI_API_KEY=(str, None), ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ From 25b3b1ffc92b34c047311e899e4a1c02572844cc Mon Sep 17 00:00:00 2001 From: Ranjan Shrestha Date: Fri, 13 Sep 2024 14:53:43 +0545 Subject: [PATCH 20/20] note added regarding embedding model; --- main/settings.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/main/settings.py b/main/settings.py index 7355c28..2dde413 100644 --- a/main/settings.py +++ b/main/settings.py @@ -70,6 +70,12 @@ ALLOWED_HOSTS = env("DJANGO_ALLOWED_HOST") + +# Note: The embedding model and vector size both should be compatible +# and chosen carefully +# If the embedding model is changed, we need to re-vectorize all the +# texts in the vector database + # Embedding Model selection EMBEDDING_MODEL_TYPE = env("EMBEDDING_MODEL_TYPE") EMBEDDING_MODEL_URL = env("EMBEDDING_MODEL_URL")