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 \ diff --git a/chatbot-core/custom_embeddings.py b/chatbot-core/custom_embeddings.py new file mode 100644 index 0000000..ac1b875 --- /dev/null +++ b/chatbot-core/custom_embeddings.py @@ -0,0 +1,51 @@ +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 new file mode 100644 index 0000000..cd77e3c --- /dev/null +++ b/chatbot-core/database.py @@ -0,0 +1,67 @@ +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 + +logger = logging.getLogger(__name__) + + +@dataclass +class QdrantDatabase: + """Qdrant Vector Database""" + + collection_name: str + host: str + port: int + db_client: QdrantClient = field(init=False) + + def __post_init__(self): + """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""" + try: + self.db_client.get_collection(collection_name=collection_name) + return True + except UnexpectedResponse: + return False + + 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), + ) + 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""" + 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 diff --git a/chatbot-core/doc_loaders.py b/chatbot-core/doc_loaders.py new file mode 100644 index 0000000..c840d22 --- /dev/null +++ b/chatbot-core/doc_loaders.py @@ -0,0 +1,61 @@ +from dataclasses import dataclass +from typing import List + +from langchain.schema import Document +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 + + 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 + """ + + text: str + + def create_document_chunks(self): + """ + Creates multiple documents from the input texts + """ + documents = [Document(page_content=self.text)] + 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/llm.py b/chatbot-core/llm.py new file mode 100644 index 0000000..3e3c3ba --- /dev/null +++ b/chatbot-core/llm.py @@ -0,0 +1,162 @@ +import logging +from dataclasses import dataclass, field +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 +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 + +logger = logging.getLogger(__name__) + + +@dataclass +class LLMBase: + """LLM Base containing common methods""" + + 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 + + try: + 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=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): + """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, \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_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, 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=self.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.") + + 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) + + 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 = settings.QDRANT_DB_COLLECTION_NAME): + """ + Executes the chain + """ + if not self.rag_chain: + self.rag_chain = self.create_chain(db_collection_name=db_collection_name) + + 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 "" + + def get_message_history(self): + """ + Returns the historical conversational data + """ + return self.memory.load_memory_variables({}) + + +@dataclass +class OpenAIHandler(LLMBase): + """LLM handler using OpenAI for RAG""" + + temperature: float = 0.2 + + def __post_init__(self): + super().__post_init__() + try: + 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)}") + + +@dataclass +class OllamaHandler(LLMBase): + """LLM Handler using Ollama for RAG""" + + temperature: float = 0.2 + + 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 + ) + 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 new file mode 100644 index 0000000..09b3960 --- /dev/null +++ b/chatbot-core/utils.py @@ -0,0 +1,27 @@ +import re +from enum import Enum + + +class EmbeddingModelType(Enum): + """Embedding Model Types""" + + SENTENCE_TRANSFORMES = 1 + 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 + """ + pattern = r"\s+" + + results = [re.sub(pattern, "", text) for text in texts] + return results diff --git a/docker-compose.yml b/docker-compose.yml index c473353..78e0678 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -16,12 +16,15 @@ 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_HOST=0.0.0.0 + # 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 web: build: . @@ -36,6 +39,18 @@ services: DATABASE_PASSWORD: ${DATABASE_PASSWORD:-postgres} DATABASE_PORT: ${DATABASE_PORT:-5432} 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_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} + 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/env.sample b/env.sample index f2b28e4..ac02783 100644 --- a/env.sample +++ b/env.sample @@ -1,2 +1,14 @@ # additional env-variable in main/settings.py DJANGO_SECRET_KEY="" +OPENAI_API_KEY= +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 +LLM_TYPE= +LLM_MODEL_NAME= +LLM_OLLAMA_BASE_URL= diff --git a/main/settings.py b/main/settings.py index 6db134f..2dde413 100644 --- a/main/settings.py +++ b/main/settings.py @@ -42,6 +42,22 @@ 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_URL=str, + EMBEDDING_MODEL_NAME=str, + EMBEDDING_MODEL_VECTOR_SIZE=int, + EMBEDDING_MODEL_TYPE=int, + OLLAMA_EMBEDDING_MODEL_BASE_URL=(str, None), + # LLM Type + LLM_TYPE=int, + LLM_MODEL_NAME=str, + LLM_OLLAMA_BASE_URL=(str, None), + # OpenAI API key + OPENAI_API_KEY=(str, None), ) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/ @@ -55,6 +71,38 @@ 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") +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") +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") +# 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.") + + # 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