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import os
import warnings
import shutil
from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader, WikipediaLoader
from typing import List, Optional, Dict, Any
from langchain.schema import Document
import chromadb
# from langchain_community.embeddings.sentence_transformer import (SentenceTransformerEmbeddings)
from langchain_community.vectorstores import FAISS
warnings.filterwarnings("ignore")
CHROMA_DB_PATH = os.path.join(os.getcwd(), "Stock Sentiment Analysis", "chroma_db")
# FAISS_DB_PATH = os.path.join(os.getcwd(), "Stock Sentiment Analysis", "faiss_index")
tesla_10k_collection = 'tesla-10k-2019-to-2023'
embedding_model = ""
# embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
class DBStorage:
def __init__(self):
self.CHROMA_PATH = CHROMA_DB_PATH
self.vector_store = None
self.client = chromadb.PersistentClient(path=CHROMA_DB_PATH)
print(self.client.list_collections())
self.collection = self.client.get_or_create_collection(name=tesla_10k_collection)
print(self.collection.count())
def chunk_data(self, data, chunk_size=10000):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0)
return text_splitter.split_documents(data)
def create_embeddings(self, chunks):
embeddings = AzureOpenAIEmbeddings(
model=os.getenv("AZURE_OPENAI_EMBEDDING_NAME"),
api_key=os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY"),
api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION"),
azure_endpoint=os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
)
self.vector_store = Chroma.from_documents(documents=chunks,
# embedding=embeddings,
embedding=embedding_model,
collection_name=tesla_10k_collection,
persist_directory=self.CHROMA_PATH)
print("Here B")
self.collection = self.client.get_or_create_collection(name=tesla_10k_collection)
print("here"+str(self.collection.count()))
# return self.vector_store
def create_vector_store(self, chunks):
embeddings = AzureOpenAIEmbeddings(
model=os.getenv("AZURE_OPENAI_EMBEDDING_NAME"),
api_key=os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY"),
api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION"),
azure_endpoint=os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
)
return FAISS.from_documents(chunks, embedding=embeddings)
# vector_store.save_local(FAISS_DB_PATH)
def load_embeddings(self):
embeddings = AzureOpenAIEmbeddings(
model=os.getenv("AZURE_OPENAI_EMBEDDING_NAME"),
api_key=os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY"),
api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION"),
azure_endpoint=os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
)
self.vector_store = Chroma(collection_name=tesla_10k_collection,
persist_directory=CHROMA_DB_PATH,
# embedding_function=embeddings
embedding_function=embedding_model
)
print("loaded vector store: ")
print(self.vector_store)
# return self.vector_store
def load_vectors(self,FAISS_DB_PATH):
embeddings = AzureOpenAIEmbeddings(
model=os.getenv("AZURE_OPENAI_EMBEDDING_NAME"),
api_key=os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY"),
api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION"),
azure_endpoint=os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
)
self.vector_store = FAISS.load_local(folder_path=FAISS_DB_PATH,
embeddings=embeddings,
allow_dangerous_deserialization=True)
def fetch_documents(self, metadata_filter: Dict[str, Any]) -> List[Document]:
results = self.collection.get(
where=metadata_filter,
include=["documents", "metadatas"],
)
documents = []
for content, metadata in zip(results['documents'][0], results['metadatas'][0]):
documents.append(Document(page_content=content, metadata=metadata))
return documents
def get_context_for_query(self, question, k=3):
print(self.vector_store)
# if not self.vector_store:
# raise ValueError("Vector store not initialized. Call create_embeddings() or load_embeddings() first.")
# relevant_document_chunks=self.fetch_documents({"company": question})
# retriever = self.vector_store.as_retriever(search_type='similarity', search_kwargs={'k': k})
# relevant_document_chunks = retriever.get_relevant_documents(question)
relevant_document_chunks = self.vector_store.similarity_search(question)
# chain = get_conversational_chain(models.llm)
# response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
# print(response)
print(relevant_document_chunks)
context_list = [d.page_content for d in relevant_document_chunks]
context_for_query = ". ".join(context_list)
print("context_for_query: "+ str(len(context_for_query)))
return context_for_query
# def ask_question(self, question, k=3):
# if not self.vector_store:
# raise ValueError("Vector store not initialized. Call create_embeddings() or load_embeddings() first.")
# llm = AzureChatOpenAI(
# temperature=0,
# api_key=os.getenv("AZURE_OPENAI_API_KEY"),
# api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
# azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
# model=os.getenv("AZURE_OPENAI_MODEL_NAME")
# )
# retriever = self.vector_store.as_retriever(search_type='similarity', search_kwargs={'k': k})
# chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
# return chain.invoke(question)
def embed_vectors(self,social_media_document,FAISS_DB_PATH):
print("here A")
chunks = self.chunk_data(social_media_document)
print(len(chunks))
# self.create_embeddings(chunks)
vector_store = self.create_vector_store(chunks)
check_and_delete(FAISS_DB_PATH)
vector_store.save_local(FAISS_DB_PATH)
def check_and_delete(PATH):
if os.path.isdir(PATH):
shutil.rmtree(PATH, onexc=lambda func, path, exc: os.chmod(path, 0o777))
print(f'Deleted {PATH}')
def clear_db():
check_and_delete(CHROMA_DB_PATH)
# check_and_delete(FAISS_DB_PATH)
# Usage example
if __name__ == "__main__":
qa_system = DBStorage()
# Load and process document
social_media_document = []
chunks = qa_system.chunk_data(social_media_document)
# Create embeddings
qa_system.create_embeddings(chunks)
# # Ask a question
# question = 'Summarize the whole input in 150 words'
# answer = qa_system.ask_question(question)
# print(answer) |