vector_tool.py

import os
import time
import pickle
import streamlit as st
from dotenv import load_dotenv
from pinecone import Pinecone, ServerlessSpec
from utils import load_pickle, initialize_embedding_model
from langchain_community.retrievers import BM25Retriever
from langchain_pinecone import PineconeVectorStore
from langchain.retrievers import EnsembleRetriever
from langchain.tools.retriever import create_retriever_tool



# Load .env file
load_dotenv()

# Constants
INDEX_NAME = "veda-index-v2"
MODEL_NAME = "BAAI/bge-large-en-v1.5"



# Initialize Pinecone client
os.environ['PINECONE_API_KEY'] = os.getenv("PINECONE_API_KEY")
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
pc = Pinecone(api_key=PINECONE_API_KEY)

#@st.cache_resource
def create_or_load_index():
    # Check if index already exists
    if INDEX_NAME not in pc.list_indexes().names():
        # Create index if it does not exist
        pc.create_index(
            INDEX_NAME,
            dimension=1024,
            metric='dotproduct',
            spec=ServerlessSpec(
                cloud="aws",
                region="us-east-1"
            )
        )
        # Wait for index to be initialized
        while not pc.describe_index(INDEX_NAME).status['ready']:
            time.sleep(1)
    # Connect to index
    return pc.Index(INDEX_NAME)

# Load documents
docs = load_pickle("ramana_docs_ids.pkl")
# Initialize embedding model
embedding = initialize_embedding_model(MODEL_NAME)
# Create or load index
index = create_or_load_index()

# Initialize BM25 retriever
bm25_retriever = BM25Retriever.from_texts(
    [text['document'].page_content for text in docs],
    metadatas=[text['document'].metadata for text in docs]
)
bm25_retriever.k = 2

# Switch back to normal index for LangChain
vector_store = PineconeVectorStore(index, embedding)
retriever = vector_store.as_retriever(search_type="mmr")

# Initialize the ensemble retriever
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, retriever], weights=[0.2, 0.8]
)


vector_tools = create_retriever_tool(
    retriever = ensemble_retriever,
    name = "vector_retrieve",
    description="Search and return documents related user query from the vector index.",
)

from langchain import hub

prompt = hub.pull("hwchase17/openai-tools-agent")
prompt.messages

from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
import streamlit as st

os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
#load llm model
llm_AI4 = ChatOpenAI(model="gpt-4-1106-preview", temperature=0)

agent = create_openai_tools_agent(llm_AI4, [vector_tools], prompt)
agent_executor = AgentExecutor(agent=agent, tools=[vector_tools])