'tool_added'

vector_tool.py

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+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"
+DOCS_DIRECTORY = r"Docs\ramana_docs_ids.pkl"
+
+
+
+# Initialize Pinecone client
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY_SAM")
+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(DOCS_DIRECTORY)
+# 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])