'packages_changed'

function_tools.py

@@ -500,92 +500,6 @@ vedamantra_summary_tool =StructuredTool.from_function(
     return_direct=False
     )
 
-## vector tool
-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"
-CURRENT_DIRECTORY = os.getcwd()
-
-
-# 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]
-)
-
-class VectorResponse(BaseModel):
-    query:str = Field(description="user query")
-
-def vector_retrieve(query):
-    response = retriever.get_relevant_documents(query)
-    return response
-
-vector_tool = StructuredTool.from_function(
-    func = vector_retrieve,
-    name = "vector_retrieve",
-    description="Search and return documents related user query from the vector index.",
-    args_schema=VectorResponse,
-    return_direct=False
-)
 
 tools_list = [pada_morphological_tool, sql_tool, pada_meaning_tool, pada_word_sense_tool, vedamantra_tool, vedamantra_summary_tool]
 #vector_tool,