Update utils.py

utils.py

@@ -50,49 +50,6 @@ def create_embeddings_load_data():
     return embeddings
 
 
-#Function to push data to Vector Store - Pinecone here
-def push_to_pinecone(pinecone_apikey,pinecone_environment,pinecone_index_name,embeddings,docs):
-
-    pinecone.init(
-    api_key=pinecone_apikey,
-    environment=pinecone_environment
-    )
-    print("done......2")
-    Pinecone.from_documents(docs, embeddings, index_name=pinecone_index_name)
-    
-
-
-#Function to pull infrmation from Vector Store - Pinecone here
-def pull_from_pinecone(pinecone_apikey,pinecone_environment,pinecone_index_name,embeddings):
-
-    pinecone.init(
-    api_key=pinecone_apikey,
-    environment=pinecone_environment
-    )
-
-    index_name = pinecone_index_name
-
-    index = Pinecone.from_existing_index(index_name, embeddings)
-    return index
-
-
-
-#Function to help us get relavant documents from vector store - based on user input
-def similar_docs(query,k,pinecone_apikey,pinecone_environment,pinecone_index_name,embeddings,unique_id):
-
-    pinecone.init(
-    api_key=pinecone_apikey,
-    environment=pinecone_environment
-    )
-
-    index_name = pinecone_index_name
-
-    index = pull_from_pinecone(pinecone_apikey,pinecone_environment,index_name,embeddings)
-    #similar_docs = index.similarity_search_with_score(query, int(k),{"unique_id":unique_id})
-    similar_docs = index.similarity_search_with_score(query, int(k))
-    #print(similar_docs)
-    return similar_docs
-
 def close_matches(query,k,docs,embeddings):
     #https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html#langchain.vectorstores.faiss.FAISS.similarity_search_with_score
     db = FAISS.from_documents(docs, embeddings)