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#Pinecone team has been making a lot of changes to there code and here is how it should be used going forward :)
from pinecone import Pinecone as PineconeClient
#from langchain.vectorstores import Pinecone #This import has been replaced by the below one :)
from langchain_community.vectorstores import Pinecone
from transformers import pipeline
#from langchain.llms import OpenAI #This import has been replaced by the below one :)
from langchain_openai import OpenAI
from langchain.chains.question_answering import load_qa_chain
#from langchain.callbacks import get_openai_callback #This import has been replaced by the below one :)
from langchain_community.callbacks import get_openai_callback
from langchain_community.embeddings import SentenceTransformerEmbeddings
import joblib
qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
#Function to pull index data from Pinecone...
def pull_from_pinecone(pinecone_apikey,pinecone_environment,pinecone_index_name,embeddings):
PineconeClient(
api_key=pinecone_apikey,
environment=pinecone_environment
)
index_name = pinecone_index_name
index = Pinecone.from_existing_index(index_name, embeddings)
return index
def create_embeddings():
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
return embeddings
#This function will help us in fetching the top relevent documents from our vector store - Pinecone Index
def get_similar_docs(index,query,k=2):
similar_docs = index.similarity_search(query, k=k)
return similar_docs
def get_answer(docs, user_input):
# Concatenate all the documents into one large context
# Assuming 'doc.page_content' is how the content is stored in your 'Document' object
context = " ".join([doc.page_content for doc in docs])
# Use Hugging Face's QA model to get the answer
response = qa_pipeline(question=user_input, context=context)
return response['answer']
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