Talmud_read_write.py

"""
This module provides functionality for
    creating the Talmud Illuminated corpus
    creating a Pinecone index with the TI data
    reading the resulting pinecone index

Functions:
-----------
    create_order(user_id, product_id, quantity): Creates an order.
    get_order(order_id): Retrieves the details of an order.
"""
import os
import openai
from dotenv import load_dotenv, find_dotenv
import pinecone
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain

_ = load_dotenv(find_dotenv())  # read local .env file
MODEL = "gpt-4"
embeddings = OpenAIEmbeddings(openai_api_key=openai.api_key)

# query_result = embeddings.embed_query("Hello world")
# print(len(query_result))

# initialize pinecone
pinecone.init(
    api_key=os.getenv('PINECONE_API_KEY'),
    environment="us-central1-gcp"  # next to api key in console
)

#index_name = "langchain-demo"
index_name = "talmud-pages"

# To create index...
# index = Pinecone.from_documents(docs, embeddings, index_name=index_name)

# if you already have an index, you can load it like this
index = Pinecone.from_existing_index(index_name, embeddings)

def get_similar_docs(query, num_sources=20, score=False):
    if score:
        similar_docs = index.similarity_search_with_score(query, k=num_sources)
    else:
        similar_docs = index.similarity_search(query, k=num_sources)
    return similar_docs


# query = "When do you say Shema?"
# similar_docs= get_similar_docs(query)
# len(similar_docs)


llm = OpenAI(model_name=MODEL)

chain = load_qa_chain(llm, chain_type="stuff")


def get_answer(query, num_sources=20):
    similar_docs_list = get_similar_docs(query, num_sources=num_sources)
    # print(similar_docs)
    return chain.run(input_documents=similar_docs_list, question=query)