corev2.py

import json
from pathlib import Path
from pprint import pprint
from dotenv import load_dotenv
# from langchain_core.documents import Document
from langchain_openai import ChatOpenAI
# from langchain_huggingface import HuggingFaceEmbeddings
# from langchain_chroma import Chroma
# from langchain.chains.combine_documents import create_stuff_documents_chain
# from langchain_core.prompts import ChatPromptTemplate
# from langchain.chains import create_retrieval_chain
load_dotenv()

llm = ChatOpenAI(model='gpt-4o-mini')

# embeddings = HuggingFaceEmbeddings(  
# model_name='sentence-transformers/all-mpnet-base-v2',    
# )

# embeddings = OpenAIEmbeddings(model='text-embedding-3-small')

# vector_store = Chroma(
#     collection_name="example_collection",
#     embedding_function=embeddings,
#     persist_directory="./chroma_langchain_db",  
# )

# file_path='./constitution_of_india_edited.json'
# data = json.loads(Path(file_path).read_text(encoding='utf-8'))

# all_splits = [Document(page_content=obj['description'],
#     metadata={'title': obj['title'], 'article': obj['article']}) for obj in data]

# retriever = vector_store.as_retriever(search_kwargs={'k': 3})

# print(retriever,"THis is the retriever")

# vector_store.add_documents(documents=all_splits)

# system_prompt = (
#     '''
# Always provide concise answers(within 40-50 words) and ensure relevance.
# Use {context}
# '''
# )

# prompt = ChatPromptTemplate.from_messages(
#     [
#         ("system", system_prompt),
#         ("human", "{input}"),
#     ]
# )                                                                                                       


# question_answer_chain = create_stuff_documents_chain(llm, prompt)
# rag_chain = create_retrieval_chain(retriever, question_answer_chain)

# results = rag_chain.invoke({"input": "How do Articles 14, 19, and 21 interact with each other to protect fundamental rights?"})

# print(results['answer'])