Modules/rag.py

import os
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
from dotenv import load_dotenv
load_dotenv() # load .env api keys 

mistral_api_key = os.getenv("MISTRAL_API_KEY")

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma, FAISS
from langchain_mistralai import MistralAIEmbeddings
from langchain import hub
from typing import Literal
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_mistralai import ChatMistralAI
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.tools import DuckDuckGoSearchRun

def load_chunk_persist_pdf() -> Chroma:
    pdf_folder_path = "data/pdf_folder/"
    documents = []
    for file in os.listdir(pdf_folder_path):
        if file.endswith('.pdf'):
            pdf_path = os.path.join(pdf_folder_path, file)
            loader = PyPDFLoader(pdf_path)
            documents.extend(loader.load())
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
    chunked_documents = text_splitter.split_documents(documents)
    os.makedirs("data/chroma_store/", exist_ok=True)
    vectorstore = Chroma.from_documents(
        documents=chunked_documents,
        embedding=MistralAIEmbeddings(),
        persist_directory="data/chroma_store/"
    )
    vectorstore.persist()
    return vectorstore

vectorstore = load_chunk_persist_pdf()
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")


# Data model
class RouteQuery(BaseModel):
    """Route a user query to the most relevant datasource."""

    datasource: Literal["vectorstore", "websearch"] = Field(
        ...,
        description="Given a user question choose to route it to web search or a vectorstore.",
    )

# LLM with function call 
llm = ChatMistralAI(model="mistral-large-latest", mistral_api_key=mistral_api_key, temperature=0)

# structured_llm_router = llm.with_structured_output(RouteQuery, method="json_mode")

# Prompt 
system = """You are an expert at routing a user question to a vectorstore or web search.
The vectorstore contains documents related to agents, prompt engineering, and adversarial attacks.
Use the vectorstore for questions on these topics. For all else, use web-search."""
route_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)
prompt = hub.pull("rlm/rag-prompt")
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

def format_docs(docs):
        return "\n\n".join(doc.page_content for doc in docs)
    
    
rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )

print(rag_chain.invoke("Build a fitness program for me. Be precise in terms of exercises"))

# print(rag_chain.invoke("I am a 45 years old woman and I have to loose weight for the summer. Provide me with a fitness program"))