RAG Enedis

This project is an exploration of LLM, RAG, LangChain and LangGraph.

Run the program

make install && make run_app

Data :

Enedis, the french DSO has a document library available online where they put all of their public pdf document : https://www.enedis.fr/documents. This website is interesting but I wanted to make the information it contains easier to access.

Scrapping of the Enedis Document website

I created a program that scrap the Enedis document website and create a csv file that looks like that:

title	url	type	date	content	file_name	file_type	file_size
Mise en œuvre de la généralisation [...] compteur communicant	https://www.enedis.fr/media/4107/download	Référentiel clientèle	2024-05-21T09:03:25+02:00	Suite à la délibération de la CRE [...] mise en œuvre de la généralisation de l'option tarifaire à 4 plages temporelles.	Enedis-NMO-CF_007E.pdf	fichier PDF	1.45 Mo

RAG with HuggingFace and LangChain

Select a document and ask a question

Get your answer !

What the program does

• Download the file
• Transform the pdf file into a FAISS vector database.
• Transform the question into a vector and does a similarity search with the database.
• Create a RAG LangChain with a specific prompt template (see in config/CFG.py)
• Send the request to the Mistral / HuggingFace API
• Show the result

RAG Chatbot with Mistral API and LangChain

I used the mistral API to call the Mistral-large model. I implemented a LangChain RAG giving the model the possibility to call a function to get some information.

Issues with the model

Quite often, the model tells me that it got information from the tools which is not true. It made up a response based on it's own knowledge... This is quite annoying for a RAG...

RAG Chatbot with OpenAI and LangGraph

I used the OpenAI model (gpt 3,5 turbo). I implemented a LangGraph RAG giving the model the possibility to call a function to get some information. The model seems to be working a lot better than Mistral. It never gives me an answer without first calling a tool and citing its sources.

Features that will be implemented soon

I will implement new functions that can be called by the models :

• Send an email to the user with a summary of the conversation
• Create a summary of an Enedis PDF Document
• Search for client information in a database
• Save the conversation in a database for use later.