🧐 Chat with your CV and Cove letter
=================================
Introduction
------------
The notebook provides a step-by-step guide to building a simple pdf-RAG system. It involves:
- Using your **CV** and **Cove Letter** as the knowledge base for RAG operations.
- Leveraging **LangChain** for retrieval and generation tasks.
- Employing **Ollama** as the local large language model backend.
Dependencies
------------
The required libraries are installed using the following commands:
.. code-block:: python
pip install -qU langchain langchain_community
pip install -qU langchain_chroma
pip install -qU langchain_ollama
pip install pypdf
Additionally, if running in environments like Google Colab, the notebook includes special setups for using **Ollama**.
Notebook Overview
-----------------
1. **Setting Up the Environment**:
- Installing dependencies.
- Initializing the tools for retrieval and embedding.
2. **Loading the Pdfs**:
- In the notebook I include the github link to the my PDFs, but feel free to use your own.
3. **Building the RAG Pipeline**:
- **Chunking**: Breaks the PDFs into smaller chunks for embedding.
- **Create ChromaDB**: Creates a vector database for storing the embeddings.
- **Clone a prompt**: we use a predifined prompt to interact with our RAG pipeline, but you can create your own.
- **Retrieve**: Retrieves relevant chunks using semantic search.
- **Generate**: Combines the retrieved context with the query and sends it to the LLM.
4. **Testing the System**:
- Users can input queries to test how the RAG pipeline responds.
- The outputs are evaluated for relevance and accuracy.
.. raw:: html
Streamlit Interface
-------------------
You can build a simple python interface with Streamlit library to interact with your RAG system.
.. code-block:: python
from langchain_community.llms import Ollama
from langchain_community.embeddings import OllamaEmbeddings
import streamlit as st
from langchain.vectorstores import Chroma
from langchain.chains import create_retrieval_chain
from langchain import hub
from langchain.chains.combine_documents import create_stuff_documents_chain
.. hint::
- You need to save your ChromaDB folder in your machine, or like I did in Github. So you don't need to create it again.
.. code-block:: python
# The Chroma is On Github https://github.com/NourTechNerd/data
persist_directory ="CHROMA"
embed_model = OllamaEmbeddings(model="llama3.1:8b",base_url="http://127.0.0.1:11434")
vector_store = Chroma(persist_directory=persist_directory,embedding_function=embed_model)
llm = Ollama(model="llama3.1:8b",base_url="http://127.0.0.1:11434")
retriever = vector_store.as_retriever(search_kwargs={"k":3})
retrieval_qa_chat_prompt = hub.pull("langchain-ai/retrieval-qa-chat")
combined_docs_chain = create_stuff_documents_chain(llm,retrieval_qa_chat_prompt)
retrieval_chain = create_retrieval_chain(retriever,combined_docs_chain)
1. **Chroma Vector Store**
- `persist_directory`: Specifies the directory where ChromaDB will persist data.
- `vector_store`: Initializes a Chroma vector database with the embedding function.
2. **Embedding Model**
- `OllamaEmbeddings`: A model to compute vector representations of text using the `llama3.1:8b` model hosted locally at `127.0.0.1:11434`.
3. **Local Language Model (LLM)**
- `llm`: Instantiates the `llama3.1:8b` model for generating text and answering queries.
4. **Retriever**
- `retriever`: Configures the vector store to return the top 3 (`k=3`) most relevant documents for a query.
5. **Prompt and Chain**
- `retrieval_qa_chat_prompt`: Fetches a pre-defined prompt template for retrieval-based Q&A tasks.
- `combined_docs_chain`: Combines the retrieval system with the LLM for document-based answers.
- `retrieval_chain`: Creates the full pipeline that integrates retrieval and generation.
.. code-block:: python
st.title("RAG Chatbot")
question = st.text_input("poser votre question")
if st.button("obtenir la reponse"):
if question:
with st.spinner("Recherche de la réponse..."):
response = retrieval_chain.invoke({"input":question})
st.write("**réponse:**")
st.write(response["answer"])
else:
st.write("veuillez poser une question")
Demo Video
----------
Here is a video of the RAG pipeline in action:
.. raw:: html
