langchain_RAG

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Using langchain module to generate RAG prompt for open AI

langchain_RAG 최신버젼 다운로드

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# langchain_RAG for a CPU computer
Using langchain module to generate RAG prompt for open AI

The code aims to create a document retrieval and question-answering system using a Retrieval-Augmented Generation (RAG) model or similar language model (LLM). First, it downloads PDF documents from specified URLs and saves them locally. These documents are then chunked into smaller pieces and processed to generate embeddings. These embeddings serve as a "document store" in memory, which can be queried for relevant information. The FAISS library is used as the vector store to manage these embeddings, allowing efficient similarity-based lookup.

When a user submits a question (query), the code generates an embedding for that question and uses it to find the most similar document chunks from the FAISS vector store. Once the relevant documents are retrieved, they are then further processed to extract answers to the user's query. If the list of answers is not empty, the OpenAI API is called to generate a summary or interpretation of the answers using ChatGPT. This essentially couples document retrieval with further natural language processing to answer questions based on a corpus of documents.

Step 1 Import Libraries: Import the necessary Python libraries and modules like Hugging Face's Transformers, FAISS for similarity search, and other custom modules for text and document processing.

Step 2 Initialize Embedding Model: Initialize a language model like "sentence-transformers/all-mpnet-base-v2" to generate embeddings for documents and queries.

Step 3 Create Data Repository: Create a directory where downloaded PDF files will be stored.

Step 4 Download PDFs: Download PDF documents from given URLs and save them in the data repository. HTTP headers are set to mimic a web browser to avoid 403 errors.

Step 5 Load and Chunk Documents: Use a PDF loader to read the saved PDF files and chunk them into smaller text segments.

Step 6 Generate Document Embeddings: Use the initialized language model to create embeddings for each of the text segments and store them in a FAISS-based vector store.

Step 7 Initialize Vectorstore Wrapper: Create a wrapper around the FAISS vector store to facilitate efficient similarity searches.

Step 8 Process User Query: Accept a user question and generate its embedding using the same language model.

Step 9 Query Vectorstore: Use the query's embedding to search the FAISS vector store for relevant document chunks.

Step 10 Extract and Present Answers: List out relevant document chunks as the answer to the user's query.

Step 11  Conditional OpenAI API Call: Check if any answers were found. If so, use OpenAI's API to further summarize or interpret these answers using ChatGPT.

Step 12    Output: Display the final result, which can be either the summarized/interpreted answer or a message indicating that no answer was found.

Each of these steps plays a crucial role in building a comprehensive document retrieval and question-answering system.

Here is how you set it up on your computer
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Requirements: python 3

Git clone this repository:

git clone https://github.com/pnmeka/langchain_RAG/

Install the requirements- open AI, Hugging face transformer model, lanchain, pypdf etc

pip install -r requirements.txt

Update the open AI API in the code line 99

python3 RAG_langchain.py

langchain_RAG

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