https://github.com/ahmadhuss/rag-chromadb

A RAG overview that utilizes a PDF and JSON file using OpenAI's language model (LLM).
https://github.com/ahmadhuss/rag-chromadb

chromadb langchain llm openai rag

Last synced: about 1 month ago
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A RAG overview that utilizes a PDF and JSON file using OpenAI's language model (LLM).

• Host: GitHub
• URL: https://github.com/ahmadhuss/rag-chromadb
• Owner: ahmadhuss
• Created: 2024-09-26T11:55:29.000Z (3 months ago)
• Default Branch: master
• Last Pushed: 2024-09-26T19:41:58.000Z (3 months ago)
• Last Synced: 2024-12-01T00:06:40.535Z (about 1 month ago)
• Topics: chromadb, langchain, llm, openai, rag
• Language: Python
• Homepage:
• Size: 1.78 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Knowledge-Based CLI RAG Application

This repository contains a knowledge-based CLI-type RAG (Retrieval-Augmented Generation) application. RAG applications typically work with textual data. In this repository, we can pass the textutal data in two formats: `.pdf` and `.json`.

This CLI-based RAG application uses the Langchain framework along with various ecosystem packages, such as:

-   langchain-core

-   langchain-community

-   langchain-chroma

-   langchain-openai

The repository utilizes the OpenAI LLM model for query retrieval from the vector embeddings.

## Installation  

  

Ensure you have Python >=3.10 <=3.13 installed on your system.

### Create virtual environment:  

```bash

python3 -m venv env

```

After that, Activate the virtual environment.

  

### Windows:  

  

```bash  

env\Scripts\activate  

```  

  

### Unix:  

  

```bash  

source env/bin/activate  

```  

### Install Dependencies:  

```bash  

pip install -r requirements.txt

```  

### Create Vector Database - Embedding based on the provided data (PDF, JSON files):

```bash  

python3 create_database.py

```

### Direct Query from the Vector Database:

You can perform simple direct queries on the vector database with the following commands:

```bash  

python3 query_data.py "What is Lee Kim?"

```

```bash  

python3 query_data.py "Who is Mr. Ken?"

```

```bash  

python3 query_data.py "What company type does Test Company have?"

```

### LLM-Based Queries on Our Vector Database:

You can leverage a language model for querying the vector database with more complex requests:

```bash  

python3 quality_response.py "What is Lee Kim?"

```

```bash  

python3 quality_response.py "Who is Mr. Ken?"

```

```bash  

python3 quality_response.py "What company type does Test Company have?"

```

```bash  

python3 quality_response.py "Provide information about Test Company in tabular format."

```

```bash  

python3 quality_response.py "How many resources are working in Lee Kim? List them in tabular format."

```

### License:

MIT