https://github.com/greathayat/langgraph-corrective-rag
A repository to learn & explore the CRAG - A better version of RAG.
https://github.com/greathayat/langgraph-corrective-rag
agentic-ai agentic-rag ai langgraph langgraph-python
Last synced: about 2 months ago
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A repository to learn & explore the CRAG - A better version of RAG.
- Host: GitHub
- URL: https://github.com/greathayat/langgraph-corrective-rag
- Owner: GreatHayat
- Created: 2025-01-13T02:39:55.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-14T06:26:45.000Z (4 months ago)
- Last Synced: 2025-02-03T08:43:06.459Z (4 months ago)
- Topics: agentic-ai, agentic-rag, ai, langgraph, langgraph-python
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Corrective RAG (CRAG)
**Corrective RAG (CRAG)** is a strategy for Retrieval-Augmented Generation (RAG) that integrates self-reflection and self-grading mechanisms to enhance the accuracy of responses by evaluating the relevance of retrieved documents.
**Reference & Inspiration:**
[Learn more about CRAG in LangGraph](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_crag/)
## Prerequisites
To get started with this project, ensure you have access to the following:
- **OpenAI API** – For language model operations.
- **Qdrant Vector Database** – For vector storage and retrieval.
- **Tavily API Key** – Required for specific integrations.
---
## Getting Started
Follow these steps to set up and run the project:
1. **Set up a virtual environment**
```bash
python -m venv env
source env/bin/activate # For Linux/macOS
env\Scripts\activate # For Windows
```
2. **Install dependencies**
Navigate to the project directory and run:
```bash
pip install -r my-agents/requirements.txt
```
3. **Open in LangGraph Studio**
- Launch the **LangGraph Studio** desktop application.
- Open the project folder within the application.