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https://github.com/qredence/graphfleet
An open-source integration of GraphRAG for Agentic System with NoCode
https://github.com/qredence/graphfleet
ai-agent graph graphrag knowledge-graph llm llms nocode rag streamlit ui
Last synced: 3 months ago
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An open-source integration of GraphRAG for Agentic System with NoCode
- Host: GitHub
- URL: https://github.com/qredence/graphfleet
- Owner: Qredence
- License: apache-2.0
- Created: 2024-07-19T01:40:51.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-02T18:25:45.000Z (3 months ago)
- Last Synced: 2024-11-02T19:23:45.548Z (3 months ago)
- Topics: ai-agent, graph, graphrag, knowledge-graph, llm, llms, nocode, rag, streamlit, ui
- Language: Jupyter Notebook
- Homepage: https://www.qredence.ai
- Size: 1.07 GB
- Stars: 9
- Watchers: 2
- Forks: 3
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
# Overview
https://github.com/user-attachments/assets/ac3271fa-cab5-412f-a441-bbedfe4a009f
# ** /!\ DEPRECATED NEW FULL REVAMP COMING SOON /!\**
GraphFleet is an advanced implementation of [GraphRAG from Microsoft](https://github.com/microsoft/graphrag), designed to enhance large language models' ability to reason about complex information and private datasets. It builds upon GraphRAG (Retrieval Augmented Generation using Graph structures) and will gradually adopt its own path to fulfill our roadmap at Qredence.
## GraphFleet
GraphFleet uses knowledge graphs to provide substantial improvements in question-and-answer performance when reasoning about complex information. It addresses limitations of traditional RAG approaches:
## Roadmap
- [ ] Provide a FleetUI Design Kit and a quicker way of starting GraphFleet locally.
- [ ] Provide a Toddle interface ready to use for GraphFleet
- [ ] Add integrations of Composio
- [ ] Add integrations of LangSmith
- [ ] Add few selfhosting one click deploy solutions.
- [ ] Access GraphFleet through a secure and enterprise-ready Azure Cloud hosting version.
- [ ] And way more... 👀
## Key Features
- Structured, hierarchical approach to Retrieval Augmented Generation.
- Knowledge graph extraction from raw text.
- Community hierarchy building.
- Hierarchical summarization.
- Enhanced reasoning capabilities for LLMs on private datasets.
Our current API Endpoints : https://agenticfleet.apidocumentation.com/reference#tag/search/POST/search/global
## Contribute
- Leave us a star ♥
- Fork and contribute to the project
-
## Getting Started
### Prerequisites
- Python 3.11
- Poetry
- Make sure to have a virtual environment manager such as `virtualenv` installed
### Installation
1. Clone the repository:
```bash
git clone https://github.com/Qredence/GraphFleet.git
cd GraphFleet
```
2. Install the dependencies:
```bash
poetry shell
poetry install
```
### Usage
1. Configuration:
Environment Variables: Set up your environment variables in a .env file (refer to the .env.example file for available options). Key variables include:
Fill in the .env file in the root folder and the one in the graphfleet folder.
```sh
export GRAPHRAG_API_KEY="your_api_key_here"
export GRAPHRAG_API_BASE=""
export GRAPHRAG_API_VERSION=""
export GRAPHRAG_DEPLOYMENT_NAME="your_deployment_name"
export GRAPHRAG_API_TYPE="azure_openai"
export GRAPHRAG_EMBEDDING_MODEL="text-embedding-ada-002"
export GRAPHRAG_LLM_MODEL="gpt-4"
export GRAPHRAG_DATA_PATH="./your_data_directory"
export GRAPHRAG_EMBEDDING_TYPE="azure_openai_embedding"
export GRAPHRAG_EMBEDDING_KEY="your_embedding_key_here"
export GRAPHRAG_EMBEDDING_ENDPOINT=""
export GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME="your_embedding_deployment_name"
```
settings.yaml: Customize GraphFleet's behavior further by modifying the settings.yaml file within the graphfleet directory.
1. Interacting with GraphFleet:
settings.yaml: Customize GraphFleet's behavior further by modifying the settings.yaml file within the graphfleet directory.
Jupyter Notebooks: Explore GraphFleet's capabilities with the provided notebooks:
get-started-graphfleet.ipynb: A comprehensive guide to indexing your data and running basic queries.
Local Search Notebook.ipynb: Demonstrates local search techniques.
app.py (FastAPI Application): Run a Streamlit-powered web interface to interact with GraphFleet using a user-friendly chat-like interface.
### Add your text files in ./graphfleet/input/ and run the auto_prompt function
``` bash
!python -m graphrag.prompt_tune \
--config ./graphfleet/settings.yaml \
--root ./graphfleet \
--no-entity-types \
--output ./graphfleet/prompts
```
### Data Indexing
Jupyter Notebook Guide: Follow the instructions provided in the get-started-graphfleet.ipynb notebook to learn how to index your data with GraphFleet. This notebook provides a hands-on experience for setting up your knowledge base.
``` bash
! python -m graphrag.index \
--verbose \
--root ./graphfleet \
--config ./graphfleet/settings.yaml
```
### Recommended Run these notebook to get started with GraphFleet
Jupyter Notebooks: Explore GraphFleet's capabilities with the provided notebooks:
([Get Started Quickly.ipynb](https://github.com/Qredence/GraphFleet/blob/a67eaeb295d99dd2ef48bcdd8f8a719b830ffb7d/notebook/Get%20Started%20Quickly.ipynb)): A comprehensive guide to indexing your data and running basic queries.
([Local Search Notebook.ipynb:](https://github.com/Qredence/GraphFleet/blob/a67eaeb295d99dd2ef48bcdd8f8a719b830ffb7d/notebook/Local%20Search%20Notebook.ipynb)) Demonstrates local search techniques.
([Global Search Notebook](https://github.com/Qredence/GraphFleet/blob/a67eaeb295d99dd2ef48bcdd8f8a719b830ffb7d/notebook/Global%20Search%20Notebook.ipynb))
### Running the API only (or run CLI commands for local search or global search)
To run the API, save the code in a file named api.py and execute the following command in your terminal:
``` bash
uvicorn app:main --reload --port 8001
```
### Running Streamlit
To run the API, save the code in a file named api.py and execute the following command in your terminal:
``` bash
streamlit run app/streamlit_app.py
```
## Security
For details about our security policy, please see [Security](SECURITY.md)
## License
This project is licensed under the Apache License 2.0. For the full license text, please see [License](LICENSE)
## Star History
[](https://star-history.com/#Qredence/GraphFleet&Date)