https://github.com/the-swarm-corporation/swarms-llamaindex-rag-template
Get started integration Llama Index into your swarms application now!
https://github.com/the-swarm-corporation/swarms-llamaindex-rag-template
agents ai llamaindex long-term-memory memory-boost memory-system ml openai rag rag-memory swarm swarms swarms-agents
Last synced: 3 months ago
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Get started integration Llama Index into your swarms application now!
- Host: GitHub
- URL: https://github.com/the-swarm-corporation/swarms-llamaindex-rag-template
- Owner: The-Swarm-Corporation
- License: mit
- Created: 2024-11-11T14:34:40.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-11T14:54:35.000Z (11 months ago)
- Last Synced: 2024-11-11T15:37:51.399Z (11 months ago)
- Topics: agents, ai, llamaindex, long-term-memory, memory-boost, memory-system, ml, openai, rag, rag-memory, swarm, swarms, swarms-agents
- Language: Python
- Homepage: https://swarms.xyz/
- Size: 21.5 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# 🚀 Swarms LlamaIndex RAG Template
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
[](https://github.com/The-Swarm-Corporation/Legal-Swarm-Template)
[](https://github.com/kyegomez/swarms)
A production-ready template for building RAG (Retrieval-Augmented Generation) applications using Swarms and LlamaIndex. This template provides everything you need to get started with document-based AI interactions.
## 🌟 Features
- 📚 Built-in RAG implementation with LlamaIndex
- 🤖 Advanced AI agent for healthcare data summarization
- 🔄 Interactive chat interface
- 📊 Sample data generator for testing
- 🛠️ Easy-to-customize architecture
## 🚀 Quick Start
1. **Clone the repository**
```bash
git clone https://github.com/The-Swarm-Corporation/Swarms-LlamaIndex-RAG-Template.git
cd Swarms-LlamaIndex-RAG-Template
```
2. **Install dependencies**
```bash
pip install -r requirements.txt
```
3. **Set up your environment**
```bash
# Create a .env file and add your API keys and configurations
touch .env
# export OPENAI_API_KEY=
# export GROQ_API_KEY=
# export WORKSPACE_DIR=
```
4. **Generate sample data or add your own**
```bash
# To generate sample data:
python -m llamaindex_rag.fake_data_generator
# OR add your own documents to the 'docs' folder
```
5. **Run the application**
```bash
python main.py
```
# Example Usage
- The `healthcare_summarizer` agent is a pre-built agent that summarizes medical data.
- You can customize the prompt to fit your needs.
- The `all_cores=True` argument is optional, it allows the agent to use all available cores on your machine for faster processing.
- The agent will first query the LlamaIndexDB for relevant documents, then use the LLM to summarize the data.
```python
from llamaindex_rag.agent import healthcare_summarizer
# Example usage
print(
healthcare_summarizer.run(
"""
What is the medical history of patient 1? Create a report with the following format:
- Chief Complaint
- Vitals
- Assessment
- Medications
- Plan
""",
all_cores=True,
)
)
```
### Custom Agent with RAG
- You can create your own agent with RAG by using the `Agent` class.
- The `system_prompt` argument is the prompt that the agent will use to summarize the data.
- The `llm` argument is the LLM model that the agent will use to summarize the data.
- The `max_loops` argument is the number of loops the agent will run.
- The `long_term_memory` argument is the LlamaIndexDB instance that the agent will use to query the vector database. It can use any RAG system you want it just needs to be a class with a `query` method that intakes a `query` string and returns a `response` string.
```python
# Initialize the healthcare summarization agent
healthcare_summarizer = Agent(
agent_name="Healthcare-Data-Summarizer",
system_prompt=HEALTHCARE_SUMMARY_PROMPT,
llm=model,
max_loops=2,
autosave=True,
dashboard=False,
verbose=True,
dynamic_temperature_enabled=False,
saved_state_path="healthcare_summarizer.json",
user_name="Human:",
retry_attempts=1,
context_length=250000,
return_step_meta=True,
output_type="json",
streaming_on=False,
long_term_memory=database,
auto_generate_prompt=False,
# output_file="healthcare_report.md",
# state_save_file_type="json",
rag_every_loop=True,
interactive=True,
)
```
## Memory Example
- LlamaIndexDB is a class that allows you to query a vector database of documents.
- Add your documents to the `docs` folder or use the fake data generator to populate the database.
- The `data_dir` argument is the directory containing your documents.
- `similarity_top_k` is the number of similar documents to retrieve, it can make the response more relevant and longer.
```python
from llamaindex_rag.memory import LlamaIndexDB
# Example usage
llama_index_db = LlamaIndexDB(
data_dir="docs",
filename_as_id=True,
recursive=True,
required_exts=[".txt", ".pdf", ".docx"],
similarity_top_k=10
)
llama_index_db.query(
"What is the medical history of patient 1?",
streaming=True,
response_mode="compact"
)
```
## 📁 Project Structure
```
.
├── llamaindex_rag/
│ ├── agent.py # Main RAG agent implementation
│ ├── memory.py # LlamaIndex database integration
│ └── fake_data_generator.py # Sample data generator
├── docs/ # Document storage directory
├── requirements.txt # Project dependencies
└── main.py # Application entry point
```
## 🛠️ Built With
- [Swarms Framework](https://github.com/kyegomez/swarms) - AI agent framework
- [LlamaIndex](https://www.llamaindex.ai/) - Data framework for LLM applications
- Python 3.10+
- GROQ/OpenAI API
## 📖 Usage
1. **Add Your Documents**
- Place your documents in the `docs` folder, or
- Use the fake data generator for testing
2. **Interact with the Agent**
- Run `main.py` to start the interactive session
- Ask questions about your documents
- Get AI-powered insights and summaries
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 🛠 Built With
- [Swarms Framework](https://github.com/kyegomez/swarms)
- Python 3.10+
- GROQ API Key or you can change it to use any model from [Swarm Models](https://github.com/The-Swarm-Corporation/swarm-models)
## 📬 Contact
Questions? Reach out:
- Twitter: [@kyegomez](https://twitter.com/kyegomez)
- Email: kye@swarms.world
---
## Want Real-Time Assistance?
[Book a call with here for real-time assistance:](https://cal.com/swarms/swarms-onboarding-session)
---
⭐ Star us on GitHub if this project helped you!
Built with ♥ using [Swarms Framework](https://github.com/kyegomez/swarms)