https://github.com/ako1983/llm_research_assistant
An AI-powered research assistant that leverages Retrieval-Augmented Generation (RAG) to provide accurate responses to user queries by retrieving relevant documents and reasoning through complex questions.
https://github.com/ako1983/llm_research_assistant
agent-based-modeling agents anthropic chromadb dspy langchain-python langgraph-python llm openai rag reasoning
Last synced: 5 months ago
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An AI-powered research assistant that leverages Retrieval-Augmented Generation (RAG) to provide accurate responses to user queries by retrieving relevant documents and reasoning through complex questions.
- Host: GitHub
- URL: https://github.com/ako1983/llm_research_assistant
- Owner: ako1983
- License: mit
- Created: 2025-03-23T22:25:52.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-24T09:48:23.000Z (6 months ago)
- Last Synced: 2025-05-06T21:08:33.015Z (5 months ago)
- Topics: agent-based-modeling, agents, anthropic, chromadb, dspy, langchain-python, langgraph-python, llm, openai, rag, reasoning
- Language: Python
- Homepage:
- Size: 56.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📚 LLM-Powered Research Assistant 🤖
An AI-powered research assistant that leverages Retrieval-Augmented Generation (RAG) to provide accurate responses to user queries by retrieving relevant documents and reasoning through complex questions.
## Features
- **Smart Query Routing**: Autonomously decides whether to answer directly from knowledge, retrieve additional context, or use specialized tools
- **RAG Pipeline**: Retrieves relevant documents to enhance responses with accurate, up-to-date information
- **Multi-step Reasoning**: Uses DSPy for structured reasoning to break down complex queries
- **Tool Integration**: Can utilize calculators, web search, and other external tools when needed
- **Evaluation Framework**: Measures response quality and relevance using DSPy's evaluation capabilities
## Architecture
```ascii
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ User Interface │────▶│ Query Router │────▶│ RAG Pipeline │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ ▲ │
▼ │ ▼
┌─────────────────┐ ┌─────────────────┐
│ Tools (Calc, │ │ Vector Store │
│ Web Search) │ │ (ChromaDB) │
└─────────────────┘ └─────────────────┘
│ ▲ │
▼ │ ▼
┌─────────────────┐ ┌─────────────────┐
│ LLM Provider │◀───▶│ DSPy Modules │
│ (OpenAI/Claude) │ │ & Metrics │
└─────────────────┘ └─────────────────┘
```
## Setup & Installation
1. Clone the repository
```bash
git clone https://github.com/ako1983//LLM_research_assistant.git
cd /LLM_research_assistant
```
2. Install dependencies
```bash
pip install -r requirements.txt
```
3. Set up environment variables
```bash
export OPENAI_API_KEY="your-api-key"
export ANTHROPIC_API_KEY="your-api-key" # If using Claude
```
4. Prepare your data
```bash
python src/vectorstore_builder.py
```
## Usage
```python
from src.agent import ResearchAssistant
from src.llm_providers import OpenAILLM
from src.rag_pipeline import RAGPipeline
# Initialize components
llm = OpenAILLM(model_name="gpt-3.5-turbo")
rag = RAGPipeline()
rag.initialize()
retriever = rag.get_retriever()
# Create and use the assistant
assistant = ResearchAssistant(llm_provider=llm, retriever=retriever)
response = assistant.process_query("How do I fix Wi-Fi connection issues?")
print(response["response"])
```
## Project Structure
```
llm-research-assistant/
├── data/
│ ├── raw/ # Original dataset files
│ ├── processed/ # Cleaned CSV files
│ └── vector_stores/ # ChromaDB vector stores
├── prompts/
│ └── query_classification_prompt_template.txt # LLM prompts
├── src/
│ ├── agent.py # Main assistant logic
│ ├── llm_providers.py # LLM abstraction layer
│ ├── rag_pipeline.py # Document retrieval system
│ ├── router.py # Query routing logic
│ ├── tools/ # External tool integrations
│ └── dspy_modules/ # DSPy components
├── tests/ # Test cases
├── main.py # Entry point
└── requirements.txt # Dependencies
```
## Requirements
- Python 3.8+
- LangChain
- DSPy
- ChromaDB
- OpenAI or Anthropic API access
## Evaluation
The system uses DSPy's evaluation framework to assess:
- Answer correctness
- Context relevance
- Reasoning quality
## Acknowledgements
- Data sourced from [Bitext Customer Support Dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset)
- Built for an assessment