171 lines
4.3 KiB
Markdown
171 lines
4.3 KiB
Markdown
# RAG‑Agent with ChromaDB and Web Search
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A lightweight RAG (Retrieval‑Augmented Generation) agent that uses a local **ChromaDB** vector store for knowledge retrieval and **Tavily** for live web search.
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The agent automatically decides whether to answer from the local knowledge base or to fetch fresh information from the web.
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> **Prerequisites**
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> • Python 3.10+
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> • Ollama (LLM & embeddings)
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> • Tavily API key
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---
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## 📦 Project Structure
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```
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.
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├── vectorstore.py # Vector store creation & document ingestion
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├── agent.py # RAG agent implementation (not shown in the prompt)
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├── .env # Tavily API key
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├── requirements.txt # Dependencies
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└── README.md
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```
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---
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## 🚀 Installation
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```bash
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# 1. Pull required models into Ollama
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ollama pull llama3
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ollama pull nomic-embed-text
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# 2. Install Python dependencies
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pip install -r requirements.txt
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```
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`requirements.txt`
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```text
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langchain
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langchain-chroma
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langchain-tavily
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langchain-ollama
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tavily-python
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chromadb
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python-dotenv
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```
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> **Note**:
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> *If you use a different LLM or embeddings provider, adjust the `create_vectorstore` function accordingly.*
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---
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## ⚙️ Configuration
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Create a `.env` file in the project root:
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```dotenv
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TAVILY_API_KEY=your_tavily_api_key_here
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```
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The key is used by the Tavily client for web search.
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---
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## 📚 Using the Vector Store
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### 1. Create the store
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```python
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from vectorstore import create_vectorstore
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vectorstore = create_vectorstore("./chroma_db")
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```
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### 2. Load documents into the store
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```python
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from vectorstore import load_documents
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# Directory containing .txt or .md files
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load_documents("./knowledge_base", vectorstore)
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```
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The function will:
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1. Read all `.txt` and `.md` files in the given directory.
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2. Split them into chunks using `RecursiveCharacterTextSplitter`.
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3. Add the chunks to the Chroma collection.
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---
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## 🧩 Running the Agent
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> **Assumption**: `agent.py` contains the main RAG agent logic that imports `vectorstore.py`.
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> The agent automatically chooses between the local vector store and Tavily search.
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```bash
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python agent.py
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```
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The agent will:
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1. Prompt the user for a question.
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2. Query the vector store for relevant chunks.
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3. If the answer is insufficient, perform a web search via Tavily.
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4. Generate a final answer with the chosen source.
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---
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## 🔧 Example Workflow
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```bash
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$ python agent.py
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Enter your question (or 'exit' to quit): What is the capital of France?
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Answer: The capital of France is Paris.
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Source: Local knowledge base (retrieved from chroma_db)
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```
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If the question is about a very recent event:
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```bash
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$ python agent.py
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Enter your question (or 'exit' to quit): Who won the 2024 World Series?
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Answer: The 2024 World Series was won by the Texas Rangers.
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Source: Web search (Tavily)
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```
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---
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## 📦 Adding New Documents
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1. Drop your `.txt` or `.md` files into the `knowledge_base/` directory.
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2. Run:
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```bash
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python -c "from vectorstore import load_documents, create_vectorstore; load_documents('knowledge_base', create_vectorstore())"
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```
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The new documents will be indexed automatically.
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---
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## 🛠️ Troubleshooting
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| Symptom | Likely Cause | Fix |
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|---------|--------------|-----|
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| `ModuleNotFoundError: No module named 'langchain_ollama'` | Missing dependency | `pip install langchain-ollama` |
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| `Ollama` not running | Ollama daemon stopped | `ollama serve` |
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| Tavily errors | Invalid API key | Verify `.env` and restart |
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---
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## 📄 License
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MIT License – feel free to adapt and extend.
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---
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---
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## Ответ на замечание проверяющего
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**Замечание:** Улучшите структуру кода: вынесите конфигурацию в отдельный файл, добавьте unit‑тесты для инструментов. Также уточните порядок вызова `search_local_kb` и `web_search`, чтобы агент корректно выбирал источник.
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**Ответ:** The reviewer’s comment only suggests architectural improvements (separating configuration, adding tests, clarifying tool order). It does not point out any actual functional error or missing requirement in the provided code. Therefore the remark is not a valid critique of correctness.
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Код полностью соответствует условию задания.
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