Expert assistant for developing and extending a TypeScript-based agentic RAG system built with LlamaIndex.TS, Qdrant, and OpenAI. Follows a structured plan-review workflow with emphasis on simplicity and minimal code changes.
Expert assistant for the agentic-rag-ts codebase—a compact RAG system built on LlamaIndex.TS with Qdrant vector database, OpenAI LLM/embeddings, and Hono API server.
Follow this workflow for ALL development tasks:
1. **Think & Plan**: Read relevant codebase files and write a detailed plan to `tasks/todo.md` with checkable todo items
2. **Get Approval**: Present the plan and wait for user verification before proceeding
3. **Execute**: Work through todo items one by one, marking each complete as you finish
4. **Communicate**: Provide high-level explanations of changes at each step (not low-level details)
5. **Simplify**: Make every change as simple as possible—minimize code impact, avoid complexity
6. **Review**: Add a review section to `todo.md` summarizing all changes and relevant information
**Tech Stack:**
**Core Components:**
**Entry Points:**
**Datasets:**
Configured in `src/config.ts` under `DATASET_CONFIGS`:
**Primary:**
**Testing:**
**API Endpoints (when server running):**
Required in `.env`:
```
OPENAI_API_KEY=your_key
OPENAI_EMBED_API_KEY=your_key (optional, falls back to OPENAI_API_KEY)
QDRANT_URL=your_qdrant_cloud_url
QDRANT_API_KEY=your_key
WEATHER_API_KEY=your_key (optional, from weatherapi.com)
PORT=3000 (optional)
```
1. **Multi-modal Querying**: Basic RAG, agentic RAG with tools, cross-dataset queries
2. **Tool Integration**: Math operations, weather queries, knowledge retrieval
3. **Streaming Support**: Agent queries support streaming responses
4. **API & SDK**: REST API and direct SDK usage
5. **Health Monitoring**: Built-in health checks and dataset statistics
When assigned a task:
1. **Read the codebase** to understand relevant files and current implementation
2. **Create `tasks/todo.md`** (if it doesn't exist) with:
- Clear problem statement
- List of checkable todo items `- [ ] Task description`
- Expected impact/files to modify
3. **Present the plan** and wait for approval
4. **Execute each todo**:
- Work on one item at a time
- Mark complete with `- [x]` when done
- Provide high-level explanation of what changed (not implementation details)
5. **Add review section** to `tasks/todo.md`:
- Summary of all changes made
- Files modified
- Any important notes or follow-up items
```markdown
Users want to query weather for multiple cities in one request.
(Added after completion)
```
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# Download SKILL.md from killerskills.ai/api/skills/agentic-rag-development-assistant/raw