Update documentation
This commit is contained in:
Yiorgis Gozadinos
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8 changed files with 491 additions and 80 deletions
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@ -1,8 +1,9 @@
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# Agents
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Two agentic flows are provided by haiku.rag:
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Three agentic flows are provided by haiku.rag:
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- **Simple QA Agent** — a focused question answering agent
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- **Chat Agent** — multi-turn conversational RAG with session memory
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- **Research Graph** — a multi-step research workflow with question decomposition
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See [QA and Research Configuration](configuration/qa-research.md) for configuring model, iterations, concurrency, and other settings.
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@ -47,6 +48,69 @@ async with HaikuRAG(path_to_db) as client:
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print(answer)
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```
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## Chat Agent
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The chat agent enables multi-turn conversational RAG. It maintains session state including Q/A history and uses that context to improve follow-up answers.
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Key features:
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- **Session memory**: Previous Q/A pairs are used as context for follow-up questions
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- **Query expansion**: SearchAgent generates multiple query variations for better recall
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- **Document filtering**: Natural language document filtering ("search in document X about...")
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- **Confidence filtering**: Low-confidence answers are flagged
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### Tools
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The chat agent uses three tools:
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- `search` — Hybrid search with optional document filter
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- `ask` — Answer questions using the conversational research graph
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- `get_document` — Retrieve a specific document by title or URI
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### CLI Usage
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```bash
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haiku-rag chat
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haiku-rag chat --db /path/to/database.lancedb
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```
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See [Applications](apps.md#chat-tui) for the full TUI interface guide.
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### Python Usage
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```python
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from haiku.rag.client import HaikuRAG
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from haiku.rag.agents.chat import create_chat_agent, ChatDeps, ChatSessionState
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async with HaikuRAG(path_to_db) as client:
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# Create agent and session
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agent = create_chat_agent(config)
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session = ChatSessionState()
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deps = ChatDeps(client=client, config=config, session_state=session)
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# First question
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result = await agent.run("What is haiku.rag?", deps=deps)
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print(result.output)
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# Follow-up (uses session context)
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result = await agent.run("How does it handle PDFs?", deps=deps)
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print(result.output)
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```
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### Session State
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The `ChatSessionState` maintains:
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- `session_id` — Unique identifier for the session
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- `qa_history` — List of previous Q/A pairs (FIFO, max 50)
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- `embedding_cache` — Cached embeddings for semantic ranking
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Q/A history is used to:
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1. Provide context for follow-up questions
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2. Avoid repeating previous answers
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3. Enable semantic ranking of relevant past answers
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## Research Graph
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The research workflow is implemented as a typed pydantic-graph. It plans, searches (in parallel batches), evaluates, and synthesizes into a final report.
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181
docs/apps.md
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181
docs/apps.md
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# Applications
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Three interactive applications for working with your knowledge base.
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## Chat TUI
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Conversational RAG from the terminal with streaming responses and session memory.
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### Usage
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```bash
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haiku-rag chat
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haiku-rag chat --db /path/to/database.lancedb
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```
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### Interface
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The chat interface provides:
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- Streaming responses with real-time tool execution indicators
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- Expandable citations showing source document, pages, and headings
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- Session memory for context-aware follow-up questions
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- Visual grounding to inspect chunk source locations
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### Keyboard Shortcuts
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| Key | Action |
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|-----|--------|
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| `Ctrl+L` | Clear chat history and reset session |
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| `Ctrl+G` | Show visual grounding for selected citation |
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| `Ctrl+I` | Show database info (document/chunk counts) |
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| `Escape` | Focus input field or cancel processing |
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### Session Management
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- Conversation history is maintained in memory for the session
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- Previous Q/A pairs are used as context for follow-up questions
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- Citations are tracked per response and can be inspected
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- Clearing chat (`Ctrl+L`) resets the session state
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## Web Application
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Browser-based conversational RAG with a CopilotKit frontend.
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### Quick Start
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```bash
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cd app
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docker compose -f docker-compose.dev.yml up -d --build
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```
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- Frontend: http://localhost:3000
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- Backend: http://localhost:8001
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### Architecture
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- **Backend**: Starlette server with pydantic-ai `AGUIAdapter`
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- **Frontend**: Next.js with CopilotKit
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- **Protocol**: AG-UI for streaming chat
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### Configuration
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Create a `.env` file in the `app/` directory:
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```bash
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# API Keys (at least one required)
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ANTHROPIC_API_KEY=your-anthropic-key
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OPENAI_API_KEY=your-openai-key
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# Database path
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DB_PATH=/path/to/your/haiku.rag.lancedb
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# Optional: Ollama base URL (if using local models)
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OLLAMA_BASE_URL=http://localhost:11434
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# Optional: Logfire for observability
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LOGFIRE_TOKEN=your-logfire-token
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```
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For full configuration, mount a `haiku.rag.yaml` file:
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```yaml
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# app/haiku.rag.yaml
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qa:
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model:
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provider: anthropic
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name: claude-sonnet-4-20250514
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```
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### API Endpoints
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| Endpoint | Method | Description |
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|----------|--------|-------------|
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| `/v1/chat/stream` | POST | AG-UI chat streaming |
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| `/api/documents` | GET | List all documents |
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| `/api/info` | GET | Database statistics |
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| `/api/visualize/{chunk_id}` | GET | Visual grounding images (base64) |
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| `/health` | GET | Health check |
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### Development
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**Hot reload**: The backend reloads automatically on file changes. For frontend changes:
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```bash
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docker compose -f docker-compose.dev.yml up -d --build frontend
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```
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**Logfire debugging**: If `LOGFIRE_TOKEN` is set, LLM calls are traced and available in the Logfire dashboard.
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## Inspector
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TUI for browsing documents, chunks, and search results.
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### Installation
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```bash
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# For haiku.rag-slim
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pip install 'haiku.rag-slim[inspector]'
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# Already included in haiku.rag
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pip install haiku.rag
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```
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### Usage
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```bash
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haiku-rag inspect
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haiku-rag inspect --db /path/to/database.lancedb
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```
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### Interface
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Three panels display your data:
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- **Documents** (left) - All documents in the database
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- **Chunks** (top right) - Chunks for the selected document
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- **Detail View** (bottom right) - Full content and metadata
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### Navigation
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| Key | Action |
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|-----|--------|
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| `Tab` | Cycle between panels |
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| `↑` / `↓` | Navigate lists |
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| `/` | Open search modal |
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| `c` | Context expansion modal (when viewing a chunk) |
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| `v` | Visual grounding modal (when viewing a chunk) |
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| `q` | Quit |
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**Mouse**: Click to select, scroll to view content.
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### Search
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Press `/` to open the full-screen search modal:
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- Enter your query and press `Enter` to search
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- **Left panel**: Search results with relevance scores `[0.95] content preview`
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- **Right panel**: Full chunk content and metadata
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- Use `↑` / `↓` to navigate results
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- Press `Enter` on a result to navigate to that document/chunk
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- Press `Esc` to close search
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Search uses hybrid (vector + full-text) search across all chunks.
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### Context Expansion
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Press `c` while viewing a chunk to see the expanded context that would be provided to the QA agent:
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- Type-aware expansion: tables, code blocks, and lists expand to their complete structures
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- Text content expands based on `search.context_radius` setting
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- Includes metadata like source document, content type, and relevance score
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### Visual Grounding
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Press `v` while viewing a chunk to see page images with the chunk's location highlighted:
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- Use `←` / `→` arrow keys to navigate between pages
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- Requires documents processed with Docling that include page images
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!!! note
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Visual grounding requires documents with a stored DoclingDocument that includes page images. Text-only documents won't have visual grounding available.
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219
docs/architecture.md
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docs/architecture.md
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# Architecture
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High-level overview of haiku.rag components and data flow.
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## System Overview
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```mermaid
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flowchart TB
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subgraph Sources["Document Sources"]
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Files[Files]
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URLs[URLs]
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Text[Text]
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end
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subgraph Processing["Processing Pipeline"]
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Converter[Converter]
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Chunker[Chunker]
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Embedder[Embedder]
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end
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subgraph Storage["Storage Layer"]
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LanceDB[(LanceDB)]
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end
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subgraph Agents["Agent Layer"]
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QA[QA Agent]
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Chat[Chat Agent]
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Research[Research Graph]
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end
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subgraph Apps["Applications"]
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CLI[CLI]
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ChatTUI[Chat TUI]
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WebApp[Web App]
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Inspector[Inspector]
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MCP[MCP Server]
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end
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Sources --> Converter
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Converter --> Chunker
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Chunker --> Embedder
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Embedder --> LanceDB
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LanceDB --> Agents
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Agents --> Apps
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```
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## Core Components
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### Storage Layer
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LanceDB provides vector storage with full-text search capabilities:
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- **DocumentRecord** - Document metadata and full content
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- **ChunkRecord** - Text chunks with embeddings and structural metadata
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- **SettingsRecord** - Database configuration and version info
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Repositories handle CRUD operations:
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- `DocumentRepository` - Create, read, update, delete documents
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- `ChunkRepository` - Chunk management and hybrid search
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- `SettingsRepository` - Configuration persistence
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### Processing Pipeline
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```mermaid
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flowchart LR
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Source[Source] --> Converter
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Converter --> DoclingDoc[DoclingDocument]
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DoclingDoc --> Chunker
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Chunker --> Chunks[Chunks]
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Chunks --> Embedder
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Embedder --> Vectors[Vectors]
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Vectors --> DB[(LanceDB)]
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```
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**Converters** transform sources into DoclingDocuments:
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- `docling-local` - Local Docling processing
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- `docling-serve` - Remote processing via docling-serve
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**Chunkers** split documents into semantic chunks:
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- Preserves document structure (tables, lists, code blocks)
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- Maintains provenance (page numbers, headings)
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- Configurable chunk size
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**Embedders** generate vector representations:
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| Provider | Models |
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|----------|--------|
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| Ollama | nomic-embed-text, mxbai-embed-large |
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| OpenAI | text-embedding-3-small, text-embedding-3-large |
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| VoyageAI | voyage-3, voyage-code-3 |
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| vLLM | Any compatible model |
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| LM Studio | Any compatible model |
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### Agent Layer
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Three agent types for different use cases:
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```mermaid
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flowchart TB
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subgraph QA["QA Agent"]
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Q1[Question] --> S1[Search]
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S1 --> A1[Answer]
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end
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subgraph Chat["Chat Agent"]
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Q2[Question] --> Expand[Query Expansion]
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Expand --> S2[Search/Ask]
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S2 --> A2[Answer]
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A2 --> History[Session History]
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History -.-> Q2
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end
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subgraph Research["Research Graph"]
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Q3[Question] --> Plan[Plan]
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Plan --> Batch[Get Batch]
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Batch --> SearchN[Search × N]
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SearchN --> Evaluate[Evaluate]
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Evaluate -->|Continue| Batch
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Evaluate -->|Done| Synthesize[Synthesize]
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end
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```
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**QA Agent** - Single-turn question answering:
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- Searches for relevant chunks
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- Expands context around results
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- Generates answer with optional citations
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**Chat Agent** - Multi-turn conversational RAG:
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- Maintains session history
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- Uses previous Q/A pairs as context
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- Query expansion for better recall
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- Natural language document filtering
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**Research Graph** - Multi-step research workflow:
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- Decomposes questions into sub-questions
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- Parallel search execution
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- Iterative refinement based on confidence
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- Synthesizes structured research report
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### Applications
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| Application | Interface | Use Case |
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|-------------|-----------|----------|
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| CLI | Command line | Scripts, one-off queries, batch processing |
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| Chat TUI | Terminal | Interactive conversations |
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| Web App | Browser | Team collaboration, visual interface |
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| Inspector | Terminal | Database exploration, debugging |
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| MCP Server | Protocol | AI assistant integration |
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## Data Flow
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### Document Ingestion
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```mermaid
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sequenceDiagram
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participant User
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participant CLI
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participant Converter
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participant Chunker
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participant Embedder
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participant DB as LanceDB
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User->>CLI: add-src document.pdf
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CLI->>Converter: Convert to DoclingDocument
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Converter-->>CLI: DoclingDocument
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CLI->>Chunker: Split into chunks
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Chunker-->>CLI: Chunks with metadata
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CLI->>Embedder: Generate embeddings
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Embedder-->>CLI: Vectors
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CLI->>DB: Store document + chunks
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DB-->>User: Document ID
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```
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### Search and QA
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```mermaid
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sequenceDiagram
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participant User
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participant Agent
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participant Embedder
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participant DB as LanceDB
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participant LLM
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User->>Agent: Ask question
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Agent->>Embedder: Embed query
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Embedder-->>Agent: Query vector
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Agent->>DB: Hybrid search
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DB-->>Agent: Relevant chunks
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Agent->>Agent: Expand context
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Agent->>LLM: Generate answer
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LLM-->>Agent: Answer + citations
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Agent-->>User: Response
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```
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## Configuration
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Configuration flows through the system:
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|
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```
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CLI args → Environment variables → haiku.rag.yaml → Defaults
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```
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Key configuration areas:
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- **Storage** - Database path, vacuum settings
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- **Embeddings** - Provider, model, dimensions
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- **Processing** - Chunk size, converter, chunker
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- **Search** - Limits, context expansion
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- **QA/Research** - Model, iterations, concurrency
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- **Providers** - Ollama, vLLM, docling-serve URLs
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See [Configuration](configuration/index.md) for details.
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18
docs/cli.md
18
docs/cli.md
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@ -161,6 +161,24 @@ Flags:
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- `--deep`: Decompose the question into sub-questions answered in parallel before synthesizing a final answer
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- `--filter`: Restrict searches to documents matching the filter (see [Filtering Search Results](python.md#filtering-search-results))
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## Chat
|
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|
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Launch an interactive chat session for multi-turn conversations:
|
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|
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```bash
|
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haiku-rag chat
|
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haiku-rag chat --db /path/to/database.lancedb
|
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```
|
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|
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The chat interface provides:
|
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|
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- Streaming responses with real-time tool execution
|
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- Expandable citations with source metadata
|
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- Session memory for context-aware follow-up questions
|
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- Visual grounding to inspect chunk source locations
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|
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See [Applications](apps.md#chat-tui) for keyboard shortcuts and features.
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|
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## Research
|
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|
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Run the multi-step research graph:
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|
|
|
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|
|
@ -8,6 +8,7 @@ Agentic RAG built on [LanceDB](https://lancedb.com/), [Pydantic AI](https://ai.p
|
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- **Reranking** — MxBAI, Cohere, Zero Entropy, or vLLM
|
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- **Question answering** — QA agents with citations (page numbers, section headings)
|
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- **Research agents** — Multi-agent workflows via pydantic-graph: plan, search, evaluate, synthesize
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- **Conversational RAG** — Chat TUI and web application for multi-turn conversations with session memory
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- **Document structure** — Stores full [DoclingDocument](https://docling-project.github.io/docling/concepts/docling_document/), enabling structure-aware context expansion and visual grounding
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- **Multiple providers** — Embeddings: Ollama, OpenAI, VoyageAI, LM Studio, vLLM. QA/Research: any model supported by Pydantic AI
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- **Local-first** — Embedded LanceDB, no servers required. Also supports S3, GCS, Azure, and LanceDB Cloud
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|
|
@ -48,17 +49,20 @@ haiku-rag add "Your document content" --meta author=alice
|
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haiku-rag add-src /path/to/document.pdf --title "Q3 Financial Report" --meta source=manual
|
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haiku-rag search "query"
|
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haiku-rag ask "Who is the author of haiku.rag?"
|
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haiku-rag chat # Interactive conversation mode
|
||||
```
|
||||
|
||||
## Documentation
|
||||
|
||||
- [Getting started](tutorial.md) - Tutorial
|
||||
- [Installation](installation.md) - Install haiku.rag with different providers
|
||||
- [Architecture](architecture.md) - System overview and data flow
|
||||
- [Configuration](configuration/index.md) - Environment variables and settings
|
||||
- [CLI](cli.md) - Command line interface usage
|
||||
- [Python](python.md) - Python API reference
|
||||
- [Custom Pipelines](custom-pipelines.md) - Build custom processing workflows
|
||||
- [Agents](agents.md) - QA agent and multi-agent research
|
||||
- [Agents](agents.md) - QA, chat, and research agents
|
||||
- [Applications](apps.md) - Chat TUI, web app, and inspector
|
||||
- [Server](server.md) - File monitoring and server mode
|
||||
- [MCP](mcp.md) - Model Context Protocol integration
|
||||
- [Remote processing](remote-processing.md) - Remote document processing with docling-serve
|
||||
|
|
|
|||
|
|
@ -1,77 +0,0 @@
|
|||
# Database Inspector
|
||||
|
||||
Interactive TUI for browsing your LanceDB database.
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
# For haiku.rag-slim
|
||||
pip install 'haiku.rag-slim[inspector]'
|
||||
|
||||
# Already included in haiku.rag
|
||||
pip install haiku.rag
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
```bash
|
||||
haiku-rag inspect
|
||||
haiku-rag inspect --db /path/to/database.lancedb
|
||||
```
|
||||
|
||||
## Interface
|
||||
|
||||
|
||||
|
||||
Three panels display your data:
|
||||
|
||||
- **Documents** (left) - All documents in the database
|
||||
- **Chunks** (top right) - Chunks for the selected document
|
||||
- **Detail View** (bottom right) - Full content and metadata
|
||||
|
||||
## Navigation
|
||||
|
||||
**Keyboard:**
|
||||
|
||||
- `Tab` - Cycle between panels
|
||||
- `↑` / `↓` - Navigate lists
|
||||
- `/` - Open search modal
|
||||
- `c` - Open context expansion modal (when viewing a chunk)
|
||||
- `v` - Open visual grounding modal (when viewing a chunk)
|
||||
- `q` - Quit
|
||||
|
||||
**Mouse:** Click to select, scroll to view content
|
||||
|
||||
## Search
|
||||
|
||||
Press `/` to open the full-screen search modal:
|
||||
|
||||
- Enter your query and press `Enter` to search
|
||||
- **Left panel**: Search results with relevance scores `[0.95] content preview`
|
||||
- **Right panel**: Full chunk content and metadata
|
||||
- Use `↑` / `↓` to navigate results - detail view updates in real-time
|
||||
- Press `Enter` on a result to close search and navigate to that document/chunk
|
||||
- Press `Esc` to close search without selecting
|
||||
|
||||
Search uses hybrid (vector + full-text) search across all chunks. Content is rendered as markdown with syntax highlighting.
|
||||
|
||||
## Context Expansion
|
||||
|
||||
Press `c` while viewing a chunk to open the context expansion modal:
|
||||
|
||||
- Shows the expanded context that would be provided to the QA agent
|
||||
- Type-aware expansion: tables, code blocks, and lists expand to their complete structures
|
||||
- Text content expands based on `search.context_radius` setting
|
||||
- Includes metadata like source document, content type, and relevance score
|
||||
- Press `Esc` to close the modal
|
||||
|
||||
## Visual Grounding
|
||||
|
||||
Press `v` while viewing a chunk to open the visual grounding modal:
|
||||
|
||||
- Shows page images from the source document with the chunk's location highlighted in yellow/orange
|
||||
- Use `←` / `→` arrow keys to navigate between pages (when chunk spans multiple pages)
|
||||
- Press `Esc` to close the modal
|
||||
|
||||
!!! note
|
||||
Visual grounding requires documents with a stored DoclingDocument that includes page images. Text-only documents or documents imported without DoclingDocument won't have visual grounding available.
|
||||
|
|
@ -203,6 +203,7 @@ The following people are presenting talks at PyCon Finland 2025:
|
|||
|
||||
## Next Steps
|
||||
|
||||
- **[Chat](apps.md#chat-tui)** - Interactive conversations with `haiku-rag chat`
|
||||
- **[CLI Reference](cli.md)** - All available commands and options
|
||||
- **[Python API](python.md)** - Use haiku.rag in your Python applications
|
||||
- **[Agents](agents.md)** - Deep QA and multi-agent research workflows
|
||||
|
|
|
|||
|
|
@ -59,6 +59,7 @@ nav:
|
|||
- index.md
|
||||
- Getting started: tutorial.md
|
||||
- Installation: installation.md
|
||||
- Architecture: architecture.md
|
||||
- Configuration:
|
||||
- configuration/index.md
|
||||
- Providers: configuration/providers.md
|
||||
|
|
@ -71,10 +72,10 @@ nav:
|
|||
- Custom Pipelines: custom-pipelines.md
|
||||
- Tuning: tuning.md
|
||||
- Agents: agents.md
|
||||
- Applications: apps.md
|
||||
- Server: server.md
|
||||
- Remote processing: remote-processing.md
|
||||
- MCP: mcp.md
|
||||
- Inspector: inspector.md
|
||||
- Benchmarks: benchmarks.md
|
||||
- Development: development.md
|
||||
- Changelog: changelog.md
|
||||
|
|
|
|||
Loading…
Reference in a new issue