Initial skeleton for docs

2025-06-25 20:16:08 +03:00 · 2025-06-25 20:16:08 +03:00 · a704f300bc
commit a704f300bc
parent ca45f8e5d4
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--- a/docs/.DS_Store
+++ b/docs/.DS_Store
--- a/docs/cli.md
+++ b/docs/cli.md
@ -0,0 +1,74 @@
+# Command Line Interface
+
+The `haiku-rag` CLI provides complete document management functionality.
+
+## Document Management
+
+### List Documents
+
+```bash
+haiku-rag list
+```
+
+### Add Documents
+
+From text:
+```bash
+haiku-rag add "Your document content here"
+```
+
+From file or URL:
+```bash
+haiku-rag add-src /path/to/document.pdf
+haiku-rag add-src https://example.com/article.html
+```
+
+### Get Document
+
+```bash
+haiku-rag get 1
+```
+
+### Delete Document
+
+```bash
+haiku-rag delete 1
+```
+
+## Search
+
+Basic search:
+```bash
+haiku-rag search "machine learning"
+```
+
+With options:
+```bash
+haiku-rag search "python programming" --limit 10 --k 100
+```
+
+## Server
+
+Start the MCP server:
+```bash
+# HTTP transport (default)
+haiku-rag serve
+
+# stdio transport
+haiku-rag serve --stdio
+
+# SSE transport
+haiku-rag serve --sse
+```
+
+## Options
+
+All commands support:
+- `--db` - Specify custom database path
+- `-h` - Show help for specific command
+
+Example:
+```bash
+haiku-rag list --db /path/to/custom.db
+haiku-rag add -h
+```
--- a/docs/configuration.md
+++ b/docs/configuration.md
@ -0,0 +1,43 @@
+# Configuration
+
+Configuration is done through environment variables.
+
+## File Monitoring
+
+Set directories to monitor for automatic indexing:
+
+```bash
+# Monitor single directory
+export MONITOR_DIRECTORIES="/path/to/documents"
+
+# Monitor multiple directories
+export MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
+```
+
+## Embedding Providers
+
+### Ollama (Default)
+
+```bash
+EMBEDDINGS_PROVIDER="ollama"
+EMBEDDINGS_MODEL="mxbai-embed-large"
+EMBEDDINGS_VECTOR_DIM=1024
+```
+
+### VoyageAI
+
+```bash
+EMBEDDINGS_PROVIDER="voyageai"
+EMBEDDINGS_MODEL="voyage-3.5"
+EMBEDDINGS_VECTOR_DIM=1024
+VOYAGE_API_KEY="your-api-key"
+```
+
+### OpenAI
+
+```bash
+EMBEDDINGS_PROVIDER="openai"
+EMBEDDINGS_MODEL="text-embedding-3-small"  # or text-embedding-3-large
+EMBEDDINGS_VECTOR_DIM=1536
+OPENAI_API_KEY="your-api-key"
+```
--- a/docs/img/.DS_Store
+++ b/docs/img/.DS_Store
--- a/docs/index.md
+++ b/docs/index.md
@ -0,0 +1,52 @@
+# haiku.rag
+
+A Retrieval-Augmented Generation (RAG) library on SQLite.
+
+## Features
+
+- **Local SQLite**: No need to run additional servers
+- **Support for various embedding providers**: Ollama, VoyageAI, OpenAI or add your own
+- **Hybrid Search**: Vector search using `sqlite-vec` combined with full-text search `FTS5`, using Reciprocal Rank Fusion
+- **File monitoring**: Automatically index files when run as a server
+- **Extended file format support**: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a URL!
+- **MCP server**: Exposes functionality as MCP tools
+- **CLI commands**: Access all functionality from your terminal
+- **Python client**: Call `haiku.rag` from your own python applications
+
+## Quick Start
+
+Install haiku.rag:
+```bash
+uv pip install haiku.rag
+```
+
+Use from Python:
+```python
+from haiku.rag.client import HaikuRAG
+
+async with HaikuRAG("database.db") as client:
+    # Add a document
+    doc = await client.create_document("Your content here")
+
+    # Search documents
+    results = await client.search("query")
+```
+
+Or use the CLI:
+```bash
+haiku-rag add "Your document content"
+haiku-rag search "query"
+```
+
+## Documentation
+
+- [Installation](installation.md) - Install haiku.rag with different providers
+- [Configuration](configuration.md) - Environment variables and settings
+- [CLI](cli.md) - Command line interface usage
+- [Server](server.md) - File monitoring and server mode
+- [MCP](mcp.md) - Model Context Protocol integration
+- [Python](python.md) - Python API reference
+
+## License
+
+This project is licensed under the [MIT License](https://raw.githubusercontent.com/ggozad/haiku.rag/main/LICENSE).
--- a/docs/installation.md
+++ b/docs/installation.md
@ -0,0 +1,31 @@
+# Installation
+
+## Basic Installation
+
+```bash
+uv pip install haiku.rag
+```
+
+By default, Ollama (with the `mxbai-embed-large` model) is used for embeddings.
+
+## Provider-Specific Installation
+
+For other embedding providers, install with extras:
+
+### VoyageAI
+
+```bash
+uv pip install haiku.rag --extra voyageai
+```
+
+### OpenAI
+
+```bash
+uv pip install haiku.rag --extra openai
+```
+
+## Requirements
+
+- Python 3.10+
+- SQLite 3.38+
+- Ollama (for default embeddings)
--- a/docs/mcp.md
+++ b/docs/mcp.md
@ -0,0 +1,40 @@
+# Model Context Protocol (MCP)
+
+The MCP server exposes RAG functionality as tools for AI assistants like Claude Desktop.
+
+## Available Tools
+
+### Document Management
+
+- `add_document_from_file` - Add documents from local file paths
+- `add_document_from_url` - Add documents from URLs
+- `add_document_from_text` - Add documents from raw text content
+- `get_document` - Retrieve specific documents by ID
+- `list_documents` - List all documents with pagination
+- `delete_document` - Delete documents by ID
+
+### Search
+
+- `search_documents` - Search documents using hybrid search (vector + full-text)
+
+## Starting MCP Server
+
+The MCP server starts automatically with the serve command:
+
+```bash
+# Default HTTP transport
+haiku-rag serve
+
+# stdio transport (for Claude Desktop)
+haiku-rag serve --stdio
+
+# SSE transport
+haiku-rag serve --sse
+```
+
+## Integration
+
+The MCP server follows the Model Context Protocol specification, making it compatible with:
+- Claude Desktop
+- Other MCP-compatible AI assistants
+- Custom MCP clients
--- a/docs/python.md
+++ b/docs/python.md
@ -0,0 +1,101 @@
+# Python API
+
+Use `haiku.rag` directly in your Python applications.
+
+## Basic Usage
+
+```python
+from pathlib import Path
+from haiku.rag.client import HaikuRAG
+
+# Use as async context manager (recommended)
+async with HaikuRAG("path/to/database.db") as client:
+    # Your code here
+    pass
+```
+
+## Document Management
+
+### Creating Documents
+
+From text:
+```python
+doc = await client.create_document(
+    content="Your document content here",
+    uri="doc://example",
+    metadata={"source": "manual", "topic": "example"}
+)
+```
+
+From file:
+```python
+doc = await client.create_document_from_source("path/to/document.pdf")
+```
+
+From URL:
+```python
+doc = await client.create_document_from_source("https://example.com/article.html")
+```
+
+### Retrieving Documents
+
+By ID:
+```python
+doc = await client.get_document_by_id(1)
+```
+
+By URI:
+```python
+doc = await client.get_document_by_uri("file:///path/to/document.pdf")
+```
+
+List all documents:
+```python
+docs = await client.list_documents(limit=10, offset=0)
+```
+
+### Updating Documents
+
+```python
+doc.content = "Updated content"
+await client.update_document(doc)
+```
+
+### Deleting Documents
+
+```python
+await client.delete_document(doc.id)
+```
+
+## Searching Documents
+
+Basic search:
+```python
+results = await client.search("machine learning algorithms", limit=5)
+for chunk, score in results:
+    print(f"Score: {score:.3f}")
+    print(f"Content: {chunk.content}")
+    print(f"Document ID: {chunk.document_id}")
+```
+
+With options:
+```python
+results = await client.search(
+    query="machine learning",
+    limit=5,  # Maximum results to return
+    k=60      # RRF parameter for reciprocal rank fusion
+)
+
+# Process results
+for chunk, relevance_score in results:
+    print(f"Relevance: {relevance_score:.3f}")
+    print(f"Content: {chunk.content}")
+    print(f"From document: {chunk.document_id}")
+```
+
+## Search Technology
+
+`haiku.rag` uses hybrid search combining:
+- **Vector search** using `sqlite-vec` for semantic similarity
+- **Full-text search** using SQLite's `FTS5` for keyword matching
+- **Reciprocal Rank Fusion** to combine and rank results
--- a/docs/server.md
+++ b/docs/server.md
@ -0,0 +1,41 @@
+# Server Mode
+
+The server provides automatic file monitoring and MCP functionality.
+
+## Starting the Server
+
+```bash
+haiku-rag serve
+```
+
+Transport options:
+- `--http` (default) - Streamable HTTP transport
+- `--stdio` - Standard input/output transport
+- `--sse` - Server-sent events transport
+
+## File Monitoring
+
+Set `MONITOR_DIRECTORIES` environment variable to enable automatic file monitoring:
+
+```bash
+export MONITOR_DIRECTORIES="/path/to/documents"
+haiku-rag serve
+```
+
+### Monitoring Features
+
+- **Startup**: Scans all monitored directories and adds new files
+- **File Added/Modified**: Automatically parses and updates documents
+- **File Deleted**: Removes corresponding documents from database
+
+### Supported Formats
+
+The server can parse 40+ file formats including:
+- PDF documents
+- Microsoft Office (DOCX, XLSX, PPTX)
+- HTML and Markdown
+- Plain text files
+- Audio files
+- And more...
+
+URLs are also supported for web content.
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -0,0 +1,77 @@
+site_name: haiku.rag
+site_description: Retrieval-Augmented Generation (RAG) library on SQLite.
+site_url: https://ggozad.github.io/haiku.rag/
+theme:
+  name: material
+  palette:
+    - media: "(prefers-color-scheme)"
+      toggle:
+        icon: material/lightbulb-auto
+        name: Switch to light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      primary: deep purple
+      accent: amber
+      toggle:
+        icon: material/lightbulb
+        name: Switch to dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      primary: deep purple
+      accent: amber
+      toggle:
+        icon: material/lightbulb-outline
+        name: Switch to system preference
+  features:
+    - content.code.annotate
+    - content.code.copy
+    - content.code.select
+    - content.footnote.tooltips
+    - content.tabs.link
+    - content.tooltips
+    - navigation.footer
+    - navigation.indexes
+    - navigation.instant
+    - navigation.instant.prefetch
+    - navigation.instant.progress
+    - navigation.path
+    - navigation.tabs
+    - navigation.tabs.sticky
+    - navigation.top
+    - navigation.tracking
+    - search.highlight
+    - search.share
+    - search.suggest
+    - toc.follow
+
+  icon:
+    repo: fontawesome/brands/github-alt
+  # logo: img/icon-white.svg
+  # favicon: img/favicon.png
+  language: en
+repo_name: ggozad/haiku.rag
+repo_url: https://github.com/ggozad/haiku.rag
+plugins:
+  # Material for MkDocs
+  search:
+nav:
+  - haiku.rag:
+      - index.md
+      - Installation: installation.md
+      - Configuration: configuration.md
+      - CLI: cli.md
+      - Server: server.md
+      - MCP: mcp.md
+      - Python: python.md
+markdown_extensions:
+  - admonition
+  - attr_list
+  - pymdownx.details
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+      use_pygments: true
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.superfences
--- a/pyproject.toml
+++ b/pyproject.toml
@ -52,6 +52,8 @@ packages = ["src/haiku"]
 [dependency-groups]
 dev = [
    "datasets>=3.6.0",
+    "mkdocs>=1.6.1",
+    "mkdocs-material>=9.6.14",
    "pre-commit>=4.2.0",
    "pyright>=1.1.402",
    "pytest>=8.4.0",
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@ -819,6 +856,8 @@ provides-extras = ["voyageai", "openai"]
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