Merge pull request #7 from ggozad/feat/documentation
Documentation site on GitHub Pages
This commit is contained in:
Yiorgis Gozadinos
GitHub
commit
e2fa20ce3a
28 changed files with 1193 additions and 179 deletions
28
.github/workflows/build-docs.yml
vendored
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28
.github/workflows/build-docs.yml
vendored
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@ -0,0 +1,28 @@
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name: build-docs
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on:
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push:
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branches:
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- main
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permissions:
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contents: write
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jobs:
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deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- name: Configure Git Credentials
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run: |
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git config user.name github-actions[bot]
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git config user.email 41898282+github-actions[bot]@users.noreply.github.com
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- uses: actions/setup-python@v5
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with:
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python-version: 3.x
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- run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV
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- uses: actions/cache@v4
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with:
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key: mkdocs-material-${{ env.cache_id }}
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path: .cache
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restore-keys: |
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mkdocs-material-
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- run: pip install mkdocs-material
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- run: mkdocs gh-deploy --force
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@ -20,3 +20,13 @@ repos:
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rev: v1.1.399
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hooks:
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- id: pyright
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- repo: https://github.com/RodrigoGonzalez/check-mkdocs
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rev: v1.2.0
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hooks:
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- id: check-mkdocs
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name: check-mkdocs
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args: ["--config", "mkdocs.yml"] # Optional, mkdocs.yml is the default
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# If you have additional plugins or libraries that are not included in
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# check-mkdocs, add them here
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additional_dependencies: ["mkdocs-material"]
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|
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9
BENCHMARKS.md
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9
BENCHMARKS.md
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# `haiku.rag` benchmarks
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We use [repliqa](https://huggingface.co/datasets/ServiceNow/repliqa) for the evaluation of `haiku.rag`
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* Recall
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We load the `News Stories` from `repliqa_3` which is 1035 documents, using `tests/generate_benchmark_db.py`, using the `mxbai-embed-large` Ollama embeddings.
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Subsequently, we run a search over the `question` for each row of the dataset and check whether we match the document that answers the question. The recall obtained is ~0.75 for matching in the top result, raising to ~0.75 for the top 3 results.
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227
README.md
227
README.md
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@ -1,194 +1,67 @@
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# Haiku SQLite RAG
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A Retrieval-Augmented Generation (RAG) library on SQLite.
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Retrieval-Augmented Generation (RAG) library on SQLite.
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`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
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## Features
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- **Local SQLite**: No need to run additional servers
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- **Support for various embedding providers**: You can use Ollama, VoyageAI, OpenAI or add your own
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- **Hybrid Search**: Vector search using `sqlite-vec` combined with full-text search `FTS5`, using Reciprocal Rank Fusion
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- **File monitoring** when run as a server automatically indexing your files
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- **Extended file format Support**: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, audio and more. Or add a url!
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- **MCP server** Exposes functionality as MCP tools.
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- **CLI commands** Access all functionality from your terminal
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- **Python client** Call `haiku.rag` from your own python applications.
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|
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## Installation
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- **Local SQLite**: No external servers required
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- **Multiple embedding providers**: Ollama, VoyageAI, OpenAI
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- **Hybrid search**: Vector + full-text search with Reciprocal Rank Fusion
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- **File monitoring**: Auto-index files when run as server
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- **40+ file formats**: PDF, DOCX, HTML, Markdown, audio, URLs
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- **MCP server**: Expose as tools for AI assistants
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- **CLI & Python API**: Use from command line or Python
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## Quick Start
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```bash
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# Install
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uv pip install haiku.rag
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# Add documents
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haiku-rag add "Your content here"
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haiku-rag add-src document.pdf
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# Search
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haiku-rag search "query"
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# Start server with file monitoring
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export MONITOR_DIRECTORIES="/path/to/docs"
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haiku-rag serve
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```
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By default Ollama (with the `mxbai-embed-large` model) is used for the embeddings.
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For other providers use:
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- **VoyageAI**: `uv pip install haiku.rag --extra voyageai`
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- **OpenAI**: `uv pip install haiku.rag --extra openai`
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## Configuration
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You can set the directories to monitor using the `MONITOR_DIRECTORIES` environment variable (as comma separated values) :
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```bash
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# Monitor single directory
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export MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
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```
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If you want to use an alternative embeddings provider (Ollama being the default) you will need to set the provider details through environment variables:
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By default:
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```bash
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EMBEDDINGS_PROVIDER="ollama"
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EMBEDDINGS_MODEL="mxbai-embed-large" # or any other model
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EMBEDDINGS_VECTOR_DIM=1024
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```
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|
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For VoyageAI:
|
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```bash
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EMBEDDINGS_PROVIDER="voyageai"
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EMBEDDINGS_MODEL="voyage-3.5" # or any other model
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EMBEDDINGS_VECTOR_DIM=1024
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VOYAGE_API_KEY="your-api-key"
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```
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|
||||
For OpenAI:
|
||||
```bash
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EMBEDDINGS_PROVIDER="openai"
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EMBEDDINGS_MODEL="text-embedding-3-small" # or text-embedding-3-large
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EMBEDDINGS_VECTOR_DIM=1536
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OPENAI_API_KEY="your-api-key"
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```
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## Command Line Interface
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`haiku.rag` includes a CLI application for managing documents and performing searches from the command line:
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### Available Commands
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```bash
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# List all documents
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haiku-rag list
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# Add document from text
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haiku-rag add "Your document content here"
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# Add document from file or URL
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haiku-rag add-src /path/to/document.pdf
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haiku-rag add-src https://example.com/article.html
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# Get and display a specific document
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haiku-rag get 1
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# Delete a document by ID
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haiku-rag delete 1
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# Search documents
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haiku-rag search "machine learning"
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# Search with custom options
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haiku-rag search "python programming" --limit 10 --k 100
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# Start file monitoring & MCP server (default HTTP transport)
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haiku-rag serve # --stdio for stdio transport or --sse for SSE transport
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```
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All commands support the `--db` option to specify a custom database path. Run
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```bash
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haiku-rag command -h
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```
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to see additional parameters for a command.
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## File Monitoring & MCP server
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You can start the server (using Streamble HTTP, stdio or SSE transports) with:
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```bash
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# Start with default HTTP transport
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haiku-rag serve # --stdio for stdio transport or --sse for SSE transport
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```
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You need to have set the `MONITOR_DIRECTORIES` environment variable for monitoring to take place.
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### File monitoring
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`haiku.rag` can watch directories for changes and automatically update the document store:
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|
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- **Startup**: Scan all monitored directories and add any new files
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- **File Added/Modified**: Automatically parse and add/update the document in the database
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||||
- **File Deleted**: Remove the corresponding document from the database
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### MCP Server
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`haiku.rag` includes a Model Context Protocol (MCP) server that exposes RAG functionality as tools for AI assistants like Claude Desktop. The MCP server provides the following tools:
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- `add_document_from_file` - Add documents from local file paths
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- `add_document_from_url` - Add documents from URLs
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- `add_document_from_text` - Add documents from raw text content
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- `search_documents` - Search documents using hybrid search
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- `get_document` - Retrieve specific documents by ID
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- `list_documents` - List all documents with pagination
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- `delete_document` - Delete documents by ID
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## Using `haiku.rag` from python
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### Managing documents
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## Python Usage
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```python
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from pathlib import Path
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from haiku.rag.client import HaikuRAG
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# Use as async context manager (recommended)
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async with HaikuRAG("path/to/database.db") as client:
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# Create document from text
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doc = await client.create_document(
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content="Your document content here",
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uri="doc://example",
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metadata={"source": "manual", "topic": "example"}
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)
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# Create document from file (auto-parses content)
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doc = await client.create_document_from_source("path/to/document.pdf")
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# Create document from URL
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doc = await client.create_document_from_source("https://example.com/article.html")
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# Retrieve documents
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doc = await client.get_document_by_id(1)
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doc = await client.get_document_by_uri("file:///path/to/document.pdf")
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# List all documents with pagination
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docs = await client.list_documents(limit=10, offset=0)
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# Update document content
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doc.content = "Updated content"
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await client.update_document(doc)
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# Delete document
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await client.delete_document(doc.id)
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# Search documents using hybrid search (vector + full-text)
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results = await client.search("machine learning algorithms", limit=5)
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for chunk, score in results:
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print(f"Score: {score:.3f}")
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print(f"Content: {chunk.content}")
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print(f"Document ID: {chunk.document_id}")
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print("---")
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```
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## Searching documents
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```python
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async with HaikuRAG("database.db") as client:
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# Add document
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doc = await client.create_document("Your content")
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results = await client.search(
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query="machine learning",
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limit=5, # Maximum results to return, defaults to 5
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k=60 # RRF parameter for reciprocal rank fusion, defaults to 60
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)
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# Process results
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for chunk, relevance_score in results:
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print(f"Relevance: {relevance_score:.3f}")
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print(f"Content: {chunk.content}")
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print(f"From document: {chunk.document_id}")
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# Search
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results = await client.search("query")
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for chunk, score in results:
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print(f"{score:.3f}: {chunk.content}")
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```
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|
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## MCP Server
|
||||
|
||||
Use with AI assistants like Claude Desktop:
|
||||
|
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```bash
|
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haiku-rag serve --stdio
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```
|
||||
|
||||
Provides tools for document management and search directly in your AI assistant.
|
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|
||||
## Documentation
|
||||
|
||||
Full documentation at: https://ggozad.github.io/haiku.rag/
|
||||
|
||||
- [Installation](https://ggozad.github.io/haiku.rag/installation/) - Provider setup
|
||||
- [Configuration](https://ggozad.github.io/haiku.rag/configuration/) - Environment variables
|
||||
- [CLI](https://ggozad.github.io/haiku.rag/cli/) - Command reference
|
||||
- [Python API](https://ggozad.github.io/haiku.rag/python/) - Complete API docs
|
||||
|
|
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BIN
docs/.DS_Store
vendored
Normal file
BIN
docs/.DS_Store
vendored
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74
docs/cli.md
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74
docs/cli.md
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# Command Line Interface
|
||||
|
||||
The `haiku-rag` CLI provides complete document management functionality.
|
||||
|
||||
## Document Management
|
||||
|
||||
### List Documents
|
||||
|
||||
```bash
|
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haiku-rag list
|
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```
|
||||
|
||||
### 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
|
||||
```
|
||||
57
docs/configuration.md
Normal file
57
docs/configuration.md
Normal file
|
|
@ -0,0 +1,57 @@
|
|||
# Configuration
|
||||
|
||||
Configuration is done through the use of environment variables.
|
||||
|
||||
## File Monitoring
|
||||
|
||||
Set directories to monitor for automatic indexing:
|
||||
|
||||
```bash
|
||||
# Monitor single directory
|
||||
MONITOR_DIRECTORIES="/path/to/documents"
|
||||
|
||||
# Monitor multiple directories
|
||||
MONITOR_DIRECTORIES="/path/to/documents,/another_path/to/documents"
|
||||
```
|
||||
|
||||
## Embedding Providers
|
||||
|
||||
If you use Ollama, you can use any pulled model that supports embeddings.
|
||||
|
||||
### Ollama (Default)
|
||||
|
||||
```bash
|
||||
EMBEDDINGS_PROVIDER="ollama"
|
||||
EMBEDDINGS_MODEL="mxbai-embed-large"
|
||||
EMBEDDINGS_VECTOR_DIM=1024
|
||||
```
|
||||
|
||||
### VoyageAI
|
||||
If you want to use VoyageAI embeddings you will need to install `haiku.rag` with the VoyageAI extras,
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag --extra voyageai
|
||||
```
|
||||
|
||||
```bash
|
||||
EMBEDDINGS_PROVIDER="voyageai"
|
||||
EMBEDDINGS_MODEL="voyage-3.5"
|
||||
EMBEDDINGS_VECTOR_DIM=1024
|
||||
VOYAGE_API_KEY="your-api-key"
|
||||
```
|
||||
|
||||
### OpenAI
|
||||
If you want to use OpenAI embeddings you will need to install `haiku.rag` with the VoyageAI extras,
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag --extra openai
|
||||
```
|
||||
|
||||
and set environment variables.
|
||||
|
||||
```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"
|
||||
```
|
||||
BIN
docs/img/.DS_Store
vendored
Normal file
BIN
docs/img/.DS_Store
vendored
Normal file
Binary file not shown.
53
docs/index.md
Normal file
53
docs/index.md
Normal file
|
|
@ -0,0 +1,53 @@
|
|||
# haiku.rag
|
||||
|
||||
`haiku.rag` is a Retrieval-Augmented Generation (RAG) library built to work on SQLite alone without the need for external vector databases. It uses [sqlite-vec](https://github.com/asg017/sqlite-vec) for storing the embeddings and performs semantic (vector) search as well as full-text search combined through Reciprocal Rank Fusion. Both open-source (Ollama) as well as commercial (OpenAI, VoyageAI) embedding providers are supported.
|
||||
|
||||
|
||||
## 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).
|
||||
31
docs/installation.md
Normal file
31
docs/installation.md
Normal file
|
|
@ -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)
|
||||
33
docs/mcp.md
Normal file
33
docs/mcp.md
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
# Model Context Protocol (MCP)
|
||||
|
||||
The MCP server exposes `haiku.rag` as MCP tools for compatible MCP clients.
|
||||
|
||||
## 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 and supports `Streamable HTTP`, `stdio` and `SSE` transports:
|
||||
|
||||
```bash
|
||||
# Default HTTP transport
|
||||
haiku-rag serve
|
||||
|
||||
# stdio transport (for Claude Desktop)
|
||||
haiku-rag serve --stdio
|
||||
|
||||
# SSE transport
|
||||
haiku-rag serve --sse
|
||||
```
|
||||
94
docs/python.md
Normal file
94
docs/python.md
Normal file
|
|
@ -0,0 +1,94 @@
|
|||
# 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}")
|
||||
```
|
||||
41
docs/server.md
Normal file
41
docs/server.md
Normal file
|
|
@ -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.
|
||||
77
mkdocs.yml
Normal file
77
mkdocs.yml
Normal file
|
|
@ -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
|
||||
|
|
@ -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",
|
||||
|
|
|
|||
|
|
@ -19,6 +19,9 @@ class AppConfig(BaseModel):
|
|||
EMBEDDINGS_MODEL: str = "mxbai-embed-large"
|
||||
EMBEDDINGS_VECTOR_DIM: int = 1024
|
||||
|
||||
QA_PROVIDER: str = "ollama"
|
||||
QA_MODEL: str = "qwen3"
|
||||
|
||||
CHUNK_SIZE: int = 256
|
||||
CHUNK_OVERLAP: int = 32
|
||||
|
||||
|
|
|
|||
|
|
@ -49,7 +49,6 @@ class FileWatcher:
|
|||
try:
|
||||
uri = file.as_uri()
|
||||
existing_doc = await self.client.get_document_by_uri(uri)
|
||||
print(uri)
|
||||
if existing_doc:
|
||||
doc = await self.client.create_document_from_source(str(file))
|
||||
logger.info(f"Updated document {existing_doc.id} from {file}")
|
||||
|
|
|
|||
0
src/haiku/rag/qa/__init__.py
Normal file
0
src/haiku/rag/qa/__init__.py
Normal file
16
src/haiku/rag/qa/base.py
Normal file
16
src/haiku/rag/qa/base.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
from haiku.rag.client import HaikuRAG
|
||||
from haiku.rag.qa.prompts import SYSTEM_PROMPT
|
||||
|
||||
|
||||
class QABase:
|
||||
_model: str = ""
|
||||
_system_prompt: str = SYSTEM_PROMPT
|
||||
|
||||
def __init__(self, client: HaikuRAG, model: str = ""):
|
||||
self._model = model
|
||||
self._client = client
|
||||
|
||||
async def answer(self, question: str) -> str:
|
||||
raise NotImplementedError(
|
||||
"QABase is an abstract class. Please implement the answer method in a subclass."
|
||||
)
|
||||
91
src/haiku/rag/qa/ollama.py
Normal file
91
src/haiku/rag/qa/ollama.py
Normal file
|
|
@ -0,0 +1,91 @@
|
|||
from ollama import AsyncClient
|
||||
|
||||
from haiku.rag.client import HaikuRAG
|
||||
from haiku.rag.config import Config
|
||||
from haiku.rag.qa.base import QABase
|
||||
|
||||
OLLAMA_OPTIONS = {"temperature": 0.0, "seed": 42, "num_ctx": 64000}
|
||||
|
||||
|
||||
class QA(QABase):
|
||||
def __init__(self, client: HaikuRAG, model: str = Config.QA_MODEL):
|
||||
super().__init__(client, model or self._model)
|
||||
|
||||
async def answer(self, question: str) -> str:
|
||||
ollama_client = AsyncClient(host=Config.OLLAMA_BASE_URL)
|
||||
|
||||
# Define the search tool
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "search_documents",
|
||||
"description": "Search the knowledge base for relevant documents",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "The search query to find relevant documents",
|
||||
},
|
||||
"limit": {
|
||||
"type": "integer",
|
||||
"description": "Maximum number of results to return",
|
||||
"default": 3,
|
||||
},
|
||||
},
|
||||
"required": ["query"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": self._system_prompt},
|
||||
{"role": "user", "content": question},
|
||||
]
|
||||
|
||||
# Initial response with tool calling
|
||||
response = await ollama_client.chat(
|
||||
model=self._model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
options=OLLAMA_OPTIONS,
|
||||
think=False,
|
||||
)
|
||||
|
||||
if response.get("message", {}).get("tool_calls"):
|
||||
for tool_call in response["message"]["tool_calls"]:
|
||||
if tool_call["function"]["name"] == "search_documents":
|
||||
args = tool_call["function"]["arguments"]
|
||||
query = args.get("query", question)
|
||||
limit = int(args.get("limit", 3))
|
||||
|
||||
search_results = await self._client.search(query, limit=limit)
|
||||
|
||||
context_chunks = []
|
||||
for chunk, score in search_results:
|
||||
context_chunks.append(
|
||||
f"Content: {chunk.content}\nScore: {score:.4f}"
|
||||
)
|
||||
|
||||
context = "\n\n".join(context_chunks)
|
||||
|
||||
messages.append(response["message"])
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"content": context,
|
||||
"tool_call_id": tool_call.get("id", "search_tool"),
|
||||
}
|
||||
)
|
||||
|
||||
final_response = await ollama_client.chat(
|
||||
model=self._model,
|
||||
messages=messages,
|
||||
think=False,
|
||||
options=OLLAMA_OPTIONS,
|
||||
)
|
||||
return final_response["message"]["content"]
|
||||
else:
|
||||
return response["message"]["content"]
|
||||
7
src/haiku/rag/qa/prompts.py
Normal file
7
src/haiku/rag/qa/prompts.py
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
SYSTEM_PROMPT = """
|
||||
You are a helpful assistant that uses a RAG library to answer the user's prompt.
|
||||
Your task is to provide a concise and accurate answer based on the provided context.
|
||||
You should ask the provided tools to find relevant documents and then use the content of those documents to answer the question.
|
||||
Never make up information, always use the context to answer the question.
|
||||
If the context does not contain enough information to answer the question, respond with "I cannot answer that based on the provided context."
|
||||
"""
|
||||
|
|
@ -325,7 +325,6 @@ class ChunkRepository(BaseRepository[Chunk]):
|
|||
words = re.findall(r"\b\w+\b", query.lower())
|
||||
# Join with OR to find chunks containing any of the keywords
|
||||
fts_query = " OR ".join(words) if words else query
|
||||
|
||||
# Perform hybrid search using RRF (Reciprocal Rank Fusion)
|
||||
cursor.execute(
|
||||
"""
|
||||
|
|
|
|||
0
tests/__init__.py
Normal file
0
tests/__init__.py
Normal file
|
|
@ -3,6 +3,8 @@ from pathlib import Path
|
|||
import pytest
|
||||
from datasets import Dataset, load_dataset, load_from_disk
|
||||
|
||||
from .llm_judge import LLMJudge
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def qa_corpus() -> Dataset:
|
||||
|
|
@ -16,3 +18,8 @@ def qa_corpus() -> Dataset:
|
|||
corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
|
||||
corpus.save_to_disk(ds_path)
|
||||
return corpus
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def llm_judge() -> LLMJudge:
|
||||
return LLMJudge()
|
||||
|
|
|
|||
129
tests/generate_benchmark_db.py
Normal file
129
tests/generate_benchmark_db.py
Normal file
|
|
@ -0,0 +1,129 @@
|
|||
import asyncio
|
||||
from pathlib import Path
|
||||
|
||||
from datasets import Dataset, load_dataset
|
||||
from llm_judge import LLMJudge
|
||||
from tqdm import tqdm
|
||||
|
||||
from haiku.rag.client import HaikuRAG
|
||||
from haiku.rag.qa.ollama import QA
|
||||
|
||||
db_path = Path(__file__).parent / "data" / "benchmark.sqlite"
|
||||
|
||||
|
||||
async def populate_db():
|
||||
if (db_path).exists():
|
||||
print("Benchmark database already exists. Skipping creation.")
|
||||
return
|
||||
|
||||
ds: Dataset = load_dataset("ServiceNow/repliqa")["repliqa_3"] # type: ignore
|
||||
corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
|
||||
|
||||
async with HaikuRAG(db_path) as rag:
|
||||
for i, doc in enumerate(tqdm(corpus)):
|
||||
await rag.create_document(
|
||||
content=doc["document_extracted"], # type: ignore
|
||||
uri=doc["document_id"], # type: ignore
|
||||
)
|
||||
|
||||
|
||||
async def run_match_benchmark():
|
||||
ds: Dataset = load_dataset("ServiceNow/repliqa")["repliqa_3"] # type: ignore
|
||||
corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
|
||||
|
||||
correct_at_1 = 0
|
||||
correct_at_2 = 0
|
||||
correct_at_3 = 0
|
||||
total_queries = 0
|
||||
|
||||
async with HaikuRAG(db_path) as rag:
|
||||
for i, doc in enumerate(tqdm(corpus)):
|
||||
doc_id = doc["document_id"] # type: ignore
|
||||
matches = await rag.search(
|
||||
query=doc["question"], # type: ignore
|
||||
limit=3,
|
||||
)
|
||||
|
||||
total_queries += 1
|
||||
|
||||
# Check position of correct document in results
|
||||
for position, (chunk, _) in enumerate(matches):
|
||||
retrieved = await rag.get_document_by_id(chunk.document_id)
|
||||
if retrieved and retrieved.uri == doc_id:
|
||||
if position == 0: # First position
|
||||
correct_at_1 += 1
|
||||
correct_at_2 += 1
|
||||
correct_at_3 += 1
|
||||
elif position == 1: # Second position
|
||||
correct_at_2 += 1
|
||||
correct_at_3 += 1
|
||||
elif position == 2: # Third position
|
||||
correct_at_3 += 1
|
||||
break
|
||||
|
||||
# Calculate recall metrics
|
||||
recall_at_1 = correct_at_1 / total_queries
|
||||
recall_at_2 = correct_at_2 / total_queries
|
||||
recall_at_3 = correct_at_3 / total_queries
|
||||
|
||||
print("\n=== Retrieval Benchmark Results ===")
|
||||
print(f"Total queries: {total_queries}")
|
||||
print(f"Recall@1: {recall_at_1:.4f}")
|
||||
print(f"Recall@2: {recall_at_2:.4f}")
|
||||
print(f"Recall@3: {recall_at_3:.4f}")
|
||||
|
||||
return {"recall@1": recall_at_1, "recall@2": recall_at_2, "recall@3": recall_at_3}
|
||||
|
||||
|
||||
async def run_qa_benchmark(k: int | None = None):
|
||||
"""Run QA benchmarking on the corpus."""
|
||||
ds: Dataset = load_dataset("ServiceNow/repliqa")["repliqa_3"] # type: ignore
|
||||
corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
|
||||
|
||||
if k is not None:
|
||||
corpus = corpus.select(range(min(k, len(corpus))))
|
||||
|
||||
judge = LLMJudge()
|
||||
correct_answers = 0
|
||||
total_questions = 0
|
||||
|
||||
async with HaikuRAG(db_path) as rag:
|
||||
qa = QA(rag)
|
||||
|
||||
for i, doc in enumerate(tqdm(corpus, desc="QA Benchmarking")):
|
||||
question = doc["question"] # type: ignore
|
||||
expected_answer = doc["answer"] # type: ignore
|
||||
|
||||
generated_answer = await qa.answer(question)
|
||||
is_equivalent = await judge.judge_answers(
|
||||
question, generated_answer, expected_answer
|
||||
)
|
||||
print(f"Question: {question}")
|
||||
print(f"Expected: {expected_answer}")
|
||||
print(f"Generated: {generated_answer}")
|
||||
print(f"Equivalent: {is_equivalent}\n")
|
||||
|
||||
if is_equivalent:
|
||||
correct_answers += 1
|
||||
total_questions += 1
|
||||
|
||||
accuracy = correct_answers / total_questions if total_questions > 0 else 0
|
||||
|
||||
print("\n=== QA Benchmark Results ===")
|
||||
print(f"Total questions: {total_questions}")
|
||||
print(f"Correct answers: {correct_answers}")
|
||||
print(f"QA Accuracy: {accuracy:.4f} ({accuracy * 100:.2f}%)")
|
||||
|
||||
|
||||
async def main():
|
||||
await populate_db()
|
||||
|
||||
print("Running retrieval benchmarks...")
|
||||
await run_match_benchmark()
|
||||
|
||||
print("\nRunning QA benchmarks...")
|
||||
await run_qa_benchmark()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
68
tests/llm_judge.py
Normal file
68
tests/llm_judge.py
Normal file
|
|
@ -0,0 +1,68 @@
|
|||
import json
|
||||
|
||||
from ollama import AsyncClient
|
||||
from pydantic import BaseModel
|
||||
|
||||
from haiku.rag.config import Config
|
||||
|
||||
|
||||
class LLMJudgeResponseSchema(BaseModel):
|
||||
equivalent: bool
|
||||
|
||||
|
||||
class LLMJudge:
|
||||
"""LLM-as-judge for evaluating answer equivalence using Ollama."""
|
||||
|
||||
def __init__(self, model: str = "qwen3"):
|
||||
self.model = model
|
||||
self.client = AsyncClient(host=Config.OLLAMA_BASE_URL)
|
||||
|
||||
async def judge_answers(
|
||||
self, question: str, answer: str, expected_answer: str
|
||||
) -> bool:
|
||||
"""
|
||||
Judge whether two answers are equivalent for a given question.
|
||||
|
||||
Args:
|
||||
question: The original question
|
||||
answer: The generated answer to evaluate
|
||||
expected_answer: The reference/expected answer
|
||||
|
||||
Returns:
|
||||
Dictionary with judgment result:
|
||||
- equivalent: bool indicating if answers are equivalent
|
||||
- explanation: str explaining the reasoning
|
||||
- score: str rating from 1-5
|
||||
"""
|
||||
|
||||
prompt = f"""
|
||||
You are an expert judge evaluating the equivalence of two answers to the same question.
|
||||
|
||||
Question: {question}
|
||||
|
||||
Generated Answer: {answer}
|
||||
|
||||
Expected Answer: {expected_answer}
|
||||
|
||||
Your task is to determine if these two answers are equivalent in meaning and both correctly answer the question. Consider:
|
||||
|
||||
1. Do both answers provide the same answer?
|
||||
2. Do both answers directly address the question asked?
|
||||
3. Minor differences in wording or style are acceptable if the meaning of the answer is the same.
|
||||
|
||||
Be strict but fair in your evaluation. Focus on factual correctness and whether both answers would satisfy someone asking the question."""
|
||||
|
||||
response = await self.client.chat(
|
||||
model=self.model,
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
format=LLMJudgeResponseSchema.model_json_schema(),
|
||||
think=False,
|
||||
)
|
||||
|
||||
answer = response["message"]["content"].strip()
|
||||
try:
|
||||
res = json.loads(answer)
|
||||
assert "equivalent" in res, "Response must contain 'equivalent' key"
|
||||
return res["equivalent"]
|
||||
except json.JSONDecodeError:
|
||||
assert False, "Response is not valid JSON"
|
||||
42
tests/test_qa.py
Normal file
42
tests/test_qa.py
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
from typing import TYPE_CHECKING
|
||||
|
||||
import pytest
|
||||
from datasets import Dataset
|
||||
|
||||
from haiku.rag.client import HaikuRAG
|
||||
from haiku.rag.qa.ollama import QA
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.append(str(Path(__file__).parent))
|
||||
from llm_judge import LLMJudge
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_qa_with_dataset_question(qa_corpus: Dataset, llm_judge: "LLMJudge"):
|
||||
"""Test QA with actual question from the dataset using LLM judge."""
|
||||
client = HaikuRAG(":memory:")
|
||||
qa = QA(client)
|
||||
|
||||
# Use the first document from the corpus
|
||||
doc = qa_corpus[1]
|
||||
|
||||
# Add the document to database
|
||||
await client.create_document(
|
||||
content=doc["document_extracted"], uri=doc["document_id"]
|
||||
)
|
||||
|
||||
question = doc["question"]
|
||||
expected_answer = doc["answer"]
|
||||
|
||||
answer = await qa.answer(question)
|
||||
# Use LLM judge to evaluate answer equivalence
|
||||
is_equivalent = await llm_judge.judge_answers(question, answer, expected_answer)
|
||||
|
||||
assert isinstance(answer, str)
|
||||
assert len(answer) > 0
|
||||
assert is_equivalent, (
|
||||
f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
|
||||
)
|
||||
271
uv.lock
271
uv.lock
|
|
@ -218,6 +218,29 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/84/29/587c189bbab1ccc8c86a03a5d0e13873df916380ef1be461ebe6acebf48d/authlib-1.6.0-py2.py3-none-any.whl", hash = "sha256:91685589498f79e8655e8a8947431ad6288831d643f11c55c2143ffcc738048d", size = 239981, upload-time = "2025-05-23T00:21:43.075Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "babel"
|
||||
version = "2.17.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852, upload-time = "2025-02-01T15:17:41.026Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537, upload-time = "2025-02-01T15:17:37.39Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "backrefs"
|
||||
version = "5.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/eb/a7/312f673df6a79003279e1f55619abbe7daebbb87c17c976ddc0345c04c7b/backrefs-5.9.tar.gz", hash = "sha256:808548cb708d66b82ee231f962cb36faaf4f2baab032f2fbb783e9c2fdddaa59", size = 5765857, upload-time = "2025-06-22T19:34:13.97Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/19/4d/798dc1f30468134906575156c089c492cf79b5a5fd373f07fe26c4d046bf/backrefs-5.9-py310-none-any.whl", hash = "sha256:db8e8ba0e9de81fcd635f440deab5ae5f2591b54ac1ebe0550a2ca063488cd9f", size = 380267, upload-time = "2025-06-22T19:34:05.252Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/55/07/f0b3375bf0d06014e9787797e6b7cc02b38ac9ff9726ccfe834d94e9991e/backrefs-5.9-py311-none-any.whl", hash = "sha256:6907635edebbe9b2dc3de3a2befff44d74f30a4562adbb8b36f21252ea19c5cf", size = 392072, upload-time = "2025-06-22T19:34:06.743Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/9d/12/4f345407259dd60a0997107758ba3f221cf89a9b5a0f8ed5b961aef97253/backrefs-5.9-py312-none-any.whl", hash = "sha256:7fdf9771f63e6028d7fee7e0c497c81abda597ea45d6b8f89e8ad76994f5befa", size = 397947, upload-time = "2025-06-22T19:34:08.172Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/10/bf/fa31834dc27a7f05e5290eae47c82690edc3a7b37d58f7fb35a1bdbf355b/backrefs-5.9-py313-none-any.whl", hash = "sha256:cc37b19fa219e93ff825ed1fed8879e47b4d89aa7a1884860e2db64ccd7c676b", size = 399843, upload-time = "2025-06-22T19:34:09.68Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/fc/24/b29af34b2c9c41645a9f4ff117bae860291780d73880f449e0b5d948c070/backrefs-5.9-py314-none-any.whl", hash = "sha256:df5e169836cc8acb5e440ebae9aad4bf9d15e226d3bad049cf3f6a5c20cc8dc9", size = 411762, upload-time = "2025-06-22T19:34:11.037Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/41/ff/392bff89415399a979be4a65357a41d92729ae8580a66073d8ec8d810f98/backrefs-5.9-py39-none-any.whl", hash = "sha256:f48ee18f6252b8f5777a22a00a09a85de0ca931658f1dd96d4406a34f3748c60", size = 380265, upload-time = "2025-06-22T19:34:12.405Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "beautifulsoup4"
|
||||
version = "4.13.4"
|
||||
|
|
@ -752,6 +775,18 @@ http = [
|
|||
{ name = "aiohttp" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ghp-import"
|
||||
version = "2.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "python-dateutil" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d9/29/d40217cbe2f6b1359e00c6c307bb3fc876ba74068cbab3dde77f03ca0dc4/ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343", size = 10943, upload-time = "2022-05-02T15:47:16.11Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619", size = 11034, upload-time = "2022-05-02T15:47:14.552Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
|
|
@ -790,6 +825,8 @@ voyageai = [
|
|||
[package.dev-dependencies]
|
||||
dev = [
|
||||
{ name = "datasets" },
|
||||
{ name = "mkdocs" },
|
||||
{ name = "mkdocs-material" },
|
||||
{ name = "pre-commit" },
|
||||
{ name = "pyright" },
|
||||
{ name = "pytest" },
|
||||
|
|
@ -819,6 +856,8 @@ provides-extras = ["voyageai", "openai"]
|
|||
[package.metadata.requires-dev]
|
||||
dev = [
|
||||
{ name = "datasets", specifier = ">=3.6.0" },
|
||||
{ name = "mkdocs", specifier = ">=1.6.1" },
|
||||
{ name = "mkdocs-material", specifier = ">=9.6.14" },
|
||||
{ name = "pre-commit", specifier = ">=4.2.0" },
|
||||
{ name = "pyright", specifier = ">=1.1.402" },
|
||||
{ name = "pytest", specifier = ">=8.4.0" },
|
||||
|
|
@ -937,6 +976,18 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760", size = 6050, upload-time = "2025-03-19T20:10:01.071Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jinja2"
|
||||
version = "3.1.6"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "markupsafe" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "jiter"
|
||||
version = "0.10.0"
|
||||
|
|
@ -1122,6 +1173,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/be/0c/3153f159b78e368ac473a00e955d69d976e4b69740ed07c76c9f72a161b8/mammoth-1.9.1-py2.py3-none-any.whl", hash = "sha256:f0569bd640cee6c77a07e7c75c5dc10d745dc4dc95d530cfcbb0a5d9536d636c", size = 52991, upload-time = "2025-05-28T19:17:54.62Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "markdown"
|
||||
version = "3.8.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/d7/c2/4ab49206c17f75cb08d6311171f2d65798988db4360c4d1485bd0eedd67c/markdown-3.8.2.tar.gz", hash = "sha256:247b9a70dd12e27f67431ce62523e675b866d254f900c4fe75ce3dda62237c45", size = 362071, upload-time = "2025-06-19T17:12:44.483Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/96/2b/34cc11786bc00d0f04d0f5fdc3a2b1ae0b6239eef72d3d345805f9ad92a1/markdown-3.8.2-py3-none-any.whl", hash = "sha256:5c83764dbd4e00bdd94d85a19b8d55ccca20fe35b2e678a1422b380324dd5f24", size = 106827, upload-time = "2025-06-19T17:12:42.994Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "markdown-it-py"
|
||||
version = "3.0.0"
|
||||
|
|
@ -1184,6 +1244,64 @@ xlsx = [
|
|||
{ name = "pandas" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "markupsafe"
|
||||
version = "3.0.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b2/97/5d42485e71dfc078108a86d6de8fa46db44a1a9295e89c5d6d4a06e23a62/markupsafe-3.0.2.tar.gz", hash = "sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0", size = 20537, upload-time = "2024-10-18T15:21:54.129Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/04/90/d08277ce111dd22f77149fd1a5d4653eeb3b3eaacbdfcbae5afb2600eebd/MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8", size = 14357, upload-time = "2024-10-18T15:20:51.44Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/04/e1/6e2194baeae0bca1fae6629dc0cbbb968d4d941469cbab11a3872edff374/MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158", size = 12393, upload-time = "2024-10-18T15:20:52.426Z" },
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]
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||||
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||||
[[package]]
|
||||
name = "watchfiles"
|
||||
version = "1.1.0"
|
||||
|
|
|
|||
Loading…
Reference in a new issue