From b1c7445dbfcd5a29b916f860d139471181086716 Mon Sep 17 00:00:00 2001 From: "Denis L." Date: Sun, 27 Apr 2025 17:58:08 +0800 Subject: [PATCH 1/4] Convert dependencies to uv. Add API/MCP server --- .flake8 | 2 + README.md | 15 +- docker/Dockerfile | 81 - docker/entrypoint.sh | 27 - docs/installation.rst | 32 +- docs/usage.rst | 11 +- pyproject.toml | 59 +- requirements.txt | 42 - sample_templates/generic/config_template.yaml | 2 +- sample_templates/llm/llamacpp.yaml | 27 - sample_templates/llm/openai.yaml | 2 +- sample_templates/obsidian_conf.yaml | 3 +- src/llmsearch/api.py | 193 +- src/llmsearch/config.py | 55 +- src/llmsearch/models/utils.py | 9 +- src/llmsearch/process.py | 55 +- src/llmsearch/utils.py | 75 +- src/llmsearch/webapp.py | 118 +- uv.lock | 5983 +++++++++++++++++ 19 files changed, 6376 insertions(+), 415 deletions(-) create mode 100644 .flake8 delete mode 100644 docker/Dockerfile delete mode 100755 docker/entrypoint.sh delete mode 100644 requirements.txt delete mode 100644 sample_templates/llm/llamacpp.yaml create mode 100644 uv.lock diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..ec4d2a5 --- /dev/null +++ b/.flake8 @@ -0,0 +1,2 @@ +[flake8] +max-line-length = 119 \ No newline at end of file diff --git a/README.md b/README.md index c92890a..036dae9 100644 --- a/README.md +++ b/README.md @@ -4,10 +4,13 @@ [Documentation](https://llm-search.readthedocs.io/en/latest/) -The purpose of this package is to offer a convenient question-answering (RAG) system with a simple YAML-based configuration that enables interaction with multiple collections of local documents. Special attention is given to improvements in various components of the system **in addition to basic LLM-based RAGs** - better document parsing, hybrid search, HyDE enabled search, chat history, deep linking, re-ranking, the ability to customize embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) – whether from OpenAI or installed locally. +The purpose of this package is to offer an advanced question-answering (RAG) system with a simple YAML-based configuration that enables interaction with a collection of local documents. Special attention is given to improvements in various components of the system **in addition to basic LLM-based RAGs** - better document parsing, hybrid search, HyDE, chat history, deep linking, re-ranking, the ability to customize embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) – whether from OpenAI or installed locally. + +Interaction with the package is supported through the built-in frontend, or by exposing an MCP server, allowing clients like Cursor, Windsurf or VSCode GH Copilot to interact with the RAG system. ## Features +* Fast, incremental parsing and embedding of medium size document bases (tested on up to few gigabytes of markdown and pdfs) * Supported document formats * Build-in parsers: * `.md` - Divides files based on logical components such as headings, subheadings, and code blocks. Supports additional features like cleaning image links, adding custom metadata, and more. @@ -17,12 +20,13 @@ The purpose of this package is to offer a convenient question-answering (RAG) sy * List of formats see [here](https://unstructured-io.github.io/unstructured/core/partition.html). * Allows interaction with embedded documents, internally supporting the following models and methods (including locally hosted): - * OpenAI models (ChatGPT 3.5/4 and Azure OpenAI). + * OpenAI compatible models and APIs. * HuggingFace models. - * Llama cpp supported models - for full list see [here](https://github.com/ggerganov/llama.cpp#description). * Interoperability with LiteLLM + Ollama via OpenAI API, supporting hundreds of different models (see [Model configuration for LiteLLM](sample_templates/llm/litellm.yaml)) +* SSE MCP Server enabling interface with popular MCP clients. + * Generates dense embeddings from a folder of documents and stores them in a vector database ([ChromaDB](https://github.com/chroma-core/chroma)). * The following embedding models are supported: * Hugging Face embeddings. @@ -50,12 +54,11 @@ The purpose of this package is to offer a convenient question-answering (RAG) sy * Supprts optional chat history with question contextualization - * Other features - * Simple CLI and web interfaces. + * Simple web interfaces. * Deep linking into document sections - jump to an individual PDF page or a header in a markdown file. * Ability to save responses to an offline database for future analysis. - * Experimental API + * FastAPI based API + MCP server, allo ## Demo diff --git a/docker/Dockerfile b/docker/Dockerfile deleted file mode 100644 index f821705..0000000 --- a/docker/Dockerfile +++ /dev/null @@ -1,81 +0,0 @@ -FROM huggingface/transformers-pytorch-gpu:4.28.1 - -ENV LANG C.UTF-8 -RUN APT_INSTALL="apt-get install -y --no-install-recommends" && \ - PIP_INSTALL="python3 -m pip --no-cache-dir install" && \ - GIT_CLONE="git clone --depth 10" && \ - - rm -rf /var/lib/apt/lists/* \ - /etc/apt/sources.list.d/cuda.list \ - /etc/apt/sources.list.d/nvidia-ml.list && \ - - apt-get update && \ - -# ================================================================== -# tools -# ------------------------------------------------------------------ - - DEBIAN_FRONTEND=noninteractive $APT_INSTALL \ - build-essential \ - apt-utils \ - ca-certificates \ - wget \ - git \ - vim \ - libssl-dev \ - curl \ - unzip \ - unrar \ - cmake \ - sudo \ - && \ - - -# ================================================================== -# Libraries -# ------------------------------------------------------------------ - - apt-get update && \ - $PIP_INSTALL \ - pandas \ - matplotlib \ - tqdm \ - python-dotenv \ - && \ - -# ================================================================== -# jupyter -# ------------------------------------------------------------------ - - $PIP_INSTALL \ - jupyter \ - && \ - -# ================================================================== -# jupyterlab -# ------------------------------------------------------------------ - - $PIP_INSTALL \ - jupyterlab \ - && \ - -# ================================================================== -# LLM libraries -# ------------------------------------------------------------------ - - $PIP_INSTALL \ - chromadb==0.3.21 \ - llama-index==0.6.9 \ - langchain \ - sentence-transformers==2.2.2 \ - && \ -# ================================================================== -# config & cleanup -# ------------------------------------------------------------------ - - ldconfig && \ - apt-get clean && \ - apt-get autoremove && \ - rm -rf /var/lib/apt/lists/* /tmp/* ~/* - -EXPOSE 8888 6006 \ No newline at end of file diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh deleted file mode 100755 index 315bc0b..0000000 --- a/docker/entrypoint.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/usr/bin/env bash -set -e - -if [[ -z "$HOST_UID" ]]; then - echo "ERROR: please set HOST_UID" >&2 - exit 1 -fi -if [[ -z "$HOST_GID" ]]; then - echo "ERROR: please set HOST_GID" >&2 - exit 1 -fi - -# Use this code if you want to create a new user account: -addgroup --gid "$HOST_GID" dockergroup -adduser --uid "$HOST_UID" --gid "$HOST_GID" --gecos "" --disabled-password app - -# -OR- -# Use this code if you want to modify an existing user account: -#groupmod --gid "$HOST_GID" app -#usermod --uid "$HOST_UID" app - -# Drop privileges and execute next container command, or 'bash' if not specified. -if [[ $# -gt 0 ]]; then - exec sudo -H -u app -- "$@" -else - exec sudo -H -u app -- bash -fi diff --git a/docs/installation.rst b/docs/installation.rst index 2b3173c..803e1f3 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -8,6 +8,7 @@ Prerequisites * Tested with CUDA 11.8 to 12.4 - https://developer.nvidia.com/cuda-toolkit * To interact with OpenAI models, create `.env` in the root directory of the repository, containing OpenAI API key. A template for the `.env` file is provided in `.env_template` * For parsing `.epub` documents, Pandoc is required - https://pandoc.org/installing.html +* `uv` - https://github.com/astral-sh/uv#installation @@ -16,21 +17,16 @@ Install Latest Version .. code-block:: bash - # Create a new environment - python3 -m venv .venv + uv venv # Activate new environment source .venv/bin/activate - # Install packages using pip - pip install pyllmsearch - # Optional dependencues for Azure parser - pip install "pyllmsearch[azureparser]" + uv pip install "pyllmsearch[azureparser]" # Preferred method (much faster) - install packages using uv - pip install uv uv pip install pyllmsearch @@ -45,20 +41,16 @@ Install from source git clone https://github.com/snexus/llm-search.git cd llm-search + # Create a new environment + uv venv + # Activate new environment + source .venv/bin/activate + # Install packages using uv - # Optional - Set variables for llama-cpp to compile with CUDA. - # Assuming Nvidia CUDA Toolkit is installed and pointing to `usr/local/cuda` on Ubuntu - - source ./setvars.sh - - # Optional - Install newest stable torch for CUDA 11.x - # pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu118 - - # or for CUDA 12.x version - # pip3 install torch torchvision + uv sync - # Install the package - pip install . # or `pip install -e .` for development + # Optional - install in the development mode + uv pip install -e . # or `pip install -e .` for development # For Azure parser, install with optional dependencies - pip install ."[azureparser]" \ No newline at end of file + uv pip install ."[azureparser]" \ No newline at end of file diff --git a/docs/usage.rst b/docs/usage.rst index 871c153..f074e36 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -46,11 +46,14 @@ To interact with the documents using one of the supported LLMs, follow these ste Here `path/to/config/folder` points to a folder of one or more document config files. The tool will scans the configs and allows to switch between them. -API (Experimental) ------------------ +API and MCP Server +------------------ -To launch an api, supply a path config file in the `FASTAPI_LLM_CONFIG` environment variable and launch `llmsearchapi` +To launch FastAPI/MCP server, supply a path semantic search config file in the `FASTAPI_RAG_CONFIG` and path to llm config in `FASTAPI_LLM_CONFIG` environment variable and launch `llmsearchapi` .. code-block:: bash - FASTAPI_LLM_CONFIG="/path/to/config.yaml" llmsearchapi + FASTAPI_RAG_CONFIG="/path/to/config.yaml" FASTAPI_LLM_CONFIG="/path/to/llm.yaml" llmsearchapi + +1. The API server will be available at `http://localhost:8000/docs` and can be used to interact with the documents using the LLMs. +2. The MCP server will be available at `http://localhost:8000/mcp` and can be configured via any MCP client, assuming SSE MCP server which should point to the same URL. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index cd8d2cc..9ad83b3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,10 +5,11 @@ build-backend = "setuptools.build_meta" [project] name = "pyllmsearch" description = "LLM Powered Advanced RAG Application" -dynamic = ["dependencies", "version"] +# dynamic = ["dependencies", "version"] +dynamic = ["version"] keywords = ["llm", "rag", "retrieval-augemented-generation","large-language-models", "local", "splade", "hyde", "reranking", "chroma", "openai"] readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10" classifiers = [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", @@ -16,6 +17,31 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", ] +dependencies = [ + "langchain-community>=0.3.22", + "langchain>=0.3.24", + "langchain-huggingface>=0.1.2", + "langchain-chroma>=0.2.3", + "python-dotenv>=1.1.0", + "loguru>=0.7.3", + "click>=8.1.8", + "openai>=1.76.0", + "streamlit>=1.44.1", + "tenacity>=9.1.2", + "tqdm>=4.67.1", + "gmft==0.2.1", + "pypdf2>=3.0.1", + "pydantic>=2.11.3", + "instructorembedding>=1.0.1", + "unstructured>=0.17.2", + "tiktoken>=0.9.0", + "tokenizers>=0.21.1", + "langchain-openai>=0.3.14", + "python-docx>=1.1.2", + "pymupdf>=1.25.5", + "termcolor>=3.0.1", + "fastapi-mcp>=0.3.3", +] [project.optional-dependencies] @@ -35,6 +61,10 @@ azureparser = [ "azure-identity==1.17.1" ] +googleparser = [ + "google-generativeai>=0.8.5", + ] + [project.urls] Homepage = "https://github.com/snexus/llm-search" Documentation = "https://llm-search.readthedocs.io/en/latest/" @@ -46,9 +76,30 @@ local_scheme = "no-local-version" [tool.setuptools.packages.find] where = ["src"] -[tool.setuptools.dynamic] -dependencies = {file = ["requirements.txt"]} +# [tool.setuptools.dynamic] +# dependencies = {file = ["requirements.txt"]} +[tool.flake8] +docstring-convention = "all" +ignore = [ + "D107", + "D212", + "E501", + "W503", + "W605", + "D203", + "D100", + "D400", + "D415", + "D104", + "D203", + "D213", + "D401", + "D406", + "D417", +] +exclude = [ "venv" ] +max-line-length = 119 [tool.ruff] # Decrease the maximum line length to 79 characters. diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index c173a66..0000000 --- a/requirements.txt +++ /dev/null @@ -1,42 +0,0 @@ -llama-cpp-python==0.2.76 -chromadb~=0.5.5 -langchain>=0.3,<0.4 -langchain-community>=0.3,<0.4 -langchain-openai>=0.2,<0.3 -langchain-huggingface>=0.1,<0.2 -langchain-chroma>=0.1.4,<0.2 -pydantic~=2.7 -transformers~=4.47 -sentence-transformers==3.3.1 -pypdf2~=3.0.1 -ebooklib==0.18 -# sentencepiece==0.20 -setuptools==67.7.2 -loguru -python-dotenv -accelerate~=1.2.0 -protobuf==3.20.2 -termcolor -openai~=1.57 -einops # required for Mosaic models -click -bitsandbytes==0.43.1 -# auto-gptq==0.2.0 -InstructorEmbedding==1.0.1 -unstructured~=0.16.9 -pymupdf==1.25.0 -streamlit~=1.40 -python-docx~=1.1 -six==1.16.0 ; python_version >= "3.10" and python_version < "4.0" -sniffio==1.3.0 ; python_version >= "3.10" and python_version < "4.0" -sqlalchemy==1.4.48 ; python_version >= "3.10" and python_version < "4.0" -starlette==0.27.0 ; python_version >= "3.10" and python_version < "4.0" -sympy==1.11.1 ; python_version >= "3.10" and python_version < "4.0" -tenacity==8.2.3 ; python_version >= "3.10" and python_version < "4.0" -threadpoolctl==3.1.0 ; python_version >= "3.10" and python_version < "4.0" -tiktoken==0.7.0 ; python_version >= "3.10" and python_version < "4.0" -tokenizers>=0.21,<0.22; python_version >= "3.10" and python_version < "4.0" -tqdm==4.65.0 ; python_version >= "3.10" and python_version < "4.0" -# transformers==4.29.2 ; python_version >= "3.10" and python_version < "4.0" -gmft==0.2.1 -google-generativeai~=0.8.3 diff --git a/sample_templates/generic/config_template.yaml b/sample_templates/generic/config_template.yaml index e658e69..1358982 100644 --- a/sample_templates/generic/config_template.yaml +++ b/sample_templates/generic/config_template.yaml @@ -7,7 +7,7 @@ embeddings: embedding_model: # Optional embedding model specification, default is e5-large-v2. Swap to a smaller model if out of CUDA memory # Supported types: "huggingface", "instruct", "openai" type: sentence_transformer # other supported types - "huggingface" and "instruct" - model_name: "intfloat/e5-large-v2" + model_name: "vsolatorio/GIST-Embedding-v0" splade_config: # Optional batch size of sparse embeddings. Reduce if getting out-of-memory errors on CUDA. n_batch: 5 diff --git a/sample_templates/llm/llamacpp.yaml b/sample_templates/llm/llamacpp.yaml deleted file mode 100644 index b9075eb..0000000 --- a/sample_templates/llm/llamacpp.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# This file contains a configuration section relevant to LLM, not the entire config - -llm: - type: llamacpp - params: - model_path: /storage/llm/cache/phi3/Phi-3-mini-4k-instruct-q4.gguf - prompt_template: | - ### Instruction: - Use the following pieces of context to provide detailed answer the question at the end. If answer isn't in the context, say that you don't know, don't try to make up an answer. - - ### Context: - --------------- - {context} - --------------- - - ### Question: {question} - ### Response: - model_init_params: - n_ctx: 4196 - n_batch: 4196 - n_gpu_layers: 40 - - model_kwargs: - max_tokens: 4196 - top_p: 0.1 - top_k: 40 - temperature: 0 diff --git a/sample_templates/llm/openai.yaml b/sample_templates/llm/openai.yaml index 74f5670..f3da14e 100644 --- a/sample_templates/llm/openai.yaml +++ b/sample_templates/llm/openai.yaml @@ -15,4 +15,4 @@ llm: ### Question: {question} model_kwargs: temperature: 0.0 - model_name: gpt-3.5-turbo \ No newline at end of file + model_name: gpt-4o-mini \ No newline at end of file diff --git a/sample_templates/obsidian_conf.yaml b/sample_templates/obsidian_conf.yaml index 0d80e9e..6abf7fd 100644 --- a/sample_templates/obsidian_conf.yaml +++ b/sample_templates/obsidian_conf.yaml @@ -8,7 +8,6 @@ embeddings: additional_kwargs: trust_remote_code: true - # model_name: "intfloat/e5-large-v2" splade_config: n_batch: 2 @@ -56,7 +55,7 @@ semantic_search: append_suffix: append_template: "&heading={heading}" - max_char_size: 4096 + max_char_size: 8192 query_prefix: "query: " multiquery: enabled: False diff --git a/src/llmsearch/api.py b/src/llmsearch/api.py index 63f0433..55e13cd 100644 --- a/src/llmsearch/api.py +++ b/src/llmsearch/api.py @@ -1,4 +1,7 @@ +"""FastAPI server for LLMSearch.""" + import os +from functools import lru_cache from typing import Any, List import langchain @@ -6,68 +9,108 @@ from dotenv import load_dotenv from fastapi import Depends, FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware +from fastapi_mcp import FastApiMCP from loguru import logger -from sqlalchemy.orm import Session -import llmsearch.database.crud as crud -from llmsearch.config import Config, ResponseModel, get_config -from llmsearch.database.config import get_local_session +# import llmsearch.database.crud as crud +from llmsearch.config import Config, ResponseModel, get_doc_with_model_config + +# from llmsearch.database.config import get_local_session from llmsearch.process import get_and_parse_response from llmsearch.ranking import get_relevant_documents from llmsearch.utils import LLMBundle, get_llm_bundle +# from sqlalchemy.orm import Session + + load_dotenv() -langchain.debug = True +langchain.debug = True # Enable debug mode for langchain # type: ignore + # Load the configuration def read_config() -> Config: - config_file = os.environ["FASTAPI_LLM_CONFIG"] + """Reads the configuration from environment variables and config files.""" + rag_config_file = os.environ["FASTAPI_RAG_CONFIG"] + llm_config_file = os.environ["FASTAPI_LLM_CONFIG"] - if not config_file: + if not rag_config_file or not llm_config_file: raise SystemError( - "Set 'FASTAPI_LLM_CONFG' environment variable to point to a model config file." + "Set 'FASTAPI_RAG_CONFIG' and 'FASTAPI_LLM_CONFIG' environment variable to point to a model config file." ) - logger.info(f"Loading configuration from {config_file}") - config = get_config(config_file) + logger.info(f"Loading configuration from {rag_config_file}") + conf = get_doc_with_model_config(rag_config_file, llm_config_file) + return conf + + +# Cache the configuration +@lru_cache() +def get_config() -> Config: + """Loads and caches the configuration.""" + return read_config() + + +# Cache the LLM bundle +@lru_cache() +def get_cached_llm_bundle() -> LLMBundle: + """Loads and caches the LLM bundle.""" + config = get_config() + logger.info("Loading LLM...") + bundle = get_llm_bundle(config) + return bundle + # return load_llm() + - return config +# Dependency for injecting the LLM bundle +def get_llm_bundle_cached() -> LLMBundle: + """Provides the cached LLM bundle.""" + return get_cached_llm_bundle() # Dependency: Used to get the database in our endpoints. -def get_db() -> Session: - """Creates a database session and makes sure to close it properly.""" +# def get_db() -> Session: +# """Creates a database session and makes sure to close it properly.""" - if config.persist_response_db_path is None: - raise Exception( - "Specify database filename in `persist_response_db_path` setting in config." - ) - db_settings = get_local_session(db_path=config.persist_response_db_path) +# if semantic_search_conf.persist_response_db_path is None: +# raise Exception("Specify database filename in `persist_response_db_path` setting in config.") +# db_settings = get_local_session(db_path=get_config().persist_response_db_path) - db = db_settings.SessionLocal() - try: - yield db - finally: - logger.info("Closing session...") - db.close() +# db = db_settings.SessionLocal() +# try: +# yield db +# finally: +# logger.info("Closing session...") +# db.close() -def load_llm() -> LLMBundle: - """Loads a chain to use with the api""" +# def load_llm() -> LLMBundle: +# """Loads a chain to use with the api""" - logger.info("Loading LLM...") - bundle = get_llm_bundle(config) - return bundle +# logger.info("Loading LLM...") +# bundle = get_llm_bundle(config) +# return bundle -config = read_config() -llm_bundle = load_llm() +# config = read_config() +# llm_bundle = load_llm() + +api_app = FastAPI() +mcp = FastApiMCP( + api_app, + name="pyLLMSearch MCP Server", + description="pyLLMSearch MCP Server", + describe_all_responses=True, # Include all possible response schemas + describe_full_response_schema=True, # Include full JSON schema in descriptions + include_operations=["rag_retrieve_chunks", "rag_generate_answer"], +) + + +mcp.mount() -app = FastAPI() # Enable CORS origins = ["*"] -app.add_middleware( +api_app.add_middleware( CORSMiddleware, allow_origins=origins, allow_methods=["*"], @@ -75,16 +118,20 @@ def load_llm() -> LLMBundle: ) -@app.get("/") +@api_app.get("/") def test(): + """Test endpoint to check if the API is running.""" return {"message": "Welcome to LLMSearch API"} -@app.get("/llm", response_model=ResponseModel) +@api_app.get("/llm", response_model=ResponseModel, operation_id="rag_generate_answer") async def llmsearch( - question: str, label: str = "", db: Session = Depends(get_db) + question: str, + label: str = "", + llm_bundle: LLMBundle = Depends(get_llm_bundle_cached), ) -> Any: # switch to async to block execution - if label and (label not in config.embeddings.labels): + """Retrieves answer to the question from the embedded documents, using semantic search.""" + if label and (label not in get_config().embeddings.labels): raise HTTPException( status_code=404, detail=f"Label '{label}' doesn't exist. Use GET /labels to get a list of labels.", @@ -93,52 +140,62 @@ async def llmsearch( output = get_and_parse_response( query=question, llm_bundle=llm_bundle, - config=config, - persist_db_session=db, + config=get_config(), label=label, ) - return output.dict() + return output.model_dump() -@app.get("/semantic") +@api_app.get("/semantic/{question}", operation_id="rag_retrieve_chunks") async def semanticsearch(question: str): + """Retrieves information relevant to the question from the embedded documents, using semantic search.""" docs = get_relevant_documents( - original_query=question, queries = [question], llm_bundle=llm_bundle, config=config.semantic_search, label="" + original_query=question, + queries=[question], + llm_bundle=get_llm_bundle_cached(), + config=get_config().semantic_search, + label="", ) return {"sources": docs} -@app.get("/labels") +@api_app.get("/labels") async def labels() -> List[str]: - return config.embeddings.labels - - -@app.post("/feedback") -async def llmfeedback( - response_id: str, - is_positive: bool, - feedback_text: str = "", - db: Session = Depends(get_db), -): - try: - crud.create_feedback( - response_id=response_id, - session=db, - is_positive=is_positive, - feedback_text=feedback_text, - ) - except crud.ResponseInteractionLookupError: - return HTTPException( - status_code=404, - detail=f"Reponse interaction with id '{response_id}' doesn't exist", - ) - - return {"status": "Update complete"} + """Returns a list of labels for the embeddings.""" + return get_config().embeddings.labels + + +# @api_app.post("/feedback") +# async def llmfeedback( +# response_id: str, +# is_positive: bool, +# feedback_text: str = "", +# db: Session = Depends(get_db), +# ): +# """Saves feedback for a given response id.""" +# try: +# crud.create_feedback( +# response_id=response_id, +# session=db, +# is_positive=is_positive, +# feedback_text=feedback_text, +# ) +# except crud.ResponseInteractionLookupError: +# return HTTPException( +# status_code=404, +# detail=f"Reponse interaction with id '{response_id}' doesn't exist", +# ) + +# return {"status": "Update complete"} def main(): - uvicorn.run(app, host="0.0.0.0", port=8000) + """Main function to run the FastAPI app.""" + uvicorn.run(api_app, host="0.0.0.0", port=8000) + +# Refresh mcp server +mcp.setup_server() if __name__ == "__main__": main() diff --git a/src/llmsearch/config.py b/src/llmsearch/config.py index 14c1c6b..f9034a4 100644 --- a/src/llmsearch/config.py +++ b/src/llmsearch/config.py @@ -6,12 +6,21 @@ import yaml from loguru import logger -from pydantic import (BaseModel, ConfigDict, DirectoryPath, Field, - ValidationInfo, field_validator) - -from llmsearch.models.config import (AzureOpenAIModelConfig, - HuggingFaceModelConfig, LlamaModelConfig, - OpenAIModelConfig) +from pydantic import ( + BaseModel, + ConfigDict, + DirectoryPath, + Field, + ValidationInfo, + field_validator, +) + +from llmsearch.models.config import ( + AzureOpenAIModelConfig, + HuggingFaceModelConfig, + LlamaModelConfig, + OpenAIModelConfig, +) # from pydantic.typing import Literal # type: ignore @@ -55,7 +64,8 @@ class PDFTableParser(str, Enum): class PDFImageParser(str, Enum): GEMINI_15_FLASH = "gemini-1.5-flash" - GEMINI_15_PRO= "gemini-1.5-pro" + GEMINI_15_PRO = "gemini-1.5-pro" + class PDFImageParseSettings(BaseModel): image_parser: PDFImageParser @@ -64,7 +74,10 @@ class PDFImageParseSettings(BaseModel): - Second line is empty - From the third line on - detailed data points and related metadata, extracted from the image, in Markdown format. Don't use Markdown tables. """ - user_instruction: str = """From the image, extract detailed quantitative and qualitative data points.""" + user_instruction: str = ( + """From the image, extract detailed quantitative and qualitative data points.""" + ) + class EmbeddingModelType(str, Enum): huggingface = "huggingface" @@ -92,7 +105,7 @@ class DocumentPathSettings(BaseModel): scan_extensions: List[str] """List of extensions to scan.""" - pdf_table_parser: Optional[PDFTableParser] = None + pdf_table_parser: Optional[PDFTableParser] = None """If enabled, will parse tables in pdf files using a specific of a parser.""" pdf_image_parser: Optional[PDFImageParseSettings] = None @@ -103,7 +116,6 @@ class DocumentPathSettings(BaseModel): passage_prefix: str = "" - label: str = "" # Optional label, will be included in the metadata """Optional label for the document set, will be included in the metadata.""" @@ -141,7 +153,6 @@ class EmbeddingsConfig(BaseModel): splade_config: EmbedddingsSpladeConfig = EmbedddingsSpladeConfig(n_batch=5) """Specifies settings for sparse embeddings (SPLADE).""" - @property def labels(self) -> List[str]: """Returns list of labels in document settings""" @@ -220,7 +231,7 @@ class ConversrationHistorySettings(BaseModel): User question: {user_question} """ - + template_header: str = "\nChat History:\n=============\n" template_qa_pairs: str = "User: {question}\nAssistant: {answer}\n\n" @@ -253,25 +264,25 @@ class SemanticSearchConfig(BaseModel): replace_output_path: List[ReplaceOutputPath] = Field(default_factory=list) obsidian_advanced_uri: Optional[ObsidianAdvancedURI] = None - + append_suffix: Optional[SuffixAppend] = None """Allows to append suffix to document URL. Useful for deep linking to allow opening with external application, e.g. Obsidian.""" - + reranker: RerankerSettings = RerankerSettings() """Configures re-ranker settings.""" - + max_k: int = 15 """Maximum number of documents to retrieve for dense OR sparse embedding (if using both, number of documents will be k*2)""" - + max_char_size: int = 2048 """Maximum character size for query + documents to fit into context window of LLM.""" - + query_prefix: str = "" """Prefix query with string BEFORE retrieval using embedding model.""" - + hyde: HydeSettings = HydeSettings() """Optional configuration for HyDE.""" - + multiquery: MultiQuerySettings = MultiQuerySettings() """Optional configuration for multi-query""" @@ -297,7 +308,7 @@ def validate_params(cls, value, info: ValidationInfo): if type_ not in models_config: raise TypeError(f"Uknown model type {value}. Allowed types: ") - config_type = models_config[type_] # type: ignore + config_type = models_config[type_] # type: ignore logger.info( f"Loading model paramaters in configuration class {config_type.__name__}" ) @@ -366,9 +377,10 @@ def get_doc_with_model_config(doc_config, model_config) -> Config: def load_yaml_file(config) -> dict: + """Loads YAML file or string and returns a dictionary""" if isinstance(config, str): logger.info(f"Loading doc config from a file: {config}") - with open(config, "r") as f: + with open(config, "r", encoding="utf-8") as f: string_data = f.read() else: stringio = StringIO(config.getvalue().decode("utf-8")) @@ -376,4 +388,3 @@ def load_yaml_file(config) -> dict: config_dict = yaml.safe_load(string_data) return config_dict - diff --git a/src/llmsearch/models/utils.py b/src/llmsearch/models/utils.py index a4824c2..d6fc56a 100644 --- a/src/llmsearch/models/utils.py +++ b/src/llmsearch/models/utils.py @@ -2,16 +2,17 @@ from llmsearch.models.azureopenai import AzureOpenAIModel from llmsearch.models.config import ( # AutoGPTQModelConfig, - AzureOpenAIModelConfig, HuggingFaceModelConfig, LlamaModelConfig, + AzureOpenAIModelConfig, HuggingFaceModelConfig, + # LlamaModelConfig, OpenAIModelConfig) from llmsearch.models.hf import HuggingFaceModel -from llmsearch.models.llama import LlamaModel +# from llmsearch.models.llama import LlamaModel from llmsearch.models.openai import OpenAIModel # from llmsearch.models.autogptq import AutoGPTQModel model_mappings = { - LlamaModelConfig: LlamaModel, + # LlamaModelConfig: LlamaModel, HuggingFaceModelConfig: HuggingFaceModel, OpenAIModelConfig: OpenAIModel, AzureOpenAIModelConfig: AzureOpenAIModel, @@ -22,7 +23,7 @@ def get_llm( llm_config: Union[ OpenAIModelConfig, - LlamaModelConfig, + # LlamaModelConfig, # AutoGPTQModelConfig, HuggingFaceModelConfig, ] diff --git a/src/llmsearch/process.py b/src/llmsearch/process.py index f033a7d..ff4a11b 100644 --- a/src/llmsearch/process.py +++ b/src/llmsearch/process.py @@ -3,9 +3,14 @@ from loguru import logger -from llmsearch.config import (Config, Document, ObsidianAdvancedURI, - ResponseModel, SemanticSearchOutput, - SuffixAppend) +from llmsearch.config import ( + Config, + Document, + ObsidianAdvancedURI, + ResponseModel, + SemanticSearchOutput, + SuffixAppend, +) from llmsearch.database.crud import create_response from llmsearch.ranking import get_relevant_documents from llmsearch.utils import LLMBundle @@ -17,7 +22,7 @@ def get_and_parse_response( config: Config, persist_db_session=None, label: str = "", - source_chunk_type: str = "" + source_chunk_type: str = "", ) -> ResponseModel: """Performs retieval augmented search (RAG). @@ -57,11 +62,16 @@ def get_and_parse_response( offset_max_chars = len(llm_bundle.conversation_history_settings.chat_history) else: offset_max_chars = 0 - + semantic_search_config = config.semantic_search most_relevant_docs, score = get_relevant_documents( - original_query, queries, llm_bundle, semantic_search_config, label=label, - offset_max_chars = offset_max_chars, source_chunk_type = source_chunk_type + original_query, + queries, + llm_bundle, + semantic_search_config, + label=label, + offset_max_chars=offset_max_chars, + source_chunk_type=source_chunk_type, ) # Append chat history to the documments, if enabled @@ -77,12 +87,11 @@ def get_and_parse_response( {"context": most_relevant_docs, "question": original_query}, return_only_outputs=False, ) - save_chat_history(llm_bundle, question=original_query, answer=res) out = ResponseModel( - response=res, #type: ignore + response=res, # type: ignore question=query, average_score=score, hyde_response=hyde_response, @@ -202,7 +211,9 @@ def get_multiquery_response( ) -> List[str]: if llm_bundle.multiquery_chain is None: raise TypeError("MultiQuery chain is not initialised. exiting.") - res = llm_bundle.multiquery_chain.invoke(dict(question=query, n_versions=n_versions)) + res = llm_bundle.multiquery_chain.invoke( + dict(question=query, n_versions=n_versions) + ) logger.info(f"MultiQuery: got response: {res}") queries = [q.strip() for q in res.strip().split("\n") if q.strip()] @@ -215,17 +226,17 @@ def get_multiquery_response( return queries -#def expand_query_with_history(llm_bundle: LLMBundle, original_query: str) -> str: - #"""If enabled, adds chat history to the original question.""" +# def expand_query_with_history(llm_bundle: LLMBundle, original_query: str) -> str: +# """If enabled, adds chat history to the original question.""" - #if llm_bundle.conversation_history_settings is None: - #return original_query +# if llm_bundle.conversation_history_settings is None: +# return original_query - #expanded_query = ( - #original_query + llm_bundle.conversation_history_settings.chat_history - #) - #logger.info(f"Expanded query by chat history: {expanded_query}") - #return expanded_query +# expanded_query = ( +# original_query + llm_bundle.conversation_history_settings.chat_history +# ) +# logger.info(f"Expanded query by chat history: {expanded_query}") +# return expanded_query def save_chat_history(llm_bundle: LLMBundle, question: str, answer: str) -> None: @@ -247,8 +258,10 @@ def contextualize_query_with_history_chat( return user_question res = llm_bundle.history_contextualization_chain.invoke( - {'chat_history': llm_bundle.conversation_history_settings.chat_history, - 'user_question':user_question} + { + "chat_history": llm_bundle.conversation_history_settings.chat_history, + "user_question": user_question, + } ) logger.info("Got history contextualized query: %s", res) return res diff --git a/src/llmsearch/utils.py b/src/llmsearch/utils.py index cf5cf86..d89bb8f 100644 --- a/src/llmsearch/utils.py +++ b/src/llmsearch/utils.py @@ -3,18 +3,18 @@ from typing import List, Optional, Union from langchain.chains.base import Chain -from langchain.chains.question_answering import load_qa_chain +# from langchain.chains.question_answering import load_qa_chain from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_core.caches import BaseCache, InMemoryCache from langchain_core.output_parsers.string import StrOutputParser from langchain_core.prompts import PromptTemplate + # from langchain.chains import LLMChain from langchain_core.runnables.base import RunnableSequence from loguru import logger from llmsearch.chroma import VectorStoreChroma -from llmsearch.config import (Config, ConversrationHistorySettings, - RerankerModel) +from llmsearch.config import Config, ConversrationHistorySettings, RerankerModel from llmsearch.database.config import Base, DBSettings, get_local_session from llmsearch.embeddings import VectorStore from llmsearch.models.utils import get_llm @@ -26,6 +26,8 @@ @dataclass class LLMBundle: + """Model bundle for LLM and retriever""" + chain: Chain store: VectorStore reranker: Optional[Union[BGEReranker, MarcoReranker]] @@ -119,57 +121,58 @@ def get_llm_bundle(config: Config) -> LLMBundle: hyde_enabled=config.semantic_search.hyde.enabled, multiquery_chain=multiquery_chain, multiquery_enabled=config.semantic_search.multiquery.enabled, - conversation_history_settings=config.semantic_search.conversation_history_settings, - history_contextualization_chain = history_contextualization_chain, - llm_cache=InMemoryCache() + conversation_history_settings=config.semantic_search.conversation_history_settings, + history_contextualization_chain=history_contextualization_chain, + llm_cache=InMemoryCache(), ) def get_hyde_chain(config, llm_model) -> RunnableSequence: logger.info("Creating HyDE chain...") - prompt=PromptTemplate( - template=config.semantic_search.hyde.hyde_prompt, - input_variables=["question"], - ) - return RunnableSequence(prompt, llm_model, StrOutputParser() ) + prompt = PromptTemplate( + template=config.semantic_search.hyde.hyde_prompt, + input_variables=["question"], + ) + return RunnableSequence(prompt, llm_model, StrOutputParser()) # return RunnableSequence( - # llm=llm_model, - # prompt=PromptTemplate( - # template=config.semantic_search.hyde.hyde_prompt, - # input_variables=["question"], - # ), + # llm=llm_model, + # prompt=PromptTemplate( + # template=config.semantic_search.hyde.hyde_prompt, + # input_variables=["question"], + # ), # ) def get_multiquery_chain(config, llm_model) -> RunnableSequence: logger.info("Creating MultiQUery chain...") - prompt=PromptTemplate( - template=config.semantic_search.multiquery.multiquery_prompt, - input_variables=["question", "n_versions"], - ) - return RunnableSequence(prompt, llm_model, StrOutputParser() ) + prompt = PromptTemplate( + template=config.semantic_search.multiquery.multiquery_prompt, + input_variables=["question", "n_versions"], + ) + return RunnableSequence(prompt, llm_model, StrOutputParser()) # return RunnableSequence( - # llm=llm_model, - # prompt=PromptTemplate( - # template=config.semantic_search.multiquery.multiquery_prompt, - # input_variables=["question", "n_versions"], - # ), + # llm=llm_model, + # prompt=PromptTemplate( + # template=config.semantic_search.multiquery.multiquery_prompt, + # input_variables=["question", "n_versions"], + # ), # ) + def get_history_contextualize_chain(config: Config, llm_model) -> RunnableSequence: """LLM chain for history contextualization queries""" logger.info("Creating chat history contextualization chain...") - prompt=PromptTemplate( - template=config.semantic_search.conversation_history_settings.template_contextualize, - input_variables=["chat_history", "user_question"], - ) + prompt = PromptTemplate( + template=config.semantic_search.conversation_history_settings.template_contextualize, + input_variables=["chat_history", "user_question"], + ) - return RunnableSequence(prompt, llm_model, StrOutputParser() ) + return RunnableSequence(prompt, llm_model, StrOutputParser()) # return RunnableSequence( - # llm=llm_model, - # prompt=PromptTemplate( - # template=config.semantic_search.conversation_history_settings.template_contextualize, - # input_variables=["chat_history", "user_question"], - # ), + # llm=llm_model, + # prompt=PromptTemplate( + # template=config.semantic_search.conversation_history_settings.template_contextualize, + # input_variables=["chat_history", "user_question"], + # ), # ) diff --git a/src/llmsearch/webapp.py b/src/llmsearch/webapp.py index 781fee2..b97577c 100644 --- a/src/llmsearch/webapp.py +++ b/src/llmsearch/webapp.py @@ -1,9 +1,12 @@ +"""Web application for LLMSearch""" + import argparse import gc +import io import os from io import StringIO from pathlib import Path -from typing import Dict, List +from typing import Dict, Union import langchain import streamlit as st @@ -16,8 +19,12 @@ from llmsearch.chroma import VectorStoreChroma from llmsearch.config import Config -from llmsearch.embeddings import (EmbeddingsHashNotExistError, - create_embeddings, update_embeddings, load_document_labels) +from llmsearch.embeddings import ( + EmbeddingsHashNotExistError, + create_embeddings, + load_document_labels, + update_embeddings, +) from llmsearch.process import get_and_parse_response from llmsearch.utils import get_llm_bundle, set_cache_folder @@ -25,7 +32,10 @@ load_dotenv() -langchain.debug = True +langchain.debug = True # type: ignore + +# See https://github.com/VikParuchuri/marker/issues/442 +torch.classes.__path__ = [] # add this line to manually set it to empty. @st.cache_data @@ -78,15 +88,15 @@ def udpate_index(doc_config_path: str, model_config_file): with st.spinner("Updating index, please wait..."): logger.debug("Unloading model...") unload_model() - config = load_config(doc_config_path, model_config_file) - set_cache_folder(str(config.cache_folder)) + conf = load_config(doc_config_path, model_config_file) + set_cache_folder(str(conf.cache_folder)) vs = VectorStoreChroma( - persist_folder=str(config.embeddings.embeddings_path), config=config + persist_folder=str(conf.embeddings.embeddings_path), config=conf ) try: logger.debug("Updating embeddings") - stats = update_embeddings(config, vs) + stats = update_embeddings(conf, vs) except EmbeddingsHashNotExistError: st.error( "Couldn't find hash files. Please re-create the index using current version of the app." @@ -120,20 +130,23 @@ def load_config(doc_config, model_config) -> Config: config_dict = {**doc_config_dict, **model_config_dict} return Config(**config_dict) + @st.cache_data def load_labels(embedding_path: str) -> Dict[str, str]: labels_fn = Path(os.path.join(embedding_path, "labels.txt")) all_labels = {Path(label).name: label for label in load_document_labels(labels_fn)} return all_labels + @st.cache_data -def load_yaml_file(config) -> dict: - if isinstance(config, str): - logger.info(f"Loading doc config from a file: {config}") - with open(config, "r") as f: +def load_yaml_file(conf: Union[str, io.BytesIO]) -> dict: + """Loads YAML file and returns a dictionary""" + if isinstance(conf, str): + logger.info(f"Loading doc config from a file: {conf}") + with open(conf, "r", encoding="utf-8") as f: string_data = f.read() else: - stringio = StringIO(config.getvalue().decode("utf-8")) + stringio = StringIO(conf.getvalue().decode("utf-8")) string_data = stringio.read() config_dict = yaml.safe_load(string_data) @@ -160,16 +173,18 @@ def unload_model(): with torch.no_grad(): torch.cuda.empty_cache() + def clear_chat_history(): if st.session_state["llm_bundle"] is not None: st.session_state["llm_bundle"].conversation_history_settings.history = [] - + # Clear LLM Cache st.session_state["llm_bundle"].llm_cache.clear() - + # Clear Streamlit Cache st.cache_data.clear() + @st.cache_data def generate_response( question: str, @@ -178,12 +193,16 @@ def generate_response( _config: Config, _bundle, label_filter: str = "", - source_chunk_type_filter: str = "" + source_chunk_type_filter: str = "", ): # _config and _bundle are under scored so paratemeters aren't hashed output = get_and_parse_response( - query=question, config=_config, llm_bundle=_bundle, label=label_filter, source_chunk_type=source_chunk_type_filter + query=question, + config=_config, + llm_bundle=_bundle, + label=label_filter, + source_chunk_type=source_chunk_type_filter, ) return output @@ -191,7 +210,7 @@ def generate_response( @st.cache_data def get_config_paths(config_dir: str) -> Dict[str, str]: root = Path(config_dir) - config_paths = sorted([p for p in root.glob("*.yaml")]) + # config_paths = sorted([p for p in root.glob("*.yaml")]) config_path_names = {p.name: str(p) for p in root.glob("*.yaml")} return config_path_names @@ -209,24 +228,23 @@ def reload_model(doc_config_path: str, model_config_file: str): logger.debug(f"Reload model got DOC CONFIG FILE NAME: {doc_config_path}") logger.debug(f"Reload model got MODEL CONFIG FILE NAME: {model_config_file}") with st.spinner("Loading configuration"): - config = load_config(doc_config_path, model_config_file) - if config.check_embeddings_exist(): - st.session_state["llm_bundle"] = get_llm_bundle(config) + conf = load_config(doc_config_path, model_config_file) + if conf.check_embeddings_exist(): + st.session_state["llm_bundle"] = get_llm_bundle(conf) st.session_state["llm_config"] = { - "config": config, + "config": conf, "doc_config_path": doc_config_path, "model_config_file": model_config_file, } else: st.error( "Couldn't find embeddings in {}. Please generate first.".format( - config.embeddings.embeddings_path + conf.embeddings.embeddings_path ) ) - update_embeddings_button = st.button( - "Generate", on_click=generate_index, args=(config,), type="secondary" + _ = st.button( + "Generate", on_click=generate_index, args=(conf,), type="secondary" ) - logger.debug("Setting LLM Cache") set_llm_cache(st.session_state["llm_bundle"].llm_cache) @@ -256,7 +274,7 @@ def reload_model(doc_config_path: str, model_config_file: str): doc_config_name = st.sidebar.selectbox( label="Choose config", options=sorted(list(config_paths.keys())), index=0 ) - doc_config_path = config_paths[doc_config_name] # type: ignore + doc_config_path = config_paths[doc_config_name] # type: ignore model_config_file = args.cli_model_config_path logger.debug(f"CONFIG FILE: {doc_config_path}") @@ -305,19 +323,19 @@ def reload_model(doc_config_path: str, model_config_file: str): if document_labels: label_filter = st.sidebar.selectbox( - label="Restrict search to:", options=["-"] + sorted(list(document_labels.keys())) + label="Restrict search to:", + options=["-"] + sorted(list(document_labels.keys())), ) if label_filter is None or label_filter == "-": label_filter = "" - - + # tables_only_filter = st.sidebar.checkbox(label="Prioritize tables") # if tables_only_filter: - # source_chunk_type_filter="table" + # source_chunk_type_filter="table" # else: - # source_chunk_type_filter="" - - source_chunk_type_filter="" + # source_chunk_type_filter="" + + source_chunk_type_filter = "" text = st.chat_input("Enter text", disabled=False) is_hyde = st.sidebar.checkbox( @@ -341,29 +359,31 @@ def reload_model(doc_config_path: str, model_config_file: str): "llm_bundle" ].conversation_history_settings.max_history_length, ) - conv_history_rewrite_query = st.sidebar.checkbox( - label = "Contextualize user question (LLM calls: 2)", - value=True ) - - clear_conv_history = st.sidebar.button("Clear history", on_click=clear_chat_history, type = "secondary") + conv_history_rewrite_query = st.sidebar.checkbox( + label="Contextualize user question (LLM calls: 2)", value=True + ) + + clear_conv_history = st.sidebar.button( + "Clear history", on_click=clear_chat_history, type="secondary" + ) if text: # Dynamically switch hyde st.session_state["llm_bundle"].hyde_enabled = is_hyde st.session_state["llm_bundle"].multiquery_enabled = is_multiquery st.session_state["llm_bundle"].conversation_history_settings.enabled = ( - conv_history_enabled - ) + conv_history_enabled + ) # Set conversation history settings if conv_history_enabled: - st.session_state["llm_bundle"].conversation_history_settings.max_history_length = ( - conv_history_max_length - ) - st.session_state["llm_bundle"].conversation_history_settings.rewrite_query = ( - conv_history_rewrite_query - ) + st.session_state[ + "llm_bundle" + ].conversation_history_settings.max_history_length = conv_history_max_length + st.session_state[ + "llm_bundle" + ].conversation_history_settings.rewrite_query = conv_history_rewrite_query output = generate_response( question=text, @@ -371,8 +391,8 @@ def reload_model(doc_config_path: str, model_config_file: str): use_multiquery=st.session_state["llm_bundle"].multiquery_enabled, _bundle=st.session_state["llm_bundle"], _config=config, - label_filter=document_labels.get(label_filter,""), - source_chunk_type_filter = source_chunk_type_filter + label_filter=document_labels.get(label_filter, ""), + source_chunk_type_filter=source_chunk_type_filter, ) # Add assistant response to chat history diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..f81b622 --- /dev/null +++ b/uv.lock @@ -0,0 +1,5983 @@ +version = 1 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version < '3.11'", + "python_full_version == '3.11.*'", + "python_full_version >= '3.12' and python_full_version < '3.12.4'", + "python_full_version >= '3.12.4' and python_full_version < '3.13'", + 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Date: Sun, 27 Apr 2025 18:07:25 +0800 Subject: [PATCH 2/4] Update Github workflow --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 174d448..958cd55 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -8,7 +8,7 @@ jobs: name: Build source distribution runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v4.0.0 with: From decdfeeba43962fc6a8d5278190a5d73c706e40c Mon Sep 17 00:00:00 2001 From: "Denis L." Date: Sun, 27 Apr 2025 18:10:03 +0800 Subject: [PATCH 3/4] Update Github workflow --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 958cd55..9fc99b7 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -22,7 +22,7 @@ jobs: - name: Run build run: python -m build - - uses: actions/upload-artifact@v3 + - uses: actions/upload-artifact@v4 with: path: ./dist/* From 864c22c98ac26ce46c2ef06c06727c56837b3c3f Mon Sep 17 00:00:00 2001 From: "Denis L." Date: Sun, 27 Apr 2025 18:11:27 +0800 Subject: [PATCH 4/4] Update Github workflow --- .github/workflows/release.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 9fc99b7..b591d54 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -34,7 +34,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/download-artifact@v3 + - uses: actions/download-artifact@v4 with: name: artifact path: ./dist