From b46c1087e26a49a0e4f6118ab24bcb5f095d49fd Mon Sep 17 00:00:00 2001 From: Pablo Orgaz Date: Fri, 20 Oct 2023 14:36:50 +0200 Subject: [PATCH 001/191] chore: add linux instructions and C++ guide (#1082) * fix: add linux instructions Co-authored-by: BW-Projects * chore: Add C++ as a base requirement in the docs * chore: Add clang for OSX * chore: Update docs for OSX and gcc * chore: make docs --------- Co-authored-by: Pablo Orgaz --- docs/description.md | 114 ++++++++++++++++++++++++++++++-------------- docs/openapi.json | 2 +- 2 files changed, 79 insertions(+), 37 deletions(-) diff --git a/docs/description.md b/docs/description.md index 5e02b5a..56c1fd4 100644 --- a/docs/description.md +++ b/docs/description.md @@ -21,7 +21,8 @@ The API is divided in two logical blocks: > watch, etc. ## Quick Local Installation steps -The steps in `Installation and Settings` section are better explained and cover more + +The steps in `Installation and Settings` section are better explained and cover more setup scenarios. But if you are looking for a quick setup guide, here it is: ``` @@ -53,28 +54,32 @@ being used http://localhost:8001/ ``` - ## Installation and Settings ### Base requirements to run PrivateGPT * Git clone PrivateGPT repository, and navigate to it: + ``` git clone https://github.com/imartinez/privateGPT cd privateGPT ``` -* Install Python 3.11. Ideally through a python version manager like `pyenv`. - Python 3.12 + +* Install Python 3.11. Ideally through a python version manager like `pyenv`. + Python 3.12 should work too. Earlier python versions are not supported. * osx/linux: [pyenv](https://github.com/pyenv/pyenv) * windows: [pyenv-win](https://github.com/pyenv-win/pyenv-win) - + ``` pyenv install 3.11 pyenv local 3.11 ``` -* Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management: - + +* Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management: + +* Have a valid C++ compiler like gcc. See [Troubleshooting: C++ Compiler](#troubleshooting-c-compiler) for more details. + * Install `make` for scripts: * osx: (Using homebrew): `brew install make` * windows: (Using chocolatey) `choco install make` @@ -171,16 +176,16 @@ is used. #### OSX GPU support -You will need to build [llama.cpp](https://github.com/ggerganov/llama.cpp) with +You will need to build [llama.cpp](https://github.com/ggerganov/llama.cpp) with metal support. To do that run: ```bash CMAKE_ARGS="-DLLAMA_METAL=on" pip install --force-reinstall --no-cache-dir llama-cpp-python ``` -#### Windows GPU support +#### Windows NVIDIA GPU support -Windows GPU support is done through CUDA or similar open source technologies. +Windows GPU support is done through CUDA. Follow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required dependencies. @@ -188,6 +193,8 @@ Some tips to get it working with an NVIDIA card and CUDA (Tested on Windows 10 w * Install latest VS2022 (and build tools) https://visualstudio.microsoft.com/vs/community/ * Install CUDA toolkit https://developer.nvidia.com/cuda-downloads +* Verify your installation is correct by running `nvcc --version` and `nvidia-smi`, ensure your CUDA version is up to + date and your GPU is detected. * [Optional] Install CMake to troubleshoot building issues by compiling llama.cpp directly https://cmake.org/download/ If you have all required dependencies properly configured running the @@ -209,9 +216,33 @@ Note that llama.cpp offloads matrix calculations to the GPU but the performance still hit heavily due to latency between CPU and GPU communication. You might need to tweak batch sizes and other parameters to get the best performance for your particular system. -#### Linux GPU support +#### Linux NVIDIA GPU support and Windows-WSL -🚧 Under construction 🚧 +Linux GPU support is done through CUDA. +Follow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required +external +dependencies. + +Some tips: + +* Make sure you have an up-to-date C++ compiler +* Install CUDA toolkit https://developer.nvidia.com/cuda-downloads +* Verify your installation is correct by running `nvcc --version` and `nvidia-smi`, ensure your CUDA version is up to + date and your GPU is detected. + +After that running the following command in the repository will install llama.cpp with GPU support: + +` +CMAKE_ARGS='-DLLAMA_CUBLAS=on' poetry run pip install --force-reinstall --no-cache-dir llama-cpp-python +` + +If your installation was correct, you should see a message similar to the following next +time you start the server `BLAS = 1`. + +``` +llama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, context: 762.87 MB) +AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | +``` #### Known issues and Troubleshooting @@ -226,7 +257,9 @@ You might encounter several issues: If you encounter any of these issues, please open an issue and we'll try to help. #### Troubleshooting: C++ Compiler -If you encounter an error while building a wheel during the `pip install` process, you may need to install a C++ compiler on your computer. + +If you encounter an error while building a wheel during the `pip install` process, you may need to install a C++ +compiler on your computer. **For Windows 10/11** @@ -234,13 +267,20 @@ To install a C++ compiler on Windows 10/11, follow these steps: 1. Install Visual Studio 2022. 2. Make sure the following components are selected: - * Universal Windows Platform development - * C++ CMake tools for Windows + * Universal Windows Platform development + * C++ CMake tools for Windows 3. Download the MinGW installer from the [MinGW website](https://sourceforge.net/projects/mingw/). 4. Run the installer and select the `gcc` component. +** For OSX ** + +1. Check if you have a C++ compiler installed, Xcode might have done it for you. for example running `gcc`. +2. If not, you can install clang or gcc with homebrew `brew install gcc` + #### Troubleshooting: Mac Running Intel -When running a Mac with Intel hardware (not M1), you may run into _clang: error: the clang compiler does not support '-march=native'_ during pip install. + +When running a Mac with Intel hardware (not M1), you may run into _clang: error: the clang compiler does not support ' +-march=native'_ during pip install. If so set your archflags during pip install. eg: _ARCHFLAGS="-arch x86_64" pip3 install -r requirements.txt_ @@ -313,35 +353,36 @@ Gradio UI is a ready to use way of testing most of PrivateGPT API functionalitie ### Execution Modes It has 3 modes of execution (you can select in the top-left): -* Query Documents: uses the context from the + +* Query Documents: uses the context from the ingested documents to answer the questions posted in the chat. It also takes - into account previous chat messages as context. - * Makes use of `/chat/completions` API with `use_context=true` and no - `context_filter`. + into account previous chat messages as context. + * Makes use of `/chat/completions` API with `use_context=true` and no + `context_filter`. * LLM Chat: simple, non-contextual chat with the LLM. The ingested documents won't be taken into account, only the previous messages. - * Makes use of `/chat/completions` API with `use_context=false`. + * Makes use of `/chat/completions` API with `use_context=false`. * Context Chunks: returns the JSON representation of the 2 most related text - chunks, together with their metadata, source document and previous and next - chunks. - * Makes use of `/chunks` API with no `context_filter`, `limit=2` and - `prev_next_chunks=1`. + chunks, together with their metadata, source document and previous and next + chunks. + * Makes use of `/chunks` API with no `context_filter`, `limit=2` and + `prev_next_chunks=1`. ### Document Ingestion -Ingest documents by using the `Upload a File` button. You can check the progress of -the ingestion in the console logs of the server. +Ingest documents by using the `Upload a File` button. You can check the progress of +the ingestion in the console logs of the server. The list of ingested files is shown below the button. -If you want to delete the ingested documents, refer to *Reset Local documents +If you want to delete the ingested documents, refer to *Reset Local documents database* section in the documentation. ### Chat -Normal chat interface, self-explanatory ;) +Normal chat interface, self-explanatory ;) -You can check the actual prompt being passed to the LLM by looking at the logs of +You can check the actual prompt being passed to the LLM by looking at the logs of the server. We'll add better observability in future releases. ## Deployment options @@ -350,16 +391,17 @@ the server. We'll add better observability in future releases. ## Observability -Basic logs are enabled using LlamaIndex -basic logging (for example ingestion progress or LLM prompts and answers). +Basic logs are enabled using LlamaIndex +basic logging (for example ingestion progress or LLM prompts and answers). -🚧 We are working on improved Observability. 🚧 +🚧 We are working on improved Observability. 🚧 ## Ingesting & Managing Documents 🚧 Document Update and Delete are still WIP. 🚧 The ingestion of documents can be done in different ways: + * Using the `/ingest` API * Using the Gradio UI * Using the Bulk Local Ingestion functionality (check next section) @@ -380,10 +422,10 @@ or using the completions / chat API. ### Reset Local documents database -When running in a local setup, you can remove all ingested documents by simply -deleting all contents of `local_data` folder (except .gitignore). +When running in a local setup, you can remove all ingested documents by simply +deleting all contents of `local_data` folder (except .gitignore). ## API As explained in the introduction, the API contains high level APIs (ingestion and chat/completions) and low level APIs -(embeddings and chunk retrieval). In this section the different specific API calls are explained. \ No newline at end of file +(embeddings and chunk retrieval). In this section the different specific API calls are explained. diff --git a/docs/openapi.json b/docs/openapi.json index fa172bf..548c165 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -3,7 +3,7 @@ "info": { "title": "PrivateGPT", "summary": "PrivateGPT is a production-ready AI project that allows you to ask questions to your documents using the power of Large Language Models (LLMs), even in scenarios without Internet connection. 100% private, no data leaves your execution environment at any point.", - "description": "## Introduction\n\nPrivateGPT provides an **API** containing all the building blocks required to build\n**private, context-aware AI applications**. The API follows and extends OpenAI API standard, and supports\nboth normal and streaming responses.\n\nThe API is divided in two logical blocks:\n\n- High-level API, abstracting all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation:\n - Ingestion of documents: internally managing document parsing, splitting, metadata extraction,\n embedding generation and storage.\n - Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt\n engineering and the response generation.\n- Low-level API, allowing advanced users to implement their own complex pipelines:\n - Embeddings generation: based on a piece of text.\n - Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested\n documents.\n\n> A working **Gradio UI client** is provided to test the API, together with a set of\n> useful tools such as bulk model download script, ingestion script, documents folder\n> watch, etc.\n\n## Quick Local Installation steps\nThe steps in `Installation and Settings` section are better explained and cover more \nsetup scenarios. But if you are looking for a quick setup guide, here it is:\n\n```\n# Clone the repo\ngit clone https://github.com/imartinez/privateGPT\ncd privateGPT\n\n# Install Python 3.11\npyenv install 3.11\npyenv local 3.11\n\n# Install dependencies\npoetry install --with ui,local\n\n# Download Embedding and LLM models\npoetry run python scripts/setup\n\n# (Optional) For Mac with Metal GPU, enable it. Check Installation and Settings section \nto know how to enable GPU on other platforms\nCMAKE_ARGS=\"-DLLAMA_METAL=on\" pip install --force-reinstall --no-cache-dir llama-cpp-python\n\n# Run the local server \nPGPT_PROFILES=local make run\n\n# Note: on Mac with Metal you should see a ggml_metal_add_buffer log, stating GPU is \nbeing used\n\n# Navigate to the UI and try it out! \nhttp://localhost:8001/\n```\n\n\n## Installation and Settings\n\n### Base requirements to run PrivateGPT\n\n* Git clone PrivateGPT repository, and navigate to it:\n```\n git clone https://github.com/imartinez/privateGPT\n cd privateGPT\n```\n* Install Python 3.11. Ideally through a python version manager like `pyenv`. \n Python 3.12 \n should work too. Earlier python versions are not supported.\n * osx/linux: [pyenv](https://github.com/pyenv/pyenv)\n * windows: [pyenv-win](https://github.com/pyenv-win/pyenv-win)\n \n``` \npyenv install 3.11\npyenv local 3.11\n```\n* Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management: \n \n* Install `make` for scripts:\n * osx: (Using homebrew): `brew install make`\n * windows: (Using chocolatey) `choco install make`\n\n### Install dependencies\n\nInstall the dependencies:\n\n```bash\npoetry install --with ui\n```\n\nVerify everything is working by running `make run` (or `poetry run python -m private_gpt`) and navigate to\nhttp://localhost:8001. You should see a [Gradio UI](https://gradio.app/) **configured with a mock LLM** that will\necho back the input. Later we'll see how to configure a real LLM.\n\n### Settings\n\n> Note: the default settings of PrivateGPT work out-of-the-box for a 100% local setup. Skip this section if you just\n> want to test PrivateGPT locally, and come back later to learn about more configuration options.\n\nPrivateGPT is configured through *profiles* that are defined using yaml files, and selected through env variables.\nThe full list of properties configurable can be found in `settings.yaml`\n\n#### env var `PGPT_SETTINGS_FOLDER`\n\nThe location of the settings folder. Defaults to the root of the project.\nShould contain the default `settings.yaml` and any other `settings-{profile}.yaml`.\n\n#### env var `PGPT_PROFILES`\n\nBy default, the profile definition in `settings.yaml` is loaded.\nUsing this env var you can load additional profiles; format is a comma separated list of profile names.\nThis will merge `settings-{profile}.yaml` on top of the base settings file.\n\nFor example:\n`PGPT_PROFILES=local,cuda` will load `settings-local.yaml`\nand `settings-cuda.yaml`, their contents will be merged with\nlater profiles properties overriding values of earlier ones like `settings.yaml`.\n\nDuring testing, the `test` profile will be active along with the default, therefore `settings-test.yaml`\nfile is required.\n\n#### Environment variables expansion\n\nConfiguration files can contain environment variables,\nthey will be expanded at runtime.\n\nExpansion must follow the pattern `${VARIABLE_NAME:default_value}`.\n\nFor example, the following configuration will use the value of the `PORT`\nenvironment variable or `8001` if it's not set.\nMissing variables with no default will produce an error.\n\n```yaml\nserver:\n port: ${PORT:8001}\n```\n\n### Local LLM requirements\n\nInstall extra dependencies for local execution:\n\n```bash\npoetry install --with local\n```\n\nFor PrivateGPT to run fully locally GPU acceleration is required\n(CPU execution is possible, but very slow), however,\ntypical Macbook laptops or window desktops with mid-range GPUs lack VRAM to run\neven the smallest LLMs. For that reason\n**local execution is only supported for models compatible with [llama.cpp](https://github.com/ggerganov/llama.cpp)**\n\nThese two models are known to work well:\n\n* https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF\n* https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF (recommended)\n\nTo ease the installation process, use the `setup` script that will download both\nthe embedding and the LLM model and place them in the correct location (under `models` folder):\n\n```bash\npoetry run python scripts/setup\n```\n\nIf you are ok with CPU execution, you can skip the rest of this section.\n\nAs stated before, llama.cpp is required and in\nparticular [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)\nis used.\n\n> It's highly encouraged that you fully read llama-cpp and llama-cpp-python documentation relevant to your platform.\n> Running into installation issues is very likely, and you'll need to troubleshoot them yourself.\n\n#### OSX GPU support\n\nYou will need to build [llama.cpp](https://github.com/ggerganov/llama.cpp) with \nmetal support. To do that run:\n\n```bash\nCMAKE_ARGS=\"-DLLAMA_METAL=on\" pip install --force-reinstall --no-cache-dir llama-cpp-python\n```\n\n#### Windows GPU support\n\nWindows GPU support is done through CUDA or similar open source technologies.\nFollow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required\ndependencies.\n\nSome tips to get it working with an NVIDIA card and CUDA (Tested on Windows 10 with CUDA 11.5 RTX 3070):\n\n* Install latest VS2022 (and build tools) https://visualstudio.microsoft.com/vs/community/\n* Install CUDA toolkit https://developer.nvidia.com/cuda-downloads\n* [Optional] Install CMake to troubleshoot building issues by compiling llama.cpp directly https://cmake.org/download/\n\nIf you have all required dependencies properly configured running the\nfollowing powershell command should succeed.\n\n```powershell\n$env:CMAKE_ARGS='-DLLAMA_CUBLAS=on'; poetry run pip install --force-reinstall --no-cache-dir llama-cpp-python\n```\n\nIf your installation was correct, you should see a message similar to the following next\ntime you start the server `BLAS = 1`.\n\n```\nllama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, context: 762.87 MB)\nAVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | \n```\n\nNote that llama.cpp offloads matrix calculations to the GPU but the performance is\nstill hit heavily due to latency between CPU and GPU communication. You might need to tweak\nbatch sizes and other parameters to get the best performance for your particular system.\n\n#### Linux GPU support\n\n\ud83d\udea7 Under construction \ud83d\udea7\n\n#### Known issues and Troubleshooting\n\nExecution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms.\nYou might encounter several issues:\n\n* Performance: RAM or VRAM usage is very high, your computer might experience slowdowns or even crashes.\n* GPU Virtualization on Windows and OSX: Simply not possible with docker desktop, you have to run the server directly on\n the host.\n* Building errors: Some of PrivateGPT dependencies need to build native code, and they might fail on some platforms.\n Most likely you are missing some dev tools in your machine (updated C++ compiler, CUDA is not on PATH, etc.).\n If you encounter any of these issues, please open an issue and we'll try to help.\n\n#### Troubleshooting: C++ Compiler\nIf you encounter an error while building a wheel during the `pip install` process, you may need to install a C++ compiler on your computer.\n\n**For Windows 10/11**\n\nTo install a C++ compiler on Windows 10/11, follow these steps:\n\n1. Install Visual Studio 2022.\n2. Make sure the following components are selected:\n * Universal Windows Platform development\n * C++ CMake tools for Windows\n3. Download the MinGW installer from the [MinGW website](https://sourceforge.net/projects/mingw/).\n4. Run the installer and select the `gcc` component.\n\n#### Troubleshooting: Mac Running Intel\nWhen running a Mac with Intel hardware (not M1), you may run into _clang: error: the clang compiler does not support '-march=native'_ during pip install.\n\nIf so set your archflags during pip install. eg: _ARCHFLAGS=\"-arch x86_64\" pip3 install -r requirements.txt_\n\n## Running the Server\n\nAfter following the installation steps you should be ready to go. Here are some common run setups:\n\n### Running 100% locally\n\nMake sure you have followed the *Local LLM requirements* section before moving on.\n\nThis command will start PrivateGPT using the `settings.yaml` (default profile) together with the `settings-local.yaml`\nconfiguration files. By default, it will enable both the API and the Gradio UI. Run:\n\n```\nPGPT_PROFILES=local make run\n``` \n\nor\n\n```\nPGPT_PROFILES=local poetry run python -m private_gpt\n```\n\nWhen the server is started it will print a log *Application startup complete*.\nNavigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API\nusing Swagger UI.\n\n### Local server using OpenAI as LLM\n\nIf you cannot run a local model (because you don't have a GPU, for example) or for testing purposes, you may\ndecide to run PrivateGPT using OpenAI as the LLM.\n\nIn order to do so, create a profile `settings-openai.yaml` with the following contents:\n\n```yaml\nllm:\n mode: openai\n\nopenai:\n api_key: # You could skip this configuration and use the OPENAI_API_KEY env var instead\n```\n\nAnd run PrivateGPT loading that profile you just created:\n\n```PGPT_PROFILES=openai make run```\n\nor\n\n```PGPT_PROFILES=openai poetry run python -m private_gpt```\n\n> Note this will still use the local Embeddings model, as it is ok to use it on a CPU.\n> We'll support using OpenAI embeddings in a future release.\n\nWhen the server is started it will print a log *Application startup complete*.\nNavigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.\nYou'll notice the speed and quality of response is higher, given you are using OpenAI's servers for the heavy\ncomputations.\n\n### Use AWS's Sagemaker\n\n\ud83d\udea7 Under construction \ud83d\udea7\n\n## Gradio UI user manual\n\nGradio UI is a ready to use way of testing most of PrivateGPT API functionalities.\n\n![Gradio PrivateGPT](https://lh3.googleusercontent.com/drive-viewer/AK7aPaBTlIX9j5nsQ87XvcRgf3vhv6UG6pgy4j4IH5mYIo6dHcfJ5IUMiVHoqyQwjTnjRITxYTQ3TcF3pfPXyyWB3HS8hKMWDA=s1600)\n\n### Execution Modes\n\nIt has 3 modes of execution (you can select in the top-left):\n* Query Documents: uses the context from the \n ingested documents to answer the questions posted in the chat. It also takes\n into account previous chat messages as context. \n * Makes use of `/chat/completions` API with `use_context=true` and no \n `context_filter`.\n* LLM Chat: simple, non-contextual chat with the LLM. The ingested documents won't\n be taken into account, only the previous messages.\n * Makes use of `/chat/completions` API with `use_context=false`.\n* Context Chunks: returns the JSON representation of the 2 most related text\n chunks, together with their metadata, source document and previous and next \n chunks. \n * Makes use of `/chunks` API with no `context_filter`, `limit=2` and \n `prev_next_chunks=1`. \n\n### Document Ingestion\n\nIngest documents by using the `Upload a File` button. You can check the progress of \nthe ingestion in the console logs of the server. \n\nThe list of ingested files is shown below the button.\n\nIf you want to delete the ingested documents, refer to *Reset Local documents \ndatabase* section in the documentation.\n\n### Chat\n\nNormal chat interface, self-explanatory ;) \n\nYou can check the actual prompt being passed to the LLM by looking at the logs of \nthe server. We'll add better observability in future releases.\n\n## Deployment options\n\n\ud83d\udea7 We are working on Dockerized deployment guidelines \ud83d\udea7\n\n## Observability\n\nBasic logs are enabled using LlamaIndex \nbasic logging (for example ingestion progress or LLM prompts and answers). \n\n\ud83d\udea7 We are working on improved Observability. \ud83d\udea7 \n\n## Ingesting & Managing Documents\n\n\ud83d\udea7 Document Update and Delete are still WIP. \ud83d\udea7\n\nThe ingestion of documents can be done in different ways:\n* Using the `/ingest` API\n* Using the Gradio UI\n* Using the Bulk Local Ingestion functionality (check next section)\n\n### Bulk Local Ingestion\n\nWhen you are running PrivateGPT in a fully local setup, you can ingest a complete folder for convenience (containing\npdf, text files, etc.)\nand optionally watch changes on it with the command:\n\n```bash\nmake ingest /path/to/folder -- --watch\n```\n\nAfter ingestion is complete, you should be able to chat with your documents\nby navigating to http://localhost:8001 and using the option `Query documents`,\nor using the completions / chat API.\n\n### Reset Local documents database\n\nWhen running in a local setup, you can remove all ingested documents by simply \ndeleting all contents of `local_data` folder (except .gitignore). \n\n## API\n\nAs explained in the introduction, the API contains high level APIs (ingestion and chat/completions) and low level APIs\n(embeddings and chunk retrieval). In this section the different specific API calls are explained.", + "description": "## Introduction\n\nPrivateGPT provides an **API** containing all the building blocks required to build\n**private, context-aware AI applications**. The API follows and extends OpenAI API standard, and supports\nboth normal and streaming responses.\n\nThe API is divided in two logical blocks:\n\n- High-level API, abstracting all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation:\n - Ingestion of documents: internally managing document parsing, splitting, metadata extraction,\n embedding generation and storage.\n - Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt\n engineering and the response generation.\n- Low-level API, allowing advanced users to implement their own complex pipelines:\n - Embeddings generation: based on a piece of text.\n - Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested\n documents.\n\n> A working **Gradio UI client** is provided to test the API, together with a set of\n> useful tools such as bulk model download script, ingestion script, documents folder\n> watch, etc.\n\n## Quick Local Installation steps\n\nThe steps in `Installation and Settings` section are better explained and cover more\nsetup scenarios. But if you are looking for a quick setup guide, here it is:\n\n```\n# Clone the repo\ngit clone https://github.com/imartinez/privateGPT\ncd privateGPT\n\n# Install Python 3.11\npyenv install 3.11\npyenv local 3.11\n\n# Install dependencies\npoetry install --with ui,local\n\n# Download Embedding and LLM models\npoetry run python scripts/setup\n\n# (Optional) For Mac with Metal GPU, enable it. Check Installation and Settings section \nto know how to enable GPU on other platforms\nCMAKE_ARGS=\"-DLLAMA_METAL=on\" pip install --force-reinstall --no-cache-dir llama-cpp-python\n\n# Run the local server \nPGPT_PROFILES=local make run\n\n# Note: on Mac with Metal you should see a ggml_metal_add_buffer log, stating GPU is \nbeing used\n\n# Navigate to the UI and try it out! \nhttp://localhost:8001/\n```\n\n## Installation and Settings\n\n### Base requirements to run PrivateGPT\n\n* Git clone PrivateGPT repository, and navigate to it:\n\n```\n git clone https://github.com/imartinez/privateGPT\n cd privateGPT\n```\n\n* Install Python 3.11. Ideally through a python version manager like `pyenv`.\n Python 3.12\n should work too. Earlier python versions are not supported.\n * osx/linux: [pyenv](https://github.com/pyenv/pyenv)\n * windows: [pyenv-win](https://github.com/pyenv-win/pyenv-win)\n\n``` \npyenv install 3.11\npyenv local 3.11\n```\n\n* Install [Poetry](https://python-poetry.org/docs/#installing-with-the-official-installer) for dependency management:\n\n* Have a valid C++ compiler like gcc. See [Troubleshooting: C++ Compiler](#troubleshooting-c-compiler) for more details.\n\n* Install `make` for scripts:\n * osx: (Using homebrew): `brew install make`\n * windows: (Using chocolatey) `choco install make`\n\n### Install dependencies\n\nInstall the dependencies:\n\n```bash\npoetry install --with ui\n```\n\nVerify everything is working by running `make run` (or `poetry run python -m private_gpt`) and navigate to\nhttp://localhost:8001. You should see a [Gradio UI](https://gradio.app/) **configured with a mock LLM** that will\necho back the input. Later we'll see how to configure a real LLM.\n\n### Settings\n\n> Note: the default settings of PrivateGPT work out-of-the-box for a 100% local setup. Skip this section if you just\n> want to test PrivateGPT locally, and come back later to learn about more configuration options.\n\nPrivateGPT is configured through *profiles* that are defined using yaml files, and selected through env variables.\nThe full list of properties configurable can be found in `settings.yaml`\n\n#### env var `PGPT_SETTINGS_FOLDER`\n\nThe location of the settings folder. Defaults to the root of the project.\nShould contain the default `settings.yaml` and any other `settings-{profile}.yaml`.\n\n#### env var `PGPT_PROFILES`\n\nBy default, the profile definition in `settings.yaml` is loaded.\nUsing this env var you can load additional profiles; format is a comma separated list of profile names.\nThis will merge `settings-{profile}.yaml` on top of the base settings file.\n\nFor example:\n`PGPT_PROFILES=local,cuda` will load `settings-local.yaml`\nand `settings-cuda.yaml`, their contents will be merged with\nlater profiles properties overriding values of earlier ones like `settings.yaml`.\n\nDuring testing, the `test` profile will be active along with the default, therefore `settings-test.yaml`\nfile is required.\n\n#### Environment variables expansion\n\nConfiguration files can contain environment variables,\nthey will be expanded at runtime.\n\nExpansion must follow the pattern `${VARIABLE_NAME:default_value}`.\n\nFor example, the following configuration will use the value of the `PORT`\nenvironment variable or `8001` if it's not set.\nMissing variables with no default will produce an error.\n\n```yaml\nserver:\n port: ${PORT:8001}\n```\n\n### Local LLM requirements\n\nInstall extra dependencies for local execution:\n\n```bash\npoetry install --with local\n```\n\nFor PrivateGPT to run fully locally GPU acceleration is required\n(CPU execution is possible, but very slow), however,\ntypical Macbook laptops or window desktops with mid-range GPUs lack VRAM to run\neven the smallest LLMs. For that reason\n**local execution is only supported for models compatible with [llama.cpp](https://github.com/ggerganov/llama.cpp)**\n\nThese two models are known to work well:\n\n* https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF\n* https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF (recommended)\n\nTo ease the installation process, use the `setup` script that will download both\nthe embedding and the LLM model and place them in the correct location (under `models` folder):\n\n```bash\npoetry run python scripts/setup\n```\n\nIf you are ok with CPU execution, you can skip the rest of this section.\n\nAs stated before, llama.cpp is required and in\nparticular [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)\nis used.\n\n> It's highly encouraged that you fully read llama-cpp and llama-cpp-python documentation relevant to your platform.\n> Running into installation issues is very likely, and you'll need to troubleshoot them yourself.\n\n#### OSX GPU support\n\nYou will need to build [llama.cpp](https://github.com/ggerganov/llama.cpp) with\nmetal support. To do that run:\n\n```bash\nCMAKE_ARGS=\"-DLLAMA_METAL=on\" pip install --force-reinstall --no-cache-dir llama-cpp-python\n```\n\n#### Windows NVIDIA GPU support\n\nWindows GPU support is done through CUDA.\nFollow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required\ndependencies.\n\nSome tips to get it working with an NVIDIA card and CUDA (Tested on Windows 10 with CUDA 11.5 RTX 3070):\n\n* Install latest VS2022 (and build tools) https://visualstudio.microsoft.com/vs/community/\n* Install CUDA toolkit https://developer.nvidia.com/cuda-downloads\n* Verify your installation is correct by running `nvcc --version` and `nvidia-smi`, ensure your CUDA version is up to\n date and your GPU is detected.\n* [Optional] Install CMake to troubleshoot building issues by compiling llama.cpp directly https://cmake.org/download/\n\nIf you have all required dependencies properly configured running the\nfollowing powershell command should succeed.\n\n```powershell\n$env:CMAKE_ARGS='-DLLAMA_CUBLAS=on'; poetry run pip install --force-reinstall --no-cache-dir llama-cpp-python\n```\n\nIf your installation was correct, you should see a message similar to the following next\ntime you start the server `BLAS = 1`.\n\n```\nllama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, context: 762.87 MB)\nAVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | \n```\n\nNote that llama.cpp offloads matrix calculations to the GPU but the performance is\nstill hit heavily due to latency between CPU and GPU communication. You might need to tweak\nbatch sizes and other parameters to get the best performance for your particular system.\n\n#### Linux NVIDIA GPU support and Windows-WSL\n\nLinux GPU support is done through CUDA.\nFollow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required\nexternal\ndependencies.\n\nSome tips:\n\n* Make sure you have an up-to-date C++ compiler\n* Install CUDA toolkit https://developer.nvidia.com/cuda-downloads\n* Verify your installation is correct by running `nvcc --version` and `nvidia-smi`, ensure your CUDA version is up to\n date and your GPU is detected.\n\nAfter that running the following command in the repository will install llama.cpp with GPU support:\n\n`\nCMAKE_ARGS='-DLLAMA_CUBLAS=on' poetry run pip install --force-reinstall --no-cache-dir llama-cpp-python\n`\n\nIf your installation was correct, you should see a message similar to the following next\ntime you start the server `BLAS = 1`.\n\n```\nllama_new_context_with_model: total VRAM used: 4857.93 MB (model: 4095.05 MB, context: 762.87 MB)\nAVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 | \n```\n\n#### Known issues and Troubleshooting\n\nExecution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms.\nYou might encounter several issues:\n\n* Performance: RAM or VRAM usage is very high, your computer might experience slowdowns or even crashes.\n* GPU Virtualization on Windows and OSX: Simply not possible with docker desktop, you have to run the server directly on\n the host.\n* Building errors: Some of PrivateGPT dependencies need to build native code, and they might fail on some platforms.\n Most likely you are missing some dev tools in your machine (updated C++ compiler, CUDA is not on PATH, etc.).\n If you encounter any of these issues, please open an issue and we'll try to help.\n\n#### Troubleshooting: C++ Compiler\n\nIf you encounter an error while building a wheel during the `pip install` process, you may need to install a C++\ncompiler on your computer.\n\n**For Windows 10/11**\n\nTo install a C++ compiler on Windows 10/11, follow these steps:\n\n1. Install Visual Studio 2022.\n2. Make sure the following components are selected:\n * Universal Windows Platform development\n * C++ CMake tools for Windows\n3. Download the MinGW installer from the [MinGW website](https://sourceforge.net/projects/mingw/).\n4. Run the installer and select the `gcc` component.\n\n** For OSX **\n\n1. Check if you have a C++ compiler installed, Xcode might have done it for you. for example running `gcc`.\n2. If not, you can install clang or gcc with homebrew `brew install gcc`\n\n#### Troubleshooting: Mac Running Intel\n\nWhen running a Mac with Intel hardware (not M1), you may run into _clang: error: the clang compiler does not support '\n-march=native'_ during pip install.\n\nIf so set your archflags during pip install. eg: _ARCHFLAGS=\"-arch x86_64\" pip3 install -r requirements.txt_\n\n## Running the Server\n\nAfter following the installation steps you should be ready to go. Here are some common run setups:\n\n### Running 100% locally\n\nMake sure you have followed the *Local LLM requirements* section before moving on.\n\nThis command will start PrivateGPT using the `settings.yaml` (default profile) together with the `settings-local.yaml`\nconfiguration files. By default, it will enable both the API and the Gradio UI. Run:\n\n```\nPGPT_PROFILES=local make run\n``` \n\nor\n\n```\nPGPT_PROFILES=local poetry run python -m private_gpt\n```\n\nWhen the server is started it will print a log *Application startup complete*.\nNavigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API\nusing Swagger UI.\n\n### Local server using OpenAI as LLM\n\nIf you cannot run a local model (because you don't have a GPU, for example) or for testing purposes, you may\ndecide to run PrivateGPT using OpenAI as the LLM.\n\nIn order to do so, create a profile `settings-openai.yaml` with the following contents:\n\n```yaml\nllm:\n mode: openai\n\nopenai:\n api_key: # You could skip this configuration and use the OPENAI_API_KEY env var instead\n```\n\nAnd run PrivateGPT loading that profile you just created:\n\n```PGPT_PROFILES=openai make run```\n\nor\n\n```PGPT_PROFILES=openai poetry run python -m private_gpt```\n\n> Note this will still use the local Embeddings model, as it is ok to use it on a CPU.\n> We'll support using OpenAI embeddings in a future release.\n\nWhen the server is started it will print a log *Application startup complete*.\nNavigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API.\nYou'll notice the speed and quality of response is higher, given you are using OpenAI's servers for the heavy\ncomputations.\n\n### Use AWS's Sagemaker\n\n\ud83d\udea7 Under construction \ud83d\udea7\n\n## Gradio UI user manual\n\nGradio UI is a ready to use way of testing most of PrivateGPT API functionalities.\n\n![Gradio PrivateGPT](https://lh3.googleusercontent.com/drive-viewer/AK7aPaBTlIX9j5nsQ87XvcRgf3vhv6UG6pgy4j4IH5mYIo6dHcfJ5IUMiVHoqyQwjTnjRITxYTQ3TcF3pfPXyyWB3HS8hKMWDA=s1600)\n\n### Execution Modes\n\nIt has 3 modes of execution (you can select in the top-left):\n\n* Query Documents: uses the context from the\n ingested documents to answer the questions posted in the chat. It also takes\n into account previous chat messages as context.\n * Makes use of `/chat/completions` API with `use_context=true` and no\n `context_filter`.\n* LLM Chat: simple, non-contextual chat with the LLM. The ingested documents won't\n be taken into account, only the previous messages.\n * Makes use of `/chat/completions` API with `use_context=false`.\n* Context Chunks: returns the JSON representation of the 2 most related text\n chunks, together with their metadata, source document and previous and next\n chunks.\n * Makes use of `/chunks` API with no `context_filter`, `limit=2` and\n `prev_next_chunks=1`.\n\n### Document Ingestion\n\nIngest documents by using the `Upload a File` button. You can check the progress of\nthe ingestion in the console logs of the server.\n\nThe list of ingested files is shown below the button.\n\nIf you want to delete the ingested documents, refer to *Reset Local documents\ndatabase* section in the documentation.\n\n### Chat\n\nNormal chat interface, self-explanatory ;)\n\nYou can check the actual prompt being passed to the LLM by looking at the logs of\nthe server. We'll add better observability in future releases.\n\n## Deployment options\n\n\ud83d\udea7 We are working on Dockerized deployment guidelines \ud83d\udea7\n\n## Observability\n\nBasic logs are enabled using LlamaIndex\nbasic logging (for example ingestion progress or LLM prompts and answers).\n\n\ud83d\udea7 We are working on improved Observability. \ud83d\udea7\n\n## Ingesting & Managing Documents\n\n\ud83d\udea7 Document Update and Delete are still WIP. \ud83d\udea7\n\nThe ingestion of documents can be done in different ways:\n\n* Using the `/ingest` API\n* Using the Gradio UI\n* Using the Bulk Local Ingestion functionality (check next section)\n\n### Bulk Local Ingestion\n\nWhen you are running PrivateGPT in a fully local setup, you can ingest a complete folder for convenience (containing\npdf, text files, etc.)\nand optionally watch changes on it with the command:\n\n```bash\nmake ingest /path/to/folder -- --watch\n```\n\nAfter ingestion is complete, you should be able to chat with your documents\nby navigating to http://localhost:8001 and using the option `Query documents`,\nor using the completions / chat API.\n\n### Reset Local documents database\n\nWhen running in a local setup, you can remove all ingested documents by simply\ndeleting all contents of `local_data` folder (except .gitignore).\n\n## API\n\nAs explained in the introduction, the API contains high level APIs (ingestion and chat/completions) and low level APIs\n(embeddings and chunk retrieval). In this section the different specific API calls are explained.\n", "contact": { "url": "https://github.com/imartinez/privateGPT" }, From f5a9bf4e374b2d4c76438cf8a97cccf222ec8e6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Iv=C3=A1n=20Mart=C3=ADnez?= Date: Fri, 20 Oct 2023 18:24:56 +0200 Subject: [PATCH 002/191] fix: chromadb max batch size (#1087) --- poetry.lock | 82 +++++------------ .../components/vector_store/batched_chroma.py | 87 +++++++++++++++++++ .../vector_store/vector_store_component.py | 10 ++- pyproject.toml | 4 +- tests/server/ingest/test_ingest_service.py | 27 ++++++ 5 files changed, 142 insertions(+), 68 deletions(-) create mode 100644 private_gpt/components/vector_store/batched_chroma.py create mode 100644 tests/server/ingest/test_ingest_service.py diff --git a/poetry.lock b/poetry.lock index 4ca1626..f1e3ac2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -261,24 +261,6 @@ files = [ tests = ["pytest (>=3.2.1,!=3.3.0)"] typecheck = ["mypy"] -[[package]] -name = "beautifulsoup4" -version = "4.12.2" -description = "Screen-scraping library" -optional = false -python-versions = ">=3.6.0" -files = [ - 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{version = ">=1.26.0", markers = "python_version >= \"3.12\""}, -] +numpy = {version = ">=1.23.2", markers = "python_version == \"3.11\""} python-dateutil = ">=2.8.2" pytz = ">=2020.1" tzdata = ">=2022.1" @@ -3469,11 +3443,6 @@ files = [ {file = "scikit_learn-1.3.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f66eddfda9d45dd6cadcd706b65669ce1df84b8549875691b1f403730bdef217"}, {file = "scikit_learn-1.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c6448c37741145b241eeac617028ba6ec2119e1339b1385c9720dae31367f2be"}, {file = "scikit_learn-1.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:c413c2c850241998168bbb3bd1bb59ff03b1195a53864f0b80ab092071af6028"}, - {file = "scikit_learn-1.3.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ef540e09873e31569bc8b02c8a9f745ee04d8e1263255a15c9969f6f5caa627f"}, - {file = "scikit_learn-1.3.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:9147a3a4df4d401e618713880be023e36109c85d8569b3bf5377e6cd3fecdeac"}, - {file = "scikit_learn-1.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2cd3634695ad192bf71645702b3df498bd1e246fc2d529effdb45a06ab028b4"}, - {file = "scikit_learn-1.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0c275a06c5190c5ce00af0acbb61c06374087949f643ef32d355ece12c4db043"}, - {file = "scikit_learn-1.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:0e1aa8f206d0de814b81b41d60c1ce31f7f2c7354597af38fae46d9c47c45122"}, {file = "scikit_learn-1.3.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:52b77cc08bd555969ec5150788ed50276f5ef83abb72e6f469c5b91a0009bbca"}, {file = "scikit_learn-1.3.1-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:a683394bc3f80b7c312c27f9b14ebea7766b1f0a34faf1a2e9158d80e860ec26"}, {file = "scikit_learn-1.3.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a15d964d9eb181c79c190d3dbc2fff7338786bf017e9039571418a1d53dab236"}, @@ -3690,17 +3659,6 @@ files = [ {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"}, ] -[[package]] -name = "soupsieve" -version = "2.5" -description = "A modern CSS selector implementation for Beautiful Soup." -optional = false -python-versions = ">=3.8" -files = [ - {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"}, - {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"}, -] - [[package]] name = "sqlalchemy" version = "2.0.22" @@ -3760,7 +3718,7 @@ files = [ ] [package.dependencies] -greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""} +greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or extra == \"asyncio\""} typing-extensions = ">=4.2.0" [package.extras] @@ -4738,5 +4696,5 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" -python-versions = ">=3.11,<3.13" -content-hash = "c1fa5accdcd9cd81430839398e16d2596b43b1c314b5d2a8a76aa05bbb83a39c" +python-versions = ">=3.11,<3.12" +content-hash = "56b78ce6a8a6dfbe42b490bcf4ffbf2820f10d5ce70a28c61ee4e357172dab33" diff --git a/private_gpt/components/vector_store/batched_chroma.py b/private_gpt/components/vector_store/batched_chroma.py new file mode 100644 index 0000000..9458e78 --- /dev/null +++ b/private_gpt/components/vector_store/batched_chroma.py @@ -0,0 +1,87 @@ +from typing import Any + +from llama_index.schema import BaseNode, MetadataMode +from llama_index.vector_stores import ChromaVectorStore +from llama_index.vector_stores.chroma import chunk_list +from llama_index.vector_stores.utils import node_to_metadata_dict + + +class BatchedChromaVectorStore(ChromaVectorStore): + """Chroma vector store, batching additions to avoid reaching the max batch limit. + + In this vector store, embeddings are stored within a ChromaDB collection. + + During query time, the index uses ChromaDB to query for the top + k most similar nodes. + + Args: + chroma_client (from chromadb.api.API): + API instance + chroma_collection (chromadb.api.models.Collection.Collection): + ChromaDB collection instance + + """ + + chroma_client: Any | None + + def __init__( + self, + chroma_client: Any, + chroma_collection: Any, + host: str | None = None, + port: str | None = None, + ssl: bool = False, + headers: dict[str, str] | None = None, + collection_kwargs: dict[Any, Any] | None = None, + ) -> None: + super().__init__( + chroma_collection=chroma_collection, + host=host, + port=port, + ssl=ssl, + headers=headers, + collection_kwargs=collection_kwargs or {}, + ) + self.chroma_client = chroma_client + + def add(self, nodes: list[BaseNode]) -> list[str]: + """Add nodes to index, batching the insertion to avoid issues. + + Args: + nodes: List[BaseNode]: list of nodes with embeddings + + """ + if not self.chroma_client: + raise ValueError("Client not initialized") + + if not self._collection: + raise ValueError("Collection not initialized") + + max_chunk_size = self.chroma_client.max_batch_size + node_chunks = chunk_list(nodes, max_chunk_size) + + all_ids = [] + for node_chunk in node_chunks: + embeddings = [] + metadatas = [] + ids = [] + documents = [] + for node in node_chunk: + embeddings.append(node.get_embedding()) + metadatas.append( + node_to_metadata_dict( + node, remove_text=True, flat_metadata=self.flat_metadata + ) + ) + ids.append(node.node_id) + documents.append(node.get_content(metadata_mode=MetadataMode.NONE)) + + self._collection.add( + embeddings=embeddings, + ids=ids, + metadatas=metadatas, + documents=documents, + ) + all_ids.extend(ids) + + return all_ids diff --git a/private_gpt/components/vector_store/vector_store_component.py b/private_gpt/components/vector_store/vector_store_component.py index 02f4a91..5d173fb 100644 --- a/private_gpt/components/vector_store/vector_store_component.py +++ b/private_gpt/components/vector_store/vector_store_component.py @@ -4,9 +4,9 @@ import chromadb from injector import inject, singleton from llama_index import VectorStoreIndex from llama_index.indices.vector_store import VectorIndexRetriever -from llama_index.vector_stores import ChromaVectorStore from llama_index.vector_stores.types import VectorStore +from private_gpt.components.vector_store.batched_chroma import BatchedChromaVectorStore from private_gpt.open_ai.extensions.context_filter import ContextFilter from private_gpt.paths import local_data_path @@ -36,14 +36,16 @@ class VectorStoreComponent: @inject def __init__(self) -> None: - db = chromadb.PersistentClient( + chroma_client = chromadb.PersistentClient( path=str((local_data_path / "chroma_db").absolute()) ) - chroma_collection = db.get_or_create_collection( + chroma_collection = chroma_client.get_or_create_collection( "make_this_parameterizable_per_api_call" ) # TODO - self.vector_store = ChromaVectorStore(chroma_collection=chroma_collection) + self.vector_store = BatchedChromaVectorStore( + chroma_client=chroma_client, chroma_collection=chroma_collection + ) @staticmethod def get_retriever( diff --git a/pyproject.toml b/pyproject.toml index 4991ec7..05dab2f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ description = "Private GPT" authors = ["Zylon "] [tool.poetry.dependencies] -python = ">=3.11,<3.13" +python = ">=3.11,<3.12" fastapi = { extras = ["all"], version = "^0.103.1" } loguru = "^0.7.2" boto3 = "^1.28.56" @@ -13,7 +13,7 @@ injector = "^0.21.0" pyyaml = "^6.0.1" python-multipart = "^0.0.6" pypdf = "^3.16.2" -llama-index = "v0.8.35" +llama-index = "0.8.47" chromadb = "^0.4.13" watchdog = "^3.0.0" transformers = "^4.34.0" diff --git a/tests/server/ingest/test_ingest_service.py b/tests/server/ingest/test_ingest_service.py new file mode 100644 index 0000000..a855d84 --- /dev/null +++ b/tests/server/ingest/test_ingest_service.py @@ -0,0 +1,27 @@ +from unittest.mock import PropertyMock, patch + +from llama_index import Document + +from private_gpt.server.ingest.ingest_service import IngestService +from tests.fixtures.mock_injector import MockInjector + + +def test_save_many_nodes(injector: MockInjector) -> None: + """This is a specific test for a local Chromadb Vector Database setup. + + Extend it when we add support for other vector databases in VectorStoreComponent. + """ + with patch( + "chromadb.api.segment.SegmentAPI.max_batch_size", new_callable=PropertyMock + ) as max_batch_size: + # Make max batch size of Chromadb very small + max_batch_size.return_value = 10 + + ingest_service = injector.get(IngestService) + + documents = [] + for _i in range(100): + documents.append(Document(text="This is a sentence.")) + + ingested_docs = ingest_service._save_docs(documents) + assert len(ingested_docs) == len(documents) From b8383e00a64a7da11e0d272bd4995c30f7c9e6d0 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 20 Oct 2023 18:29:21 +0200 Subject: [PATCH 003/191] chore(main): release 0.0.2 (#1088) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- CHANGELOG.md | 7 +++++++ version.txt | 2 +- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7dc1b9d..6ec7339 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,12 @@ # Changelog +## [0.0.2](https://github.com/imartinez/privateGPT/compare/v0.0.1...v0.0.2) (2023-10-20) + + +### Bug Fixes + +* chromadb max batch size ([#1087](https://github.com/imartinez/privateGPT/issues/1087)) ([f5a9bf4](https://github.com/imartinez/privateGPT/commit/f5a9bf4e374b2d4c76438cf8a97cccf222ec8e6f)) + ## 0.0.1 (2023-10-20) ### Miscellaneous Chores diff --git a/version.txt b/version.txt index 8acdd82..4e379d2 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.0.1 +0.0.2 From ba23443a70d323cd4f9a242b33fd9dce1bacd2db Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Mon, 23 Oct 2023 15:54:12 +0900 Subject: [PATCH 004/191] fix: typo in README.md (#1091) componentes -> components --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 00f6e39..dfdc040 100644 --- a/README.md +++ b/README.md @@ -87,7 +87,7 @@ primitives. The design of PrivateGPT allows to easily extend and adapt both the API and the RAG implementation. Some key architectural decisions are: -* Dependency Injection, decoupling the different componentes and layers. +* Dependency Injection, decoupling the different components and layers. * Usage of LlamaIndex abstractions such as `LLM`, `BaseEmbedding` or `VectorStore`, making it immediate to change the actual implementations of those abstractions. * Simplicity, adding as few layers and new abstractions as possible. From 769a047b54f675d45819392c37e4e2e2e63cd405 Mon Sep 17 00:00:00 2001 From: Federico Grandi Date: Mon, 23 Oct 2023 10:49:02 +0200 Subject: [PATCH 005/191] chore: add GitHub metadata (#1085) * chore: add citation file * chore: add LICENSE * chore: update email Co-authored-by: Daniel Gallego * chore: update email in citation file Co-authored-by: Pablo Orgaz * chore: add ORCIDs to citation file * docs: update README with citation info --------- Co-authored-by: Daniel Gallego Co-authored-by: Pablo Orgaz --- CITATION.cff | 25 +++++++ LICENSE | 201 +++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 22 ++++-- 3 files changed, 242 insertions(+), 6 deletions(-) create mode 100644 CITATION.cff create mode 100644 LICENSE diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..73a213d --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,25 @@ +# This CITATION.cff file was generated with cffinit. +# Visit https://bit.ly/cffinit to generate yours today! + +cff-version: 1.2.0 +title: PrivateGPT +message: >- + If you use this software, please cite it using the + metadata from this file. +type: software +authors: + - given-names: Iván + family-names: Martínez Toro + email: ivanmartit@gmail.com + orcid: 'https://orcid.org/0009-0004-5065-2311' + - family-names: Gallego Vico + given-names: Daniel + email: danielgallegovico@gmail.com + orcid: 'https://orcid.org/0009-0006-8582-4384' + - given-names: Pablo + family-names: Orgaz + email: pabloogc+gh@gmail.com + orcid: 'https://orcid.org/0009-0008-0080-1437' +repository-code: 'https://github.com/imartinez/privateGPT' +license: Apache-2.0 +date-released: '2023-05-02' diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index dfdc040..bad7195 100644 --- a/README.md +++ b/README.md @@ -149,14 +149,24 @@ Join the conversation around PrivateGPT on our: - [Discord](https://discord.gg/bK6mRVpErU) ## 📖 Citation -Reference to cite if you use PrivateGPT in a paper: +If you use PrivateGPT in a paper, check out the [Citation file](CITATION.cff) for the correct citation. +You can also use the "Cite this repository" button in this repo to get the citation in different formats. -``` -@software{PrivateGPT_2023, -authors = {Martinez, I., Gallego, D. Orgaz, P.}, -month = {5}, -title = {PrivateGPT}, +Here are a couple of examples: + +#### BibTeX +```bibtex +@software{Martinez_Toro_PrivateGPT_2023, +author = {Martínez Toro, Iván and Gallego Vico, Daniel and Orgaz, Pablo}, +license = {Apache-2.0}, +month = may, +title = {{PrivateGPT}}, url = {https://github.com/imartinez/privateGPT}, year = {2023} } ``` + +#### APA +``` +Martínez Toro, I., Gallego Vico, D., & Orgaz, P. (2023). PrivateGPT [Computer software]. https://github.com/imartinez/privateGPT +``` From 78546524d0af18b26ddf6d3ac0cb2760ba4fa5e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Iv=C3=A1n=20Mart=C3=ADnez?= Date: Mon, 23 Oct 2023 10:50:42 +0200 Subject: [PATCH 006/191] Use OpenAI for embeddings when openai mode is selected (#1096) --- .../components/embedding/embedding_component.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/private_gpt/components/embedding/embedding_component.py b/private_gpt/components/embedding/embedding_component.py index 41b4e1f..61fe6fa 100644 --- a/private_gpt/components/embedding/embedding_component.py +++ b/private_gpt/components/embedding/embedding_component.py @@ -13,14 +13,19 @@ class EmbeddingComponent: @inject def __init__(self) -> None: match settings.llm.mode: - case "mock": - # Not a random number, is the dimensionality used by - # the default embedding model - self.embedding_model = MockEmbedding(384) - case _: + case "local": from llama_index.embeddings import HuggingFaceEmbedding self.embedding_model = HuggingFaceEmbedding( model_name=settings.local.embedding_hf_model_name, cache_folder=str(models_cache_path), ) + case "openai": + from llama_index import OpenAIEmbedding + + openai_settings = settings.openai.api_key + self.embedding_model = OpenAIEmbedding(api_key=openai_settings) + case "mock": + # Not a random number, is the dimensionality used by + # the default embedding model + self.embedding_model = MockEmbedding(384) From 768e5ff50529caf2032ddabb4c6beb66891e1f12 Mon Sep 17 00:00:00 2001 From: Shivam Singh <103785990+Shivam250702@users.noreply.github.com> Date: Tue, 24 Oct 2023 16:13:41 +0530 Subject: [PATCH 007/191] chore: Update README.md (#1102) Removed Grammatical errors --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index bad7195..9fd9a7c 100644 --- a/README.md +++ b/README.md @@ -6,12 +6,12 @@ demo -PrivateGPT is a production-ready AI project that allows you to ask questions to your documents using the power -of Large Language Models (LLMs), even in scenarios without Internet connection. 100% private, no data leaves your +PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using the power +of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% private, no data leaves your execution environment at any point. The project provides an API offering all the primitives required to build private, context-aware AI applications. -It follows and extends [OpenAI API standard](https://openai.com/blog/openai-api), +It follows and extends the [OpenAI API standard](https://openai.com/blog/openai-api), and supports both normal and streaming responses. The API is divided into two logical blocks: @@ -40,14 +40,14 @@ DISCLAIMER: This README is not updated as frequently as the [documentation](http Please check it out for the latest updates! ### Motivation behind PrivateGPT -Generative AI is a game changer for our society, but adoption in companies of all size and data-sensitive +Generative AI is a game changer for our society, but adoption in companies of all sizes and data-sensitive domains like healthcare or legal is limited by a clear concern: **privacy**. Not being able to ensure that your data is fully under your control when using third-party AI tools is a risk those industries cannot take. ### Primordial version The first version of PrivateGPT was launched in May 2023 as a novel approach to address the privacy -concern by using LLMs in a complete offline way. +concerns by using LLMs in a complete offline way. This was done by leveraging existing technologies developed by the thriving Open Source AI community: [LangChain](https://github.com/hwchase17/langchain), [LlamaIndex](https://www.llamaindex.ai/), [GPT4All](https://github.com/nomic-ai/gpt4all), @@ -69,10 +69,10 @@ PrivateGPT if you come from the previous, primordial version. ### Present and Future of PrivateGPT PrivateGPT is now evolving towards becoming a gateway to generative AI models and primitives, including completions, document ingestion, RAG pipelines and other low-level building blocks. -We want to make easier for any developer to build AI applications and experiences, as well as providing +We want to make it easier for any developer to build AI applications and experiences, as well as provide a suitable extensive architecture for the community to keep contributing. -Stay tuned to our [releases](https://github.com/imartinez/privateGPT/releases) to check all the new features and changes included. +Stay tuned to our [releases](https://github.com/imartinez/privateGPT/releases) to check out all the new features and changes included. ## 📄 Documentation Full documentation on installation, dependencies, configuration, running the server, deployment options, From 895588b82a06c2bc71a9e22fb840c7f6442a3b5b Mon Sep 17 00:00:00 2001 From: Pablo Orgaz Date: Fri, 27 Oct 2023 13:29:29 +0200 Subject: [PATCH 008/191] fix: Docker and sagemaker setup (#1118) * fix: docker copying extra files * feat: allow configuring mode through env vars * feat: Attempt to build and tag a docker image * fix: run docker on release * fix: typing in prompt transformation * chore: remove tutorial comments --- .dockerignore | 3 ++ .github/workflows/docker.yml | 47 +++++++++++++++++++ Dockerfile | 11 ++--- .../embedding/embedding_component.py | 2 +- .../components/llm/custom/sagemaker.py | 6 +-- private_gpt/components/llm/llm_component.py | 2 + settings-docker.yaml | 11 +++-- 7 files changed, 67 insertions(+), 15 deletions(-) create mode 100644 .github/workflows/docker.yml diff --git a/.dockerignore b/.dockerignore index 35b2f4c..c156a9b 100644 --- a/.dockerignore +++ b/.dockerignore @@ -3,6 +3,9 @@ models .github .vscode .DS_Store +.mypy_cache +.ruff_cache +local_data terraform tests Dockerfile diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml new file mode 100644 index 0000000..a0a0ec1 --- /dev/null +++ b/.github/workflows/docker.yml @@ -0,0 +1,47 @@ +name: Create and publish a Docker image + +on: + release: + types: [ published ] + push: + branches: + - main + pull_request: + +env: + REGISTRY: ghcr.io + IMAGE_NAME: ${{ github.repository }} + +jobs: + build-and-push-image: + runs-on: ubuntu-latest + permissions: + contents: read + packages: write + steps: + - name: Checkout repository + uses: actions/checkout@v4 + - name: Log in to the Container registry + uses: docker/login-action@v3 + with: + registry: ${{ env.REGISTRY }} + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + - name: Extract metadata (tags, labels) for Docker + id: meta + uses: docker/metadata-action@v5 + with: + images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} + tags: | + type=ref,event=branch + type=ref,event=pr + type=semver,pattern={{version}} + type=semver,pattern={{major}}.{{minor}} + type=sha + - name: Build and push Docker image + uses: docker/build-push-action@v5 + with: + context: . + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 170531a..309c2a1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -23,10 +23,7 @@ FROM base as dependencies WORKDIR /home/worker/app COPY pyproject.toml poetry.lock ./ -RUN poetry install --with local RUN poetry install --with ui -RUN CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS"\ - poetry run pip install --force-reinstall --no-cache-dir llama-cpp-python FROM base as app @@ -39,9 +36,11 @@ EXPOSE 8080 RUN adduser --system worker WORKDIR /home/worker/app -# Copy everything, including the virtual environment -COPY --chown=worker --from=dependencies /home/worker/app . -COPY --chown=worker . . +RUN mkdir "local_data"; chown worker local_data +COPY --chown=worker --from=dependencies /home/worker/app/.venv/ .venv +COPY --chown=worker private_gpt/ private_gpt +COPY --chown=worker docs/ docs +COPY --chown=worker *.yaml *.md ./ USER worker ENTRYPOINT .venv/bin/python -m private_gpt \ No newline at end of file diff --git a/private_gpt/components/embedding/embedding_component.py b/private_gpt/components/embedding/embedding_component.py index 61fe6fa..079e72b 100644 --- a/private_gpt/components/embedding/embedding_component.py +++ b/private_gpt/components/embedding/embedding_component.py @@ -13,7 +13,7 @@ class EmbeddingComponent: @inject def __init__(self) -> None: match settings.llm.mode: - case "local": + case "local" | "sagemaker": from llama_index.embeddings import HuggingFaceEmbedding self.embedding_model = HuggingFaceEmbedding( diff --git a/private_gpt/components/llm/custom/sagemaker.py b/private_gpt/components/llm/custom/sagemaker.py index 9286de6..08f56a9 100644 --- a/private_gpt/components/llm/custom/sagemaker.py +++ b/private_gpt/components/llm/custom/sagemaker.py @@ -21,8 +21,6 @@ from llama_index.llms.llama_utils import ( ) if TYPE_CHECKING: - from collections.abc import Callable - from llama_index.callbacks import CallbackManager from llama_index.llms import ( CompletionResponseGen, @@ -113,10 +111,10 @@ class SagemakerLLM(CustomLLM): context_window: int = Field( description="The maximum number of context tokens for the model." ) - messages_to_prompt: Callable[..., str] = Field( + messages_to_prompt: Any = Field( description="The function to convert messages to a prompt.", exclude=True ) - completion_to_prompt: Callable[..., str] = Field( + completion_to_prompt: Any = Field( description="The function to convert a completion to a prompt.", exclude=True ) generate_kwargs: dict[str, Any] = Field( diff --git a/private_gpt/components/llm/llm_component.py b/private_gpt/components/llm/llm_component.py index 2c32897..cbd71ce 100644 --- a/private_gpt/components/llm/llm_component.py +++ b/private_gpt/components/llm/llm_component.py @@ -37,6 +37,8 @@ class LLMComponent: self.llm = SagemakerLLM( endpoint_name=settings.sagemaker.endpoint_name, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, ) case "openai": from llama_index.llms import OpenAI diff --git a/settings-docker.yaml b/settings-docker.yaml index 73255da..12fea9f 100644 --- a/settings-docker.yaml +++ b/settings-docker.yaml @@ -3,12 +3,15 @@ server: port: ${PORT:8080} llm: - mode: local + mode: ${PGPT_MODE:mock} local: - llm_hf_repo_id: TheBloke/Mistral-7B-Instruct-v0.1-GGUF - llm_hf_model_file: mistral-7b-instruct-v0.1.Q4_K_M.gguf - embedding_hf_model_name: BAAI/bge-small-en-v1.5 + llm_hf_repo_id: ${PGPT_HF_REPO_ID:TheBloke/Mistral-7B-Instruct-v0.1-GGUF} + llm_hf_model_file: ${PGPT_HF_MODEL_FILE:mistral-7b-instruct-v0.1.Q4_K_M.gguf} + embedding_hf_model_name: ${PGPT_EMBEDDING_HF_MODEL_NAME:BAAI/bge-small-en-v1.5} + +sagemaker: + endpoint_name: ${PGPT_SAGEMAKER_ENDPOINT_NAME:} ui: enabled: true From 24cfddd60f74aadd2dade4c63f6012a2489938a1 Mon Sep 17 00:00:00 2001 From: Pablo Orgaz Date: Fri, 27 Oct 2023 20:27:40 +0200 Subject: [PATCH 009/191] fix: fix pytorch version to avoid wheel bug (#1123) * fix: fix pytorch version * fix: settings env var regex and split * fix: add models folder for docker user --- Dockerfile | 3 +- poetry.lock | 1608 +++++++++++++++++------- private_gpt/settings/yaml.py | 4 +- pyproject.toml | 3 +- tests/settings/test_settings_loader.py | 12 +- 5 files changed, 1150 insertions(+), 480 deletions(-) diff --git a/Dockerfile b/Dockerfile index 309c2a1..e9ebd15 100644 --- a/Dockerfile +++ b/Dockerfile @@ -36,7 +36,8 @@ EXPOSE 8080 RUN adduser --system worker WORKDIR /home/worker/app -RUN mkdir "local_data"; chown worker local_data +RUN mkdir local_data; chown worker local_data +RUN mkdir models; chown worker models COPY --chown=worker --from=dependencies /home/worker/app/.venv/ .venv COPY --chown=worker private_gpt/ private_gpt COPY --chown=worker docs/ docs diff --git a/poetry.lock b/poetry.lock index f1e3ac2..4b36f6f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -296,17 +296,17 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "boto3" -version = "1.28.63" +version = "1.28.72" description = "The AWS SDK for Python" optional = false python-versions = ">= 3.7" files = [ - {file = "boto3-1.28.63-py3-none-any.whl", hash = "sha256:65d052ec13197460586ee385aa2d6bba0e7378d2d2c7f3e93c044c43ae1ca782"}, - {file = "boto3-1.28.63.tar.gz", hash = "sha256:94218aba2feb5b404b665b8d76c172dc654f79b4c5fa0e9e92459c098da87bf4"}, + {file = "boto3-1.28.72-py3-none-any.whl", hash = "sha256:73e2b56dcbe1b856274185c908dcb5973b007d62c74b646e6bd2ac09a303983a"}, + {file = "boto3-1.28.72.tar.gz", hash = "sha256:cc7d939cdbb4ad3980274c96c589e95f028f740ac49a3547c8f4aaec6a7a6409"}, ] [package.dependencies] -botocore = ">=1.31.63,<1.32.0" +botocore = ">=1.31.72,<1.32.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.7.0,<0.8.0" @@ -315,13 +315,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.31.63" +version = "1.31.72" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">= 3.7" files = [ - {file = "botocore-1.31.63-py3-none-any.whl", hash = "sha256:cb9db5db5af865b1fc2e1405b967db5d78dd0f4d84e5dc1974e082733c1034b7"}, - {file = "botocore-1.31.63.tar.gz", hash = "sha256:6e582c811ea74f25bdb490ac372b2645de4a60286b42ddd8c69f3b6df82b6b12"}, + {file = "botocore-1.31.72-py3-none-any.whl", hash = "sha256:1a0f5063a9a5e1ef40907c67778625cf14a8c47c8e09cd2b3f9dc53a8adb52cf"}, + {file = "botocore-1.31.72.tar.gz", hash = "sha256:37763f40c2fcc48114015fe43bbd75ec1aabef48b418e1a5e28cb1c350967260"}, ] [package.dependencies] @@ -332,6 +332,17 @@ urllib3 = {version = ">=1.25.4,<2.1", markers = "python_version >= \"3.10\""} [package.extras] crt = ["awscrt (==0.16.26)"] +[[package]] +name = "cachetools" +version = "5.3.2" +description = "Extensible memoizing collections and decorators" +optional = false +python-versions = ">=3.7" +files = [ + {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"}, + {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"}, +] + [[package]] name = "certifi" version = "2023.7.22" @@ -356,101 +367,101 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.3.0" +version = "3.3.1" description = "The Real First Universal Charset Detector. 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= re.compile(r"\$\{(\w|_)+(:(\w|_)*)?}") +_env_replace_matcher = re.compile(r"\$\{(\w|_)+:?.*}") @typing.no_type_check # pyaml does not have good hints, everything is Any @@ -22,7 +22,7 @@ def load_yaml_with_envvars( def load_env_var(_, node) -> str: """Extract the matched value, expand env variable, and replace the match.""" value = str(node.value).removeprefix("${").removesuffix("}") - split = value.split(":") + split = value.split(":", 1) env_var = split[0] value = environ.get(env_var) default = None if len(split) == 1 else split[1] diff --git a/pyproject.toml b/pyproject.toml index 05dab2f..7ac7330 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,8 @@ llama-index = "0.8.47" chromadb = "^0.4.13" watchdog = "^3.0.0" transformers = "^4.34.0" -torch = "^2.1.0" +# https://stackoverflow.com/questions/76327419/valueerror-libcublas-so-0-9-not-found-in-the-system-path +torch = ">=2.0.0, !=2.0.1, !=2.1.0" [tool.poetry.group.dev.dependencies] black = "^22" diff --git a/tests/settings/test_settings_loader.py b/tests/settings/test_settings_loader.py index af29bc6..8183c4e 100644 --- a/tests/settings/test_settings_loader.py +++ b/tests/settings/test_settings_loader.py @@ -18,10 +18,18 @@ def test_environment_variables_are_loaded() -> None: def test_environment_defaults_variables_are_loaded() -> None: sample_yaml = """ - replaced: ${TEST_REPLACE_ME:default} + replaced: ${PGPT_EMBEDDING_HF_MODEL_NAME:BAAI/bge-small-en-v1.5} """ loaded = load_yaml_with_envvars(io.StringIO(sample_yaml), {}) - assert loaded["replaced"] == "default" + assert loaded["replaced"] == "BAAI/bge-small-en-v1.5" + + +def test_environment_defaults_variables_are_loaded_with_duplicated_delimiters() -> None: + sample_yaml = """ + replaced: ${PGPT_EMBEDDING_HF_MODEL_NAME::duped::} + """ + loaded = load_yaml_with_envvars(io.StringIO(sample_yaml), {}) + assert loaded["replaced"] == ":duped::" def test_environment_without_defaults_fails() -> None: From 64c5ae214a9520151c9c2d52ece535867d799367 Mon Sep 17 00:00:00 2001 From: lopagela Date: Sun, 29 Oct 2023 19:11:02 +0100 Subject: [PATCH 010/191] feat: Drop loguru and use builtin `logging` (#1133) * Configure simple builtin logging Changed the 2 existing `print` in the `private_gpt` code base into actual python logging, stop using loguru (dependency will be dropped in a later commit). Try to use the `key=value` logging convention in logs (to indicate what dynamic values represents, and what is dynamic vs not). Using `%s` log style, so that the string formatting is pushed inside the logger, giving the ability to the logger to determine if the string need to be formatted or not (i.e. strings from debug logs might not be formatted if the log level is not debug) The (basic) builtin log configuration have been placed in `private_gpt/__init__.py` in order to initialize the logging system even before we start to launch any python code in `private_gpt` package (ensuring we get any initialization log formatted as we want to) Disabled `uvicorn` custom logging format, resulting in having uvicorn logs being outputted in our formatted. Some more concise format could be used if we want to, especially: ``` COMPACT_LOG_FORMAT = '%(asctime)s.%(msecs)03d [%(levelname)s] %(name)s - %(message)s' ``` Python documentation and cookbook on logging for reference: * https://docs.python.org/3/library/logging.html * https://docs.python.org/3/howto/logging.html * Removing loguru from the dependencies Result of `poetry remove loguru` * PR feedback: using `logger` variable name instead of `log` --------- Co-authored-by: Louis Melchior --- poetry.lock | 34 +------------------ private_gpt/__init__.py | 10 ++++++ private_gpt/__main__.py | 5 ++- .../components/llm/custom/sagemaker.py | 5 ++- private_gpt/main.py | 17 ---------- private_gpt/settings/settings_loader.py | 5 ++- pyproject.toml | 1 - 7 files changed, 23 insertions(+), 54 deletions(-) diff --git a/poetry.lock b/poetry.lock index 4b36f6f..6739a3a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1916,24 +1916,6 @@ typing-extensions = ">=4.5.0" typing-inspect = ">=0.8.0" urllib3 = "<2" -[[package]] -name = "loguru" -version = "0.7.2" -description = "Python logging made (stupidly) simple" -optional = false -python-versions = ">=3.5" -files = [ - {file = "loguru-0.7.2-py3-none-any.whl", hash = "sha256:003d71e3d3ed35f0f8984898359d65b79e5b21943f78af86aa5491210429b8eb"}, - {file = "loguru-0.7.2.tar.gz", hash = "sha256:e671a53522515f34fd406340ee968cb9ecafbc4b36c679da03c18fd8d0bd51ac"}, -] - -[package.dependencies] -colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""} -win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""} - -[package.extras] -dev = ["Sphinx (==7.2.5)", "colorama (==0.4.5)", "colorama (==0.4.6)", "exceptiongroup (==1.1.3)", "freezegun (==1.1.0)", "freezegun (==1.2.2)", "mypy (==v0.910)", "mypy (==v0.971)", "mypy (==v1.4.1)", "mypy (==v1.5.1)", "pre-commit (==3.4.0)", "pytest (==6.1.2)", "pytest (==7.4.0)", "pytest-cov (==2.12.1)", "pytest-cov (==4.1.0)", "pytest-mypy-plugins (==1.9.3)", "pytest-mypy-plugins (==3.0.0)", "sphinx-autobuild (==2021.3.14)", "sphinx-rtd-theme (==1.3.0)", "tox (==3.27.1)", "tox (==4.11.0)"] - [[package]] name = "markupsafe" version = "2.1.3" @@ -5154,20 +5136,6 @@ files = [ [package.extras] test = ["pytest (>=6.0.0)", "setuptools (>=65)"] -[[package]] -name = "win32-setctime" -version = "1.1.0" -description = "A small Python utility to set file creation time on Windows" -optional = false -python-versions = ">=3.5" -files = [ - {file = "win32_setctime-1.1.0-py3-none-any.whl", hash = "sha256:231db239e959c2fe7eb1d7dc129f11172354f98361c4fa2d6d2d7e278baa8aad"}, - {file = "win32_setctime-1.1.0.tar.gz", hash = "sha256:15cf5750465118d6929ae4de4eb46e8edae9a5634350c01ba582df868e932cb2"}, -] - -[package.extras] -dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] - [[package]] name = "wrapt" version = "1.15.0" @@ -5357,4 +5325,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.12" -content-hash = "02ab2873e2074cbed0b415f5fa8addce0fd945487d12944f4c8043d4fc617de9" +content-hash = "28fe3510739b539671c6cf447706c17934c31094f1a860e9e95452d9e63f5a46" diff --git a/private_gpt/__init__.py b/private_gpt/__init__.py index dd7fcef..1f40983 100644 --- a/private_gpt/__init__.py +++ b/private_gpt/__init__.py @@ -1 +1,11 @@ """private-gpt.""" +import logging + +# Set to 'DEBUG' to have extensive logging turned on, even for libraries +ROOT_LOG_LEVEL = "INFO" + +PRETTY_LOG_FORMAT = ( + "%(asctime)s.%(msecs)03d [%(levelname)-8s] %(name)+25s - %(message)s" +) +logging.basicConfig(level=ROOT_LOG_LEVEL, format=PRETTY_LOG_FORMAT, datefmt="%H:%M:%S") +logging.captureWarnings(True) diff --git a/private_gpt/__main__.py b/private_gpt/__main__.py index 100dfdc..6bf2f15 100644 --- a/private_gpt/__main__.py +++ b/private_gpt/__main__.py @@ -5,4 +5,7 @@ import uvicorn from private_gpt.main import app from private_gpt.settings.settings import settings -uvicorn.run(app, host="0.0.0.0", port=settings.server.port) +# Set log_config=None to do not use the uvicorn logging configuration, and +# use ours instead. For reference, see below: +# https://github.com/tiangolo/fastapi/discussions/7457#discussioncomment-5141108 +uvicorn.run(app, host="0.0.0.0", port=settings.server.port, log_config=None) diff --git a/private_gpt/components/llm/custom/sagemaker.py b/private_gpt/components/llm/custom/sagemaker.py index 08f56a9..070f8ed 100644 --- a/private_gpt/components/llm/custom/sagemaker.py +++ b/private_gpt/components/llm/custom/sagemaker.py @@ -3,6 +3,7 @@ from __future__ import annotations import io import json +import logging from typing import TYPE_CHECKING, Any import boto3 # type: ignore @@ -26,6 +27,8 @@ if TYPE_CHECKING: CompletionResponseGen, ) +logger = logging.getLogger(__name__) + class LineIterator: r"""A helper class for parsing the byte stream input from TGI container. @@ -81,7 +84,7 @@ class LineIterator: continue raise if "PayloadPart" not in chunk: - print("Unknown event type:" + chunk) + logger.warning("Unknown event type=%s", chunk) continue self.buffer.seek(0, io.SEEK_END) self.buffer.write(chunk["PayloadPart"]["Bytes"]) diff --git a/private_gpt/main.py b/private_gpt/main.py index 7a6dc66..f5f193e 100644 --- a/private_gpt/main.py +++ b/private_gpt/main.py @@ -1,11 +1,9 @@ """FastAPI app creation, logger configuration and main API routes.""" -import sys from typing import Any import llama_index from fastapi import FastAPI from fastapi.openapi.utils import get_openapi -from loguru import logger from private_gpt.paths import docs_path from private_gpt.server.chat.chat_router import chat_router @@ -16,21 +14,6 @@ from private_gpt.server.health.health_router import health_router from private_gpt.server.ingest.ingest_router import ingest_router from private_gpt.settings.settings import settings -# Remove pre-configured logging handler -logger.remove(0) -# Create a new logging handler same as the pre-configured one but with the extra -# attribute `request_id` -logger.add( - sys.stdout, - level="INFO", - format=( - "{time:YYYY-MM-DD HH:mm:ss.SSS} | " - "{level: <8} | " - "{name}:{function}:{line} | " - "ID: {extra[request_id]} - {message}" - ), -) - # Add LlamaIndex simple observability llama_index.set_global_handler("simple") diff --git a/private_gpt/settings/settings_loader.py b/private_gpt/settings/settings_loader.py index d4b3d55..99c2ca4 100644 --- a/private_gpt/settings/settings_loader.py +++ b/private_gpt/settings/settings_loader.py @@ -1,4 +1,5 @@ import functools +import logging import os import sys from pathlib import Path @@ -9,6 +10,8 @@ from pydantic.v1.utils import deep_update, unique_list from private_gpt.constants import PROJECT_ROOT_PATH from private_gpt.settings.yaml import load_yaml_with_envvars +logger = logging.getLogger(__name__) + _settings_folder = os.environ.get("PGPT_SETTINGS_FOLDER", PROJECT_ROOT_PATH) # if running in unittest, use the test profile @@ -41,7 +44,7 @@ def load_profile(profile: str) -> dict[str, Any]: def load_active_profiles() -> dict[str, Any]: """Load active profiles and merge them.""" - print(f"Starting application with profiles: {active_profiles}") + logger.info("Starting application with profiles=%s", active_profiles) loaded_profiles = [load_profile(profile) for profile in active_profiles] merged: dict[str, Any] = functools.reduce(deep_update, loaded_profiles, {}) return merged diff --git a/pyproject.toml b/pyproject.toml index 7ac7330..bc1d075 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,6 @@ authors = ["Zylon "] [tool.poetry.dependencies] python = ">=3.11,<3.12" fastapi = { extras = ["all"], version = "^0.103.1" } -loguru = "^0.7.2" boto3 = "^1.28.56" injector = "^0.21.0" pyyaml = "^6.0.1" From 5d1be6e94c0de136f6138b86ef2c67b990679649 Mon Sep 17 00:00:00 2001 From: Pablo Orgaz Date: Sun, 29 Oct 2023 21:48:16 +0100 Subject: [PATCH 011/191] chore: only generate docker images on demand (#1134) * chore: only generate docker images on demand * chore: consistent naming --- .github/workflows/docker.yml | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index a0a0ec1..803075f 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -1,12 +1,9 @@ -name: Create and publish a Docker image +name: docker on: release: types: [ published ] - push: - branches: - - main - pull_request: + workflow_dispatch: env: REGISTRY: ghcr.io @@ -44,4 +41,4 @@ jobs: context: . push: true tags: ${{ steps.meta.outputs.tags }} - labels: ${{ steps.meta.outputs.labels }} \ No newline at end of file + labels: ${{ steps.meta.outputs.labels }} From b0e258265f3b3403b6ab98b34f325157fdfe369e Mon Sep 17 00:00:00 2001 From: NetroScript Date: Mon, 30 Oct 2023 21:54:09 +0100 Subject: [PATCH 012/191] Improve logging and error handling when ingesting an entire folder (#1132) --- docs/description.md | 6 +++++ scripts/ingest_folder.py | 54 +++++++++++++++++++++++++++++++++++----- 2 files changed, 54 insertions(+), 6 deletions(-) diff --git a/docs/description.md b/docs/description.md index 56c1fd4..c558fb5 100644 --- a/docs/description.md +++ b/docs/description.md @@ -416,6 +416,12 @@ and optionally watch changes on it with the command: make ingest /path/to/folder -- --watch ``` +To log the processed and failed files to an additional file, use: + +```bash +make ingest /path/to/folder -- --watch --log-file /path/to/log/file.log +``` + After ingestion is complete, you should be able to chat with your documents by navigating to http://localhost:8001 and using the option `Query documents`, or using the completions / chat API. diff --git a/scripts/ingest_folder.py b/scripts/ingest_folder.py index 8936d91..b59d70e 100644 --- a/scripts/ingest_folder.py +++ b/scripts/ingest_folder.py @@ -1,13 +1,14 @@ import argparse -import sys +import logging from pathlib import Path from private_gpt.di import root_injector from private_gpt.server.ingest.ingest_service import IngestService from private_gpt.server.ingest.ingest_watcher import IngestWatcher -ingest_service = root_injector.get(IngestService) +logger = logging.getLogger(__name__) +ingest_service = root_injector.get(IngestService) parser = argparse.ArgumentParser(prog="ingest_folder.py") parser.add_argument("folder", help="Folder to ingest") @@ -17,29 +18,70 @@ parser.add_argument( action=argparse.BooleanOptionalAction, default=False, ) +parser.add_argument( + "--log-file", + help="Optional path to a log file. If provided, logs will be written to this file.", + type=str, + default=None, +) args = parser.parse_args() +# Set up logging to a file if a path is provided +if args.log_file: + file_handler = logging.FileHandler(args.log_file, mode="a") + file_handler.setFormatter( + logging.Formatter( + "[%(asctime)s.%(msecs)03d] [%(levelname)s] %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + ) + logger.addHandler(file_handler) + + +total_documents = 0 +current_document_count = 0 + + +def count_documents(folder_path: Path) -> None: + global total_documents + for file_path in folder_path.iterdir(): + if file_path.is_file(): + total_documents += 1 + elif file_path.is_dir(): + count_documents(file_path) + def _recursive_ingest_folder(folder_path: Path) -> None: + global current_document_count, total_documents for file_path in folder_path.iterdir(): if file_path.is_file(): + current_document_count += 1 + progress_msg = f"Document {current_document_count} of {total_documents} ({(current_document_count / total_documents) * 100:.2f}%)" + logger.info(progress_msg) _do_ingest(file_path) elif file_path.is_dir(): _recursive_ingest_folder(file_path) def _do_ingest(changed_path: Path) -> None: - if changed_path.exists(): - print(f"\nIngesting {changed_path}") - ingest_service.ingest(changed_path.name, changed_path) + try: + if changed_path.exists(): + logger.info(f"Started ingesting {changed_path}") + ingest_service.ingest(changed_path.name, changed_path) + logger.info(f"Completed ingesting {changed_path}") + except Exception as e: + logger.error(f"Failed to ingest document: {changed_path}. Error: {e}") path = Path(args.folder) if not path.exists(): raise ValueError(f"Path {args.folder} does not exist") +# Count total documents before ingestion +count_documents(path) + _recursive_ingest_folder(path) if args.watch: - print(f"Watching {args.folder} for changes, press Ctrl+C to stop...") + logger.info(f"Watching {args.folder} for changes, press Ctrl+C to stop...") watcher = IngestWatcher(args.folder, _do_ingest) watcher.start() From a517a588c4927aa5c5c2a93e4f82a58f0599d251 Mon Sep 17 00:00:00 2001 From: Pablo Orgaz Date: Mon, 30 Oct 2023 21:54:41 +0100 Subject: [PATCH 013/191] fix: sagemaker config and chat methods (#1142) --- .../components/llm/custom/sagemaker.py | 31 +++++++++++++++++-- private_gpt/components/llm/llm_component.py | 2 -- 2 files changed, 29 insertions(+), 4 deletions(-) diff --git a/private_gpt/components/llm/custom/sagemaker.py b/private_gpt/components/llm/custom/sagemaker.py index 070f8ed..284ee2c 100644 --- a/private_gpt/components/llm/custom/sagemaker.py +++ b/private_gpt/components/llm/custom/sagemaker.py @@ -4,7 +4,7 @@ from __future__ import annotations import io import json import logging -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING import boto3 # type: ignore from llama_index.bridge.pydantic import Field @@ -13,7 +13,14 @@ from llama_index.llms import ( CustomLLM, LLMMetadata, ) -from llama_index.llms.base import llm_completion_callback +from llama_index.llms.base import ( + llm_chat_callback, + llm_completion_callback, +) +from llama_index.llms.generic_utils import ( + completion_response_to_chat_response, + stream_completion_response_to_chat_response, +) from llama_index.llms.llama_utils import ( completion_to_prompt as generic_completion_to_prompt, ) @@ -22,8 +29,14 @@ from llama_index.llms.llama_utils import ( ) if TYPE_CHECKING: + from collections.abc import Sequence + from typing import Any + from llama_index.callbacks import CallbackManager from llama_index.llms import ( + ChatMessage, + ChatResponse, + ChatResponseGen, CompletionResponseGen, ) @@ -247,3 +260,17 @@ class SagemakerLLM(CustomLLM): yield CompletionResponse(delta=delta, text=text, raw=data) return get_stream() + + @llm_chat_callback() + def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse: + prompt = self.messages_to_prompt(messages) + completion_response = self.complete(prompt, formatted=True, **kwargs) + return completion_response_to_chat_response(completion_response) + + @llm_chat_callback() + def stream_chat( + self, messages: Sequence[ChatMessage], **kwargs: Any + ) -> ChatResponseGen: + prompt = self.messages_to_prompt(messages) + completion_response = self.stream_complete(prompt, formatted=True, **kwargs) + return stream_completion_response_to_chat_response(completion_response) diff --git a/private_gpt/components/llm/llm_component.py b/private_gpt/components/llm/llm_component.py index cbd71ce..2c32897 100644 --- a/private_gpt/components/llm/llm_component.py +++ b/private_gpt/components/llm/llm_component.py @@ -37,8 +37,6 @@ class LLMComponent: self.llm = SagemakerLLM( endpoint_name=settings.sagemaker.endpoint_name, - messages_to_prompt=messages_to_prompt, - completion_to_prompt=completion_to_prompt, ) case "openai": from llama_index.llms import OpenAI From f29df84301ff2894e26f520c488310750c5b4f7c Mon Sep 17 00:00:00 2001 From: Pierre Marais Date: Thu, 2 Nov 2023 12:45:48 +0100 Subject: [PATCH 014/191] Disable chromaDB anonymous information collection (#1144) See https://docs.trychroma.com/telemetry --- .../components/vector_store/vector_store_component.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/private_gpt/components/vector_store/vector_store_component.py b/private_gpt/components/vector_store/vector_store_component.py index 5d173fb..ce7010b 100644 --- a/private_gpt/components/vector_store/vector_store_component.py +++ b/private_gpt/components/vector_store/vector_store_component.py @@ -1,6 +1,7 @@ import typing import chromadb +from chromadb.config import Settings from injector import inject, singleton from llama_index import VectorStoreIndex from llama_index.indices.vector_store import VectorIndexRetriever @@ -36,8 +37,10 @@ class VectorStoreComponent: @inject def __init__(self) -> None: + chroma_settings = Settings(anonymized_telemetry=False) chroma_client = chromadb.PersistentClient( - path=str((local_data_path / "chroma_db").absolute()) + path=str((local_data_path / "chroma_db").absolute()), + settings=chroma_settings, ) chroma_collection = chroma_client.get_or_create_collection( "make_this_parameterizable_per_api_call" From ad512e3c4222c26d57656a3735edc8ef4087c634 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Iv=C3=A1n=20Mart=C3=ADnez?= Date: Sun, 5 Nov 2023 16:16:49 +0100 Subject: [PATCH 015/191] Feature/sagemaker embedding (#1161) * Sagemaker deployed embedding model support --------- Co-authored-by: Pablo Orgaz --- .../components/embedding/custom/__init__.py | 0 .../components/embedding/custom/sagemaker.py | 82 +++++++++++++++++++ .../embedding/embedding_component.py | 11 ++- .../components/llm/custom/sagemaker.py | 3 +- private_gpt/components/llm/llm_component.py | 2 +- private_gpt/settings/settings.py | 3 +- settings-docker.yaml | 3 +- settings-sagemaker.yaml | 14 ++++ settings.yaml | 3 +- 9 files changed, 114 insertions(+), 7 deletions(-) create mode 100644 private_gpt/components/embedding/custom/__init__.py create mode 100644 private_gpt/components/embedding/custom/sagemaker.py create mode 100644 settings-sagemaker.yaml diff --git a/private_gpt/components/embedding/custom/__init__.py b/private_gpt/components/embedding/custom/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/private_gpt/components/embedding/custom/sagemaker.py b/private_gpt/components/embedding/custom/sagemaker.py new file mode 100644 index 0000000..a1dddd0 --- /dev/null +++ b/private_gpt/components/embedding/custom/sagemaker.py @@ -0,0 +1,82 @@ +# mypy: ignore-errors +import json +from typing import Any + +import boto3 +from llama_index.embeddings.base import BaseEmbedding +from pydantic import Field, PrivateAttr + + +class SagemakerEmbedding(BaseEmbedding): + """Sagemaker Embedding Endpoint. + + To use, you must supply the endpoint name from your deployed + Sagemaker embedding model & the region where it is deployed. + + To authenticate, the AWS client uses the following methods to + automatically load credentials: + https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html + + If a specific credential profile should be used, you must pass + the name of the profile from the ~/.aws/credentials file that is to be used. + + Make sure the credentials / roles used have the required policies to + access the Sagemaker endpoint. + See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html + """ + + endpoint_name: str = Field(description="") + + _boto_client: Any = boto3.client( + "sagemaker-runtime", + ) # TODO make it an optional field + + _async_not_implemented_warned: bool = PrivateAttr(default=False) + + @classmethod + def class_name(cls) -> str: + return "SagemakerEmbedding" + + def _async_not_implemented_warn_once(self) -> None: + if not self._async_not_implemented_warned: + print("Async embedding not available, falling back to sync method.") + self._async_not_implemented_warned = True + + def _embed(self, sentences: list[str]) -> list[list[float]]: + request_params = { + "inputs": sentences, + } + + resp = self._boto_client.invoke_endpoint( + EndpointName=self.endpoint_name, + Body=json.dumps(request_params), + ContentType="application/json", + ) + + response_body = resp["Body"] + response_str = response_body.read().decode("utf-8") + response_json = json.loads(response_str) + + return response_json["vectors"] + + def _get_query_embedding(self, query: str) -> list[float]: + """Get query embedding.""" + return self._embed([query])[0] + + async def _aget_query_embedding(self, query: str) -> list[float]: + # Warn the user that sync is being used + self._async_not_implemented_warn_once() + return self._get_query_embedding(query) + + async def _aget_text_embedding(self, text: str) -> list[float]: + # Warn the user that sync is being used + self._async_not_implemented_warn_once() + return self._get_text_embedding(text) + + def _get_text_embedding(self, text: str) -> list[float]: + """Get text embedding.""" + return self._embed([text])[0] + + def _get_text_embeddings(self, texts: list[str]) -> list[list[float]]: + """Get text embeddings.""" + return self._embed(texts) diff --git a/private_gpt/components/embedding/embedding_component.py b/private_gpt/components/embedding/embedding_component.py index 079e72b..f71be0a 100644 --- a/private_gpt/components/embedding/embedding_component.py +++ b/private_gpt/components/embedding/embedding_component.py @@ -13,13 +13,22 @@ class EmbeddingComponent: @inject def __init__(self) -> None: match settings.llm.mode: - case "local" | "sagemaker": + case "local": from llama_index.embeddings import HuggingFaceEmbedding self.embedding_model = HuggingFaceEmbedding( model_name=settings.local.embedding_hf_model_name, cache_folder=str(models_cache_path), ) + case "sagemaker": + + from private_gpt.components.embedding.custom.sagemaker import ( + SagemakerEmbedding, + ) + + self.embedding_model = SagemakerEmbedding( + endpoint_name=settings.sagemaker.embedding_endpoint_name, + ) case "openai": from llama_index import OpenAIEmbedding diff --git a/private_gpt/components/llm/custom/sagemaker.py b/private_gpt/components/llm/custom/sagemaker.py index 284ee2c..2eedb1d 100644 --- a/private_gpt/components/llm/custom/sagemaker.py +++ b/private_gpt/components/llm/custom/sagemaker.py @@ -4,7 +4,7 @@ from __future__ import annotations import io import json import logging -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import boto3 # type: ignore from llama_index.bridge.pydantic import Field @@ -30,7 +30,6 @@ from llama_index.llms.llama_utils import ( if TYPE_CHECKING: from collections.abc import Sequence - from typing import Any from llama_index.callbacks import CallbackManager from llama_index.llms import ( diff --git a/private_gpt/components/llm/llm_component.py b/private_gpt/components/llm/llm_component.py index 2c32897..cad6ed6 100644 --- a/private_gpt/components/llm/llm_component.py +++ b/private_gpt/components/llm/llm_component.py @@ -36,7 +36,7 @@ class LLMComponent: from private_gpt.components.llm.custom.sagemaker import SagemakerLLM self.llm = SagemakerLLM( - endpoint_name=settings.sagemaker.endpoint_name, + endpoint_name=settings.sagemaker.llm_endpoint_name, ) case "openai": from llama_index.llms import OpenAI diff --git a/private_gpt/settings/settings.py b/private_gpt/settings/settings.py index 73e5c92..16b1d4b 100644 --- a/private_gpt/settings/settings.py +++ b/private_gpt/settings/settings.py @@ -28,7 +28,8 @@ class LocalSettings(BaseModel): class SagemakerSettings(BaseModel): - endpoint_name: str + llm_endpoint_name: str + embedding_endpoint_name: str class OpenAISettings(BaseModel): diff --git a/settings-docker.yaml b/settings-docker.yaml index 12fea9f..49b3961 100644 --- a/settings-docker.yaml +++ b/settings-docker.yaml @@ -11,7 +11,8 @@ local: embedding_hf_model_name: ${PGPT_EMBEDDING_HF_MODEL_NAME:BAAI/bge-small-en-v1.5} sagemaker: - endpoint_name: ${PGPT_SAGEMAKER_ENDPOINT_NAME:} + llm_endpoint_name: ${PGPT_SAGEMAKER_LLM_ENDPOINT_NAME:} + embedding_endpoint_name: ${PGPT_SAGEMAKER_EMBEDDING_ENDPOINT_NAME:} ui: enabled: true diff --git a/settings-sagemaker.yaml b/settings-sagemaker.yaml new file mode 100644 index 0000000..774b8cb --- /dev/null +++ b/settings-sagemaker.yaml @@ -0,0 +1,14 @@ +server: + env_name: ${APP_ENV:prod} + port: ${PORT:8001} + +ui: + enabled: true + path: / + +llm: + mode: sagemaker + +sagemaker: + llm_endpoint_name: huggingface-pytorch-tgi-inference-2023-09-25-19-53-32-140 + embedding_endpoint_name: huggingface-pytorch-inference-2023-11-03-07-41-36-479 \ No newline at end of file diff --git a/settings.yaml b/settings.yaml index 5cdb556..fba278d 100644 --- a/settings.yaml +++ b/settings.yaml @@ -18,7 +18,8 @@ local: embedding_hf_model_name: BAAI/bge-small-en-v1.5 sagemaker: - endpoint_name: huggingface-pytorch-tgi-inference-2023-09-25-19-53-32-140 + llm_endpoint_name: huggingface-pytorch-tgi-inference-2023-09-25-19-53-32-140 + embedding_endpoint_name: huggingface-pytorch-inference-2023-11-03-07-41-36-479 openai: api_key: ${OPENAI_API_KEY:} From 0d677e10b970aec222ec04837d0f08f1631b6d4a Mon Sep 17 00:00:00 2001 From: Pablo Orgaz Date: Mon, 6 Nov 2023 14:24:16 +0100 Subject: [PATCH 016/191] feat: move torch and transformers to local group (#1172) --- .github/workflows/docker.yml | 1 + Dockerfile.external | 36 ++ Dockerfile => Dockerfile.local | 4 +- poetry.lock | 1075 ++++++++++++++++---------------- pyproject.toml | 10 +- 5 files changed, 566 insertions(+), 560 deletions(-) create mode 100644 Dockerfile.external rename Dockerfile => Dockerfile.local (90%) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 803075f..259cf5d 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -39,6 +39,7 @@ jobs: uses: docker/build-push-action@v5 with: context: . + file: Dockerfile.external push: true tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} diff --git a/Dockerfile.external b/Dockerfile.external new file mode 100644 index 0000000..292eb38 --- /dev/null +++ b/Dockerfile.external @@ -0,0 +1,36 @@ +FROM python:3.11.6-slim-bookworm as base + +# Install poetry +RUN pip install pipx +RUN python3 -m pipx ensurepath +RUN pipx install poetry +ENV PATH="/root/.local/bin:$PATH" + +# https://python-poetry.org/docs/configuration/#virtualenvsin-project +ENV POETRY_VIRTUALENVS_IN_PROJECT=true + +FROM base as dependencies +WORKDIR /home/worker/app +COPY pyproject.toml poetry.lock ./ + +RUN poetry install --with ui + +FROM base as app + +ENV PYTHONUNBUFFERED=1 +ENV PORT=8080 +EXPOSE 8080 + +# Prepare a non-root user +RUN adduser --system worker +WORKDIR /home/worker/app + +RUN mkdir local_data; chown worker local_data +RUN mkdir models; chown worker models +COPY --chown=worker --from=dependencies /home/worker/app/.venv/ .venv +COPY --chown=worker private_gpt/ private_gpt +COPY --chown=worker docs/ docs +COPY --chown=worker *.yaml *.md ./ + +USER worker +ENTRYPOINT .venv/bin/python -m private_gpt \ No newline at end of file diff --git a/Dockerfile b/Dockerfile.local similarity index 90% rename from Dockerfile rename to Dockerfile.local index e9ebd15..86f717c 100644 --- a/Dockerfile +++ b/Dockerfile.local @@ -8,7 +8,7 @@ RUN python3 -m pipx ensurepath RUN pipx install poetry ENV PATH="/root/.local/bin:$PATH" -# Dependencies to build llama-cpp and wget +# Dependencies to build llama-cpp RUN apt update && apt install -y \ libopenblas-dev\ ninja-build\ @@ -23,13 +23,13 @@ FROM base as dependencies WORKDIR /home/worker/app COPY pyproject.toml poetry.lock ./ +RUN poetry install --with local RUN poetry install --with ui FROM base as app ENV PYTHONUNBUFFERED=1 ENV PORT=8080 -ENV PGPT_PROFILES=docker EXPOSE 8080 # Prepare a non-root user diff --git a/poetry.lock b/poetry.lock index 6739a3a..1c10aec 100644 --- a/poetry.lock +++ b/poetry.lock @@ -296,17 +296,17 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "boto3" -version = "1.28.72" +version = "1.28.78" description = "The AWS SDK for Python" optional = false python-versions = ">= 3.7" files = [ - 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(<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.15)", "urllib3 (<2.0.0)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.15)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"] docs-specific = ["hf-doc-builder"] -fairscale = ["fairscale (>0.3)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] @@ -4607,7 +4576,7 @@ serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"] tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"] tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] @@ -4814,13 +4783,13 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] [[package]] name = "uvicorn" -version = "0.23.2" +version = "0.24.0.post1" description = "The lightning-fast ASGI server." optional = false python-versions = ">=3.8" files = [ - {file = "uvicorn-0.23.2-py3-none-any.whl", hash = "sha256:1f9be6558f01239d4fdf22ef8126c39cb1ad0addf76c40e760549d2c2f43ab53"}, - {file = "uvicorn-0.23.2.tar.gz", hash = "sha256:4d3cc12d7727ba72b64d12d3cc7743124074c0a69f7b201512fc50c3e3f1569a"}, + {file = "uvicorn-0.24.0.post1-py3-none-any.whl", hash = "sha256:7c84fea70c619d4a710153482c0d230929af7bcf76c7bfa6de151f0a3a80121e"}, + {file = "uvicorn-0.24.0.post1.tar.gz", hash = "sha256:09c8e5a79dc466bdf28dead50093957db184de356fcdc48697bad3bde4c2588e"}, ] [package.dependencies] @@ -5124,13 +5093,13 @@ files = [ [[package]] name = "wheel" -version = "0.41.2" +version = "0.41.3" description = "A built-package format for Python" optional = false python-versions = ">=3.7" files = [ - {file = "wheel-0.41.2-py3-none-any.whl", hash = "sha256:75909db2664838d015e3d9139004ee16711748a52c8f336b52882266540215d8"}, - {file = "wheel-0.41.2.tar.gz", hash = "sha256:0c5ac5ff2afb79ac23ab82bab027a0be7b5dbcf2e54dc50efe4bf507de1f7985"}, + {file = "wheel-0.41.3-py3-none-any.whl", hash = "sha256:488609bc63a29322326e05560731bf7bfea8e48ad646e1f5e40d366607de0942"}, + {file = "wheel-0.41.3.tar.gz", hash = "sha256:4d4987ce51a49370ea65c0bfd2234e8ce80a12780820d9dc462597a6e60d0841"}, ] [package.extras] @@ -5325,4 +5294,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.12" -content-hash = "28fe3510739b539671c6cf447706c17934c31094f1a860e9e95452d9e63f5a46" +content-hash = "2e11477e0b1a37de6711a1962d37b04f57760ab892815b46d089ae9e63cfd6d7" diff --git a/pyproject.toml b/pyproject.toml index bc1d075..371783d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,9 +15,6 @@ pypdf = "^3.16.2" llama-index = "0.8.47" chromadb = "^0.4.13" watchdog = "^3.0.0" -transformers = "^4.34.0" -# https://stackoverflow.com/questions/76327419/valueerror-libcublas-so-0-9-not-found-in-the-system-path -torch = ">=2.0.0, !=2.0.1, !=2.1.0" [tool.poetry.group.dev.dependencies] black = "^22" @@ -38,9 +35,12 @@ gradio = "^3.45.2" [tool.poetry.group.local] optional = true [tool.poetry.group.local.dependencies] -sentence-transformers = "^2.2.2" -numpy = "1.26.0" llama-cpp-python = "^0.2.11" +numpy = "1.26.0" +sentence-transformers = "^2.2.2" +# https://stackoverflow.com/questions/76327419/valueerror-libcublas-so-0-9-not-found-in-the-system-path +torch = ">=2.0.0, !=2.0.1, !=2.1.0" +transformers = "^4.34.0" [build-system] requires = ["poetry-core>=1.0.0"] From 6583dc84c082773443fc3973b1cdf8095fa3fec3 Mon Sep 17 00:00:00 2001 From: lopagela Date: Mon, 6 Nov 2023 14:31:26 +0100 Subject: [PATCH 017/191] feat: Disable Gradio Analytics (#1165) * Disable Gradio Analytics Gradio analytics can be disabled by either using the kwargs `enable_analytics` on `gr.Blocks`, or by setting the env variable `GRADIO_ANALYTICS_ENABLED` to something different from `True`. Since that Gradio does not seem to respect their code contract (around `enable_analytics`), and that they are performing other operations only based on the value of `GRADIO_ANALYTICS_ENABLED` (c.f. `gradio.strings` https://github.com/gradio-app/gradio/blob/main/gradio/strings.py#L39), we are disabling gradio analytics by setting the required env variable to `False`. Note: Setting an environment variables using `os.environ['foo'] = 'bar'` on system that are not based on unix might not work. c.f. https://docs.python.org/3/library/os.html#os.environ for details on how `os.environ` works and all its caveats * Update private_gpt/__init__.py --- private_gpt/__init__.py | 12 ++++++++++++ private_gpt/ui/ui.py | 2 +- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/private_gpt/__init__.py b/private_gpt/__init__.py index 1f40983..995365c 100644 --- a/private_gpt/__init__.py +++ b/private_gpt/__init__.py @@ -1,5 +1,6 @@ """private-gpt.""" import logging +import os # Set to 'DEBUG' to have extensive logging turned on, even for libraries ROOT_LOG_LEVEL = "INFO" @@ -9,3 +10,14 @@ PRETTY_LOG_FORMAT = ( ) logging.basicConfig(level=ROOT_LOG_LEVEL, format=PRETTY_LOG_FORMAT, datefmt="%H:%M:%S") logging.captureWarnings(True) + +# Disable gradio analytics +# This is done this way because gradio does not solely rely on what values are +# passed to gr.Blocks(enable_analytics=...) but also on the environment +# variable GRADIO_ANALYTICS_ENABLED. `gradio.strings` actually reads this env +# directly, so to fully disable gradio analytics we need to set this env var. +os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" + +# Disable chromaDB telemetry +# It is already disabled, see PR#1144 +# os.environ["ANONYMIZED_TELEMETRY"] = "False" diff --git a/private_gpt/ui/ui.py b/private_gpt/ui/ui.py index 9a276e6..a5a6ff6 100644 --- a/private_gpt/ui/ui.py +++ b/private_gpt/ui/ui.py @@ -111,7 +111,7 @@ with gr.Blocks( "}" ".logo img { height: 25% }", ) as blocks: - with gr.Blocks(), gr.Row(): + with gr.Row(): gr.HTML(f"