diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..35b2f4cf --- /dev/null +++ b/.dockerignore @@ -0,0 +1,9 @@ +.venv +models +.github +.vscode +.DS_Store +terraform +tests +Dockerfile +Dockerfile.* \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 67e50c23..00000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug -assignees: '' - ---- - -Note: if you'd like to *ask a question* or *open a discussion*, head over to the [Discussions](https://github.com/imartinez/privateGPT/discussions) section and post it there. - -**Describe the bug and how to reproduce it** -A clear and concise description of what the bug is and the steps to reproduce the behavior. - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Environment (please complete the following information):** - - OS / hardware: [e.g. macOS 12.6 / M1] - - Python version [e.g. 3.11.3] -- Other relevant information - -**Additional context** -Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index 75a14598..00000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: enhancement -assignees: '' - ---- - -Note: if you'd like to *ask a question* or *open a discussion*, head over to the [Discussions](https://github.com/imartinez/privateGPT/discussions) section and post it there. - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/actions/install_dependencies/action.yml b/.github/workflows/actions/install_dependencies/action.yml new file mode 100644 index 00000000..e16c6337 --- /dev/null +++ b/.github/workflows/actions/install_dependencies/action.yml @@ -0,0 +1,30 @@ +name: "Install Dependencies" +description: "Action to build the project dependencies from the main versions" +inputs: + python_version: + required: true + type: string + default: "3.11.4" + poetry_version: + required: true + type: string + default: "1.5.1" + +runs: + using: composite + steps: + - name: Install Poetry + uses: snok/install-poetry@v1 + with: + version: ${{ inputs.poetry_version }} + virtualenvs-create: true + virtualenvs-in-project: false + installer-parallel: true + - uses: actions/setup-python@v4 + with: + python-version: ${{ inputs.python_version }} + cache: "poetry" + - name: Install Dependencies + run: poetry install --with ui --no-root + shell: bash + diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 00000000..91360c58 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,66 @@ +name: Tests + +on: + push: + branches: [main] + pull_request: + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.head_ref || github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' }} + +jobs: + setup: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: ./.github/workflows/actions/install_dependencies + + checks: + needs: setup + runs-on: ubuntu-latest + name: ${{ matrix.quality-command }} + strategy: + matrix: + quality-command: + - black + - ruff + - mypy + steps: + - uses: actions/checkout@v3 + - uses: ./.github/workflows/actions/install_dependencies + - name: run ${{ matrix.quality-command }} + run: make ${{ matrix.quality-command }} + + test: + needs: setup + runs-on: ubuntu-latest + name: test + steps: + - uses: actions/checkout@v3 + - uses: ./.github/workflows/actions/install_dependencies + - name: run test + run: make test-coverage + # Run even if make test fails for coverage reports + # TODO: select a better xml results displayer + - name: Archive test results coverage results + uses: actions/upload-artifact@v3 + if: always() + with: + name: test_results + path: tests-results.xml + - name: Archive code coverage results + uses: actions/upload-artifact@v3 + if: always() + with: + name: code-coverage-report + path: htmlcov/ + + all_checks_passed: + # Used to easily force requirements checks in GitHub + needs: + - checks + - test + runs-on: ubuntu-latest + steps: + - run: echo "All checks passed" diff --git a/.gitignore b/.gitignore index eb26736b..bc7d2125 100644 --- a/.gitignore +++ b/.gitignore @@ -1,174 +1,29 @@ -# OSX -.DS_STORE +.venv -# Models -models/ +settings-me.yaml -# Local Chroma db -.chroma/ -db/ -persist_directory/chroma.sqlite +.ruff_cache +.pytest_cache +.mypy_cache -# Byte-compiled / optimized / DLL files +# byte-compiled / optimized / DLL files __pycache__/ *.py[cod] -*$py.class -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py +# unit tests / coverage reports +/tests-results.xml +/.coverage +/coverage.xml +/htmlcov/ # pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version +/.python-version -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock +# IDE +.idea/ +.vscode/ +/.run/ +.fleet/ -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - -# vscode -.vscode/launch.json +# macOS +.DS_Store diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8826ee1b..fc10e87e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,44 +1,43 @@ ---- -files: ^(.*\.(py|json|md|sh|yaml|cfg|txt))$ -exclude: ^(\.[^/]*cache/.*|.*/_user.py|source_documents/)$ +default_install_hook_types: +# Mandatory to install both pre-commit and pre-push hooks (see https://pre-commit.com/#top_level-default_install_hook_types) +# Add new hook types here to ensure automatic installation when running `pre-commit install` +- pre-commit +- pre-push repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 - hooks: - #- id: no-commit-to-branch - # args: [--branch, main] - - id: check-yaml - args: [--unsafe] - # - id: debug-statements - - id: end-of-file-fixer - - id: trailing-whitespace - exclude-files: \.md$ - - id: check-json - - id: mixed-line-ending - # - id: check-builtin-literals - # - id: check-ast - - id: check-merge-conflict - - id: check-executables-have-shebangs - - id: check-shebang-scripts-are-executable - - id: check-docstring-first - - id: fix-byte-order-marker - - id: check-case-conflict - # - id: check-toml - - repo: https://github.com/adrienverge/yamllint.git - rev: v1.29.0 - hooks: - - id: yamllint - args: - - --no-warnings - - -d - - '{extends: relaxed, rules: {line-length: {max: 90}}}' - - repo: https://github.com/codespell-project/codespell - rev: v2.2.2 - hooks: - - id: codespell - args: - # - --builtin=clear,rare,informal,usage,code,names,en-GB_to_en-US - - --builtin=clear,rare,informal,usage,code,names - - --ignore-words-list=hass,master - - --skip="./.*" - - --quiet-level=2 +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-json + - id: check-added-large-files + +- repo: local + hooks: + - id: black + name: Formatting (black) + entry: black + language: system + types: [python] + stages: [commit] + - id: ruff + name: Linter (ruff) + entry: ruff + language: system + types: [python] + stages: [commit] + - id: mypy + name: Type checking (mypy) + entry: make mypy + pass_filenames: false + language: system + types: [python] + stages: [commit] + - id: test + name: Unit tests (pytest) + entry: make test + pass_filenames: false + language: system + types: [python] + stages: [push] \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..170531a8 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,47 @@ +### IMPORTANT, THIS IMAGE CAN ONLY BE RUN IN LINUX DOCKER +### You will run into a segfault in mac +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" + +# Dependencies to build llama-cpp and wget +RUN apt update && apt install -y \ + libopenblas-dev\ + ninja-build\ + build-essential\ + pkg-config\ + wget + +# 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 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 + +ENV PYTHONUNBUFFERED=1 +ENV PORT=8080 +ENV PGPT_PROFILES=docker +EXPOSE 8080 + +# Prepare a non-root user +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 . . + +USER worker +ENTRYPOINT .venv/bin/python -m private_gpt \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 261eeb9e..00000000 --- a/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - 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/Makefile b/Makefile new file mode 100644 index 00000000..33be1633 --- /dev/null +++ b/Makefile @@ -0,0 +1,52 @@ +# Any args passed to the make script, use with $(call args, default_value) +args = `arg="$(filter-out $@,$(MAKECMDGOALS))" && echo $${arg:-${1}}` + +######################################################################################################################## +# Quality checks +######################################################################################################################## + +test: + PYTHONPATH=. poetry run pytest tests + +test-coverage: + PYTHONPATH=. poetry run pytest tests --cov private_gpt --cov-report term --cov-report=html --cov-report xml --junit-xml=tests-results.xml + +black: + poetry run black . --check + +ruff: + poetry run ruff check private_gpt tests + +format: + poetry run black . + poetry run ruff check private_gpt tests --fix + +mypy: + poetry run mypy private_gpt + +check: + make format + make mypy + +######################################################################################################################## +# Run +######################################################################################################################## + +run: + poetry run python -m private_gpt + +dev-windows: + (set PGPT_PROFILES=local & poetry run python -m uvicorn private_gpt.main:app --reload --port 8001) + +dev: + PYTHONUNBUFFERED=1 PGPT_PROFILES=local poetry run python -m uvicorn private_gpt.main:app --reload --port 8001 + +######################################################################################################################## +# Misc +######################################################################################################################## + +api-docs: + poetry run python scripts/extract_openapi.py private_gpt.main:app --out docs/openapi.json + +ingest: + @poetry run python scripts/ingest_folder.py $(call args) \ No newline at end of file diff --git a/README.md b/README.md index cab6f381..83f4d327 100644 --- a/README.md +++ b/README.md @@ -1,152 +1,158 @@ -# privateGPT -Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection! +# 🔒 PrivateGPT 📑 -> :ear: **Need help applying PrivateGPT to your specific use case?** [Let us know more about it](https://forms.gle/4cSDmH13RZBHV9at7) and we'll try to help! We are refining PrivateGPT through your feedback. +demo -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 +execution environment at any point. -Built with [LangChain](https://github.com/hwchase17/langchain), [LlamaIndex](https://www.llamaindex.ai/), [GPT4All](https://github.com/nomic-ai/gpt4all), [LlamaCpp](https://github.com/ggerganov/llama.cpp), [Chroma](https://www.trychroma.com/) and [SentenceTransformers](https://www.sbert.net/). +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), +and supports both normal and streaming responses. -# Environment Setup -In order to set your environment up to run the code here, first install all requirements: +The API is divided into two logical blocks: + +**High-level API**, which abstracts all the complexity of a RAG (Retrieval Augmented Generation) +pipeline implementation: +- Ingestion of documents: internally managing document parsing, +splitting, metadata extraction, embedding generation and storage. +- Chat & Completions using context from ingested documents: +abstracting the retrieval of context, the prompt engineering and the response generation. + +**Low-level API**, which allows advanced users to implement their own complex pipelines: +- Embeddings generation: based on a piece of text. +- Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested documents. + +In addition to this, a working [Gradio UI](https://www.gradio.app/) +client is provided to test the API, together with a set of useful tools such as bulk model +download script, ingestion script, documents folder watch, etc. + +> 👂 **Need help applying PrivateGPT to your specific use case?** +> [Let us know more about it](https://forms.gle/4cSDmH13RZBHV9at7) +> and we'll try to help! We are refining PrivateGPT through your feedback. + +## 🎞️ Overview +DISCLAIMER: This README is not updated as frequently as the [documentation](https://docs.privategpt.dev/). +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 +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. +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), +[LlamaCpp](https://github.com/ggerganov/llama.cpp), +[Chroma](https://www.trychroma.com/) +and [SentenceTransformers](https://www.sbert.net/). + +That version, which rapidly became a go-to project for privacy-sensitive setups and served as the seed +for thousands of local-focused generative AI projects, was the foundation of what PrivateGPT is becoming nowadays; +thus a simpler and more educational implementation to understand the basic concepts required +to build a fully local -and therefore, private- chatGPT-like tool. + +If you want to keep experimenting with it, we have saved it in the +[primordial branch](https://github.com/imartinez/privateGPT/branches) of the project. + +> It is strongly recommended to do a clean clone and install of this new version of +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 +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. + +## 📄 Documentation +Full documentation on installation, dependencies, configuration, running the server, deployment options, +ingesting local documents, API details and UI features can be found here: https://docs.privategpt.dev/ + +## 🧩 Architecture +Conceptually, PrivateGPT is an API that wraps a RAG pipeline and exposes its +primitives. +* The API is built using [FastAPI](https://fastapi.tiangolo.com/) and follows + [OpenAI's API scheme](https://platform.openai.com/docs/api-reference). +* The RAG pipeline is based on [LlamaIndex](https://www.llamaindex.ai/). + +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. +* 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. +* Ready to use, providing a full implementation of the API and RAG + pipeline. + +Main building blocks: +* APIs are defined in `private_gpt:server:`. Each package contains an + `_router.py` (FastAPI layer) and an `_service.py` (the + service implementation). Each *Service* uses LlamaIndex base abstractions instead + of specific implementations, + decoupling the actual implementation from its usage. +* Components are placed in + `private_gpt:components:`. Each *Component* is in charge of providing + actual implementations to the base abstractions used in the Services - for example + `LLMComponent` is in charge of providing an actual implementation of an `LLM` + (for example `LlamaCPP` or `OpenAI`). + +## 💡 Contributing +Contributions are welcomed! To ensure code quality we have enabled several format and +typing checks, just run `make check` before committing to make sure your code is ok. +Remember to test your code! You'll find a tests folder with helpers, and you can run +tests using `make test` command. + +Interested in contributing to PrivateGPT? We have the following challenges ahead of us in case +you want to give a hand: + +### Improvements +- Better RAG pipeline implementation (improvements to both indexing and querying stages) +- Code documentation +- Expose execution parameters such as top_p, temperature, max_tokens... in Completions and Chat Completions +- Expose chunk size in Ingest API +- Implement Update and Delete document in Ingest API +- Add information about tokens consumption in each response +- Add to Completion APIs (chat and completion) the context docs used to answer the question +- In “model” field return the actual LLM or Embeddings model name used + +### Features +- Implement concurrency lock to avoid errors when there are several calls to the local LlamaCPP model +- API key-based request control to the API +- CORS support +- Support for Sagemaker +- Support Function calling +- Add md5 to check files already ingested +- Select a document to query in the UI +- Better observability of the RAG pipeline + +### Project Infrastructure +- Create a “wipe” shortcut in `make` to remove all contents of local_data folder except .gitignore +- Packaged version as a local desktop app (windows executable, mac app, linux app) +- Dockerize the application for platforms outside linux (Docker Desktop for Mac and Windows) +- Document how to deploy to AWS, GCP and Azure. + +## + +## 💬 Community +Join the conversation around PrivateGPT on our: +- [Twitter (aka X)](https://twitter.com/PrivateGPT_AI) +- [Discord](https://discord.gg/bK6mRVpErU) + +## 📖 Citation +Reference to cite if you use PrivateGPT in a paper: -```shell -pip3 install -r requirements.txt ``` - -*Alternative requirements installation with poetry* -1. Install [poetry](https://python-poetry.org/docs/#installation) - -2. Run this commands -```shell -cd privateGPT -poetry install -poetry shell +@software{PrivateGPT_2023, +authors = {Martinez, I., Gallego, D. Orgaz, P.}, +month = {5}, +title = {PrivateGPT}, +url = {https://github.com/imartinez/privateGPT}, +year = {2023} +} ``` - -Then, download the LLM model and place it in a directory of your choice: -- LLM: default to [ggml-gpt4all-j-v1.3-groovy.bin](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin). If you prefer a different GPT4All-J compatible model, just download it and reference it in your `.env` file. - -Copy the `example.env` template into `.env` -```shell -cp example.env .env -``` - -and edit the variables appropriately in the `.env` file. -``` -MODEL_TYPE: supports LlamaCpp or GPT4All -PERSIST_DIRECTORY: is the folder you want your vectorstore in -MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM -MODEL_N_CTX: Maximum token limit for the LLM model -MODEL_N_BATCH: Number of tokens in the prompt that are fed into the model at a time. Optimal value differs a lot depending on the model (8 works well for GPT4All, and 1024 is better for LlamaCpp) -EMBEDDINGS_MODEL_NAME: SentenceTransformers embeddings model name (see https://www.sbert.net/docs/pretrained_models.html) -TARGET_SOURCE_CHUNKS: The amount of chunks (sources) that will be used to answer a question -``` - -Note: because of the way `langchain` loads the `SentenceTransformers` embeddings, the first time you run the script it will require internet connection to download the embeddings model itself. - -## Test dataset -This repo uses a [state of the union transcript](https://github.com/imartinez/privateGPT/blob/main/source_documents/state_of_the_union.txt) as an example. - -## Instructions for ingesting your own dataset - -Put any and all your files into the `source_documents` directory - -The supported extensions are: - - - `.csv`: CSV, - - `.docx`: Word Document, - - `.doc`: Word Document, - - `.enex`: EverNote, - - `.eml`: Email, - - `.epub`: EPub, - - `.html`: HTML File, - - `.md`: Markdown, - - `.msg`: Outlook Message, - - `.odt`: Open Document Text, - - `.pdf`: Portable Document Format (PDF), - - `.pptx` : PowerPoint Document, - - `.ppt` : PowerPoint Document, - - `.txt`: Text file (UTF-8), - -Run the following command to ingest all the data. - -```shell -python ingest.py -``` - -Output should look like this: - -```shell -Creating new vectorstore -Loading documents from source_documents -Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00, 1.73s/it] -Loaded 1 new documents from source_documents -Split into 90 chunks of text (max. 500 tokens each) -Creating embeddings. May take some minutes... -Using embedded DuckDB with persistence: data will be stored in: db -Ingestion complete! You can now run privateGPT.py to query your documents -``` - -It will create a `db` folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document. -You can ingest as many documents as you want, and all will be accumulated in the local embeddings database. -If you want to start from an empty database, delete the `db` folder. - -Note: during the ingest process no data leaves your local environment. You could ingest without an internet connection, except for the first time you run the ingest script, when the embeddings model is downloaded. - -## Ask questions to your documents, locally! -In order to ask a question, run a command like: - -```shell -python privateGPT.py -``` - -And wait for the script to require your input. - -```plaintext -> Enter a query: -``` - -Hit enter. You'll need to wait 20-30 seconds (depending on your machine) while the LLM model consumes the prompt and prepares the answer. Once done, it will print the answer and the 4 sources it used as context from your documents; you can then ask another question without re-running the script, just wait for the prompt again. - -Note: you could turn off your internet connection, and the script inference would still work. No data gets out of your local environment. - -Type `exit` to finish the script. - - -### CLI -The script also supports optional command-line arguments to modify its behavior. You can see a full list of these arguments by running the command ```python privateGPT.py --help``` in your terminal. - - -# How does it work? -Selecting the right local models and the power of `LangChain` you can run the entire pipeline locally, without any data leaving your environment, and with reasonable performance. - -- `ingest.py` uses `LangChain` tools to parse the document and create embeddings locally using `HuggingFaceEmbeddings` (`SentenceTransformers`). It then stores the result in a local vector database using `Chroma` vector store. -- `privateGPT.py` uses a local LLM based on `GPT4All-J` or `LlamaCpp` to understand questions and create answers. The context for the answers is extracted from the local vector store using a similarity search to locate the right piece of context from the docs. -- `GPT4All-J` wrapper was introduced in LangChain 0.0.162. - -# System Requirements - -## Python Version -To use this software, you must have Python 3.10 or later installed. Earlier versions of Python will not compile. - -## 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. - -### For Windows 10/11 -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 -3. Download the MinGW installer from the [MinGW website](https://sourceforge.net/projects/mingw/). -4. Run the installer and select the `gcc` component. - -## 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. - -If so set your archflags during pip install. eg: _ARCHFLAGS="-arch x86_64" pip3 install -r requirements.txt_ - -# Disclaimer -This is a test project to validate the feasibility of a fully private solution for question answering using LLMs and Vector embeddings. It is not production ready, and it is not meant to be used in production. The models selection is not optimized for performance, but for privacy; but it is possible to use different models and vectorstores to improve performance. diff --git a/constants.py b/constants.py deleted file mode 100644 index 5a10f5d1..00000000 --- a/constants.py +++ /dev/null @@ -1,16 +0,0 @@ -import os -from dotenv import load_dotenv -from chromadb.config import Settings - -load_dotenv() - -# Define the folder for storing database -PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY') -if PERSIST_DIRECTORY is None: - raise Exception("Please set the PERSIST_DIRECTORY environment variable") - -# Define the Chroma settings -CHROMA_SETTINGS = Settings( - persist_directory=PERSIST_DIRECTORY, - anonymized_telemetry=False -) diff --git a/docs/.nojekyll b/docs/.nojekyll new file mode 100644 index 00000000..e69de29b diff --git a/docs/CNAME b/docs/CNAME new file mode 100644 index 00000000..49a32761 --- /dev/null +++ b/docs/CNAME @@ -0,0 +1 @@ +docs.privategpt.dev \ No newline at end of file diff --git a/docs/description.md b/docs/description.md new file mode 100644 index 00000000..5e02b5a2 --- /dev/null +++ b/docs/description.md @@ -0,0 +1,389 @@ +## Introduction + +PrivateGPT provides an **API** containing all the building blocks required to build +**private, context-aware AI applications**. The API follows and extends OpenAI API standard, and supports +both normal and streaming responses. + +The API is divided in two logical blocks: + +- High-level API, abstracting all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation: + - Ingestion of documents: internally managing document parsing, splitting, metadata extraction, + embedding generation and storage. + - Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt + engineering and the response generation. +- Low-level API, allowing advanced users to implement their own complex pipelines: + - Embeddings generation: based on a piece of text. + - Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested + documents. + +> A working **Gradio UI client** is provided to test the API, together with a set of +> useful tools such as bulk model download script, ingestion script, documents folder +> watch, etc. + +## Quick Local Installation steps +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: + +``` +# Clone the repo +git clone https://github.com/imartinez/privateGPT +cd privateGPT + +# Install Python 3.11 +pyenv install 3.11 +pyenv local 3.11 + +# Install dependencies +poetry install --with ui,local + +# Download Embedding and LLM models +poetry run python scripts/setup + +# (Optional) For Mac with Metal GPU, enable it. Check Installation and Settings section +to know how to enable GPU on other platforms +CMAKE_ARGS="-DLLAMA_METAL=on" pip install --force-reinstall --no-cache-dir llama-cpp-python + +# Run the local server +PGPT_PROFILES=local make run + +# Note: on Mac with Metal you should see a ggml_metal_add_buffer log, stating GPU is +being used + +# Navigate to the UI and try it out! +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 + 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 `make` for scripts: + * osx: (Using homebrew): `brew install make` + * windows: (Using chocolatey) `choco install make` + +### Install dependencies + +Install the dependencies: + +```bash +poetry install --with ui +``` + +Verify everything is working by running `make run` (or `poetry run python -m private_gpt`) and navigate to +http://localhost:8001. You should see a [Gradio UI](https://gradio.app/) **configured with a mock LLM** that will +echo back the input. Later we'll see how to configure a real LLM. + +### Settings + +> Note: the default settings of PrivateGPT work out-of-the-box for a 100% local setup. Skip this section if you just +> want to test PrivateGPT locally, and come back later to learn about more configuration options. + +PrivateGPT is configured through *profiles* that are defined using yaml files, and selected through env variables. +The full list of properties configurable can be found in `settings.yaml` + +#### env var `PGPT_SETTINGS_FOLDER` + +The location of the settings folder. Defaults to the root of the project. +Should contain the default `settings.yaml` and any other `settings-{profile}.yaml`. + +#### env var `PGPT_PROFILES` + +By default, the profile definition in `settings.yaml` is loaded. +Using this env var you can load additional profiles; format is a comma separated list of profile names. +This will merge `settings-{profile}.yaml` on top of the base settings file. + +For example: +`PGPT_PROFILES=local,cuda` will load `settings-local.yaml` +and `settings-cuda.yaml`, their contents will be merged with +later profiles properties overriding values of earlier ones like `settings.yaml`. + +During testing, the `test` profile will be active along with the default, therefore `settings-test.yaml` +file is required. + +#### Environment variables expansion + +Configuration files can contain environment variables, +they will be expanded at runtime. + +Expansion must follow the pattern `${VARIABLE_NAME:default_value}`. + +For example, the following configuration will use the value of the `PORT` +environment variable or `8001` if it's not set. +Missing variables with no default will produce an error. + +```yaml +server: + port: ${PORT:8001} +``` + +### Local LLM requirements + +Install extra dependencies for local execution: + +```bash +poetry install --with local +``` + +For PrivateGPT to run fully locally GPU acceleration is required +(CPU execution is possible, but very slow), however, +typical Macbook laptops or window desktops with mid-range GPUs lack VRAM to run +even the smallest LLMs. For that reason +**local execution is only supported for models compatible with [llama.cpp](https://github.com/ggerganov/llama.cpp)** + +These two models are known to work well: + +* https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF +* https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF (recommended) + +To ease the installation process, use the `setup` script that will download both +the embedding and the LLM model and place them in the correct location (under `models` folder): + +```bash +poetry run python scripts/setup +``` + +If you are ok with CPU execution, you can skip the rest of this section. + +As stated before, llama.cpp is required and in +particular [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) +is used. + +> It's highly encouraged that you fully read llama-cpp and llama-cpp-python documentation relevant to your platform. +> Running into installation issues is very likely, and you'll need to troubleshoot them yourself. + +#### OSX GPU support + +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 GPU support is done through CUDA or similar open source technologies. +Follow the instructions on the original [llama.cpp](https://github.com/ggerganov/llama.cpp) repo to install the required +dependencies. + +Some tips to get it working with an NVIDIA card and CUDA (Tested on Windows 10 with CUDA 11.5 RTX 3070): + +* Install latest VS2022 (and build tools) https://visualstudio.microsoft.com/vs/community/ +* Install CUDA toolkit https://developer.nvidia.com/cuda-downloads +* [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 +following powershell command should succeed. + +```powershell +$env: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 | +``` + +Note that llama.cpp offloads matrix calculations to the GPU but the performance is +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 + +🚧 Under construction 🚧 + +#### Known issues and Troubleshooting + +Execution of LLMs locally still has a lot of sharp edges, specially when running on non Linux platforms. +You might encounter several issues: + +* Performance: RAM or VRAM usage is very high, your computer might experience slowdowns or even crashes. +* GPU Virtualization on Windows and OSX: Simply not possible with docker desktop, you have to run the server directly on + the host. +* Building errors: Some of PrivateGPT dependencies need to build native code, and they might fail on some platforms. + Most likely you are missing some dev tools in your machine (updated C++ compiler, CUDA is not on PATH, etc.). + 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. + +**For Windows 10/11** + +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 +3. Download the MinGW installer from the [MinGW website](https://sourceforge.net/projects/mingw/). +4. Run the installer and select the `gcc` component. + +#### 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. + +If so set your archflags during pip install. eg: _ARCHFLAGS="-arch x86_64" pip3 install -r requirements.txt_ + +## Running the Server + +After following the installation steps you should be ready to go. Here are some common run setups: + +### Running 100% locally + +Make sure you have followed the *Local LLM requirements* section before moving on. + +This command will start PrivateGPT using the `settings.yaml` (default profile) together with the `settings-local.yaml` +configuration files. By default, it will enable both the API and the Gradio UI. Run: + +``` +PGPT_PROFILES=local make run +``` + +or + +``` +PGPT_PROFILES=local poetry run python -m private_gpt +``` + +When the server is started it will print a log *Application startup complete*. +Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API +using Swagger UI. + +### Local server using OpenAI as LLM + +If you cannot run a local model (because you don't have a GPU, for example) or for testing purposes, you may +decide to run PrivateGPT using OpenAI as the LLM. + +In order to do so, create a profile `settings-openai.yaml` with the following contents: + +```yaml +llm: + mode: openai + +openai: + api_key: # You could skip this configuration and use the OPENAI_API_KEY env var instead +``` + +And run PrivateGPT loading that profile you just created: + +```PGPT_PROFILES=openai make run``` + +or + +```PGPT_PROFILES=openai poetry run python -m private_gpt``` + +> Note this will still use the local Embeddings model, as it is ok to use it on a CPU. +> We'll support using OpenAI embeddings in a future release. + +When the server is started it will print a log *Application startup complete*. +Navigate to http://localhost:8001 to use the Gradio UI or to http://localhost:8001/docs (API section) to try the API. +You'll notice the speed and quality of response is higher, given you are using OpenAI's servers for the heavy +computations. + +### Use AWS's Sagemaker + +🚧 Under construction 🚧 + +## Gradio UI user manual + +Gradio UI is a ready to use way of testing most of PrivateGPT API functionalities. + +![Gradio PrivateGPT](https://lh3.googleusercontent.com/drive-viewer/AK7aPaBTlIX9j5nsQ87XvcRgf3vhv6UG6pgy4j4IH5mYIo6dHcfJ5IUMiVHoqyQwjTnjRITxYTQ3TcF3pfPXyyWB3HS8hKMWDA=s1600) + +### Execution Modes + +It has 3 modes of execution (you can select in the top-left): +* 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`. +* 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`. +* 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`. + +### 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. + +The list of ingested files is shown below the button. + +If you want to delete the ingested documents, refer to *Reset Local documents +database* section in the documentation. + +### Chat + +Normal chat interface, self-explanatory ;) + +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 + +🚧 We are working on Dockerized deployment guidelines 🚧 + +## Observability + +Basic logs are enabled using LlamaIndex +basic logging (for example ingestion progress or LLM prompts and answers). + +🚧 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) + +### Bulk Local Ingestion + +When you are running PrivateGPT in a fully local setup, you can ingest a complete folder for convenience (containing +pdf, text files, etc.) +and optionally watch changes on it with the command: + +```bash +make ingest /path/to/folder -- --watch +``` + +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. + +### 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). + +## 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 diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 00000000..5ed134d0 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,22 @@ + + + + + PrivateGPT Docs + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/logo.png b/docs/logo.png new file mode 100644 index 00000000..ec716470 Binary files /dev/null and b/docs/logo.png differ diff --git a/docs/openapi.json b/docs/openapi.json new file mode 100644 index 00000000..fa172bf9 --- /dev/null +++ b/docs/openapi.json @@ -0,0 +1,959 @@ +{ + "openapi": "3.1.0", + "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.", + "contact": { + "url": "https://github.com/imartinez/privateGPT" + }, + "license": { + "name": "Apache 2.0", + "url": "https://www.apache.org/licenses/LICENSE-2.0.html" + }, + "version": "0.1.0", + "x-logo": { + "url": "https://lh3.googleusercontent.com/drive-viewer/AK7aPaD_iNlMoTquOBsw4boh4tIYxyEuhz6EtEs8nzq3yNkNAK00xGjE1KUCmPJSk3TYOjcs6tReG6w_cLu1S7L_gPgT9z52iw=s2560" + } + }, + "paths": { + "/v1/completions": { + "post": { + "tags": [ + "Contextual Completions" + ], + "summary": "Completion", + "description": "We recommend most users use our Chat completions API.\n\nGiven a prompt, the model will return one predicted completion. If `use_context`\nis set to `true`, the model will use context coming from the ingested documents\nto create the response. The documents being used can be filtered using the\n`context_filter` and passing the document IDs to be used. Ingested documents IDs\ncan be found using `/ingest/list` endpoint. If you want all ingested documents to\nbe used, remove `context_filter` altogether.\n\nWhen using `'stream': true`, the API will return data chunks following [OpenAI's\nstreaming model](https://platform.openai.com/docs/api-reference/chat/streaming):\n```\n{\"id\":\"12345\",\"object\":\"completion.chunk\",\"created\":1694268190,\n\"model\":\"private-gpt\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"Hello\"},\n\"finish_reason\":null}]}\n```", + "operationId": "prompt_completion_v1_completions_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CompletionsBody" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenAICompletion" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/v1/chat/completions": { + "post": { + "tags": [ + "Contextual Completions" + ], + "summary": "Chat Completion", + "description": "Given a list of messages comprising a conversation, return a response.\n\nIf `use_context` is set to `true`, the model will use context coming\nfrom the ingested documents to create the response. The documents being used can\nbe filtered using the `context_filter` and passing the document IDs to be used.\nIngested documents IDs can be found using `/ingest/list` endpoint. If you want\nall ingested documents to be used, remove `context_filter` altogether.\n\nWhen using `'stream': true`, the API will return data chunks following [OpenAI's\nstreaming model](https://platform.openai.com/docs/api-reference/chat/streaming):\n```\n{\"id\":\"12345\",\"object\":\"completion.chunk\",\"created\":1694268190,\n\"model\":\"private-gpt\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"Hello\"},\n\"finish_reason\":null}]}\n```", + "operationId": "chat_completion_v1_chat_completions_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ChatBody" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OpenAICompletion" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/v1/chunks": { + "post": { + "tags": [ + "Context Chunks" + ], + "summary": "Chunks Retrieval", + "description": "Given a `text`, returns the most relevant chunks from the ingested documents.\n\nThe returned information can be used to generate prompts that can be\npassed to `/completions` or `/chat/completions` APIs. Note: it is usually a very\nfast API, because only the Embeddings model is involved, not the LLM. The\nreturned information contains the relevant chunk `text` together with the source\n`document` it is coming from. It also contains a score that can be used to\ncompare different results.\n\nThe max number of chunks to be returned is set using the `limit` param.\n\nPrevious and next chunks (pieces of text that appear right before or after in the\ndocument) can be fetched by using the `prev_next_chunks` field.\n\nThe documents being used can be filtered using the `context_filter` and passing\nthe document IDs to be used. Ingested documents IDs can be found using\n`/ingest/list` endpoint. If you want all ingested documents to be used,\nremove `context_filter` altogether.", + "operationId": "chunks_retrieval_v1_chunks_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ChunksBody" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ChunksResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/v1/ingest": { + "post": { + "tags": [ + "Ingestion" + ], + "summary": "Ingest", + "description": "Ingests and processes a file, storing its chunks to be used as context.\n\nThe context obtained from files is later used in\n`/chat/completions`, `/completions`, and `/chunks` APIs.\n\nMost common document\nformats are supported, but you may be prompted to install an extra dependency to\nmanage a specific file type.\n\nA file can generate different Documents (for example a PDF generates one Document\nper page). All Documents IDs are returned in the response, together with the\nextracted Metadata (which is later used to improve context retrieval). Those IDs\ncan be used to filter the context used to create responses in\n`/chat/completions`, `/completions`, and `/chunks` APIs.", + "operationId": "ingest_v1_ingest_post", + "requestBody": { + "content": { + "multipart/form-data": { + "schema": { + "$ref": "#/components/schemas/Body_ingest_v1_ingest_post" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/IngestResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/v1/ingest/list": { + "get": { + "tags": [ + "Ingestion" + ], + "summary": "List Ingested", + "description": "Lists already ingested Documents including their Document ID and metadata.\n\nThose IDs can be used to filter the context used to create responses\nin `/chat/completions`, `/completions`, and `/chunks` APIs.", + "operationId": "list_ingested_v1_ingest_list_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/IngestResponse" + } + } + } + } + } + } + }, + "/v1/embeddings": { + "post": { + "tags": [ + "Embeddings" + ], + "summary": "Embeddings Generation", + "description": "Get a vector representation of a given input.\n\nThat vector representation can be easily consumed\nby machine learning models and algorithms.", + "operationId": "embeddings_generation_v1_embeddings_post", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/EmbeddingsBody" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/EmbeddingsResponse" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HTTPValidationError" + } + } + } + } + } + } + }, + "/health": { + "get": { + "tags": [ + "Health" + ], + "summary": "Health", + "description": "Return ok if the system is up.", + "operationId": "health_health_get", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/HealthResponse" + } + } + } + } + } + } + } + }, + "components": { + "schemas": { + "Body_ingest_v1_ingest_post": { + "properties": { + "file": { + "type": "string", + "format": "binary", + "title": "File" + } + }, + "type": "object", + "required": [ + "file" + ], + "title": "Body_ingest_v1_ingest_post" + }, + "ChatBody": { + "properties": { + "messages": { + "items": { + "$ref": "#/components/schemas/OpenAIMessage" + }, + "type": "array", + "title": "Messages" + }, + "use_context": { + "type": "boolean", + "title": "Use Context", + "default": false + }, + "context_filter": { + "anyOf": [ + { + "$ref": "#/components/schemas/ContextFilter" + }, + { + "type": "null" + } + ] + }, + "stream": { + "type": "boolean", + "title": "Stream", + "default": false + } + }, + "type": "object", + "required": [ + "messages" + ], + "title": "ChatBody", + "examples": [ + { + "context_filter": { + "docs_ids": [ + "c202d5e6-7b69-4869-81cc-dd574ee8ee11" + ] + }, + "messages": [ + { + "content": "How do you fry an egg?", + "role": "user" + } + ], + "stream": false, + "use_context": true + } + ] + }, + "Chunk": { + "properties": { + "object": { + "type": "string", + "enum": [ + "context.chunk" + ], + "title": "Object" + }, + "score": { + "type": "number", + "title": "Score", + "examples": [ + 0.023 + ] + }, + "document": { + "$ref": "#/components/schemas/IngestedDoc" + }, + "text": { + "type": "string", + "title": "Text", + "examples": [ + "Outbound sales increased 20%, driven by new leads." + ] + }, + "previous_texts": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Previous Texts", + "examples": [ + [ + "SALES REPORT 2023", + "Inbound didn't show major changes." + ] + ] + }, + "next_texts": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Next Texts", + "examples": [ + [ + "New leads came from Google Ads campaign.", + "The campaign was run by the Marketing Department" + ] + ] + } + }, + "type": "object", + "required": [ + "object", + "score", + "document", + "text", + "previous_texts", + "next_texts" + ], + "title": "Chunk" + }, + "ChunksBody": { + "properties": { + "text": { + "type": "string", + "title": "Text", + "examples": [ + "Q3 2023 sales" + ] + }, + "context_filter": { + "anyOf": [ + { + "$ref": "#/components/schemas/ContextFilter" + }, + { + "type": "null" + } + ] + }, + "limit": { + "type": "integer", + "title": "Limit", + "default": 10 + }, + "prev_next_chunks": { + "type": "integer", + "title": "Prev Next Chunks", + "default": 0, + "examples": [ + 2 + ] + } + }, + "type": "object", + "required": [ + "text" + ], + "title": "ChunksBody" + }, + "ChunksResponse": { + "properties": { + "object": { + "type": "string", + "enum": [ + "list" + ], + "title": "Object" + }, + "model": { + "type": "string", + "enum": [ + "private-gpt" + ], + "title": "Model" + }, + "data": { + "items": { + "$ref": "#/components/schemas/Chunk" + }, + "type": "array", + "title": "Data" + } + }, + "type": "object", + "required": [ + "object", + "model", + "data" + ], + "title": "ChunksResponse" + }, + "CompletionsBody": { + "properties": { + "prompt": { + "type": "string", + "title": "Prompt" + }, + "use_context": { + "type": "boolean", + "title": "Use Context", + "default": false + }, + "context_filter": { + "anyOf": [ + { + "$ref": "#/components/schemas/ContextFilter" + }, + { + "type": "null" + } + ] + }, + "stream": { + "type": "boolean", + "title": "Stream", + "default": false + } + }, + "type": "object", + "required": [ + "prompt" + ], + "title": "CompletionsBody", + "examples": [ + { + "prompt": "How do you fry an egg?", + "stream": false, + "use_context": false + } + ] + }, + "ContextFilter": { + "properties": { + "docs_ids": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "title": "Docs Ids", + "examples": [ + [ + "c202d5e6-7b69-4869-81cc-dd574ee8ee11" + ] + ] + } + }, + "type": "object", + "required": [ + "docs_ids" + ], + "title": "ContextFilter" + }, + "Embedding": { + "properties": { + "index": { + "type": "integer", + "title": "Index" + }, + "object": { + "type": "string", + "enum": [ + "embedding" + ], + "title": "Object" + }, + "embedding": { + "items": { + "type": "number" + }, + "type": "array", + "title": "Embedding", + "examples": [ + [ + 0.0023064255, + -0.009327292 + ] + ] + } + }, + "type": "object", + "required": [ + "index", + "object", + "embedding" + ], + "title": "Embedding" + }, + "EmbeddingsBody": { + "properties": { + "input": { + "anyOf": [ + { + "type": "string" + }, + { + "items": { + "type": "string" + }, + "type": "array" + } + ], + "title": "Input" + } + }, + "type": "object", + "required": [ + "input" + ], + "title": "EmbeddingsBody" + }, + "EmbeddingsResponse": { + "properties": { + "object": { + "type": "string", + "enum": [ + "list" + ], + "title": "Object" + }, + "model": { + "type": "string", + "enum": [ + "private-gpt" + ], + "title": "Model" + }, + "data": { + "items": { + "$ref": "#/components/schemas/Embedding" + }, + "type": "array", + "title": "Data" + } + }, + "type": "object", + "required": [ + "object", + "model", + "data" + ], + "title": "EmbeddingsResponse" + }, + "HTTPValidationError": { + "properties": { + "detail": { + "items": { + "$ref": "#/components/schemas/ValidationError" + }, + "type": "array", + "title": "Detail" + } + }, + "type": "object", + "title": "HTTPValidationError" + }, + "HealthResponse": { + "properties": { + "status": { + "type": "string", + "enum": [ + "ok" + ], + "title": "Status" + } + }, + "type": "object", + "required": [ + "status" + ], + "title": "HealthResponse" + }, + "IngestResponse": { + "properties": { + "object": { + "type": "string", + "enum": [ + "list" + ], + "title": "Object" + }, + "model": { + "type": "string", + "enum": [ + "private-gpt" + ], + "title": "Model" + }, + "data": { + "items": { + "$ref": "#/components/schemas/IngestedDoc" + }, + "type": "array", + "title": "Data" + } + }, + "type": "object", + "required": [ + "object", + "model", + "data" + ], + "title": "IngestResponse" + }, + "IngestedDoc": { + "properties": { + "object": { + "type": "string", + "enum": [ + "ingest.document" + ], + "title": "Object" + }, + "doc_id": { + "type": "string", + "title": "Doc Id", + "examples": [ + "c202d5e6-7b69-4869-81cc-dd574ee8ee11" + ] + }, + "doc_metadata": { + "anyOf": [ + { + "type": "object" + }, + { + "type": "null" + } + ], + "title": "Doc Metadata", + "examples": [ + { + "file_name": "Sales Report Q3 2023.pdf", + "page_label": "2" + } + ] + } + }, + "type": "object", + "required": [ + "object", + "doc_id", + "doc_metadata" + ], + "title": "IngestedDoc" + }, + "OpenAIChoice": { + "properties": { + "finish_reason": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Finish Reason", + "examples": [ + "stop" + ] + }, + "delta": { + "anyOf": [ + { + "$ref": "#/components/schemas/OpenAIDelta" + }, + { + "type": "null" + } + ] + }, + "message": { + "anyOf": [ + { + "$ref": "#/components/schemas/OpenAIMessage" + }, + { + "type": "null" + } + ] + }, + "index": { + "type": "integer", + "title": "Index", + "default": 0 + } + }, + "type": "object", + "required": [ + "finish_reason" + ], + "title": "OpenAIChoice", + "description": "Response from AI.\n\nEither the delta or the message will be present, but never both." + }, + "OpenAICompletion": { + "properties": { + "id": { + "type": "string", + "title": "Id" + }, + "object": { + "type": "string", + "enum": [ + "completion", + "completion.chunk" + ], + "title": "Object", + "default": "completion" + }, + "created": { + "type": "integer", + "title": "Created", + "examples": [ + 1623340000 + ] + }, + "model": { + "type": "string", + "enum": [ + "private-gpt" + ], + "title": "Model" + }, + "choices": { + "items": { + "$ref": "#/components/schemas/OpenAIChoice" + }, + "type": "array", + "title": "Choices" + } + }, + "type": "object", + "required": [ + "id", + "created", + "model", + "choices" + ], + "title": "OpenAICompletion", + "description": "Clone of OpenAI Completion model.\n\nFor more information see: https://platform.openai.com/docs/api-reference/chat/object" + }, + "OpenAIDelta": { + "properties": { + "content": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Content" + } + }, + "type": "object", + "required": [ + "content" + ], + "title": "OpenAIDelta", + "description": "A piece of completion that needs to be concatenated to get the full message." + }, + "OpenAIMessage": { + "properties": { + "role": { + "type": "string", + "enum": [ + "assistant", + "system", + "user" + ], + "title": "Role", + "default": "user" + }, + "content": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "title": "Content" + } + }, + "type": "object", + "required": [ + "content" + ], + "title": "OpenAIMessage", + "description": "Inference result, with the source of the message.\n\nRole could be the assistant or system\n(providing a default response, not AI generated)." + }, + "ValidationError": { + "properties": { + "loc": { + "items": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "integer" + } + ] + }, + "type": "array", + "title": "Location" + }, + "msg": { + "type": "string", + "title": "Message" + }, + "type": { + "type": "string", + "title": "Error Type" + } + }, + "type": "object", + "required": [ + "loc", + "msg", + "type" + ], + "title": "ValidationError" + } + } + }, + "tags": [ + { + "name": "Ingestion", + "description": "High-level APIs covering document ingestion -internally managing document parsing, splitting,metadata extraction, embedding generation and storage- and ingested documents CRUD.Each ingested document is identified by an ID that can be used to filter the contextused in *Contextual Completions* and *Context Chunks* APIs." + }, + { + "name": "Contextual Completions", + "description": "High-level APIs covering contextual Chat and Completions. They follow OpenAI's format, extending it to allow using the context coming from ingested documents to create the response. Internallymanage context retrieval, prompt engineering and the response generation." + }, + { + "name": "Context Chunks", + "description": "Low-level API that given a query return relevant chunks of text coming from the ingesteddocuments." + }, + { + "name": "Embeddings", + "description": "Low-level API to obtain the vector representation of a given text, using an Embeddings model.Follows OpenAI's embeddings API format." + }, + { + "name": "Health", + "description": "Simple health API to make sure the server is up and running." + } + ] +} \ No newline at end of file diff --git a/example.env b/example.env deleted file mode 100644 index 143db76d..00000000 --- a/example.env +++ /dev/null @@ -1,7 +0,0 @@ -PERSIST_DIRECTORY=db -MODEL_TYPE=GPT4All -MODEL_PATH=models/ggml-gpt4all-j-v1.3-groovy.bin -EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2 -MODEL_N_CTX=1000 -MODEL_N_BATCH=8 -TARGET_SOURCE_CHUNKS=4 diff --git a/ingest.py b/ingest.py deleted file mode 100755 index 77e1040e..00000000 --- a/ingest.py +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env python3 -import os -import glob -from typing import List -from dotenv import load_dotenv -from multiprocessing import Pool -from tqdm import tqdm - -from langchain.document_loaders import ( - CSVLoader, - EverNoteLoader, - PyMuPDFLoader, - TextLoader, - UnstructuredEmailLoader, - UnstructuredEPubLoader, - UnstructuredHTMLLoader, - UnstructuredMarkdownLoader, - UnstructuredODTLoader, - UnstructuredPowerPointLoader, - UnstructuredWordDocumentLoader, -) - -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.vectorstores import Chroma -from langchain.embeddings import HuggingFaceEmbeddings -from langchain.docstore.document import Document - -if not load_dotenv(): - print("Could not load .env file or it is empty. Please check if it exists and is readable.") - exit(1) - -from constants import CHROMA_SETTINGS -import chromadb -from chromadb.api.segment import API - -# Load environment variables -persist_directory = os.environ.get('PERSIST_DIRECTORY') -source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents') -embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME') -chunk_size = 500 -chunk_overlap = 50 - - -# Custom document loaders -class MyElmLoader(UnstructuredEmailLoader): - """Wrapper to fallback to text/plain when default does not work""" - - def load(self) -> List[Document]: - """Wrapper adding fallback for elm without html""" - try: - try: - doc = UnstructuredEmailLoader.load(self) - except ValueError as e: - if 'text/html content not found in email' in str(e): - # Try plain text - self.unstructured_kwargs["content_source"]="text/plain" - doc = UnstructuredEmailLoader.load(self) - else: - raise - except Exception as e: - # Add file_path to exception message - raise type(e)(f"{self.file_path}: {e}") from e - - return doc - - -# Map file extensions to document loaders and their arguments -LOADER_MAPPING = { - ".csv": (CSVLoader, {}), - # ".docx": (Docx2txtLoader, {}), - ".doc": (UnstructuredWordDocumentLoader, {}), - ".docx": (UnstructuredWordDocumentLoader, {}), - ".enex": (EverNoteLoader, {}), - ".eml": (MyElmLoader, {}), - ".epub": (UnstructuredEPubLoader, {}), - ".html": (UnstructuredHTMLLoader, {}), - ".md": (UnstructuredMarkdownLoader, {}), - ".odt": (UnstructuredODTLoader, {}), - ".pdf": (PyMuPDFLoader, {}), - ".ppt": (UnstructuredPowerPointLoader, {}), - ".pptx": (UnstructuredPowerPointLoader, {}), - ".txt": (TextLoader, {"encoding": "utf8"}), - # Add more mappings for other file extensions and loaders as needed -} - - -def load_single_document(file_path: str) -> List[Document]: - ext = "." + file_path.rsplit(".", 1)[-1].lower() - if ext in LOADER_MAPPING: - loader_class, loader_args = LOADER_MAPPING[ext] - loader = loader_class(file_path, **loader_args) - return loader.load() - - raise ValueError(f"Unsupported file extension '{ext}'") - -def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]: - """ - Loads all documents from the source documents directory, ignoring specified files - """ - all_files = [] - for ext in LOADER_MAPPING: - all_files.extend( - glob.glob(os.path.join(source_dir, f"**/*{ext.lower()}"), recursive=True) - ) - all_files.extend( - glob.glob(os.path.join(source_dir, f"**/*{ext.upper()}"), recursive=True) - ) - filtered_files = [file_path for file_path in all_files if file_path not in ignored_files] - - with Pool(processes=os.cpu_count()) as pool: - results = [] - with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar: - for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)): - results.extend(docs) - pbar.update() - - return results - -def process_documents(ignored_files: List[str] = []) -> List[Document]: - """ - Load documents and split in chunks - """ - print(f"Loading documents from {source_directory}") - documents = load_documents(source_directory, ignored_files) - if not documents: - print("No new documents to load") - exit(0) - print(f"Loaded {len(documents)} new documents from {source_directory}") - text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) - documents = text_splitter.split_documents(documents) - print(f"Split into {len(documents)} chunks of text (max. {chunk_size} tokens each)") - return documents - -def batch_chromadb_insertions(chroma_client: API, documents: List[Document]) -> List[Document]: - """ - Split the total documents to be inserted into batches of documents that the local chroma client can process - """ - # Get max batch size. - max_batch_size = chroma_client.max_batch_size - for i in range(0, len(documents), max_batch_size): - yield documents[i:i + max_batch_size] - - -def does_vectorstore_exist(persist_directory: str, embeddings: HuggingFaceEmbeddings) -> bool: - """ - Checks if vectorstore exists - """ - db = Chroma(persist_directory=persist_directory, embedding_function=embeddings) - if not db.get()['documents']: - return False - return True - -def main(): - # Create embeddings - embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name) - # Chroma client - chroma_client = chromadb.PersistentClient(settings=CHROMA_SETTINGS , path=persist_directory) - - if does_vectorstore_exist(persist_directory, embeddings): - # Update and store locally vectorstore - print(f"Appending to existing vectorstore at {persist_directory}") - db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS, client=chroma_client) - collection = db.get() - documents = process_documents([metadata['source'] for metadata in collection['metadatas']]) - print(f"Creating embeddings. May take some minutes...") - for batched_chromadb_insertion in batch_chromadb_insertions(chroma_client, documents): - db.add_documents(batched_chromadb_insertion) - else: - # Create and store locally vectorstore - print("Creating new vectorstore") - documents = process_documents() - print(f"Creating embeddings. May take some minutes...") - # Create the db with the first batch of documents to insert - batched_chromadb_insertions = batch_chromadb_insertions(chroma_client, documents) - first_insertion = next(batched_chromadb_insertions) - db = Chroma.from_documents(first_insertion, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS, client=chroma_client) - # Add the rest of batches of documents - for batched_chromadb_insertion in batched_chromadb_insertions: - db.add_documents(batched_chromadb_insertion) - - print(f"Ingestion complete! You can now run privateGPT.py to query your documents") - - -if __name__ == "__main__": - main() diff --git a/local_data/.gitignore b/local_data/.gitignore new file mode 100644 index 00000000..c96a04f0 --- /dev/null +++ b/local_data/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/models/.gitignore b/models/.gitignore new file mode 100644 index 00000000..c96a04f0 --- /dev/null +++ b/models/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 4f3c9c6f..4ca1626f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,99 +1,110 @@ # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +[[package]] +name = "aiofiles" +version = "23.2.1" +description = "File support for asyncio." +optional = false +python-versions = ">=3.7" +files = [ + {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"}, + 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+[package.extras] +dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] + [[package]] name = "yarl" version = "1.9.2" @@ -3499,5 +4738,5 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" -python-versions = "^3.10" -content-hash = "111b08c8b4a98f2efb0ad223dab9777c171cea626211aa7efef03a4e4605bc08" +python-versions = ">=3.11,<3.13" +content-hash = "c1fa5accdcd9cd81430839398e16d2596b43b1c314b5d2a8a76aa05bbb83a39c" diff --git a/privateGPT.py b/privateGPT.py deleted file mode 100755 index 75603c2c..00000000 --- a/privateGPT.py +++ /dev/null @@ -1,87 +0,0 @@ -#!/usr/bin/env python3 -from dotenv import load_dotenv -from langchain.chains import RetrievalQA -from langchain.embeddings import HuggingFaceEmbeddings -from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler -from langchain.vectorstores import Chroma -from langchain.llms import GPT4All, LlamaCpp -import chromadb -import os -import argparse -import time - -if not load_dotenv(): - print("Could not load .env file or it is empty. Please check if it exists and is readable.") - exit(1) - -embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME") -persist_directory = os.environ.get('PERSIST_DIRECTORY') - -model_type = os.environ.get('MODEL_TYPE') -model_path = os.environ.get('MODEL_PATH') -model_n_ctx = os.environ.get('MODEL_N_CTX') -model_n_batch = int(os.environ.get('MODEL_N_BATCH',8)) -target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4)) - -from constants import CHROMA_SETTINGS - -def main(): - # Parse the command line arguments - args = parse_arguments() - embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name) - chroma_client = chromadb.PersistentClient(settings=CHROMA_SETTINGS , path=persist_directory) - db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS, client=chroma_client) - retriever = db.as_retriever(search_kwargs={"k": target_source_chunks}) - # activate/deactivate the streaming StdOut callback for LLMs - callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()] - # Prepare the LLM - match model_type: - case "LlamaCpp": - llm = LlamaCpp(model_path=model_path, max_tokens=model_n_ctx, n_batch=model_n_batch, callbacks=callbacks, verbose=False) - case "GPT4All": - llm = GPT4All(model=model_path, max_tokens=model_n_ctx, backend='gptj', n_batch=model_n_batch, callbacks=callbacks, verbose=False) - case _default: - # raise exception if model_type is not supported - raise Exception(f"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All") - - qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source) - # Interactive questions and answers - while True: - query = input("\nEnter a query: ") - if query == "exit": - break - if query.strip() == "": - continue - - # Get the answer from the chain - start = time.time() - res = qa(query) - answer, docs = res['result'], [] if args.hide_source else res['source_documents'] - end = time.time() - - # Print the result - print("\n\n> Question:") - print(query) - print(f"\n> Answer (took {round(end - start, 2)} s.):") - print(answer) - - # Print the relevant sources used for the answer - for document in docs: - print("\n> " + document.metadata["source"] + ":") - print(document.page_content) - -def parse_arguments(): - parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, ' - 'using the power of LLMs.') - parser.add_argument("--hide-source", "-S", action='store_true', - help='Use this flag to disable printing of source documents used for answers.') - - parser.add_argument("--mute-stream", "-M", - action='store_true', - help='Use this flag to disable the streaming StdOut callback for LLMs.') - - return parser.parse_args() - - -if __name__ == "__main__": - main() diff --git a/private_gpt/__init__.py b/private_gpt/__init__.py new file mode 100644 index 00000000..dd7fcef4 --- /dev/null +++ b/private_gpt/__init__.py @@ -0,0 +1 @@ +"""private-gpt.""" diff --git a/private_gpt/__main__.py b/private_gpt/__main__.py new file mode 100644 index 00000000..100dfdc3 --- /dev/null +++ b/private_gpt/__main__.py @@ -0,0 +1,8 @@ +# start a fastapi server with uvicorn + +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) diff --git a/private_gpt/components/__init__.py b/private_gpt/components/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/components/embedding/__init__.py b/private_gpt/components/embedding/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/components/embedding/embedding_component.py b/private_gpt/components/embedding/embedding_component.py new file mode 100644 index 00000000..41b4e1f4 --- /dev/null +++ b/private_gpt/components/embedding/embedding_component.py @@ -0,0 +1,26 @@ +from injector import inject, singleton +from llama_index import MockEmbedding +from llama_index.embeddings.base import BaseEmbedding + +from private_gpt.paths import models_cache_path +from private_gpt.settings.settings import settings + + +@singleton +class EmbeddingComponent: + embedding_model: BaseEmbedding + + @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 _: + from llama_index.embeddings import HuggingFaceEmbedding + + self.embedding_model = HuggingFaceEmbedding( + model_name=settings.local.embedding_hf_model_name, + cache_folder=str(models_cache_path), + ) diff --git a/private_gpt/components/llm/__init__.py b/private_gpt/components/llm/__init__.py new file mode 100644 index 00000000..ffdd59b9 --- /dev/null +++ b/private_gpt/components/llm/__init__.py @@ -0,0 +1 @@ +"""LLM implementations.""" diff --git a/private_gpt/components/llm/custom/__init__.py b/private_gpt/components/llm/custom/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/components/llm/custom/sagemaker.py b/private_gpt/components/llm/custom/sagemaker.py new file mode 100644 index 00000000..9286de61 --- /dev/null +++ b/private_gpt/components/llm/custom/sagemaker.py @@ -0,0 +1,248 @@ +# mypy: ignore-errors +from __future__ import annotations + +import io +import json +from typing import TYPE_CHECKING, Any + +import boto3 # type: ignore +from llama_index.bridge.pydantic import Field +from llama_index.llms import ( + CompletionResponse, + CustomLLM, + LLMMetadata, +) +from llama_index.llms.base import llm_completion_callback +from llama_index.llms.llama_utils import ( + completion_to_prompt as generic_completion_to_prompt, +) +from llama_index.llms.llama_utils import ( + messages_to_prompt as generic_messages_to_prompt, +) + +if TYPE_CHECKING: + from collections.abc import Callable + + from llama_index.callbacks import CallbackManager + from llama_index.llms import ( + CompletionResponseGen, + ) + + +class LineIterator: + r"""A helper class for parsing the byte stream input from TGI container. + + The output of the model will be in the following format: + ``` + b'data:{"token": {"text": " a"}}\n\n' + b'data:{"token": {"text": " challenging"}}\n\n' + b'data:{"token": {"text": " problem" + b'}}' + ... + ``` + + While usually each PayloadPart event from the event stream will contain a byte array + with a full json, this is not guaranteed and some of the json objects may be split + across PayloadPart events. For example: + ``` + {'PayloadPart': {'Bytes': b'{"outputs": '}} + {'PayloadPart': {'Bytes': b'[" problem"]}\n'}} + ``` + + + This class accounts for this by concatenating bytes written via the 'write' function + and then exposing a method which will return lines (ending with a '\n' character) + within the buffer via the 'scan_lines' function. It maintains the position of the + last read position to ensure that previous bytes are not exposed again. It will + also save any pending lines that doe not end with a '\n' to make sure truncations + are concatinated + """ + + def __init__(self, stream: Any) -> None: + """Line iterator initializer.""" + self.byte_iterator = iter(stream) + self.buffer = io.BytesIO() + self.read_pos = 0 + + def __iter__(self) -> Any: + """Self iterator.""" + return self + + def __next__(self) -> Any: + """Next element from iterator.""" + while True: + self.buffer.seek(self.read_pos) + line = self.buffer.readline() + if line and line[-1] == ord("\n"): + self.read_pos += len(line) + return line[:-1] + try: + chunk = next(self.byte_iterator) + except StopIteration: + if self.read_pos < self.buffer.getbuffer().nbytes: + continue + raise + if "PayloadPart" not in chunk: + print("Unknown event type:" + chunk) + continue + self.buffer.seek(0, io.SEEK_END) + self.buffer.write(chunk["PayloadPart"]["Bytes"]) + + +class SagemakerLLM(CustomLLM): + """Sagemaker Inference Endpoint models. + + To use, you must supply the endpoint name from your deployed + Sagemaker 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="") + temperature: float = Field(description="The temperature to use for sampling.") + max_new_tokens: int = Field(description="The maximum number of tokens to generate.") + context_window: int = Field( + description="The maximum number of context tokens for the model." + ) + messages_to_prompt: Callable[..., str] = Field( + description="The function to convert messages to a prompt.", exclude=True + ) + completion_to_prompt: Callable[..., str] = Field( + description="The function to convert a completion to a prompt.", exclude=True + ) + generate_kwargs: dict[str, Any] = Field( + default_factory=dict, description="Kwargs used for generation." + ) + model_kwargs: dict[str, Any] = Field( + default_factory=dict, description="Kwargs used for model initialization." + ) + verbose: bool = Field(description="Whether to print verbose output.") + + _boto_client: Any = boto3.client( + "sagemaker-runtime", + ) # TODO make it an optional field + + def __init__( + self, + endpoint_name: str | None = "", + temperature: float = 0.1, + max_new_tokens: int = 512, # to review defaults + context_window: int = 2048, # to review defaults + messages_to_prompt: Any = None, + completion_to_prompt: Any = None, + callback_manager: CallbackManager | None = None, + generate_kwargs: dict[str, Any] | None = None, + model_kwargs: dict[str, Any] | None = None, + verbose: bool = True, + ) -> None: + """SagemakerLLM initializer.""" + model_kwargs = model_kwargs or {} + model_kwargs.update({"n_ctx": context_window, "verbose": verbose}) + + messages_to_prompt = messages_to_prompt or generic_messages_to_prompt + completion_to_prompt = completion_to_prompt or generic_completion_to_prompt + + generate_kwargs = generate_kwargs or {} + generate_kwargs.update( + {"temperature": temperature, "max_tokens": max_new_tokens} + ) + + super().__init__( + endpoint_name=endpoint_name, + temperature=temperature, + context_window=context_window, + max_new_tokens=max_new_tokens, + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + callback_manager=callback_manager, + generate_kwargs=generate_kwargs, + model_kwargs=model_kwargs, + verbose=verbose, + ) + + @property + def inference_params(self): + # TODO expose the rest of params + return { + "do_sample": True, + "top_p": 0.7, + "temperature": self.temperature, + "top_k": 50, + "max_new_tokens": self.max_new_tokens, + } + + @property + def metadata(self) -> LLMMetadata: + """Get LLM metadata.""" + return LLMMetadata( + context_window=self.context_window, + num_output=self.max_new_tokens, + model_name="Sagemaker LLama 2", + ) + + @llm_completion_callback() + def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse: + self.generate_kwargs.update({"stream": False}) + + is_formatted = kwargs.pop("formatted", False) + if not is_formatted: + prompt = self.completion_to_prompt(prompt) + + request_params = { + "inputs": prompt, + "stream": False, + "parameters": self.inference_params, + } + + 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_dict = eval(response_str) + + return CompletionResponse( + text=response_dict[0]["generated_text"][len(prompt) :], raw=resp + ) + + @llm_completion_callback() + def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen: + def get_stream(): + text = "" + + request_params = { + "inputs": prompt, + "stream": True, + "parameters": self.inference_params, + } + resp = self._boto_client.invoke_endpoint_with_response_stream( + EndpointName=self.endpoint_name, + Body=json.dumps(request_params), + ContentType="application/json", + ) + + event_stream = resp["Body"] + start_json = b"{" + stop_token = "<|endoftext|>" + + for line in LineIterator(event_stream): + if line != b"" and start_json in line: + data = json.loads(line[line.find(start_json) :].decode("utf-8")) + if data["token"]["text"] != stop_token: + delta = data["token"]["text"] + text += delta + yield CompletionResponse(delta=delta, text=text, raw=data) + + return get_stream() diff --git a/private_gpt/components/llm/llm_component.py b/private_gpt/components/llm/llm_component.py new file mode 100644 index 00000000..2c32897c --- /dev/null +++ b/private_gpt/components/llm/llm_component.py @@ -0,0 +1,47 @@ +from injector import inject, singleton +from llama_index.llms import MockLLM +from llama_index.llms.base import LLM +from llama_index.llms.llama_utils import completion_to_prompt, messages_to_prompt + +from private_gpt.paths import models_path +from private_gpt.settings.settings import settings + + +@singleton +class LLMComponent: + llm: LLM + + @inject + def __init__(self) -> None: + match settings.llm.mode: + case "local": + from llama_index.llms import LlamaCPP + + self.llm = LlamaCPP( + model_path=str(models_path / settings.local.llm_hf_model_file), + temperature=0.1, + # llama2 has a context window of 4096 tokens, + # but we set it lower to allow for some wiggle room + context_window=3900, + generate_kwargs={}, + # All to GPU + model_kwargs={"n_gpu_layers": -1}, + # transform inputs into Llama2 format + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + verbose=True, + ) + + case "sagemaker": + from private_gpt.components.llm.custom.sagemaker import SagemakerLLM + + self.llm = SagemakerLLM( + endpoint_name=settings.sagemaker.endpoint_name, + ) + case "openai": + from llama_index.llms import OpenAI + + openai_settings = settings.openai.api_key + self.llm = OpenAI(api_key=openai_settings) + case "mock": + self.llm = MockLLM() diff --git a/private_gpt/components/node_store/__init__.py b/private_gpt/components/node_store/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/components/node_store/node_store_component.py b/private_gpt/components/node_store/node_store_component.py new file mode 100644 index 00000000..c20f98c5 --- /dev/null +++ b/private_gpt/components/node_store/node_store_component.py @@ -0,0 +1,28 @@ +from injector import inject, singleton +from llama_index.storage.docstore import BaseDocumentStore, SimpleDocumentStore +from llama_index.storage.index_store import SimpleIndexStore +from llama_index.storage.index_store.types import BaseIndexStore + +from private_gpt.paths import local_data_path + + +@singleton +class NodeStoreComponent: + index_store: BaseIndexStore + doc_store: BaseDocumentStore + + @inject + def __init__(self) -> None: + try: + self.index_store = SimpleIndexStore.from_persist_dir( + persist_dir=str(local_data_path) + ) + except FileNotFoundError: + self.index_store = SimpleIndexStore() + + try: + self.doc_store = SimpleDocumentStore.from_persist_dir( + persist_dir=str(local_data_path) + ) + except FileNotFoundError: + self.doc_store = SimpleDocumentStore() diff --git a/private_gpt/components/vector_store/__init__.py b/private_gpt/components/vector_store/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/components/vector_store/vector_store_component.py b/private_gpt/components/vector_store/vector_store_component.py new file mode 100644 index 00000000..02f4a91d --- /dev/null +++ b/private_gpt/components/vector_store/vector_store_component.py @@ -0,0 +1,61 @@ +import typing + +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.open_ai.extensions.context_filter import ContextFilter +from private_gpt.paths import local_data_path + + +@typing.no_type_check +def _chromadb_doc_id_metadata_filter( + context_filter: ContextFilter | None, +) -> dict | None: + if context_filter is None or context_filter.docs_ids is None: + return {} # No filter + elif len(context_filter.docs_ids) < 1: + return {"doc_id": "-"} # Effectively filtering out all docs + else: + doc_filter_items = [] + if len(context_filter.docs_ids) > 1: + doc_filter = {"$or": doc_filter_items} + for doc_id in context_filter.docs_ids: + doc_filter_items.append({"doc_id": doc_id}) + else: + doc_filter = {"doc_id": context_filter.docs_ids[0]} + return doc_filter + + +@singleton +class VectorStoreComponent: + vector_store: VectorStore + + @inject + def __init__(self) -> None: + db = chromadb.PersistentClient( + path=str((local_data_path / "chroma_db").absolute()) + ) + chroma_collection = db.get_or_create_collection( + "make_this_parameterizable_per_api_call" + ) # TODO + + self.vector_store = ChromaVectorStore(chroma_collection=chroma_collection) + + @staticmethod + def get_retriever( + index: VectorStoreIndex, + context_filter: ContextFilter | None = None, + similarity_top_k: int = 2, + ) -> VectorIndexRetriever: + # TODO this 'where' is specific to chromadb. Implement other vector stores + return VectorIndexRetriever( + index=index, + similarity_top_k=similarity_top_k, + vector_store_kwargs={ + "where": _chromadb_doc_id_metadata_filter(context_filter) + }, + ) diff --git a/private_gpt/constants.py b/private_gpt/constants.py new file mode 100644 index 00000000..3a1d6fb1 --- /dev/null +++ b/private_gpt/constants.py @@ -0,0 +1,3 @@ +from pathlib import Path + +PROJECT_ROOT_PATH: Path = Path(__file__).parents[1] diff --git a/private_gpt/di.py b/private_gpt/di.py new file mode 100644 index 00000000..115c8892 --- /dev/null +++ b/private_gpt/di.py @@ -0,0 +1,9 @@ +from injector import Injector + + +def create_application_injector() -> Injector: + injector = Injector(auto_bind=True) + return injector + + +root_injector: Injector = create_application_injector() diff --git a/private_gpt/main.py b/private_gpt/main.py new file mode 100644 index 00000000..7a6dc66b --- /dev/null +++ b/private_gpt/main.py @@ -0,0 +1,125 @@ +"""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 +from private_gpt.server.chunks.chunks_router import chunks_router +from private_gpt.server.completions.completions_router import completions_router +from private_gpt.server.embeddings.embeddings_router import embeddings_router +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") + +# Start the API +with open(docs_path / "description.md") as description_file: + description = description_file.read() + +tags_metadata = [ + { + "name": "Ingestion", + "description": "High-level APIs covering document ingestion -internally " + "managing document parsing, splitting," + "metadata extraction, embedding generation and storage- and ingested " + "documents CRUD." + "Each ingested document is identified by an ID that can be used to filter the " + "context" + "used in *Contextual Completions* and *Context Chunks* APIs.", + }, + { + "name": "Contextual Completions", + "description": "High-level APIs covering contextual Chat and Completions. They " + "follow OpenAI's format, extending it to " + "allow using the context coming from ingested documents to create the " + "response. Internally" + "manage context retrieval, prompt engineering and the response generation.", + }, + { + "name": "Context Chunks", + "description": "Low-level API that given a query return relevant chunks of " + "text coming from the ingested" + "documents.", + }, + { + "name": "Embeddings", + "description": "Low-level API to obtain the vector representation of a given " + "text, using an Embeddings model." + "Follows OpenAI's embeddings API format.", + }, + { + "name": "Health", + "description": "Simple health API to make sure the server is up and running.", + }, +] + +app = FastAPI() + + +def custom_openapi() -> dict[str, Any]: + if app.openapi_schema: + return app.openapi_schema + openapi_schema = get_openapi( + title="PrivateGPT", + description=description, + version="0.1.0", + 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.", + contact={ + "url": "https://github.com/imartinez/privateGPT", + }, + license_info={ + "name": "Apache 2.0", + "url": "https://www.apache.org/licenses/LICENSE-2.0.html", + }, + routes=app.routes, + tags=tags_metadata, + ) + openapi_schema["info"]["x-logo"] = { + "url": "https://lh3.googleusercontent.com/drive-viewer" + "/AK7aPaD_iNlMoTquOBsw4boh4tIYxyEuhz6EtEs8nzq3yNkNAK00xGj" + "E1KUCmPJSk3TYOjcs6tReG6w_cLu1S7L_gPgT9z52iw=s2560" + } + + app.openapi_schema = openapi_schema + return app.openapi_schema + + +app.openapi = custom_openapi # type: ignore[method-assign] + +app.include_router(completions_router) +app.include_router(chat_router) +app.include_router(chunks_router) +app.include_router(ingest_router) +app.include_router(embeddings_router) +app.include_router(health_router) + + +if settings.ui.enabled: + from private_gpt.ui.ui import mount_in_app + + mount_in_app(app) diff --git a/private_gpt/open_ai/__init__.py b/private_gpt/open_ai/__init__.py new file mode 100644 index 00000000..25a0df47 --- /dev/null +++ b/private_gpt/open_ai/__init__.py @@ -0,0 +1 @@ +"""OpenAI compatibility utilities.""" diff --git a/private_gpt/open_ai/extensions/__init__.py b/private_gpt/open_ai/extensions/__init__.py new file mode 100644 index 00000000..f893034b --- /dev/null +++ b/private_gpt/open_ai/extensions/__init__.py @@ -0,0 +1 @@ +"""OpenAI API extensions.""" diff --git a/private_gpt/open_ai/extensions/context_filter.py b/private_gpt/open_ai/extensions/context_filter.py new file mode 100644 index 00000000..c1a09c98 --- /dev/null +++ b/private_gpt/open_ai/extensions/context_filter.py @@ -0,0 +1,7 @@ +from pydantic import BaseModel, Field + + +class ContextFilter(BaseModel): + docs_ids: list[str] | None = Field( + examples=[["c202d5e6-7b69-4869-81cc-dd574ee8ee11"]] + ) diff --git a/private_gpt/open_ai/openai_models.py b/private_gpt/open_ai/openai_models.py new file mode 100644 index 00000000..260dde51 --- /dev/null +++ b/private_gpt/open_ai/openai_models.py @@ -0,0 +1,103 @@ +import time +import uuid +from collections.abc import Iterator + +from llama_index.llms import ChatResponse, CompletionResponse +from pydantic import BaseModel, Field + + +class OpenAIDelta(BaseModel): + """A piece of completion that needs to be concatenated to get the full message.""" + + content: str | None + + +class OpenAIMessage(BaseModel): + """Inference result, with the source of the message. + + Role could be the assistant or system + (providing a default response, not AI generated). + """ + + role: str = Field(default="user", enum=["assistant", "system", "user"]) + content: str | None + + +class OpenAIChoice(BaseModel): + """Response from AI. + + Either the delta or the message will be present, but never both. + """ + + finish_reason: str | None = Field(examples=["stop"]) + delta: OpenAIDelta | None = None + message: OpenAIMessage | None = None + index: int = 0 + + +class OpenAICompletion(BaseModel): + """Clone of OpenAI Completion model. + + For more information see: https://platform.openai.com/docs/api-reference/chat/object + """ + + id: str + object: str = Field("completion", enum=["completion", "completion.chunk"]) + created: int = Field(..., examples=[1623340000]) + model: str = Field(enum=["private-gpt"]) + choices: list[OpenAIChoice] + + @classmethod + def from_text( + cls, text: str | None, finish_reason: str | None = None + ) -> "OpenAICompletion": + return OpenAICompletion( + id=str(uuid.uuid4()), + object="completion", + created=int(time.time()), + model="private-gpt", + choices=[ + OpenAIChoice( + message=OpenAIMessage(role="assistant", content=text), + finish_reason=finish_reason, + ) + ], + ) + + @classmethod + def json_from_delta( + cls, *, text: str | None, finish_reason: str | None = None + ) -> str: + chunk = OpenAICompletion( + id=str(uuid.uuid4()), + object="completion.chunk", + created=int(time.time()), + model="private-gpt", + choices=[ + OpenAIChoice( + delta=OpenAIDelta(content=text), + finish_reason=finish_reason, + ) + ], + ) + + return chunk.model_dump_json() + + +def to_openai_response(response: str | ChatResponse) -> OpenAICompletion: + if isinstance(response, ChatResponse): + return OpenAICompletion.from_text(response.delta, finish_reason="stop") + else: + return OpenAICompletion.from_text(response, finish_reason="stop") + + +def to_openai_sse_stream( + response_generator: Iterator[str | CompletionResponse | ChatResponse], +) -> Iterator[str]: + for response in response_generator: + if isinstance(response, CompletionResponse | ChatResponse): + yield f"data: {OpenAICompletion.json_from_delta(text=response.delta)}\n\n" + else: + yield f"data: {OpenAICompletion.json_from_delta(text=response)}\n\n" + yield f"data: {OpenAICompletion.json_from_delta(text=None, finish_reason='stop')}\n\n" + yield "data: [DONE]\n\n" diff --git a/private_gpt/paths.py b/private_gpt/paths.py new file mode 100644 index 00000000..88310519 --- /dev/null +++ b/private_gpt/paths.py @@ -0,0 +1,16 @@ +from pathlib import Path + +from private_gpt.constants import PROJECT_ROOT_PATH +from private_gpt.settings.settings import settings + + +def _absolute_or_from_project_root(path: str) -> Path: + if path.startswith("/"): + return Path(path) + return PROJECT_ROOT_PATH / path + + +models_path: Path = PROJECT_ROOT_PATH / "models" +models_cache_path: Path = models_path / "cache" +docs_path: Path = PROJECT_ROOT_PATH / "docs" +local_data_path: Path = _absolute_or_from_project_root(settings.data.local_data_folder) diff --git a/private_gpt/server/__init__.py b/private_gpt/server/__init__.py new file mode 100644 index 00000000..5fcc285b --- /dev/null +++ b/private_gpt/server/__init__.py @@ -0,0 +1 @@ +"""private-gpt server.""" diff --git a/private_gpt/server/chat/__init__.py b/private_gpt/server/chat/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/server/chat/chat_router.py b/private_gpt/server/chat/chat_router.py new file mode 100644 index 00000000..a43df438 --- /dev/null +++ b/private_gpt/server/chat/chat_router.py @@ -0,0 +1,82 @@ +from fastapi import APIRouter +from llama_index.llms import ChatMessage, MessageRole +from pydantic import BaseModel +from starlette.responses import StreamingResponse + +from private_gpt.di import root_injector +from private_gpt.open_ai.extensions.context_filter import ContextFilter +from private_gpt.open_ai.openai_models import ( + OpenAICompletion, + OpenAIMessage, + to_openai_response, + to_openai_sse_stream, +) +from private_gpt.server.chat.chat_service import ChatService + +chat_router = APIRouter(prefix="/v1") + + +class ChatBody(BaseModel): + messages: list[OpenAIMessage] + use_context: bool = False + context_filter: ContextFilter | None = None + stream: bool = False + + model_config = { + "json_schema_extra": { + "examples": [ + { + "messages": [ + { + "role": "user", + "content": "How do you fry an egg?", + } + ], + "stream": False, + "use_context": True, + "context_filter": { + "docs_ids": ["c202d5e6-7b69-4869-81cc-dd574ee8ee11"] + }, + } + ] + } + } + + +@chat_router.post( + "/chat/completions", + response_model=None, + responses={200: {"model": OpenAICompletion}}, + tags=["Contextual Completions"], +) +def chat_completion(body: ChatBody) -> OpenAICompletion | StreamingResponse: + """Given a list of messages comprising a conversation, return a response. + + If `use_context` is set to `true`, the model will use context coming + from the ingested documents to create the response. The documents being used can + be filtered using the `context_filter` and passing the document IDs to be used. + Ingested documents IDs can be found using `/ingest/list` endpoint. If you want + all ingested documents to be used, remove `context_filter` altogether. + + When using `'stream': true`, the API will return data chunks following [OpenAI's + streaming model](https://platform.openai.com/docs/api-reference/chat/streaming): + ``` + {"id":"12345","object":"completion.chunk","created":1694268190, + "model":"private-gpt","choices":[{"index":0,"delta":{"content":"Hello"}, + "finish_reason":null}]} + ``` + """ + service = root_injector.get(ChatService) + all_messages = [ + ChatMessage(content=m.content, role=MessageRole(m.role)) for m in body.messages + ] + if body.stream: + stream = service.stream_chat( + all_messages, body.use_context, body.context_filter + ) + return StreamingResponse( + to_openai_sse_stream(stream), media_type="text/event-stream" + ) + else: + response = service.chat(all_messages, body.use_context, body.context_filter) + return to_openai_response(response) diff --git a/private_gpt/server/chat/chat_service.py b/private_gpt/server/chat/chat_service.py new file mode 100644 index 00000000..9b83b485 --- /dev/null +++ b/private_gpt/server/chat/chat_service.py @@ -0,0 +1,116 @@ +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +from injector import inject, singleton +from llama_index import ServiceContext, StorageContext, VectorStoreIndex +from llama_index.chat_engine import ContextChatEngine +from llama_index.indices.postprocessor import MetadataReplacementPostProcessor +from llama_index.llm_predictor.utils import stream_chat_response_to_tokens +from llama_index.llms import ChatMessage +from llama_index.types import TokenGen + +from private_gpt.components.embedding.embedding_component import EmbeddingComponent +from private_gpt.components.llm.llm_component import LLMComponent +from private_gpt.components.node_store.node_store_component import NodeStoreComponent +from private_gpt.components.vector_store.vector_store_component import ( + VectorStoreComponent, +) +from private_gpt.open_ai.extensions.context_filter import ContextFilter + +if TYPE_CHECKING: + from llama_index.chat_engine.types import ( + AgentChatResponse, + StreamingAgentChatResponse, + ) + + +@singleton +class ChatService: + @inject + def __init__( + self, + llm_component: LLMComponent, + vector_store_component: VectorStoreComponent, + embedding_component: EmbeddingComponent, + node_store_component: NodeStoreComponent, + ) -> None: + self.llm_service = llm_component + self.vector_store_component = vector_store_component + self.storage_context = StorageContext.from_defaults( + vector_store=vector_store_component.vector_store, + docstore=node_store_component.doc_store, + index_store=node_store_component.index_store, + ) + self.service_context = ServiceContext.from_defaults( + llm=llm_component.llm, embed_model=embedding_component.embedding_model + ) + self.index = VectorStoreIndex.from_vector_store( + vector_store_component.vector_store, + storage_context=self.storage_context, + service_context=self.service_context, + show_progress=True, + ) + + def _chat_with_contex( + self, + message: str, + context_filter: ContextFilter | None = None, + chat_history: Sequence[ChatMessage] | None = None, + streaming: bool = False, + ) -> Any: + vector_index_retriever = self.vector_store_component.get_retriever( + index=self.index, context_filter=context_filter + ) + chat_engine = ContextChatEngine.from_defaults( + retriever=vector_index_retriever, + service_context=self.service_context, + node_postprocessors=[ + MetadataReplacementPostProcessor(target_metadata_key="window"), + ], + ) + if streaming: + result = chat_engine.stream_chat(message, chat_history) + else: + result = chat_engine.chat(message, chat_history) + return result + + def stream_chat( + self, + messages: list[ChatMessage], + use_context: bool = False, + context_filter: ContextFilter | None = None, + ) -> TokenGen: + if use_context: + last_message = messages[-1].content + response: StreamingAgentChatResponse = self._chat_with_contex( + message=last_message if last_message is not None else "", + chat_history=messages[:-1], + context_filter=context_filter, + streaming=True, + ) + response_gen = response.response_gen + else: + stream = self.llm_service.llm.stream_chat(messages) + response_gen = stream_chat_response_to_tokens(stream) + return response_gen + + def chat( + self, + messages: list[ChatMessage], + use_context: bool = False, + context_filter: ContextFilter | None = None, + ) -> str: + if use_context: + last_message = messages[-1].content + wrapped_response: AgentChatResponse = self._chat_with_contex( + message=last_message if last_message is not None else "", + chat_history=messages[:-1], + context_filter=context_filter, + streaming=False, + ) + response = wrapped_response.response + else: + chat_response = self.llm_service.llm.chat(messages) + response_content = chat_response.message.content + response = response_content if response_content is not None else "" + return response diff --git a/private_gpt/server/chunks/__init__.py b/private_gpt/server/chunks/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/server/chunks/chunks_router.py b/private_gpt/server/chunks/chunks_router.py new file mode 100644 index 00000000..7407c312 --- /dev/null +++ b/private_gpt/server/chunks/chunks_router.py @@ -0,0 +1,53 @@ +from fastapi import APIRouter +from pydantic import BaseModel, Field + +from private_gpt.di import root_injector +from private_gpt.open_ai.extensions.context_filter import ContextFilter +from private_gpt.server.chunks.chunks_service import Chunk, ChunksService + +chunks_router = APIRouter(prefix="/v1") + + +class ChunksBody(BaseModel): + text: str = Field(examples=["Q3 2023 sales"]) + context_filter: ContextFilter | None = None + limit: int = 10 + prev_next_chunks: int = Field(default=0, examples=[2]) + + +class ChunksResponse(BaseModel): + object: str = Field(enum=["list"]) + model: str = Field(enum=["private-gpt"]) + data: list[Chunk] + + +@chunks_router.post("/chunks", tags=["Context Chunks"]) +def chunks_retrieval(body: ChunksBody) -> ChunksResponse: + """Given a `text`, returns the most relevant chunks from the ingested documents. + + The returned information can be used to generate prompts that can be + passed to `/completions` or `/chat/completions` APIs. Note: it is usually a very + fast API, because only the Embeddings model is involved, not the LLM. The + returned information contains the relevant chunk `text` together with the source + `document` it is coming from. It also contains a score that can be used to + compare different results. + + The max number of chunks to be returned is set using the `limit` param. + + Previous and next chunks (pieces of text that appear right before or after in the + document) can be fetched by using the `prev_next_chunks` field. + + The documents being used can be filtered using the `context_filter` and passing + the document IDs to be used. Ingested documents IDs can be found using + `/ingest/list` endpoint. If you want all ingested documents to be used, + remove `context_filter` altogether. + """ + service = root_injector.get(ChunksService) + results = service.retrieve_relevant( + body.text, body.context_filter, body.limit, body.prev_next_chunks + ) + return ChunksResponse( + object="list", + model="private-gpt", + data=results, + ) diff --git a/private_gpt/server/chunks/chunks_service.py b/private_gpt/server/chunks/chunks_service.py new file mode 100644 index 00000000..ee011bfb --- /dev/null +++ b/private_gpt/server/chunks/chunks_service.py @@ -0,0 +1,119 @@ +from typing import TYPE_CHECKING + +from injector import inject, singleton +from llama_index import ServiceContext, StorageContext, VectorStoreIndex +from llama_index.schema import NodeWithScore +from pydantic import BaseModel, Field + +from private_gpt.components.embedding.embedding_component import EmbeddingComponent +from private_gpt.components.llm.llm_component import LLMComponent +from private_gpt.components.node_store.node_store_component import NodeStoreComponent +from private_gpt.components.vector_store.vector_store_component import ( + VectorStoreComponent, +) +from private_gpt.open_ai.extensions.context_filter import ContextFilter +from private_gpt.server.ingest.ingest_service import IngestedDoc + +if TYPE_CHECKING: + from llama_index.schema import RelatedNodeInfo + + +class Chunk(BaseModel): + object: str = Field(enum=["context.chunk"]) + score: float = Field(examples=[0.023]) + document: IngestedDoc + text: str = Field(examples=["Outbound sales increased 20%, driven by new leads."]) + previous_texts: list[str] | None = Field( + examples=[["SALES REPORT 2023", "Inbound didn't show major changes."]] + ) + next_texts: list[str] | None = Field( + examples=[ + [ + "New leads came from Google Ads campaign.", + "The campaign was run by the Marketing Department", + ] + ] + ) + + +@singleton +class ChunksService: + @inject + def __init__( + self, + llm_component: LLMComponent, + vector_store_component: VectorStoreComponent, + embedding_component: EmbeddingComponent, + node_store_component: NodeStoreComponent, + ) -> None: + self.vector_store_component = vector_store_component + self.storage_context = StorageContext.from_defaults( + vector_store=vector_store_component.vector_store, + docstore=node_store_component.doc_store, + index_store=node_store_component.index_store, + ) + self.query_service_context = ServiceContext.from_defaults( + llm=llm_component.llm, embed_model=embedding_component.embedding_model + ) + + def _get_sibling_nodes_text( + self, node_with_score: NodeWithScore, related_number: int, forward: bool = True + ) -> list[str]: + explored_nodes_texts = [] + current_node = node_with_score.node + for _ in range(related_number): + explored_node_info: RelatedNodeInfo | None = ( + current_node.next_node if forward else current_node.prev_node + ) + if explored_node_info is None: + break + + explored_node = self.storage_context.docstore.get_node( + explored_node_info.node_id + ) + + explored_nodes_texts.append(explored_node.get_content()) + current_node = explored_node + + return explored_nodes_texts + + def retrieve_relevant( + self, + text: str, + context_filter: ContextFilter | None = None, + limit: int = 10, + prev_next_chunks: int = 0, + ) -> list[Chunk]: + index = VectorStoreIndex.from_vector_store( + self.vector_store_component.vector_store, + storage_context=self.storage_context, + service_context=self.query_service_context, + show_progress=True, + ) + vector_index_retriever = self.vector_store_component.get_retriever( + index=index, context_filter=context_filter, similarity_top_k=limit + ) + nodes = vector_index_retriever.retrieve(text) + nodes.sort(key=lambda n: n.score or 0.0, reverse=True) + + retrieved_nodes = [] + for node in nodes: + doc_id = node.node.ref_doc_id if node.node.ref_doc_id is not None else "-" + retrieved_nodes.append( + Chunk( + object="context.chunk", + score=node.score or 0.0, + document=IngestedDoc( + object="ingest.document", + doc_id=doc_id, + doc_metadata=node.metadata, + ), + text=node.get_content(), + previous_texts=self._get_sibling_nodes_text( + node, prev_next_chunks, False + ), + next_texts=self._get_sibling_nodes_text(node, prev_next_chunks), + ) + ) + + return retrieved_nodes diff --git a/private_gpt/server/completions/__init__.py b/private_gpt/server/completions/__init__.py new file mode 100644 index 00000000..ae635df4 --- /dev/null +++ b/private_gpt/server/completions/__init__.py @@ -0,0 +1 @@ +"""Deprecated Openai compatibility endpoint.""" diff --git a/private_gpt/server/completions/completions_router.py b/private_gpt/server/completions/completions_router.py new file mode 100644 index 00000000..ce720d4a --- /dev/null +++ b/private_gpt/server/completions/completions_router.py @@ -0,0 +1,66 @@ +from fastapi import APIRouter +from pydantic import BaseModel +from starlette.responses import StreamingResponse + +from private_gpt.open_ai.extensions.context_filter import ContextFilter +from private_gpt.open_ai.openai_models import ( + OpenAICompletion, + OpenAIMessage, +) +from private_gpt.server.chat.chat_router import ChatBody, chat_completion + +completions_router = APIRouter(prefix="/v1") + + +class CompletionsBody(BaseModel): + prompt: str + use_context: bool = False + context_filter: ContextFilter | None = None + stream: bool = False + + model_config = { + "json_schema_extra": { + "examples": [ + { + "prompt": "How do you fry an egg?", + "stream": False, + "use_context": False, + } + ] + } + } + + +@completions_router.post( + "/completions", + response_model=None, + summary="Completion", + responses={200: {"model": OpenAICompletion}}, + tags=["Contextual Completions"], +) +def prompt_completion(body: CompletionsBody) -> OpenAICompletion | StreamingResponse: + """We recommend most users use our Chat completions API. + + Given a prompt, the model will return one predicted completion. If `use_context` + is set to `true`, the model will use context coming from the ingested documents + to create the response. The documents being used can be filtered using the + `context_filter` and passing the document IDs to be used. Ingested documents IDs + can be found using `/ingest/list` endpoint. If you want all ingested documents to + be used, remove `context_filter` altogether. + + When using `'stream': true`, the API will return data chunks following [OpenAI's + streaming model](https://platform.openai.com/docs/api-reference/chat/streaming): + ``` + {"id":"12345","object":"completion.chunk","created":1694268190, + "model":"private-gpt","choices":[{"index":0,"delta":{"content":"Hello"}, + "finish_reason":null}]} + ``` + """ + message = OpenAIMessage(content=body.prompt, role="user") + chat_body = ChatBody( + messages=[message], + use_context=body.use_context, + stream=body.stream, + context_filter=body.context_filter, + ) + return chat_completion(chat_body) diff --git a/private_gpt/server/embeddings/__init__.py b/private_gpt/server/embeddings/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/server/embeddings/embeddings_router.py b/private_gpt/server/embeddings/embeddings_router.py new file mode 100644 index 00000000..f82bc871 --- /dev/null +++ b/private_gpt/server/embeddings/embeddings_router.py @@ -0,0 +1,33 @@ +from fastapi import APIRouter +from pydantic import BaseModel, Field + +from private_gpt.di import root_injector +from private_gpt.server.embeddings.embeddings_service import ( + Embedding, + EmbeddingsService, +) + +embeddings_router = APIRouter(prefix="/v1") + + +class EmbeddingsBody(BaseModel): + input: str | list[str] + + +class EmbeddingsResponse(BaseModel): + object: str = Field(enum=["list"]) + model: str = Field(enum=["private-gpt"]) + data: list[Embedding] + + +@embeddings_router.post("/embeddings", tags=["Embeddings"]) +def embeddings_generation(body: EmbeddingsBody) -> EmbeddingsResponse: + """Get a vector representation of a given input. + + That vector representation can be easily consumed + by machine learning models and algorithms. + """ + service = root_injector.get(EmbeddingsService) + input_texts = body.input if isinstance(body.input, list) else [body.input] + embeddings = service.texts_embeddings(input_texts) + return EmbeddingsResponse(object="list", model="private-gpt", data=embeddings) diff --git a/private_gpt/server/embeddings/embeddings_service.py b/private_gpt/server/embeddings/embeddings_service.py new file mode 100644 index 00000000..70bffb77 --- /dev/null +++ b/private_gpt/server/embeddings/embeddings_service.py @@ -0,0 +1,28 @@ +from injector import inject, singleton +from pydantic import BaseModel, Field + +from private_gpt.components.embedding.embedding_component import EmbeddingComponent + + +class Embedding(BaseModel): + index: int + object: str = Field(enum=["embedding"]) + embedding: list[float] = Field(examples=[[0.0023064255, -0.009327292]]) + + +@singleton +class EmbeddingsService: + @inject + def __init__(self, embedding_component: EmbeddingComponent) -> None: + self.embedding_model = embedding_component.embedding_model + + def texts_embeddings(self, texts: list[str]) -> list[Embedding]: + texts_embeddings = self.embedding_model.get_text_embedding_batch(texts) + return [ + Embedding( + index=texts_embeddings.index(embedding), + object="embedding", + embedding=embedding, + ) + for embedding in texts_embeddings + ] diff --git a/private_gpt/server/health/__init__.py b/private_gpt/server/health/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/server/health/health_router.py b/private_gpt/server/health/health_router.py new file mode 100644 index 00000000..d9e4589c --- /dev/null +++ b/private_gpt/server/health/health_router.py @@ -0,0 +1,14 @@ +from fastapi import APIRouter +from pydantic import BaseModel, Field + +health_router = APIRouter() + + +class HealthResponse(BaseModel): + status: str = Field(enum=["ok"]) + + +@health_router.get("/health", tags=["Health"]) +def health() -> HealthResponse: + """Return ok if the system is up.""" + return HealthResponse(status="ok") diff --git a/private_gpt/server/ingest/__init__.py b/private_gpt/server/ingest/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/private_gpt/server/ingest/ingest_router.py b/private_gpt/server/ingest/ingest_router.py new file mode 100644 index 00000000..dd49b5a8 --- /dev/null +++ b/private_gpt/server/ingest/ingest_router.py @@ -0,0 +1,49 @@ +from fastapi import APIRouter, HTTPException, UploadFile +from pydantic import BaseModel, Field + +from private_gpt.di import root_injector +from private_gpt.server.ingest.ingest_service import IngestedDoc, IngestService + +ingest_router = APIRouter(prefix="/v1") + + +class IngestResponse(BaseModel): + object: str = Field(enum=["list"]) + model: str = Field(enum=["private-gpt"]) + data: list[IngestedDoc] + + +@ingest_router.post("/ingest", tags=["Ingestion"]) +def ingest(file: UploadFile) -> IngestResponse: + """Ingests and processes a file, storing its chunks to be used as context. + + The context obtained from files is later used in + `/chat/completions`, `/completions`, and `/chunks` APIs. + + Most common document + formats are supported, but you may be prompted to install an extra dependency to + manage a specific file type. + + A file can generate different Documents (for example a PDF generates one Document + per page). All Documents IDs are returned in the response, together with the + extracted Metadata (which is later used to improve context retrieval). Those IDs + can be used to filter the context used to create responses in + `/chat/completions`, `/completions`, and `/chunks` APIs. + """ + service = root_injector.get(IngestService) + if file.filename is None: + raise HTTPException(400, "No file name provided") + ingested_documents = service.ingest(file.filename, file.file.read()) + return IngestResponse(object="list", model="private-gpt", data=ingested_documents) + + +@ingest_router.get("/ingest/list", tags=["Ingestion"]) +def list_ingested() -> IngestResponse: + """Lists already ingested Documents including their Document ID and metadata. + + Those IDs can be used to filter the context used to create responses + in `/chat/completions`, `/completions`, and `/chunks` APIs. + """ + service = root_injector.get(IngestService) + ingested_documents = service.list_ingested() + return IngestResponse(object="list", model="private-gpt", data=ingested_documents) diff --git a/private_gpt/server/ingest/ingest_service.py b/private_gpt/server/ingest/ingest_service.py new file mode 100644 index 00000000..6a34e6fb --- /dev/null +++ b/private_gpt/server/ingest/ingest_service.py @@ -0,0 +1,159 @@ +import tempfile +from pathlib import Path +from typing import TYPE_CHECKING, Any, AnyStr + +from injector import inject, singleton +from llama_index import ( + Document, + ServiceContext, + StorageContext, + StringIterableReader, + VectorStoreIndex, +) +from llama_index.node_parser import SentenceWindowNodeParser +from llama_index.readers.file.base import DEFAULT_FILE_READER_CLS +from pydantic import BaseModel, Field + +from private_gpt.components.embedding.embedding_component import EmbeddingComponent +from private_gpt.components.llm.llm_component import LLMComponent +from private_gpt.components.node_store.node_store_component import NodeStoreComponent +from private_gpt.components.vector_store.vector_store_component import ( + VectorStoreComponent, +) +from private_gpt.paths import local_data_path + +if TYPE_CHECKING: + from llama_index.readers.base import BaseReader + + +class IngestedDoc(BaseModel): + object: str = Field(enum=["ingest.document"]) + doc_id: str = Field(examples=["c202d5e6-7b69-4869-81cc-dd574ee8ee11"]) + doc_metadata: dict[str, Any] | None = Field( + examples=[ + { + "page_label": "2", + "file_name": "Sales Report Q3 2023.pdf", + } + ] + ) + + @staticmethod + def curate_metadata(metadata: dict[str, Any]) -> dict[str, Any]: + """Remove unwanted metadata keys.""" + metadata.pop("doc_id", None) + metadata.pop("window", None) + metadata.pop("original_text", None) + return metadata + + +@singleton +class IngestService: + @inject + def __init__( + self, + llm_component: LLMComponent, + vector_store_component: VectorStoreComponent, + embedding_component: EmbeddingComponent, + node_store_component: NodeStoreComponent, + ) -> None: + self.llm_service = llm_component + self.storage_context = StorageContext.from_defaults( + vector_store=vector_store_component.vector_store, + docstore=node_store_component.doc_store, + index_store=node_store_component.index_store, + ) + self.ingest_service_context = ServiceContext.from_defaults( + llm=self.llm_service.llm, + embed_model=embedding_component.embedding_model, + node_parser=SentenceWindowNodeParser.from_defaults(), + ) + + def ingest(self, file_name: str, file_data: AnyStr | Path) -> list[IngestedDoc]: + extension = Path(file_name).suffix + reader_cls = DEFAULT_FILE_READER_CLS.get(extension) + documents: list[Document] + if reader_cls is None: + # Read as a plain text + string_reader = StringIterableReader() + if isinstance(file_data, Path): + text = file_data.read_text() + documents = string_reader.load_data([text]) + elif isinstance(file_data, bytes): + documents = string_reader.load_data([file_data.decode("utf-8")]) + elif isinstance(file_data, str): + documents = string_reader.load_data([file_data]) + else: + raise ValueError(f"Unsupported data type {type(file_data)}") + else: + reader: BaseReader = reader_cls() + if isinstance(file_data, Path): + # Already a path, nothing to do + documents = reader.load_data(file_data) + else: + # llama-index mainly supports reading from files, so + # we have to create a tmp file to read for it to work + with tempfile.NamedTemporaryFile() as tmp: + path_to_tmp = Path(tmp.name) + if isinstance(file_data, bytes): + path_to_tmp.write_bytes(file_data) + else: + path_to_tmp.write_text(str(file_data)) + documents = reader.load_data(path_to_tmp) + + for document in documents: + document.metadata["file_name"] = file_name + return self._save_docs(documents) + + def _save_docs(self, documents: list[Document]) -> list[IngestedDoc]: + for document in documents: + document.metadata["doc_id"] = document.doc_id + # We don't want the Embeddings search to receive this metadata + document.excluded_embed_metadata_keys = ["doc_id"] + # We don't want the LLM to receive these metadata in the context + document.excluded_llm_metadata_keys = ["file_name", "doc_id", "page_label"] + # create vectorStore index + VectorStoreIndex.from_documents( + documents, + storage_context=self.storage_context, + service_context=self.ingest_service_context, + store_nodes_override=True, # Force store nodes in index and document stores + show_progress=True, + ) + # persist the index and nodes + self.storage_context.persist(persist_dir=local_data_path) + return [ + IngestedDoc( + object="ingest.document", + doc_id=document.doc_id, + doc_metadata=IngestedDoc.curate_metadata(document.metadata), + ) + for document in documents + ] + + def list_ingested(self) -> list[IngestedDoc]: + ingested_docs = [] + try: + docstore = self.storage_context.docstore + ingested_docs_ids: set[str] = set() + + for node in docstore.docs.values(): + if node.ref_doc_id is not None: + ingested_docs_ids.add(node.ref_doc_id) + + for doc_id in ingested_docs_ids: + ref_doc_info = docstore.get_ref_doc_info(ref_doc_id=doc_id) + doc_metadata = None + if ref_doc_info is not None and ref_doc_info.metadata is not None: + doc_metadata = IngestedDoc.curate_metadata(ref_doc_info.metadata) + ingested_docs.append( + IngestedDoc( + object="ingest.document", + doc_id=doc_id, + doc_metadata=doc_metadata, + ) + ) + return ingested_docs + except ValueError: + pass + return ingested_docs diff --git a/private_gpt/server/ingest/ingest_watcher.py b/private_gpt/server/ingest/ingest_watcher.py new file mode 100644 index 00000000..51bba54a --- /dev/null +++ b/private_gpt/server/ingest/ingest_watcher.py @@ -0,0 +1,46 @@ +from collections.abc import Callable +from pathlib import Path +from typing import Any + +from watchdog.events import ( + DirCreatedEvent, + DirModifiedEvent, + FileCreatedEvent, + FileModifiedEvent, + FileSystemEventHandler, +) +from watchdog.observers import Observer + + +class IngestWatcher: + def __init__( + self, watch_path: Path, on_file_changed: Callable[[Path], None] + ) -> None: + self.watch_path = watch_path + self.on_file_changed = on_file_changed + + class Handler(FileSystemEventHandler): + def on_modified(self, event: DirModifiedEvent | FileModifiedEvent) -> None: + if isinstance(event, FileModifiedEvent): + on_file_changed(Path(event.src_path)) + + def on_created(self, event: DirCreatedEvent | FileCreatedEvent) -> None: + if isinstance(event, FileCreatedEvent): + on_file_changed(Path(event.src_path)) + + event_handler = Handler() + observer: Any = Observer() + self._observer = observer + self._observer.schedule(event_handler, str(watch_path), recursive=True) + + def start(self) -> None: + self._observer.start() + while self._observer.is_alive(): + try: + self._observer.join(1) + except KeyboardInterrupt: + break + + def stop(self) -> None: + self._observer.stop() + self._observer.join() diff --git a/private_gpt/settings/__init__.py b/private_gpt/settings/__init__.py new file mode 100644 index 00000000..ebcc5dc6 --- /dev/null +++ b/private_gpt/settings/__init__.py @@ -0,0 +1 @@ +"""Settings.""" diff --git a/private_gpt/settings/settings.py b/private_gpt/settings/settings.py new file mode 100644 index 00000000..73e5c929 --- /dev/null +++ b/private_gpt/settings/settings.py @@ -0,0 +1,53 @@ +from pydantic import BaseModel, Field + +from private_gpt.settings.settings_loader import load_active_profiles + + +class ServerSettings(BaseModel): + env_name: str = Field( + description="Name of the environment (prod, staging, local...)" + ) + port: int = Field("Port of PrivateGPT FastAPI server, defaults to 8001") + + +class DataSettings(BaseModel): + local_data_folder: str = Field( + description="Path to local storage." + "It will be treated as an absolute path if it starts with /" + ) + + +class LLMSettings(BaseModel): + mode: str = Field(enum=["local", "open_ai", "sagemaker", "mock"]) + + +class LocalSettings(BaseModel): + llm_hf_repo_id: str + llm_hf_model_file: str + embedding_hf_model_name: str + + +class SagemakerSettings(BaseModel): + endpoint_name: str + + +class OpenAISettings(BaseModel): + api_key: str + + +class UISettings(BaseModel): + enabled: bool + path: str + + +class Settings(BaseModel): + server: ServerSettings + data: DataSettings + ui: UISettings + llm: LLMSettings + local: LocalSettings + sagemaker: SagemakerSettings + openai: OpenAISettings + + +settings = Settings(**load_active_profiles()) diff --git a/private_gpt/settings/settings_loader.py b/private_gpt/settings/settings_loader.py new file mode 100644 index 00000000..d4b3d55c --- /dev/null +++ b/private_gpt/settings/settings_loader.py @@ -0,0 +1,47 @@ +import functools +import os +import sys +from pathlib import Path +from typing import Any + +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 + +_settings_folder = os.environ.get("PGPT_SETTINGS_FOLDER", PROJECT_ROOT_PATH) + +# if running in unittest, use the test profile +_test_profile = ["test"] if "unittest" in sys.modules else [] + +active_profiles: list[str] = unique_list( + ["default"] + + [ + item.strip() + for item in os.environ.get("PGPT_PROFILES", "").split(",") + if item.strip() + ] + + _test_profile +) + + +def load_profile(profile: str) -> dict[str, Any]: + if profile == "default": + profile_file_name = "settings.yaml" + else: + profile_file_name = f"settings-{profile}.yaml" + + path = Path(_settings_folder) / profile_file_name + with Path(path).open("r") as f: + config = load_yaml_with_envvars(f) + if not isinstance(config, dict): + raise TypeError(f"Config file has no top-level mapping: {path}") + return config + + +def load_active_profiles() -> dict[str, Any]: + """Load active profiles and merge them.""" + print(f"Starting application with profiles: {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/private_gpt/settings/yaml.py b/private_gpt/settings/yaml.py new file mode 100644 index 00000000..69bffffe --- /dev/null +++ b/private_gpt/settings/yaml.py @@ -0,0 +1,41 @@ +import os +import re +import typing +from typing import Any, TextIO + +from yaml import SafeLoader + +_env_replace_matcher = re.compile(r"\$\{(\w|_)+(:(\w|_)*)?}") + + +@typing.no_type_check # pyaml does not have good hints, everything is Any +def load_yaml_with_envvars( + stream: TextIO, environ: dict[str, Any] = os.environ +) -> dict[str, Any]: + """Load yaml file with environment variable expansion. + + The pattern ${VAR} or ${VAR:default} will be replaced with + the value of the environment variable. + """ + loader = SafeLoader(stream) + + def load_env_var(_, node) -> str: + """Extract the matched value, expand env variable, and replace the match.""" + value = str(node.value).removeprefix("${").removesuffix("}") + split = value.split(":") + env_var = split[0] + value = environ.get(env_var) + default = None if len(split) == 1 else split[1] + if value is None and default is None: + raise ValueError( + f"Environment variable {env_var} is not set and not default was provided" + ) + return value or default + + loader.add_implicit_resolver("env_var_replacer", _env_replace_matcher, None) + loader.add_constructor("env_var_replacer", load_env_var) + + try: + return loader.get_single_data() + finally: + loader.dispose() diff --git a/private_gpt/ui/__init__.py b/private_gpt/ui/__init__.py new file mode 100644 index 00000000..8fe4022a --- /dev/null +++ b/private_gpt/ui/__init__.py @@ -0,0 +1 @@ +"""Gradio based UI.""" diff --git a/private_gpt/ui/images.py b/private_gpt/ui/images.py new file mode 100644 index 00000000..6705b57e --- /dev/null +++ b/private_gpt/ui/images.py @@ -0,0 +1 @@ +logo_svg = "data:image/svg+xml;base64,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" diff --git a/private_gpt/ui/ui.py b/private_gpt/ui/ui.py new file mode 100644 index 00000000..df8904e8 --- /dev/null +++ b/private_gpt/ui/ui.py @@ -0,0 +1,166 @@ +import itertools +import json +from collections.abc import Iterable +from pathlib import Path +from typing import Any, TextIO + +import gradio as gr # type: ignore +from fastapi import FastAPI +from gradio.themes.utils.colors import slate # type: ignore +from llama_index.llms import ChatMessage, ChatResponse, MessageRole + +from private_gpt.di import root_injector +from private_gpt.server.chat.chat_service import ChatService +from private_gpt.server.chunks.chunks_service import ChunksService +from private_gpt.server.ingest.ingest_service import IngestService +from private_gpt.settings.settings import settings +from private_gpt.ui.images import logo_svg + +ingest_service = root_injector.get(IngestService) +chat_service = root_injector.get(ChatService) +chunks_service = root_injector.get(ChunksService) + + +def _chat(message: str, history: list[list[str]], mode: str, *_: Any) -> Any: + def yield_deltas(stream: Iterable[ChatResponse | str]) -> Iterable[str]: + full_response: str = "" + for delta in stream: + if isinstance(delta, str): + full_response += str(delta) + elif isinstance(delta, ChatResponse): + full_response += delta.delta or "" + yield full_response + + def build_history() -> list[ChatMessage]: + history_messages: list[ChatMessage] = list( + itertools.chain( + *[ + [ + ChatMessage(content=interaction[0], role=MessageRole.USER), + ChatMessage(content=interaction[1], role=MessageRole.ASSISTANT), + ] + for interaction in history + ] + ) + ) + + # max 20 messages to try to avoid context overflow + return history_messages[:20] + + new_message = ChatMessage(content=message, role=MessageRole.USER) + all_messages = [*build_history(), new_message] + match mode: + case "Query Documents": + query_stream = chat_service.stream_chat( + messages=all_messages, + use_context=True, + ) + yield from yield_deltas(query_stream) + + case "LLM Chat": + llm_stream = chat_service.stream_chat( + messages=all_messages, + use_context=False, + ) + yield from yield_deltas(llm_stream) + + case "Context Chunks": + response = chunks_service.retrieve_relevant( + text=message, + limit=2, + prev_next_chunks=1, + ).__iter__() + yield "```" + json.dumps( + [node.__dict__ for node in response], + default=lambda o: o.__dict__, + indent=2, + ) + + +def _list_ingested_files() -> list[str]: + files = set() + for ingested_document in ingest_service.list_ingested(): + if ingested_document.doc_metadata is not None: + files.add( + ingested_document.doc_metadata.get("file_name") or "[FILE NAME MISSING]" + ) + return list(files) + + +# Global state +_uploaded_file_list = [[row] for row in _list_ingested_files()] + + +def _upload_file(file: TextIO) -> list[list[str]]: + path = Path(file.name) + ingest_service.ingest(file_name=path.name, file_data=path) + _uploaded_file_list.append([path.name]) + return _uploaded_file_list + + +with gr.Blocks( + theme=gr.themes.Soft(primary_hue=slate), + css=".logo { " + "display:flex;" + "background-color: #C7BAFF;" + "height: 80px;" + "border-radius: 8px;" + "align-content: center;" + "justify-content: center;" + "align-items: center;" + "}" + ".logo img { height: 25% }", +) as blocks: + with gr.Blocks(), gr.Row(): + gr.HTML(f"