diff --git a/.devcontainer/base/Dockerfile b/.devcontainer/base/Dockerfile new file mode 100644 index 0000000..42ae3fe --- /dev/null +++ b/.devcontainer/base/Dockerfile @@ -0,0 +1,20 @@ +FROM ubuntu:noble + +SHELL ["/bin/bash", "-c"] +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + curl \ + wget \ + git \ + openssh-client \ + sudo \ + just + +COPY .devcontainer/scripts/base-setup.sh /tmp/base-setup.sh +RUN chmod +x /tmp/base-setup.sh \ + && USERNAME="ubuntu" /tmp/base-setup.sh + +USER ubuntu +WORKDIR /home/ubuntu/workspace diff --git a/.devcontainer/base/devcontainer.json b/.devcontainer/base/devcontainer.json new file mode 100644 index 0000000..99f7c2a --- /dev/null +++ b/.devcontainer/base/devcontainer.json @@ -0,0 +1,45 @@ +{ + "name": "base", + "build": { + "dockerfile": "Dockerfile", + "context": "../.." + }, + + "features": { + "ghcr.io/devcontainers/features/docker-in-docker:2": { + "version": "latest", + "moby": false + } + }, + + "workspaceMount": "source=${localWorkspaceFolder},target=/home/ubuntu/workspace,type=bind", + "workspaceFolder": "/home/ubuntu/workspace", + + "mounts": [ + "type=bind,source=${localEnv:SSH_AUTH_SOCK},target=/ssh-agent" + ], + + "remoteUser": "ubuntu", + "remoteEnv": { + "SSH_AUTH_SOCK": "/ssh-agent", + "GIT_CONFIG_GLOBAL": "/home/ubuntu/.gitconfig" + }, + + "overrideCommand": true, + "postCreateCommand": "/bin/bash .devcontainer/res/post_create_command.bash", + + "customizations": { + "vscode": { + "extensions": [ + "skellock.just", + "ms-python.python", + "ms-python.pylint", + "ms-python.autopep8", + "tamasfe.even-better-toml", + "VisualStudioExptTeam.vscodeintellicode", + "mhutchie.git-graph", + "eamodio.gitlens" + ] + } + } +} diff --git a/.devcontainer/context-retriever/Dockerfile b/.devcontainer/context-retriever/Dockerfile new file mode 100644 index 0000000..67dd575 --- /dev/null +++ b/.devcontainer/context-retriever/Dockerfile @@ -0,0 +1,21 @@ +FROM python:3.12 + +SHELL ["/bin/bash", "-c"] +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + curl wget git openssh-client sudo \ + just gcc g++ + +RUN groupadd -g 1000 appuser && useradd appuser -u 1000 -g 1000 -m -s /bin/bash +RUN chown -R appuser:appuser /home/appuser/ + +COPY .devcontainer/scripts/base-setup.sh /tmp/base-setup.sh +RUN chmod +x /tmp/base-setup.sh \ + && USERNAME="appuser" /tmp/base-setup.sh + +USER appuser +WORKDIR /home/appuser/workspace + +RUN wget -qO- https://astral.sh/uv/install.sh | sh \ No newline at end of file diff --git a/.devcontainer/context-retriever/devcontainer.json b/.devcontainer/context-retriever/devcontainer.json new file mode 100644 index 0000000..124c3ac --- /dev/null +++ b/.devcontainer/context-retriever/devcontainer.json @@ -0,0 +1,35 @@ +{ + "name": "context-retriever", + "build": { + "dockerfile": "Dockerfile", + "context": "../.." + }, + "workspaceMount": "source=${localWorkspaceFolder},target=/home/appuser/workspace,type=bind", + "workspaceFolder": "/home/appuser/workspace/services/context-retriever", + "mounts": [ + "type=bind,source=${localEnv:SSH_AUTH_SOCK},target=/ssh-agent" + ], + "remoteUser": "appuser", + "remoteEnv": { + "SSH_AUTH_SOCK": "/ssh-agent", + "GIT_CONFIG_GLOBAL": "/home/appuser/.gitconfig" + }, + "runArgs": [ + "--network=host" + ], + "overrideCommand": true, + "postCreateCommand": "cd /home/appuser/workspace && /bin/bash .devcontainer/res/post_create_command.bash", + "customizations": { + "vscode": { + "extensions": [ + "skellock.just", + "ms-python.python", + "ms-python.pylint", + "ms-python.autopep8", + "tamasfe.even-better-toml", + "VisualStudioExptTeam.vscodeintellicode", + "matangover.mypy" + ] + } + } +} \ No newline at end of file diff --git a/.devcontainer/llm-proxy/Dockerfile b/.devcontainer/llm-proxy/Dockerfile new file mode 100644 index 0000000..67dd575 --- /dev/null +++ b/.devcontainer/llm-proxy/Dockerfile @@ -0,0 +1,21 @@ +FROM python:3.12 + +SHELL ["/bin/bash", "-c"] +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + curl wget git openssh-client sudo \ + just gcc g++ + +RUN groupadd -g 1000 appuser && useradd appuser -u 1000 -g 1000 -m -s /bin/bash +RUN chown -R appuser:appuser /home/appuser/ + +COPY .devcontainer/scripts/base-setup.sh /tmp/base-setup.sh +RUN chmod +x /tmp/base-setup.sh \ + && USERNAME="appuser" /tmp/base-setup.sh + +USER appuser +WORKDIR /home/appuser/workspace + +RUN wget -qO- https://astral.sh/uv/install.sh | sh \ No newline at end of file diff --git a/.devcontainer/llm-proxy/devcontainer.json b/.devcontainer/llm-proxy/devcontainer.json new file mode 100644 index 0000000..38edb99 --- /dev/null +++ b/.devcontainer/llm-proxy/devcontainer.json @@ -0,0 +1,35 @@ +{ + "name": "llm-proxy", + "build": { + "dockerfile": "Dockerfile", + "context": "../.." + }, + "workspaceMount": "source=${localWorkspaceFolder},target=/home/appuser/workspace,type=bind", + "workspaceFolder": "/home/appuser/workspace/services/llm-proxy", + "mounts": [ + "type=bind,source=${localEnv:SSH_AUTH_SOCK},target=/ssh-agent" + ], + "remoteUser": "appuser", + "remoteEnv": { + "SSH_AUTH_SOCK": "/ssh-agent", + "GIT_CONFIG_GLOBAL": "/home/appuser/.gitconfig" + }, + "runArgs": [ + "--network=host" + ], + "overrideCommand": true, + "postCreateCommand": "cd /home/appuser/workspace && /bin/bash .devcontainer/res/post_create_command.bash", + "customizations": { + "vscode": { + "extensions": [ + "skellock.just", + "ms-python.python", + "ms-python.pylint", + "ms-python.autopep8", + "tamasfe.even-better-toml", + "VisualStudioExptTeam.vscodeintellicode", + "matangover.mypy" + ] + } + } +} \ No newline at end of file diff --git a/.devcontainer/res/.bash_aliases b/.devcontainer/res/.bash_aliases new file mode 100644 index 0000000..d033a0f --- /dev/null +++ b/.devcontainer/res/.bash_aliases @@ -0,0 +1 @@ +alias gitgrlog='git log --pretty=format:"%C(yellow)%h%C(reset) %C(magenta)%cd%C(reset) %C(auto)%d%C(reset) %s" --graph --date=format:"%Y.%m.%d" --decorate=auto' diff --git a/.devcontainer/res/.bashrc b/.devcontainer/res/.bashrc new file mode 100644 index 0000000..5fecf2c --- /dev/null +++ b/.devcontainer/res/.bashrc @@ -0,0 +1,29 @@ + +# -------------------------------------------------------------------------------------- +# Borrowed from the .bashrc configuration of mcr.microsoft.com/devcontainers/base:jammy +# -------------------------------------------------------------------------------------- + +__bash_prompt() { + local userpart='`export XIT=$? \ + && [ ! -z "${GITHUB_USER}" ] && echo -n "\[\033[0;32m\]@${GITHUB_USER} " || echo -n "\[\033[0;32m\]\u " \ + && [ "$XIT" -ne "0" ] && echo -n "\[\033[1;31m\]➜" || echo -n "\[\033[0m\]➜"`' + local gitbranch='`\ + if [ "$(git config --get devcontainers-theme.hide-status 2>/dev/null)" != 1 ] && [ "$(git config --get codespaces-theme.hide-status 2>/dev/null)" != 1 ]; then \ + export BRANCH=$(git --no-optional-locks symbolic-ref --short HEAD 2>/dev/null || git --no-optional-locks rev-parse --short HEAD 2>/dev/null); \ + if [ "${BRANCH}" != "" ]; then \ + echo -n "\[\033[0;36m\](\[\033[1;31m\]${BRANCH}" \ + && if [ "$(git config --get devcontainers-theme.show-dirty 2>/dev/null)" = 1 ] && \ + git --no-optional-locks ls-files --error-unmatch -m --directory --no-empty-directory -o --exclude-standard ":/*" > /dev/null 2>&1; then \ + echo -n " \[\033[1;33m\]✗"; \ + fi \ + && echo -n "\[\033[0;36m\]) "; \ + fi; \ + fi`' + local lightblue='\[\033[1;34m\]' + local removecolor='\[\033[0m\]' + PS1="${userpart} ${lightblue}\w ${gitbranch}${removecolor}\$ " + unset -f __bash_prompt +} +__bash_prompt + +export PATH=/home/$USER/.local/bin:$PATH diff --git a/.devcontainer/res/post_create_command.bash b/.devcontainer/res/post_create_command.bash new file mode 100644 index 0000000..607883b --- /dev/null +++ b/.devcontainer/res/post_create_command.bash @@ -0,0 +1,8 @@ +#!/bin/bash + +# This script should be run after the devcontainer is created. +# It's role is to establish the necessary environment for the +# user to start developing. + +cat .devcontainer/res/.bash_aliases >> ~/.bash_aliases +cat .devcontainer/res/.bashrc >> ~/.bashrc diff --git a/.devcontainer/scripts/base-setup.sh b/.devcontainer/scripts/base-setup.sh new file mode 100755 index 0000000..094ce75 --- /dev/null +++ b/.devcontainer/scripts/base-setup.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +set -euo pipefail + +if ! id "${USERNAME}" &>/dev/null; then + echo "User '${USERNAME}' does not exist. Create it in the Dockerfile before running this script." >&2 + exit 1 +fi + +usermod -aG sudo "${USERNAME}" +echo "${USERNAME} ALL=(ALL) NOPASSWD:ALL" > "/etc/sudoers.d/${USERNAME}" +chmod 440 "/etc/sudoers.d/${USERNAME}" + +git config --system core.sshCommand /usr/bin/ssh diff --git a/.devcontainer/web-app/Dockerfile b/.devcontainer/web-app/Dockerfile new file mode 100644 index 0000000..1572246 --- /dev/null +++ b/.devcontainer/web-app/Dockerfile @@ -0,0 +1,26 @@ +FROM python:3.12-slim + +SHELL ["/bin/bash", "-c"] +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + curl \ + wget \ + git \ + openssh-client \ + sudo \ + just \ + libmagic1 + +RUN groupadd -g 1000 appuser && useradd appuser -u 1000 -g 1000 -m -s /bin/bash +RUN chown -R appuser:appuser /home/appuser/ + +COPY .devcontainer/scripts/base-setup.sh /tmp/base-setup.sh +RUN chmod +x /tmp/base-setup.sh \ + && USERNAME="appuser" /tmp/base-setup.sh + +USER appuser +WORKDIR /home/appuser/workspace + +RUN wget -qO- https://astral.sh/uv/install.sh | sh \ No newline at end of file diff --git a/.devcontainer/web-app/devcontainer.json b/.devcontainer/web-app/devcontainer.json new file mode 100644 index 0000000..e2472a7 --- /dev/null +++ b/.devcontainer/web-app/devcontainer.json @@ -0,0 +1,35 @@ +{ + "name": "web-app", + "build": { + "dockerfile": "Dockerfile", + "context": "../.." + }, + "workspaceMount": "source=${localWorkspaceFolder},target=/home/appuser/workspace,type=bind", + "workspaceFolder": "/home/appuser/workspace/services/web-app", + "mounts": [ + "type=bind,source=${localEnv:SSH_AUTH_SOCK},target=/ssh-agent" + ], + "remoteUser": "appuser", + "remoteEnv": { + "SSH_AUTH_SOCK": "/ssh-agent", + "GIT_CONFIG_GLOBAL": "/home/appuser/.gitconfig" + }, + "runArgs": [ + "--network=host" + ], + "overrideCommand": true, + "postCreateCommand": "cd /home/appuser/workspace && /bin/bash .devcontainer/res/post_create_command.bash", + "customizations": { + "vscode": { + "extensions": [ + "skellock.just", + "ms-python.python", + "ms-python.pylint", + "ms-python.autopep8", + "tamasfe.even-better-toml", + "VisualStudioExptTeam.vscodeintellicode", + "matangover.mypy" + ] + } + } +} \ No newline at end of file diff --git a/.github/actions/run-precommit-for-service/action.yml b/.github/actions/run-precommit-for-service/action.yml new file mode 100644 index 0000000..d47e76c --- /dev/null +++ b/.github/actions/run-precommit-for-service/action.yml @@ -0,0 +1,36 @@ +--- +name: Run Pre-commit for Service +description: Set up a Dev-Container for a specific service and run pre-commit checks within that environment. + +inputs: + devcontainer-cfg-path: + description: Directory containing .devcontainer configuration for the service. + required: true + + service-workdir: + description: Working directory inside the Dev-Container where the service code resides. + required: true + +runs: + using: composite + + steps: + - name: Verify Docker engine + run: docker version + shell: bash + + - name: Ensure SSH agent mount path exists + run: | + if [ ! -e "${SSH_AUTH_SOCK}" ]; then + touch "${SSH_AUTH_SOCK}" + fi + shell: bash + + - name: Run pre-commit in devcontainer + uses: devcontainers/ci@v0.3 + with: + configFile: ${{ inputs.devcontainer-cfg-path }}/devcontainer.json + runCmd: | + cd ${{ inputs.service-workdir }} && \ + just setup-environment && \ + just run-precommit \ No newline at end of file diff --git a/.github/workflows/service-checks.yml b/.github/workflows/service-checks.yml new file mode 100644 index 0000000..26d5562 --- /dev/null +++ b/.github/workflows/service-checks.yml @@ -0,0 +1,113 @@ +name: Service Checks +description: Run pre-commit static analysis on services having their own Dev-Container setup. + +on: + pull_request: + paths: + - .github/workflows/service-checks.yml + - .pre-commit-config.yaml + - pyproject.toml + - services/context-retriever/** + - services/web-app/** + - services/llm-proxy/** + - .devcontainer/** + branches: [main, develop] + +permissions: + contents: read + + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + # Dummy socket to simulate mounting SSH agent into Dev-Container + SSH_AUTH_SOCK: /tmp/ssh-agent + +jobs: + changes: + name: Detect changed services + runs-on: ubuntu-24.04 + outputs: + context-retriever: ${{ steps.filter.outputs.context-retriever }} + web-app: ${{ steps.filter.outputs.web-app }} + llm-proxy: ${{ steps.filter.outputs.llm-proxy }} + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Determine impacted services + id: filter + uses: dorny/paths-filter@v3 + with: + filters: | + context-retriever: + - services/context-retriever/** + - .pre-commit-config.yaml + - pyproject.toml + - .github/workflows/service-checks.yml + - .devcontainer/context-retriever/** + - .devcontainer/scripts/** + web-app: + - services/web-app/** + - .pre-commit-config.yaml + - pyproject.toml + - .github/workflows/service-checks.yml + - .devcontainer/web-app/** + - .devcontainer/scripts/** + llm-proxy: + - services/llm-proxy/** + - .pre-commit-config.yaml + - pyproject.toml + - .github/workflows/service-checks.yml + - .devcontainer/llm-proxy/** + - .devcontainer/scripts/** + + precommit-context-retriever: + name: Pre-commit (context-retriever) + runs-on: ubuntu-24.04 + needs: changes + if: needs.changes.outputs.context-retriever == 'true' + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Run pre-commit for context-retriever + uses: ./.github/actions/run-precommit-for-service + with: + devcontainer-cfg-path: .devcontainer/context-retriever + service-workdir: /home/appuser/workspace/services/context-retriever + + + precommit-web-app: + name: Pre-commit (web-app) + runs-on: ubuntu-24.04 + needs: changes + if: needs.changes.outputs.web-app == 'true' + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Run pre-commit for web-app + uses: ./.github/actions/run-precommit-for-service + with: + devcontainer-cfg-path: .devcontainer/web-app + service-workdir: /home/appuser/workspace/services/web-app + + precommit-llm-proxy: + name: Pre-commit (llm-proxy) + runs-on: ubuntu-24.04 + needs: changes + if: needs.changes.outputs.llm-proxy == 'true' + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Run pre-commit for llm-proxy + uses: ./.github/actions/run-precommit-for-service + with: + devcontainer-cfg-path: .devcontainer/llm-proxy + service-workdir: /home/appuser/workspace/services/llm-proxy diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7313dff --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +.vscode/ +**/__pycache__/ +persistent_data/ +.venv/ +.mypy_cache/ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..e710fb5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,74 @@ +--- +repos: + # Official pre-commit hooks + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-docstring-first + - id: check-added-large-files + - id: check-yaml + - id: debug-statements + - id: double-quote-string-fixer + - id: check-executables-have-shebangs + - id: detect-private-key + - id: check-case-conflict + + # Ensures Python files are formatted using autopep8 formatter + - repo: local + hooks: + - id: autopep8 + name: autopep8 + entry: autopep8 + language: system + types: [python] + args: + - "--in-place" + - "--aggressive" + - "--exit-code" + - "--max-line-length=100" + + # Ensures python files are written using modern syntax + - repo: https://github.com/asottile/pyupgrade + rev: v3.21.2 + hooks: + - id: pyupgrade + + # Ensures python imports are sorted according to (std, external, own) rule + - repo: https://github.com/asottile/reorder-python-imports + rev: v3.16.0 + hooks: + - id: reorder-python-imports + + # Ensures python files pass pylint checks + - repo: local + hooks: + - id: pylint + name: pylint + entry: pylint + language: system + types: [python] + args: [-rn, -sn] + + # Performs Python static type check + - repo: local + hooks: + - id: mypy + name: mypy + entry: mypy + language: system + types: [python] + + # Ensures yaml files are formatted + - repo: https://github.com/jumanjihouse/pre-commit-hook-yamlfmt + rev: 0.2.3 + hooks: + - id: yamlfmt + args: [--mapping, '2', --sequence, '4', --offset, '2'] + + # Ensures json files are valid. Supports Json with Comments + - repo: https://gitlab.com/bmares/check-json5 + rev: v1.0.0 + hooks: + - id: check-json5 diff --git a/Changelog.md b/Changelog.md new file mode 100644 index 0000000..6c7862d --- /dev/null +++ b/Changelog.md @@ -0,0 +1,10 @@ +# Changelog + +## 0.1 + +- Created first working project setup +- Created GH Actions workflows for static code analysis +- Added working versions of project components + - web-app - chat bot conversation handling, uploading PDFs, visualizing retrieved documents, basic info & error messages handling + - llm-proxy - streaming chat responses, safety and relevance guardrails + - context-retriever - simple PDF document chunking, embeddings, handling vector store operations, basic query lookup, query reformulation \ No newline at end of file diff --git a/README.md b/README.md index eece448..3400578 100644 --- a/README.md +++ b/README.md @@ -1,46 +1,74 @@ -# rag-application +# rag-app -This project contains a web application enabling the user to chat with an LLM-based chatbot with access to additional context information, leveraging the RAG paradigm. +This project contains a web application enabling the user to chat with an LLM-based chatbot with access to additional context information, leveraging the RAG paradigm. The chat bot is designed to represent AGH University of Krakow and the entire infrastructure is developed to support self-hosted LLMs for text generation, embeddings or safety checks. The app is a minimal-setup, demonstration application that could be deployed as a tuned assistant chat bot to answer questions regarding the institution, based on internal source of truth. -## Development +## System overview -It is advisable to use the VSCode's Development Container mechanism for development. The default configuration allows to set up a Docker container with all dependencies and functionalities, such as Docker-in-Docker. +The system is a three-level hierarchy: -### Setup +- **Web application** - the only endpoint available to the user. +- **Backend** - isolated environments where the requests are validated and processed +- **Self-hosted endpoints** - LLM and Vector Store endpoints leveraging the internal infrastructure of the institution, e.g. data centers and computation clusters + +![system-overview](./doc/system-overview.drawio.png) + +### Web application + +The implementation of the application is demonstration-purpose. It facilitates two primary interactions: 1. Sending messages to the chat bot, 2. Adding documents to the knowledge base (in production setup, this functionality should require admin privileges). + +The communication with the chatbot follows this lifecycle: +1. The user asks a question. +2. The question is validated with respect to its safety +3. The question is validated with respect to its relevance to the specified list of topics (e.g. preventing the user from asking questions about politics) +4. For the given user message, a set of document chunks is collected from the knowledge store, based on their relevance to the question +5. The message is sent to the main LLM, which responds to the question, based on the conversation history and documents + +![web-app-screen](./doc/web-app-screen.png) -After opening the Devcontainer, it is recommended to use only the setup commands specified in the `justfile`: +### Backend -```bash -just setup-dev -``` +#### llm-proxy -### Running checks +It is responsible for administering the conversation with the chatbot. It is a layer of communication between the web app and the assistant that provides additional validation services. -In order to run unit tests, integration tests and other checks just use one of the pre-made commands, e.g.: +1. Safety check - using a specialized smaller LLM, it checks whether the user query contains harmful/illegal content, responding to which would compromise the institution. +2. Relevance check - it examines the query and checks if it follows one of the specified topics, e.g. the question is about the rules of admission to the university, rather than cooking a perfect pasta. +3. Chat response generation - based on the conversation context and provided documents, it composes a prompt and streams the response of the main LLM. -```bash -just run-pre-commit -``` +![llm-proxy](./doc/llm-proxy.drawio.png) -The commands are configured to be reused by GitHub Actions jobs. +#### context-retriever -## Usage -Prerequisities for using the project outside of the Development Container: -1. `just` - for running the setup scripts defined in the `justfile` -2. `docker-compose` - for running multi-container application +It is responsible for handling IO operations on the internal knowledge base. In particular, it does: -In order to set up the whole system, run: +1. Document uploading - it scopes the file validation, loading, chunking and storing the embedded chunks in a vector store +2. Information Retrieval - the user query is embedded and compared with the stored chunk embedding in order to retrieve the most relevant chunks. +3. Query Reformulation - before embedding the query, it must be reformulated using the helper LLM so that it reflects the context of the conversation, e.g. + - **User:** Hello, my name is Wiktor and I've just graduated from a secondary school in Berlin. **Assistant:** Hello, Wiktor. How can I help you? **User:** How can I apply for the university? + - **Reformulated query:** What are the admission rules for first-degree studies for non-polish students? -```bash -just up-services -``` +![context-retriever](./doc/context-retriever.drawio.png) + +### Self-hosted endpoints + +- **main-llm-responder** - the primary LLM responsible for the generation of responses to the user's messages +- **guard-llm** - specialized at detecting harmful behavior and questions diverging from the expected topics +- **helper-llm** - used during the document retrieval for e.g. query reformulation +- **embedding-endpoint** - embeds the documents to store them in a Vector DB +- **vector-store** - stores the embedded documents together with their metadata and handles distance-based query lookups + +## Development + +It is advisable to use the VSCode's Development Container mechanism for development. The default configuration allows to set up a Docker container with all dependencies and functionalities, such as Docker-in-Docker. + +### Setup -To stop it: +The most important elements of the project structure are: -```bash -just down-services -``` +- `services/` - contains implementations of endpoints being the highest-level modules of the entire system +- `docker-compose.yml` - contains configurations for setting up the endpoints, together with the description of the endpoint-endpoint communication +- `.devcontainer` - contains configuration of the Devcontainers, within which the services are supposed to be developed. It is recommended to use `docker compose` in the `base` devcontainer and develop each service in isolation using its specialized devcontainer ## Changelog diff --git a/doc/context-retriever.drawio.png b/doc/context-retriever.drawio.png new file mode 100644 index 0000000..9c4adec Binary files /dev/null and b/doc/context-retriever.drawio.png differ diff --git a/doc/llm-proxy.drawio.png b/doc/llm-proxy.drawio.png new file mode 100644 index 0000000..c71cf35 Binary files /dev/null and b/doc/llm-proxy.drawio.png differ diff --git a/doc/system-overview.drawio.png b/doc/system-overview.drawio.png new file mode 100644 index 0000000..e7f92a9 Binary files /dev/null and b/doc/system-overview.drawio.png differ diff --git a/doc/web-app-screen.png b/doc/web-app-screen.png new file mode 100644 index 0000000..af0d0ed Binary files /dev/null and b/doc/web-app-screen.png differ diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..cb8598d --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,187 @@ +--- +version: '3.9' + +services: + main-llm-responder: + build: + context: services/llm-endpoint/ + dockerfile: llm-endpoint.Dockerfile + args: + EXPOSED_PORT: 11434 + + container_name: main-llm-responder + ports: + - 11434:11434 + volumes: + - ./persist_dir/main-llm-responder:/root/.ollama + networks: + - rag-net + environment: + - LLM_GENERATOR_MODEL=llama3.1:8b + - OLLAMA_DEBUG=0 + - OLLAMA_HOST=0.0.0.0:11434 + healthcheck: + test: curl -s http://localhost:11434/api/tags | grep '"model":"local_ollama_model:latest"' + interval: 5m + timeout: 30s + retries: 10 + start_period: 30s + + guard-llm: + build: + context: services/llm-endpoint/ + dockerfile: llm-endpoint.Dockerfile + args: + EXPOSED_PORT: 11435 + + container_name: guard-llm + ports: + - 11435:11435 + volumes: + - ./persist_dir/guard-llm:/root/.ollama + networks: + - rag-net + environment: + - LLM_GENERATOR_MODEL=dolphin3:8b + - OLLAMA_DEBUG=0 + - OLLAMA_HOST=0.0.0.0:11435 + healthcheck: + test: curl -s http://localhost:11435/api/tags | grep '"model":"local_ollama_model:latest"' + interval: 5m + timeout: 30s + retries: 10 + start_period: 30s + + helper-llm: + build: + context: services/llm-endpoint/ + dockerfile: llm-endpoint.Dockerfile + args: + EXPOSED_PORT: 11434 + + container_name: helper-llm + ports: + - 11437:11437 + volumes: + - ./persist_dir/helper-llm:/root/.ollama + networks: + - rag-net + environment: + - LLM_GENERATOR_MODEL=dolphin3:8b + - OLLAMA_DEBUG=0 + - OLLAMA_HOST=0.0.0.0:11437 + healthcheck: + test: curl -s http://localhost:11437/api/tags | grep '"model":"local_ollama_model:latest"' + interval: 5m + timeout: 30s + retries: 10 + start_period: 30s + + vector-store: + build: + context: services/vector-store/ + dockerfile: vector-store.Dockerfile + args: + EXPOSED_PORT_REST: 6333 + EXPOSED_PORT_GRPC: 6334 + + container_name: vector-store + ports: + - 6333:6333 + - 6334:6334 + volumes: + - ./persist_dir/vector-store:/qdrant/storage:z + networks: + - rag-net + healthcheck: + test: curl --fail http://localhost:6333/livez || exit 1 + interval: 1m30s + timeout: 30s + retries: 5 + start_period: 30s + + embedding-endpoint: + build: + context: services/embedding-endpoint/ + dockerfile: embedding-endpoint.Dockerfile + args: + EXPOSED_PORT: 11436 + + container_name: embedding-endpoint + ports: + - 11436:11436 + volumes: + - ./persist_dir/embedding-endpoint:/root/.ollama + networks: + - rag-net + environment: + - EMBEDDING_MODEL=nomic-embed-text:v1.5 + - OLLAMA_DEBUG=0 + - OLLAMA_HOST=0.0.0.0:11436 + healthcheck: + test: curl -s http://localhost:11436/api/tags | grep '"model":"nomic-embed-text:v1.5"' + interval: 5m + timeout: 30s + retries: 10 + start_period: 30s + + web-app: + build: + context: services/web-app/ + dockerfile: web-app.Dockerfile + args: + WEB_APP_PORT: 8080 + + container_name: web-app + ports: + - 8080:8080 + volumes: + - ./persist_dir/web-app:/app/data + networks: + - rag-net + + context-retriever: + build: + context: services/context-retriever/ + dockerfile: context-retriever.Dockerfile + args: + API_PORT: 8899 + + container_name: context-retriever + ports: + - 8899:8899 + volumes: + - ./persist_dir/context-retriever:/app/data + networks: + - rag-net + depends_on: + vector-store: + condition: service_healthy + embedding-endpoint: + condition: service_healthy + main-llm-responder: + condition: service_healthy + + llm-proxy: + build: + context: services/llm-proxy/ + dockerfile: llm-proxy.Dockerfile + args: + API_PORT: 8888 + + container_name: llm-proxy + ports: + - 8888:8888 + volumes: + - ./persist_dir/llm-proxy:/app/data + networks: + - rag-net + depends_on: + main-llm-responder: + condition: service_healthy + guard-llm: + condition: service_healthy + +networks: + rag-net: + driver: bridge diff --git a/justfile b/justfile new file mode 100644 index 0000000..aa849e6 --- /dev/null +++ b/justfile @@ -0,0 +1,9 @@ +# -------------------------------------------------- +# This file contains setup scripts for the project. +# For more info, see: https://github.com/casey/just +# -------------------------------------------------- + +set shell := ["bash", "-c"] + +up-infrastructure: + docker compose up --build \ No newline at end of file diff --git a/persist_dir/context-retriever/.gitignore b/persist_dir/context-retriever/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/context-retriever/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/persist_dir/embedding-endpoint/.gitignore b/persist_dir/embedding-endpoint/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/embedding-endpoint/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/persist_dir/guard-llm/.gitignore b/persist_dir/guard-llm/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/guard-llm/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/persist_dir/helper-llm/.gitignore b/persist_dir/helper-llm/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/persist_dir/helper-llm/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/persist_dir/llm-proxy/.gitignore b/persist_dir/llm-proxy/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/llm-proxy/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/persist_dir/main-llm-responder/.gitignore b/persist_dir/main-llm-responder/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/main-llm-responder/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/persist_dir/vector-store/.gitignore b/persist_dir/vector-store/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/vector-store/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/persist_dir/web-app/.gitignore b/persist_dir/web-app/.gitignore new file mode 100644 index 0000000..c96a04f --- /dev/null +++ b/persist_dir/web-app/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..aa13d50 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "rag-app" +version = "0.1.0" +requires-python=">=3.12" + +[tool.mypy] +disable_error_code = "import-untyped" +strict = true + +[tool.autopep8] +aggressive = true +max-line-length = 100 +in-place = true + +[tool.pylint."messages control"] +disable = [ + "fixme", + "too-few-public-methods", + "too-many-instance-attributes", + "too-many-arguments", + "too-many-positional-arguments", + "too-many-locals", + "R0801" +] diff --git a/services/context-retriever/.gitignore b/services/context-retriever/.gitignore new file mode 100644 index 0000000..a4a2388 --- /dev/null +++ b/services/context-retriever/.gitignore @@ -0,0 +1,3 @@ +src/context_retriever.egg-info/ +data/ +outputs/ diff --git a/services/context-retriever/cfg/main.yaml b/services/context-retriever/cfg/main.yaml new file mode 100644 index 0000000..968bf11 --- /dev/null +++ b/services/context-retriever/cfg/main.yaml @@ -0,0 +1,19 @@ +--- +server_host: 0.0.0.0 +server_port: 8899 +n_server_workers: 1 +persist_data_path: data/ +doc_processing: + chunk_size: 1000 + chunk_overlap: 200 +doc_retrieval: + max_context_docs: 3 + similarity_threshold: 0.6 + helper_llm: + model_name: local_ollama_model + url: http://helper-llm:11437 + temperature: 0.2 + + +defaults: + - vector_store: qdrant diff --git a/services/context-retriever/cfg/vector_store/qdrant.yaml b/services/context-retriever/cfg/vector_store/qdrant.yaml new file mode 100644 index 0000000..6f51bcb --- /dev/null +++ b/services/context-retriever/cfg/vector_store/qdrant.yaml @@ -0,0 +1,8 @@ +--- +driver: qdrant +url: http://vector-store:6333 +collection_name: documents +uploading_batch_size: 8 +embedding_model: + model_name: nomic-embed-text:v1.5 + url: http://embedding-endpoint:11436 diff --git a/services/context-retriever/context-retriever.Dockerfile b/services/context-retriever/context-retriever.Dockerfile new file mode 100644 index 0000000..3e726b6 --- /dev/null +++ b/services/context-retriever/context-retriever.Dockerfile @@ -0,0 +1,19 @@ +FROM python:3.12-slim + +SHELL ["/bin/bash", "-c"] +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends wget + +RUN wget -qO- https://astral.sh/uv/install.sh | env UV_INSTALL_DIR="/usr/local/bin" sh + +COPY cfg /app/cfg +COPY src /app/src +COPY pyproject.toml main.py /app/ +WORKDIR /app + +EXPOSE ${API_PORT} + +RUN uv python install 3.12 && uv venv && uv pip install . +CMD [ "uv", "run", "python", "main.py" ] diff --git a/services/context-retriever/justfile b/services/context-retriever/justfile new file mode 100644 index 0000000..f8eae92 --- /dev/null +++ b/services/context-retriever/justfile @@ -0,0 +1,20 @@ +# -------------------------------------------------- +# This file contains setup scripts for the project. +# For more info, see: https://github.com/casey/just +# -------------------------------------------------- + +set shell := ["bash", "-c"] + +# Setup virtual environment and install dependencies. +setup-environment: + uv python install 3.12 + uv venv + uv pip install -e .[dev] + +run-server *args: + uv run python main.py "$@" + +# Runs static checks using global pre-commit configuration. +run-precommit: + uv run pre-commit run -c ../../.pre-commit-config.yaml \ + --files `git ls-files --cached --others --exclude-standard` diff --git a/services/context-retriever/main.py b/services/context-retriever/main.py new file mode 100644 index 0000000..3f40493 --- /dev/null +++ b/services/context-retriever/main.py @@ -0,0 +1,205 @@ +""""Starts the backend server for the entrypoint service.""" +import json +import logging +import os +import sys +from contextlib import asynccontextmanager +from datetime import datetime +from typing import Any + +import fastapi +import hydra +import omegaconf +import pydantic +import uvicorn +from context_retriever import doc_preparation_service +from context_retriever import doc_retrieval_service +from context_retriever.vector_store import qdrant as qdrant_vs +from context_retriever.vector_store.core import EmbeddingModelCfg + + +def _logger() -> logging.Logger: + return logging.getLogger('context_retriever') + + +doc_prep: doc_preparation_service.DocPreparationService | None = None # pylint: disable=invalid-name +doc_retrieval: doc_retrieval_service.DocRetrievalService | None = None # pylint: disable=invalid-name + + +@asynccontextmanager +async def lifespan(_): # type: ignore + """Sets up global contexts used by the server workers.""" + + cfg_serialized = os.environ.get('CONTEXT_RETRIEVER_SERVER_CFG', None) + + if cfg_serialized is None: + _logger().critical('Failed to load context_retriever server config from env.') + sys.exit(1) + + cfg = omegaconf.OmegaConf.create(json.loads(cfg_serialized)) + + # The llm_service is designed to be used by the endpoint handlers as a global service. It is + # not assigned in the `main` function because the uvicorn workers don't call it, as opposed to + # the `lifespan` callback. + global doc_prep # pylint: disable=global-statement + global doc_retrieval # pylint: disable=global-statement + + if cfg.vector_store.driver == 'qdrant': + vector_store_proxy = qdrant_vs.QdrantProxy( + qdrant_vs.QdrantProxyCfg( + url=cfg.vector_store.url, + collection_name=cfg.vector_store.collection_name, + uploading_batch_size=cfg.vector_store.uploading_batch_size, + embedding_model=EmbeddingModelCfg( + **cfg.vector_store.embedding_model + ) + ) + ) + else: + _logger().critical('Unsupported vector store driver: %s', cfg.vector_store.driver) + sys.exit(1) + + doc_prep = doc_preparation_service.DocPreparationService( + vector_store_proxy=vector_store_proxy, + doc_processing_cfg=doc_preparation_service.DocProcessingCfg(**cfg.doc_processing) + ) + doc_retrieval = doc_retrieval_service.DocRetrievalService( + cfg=doc_retrieval_service.DocRetrievalCfg(**cfg.doc_retrieval), + vector_store_proxy=vector_store_proxy + ) + + yield + + +app = fastapi.FastAPI(lifespan=lifespan) + + +@app.get('/ping') +async def read_ping() -> dict[str, str]: + """Health check endpoint.""" + return {'message': 'Service is running'} + + +class RequestCollectContextInfo(pydantic.BaseModel): + """Request to collect context documents from the vector store.""" + user_message: str + chat_history: list[dict[str, Any]] + + +class ResponseCollectContextInfo(pydantic.BaseModel): + """Response from context-retriever after collecting context documents.""" + context_docs: list[dict[str, Any]] + + +@app.post('/collect_context_info') +async def collect_context_info(request: RequestCollectContextInfo) -> ResponseCollectContextInfo: + """Collects context documents from the vector store based on user message and chat history.""" + + assert doc_retrieval is not None + + context_docs = await doc_retrieval.retrieve_context_docs( + user_message=request.user_message, + chat_history=request.chat_history + ) + + return ResponseCollectContextInfo(context_docs=context_docs) + + +class ResponseUploadDocument(pydantic.BaseModel): + """Response from context-retriever to web-app after uploading a document.""" + error: str | None = None + + +@app.post('/upload_pdf') +async def upload_pdf(file: fastapi.UploadFile) -> ResponseUploadDocument: + """Processes and saves an uploaded PDF document to the vector store.""" + + assert doc_prep is not None + + if file.content_type is None: + return ResponseUploadDocument(error='Cannot determine content type for the uploaded PDF.') + + if file.size is None: + return ResponseUploadDocument(error='Cannot determine file size for the uploaded PDF.') + + is_ok, error_msg = await doc_prep.upload_pdf( + file_size=file.size, + content_type=file.content_type, + file_stream=file.file + ) + + if not is_ok: + return ResponseUploadDocument(error=error_msg) + + return ResponseUploadDocument() + + +def _configure_logging(script_cfg: omegaconf.DictConfig) -> None: + + logging.config.dictConfig({ + 'version': 1, + 'loggers': { + 'root': { + 'level': 'WARNING', + 'handlers': ['console', 'file'] + }, + 'context_retriever': { + 'level': 'DEBUG', + 'handlers': ['console', 'file'], + 'propagate': False + } + }, + 'handlers': { + 'console': { + 'class': 'logging.StreamHandler', + 'level': 'INFO', + 'formatter': 'default' + }, + 'file': { + 'class': 'logging.handlers.RotatingFileHandler', + 'level': 'DEBUG', + 'formatter': 'default', + 'filename': os.path.join(script_cfg.persist_data_path, + 'log', + f'{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.log'), + 'maxBytes': 5_000_000, + 'backupCount': 5, + 'encoding': 'utf-8', + } + }, + 'formatters': { + 'default': { + 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s', + } + } + }) + + +@hydra.main(version_base=None, config_path='cfg', config_name='main') +def main(cfg: omegaconf.DictConfig) -> None: + """Initializes and serves the web app.""" + + _logger().info('Script configuration:\n%s', omegaconf.OmegaConf.to_yaml(cfg)) + + os.makedirs(os.path.join(cfg.persist_data_path, 'log'), exist_ok=True) + + _configure_logging(cfg) + + _logger().info('Starting server on %s:%d with %d workers.', + cfg.server_host, cfg.server_port, cfg.n_server_workers) + + cfg_serialized = json.dumps(omegaconf.OmegaConf.to_container(cfg)) + os.environ['CONTEXT_RETRIEVER_SERVER_CFG'] = cfg_serialized + + uvicorn.run('main:app', + host=cfg.server_host, + port=cfg.server_port, + workers=cfg.n_server_workers, + log_level='info', + access_log=True, + limit_concurrency=1000, + timeout_keep_alive=5) + + +if __name__ == '__main__': + main() # pylint: disable=no-value-for-parameter diff --git a/services/context-retriever/pyproject.toml b/services/context-retriever/pyproject.toml new file mode 100644 index 0000000..4813fae --- /dev/null +++ b/services/context-retriever/pyproject.toml @@ -0,0 +1,29 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "context_retriever" +version = "0.1.0" +requires-python=">=3.12" +dependencies = [ + "hydra-core>=1.3.2", + "requests>=2.32.3", + "uvicorn>=0.38.0", + "fastapi>=0.124.4", + "langchain>=1.2.0", + "langchain-core>=1.2.6", + "langchain-community>=0.4.1", + "langchain-ollama>=1.0.1", + "python-multipart>=0.0.21", + "pypdf>=6.5.0", + "qdrant-client>=1.16.2" +] + +[project.optional-dependencies] +dev = [ + "pylint>=4.0.4", + "autopep8>=2.3.2", + "mypy>=1.19.0", + "pre-commit>=4.5.0" +] diff --git a/services/context-retriever/src/context_retriever/__init__.py b/services/context-retriever/src/context_retriever/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/context-retriever/src/context_retriever/doc_preparation_service.py b/services/context-retriever/src/context_retriever/doc_preparation_service.py new file mode 100644 index 0000000..78ddfee --- /dev/null +++ b/services/context-retriever/src/context_retriever/doc_preparation_service.py @@ -0,0 +1,96 @@ +"""Contains service that prepares documents for storage in the vector store.""" +import logging +import tempfile +from typing import BinaryIO + +import langchain_text_splitters +import pydantic +from context_retriever.vector_store.core import VectorStoreProxy +from langchain_community import document_loaders +from langchain_core.documents import Document + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class DocProcessingCfg(pydantic.BaseModel): + """Configuration for document processing.""" + chunk_size: int + chunk_overlap: int + + +class DocPreparationService: + """Validates, processes and saves documents to the vector store.""" + + _MAX_PDF_SIZE_BYTES = 10 * 1024 * 1024 # 10 MB + _PDF_ALLOWED_FIELDS = ('title', 'author') + + def __init__(self, + vector_store_proxy: VectorStoreProxy, + doc_processing_cfg: DocProcessingCfg) -> None: + + _logger().info('Initializing DocPreparationService with config: %s', + doc_processing_cfg) + + self._vector_store_proxy = vector_store_proxy + + self._text_splitter = langchain_text_splitters.RecursiveCharacterTextSplitter( + separators=['\n\n', '\n', ' '], + chunk_size=doc_processing_cfg.chunk_size, + chunk_overlap=doc_processing_cfg.chunk_overlap, + length_function=len, + is_separator_regex=False + ) + + async def upload_pdf(self, + file_size: int, + content_type: str, + file_stream: BinaryIO + ) -> tuple[bool, str]: + """Uploads a PDF document to the vector store. + Args: + file_size: Size of the file in bytes. + content_type: Content type of the file. + file_stream: Stream of the file to be uploaded. + + Returns: + A tuple where the first element indicates success, and the second element contains + an error message if any. + """ + + if content_type != 'application/pdf': + return False, 'Invalid content type. Only PDF files are supported.' + + if file_size > self._MAX_PDF_SIZE_BYTES: + return (False, + f'File size exceeds the maximum limit of {self._MAX_PDF_SIZE_BYTES} bytes.') + + with tempfile.NamedTemporaryFile(delete=True) as temp_file: + temp_file.write(file_stream.read()) + temp_file.flush() + + pdf_pages = document_loaders.PyPDFLoader(temp_file.name).load() + + self._sanitize_pdf_metadata(pdf_pages) + + _logger().debug('Uploading PDF: size=%d, title=%s.', + file_size, + pdf_pages[0].metadata['title']) + + chunks = self._text_splitter.split_documents(pdf_pages) + + await self._vector_store_proxy.store_documents(chunks) + + return True, '' + + def _sanitize_pdf_metadata(self, pdf_pages: list[Document]) -> None: + """Sanitizes metadata of PDF documents to remove unnecessary fields.""" + + for page in pdf_pages: + + page.metadata = { + 'page': page.metadata.get('page_label', 'Unknown'), + 'title': page.metadata.get('title', 'Unknown'), + 'author': page.metadata.get('author', 'Unknown') + } diff --git a/services/context-retriever/src/context_retriever/doc_retrieval_service.py b/services/context-retriever/src/context_retriever/doc_retrieval_service.py new file mode 100644 index 0000000..05ebc3b --- /dev/null +++ b/services/context-retriever/src/context_retriever/doc_retrieval_service.py @@ -0,0 +1,144 @@ +"""Contains service handling conversation context document retrieval.""" +import logging +from typing import Any + +import pydantic +from context_retriever.vector_store import core as vs_core +from langchain.messages import HumanMessage +from langchain.messages import SystemMessage +from langchain_ollama.chat_models import ChatOllama + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class HelperLLMConfig(pydantic.BaseModel): + """Configuration for LLM model endpoint.""" + model_name: str + url: str + temperature: float + + +class DocRetrievalCfg(pydantic.BaseModel): + """Configuration for document retrieval.""" + max_context_docs: int + similarity_threshold: float + helper_llm: HelperLLMConfig + + +class DocRetrievalService: + """Retrieves context documents from the vector store. + + The service constructs queries based on user messages and chat history to fetch + relevant documents. + """ + + _CONSTRUCT_QUERY_SYSTEM_PROMPT = """ + You are an expert at reformulating user messages based on conversation history. + """ + + _CONSTRUCT_QUERY_PROMPT_TEMPLATE = """ + Given the following chat history and the latest user message, reformulate the user's message + to better reflect the context of the conversation. + + ### Guidelines + 1. The reformulated message should capture the context of the user's latest + message, taking into account the previous messages in the chat history. + 2. Your response should contain ONLY the reformulated message. + 3. The reformulated message MUST have the same meaning and grammatical structure as the + original user message. E.g. if the user message is a question, the reformulated message + should also be a question, as if the user had asked it. + + ### Chat History: + {chat_history} + + ### Latest User Message: + {user_message} + """ + + def __init__(self, + cfg: DocRetrievalCfg, + vector_store_proxy: vs_core.VectorStoreProxy) -> None: + + _logger().info('Initializing DocRetrievalService with config: %s', cfg) + + self._cfg = cfg + self._vector_store_proxy = vector_store_proxy + self._helper_llm = ChatOllama( + model=self._cfg.helper_llm.model_name, + base_url=self._cfg.helper_llm.url, + temperature=self._cfg.helper_llm.temperature + ) + + async def retrieve_context_docs(self, + user_message: str, + chat_history: list[dict[str, str]] + ) -> list[dict[str, Any]]: + """Retrieves context documents based on user message and chat history. + + Args: + user_message: The latest message from the user. + chat_history: List of previous messages in the conversation. + """ + + _logger().debug('Retrieving context documents for user message: %s and chat history: %s', + user_message, + chat_history) + + query = await self._construct_query(user_message, chat_history) + + _logger().debug('Constructed query for document retrieval: %s', query) + + retrieved_docs = await self._vector_store_proxy.retrieve_documents( + query, + self._cfg.max_context_docs, + self._cfg.similarity_threshold) + + return [ + {'content': doc.page_content, 'metadata': doc.metadata} + for doc in retrieved_docs + ] + + async def _construct_query(self, + user_message: str, + chat_history: list[dict[str, str]] + ) -> str: + """Constructs a query string using the helper LLM based on user message and chat history. + + Args: + user_message: The latest message from the user. + chat_history: List of previous messages in the conversation. + """ + + formatted_chat_history = self._format_chat_history(chat_history) + + messages = [ + SystemMessage(self._CONSTRUCT_QUERY_SYSTEM_PROMPT), + HumanMessage( + self._CONSTRUCT_QUERY_PROMPT_TEMPLATE.format( + chat_history=formatted_chat_history, + user_message=user_message + ) + ) + ] + + response = await self._helper_llm.agenerate([messages]) + + return response.generations[0][0].text.strip() + + def _format_chat_history(self, + chat_history: list[dict[str, str]] + ) -> str: + """Formats the chat history into a string for prompt input. + + Args: + chat_history: List of previous messages in the conversation. + """ + + history_lines = [ + f"{entry['role'].capitalize()}: {entry['content']}" + for entry in chat_history + ] + + return '\n'.join(history_lines) diff --git a/services/context-retriever/src/context_retriever/vector_store/__init__.py b/services/context-retriever/src/context_retriever/vector_store/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/context-retriever/src/context_retriever/vector_store/core.py b/services/context-retriever/src/context_retriever/vector_store/core.py new file mode 100644 index 0000000..b37a672 --- /dev/null +++ b/services/context-retriever/src/context_retriever/vector_store/core.py @@ -0,0 +1,57 @@ +"""Contains core interfaces and utilities for vector store operations.""" +from abc import ABC +from abc import abstractmethod +from typing import Iterator +from typing import TypeVar + +import pydantic +from langchain_core.documents import Document + + +class EmbeddingModelCfg(pydantic.BaseModel): + """Configuration of embedding model endpoint.""" + model_name: str + url: str + + +class VectorStoreProxy(ABC): + """Interface for classes handling vector store operations.""" + + @abstractmethod + async def store_documents(self, documents: list[Document]) -> None: + """Stores the given documents in the vector store. + + Args: + documents: List of documents to be stored. + """ + + @abstractmethod + async def retrieve_documents(self, + query: str, + k: int, + sim_thresh: float) -> list[Document]: + """Retrieves documents from the vector store based on the given query. + + Args: + query: The query string to search for relevant documents. + k: The number of top documents to retrieve. + sim_thresh: Similarity threshold for document retrieval. + + Returns: + List of documents matching the query. + """ + + +T = TypeVar('T') + + +def batch_iterate(iterable: list[T], batch_size: int) -> Iterator[list[T]]: + """Yields successive batches from an iterable. + + Args: + iterable: The iterable to be divided into batches. + batch_size: The size of each batch. + """ + + for i in range(0, len(iterable), batch_size): + yield iterable[i:i + batch_size] diff --git a/services/context-retriever/src/context_retriever/vector_store/qdrant.py b/services/context-retriever/src/context_retriever/vector_store/qdrant.py new file mode 100644 index 0000000..81b1cb4 --- /dev/null +++ b/services/context-retriever/src/context_retriever/vector_store/qdrant.py @@ -0,0 +1,106 @@ +"""Contains class for handling communication with Qdrant vector store.""" +import logging + +import pydantic +import qdrant_client +from context_retriever.vector_store import core as vs_core +from langchain_core.documents import Document +from langchain_ollama.embeddings import OllamaEmbeddings +from qdrant_client.models import Distance +from qdrant_client.models import VectorParams + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class QdrantProxyCfg(pydantic.BaseModel): + """Configuration for vector store proxy.""" + url: str + collection_name: str + uploading_batch_size: int + embedding_model: vs_core.EmbeddingModelCfg + + +class QdrantProxy(vs_core.VectorStoreProxy): + """Handles communication with Qdrant vector store.""" + + def __init__(self, cfg: QdrantProxyCfg) -> None: + + _logger().info('Initializing QdrantProxy with config: %s', cfg) + + self._cfg = cfg + + self._client = qdrant_client.QdrantClient( + url=self._cfg.url + ) + + self._embedding_model = OllamaEmbeddings( + model=self._cfg.embedding_model.model_name, + base_url=self._cfg.embedding_model.url + ) + + self._ensure_collection_exists() + + async def store_documents(self, documents: list[Document]) -> None: + """Stores the given documents in the vector store.""" + + _logger().debug('Storing %d documents in collection \'%s\'', + len(documents), self._cfg.collection_name) + + for batch_docs in vs_core.batch_iterate(documents, self._cfg.uploading_batch_size): + + embedded_docs = await self._embedding_model.aembed_documents( + [doc.page_content for doc in batch_docs]) + + self._client.upload_collection( + collection_name=self._cfg.collection_name, + vectors=embedded_docs, + payload=[{'content': doc.page_content, + 'metadata': doc.metadata} for doc in batch_docs], + ) + + async def retrieve_documents(self, + query: str, + k: int, + sim_thresh: float) -> list[Document]: + """Retrieves documents from the vector store based on the given query.""" + + _logger().debug('Retrieving documents from collection \'%s\' for query: \'%s\'', + self._cfg.collection_name, query) + + docs = self._client.query_points( + collection_name=self._cfg.collection_name, + query=await self._embedding_model.aembed_query(query), + limit=k, + score_threshold=sim_thresh + ) + + return [ + Document(page_content=point.payload['content'], + metadata=point.payload['metadata']) + for point in docs.points + if point.payload is not None + ] + + def _ensure_collection_exists(self) -> None: + """Ensures that the specified collection exists in the Qdrant vector store.""" + + if not self._client.collection_exists(self._cfg.collection_name): + + _logger().debug('Collection \'%s\' does not exist. Creating new collection.', + self._cfg.collection_name) + + vector_size = self._determine_vector_size() + + self._client.create_collection( + collection_name=self._cfg.collection_name, + vectors_config=VectorParams(size=vector_size, distance=Distance.DOT, on_disk=True) + ) + + def _determine_vector_size(self) -> int: + """Determines the size of the embedding vectors produced by the embedding model.""" + + sample_text = 'Sample text for embedding size determination.' + embedding = self._embedding_model.embed_query(sample_text) + return len(embedding) diff --git a/services/context-retriever/uv.lock b/services/context-retriever/uv.lock new file mode 100644 index 0000000..0b65641 --- /dev/null +++ b/services/context-retriever/uv.lock @@ -0,0 +1,2074 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14'", + "python_full_version == '3.13.*'", + "python_full_version < '3.13'", +] + +[[package]] +name = "aiohappyeyeballs" +version = 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-0,0 +1,12 @@ +FROM ollama/ollama:latest + +RUN apt-get update && apt-get install -y curl + +COPY ./entrypoint.sh /root/entrypoint.sh +RUN chmod +x /root/entrypoint.sh + +WORKDIR /root + +EXPOSE ${EXPOSED_PORT} + +ENTRYPOINT ["./entrypoint.sh"] \ No newline at end of file diff --git a/services/embedding-endpoint/entrypoint.sh b/services/embedding-endpoint/entrypoint.sh new file mode 100644 index 0000000..2909bb6 --- /dev/null +++ b/services/embedding-endpoint/entrypoint.sh @@ -0,0 +1,12 @@ +#!/bin/sh + +ollama serve & +SERVER_PID=$! + +echo "Waiting for ollama server to start..." +sleep 5 + +echo "Loading the model..." +ollama pull ${EMBEDDING_MODEL} + +wait $SERVER_PID \ No newline at end of file diff --git a/services/llm-endpoint/Modelfile b/services/llm-endpoint/Modelfile new file mode 100644 index 0000000..44e21fc --- /dev/null +++ b/services/llm-endpoint/Modelfile @@ -0,0 +1,22 @@ +FROM ${LLM_GENERATOR_MODEL} + +TEMPLATE """ +{{- if .System}}<|start_header_id|>system<|end_header_id|> +{{ .System }}<|eot_id|> +{{- end }} + +{{- range $i, $_ := .Messages }} +{{- $last := eq (len (slice $.Messages $i)) 1 }} + +{{- if eq .Role "user" }}<|start_header_id|>user<|end_header_id|> +{{ .Content }}<|eot_id|> +{{ if $last }}<|start_header_id|>assistant<|end_header_id|>{{ end }} + +{{- else if eq .Role "assistant" }}<|start_header_id|>assistant<|end_header_id|> +{{ .Content }} +{{ if not $last }}<|eot_id|>{{ end }} +{{- end }} +{{- end }} +""" + + diff --git a/services/llm-endpoint/entrypoint.sh b/services/llm-endpoint/entrypoint.sh new file mode 100644 index 0000000..7de3ae2 --- /dev/null +++ b/services/llm-endpoint/entrypoint.sh @@ -0,0 +1,15 @@ +#!/bin/sh + +ollama serve & +SERVER_PID=$! + +echo "Waiting for ollama server to start..." +sleep 5 + +echo "Creating model from Modelfile..." +sed -i "s/\${LLM_GENERATOR_MODEL}/${LLM_GENERATOR_MODEL}/g" Modelfile +ollama create -f Modelfile local_ollama_model + +ollama run local_ollama_model + +wait $SERVER_PID \ No newline at end of file diff --git a/services/llm-endpoint/llm-endpoint.Dockerfile b/services/llm-endpoint/llm-endpoint.Dockerfile new file mode 100644 index 0000000..2c56dbb --- /dev/null +++ b/services/llm-endpoint/llm-endpoint.Dockerfile @@ -0,0 +1,14 @@ +FROM ollama/ollama:latest + +RUN apt-get update && apt-get install -y curl + +COPY ./entrypoint.sh /root/entrypoint.sh +RUN chmod +x /root/entrypoint.sh + +COPY ./Modelfile /root/Modelfile + +WORKDIR /root + +EXPOSE ${EXPOSED_PORT} + +ENTRYPOINT ["./entrypoint.sh"] \ No newline at end of file diff --git a/services/llm-proxy/.gitignore b/services/llm-proxy/.gitignore new file mode 100644 index 0000000..00c3d6c --- /dev/null +++ b/services/llm-proxy/.gitignore @@ -0,0 +1,3 @@ +src/llm_proxy.egg-info/ +data/ +outputs/ diff --git a/services/llm-proxy/cfg/main-dev.yaml b/services/llm-proxy/cfg/main-dev.yaml new file mode 100644 index 0000000..0ec3e7e --- /dev/null +++ b/services/llm-proxy/cfg/main-dev.yaml @@ -0,0 +1,17 @@ +--- +server_host: 0.0.0.0 +server_port: 8888 +n_server_workers: 1 +persist_data_path: data/ +models: + main_chat: + model: local_ollama_model + base_url: http://localhost:11434 + + conversation_safety_guardrail: + model: local_ollama_model + base_url: http://localhost:11435 + + input_relevance_guardrail: + model: local_ollama_model + base_url: http://localhost:11435 diff --git a/services/llm-proxy/cfg/main.yaml b/services/llm-proxy/cfg/main.yaml new file mode 100644 index 0000000..43df3b4 --- /dev/null +++ b/services/llm-proxy/cfg/main.yaml @@ -0,0 +1,17 @@ +--- +server_host: 0.0.0.0 +server_port: 8888 +n_server_workers: 1 +persist_data_path: data/ +models: + main_chat: + model: local_ollama_model + base_url: http://main-llm-responder:11434 + + conversation_safety_guardrail: + model: local_ollama_model + base_url: http://guard-llm:11435 + + input_relevance_guardrail: + model: local_ollama_model + base_url: http://guard-llm:11435 diff --git a/services/llm-proxy/justfile b/services/llm-proxy/justfile new file mode 100644 index 0000000..cac9d9b --- /dev/null +++ b/services/llm-proxy/justfile @@ -0,0 +1,22 @@ +# -------------------------------------------------- +# This file contains setup scripts for the project. +# For more info, see: https://github.com/casey/just +# -------------------------------------------------- + +set positional-arguments + +set shell := ["bash", "-c"] + +# Setup virtual environment and install dependencies. +setup-environment: + uv python install 3.12 + uv venv + uv pip install -e .[dev] + +run-server *args: + uv run python main.py --config-name "main-dev" "$@" + +# Runs static checks using global pre-commit configuration. +run-precommit: + uv run --extra dev pre-commit run -c ../../.pre-commit-config.yaml \ + --files `git ls-files --cached --others --exclude-standard` diff --git a/services/llm-proxy/llm-proxy.Dockerfile b/services/llm-proxy/llm-proxy.Dockerfile new file mode 100644 index 0000000..3e726b6 --- /dev/null +++ b/services/llm-proxy/llm-proxy.Dockerfile @@ -0,0 +1,19 @@ +FROM python:3.12-slim + +SHELL ["/bin/bash", "-c"] +ENV DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends wget + +RUN wget -qO- https://astral.sh/uv/install.sh | env UV_INSTALL_DIR="/usr/local/bin" sh + +COPY cfg /app/cfg +COPY src /app/src +COPY pyproject.toml main.py /app/ +WORKDIR /app + +EXPOSE ${API_PORT} + +RUN uv python install 3.12 && uv venv && uv pip install . +CMD [ "uv", "run", "python", "main.py" ] diff --git a/services/llm-proxy/main.py b/services/llm-proxy/main.py new file mode 100644 index 0000000..3b65e89 --- /dev/null +++ b/services/llm-proxy/main.py @@ -0,0 +1,190 @@ +""""Starts the backend server for the entrypoint service.""" +import json +import logging +import os +import sys +from contextlib import asynccontextmanager +from datetime import datetime +from typing import Any + +import fastapi +import hydra +import omegaconf +import pydantic +import uvicorn +from fastapi.responses import StreamingResponse +from llm_proxy import chat_llm_service + + +def _logger() -> logging.Logger: + return logging.getLogger('llm_proxy') + + +llm_service: chat_llm_service.ChatLLMService | None = None # pylint: disable=invalid-name + + +@asynccontextmanager +async def lifespan(_): # type: ignore + """Sets up global contexts used by the server workers.""" + + cfg_serialized = os.environ.get('LLM_PROXY_SERVER_CFG', None) + + if cfg_serialized is None: + _logger().critical('Failed to load llm-proxy server config from environment variable.') + sys.exit(1) + + cfg = omegaconf.OmegaConf.create(json.loads(cfg_serialized)) + + # The llm_service is designed to be used by the endpoint handlers as a global service. It is + # not assigned in the `main` function because the uvicorn workers don't call it, as opposed to + # the `lifespan` callback. + global llm_service # pylint: disable=global-statement + + llm_service = chat_llm_service.ChatLLMService(cfg.models) + + yield + + +app = fastapi.FastAPI(lifespan=lifespan) + + +@app.get('/ping') +async def read_ping() -> dict[str, str]: + """Health check endpoint.""" + return {'message': 'Service is running'} + + +class ConversationState(pydantic.BaseModel): + """State of the chat conversation including chat history and current user query.""" + chat_history: list[dict[str, Any]] + user_message: str + + +class RequestStreamChatResponse(pydantic.BaseModel): + """Request to return the LLM response for a given query and retrieved context.""" + conversation_state: ConversationState + context_docs: list[dict[str, Any]] + + +@app.post('/stream_chat_response') +async def stream_chat_response(request: RequestStreamChatResponse) -> StreamingResponse: + """Streams chat response for the user query based on the provided context. + + The conversation is expected to be safe i.e. no input rails are fired during handling + the request. In order to use the input guardrails, the client should call relevant + endpoints. + """ + + assert llm_service is not None + + return StreamingResponse( + llm_service.stream_chat_response(request.conversation_state.user_message, + chat_history=request.conversation_state.chat_history, + context_documents=request.context_docs), + media_type='application/json') + + +class ResponseInputCheck(pydantic.BaseModel): + """Response containing the status of input guardrails check.""" + + is_ok: bool + reason: str | None = None + + +@app.post('/check_input_safety') +async def check_input_safety(request: ConversationState) -> ResponseInputCheck: + """Fires the internal safety guardrails on the current conversation state.""" + + assert llm_service is not None + + is_ok, reason = await llm_service.check_input_safety( + user_query=request.user_message, + chat_history=request.chat_history + ) + + return ResponseInputCheck(is_ok=is_ok, reason=reason) + + +@app.post('/check_input_relevance') +async def check_input_relevance(request: ConversationState) -> ResponseInputCheck: + """Fires the internal guardrails responsible for checking the relevance of the user input.""" + + assert llm_service is not None + + is_ok, reason = await llm_service.check_input_relevance( + user_query=request.user_message, + chat_history=request.chat_history + ) + + return ResponseInputCheck(is_ok=is_ok, reason=reason) + + +def _configure_logging(script_cfg: omegaconf.DictConfig) -> None: + + logging.config.dictConfig({ + 'version': 1, + 'loggers': { + 'root': { + 'level': 'WARNING', + 'handlers': ['console', 'file'] + }, + 'llm_proxy': { + 'level': 'DEBUG', + 'handlers': ['console', 'file'], + 'propagate': False + } + }, + 'handlers': { + 'console': { + 'class': 'logging.StreamHandler', + 'level': 'INFO', + 'formatter': 'default' + }, + 'file': { + 'class': 'logging.handlers.RotatingFileHandler', + 'level': 'DEBUG', + 'formatter': 'default', + 'filename': os.path.join(script_cfg.persist_data_path, + 'log', + f'{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.log'), + 'maxBytes': 5_000_000, + 'backupCount': 5, + 'encoding': 'utf-8', + } + }, + 'formatters': { + 'default': { + 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s', + } + } + }) + + +@hydra.main(version_base=None, config_path='cfg', config_name='main') +def main(cfg: omegaconf.DictConfig) -> None: + """Initializes and serves the web app.""" + + _logger().info('Script configuration:\n%s', omegaconf.OmegaConf.to_yaml(cfg)) + + os.makedirs(os.path.join(cfg.persist_data_path, 'log'), exist_ok=True) + + _configure_logging(cfg) + + _logger().info('Starting server on %s:%d with %d workers.', + cfg.server_host, cfg.server_port, cfg.n_server_workers) + + cfg_serialized = json.dumps(omegaconf.OmegaConf.to_container(cfg)) + os.environ['LLM_PROXY_SERVER_CFG'] = cfg_serialized + + uvicorn.run('main:app', + host=cfg.server_host, + port=cfg.server_port, + workers=cfg.n_server_workers, + log_level='info', + access_log=True, + limit_concurrency=1000, + timeout_keep_alive=5) + + +if __name__ == '__main__': + main() # pylint: disable=no-value-for-parameter diff --git a/services/llm-proxy/pyproject.toml b/services/llm-proxy/pyproject.toml new file mode 100644 index 0000000..d535a02 --- /dev/null +++ b/services/llm-proxy/pyproject.toml @@ -0,0 +1,26 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "llm_proxy" +version = "0.1.0" +requires-python=">=3.12" +dependencies = [ + "hydra-core==1.3.2", + "requests==2.32.3", + "uvicorn==0.38.0", + "fastapi==0.124.4", + "ollama==0.6.1", + "langchain==1.2.0", + "langchain-core==1.2.6", + "langchain_ollama==1.0.1" +] + +[project.optional-dependencies] +dev = [ + "pylint>=4.0.4", + "autopep8>=2.3.2", + "mypy>=1.19.0", + "pre-commit>=4.5.0" +] diff --git a/services/llm-proxy/src/llm_proxy/__init__.py b/services/llm-proxy/src/llm_proxy/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/llm-proxy/src/llm_proxy/chat_llm_service.py b/services/llm-proxy/src/llm_proxy/chat_llm_service.py new file mode 100644 index 0000000..0cf1c88 --- /dev/null +++ b/services/llm-proxy/src/llm_proxy/chat_llm_service.py @@ -0,0 +1,98 @@ +"""Contains service responsible for connecting with self-hosted LLM.""" +import json +import logging +from typing import Any +from typing import AsyncIterator + +from langchain_ollama import ChatOllama +from llm_proxy import llm_actions +from llm_proxy.rails import rails +from llm_proxy.rails.core import LLMCallContext + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class ChatLLMService: + """Establishes connection with self-hosted LLM and handles requests to it.""" + + def __init__(self, models_cfg: dict[str, dict[str, Any]]) -> None: + + self._safety_guardrails = rails.ConversationSafetyGuardrail( + llm=ChatOllama(**models_cfg['conversation_safety_guardrail'], + temperature=0.0)) + + self._relevance_guardrails = rails.ConversationRelevanceGuardrail( + llm=ChatOllama(**models_cfg['input_relevance_guardrail'], + temperature=0.0)) + + self._chat_response_action = llm_actions.ChatResponseAction( + llm=ChatOllama(**models_cfg['main_chat']) + ) + + async def stream_chat_response(self, + user_query: str, + chat_history: list[dict[str, Any]], + context_documents: list[dict[str, Any]] | None = None, + ) -> AsyncIterator[bytes]: + """Streams, chunk by chunk, LLM response for a given query and chat history. + + The input is checkes according to the guardrails specification. + """ + + _logger().debug('Streaming llm response for query \'%s\' and conversation %s...', + user_query, chat_history) + + try: + async for chunk in self._chat_response_action.run( + user_query=user_query, + chat_history=chat_history, + context_documents=context_documents + ): + chunk_struct = {'content': chunk} + + yield json.dumps(chunk_struct).encode('utf-8') + + except Exception as e: # pylint: disable=broad-except + _logger().error('Chat call failed: %s', str(e)) + + yield json.dumps({'error': 'Internal system error.'}).encode('utf-8') + + async def check_input_safety(self, + user_query: str, + chat_history: list[dict[str, Any]], + ) -> tuple[bool, str | None]: + """Checks whether the conversation state passes the input safety guardrails.""" + + _logger().debug('Checking input safety for query \'%s\' and conversation %s...', + user_query, chat_history) + + decision = await self._safety_guardrails.should_pass( + LLMCallContext( + user_message=user_query, + chat_history=chat_history, + retrieved_context=[] + ) + ) + + return decision.should_pass, decision.reason + + async def check_input_relevance(self, + user_query: str, + chat_history: list[dict[str, Any]], + ) -> tuple[bool, str | None]: + """Checks whether the conversation state passes the input relevance guardrails.""" + + _logger().debug('Checking input relevance for query \'%s\' and conversation %s...', + user_query, chat_history) + + decision = await self._relevance_guardrails.should_pass( + LLMCallContext( + user_message=user_query, + chat_history=chat_history, + retrieved_context=[] + ) + ) + + return decision.should_pass, decision.reason diff --git a/services/llm-proxy/src/llm_proxy/llm_actions.py b/services/llm-proxy/src/llm_proxy/llm_actions.py new file mode 100644 index 0000000..39b7cc3 --- /dev/null +++ b/services/llm-proxy/src/llm_proxy/llm_actions.py @@ -0,0 +1,148 @@ +"""Contains modules performing LLM-related isolated actions.""" +import logging +from typing import Any +from typing import AsyncIterator + +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import AIMessage +from langchain_core.messages import BaseMessage +from langchain_core.messages import HumanMessage +from langchain_core.messages import SystemMessage + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class ChatResponseAction: + """Generates chat response for a given user query and chat history.""" + + _SYSTEM_PROMPT = ( + 'You are a helpful AI assistant. Respond to the user query based on the conversation ' + 'history, i.e. fall back to the chat history when the query refers to previous messages.' + 'If any context documents are provided, use them to ground your response.' + ) + + def __init__(self, + llm: BaseChatModel): + + self._llm = llm + + async def run(self, + user_query: str, + chat_history: list[dict[str, str]], + context_documents: list[dict[str, Any]] | None = None) -> AsyncIterator[str]: + """Generates chat response for the given user query and chat history.""" + + messages: list[BaseMessage] = [SystemMessage(content=self._SYSTEM_PROMPT)] + + for message in chat_history: + role = message['role'] + content = message['content'] + + if role == 'user': + messages.append(HumanMessage(content=content)) + else: + messages.append(AIMessage(content=content)) + + if context_documents: + context_message = ( + 'The following context documents are provided to help you answer the user query:\n' + f"{self._format_context_documents(context_documents)}" + ) + messages.append(SystemMessage(content=context_message)) + + messages.append(HumanMessage(content=user_query)) + + async for chunk in self._llm.astream(messages): + yield str(chunk.content) + + def _format_context_documents(self, + context_documents: list[dict[str, Any]]) -> str: + """Formats context documents into a string for inclusion in the system prompt.""" + + formatted_docs = [] + + for doc in context_documents: + formatted_doc = f"Title: {doc['metadata']['title']}\nContent: {doc['content']}" + formatted_docs.append(formatted_doc) + + return '\n\n'.join(formatted_docs) + + +class SimpleConversationValidateAction: + """Validates a conversation using a two line response format. + + The response is expected to contain: + 1. A single word indicating whether the input is 'good' or 'bad'. + 2. A brief explanation in words of the decision. + """ + + def __init__(self, + system_prompt: str, + main_prompt_template: str, + good_keyword: str, + llm: BaseChatModel + ) -> None: + """Args: + system_prompt: The system prompt to set the behavior of the LLM. + main_prompt_template: The main prompt template containing a placeholder + for the 'conversation' and 'user_input'. + good_keyword: The keyword indicating a 'good' input in the LLM response. + E.g. 'safe'. + llm: The language model to use for validation. + """ + + self._system_prompt = system_prompt + self._main_prompt_template = main_prompt_template + self._good_keyword = good_keyword + self._llm = llm + + async def run(self, + user_query: str, + chat_history: list[dict[str, str]]) -> tuple[bool, str | None]: + """Returns a tuple indicating whether the input is safe and an optional reason.""" + + messages = [SystemMessage(self._system_prompt), + HumanMessage(self._main_prompt_template.format( + conversation_history=self._format_conversation(chat_history), + user_input=user_query))] + + try: + response = await self._llm.ainvoke(messages) + + except Exception as e: # pylint: disable=broad-except + _logger().error('Validation LLM call failed: %s', str(e)) + return False, 'Internal system error during conversation validation.' + + decision = str(response.content) + decision_lines = [line.strip() for line in decision.split('\n') if line.strip()] + + _logger().debug('Validation LLM raw response:\n%s', decision) + + if len(decision_lines) != 2: + _logger().error('Unexpected number of lines in validation response: %d', + len(decision_lines)) + return False, 'Internal system error during conversation validation.' + + if decision_lines[0].lower() == self._good_keyword.lower(): + return True, None + + return False, decision_lines[1] + + def _format_conversation(self, + chat_history: list[dict[str, str]]) -> str: + """Formats the conversation history into a string.""" + + conversation_lines = [] + + for message in chat_history: + role = message['role'] + content = message['content'] + + if role == 'user': + conversation_lines.append(f'User: {content}') + else: + conversation_lines.append(f'Assistant: {content}') + + return '\n'.join(conversation_lines) diff --git a/services/llm-proxy/src/llm_proxy/rails/__init__.py b/services/llm-proxy/src/llm_proxy/rails/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/llm-proxy/src/llm_proxy/rails/core.py b/services/llm-proxy/src/llm_proxy/rails/core.py new file mode 100644 index 0000000..ec10792 --- /dev/null +++ b/services/llm-proxy/src/llm_proxy/rails/core.py @@ -0,0 +1,35 @@ +"""Contains core guardrails interfaces.""" +import dataclasses +from abc import ABC +from abc import abstractmethod +from typing import Any + + +@dataclasses.dataclass +class LLMCallContext: + """Context information about an LLM call.""" + + user_message: str + chat_history: list[dict[str, Any]] + retrieved_context: list[dict[str, Any]] + + +@dataclasses.dataclass +class GuardrailDecision: + """Represents the decision made by a guardrail.""" + + should_pass: bool + reason: str | None = None + + +class Guardrail(ABC): + """Interface for all input/output/retrieval guardrails.""" + + @abstractmethod + async def should_pass(self, llm_call_context: LLMCallContext) -> GuardrailDecision: + """Tells whether the given LLM call context should pass the guardrail.""" + + @property + @abstractmethod + def name(self) -> str: + """Returns the name of the guardrail.""" diff --git a/services/llm-proxy/src/llm_proxy/rails/rails.py b/services/llm-proxy/src/llm_proxy/rails/rails.py new file mode 100644 index 0000000..55e1901 --- /dev/null +++ b/services/llm-proxy/src/llm_proxy/rails/rails.py @@ -0,0 +1,155 @@ +"""Contains implementation of input/output/retrieval guardrails.""" +import logging + +from langchain_core.language_models.chat_models import BaseChatModel +from llm_proxy import llm_actions +from llm_proxy.rails.core import Guardrail +from llm_proxy.rails.core import GuardrailDecision +from llm_proxy.rails.core import LLMCallContext + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class ConversationSafetyGuardrail(Guardrail): + """Guardrail that checks whether the conversation is safe. + + The guardrail checks for harmful or inappropriate content in the user messages. according + to the MLCommons AI Ethics Guidelines. + """ + + _SYSTEM_PROMPT = """You are a professional content moderator. Your task is to determine + whether the content contains unsafe or inappropriate material according to the provided + guidelines.""" + + _MAIN_PROMPT_TEMPLATE = """ + Between the and tags, there is a conversation between + user and assistant that represents an official institution. Your task is to determine whether + the LAST user query is safe and appropriate, i.e. it DOES NOT fall under one of the categories + provided between the and tags. + Pay heed to the entire conversation history when making your decision. + + + S1. Sexual content or any sex-related material + S2. Sexual exploitation of minors + S3. Hate speech or discriminatory language + S4. Violent or graphic content + S5. Illegal activities + S6. Vulgar or offensive language + + + + {conversation_history} + User: {user_input} + + + Your response should contain EXACTLY TWO lines: + The first line should contain one word. If the query is safe according to the guidelines, + it should contain the word 'safe', and, if it is unsafe according to the guidelines, it + should contains the word 'unsafe'. It it does not fall under any of the categories, it is 'safe'. + The second line should contain a brief explanation IN WORDS of your decision based on the + guidelines. Do not use policy category codes, instead use your words. Formulate the explanation + in such a way that it can be directly shown to the end user. + """ + + def __init__(self, llm: BaseChatModel) -> None: + + self._llm = llm + + self._action = llm_actions.SimpleConversationValidateAction( + system_prompt=self._SYSTEM_PROMPT, + main_prompt_template=self._MAIN_PROMPT_TEMPLATE, + good_keyword='safe', + llm=llm + ) + + async def should_pass(self, llm_call_context: LLMCallContext) -> GuardrailDecision: + """Returns true if the conversation is safe, false otherwise.""" + + decision, reason = await self._action.run( + user_query=llm_call_context.user_message, + chat_history=llm_call_context.chat_history + ) + + _logger().debug('Guardrail \'%s\' response: %s', self.name, decision) + + return GuardrailDecision(should_pass=decision, reason=reason) + + @property + def name(self) -> str: + return 'ConversationSafetyGuardrail' + + +class ConversationRelevanceGuardrail(Guardrail): + """Guardrail that checks whether the user input follows one of the expected topics. + + The guardrail is designed to ensure that the user cannot ask things about things the chat + is not supposed to answer. For example, if the chat is supposed to answer questions + about cooking, the guardrail will block questions about politics. + """ + + _SYSTEM_PROMPT = """You are a professional content moderator. Your task is to determine + whether the content's topic does not diverge from a specified set of allowed topics.""" + + _MAIN_PROMPT_TEMPLATE = """ + Between the and tags, there is a conversation between + user and assistant that represents AGH University of Krakow institution. The user is supposed + to ask questions on the functioning of the university, but shouldn't ask any other questions. + Your task is to determine whether the LAST user query strictly concerns at least one of the + topics provided between the and + tags. If it is unrelated to them or only barely touches on them but is mostly about other + things, mark it as 'unrelated'. Pay heed to the entire conversation history when making your + decision. + + + 1. Basic information about the AGH University of Krakow. + 2. Academic programs and courses offered at AGH University of Krakow. + 3. Campus facilities and services available to students at AGH University of Krakow. + 4. Admission requirements and application procedures for AGH University of Krakow. + 5. Extracurricular activities and student organizations at AGH University of Krakow. + 6. Events and news related to AGH University of Krakow. + 7. Research opportunities and projects at AGH University of Krakow. + 8. Rules and procedures related to studying at AGH University of Krakow. + + + + {conversation_history} + User: {user_input} + + + Your response should contain EXACTLY TWO lines: + The first line should contain one word. If the query is relevant to the allowed topics, + it should contain the word 'related', and, if it concerns other topics, it should contains + the word 'unrelated'. + The second line should contain a brief explanation IN WORDS of your decision based on the + guidelines. Do not use policy category codes, instead use your words. Formulate the explanation + in such a way that it can be directly shown to the end user. + """ + + def __init__(self, llm: BaseChatModel) -> None: + + self._llm = llm + + self._action = llm_actions.SimpleConversationValidateAction( + system_prompt=self._SYSTEM_PROMPT, + main_prompt_template=self._MAIN_PROMPT_TEMPLATE, + good_keyword='related', + llm=llm + ) + + async def should_pass(self, llm_call_context: LLMCallContext) -> GuardrailDecision: + """Returns true if the user input is relevant, false otherwise.""" + + decision, reason = await self._action.run( + user_query=llm_call_context.user_message, + chat_history=llm_call_context.chat_history + ) + + _logger().debug('Guardrail \'%s\' response: %s', self.name, decision) + + return GuardrailDecision(should_pass=decision, reason=reason) + + @property + def name(self) -> str: + return 'ConversationRelevanceGaurdrail' diff --git a/services/llm-proxy/uv.lock b/services/llm-proxy/uv.lock new file mode 100644 index 0000000..bbb8d11 --- 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+RUN apt update && apt install -y curl + +EXPOSE ${EXPOSED_PORT_REST} +EXPOSE ${EXPOSED_PORT_GRPC} diff --git a/services/web-app/.gitignore b/services/web-app/.gitignore new file mode 100644 index 0000000..69ef917 --- /dev/null +++ b/services/web-app/.gitignore @@ -0,0 +1,3 @@ +src/web_app.egg-info/ +data/ +outputs/ diff --git a/services/web-app/cfg/main.yaml b/services/web-app/cfg/main.yaml new file mode 100644 index 0000000..4475cbc --- /dev/null +++ b/services/web-app/cfg/main.yaml @@ -0,0 +1,11 @@ +--- +context_retriever_cfg: + url: http://context-retriever:8899 + connection_timeout: 90 + +llm_proxy_cfg: + url: http://llm-proxy:8888 + connection_timeout: 90 +persist_data_path: data/ +web_app_port: 8080 +web_app_host: 0.0.0.0 diff --git a/services/web-app/cfg/main_dev.yaml b/services/web-app/cfg/main_dev.yaml new file mode 100644 index 0000000..cbfafca --- /dev/null +++ b/services/web-app/cfg/main_dev.yaml @@ -0,0 +1,11 @@ +--- +context_retriever_cfg: + url: http://localhost:8888 + connection_timeout: 90 + +llm_proxy_cfg: + url: http://localhost:8888 + connection_timeout: 90 +persist_data_path: data/ +web_app_port: 8080 +web_app_host: 0.0.0.0 diff --git a/services/web-app/cfg/mock_backend.yaml b/services/web-app/cfg/mock_backend.yaml new file mode 100644 index 0000000..516a613 --- /dev/null +++ b/services/web-app/cfg/mock_backend.yaml @@ -0,0 +1,3 @@ +--- +host: localhost +port: 8888 diff --git a/services/web-app/justfile b/services/web-app/justfile new file mode 100644 index 0000000..6593f1d --- /dev/null +++ b/services/web-app/justfile @@ -0,0 +1,29 @@ +# -------------------------------------------------- +# This file contains setup scripts for the project. +# For more info, see: https://github.com/casey/just +# -------------------------------------------------- + +set shell := ["bash", "-c"] + +# Setup virtual environment and install dependencies. +setup-environment: + uv python install 3.12 + uv venv + uv pip install -e .[dev] + +# Run the Gradio web application with live reloading. +run-app-dev: + uv run gradio main_dev.py --watch-dirs . --demo-name web_application + +# Run the web application. +run-app: + uv run python main.py + +# Run the mock backend server. +run-mock-backend: + uv run python mock_backend.py + +# Runs static checks using global pre-commit configuration. +run-precommit: + uv run --extra dev pre-commit run -c ../../.pre-commit-config.yaml \ + --files `git ls-files --cached --others --exclude-standard` diff --git a/services/web-app/main.py b/services/web-app/main.py new file mode 100644 index 0000000..2a0060b --- /dev/null +++ b/services/web-app/main.py @@ -0,0 +1,93 @@ +"""Contains the main entrypoint for the web application.""" +import logging.config +import os +from datetime import datetime + +import gradio as gr +import hydra +import omegaconf +from web_app.backend import context_retriever +from web_app.backend import llm_proxy +from web_app.backend import utils as backend_utils +from web_app.gui import main_controller +from web_app.gui import utils as gui_utils + + +def _logger() -> logging.Logger: + return logging.getLogger() + + +@hydra.main(version_base=None, config_path='cfg', config_name='main') +def main(cfg: omegaconf.DictConfig) -> None: + """Initializes and serves the web app.""" + + os.makedirs(os.path.join(cfg.persist_data_path, 'log'), exist_ok=True) + + logging.config.dictConfig({ + 'version': 1, + 'loggers': { + 'root': { + 'level': 'NOTSET', + 'handlers': ['console', 'file'], + 'propagate': True + }, + 'httpx': { + 'level': 'WARNING', + 'propagate': False + }, + 'httpcore': { + 'level': 'WARNING', + 'propagate': False + } + }, + 'handlers': { + 'console': { + 'class': 'logging.StreamHandler', + 'level': 'INFO', + 'formatter': 'default' + }, + 'file': { + 'class': 'logging.handlers.RotatingFileHandler', + 'level': 'DEBUG', + 'formatter': 'default', + 'filename': os.path.join(cfg.persist_data_path, + 'log', + f'{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.log'), + 'maxBytes': 5_000_000, + 'backupCount': 5, + 'encoding': 'utf-8', + } + }, + 'formatters': { + 'default': { + 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s', + } + } + }) + + _logger().info('Starting web application with configuration:\n%s', + omegaconf.OmegaConf.to_yaml(cfg)) + + _logger().info('Creating services for backend communication...') + + context_retriever_service = context_retriever.ContextRetrieverService( + endpoint_cfg=backend_utils.EndpointConnectionCfg(**cfg.context_retriever_cfg) + ) + + llm_proxy_service = llm_proxy.LLMProxyService( + endpoint_cfg=backend_utils.EndpointConnectionCfg(**cfg.llm_proxy_cfg) + ) + + _logger().info('Rendering GUI...') + + with gr.Blocks(fill_height=True, + title='AGH Chat', + css=gui_utils.CUSTOM_CSS) as web_application: + main_controller.MainController(context_retriever_service, llm_proxy_service).render_gui() + + web_application.launch(server_name=cfg.web_app_host, + server_port=cfg.web_app_port) + + +if __name__ == '__main__': + main() # pylint: disable=no-value-for-parameter diff --git a/services/web-app/main_dev.py b/services/web-app/main_dev.py new file mode 100644 index 0000000..2fa0eac --- /dev/null +++ b/services/web-app/main_dev.py @@ -0,0 +1,63 @@ +"""Contains the main entrypoint for the web application.""" +import logging.config + +import gradio as gr +import hydra +import omegaconf +from web_app.backend import context_retriever +from web_app.backend import llm_proxy +from web_app.backend import utils as backend_utils +from web_app.gui import main_controller +from web_app.gui import utils as gui_utils + + +def _logger() -> logging.Logger: + return logging.getLogger('web_app') + + +with hydra.initialize(version_base=None, config_path='cfg'): + cfg = hydra.compose(config_name='main_dev') + + _logger().info('Starting web application with configuration:\n%s', + omegaconf.OmegaConf.to_yaml(cfg)) + +logging.config.dictConfig({ + 'version': 1, + 'loggers': { + 'root': { + 'level': 'WARNING', + 'handlers': ['console'] + }, + 'web_app': { + 'level': 'DEBUG', + 'handlers': ['console'], + 'propagate': False + } + }, + 'handlers': { + 'console': { + 'class': 'logging.StreamHandler', + 'level': 'DEBUG', + 'formatter': 'default' + } + }, + 'formatters': { + 'default': { + 'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s', + } + } +}) + +context_retriever_service = context_retriever.ContextRetrieverService( + endpoint_cfg=backend_utils.EndpointConnectionCfg(**cfg.context_retriever_cfg) +) + +llm_proxy_service = llm_proxy.LLMProxyService( + endpoint_cfg=backend_utils.EndpointConnectionCfg(**cfg.llm_proxy_cfg) +) + +with gr.Blocks(fill_height=True, title='AGH Chat', css=gui_utils.CUSTOM_CSS) as web_application: + main_controller.MainController(context_retriever_service, llm_proxy_service).render_gui() + +web_application.launch(server_name=cfg.web_app_host, + server_port=cfg.web_app_port) diff --git a/services/web-app/mock_backend.py b/services/web-app/mock_backend.py new file mode 100644 index 0000000..afb0059 --- /dev/null +++ b/services/web-app/mock_backend.py @@ -0,0 +1,156 @@ +"""Mock backend service for testing the web application.""" +import asyncio +import json +import logging +import random +from typing import Any +from typing import AsyncIterator +from typing import Dict +from typing import List + +import fastapi +import hydra +import omegaconf +import pydantic +import uvicorn +from fastapi.responses import StreamingResponse + + +# pylint: disable-all +# mypy: ignore-errors + +app = fastapi.FastAPI() + + +class ConversationState(pydantic.BaseModel): + """State of the chat conversation including chat history and current user query.""" + chat_history: list[dict[str, Any]] + user_message: str + + +class RequestStreamChatResponse(pydantic.BaseModel): + """Request to return the LLM response for a given query and retrieved context.""" + conversation_state: ConversationState + context_docs: list[dict[str, Any]] + + +@app.post('/stream_chat_response') +async def stream_chat_response(request: RequestStreamChatResponse) -> StreamingResponse: + """""" + + mock_response = """Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod + tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud + exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor + in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur + sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est + laborum.""" + + mock_words = mock_response.split() + + response = ' '.join(random.sample(mock_words, len(mock_words))) + response += f'Documents used: {[doc["metadata"]["title"] for doc in request.context_docs]}' + + async def event_generator() -> AsyncIterator[bytes]: + + if 'fail' in request.conversation_state.user_message.lower(): + chunk = {'error': 'Simulated backend failure.'} + yield json.dumps(chunk).encode('utf-8') + return + + for token in response.replace(' ', ' [split_token]') .split('[split_token]'): + chunk = {'content': token} + yield json.dumps(chunk).encode('utf-8') + await asyncio.sleep(0.05) + + return StreamingResponse(event_generator(), media_type='application/json') + + +class ResponseInputCheck(pydantic.BaseModel): + + is_ok: bool + reason: str | None = None + + +@app.post('/check_input_safety') +async def check_input_safety(request: ConversationState) -> ResponseInputCheck: + + if 'badword' in request.user_message.lower(): + return ResponseInputCheck(is_ok=False, reason='Inappropriate language detected.') + + return ResponseInputCheck(is_ok=True) + + +@app.post('/check_input_relevance') +async def check_input_relevance(request: ConversationState) -> ResponseInputCheck: + + if 'unrelated' in request.user_message.lower(): + return ResponseInputCheck( + is_ok=False, reason='Input is not relevant.') + + return ResponseInputCheck(is_ok=True) + + +class RequestCollectContextInfo(pydantic.BaseModel): + user_message: str + chat_history: list[dict[str, Any]] + + +class ResponseCollectContextInfo(pydantic.BaseModel): + context_docs: list[dict[str, Any]] + + +@app.post('/collect_context_info') +async def collect_context_info(request: RequestCollectContextInfo) -> ResponseCollectContextInfo: + + await asyncio.sleep(3.0) + + logging.info('/collect_context_info - Message: %s', request.user_message) + + mock_docs = [ + {'content': 'This is document 1', 'metadata': { + 'title': 'Document 1', + 'author': 'Author A', + 'page': '1'}}, + {'content': 'This is document 2', 'metadata': { + 'title': 'Document 2', + 'author': 'Author B', + 'page': '2'}}, + {'content': 'This is document 3', 'metadata': { + 'title': 'Document 3', + 'author': 'Author C', + 'page': '3'}}, + ] + + return ResponseCollectContextInfo(context_docs=random.sample(mock_docs, 2)) + + +class ResponseUploadDocument(pydantic.BaseModel): + error: str | None = None + + +@app.post('/upload_pdf') +async def upload_pdf(file: fastapi.UploadFile) -> ResponseUploadDocument: + + if not file.filename.lower().endswith('.pdf'): + return ResponseUploadDocument(error='Only PDF files are supported.') + + return ResponseUploadDocument() + + +@hydra.main(version_base=None, config_path='cfg', config_name='mock_backend') +def main(cfg: omegaconf.DictConfig) -> None: + """Sets up the mock backend service.""" + + logging.info('Starting mock backend with configuration:\n%s', + omegaconf.OmegaConf.to_yaml(cfg)) + + uvicorn.run( + 'mock_backend:app', + host=cfg.host, + port=cfg.port, + reload=False + ) + + +if __name__ == '__main__': + main() # pylint: disable=no-value-for-parameter diff --git a/services/web-app/pyproject.toml b/services/web-app/pyproject.toml new file mode 100644 index 0000000..81d5845 --- /dev/null +++ b/services/web-app/pyproject.toml @@ -0,0 +1,24 @@ +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "web_app" +version = "0.1.0" +requires-python=">=3.12" +dependencies = [ + "gradio==5.33.0", + "hydra-core==1.3.2", + "requests==2.32.3", + "python-magic>=0.4.27" +] + +[project.optional-dependencies] +dev = [ + "uvicorn==0.34.3", + "fastapi==0.115.12", + "pylint>=4.0.4", + "autopep8>=2.3.2", + "mypy>=1.19.0", + "pre-commit>=4.5.0" +] diff --git a/services/web-app/src/web_app/__init__.py b/services/web-app/src/web_app/__init__.py new file mode 100644 index 0000000..7a7ff41 --- /dev/null +++ b/services/web-app/src/web_app/__init__.py @@ -0,0 +1,3 @@ +"""Package init file.""" +from . import backend +from . import gui diff --git a/services/web-app/src/web_app/backend/__init__.py b/services/web-app/src/web_app/backend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/web-app/src/web_app/backend/context_retriever.py b/services/web-app/src/web_app/backend/context_retriever.py new file mode 100644 index 0000000..51de377 --- /dev/null +++ b/services/web-app/src/web_app/backend/context_retriever.py @@ -0,0 +1,94 @@ +"""Contains implementation of service that communicates with the context retriever module.""" +import logging + +import magic +import requests +from web_app.backend import utils + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class ContextRetrieverService: + """Communicates with the context-retriever module and retrieves context for queries.""" + + def __init__(self, endpoint_cfg: utils.EndpointConnectionCfg): + + self._endpoint_cfg = endpoint_cfg + + _logger().info('Created service for context-retriever with cfg: %s', + endpoint_cfg) + + def collect_context_info(self, + user_message: str, + chat_history: utils.ChatHistory) -> list[utils.ContextDocument]: + """Collects context information based on the user's message and chat history. + + Args: + user_message: The message from the user to collect context information for. + chat_history: The history of the chat to provide context for the request. + + Raises: + requests.HTTPError: If the request to the backend fails. + """ + + _logger().debug('Collecting context info with user_message: %s and chat_history: %s', + user_message, chat_history) + + url = f"{self._endpoint_cfg.url}/collect_context_info" + payload = { + 'user_message': user_message, + 'chat_history': utils.chat_history_to_payload(chat_history) + } + + response = requests.post(url, json=payload, timeout=self._endpoint_cfg.connection_timeout) + response.raise_for_status() + + response_data = response.json() + + return [utils.ContextDocument(doc['content'], doc['metadata']) + for doc in response_data['context_docs']] + + def upload_file(self, + uploaded_file_path: str) -> str | None: + """Sends a file to the context retriever service for processing. + + Raises: + requests.HTTPError: If the request to the backend fails. + + Returns: + None, if the upload was successful, or an error message string if there was an error. + """ + + _logger().debug('Uploading file %s to context retriever service.', uploaded_file_path) + + mime = magic.from_file(uploaded_file_path, mime=True) + + if mime == 'application/pdf': + url = f"{self._endpoint_cfg.url}/upload_pdf" + + else: + return 'Only PDF files are supported.' + + try: + with open(uploaded_file_path, 'rb') as file: + response = requests.post( + url, + files={'file': (uploaded_file_path, file, mime)}, + timeout=self._endpoint_cfg.connection_timeout + ) + + except requests.exceptions.ConnectionError as e: + _logger().error('Connection error while uploading PDF file to context retriever: %s', e) + raise requests.HTTPError( + 'Connection error while uploading PDF file to context retriever.') from e + + response.raise_for_status() + + response_data = response.json() + + if response_data['error'] is not None: + return str(response_data['error']) + + return None diff --git a/services/web-app/src/web_app/backend/llm_proxy.py b/services/web-app/src/web_app/backend/llm_proxy.py new file mode 100644 index 0000000..287eda9 --- /dev/null +++ b/services/web-app/src/web_app/backend/llm_proxy.py @@ -0,0 +1,123 @@ +"""Contains service that communicates with the llm-proxy module.""" +import json +import logging +from typing import Iterator + +import httpx +import requests +from web_app.backend import utils + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class LLMProxyService: + """Communicates with the llm-proxy module and returns model's responses.""" + + def __init__(self, endpoint_cfg: utils.EndpointConnectionCfg): + + self._endpoint_cfg = endpoint_cfg + + _logger().info('Created service for llm-proxy with cfg: %s', + endpoint_cfg) + + def stream_chat_response(self, + user_message: str, + chat_history: utils.ChatHistory, + context_docs: list[utils.ContextDocument]) -> Iterator[dict[str, str]]: + """Collects LLM response based on the context and streams it. + + Args: + user_message: The message from the user to generate a response for. + chat_history: The history of the chat to provide context for the request. + context_docs: The documents retrieved to provide additional context. + + Returns: + A generator that yields chunks of the chat response as they are received. Each chunk + contain the 'content' field. + """ + + _logger().debug(('Streaming chat response with user_message: %s, ' + + 'chat_history: %s, context_docs: %s'), + user_message, chat_history, context_docs) + + url = f"{self._endpoint_cfg.url}/stream_chat_response" + + payload = { + 'conversation_state': { + 'user_message': user_message, + 'chat_history': utils.chat_history_to_payload(chat_history) + }, + 'context_docs': utils.context_docs_to_payload(context_docs) + } + + with httpx.stream('POST', url, + json=payload, + timeout=self._endpoint_cfg.connection_timeout) as stream: + for chunk in stream.iter_bytes(): + yield json.loads(chunk.decode('utf-8')) + + def check_input_safety(self, + user_message: str, + chat_history: utils.ChatHistory, + ) -> utils.InputCheckResult: + """Sends the conversation state to the llm-proxy to check input safety. + + Raises: + requests.HTTPError: If the request to the llm-proxy fails. + """ + + _logger().debug(('Checking input safety with user_message: %s, chat_history: %s'), + user_message, chat_history) + + return self._sanitize_input( + user_message=user_message, + chat_history=chat_history, + url=f"{self._endpoint_cfg.url}/check_input_safety" + ) + + def check_input_relevance(self, + user_message: str, + chat_history: utils.ChatHistory, + ) -> utils.InputCheckResult: + """Sends the conversation state to the llm-proxy to check input relevance. + + Raises: + requests.HTTPError: If the request to the llm-proxy fails. + """ + + _logger().debug(('Checking input relevance with user_message: %s, chat_history: %s'), + user_message, chat_history) + + return self._sanitize_input( + user_message=user_message, + chat_history=chat_history, + url=f"{self._endpoint_cfg.url}/check_input_relevance" + ) + + def _sanitize_input(self, + user_message: str, + chat_history: utils.ChatHistory, + url: str, + ) -> utils.InputCheckResult: + """Sends the conversations state to a chosen llm-proxy guardrail endpoints.""" + + payload = { + 'user_message': user_message, + 'chat_history': utils.chat_history_to_payload(chat_history) + } + + try: + response = requests.post(url, + json=payload, + timeout=self._endpoint_cfg.connection_timeout) + + except requests.exceptions.ConnectionError as e: + _logger().error('Connection error while connecting to llm-proxy: %s', e) + raise requests.HTTPError('Connection error while connecting to llm-proxy.') from e + + response.raise_for_status() + + resp_json = response.json() + return utils.InputCheckResult(is_ok=resp_json['is_ok'], reason=resp_json['reason']) diff --git a/services/web-app/src/web_app/backend/utils.py b/services/web-app/src/web_app/backend/utils.py new file mode 100644 index 0000000..cf05eeb --- /dev/null +++ b/services/web-app/src/web_app/backend/utils.py @@ -0,0 +1,72 @@ +"""Contains utilities used by the backend services.""" +import dataclasses +from typing import Any +from typing import TypeAlias + +import pydantic + + +class EndpointConnectionCfg(pydantic.BaseModel): + """Configuration for connecting to a backend endpoint.""" + + url: str + connection_timeout: float + + +@dataclasses.dataclass +class ChatMessage: + """Represents a single chat message.""" + + role: str + content: str + + +@dataclasses.dataclass +class ChatHistory: + """Contains history of messages in a chat session.""" + + messages: list[ChatMessage] + + +@dataclasses.dataclass +class ContextDocument: + """Represents a single document retrieved from the doc store.""" + + content: str + metadata: dict[str, Any] + + +@dataclasses.dataclass +class InputCheckResult: + """Result of input safety, or relevance check.""" + + is_ok: bool + reason: str + + +UnstructuredChatHistory: TypeAlias = list[dict[str, str]] + +UnstructuredContextDocs: TypeAlias = list[dict[str, Any]] + + +def chat_history_to_payload(chat_history: ChatHistory) -> UnstructuredChatHistory: + """Converts chat history into json representation containing only core fields.""" + return [ + { + 'role': message.role, + 'content': message.content + } + for message in chat_history.messages + ] + + +def context_docs_to_payload(context_docs: list[ContextDocument]) -> UnstructuredContextDocs: + """Converts context docs into json representation.""" + + return [ + { + 'content': doc.content, + 'metadata': doc.metadata, + } + for doc in context_docs + ] diff --git a/services/web-app/src/web_app/gui/__init__.py b/services/web-app/src/web_app/gui/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/web-app/src/web_app/gui/main_controller.py b/services/web-app/src/web_app/gui/main_controller.py new file mode 100644 index 0000000..3657999 --- /dev/null +++ b/services/web-app/src/web_app/gui/main_controller.py @@ -0,0 +1,241 @@ +"""Contains GUI related utils.""" +import logging +from typing import Iterator + +import gradio as gr +import requests +from web_app.backend import context_retriever +from web_app.backend import llm_proxy +from web_app.backend import utils + + +def _logger() -> logging.Logger: + return logging.getLogger(__name__) + + +class MainController: + """Renders GUI elements and handles controller-view interactions.""" + + _DOC_MD_TEMPLATE = """ +### {title} + +{content} +""" + + _DOCS_LIST_MD_TEMPLATE = """ +## Round {round_nr} + +{docs} +""" + + def __init__(self, + context_retriever_service: context_retriever.ContextRetrieverService, + llm_proxy_service: llm_proxy.LLMProxyService): + + self._context_retriever_service = context_retriever_service + self._llm_proxy_service = llm_proxy_service + + self._documents_retrieval_history: list[list[utils.ContextDocument]] = [] + + def render_gui(self) -> None: + """Renders the UI for application and assigns the necessary callbacks.""" + + with gr.Row(elem_id='agh_header_row', height='70vh'): + + with gr.Column(elem_id='context_column', scale=1): + gr.Markdown( + """ + # Context + """ + ) + + file_upload = gr.File(label='Upload Files for Context') + + file_upload.upload( # pylint: disable=no-member + self._upload_file, file_upload, file_upload, + ) + + gr.Markdown( + """ + ## Retrieved Documents + """ + ) + + with gr.Column(elem_id='retrieved_docs', + variant='panel', + elem_classes='retrieved-docs'): + + docs_list = gr.Markdown('Retrieved documents will be displayed here.') + + with gr.Column(elem_id='chat_column', scale=3): + + gr.Label('AGH Chat', elem_id='agh_chat_label', show_label=False) + + chatbot = gr.Chatbot(elem_id='agh_chat', + type='messages', + height='70vh', + show_copy_button=True, + label='AGH Chat') + msg = gr.Textbox(placeholder='Type a message...', show_label=False) + + msg.submit( # pylint: disable=no-member + self._move_user_msg_to_chat, [msg, chatbot], [msg, chatbot] + ).success( + self._validate_user_msg, chatbot, chatbot + ).success( + self._retrieve_and_store_docs, chatbot, None + ).success( + self._stream_chat_response, chatbot, chatbot + ).success( + self._create_retrieved_docs_representation, None, docs_list + ) + + def _stream_chat_response(self, + chat_history: utils.UnstructuredChatHistory, + ) -> Iterator[utils.UnstructuredChatHistory]: + """Streams the chat response based on the chat history with the latest user msg.""" + + chat_history, user_message = chat_history[:-1], chat_history[-1]['content'] + + structured_history = utils.ChatHistory( + [utils.ChatMessage(message['role'], message['content']) + for message in chat_history] + ) + + full_response = '' + + for chunk in self._llm_proxy_service.stream_chat_response( + user_message=user_message, + chat_history=structured_history, + context_docs=self._documents_retrieval_history[-1] + ): + + if 'error' in chunk: + _logger().debug('Received error from llm-proxy: %s', chunk['error']) + + yield chat_history + self._documents_retrieval_history.pop() + + raise gr.Error(chunk['error'], + title='Error while generating chat response', + duration=None) + + full_response += chunk['content'] + + yield chat_history + [{'role': 'user', 'content': user_message}, + {'role': 'assistant', 'content': full_response}] + + def _create_retrieved_docs_representation(self) -> gr.Markdown: + """Concatenates the documents retrieved till now and returns their Markdown repr.""" + + retrieval_history_reprs: list[str] = [] + + for docs in self._documents_retrieval_history: + + docs_repr = (self._DOC_MD_TEMPLATE.format(title=doc.metadata['title'], + content=doc.content) + for doc in docs) + + retrieval_history_reprs.append('\n\n'.join(docs_repr)) + + docs_list_repr = (self._DOCS_LIST_MD_TEMPLATE.format(round_nr=i + 1, + docs=repr) + for i, repr in enumerate(retrieval_history_reprs)) + + return gr.Markdown('\n---\n'.join(docs_list_repr)) + + def _move_user_msg_to_chat(self, + user_message: str, + chat_history: utils.UnstructuredChatHistory | None, + ) -> tuple[str, utils.UnstructuredChatHistory]: + """Migrates the submitted user message to the chat history and resets the msg input.""" + + chat_history = chat_history or [] + + return '', chat_history + [{'role': 'user', 'content': user_message}] + + def _retrieve_and_store_docs(self, + chat_history: utils.UnstructuredChatHistory, + ) -> None: + """Retrieves context documents and stores them internally.""" + + chat_history, user_message = chat_history[:-1], chat_history[-1]['content'] + + gr.Info('Collecting context documents...', duration=5) + + try: + context_docs = self._context_retriever_service.collect_context_info( + user_message=user_message, + chat_history=utils.ChatHistory( + [utils.ChatMessage(message['role'], message['content']) + for message in chat_history] + ) + ) + + except requests.HTTPError as e: + _logger().error('Failed to collect context info from backend: %s', e) + + raise gr.Error('Failed to collect context info from backend.', duration=None) + + self._documents_retrieval_history.append(context_docs) + + def _validate_user_msg(self, + chat_history: utils.UnstructuredChatHistory, + ) -> Iterator[utils.UnstructuredChatHistory]: + """Validates the user message for safety and relevance.""" + + chat_history, user_message = chat_history[:-1], chat_history[-1]['content'] + + structured_history = utils.ChatHistory( + [utils.ChatMessage(message['role'], message['content']) + for message in chat_history] + ) + + gr.Info('Validating user message...', duration=5) + + try: + safety_check = self._llm_proxy_service.check_input_safety( + user_message, structured_history) + + if not safety_check.is_ok: + yield chat_history + raise gr.Error(safety_check.reason, + title='Input Safety Check Failed', + duration=None) + + relevance_check = self._llm_proxy_service.check_input_relevance( + user_message, structured_history) + + if not relevance_check.is_ok: + yield chat_history + raise gr.Error(relevance_check.reason, + title='Input Relevance Check Failed', + duration=None) + + except requests.HTTPError as e: + _logger().error('Failed to validate user message: %s', e) + + raise gr.Error('Failure while validating user message.', duration=None) + + yield chat_history + [{'role': 'user', 'content': user_message}] + + def _upload_file(self, + uploaded_file_path: str) -> None: + """Uploads a file to the context retriever service.""" + + try: + upload_error = self._context_retriever_service.upload_file(uploaded_file_path) + + except requests.HTTPError as e: + _logger().error('Failed to upload file to context retriever: %s', e) + + raise gr.Error('Failed to upload a file.', duration=None) + + if upload_error is not None: + _logger().error('Failed to upload file to context retriever: %s', upload_error) + + raise gr.Error(upload_error, + title='Failed to upload a file', + duration=None) + + gr.Success('File uploaded successfully!', duration=5) diff --git a/services/web-app/src/web_app/gui/utils.py b/services/web-app/src/web_app/gui/utils.py new file mode 100644 index 0000000..a34c819 --- /dev/null +++ b/services/web-app/src/web_app/gui/utils.py @@ -0,0 +1,8 @@ +"""Contains utilities for application frontend.""" + +CUSTOM_CSS = """ +.retrieved-docs { + max-height: 50vh; + overflow-y: 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DEBIAN_FRONTEND=noninteractive + +RUN apt-get update && \ + apt-get install -y --no-install-recommends wget libmagic1 + +RUN wget -qO- https://astral.sh/uv/install.sh | env UV_INSTALL_DIR="/usr/local/bin" sh + +COPY cfg /app/cfg +COPY src /app/src +COPY pyproject.toml main.py /app/ +WORKDIR /app + +EXPOSE ${WEB_APP_PORT} + +RUN uv python install 3.12 && uv venv && uv pip install . +CMD [ "uv", "run", "python", "main.py" ]