Flask AI Starter

A starter application that shows a data collector architecture for retrieval augmented generation.

Technology stack

This codebase is written Python and runs on Azure's Container Apps and Functions. It uses Flask and Jinja2 Templates with the Azure OpenAI Service. It stores data in PostgreSQL and uses pgvector to write and query embeddings. A GitHub Action runs tests, builds the apps, runs migrations, then deploys to Azure.

The devcontainer.json file configures an environment for local development in a Development Container.

Architecture

The AI Starter consists of three free-running processes communicating with one Postgres database.

1. The data collector is a background process that collects data from one or more sources.
2. The data analyzer is another background process that processes collected data.
3. The web application collects a query from the user and displays a result to the user.

flowchart LR
    embeddings([Azure AI embeddings])
    user((User))
    app["Web App\n(Container App)"]
    db[("PostgreSQL\n(+pgvector)")]
    llm([Azure AI completion])
    
    user -- query --> app
    app -- create embedding --> embeddings
    app -- search embeddings --> db
    app -- retrieve documents --> db
    app -- fetch text completion --> llm

    classDef node font-weight:bold,color:white,stroke:black,stroke-width:2px;
    classDef app fill:#3185FC;
    classDef db fill:#B744B8;
    classDef external fill:#FA9F42;
    classDef user fill:#ED6A5A;

    class app,collector,analyzer app;
    class db db;
    class docs,embeddings,llm external;
    class user user;

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flowchart LR
    embeddings([Azure AI embeddings])
    docs(["RSS feeds"])
    db[("PostgreSQL\n(+pgvector)")]
    collector["Data Collector\n(Azure Function)"]
    analyzer["Data Analyzer\n(Azure Function)"]
    
    collector -- fetch documents --> docs
    collector -- save documents --> db
    analyzer -- retrieve documents --> db
    analyzer -- create embeddings --> embeddings
    analyzer -- "save embeddings \n (with reference)" --> db

    classDef node font-weight:bold,color:white,stroke:black,stroke-width:2px;
    classDef app fill:#3185FC;
    classDef db fill:#B744B8;
    class app,collector,analyzer app;
    classDef external fill:#FA9F42;
    classDef user fill:#ED6A5A;

    class db db;
    class docs,embeddings external;
    class user user;

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Collection and Analysis

The data collector fetches documents from RSS feeds sources and stores the document text in the database. It also splits documents into chunks of less than 6000 tokens to ensure embedding and text completion calls stay below their token limits. The data analyzer sends document chunks to the Azure AI Embeddings API and uses pgvector to store the embeddings in PostgreSQL.

Web Application

The web application collects the user's query and creates an embedding with the OpenAI Embeddings API. It then searches the PostgreSQL for similar embeddings (using pgvector) and provides the corresponding chunk of text as context for a query to the Azure AI Chat Completion API.

Local development

1. Install and start Docker Desktop.

2. Install and open PyCharm.

3. In the PyCharm menu, choose File > Remote Development > Dev Containers > New Dev Containers > From VCS Project, then enter [email protected]:initialcapacity/flask-ai-starter.git to start the dev container in PyCharm.

4. Once your dev container is running, open a terminal in PyCharm (Alt/Option + F12) and run tests

   source venv/bin/activate
   python -m unittest

5. Copy the example environment file and fill in the necessary values.

   cp .env.example .env 
   source .env

6. Run the collector and the analyzer to populate the database, then run the app and navigate to localhost:5001.

   python collect.py
   python analyze.py
   python -m starter