News Analyzer

Initial Capacity has worked on several production AI applications using LLMs. We see a common pattern in these applications: They collect and store large amounts of data from various sources, analyze this data with transformations and embeddings, and display the results to users on the web.

Data Collector Architecture

An architecture that we've been using for years is a great solution to this problem. Internally we call this the Data Collector architecture.

flowchart LR
    user((User))
    app[Web App]
    data(["Data Source(s)"])
    db[("Database(s)")]
    collector[Data Collector]
    analyzer[Data Analyzer]
    
    user --> app
    collector --> data
    collector --> db
    app --> db
    analyzer --> db

    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 data external;
    class user user;

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The Data Collector architecture consists of three deployed applications talking to one or more databases.

1. The web application displays data to the user and collects user input.
2. The data collector is a background process that collects data from one or more sources and stores the data in a database.
3. The data analyzer is another background process that reads the collected data, performs analysis, and stores the result in a database.

The architecture allows the various parts of the system to change and scale at different paces.

LLM Collection Architecture

A variation of the Data Collector architecture emerges in LLM applications.

Collection

flowchart LR
    embeddings([Embedding Service])
    docs(["Document Source(s)"])
    vdb[(Vector Database)]
    db[(Document Storage)]
    collector[Data Collector]
    analyzer[Data Analyzer]
    
    collector -- fetch documents --> docs
    collector -- save documents --> db
    analyzer -- retrieve documents --> db
    analyzer -- create embeddings --> embeddings
    analyzer -- "save embeddings \n (with reference)" --> vdb

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

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1. The data collector fetches documents from one or more sources and stores the document text in a database.
2. The data analyzer reads the collected data, creates embeddings, and stores these embeddings a vector database.

flowchart LR
    embeddings([Embedding Service])
    user((User))
    app[Web App]
    vdb[(Vector Database)]
    db[(Document Storage)]
    llm([LLM])
    
    user -- query --> app
    app -- create embedding --> embeddings
    app -- search embeddings --> vdb
    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,vdb db;
    class docs,embeddings,llm external;
    class user user;

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The web application collects user input, creates an embedding, searches the vector database for similar embeddings, and provides the corresponding document text as context for an LLM text-completion query.

Sample application

The News Analyzer application is an implementation of the LLM Collection architecture. It's built using Cloudflare's Workers, Workers KV, AI, and Vectorize, but the architecture can be implemented in may different stacks (Google Cloud's Cloud Run and Cloud Functions with the OpenAI API, and Go are another stack which is well-suited to the architecture).