MicroLlama

The smallest possible LLM API. Build a question and answer interface to your own content in a few minutes. Uses OpenAI embeddings, gpt-3.5 and Faiss, via Langchain.

Usage

1. Combine your source documents into a single JSON file called source.json. It should look like this:

[
    {
        "source": "Reference to the source of your content. Typically a title.",
        "url": "URL for your source. This key is optional.",
        "content": "Your content as a single string. If there's a title or summary, put these first, separated by new lines."
    }, 
    ...
]

See example.source.json for an example.

2. Install MicroLlama into a virtual environment:

pip install microllama

3. Get an OpenAI API key and add it to the environment, e.g. export OPENAI_API_KEY=sk-etc. Note that indexing and querying require OpenAI credits, which aren't free.

4. Run your server with microllama. If a vector search index doesn't exist, it'll be created from your source.json, and stored.

5. Query your documents at /api/ask?your question.

6. Microllama includes an optional web front-end, which is generated with microllama make-front-end. This command creates a single index.html file which you can edit. It's served at /.

Deploying your API

Create a Dockerfile with microllama make-dockerfile. Then:

On Fly.io

Sign up for a Fly.io account and install flyctl. Then:

fly launch # answer no to Postgres, Redis and deploying now 
fly secrets set OPENAI_API_KEY=sk-etc 
fly deploy

On Google Cloud Run

gcloud run deploy --source . --set-env-vars="OPENAI_API_KEY=sk-etc"

For Cloud Run and other serverless platforms you should generate the FAISS index at container build time, to reduce startup time. See the two commented lines in Dockerfile.

Based on

• Langchain
• Simon Willison's blog post, datasette-openai and datasette-faiss.
• FastAPI
• GPT Index
• Dagster blog post

TODO

• Use splitting which generates more meaningful fragments, e.g. text_splitter = SpacyTextSplitter(chunk_size=700, chunk_overlap=200, separator=" ")