IntegrationsChat clients

Using BeatAPI in AnythingLLM

Using BeatAPI in AnythingLLM

AnythingLLM builds a private knowledge base over your own documents and answers questions against them. BeatAPI supplies the chat model through its Generic OpenAI provider; the documents and the vector index stay wherever you deploy AnythingLLM.

Prerequisites

  1. AnythingLLM installed — desktop build for Windows, macOS, or Linux, or a Docker deployment.
  2. A BeatAPI API key — create one in Dashboard → API Keys.

Step 1: Open LLM settings

Launch AnythingLLM and click Open settings at the bottom left, then LLM Preference in the left menu.

Step 2: Configure BeatAPI

Select Generic OpenAI as the LLM Provider, then fill in:

FieldValue
API KeyYour BeatAPI API key
Base URLhttps://api.beatapi.io/v1
Chat ModelA model ID, for example claude-sonnet-5

The Base URL here does include /v1, and nothing after it. AnythingLLM appends /chat/completions.

Click Save. AnythingLLM tests the connection and reports the result.

Model IDChoose it for
claude-sonnet-5Balanced quality and cost for document Q&A
claude-fable-5-1The newest generation, for demanding synthesis
gpt-5.6-terraA strong general alternative
gemini-3.7-flashFast and inexpensive over large document sets
glm-5.3-flashThe cheapest tier for high query volume
kimi-k3Long context, useful when retrieval returns many chunks

The full catalogue is on the Text API pages and the pricing page.

Step 3: Choose an embedding model

BeatAPI does not serve an embeddings endpoint. Leave the Embedding Provider on AnythingLLM’s built-in embedder, or point it at a service that offers one. Configuring Generic OpenAI under Embedding Preference with the BeatAPI base URL will fail, because /v1/embeddings is not part of the published surface.

AnythingLLM ships with a local embedder that runs on the same machine and needs no key. For a private knowledge base that is usually the better answer anyway: the document text never leaves the deployment, and only the retrieved excerpts are sent to the chat model.

Step 4: Create a workspace and add documents

  1. Click + New Workspace and name it.
  2. Click Upload Documents and add files, or drag them in.
  3. Wait for processing — documents are chunked and embedded locally.

Supported inputs include .txt, .md, .pdf, .docx, source files, .csv, .json, and pages fetched by URL.

Step 5: Ask questions

Type a question in the workspace. AnythingLLM retrieves relevant chunks from your documents and sends them to the BeatAPI model together with the question.

Each workspace has its own documents, its own system prompt, and its own history, so one deployment can serve several unrelated knowledge bases.

FAQ

AnythingLLM cannot connect

  1. Base URL must be https://api.beatapi.io/v1 — with /v1, no trailing slash.
  2. Confirm the key is active in Dashboard → API Keys.
  3. For a Docker deployment, confirm the container itself can reach api.beatapi.io; a desktop test proves nothing about the container’s network.

Embedding configuration fails

Expected — see Step 3. BeatAPI serves chat, not embeddings. Use the built-in embedder.

Retrieval returns nothing useful

That is a retrieval problem, not a model problem. Re-upload after checking the document parsed correctly, lower the similarity threshold, or raise the number of returned chunks in workspace settings. Changing the chat model will not fix an empty retrieval.

Answers are slow

Move to a faster tier — gemini-3.7-flash or glm-5.3-flash — and reduce how many chunks are retrieved per question. Retrieval size drives the input token count directly.

402 insufficient_credits

The balance is exhausted. Add credits from the dashboard.

Checking usage and spend

The dashboard shows calls, tokens, and cost per API key; the same figures come from GET /v1/usage. Give AnythingLLM its own key so its consumption is separable from everything else.

Support

Open a support ticket from the dashboard and include the request_id from the failing response.