A pure Dart package, no SDKs, one interface (LlmClient) over Anthropic's Messages API, OpenAI's chat completions, in each known host's dialect and the local servers' (D45), and OpenAI's Responses API, stateless (D46). A model's turn keeps what its provider wants back with the tools' results: Anthropic's thinking blocks, the Responses API's reasoning items and message phase, Gemini's thought signatures. Failures come by kind, the provider's words cut and without keys. Tested on replies written from the providers' documentation, replayed in pieces of 1, 7 and 4,096 bytes; tool/record_llm.dart records real ones with a key of one's own, which the tests then run on too (D36). FakeLlmClient for the code that uses it. BSD-3-Clause, as halkyon_oidc. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Tizian 「ティツィアーン」 <raisondetredev@pm.me>
104 lines
5 KiB
Markdown
104 lines
5 KiB
Markdown
# halkyon_llm
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Language models' APIs in pure Dart, streamed, over plain HTTP, no SDKs: Anthropic's
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Messages API, OpenAI's chat completions (and every service compatible with them), and
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OpenAI's Responses API, behind one interface of what they share: a system prompt and
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messages, text as the model writes it, images, and tool calls. Halkyon's server forwards
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the operator's assistants with it, and its apps call a person's own provider with it
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(Halkyon Learn's ADR 0021, points 5 and 10).
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## Asking
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```dart
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final client = LlmClient();
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final endpoint = LlmEndpoint(
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protocol: LlmProtocol.anthropic,
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model: 'claude-sonnet-5-5',
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apiKey: key,
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);
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final request = LlmRequest(
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system: 'You help with learning Japanese.',
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messages: [
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LlmMessage.user([const LlmText('What is "dog" in Japanese?')]),
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],
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maxOutputTokens: 4096,
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);
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await for (final event in client.send(endpoint, request)) {
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switch (event) {
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case LlmTextDelta(:final text):
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stdout.write(text);
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case LlmFinished(:final message, :final stop, :final usage):
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// The model's whole turn, to send back in the next request.
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}
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}
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client.close();
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```
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`send` streams `LlmTextDelta`s as the model writes, then one `LlmFinished` with the
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model's turn, why it stopped (`LlmStop`: `end`, `maxTokens`, `toolCalls`, `refused`,
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`other`) and what the provider counted (`LlmUsage`, input with what was read from a
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cache, output with the model's thinking). A failure ends the stream with an `LlmFailure`
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of a kind a caller can act on (`key`, `rateLimited` with the provider's `retryAfter`,
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`unavailable`, `request`, `provider`, `network`, `malformed`), the usage counted so far
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and the provider's words, cut to 300 characters with anything that looks like a key
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removed. Cancelling the subscription aborts the request.
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### The protocols
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| `LlmProtocol` | Request | Default base URL |
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|---|---|---|
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| `anthropic` | `POST {base}/v1/messages` | `https://api.anthropic.com` |
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| `openai` | `POST {base}/chat/completions` | `https://api.openai.com/v1` |
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| `openaiResponses` (`openai-responses`) | `POST {base}/responses` | `https://api.openai.com/v1` |
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A base URL is `https`, but for a model on the same machine or network (`localhost`, a
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private or link-local address, a single-label name such as a container's, `.local`,
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`.internal`, `.home.arpa`), as Ollama and llama.cpp serve it; anything else is a
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`FormatException` when the endpoint is made.
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- **Chat completions** go to OpenAI, Mistral, Gemini's compatibility endpoint, OpenRouter
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and local servers. OpenAI, Mistral and Gemini refuse fields they don't know and differ
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in which they take, so each of those hosts, and OpenRouter, gets its own fields; every
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other host gets what Ollama, vLLM and llama.cpp all take. Replies are read leniently:
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usage at the end, with the last choice or as a running total; tool calls in pieces or
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whole, with or without an index; errors as a status, in a chunk, or lost.
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- **The Responses API** is stateless here (`store: false`): each request carries the
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whole conversation, with the model's encrypted reasoning as it streamed it.
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- **What goes back:** a model's turn holds, besides its text and calls, what its provider
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wants returned with the tools' results, kept as it came: Anthropic's thinking blocks
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and the Responses API's reasoning items as `LlmProviderBlock`s (sent to that protocol
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alone), Gemini's thought signatures in `LlmToolCall.extra`, the Responses API's message
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phase in `LlmText.extra`. Send the `LlmFinished.message` back unchanged.
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- **The person's opaque ID** (`LlmRequest.personId`) goes where the provider takes one:
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Anthropic's `metadata.user_id`, OpenAI's `safety_identifier`, OpenRouter's `user`.
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## Testing with it
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`package:halkyon_llm/testing.dart` has `FakeLlmClient`: it keeps what it was sent, and
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answers with what a test queues (`replyText`, `reply` with events and a failure, `fail`),
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so code that forwards or runs a tools' loop is tested without HTTP.
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## How it is tested
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- **Documented replies** (`test/fixtures/documented/<protocol>/`): each provider's stream
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written event by event from its documentation, in our own words: text, tool calls,
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errors mid-stream, the statuses, and the variants each compatible service streams.
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Each is a `.http` file: `HTTP <status>`, the headers that matter, an empty line, the
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body. The tests replay them in pieces of 1, 7 and 4,096 bytes, so a line or a character
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cut in two is read whole.
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- **Recorded replies** (`test/fixtures/recorded/<protocol>[@<host>]/`): real ones, which
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`tool/record_llm.dart` writes with a key of one's own (four small requests: a text, a
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tool call, the tool's result sent back with the model's whole turn, a wrong key). Their
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words are the model's, so only what every such reply must hold is checked. Read them
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before committing them.
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```sh
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cd packages/halkyon_llm
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dart run tool/record_llm.dart --protocol anthropic
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dart run tool/record_llm.dart --protocol openai-responses --model <a small model>
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```
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## Licence
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BSD-3-Clause (`LICENSE`), as Serverpod's own and `halkyon_oidc`, so that others can use
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it; the rest of Halkyon is AGPL. Not on pub.dev yet.
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