LLM & embeddings

Python SDK reference for the governed LLM surface — list_models, tokenize, count_tokens, chat_completions, responses, embeddings, embedding_similarity, and rerank.

These methods call Algenta's governed LLM surface: model discovery, tokenization, chat completions, the responses API, embeddings, similarity, and reranking. They are available on both AlgentaClient and AsyncAlgentaClient (await the async calls). Every model argument routes through the instance's model catalog — call list_models() first to see what is executable, as described in API overview.

Method reference

Method	Purpose
list_models()	List the LLM utility models this instance can execute (LLMModelListResult). Reads /v1/models.
tokenize(text, *, model="text.tokenizer")	Tokenize text (TokenizeResult). Posts to /v1/tokenize.
count_tokens(text, *, model="text.tokenizer")	Count tokens in text (CountTokensResult). Posts to /v1/count_tokens.
chat_completions(messages, *, model="text.tokenizer")	Run a chat completion (ChatCompletionsResult). Posts to /v1/chat/completions.
stream_chat_completions(messages, *, model="text.tokenizer")	Stream a chat completion as chunks. Posts to /v1/chat/completions.
responses(input_value, *, model="text.tokenizer", dimensions=64)	Run the responses API and return output items (ResponsesResult). Posts to /v1/responses.
stream_responses(input_value, *, model="text.tokenizer", dimensions=64)	Stream responses events. Posts to /v1/responses.
embeddings(input_value, *, model="text.hash_embedding_v1", dimensions=64)	Compute embeddings (EmbeddingsResult). Posts to /v1/embeddings.
embedding_similarity(left, right, *, model="embeddings.cosine_similarity")	Compute similarity between two vectors (EmbeddingSimilarityResult). Posts to /v1/embeddings/similarity.
rerank(query_embedding, documents, *, model="embeddings.cosine_similarity", top_n=None)	Rerank documents against a query embedding (RerankResponseResult). Posts to /v1/rerank.
resolve_artifact_bridge(*, repo_id, filename, revision=None, local_files_only=True)	Resolve a model artifact through the governed artifact bridge (ArtifactBridgeResolveResult). Posts to /v1/artifacts/resolve.

Example: discover a model and run a chat completion

import os
from decision_engine import AlgentaClient

client = AlgentaClient(api_key=os.environ["ALGENTA_API_KEY"])

# Only models listed here are executable on this instance.
models = client.list_models()
print([m.id for m in models.data])

count = client.count_tokens("How many tokens is this?")
print(count.token_count)

reply = client.chat_completions(
    [
        {"role": "system", "content": "You are concise."},
        {"role": "user", "content": "Summarize the incident in one sentence."},
    ],
)
print(reply.choices[0].message.content)

Example: embed, compare, and rerank

import os
from decision_engine import AlgentaClient

client = AlgentaClient(api_key=os.environ["ALGENTA_API_KEY"])

vectors = client.embeddings(["orders pipeline", "checkout latency"])
left = vectors.data[0].embedding
right = vectors.data[1].embedding

similarity = client.embedding_similarity(left, right)
print(similarity.score)

ranked = client.rerank(
    query_embedding=left,
    documents=[
        {"id": "doc-1", "text": "checkout latency runbook"},
        {"id": "doc-2", "text": "billing reconciliation notes"},
    ],
    top_n=1,
)
for item in ranked.data:
    print(item.rank, item.id, item.score)

Related pages

API overviewSimulations & queriesMCP server