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Chat

Conversational pipelines: build a Question, send it with client.chat(), and parse the response with Answer. Class tables in the API reference.

Chat is the conversational lane: it works against chat, webhook, and dropper pipeline sources. Under the hood the client opens a pipe with MIME type application/rocketride-question, writes the serialized Question, closes the pipe, and returns the server result.

Build a Question

from rocketride.schema import Question

question = Question(expectJson=True)
question.addInstruction('Format', 'Return a JSON object with keys: summary, keywords.')
question.addExample('Summarize X', {'summary': '...', 'keywords': ['a', 'b']})
question.addQuestion('Summarize the main points and list keywords.')

If you send the same question again and again with only its context, documents, goals or questions changing (an agent loop does), set question.cachePrefix = True. A provider that supports prompt caching (the Anthropic node, and Claude models on the Bedrock node) may then cache the unchanging start of the prompt; other providers ignore the flag.

Question(type=QuestionType.QUESTION, filter=DocFilter(), expectJson=False, cachePrefix=False, role='') — QuestionType is one of QUESTION, SEMANTIC, KEYWORD, GET, PROMPT. Steer the model with addInstruction, addExample, addContext, addHistory (for multi-turn), addDocuments, addGoal, and addQuestion.

Send it

response = await client.chat(token=token, question=question)

chat(*, token, question, on_sse=None) is keyword-only; the optional on_sse callback streams server-sent events (token-by-token output) as they arrive. The final answer is in the result body.

Parse the response with Answer

Answer extracts structure from AI text, which often arrives wrapped in markdown or code fences. The client does not attach an Answer to the result — you read the body and feed it in:

from rocketride.schema import Answer

answer_text = (response.get('answers') or [None])[0]
answer = Answer(expectJson=True)
answer.setAnswer(answer_text or '')
if answer.isJson():
    structured = answer.getJson()
else:
    structured = answer.getText()

Semantics worth knowing:

  • setAnswer(value) stores the response, validating/parsing it as JSON when expectJson is True.
  • isJson() returns the expectJson flag — it does not inspect the content.
  • getJson() returns the parsed JSON; it returns None only when no answer has been set, and raises ValueError if the stored answer is not valid JSON.
  • getText() returns the answer as plain text; parsePython(value) extracts Python code from a code block.
  • answer.tokens carries the turn-total LLM token usage reported by the server.

A complete chat program is example 6.