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Overview

Streaming returns tokens as they are generated rather than waiting for the full response. This gives users a faster perceived experience — important for long outputs like document summaries or policy analysis.

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Basic streaming

import kitefishai

client = kitefishai.Client(api_key="kf-...")

with client.chat.stream(
    model="kf-reasoning-10b",
    messages=[{"role": "user", "content": "Summarise the RBI Master Directions on KYC."}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)
print()  # newline after stream ends

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Get the full text after streaming

with client.chat.stream(
    model="kf-reasoning-10b",
    messages=[{"role": "user", "content": "List IRDAI compliance requirements."}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)

full_text = stream.get_final_text()
print(f"\nTotal characters: {len(full_text)}")

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StreamChunk fields

Each chunk yielded by the iterator has:
FieldTypeDescription
idstrRequest ID
modelstrModel that generated the chunk
deltastrThe text content of this chunk
finish_reasonstr or None"stop" on the final chunk

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With a system prompt

with client.chat.stream(
    model="kf-reasoning-10b",
    system="You are a BFSI compliance assistant. Be concise and cite regulations.",
    messages=[{"role": "user", "content": "What is Form 60?"}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)

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Collecting chunks manually

chunks = []

with client.chat.stream(model="kf-reasoning-10b", messages=[...]) as stream:
    for chunk in stream:
        chunks.append(chunk)

print(f"Received {len(chunks)} chunks")
print("".join(c.delta for c in chunks))