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Documentation Index

Fetch the complete documentation index at: https://docs.9dlabs.xyz/llms.txt

Use this file to discover all available pages before exploring further.

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Confidence Score

Every context pack includes a confidence score (0.0 - 1.0) that reflects how well the retrieved evidence matches the query. The score accounts for:
  • Relevance strength — How closely the top evidence matches the query
  • Evidence depth — Whether multiple strong matches were found
  • Budget coverage — How much of the token budget was filled with useful evidence
  • Source diversity — Whether evidence comes from multiple artifacts or just one
A high confidence score means the system found strong, diverse evidence that covers the query well. A low score means the evidence is weak, sparse, or from a single source.

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Abstention

When the system determines it cannot provide reliable evidence, it sets abstain_flag: true instead of returning low-quality context. This lets your agent decline to answer rather than hallucinate with bad context.

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Abstention Triggers

ConditionThreshold
Confidence score too low< 0.08
Insufficient evidence tokens< 20 tokens total

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Handling Abstention

Always check abstain_flag before using context_text. Ignoring abstention and feeding low-quality context to your LLM will produce unreliable answers.
pack = client.context_pack(query="...", workspace_id="ws-1")

if pack["abstain_flag"]:
    # Insufficient evidence
    print(f"Cannot answer: {pack['abstain_reason']}")
    # Options:
    # - Tell the user you don't have enough information
    # - Fall back to a general response
    # - Ask the user for more context
else:
    # Confident answer
    print(f"Confidence: {pack['confidence']:.0%}")
    # Feed context_text into your LLM

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Abstention Reasons

The abstain_reason field provides a human-readable explanation:
  • "Confidence too low" — The evidence quality is below the threshold
  • "Insufficient evidence tokens" — Too little evidence was found to form a useful context