russell@unturf.com
fc039555cc
aborist/qa/query.py exposes query() — the user-facing RAG flow:
1. FTS5 search across all shards (chunks_fts can't be UNION'd as a
view, so each shard's index is queried independently and merged
by score).
2. Top-K distinct documents are selected within a max-context-chars
budget (default 60 KB so a 768-token response fits Hermes-3's
82 K context window comfortably).
3. context_root = Merkle root over the sorted source document_roots.
That's the v9.8 'source' dimension for multi-source answers —
a verifier can recompute it from the listed source roots.
4. 8-dim cache_key over (context_root, question_hash, model_profile,
conversation, governance_policy, schema, canonicalization,
chunking). Hit returns STRICT immediately; miss calls Hermes and
persists.
CLI: aborist [--shards-dir DIR] query "<question>"
Default qa_db is <shards-dir>/qa.db (or ~/.aborist/qa.db). Uses the
same OpenAICompatibleClient/StubClient as `ask`. --dry-run skips the
LLM and returns context-only.
Search escape fix: the prior FTS5 escape ANDed every token including
stopwords + punctuation, so "What is anarcho-capitalism?" required
the doc to literally contain "what" + "is" + "anarcho-capitalism?" —
zero hits. New tokenizer drops stopwords + punctuation and ORs the
remaining content tokens; BM25 ranks the multi-token matches highest.
Live demo against the 122k-doc 4-shard cluster:
Q "What is anarcho-capitalism?" 6.1 s wall miss / 0.45 s cache hit
Q "Who was George Washington?" 10.3 s wall miss
Both answers cite the source URIs Hermes was given.
57 tests passing (4 new query tests covering search → context →
cache → audit chain).
27 lines
678 B
Python
27 lines
678 B
Python
"""Q&A layer: answer a question about a document, write a provable record."""
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from aborist.qa.client import ChatClient, OpenAICompatibleClient, StubClient
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from aborist.qa.keys import (
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cache_key,
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conversation_hash,
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governance_policy_hash,
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model_profile_hash,
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question_hash,
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)
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from aborist.qa.query import DEFAULT_QUERY_POLICY, query
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from aborist.qa.runner import DEFAULT_POLICY, ask
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__all__ = [
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"ChatClient",
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"OpenAICompatibleClient",
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"StubClient",
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"cache_key",
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"conversation_hash",
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"governance_policy_hash",
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"model_profile_hash",
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"question_hash",
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"DEFAULT_POLICY",
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"DEFAULT_QUERY_POLICY",
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"ask",
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"query",
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]
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