ducoterra
99c48cbe06
Phase 02 (story: import documents):
- fence-aware markdown chunker (heading sections, 200-char overlap,
heading anchor on every chunk, 1200-char hard cap, fence blocks
kept atomic and split under the cap)
- LLMClient over aipi (LiteLLM) reusing the openai client's httpx
transport to send a clean {model, input} payload — the openai SDK
injects encoding_format, which aipi's openai_like group rejects;
token-budget batching + halving retry for the endpoint's
~1024-token per-request input cap
- two-phase per-file upsert importer: sha256 delta (unchanged skip),
atomic commit, A9 exclusion walk, per-source prune, per-file error
tolerance (rollback + log + continue, non-zero CLI exit), adaptive
re-chunk at half target for URL-dense files the endpoint rejects
- scripts/import_docs CLI (repeatable --source, --prune, --limit,
defaults ~/Homelab + ~/Deployments)
- GET /api/docs with per-doc chunk counts; Sources page wired to the
real endpoint (stat cards, full-width a11y table, designed empty
state, DOM-built rows — no innerHTML)
- tests: 63 passed (chunker/llm/importer units, docs API + importer
integration), story E2E 3/3 (real endpoints, in-thread import);
app/ coverage 98%
- real KB imported: 672 docs / 8969 chunks in ~3m, idempotent
re-run (672 unchanged, 0 batches)
- harness: .agent/validate.sh now gates through uv (pytest +
coverage >90% + ruff + pyright) instead of system python3
25 lines
838 B
Python
25 lines
838 B
Python
"""Shared test fakes (no network, deterministic)."""
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from __future__ import annotations
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from app.config import Settings
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class FakeEmbedder:
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"""Duck-typed stand-in for :class:`app.rag.llm.LLMClient` (see the
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``Embedder`` protocol in :mod:`app.rag.importer`).
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Returns deterministic vectors of *dim* dimensions; records every call
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so tests can assert batching behaviour.
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"""
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def __init__(self, dim: int = 768) -> None:
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self.dim = dim
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self.settings = Settings(_env_file=None) # pyright: ignore[reportCallIssue]
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self.embed_batches = 0
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self.calls: list[list[str]] = []
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async def embed(self, texts: list[str]) -> list[list[float]]:
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self.calls.append(list(texts))
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self.embed_batches += 1
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return [[0.01 * (i % 97) for i in range(self.dim)] for _ in texts]
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