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
52 lines
1003 B
Python
52 lines
1003 B
Python
"""Pydantic request/response schemas (API contract)."""
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from __future__ import annotations
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from pydantic import BaseModel, Field
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class HealthResponse(BaseModel):
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status: str
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db: str
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version: str
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environment: str
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class SuggestionList(BaseModel):
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suggestions: list[str]
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class ChatRequest(BaseModel):
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message: str = Field(min_length=1, max_length=4000)
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class SourceRef(BaseModel):
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source: str
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path: str
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title: str
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class ChatDoneEvent(BaseModel):
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"""Final SSE event of a chat turn: metadata for the finished answer."""
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type: str = "done"
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deflected: bool
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sources: list[SourceRef]
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suggestions: list[str] = []
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class DocSummary(BaseModel):
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"""One indexed document as shown on the Sources page / API."""
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id: str
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source: str
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path: str
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title: str
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chunks: int
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indexed_at: str
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class DocList(BaseModel):
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"""Response of ``GET /api/docs`` (empty list → designed empty state)."""
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documents: list[DocSummary]
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