Ajay Krishnan
51dceee224
Replace the abandoned llms-txt-mcp/Chroma docs backend with an in-repo MCP service: SQLite WAL + FTS5 + sentence-transformer embeddings, transactional source replacement, persisted state across restarts, singleflight refresh with conditional requests, hybrid lexical/semantic ranking with exact-duplicate collapse, source/host filters, and explicit-by-default content retrieval. Add docs_rebuild and a docs-rebuild CLI command. Add deterministic llms-full.txt snapshot generation for machine-local menus with hash-validated provenance manifests; lifecycle commands promote a local menu to its snapshot only when the manifest validates. Switch public source profiles to content-bearing llms-full.txt feeds. Improve web search: bounded provider fallback with per-attempt diagnostics and cancellation, an optional Brave Search API provider, strict SearXNG engine selection, capped link/media extraction, and a real engine=browser renderer that routes every request through the existing SSRF vetting while blocking WebSockets, non-GET traffic, and private destinations. Extend release checks with offline unit suites and isolated candidate container tests for both images.
83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
from __future__ import annotations
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from collections import defaultdict
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import numpy as np
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from .models import SearchResult, StoredDocument
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from .store import IndexStore
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class HybridSearch:
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def __init__(self, store: IndexStore, embedder, rrf_k: int = 60):
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self.store = store
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self.embedder = embedder
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self.rrf_k = rrf_k
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async def search(
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self,
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query: str,
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limit: int = 10,
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sources: list[str] | None = None,
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hosts: list[str] | None = None,
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) -> list[SearchResult]:
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pool_size = max(limit * 8, 40)
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lexical = self.store.lexical_search(query, pool_size, sources, hosts)
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candidates = self.store.semantic_candidates(sources, hosts)
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semantic: list[StoredDocument] = []
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if candidates:
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query_vector = np.asarray(await self.embedder.encode_query(query), dtype=np.float32)
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query_norm = np.linalg.norm(query_vector)
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scored: list[tuple[float, StoredDocument]] = []
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for document in candidates:
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norm = np.linalg.norm(document.embedding) * query_norm
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score = float(np.dot(document.embedding, query_vector) / norm) if norm else 0.0
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scored.append((score, document))
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semantic = [document for _, document in sorted(scored, key=lambda item: (-item[0], item[1].id))[:pool_size]]
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lexical_ranks = {document.id: rank for rank, document in enumerate(lexical, 1)}
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semantic_ranks = {document.id: rank for rank, document in enumerate(semantic, 1)}
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documents = {document.id: document for document in [*lexical, *semantic]}
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scores = defaultdict(float)
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for identifier, rank in lexical_ranks.items():
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scores[identifier] += 1.0 / (self.rrf_k + rank)
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for identifier, rank in semantic_ranks.items():
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scores[identifier] += 1.0 / (self.rrf_k + rank)
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ordered = sorted(documents.values(), key=lambda item: (-scores[item.id], item.id))
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groups: dict[str, list[StoredDocument]] = {}
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group_order: list[str] = []
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for document in ordered:
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key = document.content_hash
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if key not in groups:
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groups[key] = []
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group_order.append(key)
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groups[key].append(document)
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results: list[SearchResult] = []
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for key in group_order[:limit]:
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group = groups[key]
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primary = group[0]
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alternates = [
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{"source": document.configured_source, "url": document.canonical_url}
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for document in group[1:]
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]
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results.append(
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SearchResult(
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id=primary.id,
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configured_source=primary.configured_source,
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canonical_url=primary.canonical_url,
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title=primary.title,
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description=primary.description,
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heading_path=primary.heading_path,
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content=primary.content,
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content_hash=primary.content_hash,
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score=min(1.0, scores[primary.id] / (2.0 / (self.rrf_k + 1))),
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lexical_rank=lexical_ranks.get(primary.id),
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semantic_rank=semantic_ranks.get(primary.id),
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duplicate_count=len(group),
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alternate_sources=alternates,
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)
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)
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return results
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