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https://github.com/trustgraph-ai/trustgraph.git
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2bdc930b2a
Adds a sparse keyword retrieval path beside the existing vector path in document-RAG, fused by weighted Reciprocal Rank Fusion on chunk_id, behind --retrieval-mode (vector | keyword | hybrid, default vector). The keyword index is a new pluggable service (KeywordIndexService / KeywordIndexClientSpec); the first backend is SQLite FTS5, consuming Chunk messages off the ingestion stream and answering BM25 queries from one process, since the index is a single local file. Query text is sanitized into per-term quoted phrases (raw text is not valid FTS5 syntax), which also makes dotted clause numbers and error codes exact-match without a trigram index. Indexes are scoped per (workspace, collection) and dropped on collection deletion. The keyword-index client spec is only registered when the sparse path is enabled, so existing flow definitions without keyword-index queues are untouched; with retrieval_mode=vector the retrieval path is unchanged. In hybrid mode a keyword-path failure degrades to vector-only.
132 lines
3.7 KiB
Python
132 lines
3.7 KiB
Python
"""
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Keyword index service base class. A single service owns both sides of the
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lexical index: it consumes Chunk messages off the ingestion stream (the last
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message in the pipeline that still carries chunk text) and answers keyword
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search requests over what it has indexed. Unlike the vector stores, ingest
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and query are not split into two processors: the first backend (SQLite FTS5)
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is a single-file index that cannot be shared between containers, so one
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process must own it. Backends with a server (Elasticsearch/OpenSearch) can
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still be split later behind the same schema.
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"""
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from __future__ import annotations
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from argparse import ArgumentParser
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import logging
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from .. schema import Chunk
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from .. schema import KeywordIndexRequest, KeywordIndexResponse
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from .. schema import Error
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from .. exceptions import TooManyRequests
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from . flow_processor import FlowProcessor
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from . consumer_spec import ConsumerSpec
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from . producer_spec import ProducerSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "kw-index"
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default_concurrency = 10
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class KeywordIndexService(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id")
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concurrency = params.get("concurrency", default_concurrency)
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super(KeywordIndexService, self).__init__(
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**params | { "id": id }
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)
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self.register_specification(
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ConsumerSpec(
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name = "input",
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schema = Chunk,
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handler = self.on_chunk,
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)
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)
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self.register_specification(
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ConsumerSpec(
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name = "request",
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schema = KeywordIndexRequest,
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handler = self.on_request,
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concurrency = concurrency,
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)
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)
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self.register_specification(
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ProducerSpec(
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name = "response",
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schema = KeywordIndexResponse,
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)
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)
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async def on_chunk(self, msg, consumer, flow):
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try:
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request = msg.value()
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# Workspace comes from the flow the message arrived on.
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await self.index_chunk(flow.workspace, request)
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except TooManyRequests as e:
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raise e
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except Exception as e:
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logger.error(f"Exception in keyword index store: {e}", exc_info=True)
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raise e
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async def on_request(self, msg, consumer, flow):
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try:
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request = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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logger.debug(f"Handling keyword index query request {id}...")
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chunks = await self.query_keyword_index(
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flow.workspace, request,
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)
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logger.debug("Sending keyword index query response...")
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r = KeywordIndexResponse(chunks=chunks, error=None)
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await flow("response").send(r, properties={"id": id})
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logger.debug("Keyword index query request completed")
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except Exception as e:
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logger.error(f"Exception in keyword index query service: {e}", exc_info=True)
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logger.info("Sending error response...")
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r = KeywordIndexResponse(
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error=Error(
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type = "keyword-index-query-error",
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message = str(e),
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),
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chunks=[],
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)
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await flow("response").send(r, properties={"id": id})
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@staticmethod
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def add_args(parser: ArgumentParser) -> None:
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FlowProcessor.add_args(parser)
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parser.add_argument(
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'-c', '--concurrency',
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type=int,
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default=default_concurrency,
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help=f'Number of concurrent requests (default: {default_concurrency})'
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)
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