mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-05-17 03:15:14 +02:00
release/v2.4 -> master (#924)
* CLI auth migration, document embeddings core lifecycle (#913) Migrate get_kg_core and put_kg_core CLI tools to use Api/SocketClient with first-frame auth (fixes broken raw websocket path). Fix wire format field names (root/vector). Remove ~600 lines of dead raw websocket code from invoke_graph_rag.py. Add document embeddings core lifecycle to the knowledge service: list/get/put/delete/load operations across schema, translator, Cassandra table store, knowledge manager, gateway registry, REST API, socket client, and CLI (tg-get-de-core, tg-put-de-core). Fix delete_kg_core to also clean up document embeddings rows. * Remove spurious workspace parameter from SPARQL algebra evaluator (#915) Fix threading of workspace paramater: - The SPARQL algebra evaluator was threading a workspace parameter through every function and passing it to TriplesClient.query(), which doesn't accept it. Workspace isolation is handled by pub/sub topic routing — the TriplesClient is already scoped to a workspace-specific flow, same as GraphRAG. Passing workspace explicitly was both incorrect and unnecessary. Update tests: - tests/unit/test_query/test_sparql_algebra.py (new) — Tests _query_pattern, _eval_bgp, and evaluate() with various algebra nodes. Key tests assert workspace is never in tc.query() kwargs, plus correctness tests for BGP, JOIN, UNION, SLICE, DISTINCT, and edge cases. - tests/unit/test_retrieval/test_graph_rag.py — Added test_triples_query_never_passes_workspace (checks query()) and test_follow_edges_never_passes_workspace (checks query_stream()). * Make all Cassandra and Qdrant I/O async-safe with proper concurrency controls (#916) Cassandra triples services were using syncronous EntityCentricKnowledgeGraph methods from async contexts, and connection state was managed with threading.local which is wrong for asyncio coroutines sharing a single thread. Qdrant services had no async wrapping at all, blocking the event loop on every network call. Rows services had unprotected shared state mutations across concurrent coroutines. - Add async methods to EntityCentricKnowledgeGraph (async_insert, async_get_s/p/o/sp/po/os/spo/all, async_collection_exists, async_create_collection, async_delete_collection) using the existing cassandra_async.async_execute bridge - Rewrite triples write + query services: replace threading.local with asyncio.Lock + dict cache for per-workspace connections, use async ECKG methods for all data operations, keep asyncio.to_thread only for one-time blocking ECKG construction - Wrap all Qdrant calls in asyncio.to_thread across all 6 services (doc/graph/row embeddings write + query), add asyncio.Lock + set cache for collection existence checks - Add asyncio.Lock to rows write + query services to protect shared state (schemas, sessions, config caches) from concurrent mutation - Update all affected tests to match new async patterns * Fixed error only returning a page of results (#921) The root cause: async_execute only materialises the first result page (by design — it says so in its docstring). The streaming query set fetch_size=20 and expected to iterate all results, but only got the first 20 rows back. The fix uses asyncio.to_thread(lambda: list(tg.session.execute(...))) which lets the sync driver iterate all pages in a worker thread — exactly what the pre-async code did. * Optional test warning suppression (#923) * Fix test collection module errors & silence upstream Pytest warnings (#823) * chore: add virtual environment and .env directories to gitignore * test: filter upstream DeprecationWarning and UserWarning messages * fix(namespace): remove empty __init__.py files to fix PEP 420 implicit namespace routing for trustgraph sub-packages * Revert __init__.py deletions * Add .ini changes but commented out, will be useful at times --------- Co-authored-by: Salil M <d2kyt@protonmail.com>
This commit is contained in:
cybermaggedon
GitHub
parent
159b1e2824
commit
142dd0231c
42 changed files with 1910 additions and 1492 deletions
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@ -1,5 +1,6 @@
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from .. schema import KnowledgeResponse, Error, Triples, GraphEmbeddings
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from .. schema import DocumentEmbeddings
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from .. knowledge import hash
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from .. exceptions import RequestError
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from .. tables.knowledge import KnowledgeTableStore
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@ -157,6 +158,98 @@ class KnowledgeManager:
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)
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)
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async def list_de_cores(self, request, respond, workspace):
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ids = await self.table_store.list_de_cores(workspace)
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await respond(
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KnowledgeResponse(
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error = None,
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ids = ids,
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eos = False,
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triples = None,
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graph_embeddings = None,
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)
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)
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async def get_de_core(self, request, respond, workspace):
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logger.info("Getting document embeddings core...")
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async def publish_de(de):
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = False,
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triples = None,
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graph_embeddings = None,
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document_embeddings = de,
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)
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)
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await self.table_store.get_document_embeddings(
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workspace,
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request.id,
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publish_de,
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)
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logger.debug("Document embeddings core retrieval complete")
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = True,
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triples = None,
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graph_embeddings = None,
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)
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)
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async def put_de_core(self, request, respond, workspace):
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if request.document_embeddings:
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await self.table_store.add_document_embeddings(
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workspace, request.document_embeddings
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)
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = False,
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triples = None,
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graph_embeddings = None,
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)
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)
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async def delete_de_core(self, request, respond, workspace):
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logger.info("Deleting document embeddings core...")
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await self.table_store.delete_document_embeddings(
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workspace, request.id
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)
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = False,
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triples = None,
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graph_embeddings = None,
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)
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)
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async def load_de_core(self, request, respond, workspace):
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if self.background_task is None:
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self.background_task = asyncio.create_task(
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self.core_loader()
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)
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await self.loader_queue.put((request, respond, workspace))
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async def core_loader(self):
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logger.info("Knowledge background processor running...")
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@ -165,7 +258,7 @@ class KnowledgeManager:
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logger.debug("Waiting for next load...")
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request, respond, workspace = await self.loader_queue.get()
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logger.info(f"Loading knowledge: {request.id}")
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logger.info(f"Loading: {request.operation} {request.id}")
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try:
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@ -187,25 +280,14 @@ class KnowledgeManager:
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if "interfaces" not in flow:
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raise RuntimeError("No defined interfaces")
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if "triples-store" not in flow["interfaces"]:
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raise RuntimeError("Flow has no triples-store")
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if "graph-embeddings-store" not in flow["interfaces"]:
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raise RuntimeError("Flow has no graph-embeddings-store")
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t_q = flow["interfaces"]["triples-store"]["flow"]
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ge_q = flow["interfaces"]["graph-embeddings-store"]["flow"]
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# Got this far, it should all work
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = False,
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triples = None,
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graph_embeddings = None
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if request.operation == "load-de-core":
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await self._load_de_core(
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request, respond, workspace, flow,
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)
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else:
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await self._load_kg_core(
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request, respond, workspace, flow,
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)
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)
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except Exception as e:
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@ -223,72 +305,145 @@ class KnowledgeManager:
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)
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)
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logger.debug("Starting knowledge loading process...")
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try:
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t_pub = None
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ge_pub = None
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logger.debug(f"Triples queue: {t_q}")
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logger.debug(f"Graph embeddings queue: {ge_q}")
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t_pub = Publisher(
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self.flow_config.pubsub, t_q,
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schema=Triples,
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)
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ge_pub = Publisher(
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self.flow_config.pubsub, ge_q,
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schema=GraphEmbeddings
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)
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logger.debug("Starting publishers...")
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await t_pub.start()
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await ge_pub.start()
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async def publish_triples(t):
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# Override collection with request collection
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if hasattr(t, 'metadata') and hasattr(t.metadata, 'collection'):
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t.metadata.collection = request.collection or "default"
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await t_pub.send(None, t)
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logger.debug("Publishing triples...")
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await self.table_store.get_triples(
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workspace,
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request.id,
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publish_triples,
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)
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async def publish_ge(g):
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# Override collection with request collection
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if hasattr(g, 'metadata') and hasattr(g.metadata, 'collection'):
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g.metadata.collection = request.collection or "default"
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await ge_pub.send(None, g)
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logger.debug("Publishing graph embeddings...")
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await self.table_store.get_graph_embeddings(
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workspace,
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request.id,
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publish_ge,
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)
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logger.debug("Knowledge loading completed")
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except Exception as e:
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logger.error(f"Knowledge exception: {e}", exc_info=True)
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finally:
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logger.debug("Stopping publishers...")
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if t_pub: await t_pub.stop()
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if ge_pub: await ge_pub.stop()
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logger.debug("Knowledge processing done")
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continue
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async def _load_kg_core(self, request, respond, workspace, flow):
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if "triples-store" not in flow["interfaces"]:
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raise RuntimeError("Flow has no triples-store")
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if "graph-embeddings-store" not in flow["interfaces"]:
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raise RuntimeError("Flow has no graph-embeddings-store")
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t_q = flow["interfaces"]["triples-store"]["flow"]
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ge_q = flow["interfaces"]["graph-embeddings-store"]["flow"]
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = False,
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triples = None,
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graph_embeddings = None
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)
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)
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t_pub = None
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ge_pub = None
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try:
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logger.debug(f"Triples queue: {t_q}")
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logger.debug(f"Graph embeddings queue: {ge_q}")
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t_pub = Publisher(
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self.flow_config.pubsub, t_q,
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schema=Triples,
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)
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ge_pub = Publisher(
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self.flow_config.pubsub, ge_q,
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schema=GraphEmbeddings
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)
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logger.debug("Starting publishers...")
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await t_pub.start()
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await ge_pub.start()
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async def publish_triples(t):
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if hasattr(t, 'metadata') and hasattr(t.metadata, 'collection'):
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t.metadata.collection = request.collection or "default"
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await t_pub.send(None, t)
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logger.debug("Publishing triples...")
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await self.table_store.get_triples(
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workspace,
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request.id,
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publish_triples,
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)
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async def publish_ge(g):
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if hasattr(g, 'metadata') and hasattr(g.metadata, 'collection'):
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g.metadata.collection = request.collection or "default"
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await ge_pub.send(None, g)
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logger.debug("Publishing graph embeddings...")
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await self.table_store.get_graph_embeddings(
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workspace,
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request.id,
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publish_ge,
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)
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logger.debug("Knowledge core loading completed")
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except Exception as e:
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logger.error(f"Knowledge exception: {e}", exc_info=True)
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finally:
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logger.debug("Stopping publishers...")
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if t_pub: await t_pub.stop()
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if ge_pub: await ge_pub.stop()
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async def _load_de_core(self, request, respond, workspace, flow):
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if "document-embeddings-store" not in flow["interfaces"]:
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raise RuntimeError("Flow has no document-embeddings-store")
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de_q = flow["interfaces"]["document-embeddings-store"]["flow"]
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await respond(
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KnowledgeResponse(
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error = None,
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ids = None,
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eos = False,
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triples = None,
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graph_embeddings = None
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)
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)
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de_pub = None
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try:
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logger.debug(f"Document embeddings queue: {de_q}")
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de_pub = Publisher(
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self.flow_config.pubsub, de_q,
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schema=DocumentEmbeddings,
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)
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logger.debug("Starting publisher...")
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await de_pub.start()
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async def publish_de(de):
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if hasattr(de, 'metadata') and hasattr(de.metadata, 'collection'):
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de.metadata.collection = request.collection or "default"
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await de_pub.send(None, de)
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logger.debug("Publishing document embeddings...")
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await self.table_store.get_document_embeddings(
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workspace,
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request.id,
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publish_de,
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)
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logger.debug("Document embeddings core loading completed")
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except Exception as e:
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logger.error(f"Knowledge exception: {e}", exc_info=True)
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finally:
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logger.debug("Stopping publisher...")
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if de_pub: await de_pub.stop()
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@ -187,6 +187,11 @@ class Processor(WorkspaceProcessor):
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"put-kg-core": self.knowledge.put_kg_core,
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"load-kg-core": self.knowledge.load_kg_core,
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"unload-kg-core": self.knowledge.unload_kg_core,
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"list-de-cores": self.knowledge.list_de_cores,
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"get-de-core": self.knowledge.get_de_core,
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"delete-de-core": self.knowledge.delete_de_core,
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"put-de-core": self.knowledge.put_de_core,
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"load-de-core": self.knowledge.load_de_core,
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}
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if v.operation not in impls:
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@ -1,10 +1,14 @@
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import datetime
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import os
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import logging
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from cassandra.cluster import Cluster
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from cassandra.auth import PlainTextAuthProvider
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from cassandra.query import BatchStatement, SimpleStatement
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from ssl import SSLContext, PROTOCOL_TLSv1_2
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import os
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import logging
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from ..tables.cassandra_async import async_execute
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# Global list to track clusters for cleanup
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_active_clusters = []
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@ -461,7 +465,6 @@ class KnowledgeGraph:
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def create_collection(self, collection):
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"""Create collection by inserting metadata row"""
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try:
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import datetime
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self.session.execute(
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f"INSERT INTO {self.collection_metadata_table} (collection, created_at) VALUES (%s, %s)",
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(collection, datetime.datetime.now())
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@ -954,7 +957,6 @@ class EntityCentricKnowledgeGraph:
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def create_collection(self, collection):
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"""Create collection by inserting metadata row"""
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try:
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import datetime
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self.session.execute(
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f"INSERT INTO {self.collection_metadata_table} (collection, created_at) VALUES (%s, %s)",
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(collection, datetime.datetime.now())
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@ -1045,6 +1047,222 @@ class EntityCentricKnowledgeGraph:
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logger.info(f"Deleted collection {collection}: {len(entities)} entity partitions, {len(quads)} quads")
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# ========================================================================
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# Async methods — use cassandra driver's native async API via async_execute
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# ========================================================================
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async def async_insert(self, collection, s, p, o, g=None, otype=None, dtype="", lang=""):
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if g is None:
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g = DEFAULT_GRAPH
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if otype is None:
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if o.startswith("http://") or o.startswith("https://"):
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otype = "u"
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else:
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otype = "l"
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batch = BatchStatement()
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batch.add(self.insert_entity_stmt, (collection, s, 'S', p, otype, s, o, g, dtype, lang))
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batch.add(self.insert_entity_stmt, (collection, p, 'P', p, otype, s, o, g, dtype, lang))
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if otype == 'u' or otype == 't':
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batch.add(self.insert_entity_stmt, (collection, o, 'O', p, otype, s, o, g, dtype, lang))
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if g != DEFAULT_GRAPH:
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batch.add(self.insert_entity_stmt, (collection, g, 'G', p, otype, s, o, g, dtype, lang))
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batch.add(self.insert_collection_stmt, (collection, g, s, p, o, otype, dtype, lang))
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await async_execute(self.session, batch)
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async def async_get_all(self, collection, limit=50):
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return await async_execute(
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self.session, self.get_collection_all_stmt, (collection, limit)
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)
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async def async_get_s(self, collection, s, g=None, limit=10):
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rows = await async_execute(
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self.session, self.get_entity_as_s_stmt, (collection, s, limit)
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)
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results = []
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for row in rows:
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d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
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if g is not None and d != g:
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continue
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results.append(QuadResult(
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s=row.s, p=row.p, o=row.o, g=d,
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otype=row.otype, dtype=row.dtype, lang=row.lang
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))
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return results
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async def async_get_p(self, collection, p, g=None, limit=10):
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rows = await async_execute(
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self.session, self.get_entity_as_p_stmt, (collection, p, limit)
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)
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results = []
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for row in rows:
|
||||
d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
|
||||
if g is not None and d != g:
|
||||
continue
|
||||
results.append(QuadResult(
|
||||
s=row.s, p=row.p, o=row.o, g=d,
|
||||
otype=row.otype, dtype=row.dtype, lang=row.lang
|
||||
))
|
||||
return results
|
||||
|
||||
async def async_get_o(self, collection, o, g=None, limit=10):
|
||||
rows = await async_execute(
|
||||
self.session, self.get_entity_as_o_stmt, (collection, o, limit)
|
||||
)
|
||||
results = []
|
||||
for row in rows:
|
||||
d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
|
||||
if g is not None and d != g:
|
||||
continue
|
||||
results.append(QuadResult(
|
||||
s=row.s, p=row.p, o=row.o, g=d,
|
||||
otype=row.otype, dtype=row.dtype, lang=row.lang
|
||||
))
|
||||
return results
|
||||
|
||||
async def async_get_sp(self, collection, s, p, g=None, limit=10):
|
||||
rows = await async_execute(
|
||||
self.session, self.get_entity_as_s_p_stmt, (collection, s, p, limit)
|
||||
)
|
||||
results = []
|
||||
for row in rows:
|
||||
d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
|
||||
if g is not None and d != g:
|
||||
continue
|
||||
results.append(QuadResult(
|
||||
s=s, p=p, o=row.o, g=d,
|
||||
otype=row.otype, dtype=row.dtype, lang=row.lang
|
||||
))
|
||||
return results
|
||||
|
||||
async def async_get_po(self, collection, p, o, g=None, limit=10):
|
||||
rows = await async_execute(
|
||||
self.session, self.get_entity_as_o_p_stmt, (collection, o, p, limit)
|
||||
)
|
||||
results = []
|
||||
for row in rows:
|
||||
d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
|
||||
if g is not None and d != g:
|
||||
continue
|
||||
results.append(QuadResult(
|
||||
s=row.s, p=p, o=o, g=d,
|
||||
otype=row.otype, dtype=row.dtype, lang=row.lang
|
||||
))
|
||||
return results
|
||||
|
||||
async def async_get_os(self, collection, o, s, g=None, limit=10):
|
||||
rows = await async_execute(
|
||||
self.session, self.get_entity_as_s_stmt, (collection, s, limit)
|
||||
)
|
||||
results = []
|
||||
for row in rows:
|
||||
if row.o != o:
|
||||
continue
|
||||
d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
|
||||
if g is not None and d != g:
|
||||
continue
|
||||
results.append(QuadResult(
|
||||
s=s, p=row.p, o=o, g=d,
|
||||
otype=row.otype, dtype=row.dtype, lang=row.lang
|
||||
))
|
||||
return results
|
||||
|
||||
async def async_get_spo(self, collection, s, p, o, g=None, limit=10):
|
||||
rows = await async_execute(
|
||||
self.session, self.get_entity_as_s_p_stmt, (collection, s, p, limit)
|
||||
)
|
||||
results = []
|
||||
for row in rows:
|
||||
if row.o != o:
|
||||
continue
|
||||
d = row.d if hasattr(row, 'd') else DEFAULT_GRAPH
|
||||
if g is not None and d != g:
|
||||
continue
|
||||
results.append(QuadResult(
|
||||
s=s, p=p, o=o, g=d,
|
||||
otype=row.otype, dtype=row.dtype, lang=row.lang
|
||||
))
|
||||
return results
|
||||
|
||||
async def async_get_g(self, collection, g, limit=50):
|
||||
if g is None:
|
||||
g = DEFAULT_GRAPH
|
||||
return await async_execute(
|
||||
self.session, self.get_collection_by_graph_stmt, (collection, g, limit)
|
||||
)
|
||||
|
||||
async def async_collection_exists(self, collection):
|
||||
try:
|
||||
result = await async_execute(
|
||||
self.session,
|
||||
f"SELECT collection FROM {self.collection_metadata_table} WHERE collection = %s LIMIT 1",
|
||||
(collection,)
|
||||
)
|
||||
return bool(result)
|
||||
except Exception as e:
|
||||
logger.error(f"Error checking collection existence: {e}")
|
||||
return False
|
||||
|
||||
async def async_create_collection(self, collection):
|
||||
await async_execute(
|
||||
self.session,
|
||||
f"INSERT INTO {self.collection_metadata_table} (collection, created_at) VALUES (%s, %s)",
|
||||
(collection, datetime.datetime.now())
|
||||
)
|
||||
logger.info(f"Created collection metadata for {collection}")
|
||||
|
||||
async def async_delete_collection(self, collection):
|
||||
rows = await async_execute(
|
||||
self.session,
|
||||
f"SELECT d, s, p, o, otype, dtype, lang FROM {self.collection_table} WHERE collection = %s",
|
||||
(collection,)
|
||||
)
|
||||
|
||||
entities = set()
|
||||
quads = []
|
||||
for row in rows:
|
||||
d, s, p, o = row.d, row.s, row.p, row.o
|
||||
otype = row.otype
|
||||
dtype = row.dtype if hasattr(row, 'dtype') else ''
|
||||
lang = row.lang if hasattr(row, 'lang') else ''
|
||||
quads.append((d, s, p, o, otype, dtype, lang))
|
||||
entities.add(s)
|
||||
entities.add(p)
|
||||
if otype == 'u' or otype == 't':
|
||||
entities.add(o)
|
||||
if d != DEFAULT_GRAPH:
|
||||
entities.add(d)
|
||||
|
||||
batch = BatchStatement()
|
||||
count = 0
|
||||
for entity in entities:
|
||||
batch.add(self.delete_entity_partition_stmt, (collection, entity))
|
||||
count += 1
|
||||
if count % 50 == 0:
|
||||
await async_execute(self.session, batch)
|
||||
batch = BatchStatement()
|
||||
if count % 50 != 0:
|
||||
await async_execute(self.session, batch)
|
||||
|
||||
batch = BatchStatement()
|
||||
count = 0
|
||||
for d, s, p, o, otype, dtype, lang in quads:
|
||||
batch.add(self.delete_collection_row_stmt, (collection, d, s, p, o, otype, dtype, lang))
|
||||
count += 1
|
||||
if count % 50 == 0:
|
||||
await async_execute(self.session, batch)
|
||||
batch = BatchStatement()
|
||||
if count % 50 != 0:
|
||||
await async_execute(self.session, batch)
|
||||
|
||||
await async_execute(
|
||||
self.session,
|
||||
f"DELETE FROM {self.collection_metadata_table} WHERE collection = %s",
|
||||
(collection,)
|
||||
)
|
||||
logger.info(f"Deleted collection {collection}: {len(entities)} entity partitions, {len(quads)} quads")
|
||||
|
||||
def close(self):
|
||||
"""Close connections"""
|
||||
if hasattr(self, 'session') and self.session:
|
||||
|
|
|
|||
|
|
@ -457,6 +457,12 @@ for _op in ("put-kg-core", "delete-kg-core",
|
|||
"load-kg-core", "unload-kg-core"):
|
||||
_register_kind_op("knowledge", _op, "knowledge:write")
|
||||
|
||||
# knowledge: document-embeddings core service.
|
||||
for _op in ("get-de-core", "list-de-cores"):
|
||||
_register_kind_op("knowledge", _op, "knowledge:read")
|
||||
for _op in ("put-de-core", "delete-de-core", "load-de-core"):
|
||||
_register_kind_op("knowledge", _op, "knowledge:write")
|
||||
|
||||
|
||||
# collection-management: workspace collection lifecycle.
|
||||
_register_kind_op("collection-management", "list-collections", "collections:read")
|
||||
|
|
|
|||
|
|
@ -4,11 +4,10 @@ Document embeddings query service. Input is vector, output is an array
|
|||
of chunk_ids
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.models import PointStruct
|
||||
from qdrant_client.models import Distance, VectorParams
|
||||
|
||||
from .... schema import DocumentEmbeddingsResponse, ChunkMatch
|
||||
from .... schema import Error
|
||||
|
|
@ -38,32 +37,6 @@ class Processor(DocumentEmbeddingsQueryService):
|
|||
)
|
||||
|
||||
self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
|
||||
self.last_collection = None
|
||||
|
||||
def ensure_collection_exists(self, collection, dim):
|
||||
"""Ensure collection exists, create if it doesn't"""
|
||||
if collection != self.last_collection:
|
||||
if not self.qdrant.collection_exists(collection):
|
||||
try:
|
||||
self.qdrant.create_collection(
|
||||
collection_name=collection,
|
||||
vectors_config=VectorParams(
|
||||
size=dim, distance=Distance.COSINE
|
||||
),
|
||||
)
|
||||
logger.info(f"Created collection: {collection}")
|
||||
except Exception as e:
|
||||
logger.error(f"Qdrant collection creation failed: {e}")
|
||||
raise e
|
||||
self.last_collection = collection
|
||||
|
||||
def collection_exists(self, collection):
|
||||
"""Check if collection exists (no implicit creation)"""
|
||||
return self.qdrant.collection_exists(collection)
|
||||
|
||||
def collection_exists(self, collection):
|
||||
"""Check if collection exists (no implicit creation)"""
|
||||
return self.qdrant.collection_exists(collection)
|
||||
|
||||
async def query_document_embeddings(self, workspace, msg):
|
||||
|
||||
|
|
@ -73,21 +46,24 @@ class Processor(DocumentEmbeddingsQueryService):
|
|||
if not vec:
|
||||
return []
|
||||
|
||||
# Use dimension suffix in collection name
|
||||
dim = len(vec)
|
||||
collection = f"d_{workspace}_{msg.collection}_{dim}"
|
||||
|
||||
# Check if collection exists - return empty if not
|
||||
if not self.collection_exists(collection):
|
||||
exists = await asyncio.to_thread(
|
||||
self.qdrant.collection_exists, collection
|
||||
)
|
||||
if not exists:
|
||||
logger.info(f"Collection {collection} does not exist, returning empty results")
|
||||
return []
|
||||
|
||||
search_result = self.qdrant.query_points(
|
||||
result = await asyncio.to_thread(
|
||||
self.qdrant.query_points,
|
||||
collection_name=collection,
|
||||
query=vec,
|
||||
limit=msg.limit,
|
||||
with_payload=True,
|
||||
).points
|
||||
)
|
||||
search_result = result.points
|
||||
|
||||
chunks = []
|
||||
for r in search_result:
|
||||
|
|
|
|||
|
|
@ -4,11 +4,10 @@ Graph embeddings query service. Input is vector, output is list of
|
|||
entities
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.models import PointStruct
|
||||
from qdrant_client.models import Distance, VectorParams
|
||||
|
||||
from .... schema import GraphEmbeddingsResponse, EntityMatch
|
||||
from .... schema import Error, Term, IRI, LITERAL
|
||||
|
|
@ -38,32 +37,6 @@ class Processor(GraphEmbeddingsQueryService):
|
|||
)
|
||||
|
||||
self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
|
||||
self.last_collection = None
|
||||
|
||||
def ensure_collection_exists(self, collection, dim):
|
||||
"""Ensure collection exists, create if it doesn't"""
|
||||
if collection != self.last_collection:
|
||||
if not self.qdrant.collection_exists(collection):
|
||||
try:
|
||||
self.qdrant.create_collection(
|
||||
collection_name=collection,
|
||||
vectors_config=VectorParams(
|
||||
size=dim, distance=Distance.COSINE
|
||||
),
|
||||
)
|
||||
logger.info(f"Created collection: {collection}")
|
||||
except Exception as e:
|
||||
logger.error(f"Qdrant collection creation failed: {e}")
|
||||
raise e
|
||||
self.last_collection = collection
|
||||
|
||||
def collection_exists(self, collection):
|
||||
"""Check if collection exists (no implicit creation)"""
|
||||
return self.qdrant.collection_exists(collection)
|
||||
|
||||
def collection_exists(self, collection):
|
||||
"""Check if collection exists (no implicit creation)"""
|
||||
return self.qdrant.collection_exists(collection)
|
||||
|
||||
def create_value(self, ent):
|
||||
if ent.startswith("http://") or ent.startswith("https://"):
|
||||
|
|
@ -79,23 +52,26 @@ class Processor(GraphEmbeddingsQueryService):
|
|||
if not vec:
|
||||
return []
|
||||
|
||||
# Use dimension suffix in collection name
|
||||
dim = len(vec)
|
||||
collection = f"t_{workspace}_{msg.collection}_{dim}"
|
||||
|
||||
# Check if collection exists - return empty if not
|
||||
if not self.collection_exists(collection):
|
||||
exists = await asyncio.to_thread(
|
||||
self.qdrant.collection_exists, collection
|
||||
)
|
||||
if not exists:
|
||||
logger.info(f"Collection {collection} does not exist")
|
||||
return []
|
||||
|
||||
# Heuristic hack, get (2*limit), so that we have more chance
|
||||
# of getting (limit) unique entities
|
||||
search_result = self.qdrant.query_points(
|
||||
result = await asyncio.to_thread(
|
||||
self.qdrant.query_points,
|
||||
collection_name=collection,
|
||||
query=vec,
|
||||
limit=msg.limit * 2,
|
||||
with_payload=True,
|
||||
).points
|
||||
)
|
||||
search_result = result.points
|
||||
|
||||
entity_set = set()
|
||||
entities = []
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ Output is matching row index information (index_name, index_value) for
|
|||
use in subsequent Cassandra lookups.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import re
|
||||
from typing import Optional
|
||||
|
|
@ -70,7 +71,7 @@ class Processor(FlowProcessor):
|
|||
safe_name = 'r_' + safe_name
|
||||
return safe_name.lower()
|
||||
|
||||
def find_collection(self, workspace: str, collection: str, schema_name: str) -> Optional[str]:
|
||||
async def find_collection(self, workspace: str, collection: str, schema_name: str) -> Optional[str]:
|
||||
"""Find the Qdrant collection for a given workspace/collection/schema"""
|
||||
prefix = (
|
||||
f"rows_{self.sanitize_name(workspace)}_"
|
||||
|
|
@ -78,14 +79,15 @@ class Processor(FlowProcessor):
|
|||
)
|
||||
|
||||
try:
|
||||
all_collections = self.qdrant.get_collections().collections
|
||||
all_collections = await asyncio.to_thread(
|
||||
lambda: self.qdrant.get_collections().collections
|
||||
)
|
||||
matching = [
|
||||
coll.name for coll in all_collections
|
||||
if coll.name.startswith(prefix)
|
||||
]
|
||||
|
||||
if matching:
|
||||
# Return first match (there should typically be only one per dimension)
|
||||
return matching[0]
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -100,8 +102,7 @@ class Processor(FlowProcessor):
|
|||
if not vec:
|
||||
return []
|
||||
|
||||
# Find the collection for this workspace/collection/schema
|
||||
qdrant_collection = self.find_collection(
|
||||
qdrant_collection = await self.find_collection(
|
||||
workspace, request.collection, request.schema_name
|
||||
)
|
||||
|
||||
|
|
@ -113,7 +114,6 @@ class Processor(FlowProcessor):
|
|||
return []
|
||||
|
||||
try:
|
||||
# Build optional filter for index_name
|
||||
query_filter = None
|
||||
if request.index_name:
|
||||
query_filter = Filter(
|
||||
|
|
@ -125,16 +125,16 @@ class Processor(FlowProcessor):
|
|||
]
|
||||
)
|
||||
|
||||
# Query Qdrant
|
||||
search_result = self.qdrant.query_points(
|
||||
result = await asyncio.to_thread(
|
||||
self.qdrant.query_points,
|
||||
collection_name=qdrant_collection,
|
||||
query=vec,
|
||||
limit=request.limit,
|
||||
with_payload=True,
|
||||
query_filter=query_filter,
|
||||
).points
|
||||
)
|
||||
search_result = result.points
|
||||
|
||||
# Convert to RowIndexMatch objects
|
||||
matches = []
|
||||
for point in search_result:
|
||||
payload = point.payload or {}
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ Queries against the unified 'rows' table with schema:
|
|||
- source: text
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
|
|
@ -97,34 +98,38 @@ class Processor(FlowProcessor):
|
|||
# Cassandra session
|
||||
self.cluster = None
|
||||
self.session = None
|
||||
self._setup_lock = asyncio.Lock()
|
||||
|
||||
# Known keyspaces
|
||||
self.known_keyspaces: Set[str] = set()
|
||||
|
||||
def connect_cassandra(self):
|
||||
async def connect_cassandra(self):
|
||||
"""Connect to Cassandra cluster"""
|
||||
if self.session:
|
||||
return
|
||||
async with self._setup_lock:
|
||||
if self.session:
|
||||
return
|
||||
|
||||
try:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
auth_provider = PlainTextAuthProvider(
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password
|
||||
)
|
||||
self.cluster = Cluster(
|
||||
contact_points=self.cassandra_host,
|
||||
auth_provider=auth_provider
|
||||
)
|
||||
else:
|
||||
self.cluster = Cluster(contact_points=self.cassandra_host)
|
||||
try:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
auth_provider = PlainTextAuthProvider(
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password
|
||||
)
|
||||
cluster = Cluster(
|
||||
contact_points=self.cassandra_host,
|
||||
auth_provider=auth_provider
|
||||
)
|
||||
else:
|
||||
cluster = Cluster(contact_points=self.cassandra_host)
|
||||
|
||||
self.session = self.cluster.connect()
|
||||
logger.info(f"Connected to Cassandra cluster at {self.cassandra_host}")
|
||||
session = await asyncio.to_thread(cluster.connect)
|
||||
self.cluster = cluster
|
||||
self.session = session
|
||||
logger.info(f"Connected to Cassandra cluster at {self.cassandra_host}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to connect to Cassandra: {e}", exc_info=True)
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to connect to Cassandra: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
def sanitize_name(self, name: str) -> str:
|
||||
"""Sanitize names for Cassandra compatibility"""
|
||||
|
|
@ -140,14 +145,17 @@ class Processor(FlowProcessor):
|
|||
f"for workspace {workspace}"
|
||||
)
|
||||
|
||||
# Replace existing schemas for this workspace
|
||||
async with self._setup_lock:
|
||||
await self._apply_schema_config(workspace, config)
|
||||
|
||||
async def _apply_schema_config(self, workspace, config):
|
||||
|
||||
ws_schemas: Dict[str, RowSchema] = {}
|
||||
self.schemas[workspace] = ws_schemas
|
||||
|
||||
builder = GraphQLSchemaBuilder()
|
||||
self.schema_builders[workspace] = builder
|
||||
|
||||
# Check if our config type exists
|
||||
if self.config_key not in config:
|
||||
logger.warning(
|
||||
f"No '{self.config_key}' type in configuration "
|
||||
|
|
@ -156,16 +164,12 @@ class Processor(FlowProcessor):
|
|||
self.graphql_schemas[workspace] = None
|
||||
return
|
||||
|
||||
# Get the schemas dictionary for our type
|
||||
schemas_config = config[self.config_key]
|
||||
|
||||
# Process each schema in the schemas config
|
||||
for schema_name, schema_json in schemas_config.items():
|
||||
try:
|
||||
# Parse the JSON schema definition
|
||||
schema_def = json.loads(schema_json)
|
||||
|
||||
# Create Field objects
|
||||
fields = []
|
||||
for field_def in schema_def.get("fields", []):
|
||||
field = SchemaField(
|
||||
|
|
@ -180,7 +184,6 @@ class Processor(FlowProcessor):
|
|||
)
|
||||
fields.append(field)
|
||||
|
||||
# Create RowSchema
|
||||
row_schema = RowSchema(
|
||||
name=schema_def.get("name", schema_name),
|
||||
description=schema_def.get("description", ""),
|
||||
|
|
@ -202,7 +205,6 @@ class Processor(FlowProcessor):
|
|||
f"{len(ws_schemas)} schemas"
|
||||
)
|
||||
|
||||
# Regenerate GraphQL schema for this workspace
|
||||
self.graphql_schemas[workspace] = builder.build(self.query_cassandra)
|
||||
|
||||
def get_index_names(self, schema: RowSchema) -> List[str]:
|
||||
|
|
@ -254,7 +256,7 @@ class Processor(FlowProcessor):
|
|||
For other queries, we need to scan and post-filter.
|
||||
"""
|
||||
# Connect if needed
|
||||
self.connect_cassandra()
|
||||
await self.connect_cassandra()
|
||||
|
||||
safe_keyspace = self.sanitize_name(workspace)
|
||||
|
||||
|
|
|
|||
|
|
@ -30,14 +30,13 @@ class EvaluationError(Exception):
|
|||
pass
|
||||
|
||||
|
||||
async def evaluate(node, triples_client, workspace, collection, limit=10000):
|
||||
async def evaluate(node, triples_client, collection, limit=10000):
|
||||
"""
|
||||
Evaluate a SPARQL algebra node.
|
||||
|
||||
Args:
|
||||
node: rdflib CompValue algebra node
|
||||
triples_client: TriplesClient instance for triple pattern queries
|
||||
workspace: workspace/keyspace identifier
|
||||
collection: collection identifier
|
||||
limit: safety limit on results
|
||||
|
||||
|
|
@ -55,24 +54,24 @@ async def evaluate(node, triples_client, workspace, collection, limit=10000):
|
|||
logger.warning(f"Unsupported algebra node: {name}")
|
||||
return [{}]
|
||||
|
||||
return await handler(node, triples_client, workspace, collection, limit)
|
||||
return await handler(node, triples_client, collection, limit)
|
||||
|
||||
|
||||
# --- Node handlers ---
|
||||
|
||||
async def _eval_select_query(node, tc, workspace, collection, limit):
|
||||
async def _eval_select_query(node, tc, collection, limit):
|
||||
"""Evaluate a SelectQuery node."""
|
||||
return await evaluate(node.p, tc, workspace, collection, limit)
|
||||
return await evaluate(node.p, tc, collection, limit)
|
||||
|
||||
|
||||
async def _eval_project(node, tc, workspace, collection, limit):
|
||||
async def _eval_project(node, tc, collection, limit):
|
||||
"""Evaluate a Project node (SELECT variable projection)."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
variables = [str(v) for v in node.PV]
|
||||
return project(solutions, variables)
|
||||
|
||||
|
||||
async def _eval_bgp(node, tc, workspace, collection, limit):
|
||||
async def _eval_bgp(node, tc, collection, limit):
|
||||
"""
|
||||
Evaluate a Basic Graph Pattern.
|
||||
|
||||
|
|
@ -107,7 +106,7 @@ async def _eval_bgp(node, tc, workspace, collection, limit):
|
|||
|
||||
# Query the triples store
|
||||
results = await _query_pattern(
|
||||
tc, s_val, p_val, o_val, workspace, collection, limit
|
||||
tc, s_val, p_val, o_val, collection, limit
|
||||
)
|
||||
|
||||
# Map results back to variable bindings,
|
||||
|
|
@ -130,17 +129,17 @@ async def _eval_bgp(node, tc, workspace, collection, limit):
|
|||
return solutions[:limit]
|
||||
|
||||
|
||||
async def _eval_join(node, tc, workspace, collection, limit):
|
||||
async def _eval_join(node, tc, collection, limit):
|
||||
"""Evaluate a Join node."""
|
||||
left = await evaluate(node.p1, tc, workspace, collection, limit)
|
||||
right = await evaluate(node.p2, tc, workspace, collection, limit)
|
||||
left = await evaluate(node.p1, tc, collection, limit)
|
||||
right = await evaluate(node.p2, tc, collection, limit)
|
||||
return hash_join(left, right)[:limit]
|
||||
|
||||
|
||||
async def _eval_left_join(node, tc, workspace, collection, limit):
|
||||
async def _eval_left_join(node, tc, collection, limit):
|
||||
"""Evaluate a LeftJoin node (OPTIONAL)."""
|
||||
left_sols = await evaluate(node.p1, tc, workspace, collection, limit)
|
||||
right_sols = await evaluate(node.p2, tc, workspace, collection, limit)
|
||||
left_sols = await evaluate(node.p1, tc, collection, limit)
|
||||
right_sols = await evaluate(node.p2, tc, collection, limit)
|
||||
|
||||
filter_fn = None
|
||||
if hasattr(node, "expr") and node.expr is not None:
|
||||
|
|
@ -153,16 +152,16 @@ async def _eval_left_join(node, tc, workspace, collection, limit):
|
|||
return left_join(left_sols, right_sols, filter_fn)[:limit]
|
||||
|
||||
|
||||
async def _eval_union(node, tc, workspace, collection, limit):
|
||||
async def _eval_union(node, tc, collection, limit):
|
||||
"""Evaluate a Union node."""
|
||||
left = await evaluate(node.p1, tc, workspace, collection, limit)
|
||||
right = await evaluate(node.p2, tc, workspace, collection, limit)
|
||||
left = await evaluate(node.p1, tc, collection, limit)
|
||||
right = await evaluate(node.p2, tc, collection, limit)
|
||||
return union(left, right)[:limit]
|
||||
|
||||
|
||||
async def _eval_filter(node, tc, workspace, collection, limit):
|
||||
async def _eval_filter(node, tc, collection, limit):
|
||||
"""Evaluate a Filter node."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
expr = node.expr
|
||||
return [
|
||||
sol for sol in solutions
|
||||
|
|
@ -170,22 +169,22 @@ async def _eval_filter(node, tc, workspace, collection, limit):
|
|||
]
|
||||
|
||||
|
||||
async def _eval_distinct(node, tc, workspace, collection, limit):
|
||||
async def _eval_distinct(node, tc, collection, limit):
|
||||
"""Evaluate a Distinct node."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
return distinct(solutions)
|
||||
|
||||
|
||||
async def _eval_reduced(node, tc, workspace, collection, limit):
|
||||
async def _eval_reduced(node, tc, collection, limit):
|
||||
"""Evaluate a Reduced node (like Distinct but implementation-defined)."""
|
||||
# Treat same as Distinct
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
return distinct(solutions)
|
||||
|
||||
|
||||
async def _eval_order_by(node, tc, workspace, collection, limit):
|
||||
async def _eval_order_by(node, tc, collection, limit):
|
||||
"""Evaluate an OrderBy node."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
|
||||
key_fns = []
|
||||
for cond in node.expr:
|
||||
|
|
@ -206,7 +205,7 @@ async def _eval_order_by(node, tc, workspace, collection, limit):
|
|||
return order_by(solutions, key_fns)
|
||||
|
||||
|
||||
async def _eval_slice(node, tc, workspace, collection, limit):
|
||||
async def _eval_slice(node, tc, collection, limit):
|
||||
"""Evaluate a Slice node (LIMIT/OFFSET)."""
|
||||
# Pass tighter limit downstream if possible
|
||||
inner_limit = limit
|
||||
|
|
@ -214,13 +213,13 @@ async def _eval_slice(node, tc, workspace, collection, limit):
|
|||
offset = node.start or 0
|
||||
inner_limit = min(limit, offset + node.length)
|
||||
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, inner_limit)
|
||||
solutions = await evaluate(node.p, tc, collection, inner_limit)
|
||||
return slice_solutions(solutions, node.start or 0, node.length)
|
||||
|
||||
|
||||
async def _eval_extend(node, tc, workspace, collection, limit):
|
||||
async def _eval_extend(node, tc, collection, limit):
|
||||
"""Evaluate an Extend node (BIND)."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
var_name = str(node.var)
|
||||
expr = node.expr
|
||||
|
||||
|
|
@ -246,9 +245,9 @@ async def _eval_extend(node, tc, workspace, collection, limit):
|
|||
return result
|
||||
|
||||
|
||||
async def _eval_group(node, tc, workspace, collection, limit):
|
||||
async def _eval_group(node, tc, collection, limit):
|
||||
"""Evaluate a Group node (GROUP BY with aggregation)."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
|
||||
# Extract grouping expressions
|
||||
group_exprs = []
|
||||
|
|
@ -289,9 +288,9 @@ async def _eval_group(node, tc, workspace, collection, limit):
|
|||
return result
|
||||
|
||||
|
||||
async def _eval_aggregate_join(node, tc, workspace, collection, limit):
|
||||
async def _eval_aggregate_join(node, tc, collection, limit):
|
||||
"""Evaluate an AggregateJoin (aggregation functions after GROUP BY)."""
|
||||
solutions = await evaluate(node.p, tc, workspace, collection, limit)
|
||||
solutions = await evaluate(node.p, tc, collection, limit)
|
||||
|
||||
result = []
|
||||
for sol in solutions:
|
||||
|
|
@ -310,7 +309,7 @@ async def _eval_aggregate_join(node, tc, workspace, collection, limit):
|
|||
return result
|
||||
|
||||
|
||||
async def _eval_graph(node, tc, workspace, collection, limit):
|
||||
async def _eval_graph(node, tc, collection, limit):
|
||||
"""Evaluate a Graph node (GRAPH clause)."""
|
||||
term = node.term
|
||||
|
||||
|
|
@ -319,16 +318,16 @@ async def _eval_graph(node, tc, workspace, collection, limit):
|
|||
# We'd need to pass graph to triples queries
|
||||
# For now, evaluate inner pattern normally
|
||||
logger.info(f"GRAPH <{term}> clause - graph filtering not yet wired")
|
||||
return await evaluate(node.p, tc, workspace, collection, limit)
|
||||
return await evaluate(node.p, tc, collection, limit)
|
||||
elif isinstance(term, Variable):
|
||||
# GRAPH ?g { ... } — variable graph
|
||||
logger.info(f"GRAPH ?{term} clause - variable graph not yet wired")
|
||||
return await evaluate(node.p, tc, workspace, collection, limit)
|
||||
return await evaluate(node.p, tc, collection, limit)
|
||||
else:
|
||||
return await evaluate(node.p, tc, workspace, collection, limit)
|
||||
return await evaluate(node.p, tc, collection, limit)
|
||||
|
||||
|
||||
async def _eval_values(node, tc, workspace, collection, limit):
|
||||
async def _eval_values(node, tc, collection, limit):
|
||||
"""Evaluate a VALUES clause (inline data)."""
|
||||
variables = [str(v) for v in node.var]
|
||||
solutions = []
|
||||
|
|
@ -343,9 +342,9 @@ async def _eval_values(node, tc, workspace, collection, limit):
|
|||
return solutions
|
||||
|
||||
|
||||
async def _eval_to_multiset(node, tc, workspace, collection, limit):
|
||||
async def _eval_to_multiset(node, tc, collection, limit):
|
||||
"""Evaluate a ToMultiSet node (subquery)."""
|
||||
return await evaluate(node.p, tc, workspace, collection, limit)
|
||||
return await evaluate(node.p, tc, collection, limit)
|
||||
|
||||
|
||||
# --- Aggregate computation ---
|
||||
|
|
@ -487,7 +486,7 @@ def _resolve_term(tmpl, solution):
|
|||
return rdflib_term_to_term(tmpl)
|
||||
|
||||
|
||||
async def _query_pattern(tc, s, p, o, workspace, collection, limit):
|
||||
async def _query_pattern(tc, s, p, o, collection, limit):
|
||||
"""
|
||||
Issue a streaming triple pattern query via TriplesClient.
|
||||
|
||||
|
|
@ -496,7 +495,6 @@ async def _query_pattern(tc, s, p, o, workspace, collection, limit):
|
|||
results = await tc.query(
|
||||
s=s, p=p, o=o,
|
||||
limit=limit,
|
||||
workspace=workspace,
|
||||
collection=collection,
|
||||
)
|
||||
return results
|
||||
|
|
|
|||
|
|
@ -141,7 +141,6 @@ class Processor(FlowProcessor):
|
|||
solutions = await evaluate(
|
||||
parsed.algebra,
|
||||
triples_client,
|
||||
workspace=flow.workspace,
|
||||
collection=request.collection or "default",
|
||||
limit=request.limit or 10000,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -6,8 +6,8 @@ null. Output is a list of quads.
|
|||
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
import json
|
||||
|
||||
from cassandra.query import SimpleStatement
|
||||
|
||||
from .... direct.cassandra_kg import (
|
||||
|
|
@ -176,45 +176,42 @@ class Processor(TriplesQueryService):
|
|||
self.cassandra_host = hosts
|
||||
self.cassandra_username = username
|
||||
self.cassandra_password = password
|
||||
self.table = None
|
||||
|
||||
def ensure_connection(self, workspace):
|
||||
"""Ensure we have a connection to the correct keyspace."""
|
||||
if workspace != self.table:
|
||||
KGClass = EntityCentricKnowledgeGraph
|
||||
self._connections = {}
|
||||
self._conn_lock = asyncio.Lock()
|
||||
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password
|
||||
)
|
||||
else:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
)
|
||||
self.table = workspace
|
||||
async def _get_connection(self, workspace):
|
||||
async with self._conn_lock:
|
||||
if workspace not in self._connections:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
tg = await asyncio.to_thread(
|
||||
EntityCentricKnowledgeGraph,
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password,
|
||||
)
|
||||
else:
|
||||
tg = await asyncio.to_thread(
|
||||
EntityCentricKnowledgeGraph,
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
)
|
||||
self._connections[workspace] = tg
|
||||
return self._connections[workspace]
|
||||
|
||||
async def query_triples(self, workspace, query):
|
||||
|
||||
try:
|
||||
|
||||
# ensure_connection may construct a fresh
|
||||
# EntityCentricKnowledgeGraph which does sync schema
|
||||
# setup against Cassandra. Push it to a worker thread
|
||||
# so the event loop doesn't block on first-use per workspace.
|
||||
await asyncio.to_thread(self.ensure_connection, workspace)
|
||||
|
||||
# Extract values from query
|
||||
s_val = get_term_value(query.s)
|
||||
p_val = get_term_value(query.p)
|
||||
o_val = get_term_value(query.o)
|
||||
g_val = query.g # Already a string or None
|
||||
g_val = query.g
|
||||
|
||||
tg = await self._get_connection(workspace)
|
||||
|
||||
def get_object_metadata(row):
|
||||
"""Extract term type metadata from result row"""
|
||||
return (
|
||||
getattr(row, 'otype', None),
|
||||
getattr(row, 'dtype', None),
|
||||
|
|
@ -223,33 +220,21 @@ class Processor(TriplesQueryService):
|
|||
|
||||
quads = []
|
||||
|
||||
# All self.tg.get_* calls below are sync wrappers around
|
||||
# cassandra session.execute. Materialise inside a worker
|
||||
# thread so iteration never triggers sync paging back on
|
||||
# the event loop.
|
||||
|
||||
# Route to appropriate query method based on which fields are specified
|
||||
if s_val is not None:
|
||||
if p_val is not None:
|
||||
if o_val is not None:
|
||||
# SPO specified - find matching graphs
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_spo(
|
||||
query.collection, s_val, p_val, o_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_spo(
|
||||
query.collection, s_val, p_val, o_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
term_type, datatype, language = get_object_metadata(t)
|
||||
quads.append((s_val, p_val, o_val, g, term_type, datatype, language))
|
||||
else:
|
||||
# SP specified
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_sp(
|
||||
query.collection, s_val, p_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_sp(
|
||||
query.collection, s_val, p_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
|
|
@ -257,24 +242,18 @@ class Processor(TriplesQueryService):
|
|||
quads.append((s_val, p_val, t.o, g, term_type, datatype, language))
|
||||
else:
|
||||
if o_val is not None:
|
||||
# SO specified
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_os(
|
||||
query.collection, o_val, s_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_os(
|
||||
query.collection, o_val, s_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
term_type, datatype, language = get_object_metadata(t)
|
||||
quads.append((s_val, t.p, o_val, g, term_type, datatype, language))
|
||||
else:
|
||||
# S only
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_s(
|
||||
query.collection, s_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_s(
|
||||
query.collection, s_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
|
|
@ -283,24 +262,18 @@ class Processor(TriplesQueryService):
|
|||
else:
|
||||
if p_val is not None:
|
||||
if o_val is not None:
|
||||
# PO specified
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_po(
|
||||
query.collection, p_val, o_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_po(
|
||||
query.collection, p_val, o_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
term_type, datatype, language = get_object_metadata(t)
|
||||
quads.append((t.s, p_val, o_val, g, term_type, datatype, language))
|
||||
else:
|
||||
# P only
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_p(
|
||||
query.collection, p_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_p(
|
||||
query.collection, p_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
|
|
@ -308,40 +281,26 @@ class Processor(TriplesQueryService):
|
|||
quads.append((t.s, p_val, t.o, g, term_type, datatype, language))
|
||||
else:
|
||||
if o_val is not None:
|
||||
# O only
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_o(
|
||||
query.collection, o_val,
|
||||
g=g_val, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_o(
|
||||
query.collection, o_val,
|
||||
g=g_val, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
g = t.g if hasattr(t, 'g') else DEFAULT_GRAPH
|
||||
term_type, datatype, language = get_object_metadata(t)
|
||||
quads.append((t.s, t.p, o_val, g, term_type, datatype, language))
|
||||
else:
|
||||
# Nothing specified - get all
|
||||
resp = await asyncio.to_thread(
|
||||
lambda: list(self.tg.get_all(
|
||||
query.collection, limit=query.limit,
|
||||
))
|
||||
resp = await tg.async_get_all(
|
||||
query.collection, limit=query.limit,
|
||||
)
|
||||
for t in resp:
|
||||
# Note: quads_by_collection uses 'd' for graph field
|
||||
g = t.d if hasattr(t, 'd') else DEFAULT_GRAPH
|
||||
# Filter by graph
|
||||
# g_val=None means all graphs (no filter)
|
||||
# g_val="" means default graph only
|
||||
# otherwise filter to specific named graph
|
||||
if g_val is not None:
|
||||
if g != g_val:
|
||||
continue
|
||||
term_type, datatype, language = get_object_metadata(t)
|
||||
quads.append((t.s, t.p, t.o, g, term_type, datatype, language))
|
||||
|
||||
# Convert to Triple objects (with g field)
|
||||
# s and p are always IRIs in RDF
|
||||
# Object uses term_type/datatype/language metadata from database
|
||||
triples = [
|
||||
Triple(
|
||||
s=create_term(q[0], term_type='u'),
|
||||
|
|
@ -365,51 +324,41 @@ class Processor(TriplesQueryService):
|
|||
Uses Cassandra's paging to fetch results incrementally.
|
||||
"""
|
||||
try:
|
||||
await asyncio.to_thread(self.ensure_connection, workspace)
|
||||
|
||||
batch_size = query.batch_size if query.batch_size > 0 else 20
|
||||
limit = query.limit if query.limit > 0 else 10000
|
||||
|
||||
# Extract query pattern
|
||||
s_val = get_term_value(query.s)
|
||||
p_val = get_term_value(query.p)
|
||||
o_val = get_term_value(query.o)
|
||||
g_val = query.g
|
||||
|
||||
def get_object_metadata(row):
|
||||
"""Extract term type metadata from result row"""
|
||||
return (
|
||||
getattr(row, 'otype', None),
|
||||
getattr(row, 'dtype', None),
|
||||
getattr(row, 'lang', None),
|
||||
)
|
||||
|
||||
# For streaming, we need to execute with fetch_size
|
||||
# Use the collection table for get_all queries (most common streaming case)
|
||||
|
||||
# Determine which query to use based on pattern
|
||||
if s_val is None and p_val is None and o_val is None:
|
||||
# Get all - use collection table with paging
|
||||
cql = f"SELECT d, s, p, o, otype, dtype, lang FROM {self.tg.collection_table} WHERE collection = %s"
|
||||
|
||||
tg = await self._get_connection(workspace)
|
||||
|
||||
cql = f"SELECT d, s, p, o, otype, dtype, lang FROM {tg.collection_table} WHERE collection = %s"
|
||||
params = [query.collection]
|
||||
statement = SimpleStatement(cql, fetch_size=batch_size)
|
||||
# async_execute only materialises the first page;
|
||||
# this query needs all pages, so use sync execute
|
||||
# in a worker thread where page iteration can block.
|
||||
result_set = await asyncio.to_thread(
|
||||
lambda: list(tg.session.execute(statement, params))
|
||||
)
|
||||
|
||||
else:
|
||||
# For specific patterns, fall back to non-streaming
|
||||
# (these typically return small result sets anyway)
|
||||
async for batch, is_final in self._fallback_stream(workspace, query, batch_size):
|
||||
yield batch, is_final
|
||||
return
|
||||
|
||||
# Materialise in a worker thread. We lose true streaming
|
||||
# paging (the driver fetches all pages eagerly inside the
|
||||
# thread) but the event loop stays responsive, and result
|
||||
# sets at this layer are typically small enough that this
|
||||
# is acceptable. If true async paging is needed later,
|
||||
# revisit using ResponseFuture page callbacks.
|
||||
statement = SimpleStatement(cql, fetch_size=batch_size)
|
||||
result_set = await asyncio.to_thread(
|
||||
lambda: list(self.tg.session.execute(statement, params))
|
||||
)
|
||||
|
||||
batch = []
|
||||
count = 0
|
||||
|
||||
|
|
|
|||
|
|
@ -3,11 +3,13 @@
|
|||
Accepts entity/vector pairs and writes them to a Qdrant store.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import uuid
|
||||
import logging
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.models import PointStruct
|
||||
from qdrant_client.models import Distance, VectorParams
|
||||
import uuid
|
||||
import logging
|
||||
|
||||
from .... base import DocumentEmbeddingsStoreService, CollectionConfigHandler
|
||||
from .... base import AsyncProcessor, Consumer, Producer
|
||||
|
|
@ -35,13 +37,35 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
|
|||
)
|
||||
|
||||
self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
|
||||
self._cache_lock = asyncio.Lock()
|
||||
self._known_collections: set[str] = set()
|
||||
|
||||
# Register for config push notifications
|
||||
self.register_config_handler(self.on_collection_config, types=["collection"])
|
||||
|
||||
async def ensure_collection(self, collection_name, dim):
|
||||
async with self._cache_lock:
|
||||
if collection_name in self._known_collections:
|
||||
return
|
||||
exists = await asyncio.to_thread(
|
||||
self.qdrant.collection_exists, collection_name
|
||||
)
|
||||
if not exists:
|
||||
logger.info(
|
||||
f"Lazily creating Qdrant collection {collection_name} "
|
||||
f"with dimension {dim}"
|
||||
)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.create_collection,
|
||||
collection_name=collection_name,
|
||||
vectors_config=VectorParams(
|
||||
size=dim, distance=Distance.COSINE
|
||||
),
|
||||
)
|
||||
self._known_collections.add(collection_name)
|
||||
|
||||
async def store_document_embeddings(self, workspace, message):
|
||||
|
||||
# Validate collection exists in config before processing
|
||||
if not self.collection_exists(workspace, message.metadata.collection):
|
||||
logger.warning(
|
||||
f"Collection {message.metadata.collection} for workspace {workspace} "
|
||||
|
|
@ -60,24 +84,15 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
|
|||
if not vec:
|
||||
continue
|
||||
|
||||
# Create collection name with dimension suffix for lazy creation
|
||||
dim = len(vec)
|
||||
collection = (
|
||||
f"d_{workspace}_{message.metadata.collection}_{dim}"
|
||||
)
|
||||
|
||||
# Lazily create collection if it doesn't exist (but only if authorized in config)
|
||||
if not self.qdrant.collection_exists(collection):
|
||||
logger.info(f"Lazily creating Qdrant collection {collection} with dimension {dim}")
|
||||
self.qdrant.create_collection(
|
||||
collection_name=collection,
|
||||
vectors_config=VectorParams(
|
||||
size=dim,
|
||||
distance=Distance.COSINE
|
||||
)
|
||||
)
|
||||
await self.ensure_collection(collection, dim)
|
||||
|
||||
self.qdrant.upsert(
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.upsert,
|
||||
collection_name=collection,
|
||||
points=[
|
||||
PointStruct(
|
||||
|
|
@ -87,7 +102,7 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
|
|||
"chunk_id": chunk_id,
|
||||
}
|
||||
)
|
||||
]
|
||||
],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -124,8 +139,9 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
|
|||
try:
|
||||
prefix = f"d_{workspace}_{collection}_"
|
||||
|
||||
# Get all collections and filter for matches
|
||||
all_collections = self.qdrant.get_collections().collections
|
||||
all_collections = await asyncio.to_thread(
|
||||
lambda: self.qdrant.get_collections().collections
|
||||
)
|
||||
matching_collections = [
|
||||
coll.name for coll in all_collections
|
||||
if coll.name.startswith(prefix)
|
||||
|
|
@ -135,7 +151,11 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
|
|||
logger.info(f"No collections found matching prefix {prefix}")
|
||||
else:
|
||||
for collection_name in matching_collections:
|
||||
self.qdrant.delete_collection(collection_name)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.delete_collection, collection_name
|
||||
)
|
||||
async with self._cache_lock:
|
||||
self._known_collections.discard(collection_name)
|
||||
logger.info(f"Deleted Qdrant collection: {collection_name}")
|
||||
logger.info(f"Deleted {len(matching_collections)} collection(s) for {workspace}/{collection}")
|
||||
|
||||
|
|
|
|||
|
|
@ -3,11 +3,13 @@
|
|||
Accepts entity/vector pairs and writes them to a Qdrant store.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import uuid
|
||||
import logging
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.models import PointStruct
|
||||
from qdrant_client.models import Distance, VectorParams
|
||||
import uuid
|
||||
import logging
|
||||
|
||||
from .... base import GraphEmbeddingsStoreService, CollectionConfigHandler
|
||||
from .... base import AsyncProcessor, Consumer, Producer
|
||||
|
|
@ -50,13 +52,35 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
|
|||
)
|
||||
|
||||
self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
|
||||
self._cache_lock = asyncio.Lock()
|
||||
self._known_collections: set[str] = set()
|
||||
|
||||
# Register for config push notifications
|
||||
self.register_config_handler(self.on_collection_config, types=["collection"])
|
||||
|
||||
async def ensure_collection(self, collection_name, dim):
|
||||
async with self._cache_lock:
|
||||
if collection_name in self._known_collections:
|
||||
return
|
||||
exists = await asyncio.to_thread(
|
||||
self.qdrant.collection_exists, collection_name
|
||||
)
|
||||
if not exists:
|
||||
logger.info(
|
||||
f"Lazily creating Qdrant collection {collection_name} "
|
||||
f"with dimension {dim}"
|
||||
)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.create_collection,
|
||||
collection_name=collection_name,
|
||||
vectors_config=VectorParams(
|
||||
size=dim, distance=Distance.COSINE
|
||||
),
|
||||
)
|
||||
self._known_collections.add(collection_name)
|
||||
|
||||
async def store_graph_embeddings(self, workspace, message):
|
||||
|
||||
# Validate collection exists in config before processing
|
||||
if not self.collection_exists(workspace, message.metadata.collection):
|
||||
logger.warning(
|
||||
f"Collection {message.metadata.collection} for workspace {workspace} "
|
||||
|
|
@ -75,22 +99,12 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
|
|||
if not vec:
|
||||
continue
|
||||
|
||||
# Create collection name with dimension suffix for lazy creation
|
||||
dim = len(vec)
|
||||
collection = (
|
||||
f"t_{workspace}_{message.metadata.collection}_{dim}"
|
||||
)
|
||||
|
||||
# Lazily create collection if it doesn't exist (but only if authorized in config)
|
||||
if not self.qdrant.collection_exists(collection):
|
||||
logger.info(f"Lazily creating Qdrant collection {collection} with dimension {dim}")
|
||||
self.qdrant.create_collection(
|
||||
collection_name=collection,
|
||||
vectors_config=VectorParams(
|
||||
size=dim,
|
||||
distance=Distance.COSINE
|
||||
)
|
||||
)
|
||||
await self.ensure_collection(collection, dim)
|
||||
|
||||
payload = {
|
||||
"entity": entity_value,
|
||||
|
|
@ -98,7 +112,8 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
|
|||
if entity.chunk_id:
|
||||
payload["chunk_id"] = entity.chunk_id
|
||||
|
||||
self.qdrant.upsert(
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.upsert,
|
||||
collection_name=collection,
|
||||
points=[
|
||||
PointStruct(
|
||||
|
|
@ -106,7 +121,7 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
|
|||
vector=vec,
|
||||
payload=payload,
|
||||
)
|
||||
]
|
||||
],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -143,8 +158,9 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
|
|||
try:
|
||||
prefix = f"t_{workspace}_{collection}_"
|
||||
|
||||
# Get all collections and filter for matches
|
||||
all_collections = self.qdrant.get_collections().collections
|
||||
all_collections = await asyncio.to_thread(
|
||||
lambda: self.qdrant.get_collections().collections
|
||||
)
|
||||
matching_collections = [
|
||||
coll.name for coll in all_collections
|
||||
if coll.name.startswith(prefix)
|
||||
|
|
@ -154,7 +170,11 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
|
|||
logger.info(f"No collections found matching prefix {prefix}")
|
||||
else:
|
||||
for collection_name in matching_collections:
|
||||
self.qdrant.delete_collection(collection_name)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.delete_collection, collection_name
|
||||
)
|
||||
async with self._cache_lock:
|
||||
self._known_collections.discard(collection_name)
|
||||
logger.info(f"Deleted Qdrant collection: {collection_name}")
|
||||
logger.info(f"Deleted {len(matching_collections)} collection(s) for {workspace}/{collection}")
|
||||
|
||||
|
|
|
|||
|
|
@ -16,10 +16,10 @@ Payload structure:
|
|||
- text: The text that was embedded (for debugging/display)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import re
|
||||
import uuid
|
||||
from typing import Set, Tuple
|
||||
|
||||
from qdrant_client import QdrantClient
|
||||
from qdrant_client.models import PointStruct, Distance, VectorParams
|
||||
|
|
@ -63,11 +63,9 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
# Register config handler for collection management
|
||||
self.register_config_handler(self.on_collection_config, types=["collection"])
|
||||
|
||||
# Cache of created Qdrant collections
|
||||
self.created_collections: Set[str] = set()
|
||||
|
||||
# Qdrant client
|
||||
self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
|
||||
self._cache_lock = asyncio.Lock()
|
||||
self._known_collections: set[str] = set()
|
||||
|
||||
def sanitize_name(self, name: str) -> str:
|
||||
"""Sanitize names for Qdrant collection naming"""
|
||||
|
|
@ -85,25 +83,28 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
safe_schema = self.sanitize_name(schema_name)
|
||||
return f"rows_{safe_user}_{safe_collection}_{safe_schema}_{dimension}"
|
||||
|
||||
def ensure_collection(self, collection_name: str, dimension: int):
|
||||
async def ensure_collection(self, collection_name: str, dimension: int):
|
||||
"""Create Qdrant collection if it doesn't exist"""
|
||||
if collection_name in self.created_collections:
|
||||
return
|
||||
|
||||
if not self.qdrant.collection_exists(collection_name):
|
||||
logger.info(
|
||||
f"Creating Qdrant collection {collection_name} "
|
||||
f"with dimension {dimension}"
|
||||
async with self._cache_lock:
|
||||
if collection_name in self._known_collections:
|
||||
return
|
||||
exists = await asyncio.to_thread(
|
||||
self.qdrant.collection_exists, collection_name
|
||||
)
|
||||
self.qdrant.create_collection(
|
||||
collection_name=collection_name,
|
||||
vectors_config=VectorParams(
|
||||
size=dimension,
|
||||
distance=Distance.COSINE
|
||||
if not exists:
|
||||
logger.info(
|
||||
f"Creating Qdrant collection {collection_name} "
|
||||
f"with dimension {dimension}"
|
||||
)
|
||||
)
|
||||
|
||||
self.created_collections.add(collection_name)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.create_collection,
|
||||
collection_name=collection_name,
|
||||
vectors_config=VectorParams(
|
||||
size=dimension,
|
||||
distance=Distance.COSINE
|
||||
),
|
||||
)
|
||||
self._known_collections.add(collection_name)
|
||||
|
||||
async def on_embeddings(self, msg, consumer, flow):
|
||||
"""Process incoming RowEmbeddings and write to Qdrant"""
|
||||
|
|
@ -143,15 +144,14 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
|
||||
dimension = len(vector)
|
||||
|
||||
# Create/get collection name (lazily on first vector)
|
||||
if qdrant_collection is None:
|
||||
qdrant_collection = self.get_collection_name(
|
||||
workspace, collection, schema_name, dimension
|
||||
)
|
||||
self.ensure_collection(qdrant_collection, dimension)
|
||||
await self.ensure_collection(qdrant_collection, dimension)
|
||||
|
||||
# Write to Qdrant
|
||||
self.qdrant.upsert(
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.upsert,
|
||||
collection_name=qdrant_collection,
|
||||
points=[
|
||||
PointStruct(
|
||||
|
|
@ -163,7 +163,7 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
"text": row_emb.text
|
||||
}
|
||||
)
|
||||
]
|
||||
],
|
||||
)
|
||||
embeddings_written += 1
|
||||
|
||||
|
|
@ -181,8 +181,9 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
try:
|
||||
prefix = f"rows_{self.sanitize_name(workspace)}_{self.sanitize_name(collection)}_"
|
||||
|
||||
# Get all collections and filter for matches
|
||||
all_collections = self.qdrant.get_collections().collections
|
||||
all_collections = await asyncio.to_thread(
|
||||
lambda: self.qdrant.get_collections().collections
|
||||
)
|
||||
matching_collections = [
|
||||
coll.name for coll in all_collections
|
||||
if coll.name.startswith(prefix)
|
||||
|
|
@ -192,8 +193,11 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
logger.info(f"No Qdrant collections found matching prefix {prefix}")
|
||||
else:
|
||||
for collection_name in matching_collections:
|
||||
self.qdrant.delete_collection(collection_name)
|
||||
self.created_collections.discard(collection_name)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.delete_collection, collection_name
|
||||
)
|
||||
async with self._cache_lock:
|
||||
self._known_collections.discard(collection_name)
|
||||
logger.info(f"Deleted Qdrant collection: {collection_name}")
|
||||
logger.info(
|
||||
f"Deleted {len(matching_collections)} collection(s) "
|
||||
|
|
@ -217,8 +221,9 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
f"{self.sanitize_name(collection)}_{self.sanitize_name(schema_name)}_"
|
||||
)
|
||||
|
||||
# Get all collections and filter for matches
|
||||
all_collections = self.qdrant.get_collections().collections
|
||||
all_collections = await asyncio.to_thread(
|
||||
lambda: self.qdrant.get_collections().collections
|
||||
)
|
||||
matching_collections = [
|
||||
coll.name for coll in all_collections
|
||||
if coll.name.startswith(prefix)
|
||||
|
|
@ -228,8 +233,11 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
logger.info(f"No Qdrant collections found matching prefix {prefix}")
|
||||
else:
|
||||
for collection_name in matching_collections:
|
||||
self.qdrant.delete_collection(collection_name)
|
||||
self.created_collections.discard(collection_name)
|
||||
await asyncio.to_thread(
|
||||
self.qdrant.delete_collection, collection_name
|
||||
)
|
||||
async with self._cache_lock:
|
||||
self._known_collections.discard(collection_name)
|
||||
logger.info(f"Deleted Qdrant collection: {collection_name}")
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
|
||||
# Cache of known keyspaces and whether tables exist
|
||||
self.known_keyspaces: Set[str] = set()
|
||||
self.tables_initialized: Set[str] = set() # keyspaces with rows/row_partitions tables
|
||||
self.tables_initialized: Set[str] = set()
|
||||
|
||||
# Cache of registered (collection, schema_name) pairs
|
||||
self.registered_partitions: Set[Tuple[str, str]] = set()
|
||||
|
|
@ -94,6 +94,9 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
self.cluster = None
|
||||
self.session = None
|
||||
|
||||
# Protects connection setup and cache mutations
|
||||
self._setup_lock = asyncio.Lock()
|
||||
|
||||
def connect_cassandra(self):
|
||||
"""Connect to Cassandra cluster"""
|
||||
if self.session:
|
||||
|
|
@ -126,6 +129,11 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
f"for workspace {workspace}"
|
||||
)
|
||||
|
||||
async with self._setup_lock:
|
||||
return await self._apply_schema_config(workspace, config, version)
|
||||
|
||||
async def _apply_schema_config(self, workspace, config, version):
|
||||
|
||||
# Track which schemas changed in this workspace
|
||||
old_schemas = self.schemas.get(workspace, {})
|
||||
old_schema_names = set(old_schemas.keys())
|
||||
|
|
@ -391,16 +399,12 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
schema_name = obj.schema_name
|
||||
source = getattr(obj.metadata, 'source', '') or ''
|
||||
|
||||
# Ensure tables exist (sync DDL — push to a worker thread
|
||||
# so the event loop stays responsive when running in a
|
||||
# processor group sharing the loop with siblings).
|
||||
await asyncio.to_thread(self.ensure_tables, keyspace)
|
||||
|
||||
# Register partitions if first time seeing this (collection, schema_name)
|
||||
await asyncio.to_thread(
|
||||
self.register_partitions,
|
||||
keyspace, collection, schema_name, workspace,
|
||||
)
|
||||
async with self._setup_lock:
|
||||
await asyncio.to_thread(self.ensure_tables, keyspace)
|
||||
await asyncio.to_thread(
|
||||
self.register_partitions,
|
||||
keyspace, collection, schema_name, workspace,
|
||||
)
|
||||
|
||||
safe_keyspace = self.sanitize_name(keyspace)
|
||||
|
||||
|
|
@ -461,35 +465,27 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
|
||||
async def create_collection(self, workspace: str, collection: str, metadata: dict):
|
||||
"""Create/verify collection exists in Cassandra row store"""
|
||||
# Connect if not already connected (sync, push to thread)
|
||||
await asyncio.to_thread(self.connect_cassandra)
|
||||
|
||||
# Ensure tables exist (sync DDL, push to thread)
|
||||
await asyncio.to_thread(self.ensure_tables, workspace)
|
||||
async with self._setup_lock:
|
||||
await asyncio.to_thread(self.connect_cassandra)
|
||||
await asyncio.to_thread(self.ensure_tables, workspace)
|
||||
|
||||
logger.info(f"Collection {collection} ready for workspace {workspace}")
|
||||
|
||||
async def delete_collection(self, workspace: str, collection: str):
|
||||
"""Delete all data for a specific collection using partition tracking"""
|
||||
# Connect if not already connected
|
||||
await asyncio.to_thread(self.connect_cassandra)
|
||||
async with self._setup_lock:
|
||||
await asyncio.to_thread(self.connect_cassandra)
|
||||
if workspace not in self.known_keyspaces:
|
||||
safe_ks = self.sanitize_name(workspace)
|
||||
check_cql = "SELECT keyspace_name FROM system_schema.keyspaces WHERE keyspace_name = %s"
|
||||
result = await async_execute(self.session, check_cql, (safe_ks,))
|
||||
if not result:
|
||||
logger.info(f"Keyspace {safe_ks} does not exist, nothing to delete")
|
||||
return
|
||||
self.known_keyspaces.add(workspace)
|
||||
|
||||
safe_keyspace = self.sanitize_name(workspace)
|
||||
|
||||
# Check if keyspace exists
|
||||
if workspace not in self.known_keyspaces:
|
||||
check_keyspace_cql = """
|
||||
SELECT keyspace_name FROM system_schema.keyspaces
|
||||
WHERE keyspace_name = %s
|
||||
"""
|
||||
result = await async_execute(
|
||||
self.session, check_keyspace_cql, (safe_keyspace,)
|
||||
)
|
||||
if not result:
|
||||
logger.info(f"Keyspace {safe_keyspace} does not exist, nothing to delete")
|
||||
return
|
||||
self.known_keyspaces.add(workspace)
|
||||
|
||||
# Discover all partitions for this collection
|
||||
select_partitions_cql = f"""
|
||||
SELECT schema_name, index_name FROM {safe_keyspace}.row_partitions
|
||||
|
|
@ -540,11 +536,11 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
logger.error(f"Failed to clean up row_partitions for {collection}: {e}")
|
||||
raise
|
||||
|
||||
# Clear from local cache
|
||||
self.registered_partitions = {
|
||||
(col, sch) for col, sch in self.registered_partitions
|
||||
if col != collection
|
||||
}
|
||||
async with self._setup_lock:
|
||||
self.registered_partitions = {
|
||||
(col, sch) for col, sch in self.registered_partitions
|
||||
if col != collection
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"Deleted collection {collection}: {partitions_deleted} partitions "
|
||||
|
|
@ -553,8 +549,8 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
|
||||
async def delete_collection_schema(self, workspace: str, collection: str, schema_name: str):
|
||||
"""Delete all data for a specific collection + schema combination"""
|
||||
# Connect if not already connected
|
||||
await asyncio.to_thread(self.connect_cassandra)
|
||||
async with self._setup_lock:
|
||||
await asyncio.to_thread(self.connect_cassandra)
|
||||
|
||||
safe_keyspace = self.sanitize_name(workspace)
|
||||
|
||||
|
|
@ -614,8 +610,8 @@ class Processor(CollectionConfigHandler, FlowProcessor):
|
|||
)
|
||||
raise
|
||||
|
||||
# Clear from local cache
|
||||
self.registered_partitions.discard((collection, schema_name))
|
||||
async with self._setup_lock:
|
||||
self.registered_partitions.discard((collection, schema_name))
|
||||
|
||||
logger.info(
|
||||
f"Deleted {collection}/{schema_name}: {partitions_deleted} partitions "
|
||||
|
|
|
|||
|
|
@ -4,12 +4,7 @@ Graph writer. Input is graph edge. Writes edges to Cassandra graph.
|
|||
"""
|
||||
|
||||
import asyncio
|
||||
import base64
|
||||
import os
|
||||
import argparse
|
||||
import time
|
||||
import logging
|
||||
import json
|
||||
|
||||
from .... direct.cassandra_kg import (
|
||||
EntityCentricKnowledgeGraph, DEFAULT_GRAPH
|
||||
|
|
@ -28,6 +23,8 @@ default_ident = "triples-write"
|
|||
|
||||
def serialize_triple(triple):
|
||||
"""Serialize a Triple object to JSON for storage."""
|
||||
import json
|
||||
|
||||
if triple is None:
|
||||
return None
|
||||
|
||||
|
|
@ -141,156 +138,84 @@ class Processor(CollectionConfigHandler, TriplesStoreService):
|
|||
self.cassandra_host = hosts
|
||||
self.cassandra_username = username
|
||||
self.cassandra_password = password
|
||||
self.table = None
|
||||
self.tg = None
|
||||
|
||||
self._connections = {}
|
||||
self._conn_lock = asyncio.Lock()
|
||||
|
||||
# Register for config push notifications
|
||||
self.register_config_handler(self.on_collection_config, types=["collection"])
|
||||
|
||||
async def _get_connection(self, workspace):
|
||||
async with self._conn_lock:
|
||||
if workspace not in self._connections:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
tg = await asyncio.to_thread(
|
||||
EntityCentricKnowledgeGraph,
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password,
|
||||
)
|
||||
else:
|
||||
tg = await asyncio.to_thread(
|
||||
EntityCentricKnowledgeGraph,
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
)
|
||||
self._connections[workspace] = tg
|
||||
return self._connections[workspace]
|
||||
|
||||
async def store_triples(self, workspace, message):
|
||||
|
||||
# The cassandra-driver work below — connection, schema
|
||||
# setup, and per-triple inserts — is all synchronous.
|
||||
# Wrap the whole batch in a worker thread so the event
|
||||
# loop stays responsive for sibling processors when
|
||||
# running in a processor group.
|
||||
tg = await self._get_connection(workspace)
|
||||
|
||||
def _do_store():
|
||||
for t in message.triples:
|
||||
s_val = get_term_value(t.s)
|
||||
p_val = get_term_value(t.p)
|
||||
o_val = get_term_value(t.o)
|
||||
g_val = t.g if t.g is not None else DEFAULT_GRAPH
|
||||
|
||||
if self.table is None or self.table != workspace:
|
||||
otype = get_term_otype(t.o)
|
||||
dtype = get_term_dtype(t.o)
|
||||
lang = get_term_lang(t.o)
|
||||
|
||||
self.tg = None
|
||||
|
||||
# Use factory function to select implementation
|
||||
KGClass = EntityCentricKnowledgeGraph
|
||||
|
||||
try:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password,
|
||||
)
|
||||
else:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Exception: {e}", exc_info=True)
|
||||
time.sleep(1)
|
||||
raise e
|
||||
|
||||
self.table = workspace
|
||||
|
||||
for t in message.triples:
|
||||
# Extract values from Term objects
|
||||
s_val = get_term_value(t.s)
|
||||
p_val = get_term_value(t.p)
|
||||
o_val = get_term_value(t.o)
|
||||
# t.g is None for default graph, or a graph IRI
|
||||
g_val = t.g if t.g is not None else DEFAULT_GRAPH
|
||||
|
||||
# Extract object type metadata for entity-centric storage
|
||||
otype = get_term_otype(t.o)
|
||||
dtype = get_term_dtype(t.o)
|
||||
lang = get_term_lang(t.o)
|
||||
|
||||
self.tg.insert(
|
||||
message.metadata.collection,
|
||||
s_val,
|
||||
p_val,
|
||||
o_val,
|
||||
g=g_val,
|
||||
otype=otype,
|
||||
dtype=dtype,
|
||||
lang=lang,
|
||||
)
|
||||
|
||||
await asyncio.to_thread(_do_store)
|
||||
await tg.async_insert(
|
||||
message.metadata.collection,
|
||||
s_val,
|
||||
p_val,
|
||||
o_val,
|
||||
g=g_val,
|
||||
otype=otype,
|
||||
dtype=dtype,
|
||||
lang=lang,
|
||||
)
|
||||
|
||||
async def create_collection(self, workspace: str, collection: str, metadata: dict):
|
||||
"""Create a collection in Cassandra triple store via config push"""
|
||||
try:
|
||||
tg = await self._get_connection(workspace)
|
||||
|
||||
def _do_create():
|
||||
# Create or reuse connection for this workspace's keyspace
|
||||
if self.table is None or self.table != workspace:
|
||||
self.tg = None
|
||||
|
||||
# Use factory function to select implementation
|
||||
KGClass = EntityCentricKnowledgeGraph
|
||||
|
||||
try:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password,
|
||||
)
|
||||
else:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to connect to Cassandra for workspace {workspace}: {e}")
|
||||
raise
|
||||
|
||||
self.table = workspace
|
||||
|
||||
# Create collection using the built-in method
|
||||
logger.info(f"Creating collection {collection} for workspace {workspace}")
|
||||
|
||||
if self.tg.collection_exists(collection):
|
||||
exists = await tg.async_collection_exists(collection)
|
||||
if exists:
|
||||
logger.info(f"Collection {collection} already exists")
|
||||
else:
|
||||
self.tg.create_collection(collection)
|
||||
await tg.async_create_collection(collection)
|
||||
logger.info(f"Created collection {collection}")
|
||||
|
||||
try:
|
||||
await asyncio.to_thread(_do_create)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create collection {workspace}/{collection}: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def delete_collection(self, workspace: str, collection: str):
|
||||
"""Delete all data for a specific collection from the unified triples table"""
|
||||
try:
|
||||
tg = await self._get_connection(workspace)
|
||||
|
||||
def _do_delete():
|
||||
# Create or reuse connection for this workspace's keyspace
|
||||
if self.table is None or self.table != workspace:
|
||||
self.tg = None
|
||||
|
||||
# Use factory function to select implementation
|
||||
KGClass = EntityCentricKnowledgeGraph
|
||||
|
||||
try:
|
||||
if self.cassandra_username and self.cassandra_password:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
username=self.cassandra_username,
|
||||
password=self.cassandra_password,
|
||||
)
|
||||
else:
|
||||
self.tg = KGClass(
|
||||
hosts=self.cassandra_host,
|
||||
keyspace=workspace,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to connect to Cassandra for workspace {workspace}: {e}")
|
||||
raise
|
||||
|
||||
self.table = workspace
|
||||
|
||||
# Delete all triples for this collection using the built-in method
|
||||
self.tg.delete_collection(collection)
|
||||
await tg.async_delete_collection(collection)
|
||||
logger.info(f"Deleted all triples for collection {collection} from keyspace {workspace}")
|
||||
|
||||
try:
|
||||
await asyncio.to_thread(_do_delete)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete collection {workspace}/{collection}: {e}", exc_info=True)
|
||||
raise
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
|
||||
from .. schema import KnowledgeResponse, Triple, Triples, EntityEmbeddings
|
||||
from .. schema import Metadata, Term, IRI, LITERAL, GraphEmbeddings
|
||||
from .. schema import DocumentEmbeddings, ChunkEmbeddings
|
||||
|
||||
from cassandra.cluster import Cluster
|
||||
|
||||
|
|
@ -217,6 +218,16 @@ class KnowledgeTableStore:
|
|||
WHERE workspace = ? AND document_id = ?
|
||||
""")
|
||||
|
||||
self.delete_document_embeddings_stmt = self.cassandra.prepare("""
|
||||
DELETE FROM document_embeddings
|
||||
WHERE workspace = ? AND document_id = ?
|
||||
""")
|
||||
|
||||
self.list_de_cores_stmt = self.cassandra.prepare("""
|
||||
SELECT DISTINCT workspace, document_id FROM document_embeddings
|
||||
WHERE workspace = ?
|
||||
""")
|
||||
|
||||
async def add_triples(self, workspace, m):
|
||||
|
||||
when = int(time.time() * 1000)
|
||||
|
|
@ -338,6 +349,50 @@ class KnowledgeTableStore:
|
|||
logger.error("Exception occurred", exc_info=True)
|
||||
raise
|
||||
|
||||
try:
|
||||
await async_execute(
|
||||
self.cassandra,
|
||||
self.delete_document_embeddings_stmt,
|
||||
(workspace, document_id),
|
||||
)
|
||||
except Exception:
|
||||
logger.error("Exception occurred", exc_info=True)
|
||||
raise
|
||||
|
||||
async def delete_document_embeddings(self, workspace, document_id):
|
||||
|
||||
logger.debug("Delete document embeddings...")
|
||||
|
||||
try:
|
||||
await async_execute(
|
||||
self.cassandra,
|
||||
self.delete_document_embeddings_stmt,
|
||||
(workspace, document_id),
|
||||
)
|
||||
except Exception:
|
||||
logger.error("Exception occurred", exc_info=True)
|
||||
raise
|
||||
|
||||
async def list_de_cores(self, workspace):
|
||||
|
||||
logger.debug("List DE cores...")
|
||||
|
||||
try:
|
||||
rows = await async_execute(
|
||||
self.cassandra,
|
||||
self.list_de_cores_stmt,
|
||||
(workspace,),
|
||||
)
|
||||
except Exception:
|
||||
logger.error("Exception occurred", exc_info=True)
|
||||
raise
|
||||
|
||||
lst = [row[1] for row in rows]
|
||||
|
||||
logger.debug("Done")
|
||||
|
||||
return lst
|
||||
|
||||
async def get_triples(self, workspace, document_id, receiver):
|
||||
|
||||
logger.debug("Get triples...")
|
||||
|
|
@ -417,3 +472,42 @@ class KnowledgeTableStore:
|
|||
|
||||
logger.debug("Done")
|
||||
|
||||
async def get_document_embeddings(self, workspace, document_id, receiver):
|
||||
|
||||
logger.debug("Get DE...")
|
||||
|
||||
try:
|
||||
rows = await async_execute(
|
||||
self.cassandra,
|
||||
self.get_document_embeddings_stmt,
|
||||
(workspace, document_id),
|
||||
)
|
||||
except Exception:
|
||||
logger.error("Exception occurred", exc_info=True)
|
||||
raise
|
||||
|
||||
for row in rows:
|
||||
|
||||
if row[3]:
|
||||
chunks = [
|
||||
ChunkEmbeddings(
|
||||
chunk_id=ch[0],
|
||||
vector=ch[1],
|
||||
)
|
||||
for ch in row[3]
|
||||
]
|
||||
else:
|
||||
chunks = []
|
||||
|
||||
await receiver(
|
||||
DocumentEmbeddings(
|
||||
metadata = Metadata(
|
||||
id = document_id,
|
||||
collection = "default",
|
||||
),
|
||||
chunks = chunks
|
||||
)
|
||||
)
|
||||
|
||||
logger.debug("Done")
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue