mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 00:16:23 +02:00
25995d03f4
Warning generated by librarian responses meant for other services (chunker, embeddings, etc.) arriving on the shared response queue. The decoder's subscription picks them up, can't match them to a pending request, and logs a warning. Removed the warnings, as not serving a purpose.
463 lines
16 KiB
Python
Executable file
463 lines
16 KiB
Python
Executable file
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"""
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Simple RAG service, performs query using graph RAG an LLM.
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Input is query, output is response.
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"""
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import asyncio
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import base64
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import logging
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import uuid
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from ... schema import GraphRagQuery, GraphRagResponse, Error
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from ... schema import Triples, Metadata
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from ... schema import LibrarianRequest, LibrarianResponse, DocumentMetadata
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from ... schema import librarian_request_queue, librarian_response_queue
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from ... provenance import GRAPH_RETRIEVAL
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from . graph_rag import GraphRag
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from ... base import FlowProcessor, ConsumerSpec, ProducerSpec
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from ... base import PromptClientSpec, EmbeddingsClientSpec
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from ... base import GraphEmbeddingsClientSpec, TriplesClientSpec
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from ... base import Consumer, Producer, ConsumerMetrics, ProducerMetrics
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "graph-rag"
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default_concurrency = 1
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default_librarian_request_queue = librarian_request_queue
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default_librarian_response_queue = librarian_response_queue
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class Processor(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id", default_ident)
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concurrency = params.get("concurrency", 1)
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entity_limit = params.get("entity_limit", 50)
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triple_limit = params.get("triple_limit", 30)
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max_subgraph_size = params.get("max_subgraph_size", 150)
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max_path_length = params.get("max_path_length", 2)
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edge_score_limit = params.get("edge_score_limit", 30)
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edge_limit = params.get("edge_limit", 25)
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super(Processor, self).__init__(
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**params | {
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"id": id,
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"concurrency": concurrency,
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"entity_limit": entity_limit,
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"triple_limit": triple_limit,
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"max_subgraph_size": max_subgraph_size,
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"max_path_length": max_path_length,
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"edge_score_limit": edge_score_limit,
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"edge_limit": edge_limit,
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}
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)
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self.default_entity_limit = entity_limit
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self.default_triple_limit = triple_limit
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self.default_max_subgraph_size = max_subgraph_size
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self.default_max_path_length = max_path_length
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self.default_edge_score_limit = edge_score_limit
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self.default_edge_limit = edge_limit
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# CRITICAL SECURITY: NEVER share data between users or collections
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# Each user/collection combination MUST have isolated data access
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# Caching must NEVER allow information leakage across these boundaries
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self.register_specification(
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ConsumerSpec(
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name = "request",
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schema = GraphRagQuery,
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handler = self.on_request,
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concurrency = concurrency,
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)
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)
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self.register_specification(
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EmbeddingsClientSpec(
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request_name = "embeddings-request",
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response_name = "embeddings-response",
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)
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)
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self.register_specification(
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GraphEmbeddingsClientSpec(
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request_name = "graph-embeddings-request",
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response_name = "graph-embeddings-response",
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)
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)
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self.register_specification(
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TriplesClientSpec(
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request_name = "triples-request",
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response_name = "triples-response",
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)
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)
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self.register_specification(
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PromptClientSpec(
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request_name = "prompt-request",
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response_name = "prompt-response",
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)
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)
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self.register_specification(
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ProducerSpec(
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name = "response",
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schema = GraphRagResponse,
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)
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)
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self.register_specification(
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ProducerSpec(
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name = "explainability",
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schema = Triples,
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)
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)
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# Librarian client for storing answer content
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librarian_request_q = params.get(
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"librarian_request_queue", default_librarian_request_queue
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)
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librarian_response_q = params.get(
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"librarian_response_queue", default_librarian_response_queue
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)
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librarian_request_metrics = ProducerMetrics(
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processor=id, flow=None, name="librarian-request"
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)
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self.librarian_request_producer = Producer(
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backend=self.pubsub,
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topic=librarian_request_q,
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schema=LibrarianRequest,
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metrics=librarian_request_metrics,
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)
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librarian_response_metrics = ConsumerMetrics(
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processor=id, flow=None, name="librarian-response"
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)
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self.librarian_response_consumer = Consumer(
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taskgroup=self.taskgroup,
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backend=self.pubsub,
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flow=None,
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topic=librarian_response_q,
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subscriber=f"{id}-librarian",
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schema=LibrarianResponse,
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handler=self.on_librarian_response,
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metrics=librarian_response_metrics,
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)
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# Pending librarian requests: request_id -> asyncio.Future
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self.pending_librarian_requests = {}
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logger.info("Graph RAG service initialized")
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async def start(self):
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await super(Processor, self).start()
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await self.librarian_request_producer.start()
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await self.librarian_response_consumer.start()
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async def on_librarian_response(self, msg, consumer, flow):
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"""Handle responses from the librarian service."""
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response = msg.value()
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request_id = msg.properties().get("id")
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if request_id and request_id in self.pending_librarian_requests:
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future = self.pending_librarian_requests.pop(request_id)
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future.set_result(response)
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async def save_answer_content(self, doc_id, user, content, title=None, timeout=120):
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"""
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Save answer content to the librarian.
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Args:
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doc_id: ID for the answer document
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user: User ID
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content: Answer text content
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title: Optional title
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timeout: Request timeout in seconds
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Returns:
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The document ID on success
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"""
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request_id = str(uuid.uuid4())
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doc_metadata = DocumentMetadata(
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id=doc_id,
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user=user,
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kind="text/plain",
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title=title or "GraphRAG Answer",
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document_type="answer",
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)
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request = LibrarianRequest(
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operation="add-document",
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document_id=doc_id,
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document_metadata=doc_metadata,
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content=base64.b64encode(content.encode("utf-8")).decode("utf-8"),
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user=user,
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)
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# Create future for response
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future = asyncio.get_event_loop().create_future()
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self.pending_librarian_requests[request_id] = future
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try:
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# Send request
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await self.librarian_request_producer.send(
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request, properties={"id": request_id}
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)
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# Wait for response
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response = await asyncio.wait_for(future, timeout=timeout)
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if response.error:
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raise RuntimeError(
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f"Librarian error saving answer: {response.error.type}: {response.error.message}"
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)
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return doc_id
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except asyncio.TimeoutError:
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self.pending_librarian_requests.pop(request_id, None)
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raise RuntimeError(f"Timeout saving answer document {doc_id}")
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async def on_request(self, msg, consumer, flow):
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try:
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v = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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logger.info(f"Handling input {id}...")
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# Track explainability refs for end_of_session signaling
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explainability_refs_emitted = []
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# Real-time explainability callback - emits triples and IDs as they're generated
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# Triples are stored in the user's collection with a named graph (urn:graph:retrieval)
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async def send_explainability(triples, explain_id):
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# Send triples to explainability queue - stores in same collection with named graph
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await flow("explainability").send(Triples(
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metadata=Metadata(
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id=explain_id,
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user=v.user,
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collection=v.collection, # Store in user's collection, not separate explainability collection
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),
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triples=triples,
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))
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# Send explain ID and graph to response queue
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await flow("response").send(
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GraphRagResponse(
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message_type="explain",
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explain_id=explain_id,
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explain_graph=GRAPH_RETRIEVAL,
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),
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properties={"id": id}
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)
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explainability_refs_emitted.append(explain_id)
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# CRITICAL SECURITY: Create new GraphRag instance per request
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# This ensures proper isolation between users and collections
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# Flow clients are request-scoped and must not be shared
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rag = GraphRag(
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embeddings_client=flow("embeddings-request"),
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graph_embeddings_client=flow("graph-embeddings-request"),
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triples_client=flow("triples-request"),
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prompt_client=flow("prompt-request"),
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verbose=True,
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)
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if v.entity_limit:
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entity_limit = v.entity_limit
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else:
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entity_limit = self.default_entity_limit
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if v.triple_limit:
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triple_limit = v.triple_limit
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else:
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triple_limit = self.default_triple_limit
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if v.max_subgraph_size:
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max_subgraph_size = v.max_subgraph_size
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else:
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max_subgraph_size = self.default_max_subgraph_size
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if v.max_path_length:
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max_path_length = v.max_path_length
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else:
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max_path_length = self.default_max_path_length
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if v.edge_score_limit:
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edge_score_limit = v.edge_score_limit
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else:
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edge_score_limit = self.default_edge_score_limit
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if v.edge_limit:
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edge_limit = v.edge_limit
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else:
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edge_limit = self.default_edge_limit
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# Callback to save answer content to librarian
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async def save_answer(doc_id, answer_text):
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await self.save_answer_content(
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doc_id=doc_id,
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user=v.user,
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content=answer_text,
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title=f"GraphRAG Answer: {v.query[:50]}...",
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)
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# Check if streaming is requested
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if v.streaming:
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# Define async callback for streaming chunks
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# Receives chunk text and end_of_stream flag from prompt client
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async def send_chunk(chunk, end_of_stream):
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await flow("response").send(
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GraphRagResponse(
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message_type="chunk",
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response=chunk,
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end_of_stream=end_of_stream,
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error=None
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),
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properties={"id": id}
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)
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# Query with streaming and real-time explain
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response = await rag.query(
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query = v.query, user = v.user, collection = v.collection,
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entity_limit = entity_limit, triple_limit = triple_limit,
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max_subgraph_size = max_subgraph_size,
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max_path_length = max_path_length,
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edge_score_limit = edge_score_limit,
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edge_limit = edge_limit,
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streaming = True,
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chunk_callback = send_chunk,
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explain_callback = send_explainability,
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save_answer_callback = save_answer,
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)
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else:
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# Non-streaming path with real-time explain
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response = await rag.query(
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query = v.query, user = v.user, collection = v.collection,
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entity_limit = entity_limit, triple_limit = triple_limit,
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max_subgraph_size = max_subgraph_size,
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max_path_length = max_path_length,
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edge_score_limit = edge_score_limit,
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edge_limit = edge_limit,
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explain_callback = send_explainability,
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save_answer_callback = save_answer,
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)
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# Send chunk with response
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await flow("response").send(
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GraphRagResponse(
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message_type="chunk",
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response=response,
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end_of_stream=True,
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error=None,
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),
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properties={"id": id}
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)
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# Send final message to close session
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await flow("response").send(
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GraphRagResponse(
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message_type="chunk",
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response="",
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end_of_session=True,
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),
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properties={"id": id}
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)
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logger.info("Request processing complete")
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except Exception as e:
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logger.error(f"Graph RAG service exception: {e}", exc_info=True)
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logger.debug("Sending error response...")
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# Send error response and close session
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await flow("response").send(
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GraphRagResponse(
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message_type="chunk",
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error=Error(
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type="graph-rag-error",
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message=str(e),
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),
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end_of_stream=True,
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end_of_session=True,
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),
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properties={"id": id}
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)
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@staticmethod
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def add_args(parser):
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parser.add_argument(
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'-c', '--concurrency',
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type=int,
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default=default_concurrency,
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help=f'Concurrent processing threads (default: {default_concurrency})'
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)
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FlowProcessor.add_args(parser)
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parser.add_argument(
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'-e', '--entity-limit',
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type=int,
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default=50,
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help=f'Default entity vector fetch limit (default: 50)'
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)
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parser.add_argument(
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'-t', '--triple-limit',
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type=int,
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default=30,
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help=f'Default triple query limit, per query (default: 30)'
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)
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parser.add_argument(
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'-u', '--max-subgraph-size',
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type=int,
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default=150,
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help=f'Default max subgraph size (default: 150)'
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)
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parser.add_argument(
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'-a', '--max-path-length',
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type=int,
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default=2,
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help=f'Default max path length (default: 2)'
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)
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parser.add_argument(
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'--edge-score-limit',
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type=int,
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default=30,
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help=f'Semantic pre-filter limit before LLM scoring (default: 30)'
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)
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parser.add_argument(
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'--edge-limit',
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type=int,
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default=25,
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help=f'Max edges after LLM scoring (default: 25)'
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)
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# Note: Explainability triples are now stored in the user's collection
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# with the named graph urn:graph:retrieval (no separate collection needed)
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def run():
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Processor.launch(default_ident, __doc__)
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