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https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-28 09:56:22 +02:00
Implement logging strategy (#444)
* Logging strategy and convert all prints() to logging invocations
This commit is contained in:
cybermaggedon
GitHub
parent
3e0651222b
commit
dd70aade11
117 changed files with 1216 additions and 667 deletions
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@ -1,5 +1,9 @@
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import asyncio
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import logging
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# Module logger
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logger = logging.getLogger(__name__)
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LABEL="http://www.w3.org/2000/01/rdf-schema#label"
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@ -18,12 +22,12 @@ class Query:
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async def get_vector(self, query):
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if self.verbose:
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print("Compute embeddings...", flush=True)
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logger.debug("Computing embeddings...")
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qembeds = await self.rag.embeddings_client.embed(query)
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if self.verbose:
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print("Done.", flush=True)
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logger.debug("Embeddings computed")
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return qembeds
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@ -32,7 +36,7 @@ class Query:
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vectors = await self.get_vector(query)
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if self.verbose:
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print("Get docs...", flush=True)
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logger.debug("Getting documents...")
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docs = await self.rag.doc_embeddings_client.query(
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vectors, limit=self.doc_limit,
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@ -40,9 +44,9 @@ class Query:
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)
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if self.verbose:
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print("Docs:", flush=True)
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logger.debug("Documents:")
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for doc in docs:
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print(doc, flush=True)
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logger.debug(f" {doc}")
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return docs
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@ -60,7 +64,7 @@ class DocumentRag:
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self.doc_embeddings_client = doc_embeddings_client
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if self.verbose:
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print("Initialised", flush=True)
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logger.debug("DocumentRag initialized")
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async def query(
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self, query, user="trustgraph", collection="default",
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@ -68,7 +72,7 @@ class DocumentRag:
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):
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if self.verbose:
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print("Construct prompt...", flush=True)
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logger.debug("Constructing prompt...")
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q = Query(
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rag=self, user=user, collection=collection, verbose=self.verbose,
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@ -78,9 +82,9 @@ class DocumentRag:
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docs = await q.get_docs(query)
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if self.verbose:
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print("Invoke LLM...", flush=True)
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print(docs)
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print(query)
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logger.debug("Invoking LLM...")
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logger.debug(f"Documents: {docs}")
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logger.debug(f"Query: {query}")
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resp = await self.prompt_client.document_prompt(
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query = query,
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@ -88,7 +92,7 @@ class DocumentRag:
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)
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if self.verbose:
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print("Done", flush=True)
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logger.debug("Query processing complete")
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return resp
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@ -4,12 +4,16 @@ Simple RAG service, performs query using document RAG an LLM.
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Input is query, output is response.
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"""
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import logging
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from ... schema import DocumentRagQuery, DocumentRagResponse, Error
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from . document_rag import DocumentRag
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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 DocumentEmbeddingsClientSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "document-rag"
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class Processor(FlowProcessor):
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@ -81,7 +85,7 @@ class Processor(FlowProcessor):
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# Sender-produced ID
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id = msg.properties()["id"]
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print(f"Handling input {id}...", flush=True)
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logger.info(f"Handling input {id}...")
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if v.doc_limit:
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doc_limit = v.doc_limit
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@ -98,13 +102,13 @@ class Processor(FlowProcessor):
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properties = {"id": id}
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)
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print("Done.", flush=True)
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logger.info("Request processing complete")
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except Exception as e:
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print(f"Exception: {e}")
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logger.error(f"Document RAG service exception: {e}", exc_info=True)
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print("Send error response...", flush=True)
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logger.debug("Sending error response...")
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await flow("response").send(
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DocumentRagResponse(
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@ -1,5 +1,9 @@
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import asyncio
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import logging
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# Module logger
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logger = logging.getLogger(__name__)
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LABEL="http://www.w3.org/2000/01/rdf-schema#label"
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@ -22,12 +26,12 @@ class Query:
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async def get_vector(self, query):
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if self.verbose:
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print("Compute embeddings...", flush=True)
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logger.debug("Computing embeddings...")
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qembeds = await self.rag.embeddings_client.embed(query)
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if self.verbose:
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print("Done.", flush=True)
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logger.debug("Done.")
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return qembeds
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@ -36,7 +40,7 @@ class Query:
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vectors = await self.get_vector(query)
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if self.verbose:
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print("Get entities...", flush=True)
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logger.debug("Getting entities...")
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entities = await self.rag.graph_embeddings_client.query(
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vectors=vectors, limit=self.entity_limit,
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@ -49,9 +53,9 @@ class Query:
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]
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if self.verbose:
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print("Entities:", flush=True)
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logger.debug("Entities:")
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for ent in entities:
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print(" ", ent, flush=True)
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logger.debug(f" {ent}")
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return entities
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@ -126,7 +130,7 @@ class Query:
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entities = await self.get_entities(query)
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if self.verbose:
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print("Get subgraph...", flush=True)
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logger.debug("Getting subgraph...")
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subgraph = set()
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@ -157,12 +161,12 @@ class Query:
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sg2 = sg2[0:self.max_subgraph_size]
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if self.verbose:
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print("Subgraph:", flush=True)
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logger.debug("Subgraph:")
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for edge in sg2:
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print(" ", str(edge), flush=True)
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logger.debug(f" {str(edge)}")
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if self.verbose:
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print("Done.", flush=True)
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logger.debug("Done.")
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return sg2
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@ -183,7 +187,7 @@ class GraphRag:
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self.label_cache = {}
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if self.verbose:
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print("Initialised", flush=True)
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logger.debug("GraphRag initialized")
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async def query(
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self, query, user = "trustgraph", collection = "default",
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@ -192,7 +196,7 @@ class GraphRag:
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):
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if self.verbose:
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print("Construct prompt...", flush=True)
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logger.debug("Constructing prompt...")
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q = Query(
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rag = self, user = user, collection = collection,
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@ -205,14 +209,14 @@ class GraphRag:
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kg = await q.get_labelgraph(query)
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if self.verbose:
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print("Invoke LLM...", flush=True)
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print(kg)
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print(query)
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logger.debug("Invoking LLM...")
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logger.debug(f"Knowledge graph: {kg}")
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logger.debug(f"Query: {query}")
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resp = await self.prompt_client.kg_prompt(query, kg)
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if self.verbose:
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print("Done", flush=True)
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logger.debug("Query processing complete")
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return resp
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@ -4,12 +4,16 @@ 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 logging
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from ... schema import GraphRagQuery, GraphRagResponse, Error
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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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# 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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@ -102,7 +106,7 @@ class Processor(FlowProcessor):
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# Sender-produced ID
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id = msg.properties()["id"]
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print(f"Handling input {id}...", flush=True)
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logger.info(f"Handling input {id}...")
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if v.entity_limit:
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entity_limit = v.entity_limit
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@ -139,13 +143,13 @@ class Processor(FlowProcessor):
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properties = {"id": id}
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)
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print("Done.", flush=True)
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logger.info("Request processing complete")
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except Exception as e:
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print(f"Exception: {e}")
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logger.error(f"Graph RAG service exception: {e}", exc_info=True)
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print("Send error response...", flush=True)
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logger.debug("Sending error response...")
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await flow("response").send(
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GraphRagResponse(
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