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https://github.com/trustgraph-ai/trustgraph.git
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a9197d11ee
- Keeps processing in different flows separate so that data can go to different stores / collections etc. - Potentially supports different processing flows - Tidies the processing API with common base-classes for e.g. LLMs, and automatic configuration of 'clients' to use the right queue names in a flow
167 lines
4.5 KiB
Python
Executable file
167 lines
4.5 KiB
Python
Executable file
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"""
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Graph embeddings query service. Input is vector, output is list of
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entities. Pinecone implementation.
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"""
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from pinecone import Pinecone, ServerlessSpec
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from pinecone.grpc import PineconeGRPC, GRPCClientConfig
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import uuid
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import os
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from .... schema import GraphEmbeddingsRequest, GraphEmbeddingsResponse
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from .... schema import Error, Value
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from .... schema import graph_embeddings_request_queue
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from .... schema import graph_embeddings_response_queue
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from .... base import ConsumerProducer
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module = "ge-query"
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default_input_queue = graph_embeddings_request_queue
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default_output_queue = graph_embeddings_response_queue
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default_subscriber = module
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default_api_key = os.getenv("PINECONE_API_KEY", "not-specified")
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class Processor(ConsumerProducer):
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def __init__(self, **params):
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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self.url = params.get("url", None)
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self.api_key = params.get("api_key", default_api_key)
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if self.url:
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self.pinecone = PineconeGRPC(
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api_key = self.api_key,
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host = self.url
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)
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else:
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self.pinecone = Pinecone(api_key = self.api_key)
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super(Processor, self).__init__(
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**params | {
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"input_queue": input_queue,
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"output_queue": output_queue,
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"subscriber": subscriber,
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"input_schema": GraphEmbeddingsRequest,
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"output_schema": GraphEmbeddingsResponse,
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"url": self.url,
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}
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)
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def create_value(self, ent):
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if ent.startswith("http://") or ent.startswith("https://"):
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return Value(value=ent, is_uri=True)
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else:
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return Value(value=ent, is_uri=False)
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async def handle(self, msg):
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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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print(f"Handling input {id}...", flush=True)
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entity_set = set()
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entities = []
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for vec in v.vectors:
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dim = len(vec)
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index_name = (
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"t-" + v.user + "-" + str(dim)
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)
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index = self.pinecone.Index(index_name)
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# Heuristic hack, get (2*limit), so that we have more chance
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# of getting (limit) entities
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results = index.query(
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namespace=v.collection,
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vector=vec,
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top_k=v.limit * 2,
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include_values=False,
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include_metadata=True
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)
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for r in results.matches:
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ent = r.metadata["entity"]
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# De-dupe entities
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if ent not in entity_set:
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entity_set.add(ent)
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entities.append(ent)
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# Keep adding entities until limit
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if len(entity_set) >= v.limit: break
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# Keep adding entities until limit
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if len(entity_set) >= v.limit: break
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ents2 = []
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for ent in entities:
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ents2.append(self.create_value(ent))
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entities = ents2
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print("Send response...", flush=True)
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r = GraphEmbeddingsResponse(entities=entities, error=None)
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await self.send(r, properties={"id": id})
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print("Done.", flush=True)
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except Exception as e:
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print(f"Exception: {e}")
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print("Send error response...", flush=True)
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r = GraphEmbeddingsResponse(
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error=Error(
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type = "llm-error",
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message = str(e),
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),
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entities=None,
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)
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await self.send(r, properties={"id": id})
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self.consumer.acknowledge(msg)
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@staticmethod
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def add_args(parser):
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ConsumerProducer.add_args(
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parser, default_input_queue, default_subscriber,
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default_output_queue,
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)
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parser.add_argument(
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'-a', '--api-key',
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default=default_api_key,
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help='Pinecone API key. (default from PINECONE_API_KEY)'
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)
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parser.add_argument(
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'-u', '--url',
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help='Pinecone URL. If unspecified, serverless is used'
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)
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def run():
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Processor.launch(module, __doc__)
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