mirror of
https://github.com/trustgraph-ai/trustgraph.git
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6129bb68c1
* Fixed hard-coded embeddings store size * Vector store lazy-creates collections, different collections for different dimension lengths. * Added tech spec for vector store lifecycle * Fixed some tests for the new spec
277 lines
9 KiB
Python
Executable file
277 lines
9 KiB
Python
Executable file
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"""
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Accepts entity/vector pairs and writes them to a Pinecone store.
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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 time
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import uuid
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import os
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import logging
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from .... base import GraphEmbeddingsStoreService
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from .... base import AsyncProcessor, Consumer, Producer
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from .... base import ConsumerMetrics, ProducerMetrics
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from .... schema import StorageManagementRequest, StorageManagementResponse, Error
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from .... schema import vector_storage_management_topic, storage_management_response_topic
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "ge-write"
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default_api_key = os.getenv("PINECONE_API_KEY", "not-specified")
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default_cloud = "aws"
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default_region = "us-east-1"
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class Processor(GraphEmbeddingsStoreService):
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def __init__(self, **params):
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self.url = params.get("url", None)
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self.cloud = params.get("cloud", default_cloud)
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self.region = params.get("region", default_region)
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self.api_key = params.get("api_key", default_api_key)
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if self.api_key is None or self.api_key == "not-specified":
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raise RuntimeError("Pinecone API key must be specified")
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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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"url": self.url,
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"cloud": self.cloud,
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"region": self.region,
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"api_key": self.api_key,
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}
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)
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self.last_index_name = None
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# Set up metrics for storage management
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storage_request_metrics = ConsumerMetrics(
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processor=self.id, flow=None, name="storage-request"
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)
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storage_response_metrics = ProducerMetrics(
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processor=self.id, flow=None, name="storage-response"
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)
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# Set up consumer for storage management requests
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self.storage_request_consumer = Consumer(
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taskgroup=self.taskgroup,
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client=self.pulsar_client,
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flow=None,
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topic=vector_storage_management_topic,
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subscriber=f"{self.id}-storage",
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schema=StorageManagementRequest,
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handler=self.on_storage_management,
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metrics=storage_request_metrics,
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)
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# Set up producer for storage management responses
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self.storage_response_producer = Producer(
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client=self.pulsar_client,
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topic=storage_management_response_topic,
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schema=StorageManagementResponse,
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metrics=storage_response_metrics,
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)
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def create_index(self, index_name, dim):
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self.pinecone.create_index(
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name = index_name,
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dimension = dim,
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metric = "cosine",
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spec = ServerlessSpec(
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cloud = self.cloud,
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region = self.region,
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)
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)
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for i in range(0, 1000):
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if self.pinecone.describe_index(
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index_name
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).status["ready"]:
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break
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time.sleep(1)
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if not self.pinecone.describe_index(
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index_name
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).status["ready"]:
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raise RuntimeError(
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"Gave up waiting for index creation"
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)
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async def start(self):
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"""Start the processor and its storage management consumer"""
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await super().start()
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await self.storage_request_consumer.start()
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await self.storage_response_producer.start()
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async def store_graph_embeddings(self, message):
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for entity in message.entities:
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if entity.entity.value == "" or entity.entity.value is None:
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continue
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for vec in entity.vectors:
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# Create index name with dimension suffix for lazy creation
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dim = len(vec)
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index_name = (
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f"t-{message.metadata.user}-{message.metadata.collection}-{dim}"
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)
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# Lazily create index if it doesn't exist
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if not self.pinecone.has_index(index_name):
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logger.info(f"Lazily creating Pinecone index {index_name} with dimension {dim}")
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self.create_index(index_name, dim)
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index = self.pinecone.Index(index_name)
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# Generate unique ID for each vector
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vector_id = str(uuid.uuid4())
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records = [
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{
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"id": vector_id,
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"values": vec,
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"metadata": { "entity": entity.entity.value },
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}
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]
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index.upsert(
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vectors = records,
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)
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@staticmethod
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def add_args(parser):
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GraphEmbeddingsStoreService.add_args(parser)
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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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parser.add_argument(
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'--cloud',
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default=default_cloud,
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help=f'Pinecone cloud, (default: {default_cloud}'
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)
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parser.add_argument(
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'--region',
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default=default_region,
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help=f'Pinecone region, (default: {default_region}'
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)
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async def on_storage_management(self, message, consumer, flow):
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"""Handle storage management requests"""
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request = message.value()
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logger.info(f"Storage management request: {request.operation} for {request.user}/{request.collection}")
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try:
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if request.operation == "create-collection":
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await self.handle_create_collection(request)
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elif request.operation == "delete-collection":
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await self.handle_delete_collection(request)
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else:
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response = StorageManagementResponse(
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error=Error(
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type="invalid_operation",
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message=f"Unknown operation: {request.operation}"
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)
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)
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await self.storage_response_producer.send(response)
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except Exception as e:
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logger.error(f"Error processing storage management request: {e}", exc_info=True)
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response = StorageManagementResponse(
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error=Error(
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type="processing_error",
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message=str(e)
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)
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)
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await self.storage_response_producer.send(response)
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async def handle_create_collection(self, request):
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"""
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No-op for collection creation - indexes are created lazily on first write
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with the correct dimension determined from the actual embeddings.
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"""
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try:
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logger.info(f"Collection create request for {request.user}/{request.collection} - will be created lazily on first write")
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# Send success response
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response = StorageManagementResponse(error=None)
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await self.storage_response_producer.send(response)
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except Exception as e:
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logger.error(f"Failed to handle create collection request: {e}", exc_info=True)
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response = StorageManagementResponse(
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error=Error(
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type="creation_error",
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message=str(e)
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)
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)
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await self.storage_response_producer.send(response)
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async def handle_delete_collection(self, request):
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"""
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Delete all dimension variants of the index for graph embeddings.
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Since indexes are created with dimension suffixes (e.g., t-user-coll-384),
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we need to find and delete all matching indexes.
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"""
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try:
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prefix = f"t-{request.user}-{request.collection}-"
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# Get all indexes and filter for matches
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all_indexes = self.pinecone.list_indexes()
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matching_indexes = [
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idx.name for idx in all_indexes
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if idx.name.startswith(prefix)
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]
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if not matching_indexes:
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logger.info(f"No indexes found matching prefix {prefix}")
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else:
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for index_name in matching_indexes:
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self.pinecone.delete_index(index_name)
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logger.info(f"Deleted Pinecone index: {index_name}")
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logger.info(f"Deleted {len(matching_indexes)} index(es) for {request.user}/{request.collection}")
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# Send success response
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response = StorageManagementResponse(
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error=None # No error means success
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)
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await self.storage_response_producer.send(response)
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except Exception as e:
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logger.error(f"Failed to delete collection: {e}")
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raise
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def run():
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Processor.launch(default_ident, __doc__)
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