mirror of
https://github.com/trustgraph-ai/trustgraph.git
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170 lines
4.9 KiB
Python
170 lines
4.9 KiB
Python
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"""
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Accepts entity/vector pairs and writes them to a Qdrant store.
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"""
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from qdrant_client import QdrantClient
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from qdrant_client.models import PointStruct
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from qdrant_client.models import Distance, VectorParams
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import time
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import uuid
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import os
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from .... schema import DocumentEmbeddings
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from .... schema import document_embeddings_store_queue
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from .... log_level import LogLevel
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from .... base import Consumer
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = document_embeddings_store_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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default_cloud = "aws"
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default_region = "us-east-1"
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class Processor(Consumer):
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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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subscriber = params.get("subscriber", default_subscriber)
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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:
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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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"input_queue": input_queue,
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"subscriber": subscriber,
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"input_schema": DocumentEmbeddings,
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"url": self.url,
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}
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)
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self.last_index_name = None
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async def handle(self, msg):
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v = msg.value()
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for emb in v.chunks:
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chunk = emb.chunk.decode("utf-8")
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if chunk == "" or chunk is None: continue
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for vec in emb.vectors:
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for vec in v.vectors:
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dim = len(vec)
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collection = (
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"d-" + v.metadata.user + "-" + str(dim)
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)
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if index_name != self.last_index_name:
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if not self.pinecone.has_index(index_name):
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try:
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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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except Exception as e:
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print("Pinecone index creation failed")
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raise e
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print(f"Index {index_name} created", flush=True)
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self.last_index_name = index_name
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index = self.pinecone.Index(index_name)
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records = [
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{
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"id": id,
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"values": vec,
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"metadata": { "doc": chunk },
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}
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]
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index.upsert(
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vectors = records,
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namespace = v.metadata.collection,
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)
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@staticmethod
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def add_args(parser):
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Consumer.add_args(
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parser, default_input_queue, default_subscriber,
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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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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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def run():
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Processor.launch(module, __doc__)
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