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https://github.com/trustgraph-ai/trustgraph.git
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f7df2df266
* Bring QDrant up-to-date * Tables for data from queue outputs - Pass single Pulsar client to everything in gateway & librarian - Pulsar listener-name support in gateway - PDF and text load working in librarian * Complete Cassandra schema * Add librarian support to templates
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
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import asyncio
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from pulsar.schema import JsonSchema
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import uuid
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from aiohttp import WSMsgType
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from .. schema import Metadata
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from .. schema import DocumentEmbeddings, ChunkEmbeddings
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from .. schema import document_embeddings_store_queue
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from .. base import Publisher
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from . socket import SocketEndpoint
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from . serialize import to_subgraph
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class DocumentEmbeddingsLoadEndpoint(SocketEndpoint):
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def __init__(
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self, pulsar_client, auth, path="/api/v1/load/document-embeddings",
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):
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super(DocumentEmbeddingsLoadEndpoint, self).__init__(
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endpoint_path=path, auth=auth,
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)
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self.pulsar_client=pulsar_client
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self.publisher = Publisher(
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self.pulsar_client, document_embeddings_store_queue,
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schema=JsonSchema(DocumentEmbeddings)
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)
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async def start(self):
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self.publisher.start()
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async def listener(self, ws, running):
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async for msg in ws:
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# On error, finish
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if msg.type == WSMsgType.ERROR:
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break
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else:
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data = msg.json()
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elt = DocumentEmbeddings(
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metadata=Metadata(
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id=data["metadata"]["id"],
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metadata=to_subgraph(data["metadata"]["metadata"]),
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user=data["metadata"]["user"],
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collection=data["metadata"]["collection"],
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),
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chunks=[
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ChunkEmbeddings(
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chunk=de["chunk"].encode("utf-8"),
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vectors=de["vectors"],
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)
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for de in data["chunks"]
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],
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)
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await self.publisher.send(None, elt)
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running.stop()
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