mirror of
https://github.com/trustgraph-ai/trustgraph.git
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b0f4c58200
* Update schema defs for source -> metadata * Migrate to use metadata part of schema, also add metadata to triples & vecs * Add user/collection metadata to query * Use user/collection in RAG * Write and query working on triples
103 lines
3 KiB
Python
Executable file
103 lines
3 KiB
Python
Executable file
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"""
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Vectorizer, calls the embeddings service to get embeddings for a chunk.
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Input is text chunk, output is chunk and vectors.
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"""
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from ... schema import Chunk, ChunkEmbeddings
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from ... schema import chunk_ingest_queue, chunk_embeddings_ingest_queue
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from ... schema import embeddings_request_queue, embeddings_response_queue
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from ... clients.embeddings_client import EmbeddingsClient
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from ... log_level import LogLevel
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from ... base import ConsumerProducer
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = chunk_ingest_queue
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default_output_queue = chunk_embeddings_ingest_queue
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default_subscriber = module
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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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emb_request_queue = params.get(
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"embeddings_request_queue", embeddings_request_queue
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)
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emb_response_queue = params.get(
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"embeddings_response_queue", embeddings_response_queue
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)
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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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"embeddings_request_queue": emb_request_queue,
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"embeddings_response_queue": emb_response_queue,
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"subscriber": subscriber,
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"input_schema": Chunk,
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"output_schema": ChunkEmbeddings,
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}
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)
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self.embeddings = EmbeddingsClient(
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pulsar_host=self.pulsar_host,
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input_queue=emb_request_queue,
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output_queue=emb_response_queue,
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subscriber=module + "-emb",
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)
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def emit(self, metadata, chunk, vectors):
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r = ChunkEmbeddings(metadata=metadata, chunk=chunk, vectors=vectors)
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self.producer.send(r)
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def handle(self, msg):
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v = msg.value()
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print(f"Indexing {v.metadata.id}...", flush=True)
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chunk = v.chunk.decode("utf-8")
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try:
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vectors = self.embeddings.request(chunk)
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self.emit(
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metadata=v.metadata,
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chunk=chunk.encode("utf-8"),
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vectors=vectors
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)
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except Exception as e:
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print("Exception:", e, flush=True)
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print("Done.", flush=True)
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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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'--embeddings-request-queue',
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default=embeddings_request_queue,
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help=f'Embeddings request queue (default: {embeddings_request_queue})',
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)
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parser.add_argument(
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'--embeddings-response-queue',
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default=embeddings_response_queue,
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help=f'Embeddings request queue (default: {embeddings_response_queue})',
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)
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def run():
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Processor.start(module, __doc__)
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