mirror of
https://github.com/trustgraph-ai/trustgraph.git
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4fb0b4d8e8
Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
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from .. schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse
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from .. schema import document_embeddings_request_queue
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from .. schema import document_embeddings_response_queue
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from . base import BaseClient
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# Ugly
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class DocumentEmbeddingsClient(BaseClient):
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def __init__(
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self,
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subscriber=None,
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input_queue=None,
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output_queue=None,
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pulsar_host="pulsar://pulsar:6650",
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pulsar_api_key=None,
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):
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if input_queue == None:
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input_queue = document_embeddings_request_queue
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if output_queue == None:
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output_queue = document_embeddings_response_queue
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super(DocumentEmbeddingsClient, self).__init__(
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subscriber=subscriber,
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input_queue=input_queue,
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output_queue=output_queue,
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pulsar_host=pulsar_host,
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pulsar_api_key=pulsar_api_key,
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input_schema=DocumentEmbeddingsRequest,
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output_schema=DocumentEmbeddingsResponse,
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)
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def request(
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self, vector, user="trustgraph", collection="default",
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limit=10, timeout=300
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):
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return self.call(
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user=user, collection=collection,
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vector=vector, limit=limit, timeout=timeout
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).chunks
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