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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.
55 lines
1.2 KiB
Python
55 lines
1.2 KiB
Python
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from dataclasses import dataclass, field
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from ..core.primitives import Error
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############################################################################
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# LLM text completion
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@dataclass
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class TextCompletionRequest:
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system: str = ""
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prompt: str = ""
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streaming: bool = False # Default false for backward compatibility
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@dataclass
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class TextCompletionResponse:
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error: Error | None = None
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response: str = ""
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in_token: int = 0
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out_token: int = 0
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model: str = ""
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end_of_stream: bool = False # Indicates final message in stream
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############################################################################
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# Embeddings
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@dataclass
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class EmbeddingsRequest:
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texts: list[str] = field(default_factory=list)
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@dataclass
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class EmbeddingsResponse:
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error: Error | None = None
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vectors: list[list[float]] = field(default_factory=list)
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############################################################################
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# Tool request/response
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@dataclass
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class ToolRequest:
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name: str = ""
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# Parameters are JSON encoded
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parameters: str = ""
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@dataclass
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class ToolResponse:
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error: Error | None = None
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# Plain text aka "unstructured"
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text: str = ""
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# JSON-encoded object aka "structured"
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object: str = ""
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