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.
270 lines
8.9 KiB
Python
270 lines
8.9 KiB
Python
"""
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Contract tests for document embeddings message schemas and translators
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Ensures that message formats remain consistent across services
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"""
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import pytest
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from unittest.mock import MagicMock
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from trustgraph.schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse, ChunkMatch, Error
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from trustgraph.messaging.translators.embeddings_query import (
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DocumentEmbeddingsRequestTranslator,
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DocumentEmbeddingsResponseTranslator
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)
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class TestDocumentEmbeddingsRequestContract:
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"""Test DocumentEmbeddingsRequest schema contract"""
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def test_request_schema_fields(self):
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"""Test that DocumentEmbeddingsRequest has expected fields"""
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# Create a request
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request = DocumentEmbeddingsRequest(
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vector=[0.1, 0.2, 0.3],
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limit=10,
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user="test_user",
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collection="test_collection"
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)
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# Verify all expected fields exist
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assert hasattr(request, 'vector')
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assert hasattr(request, 'limit')
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assert hasattr(request, 'user')
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assert hasattr(request, 'collection')
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# Verify field values
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assert request.vector == [0.1, 0.2, 0.3]
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assert request.limit == 10
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assert request.user == "test_user"
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assert request.collection == "test_collection"
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def test_request_translator_decode(self):
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"""Test request translator converts dict to Pulsar schema"""
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translator = DocumentEmbeddingsRequestTranslator()
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data = {
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"vector": [0.1, 0.2, 0.3, 0.4],
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"limit": 5,
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"user": "custom_user",
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"collection": "custom_collection"
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}
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result = translator.decode(data)
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assert isinstance(result, DocumentEmbeddingsRequest)
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assert result.vector == [0.1, 0.2, 0.3, 0.4]
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assert result.limit == 5
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assert result.user == "custom_user"
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assert result.collection == "custom_collection"
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def test_request_translator_decode_with_defaults(self):
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"""Test request translator uses correct defaults"""
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translator = DocumentEmbeddingsRequestTranslator()
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data = {
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"vector": [0.1, 0.2]
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# No limit, user, or collection provided
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}
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result = translator.decode(data)
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assert isinstance(result, DocumentEmbeddingsRequest)
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assert result.vector == [0.1, 0.2]
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assert result.limit == 10 # Default
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assert result.user == "trustgraph" # Default
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assert result.collection == "default" # Default
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def test_request_translator_encode(self):
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"""Test request translator converts Pulsar schema to dict"""
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translator = DocumentEmbeddingsRequestTranslator()
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request = DocumentEmbeddingsRequest(
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vector=[0.5, 0.6],
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limit=20,
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user="test_user",
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collection="test_collection"
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)
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result = translator.encode(request)
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assert isinstance(result, dict)
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assert result["vector"] == [0.5, 0.6]
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assert result["limit"] == 20
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assert result["user"] == "test_user"
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assert result["collection"] == "test_collection"
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class TestDocumentEmbeddingsResponseContract:
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"""Test DocumentEmbeddingsResponse schema contract"""
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def test_response_schema_fields(self):
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"""Test that DocumentEmbeddingsResponse has expected fields"""
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# Create a response with chunks
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response = DocumentEmbeddingsResponse(
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error=None,
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chunks=[
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ChunkMatch(chunk_id="chunk1", score=0.9),
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ChunkMatch(chunk_id="chunk2", score=0.8),
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ChunkMatch(chunk_id="chunk3", score=0.7)
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]
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)
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# Verify all expected fields exist
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assert hasattr(response, 'error')
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assert hasattr(response, 'chunks')
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# Verify field values
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assert response.error is None
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assert len(response.chunks) == 3
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assert response.chunks[0].chunk_id == "chunk1"
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assert response.chunks[0].score == 0.9
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def test_response_schema_with_error(self):
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"""Test response schema with error"""
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error = Error(
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type="query_error",
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message="Database connection failed"
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)
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response = DocumentEmbeddingsResponse(
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error=error,
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chunks=[]
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)
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assert response.error == error
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assert response.chunks == []
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def test_response_translator_encode_with_chunks(self):
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"""Test response translator converts Pulsar schema with chunks to dict"""
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translator = DocumentEmbeddingsResponseTranslator()
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response = DocumentEmbeddingsResponse(
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error=None,
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chunks=[
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ChunkMatch(chunk_id="doc1/c1", score=0.95),
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ChunkMatch(chunk_id="doc2/c2", score=0.85),
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ChunkMatch(chunk_id="doc3/c3", score=0.75)
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]
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)
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result = translator.encode(response)
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assert isinstance(result, dict)
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assert "chunks" in result
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assert len(result["chunks"]) == 3
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assert result["chunks"][0]["chunk_id"] == "doc1/c1"
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assert result["chunks"][0]["score"] == 0.95
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def test_response_translator_encode_with_empty_chunks(self):
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"""Test response translator handles empty chunks list"""
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translator = DocumentEmbeddingsResponseTranslator()
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response = DocumentEmbeddingsResponse(
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error=None,
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chunks=[]
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)
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result = translator.encode(response)
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assert isinstance(result, dict)
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assert "chunks" in result
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assert result["chunks"] == []
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def test_response_translator_encode_with_none_chunks(self):
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"""Test response translator handles None chunks"""
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translator = DocumentEmbeddingsResponseTranslator()
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response = MagicMock()
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response.chunks = None
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result = translator.encode(response)
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assert isinstance(result, dict)
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assert "chunks" not in result or result.get("chunks") is None
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def test_response_translator_encode_with_completion(self):
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"""Test response translator with completion flag"""
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translator = DocumentEmbeddingsResponseTranslator()
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response = DocumentEmbeddingsResponse(
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error=None,
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chunks=[
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ChunkMatch(chunk_id="chunk1", score=0.9),
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ChunkMatch(chunk_id="chunk2", score=0.8)
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]
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)
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result, is_final = translator.encode_with_completion(response)
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assert isinstance(result, dict)
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assert "chunks" in result
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assert len(result["chunks"]) == 2
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assert result["chunks"][0]["chunk_id"] == "chunk1"
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assert is_final is True # Document embeddings responses are always final
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def test_response_translator_decode_not_implemented(self):
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"""Test that decode raises NotImplementedError for responses"""
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translator = DocumentEmbeddingsResponseTranslator()
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with pytest.raises(NotImplementedError):
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translator.decode({"chunks": [{"chunk_id": "test", "score": 0.9}]})
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class TestDocumentEmbeddingsMessageCompatibility:
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"""Test compatibility between request and response messages"""
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def test_request_response_flow(self):
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"""Test complete request-response flow maintains data integrity"""
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# Create request
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request_data = {
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"vector": [0.1, 0.2, 0.3],
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"limit": 5,
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"user": "test_user",
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"collection": "test_collection"
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}
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# Convert to Pulsar request
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req_translator = DocumentEmbeddingsRequestTranslator()
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pulsar_request = req_translator.decode(request_data)
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# Simulate service processing and creating response
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response = DocumentEmbeddingsResponse(
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error=None,
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chunks=[
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ChunkMatch(chunk_id="doc1/c1", score=0.95),
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ChunkMatch(chunk_id="doc2/c2", score=0.85)
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]
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)
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# Convert response back to dict
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resp_translator = DocumentEmbeddingsResponseTranslator()
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response_data = resp_translator.encode(response)
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# Verify data integrity
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assert isinstance(pulsar_request, DocumentEmbeddingsRequest)
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assert isinstance(response_data, dict)
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assert "chunks" in response_data
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assert len(response_data["chunks"]) == 2
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def test_error_response_flow(self):
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"""Test error response flow"""
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# Create error response
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error = Error(
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type="vector_db_error",
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message="Collection not found"
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)
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response = DocumentEmbeddingsResponse(
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error=error,
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chunks=[]
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)
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# Convert response to dict
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translator = DocumentEmbeddingsResponseTranslator()
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response_data = translator.encode(response)
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# Verify error handling
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assert isinstance(response_data, dict)
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# The translator doesn't include error in the dict, only chunks
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assert "chunks" in response_data
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assert response_data["chunks"] == []
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