mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-04-25 16:36:21 +02:00
Release/v1.2 (#457)
* Bump setup.py versions for 1.1 * PoC MCP server (#419) * Very initial MCP server PoC for TrustGraph * Put service on port 8000 * Add MCP container and packages to buildout * Update docs for API/CLI changes in 1.0 (#421) * Update some API basics for the 0.23/1.0 API change * Add MCP container push (#425) * Add command args to the MCP server (#426) * Host and port parameters * Added websocket arg * More docs * MCP client support (#427) - MCP client service - Tool request/response schema - API gateway support for mcp-tool - Message translation for tool request & response - Make mcp-tool using configuration service for information about where the MCP services are. * Feature/react call mcp (#428) Key Features - MCP Tool Integration: Added core MCP tool support with ToolClientSpec and ToolClient classes - API Enhancement: New mcp_tool method for flow-specific tool invocation - CLI Tooling: New tg-invoke-mcp-tool command for testing MCP integration - React Agent Enhancement: Fixed and improved multi-tool invocation capabilities - Tool Management: Enhanced CLI for tool configuration and management Changes - Added MCP tool invocation to API with flow-specific integration - Implemented ToolClientSpec and ToolClient for tool call handling - Updated agent-manager-react to invoke MCP tools with configurable types - Enhanced CLI with new commands and improved help text - Added comprehensive documentation for new CLI commands - Improved tool configuration management Testing - Added tg-invoke-mcp-tool CLI command for isolated MCP integration testing - Enhanced agent capability to invoke multiple tools simultaneously * Test suite executed from CI pipeline (#433) * Test strategy & test cases * Unit tests * Integration tests * Extending test coverage (#434) * Contract tests * Testing embeedings * Agent unit tests * Knowledge pipeline tests * Turn on contract tests * Increase storage test coverage (#435) * Fixing storage and adding tests * PR pipeline only runs quick tests * Empty configuration is returned as empty list, previously was not in response (#436) * Update config util to take files as well as command-line text (#437) * Updated CLI invocation and config model for tools and mcp (#438) * Updated CLI invocation and config model for tools and mcp * CLI anomalies * Tweaked the MCP tool implementation for new model * Update agent implementation to match the new model * Fix agent tools, now all tested * Fixed integration tests * Fix MCP delete tool params * Update Python deps to 1.2 * Update to enable knowledge extraction using the agent framework (#439) * Implement KG extraction agent (kg-extract-agent) * Using ReAct framework (agent-manager-react) * ReAct manager had an issue when emitting JSON, which conflicts which ReAct manager's own JSON messages, so refactored ReAct manager to use traditional ReAct messages, non-JSON structure. * Minor refactor to take the prompt template client out of prompt-template so it can be more readily used by other modules. kg-extract-agent uses this framework. * Migrate from setup.py to pyproject.toml (#440) * Converted setup.py to pyproject.toml * Modern package infrastructure as recommended by py docs * Install missing build deps (#441) * Install missing build deps (#442) * Implement logging strategy (#444) * Logging strategy and convert all prints() to logging invocations * Fix/startup failure (#445) * Fix loggin startup problems * Fix logging startup problems (#446) * Fix logging startup problems (#447) * Fixed Mistral OCR to use current API (#448) * Fixed Mistral OCR to use current API * Added PDF decoder tests * Fix Mistral OCR ident to be standard pdf-decoder (#450) * Fix Mistral OCR ident to be standard pdf-decoder * Correct test * Schema structure refactor (#451) * Write schema refactor spec * Implemented schema refactor spec * Structure data mvp (#452) * Structured data tech spec * Architecture principles * New schemas * Updated schemas and specs * Object extractor * Add .coveragerc * New tests * Cassandra object storage * Trying to object extraction working, issues exist * Validate librarian collection (#453) * Fix token chunker, broken API invocation (#454) * Fix token chunker, broken API invocation (#455) * Knowledge load utility CLI (#456) * Knowledge loader * More tests
This commit is contained in:
cybermaggedon
GitHub
parent
c85ba197be
commit
89be656990
509 changed files with 49632 additions and 5159 deletions
148
tests/unit/test_query/conftest.py
Normal file
148
tests/unit/test_query/conftest.py
Normal file
|
|
@ -0,0 +1,148 @@
|
|||
"""
|
||||
Shared fixtures for query tests
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def base_query_config():
|
||||
"""Base configuration for query processors"""
|
||||
return {
|
||||
'taskgroup': AsyncMock(),
|
||||
'id': 'test-query-processor'
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def qdrant_query_config(base_query_config):
|
||||
"""Configuration for Qdrant query processors"""
|
||||
return base_query_config | {
|
||||
'store_uri': 'http://localhost:6333',
|
||||
'api_key': 'test-api-key'
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_qdrant_client():
|
||||
"""Mock Qdrant client"""
|
||||
mock_client = MagicMock()
|
||||
mock_client.query_points.return_value = []
|
||||
return mock_client
|
||||
|
||||
|
||||
# Graph embeddings query fixtures
|
||||
@pytest.fixture
|
||||
def mock_graph_embeddings_request():
|
||||
"""Mock graph embeddings request message"""
|
||||
mock_message = MagicMock()
|
||||
mock_message.vectors = [[0.1, 0.2, 0.3]]
|
||||
mock_message.limit = 5
|
||||
mock_message.user = 'test_user'
|
||||
mock_message.collection = 'test_collection'
|
||||
return mock_message
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_graph_embeddings_multiple_vectors():
|
||||
"""Mock graph embeddings request with multiple vectors"""
|
||||
mock_message = MagicMock()
|
||||
mock_message.vectors = [[0.1, 0.2], [0.3, 0.4]]
|
||||
mock_message.limit = 3
|
||||
mock_message.user = 'multi_user'
|
||||
mock_message.collection = 'multi_collection'
|
||||
return mock_message
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_graph_embeddings_query_response():
|
||||
"""Mock graph embeddings query response from Qdrant"""
|
||||
mock_point1 = MagicMock()
|
||||
mock_point1.payload = {'entity': 'entity1'}
|
||||
mock_point2 = MagicMock()
|
||||
mock_point2.payload = {'entity': 'entity2'}
|
||||
return [mock_point1, mock_point2]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_graph_embeddings_uri_response():
|
||||
"""Mock graph embeddings query response with URIs"""
|
||||
mock_point1 = MagicMock()
|
||||
mock_point1.payload = {'entity': 'http://example.com/entity1'}
|
||||
mock_point2 = MagicMock()
|
||||
mock_point2.payload = {'entity': 'https://secure.example.com/entity2'}
|
||||
mock_point3 = MagicMock()
|
||||
mock_point3.payload = {'entity': 'regular entity'}
|
||||
return [mock_point1, mock_point2, mock_point3]
|
||||
|
||||
|
||||
# Document embeddings query fixtures
|
||||
@pytest.fixture
|
||||
def mock_document_embeddings_request():
|
||||
"""Mock document embeddings request message"""
|
||||
mock_message = MagicMock()
|
||||
mock_message.vectors = [[0.1, 0.2, 0.3]]
|
||||
mock_message.limit = 5
|
||||
mock_message.user = 'test_user'
|
||||
mock_message.collection = 'test_collection'
|
||||
return mock_message
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_document_embeddings_multiple_vectors():
|
||||
"""Mock document embeddings request with multiple vectors"""
|
||||
mock_message = MagicMock()
|
||||
mock_message.vectors = [[0.1, 0.2], [0.3, 0.4]]
|
||||
mock_message.limit = 3
|
||||
mock_message.user = 'multi_user'
|
||||
mock_message.collection = 'multi_collection'
|
||||
return mock_message
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_document_embeddings_query_response():
|
||||
"""Mock document embeddings query response from Qdrant"""
|
||||
mock_point1 = MagicMock()
|
||||
mock_point1.payload = {'doc': 'first document chunk'}
|
||||
mock_point2 = MagicMock()
|
||||
mock_point2.payload = {'doc': 'second document chunk'}
|
||||
return [mock_point1, mock_point2]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_document_embeddings_utf8_response():
|
||||
"""Mock document embeddings query response with UTF-8 content"""
|
||||
mock_point1 = MagicMock()
|
||||
mock_point1.payload = {'doc': 'Document with UTF-8: café, naïve, résumé'}
|
||||
mock_point2 = MagicMock()
|
||||
mock_point2.payload = {'doc': 'Chinese text: 你好世界'}
|
||||
return [mock_point1, mock_point2]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_empty_query_response():
|
||||
"""Mock empty query response"""
|
||||
return []
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_large_query_response():
|
||||
"""Mock large query response with many results"""
|
||||
mock_points = []
|
||||
for i in range(10):
|
||||
mock_point = MagicMock()
|
||||
mock_point.payload = {'doc': f'document chunk {i}'}
|
||||
mock_points.append(mock_point)
|
||||
return mock_points
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_mixed_dimension_vectors():
|
||||
"""Mock request with vectors of different dimensions"""
|
||||
mock_message = MagicMock()
|
||||
mock_message.vectors = [[0.1, 0.2], [0.3, 0.4, 0.5]] # 2D and 3D
|
||||
mock_message.limit = 5
|
||||
mock_message.user = 'dim_user'
|
||||
mock_message.collection = 'dim_collection'
|
||||
return mock_message
|
||||
Loading…
Add table
Add a link
Reference in a new issue