Benchmark

[spyrchat]

This pull request introduces a production-ready, modular Retrieval-Augmented Generation (RAG) system with LangGraph agent integration. It adds a robust, configurable agent pipeline, modernizes the documentation, and provides full CI/CD test coverage, including security checks. The changes focus on making the system extensible, maintainable, and ready for deployment.

Agent Pipeline Implementation:

• Added a new LangGraph-based agent pipeline in agent/graph.py, featuring modular nodes for query interpretation, retrieval, generation, and memory updating, all configurable via YAML.
• Implemented core agent nodes:
  • query_interpreter for intent and routing
  • retriever (referenced in graph.py)
  • generator for answer synthesis
  • memory_updater for chat history management

Documentation and Developer Experience:

• Overhauled README.md with architecture diagrams, feature highlights, quick start instructions, extensibility guides, and migration steps from legacy code.

Testing and CI/CD:

• Added a comprehensive GitHub Actions workflow (.github/workflows/pipeline-tests.yml) for minimal, integration, end-to-end, and security/config validation tests, ensuring reliability and code quality.

Deployment and Packaging:

• Introduced a new Dockerfile using a slim Python image, installing dependencies and copying the full source for streamlined containerization and deployment.

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References:
[1] [2] [3] [4] [5] [6] [7]

[Copilot]

Pull Request Overview

This pull request introduces a comprehensive, production-ready Retrieval-Augmented Generation (RAG) system with extensive testing infrastructure. The changes transform the existing codebase into a modern, modular pipeline with LangGraph agent integration, comprehensive CI/CD testing, and deployment readiness.

• Introduces modular RAG pipeline with configurable agents, retrievers, and embeddings
• Adds comprehensive three-tier testing system (minimal, integration, end-to-end)
• Implements modern retrievers with hybrid search capabilities and pipeline integration
• Provides standalone processing scripts and deployment containerization

Reviewed Changes

Copilot reviewed 127 out of 142 changed files in this pull request and generated 7 comments.

Show a summary per file

File	Description
tests/test_local_setup.py	Local end-to-end test orchestration with progressive validation levels
tests/requirements-minimal.txt	Minimal testing dependencies for CI/CD environments
tests/requirements-README.md	Comprehensive documentation for testing dependency management
tests/pipeline/test_runner.py	Test runner combining configuration, pipeline, and database validation
tests/pipeline/test_qdrant_connectivity.py	Qdrant database connectivity tests with optional execution
tests/pipeline/test_qdrant.py	Simplified Qdrant operations testing for CI environments
tests/pipeline/test_minimal_pipeline.py	Core pipeline tests using Google Gemini embeddings only
tests/pipeline/test_minimal.py	Minimal pipeline validation without local model dependencies
tests/pipeline/test_end_to_end.py	Complete pipeline testing with real data and API integration
tests/pipeline/test_config.py	Configuration validation tests for YAML structure and completeness
tests/pipeline/test_components.py	Component integration tests without external service requirements
tests/pipeline/run_tests.py	Comprehensive test suite runner with auto-detection and reporting
tests/pipeline/init.py	Package initialization for pipeline tests
tests/pipeline/README_NEW.md	Detailed documentation for the three-tier testing system
tests/pipeline/.github-actions-example.yml	GitHub Actions CI configuration example for automated testing
tests/init.py	Package initialization for tests directory
scripts/standalone_sosum_processor.py	Standalone SOSum dataset processor without pipeline dependencies
scripts/setup_sosum.sh	SOSum dataset setup script with ingestion examples
retrievers/sparse_retriever.py	Modern sparse retriever with Qdrant integration and pipeline compliance
retrievers/semantic_retriever.py	Advanced semantic retriever with intelligent strategy selection
retrievers/hybrid_retriever.py	Hybrid retriever combining dense and sparse search with fusion methods
retrievers/dense_retriever.py	Modern dense retriever with direct Qdrant API usage
retrievers/base_retriever.py	Modern base retriever implementing pipeline interface
retrievers/base.py	Removed legacy base retriever implementation
retrievers/init.py	Updated exports for modern retriever implementations
pytest.ini	Pytest configuration with markers and test discovery settings

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[Copilot]

[nitpick] The fallback logic when health endpoint returns 404 is duplicating the collections endpoint test. Consider extracting this into a helper method to reduce code duplication and improve maintainability.

[Copilot]

[nitpick] The hardcoded force_recreate = False setting could cause issues if the test collection already exists with different configuration. Consider using True for test collections to ensure clean state, or add logic to handle collection conflicts.

Suggested change

	config["retrieval_pipeline"]["retriever"]["qdrant"]["force_recreate"] = False
	config["retrieval_pipeline"]["retriever"]["qdrant"]["force_recreate"] = True

[Copilot]

[nitpick] The default sparse embedding configuration is hardcoded. Consider moving these defaults to a configuration file or constants to improve maintainability and allow easier customization.

[Copilot]

[nitpick] The default dense embedding configuration is hardcoded with Google-specific settings. This creates tight coupling and should be extracted to configuration constants or use a configuration factory pattern.

[Copilot]

Taking only the first 16 bytes of SHA256 reduces the hash space and could increase collision probability. Consider using the full hash or a UUID5 which is designed for deterministic UUIDs from names.

Suggested change

	"""Convert a string to a deterministic UUID."""
	# Create a deterministic UUID from string using SHA256
	hash_bytes = hashlib.sha256(text.encode('utf-8')).digest()[:16]
	return str(uuid.UUID(bytes=hash_bytes))
	"""Convert a string to a deterministic UUID using UUID5."""
	# Use UUID5 for deterministic UUID from string and namespace
	return str(uuid.uuid5(uuid.NAMESPACE_URL, text))

[Copilot]

[nitpick] The 50% threshold for character repetition is hardcoded. Consider making this configurable through the validator config to allow different content types (e.g., code, structured data) that may legitimately have high character repetition.

[Copilot]

[nitpick] The vector configuration detection logic assumes specific attribute names ('dense') and structure. This is fragile and could break with Qdrant API changes. Consider using a more robust approach with try-catch blocks or checking attribute existence more defensively.

Suggested change

	if hasattr(vector_config, 'dense'):
	# Named vectors (hybrid setup)
	vector_size = vector_config.dense.size
	vector_name = "dense"
	else:
	# Single vector config
	vector_size = vector_config.size
	vector_name = None
	vector_size = None
	vector_name = None
	# Defensive checks for vector config structure
	if hasattr(vector_config, 'dense') and hasattr(vector_config.dense, 'size'):
	# Named vectors (hybrid setup)
	vector_size = vector_config.dense.size
	vector_name = "dense"
	elif hasattr(vector_config, 'size'):
	# Single vector config
	vector_size = vector_config.size
	vector_name = None
	elif isinstance(vector_config, dict):
	# Sometimes vector_config may be a dict (API change)
	if "dense" in vector_config and "size" in vector_config["dense"]:
	vector_size = vector_config["dense"]["size"]
	vector_name = "dense"
	elif "size" in vector_config:
	vector_size = vector_config["size"]
	vector_name = None
	if vector_size is None:
	raise ValueError("Could not determine vector size from collection config. Unexpected vector config structure: {}".format(vector_config))