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feat: add mock vLLM infrastructure for lightweight e2e testing (#228)
* feat: add mock vLLM infrastructure for lightweight e2e testing This commit introduces a mock vLLM server infrastructure to enable e2e testing without requiring GPU resources. The mock infrastructure simulates intelligent routing behavior while maintaining compatibility with the existing semantic router. Key changes: - Add mock-vllm-server.py: Simulates vLLM OpenAI-compatible API with intelligent content-based routing (math queries → TinyLlama, general → Qwen) - Add start-mock-servers.sh: Launch mock servers in foreground mode - Update config.yaml: Add minimal vLLM endpoint configuration for Qwen (port 8000) and TinyLlama (port 8001) with smart routing preference - Update 00-client-request-test.py: Fix import path and use configured model - Update e2e-tests/README.md: Document mock infrastructure usage - Update build-run-test.mk: Add mock server management targets The mock infrastructure enables: - Fast e2e testing without GPU dependencies - Content-aware model selection simulation - vLLM API compatibility testing - Smart routing behavior validation Signed-off-by: Yossi Ovadia <[email protected]> * feat: replace mock vLLM infrastructure with LLM Katan package Replace the mock vLLM server with a real FastAPI-based implementation using HuggingFace transformers and tiny models. The new LLM Katan package provides actual inference while maintaining lightweight testing benefits. Key changes: - Add complete LLM Katan PyPI package (v0.1.4) under e2e-tests/ - FastAPI server with OpenAI-compatible endpoints (/v1/chat/completions, /v1/models, /health, /metrics) - Real Qwen/Qwen3-0.6B model with name aliasing for multi-model testing - Enhanced logging and Prometheus metrics endpoint - CLI tool with comprehensive configuration options - Replace start-mock-servers.sh with start-llm-katan.sh - Update e2e-tests README with new LLM Katan usage instructions - Remove obsolete mock-vllm-server.py and start-mock-servers.sh Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * docs: add HuggingFace token setup instructions to LLM Katan README Add comprehensive setup section covering HuggingFace token requirements with three authentication methods: - Environment variable (HUGGINGFACE_HUB_TOKEN) - CLI login (huggingface-cli login) - Token file in home directory Explains why token is needed (private models, rate limits, reliable downloads) and provides direct link to HuggingFace token settings. Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * fix: add Python build artifacts to .gitignore - Add dist/, build/, *.egg-info/, *.whl to ignore Python build outputs - Prevents accidentally committing generated files Signed-off-by: Yossi Ovadia <[email protected]> * refactor: separate e2e and production configs - Create config.e2e.yaml with LLM Katan endpoints for e2e tests - Restore config.yaml to original production endpoints (matches origin/main) - Add run-router-e2e target to use e2e config (config/config.e2e.yaml) - Add start-llm-katan and test-e2e-vllm targets for LLM Katan testing - Update Makefile help with new e2e test targets - Remove egg-info directory from git tracking (now in .gitignore) - Keep pyproject.toml at stable version 0.1.4, always install latest via pip This separation allows: - Production config stays clean with real vLLM endpoints - E2E tests use lightweight LLM Katan servers - Clear distinction between test and production environments - Always use latest LLM Katan features via unpinned pip installation Signed-off-by: Yossi Ovadia <[email protected]> * fix: update e2e test to use model from config.e2e.yaml - Change test model from 'gemma3:27b' to 'Qwen/Qwen2-0.5B-Instruct' - Ensures Envoy health check uses model available in e2e config - Fixes 503 errors when checking if Envoy proxy is running Signed-off-by: Yossi Ovadia <[email protected]> * Update llm-katan package metadata - Bump version to 0.1.6 for PyPI publishing - Change license from MIT to Apache-2.0 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * Fix Apache license classifier in pyproject.toml - Update license classifier from MIT to Apache Software License - Bump version to 0.1.7 for corrected license display on PyPI 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * fix: resolve pre-commit hook failures - Fix markdown linting issues (MD032, MD031, MD047) in README files - Remove binary distribution files from git tracking - Add Python build artifacts to .gitignore - Auto-format Python files with black and isort - Add CLAUDE.md exclusion to prevent future commits 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * fix: update llm-katan project URLs to vllm-project repository Update repository URLs in pyproject.toml to point to the correct vllm-project organization instead of personal fork. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * fix: revert config.yaml to original main branch version Revert production config.yaml to original state from main branch. The config modifications were not intended for this PR and should remain unchanged to preserve production configuration. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> * fix: restore config.yaml to match upstream main exactly Copy config.yaml from upstream main to ensure it matches exactly and includes the health_check_path and other missing fields. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> Signed-off-by: Yossi Ovadia <[email protected]> --------- Signed-off-by: Yossi Ovadia <[email protected]> Co-authored-by: Claude <[email protected]>
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‎.gitignore‎

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.venv/
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pii_env/
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# Python build artifacts
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dist/
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build/
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*.egg-info/
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*.whl
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*.tar.gz
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# Go
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*.exe
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*.exe~
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.cursorrules.*
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# augment editor rules
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.augment
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.augment
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# Claude Code configuration (should not be committed)
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CLAUDE.md

‎.pre-commit-config.yaml‎

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entry: bash -c "make markdown-lint"
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language: system
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files: \.md$
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exclude: ^(\node_modules/)
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exclude: ^(\node_modules/|CLAUDE\.md)
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# Yaml specific hooks
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- repo: local

‎config/config.e2e.yaml‎

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bert_model:
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model_id: sentence-transformers/all-MiniLM-L12-v2
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threshold: 0.6
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use_cpu: true
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semantic_cache:
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enabled: true
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backend_type: "memory" # Options: "memory" or "milvus"
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similarity_threshold: 0.8
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max_entries: 1000 # Only applies to memory backend
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ttl_seconds: 3600
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# For production environments, use Milvus for scalable caching:
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# backend_type: "milvus"
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# backend_config_path: "config/cache/milvus.yaml"
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# Development/Testing: Use in-memory cache (current configuration)
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# - Fast startup and no external dependencies
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# - Limited to single instance scaling
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# - Data lost on restart
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# Production: Use Milvus vector database
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# - Horizontally scalable and persistent
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# - Supports distributed deployments
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# - Requires Milvus cluster setup
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# - To enable: uncomment the lines above and install Milvus dependencies
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tools:
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enabled: true # Set to true to enable automatic tool selection
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top_k: 3 # Number of most relevant tools to select
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similarity_threshold: 0.2 # Threshold for tool similarity
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tools_db_path: "config/tools_db.json"
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fallback_to_empty: true # If true, return no tools on failure; if false, return error
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prompt_guard:
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enabled: true
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use_modernbert: true
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model_id: "models/jailbreak_classifier_modernbert-base_model"
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threshold: 0.7
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use_cpu: true
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jailbreak_mapping_path: "models/jailbreak_classifier_modernbert-base_model/jailbreak_type_mapping.json"
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# vLLM Endpoints Configuration - supports multiple endpoints, each can serve multiple models
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vllm_endpoints:
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- name: "endpoint1"
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address: "127.0.0.1"
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port: 11434
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models:
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- "phi4"
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- "gemma3:27b"
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weight: 1 # Load balancing weight
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health_check_path: "/health" # Optional health check endpoint
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- name: "endpoint2"
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address: "127.0.0.1"
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port: 11434
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models:
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- "mistral-small3.1"
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weight: 1
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health_check_path: "/health"
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- name: "endpoint3"
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address: "127.0.0.1"
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port: 11434
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models:
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- "phi4" # Same model can be served by multiple endpoints for redundancy
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- "mistral-small3.1"
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weight: 2 # Higher weight for more powerful endpoint
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- name: "qwen-endpoint"
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address: "127.0.0.1"
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port: 8000
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models:
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- "Qwen/Qwen2-0.5B-Instruct"
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weight: 1
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health_check_path: "/health"
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- name: "tinyllama-endpoint"
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address: "127.0.0.1"
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port: 8001
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models:
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- "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
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weight: 1
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health_check_path: "/health"
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model_config:
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phi4:
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pricing:
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currency: USD
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prompt_per_1m: 0.07
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completion_per_1m: 0.35
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pii_policy:
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allow_by_default: false # Deny all PII by default
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pii_types_allowed: ["EMAIL_ADDRESS", "PERSON", "GPE", "PHONE_NUMBER"] # Only allow these specific PII types
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# Specify which endpoints can serve this model (optional - if not specified, uses all endpoints that list this model)
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preferred_endpoints: ["endpoint1", "endpoint3"]
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# Reasoning family - phi4 doesn't support reasoning, so omit this field
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# Example: DeepSeek model with custom name
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"ds-v31-custom":
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reasoning_family: "deepseek" # This model uses DeepSeek reasoning syntax
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preferred_endpoints: ["endpoint1"]
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pii_policy:
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allow_by_default: true
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# Example: Qwen3 model with custom name
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"my-qwen3-model":
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reasoning_family: "qwen3" # This model uses Qwen3 reasoning syntax
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preferred_endpoints: ["endpoint2"]
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pii_policy:
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allow_by_default: true
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# Example: GPT-OSS model with custom name
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"custom-gpt-oss":
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reasoning_family: "gpt-oss" # This model uses GPT-OSS reasoning syntax
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preferred_endpoints: ["endpoint1"]
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pii_policy:
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allow_by_default: true
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gemma3:27b:
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pricing:
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currency: USD
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prompt_per_1m: 0.067
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completion_per_1m: 0.267
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pii_policy:
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allow_by_default: false # Deny all PII by default
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pii_types_allowed: ["EMAIL_ADDRESS", "PERSON", "GPE", "PHONE_NUMBER"] # Only allow these specific PII types
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preferred_endpoints: ["endpoint1"]
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"mistral-small3.1":
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pricing:
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currency: USD
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prompt_per_1m: 0.1
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completion_per_1m: 0.3
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pii_policy:
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allow_by_default: false # Deny all PII by default
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pii_types_allowed: ["EMAIL_ADDRESS", "PERSON", "GPE", "PHONE_NUMBER"] # Only allow these specific PII types
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preferred_endpoints: ["endpoint2", "endpoint3"]
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"Qwen/Qwen2-0.5B-Instruct":
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reasoning_family: "qwen3" # This model uses Qwen reasoning syntax
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preferred_endpoints: ["qwen-endpoint"]
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pii_policy:
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allow_by_default: true
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pii_types_allowed: ["EMAIL_ADDRESS", "PERSON", "GPE", "PHONE_NUMBER"]
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"TinyLlama/TinyLlama-1.1B-Chat-v1.0":
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preferred_endpoints: ["tinyllama-endpoint"]
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pii_policy:
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allow_by_default: true
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pii_types_allowed: ["EMAIL_ADDRESS", "PERSON", "GPE", "PHONE_NUMBER"]
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# Classifier configuration for text classification
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classifier:
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category_model:
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model_id: "models/category_classifier_modernbert-base_model" # TODO: Use local model for now before the code can download the entire model from huggingface
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use_modernbert: true
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threshold: 0.6
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use_cpu: true
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category_mapping_path: "models/category_classifier_modernbert-base_model/category_mapping.json"
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pii_model:
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model_id: "models/pii_classifier_modernbert-base_presidio_token_model" # TODO: Use local model for now before the code can download the entire model from huggingface
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use_modernbert: true
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threshold: 0.7
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use_cpu: true
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pii_mapping_path: "models/pii_classifier_modernbert-base_presidio_token_model/pii_type_mapping.json"
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categories:
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- name: business
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use_reasoning: false
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reasoning_description: "Business content is typically conversational"
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reasoning_effort: low # Business conversations need low reasoning effort
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model_scores:
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- model: phi4
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score: 0.8
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- model: gemma3:27b
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score: 0.4
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- model: mistral-small3.1
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score: 0.2
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- name: law
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use_reasoning: false
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reasoning_description: "Legal content is typically explanatory"
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model_scores:
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- model: gemma3:27b
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score: 0.8
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- model: phi4
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score: 0.6
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- model: mistral-small3.1
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score: 0.4
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- name: psychology
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use_reasoning: false
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reasoning_description: "Psychology content is usually explanatory"
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model_scores:
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- model: mistral-small3.1
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score: 0.6
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- model: gemma3:27b
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score: 0.4
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- model: phi4
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score: 0.4
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- name: biology
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use_reasoning: true
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reasoning_description: "Biological processes benefit from structured analysis"
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model_scores:
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- model: mistral-small3.1
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score: 0.8
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- model: gemma3:27b
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score: 0.6
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- model: phi4
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score: 0.2
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- name: chemistry
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use_reasoning: true
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reasoning_description: "Chemical reactions and formulas require systematic thinking"
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reasoning_effort: high # Chemistry requires high reasoning effort
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model_scores:
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- model: mistral-small3.1
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score: 0.8
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- model: gemma3:27b
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score: 0.6
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- model: phi4
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score: 0.6
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- name: history
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use_reasoning: false
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reasoning_description: "Historical content is narrative-based"
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model_scores:
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- model: mistral-small3.1
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score: 0.8
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- model: phi4
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score: 0.6
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- model: gemma3:27b
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score: 0.4
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- name: other
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use_reasoning: false
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reasoning_description: "General content doesn't require reasoning"
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model_scores:
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- model: gemma3:27b
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score: 0.8
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- model: phi4
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score: 0.6
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- model: mistral-small3.1
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score: 0.6
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- name: health
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use_reasoning: false
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reasoning_description: "Health information is typically informational"
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model_scores:
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- model: gemma3:27b
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score: 0.8
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- model: phi4
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score: 0.8
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- model: mistral-small3.1
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score: 0.6
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- name: economics
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use_reasoning: false
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reasoning_description: "Economic discussions are usually explanatory"
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model_scores:
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- model: gemma3:27b
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score: 0.8
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- model: mistral-small3.1
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score: 0.8
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- model: phi4
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score: 0.0
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- name: math
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use_reasoning: true
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reasoning_description: "Mathematical problems require step-by-step reasoning"
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reasoning_effort: high # Math problems need high reasoning effort
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model_scores:
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- model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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score: 1.0
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- model: phi4
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score: 0.9
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- model: mistral-small3.1
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score: 0.8
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- model: gemma3:27b
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score: 0.6
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- name: physics
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use_reasoning: true
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reasoning_description: "Physics concepts need logical analysis"
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model_scores:
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- model: gemma3:27b
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score: 0.4
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- model: phi4
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score: 0.4
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- model: mistral-small3.1
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score: 0.4
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- name: computer science
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use_reasoning: true
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reasoning_description: "Programming and algorithms need logical reasoning"
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model_scores:
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- model: gemma3:27b
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score: 0.6
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- model: mistral-small3.1
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score: 0.6
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- model: phi4
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score: 0.0
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- name: philosophy
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use_reasoning: false
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reasoning_description: "Philosophical discussions are conversational"
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model_scores:
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- model: phi4
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score: 0.6
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- model: gemma3:27b
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score: 0.2
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- model: mistral-small3.1
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score: 0.2
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- name: engineering
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use_reasoning: true
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reasoning_description: "Engineering problems require systematic problem-solving"
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model_scores:
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- model: gemma3:27b
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score: 0.6
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- model: mistral-small3.1
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score: 0.6
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- model: phi4
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score: 0.2
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default_model: mistral-small3.1
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# API Configuration
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api:
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batch_classification:
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# Metrics configuration for monitoring batch classification performance
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metrics:
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enabled: true # Enable comprehensive metrics collection
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detailed_goroutine_tracking: true # Track individual goroutine lifecycle
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high_resolution_timing: false # Use nanosecond precision timing
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sample_rate: 1.0 # Collect metrics for all requests (1.0 = 100%, 0.5 = 50%)
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# Histogram buckets for metrics (directly configure what you need)
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duration_buckets: [0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10, 30]
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size_buckets: [1, 2, 5, 10, 20, 50, 100, 200]
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# Reasoning family configurations - define how different model families handle reasoning syntax
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reasoning_families:
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deepseek:
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type: "chat_template_kwargs"
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parameter: "thinking"
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qwen3:
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type: "chat_template_kwargs"
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parameter: "enable_thinking"
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gpt-oss:
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type: "reasoning_effort"
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parameter: "reasoning_effort"
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gpt:
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type: "reasoning_effort"
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parameter: "reasoning_effort"
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# Global default reasoning effort level
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default_reasoning_effort: medium # Default reasoning effort level (low, medium, high)

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