mirror of
https://github.com/headroomlabs-ai/headroom.git
synced 2026-08-27 14:17:10 -04:00
This commit adds comprehensive batch API support for all three major LLM
providers (Anthropic, OpenAI, Google/Gemini) with integrated CCR
(Compress-Cache-Retrieve) functionality for asynchronous batch processing.
## Batch CCR Post-Processing Architecture
When batch APIs are used, responses are processed asynchronously. If the
model calls the CCR retrieval tool (`headroom_retrieve`) within a batch
response, the system now handles this automatically:
1. **Batch Submit**: Request context (messages, tools, model) is stored
in BatchContextStore keyed by batch_id
2. **Batch Results**: When results are retrieved, CCR tool calls are
detected in the responses
3. **Continuation**: For each CCR tool call, the system executes local
retrieval and makes a continuation API call to complete the response
4. **Result Update**: The batch result is updated with the complete
response, transparent to the caller
## New Components
- `headroom/ccr/batch_store.py`: TTL-based context storage for batch
requests, enabling CCR retrieval during result processing
- `headroom/ccr/batch_processor.py`: Processes batch results, detects
CCR tool calls across all provider formats, executes continuations
## Provider Support
### Anthropic
- POST /v1/messages/batches (create with compression)
- GET /v1/messages/batches (list)
- GET /v1/messages/batches/{id} (status)
- GET /v1/messages/batches/{id}/results (with CCR post-processing)
### OpenAI
- POST /v1/batches (create)
- GET /v1/batches (list)
- GET /v1/batches/{id} (status)
- Batch file upload/download support
### Google/Gemini
- Native API support: /v1beta/models/{model}:generateContent
- Batch API: /v1beta/models/{model}:batchGenerateContent
- Token counting: /v1beta/models/{model}:countTokens
- OpenAI-compatible endpoint support
## CCR Enhancements
- Added Google/Gemini format support to response_handler.py
- Extended tool_injection.py with multiple marker patterns for different
compressors (SmartCrusher, TextCompressor, LogCompressor, etc.)
- Added Google functionCall/functionResponse handling
## Proxy Server Updates
- Added Gemini native API handlers alongside OpenAI-compatible endpoints
- Integrated batch context storage on submission
- Added batch result processing with CCR continuation
- Rate limiting and metrics tracking for all providers
## Test Coverage
Added comprehensive integration tests (all skip gracefully without API keys):
- test_proxy_batch_integration.py: Anthropic and OpenAI batch APIs
- test_proxy_gemini_integration.py: Gemini via OpenAI-compatible endpoint
- test_proxy_gemini_native_integration.py: Gemini native API
- test_proxy_count_tokens_integration.py: Token counting endpoint
- test_proxy_openai_responses_integration.py: OpenAI responses API
- test_proxy_passthrough_integration.py: Passthrough endpoints
## Compression Results (Real API Testing)
- Token savings: 83-98% on tool result content
- CCR tool injection: Working across all providers
- Model behavior: OpenAI gpt-4o-mini successfully called headroom_retrieve
when presented with compressed data, proving end-to-end CCR functionality
374 lines
15 KiB
Python
374 lines
15 KiB
Python
"""Integration tests for Gemini native API endpoint with real API calls.
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These tests require a valid GEMINI_API_KEY environment variable.
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They test the /v1beta/models/{model}:generateContent endpoint with compression.
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Run with:
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GEMINI_API_KEY=your-key pytest tests/test_proxy_gemini_native_integration.py -v
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"""
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import json
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import os
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import pytest
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# Skip entire module if no API key
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pytestmark = pytest.mark.skipif(
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not os.environ.get("GEMINI_API_KEY"), reason="GEMINI_API_KEY not set"
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)
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pytest.importorskip("fastapi")
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pytest.importorskip("httpx")
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from fastapi.testclient import TestClient # noqa: E402
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from headroom.proxy.server import ProxyConfig, create_app # noqa: E402
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@pytest.fixture
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def gemini_native_client():
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"""Create test client for Gemini native API with optimization enabled."""
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config = ProxyConfig(
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optimize=True, # Enable compression
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cache_enabled=False,
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rate_limit_enabled=False,
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cost_tracking_enabled=False,
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)
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app = create_app(config)
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with TestClient(app) as client:
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yield client
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@pytest.fixture
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def api_key():
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"""Get Gemini API key from environment."""
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return os.environ.get("GEMINI_API_KEY")
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class TestGeminiNativeGenerateContent:
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"""Test /v1beta/models/{model}:generateContent endpoint."""
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def test_basic_generation(self, gemini_native_client, api_key):
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"""Basic text generation works."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={"contents": [{"parts": [{"text": "What is 2+2? Reply with just the number."}]}]},
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)
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assert response.status_code == 200
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data = response.json()
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# Verify Gemini native response format
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assert "candidates" in data
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assert len(data["candidates"]) > 0
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assert "content" in data["candidates"][0]
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assert "parts" in data["candidates"][0]["content"]
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text = data["candidates"][0]["content"]["parts"][0]["text"]
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assert "4" in text
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# Verify usage metadata
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assert "usageMetadata" in data
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assert "promptTokenCount" in data["usageMetadata"]
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def test_with_system_instruction(self, gemini_native_client, api_key):
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"""System instruction works correctly."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={
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"contents": [{"parts": [{"text": "Hello"}]}],
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"systemInstruction": {"parts": [{"text": "Always respond with exactly one word."}]},
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},
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)
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assert response.status_code == 200
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data = response.json()
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text = data["candidates"][0]["content"]["parts"][0]["text"]
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# Should be a short response due to system instruction
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assert len(text.split()) <= 3
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def test_multi_turn_conversation(self, gemini_native_client, api_key):
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"""Multi-turn conversations maintain context."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={
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"contents": [
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{"role": "user", "parts": [{"text": "My name is TestUser456."}]},
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{"role": "model", "parts": [{"text": "Nice to meet you, TestUser456!"}]},
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{"role": "user", "parts": [{"text": "What is my name?"}]},
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]
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},
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)
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assert response.status_code == 200
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data = response.json()
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text = data["candidates"][0]["content"]["parts"][0]["text"].lower()
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assert "testuser456" in text
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def test_function_calling(self, gemini_native_client, api_key):
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"""Function calling / tools work correctly."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={
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"contents": [{"parts": [{"text": "What is the weather in Tokyo?"}]}],
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"tools": [
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{
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"functionDeclarations": [
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{
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"name": "get_weather",
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"description": "Get current weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "City name"}
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},
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"required": ["location"],
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},
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}
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]
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}
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],
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},
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)
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assert response.status_code == 200
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data = response.json()
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# Verify function call response
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parts = data["candidates"][0]["content"]["parts"]
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function_call = None
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for part in parts:
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if "functionCall" in part:
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function_call = part["functionCall"]
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break
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assert function_call is not None
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assert function_call["name"] == "get_weather"
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assert "tokyo" in function_call["args"]["location"].lower()
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def test_generation_config(self, gemini_native_client, api_key):
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"""Generation config parameters are respected."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={
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"contents": [{"parts": [{"text": "Write a very short poem about AI."}]}],
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"generationConfig": {"maxOutputTokens": 50, "temperature": 0.1},
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},
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)
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assert response.status_code == 200
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data = response.json()
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# Response should be limited by maxOutputTokens
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assert data["usageMetadata"]["candidatesTokenCount"] <= 60 # Some buffer
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class TestGeminiNativeCompression:
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"""Test that compression works with Gemini native API."""
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def test_compression_on_model_message(self, gemini_native_client, api_key):
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"""Large data in model message gets compressed."""
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# Create large JSON data (simulating tool output)
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items = [
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{"id": i, "name": f"Item {i}", "desc": f"Description for item {i}"} for i in range(100)
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]
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tool_output = json.dumps(items)
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# Send as model message (like tool returning data)
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={
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"contents": [
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{"role": "user", "parts": [{"text": "Get items from database"}]},
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{"role": "model", "parts": [{"text": f"Here are the results:\n{tool_output}"}]},
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{"role": "user", "parts": [{"text": "How many items are there?"}]},
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]
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},
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)
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assert response.status_code == 200
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data = response.json()
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text = data["candidates"][0]["content"]["parts"][0]["text"]
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# Model should correctly count the items
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assert "100" in text
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# Check that compression happened via stats
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stats = gemini_native_client.get("/stats").json()
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# At least some tokens should have been saved
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assert stats["tokens"]["saved"] >= 0 # May or may not compress depending on size
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def test_user_messages_protected(self, gemini_native_client, api_key):
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"""User messages are not compressed (by design)."""
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# Large data in user message
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items = [{"id": i} for i in range(50)]
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user_data = json.dumps(items)
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# First request with data in user message
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={
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"contents": [
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{"role": "user", "parts": [{"text": f"Analyze this data: {user_data}"}]}
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]
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},
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)
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assert response.status_code == 200
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# The request should succeed - user messages are protected from compression
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class TestGeminiNativeStats:
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"""Test that proxy stats track Gemini native requests correctly."""
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def test_stats_track_gemini_provider(self, gemini_native_client, api_key):
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"""Stats show requests under 'gemini' provider."""
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# Make a request
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gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={"contents": [{"parts": [{"text": "Hi"}]}]},
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)
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stats = gemini_native_client.get("/stats").json()
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assert "gemini" in stats["requests"]["by_provider"]
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assert stats["requests"]["by_provider"]["gemini"] >= 1
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def test_stats_track_model(self, gemini_native_client, api_key):
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"""Stats track the specific model used."""
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gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}",
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json={"contents": [{"parts": [{"text": "Hi"}]}]},
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)
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stats = gemini_native_client.get("/stats").json()
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assert "gemini-2.0-flash" in stats["requests"]["by_model"]
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class TestGeminiNativeErrorHandling:
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"""Test error handling for Gemini native API."""
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def test_invalid_api_key(self, gemini_native_client):
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"""Invalid API key returns appropriate error."""
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response = gemini_native_client.post(
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"/v1beta/models/gemini-2.0-flash:generateContent?key=invalid-key-123",
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json={"contents": [{"parts": [{"text": "Hi"}]}]},
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)
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assert response.status_code >= 400
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def test_invalid_model(self, gemini_native_client, api_key):
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"""Invalid model returns appropriate error."""
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response = gemini_native_client.post(
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f"/v1beta/models/nonexistent-model-xyz:generateContent?key={api_key}",
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json={"contents": [{"parts": [{"text": "Hi"}]}]},
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)
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assert response.status_code >= 400
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def test_empty_contents(self, gemini_native_client, api_key):
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"""Empty contents handled gracefully."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:generateContent?key={api_key}", json={"contents": []}
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)
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# Should either return error or handle gracefully
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assert response.status_code in [200, 400]
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class TestGeminiNativeHeaderAuth:
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"""Test authentication via x-goog-api-key header."""
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def test_header_auth(self, gemini_native_client, api_key):
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"""API key in header works."""
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response = gemini_native_client.post(
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"/v1beta/models/gemini-2.0-flash:generateContent",
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headers={"x-goog-api-key": api_key},
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json={"contents": [{"parts": [{"text": "Hi"}]}]},
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)
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assert response.status_code == 200
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class TestGeminiNativeCountTokens:
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"""Test /v1beta/models/{model}:countTokens endpoint with compression."""
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def test_count_tokens_basic(self, gemini_native_client, api_key):
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"""Basic token counting works."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:countTokens?key={api_key}",
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json={"contents": [{"parts": [{"text": "Hello, world!"}]}]},
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)
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assert response.status_code == 200
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data = response.json()
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# Verify response format
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assert "totalTokens" in data
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assert isinstance(data["totalTokens"], int)
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assert data["totalTokens"] > 0
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def test_count_tokens_with_system_instruction(self, gemini_native_client, api_key):
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"""Token counting includes system instruction."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:countTokens?key={api_key}",
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json={
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"contents": [{"parts": [{"text": "Hello"}]}],
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"systemInstruction": {"parts": [{"text": "You are a helpful assistant."}]},
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},
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)
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# Note: systemInstruction may not be supported by countTokens in all versions
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assert response.status_code in [200, 400]
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if response.status_code == 200:
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data = response.json()
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assert "totalTokens" in data
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assert data["totalTokens"] > 0
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def test_count_tokens_reflects_compression(self, gemini_native_client, api_key):
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"""Token count reflects compressed content size."""
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# Create large repetitive JSON data that should compress
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items = [
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{
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"id": i,
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"name": f"Item {i}",
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"description": f"This is the description for item number {i}",
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}
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for i in range(100)
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]
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tool_output = json.dumps(items)
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# Count tokens with large data in model message (which gets compressed)
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:countTokens?key={api_key}",
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json={
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"contents": [
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{"role": "user", "parts": [{"text": "Get items from database"}]},
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{"role": "model", "parts": [{"text": f"Here are the results:\n{tool_output}"}]},
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{"role": "user", "parts": [{"text": "Summarize these items"}]},
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]
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},
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)
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assert response.status_code == 200
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data = response.json()
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# Verify we got a token count
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assert "totalTokens" in data
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compressed_tokens = data["totalTokens"]
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assert compressed_tokens > 0
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# Check stats to verify compression was applied
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stats = gemini_native_client.get("/stats").json()
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# The request should have been tracked
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assert stats["requests"]["by_provider"].get("gemini", 0) >= 1
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def test_count_tokens_multi_turn(self, gemini_native_client, api_key):
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"""Token counting works for multi-turn conversations."""
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response = gemini_native_client.post(
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f"/v1beta/models/gemini-2.0-flash:countTokens?key={api_key}",
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json={
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"contents": [
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{"role": "user", "parts": [{"text": "My name is Alice."}]},
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{"role": "model", "parts": [{"text": "Nice to meet you, Alice!"}]},
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{"role": "user", "parts": [{"text": "What is my name?"}]},
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]
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},
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)
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assert response.status_code == 200
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data = response.json()
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assert "totalTokens" in data
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assert data["totalTokens"] > 0
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def test_count_tokens_header_auth(self, gemini_native_client, api_key):
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"""API key in header works for countTokens."""
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response = gemini_native_client.post(
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"/v1beta/models/gemini-2.0-flash:countTokens",
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headers={"x-goog-api-key": api_key},
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json={"contents": [{"parts": [{"text": "Hello"}]}]},
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)
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assert response.status_code == 200
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data = response.json()
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assert "totalTokens" in data
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