bb1f9cb221
* feat: support for gemini-3-pro and gpt-5.2 * feat: support for gemini-3-pro and gpt-5.2 * feat: support for gemini-3-pro and gpt-5.2 * feat: support for gemini-3-pro and gpt-5.2 * feat: add local mcp server * docs * docs
110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
"""
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Test LLM provider with different models using actual memory operations.
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"""
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import os
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from datetime import datetime
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import pytest
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from hindsight_api.engine.llm_wrapper import LLMProvider
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from hindsight_api.engine.utils import extract_facts
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from hindsight_api.engine.search.think_utils import reflect
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# Model matrix: (provider, model)
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MODEL_MATRIX = [
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# OpenAI models
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("openai", "gpt-4o-mini"),
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("openai", "gpt-4.1-mini"),
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("openai", "gpt-4.1-nano"),
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("openai", "gpt-5-mini"),
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("openai", "gpt-5-nano"),
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("openai", "gpt-5"),
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("openai", "gpt-5.2"),
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# Groq models
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("groq", "openai/gpt-oss-120b"),
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("groq", "openai/gpt-oss-20b"),
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# Gemini models
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("gemini", "gemini-2.5-flash"),
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("gemini", "gemini-2.5-flash-lite"),
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("gemini", "gemini-3-pro-preview"),
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]
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def get_api_key_for_provider(provider: str) -> str | None:
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"""Get API key for provider from environment variables."""
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provider_key_map = {
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"openai": "OPENAI_API_KEY",
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"groq": "GROQ_API_KEY",
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"gemini": "GEMINI_API_KEY",
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}
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env_var = provider_key_map.get(provider)
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return os.getenv(env_var) if env_var else None
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@pytest.mark.parametrize("provider,model", MODEL_MATRIX)
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@pytest.mark.asyncio
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async def test_llm_provider_memory_operations(provider: str, model: str):
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"""
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Test LLM provider with actual memory operations: fact extraction and reflect.
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All models must pass this test.
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"""
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api_key = get_api_key_for_provider(provider)
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if not api_key:
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pytest.skip(f"Skipping {provider}/{model}: no API key available")
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llm = LLMProvider(
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provider=provider,
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api_key=api_key,
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base_url="",
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model=model,
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)
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# Test 1: Fact extraction (structured output)
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test_text = """
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User: I just got back from my trip to Paris last week. The Eiffel Tower was amazing!
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Assistant: That sounds wonderful! How long were you there?
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User: About 5 days. I also visited the Louvre and saw the Mona Lisa.
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"""
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event_date = datetime(2024, 12, 10)
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facts, chunks = await extract_facts(
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text=test_text,
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event_date=event_date,
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context="Travel conversation",
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llm_config=llm,
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)
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print(f"\n{provider}/{model} - Fact extraction:")
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print(f" Extracted {len(facts)} facts from {len(chunks)} chunks")
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for fact in facts:
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print(f" - {fact.fact}")
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assert facts is not None, f"{provider}/{model} fact extraction returned None"
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assert len(facts) > 0, f"{provider}/{model} should extract at least one fact"
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# Verify facts have required fields
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for fact in facts:
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assert fact.fact, f"{provider}/{model} fact missing text"
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assert fact.fact_type in ["world", "experience", "opinion"], f"{provider}/{model} invalid fact_type: {fact.fact_type}"
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# Test 2: Reflect (actual reflect function)
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response = await reflect(
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llm_config=llm,
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query="What was the highlight of my Paris trip?",
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experience_facts=[
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"I visited Paris in December 2024",
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"I saw the Eiffel Tower and it was amazing",
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"I visited the Louvre and saw the Mona Lisa",
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"The trip lasted 5 days",
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],
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world_facts=[
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"The Eiffel Tower is a famous landmark in Paris",
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"The Mona Lisa is displayed at the Louvre museum",
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],
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name="Traveler",
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)
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print(f"\n{provider}/{model} - Reflect response:")
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print(f" {response[:200]}...")
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assert response is not None, f"{provider}/{model} reflect returned None"
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assert len(response) > 10, f"{provider}/{model} reflect response too short"
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