* feat: support for codex and claude-code as llm * Remove refactoring plan file * Consolidate Anthropic tests into main LLM provider test suite - Add Anthropic models (Sonnet, Opus, Haiku) to MODEL_MATRIX - Remove separate test_anthropic_provider.py file - All Anthropic models now tested with standard memory operations * Add provider-specific default models Each LLM provider now has a sensible default model that's used when HINDSIGHT_API_LLM_MODEL is not explicitly set. This simplifies configuration - users can specify just the provider and API key. Changes: - Add PROVIDER_DEFAULT_MODELS mapping in config.py - Update config logic to use provider defaults for both global and per-operation LLM configs - Add comprehensive tests for provider default model selection - Document provider defaults in models.md Example usage: export HINDSIGHT_API_LLM_PROVIDER=anthropic export HINDSIGHT_API_LLM_API_KEY=sk-ant-xxx # Automatically uses claude-sonnet-4-20250514 Provider defaults: - openai: gpt-5-mini - anthropic: claude-sonnet-4-20250514 - gemini: gemini-2.5-flash - groq: openai/gpt-oss-120b - ollama: gemma3:12b - lmstudio: local-model - vertexai: gemini-2.0-flash-001 - openai-codex: o3-mini - claude-code: claude-sonnet-4-20250514 - mock: mock-model * Update provider default models - openai: gpt-5-mini -> o3-mini - anthropic: claude-sonnet-4-20250514 -> claude-haiku-4-5-20251001 - openai-codex: o3-mini -> gpt-5.2-codex - claude-code: claude-sonnet-4-20250514 -> claude-sonnet-4-5-20250929 Updated tests and documentation to reflect new defaults. * Move OpenAI Codex and Claude Code setup to models.md Moved detailed setup instructions for OpenAI Codex and Claude Code from configuration.md to models.md where they better fit with model-specific documentation. Changes: - Move "OpenAI Codex Setup" section from configuration.md to models.md - Move "Claude Code Setup" section from configuration.md to models.md - Add cross-reference tip in configuration.md pointing to models.md - Update default model in Claude Code example to claude-sonnet-4-5-20250929 - Keep basic provider examples in configuration.md for quick reference This makes the configuration.md page more focused on environment variables while models.md contains provider-specific setup details.
124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
"""
|
|
Test LLM provider with different models using actual memory operations.
|
|
"""
|
|
import os
|
|
from datetime import datetime
|
|
import pytest
|
|
from hindsight_api.engine.llm_wrapper import LLMProvider
|
|
from hindsight_api.engine.utils import extract_facts
|
|
from hindsight_api.engine.search.think_utils import reflect
|
|
|
|
|
|
# Model matrix: (provider, model)
|
|
MODEL_MATRIX = [
|
|
# OpenAI models
|
|
("openai", "gpt-4o-mini"),
|
|
("openai", "gpt-4.1-mini"),
|
|
("openai", "gpt-4.1-nano"),
|
|
("openai", "gpt-5-mini"),
|
|
("openai", "gpt-5-nano"),
|
|
("openai", "gpt-5"),
|
|
("openai", "gpt-5.2"),
|
|
# Anthropic models
|
|
("anthropic", "claude-sonnet-4-20250514"),
|
|
("anthropic", "claude-opus-4-5-20251101"),
|
|
("anthropic", "claude-haiku-4-20250514"),
|
|
# Groq models
|
|
("groq", "openai/gpt-oss-120b"),
|
|
("groq", "openai/gpt-oss-20b"),
|
|
# Gemini models
|
|
("gemini", "gemini-2.5-flash"),
|
|
("gemini", "gemini-2.5-flash-lite"),
|
|
("gemini", "gemini-3-pro-preview"),
|
|
# Ollama models (local)
|
|
("ollama", "gemma3:12b"),
|
|
("ollama", "gemma3:1b"),
|
|
]
|
|
|
|
|
|
def get_api_key_for_provider(provider: str) -> str | None:
|
|
"""Get API key for provider from environment variables."""
|
|
provider_key_map = {
|
|
"openai": "OPENAI_API_KEY",
|
|
"anthropic": "ANTHROPIC_API_KEY",
|
|
"groq": "GROQ_API_KEY",
|
|
"gemini": "GEMINI_API_KEY",
|
|
}
|
|
env_var = provider_key_map.get(provider)
|
|
return os.getenv(env_var) if env_var else None
|
|
|
|
|
|
@pytest.mark.parametrize("provider,model", MODEL_MATRIX)
|
|
@pytest.mark.asyncio
|
|
async def test_llm_provider_memory_operations(provider: str, model: str):
|
|
"""
|
|
Test LLM provider with actual memory operations: fact extraction and reflect.
|
|
All models must pass this test.
|
|
"""
|
|
api_key = get_api_key_for_provider(provider)
|
|
|
|
# Skip Ollama tests in CI (no models available)
|
|
if provider == "ollama" and os.getenv("CI"):
|
|
pytest.skip(f"Skipping {provider}/{model}: Ollama not available in CI")
|
|
|
|
# Other providers need an API key
|
|
if provider != "ollama" and not api_key:
|
|
pytest.skip(f"Skipping {provider}/{model}: no API key available")
|
|
|
|
llm = LLMProvider(
|
|
provider=provider,
|
|
api_key=api_key or "",
|
|
base_url="",
|
|
model=model,
|
|
)
|
|
|
|
# Test 1: Fact extraction (structured output)
|
|
test_text = """
|
|
User: I just got back from my trip to Paris last week. The Eiffel Tower was amazing!
|
|
Assistant: That sounds wonderful! How long were you there?
|
|
User: About 5 days. I also visited the Louvre and saw the Mona Lisa.
|
|
"""
|
|
event_date = datetime(2024, 12, 10)
|
|
|
|
facts, chunks = await extract_facts(
|
|
text=test_text,
|
|
event_date=event_date,
|
|
context="Travel conversation",
|
|
llm_config=llm,
|
|
)
|
|
|
|
print(f"\n{provider}/{model} - Fact extraction:")
|
|
print(f" Extracted {len(facts)} facts from {len(chunks)} chunks")
|
|
for fact in facts:
|
|
print(f" - {fact.fact}")
|
|
|
|
assert facts is not None, f"{provider}/{model} fact extraction returned None"
|
|
assert len(facts) > 0, f"{provider}/{model} should extract at least one fact"
|
|
|
|
# Verify facts have required fields
|
|
for fact in facts:
|
|
assert fact.fact, f"{provider}/{model} fact missing text"
|
|
assert fact.fact_type in ["world", "experience", "opinion"], f"{provider}/{model} invalid fact_type: {fact.fact_type}"
|
|
|
|
# Test 2: Reflect (actual reflect function)
|
|
response = await reflect(
|
|
llm_config=llm,
|
|
query="What was the highlight of my Paris trip?",
|
|
experience_facts=[
|
|
"I visited Paris in December 2024",
|
|
"I saw the Eiffel Tower and it was amazing",
|
|
"I visited the Louvre and saw the Mona Lisa",
|
|
"The trip lasted 5 days",
|
|
],
|
|
world_facts=[
|
|
"The Eiffel Tower is a famous landmark in Paris",
|
|
"The Mona Lisa is displayed at the Louvre museum",
|
|
],
|
|
name="Traveler",
|
|
)
|
|
|
|
print(f"\n{provider}/{model} - Reflect response:")
|
|
print(f" {response[:200]}...")
|
|
|
|
assert response is not None, f"{provider}/{model} reflect returned None"
|
|
assert len(response) > 10, f"{provider}/{model} reflect response too short"
|