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Honour QA_MODEL for anthropic and OpenAI

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Yiorgis Gozadinos 2025-07-04 12:14:13 +03:00
parent ae1188da6b
commit 1705e7a21c
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2 changed files with 4 additions and 7 deletions
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7
src/haiku/rag/qa/__init__.py
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@ -8,7 +8,6 @@ def get_qa_agent(client: HaikuRAG, model: str = "") -> QuestionAnswerAgentBase:
""" """
Factory function to get the appropriate QA agent based on the configuration. Factory function to get the appropriate QA agent based on the configuration.
""" """
if Config.QA_PROVIDER == "ollama": if Config.QA_PROVIDER == "ollama":
return QuestionAnswerOllamaAgent(client, model or Config.QA_MODEL) return QuestionAnswerOllamaAgent(client, model or Config.QA_MODEL)
@ -21,7 +20,7 @@ def get_qa_agent(client: HaikuRAG, model: str = "") -> QuestionAnswerAgentBase:
"Please install haiku.rag with the 'openai' extra:" "Please install haiku.rag with the 'openai' extra:"
"uv pip install haiku.rag --extra openai" "uv pip install haiku.rag --extra openai"
) )
return QuestionAnswerOpenAIAgent(client, model or "gpt-4o-mini") return QuestionAnswerOpenAIAgent(client, model or Config.QA_MODEL)
if Config.QA_PROVIDER == "anthropic": if Config.QA_PROVIDER == "anthropic":
try: try:
@ -32,8 +31,6 @@ def get_qa_agent(client: HaikuRAG, model: str = "") -> QuestionAnswerAgentBase:
"Please install haiku.rag with the 'anthropic' extra:" "Please install haiku.rag with the 'anthropic' extra:"
"uv pip install haiku.rag --extra anthropic" "uv pip install haiku.rag --extra anthropic"
) )
return QuestionAnswerAnthropicAgent( return QuestionAnswerAnthropicAgent(client, model or Config.QA_MODEL)
client, model or "claude-3-5-haiku-20241022"
)
raise ValueError(f"Unsupported QA provider: {Config.QA_PROVIDER}") raise ValueError(f"Unsupported QA provider: {Config.QA_PROVIDER}")

4
tests/generate_benchmark_db.py
View file

@ -6,7 +6,7 @@ from llm_judge import LLMJudge
from tqdm import tqdm from tqdm import tqdm
from haiku.rag.client import HaikuRAG from haiku.rag.client import HaikuRAG
from haiku.rag.qa.ollama import QuestionAnswerOllamaAgent from haiku.rag.qa import get_qa_agent
db_path = Path(__file__).parent / "data" / "benchmark.sqlite" db_path = Path(__file__).parent / "data" / "benchmark.sqlite"
@ -88,7 +88,7 @@ async def run_qa_benchmark(k: int | None = None):
total_questions = 0 total_questions = 0
async with HaikuRAG(db_path) as rag: async with HaikuRAG(db_path) as rag:
qa = QuestionAnswerOllamaAgent(rag) qa = get_qa_agent(rag)
for i, doc in enumerate(tqdm(corpus, desc="QA Benchmarking")): for i, doc in enumerate(tqdm(corpus, desc="QA Benchmarking")):
question = doc["question"] # type: ignore question = doc["question"] # type: ignore