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Use gemini 2.0 flash as evaluator. Set seed for it to reduce eval variance.

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Gemini 2.0 flash model is cheaper and better than Gemini 1.5 pro
This commit is contained in:
Debanjum
2025-04-04 00:08:48 +05:30
parent 0dcb2544d7
commit 911e1bf981
1 changed files with 3 additions and 2 deletions
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5
tests/evals/eval.py
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@@ -37,8 +37,9 @@ KHOJ_API_KEY = os.getenv("KHOJ_API_KEY")
KHOJ_MODE = os.getenv("KHOJ_MODE", "default").lower() # E.g research, general, notes etc. KHOJ_MODE = os.getenv("KHOJ_MODE", "default").lower() # E.g research, general, notes etc.
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
GEMINI_EVAL_MODEL = os.getenv("GEMINI_EVAL_MODEL", "gemini-1.5-pro-002") GEMINI_EVAL_MODEL = os.getenv("GEMINI_EVAL_MODEL", "gemini-2.0-flash-001")
LLM_SEED = int(os.getenv("KHOJ_LLM_SEED")) if os.getenv("KHOJ_LLM_SEED") else None
SAMPLE_SIZE = os.getenv("SAMPLE_SIZE") # Number of examples to evaluate SAMPLE_SIZE = os.getenv("SAMPLE_SIZE") # Number of examples to evaluate
RANDOMIZE = os.getenv("RANDOMIZE", "false").lower() == "true" # Randomize examples RANDOMIZE = os.getenv("RANDOMIZE", "false").lower() == "true" # Randomize examples
BATCH_SIZE = int( BATCH_SIZE = int(
@@ -469,7 +470,7 @@ def evaluate_response_with_gemini(
headers={"Content-Type": "application/json"}, headers={"Content-Type": "application/json"},
json={ json={
"contents": [{"parts": [{"text": evaluation_prompt}]}], "contents": [{"parts": [{"text": evaluation_prompt}]}],
"generationConfig": {"response_mime_type": "application/json"}, "generationConfig": {"response_mime_type": "application/json", "seed": LLM_SEED},
}, },
) )
response.raise_for_status() response.raise_for_status()