The introduction of logprobs in the Gemini API on Vertex AI finally lifts the curtain, offering developers a transparent look into the model’s decision-making process. This feature is a game changer, providing probability scores for both chosen and alternative tokens, allowing you to build smarter, context-aware applications and streamline debugging.
Getting Started: Environment Setup
To take advantage of logprobs, begin by setting up your development environment. Install the Google GenAI SDK for Python, configure your Google Cloud Project credentials, and initialize the Vertex AI client using your preferred Gemini model. This preparation ensures seamless interaction with the API and access to advanced features like logprobs.
What Are Logprobs?
Logprobs are the natural logarithms of a token’s probability. A value closer to zero signals higher confidence, while more negative values indicate uncertainty. By analyzing these scores, you gain quantitative insight into the model’s confidence for each generated token, turning opaque AI outputs into actionable intelligence.
How to Enable and Use Logprobs
Activating logprobs is simple: set response_logprobs=True in the generation_config of your request, and specify the number of alternatives (up to 20) with logprobs=[integer]. The response will include detailed logprob data, which you can parse and visualize using helper functions, giving you a window into the AI’s reasoning process.
from google.genai.types import GenerateContentConfig
prompt = "I am not sure if I really like this restaurant a lot."
response_schema = {"type": "STRING", "enum": ["Positive", "Negative", "Neutral"]}
response = client.models.generate_content(
model=MODEL_ID,
contents=prompt,
generation_config=GenerateContentConfig(
response_mime_type="application/json",
response_schema=response_schema,
response_logprobs=True,
logprobs=3,
),
)
Interpreting Logprob Outputs
Consider a scenario where Gemini classifies text as “Neutral” with a logprob of -0.0214, compared to -4.82 for “Positive” and -5.63 for “Negative.” The least negative logprob (“Neutral”) indicates the highest confidence. This level of detail informs nuanced application logic, such as error handling or dynamic user feedback.
def print_logprobs(response):
"""
Print log probabilities for each token in the response
"""
if response.candidates and response.candidates[0].logprobs_result:
logprobs_result = response.candidates[0].logprobs_result
for i, chosen_candidate in enumerate(logprobs_result.chosen_candidates):
print(
f"Token: '{chosen_candidate.token}' ({chosen_candidate.log_probability:.4f})"
)
if i < len(logprobs_result.top_candidates):
top_alternatives = logprobs_result.top_candidates[i].candidates
alternatives = [
alt
for alt in top_alternatives
if alt.token != chosen_candidate.token
]
if alternatives:
print("Alternative Tokens:")
for alt_token_info in alternatives:
print(
f" - '{alt_token_info.token}': ({alt_token_info.log_probability:.4f})"
)
print("-" * 20)
Key Use Cases for Logprobs
- Smarter Classification: Logprobs reveal the model’s confidence, enabling you to flag ambiguous results or enforce confidence thresholds, critical for robust, reliable systems.
- Dynamic Autocomplete: By tracking logprobs in real time, you can create autocomplete features that adapt suggestions based on evolving model confidence, enhancing the user experience.
- Quantitative RAG Evaluation: In retrieval-augmented generation (RAG) systems, averaging logprobs across answers provides a “grounding score,” automating quality checks and improving factual reliability.
Building Transparent and Trustworthy AI
With logprobs, Gemini evolves from a black box to a glass box, empowering developers to understand and confidently act on model reasoning. Whether you’re building classifiers, autocomplete tools, or RAG evaluators, logprobs offer the clarity needed for robust, trustworthy AI solutions. For hands-on examples, explore the introductory notebook or consult the official Gemini API documentation.
Unlocking Gemini's Reasoning: How Logprobs Bring Transparency to Vertex AI