python-genai

Not so fast! Check this out https://github.com/googleapis/python-genai/issues/460 In my experience, you can only use Gemini structured outputs for the most trivial of schemas. No integer literals, no discriminated unions and many more paper cuts. So at least for me, it was completely unusable for what I do at work.

Hi if the Gemini API team is reading this can you please be more transparent about 'The specified schema produces a constraint that has too many states for serving. ...' when using Structured Outputs. I assume it has something to do with the underlying constraint grammar/token masks becoming too long/taking too long to compute. But as end users we have no way of figuring out what the actual limits are. OpenAI has more generous limits on the schemas and clearer docs. https://platform.openai.com/docs/guides/structured-outputs#s.... You guys closed this issue for no reason: https://github.com/googleapis/python-genai/issues/660 Other than that, good work! I love how fast the Gemini models are. The current API is significantly less of a shitshow compared to last year with property ordering etc.

Thoughts used to be available in the Gemini/Vertex APIs when Gemini 2.0 Flash Thinking Experimental was initially introduced [1][2], and subsequently disabled to the public (I assume hidden behind a visibility flag) shortly after DeepSeek R1's release [3] regardless of the `include_thoughts` setting. At ~10:15AM UTC 04 May, a change was rolled out to the Vertex API (but not the Gemini API) that caused the API to respect the `include_thoughts` setting and return the thoughts. For consumers that don't handle the thoughts correctly and had specified `include_thoughts = true`, the thinking traces then leaked into responses. [1]: https://googleapis.github.io/python-genai/genai.html#genai.t... [2]: https://ai.google.dev/api/generate-content#ThinkingConfig [3]: https://github.com/googleapis/python-genai/blob/157b16b8df40...

lemming, this is super helpful, thank you. We provide the genai SDK (https://github.com/googleapis/python-genai) to reduce the learning curve in 4 languages (GA: Python, Go Preview: Node.JS, Java). The SDK works for all Gemini APIs provided by Google AI Studio (https://ai.google.dev/) and Vertex AI.

We built the OpenAI Compatible API (https://cloud.google.com/vertex-ai/generative-ai/docs/multim...) layer to help customers that are already using OAI library to test out Gemini easily with basic inference but not as a replacement library for the genai sdk (https://github.com/googleapis/python-genai). We recommend using th genai SDK for working with Gemini.

I maintain an alternative client which I build from the API definitions at https://github.com/googleapis/googleapis, which according to https://github.com/googleapis/python-genai/issues/345 should be the right place. But neither the AI Studio nor the Vertex definitions even have ThinkingConfig yet - very frustrating. In general it's amazing how much API munging is required to get a working client from the public API definitions.

They removed the docs and support for it https://github.com/googleapis/python-genai/commit/af3b339a9d.... You can see the thoughts in AI Studio UI as per https://ai.google.dev/gemini-api/docs/thinking#debugging-and....

I spotted something interesting in the Python API library code: https://github.com/googleapis/python-genai/blob/473bf4b6b5a6... class ThinkingConfig(_common.BaseModel): """The thinking features configuration.""" include_thoughts: Optional[bool] = Field( default=None, description="""Indicates whether to include thoughts in the response. If true, thoughts are returned only if the model supports thought and thoughts are available. """, ) thinking_budget: Optional[int] = Field( default=None, description="""Indicates the thinking budget in tokens. """, ) That thinking_budget thing is documented, but what's the deal with include_thoughts? It sounds like it's an option to have the API return the thought summary... but I can't figure out how to get it t

Oh wow, it supports directly specifying a Pydantic model as an output schema that it will adhere to for structured JSON output. That’s fantastic! https://github.com/googleapis/python-genai?tab=readme-ov-fil...

Buried in the announcement is the real gem — they’re releasing a new SDK that actually looks like it follows modern best practices. Could be a game-changer for usability. They’ve had OpenAI-compatible endpoints for a while, but it’s never been clear how serious they were about supporting them long-term. Nice to see another option showing up. For reference, their main repo (not kidding) recommends setting up a Kubernetes cluster and a GCP bucket to submit batch requests. [1]https://github.com/googleapis/python-genai