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Releases: google-gemini/generative-ai-python

v0.7.2 - Bugfix

08 Jul 20:48
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Bugfixes

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Full Changelog: v0.7.1...v0.7.2

v0.7.1 - Code Execution

27 Jun 00:48
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v0.7.0 - Context caching

17 Jun 23:17
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For a detailed introduction, check out the context caching guide on the site.

Features

Bugfixes and small changes

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Full Changelog: v0.6.0...v0.7.0

v0.6.0

03 Jun 16:46
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Full Changelog: v0.5.4...v0.6.0

v0.5.4 - Bugfix: Update client library version to clear timeouts.

17 May 01:23
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  • Update version of generativelanguage to fix timeout errors. by @MarkDaoust in #345

v0.5.3

13 May 20:50
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Features

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Full Changelog: v0.5.2...v0.5.3

v0.5.2 - File upload bugfix

18 Apr 20:53
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Full Changelog: v0.5.1...v0.5.2

v0.5.1 - Add json mode

16 Apr 18:26
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Docs and small fixes

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Full Changelog: v0.5.0...v0.5.1

v0.5.0 - Files

08 Apr 18:18
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What's Changed

New features

  • Files API

    You can now upload files to the api. Then instead of resending an image with each request, just pass a file reference in your prompt:

    f = genai.upload_file(path="image.png")
    m = genai.GenerativeModel(model_name=...)
    response = m.generate_content(["Please describe this file:", f])
    
    • Add initial prototype supporting the files API by @MarkDaoust in #249
    • Files API: improve error messages, add delete_file, add .uri property by @TYMichaelChen in #260
  • Semantic retriever

    Using answer.generate_answer, you can now either pass "inline_passages" to include the reference material in the request:

    from google.generativeai import answer
    answer.generate_answer(
        content=question,
        inline_passages=splitter.split(document)
    )
    

    Or pass a reference to a retriever Document or Corpus:

    from google.generativeai import answer
    from google.generativeai import retriever
    my_corpus = retriever.get_corpus('my_corpus')
    genai.generate_answer(
        content=question,
        semantic_retreiver=my_corpus
    )
    
  • System instructions

    When creating a model you can pass a string (or Content) as system_instructions: genai.GenerativeModel(system_instructions="Be good!")

  • Function Calling - Tool Config

    The list of tools available for function calling during a chat session is typically constant. The new tool_config argument lets you switch the function calling mode between None (No function calls), Auto (The model chooses to return text or a FunctionCall), and Any (the model must call a function).

    model = genai.GenerativeModel(tools=[my_functions])
    chat = model.start_chat()
    chat.send_message(tool_config={"function_calling_config": "ANY"})
    

    In Any mode the model will return a function call. You may also pass allowed_function_names to restrict the calls to a subset of the available functions: `chat.send_message(tool_config={"function_calling_config": "ANY", "allowed_function_names": [...]})

    In Any mode the model can use constrained decoding to more strictly follow the argument specifications in the function declarations.

Docs, bug fixes, and minor updates.

New Contributors

Full Changelog: v0.4.1...v0.5.0

v0.4.1 - Bugfix

13 Mar 16:03
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Bugfix

  • The send_message_async method should call the asynchronous method generate_content_async by @xnny in #229

Docs

New Contributors

Full Changelog: v0.4.0...v0.4.1