mirror of
https://github.com/microsoft/agent-framework.git
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Eduard van Valkenburg
90a633967c
* Add Gemini and Vertex AI client support Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address Gemini PR review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * removed sample run readme part --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
37 lines
1.6 KiB
Markdown
37 lines
1.6 KiB
Markdown
# Gemini Package (agent-framework-gemini)
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Integration with Google's Gemini Developer API and Vertex AI via the `google-genai` SDK.
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## Core Classes
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- **`RawGeminiChatClient`** - Lightweight chat client without any layers, for custom pipeline composition
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- **`GeminiChatClient`** - Full-featured chat client with function invocation, middleware, and telemetry
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- **`GeminiChatOptions`** - Options TypedDict for Gemini-specific parameters
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- **`GeminiSettings`** - Settings loaded from environment variables
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- **`GoogleGeminiSettings`** - SDK-standard `GOOGLE_*` settings loaded from environment variables
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- **`ThinkingConfig`** - Configuration for extended thinking
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## Gemini-specific Options
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- **`thinking_config`** - Enable extended thinking via `ThinkingConfig`
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- **`response_schema`** - Raw JSON schema dict for structured output (alternative to `response_format`)
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- **`top_k`** - Top-K sampling parameter
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## Built-in Tool Factory Methods
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- **`get_web_search_tool()`** - Google Search grounding for up-to-date web answers
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- **`get_code_interpreter_tool()`** - Sandboxed code execution
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- **`get_maps_grounding_tool()`** - Google Maps grounding for location and mapping
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- **`get_file_search_tool()`** - Retrieval from Gemini file search stores
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- **`get_mcp_tool()`** - Model Context Protocol server integration
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## Usage
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```python
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from agent_framework import Content, Message
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from agent_framework_gemini import GeminiChatClient
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client = GeminiChatClient(model="gemini-2.5-flash")
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response = await client.get_response([Message(role="user", contents=[Content.from_text("Hello")])])
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```
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