chore: import upstream snapshot with attribution

2026-07-13 13:39:25 +08:00
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+# Gemini Package (agent-framework-gemini)
+
+Integration with Google's Gemini Developer API and Vertex AI via the `google-genai` SDK.
+
+## Core Classes
+
+- **`RawGeminiChatClient`** - Lightweight chat client without any layers, for custom pipeline composition
+- **`GeminiChatClient`** - Full-featured chat client with function invocation, middleware, and telemetry
+- **`GeminiChatOptions`** - Options TypedDict for Gemini-specific parameters
+- **`GeminiSettings`** - Settings loaded from environment variables
+- **`GoogleGeminiSettings`** - SDK-standard `GOOGLE_*` settings loaded from environment variables
+- **`ThinkingConfig`** - Configuration for extended thinking
+
+## Gemini-specific Options
+
+- **`thinking_config`** - Enable extended thinking via `ThinkingConfig`
+- **`response_schema`** - Raw JSON schema dict for structured output (alternative to `response_format`)
+- **`top_k`** - Top-K sampling parameter
+
+## Built-in Tool Factory Methods
+
+- **`get_web_search_tool()`** - Google Search grounding for up-to-date web answers
+- **`get_code_interpreter_tool()`** - Sandboxed code execution
+- **`get_maps_grounding_tool()`** - Google Maps grounding for location and mapping
+- **`get_file_search_tool()`** - Retrieval from Gemini file search stores
+- **`get_mcp_tool()`** - Model Context Protocol server integration
+
+## Usage
+
+```python
+from agent_framework import Content, Message
+from agent_framework_gemini import GeminiChatClient
+
+client = GeminiChatClient(model="gemini-2.5-flash")
+response = await client.get_response([Message(role="user", contents=[Content.from_text("Hello")])])
+```