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Merge https://github.com/google/adk-python/pull/4175 ### Link to Issue or Description of Change **1. Link to an existing issue (if applicable):** N/A: just fixing typos discovered while reading the repo **2. Or, if no issue exists, describe the change:** No code change, just typo fixes: see commit diffs for all details **Problem:** Trying to improve overall repo quality **Solution:** Fixing typos as they get discovered ### Testing Plan N/A **Unit Tests:** N/A **Manual End-to-End (E2E) Tests:** N/A ### Checklist - [X] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document. - [X] I have performed a self-review of my own code. - [ ] I have commented my code, particularly in hard-to-understand areas. - [ ] I have added tests that prove my fix is effective or that my feature works. - [X] New and existing unit tests pass locally with my changes. - [ ] I have manually tested my changes end-to-end. - [ ] Any dependent changes have been merged and published in downstream modules. COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4175 from didier-durand:fix-typos-c 16e93ed2d9bc153fa0332ab1ae39633fcc5056e9 PiperOrigin-RevId: 858751240
67 lines
3.3 KiB
Markdown
67 lines
3.3 KiB
Markdown
# Sales Assistant Agent with Context Offloading
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This agent acts as a sales assistant, capable of generating and retrieving large
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sales reports for different regions (North America, EMEA, APAC).
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## The Challenge: Large Context Windows
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Storing large pieces of data, like full sales reports, directly in conversation
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history consumes valuable LLM context window space. This limits how much
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conversation history the model can see, potentially degrading response quality
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in longer conversations and increasing token costs.
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## The Solution: Context Offloading with Artifacts
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This agent demonstrates how to use ADK's artifact feature to offload large data
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from the main conversation context, while still making it available to the agent
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on-demand. Large reports are generated by the `query_large_data` tool but are
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immediately saved as artifacts instead of being returned in the function call
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response. This keeps the turn events small, saving context space.
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### How it Works
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1. **Saving Artifacts**: When the user asks for a sales report (e.g., "Get EMEA
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sales report"), the `query_large_data` tool is called. It generates a mock
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report, saves it as an artifact (`EMEA_sales_report_q3_2025.txt`), and saves
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a brief description in the artifact's metadata (e.g., `{'summary': 'Sales
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report for EMEA Q3 2025'}`). The tool returns only a confirmation message to
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the agent, not the large report itself.
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2. **Immediate Loading**: The `QueryLargeDataTool` then runs its
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`process_llm_request` hook. It detects that `query_large_data` was just
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called, loads the artifact that was just saved, and injects its content into
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the *next* request to the LLM. This makes the report data available
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immediately, allowing the agent to summarize it or answer questions in the
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same turn, as seen in the logs. This artifact is only appended for that
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round and not saved to session. For future rounds of conversation, it will
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be removed from context.
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3. **Loading on Demand**: The `CustomLoadArtifactsTool` enhances the default
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`load_artifacts` behavior.
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* It reads the `summary` metadata from all available artifacts and includes
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these summaries in the instructions sent to the LLM (e.g., `You have
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access to artifacts: ["APAC_sales_report_q3_2025.txt: Sales report for
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APAC Q3 2025", ...]`). This lets the agent know *what* data is
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available in artifacts, without having to load the full content.
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* It instructs the agent to use data from the most recent turn if
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available, but to call `load_artifacts` if it needs to access data from
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an *older* turn that is no longer in the immediate context (e.g., if
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comparing North America data after having discussed EMEA and APAC).
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* When `load_artifacts` is called, this tool intercepts it and injects the
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requested artifact content into the LLM request.
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* Note that artifacts are never saved to session.
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This pattern ensures that large data is only loaded into the LLM's context
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window when it is immediately relevant—either just after being generated or when
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explicitly requested later—thereby managing context size more effectively.
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### How to Run
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```bash
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adk web
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```
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Then, ask the agent:
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* "Hi, help me query the North America sales report"
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* "help me query EMEA and APAC sales report"
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* "Summarize sales report for North America?"
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