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37 lines
2.0 KiB
Markdown
37 lines
2.0 KiB
Markdown
# Context Compaction Samples
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This folder demonstrates context compaction patterns introduced by ADR-0019.
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## Files
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- `basics.py` — builds a local message list and applies each built-in strategy one at a time.
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- `summarization.py` — runs `SummarizationStrategy` directly with a real summarizing chat client.
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- `advanced.py` — composes multiple strategies with `TokenBudgetComposedStrategy`, including a real summarizer and tool-call groups.
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- `agent_client_overrides.py` — shows client defaults, agent-level overrides, and per-run compaction overrides.
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- `custom.py` — defines a custom strategy implementing the `CompactionStrategy` protocol.
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- `tiktoken_tokenizer.py` — shows a `TokenizerProtocol` implementation backed by `tiktoken`.
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- `compaction_provider.py` — uses `CompactionProvider` with an agent and `InMemoryHistoryProvider`.
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Run samples with:
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```bash
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uv run samples/02-agents/compaction/basics.py
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uv run samples/02-agents/compaction/summarization.py # requires OPENAI_API_KEY
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uv run samples/02-agents/compaction/advanced.py # requires OPENAI_API_KEY
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uv run samples/02-agents/compaction/agent_client_overrides.py
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uv run samples/02-agents/compaction/custom.py
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uv run samples/02-agents/compaction/tiktoken_tokenizer.py
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uv run samples/02-agents/compaction/compaction_provider.py # requires OPENAI_API_KEY
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```
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## Security Considerations
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Most compaction strategies in this folder (`TruncationStrategy`, `SlidingWindowStrategy`,
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`SelectiveToolCallCompactionStrategy`, `ToolResultCompactionStrategy`) only remove or reorder
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existing messages and carry no additional risk. `SummarizationStrategy` is the exception: it
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calls out to an LLM to produce replacement summary content that permanently becomes part of
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chat history. A compromised or malicious summarization service could return a summary
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containing unsafe instructions, creating a persistent indirect-prompt-injection vector. Using
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`SummarizationStrategy` is optional and requires explicit configuration — only point its
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chat client at a summarization service you trust as much as the primary model.
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