Sebastian Husch Lee
78 lines
3.3 KiB
Python
78 lines
3.3 KiB
Python
# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
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#
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# SPDX-License-Identifier: Apache-2.0
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"""
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Example: Automatic context compaction with SummarizationCompactionTool
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This example shows how to add automatic chat history summarization to a Haystack
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Agent using the new SummarizationCompactionTool and the condition-triggered tool
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mechanism.
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The agent has a simple calculator tool and is given a pre-existing conversation
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history with alternating user/assistant messages. Once the non-system message count
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exceeds `max_messages`, the compaction tool automatically summarizes the oldest
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messages into a single concise entry — without the agent having to decide to do it.
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Run with:
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OPENAI_API_KEY=<your-key> python compaction_example.py
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"""
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from typing import Annotated
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from haystack.components.agents import Agent
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from haystack.components.agents.compaction import SummarizationCompactionTool
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from haystack.components.generators.chat import OpenAIChatGenerator
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from haystack.dataclasses import ChatMessage
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from haystack.tools import tool
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@tool
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def add(a: Annotated[float, "First number"], b: Annotated[float, "Second number"]) -> float:
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"""Add two numbers."""
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return a + b
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# A dedicated generator for the compaction summarizer (can be the same model or a cheaper/faster one — here we reuse
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# the same model for simplicity).
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summarizer_generator = OpenAIChatGenerator(model="gpt-4o-mini")
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# The compaction tool fires automatically before each LLM call once the non-system message count exceeds max_messages.
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# It summarizes the oldest half of the history and keeps the most recent messages verbatim.
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compaction = SummarizationCompactionTool(
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chat_generator=summarizer_generator,
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max_messages=3, # low threshold so it triggers quickly in this demo
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)
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agent = Agent(
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chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
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tools=[add, compaction],
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system_prompt="You are a helpful assistant that can perform calculations.",
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)
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# Pre-existing conversation history with alternating user/assistant messages.
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# This simulates a session that has already been running for a while and will
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# push the message count past the compaction threshold on the next agent call.
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messages = [
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ChatMessage.from_user("What is quantum mechanics?"),
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ChatMessage.from_assistant(
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"Quantum mechanics is a fundamental theory in physics that describes the behavior of matter and energy at "
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"the smallest scales."
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),
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ChatMessage.from_user("Can you explain it in simple terms?"),
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ChatMessage.from_assistant(
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"Sure! Quantum mechanics is like a set of rules that govern how tiny particles, like electrons and photons,"
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" behave. It tells us that these particles can exist in multiple states at once (superposition) and can be "
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"connected in ways that seem to defy classical physics (entanglement). It's a fascinating and complex field "
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"that has led to many technological advancements, like semiconductors and quantum computing."
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),
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ChatMessage.from_user("Thanks! Now, can you do some math for me? What's 3 + 4?"),
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]
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print(f"History length before agent call: {len(messages)} messages")
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result = agent.run(messages=messages)
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print(f"History length after agent call: {len(result['messages'])} messages")
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print(f"\nAgent reply: {result['last_message'].text}")
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