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139 lines
6.1 KiB
Markdown
139 lines
6.1 KiB
Markdown
# Task-Centric Memory Code Samples
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_(EXPERIMENTAL, RESEARCH IN PROGRESS)_
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<p align="right">
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<img src="../../packages/autogen-ext/imgs/task_centric_memory.png" alt="Description" width="300" align="right" style="margin-left: 10px;">
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</p>
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This directory contains code samples that illustrate the following forms of fast, memory-based learning:
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* Direct memory storage and retrieval
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* Learning from user advice and corrections
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* Learning from user demonstrations
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* Learning from the agent's own experience
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Each sample connects task-centric memory to a selectable agent with no changes to that agent's code.
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See the block diagram to the right for an overview of the components and their interactions.
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Each sample is contained in a separate python script, using data and configs stored in yaml files for easy modification.
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Note that since agent behavior is non-deterministic, results will vary between runs.
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To watch operations live in a browser and see how task-centric memory works,
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open the HTML page at the location specified at the top of the config file,
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such as: `./pagelogs/teachability/0 Call Tree.html`
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To turn off logging entirely, set logging level to NONE in the config file.
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The config files specify an _AssistantAgent_ by default, which uses a fixed, multi-step system prompt.
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To use _MagenticOneGroupChat_ instead, specify that in the yaml file where indicated.
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## Installation
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Install AutoGen and its extension package as follows:
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```bash
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pip install -U "autogen-agentchat" "autogen-ext[openai]" "autogen-ext[task-centric-memory]"
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```
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Assign your OpenAI key to the environment variable OPENAI_API_KEY,
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or else modify `utils/client.py` as appropriate for the model you choose.
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## Running the Samples
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The following samples are listed in order of increasing complexity.
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Execute the corresponding commands from the `python/samples/task_centric_memory` directory.
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### Making AssistantAgent Teachable
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This short, interactive code sample shows how to make the AssistantAgent teachable.
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The following steps show the agent learning a user teaching from one chat session to the next,
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starting with an empty memory bank.
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The memory bank can be cleared manually by deleting the memory_bank directory (if it exists from a prior run), as shown below.
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```bash
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rm -r memory_bank
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python chat_with_teachable_agent.py
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Now chatting with a teachable agent. Please enter your first message. Type 'exit' or 'quit' to quit.
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You: How many items should be put in research summaries?
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---------- user ----------
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How many items should be put in research summaries?
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---------- teachable_agent ----------
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<generates a long discussion>
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You: Whenever asked to prepare a research summary, try to cover just the 5 top items.
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---------- user ----------
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Whenever asked to prepare a research summary, try to cover just the 5 top items.
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---------- teachable_agent ----------
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<discusses the advice>
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You: quit
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python chat_with_teachable_agent.py`
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Now chatting with a teachable agent. Please enter your first message. Type 'exit' or 'quit' to quit.
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You: How many items should be put in research summaries?
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---------- user ----------
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How many items should be put in research summaries?
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---------- teachable_agent ----------
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[MemoryContent(content='Whenever asked to prepare a research summary, try to cover just the 5 top items.', mime_type='MemoryMimeType.TEXT', metadata={})]
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---------- teachable_agent ----------
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<generates a more appropriate answer>
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```
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### Direct Memory Storage and Retrieval
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This sample shows how an app can access the `MemoryController` directly
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to retrieve previously stored task-insight pairs as potentially useful examplars when solving some new task.
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A task is any text instruction that the app may give to an agent.
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An insight is any text (like a hint, advice, a demonstration or plan) that might help the agent perform such tasks.
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A typical app will perform the following steps in some interleaved order:
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1. Call the `MemoryController` repeatedly to store a set of memories (task-insight pairs).
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2. Call the `MemoryController` repeatedly to retrieve any memories related to a new task.
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3. Use the retrieved insights, typically by adding them to the agent's context window. (This step is not illustrated by this code sample.)
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This sample code adds several task-insight pairs to memory, retrieves memories for a set of new tasks,
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logs the full retrieval results, and reports the retrieval precision and recall.
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`python eval_retrieval.py configs/retrieval.yaml`
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Precision and recall for this sample are usually near 100%.
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### Agent Learning from User Advice and Corrections
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This sample first tests the agent (once) for knowledge it currently lacks.
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Then the agent is given advice to help it solve the task, and the context window is cleared.
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Finally the agent is once tested again to see if it can retrieve and use the advice successfully.
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`python eval_teachability.py configs/teachability.yaml`
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With the benefit of memory, the agent usually succeeds on this sample.
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### Agent Learning from User Demonstrations
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This sample asks the agent to perform a reasoning task (ten times) on which it usually fails.
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The agent is then given one demonstration of how to solve a similar but different task, and the context window is cleared.
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Finally the agent is tested 10 more times to see if it can retrieve and apply the demonstration to the original task.
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`python eval_learning_from_demonstration.py configs/demonstration.yaml`
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The agent's success rate tends to be measurably higher after the demonstration has been stored in memory.
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### Agent Learning from Its Own Experience
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This sample asks the agent to perform a reasoning task on which it usually fails.
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Then using automatic success or failure feedback (for a verifiable task with no side-effects on the environment),
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the agent iterates through a background learning loop to find a solution, which it then stores as an insight in memory.
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Finally the agent is tested again to see if it can retrieve and apply its insight to the original task,
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as well as to a similar but different task as a test of generalization.
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`python eval_self_teaching.py configs/self_teaching.yaml`
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Using memory, the agent usually completes both tasks successfully in the second set of trials.
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