This PR refactored `AgentEvent` and `ChatMessage` union types to
abstract base classes. This allows for user-defined message types that
subclass one of the base classes to be used in AgentChat.
To support a unified interface for working with the messages, the base
classes added abstract methods for:
- Convert content to string
- Convert content to a `UserMessage` for model client
- Convert content for rendering in console.
- Dump into a dictionary
- Load and create a new instance from a dictionary
This way, all agents such as `AssistantAgent` and `SocietyOfMindAgent`
can utilize the unified interface to work with any built-in and
user-defined message type.
This PR also introduces a new message type, `StructuredMessage` for
AgentChat (Resolves#5131), which is a generic type that requires a
user-specified content type.
You can create a `StructuredMessage` as follow:
```python
class MessageType(BaseModel):
data: str
references: List[str]
message = StructuredMessage[MessageType](content=MessageType(data="data", references=["a", "b"]), source="user")
# message.content is of type `MessageType`.
```
This PR addresses the receving side of this message type. To produce
this message type from `AssistantAgent`, the work continue in #5934.
Added unit tests to verify this message type works with agents and
teams.
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
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## Why are these changes needed?
<!-- Please give a short summary of the change and the problem this
solves. -->
This PR makes it clear which agent is speaking per message in the
Chainlit team sample. Previously, messages would be exchanged without
showing who is communicating.
## Related issue number
<!-- For example: "Closes #1234" -->
Closes#5609
## Checks
- [x] I've included any doc changes needed for
<https://microsoft.github.io/autogen/>. See
<https://github.com/microsoft/autogen/blob/main/CONTRIBUTING.md> to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
_(EXPERIMENTAL, RESEARCH IN PROGRESS)_
In 2023 AutoGen introduced [Teachable
Agents](https://microsoft.github.io/autogen/0.2/blog/2023/10/26/TeachableAgent/)
that users could teach new facts, preferences and skills. But teachable
agents were limited in several ways: They could only be
`ConversableAgent` subclasses, they couldn't learn a new skill unless
the user stated (in a single turn) both the task and how to solve it,
and they couldn't learn on their own. **Task-Centric Memory** overcomes
these limitations, allowing users to teach arbitrary agents (or teams)
more flexibly and reliably, and enabling agents to learn from their own
trial-and-error experiences.
This PR is large and complex. All of the files are new, and most of the
added components depend on the others to run at all. But the review
process can be accelerated if approached in the following order.
1. Start with the [Task-Centric Memory
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/packages/autogen-ext/src/autogen_ext/task_centric_memory).
1. Install the memory extension locally, since it won't be in pypi until
it's merged. In the `agentic_memory` branch, and the `python/packages`
directory:
- `pip install -e autogen-agentchat`
- `pip install -e autogen-ext[openai]`
- `pip install -e autogen-ext[task-centric-memory]`
2. Run the Quickstart sample code, then immediately open the
`./pagelogs/quick/0 Call Tree.html` file in a browser to view the work
in progress.
3. Click through the web page links to see the details.
2. Continue through the rest of the main README to get a high-level
overview of the architecture.
3. Read through the [code samples
README](https://github.com/microsoft/autogen/tree/agentic_memory/python/samples/task_centric_memory),
running each of the 4 code samples while viewing their page logs.
4. Skim through the 4 code samples, along with their corresponding yaml
config files:
1. `chat_with_teachable_agent.py`
2. `eval_retrieval.py`
3. `eval_teachability.py`
4. `eval_learning_from_demonstration.py`
5. `eval_self_teaching.py`
6. Read `task_centric_memory_controller.py`, referring back to the
previously generated page logs as needed. This is the most important and
complex file in the PR.
7. Read the remaining core files.
1. `_task_centric_memory_bank.py`
2. `_string_similarity_map.py`
3. `_prompter.py`
8. Read the supporting files in the utils dir.
1. `teachability.py`
2. `apprentice.py`
3. `grader.py`
4. `page_logger.py`
5. `_functions.py`
* integration tests used to use the samples - now they are separate
* patch dictionary problem in serializer
* add Message Registry with dead letter queue that gets checked on new
subs.
Right now we rely on opening the channel to associate a ClientId with an
entry on the gateway side. This causes a race when the channel is being
opened in the background while an RPC (e.g. MyAgent.register()) is
invoked.
If the RPC is processed first, the gateway rejects it due to "invalid"
clientId.
This fix makes this condition less likely to trigger, but there is still
a piece of the puzzle that needs to be solved on the Gateway side.
Restoring the grpc + Orleans server into the project
## Why are these changes needed?
This is the distributed agent runtime for .NET that can manage routing
messages amongst a fleet of grpc agent runtimes.
## Related issue number
## Checks
- [ ] I've included any doc changes needed for
https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation to
build and test documentation locally.
- [x] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [x] I've made sure all auto checks have passed.
---------
Co-authored-by: Jack Gerrits <jack@jackgerrits.com>
Co-authored-by: Jacob Alber <jaalber@microsoft.com>
Introduce a sample chat application using AgentChat and FastAPI,
demonstrating single-agent and team chat functionalities, along with
state persistence and conversation history management.
Resolves#5423
---------
Co-authored-by: Victor Dibia <victor.dibia@gmail.com>
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
<!-- Thank you for your contribution! Please review
https://microsoft.github.io/autogen/docs/Contribute before opening a
pull request. -->
<!-- Please add a reviewer to the assignee section when you create a PR.
If you don't have the access to it, we will shortly find a reviewer and
assign them to your PR. -->
## Why are these changes needed?
Wrong file names in the README.md
## Related issue number
## Checks
- [x] I've included any doc changes needed for
https://microsoft.github.io/autogen/. See
https://microsoft.github.io/autogen/docs/Contribute#documentation to
build and test documentation locally.
- [ ] I've added tests (if relevant) corresponding to the changes
introduced in this PR.
- [ ] I've made sure all auto checks have passed.
Co-authored-by: Mohammad Mazraeh <Mazraeh.Mohammad@Gmail.com>
* New sample of chess playing showing R1's thought process in streaming
mode
* Modify existing samples to use `model_config.yml` instead of JSON
configs for better clarity.
---------
Co-authored-by: Mohammad Mazraeh <Mazraeh.Mohammad@Gmail.com>
* Separate agent and team examples
* Add streaming output
* Refactor to better use the chainlit API
* Removed the user proxy example -- this needs a bit more work to
improve the presentation on the ChainLit interface.
---------
Co-authored-by: Victor Dibia <victordibia@microsoft.com>
* add chainlit sample
* readme updates
* put team inside run to avoid problems
* simplify example and add readme
* inform user team is reset
---------
Co-authored-by: Hussein Mozannar <hmozannar@microsoft.com>
Eric Zhu
WuYunlong
Federico Villa
DavidYu00
gagb
Ricky Loynd
Ryan Sweet
Jacob Alber
Juan
Hussein Koprly
Griffin Bassman
linznin
Mohammad Mazraeh
Jack Gerrits
Leonardo Pinheiro
Matthew Wyman
Victor Dibia