* Calculate the dimension size based off model chosen.
* Added example docstring.
* Validated working notebook with sentence models of different dimensions.
* Validated removal of model_name working.
* Second example uses conn object.
* embedding_function no longer directly references .encode
* Fixed pre-commit issue.
* Use try/except to raise error when shape is not found in embedding function.
* Re-ran notebook.
* Update autogen/agentchat/contrib/vectordb/pgvectordb.py
Co-authored-by: Li Jiang <bnujli@gmail.com>
* Update autogen/agentchat/contrib/vectordb/pgvectordb.py
Co-authored-by: Li Jiang <bnujli@gmail.com>
* Added .encode
* Removed example comment.
* Fix overwrite doesn't work with existing collection when custom embedding function has different dimension from default one
---------
Co-authored-by: Li Jiang <bnujli@gmail.com>
* Refactor hook registration and processing methods
- Refactored the `hook_lists` dictionary to use type hints for better readability.
- Updated the `register_hook` method signature to include type hints for the `hook` parameter.
- Added type hints to the `process_last_received_message` method parameters and return value.
This commit refactors the code related to hook registration and processing in the `conversable_agent.py` file. The changes improve code readability and maintainability by using type hints and updating method signatures.
* Refactor hook_lists initialization and add type hints
- Refactored the initialization of `hook_lists` to use a colon instead of an equal sign.
- Added type hints for the parameters and return types of `process_last_received_message` method.
* Refactor hook registration and processing in conversable_agent.py
- Refactored the `hook_lists` dictionary to use a more generic type for the list of hooks.
- Updated the signature check for `process_message_before_send`, `process_all_messages_before_reply`, and `process_last_received_message` hooks to ensure they are callable with the correct signatures.
- Added error handling to raise a ValueError or TypeError if any hook does not have the expected signature.
* Refactor hook processing in conversable_agent.py
- Simplify the code by removing unnecessary type checks and error handling.
- Consolidate the logic for processing hooks in `_process_message_before_send`, `process_all_messages_before_reply`, and `process_last_received_message` methods.
* Refactor register_hook method signature for flexibility
The commit changes the signature of the `register_hook` method in `conversable_agent.py`. The second argument, `hook`, is now of type `Callable` instead of `Callable[[List[Dict]], List[Dict]]`. This change allows for more flexibility when registering hooks.
* google default auth and svc keyfile for Gemini
* [.Net] Release note for 0.0.14 (#2815)
* update release note
* update trigger
* [.Net] Update website for AutoGen.SemanticKernel and AutoGen.Ollama (#2814)
support vertex ai compute region
* [CAP] User supplied threads for agents (#2812)
* First pass: message loop in main thread
* pypi version bump
* Fix readme
* Better example
* Fixed docs
* pre-commit fixes
* refactoring, minor fixes, update gemini demo ipynb
* add new deps again and reset line endings
* Docstring for the init function. Use private methods
* improve docstring
---------
Co-authored-by: Xiaoyun Zhang <bigmiao.zhang@gmail.com>
Co-authored-by: Rajan <rajan.chari@yahoo.com>
Co-authored-by: Zoltan Lux <z.lux@campus.tu-berlin.de>
* white spaces
* add llamaindex agent wrapper for autogen
* formatting
* formatting fixes
* add support for llamaindex agents
* fix style
* fix style
* delete file
* re-add file
* fixes pre-commit errors
* feat: Add agentchat_group_chat_with_llamaindex_agents notebook
This commit adds the notebook "agentchat_group_chat_with_llamaindex_agents.ipynb" which demonstrates how to integrate Llamaindex agents into Autogen. The notebook includes code for setting up the API endpoint, creating Llamaindex agents, and setting up a group chat.
* Refactor code
* feat: Add test for LLamaIndexConversableAgent
This commit adds a new test file `test_llamaindex_conversable_agent.py` that contains a test case for the `LLamaIndexConversableAgent` class. The test verifies the functionality of group chat with two MultimodalConversable Agents, limiting the chat by the `max_round` parameter. It also checks if the number of rounds does not exceed the maximum specified rounds.
The purpose of this change is to ensure that the `LLamaIndexConversableAgent` behaves as expected and correctly handles group chats with limited rounds.
Note: This commit includes import statements and setup code necessary for running the test case.
* fix formatting
* feat: Add LlamaIndexAgent job to GitHub Actions workflow
This commit adds a new job called "LlamaIndexAgent" to the GitHub Actions workflow. The job runs on multiple operating systems (ubuntu-latest, macos-latest, windows-2019) and uses Python version 3.11. It sets up the Python environment, installs necessary packages and dependencies for LMM, performs coverage testing using pytest, and uploads the coverage report to Codecov.
The commit also includes changes to the test_llamaindex_conversable_agent.py file. It imports os and sys modules, appends a path to sys.path, and adds skip conditions for tests based on certain conditions.
These changes improve the CI/CD pipeline by adding a new job for LlamaIndexAgent and enhancing test conditions in test_llamaindex_conversable_agent.py.
* fix test yaml
* cleanup tests
* fix test run
* formatting
* add test
* fix yaml
* pr feedback
* add documentation to website
* fixed style
* edit to document page
* newline
* make skip reason easier to see
* compose skip reasons
* fix env variable name
* refactor: Update package installation in contrib workflows
- Replaced specific package installations with more general ones
- Updated the installation of llama-index packages and dependencies
- Added new packages for llama-index and Wikipedia tools
* Update dependencies and add new agents for group chat
- Update dependencies to specific versions
- Add new agent `entertainent_specialist` for discovering entertainment opportunities in a location
- Modify the `test_group_chat_with_llama_index_conversable_agent` function to include the new agent in the group chat
* Update pydantic version requirement in setup.py
- Update pydantic version requirement from "pydantic>=1.10,<3,!=2.6.0" to "pydantic>=1.10,<3"
- Remove comment about issue with pydantic 2.6.0
* Refactor ChatMessage instantiation in _extract_message_and_history()
- Refactored the instantiation of ChatMessage in the _extract_message_and_history() function.
- Added an empty dictionary as additional_kwargs to the ChatMessage constructor.
* Refactor test_llamaindex_conversable_agent.py
- Removed unused import and variable
- Updated OpenAI model to gpt-4
- Reduced max_iterations for location_specialist and entertainment_specialist from 30 to 5
- Changed human_input_mode for user_proxy to "NEVER"
- Added assertion for max_rounds in entertainent_assistant
These changes improve code efficiency and ensure proper functionality.
* Remove entertainment_specialist agent and update user_proxy settings in test_llamaindex_conversable_agent.py
- Remove the creation of entertainent_specialist agent
- Update the max_consecutive_auto_reply setting for user_proxy to 10
- Update the default_auto_reply setting for user_proxy to "Thank you. TERMINATE"
* Refactor installation of LlamaIndex packages and dependencies
- Simplify installation commands for LlamaIndex packages
- Remove specific version numbers from pip install commands
* Update test_llamaindex_conversable_agent.py to include verbose output during pytest.
- Add the -v flag to the pytest command in contrib-openai.yml.
- Print a message when skipping the test due to missing dependencies or key.
* Refactor OpenAI workflow, remove LlamaIndexAgent
This commit removes the LlamaIndexAgent from the OpenAI workflow in order to streamline and simplify the code. The LlamaIndexAgent was no longer necessary and its removal improves overall code organization and maintainability.
* feat: Add test for group chat functionality with LLamaIndexConversableAgent
use mock reactagent in test
* Update Dockerfile for devcontainer
- Updated the Dockerfile for the devcontainer environment.
- Added installation of build-essential, npm, git-lfs, and other packages.
- Upgraded pip and installed pydoc-markdown, pyyaml, and colored libraries.
* Update devcontainer.json with new VS Code extensions and terminal settings
- Updated the list of VS Code extensions in devcontainer.json to include "GitHub.copilot"
- Added a new terminal profile for Linux with the path set to "/bin/bash"
- Set the default Linux terminal profile to "bash"
* removeall
* feat: Add Dockerfiles and devcontainer configurations
This commit adds Dockerfiles and devcontainer configurations for different use cases in the `.devcontainer` directory. The following changes were made:
- Added `Dockerfile` for basic setups (`base`)
- Added `Dockerfile` for advanced features (`full`)
- Added `Dockerfile` for AutoGen project developers (`dev`)
- Added `Dockerfile` for AutoGen project developers using Studio (`studio`)
- Updated existing files with necessary dependencies and configurations
- Modified README.md to provide instructions on customizing Dockerfiles and managing the Docker environment
These changes allow users to easily set up their AutoGen development environment using Docker containers.
* delete
* Add authors.yml file with author information
This commit adds the authors.yml file, which contains information about various authors contributing to the project. Each author entry includes their name, title, URL, and image URL. This file will be used to display author information on the website.
* delete
* Add test cases for agent chat functionality
This commit adds new test cases for the agent chat functionality. The test cases include scenarios such as auto feedback from code execution, function calls, currency calculator, async function calls, group chat finite state machine, cost token tracking, and group chat state flow. These test cases cover different versions of Python (3.10, 3.11, and 3.12) and are skipped if OpenAI is not installed or the Python version does not match.
* delete
* feat: Add LLM configuration documentation
This commit adds documentation for configuring an agent's access to LLMs. It includes information on the `llm_config` argument, `config_list`, and other configuration parameters. The commit also provides examples of filtering the `config_list` based on model names and tags. Additionally, it demonstrates how to add an HTTP client in `llm_config` for proxy usage. Finally, it mentions helper functions for loading a config list from API keys, environment variables, files, or `.env` files.
Closes#1234
* delete
* feat: Add LLM configuration documentation
This commit adds documentation for configuring an agent's access to LLMs. It includes information on the `llm_config` argument, `config_list`, and other configuration parameters. The commit also provides examples of filtering the `config_list` based on model names and tags. Additionally, it demonstrates how to add an HTTP client in `llm_config` for proxy usage. Finally, it mentions helper functions for loading a config list from various sources.
Closes#1234
* delete
* adding back notebooks
* reset
* feat: Add setup.py for package installation
This commit adds a new file, `setup.py`, which is used for installing the package. The `setup.py` file includes information such as the author, description, and dependencies of the package. This allows users to easily install and use the package in their projects.
The `setup.py` file also includes different extra requirements for specific functionalities, such as retrieving chat data or running Jupyter notebooks. These extra requirements are installed when specified during installation.
Overall, this addition improves the usability and installation process of the package.
