Dev/v0.2

.github/workflows/build.yml

@@ -40,7 +40,7 @@ jobs:
           python -m pip install --upgrade pip wheel
           pip install -e .
           python -c "import autogen"
-          pip install -e.[mathchat,retrievechat,test] datasets pytest
+          pip install -e. pytest
           pip uninstall -y openai
       - name: Test with pytest
         if: matrix.python-version != '3.10'
@@ -49,7 +49,8 @@ jobs:
       - name: Coverage
         if: matrix.python-version == '3.10'
         run: |
-          pip install coverage
+          pip install -e .[mathchat,test]
+          pip uninstall -y openai
           coverage run -a -m pytest test
           coverage xml
       - name: Upload coverage to Codecov

.github/workflows/contrib-openai.yml

@@ -0,0 +1,58 @@
+# This workflow will install Python dependencies and run tests
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: OpenAI4ContribTests
+
+on:
+  pull_request_target:
+    branches: ['main']
+    paths:
+      - 'autogen/**'
+      - 'test/agentchat/contrib/**'
+      - '.github/workflows/contrib-openai.yml'
+      - 'setup.py'
+
+jobs:
+  RetrieveChatTest:
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.10"]
+    runs-on: ${{ matrix.os }}
+    environment: openai1
+    steps:
+      # checkout to pr branch
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.event.pull_request.head.sha }}
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install packages and dependencies
+        run: |
+          docker --version
+          python -m pip install --upgrade pip wheel
+          pip install -e .
+          python -c "import autogen"
+          pip install coverage pytest-asyncio
+      - name: Install packages for test when needed
+        run: |
+          pip install docker
+          pip install qdrant_client[fastembed]
+          pip install -e .[retrievechat]
+      - name: Coverage
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
+          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
+          OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }}
+        run: |
+          coverage run -a -m pytest test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py
+          coverage xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3
+        with:
+          file: ./coverage.xml
+          flags: unittests

.github/workflows/contrib-tests.yml

@@ -0,0 +1,59 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: ContribTests
+
+on:
+  pull_request:
+    branches: ['main', 'dev/v0.2']
+    paths:
+      - 'autogen/**'
+      - 'test/agentchat/contrib/**'
+      - '.github/workflows/contrib-tests.yml'
+      - 'setup.py'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref }}
+  cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
+
+jobs:
+  RetrieveChatTest:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-2019]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install packages and dependencies for all tests
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install pytest
+      - name: Install qdrant_client when python-version is 3.10
+        if: matrix.python-version == '3.10' || matrix.python-version == '3.8'
+        run: |
+          pip install qdrant_client[fastembed]
+      - name: Install packages and dependencies for RetrieveChat
+        run: |
+          pip install -e .[retrievechat]
+          pip uninstall -y openai
+      - name: Test RetrieveChat
+        run: |
+          pytest test/test_retrieve_utils.py test/agentchat/contrib/test_retrievechat.py test/agentchat/contrib/test_qdrant_retrievechat.py
+      - name: Coverage
+        if: matrix.python-version == '3.10'
+        run: |
+          pip install coverage>=5.3
+          coverage run -a -m pytest test/test_retrieve_utils.py test/agentchat/contrib
+          coverage xml
+      - name: Upload coverage to Codecov
+        if: matrix.python-version == '3.10'
+        uses: codecov/codecov-action@v3
+        with:
+          file: ./coverage.xml
+          flags: unittests

.github/workflows/openai.yml

@@ -1,4 +1,4 @@
-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# This workflow will install Python dependencies and run tests with a variety of Python versions
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
 name: OpenAI
@@ -11,9 +11,6 @@ on:
       - 'test/**'
       - 'notebook/agentchat_auto_feedback_from_code_execution.ipynb'
       - 'notebook/agentchat_function_call.ipynb'
-      - 'notebook/agentchat_MathChat.ipynb'
-      - 'notebook/oai_completion.ipynb'
-      - 'notebook/oai_chatgpt_gpt4.ipynb'
       - '.github/workflows/openai.yml'
 
 jobs:
@@ -23,7 +20,7 @@ jobs:
         os: [ubuntu-latest]
         python-version: ["3.9", "3.10", "3.11"]
     runs-on: ${{ matrix.os }}
-    environment: openai
+    environment: openai1
     steps:
       # checkout to pr branch
       - name: Checkout
@@ -38,28 +35,17 @@ jobs:
         run: |
           docker --version
           python -m pip install --upgrade pip wheel
-          pip install -e.[blendsearch]
+          pip install -e.
           python -c "import autogen"
-          pip install coverage pytest-asyncio datasets
+          pip install coverage pytest-asyncio
       - name: Install packages for test when needed
         if: matrix.python-version == '3.9'
         run: |
           pip install docker
-      - name: Install packages for MathChat when needed
-        if: matrix.python-version != '3.11'
+      - name: Install dependencies for test when needed
+        if: matrix.python-version == '3.10'  # test_agentchat_function_call
         run: |
-          pip install -e .[mathchat]
-      - name: Install packages for RetrieveChat when needed
-        if: matrix.python-version == '3.9'
-        run: |
-          pip install -e .[retrievechat]
-      - name: Install packages for Teachable when needed
-        run: |
-          pip install -e .[teachable]
-      - name: Install packages for RetrieveChat with QDrant when needed
-        if: matrix.python-version == '3.11'
-        run: |
-          pip install -e .[retrievechat] qdrant_client[fastembed]
+          pip install -e.[mathchat]
       - name: Coverage
         if: matrix.python-version == '3.9'
         env:
@@ -80,8 +66,7 @@ jobs:
           OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }}
         run: |
           pip install nbconvert nbformat ipykernel
-          coverage run -a -m pytest test/agentchat/test_qdrant_retrievechat.py
-          coverage run -a -m pytest test/test_with_openai.py
+          coverage run -a -m pytest test/agentchat/test_function_call_groupchat.py
           coverage run -a -m pytest test/test_notebook.py
           coverage xml
           cat "$(pwd)/test/executed_openai_notebook_output.txt"

OAI_CONFIG_LIST_sample

@@ -7,14 +7,14 @@
     {
         "model": "gpt-4",
         "api_key": "<your Azure OpenAI API key here>",
-        "api_base": "<your Azure OpenAI API base here>",
+        "base_url": "<your Azure OpenAI API base here>",
         "api_type": "azure",
         "api_version": "2023-07-01-preview"
     },
     {
         "model": "gpt-3.5-turbo",
         "api_key": "<your Azure OpenAI API key here>",
-        "api_base": "<your Azure OpenAI API base here>",
+        "base_url": "<your Azure OpenAI API base here>",
         "api_type": "azure",
         "api_version": "2023-07-01-preview"
     }

README.md

@@ -28,11 +28,11 @@ AutoGen is a framework that enables the development of LLM applications using mu
 
 ![AutoGen Overview](https://github.com/microsoft/autogen/blob/main/website/static/img/autogen_agentchat.png)
 
-- AutoGen enables building next-gen LLM applications based on **multi-agent conversations** with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
-- It supports **diverse conversation patterns** for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
+- AutoGen enables building next-gen LLM applications based on [multi-agent conversations](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
+- It supports [diverse conversation patterns](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat#supporting-diverse-conversation-patterns) for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
   the number of agents, and agent conversation topology.
-- It provides a collection of working systems with different complexities. These systems span a **wide range of applications** from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
-- AutoGen provides **enhanced LLM inference**. It offers easy performance tuning, plus utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
+- It provides a collection of working systems with different complexities. These systems span a [wide range of applications](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat#diverse-applications-implemented-with-autogen) from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
+- AutoGen provides [enhanced LLM inference](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#api-unification). It offers utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
 
 AutoGen is powered by collaborative [research studies](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and the University of Washington.
 
@@ -42,14 +42,14 @@ The easiest way to start playing is
 
     [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/autogen?quickstart=1)
 
- 2. Copy OAI_CONFIG_LIST_sample to /notebook folder, name to OAI_CONFIG_LIST, and set the correct configuration.
+ 2. Copy OAI_CONFIG_LIST_sample to ./notebook folder, name to OAI_CONFIG_LIST, and set the correct configuration.
  3. Start playing with the notebooks!
 
 
 
 ## Installation
 
-AutoGen requires **Python version >= 3.8**. It can be installed from pip:
+AutoGen requires **Python version >= 3.8, < 3.12**. It can be installed from pip:
 
 ```bash
 pip install pyautogen
@@ -72,7 +72,7 @@ For LLM inference configurations, check the [FAQs](https://microsoft.github.io/a
 
 ## Multi-Agent Conversation Framework
 
-Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents that integrate LLMs, tools, and humans.
+Autogen enables the next-gen LLM applications with a generic [multi-agent conversation](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) framework. It offers customizable and conversable agents that integrate LLMs, tools, and humans.
 By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.
 
 Features of this use case include:
@@ -106,14 +106,16 @@ After the repo is cloned.
 The figure below shows an example conversation flow with AutoGen.
 ![Agent Chat Example](https://github.com/microsoft/autogen/blob/main/website/static/img/chat_example.png)
 
-Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat) for this feature.
+Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AgentChat) for this feature.
 
 ## Enhanced LLM Inferences
 
-Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers enhanced LLM inference with powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.
+Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers [enhanced LLM inference](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#api-unification) with powerful functionalities like caching, error handling, multi-config inference and templating.
+
+<!-- For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.
 
 ```python
-# perform tuning
+# perform tuning for openai<1
 config, analysis = autogen.Completion.tune(
     data=tune_data,
     metric="success",
@@ -127,7 +129,7 @@ config, analysis = autogen.Completion.tune(
 response = autogen.Completion.create(context=test_instance, **config)
 ```
 
-Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-Inference) for this feature.
+Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/Inference) for this feature. -->
 
 ## Documentation
 

autogen/agentchat/assistant_agent.py

@@ -43,7 +43,7 @@ def __init__(
             system_message (str): system message for the ChatCompletion inference.
                 Please override this attribute if you want to reprogram the agent.
             llm_config (dict): llm inference configuration.
-                Please refer to [Completion.create](/docs/reference/oai/completion#create)
+                Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create)
                 for available options.
             is_termination_msg (function): a function that takes a message in the form of a dictionary
                 and returns a boolean value indicating if this received message is a termination message.

autogen/agentchat/contrib/teachable_agent.py

@@ -18,7 +18,7 @@ def colored(x, *args, **kwargs):
 
 
 class TeachableAgent(ConversableAgent):
-    """Teachable Agent, a subclass of ConversableAgent using a vector database to remember user teachings.
+    """(Experimental) Teachable Agent, a subclass of ConversableAgent using a vector database to remember user teachings.
     In this class, the term 'user' refers to any caller (human or not) sending messages to this agent.
     Not yet tested in the group-chat setting."""
 
@@ -40,7 +40,7 @@ def __init__(
             system_message (str): system message for the ChatCompletion inference.
             human_input_mode (str): This agent should NEVER prompt the human for input.
             llm_config (dict or False): llm inference configuration.
-                Please refer to [Completion.create](/docs/reference/oai/completion#create)
+                Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create)
                 for available options.
                 To disable llm-based auto reply, set to False.
             analyzer_llm_config (dict or False): llm inference configuration passed to TextAnalyzerAgent.
@@ -125,11 +125,8 @@ def _generate_teachable_assistant_reply(
                 messages = messages.copy()
                 messages[-1]["content"] = new_user_text
 
-        # Generate a response.
-        msgs = self._oai_system_message + messages
-        response = oai.ChatCompletion.create(messages=msgs, **self.llm_config)
-        response_text = oai.ChatCompletion.extract_text_or_function_call(response)[0]
-        return True, response_text
+        # Generate a response by reusing existing generate_oai_reply
+        return self.generate_oai_reply(messages, sender, config)
 
     def learn_from_user_feedback(self):
         """Reviews the user comments from the last chat, and decides what teachings to store as memos."""
@@ -265,12 +262,14 @@ def analyze(self, text_to_analyze, analysis_instructions):
             self.send(recipient=self.analyzer, message=analysis_instructions, request_reply=True)  # Request the reply.
             return self.last_message(self.analyzer)["content"]
         else:
+            # TODO: This is not an encouraged usage pattern. It breaks the conversation-centric design.
+            # consider using the arg "silent"
             # Use the analyzer's method directly, to leave analyzer message out of the printed chat.
             return self.analyzer.analyze_text(text_to_analyze, analysis_instructions)
 
 
 class MemoStore:
-    """
+    """(Experimental)
     Provides memory storage and retrieval for a TeachableAgent, using a vector database.
     Each DB entry (called a memo) is a pair of strings: an input text and an output text.
     The input text might be a question, or a task to perform.

autogen/agentchat/contrib/text_analyzer_agent.py

@@ -10,7 +10,7 @@
 
 
 class TextAnalyzerAgent(ConversableAgent):
-    """Text Analysis agent, a subclass of ConversableAgent designed to analyze text as instructed."""
+    """(Experimental) Text Analysis agent, a subclass of ConversableAgent designed to analyze text as instructed."""
 
     def __init__(
         self,
@@ -26,7 +26,7 @@ def __init__(
             system_message (str): system message for the ChatCompletion inference.
             human_input_mode (str): This agent should NEVER prompt the human for input.
             llm_config (dict or False): llm inference configuration.
-                Please refer to [Completion.create](/docs/reference/oai/completion#create)
+                Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create)
                 for available options.
                 To disable llm-based auto reply, set to False.
             teach_config (dict or None): Additional parameters used by TeachableAgent.
@@ -74,9 +74,5 @@ def analyze_text(self, text_to_analyze, analysis_instructions):
         msg_text = "\n".join(
             [analysis_instructions, text_to_analyze, analysis_instructions]
         )  # Repeat the instructions.
-        messages = self._oai_system_message + [{"role": "user", "content": msg_text}]
-
         # Generate and return the analysis string.
-        response = oai.ChatCompletion.create(context=None, messages=messages, **self.llm_config)
-        output_text = oai.ChatCompletion.extract_text_or_function_call(response)[0]
-        return output_text
+        return self.generate_oai_reply([{"role": "user", "content": msg_text}], None, None)[1]