Update config_list_from_json function to support YAML parsing

OAI_CONFIG_LIST_yaml_sample

@@ -0,0 +1,21 @@
+# Please modify the content, remove these four lines of comment and rename this file to OAI_CONFIG_LIST to run the sample code.
+# If using pyautogen v0.1.x with Azure OpenAI, please replace "base_url" with "api_base" (line 13 and line 19 below). Use "pip list" to check version of pyautogen installed.
+
+# NOTE: This configuration lists GPT-4 as the default model, as this represents our current recommendation, and is known to work well with AutoGen. If you use a model other than GPT-4, you may need to revise various system prompts (especially if using weaker models like GPT-3.5-turbo). Moreover, if you use models other than those hosted by OpenAI or Azure, you may incur additional risks related to alignment and safety. Proceed with caution if updating this default.
+- model: gpt-4
+  api_key: <your OpenAI API key here>
+  tags:
+    - gpt-4
+    - tool
+
+- model: <your Azure OpenAI deployment name>
+  api_key: <your Azure OpenAI API key here>
+  base_url: <your Azure OpenAI API base here>
+  api_type: azure
+  api_version: 2024-02-15-preview
+
+- model: <your Azure OpenAI deployment name>
+  api_key: <your Azure OpenAI API key here>
+  base_url: <your Azure OpenAI API base here>
+  api_type: azure
+  api_version: 2024-02-15-preview

README.md

@@ -90,10 +90,10 @@ 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 or OAI_CONFIG_LIST_yaml_sample to ./notebook folder, name to OAI_CONFIG_LIST, and set the correct configuration.
  3. Start playing with the notebooks!
 
-*NOTE*: OAI_CONFIG_LIST_sample lists GPT-4 as the default model, as this represents our current recommendation, and is known to work well with AutoGen. If you use a model other than GPT-4, you may need to revise various system prompts (especially if using weaker models like GPT-3.5-turbo). Moreover, if you use models other than those hosted by OpenAI or Azure, you may incur additional risks related to alignment and safety. Proceed with caution if updating this default.
+*NOTE*: OAI_CONFIG_LIST_sample/OAI_CONFIG_LIST_yaml_sample lists GPT-4 as the default model, as this represents our current recommendation, and is known to work well with AutoGen. If you use a model other than GPT-4, you may need to revise various system prompts (especially if using weaker models like GPT-3.5-turbo). Moreover, if you use models other than those hosted by OpenAI or Azure, you may incur additional risks related to alignment and safety. Proceed with caution if updating this default.
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
   <a href="#readme-top" style="text-decoration: none; color: blue; font-weight: bold;">
@@ -152,7 +152,7 @@ For [example](https://github.com/microsoft/autogen/blob/main/test/twoagent.py),
 from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
 # Load LLM inference endpoints from an env variable or a file
 # See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
-# and OAI_CONFIG_LIST_sample
+# and OAI_CONFIG_LIST_sample/OAI_CONFIG_LIST_yaml_sample
 config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
 # You can also set config_list directly as a list, for example, config_list = [{'model': 'gpt-4', 'api_key': '<your OpenAI API key here>'},]
 assistant = AssistantAgent("assistant", llm_config={"config_list": config_list})

autogen/oai/openai_utils.py

@@ -8,6 +8,7 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Union
 
+import yaml
 from dotenv import find_dotenv, load_dotenv
 from openai import OpenAI
 from openai.types.beta.assistant import Assistant
@@ -462,10 +463,10 @@ def config_list_from_json(
     filter_dict: Optional[Dict[str, Union[List[Union[str, None]], Set[Union[str, None]]]]] = None,
 ) -> List[Dict[str, Any]]:
     """
-    Retrieves a list of API configurations from a JSON stored in an environment variable or a file.
+    Retrieves a list of API configurations from a JSON/YAML stored in an environment variable or a file.
 
-    This function attempts to parse JSON data from the given `env_or_file` parameter. If `env_or_file` is an
-    environment variable containing JSON data, it will be used directly. Otherwise, it is assumed to be a filename,
+    This function attempts to parse JSON/YAML data from the given `env_or_file` parameter. If `env_or_file` is an
+    environment variable containing JSON/YAML data, it will be used directly. Otherwise, it is assumed to be a filename,
     and the function will attempt to read the file from the specified `file_location`.
 
     The `filter_dict` parameter allows for filtering the configurations based on specified criteria. Each key in the
@@ -475,21 +476,38 @@ def config_list_from_json(
 
     Args:
         env_or_file (str): The name of the environment variable, the filename, or the environment variable of the filename
-            that containing the JSON data.
+            that containing the JSON/YAML data.
         file_location (str, optional): The directory path where the file is located, if `env_or_file` is a filename.
         filter_dict (dict, optional): A dictionary specifying the filtering criteria for the configurations, with
             keys representing field names and values being lists or sets of acceptable values for those fields.
 
     Example:
-    ```python
-    # Suppose we have an environment variable 'CONFIG_JSON' with the following content:
-    # '[{"model": "gpt-3.5-turbo", "api_type": "azure"}, {"model": "gpt-4"}]'
 
-    # We can retrieve a filtered list of configurations like this:
-    filter_criteria = {"model": ["gpt-3.5-turbo"]}
-    configs = config_list_from_json('CONFIG_JSON', filter_dict=filter_criteria)
-    # The 'configs' variable will now contain only the configurations that match the filter criteria.
-    ```
+    JSON:
+        ```python
+        # Suppose we have an environment variable 'CONFIG_JSON' with the following content:
+        # '[{"model": "gpt-3.5-turbo", "api_type": "azure"}, {"model": "gpt-4"}]'
+
+        # We can retrieve a filtered list of configurations like this:
+        filter_criteria = {"model": ["gpt-3.5-turbo"]}
+        configs = config_list_from_json('CONFIG_JSON', filter_dict=filter_criteria)
+        # The 'configs' variable will now contain only the configurations that match the filter criteria.
+        ```
+
+    YAML:
+        ```python
+        # Suppose we have an environment variable 'CONFIG_YAML' with the following content:
+        '''
+        - model: gpt-3.5-turbo
+          api_type: azure
+        - model: gpt-4
+        '''
+
+        # We can retrieve a filtered list of configurations like this:
+        filter_criteria = {"model": ["gpt-3.5-turbo"]}
+        configs = config_list_from_json('CONFIG_YAML', filter_dict=filter_criteria)
+        # The 'configs' variable will now contain only the configurations that match the filter criteria.
+        ```
 
     Returns:
         List[Dict]: A list of configuration dictionaries that match the filtering criteria specified in `filter_dict`.
@@ -504,11 +522,11 @@ def config_list_from_json(
         if os.path.exists(env_str):
             # It is a file location, and we need to load the json from the file.
             with open(env_str, "r") as file:
-                json_str = file.read()
+                json_or_yaml_str = file.read()
         else:
             # Else, it should be a JSON string by itself.
-            json_str = env_str
-        config_list = json.loads(json_str)
+            json_or_yaml_str = env_str
+        config_list = yaml.safe_load(json_or_yaml_str)
     else:
         # The environment variable does not exist.
         # So, `env_or_file` is a filename. We should use the file location.
@@ -518,7 +536,7 @@ def config_list_from_json(
             config_list_path = env_or_file
 
         with open(config_list_path) as json_file:
-            config_list = json.load(json_file)
+            config_list = yaml.safe_load(json_file)
     return filter_config(config_list, filter_dict)
 
 

website/blog/2023-10-26-TeachableAgent/index.mdx

@@ -55,7 +55,7 @@ from autogen import ConversableAgent  # As an example
 ```python
 # Load LLM inference endpoints from an env variable or a file
 # See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
-# and OAI_CONFIG_LIST_sample
+# and OAI_CONFIG_LIST_sample/OAI_CONFIG_LIST_yaml_sample
 filter_dict = {"model": ["gpt-4"]}  # GPT-3.5 is less reliable than GPT-4 at learning from user feedback.
 config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST", filter_dict=filter_dict)
 llm_config={"config_list": config_list, "timeout": 120}