cleaned up .env to move Azure config to separate azure.yaml file

updated README.md to explain new config
added Azure yaml loader to config class
centralized model retrieval into config class
this commit effectively combines and replaces #700 and #580

.env.template

@@ -7,10 +7,6 @@ FAST_LLM_MODEL=gpt-3.5-turbo
 GOOGLE_API_KEY=
 CUSTOM_SEARCH_ENGINE_ID=
 USE_AZURE=False
-OPENAI_AZURE_API_BASE=your-base-url-for-azure
-OPENAI_AZURE_API_VERSION=api-version-for-azure
-OPENAI_AZURE_DEPLOYMENT_ID=deployment-id-for-azure
-OPENAI_AZURE_EMBEDDING_DEPLOYMENT_ID=embedding-deployment-id-for-azure
 IMAGE_PROVIDER=dalle
 HUGGINGFACE_API_TOKEN=
 USE_MAC_OS_TTS=False

README.md

@@ -93,7 +93,13 @@ pip install -r requirements.txt
 4. Rename `.env.template` to `.env` and fill in your `OPENAI_API_KEY`. If you plan to use Speech Mode, fill in your `ELEVEN_LABS_API_KEY` as well.
   - Obtain your OpenAI API key from: https://platform.openai.com/account/api-keys.
   - Obtain your ElevenLabs API key from: https://elevenlabs.io. You can view your xi-api-key using the "Profile" tab on the website.
-  - If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and provide the `OPENAI_AZURE_API_BASE`, `OPENAI_AZURE_API_VERSION` and `OPENAI_AZURE_DEPLOYMENT_ID` values as explained here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section. In addition to your GPT deployment, you will need to deploy a `text-embedding-ada-002 (Version 2)` model which will have a different deployment id, please set `OPENAI_AZURE_EMBEDDING_DEPLOYMENT_ID` accordingly (see here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line).
+  - If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and then:
+    - Rename `azure.yaml.template` to `azure.yaml` and provide the relevant `azure_api_base`, `azure_api_version` and all of the deployment ids for the relevant models in the `azure_model_map` section:
+      - `fast_llm_model_deployment_id` - your gpt-3.5-turbo or gpt-4 deployment id
+      - `smart_llm_model_deployment_id` - your gpt-4 deployment id
+      - `embedding_model_deployment_id` - your text-embedding-ada-002 v2 deployment id
+    - Please specify all of these values as double quoted strings
+    - details can be found here: https://pypi.org/project/openai/ in the `Microsoft Azure Endpoints` section and here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line for the embedding model.
 
 ## 🔧 Usage
 

ai_settings.yaml

@@ -1,7 +1,9 @@
 ai_goals:
-- Increase net worth.
+- Explain the peripherals in detail with examples of how to use them effectively
-- Develop and manage multiple businesses autonomously.
+- Give detailed information on how to use I/O co-processing
-- Play to your strengths as a Large Language Model.
+- Explain which GPIO pins can be used for which tasks
-ai_name: Entrepreneur-GPT
+- Use C, C++ or assembly as the target languages for all examples and research
-ai_role: an AI designed to autonomously develop and run businesses with the sole goal
+- Use the PlatformIO system and support library for all examples and research
-  of increasing your net worth.
+ai_name: PicoGPT
+ai_role: an AI to provide detailed research into how to effectively program the raspberry
+  pi pico and all of it's peripherals

azure.yaml.template

@@ -0,0 +1,6 @@
+azure_api_base: your-base-url-for-azure
+azure_api_version: api-version-for-azure
+azure_model_map:
+    fast_llm_model_deployment_id: gpt35-deployment-id-for-azure
+    smart_llm_model_deployment_id: gpt4-deployment-id-for-azure 
+    embedding_model_deployment_id: embedding-deployment-id-for-azure

scripts/config.py

@@ -1,6 +1,7 @@
 import abc
 import os
 import openai
+import yaml
 from dotenv import load_dotenv
 # Load environment variables from .env file
 load_dotenv()
@@ -46,10 +47,7 @@ class Config(metaclass=Singleton):
         self.use_azure = False
         self.use_azure = os.getenv("USE_AZURE") == 'True'
         if self.use_azure:
-            self.openai_api_base = os.getenv("OPENAI_AZURE_API_BASE")
+            self.load_azure_config()
-            self.openai_api_version = os.getenv("OPENAI_AZURE_API_VERSION")
-            self.openai_deployment_id = os.getenv("OPENAI_AZURE_DEPLOYMENT_ID")
-            self.openai_embedding_deployment_id = os.getenv("OPENAI_AZURE_EMBEDDING_DEPLOYMENT_ID")
             openai.api_type = "azure"
             openai.api_base = self.openai_api_base
             openai.api_version = self.openai_api_version
@@ -82,6 +80,47 @@ class Config(metaclass=Singleton):
         # Initialize the OpenAI API client
         openai.api_key = self.openai_api_key
 
+    def get_azure_deployment_id_for_model(self, model: str) -> str:
+        """
+        Returns the relevant deployment id for the model specified.
+
+        Parameters:
+            model(str): The model to map to the deployment id.
+
+        Returns:
+            The matching deployment id if found, otherwise an empty string.
+        """
+        match model:
+            case self.fast_llm_model:
+                return self.azure_model_to_deployment_id_map["fast_llm_model_deployment_id"]
+            case self.smart_llm_model:
+                return self.azure_model_to_deployment_id_map["smart_llm_model_deployment_id"]
+            case "text-embedding-ada-002":
+                return self.azure_model_to_deployment_id_map["embedding_model_deployment_id"]
+            case default: 
+                return ""            
+
+    AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), '..', 'azure.yaml')
+
+    def load_azure_config(self, config_file: str=AZURE_CONFIG_FILE) -> None:
+        """
+        Loads the configuration parameters for Azure hosting from the specified file path as a yaml file.
+
+        Parameters:
+            config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml"
+
+        Returns:
+            None
+        """
+        try:
+            with open(config_file) as file:
+                config_params = yaml.load(file, Loader=yaml.FullLoader)
+        except FileNotFoundError:
+            config_params = {}
+        self.openai_api_base = config_params.get("azure_api_base", "")
+        self.openai_api_version = config_params.get("azure_api_version", "")
+        self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", [])
+
     def set_continuous_mode(self, value: bool):
         """Set the continuous mode value."""
         self.continuous_mode = value

scripts/llm_utils.py

@@ -9,7 +9,7 @@ def create_chat_completion(messages, model=None, temperature=None, max_tokens=No
     """Create a chat completion using the OpenAI API"""
     if cfg.use_azure:
         response = openai.ChatCompletion.create(
-            deployment_id=cfg.openai_deployment_id,
+            deployment_id=cfg.get_azure_deployment_id_for_model(model),
             model=model,
             messages=messages,
             temperature=temperature,

scripts/memory/base.py

@@ -9,7 +9,7 @@ cfg = Config()
 def get_ada_embedding(text):
     text = text.replace("\n", " ")
     if cfg.use_azure:
-        return openai.Embedding.create(input=[text], engine=cfg.openai_embedding_deployment_id)["data"][0]["embedding"]
+        return openai.Embedding.create(input=[text], engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"))["data"][0]["embedding"]
     else:
         return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]