Support Dedicated OpenAI API Deployments on Azure.

README.md

@@ -103,6 +103,8 @@ voyager.learn()
   3. After the world is created, press `Esc` key and press `Open to LAN`.
   4. Select `Allow cheats: ON` and press `Start LAN World`. You will see the bot join the world soon. 
 
+Or if you are using dedicated OpenAI API deployments on Azure, you can config your APIs by inferring [this](installation/run_with_azure_api_deployments.md).
+
 # Resume from a checkpoint during learning
 
 If you stop the learning process and want to resume from a checkpoint later, you can instantiate Voyager by:

installation/run_with_azure_api_deployments.md

@@ -0,0 +1,52 @@
+# Run With Azure API Deployments
+
+If you are using dedicated OpenAI API deployments on Azure, you can run Voyager by:
+
+```python
+from voyager import Voyager
+from voyager.agents import AzureChatModelConfig, AzureOpenAIEmbeddingsConfig
+
+# You can also use mc_port instead of azure_login, but azure_login is highly recommended
+azure_login = {
+    "client_id": "YOUR_CLIENT_ID",
+    "redirect_url": "https://127.0.0.1/auth-response",
+    "secret_value": "[OPTIONAL] YOUR_SECRET_VALUE",
+    "version": "fabric-loader-0.14.18-1.19", # the version Voyager is tested on
+}
+openai_api_key = "YOUR_API_KEY"
+
+# If you are using OpenAI LLM deployments on Azure, you can config them here
+azure_gpt_4_config = AzureChatModelConfig(
+    openai_api_base="BASE_URL_FOR_AZURE_GPT4_DEPLOYMENT",
+    openai_api_version="GPT4_API_VERSION",
+    deployment_name="GPT4_DEPLOYMENT_NAME",
+    openai_api_type="azure",
+    openai_api_key="YOUR_AZURE_API_KEY",	# Not API keys with prefix "sk-"
+)
+azure_gpt_35_config = AzureChatModelConfig(
+    openai_api_base="BASE_URL_FOR_AZURE_GPT35_DEPLOYMENT",
+    openai_api_version="GPT35_API_VERSION",
+    deployment_name="GPT35_DEPLOYMENT_NAME",
+    openai_api_type="azure",
+    openai_api_key="YOUR_AZURE_API_KEY",	# Not API keys with prefix "sk-"
+)
+azure_openai_embeddings_config = AzureOpenAIEmbeddingsConfig(
+    openai_api_base="BASE_URL_FOR_AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT",
+    model="MODEL_NAME",		# Check https://platform.openai.com/docs/guides/embeddings/embedding-models
+    openai_api_type="azure",
+    deployment="YOUR_DEPLOYMENT_NAME",
+    openai_api_key="YOUR_AZURE_API_KEY",	# Not API keys with prefix "sk-"
+)
+
+voyager = Voyager(
+    azure_login=azure_login,
+    openai_api_type="azure",
+    azure_gpt_4_config=azure_gpt_4_config,
+    azure_gpt_35_config=azure_gpt_35_config,
+    azure_openai_embeddings_config=azure_openai_embeddings_config,
+)
+
+# start lifelong learning
+voyager.learn()
+```
+

voyager/agents/__init__.py

@@ -2,3 +2,4 @@
 from .critic import CriticAgent
 from .curriculum import CurriculumAgent
 from .skill import SkillManager
+from .azure_model_config import AzureChatModelConfig, AzureOpenAIEmbeddingsConfig

voyager/agents/action.py

@@ -3,12 +3,13 @@
 
 import voyager.utils as U
 from javascript import require
-from langchain.chat_models import ChatOpenAI
 from langchain.prompts import SystemMessagePromptTemplate
 from langchain.schema import AIMessage, HumanMessage, SystemMessage
 
 from voyager.prompts import load_prompt
 from voyager.control_primitives_context import load_control_primitives_context
+from voyager.agents.azure_model_config import AzureChatModelConfig
+from voyager.agents.get_llm import get_llm
 
 
 class ActionAgent:
@@ -21,6 +22,9 @@ def __init__(
         resume=False,
         chat_log=True,
         execution_error=True,
+        openai_api_type="",
+        azure_gpt_4_config=AzureChatModelConfig(),
+        azure_gpt_35_config=AzureChatModelConfig(),
     ):
         self.ckpt_dir = ckpt_dir
         self.chat_log = chat_log
@@ -31,10 +35,13 @@ def __init__(
             self.chest_memory = U.load_json(f"{ckpt_dir}/action/chest_memory.json")
         else:
             self.chest_memory = {}
-        self.llm = ChatOpenAI(
+        self.llm = get_llm(
             model_name=model_name,
             temperature=temperature,
-            request_timeout=request_timout,
+            request_timout=request_timout,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
 
     def update_chest_memory(self, chests):

voyager/agents/azure_model_config.py

@@ -0,0 +1,19 @@
+from pydantic import BaseModel
+
+
+class AzureChatModelConfig(BaseModel):
+    """AzureChatOpenAI config profile"""
+    openai_api_base: str = ''
+    openai_api_version: str = ''
+    deployment_name: str = ''
+    openai_api_type: str = 'azure'
+    openai_api_key: str = ''
+
+
+class AzureOpenAIEmbeddingsConfig(BaseModel):
+    """OpenAIEmbeddings config profile"""
+    openai_api_base: str = ''
+    model: str = ''
+    openai_api_type: str = 'azure'
+    deployment: str = ''
+    openai_api_key: str = ''

voyager/agents/critic.py

@@ -1,6 +1,7 @@
 from voyager.prompts import load_prompt
 from voyager.utils.json_utils import fix_and_parse_json
-from langchain.chat_models import ChatOpenAI
+from voyager.agents.azure_model_config import AzureChatModelConfig
+from voyager.agents.get_llm import get_llm
 from langchain.schema import HumanMessage, SystemMessage
 
 
@@ -11,11 +12,17 @@ def __init__(
         temperature=0,
         request_timout=120,
         mode="auto",
+        openai_api_type="",
+        azure_gpt_4_config=AzureChatModelConfig(),
+        azure_gpt_35_config=AzureChatModelConfig(),
     ):
-        self.llm = ChatOpenAI(
+        self.llm = get_llm(
             model_name=model_name,
             temperature=temperature,
-            request_timeout=request_timout,
+            request_timout=request_timout,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
         assert mode in ["auto", "manual"]
         self.mode = mode

voyager/agents/curriculum.py

@@ -6,7 +6,8 @@
 import voyager.utils as U
 from voyager.prompts import load_prompt
 from voyager.utils.json_utils import fix_and_parse_json
-from langchain.chat_models import ChatOpenAI
+from voyager.agents.azure_model_config import AzureChatModelConfig, AzureOpenAIEmbeddingsConfig
+from voyager.agents.get_llm import get_llm
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.schema import HumanMessage, SystemMessage
 from langchain.vectorstores import Chroma
@@ -25,16 +26,26 @@ def __init__(
         mode="auto",
         warm_up=None,
         core_inventory_items: str | None = None,
+        openai_api_type="",
+        azure_gpt_4_config=AzureChatModelConfig(),
+        azure_gpt_35_config=AzureChatModelConfig(),
+        azure_openai_embeddings_config=AzureOpenAIEmbeddingsConfig(),
     ):
-        self.llm = ChatOpenAI(
+        self.llm = get_llm(
             model_name=model_name,
             temperature=temperature,
-            request_timeout=request_timout,
+            request_timout=request_timout,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
-        self.qa_llm = ChatOpenAI(
+        self.qa_llm = get_llm(
             model_name=qa_model_name,
             temperature=qa_temperature,
-            request_timeout=request_timout,
+            request_timout=request_timout,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
         assert mode in [
             "auto",
@@ -57,7 +68,7 @@ def __init__(
         # vectordb for qa cache
         self.qa_cache_questions_vectordb = Chroma(
             collection_name="qa_cache_questions_vectordb",
-            embedding_function=OpenAIEmbeddings(),
+            embedding_function=OpenAIEmbeddings(**azure_openai_embeddings_config.dict()) if openai_api_type == "azure" else OpenAIEmbeddings(),
             persist_directory=f"{ckpt_dir}/curriculum/vectordb",
         )
         assert self.qa_cache_questions_vectordb._collection.count() == len(

voyager/agents/get_llm.py

@@ -0,0 +1,27 @@
+from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
+
+from voyager.agents.azure_model_config import AzureChatModelConfig
+
+
+def get_llm(
+    model_name: str = "gpt-3.5-turbo",
+    temperature: float = 0,
+    request_timout: float = 120,
+    azure_gpt_4_config: AzureChatModelConfig = AzureChatModelConfig(),
+    azure_gpt_35_config: AzureChatModelConfig = AzureChatModelConfig(),
+    openai_api_type: str = "",
+) -> ChatOpenAI | AzureChatOpenAI:
+    if openai_api_type == "azure":
+        azure_model_config = azure_gpt_4_config if model_name == "gpt-4" else azure_gpt_35_config
+        llm = AzureChatOpenAI(
+            temperature=temperature,
+            **azure_model_config.dict(),
+        )
+    else:
+        llm = ChatOpenAI(
+            model_name=model_name,
+            temperature=temperature,
+            request_timeout=request_timout,
+        )
+
+    return llm

voyager/agents/skill.py

@@ -1,7 +1,8 @@
 import os
 
 import voyager.utils as U
-from langchain.chat_models import ChatOpenAI
+from voyager.agents.azure_model_config import AzureChatModelConfig, AzureOpenAIEmbeddingsConfig
+from voyager.agents.get_llm import get_llm
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.schema import HumanMessage, SystemMessage
 from langchain.vectorstores import Chroma
@@ -19,11 +20,18 @@ def __init__(
         request_timout=120,
         ckpt_dir="ckpt",
         resume=False,
+        openai_api_type="",
+        azure_gpt_4_config=AzureChatModelConfig(),
+        azure_gpt_35_config=AzureChatModelConfig(),
+        azure_openai_embeddings_config=AzureOpenAIEmbeddingsConfig(),
     ):
-        self.llm = ChatOpenAI(
+        self.llm = get_llm(
             model_name=model_name,
             temperature=temperature,
-            request_timeout=request_timout,
+            request_timout=request_timout,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
         U.f_mkdir(f"{ckpt_dir}/skill/code")
         U.f_mkdir(f"{ckpt_dir}/skill/description")
@@ -39,7 +47,7 @@ def __init__(
         self.ckpt_dir = ckpt_dir
         self.vectordb = Chroma(
             collection_name="skill_vectordb",
-            embedding_function=OpenAIEmbeddings(),
+            embedding_function=OpenAIEmbeddings(**azure_openai_embeddings_config.dict()) if openai_api_type == "azure" else OpenAIEmbeddings(),
             persist_directory=f"{ckpt_dir}/skill/vectordb",
         )
         assert self.vectordb._collection.count() == len(self.skills), (

voyager/voyager.py

@@ -11,6 +11,7 @@
 from .agents import CriticAgent
 from .agents import CurriculumAgent
 from .agents import SkillManager
+from .agents import AzureChatModelConfig, AzureOpenAIEmbeddingsConfig
 
 
 # TODO: remove event memory
@@ -48,6 +49,10 @@ def __init__(
         ckpt_dir: str = "ckpt",
         skill_library_dir: str = None,
         resume: bool = False,
+        openai_api_type="",
+        azure_gpt_4_config=AzureChatModelConfig(),
+        azure_gpt_35_config=AzureChatModelConfig(),
+        azure_openai_embeddings_config=AzureOpenAIEmbeddingsConfig(),
     ):
         """
         The main class for Voyager.
@@ -123,6 +128,9 @@ def __init__(
             resume=resume,
             chat_log=action_agent_show_chat_log,
             execution_error=action_agent_show_execution_error,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
         self.action_agent_task_max_retries = action_agent_task_max_retries
         self.curriculum_agent = CurriculumAgent(
@@ -136,12 +144,19 @@ def __init__(
             mode=curriculum_agent_mode,
             warm_up=curriculum_agent_warm_up,
             core_inventory_items=curriculum_agent_core_inventory_items,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
+            azure_openai_embeddings_config=azure_openai_embeddings_config,
         )
         self.critic_agent = CriticAgent(
             model_name=critic_agent_model_name,
             temperature=critic_agent_temperature,
             request_timout=openai_api_request_timeout,
             mode=critic_agent_mode,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
         )
         self.skill_manager = SkillManager(
             model_name=skill_manager_model_name,
@@ -150,6 +165,10 @@ def __init__(
             request_timout=openai_api_request_timeout,
             ckpt_dir=skill_library_dir if skill_library_dir else ckpt_dir,
             resume=True if resume or skill_library_dir else False,
+            openai_api_type=openai_api_type,
+            azure_gpt_4_config=azure_gpt_4_config,
+            azure_gpt_35_config=azure_gpt_35_config,
+            azure_openai_embeddings_config=azure_openai_embeddings_config,
         )
         self.recorder = U.EventRecorder(ckpt_dir=ckpt_dir, resume=resume)
         self.resume = resume