Merge pull request #448 from cangming/support_openai_o_series_model

feat(model): support openai o series model

.github/workflows/docs.yaml

@@ -11,6 +11,6 @@ jobs:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
         with:
-          python-version: '3.9'
+          python-version: '3.10'
       - run: pip install mkdocs mkdocs-material
       - run: mkdocs gh-deploy --force

.github/workflows/make_test_ebook.yaml

@@ -27,10 +27,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-      - name: install python 3.9 
+      - name: install python 3.10
         uses: actions/setup-python@v4
         with:
-          python-version: '3.9'
+          python-version: '3.10'
           cache: 'pip' # caching pip dependencies
       - name: Check formatting (black)
         run: |
@@ -71,7 +71,7 @@ jobs:
             
       - name: Rename and Upload ePub
         if: env.OPENAI_API_KEY != null
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: epub_output
           path: "test_books/lemo_bilingual.epub"

book_maker/cli.py

@@ -17,6 +17,7 @@ def parse_prompt_arg(prompt_arg):
     if prompt_arg.endswith(".md") and os.path.exists(prompt_arg):
         try:
             from promptdown import StructuredPrompt
+
             structured_prompt = StructuredPrompt.from_promptdown_file(prompt_arg)
 
             # Initialize our prompt structure
@@ -24,27 +25,40 @@ def parse_prompt_arg(prompt_arg):
 
             # Handle developer_message or system_message
             # Developer message takes precedence if both are present
-            if hasattr(structured_prompt, 'developer_message') and structured_prompt.developer_message:
+            if (
-                prompt['system'] = structured_prompt.developer_message
+                hasattr(structured_prompt, "developer_message")
-            elif hasattr(structured_prompt, 'system_message') and structured_prompt.system_message:
+                and structured_prompt.developer_message
-                prompt['system'] = structured_prompt.system_message
+            ):
+                prompt["system"] = structured_prompt.developer_message
+            elif (
+                hasattr(structured_prompt, "system_message")
+                and structured_prompt.system_message
+            ):
+                prompt["system"] = structured_prompt.system_message
 
             # Extract user message from conversation
-            if hasattr(structured_prompt, 'conversation') and structured_prompt.conversation:
+            if (
+                hasattr(structured_prompt, "conversation")
+                and structured_prompt.conversation
+            ):
                 for message in structured_prompt.conversation:
-                    if message.role.lower() == 'user':
+                    if message.role.lower() == "user":
-                        prompt['user'] = message.content
+                        prompt["user"] = message.content
                         break
 
             # Ensure we found a user message
-            if 'user' not in prompt or not prompt['user']:
+            if "user" not in prompt or not prompt["user"]:
-                raise ValueError("PromptDown file must contain at least one user message")
+                raise ValueError(
+                    "PromptDown file must contain at least one user message"
+                )
 
             print(f"Successfully loaded PromptDown file: {prompt_arg}")
 
             # Validate required placeholders
             if any(c not in prompt["user"] for c in ["{text}"]):
-                raise ValueError("User message in PromptDown must contain `{text}` placeholder")
+                raise ValueError(
+                    "User message in PromptDown must contain `{text}` placeholder"
+                )
 
             return prompt
         except Exception as e:
@@ -388,7 +402,17 @@ So you are close to reaching the limit. You have to choose your own value, there
     translate_model = MODEL_DICT.get(options.model)
     assert translate_model is not None, "unsupported model"
     API_KEY = ""
-    if options.model in ["openai", "chatgptapi", "gpt4", "gpt4omini", "gpt4o"]:
+    if options.model in [
+        "openai",
+        "chatgptapi",
+        "gpt4",
+        "gpt4omini",
+        "gpt4o",
+        "o1preview",
+        "o1",
+        "o1mini",
+        "o3mini",
+    ]:
         if OPENAI_API_KEY := (
             options.openai_key
             or env.get(
@@ -510,6 +534,10 @@ So you are close to reaching the limit. You have to choose your own value, there
             "gpt4",
             "gpt4omini",
             "gpt4o",
+            "o1",
+            "o1preview",
+            "o1mini",
+            "o3mini",
         ], "only support chatgptapi for deployment_id"
         if not options.api_base:
             raise ValueError("`api_base` must be provided when using `deployment_id`")
@@ -534,6 +562,14 @@ So you are close to reaching the limit. You have to choose your own value, there
         e.translate_model.set_gpt4omini_models()
     if options.model == "gpt4o":
         e.translate_model.set_gpt4o_models()
+    if options.model == "o1preview":
+        e.translate_model.set_o1preview_models()
+    if options.model == "o1":
+        e.translate_model.set_o1_models()
+    if options.model == "o1mini":
+        e.translate_model.set_o1mini_models()
+    if options.model == "o3mini":
+        e.translate_model.set_o3mini_models()
     if options.model.startswith("claude-"):
         e.translate_model.set_claude_model(options.model)
     if options.block_size > 0:

book_maker/translator/__init__.py

@@ -16,6 +16,10 @@ MODEL_DICT = {
     "gpt4": ChatGPTAPI,
     "gpt4omini": ChatGPTAPI,
     "gpt4o": ChatGPTAPI,
+    "o1preview": ChatGPTAPI,
+    "o1": ChatGPTAPI,
+    "o1mini": ChatGPTAPI,
+    "o3mini": ChatGPTAPI,
     "google": Google,
     "caiyun": Caiyun,
     "deepl": DeepL,

book_maker/translator/chatgptapi_translator.py

@@ -48,6 +48,21 @@ GPT4o_MODEL_LIST = [
     "gpt-4o-2024-08-06",
     "chatgpt-4o-latest",
 ]
+O1PREVIEW_MODEL_LIST = [
+    "o1-preview",
+    "o1-preview-2024-09-12",
+]
+O1_MODEL_LIST = [
+    "o1",
+    "o1-2024-12-17",
+]
+O1MINI_MODEL_LIST = [
+    "o1-mini",
+    "o1-mini-2024-09-12",
+]
+O3MINI_MODEL_LIST = [
+    "o3-mini",
+]
 
 
 class ChatGPTAPI(Base):
@@ -422,6 +437,54 @@ class ChatGPTAPI(Base):
             print(f"Using model list {model_list}")
             self.model_list = cycle(model_list)
 
+    def set_o1preview_models(self):
+        # for issue #375 azure can not use model list
+        if self.deployment_id:
+            self.model_list = cycle(["o1-preview"])
+        else:
+            my_model_list = [
+                i["id"] for i in self.openai_client.models.list().model_dump()["data"]
+            ]
+            model_list = list(set(my_model_list) & set(O1PREVIEW_MODEL_LIST))
+            print(f"Using model list {model_list}")
+            self.model_list = cycle(model_list)
+
+    def set_o1_models(self):
+        # for issue #375 azure can not use model list
+        if self.deployment_id:
+            self.model_list = cycle(["o1"])
+        else:
+            my_model_list = [
+                i["id"] for i in self.openai_client.models.list().model_dump()["data"]
+            ]
+            model_list = list(set(my_model_list) & set(O1_MODEL_LIST))
+            print(f"Using model list {model_list}")
+            self.model_list = cycle(model_list)
+
+    def set_o1mini_models(self):
+        # for issue #375 azure can not use model list
+        if self.deployment_id:
+            self.model_list = cycle(["o1-mini"])
+        else:
+            my_model_list = [
+                i["id"] for i in self.openai_client.models.list().model_dump()["data"]
+            ]
+            model_list = list(set(my_model_list) & set(O1MINI_MODEL_LIST))
+            print(f"Using model list {model_list}")
+            self.model_list = cycle(model_list)
+
+    def set_o3mini_models(self):
+        # for issue #375 azure can not use model list
+        if self.deployment_id:
+            self.model_list = cycle(["o3-mini"])
+        else:
+            my_model_list = [
+                i["id"] for i in self.openai_client.models.list().model_dump()["data"]
+            ]
+            model_list = list(set(my_model_list) & set(O3MINI_MODEL_LIST))
+            print(f"Using model list {model_list}")
+            self.model_list = cycle(model_list)
+
     def set_model_list(self, model_list):
         model_list = list(set(model_list))
         print(f"Using model list {model_list}")

docs/model_lang.md

@@ -2,7 +2,7 @@
 ## Models
 `-m, --model <Model>` <br>
 
-Currently `bbook_maker` supports these models: `chatgptapi` , `gpt3` , `google` , `caiyun` , `deepl` , `deeplfree` , `gpt4` , `gpt4omini` , `claude` , `customapi`.
+Currently `bbook_maker` supports these models: `chatgptapi` , `gpt3` , `google` , `caiyun` , `deepl` , `deeplfree` , `gpt4` , `gpt4omini` , `o1-preview` , `o1` , `o1-mini` , `o3-mini` , `claude` , `customapi`.
 Default model is `chatgptapi` . 
 
 ### OPENAI models