feat: +common talk role

metagpt/learn/__init__.py

@@ -5,3 +5,11 @@
 @Author  : alexanderwu
 @File    : __init__.py
 """
+
+from metagpt.learn.text_to_image import text_to_image
+from metagpt.learn.text_to_speech import text_to_speech
+
+__all__ = [
+    "text_to_image",
+    "text_to_speech",
+]

metagpt/learn/skill_loader.py

@@ -1,14 +1,26 @@
 from pathlib import Path
-from typing import List, Dict
+from typing import List, Dict, Optional
 
 import yaml
 from pydantic import BaseModel
 
 
+class Example(BaseModel):
+    ask: str
+    answer: str
+
+class Returns(BaseModel):
+    type: str
+    format: Optional[str] = None
+
 class Skill(BaseModel):
     name: str
     description: str
+    id: str
     requisite: List[str]
+    arguments: Dict
+    examples: List[Example]
+    returns: Returns
 
 
 class EntitySkills(BaseModel):
@@ -26,13 +38,26 @@ class SkillLoader:
             skills = yaml.safe_load(file)
         self._skills = SkillsDeclaration(**skills)
 
-    def get_skill_list(self, entity_name: str = "Assistant"):
-        if not self._skills or entity_name not in self._skills.entities:
+    def get_skill_list(self, entity_name: str = "Assistant") -> Dict:
+        entity_skills = self.get_entity(entity_name)
+        if not entity_skills:
             return {}
-        entity_skills = self._skills.entities.get(entity_name)
 
         description_to_name_mappings = {}
         for s in entity_skills.skills:
             description_to_name_mappings[s.description] = s.name
 
         return description_to_name_mappings
+
+    def get_skill(self, name, entity_name: str = "Assistant") -> Skill:
+        entity = self.get_entity(entity_name)
+        if not entity:
+            return None
+        for sk in entity.skills:
+            if sk.name == name:
+                return sk
+
+    def get_entity(self, name) -> EntitySkills:
+        if not self._skills:
+            return None
+        return self._skills.entities.get(name)

metagpt/learn/text_to_embedding.py

@@ -16,7 +16,7 @@ from metagpt.utils.common import initialize_environment
 @skill_metadata(name="Text to Embedding",
                 description="Convert the text into embeddings.",
                 requisite="`OPENAI_API_KEY`")
-def text_to_embedding(text, model="text-embedding-ada-002", openai_api_key=""):
+def text_to_embedding(text, model="text-embedding-ada-002", openai_api_key="", **kwargs):
     """Text to embedding
 
     :param text: The text used for embedding.

metagpt/learn/text_to_image.py

@@ -17,7 +17,7 @@ from metagpt.utils.common import initialize_environment
 @skill_metadata(name="Text to image",
                 description="Create a drawing based on the text.",
                 requisite="`OPENAI_API_KEY` or `METAGPT_TEXT_TO_IMAGE_MODEL`")
-def text_to_image(text, size_type: str = "512x512", openai_api_key="", model_url=""):
+def text_to_image(text, size_type: str = "512x512", openai_api_key="", model_url="", **kwargs):
     """Text to image
 
     :param text: The text used for image conversion.
@@ -27,8 +27,14 @@ def text_to_image(text, size_type: str = "512x512", openai_api_key="", model_url
     :return: The image data is returned in Base64 encoding.
     """
     initialize_environment()
+    image_declaration = "data:image/png;base64,"
     if os.environ.get("METAGPT_TEXT_TO_IMAGE_MODEL") or model_url:
-        return oas3_metagpt_text_to_image(text, size_type, model_url)
+        data = oas3_metagpt_text_to_image(text, size_type, model_url)
+        return image_declaration + data if data else ""
     if os.environ.get("OPENAI_API_KEY") or openai_api_key:
-        return oas3_openai_text_to_image(text, size_type, openai_api_key)
+        data = oas3_openai_text_to_image(text, size_type, openai_api_key)
+        return image_declaration + data if data else ""
+
     raise EnvironmentError
+
+

metagpt/learn/text_to_speech.py

@@ -17,7 +17,7 @@ from metagpt.utils.common import initialize_environment
                 description="Text-to-speech",
                 requisite="`AZURE_TTS_SUBSCRIPTION_KEY` and `AZURE_TTS_REGION`")
 def text_to_speech(text, lang="zh-CN", voice="zh-CN-XiaomoNeural", style="affectionate", role="Girl",
-                   subscription_key="", region=""):
+                   subscription_key="", region="", **kwargs):
     """Text to speech
     For more details, check out:`https://learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support?tabs=tts`
 
@@ -32,8 +32,10 @@ def text_to_speech(text, lang="zh-CN", voice="zh-CN-XiaomoNeural", style="affect
 
     """
     initialize_environment()
+    audio_declaration = "data:audio/wav;base64,"
     if (os.environ.get("AZURE_TTS_SUBSCRIPTION_KEY") and os.environ.get("AZURE_TTS_REGION")) or \
             (subscription_key and region):
-        return oas3_azsure_tts(text, lang, voice, style, role, subscription_key, region)
+        data = oas3_azsure_tts(text, lang, voice, style, role, subscription_key, region)
+        return audio_declaration + data if data else data
 
     raise EnvironmentError