feat: +hello.py oas3

metagpt/tools/azure_tts.py

@@ -108,7 +108,7 @@ def oas3_azsure_tts(text, lang="", voice="", style="", role="", subscription_key
 
 
 if __name__ == "__main__":
-    initalize_enviroment()
+    initialize_environment()
 
     v = oas3_azsure_tts("测试，test")
     print(v)

metagpt/tools/metagpt_oas3_api_svc.py

@@ -17,4 +17,5 @@ if __name__ == "__main__":
 
     app = connexion.AioHttpApp(__name__, specification_dir='../../.well-known/')
     app.add_api("metagpt_oas3_api.yaml")
+    app.add_api("openapi.yaml")
     app.run(port=8080)

metagpt/tools/openai_text_2_embedding.py

@@ -1,47 +1,92 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
-@Time    : 2023/8/17
+@Time    : 2023/8/18
 @Author  : mashenquan
-@File    : openai_text_2_vector.py
-@Desc    : OpenAI Text-to-Vector OAS3 api, which provides text-to-vector functionality.
+@File    : openai_text_2_embedding.py
+@Desc    : OpenAI Text-to-Embedding OAS3 api, which provides text-to-embedding functionality.
+            For more details, checkout: `https://platform.openai.com/docs/api-reference/embeddings/object`
 """
 import os
+from pathlib import Path
+from typing import List
 
-class OpenAIText2Vector:
+import requests
+from pydantic import BaseModel
+import sys
+
+sys.path.append(str(Path(__file__).resolve().parent.parent.parent))  # fix-bug: No module named 'metagpt'
+from metagpt.utils.common import initialize_environment
+from metagpt.logs import logger
+
+
+class Embedding(BaseModel):
+    """Represents an embedding vector returned by embedding endpoint."""
+    object: str  # The object type, which is always "embedding".
+    embedding: List[
+        float]  # The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide.
+    index: int  # The index of the embedding in the list of embeddings.
+
+
+class Usage(BaseModel):
+    prompt_tokens: int
+    total_tokens: int
+
+
+class ResultEmbedding(BaseModel):
+    object: str
+    data: List[Embedding]
+    model: str
+    usage: Usage
+
+
+class OpenAIText2Embedding:
     def __init__(self, openai_api_key):
         """
         :param openai_api_key: OpenAI API key, For more details, checkout: `https://platform.openai.com/account/api-keys`
         """
         self.openai_api_key = openai_api_key if openai_api_key else os.environ.get('OPENAI_API_KEY')
 
-    def text_2_vector(self, text, size_type="1024x1024"):
-        """Text to image
+    def text_2_embedding(self, text, model="text-embedding-ada-002"):
+        """Text to embedding
 
-        :param text: The text used for image conversion.
-        :param size_type: One of ['256x256', '512x512', '1024x1024']
-        :return: The image data is returned in Base64 encoding.
+        :param text: The text used for embedding.
+        :param model: One of ['text-embedding-ada-002'], ID of the model to use. For more details, checkout: `https://api.openai.com/v1/models`.
+        :return: A json object of :class:`ResultEmbedding` class if successful, otherwise `{}`.
         """
 
-        class ImageUrl(BaseModel):
-            url: str
-
-        class ImageResult(BaseModel):
-            data: List[ImageUrl]
-            created: int
-
         headers = {
             "Content-Type": "application/json",
             "Authorization": f"Bearer {self.openai_api_key}"
         }
-        data = {"prompt": text, "n": 1, "size": size_type}
+        data = {"input": text, "model": model}
         try:
-            response = requests.post("https://api.openai.com/v1/images/generations", headers=headers, json=data)
+            response = requests.post("https://api.openai.com/v1/embeddings", headers=headers, json=data)
             response.raise_for_status()  # Raise an exception for 4xx or 5xx responses
-            result = ImageResult(**response.json())
+            return response.json()
         except requests.exceptions.RequestException as e:
             logger.error(f"An error occurred:{e}")
-            return ""
-        if len(result.data) > 0:
-            return OpenAIText2Image.get_image_data(result.data[0].url)
-        return ""
+        return {}
+
+
+# Export
+def oas3_openai_text_2_embedding(text, model="text-embedding-ada-002", openai_api_key=""):
+    """Text to embedding
+
+    :param text: The text used for embedding.
+    :param model: One of ['text-embedding-ada-002'], ID of the model to use. For more details, checkout: `https://api.openai.com/v1/models`.
+    :param openai_api_key: OpenAI API key, For more details, checkout: `https://platform.openai.com/account/api-keys`
+    :return: A json object of :class:`ResultEmbedding` class if successful, otherwise `{}`.
+    """
+    if not text:
+        return ""
+    if not openai_api_key:
+        openai_api_key = os.environ.get("OPENAI_API_KEY")
+    return OpenAIText2Embedding(openai_api_key).text_2_embedding(text, model=model)
+
+
+if __name__ == "__main__":
+    initialize_environment()
+
+    v = oas3_openai_text_2_embedding("Panda emoji")
+    print(v)