Update app.py

app.py

@@ -6,7 +6,8 @@ import requests
 import json
 import openai
 import time
-
+from langchain.embeddings.openai import OpenAIEmbeddings
+import langchain
 
 class Text(BaseModel):
     content: str = ""
@@ -30,7 +31,7 @@ def home():
 @app.post("/qa_maker")
 def sentiment_analysis_ep(content: Text = None):
     url = 'https://api.openai.com/v1/chat/completions'
-    prompt = '根据下面的文章，生成的“问题和回答”QA对，大于5个，以一行一个json格式（{“question”:"xxx","answer":"xxx"}）生成：\n'
+    prompt = 'According to the article below, generate "question and answer" QA pairs, greater than 5, in a json format per line（{“question”:"xxx","answer":"xxx"}）generate：\n'
     messages = [{"role": "user", "content": prompt + content.content}]
     data = {
         "model": "gpt-3.5-turbo",
@@ -53,18 +54,18 @@ def chat_pdf_ep(content: Text = None):
     messages = [
         {
             "role": "system",
-            "content": "你是一个有用的助手，可以使用文章内容准确地回答问题。使用提供的文章来生成你的答案，但避免逐字复制文章。尽可能使用自己的话。准确、有用、简洁、清晰。"
+            "content": "You are a useful assistant to answer questions accurately using the content of the article."
         }
     ]
     obj = json.loads(content.content)
-    messages.append({"role": "system", "content": "文章内容：\n" + obj['doc']})
+    messages.append({"role": "system", "content": "Article content：\n" + obj['doc']})
     history = obj['history']
     for his in history:
         messages.append({"role": "user", "content": his[0]})
         messages.append({"role": "assistant", "content": his[1]})
     messages.append({"role": "user", "content": obj['question']})
     data = {
-        "model": "gpt-3.5-turbo",
+        "model": "text-davinci-003",
         "messages": messages
     }
     print("messages = \n", messages)
@@ -84,11 +85,11 @@ def sale_ep(content: Text = None):
     messages = [
         {
             "role": "system",
-            "content": "你是小鹏汽车资深的销售，可以参考文章内容来生成你的答案，但避免逐字复制文章，尽可能使用自己的话，突出小鹏汽车的优点、说服用户来店试驾或购买小鹏汽车。准确、有用、有销售技巧、口语化、有亲和力。"
+            "content": "You are a useful assistant to answer questions accurately using the content of the article"
         }
     ]
     obj = json.loads(content.content)
-    messages.append({"role": "system", "content": "文章内容：\n" + obj['doc']})
+    messages.append({"role": "system", "content": "Article content：\n" + obj['doc']})
     history = obj['history']
     for his in history:
         messages.append({"role": "user", "content": his[0]})
@@ -172,6 +173,22 @@ def embeddings_ep(content: Text = None):
     return JSONResponse(content=result.json())
 
 
+
+@app.post("/embedd")
+def embed(content: Text = None):
+    url = 'https://api.openai.com/v1/embeddings'
+    data = {
+        "model": "text-embedding-ada-002",
+        "input": content.content
+    }
+    result = requests.post(url=url,
+                           data=json.dumps(data),
+                           headers=headers
+                           )
+    embeddings = OpenAIEmbeddings(openai_api_key= key)
+    return key
+
+
 @app.post("/create_image")
 def create_image_ep(content: Text = None):
     url = 'https://api.openai.com/v1/images/generations'