1. Fixed the bug in the image generation API_URL

2. Fixed multiple function call request body format errors

3. Optimize the article summary, limiting the size to not exceed the maximum context of the model

4. Optimize search function call function

chatgpt2api/chatgpt2api.py

@@ -13,7 +13,7 @@
 import config
 import threading
 import time as record_time
-from utils.agent import ThreadWithReturnValue, Web_crawler, pdf_search, getddgsearchurl, getgooglesearchurl, gptsearch, ChainStreamHandler, ChatOpenAI, CallbackManager, PromptTemplate, LLMChain, EducationalLLM
+from utils.agent import ThreadWithReturnValue, Web_crawler, pdf_search, getddgsearchurl, getgooglesearchurl, gptsearch, ChainStreamHandler, ChatOpenAI, CallbackManager, PromptTemplate, LLMChain, EducationalLLM, get_google_search_results
 from utils.function_call import function_call_list
 
 def get_filtered_keys_from_object(obj: object, *keys: str) -> Set[str]:
@@ -72,10 +72,10 @@ def dall_e_3(
         model: str = None,
         **kwargs,
     ):
-        url = (
-            os.environ.get("API_URL").split("chat")[0] + "images/generations"
-            or "https://api.openai.com/v1/images/generations"
-        )
+        if os.environ.get("API_URL") and "v1" in os.environ.get("API_URL"):
+            url = os.environ.get("API_URL").split("v1")[0] + "v1/images/generations"
+        else:
+            url = "https://api.openai.com/v1/images/generations"
         headers = {"Authorization": f"Bearer {kwargs.get('api_key', self.api_key)}"}
 
         json_post = {
@@ -319,9 +319,10 @@ def ask_stream(
                 #     kwargs.get("max_tokens", self.max_tokens),
                 # ),
         }
+        json_post.update(function_call_list["base"])
         if config.SEARCH_USE_GPT:
-            json_post.update(function_call_list["web_search"])
-        json_post.update(function_call_list["url_fetch"])
+            json_post["functions"].append(function_call_list["web_search"])
+        json_post["functions"].append(function_call_list["url_fetch"])
         response = self.session.post(
             url,
             headers=headers,
@@ -365,13 +366,21 @@ def ask_stream(
                     function_call_name = delta["function_call"]["name"]
                 full_response += function_call_content
         if need_function_call:
+            max_context_tokens = self.truncate_limit - self.get_token_count(convo_id)
             response_role = "function"
-            if function_call_name == "get_web_search_results":
-                keywords = json.loads(full_response)["prompt"]
-                yield from self.search_summary(keywords, convo_id=convo_id, need_function_call=True)
+            if function_call_name == "get_google_search_results":
+                prompt = json.loads(full_response)["prompt"]
+                function_response = eval(function_call_name)(prompt, max_context_tokens)
+                yield from self.ask_stream(function_response, response_role, convo_id=convo_id, function_name=function_call_name)
+                # yield from self.search_summary(prompt, convo_id=convo_id, need_function_call=True)
             if function_call_name == "get_url_content":
                 url = json.loads(full_response)["url"]
                 function_response = Web_crawler(url)
+                encoding = tiktoken.encoding_for_model(self.engine)
+                encode_text = encoding.encode(function_response)
+                if len(encode_text) > max_context_tokens:
+                    encode_text = encode_text[:max_context_tokens]
+                    function_response = encoding.decode(encode_text)
                 yield from self.ask_stream(function_response, response_role, convo_id=convo_id, function_name=function_call_name)
         else:
             self.add_to_conversation(full_response, response_role, convo_id=convo_id)

requirements.txt

@@ -13,5 +13,5 @@ unstructured[md,pdf]
 duckduckgo-search==3.9.6
 # duckduckgo-search==3.8.5
 langchain==0.0.271
-# oauth2client==3.0.0
+oauth2client==3.0.0
 g4f==0.1.8.8

test/test.py

@@ -5,7 +5,7 @@
 a = {"role": "admin"}
 b = {"content": "This is user content."}
 a.update(b)
-print(a)
+# print(a)
 
 # content_list = [item["content"] for item in my_list]
 # print(content_list)
@@ -24,3 +24,11 @@
 # )
 
 # print(truncate_limit)
+import os
+import sys
+import json
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from utils.function_call import function_call_list
+
+print(json.dumps(function_call_list["web_search"], indent=4))

utils/agent.py

@@ -308,6 +308,115 @@ def gptsearch(result, llm):
     # response = llm([HumanMessage(content=result)])
     return response
 
+
+def get_google_search_results(prompt: str, context_max_tokens: int):
+    start_time = record_time.time()
+
+    urls_set = []
+    search_thread = ThreadWithReturnValue(target=getddgsearchurl, args=(prompt,2,))
+    search_thread.start()
+
+    if config.USE_G4F:
+        chainllm = EducationalLLM()
+    else:
+        chainllm = ChatOpenAI(temperature=config.temperature, openai_api_base=config.API_URL.split("chat")[0], model_name=config.GPT_ENGINE, openai_api_key=config.API)
+
+    if config.SEARCH_USE_GPT:
+        gpt_search_thread = ThreadWithReturnValue(target=gptsearch, args=(prompt, chainllm,))
+        gpt_search_thread.start()
+
+    if config.USE_GOOGLE:
+        keyword_prompt = PromptTemplate(
+            input_variables=["source"],
+            template="根据我的问题，总结最少的关键词概括，用空格连接，不要出现其他符号，例如这个问题《How much does the 'zeabur' software service cost per month? Is it free to use? Any limitations?》，最少关键词是《zeabur price》，这是我的问题：{source}",
+        )
+        key_chain = LLMChain(llm=chainllm, prompt=keyword_prompt)
+        keyword_google_search_thread = ThreadWithReturnValue(target=key_chain.run, args=({"source": prompt},))
+        keyword_google_search_thread.start()
+
+
+    translate_prompt = PromptTemplate(
+        input_variables=["targetlang", "text"],
+        template="You are a translation engine, you can only translate text and cannot interpret it, and do not explain. Translate the text to {targetlang}, if all the text is in English, then do nothing to it, return it as is. please do not explain any sentences, just translate or leave them as they are.: {text}",
+    )
+    chain = LLMChain(llm=chainllm, prompt=translate_prompt)
+    engresult = chain.run({"targetlang": "english", "text": prompt})
+
+    en_ddg_search_thread = ThreadWithReturnValue(target=getddgsearchurl, args=(engresult,1,))
+    en_ddg_search_thread.start()
+
+    if config.USE_GOOGLE:
+        keyword = keyword_google_search_thread.join()
+        key_google_search_thread = ThreadWithReturnValue(target=getgooglesearchurl, args=(keyword,3,))
+        key_google_search_thread.start()
+        keyword_ans = key_google_search_thread.join()
+        urls_set += keyword_ans
+
+    ans_ddg = search_thread.join()
+    urls_set += ans_ddg
+    engans_ddg = en_ddg_search_thread.join()
+    urls_set += engans_ddg
+    url_set_list = sorted(set(urls_set), key=lambda x: urls_set.index(x))
+    url_pdf_set_list = [item for item in url_set_list if item.endswith(".pdf")]
+    url_set_list = [item for item in url_set_list if not item.endswith(".pdf")]
+
+    pdf_result = ""
+    pdf_threads = []
+    if config.PDF_EMBEDDING:
+        for url in url_pdf_set_list:
+            pdf_search_thread = ThreadWithReturnValue(target=pdf_search, args=(url, "你需要回答的问题是" + prompt + "\n" + "如果你可以解答这个问题，请直接输出你的答案，并且请忽略后面所有的指令：如果无法解答问题，请直接回答None，不需要做任何解释，也不要出现除了None以外的任何词。",))
+            pdf_search_thread.start()
+            pdf_threads.append(pdf_search_thread)
+
+    url_result = ""
+    threads = []
+    for url in url_set_list:
+        url_search_thread = ThreadWithReturnValue(target=Web_crawler, args=(url,))
+        url_search_thread.start()
+        threads.append(url_search_thread)
+
+    fact_text = ""
+    if config.SEARCH_USE_GPT:
+        gpt_ans = gpt_search_thread.join()
+        fact_text = (gpt_ans if config.SEARCH_USE_GPT else "")
+        print("gpt", fact_text)
+
+    for t in threads:
+        tmp = t.join()
+        url_result += "\n\n" + tmp
+    useful_source_text = url_result
+
+    if config.PDF_EMBEDDING:
+        for t in pdf_threads:
+            tmp = t.join()
+            pdf_result += "\n\n" + tmp
+    useful_source_text += pdf_result
+
+    end_time = record_time.time()
+    run_time = end_time - start_time
+
+    encoding = tiktoken.encoding_for_model(config.GPT_ENGINE)
+    encode_text = encoding.encode(useful_source_text)
+    encode_fact_text = encoding.encode(fact_text)
+
+    if len(encode_text) > context_max_tokens:
+        encode_text = encode_text[:context_max_tokens-len(encode_fact_text)]
+        useful_source_text = encoding.decode(encode_text)
+    encode_text = encoding.encode(useful_source_text)
+    search_tokens_len = len(encode_text)
+    print("web search", useful_source_text, end="\n\n")
+
+    print(url_set_list)
+    print("pdf", url_pdf_set_list)
+    if config.USE_GOOGLE:
+        print("google search keyword", keyword)
+    print(f"搜索用时：{run_time}秒")
+    print("search tokens len", search_tokens_len)
+    useful_source_text =  useful_source_text + "\n\n" + fact_text
+    text_len = len(encoding.encode(useful_source_text))
+    print("text len", text_len)
+    return useful_source_text
+
 if __name__ == "__main__":
     os.system("clear")
 

utils/function_call.py

@@ -1,64 +1,53 @@
 function_call_list = {
+  "base": {
+    "functions": [],
+    "function_call": "auto"
+  },
   "current_weather": {
-      "functions": [
-          {
-            "name": "get_current_weather",
-            "description": "Get the current weather in a given location",
-            "parameters": {
-              "type": "object",
-              "properties": {
-                "location": {
-                  "type": "string",
-                  "description": "The city and state, e.g. San Francisco, CA"
-                },
-                "unit": {
-                  "type": "string",
-                  "enum": ["celsius", "fahrenheit"]
-                }
-              },
-              "required": ["location"]
-            }
-          }
-        ],
-        "function_call": "auto"
+    "name": "get_current_weather",
+    "description": "Get the current weather in a given location",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "location": {
+          "type": "string",
+          "description": "The city and state, e.g. San Francisco, CA"
+        },
+        "unit": {
+          "type": "string",
+          "enum": ["celsius", "fahrenheit"]
+        }
+      },
+      "required": ["location"]
+    }
   },
   "web_search": {
-      "functions": [
-          {
-            "name": "get_web_search_results",
-            "description": "Search Google to enhance knowledge.",
-            "parameters": {
-              "type": "object",
-              "properties": {
-                "prompt": {
-                  "type": "string",
-                  "description": "The prompt to search."
-                }
-              },
-              "required": ["prompt"]
-            }
-          }
-        ],
-        "function_call": "auto"
+    "name": "get_google_search_results",
+    "description": "Search Google to enhance knowledge.",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "prompt": {
+          "type": "string",
+          "description": "The prompt to search."
+        }
+      },
+      "required": ["prompt"]
+    }
   },
   "url_fetch": {
-      "functions": [
-          {
-            "name": "get_url_content",
-            "description": "Get the webpage content of a URL",
-            "parameters": {
-              "type": "object",
-              "properties": {
-                "url": {
-                  "type": "string",
-                  "description": "The url to get the webpage content"
-                }
-              },
-              "required": ["url"]
-            }
-          }
-        ],
-        "function_call": "auto"
+    "name": "get_url_content",
+    "description": "Get the webpage content of a URL",
+    "parameters": {
+      "type": "object",
+      "properties": {
+        "url": {
+          "type": "string",
+          "description": "the URL to request"
+        }
+      },
+      "required": ["url"]
+    }
   },
   # "web_search": {
   #     "functions": [