update bing_search.py

agent/bing_search.py

@@ -1,20 +1,19 @@
 #coding=utf8
 
-import os
 from langchain.utilities import BingSearchAPIWrapper
+from configs.model_config import BING_SEARCH_URL, BING_SUBSCRIPTION_KEY
 
 
-env_bing_key = os.environ.get("BING_SUBSCRIPTION_KEY")
+def bing_search(text, result_len=3):
-env_bing_url = os.environ.get("BING_SEARCH_URL")
+    if not (BING_SEARCH_URL and BING_SUBSCRIPTION_KEY):
-
+        return [{"snippet": "please set BING_SUBSCRIPTION_KEY and BING_SEARCH_URL in os ENV",
-
+                 "title": "env inof not fould",
-def search(text, result_len=3):
+                 "link": "https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html"}]
-    if not (env_bing_key and env_bing_url):
+    search = BingSearchAPIWrapper(bing_subscription_key=BING_SUBSCRIPTION_KEY,
-        return [{"snippet":"please set BING_SUBSCRIPTION_KEY and BING_SEARCH_URL in os ENV",
+                                  bing_search_url=BING_SEARCH_URL)
-            "title": "env inof not fould", "link":"https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html"}]
-    search = BingSearchAPIWrapper()
     return search.results(text, result_len)
 
 
 if __name__ == "__main__":
-    r = search('python')
+    r = bing_search('python')
+    print(r)

chains/local_doc_qa.py

@@ -4,7 +4,7 @@ from langchain.document_loaders import UnstructuredFileLoader, TextLoader
 from configs.model_config import *
 import datetime
 from textsplitter import ChineseTextSplitter
-from typing import List, Tuple
+from typing import List, Tuple, Dict
 from langchain.docstore.document import Document
 import numpy as np
 from utils import torch_gc
@@ -18,6 +18,8 @@ from models.base import (BaseAnswer,
 from models.loader.args import parser
 from models.loader import LoaderCheckPoint
 import models.shared as shared
+from agent import bing_search
+from langchain.docstore.document import Document
 
 
 def load_file(filepath, sentence_size=SENTENCE_SIZE):
@@ -58,8 +60,9 @@ def write_check_file(filepath, docs):
         fout.close()
 
 
-def generate_prompt(related_docs: List[str], query: str,
+def generate_prompt(related_docs: List[str],
-                    prompt_template=PROMPT_TEMPLATE) -> str:
+                    query: str,
+                    prompt_template: str = PROMPT_TEMPLATE, ) -> str:
     context = "\n".join([doc.page_content for doc in related_docs])
     prompt = prompt_template.replace("{question}", query).replace("{context}", context)
     return prompt
@@ -137,6 +140,16 @@ def similarity_search_with_score_by_vector(
     return docs
 
 
+def search_result2docs(search_results):
+    docs = []
+    for result in search_results:
+        doc = Document(page_content=result["snippet"] if "snippet" in result.keys() else "",
+                       metadata={"source": result["link"] if "link" in result.keys() else "",
+                                 "filename": result["title"] if "title" in result.keys() else ""})
+        docs.append(doc)
+    return docs
+
+
 class LocalDocQA:
     llm: BaseAnswer = None
     embeddings: object = None
@@ -262,7 +275,6 @@ class LocalDocQA:
                         "source_documents": related_docs_with_score}
             yield response, history
 
-
     # query      查询内容
     # vs_path    知识库路径
     # chunk_conent   是否启用上下文关联
@@ -288,6 +300,21 @@ class LocalDocQA:
                     "source_documents": related_docs_with_score}
         return response, prompt
 
+    def get_search_result_based_answer(self, query, chat_history=[], streaming: bool = STREAMING):
+        results = bing_search(query)
+        result_docs = search_result2docs(results)
+        prompt = generate_prompt(result_docs, query)
+
+        for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history,
+                                                      streaming=streaming):
+            resp = answer_result.llm_output["answer"]
+            history = answer_result.history
+            history[-1][0] = query
+            response = {"query": query,
+                        "result": resp,
+                        "source_documents": result_docs}
+            yield response, history
+
 
 if __name__ == "__main__":
     # 初始化消息
@@ -304,13 +331,17 @@ if __name__ == "__main__":
     query = "本项目使用的embedding模型是什么，消耗多少显存"
     vs_path = "/media/gpt4-pdf-chatbot-langchain/dev-langchain-ChatGLM/vector_store/test"
     last_print_len = 0
-    for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
+    # for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
-                                                                 vs_path=vs_path,
+    #                                                              vs_path=vs_path,
+    #                                                              chat_history=[],
+    #                                                              streaming=True):
+    for resp, history in local_doc_qa.get_search_result_based_answer(query=query,
                                                                      chat_history=[],
                                                                      streaming=True):
-        logger.info(resp["result"][last_print_len:], end="", flush=True)
+        print(resp["result"][last_print_len:], end="", flush=True)
         last_print_len = len(resp["result"])
-    source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
+    source_text = [f"""出处 [{inum + 1}] {doc.metadata['source'] if doc.metadata['source'].startswith("http") 
+                   else os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
                    # f"""相关度：{doc.metadata['score']}\n\n"""
                    for inum, doc in
                    enumerate(resp["source_documents"])]