fix(huixiangdou): test main.py passed

huixiangdou/frontend/lark.py

@@ -53,7 +53,7 @@ def post(self, data):
                                      headers=self.headers,
                                      data=post_data,
                                      verify=False,
-                                     timeout=5)
+                                     timeout=60)
         except requests.exceptions.HTTPError as exc:
             code = exc.response.status_code
             reason = exc.response.reason
@@ -96,5 +96,5 @@ def post(self, data):
             requests.post(self.webhook,
                           headers=self.headers,
                           data=json.dumps(error_data),
-                          timeout=5)
+                          timeout=60)
         return result

huixiangdou/main.py

@@ -45,7 +45,7 @@ def check_env(args):
             f'{CONFIG_NAME} not found, download a template from {CONFIG_URL}.')
 
         try:
-            response = requests.get(CONFIG_URL, timeout=5)
+            response = requests.get(CONFIG_URL, timeout=60)
             response.raise_for_status()
             with open(CONFIG_NAME, 'wb') as f:
                 f.write(response.content)

huixiangdou/service/feature_store.py

@@ -215,11 +215,22 @@ def load_feature(self,
                      feature_reject: str = 'db_reject'):
         """Load extracted feature."""
         # https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html#langchain.vectorstores.faiss.FAISS
+
+        resp_dir = os.path.join(work_dir, feature_response)
+        reject_dir = os.path.join(work_dir, feature_reject)
+
+        if not os.path.exists(resp_dir) or not os.path.exists(reject_dir):
+            logger.error(
+                'Please check README.md first and `python3 -m huixiangdou.service.feature_store`'  # noqa E501
+            )
+            raise Exception(
+                f'{resp_dir} or {reject_dir} not exist, please initialize with feature_store.'  # noqa E501
+            )
+
         self.vector_store_reject = Vectorstore.load_local(
-            os.path.join(work_dir, feature_response),
-            embeddings=self.embeddings)
+            reject_dir, embeddings=self.embeddings)
         self.vector_store_db = Vectorstore.load_local(
-            os.path.join(work_dir, feature_reject), embeddings=self.embeddings)
+            resp_dir, embeddings=self.embeddings)
 
     def get_doc_by_id(self, _id, vector_store):
         """Get doc by search id."""

huixiangdou/service/llm_client.py

@@ -116,7 +116,7 @@ def generate_response(self, prompt, history=[], remote=False):
             resp = requests.post(url,
                                  headers=header,
                                  data=json.dumps(data),
-                                 timeout=5)
+                                 timeout=300)
             if resp.status_code != 200:
                 raise Exception(str((resp.status_code, resp.reason)))
             return resp.json()['text']

huixiangdou/service/llm_server_hybrid.py

@@ -152,10 +152,14 @@ def generate_response(self, prompt, history=[], remote=False):
 
         else:
             prompt = prompt[0:self.local_max_length]
+            """# Caution: For the results of this software to be reliable and verifiable,  # noqa E501
+            it's essential to ensure reproducibility. Thus `GenerationMode.GREEDY_SEARCH`  # noqa E501
+            must enabled."""
             output_text, _ = self.model.chat(self.tokenizer,
                                              prompt,
                                              history,
-                                             top_k=1)
+                                             top_k=1,
+                                             do_sample=False)
             print((prompt, output_text))
         time_finish = time.time()
 

huixiangdou/service/web_search.py

@@ -95,7 +95,7 @@ def google(self, query: str, max_article):
                                     url,
                                     headers=headers,
                                     data=payload,
-                                    timeout=3)  # noqa E501
+                                    timeout=60)  # noqa E501
         jsonobj = json.loads(response.text)
 
         # 带偏序的 url 连接拾取
@@ -137,7 +137,7 @@ def google(self, query: str, max_article):
             while life < self.retry:
                 try:
                     logger.info(f'extract: {target_link}')
-                    response = requests.get(target_link, timeout=5)
+                    response = requests.get(target_link, timeout=60)
                     if len(response.text) < 1:
                         break
 
@@ -233,7 +233,7 @@ def fetch_web_content(target_link: str):
     Extracts the main content and title from the HTML of the page. Returns the
     title and content as a single string.
     """
-    response = requests.get(target_link, timeout=5)
+    response = requests.get(target_link, timeout=60)
 
     doc = Document(response.text)
     content_html = doc.summary()

huixiangdou/service/worker.py

@@ -264,7 +264,6 @@ def generate(self, query, history, groupname):
                                  default=0):
                 reborn_code = ErrorCode.BAD_ANSWER
 
-        reborn_code = ErrorCode.BAD_ANSWER
         if self.config['worker']['enable_sg_search']:
             if reborn_code == ErrorCode.BAD_ANSWER or reborn_code == ErrorCode.NO_SEARCH_RESULT:  # noqa E501
                 # reborn
@@ -292,7 +291,7 @@ def generate(self, query, history, groupname):
                                          default=0):
                         return ErrorCode.BAD_ANSWER, response
 
-        if response is not None and len(response) >= 500:
+        if response is not None and len(response) >= 800:
             # reply too long, summarize it
             response = self.llm.generate_response(
                 prompt=self.SUMMARIZE_TEMPLATE.format(response))

tests/test_internlm2.py

@@ -0,0 +1,19 @@
+import pdb
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_path = '/internlm/ampere_7b_v1_7_0'
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_path,
+                                             trust_remote_code=True,
+                                             device_map='auto',
+                                             torch_dtype='auto').eval()
+
+# 不能像某些 LLM 一样 AutoModelForCausalLM.from_pretrained(.. fp16=True) 这样写，会 Internlm2Config.__init__() 报错
+
+queries = ['how to install mmdeploy ?']
+for query in queries:
+    pdb.set_trace()
+    output_text, _ = model.chat(tokenizer, query, top_k=1, do_sample=False)
+    print(query, output_text)