Phase2/HuyDN: add request_timeout=120u

app/modules/matching_cv/models/matching_cv_logic.py

@@ -42,7 +42,7 @@ def result_matching_cv_jd(cv_text, jd_text):
     # create the chat message
     chat_message =  chat_template.format_messages(cv=cv_text, jd=jd_text)
 
-    llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY)
+    llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY, request_timeout=120)
     chain = llm | parser
     result = chain.invoke(chat_message)
 

app/modules/question_tests_retrieval/models/jd2text.py

@@ -17,7 +17,7 @@ parser = JsonOutputParser()
 
 def jobdes2text(jobdes):
     # setup the gemini pro
-    llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY)
+    llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY, request_timeout=120)
 
     # create the prompt template
     finnal_jd_chat_template = ChatPromptTemplate.from_messages(
@@ -27,7 +27,7 @@ def jobdes2text(jobdes):
                     """Return Job title, level(Fresher, Junior, Senior, ...) and Brief summary of required skills about 20 words from the job description. Use the following format: Job Title is {job title}, Level is {level}, and Brief summary of required skills is {brief summary of required skills}."""
                 )
             ),
-            HumanMessagePromptTemplate.from_template("{text}"), 
+            HumanMessagePromptTemplate.from_template("{text}"),
         ]
     )
 

app/modules/question_tests_retrieval/models/question_tests_logic.py

@@ -18,7 +18,7 @@ os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY')
 GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
 
 # Setting model embedding
-embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
+embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY, request_timeout=120)
 gemini_evaluator = load_evaluator("embedding_distance", distance_metric=EmbeddingDistance.COSINE, embeddings=embedding_model)
 
 # def compare_vector(vector_extract, vector_des):

app/modules/question_tests_retrieval/models/text2vector.py

@@ -10,6 +10,6 @@ os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY')
 GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
 
 def text2vector(text):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY, request_timeout=120)
     vector = embeddings.embed_query(text)
     return vector