chore: clean some variables begining with KUBE_CHAT_

aperag/chat/websocket/base_consumer.py

@@ -37,8 +37,8 @@
     welcome_response,
 )
 from aperag.db.ops import query_bot, query_user_quota
-from aperag.pipeline.base_pipeline import KUBE_CHAT_DOC_QA_REFERENCES, KUBE_CHAT_RELATED_QUESTIONS, \
-    KUBE_CHAT_DOCUMENT_URLS
+from aperag.pipeline.base_pipeline import DOC_QA_REFERENCES, RELATED_QUESTIONS, \
+    DOCUMENT_URLS
 from aperag.utils.constant import KEY_BOT_ID, KEY_CHAT_ID, KEY_USER_ID, KEY_WEBSOCKET_PROTOCOL
 from aperag.utils.utils import now_unix_milliseconds
 
@@ -133,14 +133,14 @@ async def receive(self, text_data=None, bytes_data=None):
                     return
 
             async for tokens in self.predict(data["data"], message_id=message_id):
-                if tokens.startswith(KUBE_CHAT_DOC_QA_REFERENCES):
-                    references = json.loads(tokens[len(KUBE_CHAT_DOC_QA_REFERENCES):])
+                if tokens.startswith(DOC_QA_REFERENCES):
+                    references = json.loads(tokens[len(DOC_QA_REFERENCES):])
                     continue
-                if tokens.startswith(KUBE_CHAT_RELATED_QUESTIONS):
-                    related_question = ast.literal_eval(tokens[len(KUBE_CHAT_RELATED_QUESTIONS):])
+                if tokens.startswith(RELATED_QUESTIONS):
+                    related_question = ast.literal_eval(tokens[len(RELATED_QUESTIONS):])
                     continue
-                if tokens.startswith(KUBE_CHAT_DOCUMENT_URLS):
-                    urls = ast.literal_eval(tokens[len(KUBE_CHAT_DOCUMENT_URLS):])
+                if tokens.startswith(DOCUMENT_URLS):
+                    urls = ast.literal_eval(tokens[len(DOCUMENT_URLS):])
                     continue
 
                 # streaming response

aperag/chat/websocket/common_consumer.py

@@ -29,7 +29,7 @@
     success_response,
 )
 from aperag.chat.websocket.base_consumer import BaseConsumer
-from aperag.pipeline.base_pipeline import KUBE_CHAT_RELATED_QUESTIONS
+from aperag.pipeline.base_pipeline import RELATED_QUESTIONS
 from aperag.pipeline.common_pipeline import CommonPipeline
 from aperag.readers.base_readers import DEFAULT_FILE_READER_CLS
 from aperag.source.utils import gen_temporary_file
@@ -97,8 +97,8 @@ async def receive(self, text_data=None, bytes_data=None):
                     return
 
             async for tokens in self.predict(data["data"], message_id=message_id, file=self.file):
-                if tokens.startswith(KUBE_CHAT_RELATED_QUESTIONS):
-                    related_question = ast.literal_eval(tokens[len(KUBE_CHAT_RELATED_QUESTIONS):])
+                if tokens.startswith(RELATED_QUESTIONS):
+                    related_question = ast.literal_eval(tokens[len(RELATED_QUESTIONS):])
                     continue
 
                 # streaming response

aperag/pipeline/base_pipeline.py

@@ -45,9 +45,9 @@ class Message(BaseModel):
     llm_context_window: Optional[int] = None
 
 
-KUBE_CHAT_DOC_QA_REFERENCES = "|KUBE_CHAT_DOC_QA_REFERENCES|"
-KUBE_CHAT_RELATED_QUESTIONS = "|KUBE_CHAT_RELATED_QUESTIONS|"
-KUBE_CHAT_DOCUMENT_URLS = "|KUBE_CHAT_DOCUMENT_URLS|"
+DOC_QA_REFERENCES = "|DOC_QA_REFERENCES|"
+RELATED_QUESTIONS = "|RELATED_QUESTIONS|"
+DOCUMENT_URLS = "|DOCUMENT_URLS|"
 
 
 class Pipeline(ABC):

aperag/pipeline/common_pipeline.py

@@ -20,7 +20,7 @@
 from langchain import PromptTemplate
 
 from aperag.llm.prompts import COMMON_FILE_TEMPLATE
-from aperag.pipeline.base_pipeline import KUBE_CHAT_RELATED_QUESTIONS, Message, Pipeline
+from aperag.pipeline.base_pipeline import RELATED_QUESTIONS, Message, Pipeline
 from aperag.utils.utils import now_unix_milliseconds
 
 logger = logging.getLogger(__name__)
@@ -111,4 +111,4 @@ async def run(self, message, gen_references=False, message_id="", file=None):
                     related_questions.update(related_question_generate)
                 related_questions = list(related_questions)
                 random.shuffle(related_questions)
-                yield KUBE_CHAT_RELATED_QUESTIONS + str(related_questions[:3])
+                yield RELATED_QUESTIONS + str(related_questions[:3])

aperag/pipeline/fake_pipeline.py

@@ -18,7 +18,7 @@
 
 from aperag.pipeline.base_pipeline import Pipeline
 
-KUBE_CHAT_DOC_QA_REFERENCES = "|KUBE_CHAT_DOC_QA_REFERENCES|"
+DOC_QA_REFERENCES = "|DOC_QA_REFERENCES|"
 
 
 class FakePipeline(Pipeline):
@@ -61,4 +61,4 @@ async def run(self, message, gen_references=False, message_id=""):
         await self.add_ai_message(message, message_id, response, references, urls=[])
 
         if gen_references:
-            yield KUBE_CHAT_DOC_QA_REFERENCES + json.dumps(references)
+            yield DOC_QA_REFERENCES + json.dumps(references)

aperag/pipeline/knowledge_pipeline.py

@@ -27,8 +27,8 @@
     DEFAULT_CHINESE_PROMPT_TEMPLATE_V3,
     DEFAULT_MODEL_MEMOTY_PROMPT_TEMPLATES,
 )
-from aperag.pipeline.base_pipeline import KUBE_CHAT_DOC_QA_REFERENCES, KUBE_CHAT_RELATED_QUESTIONS, \
-    Message, Pipeline, KUBE_CHAT_DOCUMENT_URLS
+from aperag.pipeline.base_pipeline import DOC_QA_REFERENCES, RELATED_QUESTIONS, \
+    Message, Pipeline, DOCUMENT_URLS
 from aperag.pipeline.keyword_extractor import IKExtractor
 from aperag.query.query import DocumentWithScore, get_packed_answer
 from aperag.readers.base_embedding import get_embedding_model, rerank
@@ -249,11 +249,11 @@ async def run(self, message, gen_references=False, message_id=""):
                 related_questions.update(related_question_generate)
             related_questions = list(related_questions)
             random.shuffle(related_questions)
-            yield KUBE_CHAT_RELATED_QUESTIONS + str(related_questions[:3])
+            yield RELATED_QUESTIONS + str(related_questions[:3])
 
         if gen_references:
-            yield KUBE_CHAT_DOC_QA_REFERENCES + json.dumps(references)
+            yield DOC_QA_REFERENCES + json.dumps(references)
 
         if document_url_list:
-            yield KUBE_CHAT_DOCUMENT_URLS + json.dumps(document_url_list)
+            yield DOCUMENT_URLS + json.dumps(document_url_list)