github/DB-GPT
1
0
Fork 0
mirror of https://github.com/csunny/DB-GPT.git synced 2025-08-09 20:28:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix: custom chat exception (#257)

Browse Source 
fix custom chat exception
raise user can understand error
This commit is contained in:
magic.chen 2023-06-20 20:00:36 +08:00 committed by GitHub
commit 31e7ea8df9
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 16 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
pilot/scene/chat_knowledge/custom/chat.py
View File

@ -1,3 +1,5 @@
from chromadb.errors import NoIndexException
from pilot.scene.base_chat import BaseChat, logger, headers
from pilot.scene.base import ChatScene
from pilot.common.sql_database import Database
@ -50,13 +52,19 @@ class ChatNewKnowledge(BaseChat):
)
def generate_input_values(self):
docs = self.knowledge_embedding_client.similar_search(
self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
)
context = [d.page_content for d in docs]
self.metadata = [d.metadata for d in docs]
context = context[:2000]
input_values = {"context": context, "question": self.current_user_input}
try:
docs = self.knowledge_embedding_client.similar_search(
self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
)
context = [d.page_content for d in docs]
self.metadata = [d.metadata for d in docs]
context = context[:2000]
input_values = {"context": context, "question": self.current_user_input}
except NoIndexException:
raise ValueError(
f"you have no {self.knowledge_name} knowledge store, please upload your knowledge"
)
return input_values
def do_with_prompt_response(self, prompt_response):

3
pilot/server/webserver.py
View File

@ -25,7 +25,6 @@ from pilot.configs.config import Config
from pilot.configs.model_config import (
DATASETS_DIR,
KNOWLEDGE_UPLOAD_ROOT_PATH,
LLM_MODEL_CONFIG,
LOGDIR,
)
@ -632,7 +631,7 @@ def knowledge_embedding_store(vs_id, files):
)
knowledge_embedding_client = KnowledgeEmbedding(
file_path=os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename),
model_name=LLM_MODEL_CONFIG["text2vec"],
model_name=CFG.EMBEDDING_MODEL,
vector_store_config={
"vector_store_name": vector_store_name["vs_name"],
"vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,