From c82907c963fb93202611fa783196165d8e32d521 Mon Sep 17 00:00:00 2001 From: aries-ckt <916701291@qq.com> Date: Thu, 18 May 2023 20:23:06 +0800 Subject: [PATCH] feature:knowledge embedding update --- pilot/server/webserver.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pilot/server/webserver.py b/pilot/server/webserver.py index e6ba19160..466ba30b8 100644 --- a/pilot/server/webserver.py +++ b/pilot/server/webserver.py @@ -19,7 +19,7 @@ from langchain import PromptTemplate ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.append(ROOT_PATH) -from pilot.configs.model_config import DB_SETTINGS, KNOWLEDGE_UPLOAD_ROOT_PATH, LLM_MODEL_CONFIG, TOP_RETURN_SIZE +from pilot.configs.model_config import DB_SETTINGS, KNOWLEDGE_UPLOAD_ROOT_PATH, LLM_MODEL_CONFIG, VECTOR_SEARCH_TOP_K from pilot.server.vectordb_qa import KnownLedgeBaseQA from pilot.connections.mysql import MySQLOperator from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding @@ -262,7 +262,7 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re vector_store_config={"vector_store_name": vector_store_name["vs_name"], "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH}) query = state.messages[-2][1] - docs = knowledge_embedding_client.similar_search(query, TOP_RETURN_SIZE) + docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K) context = [d.page_content for d in docs] prompt_template = PromptTemplate( template=conv_qa_prompt_template,