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Refine search query. (#5235)

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### What problem does this PR solve?

#5173
#5214

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Kevin Hu 2025-02-21 18:32:32 +08:00 committed by GitHub
parent 0151d42156
commit 3444cb15e3
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GPG Key ID: B5690EEEBB952194
2 changed files with 85 additions and 87 deletions
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26
api/db/services/dialog_service.py
View File

@ -876,12 +876,16 @@ def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LL
"Once you have all the information you need, continue your reasoning.\n\n"
"-- Example --\n"
"Question: \"Find the minimum number of vertices in a Steiner tree that includes all specified vertices in a given tree.\"\n"
"Assistant thinking steps:\n"
"- I need to understand what a Steiner tree is and how to compute the minimum number of vertices required to include all specified vertices in a given tree.\n\n"
"Assistant:\n"
f"{BEGIN_SEARCH_QUERY}Minimum Steiner Tree problem in trees{END_SEARCH_QUERY}\n\n"
"(System returns processed information from relevant web pages)\n\n"
"Assistant continues reasoning with the new information...\n\n"
" - I need to understand what a Steiner tree is.\n\n"
f" {BEGIN_SEARCH_QUERY}What's Steiner tree{END_SEARCH_QUERY}\n\n"
f" {BEGIN_SEARCH_RESULT}\n(System returns processed information from relevant web pages)\n{END_SEARCH_RESULT}\n\n"
"User:\nContinues reasoning with the new information.\n\n"
"Assistant:\n"
" - I need to understand what the difference between minimum number of vertices and edges in the Steiner tree is.\n\n"
f" {BEGIN_SEARCH_QUERY}What's the difference between minimum number of vertices and edges in the Steiner tree{END_SEARCH_QUERY}\n\n"
f" {BEGIN_SEARCH_RESULT}\n(System returns processed information from relevant web pages)\n{END_SEARCH_RESULT}\n\n"
"User:\nContinues reasoning with the new information...\n\n"
"**Remember**:\n"
f"- You have a dataset to search, so you just provide a proper search query.\n"
f"- Use {BEGIN_SEARCH_QUERY} to request a dataset search and end with {END_SEARCH_QUERY}.\n"
@ -943,7 +947,7 @@ def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LL
query_think = ""
if msg_hisotry[-1]["role"] != "user":
msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information...\n"})
msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
for ans in chat_mdl.chat_streamly(reason_prompt, msg_hisotry, {"temperature": 0.7}):
ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
if not ans:
@ -954,15 +958,13 @@ def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LL
think += rm_query_tags(query_think)
all_reasoning_steps.append(query_think)
msg_hisotry.append({"role": "assistant", "content": query_think})
search_query = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
if not search_query:
queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
if not queries:
if ii > 0:
break
search_query = question
txt = f"\n{BEGIN_SEARCH_QUERY}{question}{END_SEARCH_QUERY}\n\n"
think += txt
msg_hisotry[-1]["content"] += txt
queries = [question]
for search_query in queries:
logging.info(f"[THINK]Query: {ii}. {search_query}")
think += f"\n\n> {ii+1}. {search_query}\n\n"
yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}

8
rag/nlp/__init__.py
View File

@ -17,7 +17,6 @@
import logging
import random
from collections import Counter
from typing import Optional
from rag.utils import num_tokens_from_string
from . import rag_tokenizer
@ -604,9 +603,6 @@ def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"):
return cks, images
def extract_between(text: str, start_tag: str, end_tag: str) -> Optional[str]:
def extract_between(text: str, start_tag: str, end_tag: str) -> list[str]:
pattern = re.escape(start_tag) + r"(.*?)" + re.escape(end_tag)
matches = re.findall(pattern, text, flags=re.DOTALL)
if matches:
return matches[-1].strip()
return None
return re.findall(pattern, text, flags=re.DOTALL)