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Refine search query. (#5235)
### What problem does this PR solve? #5173 #5214 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
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Kevin Hu
GitHub
parent
0151d42156
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3444cb15e3
@ -876,12 +876,16 @@ def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LL
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"Once you have all the information you need, continue your reasoning.\n\n"
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"-- Example --\n"
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"Question: \"Find the minimum number of vertices in a Steiner tree that includes all specified vertices in a given tree.\"\n"
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"Assistant thinking steps:\n"
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"- 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"
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"Assistant:\n"
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f"{BEGIN_SEARCH_QUERY}Minimum Steiner Tree problem in trees{END_SEARCH_QUERY}\n\n"
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"(System returns processed information from relevant web pages)\n\n"
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"Assistant continues reasoning with the new information...\n\n"
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" - I need to understand what a Steiner tree is.\n\n"
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f" {BEGIN_SEARCH_QUERY}What's Steiner tree{END_SEARCH_QUERY}\n\n"
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f" {BEGIN_SEARCH_RESULT}\n(System returns processed information from relevant web pages)\n{END_SEARCH_RESULT}\n\n"
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"User:\nContinues reasoning with the new information.\n\n"
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"Assistant:\n"
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" - I need to understand what the difference between minimum number of vertices and edges in the Steiner tree is.\n\n"
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f" {BEGIN_SEARCH_QUERY}What's the difference between minimum number of vertices and edges in the Steiner tree{END_SEARCH_QUERY}\n\n"
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f" {BEGIN_SEARCH_RESULT}\n(System returns processed information from relevant web pages)\n{END_SEARCH_RESULT}\n\n"
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"User:\nContinues reasoning with the new information...\n\n"
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"**Remember**:\n"
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f"- You have a dataset to search, so you just provide a proper search query.\n"
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f"- Use {BEGIN_SEARCH_QUERY} to request a dataset search and end with {END_SEARCH_QUERY}.\n"
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@ -943,7 +947,7 @@ def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LL
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query_think = ""
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if msg_hisotry[-1]["role"] != "user":
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msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information...\n"})
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msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
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for ans in chat_mdl.chat_streamly(reason_prompt, msg_hisotry, {"temperature": 0.7}):
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ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
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if not ans:
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@ -954,86 +958,84 @@ def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LL
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think += rm_query_tags(query_think)
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all_reasoning_steps.append(query_think)
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msg_hisotry.append({"role": "assistant", "content": query_think})
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search_query = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
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if not search_query:
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queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
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if not queries:
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if ii > 0:
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break
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search_query = question
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txt = f"\n{BEGIN_SEARCH_QUERY}{question}{END_SEARCH_QUERY}\n\n"
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think += txt
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msg_hisotry[-1]["content"] += txt
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queries = [question]
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logging.info(f"[THINK]Query: {ii}. {search_query}")
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think += f"\n\n> {ii+1}. {search_query}\n\n"
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yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}
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for search_query in queries:
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logging.info(f"[THINK]Query: {ii}. {search_query}")
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think += f"\n\n> {ii+1}. {search_query}\n\n"
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yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}
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summary_think = ""
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# The search query has been searched in previous steps.
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if search_query in executed_search_queries:
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summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n"
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yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
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all_reasoning_steps.append(summary_think)
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msg_hisotry.append({"role": "assistant", "content": summary_think})
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think += summary_think
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continue
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truncated_prev_reasoning = ""
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for i, step in enumerate(all_reasoning_steps):
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truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n"
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prev_steps = truncated_prev_reasoning.split('\n\n')
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if len(prev_steps) <= 5:
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truncated_prev_reasoning = '\n\n'.join(prev_steps)
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else:
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truncated_prev_reasoning = ''
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for i, step in enumerate(prev_steps):
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if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step:
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truncated_prev_reasoning += step + '\n\n'
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else:
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if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n':
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truncated_prev_reasoning += '...\n\n'
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truncated_prev_reasoning = truncated_prev_reasoning.strip('\n')
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kbinfos = settings.retrievaler.retrieval(search_query, embd_mdl, tenant_ids, kb_ids, 1, top_n,
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similarity_threshold,
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vector_similarity_weight
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)
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# Merge chunk info for citations
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if not chunk_info["chunks"]:
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for k in chunk_info.keys():
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chunk_info[k] = kbinfos[k]
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else:
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cids = [c["chunk_id"] for c in chunk_info["chunks"]]
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for c in kbinfos["chunks"]:
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if c["chunk_id"] in cids:
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continue
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chunk_info["chunks"].append(c)
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dids = [d["doc_id"] for d in chunk_info["doc_aggs"]]
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for d in kbinfos["doc_aggs"]:
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if d["doc_id"] in dids:
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continue
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chunk_info["doc_aggs"].append(d)
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think += "\n\n"
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for ans in chat_mdl.chat_streamly(
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relevant_extraction_prompt.format(
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prev_reasoning=truncated_prev_reasoning,
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search_query=search_query,
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document="\n".join(kb_prompt(kbinfos, 512))
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),
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[{"role": "user",
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"content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
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{"temperature": 0.7}):
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ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
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if not ans:
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summary_think = ""
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# The search query has been searched in previous steps.
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if search_query in executed_search_queries:
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summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n"
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yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
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all_reasoning_steps.append(summary_think)
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msg_hisotry.append({"role": "assistant", "content": summary_think})
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think += summary_think
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continue
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summary_think = ans
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yield {"answer": think + rm_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
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all_reasoning_steps.append(summary_think)
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msg_hisotry.append(
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{"role": "assistant", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
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think += rm_result_tags(summary_think)
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logging.info(f"[THINK]Summary: {ii}. {summary_think}")
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truncated_prev_reasoning = ""
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for i, step in enumerate(all_reasoning_steps):
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truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n"
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prev_steps = truncated_prev_reasoning.split('\n\n')
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if len(prev_steps) <= 5:
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truncated_prev_reasoning = '\n\n'.join(prev_steps)
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else:
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truncated_prev_reasoning = ''
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for i, step in enumerate(prev_steps):
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if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step:
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truncated_prev_reasoning += step + '\n\n'
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else:
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if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n':
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truncated_prev_reasoning += '...\n\n'
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truncated_prev_reasoning = truncated_prev_reasoning.strip('\n')
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kbinfos = settings.retrievaler.retrieval(search_query, embd_mdl, tenant_ids, kb_ids, 1, top_n,
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similarity_threshold,
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vector_similarity_weight
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)
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# Merge chunk info for citations
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if not chunk_info["chunks"]:
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for k in chunk_info.keys():
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chunk_info[k] = kbinfos[k]
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else:
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cids = [c["chunk_id"] for c in chunk_info["chunks"]]
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for c in kbinfos["chunks"]:
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if c["chunk_id"] in cids:
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continue
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chunk_info["chunks"].append(c)
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dids = [d["doc_id"] for d in chunk_info["doc_aggs"]]
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for d in kbinfos["doc_aggs"]:
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if d["doc_id"] in dids:
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continue
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chunk_info["doc_aggs"].append(d)
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think += "\n\n"
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for ans in chat_mdl.chat_streamly(
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relevant_extraction_prompt.format(
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prev_reasoning=truncated_prev_reasoning,
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search_query=search_query,
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document="\n".join(kb_prompt(kbinfos, 512))
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),
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[{"role": "user",
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"content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
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{"temperature": 0.7}):
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ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
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if not ans:
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continue
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summary_think = ans
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yield {"answer": think + rm_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
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all_reasoning_steps.append(summary_think)
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msg_hisotry.append(
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{"role": "assistant", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
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think += rm_result_tags(summary_think)
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logging.info(f"[THINK]Summary: {ii}. {summary_think}")
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yield think + "</think>"
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@ -17,7 +17,6 @@
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import logging
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import random
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from collections import Counter
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from typing import Optional
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from rag.utils import num_tokens_from_string
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from . import rag_tokenizer
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@ -604,9 +603,6 @@ def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"):
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return cks, images
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def extract_between(text: str, start_tag: str, end_tag: str) -> Optional[str]:
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def extract_between(text: str, start_tag: str, end_tag: str) -> list[str]:
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pattern = re.escape(start_tag) + r"(.*?)" + re.escape(end_tag)
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matches = re.findall(pattern, text, flags=re.DOTALL)
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if matches:
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return matches[-1].strip()
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return None
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return re.findall(pattern, text, flags=re.DOTALL)
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