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Code refactor (#5371)
### What problem does this PR solve? #5173 ### Type of change - [x] Refactoring
This commit is contained in:
Kevin Hu
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agentic_reasoning/__init__.py
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agentic_reasoning/__init__.py
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from .deep_research import DeepResearcher as DeepResearcher
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agentic_reasoning/deep_research.py
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agentic_reasoning/deep_research.py
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#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import re
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from functools import partial
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from agentic_reasoning.prompts import BEGIN_SEARCH_QUERY, BEGIN_SEARCH_RESULT, END_SEARCH_RESULT, MAX_SEARCH_LIMIT, \
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END_SEARCH_QUERY, REASON_PROMPT, RELEVANT_EXTRACTION_PROMPT
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from api.db.services.llm_service import LLMBundle
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from rag.nlp import extract_between
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from rag.prompts import kb_prompt
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from rag.utils.tavily_conn import Tavily
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class DeepResearcher:
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def __init__(self,
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chat_mdl: LLMBundle,
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prompt_config: dict,
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kb_retrieve: partial = None,
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kg_retrieve: partial = None
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):
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self.chat_mdl = chat_mdl
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self.prompt_config = prompt_config
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self._kb_retrieve = kb_retrieve
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self._kg_retrieve = kg_retrieve
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def thinking(self, chunk_info: dict, question: str):
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def rm_query_tags(line):
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pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
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return re.sub(pattern, "", line)
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def rm_result_tags(line):
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pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
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return re.sub(pattern, "", line)
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executed_search_queries = []
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msg_hisotry = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
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all_reasoning_steps = []
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think = "<think>"
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for ii in range(MAX_SEARCH_LIMIT + 1):
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if ii == MAX_SEARCH_LIMIT - 1:
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summary_think = f"\n{BEGIN_SEARCH_RESULT}\nThe maximum search limit is exceeded. You are not allowed to search.\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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break
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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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else:
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msg_hisotry[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
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for ans in self.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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continue
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query_think = ans
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yield {"answer": think + rm_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}
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think += rm_query_tags(query_think)
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all_reasoning_steps.append(query_think)
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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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queries = [question]
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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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msg_hisotry.append({"role": "assistant", "content": 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": "user", "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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# Retrieval procedure:
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# 1. KB search
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# 2. Web search (optional)
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# 3. KG search (optional)
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kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []}
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if self.prompt_config.get("tavily_api_key"):
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tav = Tavily(self.prompt_config["tavily_api_key"])
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tav_res = tav.retrieve_chunks(" ".join(search_query))
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kbinfos["chunks"].extend(tav_res["chunks"])
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kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
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if self.prompt_config.get("use_kg") and self._kg_retrieve:
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ck = self._kg_retrieve(question=search_query)
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if ck["content_with_weight"]:
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kbinfos["chunks"].insert(0, ck)
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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 self.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, 4096))
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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": "user", "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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agentic_reasoning/prompts.py
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agentic_reasoning/prompts.py
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#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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BEGIN_SEARCH_QUERY = "<|begin_search_query|>"
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END_SEARCH_QUERY = "<|end_search_query|>"
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BEGIN_SEARCH_RESULT = "<|begin_search_result|>"
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END_SEARCH_RESULT = "<|end_search_result|>"
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MAX_SEARCH_LIMIT = 6
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REASON_PROMPT = (
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"You are a reasoning assistant with the ability to perform dataset searches to help "
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"you answer the user's question accurately. You have special tools:\n\n"
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f"- To perform a search: write {BEGIN_SEARCH_QUERY} your query here {END_SEARCH_QUERY}.\n"
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f"Then, the system will search and analyze relevant content, then provide you with helpful information in the format {BEGIN_SEARCH_RESULT} ...search results... {END_SEARCH_RESULT}.\n\n"
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f"You can repeat the search process multiple times if necessary. The maximum number of search attempts is limited to {MAX_SEARCH_LIMIT}.\n\n"
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"Once you have all the information you need, continue your reasoning.\n\n"
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"-- Example 1 --\n" ########################################
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"Question: \"Are both the directors of Jaws and Casino Royale from the same country?\"\n"
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"Assistant:\n"
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f" {BEGIN_SEARCH_QUERY}Who is the director of Jaws?{END_SEARCH_QUERY}\n\n"
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"User:\n"
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f" {BEGIN_SEARCH_RESULT}\nThe director of Jaws is Steven Spielberg...\n{END_SEARCH_RESULT}\n\n"
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"Continues reasoning with the new information.\n"
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"Assistant:\n"
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f" {BEGIN_SEARCH_QUERY}Where is Steven Spielberg from?{END_SEARCH_QUERY}\n\n"
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"User:\n"
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f" {BEGIN_SEARCH_RESULT}\nSteven Allan Spielberg is an American filmmaker...\n{END_SEARCH_RESULT}\n\n"
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"Continues reasoning with the new information...\n\n"
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"Assistant:\n"
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f" {BEGIN_SEARCH_QUERY}Who is the director of Casino Royale?{END_SEARCH_QUERY}\n\n"
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"User:\n"
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f" {BEGIN_SEARCH_RESULT}\nCasino Royale is a 2006 spy film directed by Martin Campbell...\n{END_SEARCH_RESULT}\n\n"
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"Continues reasoning with the new information...\n\n"
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"Assistant:\n"
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f" {BEGIN_SEARCH_QUERY}Where is Martin Campbell from?{END_SEARCH_QUERY}\n\n"
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"User:\n"
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f" {BEGIN_SEARCH_RESULT}\nMartin Campbell (born 24 October 1943) is a New Zealand film and television director...\n{END_SEARCH_RESULT}\n\n"
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"Continues reasoning with the new information...\n\n"
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"Assistant:\nIt's enough to answer the question\n"
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"-- Example 2 --\n" #########################################
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"Question: \"When was the founder of craigslist born?\"\n"
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"Assistant:\n"
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f" {BEGIN_SEARCH_QUERY}Who was the founder of craigslist?{END_SEARCH_QUERY}\n\n"
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"User:\n"
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f" {BEGIN_SEARCH_RESULT}\nCraigslist was founded by Craig Newmark...\n{END_SEARCH_RESULT}\n\n"
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"Continues reasoning with the new information.\n"
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"Assistant:\n"
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f" {BEGIN_SEARCH_QUERY} When was Craig Newmark born?{END_SEARCH_QUERY}\n\n"
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"User:\n"
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f" {BEGIN_SEARCH_RESULT}\nCraig Newmark was born on December 6, 1952...\n{END_SEARCH_RESULT}\n\n"
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"Continues reasoning with the new information...\n\n"
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"Assistant:\nIt's enough to answer the question\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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"- The language of query MUST be as the same as 'Question' or 'search result'.\n"
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"- When done searching, continue your reasoning.\n\n"
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'Please answer the following question. You should think step by step to solve it.\n\n'
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)
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RELEVANT_EXTRACTION_PROMPT = """**Task Instruction:**
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You are tasked with reading and analyzing web pages based on the following inputs: **Previous Reasoning Steps**, **Current Search Query**, and **Searched Web Pages**. Your objective is to extract relevant and helpful information for **Current Search Query** from the **Searched Web Pages** and seamlessly integrate this information into the **Previous Reasoning Steps** to continue reasoning for the original question.
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**Guidelines:**
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1. **Analyze the Searched Web Pages:**
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- Carefully review the content of each searched web page.
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- Identify factual information that is relevant to the **Current Search Query** and can aid in the reasoning process for the original question.
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2. **Extract Relevant Information:**
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- Select the information from the Searched Web Pages that directly contributes to advancing the **Previous Reasoning Steps**.
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- Ensure that the extracted information is accurate and relevant.
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3. **Output Format:**
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- **If the web pages provide helpful information for current search query:** Present the information beginning with `**Final Information**` as shown below.
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- The language of query **MUST BE** as the same as 'Search Query' or 'Web Pages'.\n"
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**Final Information**
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[Helpful information]
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- **If the web pages do not provide any helpful information for current search query:** Output the following text.
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**Final Information**
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No helpful information found.
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**Inputs:**
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- **Previous Reasoning Steps:**
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{prev_reasoning}
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- **Current Search Query:**
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{search_query}
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- **Searched Web Pages:**
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{document}
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"""
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