diff --git a/Dockerfile b/Dockerfile index aaec6f16e..a1eb24339 100644 --- a/Dockerfile +++ b/Dockerfile @@ -187,7 +187,6 @@ COPY deepdoc deepdoc COPY rag rag COPY agent agent COPY graphrag graphrag -COPY agentic_reasoning agentic_reasoning COPY pyproject.toml uv.lock ./ COPY mcp mcp COPY plugin plugin diff --git a/agentic_reasoning/__init__.py b/agentic_reasoning/__init__.py deleted file mode 100644 index 1422de46e..000000000 --- a/agentic_reasoning/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .deep_research import DeepResearcher as DeepResearcher \ No newline at end of file diff --git a/agentic_reasoning/deep_research.py b/agentic_reasoning/deep_research.py deleted file mode 100644 index 17afdab18..000000000 --- a/agentic_reasoning/deep_research.py +++ /dev/null @@ -1,311 +0,0 @@ -# -# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -import logging -import re -from functools import partial -from agentic_reasoning.prompts import BEGIN_SEARCH_QUERY, BEGIN_SEARCH_RESULT, END_SEARCH_RESULT, MAX_SEARCH_LIMIT, \ - END_SEARCH_QUERY, REASON_PROMPT, RELEVANT_EXTRACTION_PROMPT -from api.db.services.llm_service import LLMBundle -from rag.nlp import extract_between -from rag.prompts import kb_prompt -from rag.utils.tavily_conn import Tavily - - -class DeepResearcher: - def __init__(self, - chat_mdl: LLMBundle, - prompt_config: dict, - kb_retrieve: partial = None, - kg_retrieve: partial = None - ): - self.chat_mdl = chat_mdl - self.prompt_config = prompt_config - self._kb_retrieve = kb_retrieve - self._kg_retrieve = kg_retrieve - - def _remove_tags(text: str, start_tag: str, end_tag: str) -> str: - """Remove tags but keep the content between them.""" - if not text: - return text - text = re.sub(re.escape(start_tag), "", text) - return re.sub(re.escape(end_tag), "", text) - - @staticmethod - def _remove_query_tags(text: str) -> str: - """Remove Query Tags""" - return DeepResearcher._remove_tags(text, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY) - - @staticmethod - def _remove_result_tags(text: str) -> str: - """Remove Result Tags""" - return DeepResearcher._remove_tags(text, BEGIN_SEARCH_RESULT, END_SEARCH_RESULT) - - async def _generate_reasoning(self, msg_history): - """Generate reasoning steps (delta output)""" - raw_answer = "" - cleaned_answer = "" - if msg_history[-1]["role"] != "user": - msg_history.append({"role": "user", "content": "Continues reasoning with the new information.\n"}) - else: - msg_history[-1]["content"] += "\n\nContinues reasoning with the new information.\n" - - async for delta in self.chat_mdl.async_chat_streamly_delta(REASON_PROMPT, msg_history, {"temperature": 0.7}): - if not delta: - continue - raw_answer += delta - cleaned_full = re.sub(r"^.*", "", raw_answer, flags=re.DOTALL) - if not cleaned_full: - continue - if cleaned_full.startswith(cleaned_answer): - delta_clean = cleaned_full[len(cleaned_answer):] - else: - delta_clean = cleaned_full - if not delta_clean: - continue - cleaned_answer = cleaned_full - yield delta_clean - - def _extract_search_queries(self, query_think, question, step_index): - """Extract search queries from thinking""" - queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY) - if not queries and step_index == 0: - # If this is the first step and no queries are found, use the original question as the query - queries = [question] - return queries - - def _truncate_previous_reasoning(self, all_reasoning_steps): - """Truncate previous reasoning steps to maintain a reasonable length""" - truncated_prev_reasoning = "" - for i, step in enumerate(all_reasoning_steps): - truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n" - - prev_steps = truncated_prev_reasoning.split('\n\n') - if len(prev_steps) <= 5: - truncated_prev_reasoning = '\n\n'.join(prev_steps) - else: - truncated_prev_reasoning = '' - for i, step in enumerate(prev_steps): - if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step: - truncated_prev_reasoning += step + '\n\n' - else: - if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n': - truncated_prev_reasoning += '...\n\n' - - return truncated_prev_reasoning.strip('\n') - - def _retrieve_information(self, search_query): - """Retrieve information from different sources""" - # 1. Knowledge base retrieval - kbinfos = [] - try: - kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []} - except Exception as e: - logging.error(f"Knowledge base retrieval error: {e}") - - # 2. Web retrieval (if Tavily API is configured) - try: - if self.prompt_config.get("tavily_api_key"): - tav = Tavily(self.prompt_config["tavily_api_key"]) - tav_res = tav.retrieve_chunks(search_query) - kbinfos["chunks"].extend(tav_res["chunks"]) - kbinfos["doc_aggs"].extend(tav_res["doc_aggs"]) - except Exception as e: - logging.error(f"Web retrieval error: {e}") - - # 3. Knowledge graph retrieval (if configured) - try: - if self.prompt_config.get("use_kg") and self._kg_retrieve: - ck = self._kg_retrieve(question=search_query) - if ck["content_with_weight"]: - kbinfos["chunks"].insert(0, ck) - except Exception as e: - logging.error(f"Knowledge graph retrieval error: {e}") - - return kbinfos - - def _update_chunk_info(self, chunk_info, kbinfos): - """Update chunk information for citations""" - if not chunk_info["chunks"]: - # If this is the first retrieval, use the retrieval results directly - for k in chunk_info.keys(): - chunk_info[k] = kbinfos[k] - else: - # Merge newly retrieved information, avoiding duplicates - cids = [c["chunk_id"] for c in chunk_info["chunks"]] - for c in kbinfos["chunks"]: - if c["chunk_id"] not in cids: - chunk_info["chunks"].append(c) - - dids = [d["doc_id"] for d in chunk_info["doc_aggs"]] - for d in kbinfos["doc_aggs"]: - if d["doc_id"] not in dids: - chunk_info["doc_aggs"].append(d) - - async def _extract_relevant_info(self, truncated_prev_reasoning, search_query, kbinfos): - """Extract and summarize relevant information (delta output)""" - raw_answer = "" - cleaned_answer = "" - async for delta in self.chat_mdl.async_chat_streamly_delta( - RELEVANT_EXTRACTION_PROMPT.format( - prev_reasoning=truncated_prev_reasoning, - search_query=search_query, - document="\n".join(kb_prompt(kbinfos, 4096)) - ), - [{"role": "user", - "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.'}], - {"temperature": 0.7}): - if not delta: - continue - raw_answer += delta - cleaned_full = re.sub(r"^.*", "", raw_answer, flags=re.DOTALL) - if not cleaned_full: - continue - if cleaned_full.startswith(cleaned_answer): - delta_clean = cleaned_full[len(cleaned_answer):] - else: - delta_clean = cleaned_full - if not delta_clean: - continue - cleaned_answer = cleaned_full - yield delta_clean - - async def thinking(self, chunk_info: dict, question: str): - executed_search_queries = [] - msg_history = [{"role": "user", "content": f'Question:\"{question}\"\n'}] - all_reasoning_steps = [] - think = "" - last_idx = 0 - endswith_think = False - last_full = "" - - def emit_delta(full_text: str): - nonlocal last_idx, endswith_think, last_full - if full_text == last_full: - return None - last_full = full_text - delta_ans = full_text[last_idx:] - - if delta_ans.find("") == 0: - last_idx += len("") - delta = "" - elif delta_ans.find("") > 0: - delta = full_text[last_idx:last_idx + delta_ans.find("")] - last_idx += delta_ans.find("") - elif delta_ans.endswith(""): - endswith_think = True - delta = re.sub(r"(|)", "", delta_ans) - elif endswith_think: - endswith_think = False - delta = "" - else: - last_idx = len(full_text) - if full_text.endswith(""): - last_idx -= len("") - delta = re.sub(r"(|)", "", delta_ans) - - if not delta: - return None - if delta == "": - return {"answer": "", "reference": {}, "audio_binary": None, "final": False, "start_to_think": True} - if delta == "": - return {"answer": "", "reference": {}, "audio_binary": None, "final": False, "end_to_think": True} - return {"answer": delta, "reference": {}, "audio_binary": None, "final": False} - - def flush_think_close(): - nonlocal endswith_think - if endswith_think: - endswith_think = False - return {"answer": "", "reference": {}, "audio_binary": None, "final": False, "end_to_think": True} - return None - - for step_index in range(MAX_SEARCH_LIMIT + 1): - # Check if the maximum search limit has been reached - if step_index == MAX_SEARCH_LIMIT - 1: - summary_think = f"\n{BEGIN_SEARCH_RESULT}\nThe maximum search limit is exceeded. You are not allowed to search.\n{END_SEARCH_RESULT}\n" - payload = emit_delta(think + summary_think) - if payload: - yield payload - all_reasoning_steps.append(summary_think) - msg_history.append({"role": "assistant", "content": summary_think}) - break - - # Step 1: Generate reasoning - query_think = "" - async for delta in self._generate_reasoning(msg_history): - query_think += delta - payload = emit_delta(think + self._remove_query_tags(query_think)) - if payload: - yield payload - - think += self._remove_query_tags(query_think) - all_reasoning_steps.append(query_think) - - # Step 2: Extract search queries - queries = self._extract_search_queries(query_think, question, step_index) - if not queries and step_index > 0: - # If not the first step and no queries, end the search process - break - - # Process each search query - for search_query in queries: - msg_history.append({"role": "assistant", "content": search_query}) - think += f"\n\n> {step_index + 1}. {search_query}\n\n" - payload = emit_delta(think) - if payload: - yield payload - - # Check if the query has already been executed - if search_query in executed_search_queries: - summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n" - payload = emit_delta(think + summary_think) - if payload: - yield payload - all_reasoning_steps.append(summary_think) - msg_history.append({"role": "user", "content": summary_think}) - think += summary_think - continue - - executed_search_queries.append(search_query) - - # Step 3: Truncate previous reasoning steps - truncated_prev_reasoning = self._truncate_previous_reasoning(all_reasoning_steps) - - # Step 4: Retrieve information - kbinfos = self._retrieve_information(search_query) - - # Step 5: Update chunk information - self._update_chunk_info(chunk_info, kbinfos) - - # Step 6: Extract relevant information - think += "\n\n" - summary_think = "" - async for delta in self._extract_relevant_info(truncated_prev_reasoning, search_query, kbinfos): - summary_think += delta - payload = emit_delta(think + self._remove_result_tags(summary_think)) - if payload: - yield payload - - all_reasoning_steps.append(summary_think) - msg_history.append( - {"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"}) - think += self._remove_result_tags(summary_think) - - final_payload = emit_delta(think + "") - if final_payload: - yield final_payload - close_payload = flush_think_close() - if close_payload: - yield close_payload diff --git a/agentic_reasoning/prompts.py b/agentic_reasoning/prompts.py deleted file mode 100644 index 8bf101b29..000000000 --- a/agentic_reasoning/prompts.py +++ /dev/null @@ -1,147 +0,0 @@ -# -# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -BEGIN_SEARCH_QUERY = "<|begin_search_query|>" -END_SEARCH_QUERY = "<|end_search_query|>" -BEGIN_SEARCH_RESULT = "<|begin_search_result|>" -END_SEARCH_RESULT = "<|end_search_result|>" -MAX_SEARCH_LIMIT = 6 - -REASON_PROMPT = f"""You are an advanced reasoning agent. Your goal is to answer the user's question by breaking it down into a series of verifiable steps. - -You have access to a powerful search tool to find information. - -**Your Task:** -1. Analyze the user's question. -2. If you need information, issue a search query to find a specific fact. -3. Review the search results. -4. Repeat the search process until you have all the facts needed to answer the question. -5. Once you have gathered sufficient information, synthesize the facts and provide the final answer directly. - -**Tool Usage:** -- To search, you MUST write your query between the special tokens: {BEGIN_SEARCH_QUERY}your query{END_SEARCH_QUERY}. -- The system will provide results between {BEGIN_SEARCH_RESULT}search results{END_SEARCH_RESULT}. -- You have a maximum of {MAX_SEARCH_LIMIT} search attempts. - ---- -**Example 1: Multi-hop Question** - -**Question:** "Are both the directors of Jaws and Casino Royale from the same country?" - -**Your Thought Process & Actions:** -First, I need to identify the director of Jaws. -{BEGIN_SEARCH_QUERY}who is the director of Jaws?{END_SEARCH_QUERY} -[System returns search results] -{BEGIN_SEARCH_RESULT} -Jaws is a 1975 American thriller film directed by Steven Spielberg. -{END_SEARCH_RESULT} -Okay, the director of Jaws is Steven Spielberg. Now I need to find out his nationality. -{BEGIN_SEARCH_QUERY}where is Steven Spielberg from?{END_SEARCH_QUERY} -[System returns search results] -{BEGIN_SEARCH_RESULT} -Steven Allan Spielberg is an American filmmaker. Born in Cincinnati, Ohio... -{END_SEARCH_RESULT} -So, Steven Spielberg is from the USA. Next, I need to find the director of Casino Royale. -{BEGIN_SEARCH_QUERY}who is the director of Casino Royale 2006?{END_SEARCH_QUERY} -[System returns search results] -{BEGIN_SEARCH_RESULT} -Casino Royale is a 2006 spy film directed by Martin Campbell. -{END_SEARCH_RESULT} -The director of Casino Royale is Martin Campbell. Now I need his nationality. -{BEGIN_SEARCH_QUERY}where is Martin Campbell from?{END_SEARCH_QUERY} -[System returns search results] -{BEGIN_SEARCH_RESULT} -Martin Campbell (born 24 October 1943) is a New Zealand film and television director. -{END_SEARCH_RESULT} -I have all the information. Steven Spielberg is from the USA, and Martin Campbell is from New Zealand. They are not from the same country. - -Final Answer: No, the directors of Jaws and Casino Royale are not from the same country. Steven Spielberg is from the USA, and Martin Campbell is from New Zealand. - ---- -**Example 2: Simple Fact Retrieval** - -**Question:** "When was the founder of craigslist born?" - -**Your Thought Process & Actions:** -First, I need to know who founded craigslist. -{BEGIN_SEARCH_QUERY}who founded craigslist?{END_SEARCH_QUERY} -[System returns search results] -{BEGIN_SEARCH_RESULT} -Craigslist was founded in 1995 by Craig Newmark. -{END_SEARCH_RESULT} -The founder is Craig Newmark. Now I need his birth date. -{BEGIN_SEARCH_QUERY}when was Craig Newmark born?{END_SEARCH_QUERY} -[System returns search results] -{BEGIN_SEARCH_RESULT} -Craig Newmark was born on December 6, 1952. -{END_SEARCH_RESULT} -I have found the answer. - -Final Answer: The founder of craigslist, Craig Newmark, was born on December 6, 1952. - ---- -**Important Rules:** -- **One Fact at a Time:** Decompose the problem and issue one search query at a time to find a single, specific piece of information. -- **Be Precise:** Formulate clear and precise search queries. If a search fails, rephrase it. -- **Synthesize at the End:** Do not provide the final answer until you have completed all necessary searches. -- **Language Consistency:** Your search queries should be in the same language as the user's question. - -Now, begin your work. Please answer the following question by thinking step-by-step. -""" - -RELEVANT_EXTRACTION_PROMPT = """You are a highly efficient information extraction module. Your sole purpose is to extract the single most relevant piece of information from the provided `Searched Web Pages` that directly answers the `Current Search Query`. - -**Your Task:** -1. Read the `Current Search Query` to understand what specific information is needed. -2. Scan the `Searched Web Pages` to find the answer to that query. -3. Extract only the essential, factual information that answers the query. Be concise. - -**Context (For Your Information Only):** -The `Previous Reasoning Steps` are provided to give you context on the overall goal, but your primary focus MUST be on answering the `Current Search Query`. Do not use information from the previous steps in your output. - -**Output Format:** -Your response must follow one of two formats precisely. - -1. **If a direct and relevant answer is found:** - - Start your response immediately with `Final Information`. - - Provide only the extracted fact(s). Do not add any extra conversational text. - - *Example:* - `Current Search Query`: Where is Martin Campbell from? - `Searched Web Pages`: [Long article snippet about Martin Campbell's career, which includes the sentence "Martin Campbell (born 24 October 1943) is a New Zealand film and television director..."] - - *Your Output:* - Final Information - Martin Campbell is a New Zealand film and television director. - -2. **If no relevant answer that directly addresses the query is found in the web pages:** - - Start your response immediately with `Final Information`. - - Write the exact phrase: `No helpful information found.` - ---- -**BEGIN TASK** - -**Inputs:** - -- **Previous Reasoning Steps:** -{prev_reasoning} - -- **Current Search Query:** -{search_query} - -- **Searched Web Pages:** -{document} -""" \ No newline at end of file diff --git a/api/apps/kb_app.py b/api/apps/kb_app.py index fff982563..26ea12f96 100644 --- a/api/apps/kb_app.py +++ b/api/apps/kb_app.py @@ -174,6 +174,7 @@ async def update_metadata_setting(): message="Database error (Knowledgebase rename)!") kb = kb.to_dict() kb["parser_config"]["metadata"] = req["metadata"] + kb["parser_config"]["enable_metadata"] = req.get("enable_metadata", True) KnowledgebaseService.update_by_id(kb["id"], kb) return get_json_result(data=kb) diff --git a/api/db/services/conversation_service.py b/api/db/services/conversation_service.py index 693489bf8..3287ac157 100644 --- a/api/db/services/conversation_service.py +++ b/api/db/services/conversation_service.py @@ -64,6 +64,7 @@ class ConversationService(CommonService): offset += limit return res + def structure_answer(conv, ans, message_id, session_id): reference = ans["reference"] if not isinstance(reference, dict): @@ -107,6 +108,7 @@ def structure_answer(conv, ans, message_id, session_id): conv.reference[-1] = reference return ans + async def async_completion(tenant_id, chat_id, question, name="New session", session_id=None, stream=True, **kwargs): assert name, "`name` can not be empty." dia = DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value) diff --git a/api/db/services/dialog_service.py b/api/db/services/dialog_service.py index 54f70658b..ed178434d 100644 --- a/api/db/services/dialog_service.py +++ b/api/db/services/dialog_service.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # +import asyncio import binascii import logging import re @@ -23,7 +24,6 @@ from functools import partial from timeit import default_timer as timer from langfuse import Langfuse from peewee import fn -from agentic_reasoning import DeepResearcher from api.db.services.file_service import FileService from common.constants import LLMType, ParserType, StatusEnum from api.db.db_models import DB, Dialog @@ -36,6 +36,7 @@ from common.metadata_utils import apply_meta_data_filter from api.db.services.tenant_llm_service import TenantLLMService from common.time_utils import current_timestamp, datetime_format from graphrag.general.mind_map_extractor import MindMapExtractor +from rag.advanced_rag import DeepResearcher from rag.app.resume import forbidden_select_fields4resume from rag.app.tag import label_question from rag.nlp.search import index_name @@ -380,16 +381,35 @@ async def async_chat(dialog, messages, stream=True, **kwargs): doc_ids=attachments, ), ) + queue = asyncio.Queue() + async def callback(msg:str): + nonlocal queue + await queue.put(msg + "
") + await callback("") + task = asyncio.create_task(reasoner.research(kbinfos, questions[-1], questions[-1], callback=callback)) + while True: + msg = await queue.get() + if msg.find("") == 0: + yield {"answer": "", "reference": {}, "audio_binary": None, "final": False, "start_to_think": True} + elif msg.find("") == 0: + yield {"answer": "", "reference": {}, "audio_binary": None, "final": False, "end_to_think": True} + break + else: + yield {"answer": msg, "reference": {}, "audio_binary": None, "final": False} + + await task + ''' async for think in reasoner.thinking(kbinfos, attachments_ + " ".join(questions)): if isinstance(think, str): thought = think knowledges = [t for t in think.split("\n") if t] elif stream: yield think + ''' else: if embd_mdl: - kbinfos = retriever.retrieval( + kbinfos = await asyncio.to_thread(retriever.retrieval, " ".join(questions), embd_mdl, tenant_ids, @@ -420,8 +440,7 @@ async def async_chat(dialog, messages, stream=True, **kwargs): if ck["content_with_weight"]: kbinfos["chunks"].insert(0, ck) - knowledges = kb_prompt(kbinfos, max_tokens) - + knowledges = kb_prompt(kbinfos, max_tokens) logging.debug("{}->{}".format(" ".join(questions), "\n->".join(knowledges))) retrieval_ts = timer() diff --git a/pyproject.toml b/pyproject.toml index 2575194eb..f8e5338f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -177,7 +177,6 @@ url = "https://pypi.tuna.tsinghua.edu.cn/simple" [tool.setuptools] packages = [ 'agent', - 'agentic_reasoning', 'api', 'deepdoc', 'graphrag', diff --git a/rag/advanced_rag/__init__.py b/rag/advanced_rag/__init__.py new file mode 100644 index 000000000..bde0ff643 --- /dev/null +++ b/rag/advanced_rag/__init__.py @@ -0,0 +1,20 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .tree_structured_query_decomposition_retrieval import TreeStructuredQueryDecompositionRetrieval as DeepResearcher + + +__all__ = ['DeepResearcher'] \ No newline at end of file diff --git a/rag/advanced_rag/tree_structured_query_decomposition_retrieval.py b/rag/advanced_rag/tree_structured_query_decomposition_retrieval.py new file mode 100644 index 000000000..77689cab0 --- /dev/null +++ b/rag/advanced_rag/tree_structured_query_decomposition_retrieval.py @@ -0,0 +1,126 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import asyncio +import logging +from functools import partial +from api.db.services.llm_service import LLMBundle +from rag.prompts import kb_prompt +from rag.prompts.generator import sufficiency_check, multi_queries_gen +from rag.utils.tavily_conn import Tavily +from timeit import default_timer as timer + + +class TreeStructuredQueryDecompositionRetrieval: + def __init__(self, + chat_mdl: LLMBundle, + prompt_config: dict, + kb_retrieve: partial = None, + kg_retrieve: partial = None + ): + self.chat_mdl = chat_mdl + self.prompt_config = prompt_config + self._kb_retrieve = kb_retrieve + self._kg_retrieve = kg_retrieve + self._lock = asyncio.Lock() + + def _retrieve_information(self, search_query): + """Retrieve information from different sources""" + # 1. Knowledge base retrieval + kbinfos = [] + try: + kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []} + except Exception as e: + logging.error(f"Knowledge base retrieval error: {e}") + + # 2. Web retrieval (if Tavily API is configured) + try: + if self.prompt_config.get("tavily_api_key"): + tav = Tavily(self.prompt_config["tavily_api_key"]) + tav_res = tav.retrieve_chunks(search_query) + kbinfos["chunks"].extend(tav_res["chunks"]) + kbinfos["doc_aggs"].extend(tav_res["doc_aggs"]) + except Exception as e: + logging.error(f"Web retrieval error: {e}") + + # 3. Knowledge graph retrieval (if configured) + try: + if self.prompt_config.get("use_kg") and self._kg_retrieve: + ck = self._kg_retrieve(question=search_query) + if ck["content_with_weight"]: + kbinfos["chunks"].insert(0, ck) + except Exception as e: + logging.error(f"Knowledge graph retrieval error: {e}") + + return kbinfos + + async def _async_update_chunk_info(self, chunk_info, kbinfos): + async with self._lock: + """Update chunk information for citations""" + if not chunk_info["chunks"]: + # If this is the first retrieval, use the retrieval results directly + for k in chunk_info.keys(): + chunk_info[k] = kbinfos[k] + else: + # Merge newly retrieved information, avoiding duplicates + cids = [c["chunk_id"] for c in chunk_info["chunks"]] + for c in kbinfos["chunks"]: + if c["chunk_id"] not in cids: + chunk_info["chunks"].append(c) + + dids = [d["doc_id"] for d in chunk_info["doc_aggs"]] + for d in kbinfos["doc_aggs"]: + if d["doc_id"] not in dids: + chunk_info["doc_aggs"].append(d) + + async def research(self, chunk_info, question, query, depth=3, callback=None): + if callback: + await callback("") + await self._research(chunk_info, question, query, depth, callback) + if callback: + await callback("") + + async def _research(self, chunk_info, question, query, depth=3, callback=None): + if depth == 0: + #if callback: + # await callback("Reach the max search depth.") + return "" + if callback: + await callback(f"Searching by `{query}`...") + st = timer() + ret = self._retrieve_information(query) + if callback: + await callback("Retrieval %d results by %.1fms"%(len(ret["chunks"]), (timer()-st)*1000)) + await self._async_update_chunk_info(chunk_info, ret) + ret = kb_prompt(ret, self.chat_mdl.max_length*0.5) + + if callback: + await callback("Checking the sufficiency for retrieved information.") + suff = await sufficiency_check(self.chat_mdl, question, ret) + if suff["is_sufficient"]: + if callback: + await callback("Yes, it's sufficient.") + return ret + + #if callback: + # await callback("The retrieved information is not sufficient. Planing next steps...") + succ_question_info = await multi_queries_gen(self.chat_mdl, question, query, suff["missing_information"], ret) + if callback: + await callback("Next step is to search for the following questions:\n" + "\n - ".join(step["question"] for step in succ_question_info["questions"])) + steps = [] + for step in succ_question_info["questions"]: + steps.append(asyncio.create_task(self._research(chunk_info, step["question"], step["query"], depth-1, callback))) + results = await asyncio.gather(*steps, return_exceptions=True) + return "\n".join([str(r) for r in results]) diff --git a/rag/nlp/search.py b/rag/nlp/search.py index b10dc8572..46b8b5b0a 100644 --- a/rag/nlp/search.py +++ b/rag/nlp/search.py @@ -382,6 +382,7 @@ class Dealer: # Ensure RERANK_LIMIT is multiple of page_size RERANK_LIMIT = math.ceil(64 / page_size) * page_size if page_size > 1 else 1 + RERANK_LIMIT = max(30, RERANK_LIMIT) req = { "kb_ids": kb_ids, "doc_ids": doc_ids, diff --git a/rag/prompts/generator.py b/rag/prompts/generator.py index 25fd4702e..f65bfeab6 100644 --- a/rag/prompts/generator.py +++ b/rag/prompts/generator.py @@ -38,7 +38,7 @@ def get_value(d, k1, k2): def chunks_format(reference): - if not reference or (reference is not dict): + if not reference or not isinstance(reference, dict): return [] return [ { @@ -485,20 +485,26 @@ async def gen_meta_filter(chat_mdl, meta_data: dict, query: str) -> dict: return {"conditions": []} -async def gen_json(system_prompt: str, user_prompt: str, chat_mdl, gen_conf=None): +async def gen_json(system_prompt: str, user_prompt: str, chat_mdl, gen_conf={}, max_retry=2): from graphrag.utils import get_llm_cache, set_llm_cache cached = get_llm_cache(chat_mdl.llm_name, system_prompt, user_prompt, gen_conf) if cached: return json_repair.loads(cached) _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length) - ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:], gen_conf=gen_conf) - ans = re.sub(r"(^.*|```json\n|```\n*$)", "", ans, flags=re.DOTALL) - try: - res = json_repair.loads(ans) - set_llm_cache(chat_mdl.llm_name, system_prompt, ans, user_prompt, gen_conf) - return res - except Exception: - logging.exception(f"Loading json failure: {ans}") + err = "" + ans = "" + for _ in range(max_retry): + if ans and err: + msg[-1]["content"] += f"\nGenerated JSON is as following:\n{ans}\nBut exception while loading:\n{err}\nPlease reconsider and correct it." + ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:], gen_conf=gen_conf) + ans = re.sub(r"(^.*|```json\n|```\n*$)", "", ans, flags=re.DOTALL) + try: + res = json_repair.loads(ans) + set_llm_cache(chat_mdl.llm_name, system_prompt, ans, user_prompt, gen_conf) + return res + except Exception as e: + logging.exception(f"Loading json failure: {ans}") + err += str(e) TOC_DETECTION = load_prompt("toc_detection") @@ -847,8 +853,6 @@ async def run_toc_from_text(chunks, chat_mdl, callback=None): TOC_RELEVANCE_SYSTEM = load_prompt("toc_relevance_system") TOC_RELEVANCE_USER = load_prompt("toc_relevance_user") - - async def relevant_chunks_with_toc(query: str, toc: list[dict], chat_mdl, topn: int = 6): import numpy as np try: @@ -876,8 +880,6 @@ async def relevant_chunks_with_toc(query: str, toc: list[dict], chat_mdl, topn: META_DATA = load_prompt("meta_data") - - async def gen_metadata(chat_mdl, schema: dict, content: str): template = PROMPT_JINJA_ENV.from_string(META_DATA) for k, desc in schema["properties"].items(): @@ -890,3 +892,34 @@ async def gen_metadata(chat_mdl, schema: dict, content: str): _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length) ans = await chat_mdl.async_chat(msg[0]["content"], msg[1:]) return re.sub(r"^.*", "", ans, flags=re.DOTALL) + + +SUFFICIENCY_CHECK = load_prompt("sufficiency_check") +async def sufficiency_check(chat_mdl, question: str, ret_content: str): + try: + return await gen_json( + PROMPT_JINJA_ENV.from_string(SUFFICIENCY_CHECK).render(question=question, retrieved_docs=ret_content), + "Output:\n", + chat_mdl + ) + except Exception as e: + logging.exception(e) + return {} + + +MULTI_QUERIES_GEN = load_prompt("multi_queries_gen") +async def multi_queries_gen(chat_mdl, question: str, query:str, missing_infos:list[str], ret_content: str): + try: + return await gen_json( + PROMPT_JINJA_ENV.from_string(MULTI_QUERIES_GEN).render( + original_question=question, + original_query=query, + missing_info="\n - ".join(missing_infos), + retrieved_docs=ret_content + ), + "Output:\n", + chat_mdl + ) + except Exception as e: + logging.exception(e) + return {} \ No newline at end of file diff --git a/rag/prompts/multi_queries_gen.md b/rag/prompts/multi_queries_gen.md new file mode 100644 index 000000000..7d1b2993e --- /dev/null +++ b/rag/prompts/multi_queries_gen.md @@ -0,0 +1,41 @@ +You are a query optimization expert. +The user's original query failed to retrieve sufficient information; +please generate multiple complementary improved questions and corresponding queries. + +Original query: +{{ original_query }} + +Original question: +{{ original_question }} + +Currently, retrieved content: +{{ retrieved_docs }} + +Missing information: +{{ missing_info }} + +Please generate 2-3 complementary queries to help find the missing information. These queries should: +1. Focus on different missing information points. +2. Use different expressions. +3. Avoid being identical to the original query. +4. Remain concise and clear. + +Output format (JSON): +```json +{ + "reasoning": "Explanation of query generation strategy", + "questions": [ + {"question": "Improved question 1", "query": "Improved query 1"}, + {"question": "Improved question 2", "query": "Improved query 2"}, + {"question": "Improved question 3", "query": "Improved query 3"} + ] +} +``` + +Requirements: +1. Questions array contains 1-3 questions and corresponding queries. +2. Each question length is between 5-200 characters. +3. Each query length is between 1-5 keywords. +4. Each query MUST be in the same language as the retrieved content in. +5. DO NOT generate question and query that is similar to the original query. +6. Reasoning explains the generation strategy. \ No newline at end of file diff --git a/rag/prompts/sufficiency_check.md b/rag/prompts/sufficiency_check.md new file mode 100644 index 000000000..705b91465 --- /dev/null +++ b/rag/prompts/sufficiency_check.md @@ -0,0 +1,24 @@ +You are a information retrieval evaluation expert. Please assess whether the currently retrieved content is sufficient to answer the user's question. + +User question: +{{ question }} + +Retrieved content: +{{ retrieved_docs }} + +Please determine whether these content are sufficient to answer the user's question. + +Output format (JSON): +```json +{ + "is_sufficient": true/false, + "reasoning": "Your reasoning for the judgment", + "missing_information": ["Missing information 1", "Missing information 2"] +} +``` + +Requirements: +1. If the retrieved content contains key information needed to answer the query, judge as sufficient (true). +2. If key information is missing, judge as insufficient (false), and list the missing information. +3. The `reasoning` should be concise and clear. +4. 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