mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-06-26 22:19:57 +00:00
Code refactor (#5371)
### What problem does this PR solve? #5173 ### Type of change - [x] Refactoring
This commit is contained in:
Kevin Hu
GitHub
parent
ffb4cda475
commit
4f40f685d9
@ -196,6 +196,7 @@ COPY deepdoc deepdoc
|
||||
COPY rag rag
|
||||
COPY agent agent
|
||||
COPY graphrag graphrag
|
||||
COPY agentic_reasoning agentic_reasoning
|
||||
COPY pyproject.toml uv.lock ./
|
||||
|
||||
COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template
|
||||
|
||||
@ -18,10 +18,10 @@ from functools import partial
|
||||
import pandas as pd
|
||||
from api.db import LLMType
|
||||
from api.db.services.conversation_service import structure_answer
|
||||
from api.db.services.dialog_service import message_fit_in
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api import settings
|
||||
from agent.component.base import ComponentBase, ComponentParamBase
|
||||
from rag.prompts import message_fit_in
|
||||
|
||||
|
||||
class GenerateParam(ComponentParamBase):
|
||||
|
||||
@ -19,11 +19,11 @@ from abc import ABC
|
||||
import pandas as pd
|
||||
|
||||
from api.db import LLMType
|
||||
from api.db.services.dialog_service import label_question
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api import settings
|
||||
from agent.component.base import ComponentBase, ComponentParamBase
|
||||
from rag.app.tag import label_question
|
||||
|
||||
|
||||
class RetrievalParam(ComponentParamBase):
|
||||
|
||||
1
agentic_reasoning/__init__.py
Normal file
1
agentic_reasoning/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
from .deep_research import DeepResearcher as DeepResearcher
|
||||
167
agentic_reasoning/deep_research.py
Normal file
167
agentic_reasoning/deep_research.py
Normal file
@ -0,0 +1,167 @@
|
||||
#
|
||||
# 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 thinking(self, chunk_info: dict, question: str):
|
||||
def rm_query_tags(line):
|
||||
pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
|
||||
return re.sub(pattern, "", line)
|
||||
|
||||
def rm_result_tags(line):
|
||||
pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
|
||||
return re.sub(pattern, "", line)
|
||||
|
||||
executed_search_queries = []
|
||||
msg_hisotry = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
|
||||
all_reasoning_steps = []
|
||||
think = "<think>"
|
||||
for ii in range(MAX_SEARCH_LIMIT + 1):
|
||||
if ii == 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"
|
||||
yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
|
||||
all_reasoning_steps.append(summary_think)
|
||||
msg_hisotry.append({"role": "assistant", "content": summary_think})
|
||||
break
|
||||
|
||||
query_think = ""
|
||||
if msg_hisotry[-1]["role"] != "user":
|
||||
msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
|
||||
else:
|
||||
msg_hisotry[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
|
||||
for ans in self.chat_mdl.chat_streamly(REASON_PROMPT, msg_hisotry, {"temperature": 0.7}):
|
||||
ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
|
||||
if not ans:
|
||||
continue
|
||||
query_think = ans
|
||||
yield {"answer": think + rm_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}
|
||||
|
||||
think += rm_query_tags(query_think)
|
||||
all_reasoning_steps.append(query_think)
|
||||
queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
|
||||
if not queries:
|
||||
if ii > 0:
|
||||
break
|
||||
queries = [question]
|
||||
|
||||
for search_query in queries:
|
||||
logging.info(f"[THINK]Query: {ii}. {search_query}")
|
||||
msg_hisotry.append({"role": "assistant", "content": search_query})
|
||||
think += f"\n\n> {ii +1}. {search_query}\n\n"
|
||||
yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}
|
||||
|
||||
summary_think = ""
|
||||
# The search query has been searched in previous steps.
|
||||
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"
|
||||
yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
|
||||
all_reasoning_steps.append(summary_think)
|
||||
msg_hisotry.append({"role": "user", "content": summary_think})
|
||||
think += summary_think
|
||||
continue
|
||||
|
||||
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'
|
||||
truncated_prev_reasoning = truncated_prev_reasoning.strip('\n')
|
||||
|
||||
# Retrieval procedure:
|
||||
# 1. KB search
|
||||
# 2. Web search (optional)
|
||||
# 3. KG search (optional)
|
||||
kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []}
|
||||
|
||||
if self.prompt_config.get("tavily_api_key"):
|
||||
tav = Tavily(self.prompt_config["tavily_api_key"])
|
||||
tav_res = tav.retrieve_chunks(" ".join(search_query))
|
||||
kbinfos["chunks"].extend(tav_res["chunks"])
|
||||
kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
|
||||
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)
|
||||
|
||||
# Merge chunk info for citations
|
||||
if not chunk_info["chunks"]:
|
||||
for k in chunk_info.keys():
|
||||
chunk_info[k] = kbinfos[k]
|
||||
else:
|
||||
cids = [c["chunk_id"] for c in chunk_info["chunks"]]
|
||||
for c in kbinfos["chunks"]:
|
||||
if c["chunk_id"] in cids:
|
||||
continue
|
||||
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"] in dids:
|
||||
continue
|
||||
chunk_info["doc_aggs"].append(d)
|
||||
|
||||
think += "\n\n"
|
||||
for ans in self.chat_mdl.chat_streamly(
|
||||
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}):
|
||||
ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
|
||||
if not ans:
|
||||
continue
|
||||
summary_think = ans
|
||||
yield {"answer": think + rm_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
|
||||
|
||||
all_reasoning_steps.append(summary_think)
|
||||
msg_hisotry.append(
|
||||
{"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
|
||||
think += rm_result_tags(summary_think)
|
||||
logging.info(f"[THINK]Summary: {ii}. {summary_think}")
|
||||
|
||||
yield think + "</think>"
|
||||
112
agentic_reasoning/prompts.py
Normal file
112
agentic_reasoning/prompts.py
Normal file
@ -0,0 +1,112 @@
|
||||
#
|
||||
# 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 = (
|
||||
"You are a reasoning assistant with the ability to perform dataset searches to help "
|
||||
"you answer the user's question accurately. You have special tools:\n\n"
|
||||
f"- To perform a search: write {BEGIN_SEARCH_QUERY} your query here {END_SEARCH_QUERY}.\n"
|
||||
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"
|
||||
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"
|
||||
"Once you have all the information you need, continue your reasoning.\n\n"
|
||||
"-- Example 1 --\n" ########################################
|
||||
"Question: \"Are both the directors of Jaws and Casino Royale from the same country?\"\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Who is the director of Jaws?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nThe director of Jaws is Steven Spielberg...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information.\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Where is Steven Spielberg from?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nSteven Allan Spielberg is an American filmmaker...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Who is the director of Casino Royale?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nCasino Royale is a 2006 spy film directed by Martin Campbell...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Where is Martin Campbell from?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nMartin Campbell (born 24 October 1943) is a New Zealand film and television director...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\nIt's enough to answer the question\n"
|
||||
|
||||
"-- Example 2 --\n" #########################################
|
||||
"Question: \"When was the founder of craigslist born?\"\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Who was the founder of craigslist?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nCraigslist was founded by Craig Newmark...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information.\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY} When was Craig Newmark born?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nCraig Newmark was born on December 6, 1952...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\nIt's enough to answer the question\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"
|
||||
"- The language of query MUST be as the same as 'Question' or 'search result'.\n"
|
||||
"- When done searching, continue your reasoning.\n\n"
|
||||
'Please answer the following question. You should think step by step to solve it.\n\n'
|
||||
)
|
||||
|
||||
RELEVANT_EXTRACTION_PROMPT = """**Task Instruction:**
|
||||
|
||||
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.
|
||||
|
||||
**Guidelines:**
|
||||
|
||||
1. **Analyze the Searched Web Pages:**
|
||||
- Carefully review the content of each searched web page.
|
||||
- Identify factual information that is relevant to the **Current Search Query** and can aid in the reasoning process for the original question.
|
||||
|
||||
2. **Extract Relevant Information:**
|
||||
- Select the information from the Searched Web Pages that directly contributes to advancing the **Previous Reasoning Steps**.
|
||||
- Ensure that the extracted information is accurate and relevant.
|
||||
|
||||
3. **Output Format:**
|
||||
- **If the web pages provide helpful information for current search query:** Present the information beginning with `**Final Information**` as shown below.
|
||||
- The language of query **MUST BE** as the same as 'Search Query' or 'Web Pages'.\n"
|
||||
**Final Information**
|
||||
|
||||
[Helpful information]
|
||||
|
||||
- **If the web pages do not provide any helpful information for current search query:** Output the following text.
|
||||
|
||||
**Final Information**
|
||||
|
||||
No helpful information found.
|
||||
|
||||
**Inputs:**
|
||||
- **Previous Reasoning Steps:**
|
||||
{prev_reasoning}
|
||||
|
||||
- **Current Search Query:**
|
||||
{search_query}
|
||||
|
||||
- **Searched Web Pages:**
|
||||
{document}
|
||||
|
||||
"""
|
||||
@ -25,7 +25,7 @@ from api.db import FileType, LLMType, ParserType, FileSource
|
||||
from api.db.db_models import APIToken, Task, File
|
||||
from api.db.services import duplicate_name
|
||||
from api.db.services.api_service import APITokenService, API4ConversationService
|
||||
from api.db.services.dialog_service import DialogService, chat, keyword_extraction, label_question
|
||||
from api.db.services.dialog_service import DialogService, chat
|
||||
from api.db.services.document_service import DocumentService, doc_upload_and_parse
|
||||
from api.db.services.file2document_service import File2DocumentService
|
||||
from api.db.services.file_service import FileService
|
||||
@ -38,6 +38,8 @@ from api.utils.api_utils import server_error_response, get_data_error_result, ge
|
||||
generate_confirmation_token
|
||||
|
||||
from api.utils.file_utils import filename_type, thumbnail
|
||||
from rag.app.tag import label_question
|
||||
from rag.prompts import keyword_extraction
|
||||
from rag.utils.storage_factory import STORAGE_IMPL
|
||||
|
||||
from api.db.services.canvas_service import UserCanvasService
|
||||
|
||||
@ -19,9 +19,10 @@ import json
|
||||
from flask import request
|
||||
from flask_login import login_required, current_user
|
||||
|
||||
from api.db.services.dialog_service import keyword_extraction, label_question
|
||||
from rag.app.qa import rmPrefix, beAdoc
|
||||
from rag.app.tag import label_question
|
||||
from rag.nlp import search, rag_tokenizer
|
||||
from rag.prompts import keyword_extraction
|
||||
from rag.settings import PAGERANK_FLD
|
||||
from rag.utils import rmSpace
|
||||
from api.db import LLMType, ParserType
|
||||
|
||||
@ -25,13 +25,14 @@ from flask import request, Response
|
||||
from flask_login import login_required, current_user
|
||||
|
||||
from api.db import LLMType
|
||||
from api.db.services.dialog_service import DialogService, chat, ask, label_question
|
||||
from api.db.services.dialog_service import DialogService, chat, ask
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.db.services.llm_service import LLMBundle, TenantService
|
||||
from api import settings
|
||||
from api.utils.api_utils import get_json_result
|
||||
from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
|
||||
from graphrag.general.mind_map_extractor import MindMapExtractor
|
||||
from rag.app.tag import label_question
|
||||
|
||||
|
||||
@manager.route('/set', methods=['POST']) # noqa: F821
|
||||
|
||||
@ -152,6 +152,7 @@ def add_llm():
|
||||
|
||||
elif factory == "Tencent Cloud":
|
||||
req["api_key"] = apikey_json(["tencent_cloud_sid", "tencent_cloud_sk"])
|
||||
return set_api_key()
|
||||
|
||||
elif factory == "Bedrock":
|
||||
# For Bedrock, due to its special authentication method
|
||||
|
||||
@ -16,11 +16,11 @@
|
||||
from flask import request, jsonify
|
||||
|
||||
from api.db import LLMType
|
||||
from api.db.services.dialog_service import label_question
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api import settings
|
||||
from api.utils.api_utils import validate_request, build_error_result, apikey_required
|
||||
from rag.app.tag import label_question
|
||||
|
||||
|
||||
@manager.route('/dify/retrieval', methods=['POST']) # noqa: F821
|
||||
|
||||
@ -16,7 +16,6 @@
|
||||
import pathlib
|
||||
import datetime
|
||||
|
||||
from api.db.services.dialog_service import keyword_extraction, label_question
|
||||
from rag.app.qa import rmPrefix, beAdoc
|
||||
from rag.nlp import rag_tokenizer
|
||||
from api.db import LLMType, ParserType
|
||||
@ -39,6 +38,8 @@ from api.db.services.file_service import FileService
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.utils.api_utils import construct_json_result, get_parser_config
|
||||
from rag.nlp import search
|
||||
from rag.prompts import keyword_extraction
|
||||
from rag.app.tag import label_question
|
||||
from rag.utils import rmSpace
|
||||
from rag.utils.storage_factory import STORAGE_IMPL
|
||||
|
||||
|
||||
@ -15,31 +15,23 @@
|
||||
#
|
||||
import logging
|
||||
import binascii
|
||||
import os
|
||||
import json
|
||||
import time
|
||||
|
||||
import json_repair
|
||||
from functools import partial
|
||||
import re
|
||||
from collections import defaultdict
|
||||
from copy import deepcopy
|
||||
from timeit import default_timer as timer
|
||||
import datetime
|
||||
from datetime import timedelta
|
||||
from agentic_reasoning import DeepResearcher
|
||||
from api.db import LLMType, ParserType, StatusEnum
|
||||
from api.db.db_models import Dialog, DB
|
||||
from api.db.services.common_service import CommonService
|
||||
from api.db.services.document_service import DocumentService
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from api.db.services.llm_service import TenantLLMService, LLMBundle
|
||||
from api import settings
|
||||
from graphrag.utils import get_tags_from_cache, set_tags_to_cache
|
||||
from rag.app.resume import forbidden_select_fields4resume
|
||||
from rag.nlp import extract_between
|
||||
from rag.app.tag import label_question
|
||||
from rag.nlp.search import index_name
|
||||
from rag.settings import TAG_FLD
|
||||
from rag.utils import rmSpace, num_tokens_from_string, encoder
|
||||
from api.utils.file_utils import get_project_base_directory
|
||||
from rag.prompts import kb_prompt, message_fit_in, llm_id2llm_type, keyword_extraction, full_question
|
||||
from rag.utils import rmSpace, num_tokens_from_string
|
||||
from rag.utils.tavily_conn import Tavily
|
||||
|
||||
|
||||
@ -69,109 +61,6 @@ class DialogService(CommonService):
|
||||
return list(chats.dicts())
|
||||
|
||||
|
||||
def message_fit_in(msg, max_length=4000):
|
||||
def count():
|
||||
nonlocal msg
|
||||
tks_cnts = []
|
||||
for m in msg:
|
||||
tks_cnts.append(
|
||||
{"role": m["role"], "count": num_tokens_from_string(m["content"])})
|
||||
total = 0
|
||||
for m in tks_cnts:
|
||||
total += m["count"]
|
||||
return total
|
||||
|
||||
c = count()
|
||||
if c < max_length:
|
||||
return c, msg
|
||||
|
||||
msg_ = [m for m in msg[:-1] if m["role"] == "system"]
|
||||
if len(msg) > 1:
|
||||
msg_.append(msg[-1])
|
||||
msg = msg_
|
||||
c = count()
|
||||
if c < max_length:
|
||||
return c, msg
|
||||
|
||||
ll = num_tokens_from_string(msg_[0]["content"])
|
||||
ll2 = num_tokens_from_string(msg_[-1]["content"])
|
||||
if ll / (ll + ll2) > 0.8:
|
||||
m = msg_[0]["content"]
|
||||
m = encoder.decode(encoder.encode(m)[:max_length - ll2])
|
||||
msg[0]["content"] = m
|
||||
return max_length, msg
|
||||
|
||||
m = msg_[1]["content"]
|
||||
m = encoder.decode(encoder.encode(m)[:max_length - ll2])
|
||||
msg[1]["content"] = m
|
||||
return max_length, msg
|
||||
|
||||
|
||||
def llm_id2llm_type(llm_id):
|
||||
llm_id, _ = TenantLLMService.split_model_name_and_factory(llm_id)
|
||||
fnm = os.path.join(get_project_base_directory(), "conf")
|
||||
llm_factories = json.load(open(os.path.join(fnm, "llm_factories.json"), "r"))
|
||||
for llm_factory in llm_factories["factory_llm_infos"]:
|
||||
for llm in llm_factory["llm"]:
|
||||
if llm_id == llm["llm_name"]:
|
||||
return llm["model_type"].strip(",")[-1]
|
||||
|
||||
|
||||
def kb_prompt(kbinfos, max_tokens):
|
||||
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
|
||||
used_token_count = 0
|
||||
chunks_num = 0
|
||||
for i, c in enumerate(knowledges):
|
||||
used_token_count += num_tokens_from_string(c)
|
||||
chunks_num += 1
|
||||
if max_tokens * 0.97 < used_token_count:
|
||||
knowledges = knowledges[:i]
|
||||
logging.warning(f"Not all the retrieval into prompt: {i+1}/{len(knowledges)}")
|
||||
break
|
||||
|
||||
docs = DocumentService.get_by_ids([ck["doc_id"] for ck in kbinfos["chunks"][:chunks_num]])
|
||||
docs = {d.id: d.meta_fields for d in docs}
|
||||
|
||||
doc2chunks = defaultdict(lambda: {"chunks": [], "meta": []})
|
||||
for ck in kbinfos["chunks"][:chunks_num]:
|
||||
doc2chunks[ck["docnm_kwd"]]["chunks"].append((f"URL: {ck['url']}\n" if "url" in ck else "") + ck["content_with_weight"])
|
||||
doc2chunks[ck["docnm_kwd"]]["meta"] = docs.get(ck["doc_id"], {})
|
||||
|
||||
knowledges = []
|
||||
for nm, cks_meta in doc2chunks.items():
|
||||
txt = f"Document: {nm} \n"
|
||||
for k, v in cks_meta["meta"].items():
|
||||
txt += f"{k}: {v}\n"
|
||||
txt += "Relevant fragments as following:\n"
|
||||
for i, chunk in enumerate(cks_meta["chunks"], 1):
|
||||
txt += f"{i}. {chunk}\n"
|
||||
knowledges.append(txt)
|
||||
return knowledges
|
||||
|
||||
|
||||
def label_question(question, kbs):
|
||||
tags = None
|
||||
tag_kb_ids = []
|
||||
for kb in kbs:
|
||||
if kb.parser_config.get("tag_kb_ids"):
|
||||
tag_kb_ids.extend(kb.parser_config["tag_kb_ids"])
|
||||
if tag_kb_ids:
|
||||
all_tags = get_tags_from_cache(tag_kb_ids)
|
||||
if not all_tags:
|
||||
all_tags = settings.retrievaler.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
|
||||
set_tags_to_cache(all_tags, tag_kb_ids)
|
||||
else:
|
||||
all_tags = json.loads(all_tags)
|
||||
tag_kbs = KnowledgebaseService.get_by_ids(tag_kb_ids)
|
||||
tags = settings.retrievaler.tag_query(question,
|
||||
list(set([kb.tenant_id for kb in tag_kbs])),
|
||||
tag_kb_ids,
|
||||
all_tags,
|
||||
kb.parser_config.get("topn_tags", 3)
|
||||
)
|
||||
return tags
|
||||
|
||||
|
||||
def chat_solo(dialog, messages, stream=True):
|
||||
if llm_id2llm_type(dialog.llm_id) == "image2text":
|
||||
chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
|
||||
@ -297,7 +186,11 @@ def chat(dialog, messages, stream=True, **kwargs):
|
||||
|
||||
knowledges = []
|
||||
if prompt_config.get("reasoning", False):
|
||||
for think in reasoning(kbinfos, " ".join(questions), chat_mdl, embd_mdl, tenant_ids, dialog.kb_ids, prompt_config, MAX_SEARCH_LIMIT=3):
|
||||
reasoner = DeepResearcher(chat_mdl,
|
||||
prompt_config,
|
||||
partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3))
|
||||
|
||||
for think in reasoner.thinking(kbinfos, " ".join(questions)):
|
||||
if isinstance(think, str):
|
||||
thought = think
|
||||
knowledges = [t for t in think.split("\n") if t]
|
||||
@ -552,175 +445,6 @@ Please write the SQL, only SQL, without any other explanations or text.
|
||||
}
|
||||
|
||||
|
||||
def relevant(tenant_id, llm_id, question, contents: list):
|
||||
if llm_id2llm_type(llm_id) == "image2text":
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
|
||||
else:
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
|
||||
prompt = """
|
||||
You are a grader assessing relevance of a retrieved document to a user question.
|
||||
It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
|
||||
If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant.
|
||||
Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
|
||||
No other words needed except 'yes' or 'no'.
|
||||
"""
|
||||
if not contents:
|
||||
return False
|
||||
contents = "Documents: \n" + " - ".join(contents)
|
||||
contents = f"Question: {question}\n" + contents
|
||||
if num_tokens_from_string(contents) >= chat_mdl.max_length - 4:
|
||||
contents = encoder.decode(encoder.encode(contents)[:chat_mdl.max_length - 4])
|
||||
ans = chat_mdl.chat(prompt, [{"role": "user", "content": contents}], {"temperature": 0.01})
|
||||
if ans.lower().find("yes") >= 0:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def rewrite(tenant_id, llm_id, question):
|
||||
if llm_id2llm_type(llm_id) == "image2text":
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
|
||||
else:
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
|
||||
prompt = """
|
||||
You are an expert at query expansion to generate a paraphrasing of a question.
|
||||
I can't retrieval relevant information from the knowledge base by using user's question directly.
|
||||
You need to expand or paraphrase user's question by multiple ways such as using synonyms words/phrase,
|
||||
writing the abbreviation in its entirety, adding some extra descriptions or explanations,
|
||||
changing the way of expression, translating the original question into another language (English/Chinese), etc.
|
||||
And return 5 versions of question and one is from translation.
|
||||
Just list the question. No other words are needed.
|
||||
"""
|
||||
ans = chat_mdl.chat(prompt, [{"role": "user", "content": question}], {"temperature": 0.8})
|
||||
return ans
|
||||
|
||||
|
||||
def keyword_extraction(chat_mdl, content, topn=3):
|
||||
prompt = f"""
|
||||
Role: You're a text analyzer.
|
||||
Task: extract the most important keywords/phrases of a given piece of text content.
|
||||
Requirements:
|
||||
- Summarize the text content, and give top {topn} important keywords/phrases.
|
||||
- The keywords MUST be in language of the given piece of text content.
|
||||
- The keywords are delimited by ENGLISH COMMA.
|
||||
- Keywords ONLY in output.
|
||||
|
||||
### Text Content
|
||||
{content}
|
||||
|
||||
"""
|
||||
msg = [
|
||||
{"role": "system", "content": prompt},
|
||||
{"role": "user", "content": "Output: "}
|
||||
]
|
||||
_, msg = message_fit_in(msg, chat_mdl.max_length)
|
||||
kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2})
|
||||
if isinstance(kwd, tuple):
|
||||
kwd = kwd[0]
|
||||
kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
|
||||
if kwd.find("**ERROR**") >= 0:
|
||||
return ""
|
||||
return kwd
|
||||
|
||||
|
||||
def question_proposal(chat_mdl, content, topn=3):
|
||||
prompt = f"""
|
||||
Role: You're a text analyzer.
|
||||
Task: propose {topn} questions about a given piece of text content.
|
||||
Requirements:
|
||||
- Understand and summarize the text content, and propose top {topn} important questions.
|
||||
- The questions SHOULD NOT have overlapping meanings.
|
||||
- The questions SHOULD cover the main content of the text as much as possible.
|
||||
- The questions MUST be in language of the given piece of text content.
|
||||
- One question per line.
|
||||
- Question ONLY in output.
|
||||
|
||||
### Text Content
|
||||
{content}
|
||||
|
||||
"""
|
||||
msg = [
|
||||
{"role": "system", "content": prompt},
|
||||
{"role": "user", "content": "Output: "}
|
||||
]
|
||||
_, msg = message_fit_in(msg, chat_mdl.max_length)
|
||||
kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2})
|
||||
if isinstance(kwd, tuple):
|
||||
kwd = kwd[0]
|
||||
kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
|
||||
if kwd.find("**ERROR**") >= 0:
|
||||
return ""
|
||||
return kwd
|
||||
|
||||
|
||||
def full_question(tenant_id, llm_id, messages):
|
||||
if llm_id2llm_type(llm_id) == "image2text":
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
|
||||
else:
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
|
||||
conv = []
|
||||
for m in messages:
|
||||
if m["role"] not in ["user", "assistant"]:
|
||||
continue
|
||||
conv.append("{}: {}".format(m["role"].upper(), m["content"]))
|
||||
conv = "\n".join(conv)
|
||||
today = datetime.date.today().isoformat()
|
||||
yesterday = (datetime.date.today() - timedelta(days=1)).isoformat()
|
||||
tomorrow = (datetime.date.today() + timedelta(days=1)).isoformat()
|
||||
prompt = f"""
|
||||
Role: A helpful assistant
|
||||
|
||||
Task and steps:
|
||||
1. Generate a full user question that would follow the conversation.
|
||||
2. If the user's question involves relative date, you need to convert it into absolute date based on the current date, which is {today}. For example: 'yesterday' would be converted to {yesterday}.
|
||||
|
||||
Requirements & Restrictions:
|
||||
- Text generated MUST be in the same language of the original user's question.
|
||||
- If the user's latest question is completely, don't do anything, just return the original question.
|
||||
- DON'T generate anything except a refined question.
|
||||
|
||||
######################
|
||||
-Examples-
|
||||
######################
|
||||
|
||||
# Example 1
|
||||
## Conversation
|
||||
USER: What is the name of Donald Trump's father?
|
||||
ASSISTANT: Fred Trump.
|
||||
USER: And his mother?
|
||||
###############
|
||||
Output: What's the name of Donald Trump's mother?
|
||||
|
||||
------------
|
||||
# Example 2
|
||||
## Conversation
|
||||
USER: What is the name of Donald Trump's father?
|
||||
ASSISTANT: Fred Trump.
|
||||
USER: And his mother?
|
||||
ASSISTANT: Mary Trump.
|
||||
User: What's her full name?
|
||||
###############
|
||||
Output: What's the full name of Donald Trump's mother Mary Trump?
|
||||
|
||||
------------
|
||||
# Example 3
|
||||
## Conversation
|
||||
USER: What's the weather today in London?
|
||||
ASSISTANT: Cloudy.
|
||||
USER: What's about tomorrow in Rochester?
|
||||
###############
|
||||
Output: What's the weather in Rochester on {tomorrow}?
|
||||
######################
|
||||
|
||||
# Real Data
|
||||
## Conversation
|
||||
{conv}
|
||||
###############
|
||||
"""
|
||||
ans = chat_mdl.chat(prompt, [{"role": "user", "content": "Output: "}], {"temperature": 0.2})
|
||||
ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
|
||||
return ans if ans.find("**ERROR**") < 0 else messages[-1]["content"]
|
||||
|
||||
|
||||
def tts(tts_mdl, text):
|
||||
if not tts_mdl or not text:
|
||||
return
|
||||
@ -796,298 +520,3 @@ def ask(question, kb_ids, tenant_id):
|
||||
yield decorate_answer(answer)
|
||||
|
||||
|
||||
def content_tagging(chat_mdl, content, all_tags, examples, topn=3):
|
||||
prompt = f"""
|
||||
Role: You're a text analyzer.
|
||||
|
||||
Task: Tag (put on some labels) to a given piece of text content based on the examples and the entire tag set.
|
||||
|
||||
Steps::
|
||||
- Comprehend the tag/label set.
|
||||
- Comprehend examples which all consist of both text content and assigned tags with relevance score in format of JSON.
|
||||
- Summarize the text content, and tag it with top {topn} most relevant tags from the set of tag/label and the corresponding relevance score.
|
||||
|
||||
Requirements
|
||||
- The tags MUST be from the tag set.
|
||||
- The output MUST be in JSON format only, the key is tag and the value is its relevance score.
|
||||
- The relevance score must be range from 1 to 10.
|
||||
- Keywords ONLY in output.
|
||||
|
||||
# TAG SET
|
||||
{", ".join(all_tags)}
|
||||
|
||||
"""
|
||||
for i, ex in enumerate(examples):
|
||||
prompt += """
|
||||
# Examples {}
|
||||
### Text Content
|
||||
{}
|
||||
|
||||
Output:
|
||||
{}
|
||||
|
||||
""".format(i, ex["content"], json.dumps(ex[TAG_FLD], indent=2, ensure_ascii=False))
|
||||
|
||||
prompt += f"""
|
||||
# Real Data
|
||||
### Text Content
|
||||
{content}
|
||||
|
||||
"""
|
||||
msg = [
|
||||
{"role": "system", "content": prompt},
|
||||
{"role": "user", "content": "Output: "}
|
||||
]
|
||||
_, msg = message_fit_in(msg, chat_mdl.max_length)
|
||||
kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.5})
|
||||
if isinstance(kwd, tuple):
|
||||
kwd = kwd[0]
|
||||
kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
|
||||
if kwd.find("**ERROR**") >= 0:
|
||||
raise Exception(kwd)
|
||||
|
||||
try:
|
||||
return json_repair.loads(kwd)
|
||||
except json_repair.JSONDecodeError:
|
||||
try:
|
||||
result = kwd.replace(prompt[:-1], '').replace('user', '').replace('model', '').strip()
|
||||
result = '{' + result.split('{')[1].split('}')[0] + '}'
|
||||
return json_repair.loads(result)
|
||||
except Exception as e:
|
||||
logging.exception(f"JSON parsing error: {result} -> {e}")
|
||||
raise e
|
||||
|
||||
|
||||
def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LLMBundle,
|
||||
tenant_ids: list[str], kb_ids: list[str], prompt_config, MAX_SEARCH_LIMIT: int = 6,
|
||||
top_n: int = 5, similarity_threshold: float = 0.4, vector_similarity_weight: float = 0.3):
|
||||
BEGIN_SEARCH_QUERY = "<|begin_search_query|>"
|
||||
END_SEARCH_QUERY = "<|end_search_query|>"
|
||||
BEGIN_SEARCH_RESULT = "<|begin_search_result|>"
|
||||
END_SEARCH_RESULT = "<|end_search_result|>"
|
||||
|
||||
def rm_query_tags(line):
|
||||
pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
|
||||
return re.sub(pattern, "", line)
|
||||
|
||||
def rm_result_tags(line):
|
||||
pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
|
||||
return re.sub(pattern, "", line)
|
||||
|
||||
reason_prompt = (
|
||||
"You are a reasoning assistant with the ability to perform dataset searches to help "
|
||||
"you answer the user's question accurately. You have special tools:\n\n"
|
||||
f"- To perform a search: write {BEGIN_SEARCH_QUERY} your query here {END_SEARCH_QUERY}.\n"
|
||||
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"
|
||||
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"
|
||||
"Once you have all the information you need, continue your reasoning.\n\n"
|
||||
"-- Example 1 --\n" ########################################
|
||||
"Question: \"Are both the directors of Jaws and Casino Royale from the same country?\"\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Who is the director of Jaws?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nThe director of Jaws is Steven Spielberg...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information.\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Where is Steven Spielberg from?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nSteven Allan Spielberg is an American filmmaker...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Who is the director of Casino Royale?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nCasino Royale is a 2006 spy film directed by Martin Campbell...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Where is Martin Campbell from?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nMartin Campbell (born 24 October 1943) is a New Zealand film and television director...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\nIt's enough to answer the question\n"
|
||||
|
||||
"-- Example 2 --\n" #########################################
|
||||
"Question: \"When was the founder of craigslist born?\"\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY}Who was the founder of craigslist?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nCraigslist was founded by Craig Newmark...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information.\n"
|
||||
"Assistant:\n"
|
||||
f" {BEGIN_SEARCH_QUERY} When was Craig Newmark born?{END_SEARCH_QUERY}\n\n"
|
||||
"User:\n"
|
||||
f" {BEGIN_SEARCH_RESULT}\nCraig Newmark was born on December 6, 1952...\n{END_SEARCH_RESULT}\n\n"
|
||||
"Continues reasoning with the new information...\n\n"
|
||||
"Assistant:\nIt's enough to answer the question\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"
|
||||
"- The language of query MUST be as the same as 'Question' or 'search result'.\n"
|
||||
"- When done searching, continue your reasoning.\n\n"
|
||||
'Please answer the following question. You should think step by step to solve it.\n\n'
|
||||
)
|
||||
|
||||
relevant_extraction_prompt = """**Task Instruction:**
|
||||
|
||||
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.
|
||||
|
||||
**Guidelines:**
|
||||
|
||||
1. **Analyze the Searched Web Pages:**
|
||||
- Carefully review the content of each searched web page.
|
||||
- Identify factual information that is relevant to the **Current Search Query** and can aid in the reasoning process for the original question.
|
||||
|
||||
2. **Extract Relevant Information:**
|
||||
- Select the information from the Searched Web Pages that directly contributes to advancing the **Previous Reasoning Steps**.
|
||||
- Ensure that the extracted information is accurate and relevant.
|
||||
|
||||
3. **Output Format:**
|
||||
- **If the web pages provide helpful information for current search query:** Present the information beginning with `**Final Information**` as shown below.
|
||||
- The language of query **MUST BE** as the same as 'Search Query' or 'Web Pages'.\n"
|
||||
**Final Information**
|
||||
|
||||
[Helpful information]
|
||||
|
||||
- **If the web pages do not provide any helpful information for current search query:** Output the following text.
|
||||
|
||||
**Final Information**
|
||||
|
||||
No helpful information found.
|
||||
|
||||
**Inputs:**
|
||||
- **Previous Reasoning Steps:**
|
||||
{prev_reasoning}
|
||||
|
||||
- **Current Search Query:**
|
||||
{search_query}
|
||||
|
||||
- **Searched Web Pages:**
|
||||
{document}
|
||||
|
||||
"""
|
||||
|
||||
executed_search_queries = []
|
||||
msg_hisotry = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
|
||||
all_reasoning_steps = []
|
||||
think = "<think>"
|
||||
for ii in range(MAX_SEARCH_LIMIT + 1):
|
||||
if ii == 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"
|
||||
yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
|
||||
all_reasoning_steps.append(summary_think)
|
||||
msg_hisotry.append({"role": "assistant", "content": summary_think})
|
||||
break
|
||||
|
||||
query_think = ""
|
||||
if msg_hisotry[-1]["role"] != "user":
|
||||
msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
|
||||
else:
|
||||
msg_hisotry[-1]["content"] += "\n\nContinues 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:
|
||||
continue
|
||||
query_think = ans
|
||||
yield {"answer": think + rm_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}
|
||||
|
||||
think += rm_query_tags(query_think)
|
||||
all_reasoning_steps.append(query_think)
|
||||
queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
|
||||
if not queries:
|
||||
if ii > 0:
|
||||
break
|
||||
queries = [question]
|
||||
|
||||
for search_query in queries:
|
||||
logging.info(f"[THINK]Query: {ii}. {search_query}")
|
||||
msg_hisotry.append({"role": "assistant", "content": search_query})
|
||||
think += f"\n\n> {ii+1}. {search_query}\n\n"
|
||||
yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}
|
||||
|
||||
summary_think = ""
|
||||
# The search query has been searched in previous steps.
|
||||
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"
|
||||
yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
|
||||
all_reasoning_steps.append(summary_think)
|
||||
msg_hisotry.append({"role": "user", "content": summary_think})
|
||||
think += summary_think
|
||||
continue
|
||||
|
||||
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'
|
||||
truncated_prev_reasoning = truncated_prev_reasoning.strip('\n')
|
||||
|
||||
# Retrieval procedure:
|
||||
# 1. KB search
|
||||
# 2. Web search (optional)
|
||||
# 3. KG search (optional)
|
||||
kbinfos = settings.retrievaler.retrieval(search_query, embd_mdl, tenant_ids, kb_ids, 1, top_n,
|
||||
similarity_threshold,
|
||||
vector_similarity_weight
|
||||
)
|
||||
if prompt_config.get("tavily_api_key", "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"):
|
||||
tav = Tavily(prompt_config["tavily_api_key"])
|
||||
tav_res = tav.retrieve_chunks(" ".join(search_query))
|
||||
kbinfos["chunks"].extend(tav_res["chunks"])
|
||||
kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
|
||||
if prompt_config.get("use_kg"):
|
||||
ck = settings.kg_retrievaler.retrieval(search_query,
|
||||
tenant_ids,
|
||||
kb_ids,
|
||||
embd_mdl,
|
||||
chat_mdl)
|
||||
if ck["content_with_weight"]:
|
||||
kbinfos["chunks"].insert(0, ck)
|
||||
|
||||
# Merge chunk info for citations
|
||||
if not chunk_info["chunks"]:
|
||||
for k in chunk_info.keys():
|
||||
chunk_info[k] = kbinfos[k]
|
||||
else:
|
||||
cids = [c["chunk_id"] for c in chunk_info["chunks"]]
|
||||
for c in kbinfos["chunks"]:
|
||||
if c["chunk_id"] in cids:
|
||||
continue
|
||||
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"] in dids:
|
||||
continue
|
||||
chunk_info["doc_aggs"].append(d)
|
||||
|
||||
think += "\n\n"
|
||||
for ans in chat_mdl.chat_streamly(
|
||||
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}):
|
||||
ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
|
||||
if not ans:
|
||||
continue
|
||||
summary_think = ans
|
||||
yield {"answer": think + rm_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}
|
||||
|
||||
all_reasoning_steps.append(summary_think)
|
||||
msg_hisotry.append(
|
||||
{"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
|
||||
think += rm_result_tags(summary_think)
|
||||
logging.info(f"[THINK]Summary: {ii}. {summary_think}")
|
||||
|
||||
yield think + "</think>"
|
||||
|
||||
@ -13,7 +13,7 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
import json
|
||||
import re
|
||||
import csv
|
||||
from copy import deepcopy
|
||||
@ -121,6 +121,32 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
|
||||
"Excel, csv(txt) format files are supported.")
|
||||
|
||||
|
||||
def label_question(question, kbs):
|
||||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||||
from graphrag.utils import get_tags_from_cache, set_tags_to_cache
|
||||
from api import settings
|
||||
tags = None
|
||||
tag_kb_ids = []
|
||||
for kb in kbs:
|
||||
if kb.parser_config.get("tag_kb_ids"):
|
||||
tag_kb_ids.extend(kb.parser_config["tag_kb_ids"])
|
||||
if tag_kb_ids:
|
||||
all_tags = get_tags_from_cache(tag_kb_ids)
|
||||
if not all_tags:
|
||||
all_tags = settings.retrievaler.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
|
||||
set_tags_to_cache(all_tags, tag_kb_ids)
|
||||
else:
|
||||
all_tags = json.loads(all_tags)
|
||||
tag_kbs = KnowledgebaseService.get_by_ids(tag_kb_ids)
|
||||
tags = settings.retrievaler.tag_query(question,
|
||||
list(set([kb.tenant_id for kb in tag_kbs])),
|
||||
tag_kb_ids,
|
||||
all_tags,
|
||||
kb.parser_config.get("topn_tags", 3)
|
||||
)
|
||||
return tags
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
|
||||
@ -88,7 +88,7 @@ class FulltextQueryer:
|
||||
tks_w = [(re.sub(r"^[\+-]", "", tk), w) for tk, w in tks_w if tk]
|
||||
tks_w = [(tk.strip(), w) for tk, w in tks_w if tk.strip()]
|
||||
syns = []
|
||||
for tk, w in tks_w:
|
||||
for tk, w in tks_w[:256]:
|
||||
syn = self.syn.lookup(tk)
|
||||
syn = rag_tokenizer.tokenize(" ".join(syn)).split()
|
||||
keywords.extend(syn)
|
||||
|
||||
297
rag/prompts.py
Normal file
297
rag/prompts.py
Normal file
@ -0,0 +1,297 @@
|
||||
#
|
||||
# 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 datetime
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from collections import defaultdict
|
||||
import json_repair
|
||||
from api.db import LLMType
|
||||
from api.db.services.document_service import DocumentService
|
||||
from api.db.services.llm_service import TenantLLMService, LLMBundle
|
||||
from api.utils.file_utils import get_project_base_directory
|
||||
from rag.settings import TAG_FLD
|
||||
from rag.utils import num_tokens_from_string, encoder
|
||||
|
||||
|
||||
def llm_id2llm_type(llm_id):
|
||||
llm_id, _ = TenantLLMService.split_model_name_and_factory(llm_id)
|
||||
fnm = os.path.join(get_project_base_directory(), "conf")
|
||||
llm_factories = json.load(open(os.path.join(fnm, "llm_factories.json"), "r"))
|
||||
for llm_factory in llm_factories["factory_llm_infos"]:
|
||||
for llm in llm_factory["llm"]:
|
||||
if llm_id == llm["llm_name"]:
|
||||
return llm["model_type"].strip(",")[-1]
|
||||
|
||||
|
||||
def message_fit_in(msg, max_length=4000):
|
||||
def count():
|
||||
nonlocal msg
|
||||
tks_cnts = []
|
||||
for m in msg:
|
||||
tks_cnts.append(
|
||||
{"role": m["role"], "count": num_tokens_from_string(m["content"])})
|
||||
total = 0
|
||||
for m in tks_cnts:
|
||||
total += m["count"]
|
||||
return total
|
||||
|
||||
c = count()
|
||||
if c < max_length:
|
||||
return c, msg
|
||||
|
||||
msg_ = [m for m in msg[:-1] if m["role"] == "system"]
|
||||
if len(msg) > 1:
|
||||
msg_.append(msg[-1])
|
||||
msg = msg_
|
||||
c = count()
|
||||
if c < max_length:
|
||||
return c, msg
|
||||
|
||||
ll = num_tokens_from_string(msg_[0]["content"])
|
||||
ll2 = num_tokens_from_string(msg_[-1]["content"])
|
||||
if ll / (ll + ll2) > 0.8:
|
||||
m = msg_[0]["content"]
|
||||
m = encoder.decode(encoder.encode(m)[:max_length - ll2])
|
||||
msg[0]["content"] = m
|
||||
return max_length, msg
|
||||
|
||||
m = msg_[1]["content"]
|
||||
m = encoder.decode(encoder.encode(m)[:max_length - ll2])
|
||||
msg[1]["content"] = m
|
||||
return max_length, msg
|
||||
|
||||
|
||||
def kb_prompt(kbinfos, max_tokens):
|
||||
knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
|
||||
used_token_count = 0
|
||||
chunks_num = 0
|
||||
for i, c in enumerate(knowledges):
|
||||
used_token_count += num_tokens_from_string(c)
|
||||
chunks_num += 1
|
||||
if max_tokens * 0.97 < used_token_count:
|
||||
knowledges = knowledges[:i]
|
||||
logging.warning(f"Not all the retrieval into prompt: {i+1}/{len(knowledges)}")
|
||||
break
|
||||
|
||||
docs = DocumentService.get_by_ids([ck["doc_id"] for ck in kbinfos["chunks"][:chunks_num]])
|
||||
docs = {d.id: d.meta_fields for d in docs}
|
||||
|
||||
doc2chunks = defaultdict(lambda: {"chunks": [], "meta": []})
|
||||
for ck in kbinfos["chunks"][:chunks_num]:
|
||||
doc2chunks[ck["docnm_kwd"]]["chunks"].append((f"URL: {ck['url']}\n" if "url" in ck else "") + ck["content_with_weight"])
|
||||
doc2chunks[ck["docnm_kwd"]]["meta"] = docs.get(ck["doc_id"], {})
|
||||
|
||||
knowledges = []
|
||||
for nm, cks_meta in doc2chunks.items():
|
||||
txt = f"Document: {nm} \n"
|
||||
for k, v in cks_meta["meta"].items():
|
||||
txt += f"{k}: {v}\n"
|
||||
txt += "Relevant fragments as following:\n"
|
||||
for i, chunk in enumerate(cks_meta["chunks"], 1):
|
||||
txt += f"{i}. {chunk}\n"
|
||||
knowledges.append(txt)
|
||||
return knowledges
|
||||
|
||||
|
||||
def keyword_extraction(chat_mdl, content, topn=3):
|
||||
prompt = f"""
|
||||
Role: You're a text analyzer.
|
||||
Task: extract the most important keywords/phrases of a given piece of text content.
|
||||
Requirements:
|
||||
- Summarize the text content, and give top {topn} important keywords/phrases.
|
||||
- The keywords MUST be in language of the given piece of text content.
|
||||
- The keywords are delimited by ENGLISH COMMA.
|
||||
- Keywords ONLY in output.
|
||||
|
||||
### Text Content
|
||||
{content}
|
||||
|
||||
"""
|
||||
msg = [
|
||||
{"role": "system", "content": prompt},
|
||||
{"role": "user", "content": "Output: "}
|
||||
]
|
||||
_, msg = message_fit_in(msg, chat_mdl.max_length)
|
||||
kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2})
|
||||
if isinstance(kwd, tuple):
|
||||
kwd = kwd[0]
|
||||
kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
|
||||
if kwd.find("**ERROR**") >= 0:
|
||||
return ""
|
||||
return kwd
|
||||
|
||||
|
||||
def question_proposal(chat_mdl, content, topn=3):
|
||||
prompt = f"""
|
||||
Role: You're a text analyzer.
|
||||
Task: propose {topn} questions about a given piece of text content.
|
||||
Requirements:
|
||||
- Understand and summarize the text content, and propose top {topn} important questions.
|
||||
- The questions SHOULD NOT have overlapping meanings.
|
||||
- The questions SHOULD cover the main content of the text as much as possible.
|
||||
- The questions MUST be in language of the given piece of text content.
|
||||
- One question per line.
|
||||
- Question ONLY in output.
|
||||
|
||||
### Text Content
|
||||
{content}
|
||||
|
||||
"""
|
||||
msg = [
|
||||
{"role": "system", "content": prompt},
|
||||
{"role": "user", "content": "Output: "}
|
||||
]
|
||||
_, msg = message_fit_in(msg, chat_mdl.max_length)
|
||||
kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2})
|
||||
if isinstance(kwd, tuple):
|
||||
kwd = kwd[0]
|
||||
kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
|
||||
if kwd.find("**ERROR**") >= 0:
|
||||
return ""
|
||||
return kwd
|
||||
|
||||
|
||||
def full_question(tenant_id, llm_id, messages):
|
||||
if llm_id2llm_type(llm_id) == "image2text":
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id)
|
||||
else:
|
||||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id)
|
||||
conv = []
|
||||
for m in messages:
|
||||
if m["role"] not in ["user", "assistant"]:
|
||||
continue
|
||||
conv.append("{}: {}".format(m["role"].upper(), m["content"]))
|
||||
conv = "\n".join(conv)
|
||||
today = datetime.date.today().isoformat()
|
||||
yesterday = (datetime.date.today() - datetime.timedelta(days=1)).isoformat()
|
||||
tomorrow = (datetime.date.today() + datetime.timedelta(days=1)).isoformat()
|
||||
prompt = f"""
|
||||
Role: A helpful assistant
|
||||
|
||||
Task and steps:
|
||||
1. Generate a full user question that would follow the conversation.
|
||||
2. If the user's question involves relative date, you need to convert it into absolute date based on the current date, which is {today}. For example: 'yesterday' would be converted to {yesterday}.
|
||||
|
||||
Requirements & Restrictions:
|
||||
- Text generated MUST be in the same language of the original user's question.
|
||||
- If the user's latest question is completely, don't do anything, just return the original question.
|
||||
- DON'T generate anything except a refined question.
|
||||
|
||||
######################
|
||||
-Examples-
|
||||
######################
|
||||
|
||||
# Example 1
|
||||
## Conversation
|
||||
USER: What is the name of Donald Trump's father?
|
||||
ASSISTANT: Fred Trump.
|
||||
USER: And his mother?
|
||||
###############
|
||||
Output: What's the name of Donald Trump's mother?
|
||||
|
||||
------------
|
||||
# Example 2
|
||||
## Conversation
|
||||
USER: What is the name of Donald Trump's father?
|
||||
ASSISTANT: Fred Trump.
|
||||
USER: And his mother?
|
||||
ASSISTANT: Mary Trump.
|
||||
User: What's her full name?
|
||||
###############
|
||||
Output: What's the full name of Donald Trump's mother Mary Trump?
|
||||
|
||||
------------
|
||||
# Example 3
|
||||
## Conversation
|
||||
USER: What's the weather today in London?
|
||||
ASSISTANT: Cloudy.
|
||||
USER: What's about tomorrow in Rochester?
|
||||
###############
|
||||
Output: What's the weather in Rochester on {tomorrow}?
|
||||
######################
|
||||
|
||||
# Real Data
|
||||
## Conversation
|
||||
{conv}
|
||||
###############
|
||||
"""
|
||||
ans = chat_mdl.chat(prompt, [{"role": "user", "content": "Output: "}], {"temperature": 0.2})
|
||||
ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
|
||||
return ans if ans.find("**ERROR**") < 0 else messages[-1]["content"]
|
||||
|
||||
|
||||
def content_tagging(chat_mdl, content, all_tags, examples, topn=3):
|
||||
prompt = f"""
|
||||
Role: You're a text analyzer.
|
||||
|
||||
Task: Tag (put on some labels) to a given piece of text content based on the examples and the entire tag set.
|
||||
|
||||
Steps::
|
||||
- Comprehend the tag/label set.
|
||||
- Comprehend examples which all consist of both text content and assigned tags with relevance score in format of JSON.
|
||||
- Summarize the text content, and tag it with top {topn} most relevant tags from the set of tag/label and the corresponding relevance score.
|
||||
|
||||
Requirements
|
||||
- The tags MUST be from the tag set.
|
||||
- The output MUST be in JSON format only, the key is tag and the value is its relevance score.
|
||||
- The relevance score must be range from 1 to 10.
|
||||
- Keywords ONLY in output.
|
||||
|
||||
# TAG SET
|
||||
{", ".join(all_tags)}
|
||||
|
||||
"""
|
||||
for i, ex in enumerate(examples):
|
||||
prompt += """
|
||||
# Examples {}
|
||||
### Text Content
|
||||
{}
|
||||
|
||||
Output:
|
||||
{}
|
||||
|
||||
""".format(i, ex["content"], json.dumps(ex[TAG_FLD], indent=2, ensure_ascii=False))
|
||||
|
||||
prompt += f"""
|
||||
# Real Data
|
||||
### Text Content
|
||||
{content}
|
||||
|
||||
"""
|
||||
msg = [
|
||||
{"role": "system", "content": prompt},
|
||||
{"role": "user", "content": "Output: "}
|
||||
]
|
||||
_, msg = message_fit_in(msg, chat_mdl.max_length)
|
||||
kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.5})
|
||||
if isinstance(kwd, tuple):
|
||||
kwd = kwd[0]
|
||||
kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
|
||||
if kwd.find("**ERROR**") >= 0:
|
||||
raise Exception(kwd)
|
||||
|
||||
try:
|
||||
return json_repair.loads(kwd)
|
||||
except json_repair.JSONDecodeError:
|
||||
try:
|
||||
result = kwd.replace(prompt[:-1], '').replace('user', '').replace('model', '').strip()
|
||||
result = '{' + result.split('{')[1].split('}')[0] + '}'
|
||||
return json_repair.loads(result)
|
||||
except Exception as e:
|
||||
logging.exception(f"JSON parsing error: {result} -> {e}")
|
||||
raise e
|
||||
@ -23,6 +23,7 @@ from graphrag.general.index import WithCommunity, WithResolution, Dealer
|
||||
from graphrag.light.graph_extractor import GraphExtractor as LightKGExt
|
||||
from graphrag.general.graph_extractor import GraphExtractor as GeneralKGExt
|
||||
from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
|
||||
from rag.prompts import keyword_extraction, question_proposal, content_tagging
|
||||
|
||||
CONSUMER_NO = "0" if len(sys.argv) < 2 else sys.argv[1]
|
||||
CONSUMER_NAME = "task_executor_" + CONSUMER_NO
|
||||
@ -49,7 +50,6 @@ import numpy as np
|
||||
from peewee import DoesNotExist
|
||||
|
||||
from api.db import LLMType, ParserType, TaskStatus
|
||||
from api.db.services.dialog_service import keyword_extraction, question_proposal, content_tagging
|
||||
from api.db.services.document_service import DocumentService
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api.db.services.task_service import TaskService
|
||||
|
||||
8
uv.lock
generated
8
uv.lock
generated
@ -1084,6 +1084,10 @@ name = "datrie"
|
||||
version = "0.8.2"
|
||||
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
|
||||
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/9d/fe/db74bd405d515f06657f11ad529878fd389576dca4812bea6f98d9b31574/datrie-0.8.2.tar.gz", hash = "sha256:525b08f638d5cf6115df6ccd818e5a01298cd230b2dac91c8ff2e6499d18765d" }
|
||||
wheels = [
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/44/02/53f0cf0bf0cd629ba6c2cc13f2f9db24323459e9c19463783d890a540a96/datrie-0.8.2-pp273-pypy_73-win32.whl", hash = "sha256:b07bd5fdfc3399a6dab86d6e35c72b1dbd598e80c97509c7c7518ab8774d3fda" },
|
||||
]
|
||||
|
||||
|
||||
[[package]]
|
||||
name = "decorator"
|
||||
@ -4239,6 +4243,10 @@ wheels = [
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/48/7d/0f2b09490b98cc6a902ac15dda8760c568b9c18cfe70e0ef7a16de64d53a/pycryptodomex-3.20.0-cp35-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:7a7a8f33a1f1fb762ede6cc9cbab8f2a9ba13b196bfaf7bc6f0b39d2ba315a43" },
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/b0/1c/375adb14b71ee1c8d8232904e928b3e7af5bbbca7c04e4bec94fe8e90c3d/pycryptodomex-3.20.0-cp35-abi3-win32.whl", hash = "sha256:c39778fd0548d78917b61f03c1fa8bfda6cfcf98c767decf360945fe6f97461e" },
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/b2/e8/1b92184ab7e5595bf38000587e6f8cf9556ebd1bf0a583619bee2057afbd/pycryptodomex-3.20.0-cp35-abi3-win_amd64.whl", hash = "sha256:2a47bcc478741b71273b917232f521fd5704ab4b25d301669879e7273d3586cc" },
|
||||
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/e7/c5/9140bb867141d948c8e242013ec8a8011172233c898dfdba0a2417c3169a/pycryptodomex-3.20.0-pp27-pypy_73-manylinux2010_x86_64.whl", hash = "sha256:1be97461c439a6af4fe1cf8bf6ca5936d3db252737d2f379cc6b2e394e12a458" },
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/5e/6a/04acb4978ce08ab16890c70611ebc6efd251681341617bbb9e53356dee70/pycryptodomex-3.20.0-pp27-pypy_73-win32.whl", hash = "sha256:19764605feea0df966445d46533729b645033f134baeb3ea26ad518c9fdf212c" },
|
||||
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/eb/df/3f1ea084e43b91e6d2b6b3493cc948864c17ea5d93ff1261a03812fbfd1a/pycryptodomex-3.20.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:f2e497413560e03421484189a6b65e33fe800d3bd75590e6d78d4dfdb7accf3b" },
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/c9/f3/83ffbdfa0c8f9154bcd8866895f6cae5a3ec749da8b0840603cf936c4412/pycryptodomex-3.20.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e48217c7901edd95f9f097feaa0388da215ed14ce2ece803d3f300b4e694abea" },
|
||||
{ url = "https://mirrors.aliyun.com/pypi/packages/c9/9d/c113e640aaf02af5631ae2686b742aac5cd0e1402b9d6512b1c7ec5ef05d/pycryptodomex-3.20.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d00fe8596e1cc46b44bf3907354e9377aa030ec4cd04afbbf6e899fc1e2a7781" },
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user