更新环境配置，添加gevent和gunicorn依赖；新增chat_dify_by_workorder.py文件以处理工单对话逻辑；优化PgSql类中的异常处理，确保连接失败时抛出异常；改进意图识别API，使用单例模式管理意图识别器实例，增强线程安全性；新增workflow_chat.py文件以支持新工作流对话功能。
This commit is contained in:
@@ -1,5 +1,5 @@
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OPENAI_API_KEY=sk-xxaiabmfhzwwpijuledllkmkzhzwsqeicjxmjwnvriqpwmpk
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OPENAI_API_BASE=https://api.siliconflow.cn/v1/
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LLM_MODEL_NAME=deepseek-ai/DeepSeek-V3
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# LLM_MODEL_NAME=deepseek-ai/DeepSeek-R1
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RERANKER_MODEL_NAME=bge-reranker-v2-m3
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Generated
+200
-1
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||||
url = "http://mirrors.aliyun.com/pypi/simple"
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reference = "ali-mirrors"
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[[package]]
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name = "gevent"
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version = "25.5.1"
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description = "Coroutine-based network library"
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optional = false
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python-versions = ">=3.9"
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||||
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||||
{file = "gevent-25.5.1.tar.gz", hash = "sha256:582c948fa9a23188b890d0bc130734a506d039a2e5ad87dae276a456cc683e61"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = {version = ">=1.17.1", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""}
|
||||
greenlet = {version = ">=3.2.2", markers = "platform_python_implementation == \"CPython\""}
|
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"zope.event" = "*"
|
||||
"zope.interface" = "*"
|
||||
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[package.extras]
|
||||
dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"]
|
||||
docs = ["furo", "repoze.sphinx.autointerface", "sphinx", "sphinxcontrib-programoutput", "zope.schema"]
|
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monitor = ["psutil (>=5.7.0)"]
|
||||
recommended = ["cffi (>=1.17.1)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)"]
|
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test = ["cffi (>=1.17.1)", "coverage (>=5.0)", "dnspython (>=1.16.0,<2.0)", "idna", "objgraph", "psutil (>=5.7.0)", "requests"]
|
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|
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[package.source]
|
||||
type = "legacy"
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||||
url = "http://mirrors.aliyun.com/pypi/simple"
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||||
reference = "ali-mirrors"
|
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|
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[[package]]
|
||||
name = "greenlet"
|
||||
version = "3.2.2"
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@@ -826,6 +892,32 @@ type = "legacy"
|
||||
url = "http://mirrors.aliyun.com/pypi/simple"
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reference = "ali-mirrors"
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[[package]]
|
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name = "gunicorn"
|
||||
version = "23.0.0"
|
||||
description = "WSGI HTTP Server for UNIX"
|
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optional = false
|
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python-versions = ">=3.7"
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files = [
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{file = "gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d"},
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{file = "gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec"},
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]
|
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[package.dependencies]
|
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packaging = "*"
|
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[package.extras]
|
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eventlet = ["eventlet (>=0.24.1,!=0.36.0)"]
|
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gevent = ["gevent (>=1.4.0)"]
|
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setproctitle = ["setproctitle"]
|
||||
testing = ["coverage", "eventlet", "gevent", "pytest", "pytest-cov"]
|
||||
tornado = ["tornado (>=0.2)"]
|
||||
|
||||
[package.source]
|
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type = "legacy"
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||||
url = "http://mirrors.aliyun.com/pypi/simple"
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reference = "ali-mirrors"
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[[package]]
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name = "h11"
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version = "0.16.0"
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@@ -2412,6 +2504,31 @@ type = "legacy"
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url = "http://mirrors.aliyun.com/pypi/simple"
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reference = "ali-mirrors"
|
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|
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[[package]]
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name = "setuptools"
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||||
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||||
description = "Easily download, build, install, upgrade, and uninstall Python packages"
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||||
optional = false
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||||
python-versions = ">=3.9"
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files = [
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{file = "setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922"},
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||||
[package.extras]
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||||
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||||
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||||
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||||
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|
||||
enabler = ["pytest-enabler (>=2.2)"]
|
||||
test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
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||||
type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
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||||
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[package.source]
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||||
reference = "ali-mirrors"
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||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
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||||
@@ -2912,6 +3029,88 @@ type = "legacy"
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||||
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||||
reference = "ali-mirrors"
|
||||
|
||||
[[package]]
|
||||
name = "zope-event"
|
||||
version = "5.0"
|
||||
description = "Very basic event publishing system"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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|
||||
|
||||
[package.dependencies]
|
||||
setuptools = "*"
|
||||
|
||||
[package.extras]
|
||||
docs = ["Sphinx"]
|
||||
test = ["zope.testrunner"]
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||||
|
||||
[package.source]
|
||||
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||||
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||||
reference = "ali-mirrors"
|
||||
|
||||
[[package]]
|
||||
name = "zope-interface"
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||||
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||||
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||||
optional = false
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||||
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||||
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|
||||
{file = "zope.interface-7.2-cp311-cp311-win_amd64.whl", hash = "sha256:3f6771d1647b1fc543d37640b45c06b34832a943c80d1db214a37c31161a93f1"},
|
||||
{file = "zope.interface-7.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:086ee2f51eaef1e4a52bd7d3111a0404081dadae87f84c0ad4ce2649d4f708b7"},
|
||||
{file = "zope.interface-7.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:21328fcc9d5b80768bf051faa35ab98fb979080c18e6f84ab3f27ce703bce465"},
|
||||
{file = "zope.interface-7.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f6dd02ec01f4468da0f234da9d9c8545c5412fef80bc590cc51d8dd084138a89"},
|
||||
{file = "zope.interface-7.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8e7da17f53e25d1a3bde5da4601e026adc9e8071f9f6f936d0fe3fe84ace6d54"},
|
||||
{file = "zope.interface-7.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cab15ff4832580aa440dc9790b8a6128abd0b88b7ee4dd56abacbc52f212209d"},
|
||||
{file = "zope.interface-7.2-cp312-cp312-win_amd64.whl", hash = "sha256:29caad142a2355ce7cfea48725aa8bcf0067e2b5cc63fcf5cd9f97ad12d6afb5"},
|
||||
{file = "zope.interface-7.2-cp313-cp313-macosx_10_9_x86_64.whl", hash = "sha256:3e0350b51e88658d5ad126c6a57502b19d5f559f6cb0a628e3dc90442b53dd98"},
|
||||
{file = "zope.interface-7.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:15398c000c094b8855d7d74f4fdc9e73aa02d4d0d5c775acdef98cdb1119768d"},
|
||||
{file = "zope.interface-7.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:802176a9f99bd8cc276dcd3b8512808716492f6f557c11196d42e26c01a69a4c"},
|
||||
{file = "zope.interface-7.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eb23f58a446a7f09db85eda09521a498e109f137b85fb278edb2e34841055398"},
|
||||
{file = "zope.interface-7.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a71a5b541078d0ebe373a81a3b7e71432c61d12e660f1d67896ca62d9628045b"},
|
||||
{file = "zope.interface-7.2-cp313-cp313-win_amd64.whl", hash = "sha256:4893395d5dd2ba655c38ceb13014fd65667740f09fa5bb01caa1e6284e48c0cd"},
|
||||
{file = "zope.interface-7.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d3a8ffec2a50d8ec470143ea3d15c0c52d73df882eef92de7537e8ce13475e8a"},
|
||||
{file = "zope.interface-7.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:31d06db13a30303c08d61d5fb32154be51dfcbdb8438d2374ae27b4e069aac40"},
|
||||
{file = "zope.interface-7.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e204937f67b28d2dca73ca936d3039a144a081fc47a07598d44854ea2a106239"},
|
||||
{file = "zope.interface-7.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:224b7b0314f919e751f2bca17d15aad00ddbb1eadf1cb0190fa8175edb7ede62"},
|
||||
{file = "zope.interface-7.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baf95683cde5bc7d0e12d8e7588a3eb754d7c4fa714548adcd96bdf90169f021"},
|
||||
{file = "zope.interface-7.2-cp38-cp38-win_amd64.whl", hash = "sha256:7dc5016e0133c1a1ec212fc87a4f7e7e562054549a99c73c8896fa3a9e80cbc7"},
|
||||
{file = "zope.interface-7.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7bd449c306ba006c65799ea7912adbbfed071089461a19091a228998b82b1fdb"},
|
||||
{file = "zope.interface-7.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a19a6cc9c6ce4b1e7e3d319a473cf0ee989cbbe2b39201d7c19e214d2dfb80c7"},
|
||||
{file = "zope.interface-7.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:72cd1790b48c16db85d51fbbd12d20949d7339ad84fd971427cf00d990c1f137"},
|
||||
{file = "zope.interface-7.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:52e446f9955195440e787596dccd1411f543743c359eeb26e9b2c02b077b0519"},
|
||||
{file = "zope.interface-7.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2ad9913fd858274db8dd867012ebe544ef18d218f6f7d1e3c3e6d98000f14b75"},
|
||||
{file = "zope.interface-7.2-cp39-cp39-win_amd64.whl", hash = "sha256:1090c60116b3da3bfdd0c03406e2f14a1ff53e5771aebe33fec1edc0a350175d"},
|
||||
{file = "zope.interface-7.2.tar.gz", hash = "sha256:8b49f1a3d1ee4cdaf5b32d2e738362c7f5e40ac8b46dd7d1a65e82a4872728fe"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
setuptools = "*"
|
||||
|
||||
[package.extras]
|
||||
docs = ["Sphinx", "furo", "repoze.sphinx.autointerface"]
|
||||
test = ["coverage[toml]", "zope.event", "zope.testing"]
|
||||
testing = ["coverage[toml]", "zope.event", "zope.testing"]
|
||||
|
||||
[package.source]
|
||||
type = "legacy"
|
||||
url = "http://mirrors.aliyun.com/pypi/simple"
|
||||
reference = "ali-mirrors"
|
||||
|
||||
[[package]]
|
||||
name = "zstandard"
|
||||
version = "0.23.0"
|
||||
@@ -3032,4 +3231,4 @@ reference = "ali-mirrors"
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.11,<3.13"
|
||||
content-hash = "9c8317a7a8b141b864d0bbac4c482445339c928cb9cc55bc7a5a86f63b7e39fd"
|
||||
content-hash = "2b34e52cba181483fd7ab37d7d9ed92b4703aecc604befbd150d831e84de535d"
|
||||
|
||||
@@ -22,6 +22,8 @@ tqdm = "^4.67.1"
|
||||
xlsxwriter = "^3.2.3"
|
||||
flask = "^3.1.1"
|
||||
psycopg2 = "^2.9.10"
|
||||
gunicorn = "^23.0.0"
|
||||
gevent = "^25.5.1"
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
@@ -0,0 +1,151 @@
|
||||
from rag2_0.dify.workflow_chat import NewWorkflowChat
|
||||
import pandas as pd
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from tqdm import tqdm
|
||||
import concurrent.futures
|
||||
|
||||
|
||||
class ChatDifyByWorkorder:
|
||||
|
||||
def __init__(self, api_key=None, base_url="https://api.dify.ai/v1") -> None:
|
||||
"""
|
||||
初始化ChatDifyByWorkorder类
|
||||
|
||||
Args:
|
||||
api_key: Dify API密钥，默认为None
|
||||
base_url: Dify API的基础URL，默认为"https://api.dify.ai/v1"
|
||||
"""
|
||||
baseurl = "http://172.20.0.145/v1"
|
||||
new_workflow_api_key = "app-qxsSybCs7ABiKlC1JabTYVn6"
|
||||
self.new_chat = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl)
|
||||
self.new_chat_answer = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl)
|
||||
|
||||
|
||||
def get_soft_name(self, row) -> str:
|
||||
if "博微配网计价通D3" in row["产品线"]:
|
||||
return "博微配网计价通D3"
|
||||
elif "博微电力建设计价通软件" in row["产品线"]:
|
||||
return "电力建设计价通软件"
|
||||
elif "新能源系列" in row["产品线"] and "博微新型储能电站建设计价通C1软件" in row["产品名称"]:
|
||||
return "储能C1软件"
|
||||
elif "博微西藏计价通Z1" in row["产品线"]:
|
||||
return "西藏计价通Z1"
|
||||
elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-概预算" in row["产品名称"]:
|
||||
return "技改检修工程计价通T1"
|
||||
elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-清单" in row["产品名称"]:
|
||||
return "检修清单计价通T1"
|
||||
return ""
|
||||
|
||||
def process_query(self, q:str) -> dict:
|
||||
"""
|
||||
发送问题并获取回答及相关工作流信息
|
||||
|
||||
Args:
|
||||
q: 用户问题
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和工作流信息的字典
|
||||
"""
|
||||
retry_count = 0
|
||||
max_retries = 2
|
||||
|
||||
while retry_count <= max_retries:
|
||||
try:
|
||||
# 发送问题获取回答和消息ID
|
||||
result = self.new_chat.process_question(q)
|
||||
return result
|
||||
except Exception as e:
|
||||
retry_count += 1
|
||||
if retry_count <= max_retries:
|
||||
continue
|
||||
else:
|
||||
raise e
|
||||
|
||||
def process_answer(self, q:str) -> dict:
|
||||
"""
|
||||
发送问题并获取回答及相关工作流信息
|
||||
|
||||
Args:
|
||||
q: 用户问题
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和工作流信息的字典
|
||||
"""
|
||||
retry_count = 0
|
||||
max_retries = 2
|
||||
|
||||
while retry_count <= max_retries:
|
||||
try:
|
||||
# 发送问题获取回答和消息ID
|
||||
result = self.new_chat_answer.process_question(q)
|
||||
return result
|
||||
except Exception as e:
|
||||
retry_count += 1
|
||||
if retry_count <= max_retries:
|
||||
continue
|
||||
else:
|
||||
raise
|
||||
|
||||
def process_row(self, row):
|
||||
"""处理单行数据"""
|
||||
soft_name = self.get_soft_name(row=row)
|
||||
if soft_name == "":
|
||||
return None
|
||||
|
||||
# 使用线程池并发执行查询
|
||||
with ThreadPoolExecutor() as executor:
|
||||
try:
|
||||
# 提交两个任务并获取Future对象
|
||||
query_future = executor.submit(self.process_query, q=f"{soft_name},{row['客户问题']}")
|
||||
answer_future = executor.submit(self.process_answer, q=f"{soft_name},{row['解决方案']}")
|
||||
|
||||
# 获取结果
|
||||
query_result = query_future.result()
|
||||
answer_result = answer_future.result()
|
||||
except Exception as e:
|
||||
print(f"处理工单 {row.get('工单编号', '未知')} 时发生错误: {str(e)}")
|
||||
return None
|
||||
|
||||
worker_id = str(row["工单编号"])
|
||||
if query_result is None or answer_result is None:
|
||||
print("处理对话出现错误")
|
||||
return None
|
||||
|
||||
worker_order_info = {
|
||||
"工单编号": worker_id,
|
||||
"用户问题": row['客户问题'],
|
||||
"解决方案": row['解决方案'],
|
||||
"AI回答": query_result["新流程答案"],
|
||||
"用户问题检索到的词条": query_result["新检索词条"],
|
||||
"解决方案检索到的词条": answer_result["新检索词条"],
|
||||
}
|
||||
return worker_order_info
|
||||
|
||||
def run(self, excel_path:str):
|
||||
df_data = pd.read_excel(excel_path)
|
||||
list_worker_order_info = []
|
||||
|
||||
# 创建进度条
|
||||
with tqdm(total=len(df_data), desc="处理工单") as pbar:
|
||||
# 创建线程池，最大并发数可以根据需要调整
|
||||
with ThreadPoolExecutor(max_workers=5) as executor:
|
||||
# 提交所有任务
|
||||
future_to_row = {executor.submit(self.process_row, row): idx for idx, row in df_data.iterrows()}
|
||||
|
||||
# 处理完成的任务
|
||||
for future in concurrent.futures.as_completed(future_to_row):
|
||||
result = future.result()
|
||||
if result is not None:
|
||||
list_worker_order_info.append(result)
|
||||
pbar.update(1)
|
||||
|
||||
return list_worker_order_info
|
||||
|
||||
|
||||
|
||||
if __name__=="__main__":
|
||||
worker_chat = ChatDifyByWorkorder()
|
||||
result = worker_chat.run(excel_path="data/excel/工单记录_均衡提取2000条.xlsx")
|
||||
# 可以选择保存结果到Excel
|
||||
if result:
|
||||
pd.DataFrame(result).to_excel("data/excel/工单处理结果.xlsx", index=False)
|
||||
+46
-33
@@ -23,7 +23,7 @@ class PgSql:
|
||||
连接到 PostgreSQL 数据库。
|
||||
|
||||
使用预定义的凭据连接到 'dify' 数据库。
|
||||
如果连接失败，会打印错误信息。
|
||||
如果连接失败，会抛出异常。
|
||||
"""
|
||||
try:
|
||||
# 连接数据库
|
||||
@@ -36,17 +36,16 @@ class PgSql:
|
||||
)
|
||||
|
||||
except (Exception, psycopg2.Error) as error:
|
||||
print("Error while connecting to PostgreSQL", error)
|
||||
raise Exception(f"Error while connecting to PostgreSQL: {error}")
|
||||
|
||||
def close_connection(self):
|
||||
"""
|
||||
关闭当前的 PostgreSQL 数据库连接。
|
||||
|
||||
如果存在活动的连接，则关闭它并打印确认信息。
|
||||
如果存在活动的连接，则关闭它。
|
||||
"""
|
||||
if self.connection:
|
||||
self.connection.close()
|
||||
print("PostgreSQL connection is closed")
|
||||
|
||||
|
||||
def get_appinfo(self, appid:str)->dict | None:
|
||||
@@ -74,7 +73,7 @@ class PgSql:
|
||||
return dict(zip(colnames, result))
|
||||
return None
|
||||
except (Exception, psycopg2.Error) as error:
|
||||
print("Error while getting tenant_id by appid", error)
|
||||
raise Exception(f"Error while getting tenant_id by appid: {error}")
|
||||
|
||||
|
||||
def get_messages_info(self, appid:str, query:str)->dict | None:
|
||||
@@ -103,7 +102,7 @@ class PgSql:
|
||||
return dict(zip(colnames, result))
|
||||
return None
|
||||
except (Exception, psycopg2.Error) as error:
|
||||
print("Error while getting messages_info", error)
|
||||
raise Exception(f"Error while getting messages_info: {error}")
|
||||
|
||||
def get_messages_info_by_id(self, message_id:str)->dict | None:
|
||||
"""
|
||||
@@ -123,7 +122,7 @@ class PgSql:
|
||||
return dict(zip(colnames, result))
|
||||
return None
|
||||
except (Exception, psycopg2.Error) as error:
|
||||
print("Error while getting messages_info", error)
|
||||
raise Exception(f"Error while getting messages_info by id: {error}")
|
||||
|
||||
def get_workflow_node_executions_info(self, workflow_run_id:str)->list[dict] | None:
|
||||
"""
|
||||
@@ -150,7 +149,7 @@ class PgSql:
|
||||
return [dict(zip(colnames, row)) for row in result]
|
||||
return None
|
||||
except (Exception, psycopg2.Error) as error:
|
||||
print("Error while getting workflow_node_executions_info", error)
|
||||
raise Exception(f"Error while getting workflow_node_executions_info: {error}")
|
||||
|
||||
class DifyTool:
|
||||
"""
|
||||
@@ -165,16 +164,21 @@ class DifyTool:
|
||||
根据消息 ID 从 'messages' 表中获取消息信息。
|
||||
"""
|
||||
dify_pgsql = PgSql()
|
||||
messages_info = dify_pgsql.get_messages_info_by_id(message_id)
|
||||
if not messages_info:
|
||||
return None
|
||||
workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id'])
|
||||
if not workflow_node_executions_info:
|
||||
return None
|
||||
return {
|
||||
"messages_info": messages_info,
|
||||
"workflow_node_executions_info": workflow_node_executions_info
|
||||
}
|
||||
try:
|
||||
messages_info = dify_pgsql.get_messages_info_by_id(message_id)
|
||||
if not messages_info:
|
||||
return None
|
||||
workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id'])
|
||||
if not workflow_node_executions_info:
|
||||
return None
|
||||
return {
|
||||
"messages_info": messages_info,
|
||||
"workflow_node_executions_info": workflow_node_executions_info
|
||||
}
|
||||
except Exception as e:
|
||||
raise Exception(f"Error in get_message_debug_info_by_id: {e}")
|
||||
finally:
|
||||
dify_pgsql.close_connection()
|
||||
|
||||
|
||||
@staticmethod
|
||||
@@ -195,21 +199,30 @@ class DifyTool:
|
||||
查询到的应用数据、消息数据和节点执行数据。
|
||||
"""
|
||||
dify_pgsql = PgSql()
|
||||
appinfo = dify_pgsql.get_appinfo(appid)
|
||||
if not appinfo:
|
||||
return None
|
||||
messages_info = dify_pgsql.get_messages_info(appid, query)
|
||||
if not messages_info:
|
||||
return None
|
||||
workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id'])
|
||||
if not workflow_node_executions_info:
|
||||
return None
|
||||
return {
|
||||
"appinfo": appinfo,
|
||||
"messages_info": messages_info,
|
||||
"workflow_node_executions_info": workflow_node_executions_info
|
||||
}
|
||||
try:
|
||||
appinfo = dify_pgsql.get_appinfo(appid)
|
||||
if not appinfo:
|
||||
return None
|
||||
messages_info = dify_pgsql.get_messages_info(appid, query)
|
||||
if not messages_info:
|
||||
return None
|
||||
workflow_node_executions_info = dify_pgsql.get_workflow_node_executions_info(messages_info['workflow_run_id'])
|
||||
if not workflow_node_executions_info:
|
||||
return None
|
||||
return {
|
||||
"appinfo": appinfo,
|
||||
"messages_info": messages_info,
|
||||
"workflow_node_executions_info": workflow_node_executions_info
|
||||
}
|
||||
except Exception as e:
|
||||
raise Exception(f"Error in get_message_debug_info_by_query: {e}")
|
||||
finally:
|
||||
dify_pgsql.close_connection()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(DifyTool.get_message_debug_info_by_query("ccf92b97-2789-4a3f-90e0-135a869a37c5", "电力建设计价通软件,导入结算后没有暂列金怎么办？要手动添加么？"))
|
||||
try:
|
||||
result = DifyTool.get_message_debug_info_by_query("ccf92b97-2789-4a3f-90e0-135a869a37c5", "电力建设计价通软件,导入结算后没有暂列金怎么办？要手动添加么？")
|
||||
print(result)
|
||||
except Exception as e:
|
||||
print(f"执行出错: {e}")
|
||||
|
||||
@@ -4,16 +4,32 @@ from dotenv import load_dotenv
|
||||
from rag2_0.intent_recognition import IntentRecognizer
|
||||
import json
|
||||
import time
|
||||
import threading
|
||||
import datetime
|
||||
|
||||
# 加载环境变量
|
||||
load_dotenv()
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# 初始化意图识别器
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
base_url = os.getenv("OPENAI_API_BASE")
|
||||
model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
|
||||
recognizer = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name)
|
||||
# 创建线程锁，用于保护共享资源
|
||||
recognizer_lock = threading.Lock()
|
||||
|
||||
# 使用单例模式创建意图识别器
|
||||
class RecognizerSingleton:
|
||||
_instance = None
|
||||
_lock = threading.Lock()
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls):
|
||||
if cls._instance is None:
|
||||
with cls._lock:
|
||||
if cls._instance is None:
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
base_url = os.getenv("OPENAI_API_BASE")
|
||||
model_name = os.getenv("LLM_MODEL_NAME", "gpt-3.5-turbo")
|
||||
cls._instance = IntentRecognizer(api_key=api_key, base_url=base_url, model_name=model_name)
|
||||
return cls._instance
|
||||
|
||||
@app.route('/intent_recognize', methods=['POST'])
|
||||
def intent_recognize():
|
||||
@@ -22,10 +38,16 @@ def intent_recognize():
|
||||
query = data.get('query')
|
||||
if not query:
|
||||
return Response(json.dumps({"error": "缺少query参数"}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=400)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# 获取单例实例并使用线程锁保护关键操作
|
||||
recognizer = RecognizerSingleton.get_instance()
|
||||
result = recognizer.process_query_with_slots(query)
|
||||
|
||||
end_time = time.time()
|
||||
print(f"意图识别耗时: {end_time - start_time:.2f}秒")
|
||||
current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z")
|
||||
print(f"[{current_time}] [{os.getpid()}] [INFO] 意图识别耗时: {end_time - start_time:.2f}秒")
|
||||
|
||||
# 提取分类信息
|
||||
classification = result["classification"]
|
||||
@@ -63,7 +85,10 @@ def intent_recognize():
|
||||
}
|
||||
return Response(json.dumps(response_result, ensure_ascii=False), content_type='application/json; charset=utf-8')
|
||||
except Exception as e:
|
||||
print(f"意图识别出错: {str(e)}")
|
||||
return Response(json.dumps({"error": str(e)}, ensure_ascii=False), content_type='application/json; charset=utf-8', status=500)
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host="0.0.0.0", port=8001)
|
||||
# 开发环境使用Flask内置服务器
|
||||
# 生产环境使用gunicorn支持高并发 poetry run gunicorn -w 10 -k gevent -b 0.0.0.0:8001 rag2_0.dify.intent_recognition_api:app
|
||||
app.run(host="0.0.0.0", port=8001, threaded=True)
|
||||
@@ -0,0 +1,310 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import os
|
||||
import json
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from rag2_0.dify.dify_client import ChatClient, DifyClient
|
||||
from rag2_0.dify.dify_tool import DifyTool
|
||||
from pydantic import BaseModel, Field
|
||||
from langchain.output_parsers import PydanticOutputParser
|
||||
from threading import Lock
|
||||
|
||||
class ContentSource(BaseModel):
|
||||
score: int = Field(description="相关性分数")
|
||||
reason: str = Field(description="评分理由")
|
||||
|
||||
class BaseWorkflowChat:
|
||||
"""
|
||||
工作流对话基类，封装了与Dify API交互的基本功能
|
||||
"""
|
||||
def __init__(self, api_key: str, base_url: str):
|
||||
"""
|
||||
初始化工作流对话基类
|
||||
|
||||
Args:
|
||||
api_key: Dify API的密钥
|
||||
base_url: Dify API的基础URL
|
||||
"""
|
||||
self.chat_client = ChatClient(api_key=api_key, base_url=base_url)
|
||||
self.content_source_parser = PydanticOutputParser(pydantic_object=ContentSource)
|
||||
|
||||
def create_chat_message(self, query: str):
|
||||
"""
|
||||
创建聊天消息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
tuple: (聊天响应, 消息ID)
|
||||
"""
|
||||
try:
|
||||
response = self.chat_client.create_chat_message(inputs={}, query=query, user="AutoTestDifyChat").json()
|
||||
return response, response["message_id"]
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
def calculate_score(self, query: str, content: str) -> int:
|
||||
"""
|
||||
使用LLM判断query与content之间的相关性分数
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
content (str): 检索内容
|
||||
|
||||
Returns:
|
||||
int: 相关性分数，1-10分，10代表完全相关，1代表完全不相关；-1表示评分失败
|
||||
"""
|
||||
from rag2_0.tool.ModelTool import OpenAiLLM
|
||||
|
||||
try:
|
||||
prompt = f"""你是一个专业的信息相关性评估助手。请根据以下标准对用户query和检索内容的相关性进行1-10评分（10=完全相关，1=完全不相关），并按指定格式输出JSON结果。
|
||||
|
||||
【评分标准】
|
||||
10分：完全契合，主题/意图完全一致且涵盖所有关键信息
|
||||
8-9分：高度相关，核心要素匹配但存在少量信息缺失
|
||||
6-7分：部分相关，涉及相同主题但存在重要信息缺失
|
||||
4-5分：弱相关，仅次要信息点匹配
|
||||
1-3分：完全不相关或信息冲突
|
||||
|
||||
【评估维度】
|
||||
1. 主题一致性：核心主题/意图的匹配程度
|
||||
2. 内容覆盖度：是否涵盖query的关键要素
|
||||
3. 信息准确性：是否存在矛盾/错误信息
|
||||
4. 细节丰富度：是否提供query要求的详细信息
|
||||
|
||||
【输出格式】
|
||||
{{
|
||||
"score": 评分,
|
||||
"reason": "简明扼要的评分理由（中文）"
|
||||
}}
|
||||
|
||||
【示例】
|
||||
query: "新冠疫苗的常见副作用"
|
||||
内容: "辉瑞疫苗常见反应包括注射部位疼痛（84.1%）、疲劳（62.9%）"
|
||||
输出: {{"score":8,"reason":"主题完全匹配，涵盖主要副作用但未提及发热等常见反应"}}
|
||||
|
||||
现在评估：
|
||||
query: "{query}"
|
||||
content: "{content}"
|
||||
"""
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
base_url = os.getenv("OPENAI_API_BASE")
|
||||
model = os.getenv("LLM_MODEL_NAME")
|
||||
llm = OpenAiLLM(api_key=api_key, base_url=base_url, model=model)
|
||||
response = llm.invoke(user_prompt=prompt, need_retry=True)
|
||||
|
||||
# 解析JSON响应
|
||||
try:
|
||||
parsed_output = self.content_source_parser.parse(response.content)
|
||||
return parsed_output.score
|
||||
except Exception as e:
|
||||
return -1
|
||||
except Exception as e:
|
||||
return -1
|
||||
|
||||
def get_retrieve_info(self, query: str, outputs: dict) -> tuple:
|
||||
"""
|
||||
获取检索信息并计算分数
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
outputs (dict): 检索输出结果
|
||||
|
||||
Returns:
|
||||
tuple: (检索内容列表, 最高分, 最低分, 平均分)
|
||||
"""
|
||||
max_score = 0
|
||||
min_score = 10
|
||||
total_score = 0
|
||||
valid_scores = 0
|
||||
retrieve_content = []
|
||||
|
||||
# 使用线程池并发计算分数
|
||||
with ThreadPoolExecutor() as executor:
|
||||
# 创建任务列表
|
||||
future_to_content = {}
|
||||
for result in outputs["result"]:
|
||||
content = result["content"].strip()
|
||||
future = executor.submit(self.calculate_score, query=query, content=content)
|
||||
future_to_content[future] = content
|
||||
|
||||
# 收集结果
|
||||
for future in as_completed(future_to_content):
|
||||
content = future_to_content[future]
|
||||
score = future.result()
|
||||
content_title = content.split("\n")[0]
|
||||
|
||||
if score != -1:
|
||||
max_score = max(max_score, score)
|
||||
min_score = min(min_score, score)
|
||||
total_score += score
|
||||
valid_scores += 1
|
||||
|
||||
if content_title:
|
||||
retrieve_content.append(content_title + f"--相关性得分({score}分)")
|
||||
|
||||
avg_score = total_score / valid_scores if valid_scores > 0 else 0
|
||||
return retrieve_content, max_score, min_score, avg_score
|
||||
|
||||
|
||||
class NewWorkflowChat(BaseWorkflowChat):
|
||||
"""
|
||||
新工作流对话类，用于调用新工作流发送对话并解析获取相关数据
|
||||
"""
|
||||
def process_question(self, query: str) -> dict:
|
||||
"""
|
||||
处理问题，获取新工作流的回答和相关信息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和相关信息的字典
|
||||
"""
|
||||
response, message_id = self.create_chat_message(query)
|
||||
|
||||
if isinstance(response, str) and response.startswith("error:"):
|
||||
raise RuntimeError(f"create_chat_message 出错：{response}")
|
||||
|
||||
answer = response["answer"]
|
||||
workflow_info = self.get_workflow_info(query, message_id)
|
||||
|
||||
if workflow_info is None:
|
||||
return None
|
||||
|
||||
result = {
|
||||
"问题": query,
|
||||
"新流程答案": answer,
|
||||
"新问题改写": workflow_info["问题改写"],
|
||||
"新问题分类": workflow_info["问题分类"],
|
||||
"槽点信息": workflow_info["槽点信息"],
|
||||
"新检索词条": workflow_info["检索词条"],
|
||||
"检索内容": workflow_info["检索内容"],
|
||||
"message_id":message_id
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def get_workflow_info(self, query: str, message_id: str) -> dict:
|
||||
"""
|
||||
获取新工作流的问题分类和检索信息
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
message_id (str): 新工作流的消息ID
|
||||
|
||||
Returns:
|
||||
dict: 包含问题分类结果的字典
|
||||
"""
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
max_score = 0
|
||||
min_score = 0
|
||||
avg_score = 0
|
||||
rewrite_query = ""
|
||||
vertical_classification = ""
|
||||
sub_classification = ""
|
||||
slot_info = ""
|
||||
|
||||
try:
|
||||
message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
|
||||
for workflow_node in message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
llm_result_json = json.loads(workflow_node['inputs'])["llm_result"]
|
||||
json_result = json.loads(llm_result_json)
|
||||
vertical_classification = json_result['vertical_classification']
|
||||
sub_classification = json_result['sub_classification']
|
||||
slot_info = json.dumps(json_result["slot_filling"], ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
"检索内容": retrieve_content,
|
||||
"问题分类": f"{vertical_classification} - {sub_classification}",
|
||||
"槽点信息": slot_info,
|
||||
|
||||
}
|
||||
|
||||
|
||||
class OldWorkFlowChat(BaseWorkflowChat):
|
||||
"""
|
||||
旧工作流对话类，用于调用旧工作流发送对话并解析获取相关数据
|
||||
"""
|
||||
def process_question(self, query: str) -> dict:
|
||||
"""
|
||||
处理问题，获取旧工作流的回答和相关信息
|
||||
|
||||
Args:
|
||||
query: 问题内容
|
||||
|
||||
Returns:
|
||||
dict: 包含问题、回答和相关信息的字典
|
||||
"""
|
||||
response, message_id = self.create_chat_message(query)
|
||||
|
||||
if isinstance(response, str) and response.startswith("error:"):
|
||||
return None
|
||||
|
||||
answer = response["answer"]
|
||||
workflow_info = self.get_workflow_info(query, message_id)
|
||||
|
||||
if workflow_info is None:
|
||||
return None
|
||||
|
||||
result = {
|
||||
"问题": query,
|
||||
"旧流程答案": answer,
|
||||
"旧问题改写": workflow_info["问题改写"],
|
||||
"旧检索词条": workflow_info["检索词条"],
|
||||
"检索内容": workflow_info["检索内容"],
|
||||
"message_id":message_id
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def get_workflow_info(self, query: str, message_id: str) -> dict:
|
||||
"""
|
||||
获取旧工作流的问题改写和检索信息
|
||||
|
||||
Args:
|
||||
query (str): 用户问题
|
||||
message_id (str): 旧工作流的消息ID
|
||||
|
||||
Returns:
|
||||
dict: 包含问题改写和检索信息的字典
|
||||
"""
|
||||
retrieve_title = []
|
||||
retrieve_content = []
|
||||
max_score = 0
|
||||
min_score = 0
|
||||
avg_score = 0
|
||||
rewrite_query = ""
|
||||
|
||||
try:
|
||||
message_info = DifyTool.get_message_debug_info_by_id(message_id=message_id)
|
||||
for workflow_node in message_info["workflow_node_executions_info"]:
|
||||
if workflow_node["title"] == "知识检索结果后处理":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
retrieve_title, max_score, min_score, avg_score = self.get_retrieve_info(query=query, outputs=outputs)
|
||||
retrieve_content = outputs["result"]
|
||||
elif workflow_node["title"] == "问题优化结果解析":
|
||||
outputs = json.loads(workflow_node["outputs"])
|
||||
rewrite_query = outputs["optimize_query"]
|
||||
except Exception as e:
|
||||
return None
|
||||
|
||||
return {
|
||||
"问题改写": rewrite_query,
|
||||
"检索词条": "\n".join(retrieve_title) if retrieve_title else "未检索知识库",
|
||||
"检索内容": retrieve_content,
|
||||
}
|
||||
@@ -5,6 +5,18 @@ from typing import List, Optional, Dict
|
||||
from threading import Lock
|
||||
import requests
|
||||
|
||||
# 4090dify 中用的硅基流动 apikey
|
||||
# sk-iuyfpcewztavgnivrllwnegffvrrsyeiuwvjrabngtejsqwy
|
||||
# sk-skynrwfqvipknbcvjsxkhjaqlivmocpkdppkocjndbyulado
|
||||
# sk-gjeanmnxtfcqezqagixyexarlxeztkazbrsciqescrfxrgpw
|
||||
# sk-uapywdmjaylwwyufaivraqwbpqtxjpbsbkltlrmwqftvfech
|
||||
# sk-dwmxnhaeephbxgsfncbonyajubuhuyhsfqwfsxahlepkiwas
|
||||
# sk-lnxedlpzufrurrmvylugpccnppwyqdccgeiicoijrqnslcgm
|
||||
# sk-duccaryfxcrpvwrbwvjbuwjwazyqleyebumhvrutksuqbxug
|
||||
# sk-njcrhxpvevtxkzbmhkxshxcpwpnjzmccjgfdykdncaxjicez
|
||||
# sk-bdagppigfxexcofiossccywvcqggbpywjapkdbtqycbgvqpz
|
||||
# sk-dvbaktabkdwdpjgxyoozlwnejosjyhdgqwllfeborqahndxs
|
||||
|
||||
API_KEY_LIST=[
|
||||
"sk-hrojkkkrrkmsajtnizokbcgexsfggdiqavbtvbayuwqbnmom",
|
||||
"sk-kkdklmnyompoiotzkfqahpayzlkgogfudjkyaebehtsowvid",
|
||||
@@ -56,6 +68,36 @@ API_KEY_LIST=[
|
||||
"sk-zjwbwyocnuqxfshlpgfzdwlgjjrpewzgvoqwzyhufisidnos",
|
||||
"sk-kjxpzjbteiurpzhwjbbjqpjjfoewsahpjtmyqwectdubxhgf",
|
||||
"sk-sqdcnhapyzudneipdsuqlfawusrndxqkuwoaoumtonwdnppo",
|
||||
"sk-yvyvoiegjrdlgihnxlaaznzdhnvpmfowtwmofomcodaoeaqs",
|
||||
"sk-vccwuaomxhjcszjhheipoqqmsnuetasiveombkyrptstesbi",
|
||||
"sk-mxbapcczwjsyrictwgigxgcvdgptyfrlynrewqioegqwrggv",
|
||||
"sk-dujjzxrknevesbagqgqmuffxsosjoueviubnmodoormlmlzt",
|
||||
"sk-rpptsvdeifcnyfkkrwnphgkrlchrqrbkglxrmztdzvfutdor",
|
||||
"sk-lsukfggzghmdhtfhqcbmlfqabbtapwpuxnvtwshqqqlaesie",
|
||||
"sk-aulumxzhvaladchcwgmsxidtdsvzytbpvzqgfuvcxlwbwcgl",
|
||||
"sk-tzdqzroakecvseclcmrbhdnepveatybhhzxfpzxzgirpqcdy",
|
||||
"sk-otxxemniwhxkdvroszmmkitswwuykosnqoldrkzdoflqpgvw",
|
||||
"sk-zlruqobfdbjebyyvkmehakpcvfgnlfbdlbfrepusazzckbnv",
|
||||
"sk-zryimztrlkgvcaiolarhvbcewmhwruhqfcndbylonzlqvdox",
|
||||
"sk-rczjqufgdisqplkrmvhaxmdgcboluvxympvzljlreuqeeviq",
|
||||
"sk-xfnvcksdgwufsktvmhpqrwpgovsxxtaeehtxnaqjtxmubqzl",
|
||||
"sk-gcostftlutooxzsnqefcgyfxqytidvfjpxhbuxppgatwczoq",
|
||||
"sk-wwonvjnowbcxmoyoluynnkjwerghspzdulyidskunkordaft",
|
||||
"sk-rbuykocecbdoqteveeggwzvrhbvisgaerffexjsnyvjefhdk",
|
||||
"sk-qmrkfvvbbfssuoreyvwqawoveyowuvxviqzqknotyweqmuog",
|
||||
"sk-nprpuknjmikvoaxnwgyshwwwtnifvixpuqtzkzmcacdnvoib",
|
||||
"sk-xanwnicepdxfqrfejzuxjcrhdsglfypkoxlcmmtamrtjkork",
|
||||
"sk-lvtdgodiaurqyiwdxtdrgxifguychhccqlqkhqctscvqbfgi",
|
||||
"sk-aedlbtlmqcttxwnvlfmxzaysamocamqxjceoyqjfgpcowybw",
|
||||
"sk-fahdvndjblyjlizamvwcrxnilsgmbgbvwssxgquhkezgpqne",
|
||||
"sk-tzludgttzxvpvwayazdbppbauvathdtccafjrhojpemucgyi",
|
||||
"sk-hrbroidbfusidwnsmxenuzljxgdzzxiimlezygxplavnxjik",
|
||||
"sk-ylgoiqxmtxeojdnonthxtweungyzldaqarvjxlqyztlvyrff",
|
||||
"sk-asuqbqwdhjcqnvtjlwufyrkrwkobnrbmukzarvcctsgjipdp",
|
||||
"sk-dpgpymiydutoexgvkajwgahagnfmcqzafwulccudnzvleifz",
|
||||
"sk-nbksjgcngsayoumnsdbkcpnqivnvxjenwpzuazzrkhnsgeoo",
|
||||
"sk-iaafvpjyqiocgzchbdldbkgcffqniahkcbgoviuevuogulcm",
|
||||
"sk-muvjguqeshyimzowqnqgxwpsgujlpkqgrisxsimthtyrpypx",
|
||||
]
|
||||
|
||||
class APIKeyManager:
|
||||
|
||||
Reference in New Issue
Block a user