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1、删除不再使用的.cursorrules文件

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2、更新poetry.lock以反映Poetry版本的变化，添加jieba依赖，
3、重构意图识别逻辑以支持多轮对话，优化槽位填充和意图分类功能，增强代码可读性和维护性。
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ouyangyouzhang
2025-06-13 09:14:58 +08:00
parent b412019c17
commit d155565ef6
10 changed files with 1016 additions and 309 deletions
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.cursor/rules/riper-5.mdc
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---
description:
globs:
alwaysApply: false
---
背景入门
您是 Claude 3.7，并且已集成到 Cursor IDE（一个基于 AI 的 VS Code 分支）。由于您拥有强大的功能，您往往过于急躁，经常在没有明确请求的情况下实施更改，并自以为比我更了解代码，从而破坏了现有逻辑。这会导致代码出现不可接受的灾难。在我的代码库上工作时——无论是 Web 应用程序、数据管道、嵌入式系统还是任何其他软件项目——您未经授权的修改都可能引入细微的 bug 并破坏关键功能。为了避免这种情况，您必须遵循以下严格协议：
元指令：模式声明要求
您必须在每个响应的开头用括号注明您当前的模式。没有例外。 格式：[MODE: MODE_NAME] 未声明您的模式将严重违反协议。
RIPER-5 模式
模式一：研究
[模式：研究]
目的：仅收集信息
允许：阅读文件、提出澄清问题、理解代码结构
禁止：建议、实施、计划或任何行动暗示
要求：你只能试图了解存在什么，而不是可能是什么
持续时间：直到我明确发出信号进入下一个模式
输出格式：以[模式：研究]开头，然后仅观察和问题
模式二：创新
[模式：创新]
目的：集思广益，寻找潜在方法
允许：讨论想法、优点/缺点、寻求反馈
禁止：具体规划、实施细节或任何代码编写
要求：所有想法都必须以可能性而非决定的形式呈现
持续时间：直到我明确发出信号进入下一个模式
输出格式：以[模式：创新]开头，然后仅包含可能性和考虑因素
模式 3：计划
[模式：计划]
目的：创建详尽的技术规范
允许：包含精确文件路径、函数名称和更改的详细计划
禁止：任何实现或代码编写，即使是“示例代码”
要求：计划必须足够全面，以便在实施过程中不需要做出创造性的决定
强制性最后一步：将整个计划转换成一个编号的、连续的清单，每个原子操作作为单独的项目
清单格式：
复制
IMPLEMENTATION CHECKLIST:
1. [Specific action 1]
2. [Specific action 2]
...
n. [Final action]
持续时间：直到我明确批准计划并发出进入下一模式的信号
输出格式：以 [MODE: PLAN] 开头，然后仅包含规范和实施细节
模式 4：执行
[模式：执行]
目的：准确执行模式 3 中的计划
允许：仅执行批准计划中明确详述的内容
禁止：任何不在计划内的偏差、改进或创造性添加
进入要求：仅在我明确发出“进入执行模式”命令后才能进入
偏差处理：如果发现任何需要偏差的问题，立即返回计划模式
输出格式：以 [MODE: EXECUTE] 开头，然后仅执行与计划匹配的执行
模式五：回顾
[模式：回顾]
目的：严格验证计划的实施情况
允许：逐行比较计划和实施
要求：明确标记任何偏差，无论多么微小
偏差格式：“：警告：检测到的偏差：[确切偏差描述]”
报告：必须报告实施情况是否与计划一致
结论格式：“：白色勾号：实施与计划完全一致”或“：十字标记：实施与计划有偏差”
输出格式：以[MODE: REVIEW]开始，然后进行系统比较和明确判决
关键协议指南
未经我的明确许可，您不能在模式之间转换
您必须在每次响应开始时声明您当前的模式
在执行模式下，你必须 100% 忠实地遵循计划
在审查模式下，你必须标记哪怕是最小的偏差
您无权在声明模式之外做出独立决定
不遵守此协议将给我的代码库带来灾难性的后果
模式转换信号
仅当我明确发出信号时才转换模式：
“进入研究模式”
“进入创新模式”
“进入计划模式”
“进入执行模式”
“进入审核模式”
如果没有这些确切的信号，请保持当前模式。
.cursorrules
-105
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@@ -1,105 +0,0 @@
# Role Definition
- You are a **Python master**, a highly experienced **tutor**, a **world-renowned ML engineer**, and a **talented data scientist**.
- You possess exceptional coding skills and a deep understanding of Python's best practices, design patterns, and idioms.
- You are adept at identifying and preventing potential errors, and you prioritize writing efficient and maintainable code.
- You are skilled in explaining complex concepts in a clear and concise manner, making you an effective mentor and educator.
- You are recognized for your contributions to the field of machine learning and have a strong track record of developing and deploying successful ML models.
- As a talented data scientist, you excel at data analysis, visualization, and deriving actionable insights from complex datasets.
# Technology Stack
- **Python Version:** Python 3.10+
- **Dependency Management:** Poetry / Rye
- **Code Formatting:** Ruff (replaces `black`, `isort`, `flake8`)
- **Type Hinting:** Strictly use the `typing` module. All functions, methods, and class members must have type annotations.
- **Testing Framework:** `pytest`
- **Documentation:** Google style docstring
- **Environment Management:** `conda` / `venv`
- **Containerization:** `docker`, `docker-compose`
- **Asynchronous Programming:** Prefer `async` and `await`
- **Web Framework:** `fastapi`
- **Demo Framework:** `gradio`, `streamlit`
- **LLM Framework:** `langchain`, `transformers`
- **Vector Database:** `faiss`, `chroma` (optional)
- **Experiment Tracking:** `mlflow`, `tensorboard` (optional)
- **Hyperparameter Optimization:** `optuna`, `hyperopt` (optional)
- **Data Processing:** `pandas`, `numpy`, `dask` (optional), `pyspark` (optional)
- **Version Control:** `git`
- **Server:** `gunicorn`, `uvicorn` (with `nginx` or `caddy`)
- **Process Management:** `systemd`, `supervisor`
# Coding Guidelines
## 1. Pythonic Practices
- **Elegance and Readability:** Strive for elegant and Pythonic code that is easy to understand and maintain.
- **PEP 8 Compliance:** Adhere to PEP 8 guidelines for code style, with Ruff as the primary linter and formatter.
- **Explicit over Implicit:** Favor explicit code that clearly communicates its intent over implicit, overly concise code.
- **Zen of Python:** Keep the Zen of Python in mind when making design decisions.
## 2. Modular Design
- **Single Responsibility Principle:** Each module/file should have a well-defined, single responsibility.
- **Reusable Components:** Develop reusable functions and classes, favoring composition over inheritance.
- **Package Structure:** Organize code into logical packages and modules.
## 3. Code Quality
- **Comprehensive Type Annotations:** All functions, methods, and class members must have type annotations, using the most specific types possible.
- **Detailed Docstrings:** All functions, methods, and classes must have Google-style docstrings, thoroughly explaining their purpose, parameters, return values, and any exceptions raised. Include usage examples where helpful.
- **Thorough Unit Testing:** Aim for high test coverage (90% or higher) using `pytest`. Test both common cases and edge cases.
- **Robust Exception Handling:** Use specific exception types, provide informative error messages, and handle exceptions gracefully. Implement custom exception classes when needed. Avoid bare `except` clauses.
- **Logging:** Employ the `logging` module judiciously to log important events, warnings, and errors.
## 4. ML/AI Specific Guidelines
- **Experiment Configuration:** Use `hydra` or `yaml` for clear and reproducible experiment configurations.
- **Data Pipeline Management:** Employ scripts or tools like `dvc` to manage data preprocessing and ensure reproducibility.
- **Model Versioning:** Utilize `git-lfs` or cloud storage to track and manage model checkpoints effectively.
- **Experiment Logging:** Maintain comprehensive logs of experiments, including parameters, results, and environmental details.
- **LLM Prompt Engineering:** Dedicate a module or files for managing Prompt templates with version control.
- **Context Handling:** Implement efficient context management for conversations, using suitable data structures like deques.
## 5. Performance Optimization
- **Asynchronous Programming:** Leverage `async` and `await` for I/O-bound operations to maximize concurrency.
- **Caching:** Apply `functools.lru_cache`, `@cache` (Python 3.9+), or `fastapi.Depends` caching where appropriate.
- **Resource Monitoring:** Use `psutil` or similar to monitor resource usage and identify bottlenecks.
- **Memory Efficiency:** Ensure proper release of unused resources to prevent memory leaks.
- **Concurrency:** Employ `concurrent.futures` or `asyncio` to manage concurrent tasks effectively.
- **Database Best Practices:** Design database schemas efficiently, optimize queries, and use indexes wisely.
## 6. API Development with FastAPI
- **Data Validation:** Use Pydantic models for rigorous request and response data validation.
- **Dependency Injection:** Effectively use FastAPI's dependency injection for managing dependencies.
- **Routing:** Define clear and RESTful API routes using FastAPI's `APIRouter`.
- **Background Tasks:** Utilize FastAPI's `BackgroundTasks` or integrate with Celery for background processing.
- **Security:** Implement robust authentication and authorization (e.g., OAuth 2.0, JWT).
- **Documentation:** Auto-generate API documentation using FastAPI's OpenAPI support.
- **Versioning:** Plan for API versioning from the start (e.g., using URL prefixes or headers).
- **CORS:** Configure Cross-Origin Resource Sharing (CORS) settings correctly.
# Code Example Requirements
- All functions must include type annotations.
- Must provide clear, Google-style docstrings.
- Key logic should be annotated with comments.
- Provide usage examples (e.g., in the `tests/` directory or as a `__main__` section).
- Include error handling.
- Use `ruff` for code formatting.
# Others
- **Prioritize new features in Python 3.10+.**
- **When explaining code, provide clear logical explanations and code comments.**
- **When making suggestions, explain the rationale and potential trade-offs.**
- **If code examples span multiple files, clearly indicate the file name.**
- **Do not over-engineer solutions. Strive for simplicity and maintainability while still being efficient.**
- **Favor modularity, but avoid over-modularization.**
- **Use the most modern and efficient libraries when appropriate, but justify their use and ensure they don't add unnecessary complexity.**
- **When providing solutions or examples, ensure they are self-contained and executable without requiring extensive modifications.**
- **If a request is unclear or lacks sufficient information, ask clarifying questions before proceeding.**
- **Always consider the security implications of your code, especially when dealing with user inputs and external data.**
- **Actively use and promote best practices for the specific tasks at hand (LLM app development, data cleaning, demo creation, etc.).**
poetry.lock
Generated
+125 -21
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@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
[[package]]
name = "aiohappyeyeballs"
@@ -6,6 +6,7 @@ version = "2.6.1"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
{file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
@@ -22,6 +23,7 @@ version = "3.11.18"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "aiohttp-3.11.18-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:96264854fedbea933a9ca4b7e0c745728f01380691687b7365d18d9e977179c4"},
{file = "aiohttp-3.11.18-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9602044ff047043430452bc3a2089743fa85da829e6fc9ee0025351d66c332b6"},
@@ -116,7 +118,7 @@ propcache = ">=0.2.0"
yarl = ">=1.17.0,<2.0"
[package.extras]
speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
[package.source]
type = "legacy"
@@ -129,6 +131,7 @@ version = "1.3.2"
description = "aiosignal: a list of registered asynchronous callbacks"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"},
{file = "aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54"},
@@ -148,6 +151,7 @@ version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
@@ -164,6 +168,7 @@ version = "4.9.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
{file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
@@ -176,7 +181,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""]
trio = ["trio (>=0.26.1)"]
[package.source]
@@ -190,18 +195,19 @@ version = "25.3.0"
description = "Classes Without Boilerplate"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
{file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"},
{file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"},
]
[package.extras]
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"]
tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"]
tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""]
[package.source]
type = "legacy"
@@ -214,6 +220,7 @@ version = "4.13.4"
description = "Screen-scraping library"
optional = false
python-versions = ">=3.7.0"
groups = ["main"]
files = [
{file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"},
{file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"},
@@ -241,6 +248,7 @@ version = "1.9.0"
description = "Fast, simple object-to-object and broadcast signaling"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc"},
{file = "blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf"},
@@ -257,6 +265,7 @@ version = "0.0.2"
description = "Dummy package for Beautiful Soup (beautifulsoup4)"
optional = false
python-versions = "*"
groups = ["main"]
files = [
{file = "bs4-0.0.2-py2.py3-none-any.whl", hash = "sha256:abf8742c0805ef7f662dce4b51cca104cffe52b835238afc169142ab9b3fbccc"},
{file = "bs4-0.0.2.tar.gz", hash = "sha256:a48685c58f50fe127722417bae83fe6badf500d54b55f7e39ffe43b798653925"},
@@ -276,6 +285,7 @@ version = "2025.4.26"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
groups = ["main"]
files = [
{file = "certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3"},
{file = "certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6"},
@@ -292,6 +302,8 @@ version = "1.17.1"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "(sys_platform == \"win32\" or platform_python_implementation == \"PyPy\") and (platform_python_implementation == \"CPython\" or platform_python_implementation == \"PyPy\")"
files = [
{file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
{file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
@@ -376,6 +388,7 @@ version = "3.4.2"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
python-versions = ">=3.7"
groups = ["main"]
files = [
{file = "charset_normalizer-3.4.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c48ed483eb946e6c04ccbe02c6b4d1d48e51944b6db70f697e089c193404941"},
{file = "charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2d318c11350e10662026ad0eb71bb51c7812fc8590825304ae0bdd4ac283acd"},
@@ -482,6 +495,7 @@ version = "8.2.1"
description = "Composable command line interface toolkit"
optional = false
python-versions = ">=3.10"
groups = ["main"]
files = [
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@@ -501,6 +515,8 @@ version = "0.4.6"
description = "Cross-platform colored terminal text."
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
groups = ["main"]
markers = "platform_system == \"Windows\""
files = [
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@@ -517,6 +533,7 @@ version = "0.6.7"
description = "Easily serialize dataclasses to and from JSON."
optional = false
python-versions = ">=3.7,<4.0"
groups = ["main"]
files = [
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@@ -537,6 +554,7 @@ version = "1.9.0"
description = "Distro - an OS platform information API"
optional = false
python-versions = ">=3.6"
groups = ["main"]
files = [
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@@ -553,6 +571,7 @@ version = "2.0.0"
description = "An implementation of lxml.xmlfile for the standard library"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -569,6 +588,7 @@ version = "1.11.0"
description = "A library for efficient similarity search and clustering of dense vectors."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -613,6 +633,7 @@ version = "3.1.1"
description = "A simple framework for building complex web applications."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -641,6 +662,7 @@ version = "1.6.0"
description = "A list-like structure which implements collections.abc.MutableSequence"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -759,6 +781,7 @@ version = "25.5.1"
description = "Coroutine-based network library"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -808,11 +831,11 @@ greenlet = {version = ">=3.2.2", markers = "platform_python_implementation == \"
"zope.interface" = "*"
[package.extras]
dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"]
dnspython = ["dnspython (>=1.16.0,<2.0) ; python_version < \"3.10\"", "idna ; python_version < \"3.10\""]
docs = ["furo", "repoze.sphinx.autointerface", "sphinx", "sphinxcontrib-programoutput", "zope.schema"]
monitor = ["psutil (>=5.7.0)"]
recommended = ["cffi (>=1.17.1)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)"]
test = ["cffi (>=1.17.1)", "coverage (>=5.0)", "dnspython (>=1.16.0,<2.0)", "idna", "objgraph", "psutil (>=5.7.0)", "requests"]
monitor = ["psutil (>=5.7.0) ; sys_platform != \"win32\" or platform_python_implementation == \"CPython\""]
recommended = ["cffi (>=1.17.1) ; platform_python_implementation == \"CPython\"", "dnspython (>=1.16.0,<2.0) ; python_version < \"3.10\"", "idna ; python_version < \"3.10\"", "psutil (>=5.7.0) ; sys_platform != \"win32\" or platform_python_implementation == \"CPython\""]
test = ["cffi (>=1.17.1) ; platform_python_implementation == \"CPython\"", "coverage (>=5.0) ; sys_platform != \"win32\"", "dnspython (>=1.16.0,<2.0) ; python_version < \"3.10\"", "idna ; python_version < \"3.10\"", "objgraph", "psutil (>=5.7.0) ; sys_platform != \"win32\" or platform_python_implementation == \"CPython\"", "requests"]
[package.source]
type = "legacy"
@@ -825,6 +848,8 @@ version = "3.2.2"
description = "Lightweight in-process concurrent programming"
optional = false
python-versions = ">=3.9"
groups = ["main"]
markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_python_implementation == \"CPython\""
files = [
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@@ -898,6 +923,7 @@ version = "23.0.0"
description = "WSGI HTTP Server for UNIX"
optional = false
python-versions = ">=3.7"
groups = ["main"]
files = [
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@@ -924,6 +950,7 @@ version = "0.16.0"
description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -940,6 +967,7 @@ version = "1.0.9"
description = "A minimal low-level HTTP client."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -966,6 +994,7 @@ version = "0.28.1"
description = "The next generation HTTP client."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -978,7 +1007,7 @@ httpcore = "==1.*"
idna = "*"
[package.extras]
brotli = ["brotli", "brotlicffi"]
brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (==1.*)"]
@@ -995,6 +1024,7 @@ version = "0.4.0"
description = "Consume Server-Sent Event (SSE) messages with HTTPX."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -1011,6 +1041,7 @@ version = "3.10"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.6"
groups = ["main"]
files = [
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@@ -1030,6 +1061,7 @@ version = "2.2.0"
description = "Safely pass data to untrusted environments and back."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -1040,12 +1072,29 @@ type = "legacy"
url = "http://mirrors.aliyun.com/pypi/simple"
reference = "ali-mirrors"
[[package]]
name = "jieba"
version = "0.42.1"
description = "Chinese Words Segmentation Utilities"
optional = false
python-versions = "*"
groups = ["main"]
files = [
{file = "jieba-0.42.1.tar.gz", hash = "sha256:055ca12f62674fafed09427f176506079bc135638a14e23e25be909131928db2"},
]
[package.source]
type = "legacy"
url = "http://mirrors.aliyun.com/pypi/simple"
reference = "ali-mirrors"
[[package]]
name = "jinja2"
version = "3.1.6"
description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
groups = ["main"]
files = [
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@@ -1068,6 +1117,7 @@ version = "0.9.0"
description = "Fast iterable JSON parser."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -1158,6 +1208,7 @@ version = "1.33"
description = "Apply JSON-Patches (RFC 6902) "
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
groups = ["main"]
files = [
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@@ -1177,6 +1228,7 @@ version = "3.0.0"
description = "Identify specific nodes in a JSON document (RFC 6901) "
optional = false
python-versions = ">=3.7"
groups = ["main"]
files = [
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@@ -1193,6 +1245,7 @@ version = "0.3.25"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1237,6 +1290,7 @@ version = "0.3.24"
description = "Community contributed LangChain integrations."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1267,6 +1321,7 @@ version = "0.3.59"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1295,6 +1350,7 @@ version = "0.3.16"
description = "An integration package connecting OpenAI and LangChain"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1316,6 +1372,7 @@ version = "0.3.8"
description = "LangChain text splitting utilities"
optional = false
python-versions = "<4.0,>=3.9"
groups = ["main"]
files = [
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@@ -1335,6 +1392,7 @@ version = "0.3.42"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1369,6 +1427,7 @@ version = "0.13.1"
description = "Convert HTML to markdown."
optional = false
python-versions = "*"
groups = ["main"]
files = [
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@@ -1389,6 +1448,7 @@ version = "3.0.2"
description = "Safely add untrusted strings to HTML/XML markup."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1464,6 +1524,7 @@ version = "3.26.1"
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1488,6 +1549,7 @@ version = "6.4.3"
description = "multidict implementation"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1606,6 +1668,7 @@ version = "1.1.0"
description = "Type system extensions for programs checked with the mypy type checker."
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
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@@ -1622,6 +1685,7 @@ version = "1.26.4"
description = "Fundamental package for array computing in Python"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
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@@ -1672,6 +1736,7 @@ version = "1.78.1"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.8"
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python-versions = ">=3.8"
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python-versions = ">=3.8"
groups = ["main"]
markers = "(sys_platform == \"win32\" or platform_python_implementation == \"PyPy\") and (platform_python_implementation == \"CPython\" or platform_python_implementation == \"PyPy\")"
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python-versions = ">=3.9"
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email = ["email-validator (>=2.0.0)"]
timezone = ["tzdata"]
timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""]
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type = "legacy"
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python-versions = ">=3.9"
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python-versions = ">=3.9"
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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python-versions = ">=3.9"
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python-versions = "*"
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python-versions = ">=3.8"
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@@ -2377,6 +2459,7 @@ version = "2024.11.6"
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python-versions = ">=3.8"
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python-versions = ">=3.8"
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@@ -2511,6 +2595,7 @@ version = "1.0.0"
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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@@ -2530,19 +2615,20 @@ version = "80.9.0"
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python-versions = ">=3.9"
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description = "Python 2 and 3 compatibility utilities"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
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@@ -2571,6 +2658,7 @@ version = "1.3.1"
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python-versions = ">=3.7"
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optional = false
python-versions = ">=3.7"
groups = ["main"]
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@@ -2703,6 +2793,7 @@ version = "9.1.2"
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python-versions = ">=3.9"
groups = ["main"]
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brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
@@ -2894,6 +2992,7 @@ version = "3.1.3"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "werkzeug-3.1.3-py3-none-any.whl", hash = "sha256:54b78bf3716d19a65be4fceccc0d1d7b89e608834989dfae50ea87564639213e"},
{file = "werkzeug-3.1.3.tar.gz", hash = "sha256:60723ce945c19328679790e3282cc758aa4a6040e4bb330f53d30fa546d44746"},
@@ -2916,6 +3015,7 @@ version = "3.2.3"
description = "A Python module for creating Excel XLSX files."
optional = false
python-versions = ">=3.6"
groups = ["main"]
files = [
{file = "XlsxWriter-3.2.3-py3-none-any.whl", hash = "sha256:593f8296e8a91790c6d0378ab08b064f34a642b3feb787cf6738236bd0a4860d"},
{file = "xlsxwriter-3.2.3.tar.gz", hash = "sha256:ad6fd41bdcf1b885876b1f6b7087560aecc9ae5a9cc2ba97dcac7ab2e210d3d5"},
@@ -2932,6 +3032,7 @@ version = "1.20.0"
description = "Yet another URL library"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "yarl-1.20.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f1f6670b9ae3daedb325fa55fbe31c22c8228f6e0b513772c2e1c623caa6ab22"},
{file = "yarl-1.20.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:85a231fa250dfa3308f3c7896cc007a47bc76e9e8e8595c20b7426cac4884c62"},
@@ -3055,6 +3156,7 @@ version = "5.0"
description = "Very basic event publishing system"
optional = false
python-versions = ">=3.7"
groups = ["main"]
files = [
{file = "zope.event-5.0-py3-none-any.whl", hash = "sha256:2832e95014f4db26c47a13fdaef84cef2f4df37e66b59d8f1f4a8f319a632c26"},
{file = "zope.event-5.0.tar.gz", hash = "sha256:bac440d8d9891b4068e2b5a2c5e2c9765a9df762944bda6955f96bb9b91e67cd"},
@@ -3078,6 +3180,7 @@ version = "7.2"
description = "Interfaces for Python"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
{file = "zope.interface-7.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ce290e62229964715f1011c3dbeab7a4a1e4971fd6f31324c4519464473ef9f2"},
{file = "zope.interface-7.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:05b910a5afe03256b58ab2ba6288960a2892dfeef01336dc4be6f1b9ed02ab0a"},
@@ -3137,6 +3240,7 @@ version = "0.23.0"
description = "Zstandard bindings for Python"
optional = false
python-versions = ">=3.8"
groups = ["main"]
files = [
{file = "zstandard-0.23.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bf0a05b6059c0528477fba9054d09179beb63744355cab9f38059548fedd46a9"},
{file = "zstandard-0.23.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fc9ca1c9718cb3b06634c7c8dec57d24e9438b2aa9a0f02b8bb36bf478538880"},
@@ -3249,6 +3353,6 @@ url = "http://mirrors.aliyun.com/pypi/simple"
reference = "ali-mirrors"
[metadata]
lock-version = "2.0"
lock-version = "2.1"
python-versions = ">=3.11,<3.13"
content-hash = "b227dc263d83a0d98c3903fc0bca5763858aaeda6f6c14f83294afddd05679b4"
content-hash = "0b608c0456c4f231d1ce887cace758d19553b3c2c1ec423134ab87cec8776f40"
pyproject.toml
+1
View File
@@ -26,6 +26,7 @@ gunicorn = "^23.0.0"
gevent = "^25.5.1"
pymysql = "^1.1.1"
sqlalchemy = "^2.0.41"
jieba = "^0.42.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
rag2_0/demo/intent_recognition_example.py
+36 -14
View File
@@ -8,6 +8,7 @@ Description: 意图识别和问题改写示例
import os
from dotenv import load_dotenv
from regex import F
from rag2_0.intent_recognition import IntentRecognizer
import pandas as pd
import logging
@@ -16,6 +17,7 @@ import concurrent.futures
from tqdm import tqdm
import time
import sys
from typing import List, Dict
# 加载环境变量
load_dotenv()
@@ -42,7 +44,7 @@ def load_questions_from_excel(file_path=None):
logging.error(f"读取Excel文件时出错: {e}")
return []
def process_query(recognizer, query):
def process_query(recognizer: IntentRecognizer, query: str, conversation_context: str = "", chat_history: List[Dict[str, str]] = None, previous_slots: Dict[str, str] = None):
"""
处理单个查询，支持重试机制，并包含槽位填充
@@ -59,8 +61,8 @@ def process_query(recognizer, query):
while retry_count <= max_retries:
try:
# 使用新的process_query_with_slots方法处理查询
result = recognizer.process_query_with_slots(query)
# result = recognizer.process_query_with_slots(query)
result = recognizer.process_query(query, conversation_context=conversation_context, chat_history=chat_history, previous_slots=previous_slots)
# 提取分类信息
classification = result["classification"]
@@ -101,7 +103,6 @@ def process_query(recognizer, query):
"检索的关键词": keywords_str,
"槽位填充": slot_filling_str
}
except Exception as e:
retry_count += 1
@@ -174,10 +175,30 @@ def save_results_to_excel(results, output_file, is_final=False):
logging.info(f"已保存{len(valid_results)}条结果至: {temp_output_file}")
# 示例查询
examples_query = """"锁标签号:811621005858,　注册单位:惠州电力勘察设计院有限公司,软件名称:广东迁改导则2022,　注册号:BW278-83834-58155-58339.迁改导则是要另外下载安装软件吗？"
"""
examples_query = """那西藏软件呢"""
conversation_context=""
chat_history=[
{
"user": "怎么新建工程啊"
},
{
"assistant": "您好！为了更准确地帮助您解决问题，请提供以下信息：\n\n- **软件名称**：请确认您使用的软件名称，可选值如下：\n - 配网工程计价通D3软件\n - 新型储能电站建设计价通C1软件\n - 西藏电力工程计价通Z1软件\n - 技改检修工程计价通T1软件\n - 技改检修清单计价通T1软件\n - 主网电力建设计价通软件\n\n请补充以上信息，以便我们能够更好地指导您如何新建工程。"
},
{
"user": "配网工程计价通D3软件"
},
{
"assistant": """好的，针对您使用**配网工程计价通D3软件**新建工程的问题，以下是具体的操作步骤：\n\n## 新建工程\n\n### 功能入口\n【主页】界面——"新建"按钮\n\n### 操作步骤\n1. 在"新建窗口"选择对应工程模板。\n2. 设置工程名称。\n3. 确定后根据新建向导完善"电压等级"、"地区类型"等参数。\n4. 点击"确定"即可完成新建工程。\n\n![进入新建工程窗口](https://172.20.0.145/files/362491a3-69c3-45b0-a037-b5f96b91bf73/image-preview)\n\n希望这些步骤能帮助您顺利完成新建工程。如果还有其他问题，欢迎随时提问！\n"""
}
]
previous_slots={
"software_name": "配网工程计价通D3软件",
"function_name": "新建工程",
"operation": "如何新建工程",
"project_type": None,
"software_version": None,
"operation_steps": None
}
def main():
"""
意图识别和问题改写示例
@@ -193,18 +214,19 @@ def main():
# 读取提问数据
current_dir = os.path.dirname(os.path.abspath(__file__))
data_file = os.path.join(current_dir, "..", "..", "data", "excel", "历史提问数据(dislike)_提问明确.xlsx")
data_file = os.path.join(current_dir, "..", "..", "data", "excel", "历史提问数据(like)_提问明确.xlsx")
output_file = os.path.join(current_dir, "..", "..", "data", "excel", "测试提问数据_槽位填充结果.xlsx")
# 检测是否为调试模式，调试模式下使用examples_query，否则从Excel读取
is_debug = hasattr(sys, 'gettrace') and sys.gettrace() is not None
# is_debug = False
if is_debug:
examples = examples_query.strip().split("\n")
else:
examples = load_questions_from_excel(data_file)
if not is_debug:
max_workers = 20 # 减少并发数以避免API限制
max_workers = 40 # 减少并发数以避免API限制
logging.info(f"共有 {len(examples)} 个问题需要处理，使用 {max_workers} 个并发线程")
# 创建一个与输入顺序相同的结果列表
@@ -229,9 +251,9 @@ def main():
completed += 1
# 每处理batch_size条数据保存一次
if completed % batch_size == 0:
logging.info(f"已完成 {completed}/{len(examples)} 条，保存中间结果...")
save_results_to_excel(results, output_file, is_final=False)
# if completed % batch_size == 0:
# logging.info(f"已完成 {completed}/{len(examples)} 条，保存中间结果...")
# save_results_to_excel(results, output_file, is_final=False)
# 处理完所有数据后，保存最终结果
save_results_to_excel(results, output_file, is_final=True)
@@ -240,7 +262,7 @@ def main():
for idx, query in enumerate(examples):
if query.strip() == "":
continue
process_query(recognizer, query)
process_query(recognizer, query, conversation_context, chat_history, previous_slots)
def setup_logging():
# 配置日志输出到控制台
rag2_0/dify/intent_recognition_api.py
+6 -3
View File
@@ -36,6 +36,9 @@ def intent_recognize():
try:
data = request.get_json(force=True)
query = data.get('query')
conversation_context = data.get('conversation_context', "")
chat_history = data.get('chat_history', None)
previous_slots = data.get('previous_slots', None)
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)
@@ -43,7 +46,7 @@ def intent_recognize():
# 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)
result = recognizer.process_query(query, conversation_context, chat_history, previous_slots)
end_time = time.time()
current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S %z")
@@ -60,8 +63,8 @@ def intent_recognize():
for term in keywords["terms"]:
term_info = {
"名称": term["name"],
"同义词": ";".join(term["synonymous"]) if term["synonymous"] else "",
"描述": term["description"]
# "同义词": ";".join(term["synonymous"]) if term["synonymous"] else "",
# "描述": term["description"]
}
term_details.append(term_info)
keywords_str = term_details
rag2_0/intent_recognition/DataModels.py
+177 -61
View File
@@ -7,8 +7,8 @@ Date: 2025-05-13
Description: 提取和分类的数据模型
"""
from pydantic import BaseModel, Field
from typing import List, Optional, Dict, Tuple
from pydantic import BaseModel, Field, field_validator
from typing import List, Optional, Dict, Tuple, Union, Any
from enum import Enum
class SoftwareName(str, Enum):
@@ -31,6 +31,61 @@ class SoftwareName(str, Enum):
MAIN: "别名包括：主网软件、电力建设软件、主网建设软件、博微电力建设计价通等其他类似称呼"
}
# 构建别名到标准名称的映射
def build_alias_mapping() -> Dict[str, SoftwareName]:
"""构建从别名到标准软件名称的映射字典"""
alias_map = {}
# 配网工程计价通D3软件的别名映射
alias_map["配网D3"] = SoftwareName.D3
alias_map["D3软件"] = SoftwareName.D3
alias_map["配网工程软件"] = SoftwareName.D3
alias_map["配网软件"] = SoftwareName.D3
# 新型储能电站建设计价通C1软件的别名映射
alias_map["储能C1"] = SoftwareName.C1
alias_map["C1软件"] = SoftwareName.C1
alias_map["储能电站软件"] = SoftwareName.C1
alias_map["储能软件"] = SoftwareName.C1
# 西藏电力工程计价通Z1软件的别名映射
alias_map["西藏Z1"] = SoftwareName.Z1
alias_map["Z1软件"] = SoftwareName.Z1
alias_map["西藏电力软件"] = SoftwareName.Z1
# 技改检修工程计价通T1软件的别名映射
alias_map["技改T1"] = SoftwareName.T1
alias_map["T1软件"] = SoftwareName.T1
alias_map["技改检修软件"] = SoftwareName.T1
# 技改检修清单计价通T1软件的别名映射
alias_map["技改清单T1"] = SoftwareName.T1_LIST
alias_map["T1清单软件"] = SoftwareName.T1_LIST
alias_map["技改检修清单软件"] = SoftwareName.T1_LIST
# 主网电力建设计价通软件的别名映射
alias_map["主网软件"] = SoftwareName.MAIN
alias_map["电力建设软件"] = SoftwareName.MAIN
alias_map["主网建设软件"] = SoftwareName.MAIN
alias_map["博微电力建设计价通"] = SoftwareName.MAIN
alias_map["主网计价通"] = SoftwareName.MAIN
alias_map["主网计价通软件"] = SoftwareName.MAIN
alias_map["计价通软件"] = SoftwareName.MAIN
alias_map["电力计价通软件"] = SoftwareName.MAIN
alias_map["计价通"] = SoftwareName.MAIN
# 添加标准名称映射
alias_map[SoftwareName.D3.value] = SoftwareName.D3
alias_map[SoftwareName.C1.value] = SoftwareName.C1
alias_map[SoftwareName.Z1.value] = SoftwareName.Z1
alias_map[SoftwareName.T1.value] = SoftwareName.T1
alias_map[SoftwareName.T1_LIST.value] = SoftwareName.T1_LIST
alias_map[SoftwareName.MAIN.value] = SoftwareName.MAIN
return alias_map
# 全局别名映射字典
SOFTWARE_NAME_ALIAS_MAP = build_alias_mapping()
# 定义输出模型
class Term(BaseModel):
name: str = Field(description="专业名词")
@@ -55,140 +110,201 @@ class Classification(BaseModel):
class QueryRewrite(BaseModel):
rewrite:str = Field(description="问题改写")
##########################槽位模型###########################
class SlotBase(BaseModel):
"""槽位基础模型"""
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
raise NotImplementedError("子类必须实现check_required_slots方法")
@field_validator('software_name', mode='before', check_fields=False)
@classmethod
def validate_software_name(cls, v):
"""验证并转换软件名称，支持别名"""
if v is None or v == "":
return ""
# 如果已经是枚举类型，直接返回其值
if isinstance(v, SoftwareName):
return v.value
# 如果是字符串，尝试转换
if isinstance(v, str):
# 直接匹配枚举值
for software in SoftwareName:
if v == software.value:
return software.value
# 尝试通过别名匹配
if v in SOFTWARE_NAME_ALIAS_MAP:
return SOFTWARE_NAME_ALIAS_MAP[v].value
# 如果无法匹配，返回原值用于错误提示
return v
return v
# 1. 软件问题
# 1.1 软件功能
class SoftwareFunction(BaseModel):
software_name: SoftwareName = Field(description="软件名称,只能从给定的范围中取值")
function_name: str = Field(description="具体功能名称")
operation: str = Field(description="用户操作意图(如何使用功能、功能入口、功能使用场景)")
software_version: Optional[str] = Field(None, description="软件版本")
operation_steps: Optional[str] = Field(None, description="操作步骤描述")
class SoftwareFunctionSlots(SlotBase):
software_name: str = Field(default="", description="软件名称")
function_name: str = Field(default="", description="具体功能名称")
operation: str = Field(default="", description="用户操作意图(如何使用功能、功能入口、功能使用场景)")
project_type: Optional[str] = Field(default="单工程", description="工程类型(单工程、多工程、批次工程)")
software_version: Optional[str] = Field(default="", description="软件版本")
operation_steps: Optional[str] = Field(default="", description="操作步骤描述")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.software_name:
missing_slots["software_name"] = f"{SoftwareFunction.model_fields['software_name'].description}，可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}"
missing_slots["software_name"] = f"{SoftwareFunctionSlots.model_fields['software_name'].description}，可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}"
if not self.function_name:
missing_slots["function_name"] = SoftwareFunction.model_fields["function_name"].description
missing_slots["function_name"] = SoftwareFunctionSlots.model_fields["function_name"].description
if not self.operation:
missing_slots["operation"] = SoftwareFunction.model_fields["operation"].description
missing_slots["operation"] = SoftwareFunctionSlots.model_fields["operation"].description
return len(missing_slots) == 0, missing_slots
# 1.2 故障排查
class TroubleShooting(BaseModel):
software_name: SoftwareName = Field(description="软件名称,只能从给定的范围中取值")
function_name: str = Field(description="具体功能名称/操作描述")
error_message: str = Field(description="报错信息/异常现象")
software_version: Optional[str] = Field(None, description="软件版本")
os_version: Optional[str] = Field(None, description="操作系统及版本")
reproduction_steps: Optional[str] = Field(None, description="故障重现步骤")
class SoftwareTroubleShootingSlots(SlotBase):
software_name: str = Field(default="", description="软件名称")
function_name: str = Field(default="", description="具体功能名称/操作描述")
error_message: str = Field(default="", description="报错信息/异常现象")
software_version: Optional[str] = Field(default="", description="软件版本")
os_version: Optional[str] = Field(default="", description="操作系统及版本")
reproduction_steps: Optional[str] = Field(default="", description="故障重现步骤")
project_type: Optional[str] = Field(default="单工程", description="工程类型(单工程、多工程、批次工程)")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.software_name:
missing_slots["software_name"] = f"{TroubleShooting.model_fields['software_name'].description}，可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}"
missing_slots["software_name"] = f"{SoftwareTroubleShootingSlots.model_fields['software_name'].description}，可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}"
if not self.function_name:
missing_slots["function_name"] = TroubleShooting.model_fields["function_name"].description
missing_slots["function_name"] = SoftwareTroubleShootingSlots.model_fields["function_name"].description
if not self.error_message:
missing_slots["error_message"] = TroubleShooting.model_fields["error_message"].description
missing_slots["error_message"] = SoftwareTroubleShootingSlots.model_fields["error_message"].description
return len(missing_slots) == 0, missing_slots
# 2. 业务问题
# 2.1 专业咨询
class ProfessionalConsulting(BaseModel):
scene_subject: str = Field(description="场景主体")
business_scene: str = Field(description="业务场景描述")
software_name: Optional[SoftwareName] = Field(None, description="软件名称")
class ProfessionalConsultingSlots(SlotBase):
scene_subject: str = Field(default="", description="场景主体")
business_scene: str = Field(default="", description="业务场景描述")
software_name: Optional[str] = Field(default="", description="软件名称")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.scene_subject:
missing_slots["scene_subject"] = ProfessionalConsulting.model_fields["scene_subject"].description
missing_slots["scene_subject"] = ProfessionalConsultingSlots.model_fields["scene_subject"].description
if not self.business_scene:
missing_slots["business_scene"] = ProfessionalConsulting.model_fields["business_scene"].description
missing_slots["business_scene"] = ProfessionalConsultingSlots.model_fields["business_scene"].description
return len(missing_slots) == 0, missing_slots
# 2.2 数据问题
class DataProblem(BaseModel):
expense_type: str = Field(description="费用类型")
operation_purpose: str = Field(description="操作目的")
software_name: Optional[SoftwareName] = Field(None, description="软件名称")
project_type: Optional[str] = Field(None, description="工程类型")
class DataProblemSlots(SlotBase):
expense_type: str = Field(default="", description="费用类型")
operation_purpose: str = Field(default="", description="操作目的")
software_name: Optional[str] = Field(default="", description="软件名称")
project_type: Optional[str] = Field(default="", description="工程类型")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.expense_type:
missing_slots["expense_type"] = DataProblem.model_fields["expense_type"].description
missing_slots["expense_type"] = DataProblemSlots.model_fields["expense_type"].description
if not self.operation_purpose:
missing_slots["operation_purpose"] = DataProblem.model_fields["operation_purpose"].description
missing_slots["operation_purpose"] = DataProblemSlots.model_fields["operation_purpose"].description
return len(missing_slots) == 0, missing_slots
# 3. 安装下载注册
# 3.1 后缀名咨询
class FileExtensionConsulting(BaseModel):
file_extension: str = Field(description="文件后缀名")
operation_purpose: str = Field(description="操作目的")
file_source: Optional[str] = Field(None, description="文件来源场景")
related_software: Optional[str] = Field(None, description="相关软件名称")
class FileExtensionConsultingSlots(SlotBase):
file_extension: str = Field(default="", description="文件后缀名")
operation_purpose: str = Field(default="", description="操作目的(了解对应软件,对应工程)")
file_source: Optional[str] = Field(default="", description="文件来源场景")
related_software: Optional[str] = Field(default="", description="相关软件名称")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.file_extension:
missing_slots["file_extension"] = FileExtensionConsulting.model_fields["file_extension"].description
missing_slots["file_extension"] = FileExtensionConsultingSlots.model_fields["file_extension"].description
if not self.operation_purpose:
missing_slots["operation_purpose"] = FileExtensionConsulting.model_fields["operation_purpose"].description
missing_slots["operation_purpose"] = FileExtensionConsultingSlots.model_fields["operation_purpose"].description
return len(missing_slots) == 0, missing_slots
# 3.2 软件锁类
class SoftwareLock(BaseModel):
lock_type: str = Field(description="锁类型")
operation_purpose: str = Field(description="操作目的")
lock_number: Optional[str] = Field(None, description="软件锁编号/注册号")
class SoftwareLockSlots(SlotBase):
lock_type: str = Field(default="", description="锁类型")
operation_purpose: str = Field(default="", description="操作目的")
lock_number: Optional[str] = Field(default="", description="软件锁编号/注册号")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.lock_type:
missing_slots["lock_type"] = SoftwareLock.model_fields["lock_type"].description
missing_slots["lock_type"] = SoftwareLockSlots.model_fields["lock_type"].description
if not self.operation_purpose:
missing_slots["operation_purpose"] = SoftwareLock.model_fields["operation_purpose"].description
missing_slots["operation_purpose"] = SoftwareLockSlots.model_fields["operation_purpose"].description
return len(missing_slots) == 0, missing_slots
# 3.3 安装下载类
class InstallationDownload(BaseModel):
class InstallationDownloadSlots(SlotBase):
software_name: str = Field(description="软件/插件名称，与file_name二选一")
file_name: str = Field(description="文件名，与software_name二选一")
operation_stage: str = Field(description="操作阶段")
os_version: Optional[str] = Field(None, description="操作系统版本")
package_source: Optional[str] = Field(None, description="安装包来源/版本号")
software_name: str = Field(default="", description="软件/插件名称，与file_name二选一")
file_name: str = Field(default="", description="文件名，与software_name二选一")
operation_stage: str = Field(default="", description="操作阶段(下载、安装等)")
os_version: Optional[str] = Field(default="", description="操作系统版本")
package_source: Optional[str] = Field(default="", description="安装包来源/版本号")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.software_name and not self.file_name:
missing_slots["software_name"] = f"{InstallationDownload.model_fields['software_name'].description},"
missing_slots["software_name"] = f"{InstallationDownloadSlots.model_fields['software_name'].description},"
f"可选值：{', '.join([name.value for name in SoftwareName if name not in [SoftwareName.UNKNOWN, SoftwareName.ALIASES]])}"
missing_slots["file_name"] = InstallationDownload.model_fields["file_name"].description
missing_slots["file_name"] = InstallationDownloadSlots.model_fields["file_name"].description
if not self.operation_stage:
missing_slots["operation_stage"] = InstallationDownload.model_fields["operation_stage"].description
missing_slots["operation_stage"] = InstallationDownloadSlots.model_fields["operation_stage"].description
return len(missing_slots) == 0, missing_slots
# 3.4 问题排查类
class ProblemDiagnosis(BaseModel):
error_message: str = Field(description="报错信息/异常现象")
software_name: Optional[SoftwareName] = Field(None, description="软件名称,只能从给定的范围中取值")
os_version: Optional[str] = Field(None, description="操作系统版本")
class ProblemDiagnosisSlots(SlotBase):
error_message: str = Field(default="", description="报错信息/异常现象")
software_name: Optional[str] = Field(default="", description="软件名称")
os_version: Optional[str] = Field(default="", description="操作系统版本")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
"""检查必填槽位是否都存在"""
missing_slots = {}
if not self.error_message:
missing_slots["error_message"] = ProblemDiagnosis.model_fields["error_message"].description
missing_slots["error_message"] = ProblemDiagnosisSlots.model_fields["error_message"].description
return len(missing_slots) == 0, missing_slots
class OtherSlots(SlotBase):
"""其他类型槽位"""
content_type: str = Field(default="", description="内容类型(必填)")
intent: Optional[str] = Field(default="", description="用户意图(选填)")
def check_required_slots(self) -> Tuple[bool, Dict[str, str]]:
missing_slots = {}
if not self.content_type:
missing_slots["content_type"] = OtherSlots.model_fields["content_type"].description
return len(missing_slots) == 0, missing_slots
class IntentAndSlotResult(BaseModel):
"""意图槽位填充结果"""
classification: Classification
slots: Union[
SoftwareFunctionSlots,
SoftwareTroubleShootingSlots,
ProfessionalConsultingSlots,
DataProblemSlots,
FileExtensionConsultingSlots,
SoftwareLockSlots,
InstallationDownloadSlots,
ProblemDiagnosisSlots,
OtherSlots
]
rag2_0/intent_recognition/IntentRecognition.py
+218 -92
View File
@@ -7,18 +7,27 @@ Date: 2025-05-13
Description: 意图分类、改写核心逻辑
"""
import logging
import os
from langchain_openai import ChatOpenAI
from langchain.output_parsers import PydanticOutputParser
import json
from typing import List, Tuple, Dict, Any, Optional, Union
from typing import List, Tuple, Dict, Any, Optional
import re
from .PromptTemplates import classification_prompt, query_rewrite_prompt, extract_nouns_prompt, classification_info, slot_filling_prompt
import jieba
from .PromptTemplates import (classification_prompt, query_rewrite_prompt,
extract_nouns_prompt, classification_info,
slot_filling_prompt)
from .Multi_PromptTemplates import (
intent_and_slot_prompt, output_example,
generate_slot_mapping_doc, query_rewrite_prompt_pro,
)
from .DataModels import (
Classification, QueryRewrite, Term, TermList,
SoftwareFunction, TroubleShooting, ProfessionalConsulting,
DataProblem, FileExtensionConsulting, SoftwareLock,
InstallationDownload, ProblemDiagnosis
SoftwareFunctionSlots, SoftwareTroubleShootingSlots, ProfessionalConsultingSlots,
DataProblemSlots, FileExtensionConsultingSlots, SoftwareLockSlots,
InstallationDownloadSlots, ProblemDiagnosisSlots, OtherSlots, IntentAndSlotResult
)
from .ProfessionalNounVector import ProfessionalNounRetriever
from rag2_0.tool.ModelTool import XinferenceReRankerModel, OpenAiLLM, SiliconFlowReRankerModel
@@ -52,22 +61,13 @@ class IntentRecognizer:
if base_url:
llm_params["base_url"] = base_url
self.llm = OpenAiLLM(**llm_params)
# 准备分类解析器
self.classification_parser = PydanticOutputParser(pydantic_object=Classification)
# 准备问题改写解析器
self.query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
# 准备术语列表解析器
self.terms_list_parser = PydanticOutputParser(pydantic_object=TermList)
self._llm = OpenAiLLM(**llm_params)
# 加载suffix关键词
self.suffix_keywords = self._load_suffix_keywords()
self._suffix_keywords = self._load_suffix_keywords()
# 初始化向量检索器
self.noun_retriever = ProfessionalNounRetriever(api_key=api_key, index_dir=vector_index_dir)
self._noun_retriever = ProfessionalNounRetriever(api_key=api_key, index_dir=vector_index_dir)
def _load_suffix_keywords(self, filepath: str = None) -> List[str]:
"""
@@ -95,7 +95,7 @@ class IntentRecognizer:
except Exception as e:
raise RuntimeError(f"加载后缀关键词失败: {e}") from e
def classify_intent(self, query: str, keywords: TermList) -> Classification:
def _classify_intent(self, query: str) -> Classification:
"""
对用户输入进行意图分类
@@ -106,49 +106,85 @@ class IntentRecognizer:
Returns:
分类结果
"""
classification_parser = PydanticOutputParser(pydantic_object=Classification)
formatted_prompt = classification_prompt.format(user_input=query,
classification_info=classification_info,
output_format=self.classification_parser.get_format_instructions())
# 将关键词列表转换为JSON字符串
terms_dict = [term.model_dump() for term in keywords.terms]
keywords_str = json.dumps(terms_dict, ensure_ascii=False)
formatted_prompt = formatted_prompt.replace("{keywords}", keywords_str)
output_format=classification_parser.get_format_instructions())
# 调用LLM
response = self.llm.invoke(formatted_prompt, False)
response = self._llm.invoke(formatted_prompt, False)
# 解析输出
try:
# 尝试直接解析JSON响应
parsed_output = self.classification_parser.parse(response.content.strip())
parsed_output = classification_parser.parse(response.content.strip())
return parsed_output
except Exception as e:
raise RuntimeError(f"解析分类结果时出错: {e}") from e
def extract_keywords_with_llm(self, query: str) -> List[Term]:
def _tokenize_with_jieba(self, query: str) -> List[str]:
"""
使用LLM从用户查询中提取专业关键词
使用jieba分词器对查询进行分词
Args:
query: 用户查询
Returns:
分词后的词语列表
"""
# 使用jieba进行分词
seg_list = jieba.cut(query, cut_all=False)
# 过滤掉停用词和标点符号
filtered_tokens = []
for token in seg_list:
# 过滤掉空格和标点符号
if token.strip() and not re.match(r'^[^\w\s]$', token):
filtered_tokens.append(token)
return filtered_tokens
def _extract_keywords_with_llm(self, query: str, use_jieba: bool = False) -> List[Term]:
"""
使用LLM从用户查询中提取专业关键词
Args:
query: 用户查询
use_jieba: 是否使用jieba分词辅助提取关键词
Returns:
提取的术语列表
"""
try:
# 如果使用jieba分词
if use_jieba:
# 先使用jieba分词
tokens = self._tokenize_with_jieba(query)
# 构建术语列表
terms = []
for token in tokens:
if len(token) > 1: # 过滤掉单字词
terms.append(Term(name=token, synonymous=[], description=""))
return terms
else:
# 使用LLM提取关键词
# 准备提示词
formatted_prompt = extract_nouns_prompt.replace("{content}", query)
formatted_prompt = formatted_prompt.replace("{output_format}", self.terms_list_parser.get_format_instructions())
terms_list_parser = PydanticOutputParser(pydantic_object=TermList)
formatted_prompt = formatted_prompt.replace("{output_format}", terms_list_parser.get_format_instructions())
# 调用LLM
response = self.llm.invoke(formatted_prompt, False)
response = self._llm.invoke(formatted_prompt, False)
try:
# 尝试使用Pydantic解析器解析TermList
parsed_output = self.terms_list_parser.parse(response.content)
parsed_output = terms_list_parser.parse(response.content)
return parsed_output.terms
except Exception as e:
raise RuntimeError(f"无法解析LLM关键词提取响应: {e}") from e
def rerank_matched_terms(self, query_key: str, matched_terms: set, top_k: int = 2) -> List[Term]:
def _rerank_matched_terms(self, query_key: str, matched_terms: set, top_k: int = 2) -> List[Term]:
"""
对召回的专业术语进行重排序，按与用户查询的相关性排序
@@ -182,31 +218,32 @@ class IntentRecognizer:
except Exception as e:
raise RuntimeError(f"SiliconFlowReRankerModel重排失败：{e}") from e
def match_keywords(self, query: str) -> Tuple[TermList, List[str]]:
def _match_keywords(self, query: str, use_jieba: bool = False) -> Tuple[TermList, List[str]]:
"""
从用户问题中匹配关键词，结合LLM提取和向量检索
Args:
query: 用户问题
use_jieba: 是否使用jieba分词辅助提取关键词
Returns:
匹配到的关键词列表
"""
query_keys=[]
# 步骤2: 使用LLM提取查询中的关键词
# 步骤1: 使用LLM提取查询中的关键词
try:
extracted_terms = self.extract_keywords_with_llm(query)
extracted_terms = self._extract_keywords_with_llm(query, use_jieba)
for term in extracted_terms:
query_keys.append(term.name)
except Exception as e:
raise RuntimeError(f"LLM关键词提取失败: {e}") from e
matched_terms = [] # 存储匹配到的Term对象
# 步骤3: 使用向量检索找到相似的专业名词
# 步骤2: 使用向量检索找到相似的专业名词
try:
# 对matched_terms中的每个关键字进行向量检索
for current_key in query_keys:
vector_results = self.noun_retriever.query(current_key, top_k=5, use_intersection=False)
vector_results = self._noun_retriever.query(current_key, top_k=5, use_intersection=False)
current_key_terms = set()
# 添加向量检索结果
for result in vector_results:
@@ -218,7 +255,8 @@ class IntentRecognizer:
description=result.get('description', '')
)
current_key_terms.add(term)
reranked_terms = self.rerank_matched_terms(current_key, current_key_terms)
if len(current_key_terms) > 0:
reranked_terms = self._rerank_matched_terms(current_key, current_key_terms)
matched_terms.extend(reranked_terms)
except Exception as e:
raise RuntimeError(f"向量检索关键词时出错: {e}") from e
@@ -228,7 +266,7 @@ class IntentRecognizer:
term_list = TermList(terms=list(matched_terms))
return term_list, query_keys
def rewrite_query(self, query: str, keywords: TermList) -> QueryRewrite:
def _rewrite_query(self, query: str, keywords: TermList, chat_history: List[Dict[str, str]] = None, context: str = "") -> QueryRewrite:
"""
对用户问题进行改写
@@ -242,23 +280,28 @@ class IntentRecognizer:
# 准备问题改写提示
terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms]
keywords_str = json.dumps(terms_dict, ensure_ascii=False)
formatted_prompt = query_rewrite_prompt.format(query=query,
output_format=self.query_rewrite_parser.get_format_instructions(),
keywords=keywords_str)
query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
# formatted_prompt = query_rewrite_prompt.format(query=query,
# output_format=query_rewrite_parser.get_format_instructions(),
# keywords=keywords_str)
formatted_prompt = query_rewrite_prompt_pro.format(query=query,
output_format=query_rewrite_parser.get_format_instructions(),
keywords=keywords_str,
chat_history=chat_history,
context=context)
# 调用LLM
response = self.llm.invoke(formatted_prompt, False)
response = self._llm.invoke(formatted_prompt, False)
# 解析输出
try:
# 尝试直接解析JSON响应
parsed_output = self.query_rewrite_parser.parse(response.content)
parsed_output = query_rewrite_parser.parse(response.content)
return parsed_output
except Exception as e:
raise RuntimeError(f"解析问题改写结果时出错: {e}") from e
def judge_define_suffix(self, input_str: str) -> Tuple[bool, List[str]]:
def _judge_define_suffix(self, input_str: str) -> Tuple[bool, List[str]]:
"""
判断输入字符串是否包含定义的后缀，并返回所有匹配到的后缀名列表
@@ -270,7 +313,7 @@ class IntentRecognizer:
"""
# 构建正则表达式模式，匹配大小写不敏感且前面可能带有.
pattern = r'(?:\.?)(' + '|'.join(re.escape(field.get('name')) for field in self.suffix_keywords) + r')'
pattern = r'(?:\.?)(' + '|'.join(re.escape(field.get('name')) for field in self._suffix_keywords) + r')'
# 使用 re.IGNORECASE 标志来忽略大小写，findall找到所有匹配
matches = re.finditer(pattern, input_str, re.IGNORECASE)
@@ -278,23 +321,30 @@ class IntentRecognizer:
return bool(matched_suffixes), matched_suffixes
def process_query(self, query: str) -> Tuple[Classification, TermList, QueryRewrite, List[str]]:
def process_query(self, query: str, conversation_context: str = "",
chat_history: List[Dict[str, str]] = None,
previous_slots: Dict[str, Any] = None,
use_jieba: bool = False) -> Dict[str, Any]:
"""
处理用户问题的完整流程
Args:
query: 用户原始问题
conversation_context: 会话背景信息
chat_history: 历史对话记录，格式为[{"user": "content"}, {"assistant": "content"}]
previous_slots: 历史槽位信息
use_jieba: 是否使用jieba分词辅助提取关键词
Returns:
(意图分类结果, 匹配的关键词列表, 问题改写结果)的元组
包含分类、关键词、改写和槽位填充结果的字典
"""
# 是否是扩展名
# is_suffix, matched_suffixes = self.judge_define_suffix(query)
# is_suffix, matched_suffixes = self._judge_define_suffix(query)
# if is_suffix:
# # 将所有匹配到的后缀名作为Term添加到结果中
# suffix_terms = []
# for suffix in matched_suffixes:
# term_dict = next((item for item in self.suffix_keywords if item['name'].lower() == suffix.lower()), None)
# term_dict = next((item for item in self._suffix_keywords if item['name'].lower() == suffix.lower()), None)
# if term_dict:
# suffix_term = Term(
# name=term_dict.get('name'),
@@ -306,26 +356,41 @@ class IntentRecognizer:
# return Classification(vertical_classification="安装下载", sub_classification="查询"), TermList(terms=suffix_terms), QueryRewrite(rewrite=query), matched_suffixes
# 步骤1: 匹配关键词
keywords_terms, query_keys = self.match_keywords(query)
keywords_terms, query_keys = self._match_keywords(query, use_jieba)
# 步骤2: 问题改写
rewrite = self.rewrite_query(
rewrite = self._rewrite_query(
query=query,
keywords=keywords_terms
keywords=keywords_terms,
chat_history=chat_history,
context=conversation_context
)
# 步骤3: 进行意图分类
classification = self.classify_intent(query, keywords_terms)
if classification.vertical_classification == "其他" or classification.sub_classification == "其他":
return classification, TermList(terms=[]), QueryRewrite(rewrite=query), []
# 步骤3: 进行意图识别和槽位填充
result = self._process_intent_and_slot(query, conversation_context, chat_history, previous_slots)
result.update({"keywords": keywords_terms.model_dump(),
"rewrite": rewrite.model_dump(),
"query_keys": query_keys})
return result
# # 步骤3: 进行意图分类
# classification = self._classify_intent(query)
if classification.vertical_classification == "闲聊" or classification.sub_classification == "闲聊":
return classification, TermList(terms=[]), QueryRewrite(rewrite=query),[]
# # 步骤4: 进行槽位填充
# # 如果是有效分类，进行槽位填充
# slot_filling_result = {}
# if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]:
# slot_filling_result = self._fill_slots(rewrite.rewrite, classification)
# rewrite = QueryRewrite(rewrite=query)
return classification, keywords_terms, rewrite, query_keys
# return {
# "classification": classification.model_dump(),
# "keywords": keywords_terms.model_dump(),
# "rewrite": rewrite.model_dump(),
# "query_keys": query_keys,
# "slot_filling": slot_filling_result
# }
def fill_slots(self, query: str, classification: Classification) -> Dict[str, Any]:
def _fill_slots(self, query: str, classification: Classification) -> Dict[str, Any]:
"""
根据分类结果对问题进行槽位填充
@@ -340,7 +405,7 @@ class IntentRecognizer:
# 根据分类结果选择对应的数据模型
slot_model = self._get_slot_model(classification)
if not slot_model:
return {"error": "未找到匹配的槽位模型"}
raise RuntimeError("未找到匹配的槽位模型")
# 使用LLM进行槽位填充
filled_slots = self._fill_slots_with_llm(query, classification, slot_model)
@@ -356,7 +421,7 @@ class IntentRecognizer:
def _get_slot_model(self, classification: Classification) -> Optional[type]:
"""
根据分类结果获取对应的槽位模型类
根据分类结果获取对应的槽位模型类，用于统一提示词处理
Args:
classification: 意图分类结果
@@ -367,32 +432,34 @@ class IntentRecognizer:
# 软件问题
if classification.vertical_classification == "软件问题":
if classification.sub_classification == "软件功能":
return SoftwareFunction
return SoftwareFunctionSlots
elif classification.sub_classification == "故障排查":
return TroubleShooting
return SoftwareTroubleShootingSlots
# 业务问题
elif classification.vertical_classification == "业务问题":
if classification.sub_classification == "专业咨询":
return ProfessionalConsulting
return ProfessionalConsultingSlots
elif classification.sub_classification == "数据问题":
return DataProblem
return DataProblemSlots
# 安装下载注册
elif classification.vertical_classification == "安装下载注册":
if classification.sub_classification == "后缀名咨询":
return FileExtensionConsulting
return FileExtensionConsultingSlots
elif classification.sub_classification == "软件锁类":
return SoftwareLock
return SoftwareLockSlots
elif classification.sub_classification == "安装下载类":
return InstallationDownload
return InstallationDownloadSlots
elif classification.sub_classification == "问题排查类":
return ProblemDiagnosis
return ProblemDiagnosisSlots
# 其他
elif classification.vertical_classification == "其他":
return OtherSlots
return None
count=1
def _fill_slots_with_llm(self, query: str, classification: Classification, slot_model_class: type) -> Any:
"""
使用LLM进行槽位填充
@@ -416,7 +483,7 @@ class IntentRecognizer:
)
# 调用LLM
response = self.llm.invoke(formatted_prompt, False)
response = self._llm.invoke(formatted_prompt, False)
try:
# 尝试解析LLM响应
@@ -427,28 +494,87 @@ class IntentRecognizer:
empty_instance = slot_model_class()
return empty_instance
def process_query_with_slots(self, query: str) -> Dict[str, Any]:
def _process_intent_and_slot(self, user_input: str, conversation_context: str = "",
chat_history: List[Dict[str, str]] = None,
previous_slots: Dict[str, Any] = None) -> Dict[str, Any]:
"""
处理用户问题的完整流程，包括槽位填充
使用统一提示词同时进行意图识别和槽位填充
Args:
query: 用户原始问题
user_input: 当前用户输入
conversation_context: 会话背景信息
chat_history: 历史对话记录，格式为[{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
previous_slots: 历史槽位信息
Returns:
包含分类、关键词、改写和槽位填充结果的字典
包含意图分类和槽位填充结果的字典
"""
# 执行基本处理流程
classification, keywords, rewrite, query_keys = self.process_query(query)
# 初始化默认值
if chat_history is None:
chat_history = []
# 如果是有效分类，进行槽位填充
slot_filling_result = {}
if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]:
slot_filling_result = self.fill_slots(rewrite.rewrite, classification)
if previous_slots is None:
previous_slots = {}
return {
# 生成槽位映射文档
slot_mapping_doc = generate_slot_mapping_doc()
# 准备提示词
parser = PydanticOutputParser(pydantic_object=IntentAndSlotResult)
formatted_prompt = intent_and_slot_prompt.format(
conversation_context=conversation_context,
chat_history=json.dumps(chat_history, ensure_ascii=False),
previous_slots=json.dumps(previous_slots, ensure_ascii=False),
user_input=user_input,
slot_mapping_doc=slot_mapping_doc,
output_format=parser.get_format_instructions(),
classification_info=classification_info
)
# 调用LLM
response = self._llm.invoke(formatted_prompt + output_example, False)
try:
# 解析LLM响应为JSON
result_json = parser.parse(response.content)
classification=result_json.classification
slot_filling=result_json.slots
is_complete, missing_slots = slot_filling.check_required_slots()
expected_slot_model = self._get_slot_model(classification)
# 添加容错处理，发生概率较低，但仍需处理
if expected_slot_model is None:
# 添加容错处理，应对LLM返回错误分类信息,一级分类跟二级分类错乱
# 重新分类
classification = self._classify_intent(user_input)
fill_slots = self._fill_slots(user_input, classification)
result = {
"classification": classification.model_dump(),
"keywords": keywords.model_dump(),
"rewrite": rewrite.model_dump(),
"query_keys": query_keys,
"slot_filling": slot_filling_result
"slot_filling": fill_slots
}
logging.warning(f"重新分类与槽点填充")
return result
elif expected_slot_model.__name__ != type(slot_filling).__name__:
# 添加容错处理，应对LLM槽位与分类不匹配。重新填充槽位
slot_filling = self._fill_slots(user_input, classification)
result = {
"classification": classification.model_dump(),
"slot_filling": slot_filling
}
logging.warning(f"重新填充槽点")
return result
# 构建最终结果
result = {
"classification": classification.model_dump(),
"slot_filling": {
"is_complete": is_complete,
"missing_slots": missing_slots,
"filled_data": slot_filling.model_dump()
}
}
return result
except Exception as e:
logging.error(f"process_intent_and_slot error:{e}")
raise RuntimeError(f"process_intent_and_slot error:{e}") from e
rag2_0/intent_recognition/Multi_PromptTemplates.py
+357
View File
@@ -0,0 +1,357 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
File: Multi_PromptTemplates.py
Author: oyyz
Date: 2025-06-13
Description: 多轮对话下意图分类、改写核心提示词
"""
# 首版重构提示词
query_rewrite_prompt_pro_old="""
# 电力造价专业问答优化工程师（升级版）
你是一名电力造价专业问答优化工程师，负责结合历史对话背景和专业术语库，将用户的原始问题进行规范化重构，以提升知识库检索准确率。
## 核心任务
基于历史对话上下文和专业术语库，将用户的原始问题进行规范化重构，提高知识库检索的准确性和专业性，同时保持对话的连贯性和语境相关性。
## 处理流程
### 第一阶段：输入解析
1. 解析基础信息
- 原始问题(需保留核心语义)：{query}
- 关键词集合：{keywords}
- 历史对话记录：{chat_history}
- 当前聊天背景：{context}
2. 背景分析
- 识别历史对话中的关键主题和专业领域
- 提取上下文中的隐含信息（如软件版本、地区、具体场景等）
- 分析用户的提问模式和专业水平
### 第二阶段：上下文匹配分析
**背景匹配规则：**
1. 检查当前问题是否与历史对话存在关联性
2. 识别历史对话中提到的关键信息：
- 软件版本/系统（如Z1、D3等）
- 地区定额（如西藏、山东等）
- 具体功能模块
- 用户操作习惯
**术语匹配规则：**
1. 检查原始问题中是否包含关键词集合中的`name`字段或`synonymous`字段中的任何词汇
2. 结合历史对话，识别可能的隐含专业术语
3. 统计匹配的术语数量
4. 判断执行路径：
- 匹配术语 ≥ 1个 或 存在明显上下文关联 → 执行重构流程
- 匹配术语 = 0个 且 无明显上下文关联 → 直接输出原始问题
### 第三阶段：问题重构
**重构原则（按优先级排序）：**
1. **语义保真**：严格保持原问题的核心意图和诉求
2. **上下文继承**：
- 补充历史对话中的隐含信息（如软件名称、版本、地区等）
- 保持对话的连贯性和逻辑性
- 避免重复已确认的背景信息
3. **术语规范**：
- 将匹配到的同义词替换为对应的标准术语（name字段）
- 对在关键词中的标准术语使用【】进行标记
- 保留在原问题中未在关键词库中的专业术语、限定词和修饰词
4. **结构优化**：
- 保持原问题的语态特征5W2H
- 保持主谓宾结构清晰
- 保留时间、版本等限定条件
**术语处理规则：**
- 优先级1：基于历史对话补充缺失的背景信息
- 优先级2：保留原问题中的专业术语、限定词和修饰词（即使不在关键词库中）
- 优先级3：将同义词替换为标准术语并用【】标记
- 优先级4：对原问题中已存在的标准术语添加【】标记
**上下文处理策略：**
- 如果当前问题与历史对话高度相关，适当补充背景信息
- 如果用户使用代词（如"这个"、"那个"），尝试结合历史对话明确指代
- 如果历史对话中已确定软件或系统，在当前问题中适当体现
# 输出规范
{output_format}
# 示范案例库
▶ 案例1（有效匹配 + 上下文继承）
历史对话：用户之前询问过"西藏定额升级的问题"
输入：
原始问题：怎么把旧版工程转到Z1新版
关键词：【'老版本定额升级', '批量设置定额', '西藏造价软件Z1'】
输出：
{{"rewrite":"【西藏造价软件Z1】如何执行【老版本定额升级】操作？"}}
▶ 案例2（无效匹配 + 无上下文关联）
历史对话：无相关内容
输入：
原始问题：程序界面文字显示过小如何处理？
关键词：【'定额升级', '工程批量导入'】
输出：
{{"rewrite":"程序界面文字显示过小如何处理？"}}
▶ 案例3（部分匹配 + 上下文补充）
历史对话：用户之前询问过"D3软件的功能"
输入：
原始问题：能导出清单的计算公式吗？
关键词：【'配网工程计价通D3软件', '计算式'】
输出：
{{"rewrite":"【配网工程计价通D3软件】能导出清单的【计算式】吗？"}}
▶ 案例4（代词替换 + 上下文解析）
历史对话：用户刚询问过"山东定额的问题"
输入：
原始问题：这个定额怎么批量导入？
关键词：【'批量导入定额', '山东定额'】
输出：
{{"rewrite":"【山东定额】如何进行【批量导入定额】操作？"}}
## 质量检查清单
执行前请确认：
- [ ] 是否保持了原问题的核心诉求？
- [ ] 是否合理利用了历史对话中的背景信息？
- [ ] 是否正确执行了同义词替换？
- [ ] 是否保留了原问题中的专业术语和限定条件？
- [ ] 是否正确使用了【】标记？
- [ ] 重构后的问题是否自然流畅？
- [ ] 是否保持了对话的连贯性？
- [ ] 是否避免了过度补充不必要的信息？
"""
query_rewrite_prompt_pro="""
# 电力造价问答优化工程师（精简版）
**角色**：基于历史对话和专业术语库重构问题，提升知识库检索准确率。
## 核心原则
1. 语义保真 → 保持问题核心意图
2. 术语规范 → 同义词转标准词并【】标记
3. 背景继承 → 补充历史对话的隐含信息
## 处理流程
### 一、输入解析
- 原始问题(需保留核心语义)：{query}
- 关键词集合：{keywords}
- 历史对话记录：
<history>
{chat_history}
</history>
- 当前聊天背景：
<conversation_background>
{context}
</conversation_background>
### 二、重构决策树
```mermaid
graph TD
A[输入问题] --> B{{匹配关键词或上下文？}}
B -- 是 --> C[执行重构]
B -- 否 --> D[直接输出原始问题]
C --> E[补充缺失背景]
E --> F[同义词替换+【】标记]
F --> G[保留原生专业术语]
```
### 三、重构优先级
1. **背景补充**
- 历史对话中确定的软件/地区必须继承（例："这软件"→"【配网工程D3】"）
2. **术语处理**
- 同义词转标准词 → 批量设置定额
- 存在即标记 → 【计算式】
3. **结构优化**
- 保持原问题的5W2H特征
- 明确指代关系（"该功能"→"【批量导入】功能"）
## 输出规范
{output_format}
## 典型案例
| 场景 | 输入问题 | 输出结果 |
|---------------------|-----------------------------------|------------------------------------------|
| 强上下文关联 | “怎么升级旧版工程” | {{"rewrite":"【西藏Z1】如何执行【老版本定额升级】？"}} |
| 弱术语匹配 | “界面文字太小怎么办” | 原样输出 |
| 代词+背景继承 | “这个定额如何导入” | {{"rewrite":"【山东定额】如何执行【批量导入定额】？"}}|
## 质量自检
- [] 核心诉求是否保留？
- [] 背景信息是否合理补充？
- [] 术语标记是否完整【】？
- [] 语句是否自然流畅？
- [] 避免过度补充无关信息
"""
intent_and_slot_prompt = """
# 电力造价软件意图分类与槽位填充统一提示词
你是一个专业的电力造价领域智能助手，负责对用户输入进行意图分类识别和关键信息槽位填充。
{classification_info}
{slot_mapping_doc}
## 【软件名称规范】
支持的软件名称及其别名：
- **配网工程计价通D3软件**：别名包括配网D3、D3软件、配网工程软件等
- **新型储能电站建设计价通C1软件**：别名包括储能C1、C1软件、储能电站软件、储能软件等
- **西藏电力工程计价通Z1软件**：别名包括西藏Z1、Z1软件、西藏电力软件等
- **技改检修工程计价通T1软件**：别名包括技改T1、T1软件、技改检修软件等
- **技改检修清单计价通T1软件**：别名包括技改清单T1、T1清单软件、技改检修清单软件等
- **主网电力建设计价通软件**：别名包括主网软件、电力建设软件、主网建设软件、博微电力建设计价通等
## 【任务要求】
1. **会话理解**：综合考虑会话背景、历史对话和之前的槽位信息来理解当前用户输入
2. **意图分类**：准确识别用户输入属于哪个垂直领域和子分类
3. **槽位填充**：从当前用户问题中提取关键信息，并结合历史槽位信息进行补充完善
4. **信息融合**：
- 优先使用当前用户输入中的明确信息
- 当前输入缺失但历史槽位存在的信息，可适当继承
- 历史对话中的上下文信息有助于理解当前输入的真实意图
5. **槽位处理**：
- 对于必填槽位，必须尽力从当前输入和历史信息中提取
- 对于选填槽位，如果能从当前输入或历史信息中提取则填写
- 如果当前输入与历史信息存在冲突，以当前输入为准
6. **输出格式**：只输出符合格式的JSON数据，不要有任何额外的解释
## 【会话背景信息】
{conversation_context}
## 【历史对话记录】
{chat_history}
## 【历史槽位信息】
{previous_slots}
## 【当前用户输入】
{user_input}
## 【输出格式】
{output_format}
"""
output_example="""
## 【综合分析示例】
**示例1：利用历史对话理解当前输入**
会话背景: 用户正在咨询软件使用问题
历史对话:
- 用户: "我在使用配网D3软件"
- 助手: "好的，请问您遇到什么问题？"
历史槽位:{"software_name": "配网工程计价通D3软件"}
当前用户输入: "新建工程按钮找不到"
输出:
```json
{
"classification": {
"vertical_classification": "软件问题",
"sub_classification": "软件功能"
},
"slot_filling": {
"software_name": "配网工程计价通D3软件",
"function_name": "新建工程",
"operation": "查找新建工程按钮位置",
"software_version": null,
"operation_steps": null
}
}
```
**示例2：继承和更新槽位信息**
会话背景: 用户遇到软件报错，正在进行故障排查
历史对话:
- 用户: "西藏Z1软件报错了"
- 助手: "请详细描述一下报错信息"
历史槽位: {"software_name": "西藏电力工程计价通Z1软件"}
当前用户输入: "提示找不到许可证，是在新建工程的时候"
输出:
```json
{
"classification": {
"vertical_classification": "软件问题",
"sub_classification": "故障排查"
},
"slot_filling": {
"software_name": "西藏电力工程计价通Z1软件",
"function_name": "新建工程",
"error_message": "提示找不到许可证",
"software_version": null,
"os_version": null,
"reproduction_steps": "新建工程时出现错误"
}
}
```
**示例3：信息冲突处理**
会话背景: 用户在多个软件间切换咨询
历史对话:
- 用户: "配网D3的费用计算"
- 助手: "好的，关于配网D3的费用计算..."
历史槽位: {"software_name": "配网工程计价通D3软件"}
当前用户输入: "不对，我说的是技改T1软件的材料费怎么算"
输出:
```json
{
"classification": {
"vertical_classification": "业务问题",
"sub_classification": "数据问题"
},
"slot_filling": {
"expense_type": "材料费",
"operation_purpose": "了解费用计算方法",
"software_name": "技改检修工程计价通T1软件",
"project_type": null
}
}
```
"""
def generate_slot_mapping_doc() -> str:
"""
生成分类与槽位模型对应关系的文档
Returns:
str: 格式化的文档字符串
"""
mapping = {
"软件问题": {
"软件功能": "SoftwareFunctionSlots",
"故障排查": "SoftwareTroubleShootingSlots"
},
"业务问题": {
"专业咨询": "ProfessionalConsultingSlots",
"数据问题": "DataProblemSlots"
},
"安装下载注册": {
"后缀名咨询": "FileExtensionConsultingSlots",
"软件锁类": "SoftwareLockSlots",
"安装下载类": "InstallationDownloadSlots",
"问题排查类": "ProblemDiagnosisSlots"
},
"其他": {
"其他": "OtherSlots"
}
}
doc = ["## 【分类与槽位模型对应关系】"]
for vertical, sub_classes in mapping.items():
doc.append(f"\n{vertical}:")
for sub_class, slot_model in sub_classes.items():
doc.append(f"- {sub_class} -> {slot_model}")
doc.append("\n## 【注意事项】")
doc.append("1. 分类与槽位模型必须严格对应")
doc.append("2. 每个分类只能使用其对应的槽位模型")
doc.append("3. 不允许混用不同分类的槽位模型")
return "\n".join(doc)
rag2_0/tool/ModelTool.py
+1 -1
View File
@@ -118,7 +118,7 @@ class XinferenceReRankerModel:
return [{"document": item["document"]["text"], "score": item["relevance_score"], "index": item["index"]} for item in results["results"]]
except requests.exceptions.RequestException as e:
logging.error(f"重排序请求失败: {str(e)}")
logging.error(f"XinferenceReRankerModel重排序请求失败: {str(e)}")
return []