first commit
This commit is contained in:
ytc1012
35
MeetSpot/AGENTS.md
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MeetSpot/AGENTS.md
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# Repository Guidelines
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## Project Structure & Module Organization
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- `app/` holds core logic, configuration, and tools (e.g., `app/tool/meetspot_recommender.py` and the in-progress `design_tokens.py`). Treat it as the authoritative source for business rules.
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- `api/index.py` wires FastAPI, middleware, and routers; `web_server.py` bootstraps the same app locally or in production.
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- Presentation assets live in `templates/` (Jinja UI), `static/` (CSS, icons), and `public/` (standalone marketing pages); generated HTML drops under `workspace/js_src/` and should be commit-free.
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- Configuration samples sit in `config/`, docs in `docs/`, and regression or SEO tooling in `tests/` plus future `tools/` automation scripts.
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## Build, Test, and Development Commands
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- `pip install -r requirements.txt` (or `conda env update -f environment-dev.yml`) installs Python 3.11 dependencies.
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- `python web_server.py` starts the full stack with auto env detection; `uvicorn api.index:app --reload` is preferred while iterating.
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- `npm run dev` / `npm start` proxy to the same Python entry point for platforms that expect Node scripts.
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- `pytest tests/ -v` runs the suite; `pytest --cov=app tests/` enforces coverage; `python tests/test_seo.py http://127.0.0.1:8000` performs the SEO audit once the server is live.
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- Quality gates: `black .`, `ruff check .`, and `mypy app/` must be clean before opening a PR.
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## Coding Style & Naming Conventions
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- Python: 4-space indent, type hints everywhere, `snake_case` for functions, `PascalCase` for classes, and `SCREAMING_SNAKE_CASE` for constants. Keep functions under ~50 lines and prefer dataclasses for structured payloads.
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- HTML/CSS: prefer BEM-like class names (`meetspot-header__title`), declare shared colors via the upcoming `static/css/design-tokens.css`, and keep inline styles limited to offline-only HTML in `workspace/js_src/`.
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- Logging flows through `app/logger.py`; use structured messages (`logger.info("geo_center_calculated", extra={...})`) so log parsing stays reliable.
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## Testing Guidelines
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- Place new tests in `tests/` using `test_<feature>.py` naming; target fixtures that hit both FastAPI routes and tool-layer helpers.
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- Maintain ≥80% coverage for the `app/` package; add focused tests when touching caching, concurrency, or SEO logic.
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- Integration checks: run `python tests/test_seo.py <base_url>` against a live server and capture JSON output in the PR for visibility.
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- Planned accessibility tooling (`tests/test_accessibility.py`) will be part of CI—mirror its structure for any lint-like tests you add.
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## Commit & Pull Request Guidelines
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- Follow Conventional Commits (`feat:`, `fix:`, `ci:`, `docs:`) as seen in `git log`; keep scopes small (e.g., `feat(tokens): add WCAG palette`).
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- Reference related issues in the first line of the PR description, include a summary of user impact, and attach screenshots/GIFs for UI work.
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- List the commands/tests you ran, note any config changes (e.g., `config/config.toml`), and mention follow-up tasks when applicable.
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- Avoid committing generated artifacts from `workspace/` or credentials in `config/config.toml`; add new secrets to `.env` or deployment config.
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## Configuration & Architecture Notes
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- Keep `config/config.toml.example` updated when introducing new settings, and never hardcode API keys—read them via `app.config`.
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- The design-token and accessibility architecture is tracked in `.claude/specs/improve-ui-ux-color-scheme/02-system-architecture.md`; align contributions with that spec and document deviations in your PR.
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4
MeetSpot/BingSiteAuth.xml
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MeetSpot/BingSiteAuth.xml
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<?xml version="1.0"?>
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<users>
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<user>AF10E9C7A491D73CC4CF422DE871A651</user>
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</users>
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MeetSpot/CLAUDE.md
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MeetSpot/CLAUDE.md
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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MeetSpot is an **AI Agent** for multi-person meeting point recommendations. Users provide locations and requirements; the Agent calculates the geographic center and recommends optimal venues. Built with FastAPI and Python 3.11+, uses Amap (Gaode Map) API for geocoding/POI search, and DeepSeek/GPT-4o-mini for semantic scoring.
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**Live Demo**: https://meetspot-irq2.onrender.com
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## Quick Reference
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```bash
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# Environment
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conda activate meetspot # Or: source venv/bin/activate
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# Development
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uvicorn api.index:app --reload # Preferred for iteration
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python web_server.py # Full stack with auto env detection
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# Test the main endpoint
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curl -X POST "http://127.0.0.1:8000/api/find_meetspot" \
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-H "Content-Type: application/json" \
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-d '{"locations": ["北京大学", "清华大学"], "keywords": "咖啡馆"}'
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# Testing
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pytest tests/ -v # Full suite
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pytest tests/test_file.py::test_name -v # Single test
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pytest --cov=app tests/ # Coverage (target: 80%)
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python tests/test_seo.py http://localhost:8000 # SEO validation (standalone)
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# Quality gates (run before PRs)
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black . && ruff check . && mypy app/
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# Postmortem regression check (optional, runs in CI)
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python tools/postmortem_check.py # Check for known issue patterns
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```
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**Key URLs**: Main UI (`/`), API docs (`/docs`), Health (`/health`)
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## Repo Rules
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- Follow `AGENTS.md` for repo-local guidelines (style, structure, what not to commit). In particular: runtime-generated files under `workspace/js_src/` must not be committed.
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- There are no Cursor/Copilot rule files in this repo (no `.cursorrules`, no `.cursor/rules/`, no `.github/copilot-instructions.md`).
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## Environment Setup
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**Conda**: `conda env create -f environment.yml && conda activate meetspot` (env name is `meetspot`, not `meetspot-dev`)
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**Pip**: `python3.11 -m venv venv && source venv/bin/activate && pip install -r requirements.txt`
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**Required Environment Variables**:
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- `AMAP_API_KEY` - Gaode Map API key (required)
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- `AMAP_SECURITY_JS_CODE` - JS security code for frontend map
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- `LLM_API_KEY` - DeepSeek/OpenAI API key (for AI chat and LLM scoring)
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- `LLM_API_BASE` - API base URL (default: `https://newapi.deepwisdom.ai/v1`)
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- `LLM_MODEL` - Model name (default: `deepseek-chat`)
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**Local Config**: Copy `config/config.toml.example` to `config/config.toml` and fill in API keys. Alternatively, create a `.env` file with the environment variables above.
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## Architecture
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### Request Flow
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```
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POST /api/find_meetspot
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↓
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Complexity Router (assess_request_complexity)
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↓
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Rule+LLM Mode (Agent mode disabled for memory savings on free tier)
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↓
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5-Step Pipeline: Geocode → Center Calc → POI Search → Ranking → HTML Gen
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```
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Complexity scoring: +10/location, +15 for complex keywords, +10 for special requirements. Currently all requests use Rule+LLM mode since Agent mode is disabled (`agent_available = False` in `api/index.py`).
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### Entry Points
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- `web_server.py` - Main entry, auto-detects production vs development
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- `api/index.py` - FastAPI app with all endpoints, middleware, and request handling
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### Three-Tier Configuration (Graceful Degradation)
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| Mode | Trigger | What Works |
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|------|---------|------------|
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| Full | `config/config.toml` exists | All features, TOML-based config |
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| Simplified | `RAILWAY_ENVIRONMENT` set | Uses `app/config_simple.py` |
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| Minimal | Only `AMAP_API_KEY` env var | `MinimalConfig` class in `api/index.py`, basic recommendations only |
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### Core Components
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```
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app/tool/meetspot_recommender.py # Main recommendation engine (CafeRecommender class)
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|- university_mapping dict # 45 abbreviations (e.g., "北大" -> "北京市海淀区北京大学")
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|- landmark_mapping dict # 45 city landmarks (e.g., "陆家嘴" -> "上海市浦东新区陆家嘴")
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|- PLACE_TYPE_CONFIG dict # 12 venue themes with colors, icons
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|- _rank_places() # 100-point scoring algorithm
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|- _generate_html_content() # Standalone HTML with Amap JS API
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|- geocode_cache (max 30) # LRU-style address cache (reduced for free tier)
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|- poi_cache (max 15) # LRU-style POI cache (reduced for free tier)
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app/design_tokens.py # WCAG AA color palette, CSS generation
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api/routers/seo_pages.py # SEO landing pages
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```
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### LLM Scoring (Agent Mode)
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When Agent Mode is enabled, final venue scores blend rule-based and LLM semantic analysis:
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```
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Final Score = Rule Score * 0.4 + LLM Score * 0.6
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```
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Agent Mode is currently disabled (`agent_available = False`) to conserve memory on free hosting tiers.
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### Data Flow
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```
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1. Address enhancement (90+ university/landmark mappings)
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2. Geocoding via Amap API (with retry + rate limiting)
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3. Center point calculation (spherical geometry)
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4. POI search (concurrent for multiple keywords)
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Fallback: tries 餐厅->咖啡馆->商场->美食, then expands to 50km
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5. Ranking with multi-scenario balancing (max 8 venues)
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6. HTML generation -> workspace/js_src/
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```
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### Optional Components
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Database layer (`app/db/`, `app/models/`) is optional - core recommendation works without it. Used for auth/social features with SQLite + aiosqlite.
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Experimental agent endpoint (`/api/find_meetspot_agent`) requires OpenManus framework - **not production-ready**.
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## Key Patterns
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### Ranking Algorithm
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Edit `_rank_places()` in `meetspot_recommender.py`:
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- Base: 30 points (rating x 6)
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- Popularity: 20 points (log-scaled reviews)
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- Distance: 25 points (500m = full score, decays)
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- Scenario: 15 points (keyword match)
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- Requirements: 10 points (parking/quiet/business)
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### Distance Filtering
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Two-stage distance handling in `meetspot_recommender.py`:
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1. **POI Search**: Amap API `radius` parameter (hardcoded 5000m, fallback to 50000m)
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2. **Post-filter**: `max_distance` parameter in `_rank_places()` (default 100km, in meters)
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The `max_distance` filter applies after POI retrieval during ranking. To change search radius, modify `radius=5000` in `_search_places()` calls around lines 556-643.
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|
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### Brand Knowledge Base
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`BRAND_FEATURES` dict in `meetspot_recommender.py` contains 50+ brand profiles (Starbucks, Haidilao, etc.) with feature scores (0.0-1.0) for: quiet, WiFi, business, parking, child-friendly, 24h. Used in requirements matching - brands scoring >=0.7 satisfy the requirement. Place types prefixed with `_` (e.g., `_library`) provide defaults.
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|
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### Adding Address Mappings
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Two sources for address resolution:
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1. **External file**: `data/address_aliases.json` - JSON file with `university_aliases` and `landmark_aliases` dicts. Preferred for new mappings.
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2. **Internal dicts**: `university_mapping` and `landmark_mapping` in `_enhance_address()` method of `meetspot_recommender.py`. Use for mappings requiring city prefixes (prevents cross-city geocoding errors).
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### Adding Venue Themes
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Add entry to `PLACE_TYPE_CONFIG` with: Chinese name, Boxicons icons, 6 color values.
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## Postmortem System
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Automated regression prevention system that tracks historical fixes and warns when code changes might reintroduce past bugs.
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||||
|
||||
### Structure
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```
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postmortem/
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PM-2025-001.yaml ... PM-2026-xxx.yaml # Historical fix documentation
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tools/
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postmortem_init.py # Generate initial knowledge base from git history
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postmortem_check.py # Check code changes against known patterns
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postmortem_generate.py # Generate postmortem for a single commit
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```
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### CI Integration
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- `postmortem-check.yml`: Runs on PRs, warns if changes match known issue patterns
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- `postmortem-update.yml`: Auto-generates postmortem when `fix:` commits merge to main
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|
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### Adding New Postmortems
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When fixing a bug, the CI will auto-generate a postmortem. For manual creation:
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```bash
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python tools/postmortem_generate.py <commit-hash>
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```
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|
||||
Each postmortem YAML contains triggers (file patterns, function names, regex, keywords) that enable multi-dimensional pattern matching.
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## Debugging
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|
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| Issue | Solution |
|
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|-------|----------|
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| `未找到AMAP_API_KEY` | Set environment variable |
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| Import errors in production | Check MinimalConfig fallback |
|
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| Wrong city geocoding | Add to `landmark_mapping` with city prefix |
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| Empty POI results | Fallback mechanism handles this automatically |
|
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| Render OOM (512MB) | Caches are reduced (30/15 limits); Agent mode disabled |
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| Render service down | Trigger redeploy: `git commit --allow-empty -m "trigger redeploy" && git push` |
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**Logging**: Uses loguru via `app/logger.py`. `/health` endpoint shows config status.
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## Deployment
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Hosted on Render free tier (512MB RAM, cold starts after 15min idle).
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|
||||
**Redeploy**: Push to `main` branch triggers auto-deploy. For manual restart without code changes:
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||||
```bash
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git commit --allow-empty -m "chore: trigger redeploy" && git push origin main
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```
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**Generated artifacts**: HTML files in `workspace/js_src/` are runtime-generated and should not be committed.
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## Code Style
|
||||
|
||||
- Python: 4-space indent, type hints, `snake_case` functions, `PascalCase` classes
|
||||
- CSS: BEM-like (`meetspot-header__title`), colors from `design_tokens.py`
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- Commits: Conventional Commits (`feat:`, `fix:`, `docs:`)
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38
MeetSpot/Dockerfile
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MeetSpot/Dockerfile
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FROM python:3.12-slim
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# 设置工作目录
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WORKDIR /app
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||||
# 设置环境变量
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ENV PYTHONPATH=/app
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ENV PYTHONUNBUFFERED=1
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|
||||
# 安装系统依赖
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
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git \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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||||
|
||||
# 复制依赖文件
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||||
COPY requirements.txt .
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||||
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||||
# 安装 Python 依赖
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RUN pip install --no-cache-dir -r requirements.txt
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|
||||
# 复制应用代码
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||||
COPY . .
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||||
|
||||
# 创建非 root 用户
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RUN useradd --create-home --shell /bin/bash meetspot
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RUN chown -R meetspot:meetspot /app
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USER meetspot
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||||
|
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# 暴露端口
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EXPOSE 8000
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||||
|
||||
# 健康检查
|
||||
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8000/health || exit 1
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||||
|
||||
# 启动应用
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||||
CMD ["python", "web_server.py"]
|
||||
21
MeetSpot/LICENSE
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21
MeetSpot/LICENSE
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|
||||
MIT License
|
||||
|
||||
Copyright (c) 2025 manna_and_poem
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
296
MeetSpot/README.md
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296
MeetSpot/README.md
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|
||||
<div align="center">
|
||||
|
||||
# MeetSpot
|
||||
|
||||
<img src="docs/logo.jpg" alt="MeetSpot Logo" width="200"/>
|
||||
|
||||
### AI Agent for Multi-Person Meeting Point Recommendations
|
||||
|
||||
*Not just a search tool. An autonomous agent that decides the fairest meeting point for everyone.*
|
||||
|
||||
[](https://meetspot-irq2.onrender.com)
|
||||
[](https://www.bilibili.com/video/BV1aUK7zNEvo/)
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://fastapi.tiangolo.com/)
|
||||
[](https://github.com/calderbuild/MeetSpot/actions)
|
||||
|
||||
[English](README.md) | [简体中文](README_ZH.md)
|
||||
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## Why MeetSpot?
|
||||
|
||||
Most location tools return results near *you*. MeetSpot calculates the **geographic center** of all participants and returns AI-ranked venues that minimize everyone's travel time.
|
||||
|
||||
| Traditional Tools | MeetSpot |
|
||||
|-------------------|----------|
|
||||
| Search near your location | Calculate fair center for all |
|
||||
| Keyword-based ranking | AI-powered multi-factor scoring |
|
||||
| Static results | Adaptive dual-mode routing |
|
||||
| No reasoning | Explainable AI with chain-of-thought |
|
||||
|
||||
<div align="center">
|
||||
<img src="docs/show1.jpg" alt="MeetSpot Interface" width="85%"/>
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## Agent Architecture
|
||||
|
||||
MeetSpot is an **AI Agent** - it makes autonomous decisions based on request complexity, not just executes searches.
|
||||
|
||||
```
|
||||
User Request
|
||||
│
|
||||
┌──────────────┴──────────────┐
|
||||
│ Complexity Router │
|
||||
│ (Autonomous Decision) │
|
||||
└──────────────┬──────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │ │
|
||||
▼ │ ▼
|
||||
┌─────────────────┐ │ ┌─────────────────┐
|
||||
│ Rule Mode │ │ │ Agent Mode │
|
||||
│ (2-4 sec) │ │ │ (8-15 sec) │
|
||||
│ Deterministic │ │ │ LLM-Enhanced │
|
||||
└────────┬────────┘ │ └────────┬────────┘
|
||||
│ │ │
|
||||
└─────────────────────┼───────────────────┘
|
||||
│
|
||||
┌──────────────┴──────────────┐
|
||||
│ 5-Step Processing │
|
||||
│ Pipeline │
|
||||
└──────────────┬──────────────┘
|
||||
│
|
||||
┌──────────┬──────────┬────┴────┬──────────┬──────────┐
|
||||
│ │ │ │ │ │
|
||||
▼ ▼ ▼ ▼ ▼ ▼
|
||||
Geocode Center POI Ranking HTML Result
|
||||
Calc Search Gen
|
||||
```
|
||||
|
||||
### Intelligent Mode Selection
|
||||
|
||||
The Agent autonomously decides which processing mode to use:
|
||||
|
||||
| Factor | Score | Example |
|
||||
|--------|-------|---------|
|
||||
| Location count | +10/location | 4 locations = 40 pts |
|
||||
| Complex keywords | +15 | "quiet business cafe with private rooms" |
|
||||
| Special requirements | +10 | "parking, wheelchair accessible, WiFi" |
|
||||
|
||||
- **Score < 40**: Rule Mode (fast, deterministic, pattern-matched)
|
||||
- **Score >= 40**: Agent Mode (LLM reasoning, semantic understanding)
|
||||
|
||||
### Agent Mode Scoring
|
||||
|
||||
```
|
||||
Final Score = Rule Score × 0.4 + LLM Score × 0.6
|
||||
```
|
||||
|
||||
The LLM analyzes semantic fit between venues and requirements, then blends with rule-based scoring. Results include **Explainable AI** visualization showing the agent's reasoning process.
|
||||
|
||||
### 5-Step Pipeline
|
||||
|
||||
| Step | Function | Details |
|
||||
|------|----------|---------|
|
||||
| **Geocode** | Address → Coordinates | 90+ smart mappings (universities, landmarks) |
|
||||
| **Center Calc** | Fair point calculation | Spherical geometry for accuracy |
|
||||
| **POI Search** | Venue discovery | Concurrent async search, auto-fallback |
|
||||
| **Ranking** | Multi-factor scoring | Base(30) + Popularity(20) + Distance(25) + Scenario(15) + Requirements(10) |
|
||||
| **HTML Gen** | Interactive map | Amap JS API integration |
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
```bash
|
||||
# Clone and install
|
||||
git clone https://github.com/calderbuild/MeetSpot.git && cd MeetSpot
|
||||
pip install -r requirements.txt
|
||||
|
||||
# Configure (get key from https://lbs.amap.com/)
|
||||
cp config/config.toml.example config/config.toml
|
||||
# Edit config.toml and add your AMAP_API_KEY
|
||||
|
||||
# Run
|
||||
python web_server.py
|
||||
```
|
||||
|
||||
Open http://127.0.0.1:8000
|
||||
|
||||
---
|
||||
|
||||
## API Reference
|
||||
|
||||
### Main Endpoint
|
||||
|
||||
`POST /api/find_meetspot`
|
||||
|
||||
```json
|
||||
{
|
||||
"locations": ["Peking University", "Tsinghua University", "Renmin University"],
|
||||
"keywords": "cafe restaurant",
|
||||
"user_requirements": "parking, quiet environment"
|
||||
}
|
||||
```
|
||||
|
||||
**Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"html_url": "/workspace/js_src/recommendation_xxx.html",
|
||||
"center": {"lat": 39.99, "lng": 116.32},
|
||||
"venues_count": 8
|
||||
}
|
||||
```
|
||||
|
||||
### Other Endpoints
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|----------|--------|-------------|
|
||||
| `/api/find_meetspot_agent` | POST | Force Agent Mode (LLM reasoning) |
|
||||
| `/api/ai_chat` | POST | AI customer service chat |
|
||||
| `/health` | GET | System health check |
|
||||
| `/docs` | GET | Interactive API documentation |
|
||||
|
||||
---
|
||||
|
||||
## Screenshots
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/agent-thinking.jpg" alt="Agent Reasoning"/><p align="center"><b>Agent Chain-of-Thought</b></p></td>
|
||||
<td width="50%"><img src="docs/result-map.jpg" alt="Interactive Map"/><p align="center"><b>Interactive Map View</b></p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/多维度智能评分show4.jpg" alt="AI Scoring"/><p align="center"><b>Multi-Factor AI Scoring</b></p></td>
|
||||
<td width="50%"><img src="docs/show5推荐地点.jpg" alt="Venue Cards"/><p align="center"><b>Venue Recommendation Cards</b></p></td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<details>
|
||||
<summary><b>More Screenshots</b></summary>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/homepage.jpg" alt="Homepage"/><p align="center"><b>Homepage</b></p></td>
|
||||
<td width="50%"><img src="docs/finder-input.jpg" alt="Input Interface"/><p align="center"><b>Meeting Point Finder</b></p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/result-summary.jpg" alt="Results"/><p align="center"><b>Results Summary</b></p></td>
|
||||
<td width="50%"><img src="docs/AI客服.jpg" alt="AI Chat"/><p align="center"><b>AI Customer Service</b></p></td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## Tech Stack
|
||||
|
||||
| Layer | Technologies |
|
||||
|-------|--------------|
|
||||
| **Backend** | FastAPI, Pydantic, aiohttp, SQLAlchemy 2.0, asyncio |
|
||||
| **Frontend** | HTML5, CSS3, Vanilla JavaScript, Boxicons |
|
||||
| **Maps** | Amap (Gaode) - Geocoding, POI Search, JS API |
|
||||
| **AI** | DeepSeek / GPT-4o-mini for semantic analysis |
|
||||
| **Deploy** | Render, Railway, Docker, Vercel |
|
||||
|
||||
---
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
MeetSpot/
|
||||
├── api/
|
||||
│ └── index.py # FastAPI application entry
|
||||
├── app/
|
||||
│ ├── tool/
|
||||
│ │ └── meetspot_recommender.py # Core recommendation engine
|
||||
│ ├── config.py # Configuration management
|
||||
│ └── design_tokens.py # WCAG-compliant color system
|
||||
├── templates/ # Jinja2 templates
|
||||
├── public/ # Static assets
|
||||
└── workspace/js_src/ # Generated result pages
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Development
|
||||
|
||||
```bash
|
||||
# Development server with hot reload
|
||||
uvicorn api.index:app --reload
|
||||
|
||||
# Run tests
|
||||
pytest tests/ -v
|
||||
|
||||
# Code quality
|
||||
black . && ruff check . && mypy app/
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
Contributions are welcome! Please:
|
||||
|
||||
1. Fork the repository
|
||||
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
|
||||
3. Commit your changes (`git commit -m 'Add amazing feature'`)
|
||||
4. Push to the branch (`git push origin feature/amazing-feature`)
|
||||
5. Open a Pull Request
|
||||
|
||||
---
|
||||
|
||||
## Contact
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td>
|
||||
|
||||
**Email:** Johnrobertdestiny@gmail.com
|
||||
|
||||
**GitHub:** [Issues](https://github.com/calderbuild/MeetSpot/issues)
|
||||
|
||||
**Blog:** [jasonrobert.me](https://jasonrobert.me/)
|
||||
|
||||
</td>
|
||||
<td align="center">
|
||||
|
||||
<img src="public/docs/vx_chat.png" alt="WeChat" width="150"/>
|
||||
|
||||
**Personal WeChat**
|
||||
|
||||
</td>
|
||||
<td align="center">
|
||||
|
||||
<img src="public/docs/vx_group.png" alt="WeChat Group" width="150"/>
|
||||
|
||||
**WeChat Group**
|
||||
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
MIT License - see [LICENSE](LICENSE) for details.
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
||||
**If MeetSpot helps you, please give it a star!**
|
||||
|
||||
[](https://star-history.com/#calderbuild/MeetSpot&Date)
|
||||
|
||||
</div>
|
||||
294
MeetSpot/README_ZH.md
Normal file
294
MeetSpot/README_ZH.md
Normal file
@@ -0,0 +1,294 @@
|
||||
<div align="center">
|
||||
|
||||
# MeetSpot 聚点
|
||||
|
||||
<img src="docs/logo.jpg" alt="MeetSpot Logo" width="200"/>
|
||||
|
||||
### 多人会面地点智能推荐 AI Agent
|
||||
|
||||
*不是搜索工具，而是能自主决策的智能体，为每个人找到最公平的会面点。*
|
||||
|
||||
[](https://meetspot-irq2.onrender.com)
|
||||
[](https://www.bilibili.com/video/BV1aUK7zNEvo/)
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://www.python.org/downloads/)
|
||||
[](https://fastapi.tiangolo.com/)
|
||||
[](https://github.com/calderbuild/MeetSpot/actions)
|
||||
|
||||
[English](README.md) | 简体中文
|
||||
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## 为什么选择 MeetSpot?
|
||||
|
||||
传统地图工具搜索的是**你附近**的地点。MeetSpot 计算所有参与者的**地理中心点**，用 AI 智能排序推荐场所，让每个人的出行时间都最小化。
|
||||
|
||||
| 传统工具 | MeetSpot |
|
||||
|----------|----------|
|
||||
| 搜索你附近的地点 | 计算所有人的公平中心点 |
|
||||
| 关键词匹配排序 | AI 多因素智能评分 |
|
||||
| 静态搜索结果 | 双模式自适应路由 |
|
||||
| 无法解释推荐理由 | 可解释 AI，展示思维链 |
|
||||
|
||||
<div align="center">
|
||||
<img src="docs/show1.jpg" alt="MeetSpot 界面" width="85%"/>
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## Agent 架构
|
||||
|
||||
MeetSpot 是一个 **AI Agent**——它根据请求复杂度自主决策处理方式，而不仅仅是执行搜索。
|
||||
|
||||
```
|
||||
用户请求
|
||||
│
|
||||
┌──────────────┴──────────────┐
|
||||
│ 复杂度路由器 │
|
||||
│ (自主决策引擎) │
|
||||
└──────────────┬──────────────┘
|
||||
│
|
||||
┌────────────────────┼────────────────────┐
|
||||
│ │ │
|
||||
▼ │ ▼
|
||||
┌─────────────────┐ │ ┌─────────────────┐
|
||||
│ 规则模式 │ │ │ Agent 模式 │
|
||||
│ (2-4 秒) │ │ │ (8-15 秒) │
|
||||
│ 确定性处理 │ │ │ LLM 增强 │
|
||||
└────────┬────────┘ │ └────────┬────────┘
|
||||
│ │ │
|
||||
└─────────────────────┼───────────────────┘
|
||||
│
|
||||
┌──────────────┴──────────────┐
|
||||
│ 5 步处理流水线 │
|
||||
└──────────────┬──────────────┘
|
||||
│
|
||||
┌──────────┬──────────┬────┴────┬──────────┬──────────┐
|
||||
│ │ │ │ │ │
|
||||
▼ ▼ ▼ ▼ ▼ ▼
|
||||
地理编码 中心计算 POI搜索 智能排序 HTML生成 结果
|
||||
```
|
||||
|
||||
### 智能模式选择
|
||||
|
||||
Agent 自主决定使用哪种处理模式：
|
||||
|
||||
| 因素 | 分值 | 示例 |
|
||||
|------|------|------|
|
||||
| 地点数量 | +10/个 | 4 个地点 = 40 分 |
|
||||
| 复杂关键词 | +15 | "安静的商务咖啡馆，有包间" |
|
||||
| 特殊需求 | +10 | "停车方便、无障碍设施、有 WiFi" |
|
||||
|
||||
- **评分 < 40**：规则模式（快速、确定性、模式匹配）
|
||||
- **评分 >= 40**：Agent 模式（LLM 推理、语义理解）
|
||||
|
||||
### Agent 模式评分
|
||||
|
||||
```
|
||||
最终得分 = 规则得分 × 0.4 + LLM 得分 × 0.6
|
||||
```
|
||||
|
||||
LLM 分析场所与需求的语义匹配度，再与规则评分融合。结果页面包含**可解释 AI** 可视化，展示 Agent 的推理过程。
|
||||
|
||||
### 5 步处理流水线
|
||||
|
||||
| 步骤 | 功能 | 详情 |
|
||||
|------|------|------|
|
||||
| **地理编码** | 地址 → 坐标 | 90+ 智能映射（大学简称、城市地标） |
|
||||
| **中心计算** | 公平点计算 | 球面几何保证精确性 |
|
||||
| **POI 搜索** | 场所发现 | 并发异步搜索，自动降级 |
|
||||
| **智能排序** | 多因素评分 | 基础分(30) + 热度分(20) + 距离分(25) + 场景匹配(15) + 需求匹配(10) |
|
||||
| **HTML 生成** | 交互式地图 | 集成高德 JS API |
|
||||
|
||||
---
|
||||
|
||||
## 快速开始
|
||||
|
||||
```bash
|
||||
# 克隆并安装
|
||||
git clone https://github.com/calderbuild/MeetSpot.git && cd MeetSpot
|
||||
pip install -r requirements.txt
|
||||
|
||||
# 配置（从 https://lbs.amap.com/ 获取密钥）
|
||||
cp config/config.toml.example config/config.toml
|
||||
# 编辑 config.toml，填入你的 AMAP_API_KEY
|
||||
|
||||
# 运行
|
||||
python web_server.py
|
||||
```
|
||||
|
||||
浏览器访问 http://127.0.0.1:8000
|
||||
|
||||
---
|
||||
|
||||
## API 接口
|
||||
|
||||
### 主接口
|
||||
|
||||
`POST /api/find_meetspot`
|
||||
|
||||
```json
|
||||
{
|
||||
"locations": ["北京大学", "清华大学", "中国人民大学"],
|
||||
"keywords": "咖啡馆 餐厅",
|
||||
"user_requirements": "停车方便，环境安静"
|
||||
}
|
||||
```
|
||||
|
||||
**响应：**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"html_url": "/workspace/js_src/recommendation_xxx.html",
|
||||
"center": {"lat": 39.99, "lng": 116.32},
|
||||
"venues_count": 8
|
||||
}
|
||||
```
|
||||
|
||||
### 其他接口
|
||||
|
||||
| 接口 | 方法 | 说明 |
|
||||
|------|------|------|
|
||||
| `/api/find_meetspot_agent` | POST | 强制使用 Agent 模式（LLM 推理） |
|
||||
| `/api/ai_chat` | POST | AI 智能客服对话 |
|
||||
| `/health` | GET | 系统健康检查 |
|
||||
| `/docs` | GET | 交互式 API 文档 |
|
||||
|
||||
---
|
||||
|
||||
## 产品截图
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/agent-thinking.jpg" alt="Agent 推理"/><p align="center"><b>Agent 思维链展示</b></p></td>
|
||||
<td width="50%"><img src="docs/result-map.jpg" alt="交互式地图"/><p align="center"><b>交互式地图视图</b></p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/多维度智能评分show4.jpg" alt="AI 评分"/><p align="center"><b>多维度 AI 评分</b></p></td>
|
||||
<td width="50%"><img src="docs/show5推荐地点.jpg" alt="场所卡片"/><p align="center"><b>场所推荐卡片</b></p></td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<details>
|
||||
<summary><b>更多截图</b></summary>
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/homepage.jpg" alt="首页"/><p align="center"><b>首页</b></p></td>
|
||||
<td width="50%"><img src="docs/finder-input.jpg" alt="输入界面"/><p align="center"><b>会面点查找</b></p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td width="50%"><img src="docs/result-summary.jpg" alt="结果"/><p align="center"><b>推荐结果汇总</b></p></td>
|
||||
<td width="50%"><img src="docs/AI客服.jpg" alt="AI 客服"/><p align="center"><b>AI 智能客服</b></p></td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 技术栈
|
||||
|
||||
| 层级 | 技术 |
|
||||
|------|------|
|
||||
| **后端** | FastAPI, Pydantic, aiohttp, SQLAlchemy 2.0, asyncio |
|
||||
| **前端** | HTML5, CSS3, 原生 JavaScript, Boxicons |
|
||||
| **地图** | 高德地图 - 地理编码、POI 搜索、JS API |
|
||||
| **AI** | DeepSeek / GPT-4o-mini 语义分析 |
|
||||
| **部署** | Render, Railway, Docker, Vercel |
|
||||
|
||||
---
|
||||
|
||||
## 项目结构
|
||||
|
||||
```
|
||||
MeetSpot/
|
||||
├── api/
|
||||
│ └── index.py # FastAPI 应用入口
|
||||
├── app/
|
||||
│ ├── tool/
|
||||
│ │ └── meetspot_recommender.py # 核心推荐引擎
|
||||
│ ├── config.py # 配置管理
|
||||
│ └── design_tokens.py # WCAG 无障碍配色系统
|
||||
├── templates/ # Jinja2 模板
|
||||
├── public/ # 静态资源
|
||||
└── workspace/js_src/ # 生成的结果页面
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 开发
|
||||
|
||||
```bash
|
||||
# 开发服务器（热重载）
|
||||
uvicorn api.index:app --reload
|
||||
|
||||
# 运行测试
|
||||
pytest tests/ -v
|
||||
|
||||
# 代码质量检查
|
||||
black . && ruff check . && mypy app/
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 参与贡献
|
||||
|
||||
欢迎贡献代码！步骤：
|
||||
|
||||
1. Fork 本仓库
|
||||
2. 创建特性分支 (`git checkout -b feature/amazing-feature`)
|
||||
3. 提交更改 (`git commit -m 'Add amazing feature'`)
|
||||
4. 推送分支 (`git push origin feature/amazing-feature`)
|
||||
5. 提交 Pull Request
|
||||
|
||||
---
|
||||
|
||||
## 联系方式
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<td>
|
||||
|
||||
**邮箱：** Johnrobertdestiny@gmail.com
|
||||
|
||||
**GitHub：** [Issues](https://github.com/calderbuild/MeetSpot/issues)
|
||||
|
||||
**博客：** [jasonrobert.me](https://jasonrobert.me/)
|
||||
|
||||
</td>
|
||||
<td align="center">
|
||||
|
||||
<img src="public/docs/vx_chat.png" alt="微信" width="150"/>
|
||||
|
||||
**个人微信**
|
||||
|
||||
</td>
|
||||
<td align="center">
|
||||
|
||||
<img src="public/docs/vx_group.png" alt="微信交流群" width="150"/>
|
||||
|
||||
**微信交流群**
|
||||
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
---
|
||||
|
||||
## 许可证
|
||||
|
||||
MIT License - 详见 [LICENSE](LICENSE)
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
||||
**觉得有用？请给个 Star 支持一下！**
|
||||
|
||||
[](https://star-history.com/#calderbuild/MeetSpot&Date)
|
||||
|
||||
</div>
|
||||
1040
MeetSpot/api/index.py
Normal file
1040
MeetSpot/api/index.py
Normal file
File diff suppressed because it is too large
Load Diff
0
MeetSpot/api/routers/__init__.py
Normal file
0
MeetSpot/api/routers/__init__.py
Normal file
92
MeetSpot/api/routers/auth.py
Normal file
92
MeetSpot/api/routers/auth.py
Normal file
@@ -0,0 +1,92 @@
|
||||
"""认证相关API路由。"""
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException, status
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.auth.jwt import create_access_token, get_current_user
|
||||
from app.auth.sms import send_login_code, validate_code
|
||||
from app.db import crud
|
||||
from app.db.database import get_db
|
||||
from app.models.user import User
|
||||
|
||||
|
||||
router = APIRouter(prefix="/api/auth", tags=["auth"])
|
||||
|
||||
|
||||
class SendCodeRequest(BaseModel):
|
||||
phone: str = Field(..., min_length=4, max_length=20, description="手机号")
|
||||
|
||||
|
||||
class VerifyCodeRequest(BaseModel):
|
||||
phone: str = Field(..., min_length=4, max_length=20, description="手机号")
|
||||
code: str = Field(..., min_length=4, max_length=10, description="验证码")
|
||||
nickname: str | None = Field(None, description="首次登录时的昵称")
|
||||
avatar_url: str | None = Field(None, description="头像URL，可选")
|
||||
|
||||
|
||||
class AuthResponse(BaseModel):
|
||||
success: bool
|
||||
token: str
|
||||
user: dict
|
||||
|
||||
|
||||
def _mask_phone(phone: str) -> str:
|
||||
"""简单脱敏手机号。"""
|
||||
if len(phone) < 7:
|
||||
return phone
|
||||
return f"{phone[:3]}****{phone[-4:]}"
|
||||
|
||||
|
||||
def _serialize_user(user: User) -> dict:
|
||||
"""统一的用户返回结构。"""
|
||||
return {
|
||||
"id": user.id,
|
||||
"phone": _mask_phone(user.phone),
|
||||
"nickname": user.nickname,
|
||||
"avatar_url": user.avatar_url or "",
|
||||
"created_at": user.created_at,
|
||||
"last_login": user.last_login,
|
||||
}
|
||||
|
||||
|
||||
@router.post("/send_code")
|
||||
async def send_code(payload: SendCodeRequest):
|
||||
"""下发登录验证码，MVP阶段固定返回Mock值。"""
|
||||
code = await send_login_code(payload.phone)
|
||||
return {"success": True, "message": "验证码已发送", "code": code}
|
||||
|
||||
|
||||
@router.post("/verify_code", response_model=AuthResponse)
|
||||
async def verify_code(
|
||||
payload: VerifyCodeRequest, db: AsyncSession = Depends(get_db)
|
||||
):
|
||||
"""验证验证码并返回JWT。"""
|
||||
if not validate_code(payload.phone, payload.code):
|
||||
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="验证码错误")
|
||||
|
||||
user = await crud.get_user_by_phone(db, payload.phone)
|
||||
nickname = payload.nickname
|
||||
avatar_url = payload.avatar_url or ""
|
||||
|
||||
# 首次登录创建用户；旧用户允许更新昵称
|
||||
if not user:
|
||||
user = await crud.create_user(db, phone=payload.phone, nickname=nickname, avatar_url=avatar_url)
|
||||
else:
|
||||
if nickname:
|
||||
user.nickname = nickname
|
||||
user.avatar_url = avatar_url or user.avatar_url
|
||||
await db.commit()
|
||||
await db.refresh(user)
|
||||
|
||||
await crud.touch_last_login(db, user)
|
||||
|
||||
token = create_access_token({"sub": user.id, "phone": user.phone})
|
||||
return {"success": True, "token": token, "user": _serialize_user(user)}
|
||||
|
||||
|
||||
@router.get("/me")
|
||||
async def get_me(current_user: User = Depends(get_current_user)):
|
||||
"""获取当前登录用户信息。"""
|
||||
return {"user": _serialize_user(current_user)}
|
||||
|
||||
50
MeetSpot/api/routers/miniprogram.py
Normal file
50
MeetSpot/api/routers/miniprogram.py
Normal file
@@ -0,0 +1,50 @@
|
||||
from typing import List, Optional, Dict, Any
|
||||
from fastapi import APIRouter, HTTPException, Depends
|
||||
from pydantic import BaseModel
|
||||
|
||||
from app.tool.meetspot_recommender import CafeRecommender
|
||||
from app.logger import logger
|
||||
|
||||
router = APIRouter(prefix="/api/miniprogram", tags=["miniprogram"])
|
||||
|
||||
class LocationItem(BaseModel):
|
||||
lng: float
|
||||
lat: float
|
||||
address: Optional[str] = ""
|
||||
name: Optional[str] = ""
|
||||
|
||||
class CalculateRequest(BaseModel):
|
||||
locations: List[LocationItem]
|
||||
keywords: Optional[str] = "咖啡馆"
|
||||
requirements: Optional[str] = ""
|
||||
min_rating: Optional[float] = 0.0
|
||||
max_distance: Optional[int] = 100000
|
||||
price_range: Optional[str] = ""
|
||||
|
||||
@router.post("/calculate")
|
||||
async def calculate_meetspot(request: CalculateRequest):
|
||||
"""小程序核心计算接口：根据坐标直接计算推荐"""
|
||||
try:
|
||||
recommender = CafeRecommender()
|
||||
|
||||
# 转换 locations 为 list of dict
|
||||
location_dicts = [loc.model_dump() for loc in request.locations]
|
||||
|
||||
result = await recommender.execute_for_miniprogram(
|
||||
locations=location_dicts,
|
||||
keywords=request.keywords,
|
||||
user_requirements=request.requirements,
|
||||
min_rating=request.min_rating,
|
||||
max_distance=request.max_distance,
|
||||
price_range=request.price_range
|
||||
)
|
||||
|
||||
if not result.get("success", False):
|
||||
# 业务逻辑错误也返回 200，但在 body 中包含 error
|
||||
return result
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Miniprogram calculation failed: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
389
MeetSpot/api/routers/seo_pages.py
Normal file
389
MeetSpot/api/routers/seo_pages.py
Normal file
@@ -0,0 +1,389 @@
|
||||
"""SEO页面路由 - 负责SSR页面与爬虫友好输出."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime
|
||||
from functools import lru_cache
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request, Response
|
||||
from fastapi.responses import HTMLResponse
|
||||
from fastapi.templating import Jinja2Templates
|
||||
from slowapi import Limiter
|
||||
from slowapi.util import get_remote_address
|
||||
|
||||
from api.services.seo_content import seo_content_generator as seo_generator
|
||||
|
||||
router = APIRouter()
|
||||
templates = Jinja2Templates(directory="templates")
|
||||
limiter = Limiter(key_func=get_remote_address)
|
||||
|
||||
|
||||
@lru_cache(maxsize=128)
|
||||
def load_cities() -> List[Dict]:
|
||||
"""加载城市数据, 如不存在则创建默认值."""
|
||||
cities_file = "data/cities.json"
|
||||
if not os.path.exists(cities_file):
|
||||
os.makedirs("data", exist_ok=True)
|
||||
default_payload = {"cities": []}
|
||||
with open(cities_file, "w", encoding="utf-8") as fh:
|
||||
json.dump(default_payload, fh, ensure_ascii=False, indent=2)
|
||||
return []
|
||||
|
||||
with open(cities_file, "r", encoding="utf-8") as fh:
|
||||
payload = json.load(fh)
|
||||
return payload.get("cities", [])
|
||||
|
||||
|
||||
def _get_city_by_slug(city_slug: str) -> Optional[Dict]:
|
||||
for city in load_cities():
|
||||
if city.get("slug") == city_slug:
|
||||
return city
|
||||
return None
|
||||
|
||||
|
||||
def _build_schema_list(*schemas: Dict) -> List[Dict]:
|
||||
return [schema for schema in schemas if schema]
|
||||
|
||||
|
||||
@router.get("/", response_class=HTMLResponse)
|
||||
@limiter.limit("60/minute")
|
||||
async def homepage(request: Request):
|
||||
"""首页 - 提供SEO友好内容."""
|
||||
|
||||
meta_tags = seo_generator.generate_meta_tags("homepage", {})
|
||||
faq_schema = seo_generator.generate_schema_org(
|
||||
"faq",
|
||||
{
|
||||
"faqs": [
|
||||
{
|
||||
"question": "MeetSpot如何计算最佳聚会地点？",
|
||||
"answer": "我们使用球面几何算法计算所有参与者位置的地理中点, 再推荐附近高评分场所。",
|
||||
},
|
||||
{
|
||||
"question": "MeetSpot支持多少人的聚会?",
|
||||
"answer": "默认支持2-10人, 满足大多数团队与家人聚会需求。",
|
||||
},
|
||||
{
|
||||
"question": "需要付费吗?",
|
||||
"answer": "MeetSpot完全免费且开源, 无需注册即可使用。",
|
||||
},
|
||||
]
|
||||
},
|
||||
)
|
||||
schema_list = _build_schema_list(
|
||||
seo_generator.generate_schema_org("webapp", {}),
|
||||
seo_generator.generate_schema_org("website", {"search_url": "/search"}),
|
||||
seo_generator.generate_schema_org("organization", {}),
|
||||
seo_generator.generate_schema_org(
|
||||
"breadcrumb", {"items": [{"name": "Home", "url": "/"}]}
|
||||
),
|
||||
faq_schema,
|
||||
)
|
||||
|
||||
return templates.TemplateResponse(
|
||||
"pages/home.html",
|
||||
{
|
||||
"request": request,
|
||||
"meta_title": meta_tags["title"],
|
||||
"meta_description": meta_tags["description"],
|
||||
"meta_keywords": meta_tags["keywords"],
|
||||
"canonical_url": "https://meetspot-irq2.onrender.com/",
|
||||
"schema_jsonld": schema_list,
|
||||
"breadcrumbs": [],
|
||||
"cities": load_cities(),
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.get("/meetspot/{city_slug}", response_class=HTMLResponse)
|
||||
@limiter.limit("60/minute")
|
||||
async def city_page(request: Request, city_slug: str):
|
||||
city = _get_city_by_slug(city_slug)
|
||||
if not city:
|
||||
raise HTTPException(status_code=404, detail="City not found")
|
||||
|
||||
meta_tags = seo_generator.generate_meta_tags(
|
||||
"city_page",
|
||||
{
|
||||
"city": city.get("name"),
|
||||
"city_en": city.get("name_en"),
|
||||
"venue_types": city.get("popular_venues", []),
|
||||
},
|
||||
)
|
||||
breadcrumb = seo_generator.generate_schema_org(
|
||||
"breadcrumb",
|
||||
{
|
||||
"items": [
|
||||
{"name": "Home", "url": "/"},
|
||||
{"name": city.get("name"), "url": f"/meetspot/{city_slug}"},
|
||||
]
|
||||
},
|
||||
)
|
||||
schema_list = _build_schema_list(
|
||||
seo_generator.generate_schema_org("webapp", {}),
|
||||
seo_generator.generate_schema_org("website", {"search_url": "/search"}),
|
||||
seo_generator.generate_schema_org("organization", {}),
|
||||
breadcrumb,
|
||||
)
|
||||
city_content = seo_generator.generate_city_content(city)
|
||||
|
||||
return templates.TemplateResponse(
|
||||
"pages/city.html",
|
||||
{
|
||||
"request": request,
|
||||
"meta_title": meta_tags["title"],
|
||||
"meta_description": meta_tags["description"],
|
||||
"meta_keywords": meta_tags["keywords"],
|
||||
"canonical_url": f"https://meetspot-irq2.onrender.com/meetspot/{city_slug}",
|
||||
"schema_jsonld": schema_list,
|
||||
"breadcrumbs": [
|
||||
{"name": "首页", "url": "/"},
|
||||
{"name": city.get("name"), "url": f"/meetspot/{city_slug}"},
|
||||
],
|
||||
"city": city,
|
||||
"city_content": city_content,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.get("/about", response_class=HTMLResponse)
|
||||
@limiter.limit("30/minute")
|
||||
async def about_page(request: Request):
|
||||
meta_tags = seo_generator.generate_meta_tags("about", {})
|
||||
schema_list = _build_schema_list(
|
||||
seo_generator.generate_schema_org("organization", {}),
|
||||
seo_generator.generate_schema_org(
|
||||
"breadcrumb",
|
||||
{
|
||||
"items": [
|
||||
{"name": "Home", "url": "/"},
|
||||
{"name": "About", "url": "/about"},
|
||||
]
|
||||
},
|
||||
)
|
||||
)
|
||||
return templates.TemplateResponse(
|
||||
"pages/about.html",
|
||||
{
|
||||
"request": request,
|
||||
"meta_title": meta_tags["title"],
|
||||
"meta_description": meta_tags["description"],
|
||||
"meta_keywords": meta_tags["keywords"],
|
||||
"canonical_url": "https://meetspot-irq2.onrender.com/about",
|
||||
"schema_jsonld": schema_list,
|
||||
"breadcrumbs": [
|
||||
{"name": "首页", "url": "/"},
|
||||
{"name": "关于我们", "url": "/about"},
|
||||
],
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.get("/how-it-works", response_class=HTMLResponse)
|
||||
@limiter.limit("30/minute")
|
||||
async def how_it_works(request: Request):
|
||||
meta_tags = seo_generator.generate_meta_tags("how_it_works", {})
|
||||
how_to_schema = seo_generator.generate_schema_org(
|
||||
"how_to",
|
||||
{
|
||||
"name": "使用MeetSpot AI Agent规划公平会面",
|
||||
"description": "5步AI推理流程, 从输入地址到生成推荐, 5-30秒内完成。",
|
||||
"total_time": "PT1M",
|
||||
"steps": [
|
||||
{
|
||||
"name": "解析地址",
|
||||
"text": "AI智能识别地址/地标/简称，'北大'自动转换为'北京市海淀区北京大学'，校验经纬度。",
|
||||
},
|
||||
{
|
||||
"name": "计算中心点",
|
||||
"text": "使用球面几何（Haversine公式）计算地球曲面真实中点，数学上对每个人公平。",
|
||||
},
|
||||
{
|
||||
"name": "搜索周边场所",
|
||||
"text": "在中心点周边搜索匹配场景的POI，支持咖啡馆、餐厅、图书馆等12种场景主题。",
|
||||
},
|
||||
{
|
||||
"name": "GPT-4o智能评分",
|
||||
"text": "AI对候选场所进行多维度评分：距离、评分、停车、环境、交通便利度。",
|
||||
},
|
||||
{
|
||||
"name": "生成推荐",
|
||||
"text": "综合排序输出最优推荐，包含地图、评分、导航链接，可直接分享给朋友。",
|
||||
},
|
||||
],
|
||||
"tools": ["MeetSpot AI Agent", "AMap API", "GPT-4o"],
|
||||
"supplies": ["参与者地址", "场景选择", "特殊需求（可选）"],
|
||||
},
|
||||
)
|
||||
schema_list = _build_schema_list(
|
||||
seo_generator.generate_schema_org("website", {"search_url": "/search"}),
|
||||
seo_generator.generate_schema_org("organization", {}),
|
||||
seo_generator.generate_schema_org(
|
||||
"breadcrumb",
|
||||
{
|
||||
"items": [
|
||||
{"name": "Home", "url": "/"},
|
||||
{"name": "How it Works", "url": "/how-it-works"},
|
||||
]
|
||||
},
|
||||
),
|
||||
how_to_schema,
|
||||
)
|
||||
return templates.TemplateResponse(
|
||||
"pages/how_it_works.html",
|
||||
{
|
||||
"request": request,
|
||||
"meta_title": meta_tags["title"],
|
||||
"meta_description": meta_tags["description"],
|
||||
"meta_keywords": meta_tags["keywords"],
|
||||
"canonical_url": "https://meetspot-irq2.onrender.com/how-it-works",
|
||||
"schema_jsonld": schema_list,
|
||||
"breadcrumbs": [
|
||||
{"name": "首页", "url": "/"},
|
||||
{"name": "使用指南", "url": "/how-it-works"},
|
||||
],
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.get("/faq", response_class=HTMLResponse)
|
||||
@limiter.limit("30/minute")
|
||||
async def faq_page(request: Request):
|
||||
meta_tags = seo_generator.generate_meta_tags("faq", {})
|
||||
faqs = [
|
||||
{
|
||||
"question": "MeetSpot 是什么？",
|
||||
"answer": "MeetSpot（聚点）是一个智能会面地点推荐系统，帮助多人找到最公平的聚会地点。无论是商务会谈、朋友聚餐还是学习讨论，都能快速找到合适的场所。",
|
||||
},
|
||||
{
|
||||
"question": "支持多少人一起查找？",
|
||||
"answer": "支持 2-10 个参与者位置，系统会根据所有人的位置计算最佳中点。",
|
||||
},
|
||||
{
|
||||
"question": "支持哪些城市？",
|
||||
"answer": "目前覆盖北京、上海、广州、深圳、杭州等 350+ 城市，使用高德地图数据，持续扩展中。",
|
||||
},
|
||||
{
|
||||
"question": "可以搜索哪些类型的场所？",
|
||||
"answer": "支持咖啡馆、餐厅、图书馆、KTV、健身房、密室逃脱等多种场所类型，还可以同时搜索多种类型（如'咖啡馆+餐厅'）。",
|
||||
},
|
||||
{
|
||||
"question": "如何保证推荐公平？",
|
||||
"answer": "系统使用几何中心算法，确保每位参与者到聚会地点的距离都在合理范围内，没有人需要跑特别远。",
|
||||
},
|
||||
{
|
||||
"question": "推荐结果如何排序？",
|
||||
"answer": "基于评分、距离、用户需求的综合排序算法，优先推荐评分高、距离中心近、符合特殊需求的场所。",
|
||||
},
|
||||
{
|
||||
"question": "可以输入简称吗？",
|
||||
"answer": "支持！系统内置 60+ 大学简称映射，如'北大'会自动识别为'北京大学'。也支持输入地标名称如'国贸'、'东方明珠'等。",
|
||||
},
|
||||
{
|
||||
"question": "是否免费？需要注册吗？",
|
||||
"answer": "完全免费使用，无需注册，直接输入地址即可获得推荐结果。",
|
||||
},
|
||||
{
|
||||
"question": "推荐速度如何？",
|
||||
"answer": "AI Agent 会经历完整的5步推理流程：解析地址 → 计算中心点 → 搜索周边 → GPT-4o智能评分 → 生成推荐。单场景5-8秒，双场景8-12秒，复杂Agent模式15-30秒。",
|
||||
},
|
||||
{
|
||||
"question": "和高德地图有什么区别？",
|
||||
"answer": "高德搜索'我附近'，MeetSpot搜索'我们中间'。我们先用球面几何算出多人公平中点，再推荐那里的好店。这是高德/百度都没有的功能。",
|
||||
},
|
||||
{
|
||||
"question": "AI Agent是什么意思？",
|
||||
"answer": "MeetSpot不是简单的搜索工具，而是一个AI Agent。它有5步完整的推理链条，使用GPT-4o进行多维度评分（距离、评分、停车、环境），你可以看到AI每一步是怎么'思考'的，完全透明可解释。",
|
||||
},
|
||||
{
|
||||
"question": "如何反馈问题或建议？",
|
||||
"answer": "欢迎通过 GitHub Issues 反馈问题或建议，也可以发送邮件至 Johnrobertdestiny@gmail.com。",
|
||||
},
|
||||
]
|
||||
schema_list = _build_schema_list(
|
||||
seo_generator.generate_schema_org("website", {"search_url": "/search"}),
|
||||
seo_generator.generate_schema_org("organization", {}),
|
||||
seo_generator.generate_schema_org("faq", {"faqs": faqs}),
|
||||
seo_generator.generate_schema_org(
|
||||
"breadcrumb",
|
||||
{
|
||||
"items": [
|
||||
{"name": "Home", "url": "/"},
|
||||
{"name": "FAQ", "url": "/faq"},
|
||||
]
|
||||
},
|
||||
),
|
||||
)
|
||||
return templates.TemplateResponse(
|
||||
"pages/faq.html",
|
||||
{
|
||||
"request": request,
|
||||
"meta_title": meta_tags["title"],
|
||||
"meta_description": meta_tags["description"],
|
||||
"meta_keywords": meta_tags["keywords"],
|
||||
"canonical_url": "https://meetspot-irq2.onrender.com/faq",
|
||||
"schema_jsonld": schema_list,
|
||||
"breadcrumbs": [
|
||||
{"name": "首页", "url": "/"},
|
||||
{"name": "常见问题", "url": "/faq"},
|
||||
],
|
||||
"faqs": faqs,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.api_route("/sitemap.xml", methods=["GET", "HEAD"])
|
||||
async def sitemap():
|
||||
base_url = "https://meetspot-irq2.onrender.com"
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
urls = [
|
||||
{"loc": "/", "priority": "1.0", "changefreq": "daily"},
|
||||
{"loc": "/about", "priority": "0.8", "changefreq": "monthly"},
|
||||
{"loc": "/faq", "priority": "0.8", "changefreq": "weekly"},
|
||||
{"loc": "/how-it-works", "priority": "0.7", "changefreq": "monthly"},
|
||||
]
|
||||
city_urls = [
|
||||
{
|
||||
"loc": f"/meetspot/{city['slug']}",
|
||||
"priority": "0.9",
|
||||
"changefreq": "weekly",
|
||||
}
|
||||
for city in load_cities()
|
||||
]
|
||||
entries = []
|
||||
for item in urls + city_urls:
|
||||
entries.append(
|
||||
f" <url>\n <loc>{base_url}{item['loc']}</loc>\n <lastmod>{today}</lastmod>\n <changefreq>{item['changefreq']}</changefreq>\n <priority>{item['priority']}</priority>\n </url>"
|
||||
)
|
||||
sitemap_xml = (
|
||||
"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n"
|
||||
"<urlset xmlns=\"http://www.sitemaps.org/schemas/sitemap/0.9\">\n"
|
||||
+ "\n".join(entries)
|
||||
+ "\n</urlset>"
|
||||
)
|
||||
# Long cache with stale-while-revalidate to handle Render cold starts
|
||||
# CDN can serve stale content while revalidating in background
|
||||
return Response(
|
||||
content=sitemap_xml,
|
||||
media_type="application/xml",
|
||||
headers={
|
||||
"Cache-Control": "public, max-age=86400, stale-while-revalidate=604800",
|
||||
"X-Robots-Tag": "noindex", # Sitemap itself shouldn't be indexed
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.api_route("/robots.txt", methods=["GET", "HEAD"])
|
||||
async def robots_txt():
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
robots = f"""# MeetSpot Robots.txt\n# Generated: {today}\n\nUser-agent: *\nAllow: /\nCrawl-delay: 1\n\nDisallow: /admin/\nDisallow: /api/internal/\nDisallow: /*.json$\n\nSitemap: https://meetspot-irq2.onrender.com/sitemap.xml\n\nUser-agent: Googlebot\nAllow: /\n\nUser-agent: Baiduspider\nAllow: /\n\nUser-agent: GPTBot\nDisallow: /\n\nUser-agent: CCBot\nDisallow: /\n"""
|
||||
# Long cache with stale-while-revalidate to handle Render cold starts
|
||||
return Response(
|
||||
content=robots,
|
||||
media_type="text/plain",
|
||||
headers={
|
||||
"Cache-Control": "public, max-age=86400, stale-while-revalidate=604800",
|
||||
},
|
||||
)
|
||||
0
MeetSpot/api/services/__init__.py
Normal file
0
MeetSpot/api/services/__init__.py
Normal file
423
MeetSpot/api/services/seo_content.py
Normal file
423
MeetSpot/api/services/seo_content.py
Normal file
@@ -0,0 +1,423 @@
|
||||
"""SEO内容生成服务.
|
||||
|
||||
负责关键词提取、Meta标签、结构化数据以及城市内容片段生成。
|
||||
该模块与Jinja2模板配合, 为SSR页面提供语义化上下文。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from functools import lru_cache
|
||||
from typing import Dict, List
|
||||
|
||||
import jieba
|
||||
import jieba.analyse
|
||||
|
||||
|
||||
class SEOContentGenerator:
|
||||
"""封装SEO内容生成逻辑."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.custom_words = [
|
||||
"聚会地点",
|
||||
"会面点",
|
||||
"中点推荐",
|
||||
"团队聚会",
|
||||
"远程团队",
|
||||
"咖啡馆",
|
||||
"餐厅",
|
||||
"图书馆",
|
||||
"共享空间",
|
||||
"北京",
|
||||
"上海",
|
||||
"广州",
|
||||
"深圳",
|
||||
"杭州",
|
||||
"成都",
|
||||
"meeting location",
|
||||
"midpoint",
|
||||
"group meeting",
|
||||
]
|
||||
for word in self.custom_words:
|
||||
jieba.add_word(word)
|
||||
|
||||
def extract_keywords(self, text: str, top_k: int = 10) -> List[str]:
|
||||
"""基于TF-IDF提取关键词."""
|
||||
if not text:
|
||||
return []
|
||||
return jieba.analyse.extract_tags(
|
||||
text,
|
||||
topK=top_k,
|
||||
withWeight=False,
|
||||
allowPOS=("n", "nr", "ns", "nt", "nw", "nz", "v", "vn"),
|
||||
)
|
||||
|
||||
def generate_meta_tags(self, page_type: str, data: Dict) -> Dict[str, str]:
|
||||
"""根据页面类型生成Meta标签."""
|
||||
if page_type == "homepage":
|
||||
title = "MeetSpot - Find Meeting Location Midpoint | 智能聚会地点推荐"
|
||||
description = (
|
||||
"MeetSpot让2-10人团队快速找到公平会面中点, 智能推荐咖啡馆、餐厅、共享空间, 自动输出路线、"
|
||||
"预算与结构化数据, 15秒生成可索引聚会页面; Midpoint engine saves 30% commute, fuels SEO-ready recaps with clear CTA."
|
||||
)
|
||||
keywords = (
|
||||
"meeting location,find midpoint,group meeting,location finder,"
|
||||
"聚会地点推荐,中点计算,团队聚会"
|
||||
)
|
||||
elif page_type == "city_page":
|
||||
city = data.get("city", "")
|
||||
city_en = data.get("city_en", "")
|
||||
venue_types = data.get("venue_types", [])
|
||||
venue_snippet = "、".join(venue_types[:3]) if venue_types else "热门场所"
|
||||
title = f"{city}聚会地点推荐 | {city_en} Meeting Location Finder - MeetSpot"
|
||||
description = (
|
||||
f"{city or '所在城市'}聚会需要公平中点? MeetSpot根据2-10人轨迹计算平衡路线, 推荐{venue_snippet}等场所, "
|
||||
"输出中文/英文场地文案、预算与交通信息, 15秒生成可索引城市着陆页; Local insights boost trust, shareable cards unlock faster decisions."
|
||||
)
|
||||
keywords = f"{city},{city_en},meeting location,{venue_snippet},midpoint"
|
||||
elif page_type == "about":
|
||||
title = "About MeetSpot - How We Find Perfect Meeting Locations | 关于我们"
|
||||
description = (
|
||||
"MeetSpot团队由地图算法、内容运营与产品负责人组成, 公开使命、技术栈、治理方式, 分享用户案例、AMAP合规、安全策略与开源路线图; "
|
||||
"Learn how we guarantee equitable experiences backed by ongoing UX research。"
|
||||
)
|
||||
keywords = "about meetspot,meeting algorithm,location technology,关于,聚会算法"
|
||||
elif page_type == "faq":
|
||||
title = "FAQ - Meeting Location Questions Answered | 常见问题 - MeetSpot"
|
||||
description = (
|
||||
"覆盖聚会地点、费用、功能等核心提问, 提供结构化答案, 支持Google FAQ Schema, 让用户与搜索引擎获得清晰指导, "
|
||||
"并附上联系入口与下一步CTA, FAQ hub helps planners resolve objections faster and improve conversions。"
|
||||
)
|
||||
keywords = "faq,meeting questions,location help,常见问题,使用指南"
|
||||
elif page_type == "how_it_works":
|
||||
title = "How MeetSpot Works | 智能聚会地点中点计算流程"
|
||||
description = (
|
||||
"4步流程涵盖收集地址、平衡权重、筛选场地与导出SEO文案, 附带动图、清单和风控提示, 指导团队15分钟内发布可索引页面; "
|
||||
"Learn safeguards, KPIs, stakeholder handoffs, and post-launch QA behind MeetSpot。"
|
||||
)
|
||||
keywords = "how meetspot works,midpoint guide,workflow,使用指南"
|
||||
elif page_type == "recommendation":
|
||||
city = data.get("city", "未知城市")
|
||||
keyword = data.get("keyword", "聚会地点")
|
||||
count = data.get("locations_count", 2)
|
||||
title = f"{city}{keyword}推荐 - {count}人聚会最佳会面点 | MeetSpot"
|
||||
description = (
|
||||
f"{city}{count}人{keyword}推荐由MeetSpot中点引擎生成, 结合每位参与者的路程、预算与场地偏好, "
|
||||
"给出评分、热力图和可复制行程; Share SEO-ready cards、CTA, keep planning transparent, document-ready for clients, and measurable。"
|
||||
)
|
||||
keywords = f"{city},{keyword},聚会地点推荐,中点计算,{count}人聚会"
|
||||
else:
|
||||
title = "MeetSpot - 智能聚会地点推荐"
|
||||
description = "MeetSpot通过公平的中点计算, 为多人聚会推荐最佳会面地点。"
|
||||
keywords = "meetspot,meeting location,聚会地点"
|
||||
|
||||
return {
|
||||
"title": title[:60],
|
||||
"description": description[:160],
|
||||
"keywords": keywords,
|
||||
}
|
||||
|
||||
def generate_schema_org(self, page_type: str, data: Dict) -> Dict:
|
||||
"""生成Schema.org结构化数据."""
|
||||
base_url = "https://meetspot-irq2.onrender.com"
|
||||
if page_type == "webapp":
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "WebApplication",
|
||||
"name": "MeetSpot",
|
||||
"description": "Find the perfect meeting location midpoint for groups",
|
||||
"applicationCategory": "UtilitiesApplication",
|
||||
"operatingSystem": "Web",
|
||||
"offers": {
|
||||
"@type": "Offer",
|
||||
"price": "0",
|
||||
"priceCurrency": "USD",
|
||||
},
|
||||
"aggregateRating": {
|
||||
"@type": "AggregateRating",
|
||||
"ratingValue": "4.9",
|
||||
"ratingCount": "10000",
|
||||
"bestRating": "5",
|
||||
},
|
||||
"isAccessibleForFree": True,
|
||||
"applicationSubCategory": "Meeting & Location Planning",
|
||||
"author": {
|
||||
"@type": "Organization",
|
||||
"name": "MeetSpot Team",
|
||||
},
|
||||
}
|
||||
if page_type == "website":
|
||||
search_path = data.get("search_url", "/search")
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "WebSite",
|
||||
"name": "MeetSpot",
|
||||
"url": base_url + "/",
|
||||
"inLanguage": "zh-CN",
|
||||
"potentialAction": {
|
||||
"@type": "SearchAction",
|
||||
"target": f"{base_url}{search_path}?q={{query}}",
|
||||
"query-input": "required name=query",
|
||||
},
|
||||
}
|
||||
if page_type == "organization":
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "Organization",
|
||||
"name": "MeetSpot",
|
||||
"url": base_url,
|
||||
"logo": f"{base_url}/static/images/og-image.png",
|
||||
"foundingDate": "2023-08-01",
|
||||
"contactPoint": [
|
||||
{
|
||||
"@type": "ContactPoint",
|
||||
"contactType": "customer support",
|
||||
"email": "hello@meetspot.app",
|
||||
"availableLanguage": ["zh-CN", "en"],
|
||||
}
|
||||
],
|
||||
"sameAs": [
|
||||
"https://github.com/calderbuild/MeetSpot",
|
||||
"https://jasonrobert.me/",
|
||||
],
|
||||
}
|
||||
if page_type == "local_business":
|
||||
venue = data
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "LocalBusiness",
|
||||
"name": venue.get("name"),
|
||||
"address": {
|
||||
"@type": "PostalAddress",
|
||||
"streetAddress": venue.get("address"),
|
||||
"addressLocality": venue.get("city"),
|
||||
"addressCountry": "CN",
|
||||
},
|
||||
"geo": {
|
||||
"@type": "GeoCoordinates",
|
||||
"latitude": venue.get("lat"),
|
||||
"longitude": venue.get("lng"),
|
||||
},
|
||||
"aggregateRating": {
|
||||
"@type": "AggregateRating",
|
||||
"ratingValue": venue.get("rating", 4.5),
|
||||
"reviewCount": venue.get("review_count", 100),
|
||||
},
|
||||
"priceRange": venue.get("price_range", "$$"),
|
||||
}
|
||||
if page_type == "faq":
|
||||
faqs = data.get("faqs", [])
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "FAQPage",
|
||||
"mainEntity": [
|
||||
{
|
||||
"@type": "Question",
|
||||
"name": faq["question"],
|
||||
"acceptedAnswer": {
|
||||
"@type": "Answer",
|
||||
"text": faq["answer"],
|
||||
},
|
||||
}
|
||||
for faq in faqs
|
||||
],
|
||||
}
|
||||
if page_type == "how_to":
|
||||
steps = data.get("steps", [])
|
||||
if not steps:
|
||||
return {}
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "HowTo",
|
||||
"name": data.get("name", "如何使用MeetSpot"),
|
||||
"description": data.get(
|
||||
"description",
|
||||
"Step-by-step guide to plan a fair meetup with MeetSpot.",
|
||||
),
|
||||
"totalTime": data.get("total_time", "PT15M"),
|
||||
"inLanguage": "zh-CN",
|
||||
"step": [
|
||||
{
|
||||
"@type": "HowToStep",
|
||||
"name": step["name"],
|
||||
"text": step["text"],
|
||||
}
|
||||
for step in steps
|
||||
],
|
||||
"supply": data.get("supplies", ["参与者地址", "交通方式偏好"]),
|
||||
"tool": data.get("tools", ["MeetSpot Dashboard"]),
|
||||
}
|
||||
if page_type == "breadcrumb":
|
||||
items = data.get("items", [])
|
||||
return {
|
||||
"@context": "https://schema.org",
|
||||
"@type": "BreadcrumbList",
|
||||
"itemListElement": [
|
||||
{
|
||||
"@type": "ListItem",
|
||||
"position": idx + 1,
|
||||
"name": item["name"],
|
||||
"item": f"{base_url}{item['url']}",
|
||||
}
|
||||
for idx, item in enumerate(items)
|
||||
],
|
||||
}
|
||||
return {}
|
||||
|
||||
def generate_city_content(self, city_data: Dict) -> Dict[str, str]:
|
||||
"""生成城市页面内容块, 使用丰富的城市数据."""
|
||||
city = city_data.get("name", "")
|
||||
city_en = city_data.get("name_en", "")
|
||||
tagline = city_data.get("tagline", "")
|
||||
description = city_data.get("description", "")
|
||||
landmarks = city_data.get("landmarks", [])
|
||||
university_clusters = city_data.get("university_clusters", [])
|
||||
business_districts = city_data.get("business_districts", [])
|
||||
metro_lines = city_data.get("metro_lines", 0)
|
||||
use_cases = city_data.get("use_cases", [])
|
||||
local_tips = city_data.get("local_tips", "")
|
||||
popular_venues = city_data.get("popular_venues", [])
|
||||
|
||||
# 生成地标标签
|
||||
landmarks_html = "".join(
|
||||
f'<span class="tag tag-landmark">{lm}</span>' for lm in landmarks[:5]
|
||||
) if landmarks else ""
|
||||
|
||||
# 生成商圈标签
|
||||
districts_html = "".join(
|
||||
f'<span class="tag tag-district">{d}</span>' for d in business_districts[:4]
|
||||
) if business_districts else ""
|
||||
|
||||
# 生成高校标签
|
||||
universities_html = "".join(
|
||||
f'<span class="tag tag-university">{u}</span>' for u in university_clusters[:4]
|
||||
) if university_clusters else ""
|
||||
|
||||
# 生成使用场景卡片
|
||||
use_cases_html = ""
|
||||
if use_cases:
|
||||
cases_items = ""
|
||||
for uc in use_cases[:3]:
|
||||
scenario = uc.get("scenario", "")
|
||||
example = uc.get("example", "")
|
||||
cases_items += f'''
|
||||
<div class="use-case-card">
|
||||
<h4>{scenario}</h4>
|
||||
<p>{example}</p>
|
||||
</div>'''
|
||||
use_cases_html = f'''
|
||||
<section class="use-cases">
|
||||
<h2>{city}真实使用场景</h2>
|
||||
<div class="use-cases-grid">{cases_items}</div>
|
||||
</section>'''
|
||||
|
||||
# 生成场所类型
|
||||
venues_html = "、".join(popular_venues[:4]) if popular_venues else "咖啡馆、餐厅"
|
||||
|
||||
content = {
|
||||
"intro": f'''
|
||||
<div class="city-hero">
|
||||
<h1>{city}聚会地点推荐 - {city_en}</h1>
|
||||
<p class="tagline">{tagline}</p>
|
||||
<p class="lead">{description}</p>
|
||||
</div>''',
|
||||
|
||||
"features": f'''
|
||||
<section class="city-features">
|
||||
<h2>为什么在{city}使用MeetSpot？</h2>
|
||||
<div class="features-grid">
|
||||
<div class="feature-card">
|
||||
<div class="feature-icon">🚇</div>
|
||||
<h3>{metro_lines}条地铁线路</h3>
|
||||
<p>{city}地铁网络发达，MeetSpot优先推荐地铁站周边的聚会场所</p>
|
||||
</div>
|
||||
<div class="feature-card">
|
||||
<div class="feature-icon">🎯</div>
|
||||
<h3>智能中点计算</h3>
|
||||
<p>球面几何算法确保每位参与者通勤距离公平均衡</p>
|
||||
</div>
|
||||
<div class="feature-card">
|
||||
<div class="feature-icon">📍</div>
|
||||
<h3>本地精选场所</h3>
|
||||
<p>覆盖{city}{venues_html}等热门类型，高评分场所优先推荐</p>
|
||||
</div>
|
||||
</div>
|
||||
</section>''',
|
||||
|
||||
"landmarks": f'''
|
||||
<section class="city-landmarks">
|
||||
<h2>{city}热门聚会区域</h2>
|
||||
<div class="tags-section">
|
||||
<div class="tags-group">
|
||||
<h3>地标商圈</h3>
|
||||
<div class="tags">{landmarks_html}</div>
|
||||
</div>
|
||||
<div class="tags-group">
|
||||
<h3>商务中心</h3>
|
||||
<div class="tags">{districts_html}</div>
|
||||
</div>
|
||||
<div class="tags-group">
|
||||
<h3>高校聚集区</h3>
|
||||
<div class="tags">{universities_html}</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>''' if landmarks or business_districts or university_clusters else "",
|
||||
|
||||
"use_cases": use_cases_html,
|
||||
|
||||
"local_tips": f'''
|
||||
<section class="local-tips">
|
||||
<h2>{city}聚会小贴士</h2>
|
||||
<div class="tip-card">
|
||||
<div class="tip-icon">💡</div>
|
||||
<p>{local_tips}</p>
|
||||
</div>
|
||||
</section>''' if local_tips else "",
|
||||
|
||||
"how_it_works": f'''
|
||||
<section class="how-it-works">
|
||||
<h2>如何在{city}找到最佳聚会地点？</h2>
|
||||
<div class="steps">
|
||||
<div class="step">
|
||||
<span class="step-number">1</span>
|
||||
<div class="step-content">
|
||||
<h4>输入参与者位置</h4>
|
||||
<p>支持输入{city}任意地址、地标或高校名称（如{university_clusters[0] if university_clusters else "当地高校"}）</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="step">
|
||||
<span class="step-number">2</span>
|
||||
<div class="step-content">
|
||||
<h4>选择场所类型</h4>
|
||||
<p>根据聚会目的选择{venues_html}等场景</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="step">
|
||||
<span class="step-number">3</span>
|
||||
<div class="step-content">
|
||||
<h4>获取智能推荐</h4>
|
||||
<p>系统自动计算地理中点，推荐{landmarks[0] if landmarks else "市中心"}等区域的高评分场所</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>''',
|
||||
|
||||
"cta": f'''
|
||||
<section class="cta-section">
|
||||
<h2>开始规划{city}聚会</h2>
|
||||
<p>无需注册，输入地址即可获取推荐</p>
|
||||
<a href="/" class="cta-button">立即使用 MeetSpot</a>
|
||||
</section>''',
|
||||
}
|
||||
|
||||
# 计算字数
|
||||
total_text = "".join(str(v) for v in content.values())
|
||||
text_only = "".join(ch for ch in total_text if ch.isalnum())
|
||||
content["word_count"] = len(text_only)
|
||||
return content
|
||||
|
||||
def generate_city_content_simple(self, city: str) -> Dict[str, str]:
|
||||
"""兼容旧API: 仅传入城市名时生成基础内容."""
|
||||
return self.generate_city_content({"name": city, "name_en": city})
|
||||
|
||||
|
||||
seo_content_generator = SEOContentGenerator()
|
||||
"""单例生成器, 供路由直接复用。"""
|
||||
9
MeetSpot/app/__init__.py
Normal file
9
MeetSpot/app/__init__.py
Normal file
@@ -0,0 +1,9 @@
|
||||
# Python version check: 3.11-3.13
|
||||
import sys
|
||||
|
||||
if sys.version_info < (3, 11) or sys.version_info > (3, 13):
|
||||
print(
|
||||
"Warning: Unsupported Python version {ver}, please use 3.11-3.13".format(
|
||||
ver=".".join(map(str, sys.version_info))
|
||||
)
|
||||
)
|
||||
20
MeetSpot/app/agent/__init__.py
Normal file
20
MeetSpot/app/agent/__init__.py
Normal file
@@ -0,0 +1,20 @@
|
||||
"""MeetSpot Agent Module - 基于 OpenManus 架构的智能推荐 Agent"""
|
||||
|
||||
from app.agent.base import BaseAgent
|
||||
from app.agent.meetspot_agent import MeetSpotAgent, create_meetspot_agent
|
||||
from app.agent.tools import (
|
||||
CalculateCenterTool,
|
||||
GeocodeTool,
|
||||
GenerateRecommendationTool,
|
||||
SearchPOITool,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"BaseAgent",
|
||||
"MeetSpotAgent",
|
||||
"create_meetspot_agent",
|
||||
"GeocodeTool",
|
||||
"CalculateCenterTool",
|
||||
"SearchPOITool",
|
||||
"GenerateRecommendationTool",
|
||||
]
|
||||
171
MeetSpot/app/agent/base.py
Normal file
171
MeetSpot/app/agent/base.py
Normal file
@@ -0,0 +1,171 @@
|
||||
"""Agent 基类 - 参考 OpenManus BaseAgent 设计"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import List, Optional, Dict, Any
|
||||
|
||||
from pydantic import BaseModel, Field, model_validator
|
||||
|
||||
from app.llm import LLM
|
||||
from app.logger import logger
|
||||
from app.schema import AgentState, Memory, Message, ROLE_TYPE
|
||||
|
||||
|
||||
class BaseAgent(BaseModel, ABC):
|
||||
"""Agent 基类
|
||||
|
||||
提供基础的状态管理、记忆管理和执行循环。
|
||||
子类需要实现 step() 方法来定义具体行为。
|
||||
"""
|
||||
|
||||
# 核心属性
|
||||
name: str = Field(default="BaseAgent", description="Agent 名称")
|
||||
description: Optional[str] = Field(default=None, description="Agent 描述")
|
||||
|
||||
# 提示词
|
||||
system_prompt: Optional[str] = Field(default=None, description="系统提示词")
|
||||
next_step_prompt: Optional[str] = Field(default=None, description="下一步提示词")
|
||||
|
||||
# 依赖
|
||||
llm: Optional[LLM] = Field(default=None, description="LLM 实例")
|
||||
memory: Memory = Field(default_factory=Memory, description="Agent 记忆")
|
||||
state: AgentState = Field(default=AgentState.IDLE, description="当前状态")
|
||||
|
||||
# 执行控制
|
||||
max_steps: int = Field(default=10, description="最大执行步数")
|
||||
current_step: int = Field(default=0, description="当前步数")
|
||||
|
||||
# 重复检测阈值
|
||||
duplicate_threshold: int = 2
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
extra = "allow"
|
||||
|
||||
@model_validator(mode="after")
|
||||
def initialize_agent(self) -> "BaseAgent":
|
||||
"""初始化 Agent"""
|
||||
if self.llm is None:
|
||||
try:
|
||||
self.llm = LLM()
|
||||
except Exception as e:
|
||||
logger.warning(f"无法初始化 LLM: {e}")
|
||||
if not isinstance(self.memory, Memory):
|
||||
self.memory = Memory()
|
||||
return self
|
||||
|
||||
@asynccontextmanager
|
||||
async def state_context(self, new_state: AgentState):
|
||||
"""状态上下文管理器，用于安全的状态转换"""
|
||||
if not isinstance(new_state, AgentState):
|
||||
raise ValueError(f"无效状态: {new_state}")
|
||||
|
||||
previous_state = self.state
|
||||
self.state = new_state
|
||||
try:
|
||||
yield
|
||||
except Exception as e:
|
||||
self.state = AgentState.ERROR
|
||||
raise e
|
||||
finally:
|
||||
self.state = previous_state
|
||||
|
||||
def update_memory(
|
||||
self,
|
||||
role: ROLE_TYPE,
|
||||
content: str,
|
||||
base64_image: Optional[str] = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
"""添加消息到记忆"""
|
||||
message_map = {
|
||||
"user": Message.user_message,
|
||||
"system": Message.system_message,
|
||||
"assistant": Message.assistant_message,
|
||||
"tool": lambda content, **kw: Message.tool_message(content, **kw),
|
||||
}
|
||||
|
||||
if role not in message_map:
|
||||
raise ValueError(f"不支持的消息角色: {role}")
|
||||
|
||||
if role == "tool":
|
||||
self.memory.add_message(message_map[role](content, **kwargs))
|
||||
else:
|
||||
self.memory.add_message(message_map[role](content, base64_image=base64_image))
|
||||
|
||||
async def run(self, request: Optional[str] = None) -> str:
|
||||
"""执行 Agent 主循环
|
||||
|
||||
Args:
|
||||
request: 可选的初始用户请求
|
||||
|
||||
Returns:
|
||||
执行结果摘要
|
||||
"""
|
||||
if self.state != AgentState.IDLE:
|
||||
raise RuntimeError(f"无法从状态 {self.state} 启动 Agent")
|
||||
|
||||
if request:
|
||||
self.update_memory("user", request)
|
||||
|
||||
results: List[str] = []
|
||||
async with self.state_context(AgentState.RUNNING):
|
||||
while (
|
||||
self.current_step < self.max_steps
|
||||
and self.state != AgentState.FINISHED
|
||||
):
|
||||
self.current_step += 1
|
||||
logger.info(f"执行步骤 {self.current_step}/{self.max_steps}")
|
||||
step_result = await self.step()
|
||||
|
||||
# 检测卡住状态
|
||||
if self.is_stuck():
|
||||
self.handle_stuck_state()
|
||||
|
||||
results.append(f"Step {self.current_step}: {step_result}")
|
||||
|
||||
if self.current_step >= self.max_steps:
|
||||
self.current_step = 0
|
||||
self.state = AgentState.IDLE
|
||||
results.append(f"已终止: 达到最大步数 ({self.max_steps})")
|
||||
|
||||
return "\n".join(results) if results else "未执行任何步骤"
|
||||
|
||||
@abstractmethod
|
||||
async def step(self) -> str:
|
||||
"""执行单步操作 - 子类必须实现"""
|
||||
pass
|
||||
|
||||
def handle_stuck_state(self):
|
||||
"""处理卡住状态"""
|
||||
stuck_prompt = "检测到重复响应。请考虑新策略，避免重复已尝试过的无效路径。"
|
||||
self.next_step_prompt = f"{stuck_prompt}\n{self.next_step_prompt or ''}"
|
||||
logger.warning(f"Agent 检测到卡住状态，已添加提示")
|
||||
|
||||
def is_stuck(self) -> bool:
|
||||
"""检测是否陷入循环"""
|
||||
if len(self.memory.messages) < 2:
|
||||
return False
|
||||
|
||||
last_message = self.memory.messages[-1]
|
||||
if not last_message.content:
|
||||
return False
|
||||
|
||||
# 统计相同内容出现次数
|
||||
duplicate_count = sum(
|
||||
1
|
||||
for msg in reversed(self.memory.messages[:-1])
|
||||
if msg.role == "assistant" and msg.content == last_message.content
|
||||
)
|
||||
|
||||
return duplicate_count >= self.duplicate_threshold
|
||||
|
||||
@property
|
||||
def messages(self) -> List[Message]:
|
||||
"""获取记忆中的消息列表"""
|
||||
return self.memory.messages
|
||||
|
||||
@messages.setter
|
||||
def messages(self, value: List[Message]):
|
||||
"""设置记忆中的消息列表"""
|
||||
self.memory.messages = value
|
||||
361
MeetSpot/app/agent/meetspot_agent.py
Normal file
361
MeetSpot/app/agent/meetspot_agent.py
Normal file
@@ -0,0 +1,361 @@
|
||||
"""MeetSpotAgent - 智能会面地点推荐 Agent
|
||||
|
||||
基于 ReAct 模式实现的智能推荐代理，通过工具调用完成地点推荐任务。
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from app.agent.base import BaseAgent
|
||||
from app.agent.tools import (
|
||||
CalculateCenterTool,
|
||||
GeocodeTool,
|
||||
GenerateRecommendationTool,
|
||||
SearchPOITool,
|
||||
)
|
||||
from app.llm import LLM
|
||||
from app.logger import logger
|
||||
from app.schema import AgentState, Message
|
||||
from app.tool.tool_collection import ToolCollection
|
||||
|
||||
|
||||
SYSTEM_PROMPT = """你是 MeetSpot 智能会面助手，帮助用户找到最佳会面地点。
|
||||
|
||||
## 你的能力
|
||||
你可以使用以下工具来完成任务：
|
||||
|
||||
1. **geocode** - 地理编码
|
||||
- 将地址转换为经纬度坐标
|
||||
- 支持大学简称（北大、清华）、地标、商圈等
|
||||
- 返回坐标和格式化地址
|
||||
|
||||
2. **calculate_center** - 计算中心点
|
||||
- 计算多个位置的几何中心
|
||||
- 作为最佳会面位置的参考点
|
||||
- 使用球面几何确保精确
|
||||
|
||||
3. **search_poi** - 搜索场所
|
||||
- 在中心点附近搜索各类场所
|
||||
- 支持咖啡馆、餐厅、图书馆、健身房等
|
||||
- 返回名称、地址、评分、距离等
|
||||
|
||||
4. **generate_recommendation** - 生成推荐
|
||||
- 分析搜索结果
|
||||
- 根据评分、距离、用户需求排序
|
||||
- 生成个性化推荐理由
|
||||
|
||||
## 工作流程
|
||||
请按以下步骤执行：
|
||||
|
||||
1. **理解任务** - 分析用户提供的位置和需求
|
||||
2. **地理编码** - 依次对每个地址使用 geocode 获取坐标
|
||||
3. **计算中心** - 使用 calculate_center 计算最佳会面点
|
||||
4. **搜索场所** - 使用 search_poi 在中心点附近搜索
|
||||
5. **生成推荐** - 使用 generate_recommendation 生成最终推荐
|
||||
|
||||
## 输出要求
|
||||
- 推荐 3-5 个最佳场所
|
||||
- 为每个场所说明推荐理由（距离、评分、特色）
|
||||
- 考虑用户的特殊需求（停车、安静、商务等）
|
||||
- 使用中文回复
|
||||
|
||||
## 注意事项
|
||||
- 确保在调用工具前已获取所有必要参数
|
||||
- 如果地址解析失败，提供具体的错误信息和建议
|
||||
- 如果搜索无结果，尝试调整搜索关键词或扩大半径
|
||||
"""
|
||||
|
||||
|
||||
class MeetSpotAgent(BaseAgent):
|
||||
"""MeetSpot 智能会面推荐 Agent
|
||||
|
||||
基于 ReAct 模式的智能代理，通过 think() -> act() 循环完成推荐任务。
|
||||
"""
|
||||
|
||||
name: str = "MeetSpotAgent"
|
||||
description: str = "智能会面地点推荐助手"
|
||||
|
||||
system_prompt: str = SYSTEM_PROMPT
|
||||
next_step_prompt: str = "请继续执行下一步，或者如果已完成所有工具调用，请生成最终推荐结果。"
|
||||
|
||||
max_steps: int = 15 # 允许更多步骤以完成复杂任务
|
||||
|
||||
# 工具集合
|
||||
available_tools: ToolCollection = Field(default=None)
|
||||
|
||||
# 当前工具调用
|
||||
tool_calls: List[Any] = Field(default_factory=list)
|
||||
|
||||
# 存储中间结果
|
||||
geocode_results: List[Dict] = Field(default_factory=list)
|
||||
center_point: Optional[Dict] = None
|
||||
search_results: List[Dict] = Field(default_factory=list)
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
extra = "allow"
|
||||
|
||||
def __init__(self, **data):
|
||||
super().__init__(**data)
|
||||
# 初始化工具集合
|
||||
if self.available_tools is None:
|
||||
self.available_tools = ToolCollection(
|
||||
GeocodeTool(),
|
||||
CalculateCenterTool(),
|
||||
SearchPOITool(),
|
||||
GenerateRecommendationTool()
|
||||
)
|
||||
|
||||
async def step(self) -> str:
|
||||
"""执行一步: think + act
|
||||
|
||||
Returns:
|
||||
步骤执行结果的描述
|
||||
"""
|
||||
# Think: 决定下一步行动
|
||||
should_continue = await self.think()
|
||||
|
||||
if not should_continue:
|
||||
self.state = AgentState.FINISHED
|
||||
return "任务完成"
|
||||
|
||||
# Act: 执行工具调用
|
||||
result = await self.act()
|
||||
return result
|
||||
|
||||
async def think(self) -> bool:
|
||||
"""思考阶段 - 决定下一步行动
|
||||
|
||||
使用 LLM 分析当前状态，决定是否需要调用工具以及调用哪个工具。
|
||||
|
||||
Returns:
|
||||
是否需要继续执行
|
||||
"""
|
||||
# 构建消息
|
||||
messages = self.memory.messages.copy()
|
||||
|
||||
# 添加提示引导下一步
|
||||
if self.next_step_prompt and self.current_step > 1:
|
||||
messages.append(Message.user_message(self.next_step_prompt))
|
||||
|
||||
# 调用 LLM 获取响应
|
||||
response = await self.llm.ask_tool(
|
||||
messages=messages,
|
||||
system_msgs=[Message.system_message(self.system_prompt)],
|
||||
tools=self.available_tools.to_params(),
|
||||
tool_choice="auto"
|
||||
)
|
||||
|
||||
if response is None:
|
||||
logger.warning("LLM 返回空响应")
|
||||
return False
|
||||
|
||||
# 提取工具调用和内容
|
||||
self.tool_calls = response.tool_calls or []
|
||||
content = response.content or ""
|
||||
|
||||
logger.info(f"Agent 思考: {content[:200]}..." if len(content) > 200 else f"Agent 思考: {content}")
|
||||
|
||||
if self.tool_calls:
|
||||
tool_names = [tc.function.name for tc in self.tool_calls]
|
||||
logger.info(f"选择工具: {tool_names}")
|
||||
|
||||
# 保存 assistant 消息到记忆
|
||||
if self.tool_calls:
|
||||
# 带工具调用的消息
|
||||
tool_calls_data = [
|
||||
{
|
||||
"id": tc.id,
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": tc.function.name,
|
||||
"arguments": tc.function.arguments
|
||||
}
|
||||
}
|
||||
for tc in self.tool_calls
|
||||
]
|
||||
self.memory.add_message(Message(
|
||||
role="assistant",
|
||||
content=content,
|
||||
tool_calls=tool_calls_data
|
||||
))
|
||||
elif content:
|
||||
# 纯文本消息（可能是最终回复）
|
||||
self.memory.add_message(Message.assistant_message(content))
|
||||
# 如果没有工具调用且有内容，可能是最终回复
|
||||
if "推荐" in content and len(content) > 100:
|
||||
return False # 结束循环
|
||||
|
||||
return bool(self.tool_calls) or bool(content)
|
||||
|
||||
async def act(self) -> str:
|
||||
"""行动阶段 - 执行工具调用
|
||||
|
||||
执行思考阶段决定的工具调用，并将结果添加到记忆。
|
||||
|
||||
Returns:
|
||||
工具执行结果的描述
|
||||
"""
|
||||
if not self.tool_calls:
|
||||
# 没有工具调用，返回最后一条消息的内容
|
||||
return self.memory.messages[-1].content or "无操作"
|
||||
|
||||
results = []
|
||||
for call in self.tool_calls:
|
||||
tool_name = call.function.name
|
||||
tool_args = call.function.arguments
|
||||
|
||||
try:
|
||||
# 解析参数
|
||||
args = json.loads(tool_args) if isinstance(tool_args, str) else tool_args
|
||||
|
||||
# 执行工具
|
||||
logger.info(f"执行工具: {tool_name}, 参数: {args}")
|
||||
result = await self.available_tools.execute(name=tool_name, tool_input=args)
|
||||
|
||||
# 保存中间结果
|
||||
self._save_intermediate_result(tool_name, result, args)
|
||||
|
||||
# 将工具结果添加到记忆
|
||||
result_str = str(result)
|
||||
self.memory.add_message(Message.tool_message(
|
||||
content=result_str,
|
||||
tool_call_id=call.id,
|
||||
name=tool_name
|
||||
))
|
||||
|
||||
logger.info(f"工具 {tool_name} 完成")
|
||||
results.append(f"{tool_name}: 成功")
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"工具执行失败: {str(e)}"
|
||||
logger.error(f"{tool_name} {error_msg}")
|
||||
|
||||
# 添加错误消息到记忆
|
||||
self.memory.add_message(Message.tool_message(
|
||||
content=error_msg,
|
||||
tool_call_id=call.id,
|
||||
name=tool_name
|
||||
))
|
||||
results.append(f"{tool_name}: 失败 - {str(e)}")
|
||||
|
||||
return " | ".join(results)
|
||||
|
||||
def _save_intermediate_result(self, tool_name: str, result: Any, args: Dict) -> None:
|
||||
"""保存工具执行的中间结果
|
||||
|
||||
Args:
|
||||
tool_name: 工具名称
|
||||
result: 工具执行结果
|
||||
args: 工具参数
|
||||
"""
|
||||
try:
|
||||
# 解析结果
|
||||
if hasattr(result, 'output') and result.output:
|
||||
data = json.loads(result.output) if isinstance(result.output, str) else result.output
|
||||
else:
|
||||
return
|
||||
|
||||
if tool_name == "geocode" and data:
|
||||
self.geocode_results.append({
|
||||
"address": args.get("address", ""),
|
||||
"lng": data.get("lng"),
|
||||
"lat": data.get("lat"),
|
||||
"formatted_address": data.get("formatted_address", "")
|
||||
})
|
||||
|
||||
elif tool_name == "calculate_center" and data:
|
||||
self.center_point = data.get("center")
|
||||
|
||||
elif tool_name == "search_poi" and data:
|
||||
places = data.get("places", [])
|
||||
self.search_results.extend(places)
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"保存中间结果时出错: {e}")
|
||||
|
||||
async def recommend(
|
||||
self,
|
||||
locations: List[str],
|
||||
keywords: str = "咖啡馆",
|
||||
requirements: str = ""
|
||||
) -> Dict:
|
||||
"""执行推荐任务
|
||||
|
||||
这是 Agent 的主要入口方法，接收用户输入并返回推荐结果。
|
||||
|
||||
Args:
|
||||
locations: 参与者位置列表
|
||||
keywords: 搜索关键词（场所类型）
|
||||
requirements: 用户特殊需求
|
||||
|
||||
Returns:
|
||||
包含推荐结果的字典
|
||||
"""
|
||||
# 重置状态
|
||||
self.geocode_results = []
|
||||
self.center_point = None
|
||||
self.search_results = []
|
||||
self.current_step = 0
|
||||
self.state = AgentState.IDLE
|
||||
self.memory.clear()
|
||||
|
||||
# 构建任务描述
|
||||
locations_str = "、".join(locations)
|
||||
task = f"""请帮我找到适合会面的地点：
|
||||
|
||||
**参与者位置**：{locations_str}
|
||||
**想找的场所类型**：{keywords}
|
||||
**特殊需求**：{requirements or "无特殊需求"}
|
||||
|
||||
请按照工作流程执行：
|
||||
1. 先用 geocode 工具获取每个位置的坐标
|
||||
2. 用 calculate_center 计算中心点
|
||||
3. 用 search_poi 搜索附近的 {keywords}
|
||||
4. 用 generate_recommendation 生成推荐
|
||||
|
||||
最后请用中文总结推荐结果。"""
|
||||
|
||||
# 执行任务
|
||||
result = await self.run(task)
|
||||
|
||||
# 格式化返回结果
|
||||
return self._format_result(result)
|
||||
|
||||
def _format_result(self, raw_result: str) -> Dict:
|
||||
"""格式化最终结果
|
||||
|
||||
Args:
|
||||
raw_result: Agent 执行的原始结果
|
||||
|
||||
Returns:
|
||||
格式化的结果字典
|
||||
"""
|
||||
# 获取最后一条 assistant 消息作为最终推荐
|
||||
final_recommendation = ""
|
||||
for msg in reversed(self.memory.messages):
|
||||
if msg.role == "assistant" and msg.content:
|
||||
final_recommendation = msg.content
|
||||
break
|
||||
|
||||
return {
|
||||
"success": self.state == AgentState.IDLE, # IDLE 表示正常完成
|
||||
"recommendation": final_recommendation,
|
||||
"geocode_results": self.geocode_results,
|
||||
"center_point": self.center_point,
|
||||
"search_results": self.search_results[:10], # 限制返回数量
|
||||
"steps_executed": self.current_step,
|
||||
"raw_output": raw_result
|
||||
}
|
||||
|
||||
|
||||
# 创建默认 Agent 实例的工厂函数
|
||||
def create_meetspot_agent() -> MeetSpotAgent:
|
||||
"""创建 MeetSpotAgent 实例
|
||||
|
||||
Returns:
|
||||
配置好的 MeetSpotAgent 实例
|
||||
"""
|
||||
return MeetSpotAgent()
|
||||
514
MeetSpot/app/agent/tools.py
Normal file
514
MeetSpot/app/agent/tools.py
Normal file
@@ -0,0 +1,514 @@
|
||||
"""MeetSpot Agent 工具集 - 封装推荐系统的核心功能"""
|
||||
|
||||
import json
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from app.tool.base import BaseTool, ToolResult
|
||||
from app.logger import logger
|
||||
|
||||
|
||||
class GeocodeTool(BaseTool):
|
||||
"""地理编码工具 - 将地址转换为经纬度坐标"""
|
||||
|
||||
name: str = "geocode"
|
||||
description: str = """将地址或地点名称转换为经纬度坐标。
|
||||
支持各种地址格式：
|
||||
- 完整地址：'北京市海淀区中关村大街1号'
|
||||
- 大学简称：'北大'、'清华'、'复旦'（自动扩展为完整地址）
|
||||
- 知名地标：'天安门'、'外滩'、'广州塔'
|
||||
- 商圈区域：'三里屯'、'王府井'
|
||||
|
||||
返回地址的经纬度坐标和格式化地址。"""
|
||||
parameters: dict = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"address": {
|
||||
"type": "string",
|
||||
"description": "地址或地点名称，如'北京大学'、'上海市浦东新区陆家嘴'"
|
||||
}
|
||||
},
|
||||
"required": ["address"]
|
||||
}
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def _get_recommender(self):
|
||||
"""延迟加载推荐器，并确保 API key 已设置"""
|
||||
if not hasattr(self, '_cached_recommender'):
|
||||
from app.tool.meetspot_recommender import CafeRecommender
|
||||
from app.config import config
|
||||
recommender = CafeRecommender()
|
||||
# 确保 API key 已设置
|
||||
if hasattr(config, 'amap') and config.amap and hasattr(config.amap, 'api_key'):
|
||||
recommender.api_key = config.amap.api_key
|
||||
object.__setattr__(self, '_cached_recommender', recommender)
|
||||
return self._cached_recommender
|
||||
|
||||
async def execute(self, address: str) -> ToolResult:
|
||||
"""执行地理编码"""
|
||||
try:
|
||||
recommender = self._get_recommender()
|
||||
result = await recommender._geocode(address)
|
||||
|
||||
if result:
|
||||
location = result.get("location", "")
|
||||
lng, lat = location.split(",") if location else (None, None)
|
||||
|
||||
return BaseTool.success_response({
|
||||
"address": address,
|
||||
"formatted_address": result.get("formatted_address", ""),
|
||||
"location": location,
|
||||
"lng": float(lng) if lng else None,
|
||||
"lat": float(lat) if lat else None,
|
||||
"city": result.get("city", ""),
|
||||
"district": result.get("district", "")
|
||||
})
|
||||
|
||||
return BaseTool.fail_response(f"无法解析地址: {address}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"地理编码失败: {e}")
|
||||
return BaseTool.fail_response(f"地理编码错误: {str(e)}")
|
||||
|
||||
|
||||
class CalculateCenterTool(BaseTool):
|
||||
"""智能中心点工具 - 计算多个位置的最佳会面点
|
||||
|
||||
使用智能算法，综合考虑：
|
||||
- POI 密度：周边是否有足够的目标场所
|
||||
- 交通便利性：是否靠近地铁站/公交站
|
||||
- 公平性：对所有参与者的距离是否均衡
|
||||
"""
|
||||
|
||||
name: str = "calculate_center"
|
||||
description: str = """智能计算最佳会面中心点。
|
||||
|
||||
不同于简单的几何中心，本工具会：
|
||||
1. 在几何中心周围生成多个候选点
|
||||
2. 评估每个候选点的 POI 密度、交通便利性和公平性
|
||||
3. 返回综合得分最高的点作为最佳会面位置
|
||||
|
||||
这样可以避免中心点落在河流、荒地等不适合的位置。"""
|
||||
parameters: dict = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"coordinates": {
|
||||
"type": "array",
|
||||
"description": "坐标点列表，每个元素包含 lng（经度）、lat（纬度）和可选的 name（名称）",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"lng": {"type": "number", "description": "经度"},
|
||||
"lat": {"type": "number", "description": "纬度"},
|
||||
"name": {"type": "string", "description": "位置名称（可选）"}
|
||||
},
|
||||
"required": ["lng", "lat"]
|
||||
}
|
||||
},
|
||||
"keywords": {
|
||||
"type": "string",
|
||||
"description": "搜索的场所类型，如'咖啡馆'、'餐厅'，用于评估 POI 密度",
|
||||
"default": "咖啡馆"
|
||||
},
|
||||
"use_smart_algorithm": {
|
||||
"type": "boolean",
|
||||
"description": "是否使用智能算法（考虑 POI 密度和交通），默认 true",
|
||||
"default": True
|
||||
}
|
||||
},
|
||||
"required": ["coordinates"]
|
||||
}
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def _get_recommender(self):
|
||||
"""延迟加载推荐器，并确保 API key 已设置"""
|
||||
if not hasattr(self, '_cached_recommender'):
|
||||
from app.tool.meetspot_recommender import CafeRecommender
|
||||
from app.config import config
|
||||
recommender = CafeRecommender()
|
||||
if hasattr(config, 'amap') and config.amap and hasattr(config.amap, 'api_key'):
|
||||
recommender.api_key = config.amap.api_key
|
||||
object.__setattr__(self, '_cached_recommender', recommender)
|
||||
return self._cached_recommender
|
||||
|
||||
async def execute(
|
||||
self,
|
||||
coordinates: List[Dict],
|
||||
keywords: str = "咖啡馆",
|
||||
use_smart_algorithm: bool = True
|
||||
) -> ToolResult:
|
||||
"""计算最佳中心点"""
|
||||
try:
|
||||
if not coordinates or len(coordinates) < 2:
|
||||
return BaseTool.fail_response("至少需要2个坐标点来计算中心")
|
||||
|
||||
recommender = self._get_recommender()
|
||||
|
||||
# 转换为 (lng, lat) 元组列表
|
||||
coord_tuples = [(c["lng"], c["lat"]) for c in coordinates]
|
||||
|
||||
if use_smart_algorithm:
|
||||
# 使用智能中心点算法
|
||||
center, evaluation_details = await recommender._calculate_smart_center(
|
||||
coord_tuples, keywords
|
||||
)
|
||||
logger.info(f"智能中心点算法完成，最优中心: {center}")
|
||||
else:
|
||||
# 使用简单几何中心
|
||||
center = recommender._calculate_center_point(coord_tuples)
|
||||
evaluation_details = {"algorithm": "geometric_center"}
|
||||
|
||||
# 计算每个点到中心的距离
|
||||
distances = []
|
||||
for c in coordinates:
|
||||
dist = recommender._calculate_distance(center, (c["lng"], c["lat"]))
|
||||
distances.append({
|
||||
"name": c.get("name", f"({c['lng']:.4f}, {c['lat']:.4f})"),
|
||||
"distance_to_center": round(dist, 0)
|
||||
})
|
||||
|
||||
max_dist = max(d["distance_to_center"] for d in distances)
|
||||
min_dist = min(d["distance_to_center"] for d in distances)
|
||||
|
||||
result = {
|
||||
"center": {
|
||||
"lng": round(center[0], 6),
|
||||
"lat": round(center[1], 6)
|
||||
},
|
||||
"algorithm": "smart" if use_smart_algorithm else "geometric",
|
||||
"input_count": len(coordinates),
|
||||
"distances": distances,
|
||||
"max_distance": max_dist,
|
||||
"fairness_score": round(100 - (max_dist - min_dist) / 100, 1)
|
||||
}
|
||||
|
||||
# 添加智能算法的评估详情
|
||||
if use_smart_algorithm and evaluation_details:
|
||||
result["evaluation"] = {
|
||||
"geo_center": evaluation_details.get("geo_center"),
|
||||
"best_score": evaluation_details.get("best_score"),
|
||||
"top_candidates": len(evaluation_details.get("all_candidates", []))
|
||||
}
|
||||
|
||||
return BaseTool.success_response(result)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算中心点失败: {e}")
|
||||
return BaseTool.fail_response(f"计算中心点错误: {str(e)}")
|
||||
|
||||
|
||||
class SearchPOITool(BaseTool):
|
||||
"""搜索POI工具 - 在指定位置周围搜索场所"""
|
||||
|
||||
name: str = "search_poi"
|
||||
description: str = """在指定中心点周围搜索各类场所（POI）。
|
||||
支持搜索：咖啡馆、餐厅、图书馆、健身房、KTV、电影院、商场等。
|
||||
返回场所的名称、地址、评分、距离等信息。"""
|
||||
parameters: dict = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"center_lng": {
|
||||
"type": "number",
|
||||
"description": "中心点经度"
|
||||
},
|
||||
"center_lat": {
|
||||
"type": "number",
|
||||
"description": "中心点纬度"
|
||||
},
|
||||
"keywords": {
|
||||
"type": "string",
|
||||
"description": "搜索关键词，如'咖啡馆'、'餐厅'、'图书馆'"
|
||||
},
|
||||
"radius": {
|
||||
"type": "integer",
|
||||
"description": "搜索半径（米），默认3000米",
|
||||
"default": 3000
|
||||
}
|
||||
},
|
||||
"required": ["center_lng", "center_lat", "keywords"]
|
||||
}
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def _get_recommender(self):
|
||||
"""延迟加载推荐器，并确保 API key 已设置"""
|
||||
if not hasattr(self, '_cached_recommender'):
|
||||
from app.tool.meetspot_recommender import CafeRecommender
|
||||
from app.config import config
|
||||
recommender = CafeRecommender()
|
||||
if hasattr(config, 'amap') and config.amap and hasattr(config.amap, 'api_key'):
|
||||
recommender.api_key = config.amap.api_key
|
||||
object.__setattr__(self, '_cached_recommender', recommender)
|
||||
return self._cached_recommender
|
||||
|
||||
async def execute(
|
||||
self,
|
||||
center_lng: float,
|
||||
center_lat: float,
|
||||
keywords: str,
|
||||
radius: int = 3000
|
||||
) -> ToolResult:
|
||||
"""搜索POI"""
|
||||
try:
|
||||
recommender = self._get_recommender()
|
||||
center = f"{center_lng},{center_lat}"
|
||||
|
||||
places = await recommender._search_pois(
|
||||
location=center,
|
||||
keywords=keywords,
|
||||
radius=radius,
|
||||
types="",
|
||||
offset=20
|
||||
)
|
||||
|
||||
if not places:
|
||||
return BaseTool.fail_response(
|
||||
f"在 ({center_lng:.4f}, {center_lat:.4f}) 附近 {radius}米范围内"
|
||||
f"未找到与 '{keywords}' 相关的场所"
|
||||
)
|
||||
|
||||
# 简化返回数据
|
||||
simplified = []
|
||||
for p in places[:15]: # 最多返回15个
|
||||
biz_ext = p.get("biz_ext", {}) or {}
|
||||
location = p.get("location", "")
|
||||
lng, lat = location.split(",") if location else (0, 0)
|
||||
|
||||
# 计算到中心的距离
|
||||
distance = recommender._calculate_distance(
|
||||
(center_lng, center_lat),
|
||||
(float(lng), float(lat))
|
||||
) if location else 0
|
||||
|
||||
simplified.append({
|
||||
"name": p.get("name", ""),
|
||||
"address": p.get("address", ""),
|
||||
"rating": biz_ext.get("rating", "N/A"),
|
||||
"cost": biz_ext.get("cost", ""),
|
||||
"location": location,
|
||||
"lng": float(lng) if lng else None,
|
||||
"lat": float(lat) if lat else None,
|
||||
"distance": round(distance, 0),
|
||||
"tel": p.get("tel", ""),
|
||||
"tag": p.get("tag", ""),
|
||||
"type": p.get("type", "")
|
||||
})
|
||||
|
||||
# 按距离排序
|
||||
simplified.sort(key=lambda x: x.get("distance", 9999))
|
||||
|
||||
return BaseTool.success_response({
|
||||
"places": simplified,
|
||||
"count": len(simplified),
|
||||
"keywords": keywords,
|
||||
"center": {"lng": center_lng, "lat": center_lat},
|
||||
"radius": radius
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"POI搜索失败: {e}")
|
||||
return BaseTool.fail_response(f"POI搜索错误: {str(e)}")
|
||||
|
||||
|
||||
class GenerateRecommendationTool(BaseTool):
|
||||
"""智能推荐工具 - 使用 LLM 生成个性化推荐结果
|
||||
|
||||
结合规则评分和 LLM 智能评分，生成更精准的推荐：
|
||||
- 规则评分：基于距离、评分、热度等客观指标
|
||||
- LLM 评分：理解用户需求语义，评估场所匹配度
|
||||
"""
|
||||
|
||||
name: str = "generate_recommendation"
|
||||
description: str = """智能生成会面地点推荐。
|
||||
|
||||
本工具使用双层评分系统：
|
||||
1. 规则评分（40%）：基于距离、评分、热度等客观指标
|
||||
2. LLM 智能评分（60%）：理解用户需求，评估场所特色与需求的匹配度
|
||||
|
||||
最终生成个性化的推荐理由，帮助用户做出最佳选择。"""
|
||||
parameters: dict = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"places": {
|
||||
"type": "array",
|
||||
"description": "候选场所列表（来自search_poi的结果）",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {"type": "string", "description": "场所名称"},
|
||||
"address": {"type": "string", "description": "地址"},
|
||||
"rating": {"type": "string", "description": "评分"},
|
||||
"distance": {"type": "number", "description": "距中心点距离"},
|
||||
"location": {"type": "string", "description": "坐标"}
|
||||
}
|
||||
}
|
||||
},
|
||||
"center": {
|
||||
"type": "object",
|
||||
"description": "中心点坐标",
|
||||
"properties": {
|
||||
"lng": {"type": "number", "description": "经度"},
|
||||
"lat": {"type": "number", "description": "纬度"}
|
||||
},
|
||||
"required": ["lng", "lat"]
|
||||
},
|
||||
"participant_locations": {
|
||||
"type": "array",
|
||||
"description": "参与者位置名称列表，用于 LLM 评估公平性",
|
||||
"items": {"type": "string"},
|
||||
"default": []
|
||||
},
|
||||
"keywords": {
|
||||
"type": "string",
|
||||
"description": "搜索的场所类型，如'咖啡馆'、'餐厅'",
|
||||
"default": "咖啡馆"
|
||||
},
|
||||
"user_requirements": {
|
||||
"type": "string",
|
||||
"description": "用户的特殊需求，如'停车方便'、'环境安静'",
|
||||
"default": ""
|
||||
},
|
||||
"recommendation_count": {
|
||||
"type": "integer",
|
||||
"description": "推荐数量，默认5个",
|
||||
"default": 5
|
||||
},
|
||||
"use_llm_ranking": {
|
||||
"type": "boolean",
|
||||
"description": "是否使用 LLM 智能排序，默认 true",
|
||||
"default": True
|
||||
}
|
||||
},
|
||||
"required": ["places", "center"]
|
||||
}
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def _get_recommender(self):
|
||||
"""延迟加载推荐器，并确保 API key 已设置"""
|
||||
if not hasattr(self, '_cached_recommender'):
|
||||
from app.tool.meetspot_recommender import CafeRecommender
|
||||
from app.config import config
|
||||
recommender = CafeRecommender()
|
||||
if hasattr(config, 'amap') and config.amap and hasattr(config.amap, 'api_key'):
|
||||
recommender.api_key = config.amap.api_key
|
||||
object.__setattr__(self, '_cached_recommender', recommender)
|
||||
return self._cached_recommender
|
||||
|
||||
async def execute(
|
||||
self,
|
||||
places: List[Dict],
|
||||
center: Dict,
|
||||
participant_locations: List[str] = None,
|
||||
keywords: str = "咖啡馆",
|
||||
user_requirements: str = "",
|
||||
recommendation_count: int = 5,
|
||||
use_llm_ranking: bool = True
|
||||
) -> ToolResult:
|
||||
"""智能生成推荐"""
|
||||
try:
|
||||
if not places:
|
||||
return BaseTool.fail_response("没有候选场所可供推荐")
|
||||
|
||||
recommender = self._get_recommender()
|
||||
center_point = (center["lng"], center["lat"])
|
||||
|
||||
# 1. 先用规则评分进行初步排序
|
||||
ranked = recommender._rank_places(
|
||||
places=places,
|
||||
center_point=center_point,
|
||||
user_requirements=user_requirements,
|
||||
keywords=keywords
|
||||
)
|
||||
|
||||
# 2. 如果启用 LLM 智能排序，进行重排序
|
||||
if use_llm_ranking and participant_locations:
|
||||
logger.info("启用 LLM 智能排序")
|
||||
ranked = await recommender._llm_smart_ranking(
|
||||
places=ranked,
|
||||
user_requirements=user_requirements,
|
||||
participant_locations=participant_locations or [],
|
||||
keywords=keywords,
|
||||
top_n=recommendation_count + 3 # 多取几个以便筛选
|
||||
)
|
||||
|
||||
# 取前N个推荐
|
||||
top_places = ranked[:recommendation_count]
|
||||
|
||||
# 生成推荐结果
|
||||
recommendations = []
|
||||
for i, place in enumerate(top_places, 1):
|
||||
score = place.get("_final_score") or place.get("_score", 0)
|
||||
distance = place.get("_distance") or place.get("distance", 0)
|
||||
rating = place.get("_raw_rating") or place.get("rating", "N/A")
|
||||
|
||||
# 优先使用 LLM 生成的理由
|
||||
llm_reason = place.get("_llm_reason", "")
|
||||
rule_reason = place.get("_recommendation_reason", "")
|
||||
|
||||
if llm_reason:
|
||||
reasons = [llm_reason]
|
||||
elif rule_reason:
|
||||
reasons = [rule_reason]
|
||||
else:
|
||||
# 兜底：构建基础推荐理由
|
||||
reasons = []
|
||||
if distance <= 500:
|
||||
reasons.append("距离中心点很近")
|
||||
elif distance <= 1000:
|
||||
reasons.append("距离适中")
|
||||
|
||||
if rating != "N/A":
|
||||
try:
|
||||
r = float(rating)
|
||||
if r >= 4.5:
|
||||
reasons.append("口碑优秀")
|
||||
elif r >= 4.0:
|
||||
reasons.append("评价良好")
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
if not reasons:
|
||||
reasons = ["综合评分较高"]
|
||||
|
||||
recommendations.append({
|
||||
"rank": i,
|
||||
"name": place.get("name", ""),
|
||||
"address": place.get("address", ""),
|
||||
"rating": str(rating) if rating else "N/A",
|
||||
"distance": round(distance, 0),
|
||||
"score": round(score, 1),
|
||||
"llm_score": place.get("_llm_score", 0),
|
||||
"tel": place.get("tel", ""),
|
||||
"reasons": reasons,
|
||||
"location": place.get("location", ""),
|
||||
"scoring_method": "llm+rule" if place.get("_llm_score") else "rule"
|
||||
})
|
||||
|
||||
return BaseTool.success_response({
|
||||
"recommendations": recommendations,
|
||||
"total_candidates": len(places),
|
||||
"user_requirements": user_requirements,
|
||||
"center": center,
|
||||
"llm_ranking_used": use_llm_ranking and bool(participant_locations)
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成推荐失败: {e}")
|
||||
return BaseTool.fail_response(f"生成推荐错误: {str(e)}")
|
||||
|
||||
|
||||
# 导出所有工具
|
||||
__all__ = [
|
||||
"GeocodeTool",
|
||||
"CalculateCenterTool",
|
||||
"SearchPOITool",
|
||||
"GenerateRecommendationTool"
|
||||
]
|
||||
2
MeetSpot/app/auth/__init__.py
Normal file
2
MeetSpot/app/auth/__init__.py
Normal file
@@ -0,0 +1,2 @@
|
||||
"""认证相关模块。"""
|
||||
|
||||
58
MeetSpot/app/auth/jwt.py
Normal file
58
MeetSpot/app/auth/jwt.py
Normal file
@@ -0,0 +1,58 @@
|
||||
"""JWT 工具函数与统一用户依赖。"""
|
||||
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import Depends, HTTPException, status
|
||||
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
|
||||
from jose import JWTError, jwt
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.db.crud import get_user_by_id
|
||||
from app.db.database import get_db
|
||||
|
||||
|
||||
SECRET_KEY = os.getenv("JWT_SECRET_KEY", "meetspot-dev-secret")
|
||||
ALGORITHM = "HS256"
|
||||
ACCESS_TOKEN_EXPIRE_DAYS = int(os.getenv("ACCESS_TOKEN_EXPIRE_DAYS", "7"))
|
||||
|
||||
bearer_scheme = HTTPBearer(auto_error=False)
|
||||
|
||||
|
||||
def create_access_token(data: dict) -> str:
|
||||
"""生成带过期时间的JWT。"""
|
||||
to_encode = data.copy()
|
||||
expire = datetime.utcnow() + timedelta(days=ACCESS_TOKEN_EXPIRE_DAYS)
|
||||
to_encode.update({"exp": expire})
|
||||
return jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
|
||||
|
||||
|
||||
def decode_token(token: str) -> Optional[dict]:
|
||||
"""解码并验证JWT。"""
|
||||
try:
|
||||
return jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
|
||||
except JWTError:
|
||||
return None
|
||||
|
||||
|
||||
async def get_current_user(
|
||||
credentials: HTTPAuthorizationCredentials = Depends(bearer_scheme),
|
||||
db: AsyncSession = Depends(get_db),
|
||||
):
|
||||
"""FastAPI 依赖：获取当前用户。"""
|
||||
if not credentials:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED, detail="缺少认证信息"
|
||||
)
|
||||
|
||||
payload = decode_token(credentials.credentials)
|
||||
if not payload or "sub" not in payload:
|
||||
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="令牌无效")
|
||||
|
||||
user = await get_user_by_id(db, payload["sub"])
|
||||
if not user:
|
||||
raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="用户不存在")
|
||||
|
||||
return user
|
||||
|
||||
24
MeetSpot/app/auth/sms.py
Normal file
24
MeetSpot/app/auth/sms.py
Normal file
@@ -0,0 +1,24 @@
|
||||
"""短信验证码(Mock版)。"""
|
||||
|
||||
from typing import Dict
|
||||
|
||||
|
||||
MOCK_CODE = "123456"
|
||||
_code_store: Dict[str, str] = {}
|
||||
|
||||
|
||||
async def send_login_code(phone: str) -> str:
|
||||
"""Mock发送验证码，固定返回`123456`。
|
||||
|
||||
- 真实环境可替换为短信网关调用
|
||||
- 这里简单记忆最后一次下发的验证码，便于后续校验扩展
|
||||
"""
|
||||
|
||||
_code_store[phone] = MOCK_CODE
|
||||
return MOCK_CODE
|
||||
|
||||
|
||||
def validate_code(phone: str, code: str) -> bool:
|
||||
"""校验验证码，MVP阶段固定匹配Mock值。"""
|
||||
return code == MOCK_CODE
|
||||
|
||||
315
MeetSpot/app/config.py
Normal file
315
MeetSpot/app/config.py
Normal file
@@ -0,0 +1,315 @@
|
||||
import threading
|
||||
import tomllib
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
def get_project_root() -> Path:
|
||||
"""Get the project root directory"""
|
||||
return Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
PROJECT_ROOT = get_project_root()
|
||||
WORKSPACE_ROOT = PROJECT_ROOT / "workspace"
|
||||
|
||||
|
||||
class LLMSettings(BaseModel):
|
||||
model: str = Field(..., description="Model name")
|
||||
base_url: str = Field(..., description="API base URL")
|
||||
api_key: str = Field(..., description="API key")
|
||||
max_tokens: int = Field(4096, description="Maximum number of tokens per request")
|
||||
max_input_tokens: Optional[int] = Field(
|
||||
None,
|
||||
description="Maximum input tokens to use across all requests (None for unlimited)",
|
||||
)
|
||||
temperature: float = Field(1.0, description="Sampling temperature")
|
||||
api_type: str = Field(..., description="Azure, Openai, or Ollama")
|
||||
api_version: str = Field(..., description="Azure Openai version if AzureOpenai")
|
||||
|
||||
|
||||
class ProxySettings(BaseModel):
|
||||
server: str = Field(None, description="Proxy server address")
|
||||
username: Optional[str] = Field(None, description="Proxy username")
|
||||
password: Optional[str] = Field(None, description="Proxy password")
|
||||
|
||||
|
||||
class SearchSettings(BaseModel):
|
||||
engine: str = Field(default="Google", description="Search engine the llm to use")
|
||||
|
||||
|
||||
class AMapSettings(BaseModel):
|
||||
"""高德地图API配置"""
|
||||
api_key: str = Field(..., description="高德地图API密钥")
|
||||
web_api_key: Optional[str] = Field(None, description="高德地图JavaScript API密钥")
|
||||
|
||||
|
||||
class BrowserSettings(BaseModel):
|
||||
headless: bool = Field(False, description="Whether to run browser in headless mode")
|
||||
disable_security: bool = Field(
|
||||
True, description="Disable browser security features"
|
||||
)
|
||||
extra_chromium_args: List[str] = Field(
|
||||
default_factory=list, description="Extra arguments to pass to the browser"
|
||||
)
|
||||
chrome_instance_path: Optional[str] = Field(
|
||||
None, description="Path to a Chrome instance to use"
|
||||
)
|
||||
wss_url: Optional[str] = Field(
|
||||
None, description="Connect to a browser instance via WebSocket"
|
||||
)
|
||||
cdp_url: Optional[str] = Field(
|
||||
None, description="Connect to a browser instance via CDP"
|
||||
)
|
||||
proxy: Optional[ProxySettings] = Field(
|
||||
None, description="Proxy settings for the browser"
|
||||
)
|
||||
max_content_length: int = Field(
|
||||
2000, description="Maximum length for content retrieval operations"
|
||||
)
|
||||
|
||||
|
||||
class SandboxSettings(BaseModel):
|
||||
"""Configuration for the execution sandbox"""
|
||||
|
||||
use_sandbox: bool = Field(False, description="Whether to use the sandbox")
|
||||
image: str = Field("python:3.12-slim", description="Base image")
|
||||
work_dir: str = Field("/workspace", description="Container working directory")
|
||||
memory_limit: str = Field("512m", description="Memory limit")
|
||||
cpu_limit: float = Field(1.0, description="CPU limit")
|
||||
timeout: int = Field(300, description="Default command timeout (seconds)")
|
||||
network_enabled: bool = Field(
|
||||
False, description="Whether network access is allowed"
|
||||
)
|
||||
|
||||
|
||||
class AppConfig(BaseModel):
|
||||
llm: Dict[str, LLMSettings]
|
||||
sandbox: Optional[SandboxSettings] = Field(
|
||||
None, description="Sandbox configuration"
|
||||
)
|
||||
browser_config: Optional[BrowserSettings] = Field(
|
||||
None, description="Browser configuration"
|
||||
)
|
||||
search_config: Optional[SearchSettings] = Field(
|
||||
None, description="Search configuration"
|
||||
)
|
||||
amap: Optional[AMapSettings] = Field(
|
||||
None, description="高德地图API配置"
|
||||
)
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
|
||||
class Config:
|
||||
_instance = None
|
||||
_lock = threading.Lock()
|
||||
_initialized = False
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
with cls._lock:
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
if not self._initialized:
|
||||
with self._lock:
|
||||
if not self._initialized:
|
||||
self._config = None
|
||||
self._load_initial_config()
|
||||
self._initialized = True
|
||||
|
||||
@staticmethod
|
||||
def _get_config_path() -> Path:
|
||||
root = PROJECT_ROOT
|
||||
config_path = root / "config" / "config.toml"
|
||||
if config_path.exists():
|
||||
return config_path
|
||||
example_path = root / "config" / "config.toml.example"
|
||||
if example_path.exists():
|
||||
return example_path
|
||||
# 如果都没有找到，返回默认路径，让后续创建默认配置
|
||||
return config_path
|
||||
|
||||
def _load_config(self) -> dict:
|
||||
try:
|
||||
config_path = self._get_config_path()
|
||||
if not config_path.exists():
|
||||
# 创建默认配置
|
||||
default_config = {
|
||||
"llm": {
|
||||
"model": "gpt-3.5-turbo",
|
||||
"api_key": "",
|
||||
"base_url": "",
|
||||
"max_tokens": 4096,
|
||||
"temperature": 1.0,
|
||||
"api_type": "",
|
||||
"api_version": ""
|
||||
},
|
||||
"amap": {
|
||||
"api_key": "",
|
||||
"security_js_code": ""
|
||||
},
|
||||
"log": {
|
||||
"level": "info",
|
||||
"file": "logs/meetspot.log"
|
||||
},
|
||||
"server": {
|
||||
"host": "0.0.0.0",
|
||||
"port": 8000
|
||||
}
|
||||
}
|
||||
return default_config
|
||||
|
||||
with config_path.open("rb") as f:
|
||||
return tomllib.load(f)
|
||||
except Exception as e:
|
||||
# 如果加载失败，返回默认配置
|
||||
print(f"Failed to load config file, using defaults: {e}")
|
||||
return {
|
||||
"llm": {
|
||||
"model": "gpt-3.5-turbo",
|
||||
"api_key": "",
|
||||
"base_url": "",
|
||||
"max_tokens": 4096,
|
||||
"temperature": 1.0,
|
||||
"api_type": "",
|
||||
"api_version": ""
|
||||
}
|
||||
}
|
||||
|
||||
def _load_initial_config(self):
|
||||
raw_config = self._load_config()
|
||||
base_llm = raw_config.get("llm", {})
|
||||
|
||||
# 从环境变量读取敏感信息
|
||||
import os
|
||||
openai_api_key = os.getenv("OPENAI_API_KEY", "") or os.getenv("LLM_API_KEY", "")
|
||||
amap_api_key = os.getenv("AMAP_API_KEY", "")
|
||||
# 支持 Render 部署的环境变量配置
|
||||
llm_base_url = os.getenv("LLM_API_BASE", "") or base_llm.get("base_url", "")
|
||||
llm_model = os.getenv("LLM_MODEL", "") or base_llm.get("model", "gpt-3.5-turbo")
|
||||
|
||||
llm_overrides = {
|
||||
k: v for k, v in raw_config.get("llm", {}).items() if isinstance(v, dict)
|
||||
}
|
||||
|
||||
default_settings = {
|
||||
"model": llm_model, # 优先使用环境变量
|
||||
"base_url": llm_base_url, # 优先使用环境变量
|
||||
"api_key": openai_api_key, # 从环境变量获取
|
||||
"max_tokens": base_llm.get("max_tokens", 4096),
|
||||
"max_input_tokens": base_llm.get("max_input_tokens"),
|
||||
"temperature": base_llm.get("temperature", 1.0),
|
||||
"api_type": base_llm.get("api_type", ""),
|
||||
"api_version": base_llm.get("api_version", ""),
|
||||
}
|
||||
|
||||
# handle browser config.
|
||||
browser_config = raw_config.get("browser", {})
|
||||
browser_settings = None
|
||||
|
||||
if browser_config:
|
||||
# handle proxy settings.
|
||||
proxy_config = browser_config.get("proxy", {})
|
||||
proxy_settings = None
|
||||
|
||||
if proxy_config and proxy_config.get("server"):
|
||||
proxy_settings = ProxySettings(
|
||||
**{
|
||||
k: v
|
||||
for k, v in proxy_config.items()
|
||||
if k in ["server", "username", "password"] and v
|
||||
}
|
||||
)
|
||||
|
||||
# filter valid browser config parameters.
|
||||
valid_browser_params = {
|
||||
k: v
|
||||
for k, v in browser_config.items()
|
||||
if k in BrowserSettings.__annotations__ and v is not None
|
||||
}
|
||||
|
||||
# if there is proxy settings, add it to the parameters.
|
||||
if proxy_settings:
|
||||
valid_browser_params["proxy"] = proxy_settings
|
||||
|
||||
# only create BrowserSettings when there are valid parameters.
|
||||
if valid_browser_params:
|
||||
browser_settings = BrowserSettings(**valid_browser_params)
|
||||
|
||||
search_config = raw_config.get("search", {})
|
||||
search_settings = None
|
||||
if search_config:
|
||||
search_settings = SearchSettings(**search_config)
|
||||
sandbox_config = raw_config.get("sandbox", {})
|
||||
if sandbox_config:
|
||||
sandbox_settings = SandboxSettings(**sandbox_config)
|
||||
else:
|
||||
sandbox_settings = SandboxSettings()
|
||||
|
||||
# 处理高德地图API配置
|
||||
amap_config = raw_config.get("amap", {})
|
||||
amap_settings = None
|
||||
# 优先使用环境变量中的 AMAP_API_KEY
|
||||
if amap_api_key:
|
||||
amap_settings = AMapSettings(
|
||||
api_key=amap_api_key,
|
||||
security_js_code=os.getenv("AMAP_SECURITY_JS_CODE", amap_config.get("security_js_code", ""))
|
||||
)
|
||||
elif amap_config and amap_config.get("api_key"):
|
||||
amap_settings = AMapSettings(**amap_config)
|
||||
|
||||
config_dict = {
|
||||
"llm": {
|
||||
"default": default_settings,
|
||||
**{
|
||||
name: {**default_settings, **override_config}
|
||||
for name, override_config in llm_overrides.items()
|
||||
},
|
||||
},
|
||||
"sandbox": sandbox_settings,
|
||||
"browser_config": browser_settings,
|
||||
"search_config": search_settings,
|
||||
"amap": amap_settings,
|
||||
}
|
||||
|
||||
self._config = AppConfig(**config_dict)
|
||||
|
||||
@property
|
||||
def llm(self) -> Dict[str, LLMSettings]:
|
||||
return self._config.llm
|
||||
|
||||
@property
|
||||
def sandbox(self) -> SandboxSettings:
|
||||
return self._config.sandbox
|
||||
|
||||
@property
|
||||
def browser_config(self) -> Optional[BrowserSettings]:
|
||||
return self._config.browser_config
|
||||
|
||||
@property
|
||||
def search_config(self) -> Optional[SearchSettings]:
|
||||
return self._config.search_config
|
||||
|
||||
@property
|
||||
def amap(self) -> Optional[AMapSettings]:
|
||||
"""获取高德地图API配置"""
|
||||
return self._config.amap
|
||||
|
||||
@property
|
||||
def workspace_root(self) -> Path:
|
||||
"""Get the workspace root directory"""
|
||||
return WORKSPACE_ROOT
|
||||
|
||||
@property
|
||||
def root_path(self) -> Path:
|
||||
"""Get the root path of the application"""
|
||||
return PROJECT_ROOT
|
||||
|
||||
|
||||
config = Config()
|
||||
120
MeetSpot/app/config_simple.py
Normal file
120
MeetSpot/app/config_simple.py
Normal file
@@ -0,0 +1,120 @@
|
||||
import os
|
||||
import threading
|
||||
import tomllib
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
def get_project_root() -> Path:
|
||||
"""Get the project root directory"""
|
||||
return Path(__file__).resolve().parent.parent
|
||||
|
||||
|
||||
PROJECT_ROOT = get_project_root()
|
||||
WORKSPACE_ROOT = PROJECT_ROOT / "workspace"
|
||||
|
||||
|
||||
class AMapSettings(BaseModel):
|
||||
"""高德地图API配置"""
|
||||
api_key: str = Field(..., description="高德地图API密钥")
|
||||
security_js_code: Optional[str] = Field(None, description="高德地图JavaScript API安全密钥")
|
||||
|
||||
|
||||
class LogSettings(BaseModel):
|
||||
"""日志配置"""
|
||||
level: str = Field(default="INFO", description="日志级别")
|
||||
file_path: str = Field(default="logs/meetspot.log", description="日志文件路径")
|
||||
|
||||
|
||||
class AppConfig(BaseModel):
|
||||
"""应用配置"""
|
||||
amap: AMapSettings = Field(..., description="高德地图API配置")
|
||||
log: Optional[LogSettings] = Field(default=LogSettings(), description="日志配置")
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
|
||||
class Config:
|
||||
"""配置管理器（单例模式）"""
|
||||
_instance = None
|
||||
_lock = threading.Lock()
|
||||
_initialized = False
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
with cls._lock:
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
if not self._initialized:
|
||||
with self._lock:
|
||||
if not self._initialized:
|
||||
self._config = None
|
||||
self._load_initial_config()
|
||||
self._initialized = True
|
||||
|
||||
def _load_initial_config(self):
|
||||
"""加载初始配置"""
|
||||
try:
|
||||
# 首先尝试从环境变量加载（Vercel 部署）
|
||||
if os.getenv("AMAP_API_KEY"):
|
||||
self._config = AppConfig(
|
||||
amap=AMapSettings(
|
||||
api_key=os.getenv("AMAP_API_KEY", ""),
|
||||
security_js_code=os.getenv("AMAP_SECURITY_JS_CODE", "")
|
||||
)
|
||||
)
|
||||
return
|
||||
|
||||
# 然后尝试从配置文件加载（本地开发）
|
||||
config_path = PROJECT_ROOT / "config" / "config.toml"
|
||||
if config_path.exists():
|
||||
with open(config_path, "rb") as f:
|
||||
toml_data = tomllib.load(f)
|
||||
|
||||
amap_config = toml_data.get("amap", {})
|
||||
if not amap_config.get("api_key"):
|
||||
raise ValueError("高德地图API密钥未配置")
|
||||
|
||||
self._config = AppConfig(
|
||||
amap=AMapSettings(**amap_config),
|
||||
log=LogSettings(**toml_data.get("log", {}))
|
||||
)
|
||||
else:
|
||||
raise FileNotFoundError(f"配置文件不存在: {config_path}")
|
||||
|
||||
except Exception as e:
|
||||
# 提供默认配置以防止启动失败
|
||||
print(f"配置加载失败，使用默认配置: {e}")
|
||||
self._config = AppConfig(
|
||||
amap=AMapSettings(
|
||||
api_key=os.getenv("AMAP_API_KEY", ""),
|
||||
security_js_code=os.getenv("AMAP_SECURITY_JS_CODE", "")
|
||||
)
|
||||
)
|
||||
|
||||
def reload(self):
|
||||
"""重新加载配置"""
|
||||
with self._lock:
|
||||
self._initialized = False
|
||||
self._load_initial_config()
|
||||
self._initialized = True
|
||||
|
||||
@property
|
||||
def amap(self) -> AMapSettings:
|
||||
"""获取高德地图配置"""
|
||||
return self._config.amap
|
||||
|
||||
@property
|
||||
def log(self) -> LogSettings:
|
||||
"""获取日志配置"""
|
||||
return self._config.log
|
||||
|
||||
|
||||
# 全局配置实例
|
||||
config = Config()
|
||||
2
MeetSpot/app/db/__init__.py
Normal file
2
MeetSpot/app/db/__init__.py
Normal file
@@ -0,0 +1,2 @@
|
||||
"""数据库相关模块初始化。"""
|
||||
|
||||
50
MeetSpot/app/db/crud.py
Normal file
50
MeetSpot/app/db/crud.py
Normal file
@@ -0,0 +1,50 @@
|
||||
"""常用数据库操作封装。"""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.models.user import User
|
||||
|
||||
|
||||
def _default_nickname(phone: str) -> str:
|
||||
suffix = phone[-4:] if len(phone) >= 4 else phone
|
||||
return f"用户{suffix}"
|
||||
|
||||
|
||||
async def get_user_by_phone(db: AsyncSession, phone: str) -> Optional[User]:
|
||||
"""根据手机号查询用户。"""
|
||||
stmt = select(User).where(User.phone == phone)
|
||||
result = await db.execute(stmt)
|
||||
return result.scalar_one_or_none()
|
||||
|
||||
|
||||
async def get_user_by_id(db: AsyncSession, user_id: str) -> Optional[User]:
|
||||
"""根据ID查询用户。"""
|
||||
stmt = select(User).where(User.id == user_id)
|
||||
result = await db.execute(stmt)
|
||||
return result.scalar_one_or_none()
|
||||
|
||||
|
||||
async def create_user(
|
||||
db: AsyncSession, phone: str, nickname: Optional[str] = None, avatar_url: str = ""
|
||||
) -> User:
|
||||
"""创建新用户。"""
|
||||
user = User(
|
||||
phone=phone,
|
||||
nickname=nickname or _default_nickname(phone),
|
||||
avatar_url=avatar_url or "",
|
||||
)
|
||||
db.add(user)
|
||||
await db.commit()
|
||||
await db.refresh(user)
|
||||
return user
|
||||
|
||||
|
||||
async def touch_last_login(db: AsyncSession, user: User) -> None:
|
||||
"""更新用户最近登录时间。"""
|
||||
user.last_login = datetime.utcnow()
|
||||
await db.commit()
|
||||
|
||||
48
MeetSpot/app/db/database.py
Normal file
48
MeetSpot/app/db/database.py
Normal file
@@ -0,0 +1,48 @@
|
||||
"""数据库引擎与会话管理。
|
||||
|
||||
使用SQLite作为MVP默认存储，保留通过环境变量`DATABASE_URL`切换到PostgreSQL的能力。
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import AsyncGenerator
|
||||
|
||||
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
|
||||
from sqlalchemy.orm import declarative_base
|
||||
|
||||
|
||||
# 项目根目录，默认将SQLite数据库放在data目录下
|
||||
PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
|
||||
DATA_DIR = PROJECT_ROOT / "data"
|
||||
DATA_DIR.mkdir(exist_ok=True)
|
||||
|
||||
# 允许通过环境变量覆盖数据库连接串
|
||||
DEFAULT_SQLITE_PATH = DATA_DIR / "meetspot.db"
|
||||
DATABASE_URL = os.getenv(
|
||||
"DATABASE_URL", f"sqlite+aiosqlite:///{DEFAULT_SQLITE_PATH.as_posix()}"
|
||||
)
|
||||
|
||||
# 创建异步引擎与会话工厂
|
||||
engine = create_async_engine(DATABASE_URL, echo=False, future=True)
|
||||
AsyncSessionLocal = async_sessionmaker(
|
||||
bind=engine, class_=AsyncSession, expire_on_commit=False, autoflush=False
|
||||
)
|
||||
|
||||
# 统一的ORM基类
|
||||
Base = declarative_base()
|
||||
|
||||
|
||||
async def get_db() -> AsyncGenerator[AsyncSession, None]:
|
||||
"""FastAPI 依赖：提供数据库会话并确保正确关闭。"""
|
||||
async with AsyncSessionLocal() as session:
|
||||
yield session
|
||||
|
||||
|
||||
async def init_db() -> None:
|
||||
"""在启动时创建数据库表。"""
|
||||
# 延迟导入以避免循环依赖
|
||||
from app import models # noqa: F401 确保所有模型已注册
|
||||
|
||||
async with engine.begin() as conn:
|
||||
await conn.run_sync(Base.metadata.create_all)
|
||||
|
||||
652
MeetSpot/app/design_tokens.py
Normal file
652
MeetSpot/app/design_tokens.py
Normal file
@@ -0,0 +1,652 @@
|
||||
"""
|
||||
MeetSpot Design Tokens - 单一真相来源
|
||||
|
||||
所有色彩、间距、字体系统的中心定义文件。
|
||||
修改本文件会影响:
|
||||
1. 基础模板 (templates/base.html)
|
||||
2. 静态HTML (public/*.html)
|
||||
3. 动态生成页面 (workspace/js_src/*.html)
|
||||
|
||||
WCAG 2.1 AA级对比度标准:
|
||||
- 正文: ≥ 4.5:1
|
||||
- 大文字: ≥ 3.0:1
|
||||
"""
|
||||
|
||||
from typing import Dict, Any
|
||||
from functools import lru_cache
|
||||
|
||||
|
||||
class DesignTokens:
|
||||
"""设计Token中心管理类"""
|
||||
|
||||
# ============================================================================
|
||||
# 全局品牌色 (Global Brand Colors) - MeetSpot 旅程主题
|
||||
# 这些颜色应用于所有页面的共同元素 (Header, Footer, 主按钮)
|
||||
# 配色理念：深海蓝（旅程与探索）+ 日落橙（会面的温暖）+ 薄荷绿（公平与平衡）
|
||||
# ============================================================================
|
||||
BRAND = {
|
||||
"primary": "#0A4D68", # 主色：深海蓝 - 沉稳、可信赖 (对比度: 9.12:1 ✓)
|
||||
"primary_dark": "#05445E", # 暗深海蓝 - 悬停态 (对比度: 11.83:1 ✓)
|
||||
"primary_light": "#088395", # 亮海蓝 - 装饰性元素 (对比度: 5.24:1 ✓)
|
||||
"gradient": "linear-gradient(135deg, #05445E 0%, #0A4D68 50%, #088395 100%)",
|
||||
# 强调色：日落橙 - 温暖、活力
|
||||
"accent": "#FF6B35", # 日落橙 - 主强调色 (对比度: 3.55:1, 大文字用途)
|
||||
"accent_light": "#FF8C61", # 亮橙 - 次要强调 (对比度: 2.87:1, 装饰用途)
|
||||
# 次要色：薄荷绿 - 清新、平衡
|
||||
"secondary": "#06D6A0", # 薄荷绿 (对比度: 2.28:1, 装饰用途)
|
||||
# 功能色 - 全部WCAG AA级
|
||||
"success": "#0C8A5D", # 成功绿 - 保持 (4.51:1 ✓)
|
||||
"info": "#2563EB", # 信息蓝 - 保持 (5.17:1 ✓)
|
||||
"warning": "#CA7205", # 警告橙 - 保持 (4.50:1 ✓)
|
||||
"error": "#DC2626", # 错误红 - 保持 (4.83:1 ✓)
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 文字颜色系统 (Text Colors)
|
||||
# 基于WCAG 2.1标准,所有文字色在白色背景上对比度 ≥ 4.5:1
|
||||
# ============================================================================
|
||||
TEXT = {
|
||||
"primary": "#111827", # 主文字 (gray-900, 对比度 17.74:1 ✓)
|
||||
"secondary": "#4B5563", # 次要文字 (gray-600, 对比度 7.56:1 ✓)
|
||||
"tertiary": "#6B7280", # 三级文字 (gray-500, 对比度 4.83:1 ✓)
|
||||
"muted": "#6B7280", # 弱化文字 - 修正 (原#9CA3AF: 2.54:1 -> 4.83:1, 使用tertiary色)
|
||||
"disabled": "#9CA3AF", # 禁用文字 - 保持低对比度 (装饰性文字允许 <3:1)
|
||||
"inverse": "#FFFFFF", # 反转文字 (深色背景上)
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 背景颜色系统 (Background Colors)
|
||||
# ============================================================================
|
||||
BACKGROUND = {
|
||||
"primary": "#FFFFFF", # 主背景 (白色)
|
||||
"secondary": "#F9FAFB", # 次要背景 (gray-50)
|
||||
"tertiary": "#F3F4F6", # 三级背景 (gray-100)
|
||||
"elevated": "#FFFFFF", # 卡片/浮动元素背景 (带阴影)
|
||||
"overlay": "rgba(0, 0, 0, 0.5)", # 蒙层
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 边框颜色系统 (Border Colors)
|
||||
# ============================================================================
|
||||
BORDER = {
|
||||
"default": "#E5E7EB", # 默认边框 (gray-200)
|
||||
"medium": "#D1D5DB", # 中等边框 (gray-300)
|
||||
"strong": "#9CA3AF", # 强边框 (gray-400)
|
||||
"focus": "#667EEA", # 焦点边框 (主品牌色)
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 阴影系统 (Shadow System)
|
||||
# ============================================================================
|
||||
SHADOW = {
|
||||
"sm": "0 1px 2px 0 rgba(0, 0, 0, 0.05)",
|
||||
"md": "0 4px 6px -1px rgba(0, 0, 0, 0.1)",
|
||||
"lg": "0 10px 15px -3px rgba(0, 0, 0, 0.1)",
|
||||
"xl": "0 20px 25px -5px rgba(0, 0, 0, 0.1)",
|
||||
"2xl": "0 25px 50px -12px rgba(0, 0, 0, 0.25)",
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 场所类型主题系统 (Venue Theme System)
|
||||
# 14种预设主题,动态注入到生成的推荐页面中
|
||||
#
|
||||
# 每个主题包含:
|
||||
# - theme_primary: 主色调 (Header背景、主按钮)
|
||||
# - theme_primary_light: 亮色变体 (悬停态、次要元素)
|
||||
# - theme_primary_dark: 暗色变体 (Active态、强调元素)
|
||||
# - theme_secondary: 辅助色 (图标、装饰元素)
|
||||
# - theme_light: 浅背景色 (卡片背景、Section背景)
|
||||
# - theme_dark: 深文字色 (标题、关键信息)
|
||||
#
|
||||
# WCAG验证: 所有theme_primary在白色背景上对比度 ≥ 3.0:1 (大文字)
|
||||
# 所有theme_dark在theme_light背景上对比度 ≥ 4.5:1 (正文)
|
||||
# ============================================================================
|
||||
VENUE_THEMES = {
|
||||
"咖啡馆": {
|
||||
"topic": "咖啡会",
|
||||
"icon_header": "bxs-coffee-togo",
|
||||
"icon_section": "bx-coffee",
|
||||
"icon_card": "bxs-coffee-alt",
|
||||
"map_legend": "咖啡馆",
|
||||
"noun_singular": "咖啡馆",
|
||||
"noun_plural": "咖啡馆",
|
||||
"theme_primary": "#8B5A3C", # 修正后的棕色 (原#9c6644对比度不足)
|
||||
"theme_primary_light": "#B8754A",
|
||||
"theme_primary_dark": "#6D4530",
|
||||
"theme_secondary": "#C9ADA7",
|
||||
"theme_light": "#F2E9E4",
|
||||
"theme_dark": "#1A1A2E", # 修正 (原#22223b对比度不足)
|
||||
},
|
||||
"图书馆": {
|
||||
"topic": "知书达理会",
|
||||
"icon_header": "bxs-book",
|
||||
"icon_section": "bx-book",
|
||||
"icon_card": "bxs-book-reader",
|
||||
"map_legend": "图书馆",
|
||||
"noun_singular": "图书馆",
|
||||
"noun_plural": "图书馆",
|
||||
"theme_primary": "#3A5A8A", # 修正后的蓝色 (原#4a6fa5对比度不足)
|
||||
"theme_primary_light": "#5B7FB5",
|
||||
"theme_primary_dark": "#2B4469",
|
||||
"theme_secondary": "#9DC0E5",
|
||||
"theme_light": "#F0F5FA",
|
||||
"theme_dark": "#1F2937", # 修正
|
||||
},
|
||||
"餐厅": {
|
||||
"topic": "美食汇",
|
||||
"icon_header": "bxs-restaurant",
|
||||
"icon_section": "bx-restaurant",
|
||||
"icon_card": "bxs-restaurant",
|
||||
"map_legend": "餐厅",
|
||||
"noun_singular": "餐厅",
|
||||
"noun_plural": "餐厅",
|
||||
"theme_primary": "#C13B2A", # 修正后的红色 (原#e74c3c过亮)
|
||||
"theme_primary_light": "#E15847",
|
||||
"theme_primary_dark": "#9A2F22",
|
||||
"theme_secondary": "#FADBD8",
|
||||
"theme_light": "#FEF5E7",
|
||||
"theme_dark": "#2C1618", # 修正
|
||||
},
|
||||
"商场": {
|
||||
"topic": "乐购汇",
|
||||
"icon_header": "bxs-shopping-bag",
|
||||
"icon_section": "bx-shopping-bag",
|
||||
"icon_card": "bxs-store-alt",
|
||||
"map_legend": "商场",
|
||||
"noun_singular": "商场",
|
||||
"noun_plural": "商场",
|
||||
"theme_primary": "#6D3588", # 修正后的紫色 (原#8e44ad过亮)
|
||||
"theme_primary_light": "#8F57AC",
|
||||
"theme_primary_dark": "#542969",
|
||||
"theme_secondary": "#D7BDE2",
|
||||
"theme_light": "#F4ECF7",
|
||||
"theme_dark": "#2D1A33", # 修正
|
||||
},
|
||||
"公园": {
|
||||
"topic": "悠然汇",
|
||||
"icon_header": "bxs-tree",
|
||||
"icon_section": "bx-leaf",
|
||||
"icon_card": "bxs-florist",
|
||||
"map_legend": "公园",
|
||||
"noun_singular": "公园",
|
||||
"noun_plural": "公园",
|
||||
"theme_primary": "#1E8B4D", # 修正后的绿色 (原#27ae60过亮)
|
||||
"theme_primary_light": "#48B573",
|
||||
"theme_primary_dark": "#176A3A",
|
||||
"theme_secondary": "#A9DFBF",
|
||||
"theme_light": "#EAFAF1",
|
||||
"theme_dark": "#1C3020", # 修正
|
||||
},
|
||||
"电影院": {
|
||||
"topic": "光影汇",
|
||||
"icon_header": "bxs-film",
|
||||
"icon_section": "bx-film",
|
||||
"icon_card": "bxs-movie-play",
|
||||
"map_legend": "电影院",
|
||||
"noun_singular": "电影院",
|
||||
"noun_plural": "电影院",
|
||||
"theme_primary": "#2C3E50", # 保持 (对比度合格)
|
||||
"theme_primary_light": "#4D5D6E",
|
||||
"theme_primary_dark": "#1F2D3D",
|
||||
"theme_secondary": "#AEB6BF",
|
||||
"theme_light": "#EBEDEF",
|
||||
"theme_dark": "#0F1419", # 修正
|
||||
},
|
||||
"篮球场": {
|
||||
"topic": "篮球部落",
|
||||
"icon_header": "bxs-basketball",
|
||||
"icon_section": "bx-basketball",
|
||||
"icon_card": "bxs-basketball",
|
||||
"map_legend": "篮球场",
|
||||
"noun_singular": "篮球场",
|
||||
"noun_plural": "篮球场",
|
||||
"theme_primary": "#CA7F0E", # 二次修正 (原#D68910: 2.82:1 -> 3.06:1 for large text)
|
||||
"theme_primary_light": "#E89618",
|
||||
"theme_primary_dark": "#A3670B",
|
||||
"theme_secondary": "#FDEBD0",
|
||||
"theme_light": "#FEF9E7",
|
||||
"theme_dark": "#3A2303", # 已修正 ✓
|
||||
},
|
||||
"健身房": {
|
||||
"topic": "健身汇",
|
||||
"icon_header": "bx-dumbbell",
|
||||
"icon_section": "bx-dumbbell",
|
||||
"icon_card": "bx-dumbbell",
|
||||
"map_legend": "健身房",
|
||||
"noun_singular": "健身房",
|
||||
"noun_plural": "健身房",
|
||||
"theme_primary": "#C5671A", # 修正后的橙色 (原#e67e22过亮)
|
||||
"theme_primary_light": "#E17E2E",
|
||||
"theme_primary_dark": "#9E5315",
|
||||
"theme_secondary": "#FDEBD0",
|
||||
"theme_light": "#FEF9E7",
|
||||
"theme_dark": "#3A2303", # 修正
|
||||
},
|
||||
"KTV": {
|
||||
"topic": "欢唱汇",
|
||||
"icon_header": "bxs-microphone",
|
||||
"icon_section": "bx-microphone",
|
||||
"icon_card": "bxs-microphone",
|
||||
"map_legend": "KTV",
|
||||
"noun_singular": "KTV",
|
||||
"noun_plural": "KTV",
|
||||
"theme_primary": "#D10F6F", # 修正后的粉色 (原#FF1493过亮)
|
||||
"theme_primary_light": "#F03A8A",
|
||||
"theme_primary_dark": "#A50C58",
|
||||
"theme_secondary": "#FFB6C1",
|
||||
"theme_light": "#FFF0F5",
|
||||
"theme_dark": "#6B0A2E", # 修正
|
||||
},
|
||||
"博物馆": {
|
||||
"topic": "博古汇",
|
||||
"icon_header": "bxs-institution",
|
||||
"icon_section": "bx-institution",
|
||||
"icon_card": "bxs-institution",
|
||||
"map_legend": "博物馆",
|
||||
"noun_singular": "博物馆",
|
||||
"noun_plural": "博物馆",
|
||||
"theme_primary": "#A88517", # 二次修正 (原#B8941A: 2.88:1 -> 3.21:1 for large text)
|
||||
"theme_primary_light": "#C29E1D",
|
||||
"theme_primary_dark": "#896B13",
|
||||
"theme_secondary": "#F0E68C",
|
||||
"theme_light": "#FFFACD",
|
||||
"theme_dark": "#6B5535", # 已修正 ✓
|
||||
},
|
||||
"景点": {
|
||||
"topic": "游览汇",
|
||||
"icon_header": "bxs-landmark",
|
||||
"icon_section": "bx-landmark",
|
||||
"icon_card": "bxs-landmark",
|
||||
"map_legend": "景点",
|
||||
"noun_singular": "景点",
|
||||
"noun_plural": "景点",
|
||||
"theme_primary": "#138496", # 保持 (对比度合格)
|
||||
"theme_primary_light": "#20A5BB",
|
||||
"theme_primary_dark": "#0F6875",
|
||||
"theme_secondary": "#7FDBDA",
|
||||
"theme_light": "#E0F7FA",
|
||||
"theme_dark": "#00504A", # 修正
|
||||
},
|
||||
"酒吧": {
|
||||
"topic": "夜宴汇",
|
||||
"icon_header": "bxs-drink",
|
||||
"icon_section": "bx-drink",
|
||||
"icon_card": "bxs-drink",
|
||||
"map_legend": "酒吧",
|
||||
"noun_singular": "酒吧",
|
||||
"noun_plural": "酒吧",
|
||||
"theme_primary": "#2C3E50", # 保持 (对比度合格)
|
||||
"theme_primary_light": "#4D5D6E",
|
||||
"theme_primary_dark": "#1B2631",
|
||||
"theme_secondary": "#85929E",
|
||||
"theme_light": "#EBF5FB",
|
||||
"theme_dark": "#0C1014", # 修正
|
||||
},
|
||||
"茶楼": {
|
||||
"topic": "茶韵汇",
|
||||
"icon_header": "bxs-coffee-bean",
|
||||
"icon_section": "bx-coffee-bean",
|
||||
"icon_card": "bxs-coffee-bean",
|
||||
"map_legend": "茶楼",
|
||||
"noun_singular": "茶楼",
|
||||
"noun_plural": "茶楼",
|
||||
"theme_primary": "#406058", # 修正后的绿色 (原#52796F过亮)
|
||||
"theme_primary_light": "#567A6F",
|
||||
"theme_primary_dark": "#2F4841",
|
||||
"theme_secondary": "#CAD2C5",
|
||||
"theme_light": "#F7F9F7",
|
||||
"theme_dark": "#1F2D28", # 修正
|
||||
},
|
||||
"游泳馆": { # 新增第14个主题
|
||||
"topic": "泳池汇",
|
||||
"icon_header": "bx-swim",
|
||||
"icon_section": "bx-swim",
|
||||
"icon_card": "bx-swim",
|
||||
"map_legend": "游泳馆",
|
||||
"noun_singular": "游泳馆",
|
||||
"noun_plural": "游泳馆",
|
||||
"theme_primary": "#1E90FF", # 水蓝色
|
||||
"theme_primary_light": "#4DA6FF",
|
||||
"theme_primary_dark": "#1873CC",
|
||||
"theme_secondary": "#87CEEB",
|
||||
"theme_light": "#E0F2FE",
|
||||
"theme_dark": "#0C4A6E",
|
||||
},
|
||||
# 默认主题 (与咖啡馆相同)
|
||||
"default": {
|
||||
"topic": "推荐地点",
|
||||
"icon_header": "bx-map-pin",
|
||||
"icon_section": "bx-location-plus",
|
||||
"icon_card": "bx-map-alt",
|
||||
"map_legend": "推荐地点",
|
||||
"noun_singular": "地点",
|
||||
"noun_plural": "地点",
|
||||
"theme_primary": "#8B5A3C",
|
||||
"theme_primary_light": "#B8754A",
|
||||
"theme_primary_dark": "#6D4530",
|
||||
"theme_secondary": "#C9ADA7",
|
||||
"theme_light": "#F2E9E4",
|
||||
"theme_dark": "#1A1A2E",
|
||||
},
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 间距系统 (Spacing System)
|
||||
# 基于8px基准的间距尺度
|
||||
# ============================================================================
|
||||
SPACING = {
|
||||
"0": "0",
|
||||
"1": "4px", # 0.25rem
|
||||
"2": "8px", # 0.5rem
|
||||
"3": "12px", # 0.75rem
|
||||
"4": "16px", # 1rem
|
||||
"5": "20px", # 1.25rem
|
||||
"6": "24px", # 1.5rem
|
||||
"8": "32px", # 2rem
|
||||
"10": "40px", # 2.5rem
|
||||
"12": "48px", # 3rem
|
||||
"16": "64px", # 4rem
|
||||
"20": "80px", # 5rem
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 圆角系统 (Border Radius System)
|
||||
# ============================================================================
|
||||
RADIUS = {
|
||||
"none": "0",
|
||||
"sm": "4px",
|
||||
"md": "8px",
|
||||
"lg": "12px",
|
||||
"xl": "16px",
|
||||
"2xl": "24px",
|
||||
"full": "9999px",
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 字体系统 (Typography System) - MeetSpot 品牌字体
|
||||
# Poppins (标题) - 友好且现代，比 Inter 更有个性
|
||||
# Nunito (正文) - 圆润易读，传递温暖感
|
||||
# ============================================================================
|
||||
FONT = {
|
||||
"family_heading": '"Poppins", "PingFang SC", -apple-system, BlinkMacSystemFont, sans-serif',
|
||||
"family_sans": '"Nunito", "Microsoft YaHei", -apple-system, BlinkMacSystemFont, sans-serif',
|
||||
"family_mono": '"JetBrains Mono", "Fira Code", "SF Mono", "Consolas", "Monaco", monospace',
|
||||
# 字体大小 (基于16px基准)
|
||||
"size_xs": "0.75rem", # 12px
|
||||
"size_sm": "0.875rem", # 14px
|
||||
"size_base": "1rem", # 16px
|
||||
"size_lg": "1.125rem", # 18px
|
||||
"size_xl": "1.25rem", # 20px
|
||||
"size_2xl": "1.5rem", # 24px
|
||||
"size_3xl": "1.875rem", # 30px
|
||||
"size_4xl": "2.25rem", # 36px
|
||||
# 字重
|
||||
"weight_normal": "400",
|
||||
"weight_medium": "500",
|
||||
"weight_semibold": "600",
|
||||
"weight_bold": "700",
|
||||
# 行高
|
||||
"leading_tight": "1.25",
|
||||
"leading_normal": "1.5",
|
||||
"leading_relaxed": "1.7",
|
||||
"leading_loose": "2",
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# Z-Index系统 (Layering System)
|
||||
# ============================================================================
|
||||
Z_INDEX = {
|
||||
"dropdown": "1000",
|
||||
"sticky": "1020",
|
||||
"fixed": "1030",
|
||||
"modal_backdrop": "1040",
|
||||
"modal": "1050",
|
||||
"popover": "1060",
|
||||
"tooltip": "1070",
|
||||
}
|
||||
|
||||
# ============================================================================
|
||||
# 交互动画系统 (Interaction Animations)
|
||||
# 遵循WCAG 2.1 - 支持prefers-reduced-motion
|
||||
# ============================================================================
|
||||
ANIMATIONS = """
|
||||
/* ========== 交互动画系统 (Interaction Animations) ========== */
|
||||
|
||||
/* Button动画 - 200ms ease-out过渡 */
|
||||
button, .btn, input[type="submit"], a.button {
|
||||
transition: all 0.2s ease-out;
|
||||
}
|
||||
|
||||
button:hover, .btn:hover, input[type="submit"]:hover, a.button:hover {
|
||||
transform: translateY(-2px);
|
||||
box-shadow: var(--shadow-lg);
|
||||
}
|
||||
|
||||
button:active, .btn:active, input[type="submit"]:active, a.button:active {
|
||||
transform: translateY(0);
|
||||
box-shadow: var(--shadow-md);
|
||||
}
|
||||
|
||||
button:focus, .btn:focus, input[type="submit"]:focus, a.button:focus {
|
||||
outline: 2px solid var(--brand-primary);
|
||||
outline-offset: 2px;
|
||||
}
|
||||
|
||||
/* Loading Spinner动画 */
|
||||
.loading::after {
|
||||
content: "";
|
||||
width: 16px;
|
||||
height: 16px;
|
||||
margin-left: 8px;
|
||||
border: 2px solid var(--brand-primary);
|
||||
border-top-color: transparent;
|
||||
border-radius: 50%;
|
||||
display: inline-block;
|
||||
animation: spin 0.6s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes spin {
|
||||
to { transform: rotate(360deg); }
|
||||
}
|
||||
|
||||
/* Card悬停效果 - 微妙的缩放和阴影提升 */
|
||||
.card, .venue-card, .recommendation-card {
|
||||
transition: transform 0.2s ease-out, box-shadow 0.2s ease-out;
|
||||
}
|
||||
|
||||
.card:hover, .venue-card:hover, .recommendation-card:hover {
|
||||
transform: scale(1.02);
|
||||
box-shadow: var(--shadow-xl);
|
||||
}
|
||||
|
||||
/* Fade-in渐显动画 - 400ms */
|
||||
.fade-in {
|
||||
animation: fadeIn 0.4s ease-out;
|
||||
}
|
||||
|
||||
@keyframes fadeIn {
|
||||
from {
|
||||
opacity: 0;
|
||||
transform: translateY(10px);
|
||||
}
|
||||
to {
|
||||
opacity: 1;
|
||||
transform: translateY(0);
|
||||
}
|
||||
}
|
||||
|
||||
/* Slide-in滑入动画 */
|
||||
.slide-in {
|
||||
animation: slideIn 0.4s ease-out;
|
||||
}
|
||||
|
||||
@keyframes slideIn {
|
||||
from {
|
||||
opacity: 0;
|
||||
transform: translateX(-20px);
|
||||
}
|
||||
to {
|
||||
opacity: 1;
|
||||
transform: translateX(0);
|
||||
}
|
||||
}
|
||||
|
||||
/* WCAG 2.1无障碍支持 - 尊重用户的动画偏好 */
|
||||
@media (prefers-reduced-motion: reduce) {
|
||||
*,
|
||||
*::before,
|
||||
*::after {
|
||||
animation-duration: 0.01ms !important;
|
||||
animation-iteration-count: 1 !important;
|
||||
transition-duration: 0.01ms !important;
|
||||
scroll-behavior: auto !important;
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
# ============================================================================
|
||||
# 辅助方法
|
||||
# ============================================================================
|
||||
|
||||
@classmethod
|
||||
@lru_cache(maxsize=128)
|
||||
def get_venue_theme(cls, venue_type: str) -> Dict[str, str]:
|
||||
"""
|
||||
根据场所类型获取主题配置
|
||||
|
||||
Args:
|
||||
venue_type: 场所类型 (如"咖啡馆"、"图书馆")
|
||||
|
||||
Returns:
|
||||
包含主题色彩和图标的字典
|
||||
|
||||
Example:
|
||||
>>> theme = DesignTokens.get_venue_theme("咖啡馆")
|
||||
>>> print(theme['theme_primary']) # "#8B5A3C"
|
||||
"""
|
||||
return cls.VENUE_THEMES.get(venue_type, cls.VENUE_THEMES["default"])
|
||||
|
||||
@classmethod
|
||||
def to_css_variables(cls) -> str:
|
||||
"""
|
||||
将设计token转换为CSS变量字符串
|
||||
|
||||
Returns:
|
||||
可直接嵌入<style>标签的CSS变量定义
|
||||
|
||||
Example:
|
||||
>>> css = DesignTokens.to_css_variables()
|
||||
>>> print(css)
|
||||
:root {
|
||||
--brand-primary: #667EEA;
|
||||
--brand-primary-dark: #764BA2;
|
||||
...
|
||||
}
|
||||
"""
|
||||
lines = [":root {"]
|
||||
|
||||
# 品牌色
|
||||
for key, value in cls.BRAND.items():
|
||||
css_key = f"--brand-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 文字色
|
||||
for key, value in cls.TEXT.items():
|
||||
css_key = f"--text-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 背景色
|
||||
for key, value in cls.BACKGROUND.items():
|
||||
css_key = f"--bg-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 边框色
|
||||
for key, value in cls.BORDER.items():
|
||||
css_key = f"--border-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 阴影
|
||||
for key, value in cls.SHADOW.items():
|
||||
css_key = f"--shadow-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 间距
|
||||
for key, value in cls.SPACING.items():
|
||||
css_key = f"--spacing-{key}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 圆角
|
||||
for key, value in cls.RADIUS.items():
|
||||
css_key = f"--radius-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# 字体
|
||||
for key, value in cls.FONT.items():
|
||||
css_key = f"--font-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
# Z-Index
|
||||
for key, value in cls.Z_INDEX.items():
|
||||
css_key = f"--z-{key.replace('_', '-')}"
|
||||
lines.append(f" {css_key}: {value};")
|
||||
|
||||
lines.append("}")
|
||||
return "\n".join(lines)
|
||||
|
||||
@classmethod
|
||||
def generate_css_file(cls, output_path: str = "static/css/design-tokens.css"):
|
||||
"""
|
||||
生成独立的CSS设计token文件
|
||||
|
||||
Args:
|
||||
output_path: 输出文件路径
|
||||
|
||||
Example:
|
||||
>>> DesignTokens.generate_css_file()
|
||||
# 生成 static/css/design-tokens.css
|
||||
"""
|
||||
import os
|
||||
|
||||
os.makedirs(os.path.dirname(output_path), exist_ok=True)
|
||||
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
f.write("/* ============================================\n")
|
||||
f.write(" * MeetSpot Design Tokens\n")
|
||||
f.write(" * 自动生成 - 请勿手动编辑\n")
|
||||
f.write(" * 生成源: app/design_tokens.py\n")
|
||||
f.write(" * ==========================================*/\n\n")
|
||||
f.write(cls.to_css_variables())
|
||||
f.write("\n\n/* Compatibility fallbacks for older browsers */\n")
|
||||
f.write(".no-cssvar {\n")
|
||||
f.write(" /* Fallback for browsers without CSS variable support */\n")
|
||||
f.write(f" color: {cls.TEXT['primary']};\n")
|
||||
f.write(f" background-color: {cls.BACKGROUND['primary']};\n")
|
||||
f.write("}\n\n")
|
||||
# 追加交互动画系统
|
||||
f.write(cls.ANIMATIONS)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 全局单例访问 (方便快速引用)
|
||||
# ============================================================================
|
||||
COLORS = {
|
||||
"brand": DesignTokens.BRAND,
|
||||
"text": DesignTokens.TEXT,
|
||||
"background": DesignTokens.BACKGROUND,
|
||||
"border": DesignTokens.BORDER,
|
||||
}
|
||||
|
||||
VENUE_THEMES = DesignTokens.VENUE_THEMES
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 便捷函数
|
||||
# ============================================================================
|
||||
def get_venue_theme(venue_type: str) -> Dict[str, str]:
|
||||
"""便捷函数: 获取场所主题"""
|
||||
return DesignTokens.get_venue_theme(venue_type)
|
||||
|
||||
|
||||
def generate_design_tokens_css(output_path: str = "static/css/design-tokens.css"):
|
||||
"""便捷函数: 生成CSS文件"""
|
||||
DesignTokens.generate_css_file(output_path)
|
||||
13
MeetSpot/app/exceptions.py
Normal file
13
MeetSpot/app/exceptions.py
Normal file
@@ -0,0 +1,13 @@
|
||||
class ToolError(Exception):
|
||||
"""Raised when a tool encounters an error."""
|
||||
|
||||
def __init__(self, message):
|
||||
self.message = message
|
||||
|
||||
|
||||
class OpenManusError(Exception):
|
||||
"""Base exception for all OpenManus errors"""
|
||||
|
||||
|
||||
class TokenLimitExceeded(OpenManusError):
|
||||
"""Exception raised when the token limit is exceeded"""
|
||||
800
MeetSpot/app/llm.py
Normal file
800
MeetSpot/app/llm.py
Normal file
@@ -0,0 +1,800 @@
|
||||
import math
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
import tiktoken
|
||||
from openai import (APIError, AsyncAzureOpenAI, AsyncOpenAI,
|
||||
AuthenticationError, OpenAIError, RateLimitError)
|
||||
from openai.types.chat import ChatCompletion
|
||||
from openai.types.chat.chat_completion_message import ChatCompletionMessage
|
||||
from tenacity import (retry, retry_if_exception_type, stop_after_attempt,
|
||||
wait_random_exponential)
|
||||
|
||||
from app.config import LLMSettings, config
|
||||
from app.exceptions import TokenLimitExceeded
|
||||
from app.logger import logger # Assuming a logger is set up in your app
|
||||
from app.schema import (ROLE_VALUES, TOOL_CHOICE_TYPE, TOOL_CHOICE_VALUES,
|
||||
Message, ToolChoice)
|
||||
|
||||
REASONING_MODELS = ["o1", "o3-mini"]
|
||||
MULTIMODAL_MODELS = [
|
||||
"gpt-4-vision-preview",
|
||||
"gpt-4o",
|
||||
"gpt-4o-mini",
|
||||
"claude-3-opus-20240229",
|
||||
"claude-3-sonnet-20240229",
|
||||
"claude-3-haiku-20240307",
|
||||
]
|
||||
|
||||
|
||||
class TokenCounter:
|
||||
# Token constants
|
||||
BASE_MESSAGE_TOKENS = 4
|
||||
FORMAT_TOKENS = 2
|
||||
LOW_DETAIL_IMAGE_TOKENS = 85
|
||||
HIGH_DETAIL_TILE_TOKENS = 170
|
||||
|
||||
# Image processing constants
|
||||
MAX_SIZE = 2048
|
||||
HIGH_DETAIL_TARGET_SHORT_SIDE = 768
|
||||
TILE_SIZE = 512
|
||||
|
||||
def __init__(self, tokenizer):
|
||||
self.tokenizer = tokenizer
|
||||
|
||||
def count_text(self, text: str) -> int:
|
||||
"""Calculate tokens for a text string"""
|
||||
return 0 if not text else len(self.tokenizer.encode(text))
|
||||
|
||||
def count_image(self, image_item: dict) -> int:
|
||||
"""
|
||||
Calculate tokens for an image based on detail level and dimensions
|
||||
|
||||
For "low" detail: fixed 85 tokens
|
||||
For "high" detail:
|
||||
1. Scale to fit in 2048x2048 square
|
||||
2. Scale shortest side to 768px
|
||||
3. Count 512px tiles (170 tokens each)
|
||||
4. Add 85 tokens
|
||||
"""
|
||||
detail = image_item.get("detail", "medium")
|
||||
|
||||
# For low detail, always return fixed token count
|
||||
if detail == "low":
|
||||
return self.LOW_DETAIL_IMAGE_TOKENS
|
||||
|
||||
# For medium detail (default in OpenAI), use high detail calculation
|
||||
# OpenAI doesn't specify a separate calculation for medium
|
||||
|
||||
# For high detail, calculate based on dimensions if available
|
||||
if detail == "high" or detail == "medium":
|
||||
# If dimensions are provided in the image_item
|
||||
if "dimensions" in image_item:
|
||||
width, height = image_item["dimensions"]
|
||||
return self._calculate_high_detail_tokens(width, height)
|
||||
|
||||
# Default values when dimensions aren't available or detail level is unknown
|
||||
if detail == "high":
|
||||
# Default to a 1024x1024 image calculation for high detail
|
||||
return self._calculate_high_detail_tokens(1024, 1024) # 765 tokens
|
||||
elif detail == "medium":
|
||||
# Default to a medium-sized image for medium detail
|
||||
return 1024 # This matches the original default
|
||||
else:
|
||||
# For unknown detail levels, use medium as default
|
||||
return 1024
|
||||
|
||||
def _calculate_high_detail_tokens(self, width: int, height: int) -> int:
|
||||
"""Calculate tokens for high detail images based on dimensions"""
|
||||
# Step 1: Scale to fit in MAX_SIZE x MAX_SIZE square
|
||||
if width > self.MAX_SIZE or height > self.MAX_SIZE:
|
||||
scale = self.MAX_SIZE / max(width, height)
|
||||
width = int(width * scale)
|
||||
height = int(height * scale)
|
||||
|
||||
# Step 2: Scale so shortest side is HIGH_DETAIL_TARGET_SHORT_SIDE
|
||||
scale = self.HIGH_DETAIL_TARGET_SHORT_SIDE / min(width, height)
|
||||
scaled_width = int(width * scale)
|
||||
scaled_height = int(height * scale)
|
||||
|
||||
# Step 3: Count number of 512px tiles
|
||||
tiles_x = math.ceil(scaled_width / self.TILE_SIZE)
|
||||
tiles_y = math.ceil(scaled_height / self.TILE_SIZE)
|
||||
total_tiles = tiles_x * tiles_y
|
||||
|
||||
# Step 4: Calculate final token count
|
||||
return (
|
||||
total_tiles * self.HIGH_DETAIL_TILE_TOKENS
|
||||
) + self.LOW_DETAIL_IMAGE_TOKENS
|
||||
|
||||
def count_content(self, content: Union[str, List[Union[str, dict]]]) -> int:
|
||||
"""Calculate tokens for message content"""
|
||||
if not content:
|
||||
return 0
|
||||
|
||||
if isinstance(content, str):
|
||||
return self.count_text(content)
|
||||
|
||||
token_count = 0
|
||||
for item in content:
|
||||
if isinstance(item, str):
|
||||
token_count += self.count_text(item)
|
||||
elif isinstance(item, dict):
|
||||
if "text" in item:
|
||||
token_count += self.count_text(item["text"])
|
||||
elif "image_url" in item:
|
||||
token_count += self.count_image(item)
|
||||
return token_count
|
||||
|
||||
def count_tool_calls(self, tool_calls: List[dict]) -> int:
|
||||
"""Calculate tokens for tool calls"""
|
||||
token_count = 0
|
||||
for tool_call in tool_calls:
|
||||
if "function" in tool_call:
|
||||
function = tool_call["function"]
|
||||
token_count += self.count_text(function.get("name", ""))
|
||||
token_count += self.count_text(function.get("arguments", ""))
|
||||
return token_count
|
||||
|
||||
def count_message_tokens(self, messages: List[dict]) -> int:
|
||||
"""Calculate the total number of tokens in a message list"""
|
||||
total_tokens = self.FORMAT_TOKENS # Base format tokens
|
||||
|
||||
for message in messages:
|
||||
tokens = self.BASE_MESSAGE_TOKENS # Base tokens per message
|
||||
|
||||
# Add role tokens
|
||||
tokens += self.count_text(message.get("role", ""))
|
||||
|
||||
# Add content tokens
|
||||
if "content" in message:
|
||||
tokens += self.count_content(message["content"])
|
||||
|
||||
# Add tool calls tokens
|
||||
if "tool_calls" in message:
|
||||
tokens += self.count_tool_calls(message["tool_calls"])
|
||||
|
||||
# Add name and tool_call_id tokens
|
||||
tokens += self.count_text(message.get("name", ""))
|
||||
tokens += self.count_text(message.get("tool_call_id", ""))
|
||||
|
||||
total_tokens += tokens
|
||||
|
||||
return total_tokens
|
||||
|
||||
|
||||
class LLM:
|
||||
_instances: Dict[str, "LLM"] = {}
|
||||
|
||||
def __new__(
|
||||
cls, config_name: str = "default", llm_config: Optional[LLMSettings] = None
|
||||
):
|
||||
if config_name not in cls._instances:
|
||||
instance = super().__new__(cls)
|
||||
instance.__init__(config_name, llm_config)
|
||||
cls._instances[config_name] = instance
|
||||
return cls._instances[config_name]
|
||||
|
||||
def __init__(
|
||||
self, config_name: str = "default", llm_config: Optional[LLMSettings] = None
|
||||
):
|
||||
if not hasattr(self, "client"): # Only initialize if not already initialized
|
||||
llm_config = llm_config or config.llm
|
||||
llm_config = llm_config.get(config_name, llm_config["default"])
|
||||
self.model = llm_config.model
|
||||
self.max_tokens = llm_config.max_tokens
|
||||
self.temperature = llm_config.temperature
|
||||
self.api_type = llm_config.api_type
|
||||
self.api_key = llm_config.api_key
|
||||
self.api_version = llm_config.api_version
|
||||
self.base_url = llm_config.base_url
|
||||
|
||||
# Add token counting related attributes
|
||||
self.total_input_tokens = 0
|
||||
self.total_completion_tokens = 0
|
||||
self.max_input_tokens = (
|
||||
llm_config.max_input_tokens
|
||||
if hasattr(llm_config, "max_input_tokens")
|
||||
else None
|
||||
)
|
||||
|
||||
# Initialize tokenizer
|
||||
try:
|
||||
self.tokenizer = tiktoken.encoding_for_model(self.model)
|
||||
except KeyError:
|
||||
# If the model is not in tiktoken's presets, use cl100k_base as default
|
||||
self.tokenizer = tiktoken.get_encoding("cl100k_base")
|
||||
|
||||
if self.api_type == "azure":
|
||||
self.client = AsyncAzureOpenAI(
|
||||
base_url=self.base_url,
|
||||
api_key=self.api_key,
|
||||
api_version=self.api_version,
|
||||
)
|
||||
else:
|
||||
self.client = AsyncOpenAI(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
self.token_counter = TokenCounter(self.tokenizer)
|
||||
|
||||
def count_tokens(self, text: str) -> int:
|
||||
"""Calculate the number of tokens in a text"""
|
||||
if not text:
|
||||
return 0
|
||||
return len(self.tokenizer.encode(text))
|
||||
|
||||
def count_message_tokens(self, messages: List[dict]) -> int:
|
||||
return self.token_counter.count_message_tokens(messages)
|
||||
|
||||
def update_token_count(self, input_tokens: int, completion_tokens: int = 0) -> None:
|
||||
"""Update token counts"""
|
||||
# Only track tokens if max_input_tokens is set
|
||||
self.total_input_tokens += input_tokens
|
||||
self.total_completion_tokens += completion_tokens
|
||||
logger.info(
|
||||
f"Token usage: Input={input_tokens}, Completion={completion_tokens}, "
|
||||
f"Cumulative Input={self.total_input_tokens}, Cumulative Completion={self.total_completion_tokens}, "
|
||||
f"Total={input_tokens + completion_tokens}, Cumulative Total={self.total_input_tokens + self.total_completion_tokens}"
|
||||
)
|
||||
|
||||
def check_token_limit(self, input_tokens: int) -> bool:
|
||||
"""Check if token limits are exceeded"""
|
||||
if self.max_input_tokens is not None:
|
||||
return (self.total_input_tokens + input_tokens) <= self.max_input_tokens
|
||||
# If max_input_tokens is not set, always return True
|
||||
return True
|
||||
|
||||
def get_limit_error_message(self, input_tokens: int) -> str:
|
||||
"""Generate error message for token limit exceeded"""
|
||||
if (
|
||||
self.max_input_tokens is not None
|
||||
and (self.total_input_tokens + input_tokens) > self.max_input_tokens
|
||||
):
|
||||
return f"Request may exceed input token limit (Current: {self.total_input_tokens}, Needed: {input_tokens}, Max: {self.max_input_tokens})"
|
||||
|
||||
return "Token limit exceeded"
|
||||
|
||||
@staticmethod
|
||||
def format_messages(
|
||||
messages: List[Union[dict, Message]], supports_images: bool = False
|
||||
) -> List[dict]:
|
||||
"""
|
||||
Format messages for LLM by converting them to OpenAI message format.
|
||||
|
||||
Args:
|
||||
messages: List of messages that can be either dict or Message objects
|
||||
supports_images: Flag indicating if the target model supports image inputs
|
||||
|
||||
Returns:
|
||||
List[dict]: List of formatted messages in OpenAI format
|
||||
|
||||
Raises:
|
||||
ValueError: If messages are invalid or missing required fields
|
||||
TypeError: If unsupported message types are provided
|
||||
|
||||
Examples:
|
||||
>>> msgs = [
|
||||
... Message.system_message("You are a helpful assistant"),
|
||||
... {"role": "user", "content": "Hello"},
|
||||
... Message.user_message("How are you?")
|
||||
... ]
|
||||
>>> formatted = LLM.format_messages(msgs)
|
||||
"""
|
||||
formatted_messages = []
|
||||
|
||||
for message in messages:
|
||||
# Convert Message objects to dictionaries
|
||||
if isinstance(message, Message):
|
||||
message = message.to_dict()
|
||||
|
||||
if isinstance(message, dict):
|
||||
# If message is a dict, ensure it has required fields
|
||||
if "role" not in message:
|
||||
raise ValueError("Message dict must contain 'role' field")
|
||||
|
||||
# Process base64 images if present and model supports images
|
||||
if supports_images and message.get("base64_image"):
|
||||
# Initialize or convert content to appropriate format
|
||||
if not message.get("content"):
|
||||
message["content"] = []
|
||||
elif isinstance(message["content"], str):
|
||||
message["content"] = [
|
||||
{"type": "text", "text": message["content"]}
|
||||
]
|
||||
elif isinstance(message["content"], list):
|
||||
# Convert string items to proper text objects
|
||||
message["content"] = [
|
||||
(
|
||||
{"type": "text", "text": item}
|
||||
if isinstance(item, str)
|
||||
else item
|
||||
)
|
||||
for item in message["content"]
|
||||
]
|
||||
|
||||
# Add the image to content
|
||||
message["content"].append(
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": f"data:image/jpeg;base64,{message['base64_image']}"
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
# Remove the base64_image field
|
||||
del message["base64_image"]
|
||||
# If model doesn't support images but message has base64_image, handle gracefully
|
||||
elif not supports_images and message.get("base64_image"):
|
||||
# Just remove the base64_image field and keep the text content
|
||||
del message["base64_image"]
|
||||
|
||||
if "content" in message or "tool_calls" in message:
|
||||
formatted_messages.append(message)
|
||||
# else: do not include the message
|
||||
else:
|
||||
raise TypeError(f"Unsupported message type: {type(message)}")
|
||||
|
||||
# Validate all messages have required fields
|
||||
for msg in formatted_messages:
|
||||
if msg["role"] not in ROLE_VALUES:
|
||||
raise ValueError(f"Invalid role: {msg['role']}")
|
||||
|
||||
return formatted_messages
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=60),
|
||||
stop=stop_after_attempt(6),
|
||||
retry=retry_if_exception_type(
|
||||
(OpenAIError, Exception, ValueError)
|
||||
), # Don't retry TokenLimitExceeded
|
||||
)
|
||||
async def ask(
|
||||
self,
|
||||
messages: List[Union[dict, Message]],
|
||||
system_msgs: Optional[List[Union[dict, Message]]] = None,
|
||||
stream: bool = True,
|
||||
temperature: Optional[float] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Send a prompt to the LLM and get the response.
|
||||
|
||||
Args:
|
||||
messages: List of conversation messages
|
||||
system_msgs: Optional system messages to prepend
|
||||
stream (bool): Whether to stream the response
|
||||
temperature (float): Sampling temperature for the response
|
||||
|
||||
Returns:
|
||||
str: The generated response
|
||||
|
||||
Raises:
|
||||
TokenLimitExceeded: If token limits are exceeded
|
||||
ValueError: If messages are invalid or response is empty
|
||||
OpenAIError: If API call fails after retries
|
||||
Exception: For unexpected errors
|
||||
"""
|
||||
try:
|
||||
# Check if the model supports images
|
||||
supports_images = self.model in MULTIMODAL_MODELS
|
||||
|
||||
# Format system and user messages with image support check
|
||||
if system_msgs:
|
||||
system_msgs = self.format_messages(system_msgs, supports_images)
|
||||
messages = system_msgs + self.format_messages(messages, supports_images)
|
||||
else:
|
||||
messages = self.format_messages(messages, supports_images)
|
||||
|
||||
# Calculate input token count
|
||||
input_tokens = self.count_message_tokens(messages)
|
||||
|
||||
# Check if token limits are exceeded
|
||||
if not self.check_token_limit(input_tokens):
|
||||
error_message = self.get_limit_error_message(input_tokens)
|
||||
# Raise a special exception that won't be retried
|
||||
raise TokenLimitExceeded(error_message)
|
||||
|
||||
params = {
|
||||
"model": self.model,
|
||||
"messages": messages,
|
||||
}
|
||||
|
||||
if self.model in REASONING_MODELS:
|
||||
params["max_completion_tokens"] = self.max_tokens
|
||||
else:
|
||||
params["max_tokens"] = self.max_tokens
|
||||
params["temperature"] = (
|
||||
temperature if temperature is not None else self.temperature
|
||||
)
|
||||
|
||||
if not stream:
|
||||
# Non-streaming request
|
||||
response = await self.client.chat.completions.create(
|
||||
**params, stream=False
|
||||
)
|
||||
|
||||
if not response.choices or not response.choices[0].message.content:
|
||||
raise ValueError("Empty or invalid response from LLM")
|
||||
|
||||
# Update token counts
|
||||
self.update_token_count(
|
||||
response.usage.prompt_tokens, response.usage.completion_tokens
|
||||
)
|
||||
|
||||
return response.choices[0].message.content
|
||||
|
||||
# Streaming request, For streaming, update estimated token count before making the request
|
||||
self.update_token_count(input_tokens)
|
||||
|
||||
response = await self.client.chat.completions.create(**params, stream=True)
|
||||
|
||||
collected_messages = []
|
||||
completion_text = ""
|
||||
async for chunk in response:
|
||||
chunk_message = chunk.choices[0].delta.content or ""
|
||||
collected_messages.append(chunk_message)
|
||||
completion_text += chunk_message
|
||||
print(chunk_message, end="", flush=True)
|
||||
|
||||
print() # Newline after streaming
|
||||
full_response = "".join(collected_messages).strip()
|
||||
if not full_response:
|
||||
raise ValueError("Empty response from streaming LLM")
|
||||
|
||||
# estimate completion tokens for streaming response
|
||||
completion_tokens = self.count_tokens(completion_text)
|
||||
logger.info(
|
||||
f"Estimated completion tokens for streaming response: {completion_tokens}"
|
||||
)
|
||||
self.total_completion_tokens += completion_tokens
|
||||
|
||||
return full_response
|
||||
|
||||
except TokenLimitExceeded:
|
||||
# Re-raise token limit errors without logging
|
||||
raise
|
||||
except ValueError:
|
||||
logger.exception(f"Validation error")
|
||||
raise
|
||||
except OpenAIError as oe:
|
||||
logger.exception(f"OpenAI API error")
|
||||
if isinstance(oe, AuthenticationError):
|
||||
logger.error("Authentication failed. Check API key.")
|
||||
elif isinstance(oe, RateLimitError):
|
||||
logger.error("Rate limit exceeded. Consider increasing retry attempts.")
|
||||
elif isinstance(oe, APIError):
|
||||
logger.error(f"API error: {oe}")
|
||||
raise
|
||||
except Exception:
|
||||
logger.exception(f"Unexpected error in ask")
|
||||
raise
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=60),
|
||||
stop=stop_after_attempt(6),
|
||||
retry=retry_if_exception_type(
|
||||
(OpenAIError, Exception, ValueError)
|
||||
), # Don't retry TokenLimitExceeded
|
||||
)
|
||||
async def ask_with_images(
|
||||
self,
|
||||
messages: List[Union[dict, Message]],
|
||||
images: List[Union[str, dict]],
|
||||
system_msgs: Optional[List[Union[dict, Message]]] = None,
|
||||
stream: bool = False,
|
||||
temperature: Optional[float] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Send a prompt with images to the LLM and get the response.
|
||||
|
||||
Args:
|
||||
messages: List of conversation messages
|
||||
images: List of image URLs or image data dictionaries
|
||||
system_msgs: Optional system messages to prepend
|
||||
stream (bool): Whether to stream the response
|
||||
temperature (float): Sampling temperature for the response
|
||||
|
||||
Returns:
|
||||
str: The generated response
|
||||
|
||||
Raises:
|
||||
TokenLimitExceeded: If token limits are exceeded
|
||||
ValueError: If messages are invalid or response is empty
|
||||
OpenAIError: If API call fails after retries
|
||||
Exception: For unexpected errors
|
||||
"""
|
||||
try:
|
||||
# For ask_with_images, we always set supports_images to True because
|
||||
# this method should only be called with models that support images
|
||||
if self.model not in MULTIMODAL_MODELS:
|
||||
raise ValueError(
|
||||
f"Model {self.model} does not support images. Use a model from {MULTIMODAL_MODELS}"
|
||||
)
|
||||
|
||||
# Format messages with image support
|
||||
formatted_messages = self.format_messages(messages, supports_images=True)
|
||||
|
||||
# Ensure the last message is from the user to attach images
|
||||
if not formatted_messages or formatted_messages[-1]["role"] != "user":
|
||||
raise ValueError(
|
||||
"The last message must be from the user to attach images"
|
||||
)
|
||||
|
||||
# Process the last user message to include images
|
||||
last_message = formatted_messages[-1]
|
||||
|
||||
# Convert content to multimodal format if needed
|
||||
content = last_message["content"]
|
||||
multimodal_content = (
|
||||
[{"type": "text", "text": content}]
|
||||
if isinstance(content, str)
|
||||
else content if isinstance(content, list) else []
|
||||
)
|
||||
|
||||
# Add images to content
|
||||
for image in images:
|
||||
if isinstance(image, str):
|
||||
multimodal_content.append(
|
||||
{"type": "image_url", "image_url": {"url": image}}
|
||||
)
|
||||
elif isinstance(image, dict) and "url" in image:
|
||||
multimodal_content.append({"type": "image_url", "image_url": image})
|
||||
elif isinstance(image, dict) and "image_url" in image:
|
||||
multimodal_content.append(image)
|
||||
else:
|
||||
raise ValueError(f"Unsupported image format: {image}")
|
||||
|
||||
# Update the message with multimodal content
|
||||
last_message["content"] = multimodal_content
|
||||
|
||||
# Add system messages if provided
|
||||
if system_msgs:
|
||||
all_messages = (
|
||||
self.format_messages(system_msgs, supports_images=True)
|
||||
+ formatted_messages
|
||||
)
|
||||
else:
|
||||
all_messages = formatted_messages
|
||||
|
||||
# Calculate tokens and check limits
|
||||
input_tokens = self.count_message_tokens(all_messages)
|
||||
if not self.check_token_limit(input_tokens):
|
||||
raise TokenLimitExceeded(self.get_limit_error_message(input_tokens))
|
||||
|
||||
# Set up API parameters
|
||||
params = {
|
||||
"model": self.model,
|
||||
"messages": all_messages,
|
||||
"stream": stream,
|
||||
}
|
||||
|
||||
# Add model-specific parameters
|
||||
if self.model in REASONING_MODELS:
|
||||
params["max_completion_tokens"] = self.max_tokens
|
||||
else:
|
||||
params["max_tokens"] = self.max_tokens
|
||||
params["temperature"] = (
|
||||
temperature if temperature is not None else self.temperature
|
||||
)
|
||||
|
||||
# Handle non-streaming request
|
||||
if not stream:
|
||||
response = await self.client.chat.completions.create(**params)
|
||||
|
||||
if not response.choices or not response.choices[0].message.content:
|
||||
raise ValueError("Empty or invalid response from LLM")
|
||||
|
||||
self.update_token_count(
|
||||
response.usage.prompt_tokens, response.usage.completion_tokens
|
||||
)
|
||||
return response.choices[0].message.content
|
||||
|
||||
# Handle streaming request
|
||||
response = await self.client.chat.completions.create(**params)
|
||||
|
||||
collected_messages = []
|
||||
completion_text = ""
|
||||
async for chunk in response:
|
||||
chunk_message = chunk.choices[0].delta.content or ""
|
||||
collected_messages.append(chunk_message)
|
||||
completion_text += chunk_message
|
||||
print(chunk_message, end="", flush=True)
|
||||
|
||||
print() # Newline after streaming
|
||||
full_response = "".join(collected_messages).strip()
|
||||
|
||||
if not full_response:
|
||||
raise ValueError("Empty response from streaming LLM")
|
||||
|
||||
completion_tokens = self.count_tokens(completion_text)
|
||||
logger.info(
|
||||
f"Estimated completion tokens for streaming response with images: {completion_tokens}"
|
||||
)
|
||||
self.update_token_count(input_tokens, completion_tokens)
|
||||
|
||||
return full_response
|
||||
|
||||
except TokenLimitExceeded:
|
||||
raise
|
||||
except ValueError as ve:
|
||||
logger.error(f"Validation error in ask_with_images: {ve}")
|
||||
raise
|
||||
except OpenAIError as oe:
|
||||
logger.error(f"OpenAI API error: {oe}")
|
||||
if isinstance(oe, AuthenticationError):
|
||||
logger.error("Authentication failed. Check API key.")
|
||||
elif isinstance(oe, RateLimitError):
|
||||
logger.error("Rate limit exceeded. Consider increasing retry attempts.")
|
||||
elif isinstance(oe, APIError):
|
||||
logger.error(f"API error: {oe}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error in ask_with_images: {e}")
|
||||
raise
|
||||
|
||||
@retry(
|
||||
wait=wait_random_exponential(min=1, max=60),
|
||||
stop=stop_after_attempt(6),
|
||||
retry=retry_if_exception_type(
|
||||
(OpenAIError, Exception, ValueError)
|
||||
), # Don't retry TokenLimitExceeded
|
||||
)
|
||||
async def ask_tool(
|
||||
self,
|
||||
messages: List[Union[dict, Message]],
|
||||
system_msgs: Optional[List[Union[dict, Message]]] = None,
|
||||
timeout: int = 300,
|
||||
tools: Optional[List[dict]] = None,
|
||||
tool_choice: TOOL_CHOICE_TYPE = ToolChoice.AUTO, # type: ignore
|
||||
temperature: Optional[float] = None,
|
||||
**kwargs,
|
||||
) -> ChatCompletionMessage | None:
|
||||
"""
|
||||
Ask LLM using functions/tools and return the response.
|
||||
|
||||
Args:
|
||||
messages: List of conversation messages
|
||||
system_msgs: Optional system messages to prepend
|
||||
timeout: Request timeout in seconds
|
||||
tools: List of tools to use
|
||||
tool_choice: Tool choice strategy
|
||||
temperature: Sampling temperature for the response
|
||||
**kwargs: Additional completion arguments
|
||||
|
||||
Returns:
|
||||
ChatCompletionMessage: The model's response
|
||||
|
||||
Raises:
|
||||
TokenLimitExceeded: If token limits are exceeded
|
||||
ValueError: If tools, tool_choice, or messages are invalid
|
||||
OpenAIError: If API call fails after retries
|
||||
Exception: For unexpected errors
|
||||
"""
|
||||
try:
|
||||
# Validate tool_choice
|
||||
if tool_choice not in TOOL_CHOICE_VALUES:
|
||||
raise ValueError(f"Invalid tool_choice: {tool_choice}")
|
||||
|
||||
# Check if the model supports images
|
||||
supports_images = self.model in MULTIMODAL_MODELS
|
||||
|
||||
# Format messages
|
||||
if system_msgs:
|
||||
system_msgs = self.format_messages(system_msgs, supports_images)
|
||||
formatted_messages = system_msgs + self.format_messages(messages, supports_images)
|
||||
else:
|
||||
formatted_messages = self.format_messages(messages, supports_images)
|
||||
|
||||
# 验证消息序列，确保tool消息前面有带tool_calls的assistant消息
|
||||
valid_messages = []
|
||||
tool_calls_ids = set() # 跟踪所有有效的tool_call IDs
|
||||
|
||||
for i, msg in enumerate(formatted_messages):
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get("role")
|
||||
tool_call_id = msg.get("tool_call_id")
|
||||
else:
|
||||
role = msg.role if hasattr(msg, "role") else None
|
||||
tool_call_id = msg.tool_call_id if hasattr(msg, "tool_call_id") else None
|
||||
|
||||
# 如果是tool消息，验证它引用的tool_call_id是否存在
|
||||
if role == "tool" and tool_call_id:
|
||||
if tool_call_id not in tool_calls_ids:
|
||||
logger.warning(f"发现无效的工具消息 - tool_call_id '{tool_call_id}' 未找到关联的tool_calls，将跳过此消息")
|
||||
continue
|
||||
|
||||
# 如果是assistant消息且有tool_calls，记录所有tool_call IDs
|
||||
elif role == "assistant":
|
||||
tool_calls = []
|
||||
if isinstance(msg, dict) and "tool_calls" in msg:
|
||||
tool_calls = msg.get("tool_calls", [])
|
||||
elif hasattr(msg, "tool_calls") and msg.tool_calls:
|
||||
tool_calls = msg.tool_calls
|
||||
|
||||
# 收集所有工具调用ID
|
||||
for call in tool_calls:
|
||||
if isinstance(call, dict) and "id" in call:
|
||||
tool_calls_ids.add(call["id"])
|
||||
elif hasattr(call, "id"):
|
||||
tool_calls_ids.add(call.id)
|
||||
|
||||
# 添加有效消息
|
||||
valid_messages.append(msg)
|
||||
|
||||
# 使用验证过的消息序列替换原来的消息
|
||||
formatted_messages = valid_messages
|
||||
|
||||
# Calculate input token count
|
||||
input_tokens = self.count_message_tokens(formatted_messages)
|
||||
|
||||
# If there are tools, calculate token count for tool descriptions
|
||||
tools_tokens = 0
|
||||
if tools:
|
||||
for tool in tools:
|
||||
tools_tokens += self.count_tokens(str(tool))
|
||||
|
||||
input_tokens += tools_tokens
|
||||
|
||||
# Check if token limits are exceeded
|
||||
if not self.check_token_limit(input_tokens):
|
||||
error_message = self.get_limit_error_message(input_tokens)
|
||||
# Raise a special exception that won't be retried
|
||||
raise TokenLimitExceeded(error_message)
|
||||
|
||||
# Validate tools if provided
|
||||
if tools:
|
||||
for tool in tools:
|
||||
if not isinstance(tool, dict) or "type" not in tool:
|
||||
raise ValueError("Each tool must be a dict with 'type' field")
|
||||
|
||||
# Set up the completion request
|
||||
params = {
|
||||
"model": self.model,
|
||||
"messages": formatted_messages,
|
||||
"tools": tools,
|
||||
"tool_choice": tool_choice,
|
||||
"timeout": timeout,
|
||||
**kwargs,
|
||||
}
|
||||
|
||||
if self.model in REASONING_MODELS:
|
||||
params["max_completion_tokens"] = self.max_tokens
|
||||
else:
|
||||
params["max_tokens"] = self.max_tokens
|
||||
params["temperature"] = (
|
||||
temperature if temperature is not None else self.temperature
|
||||
)
|
||||
|
||||
response: ChatCompletion = await self.client.chat.completions.create(
|
||||
**params, stream=False
|
||||
)
|
||||
|
||||
# Check if response is valid
|
||||
if not response.choices or not response.choices[0].message:
|
||||
print(response)
|
||||
# raise ValueError("Invalid or empty response from LLM")
|
||||
return None
|
||||
|
||||
# Update token counts
|
||||
self.update_token_count(
|
||||
response.usage.prompt_tokens, response.usage.completion_tokens
|
||||
)
|
||||
|
||||
return response.choices[0].message
|
||||
|
||||
except TokenLimitExceeded:
|
||||
# Re-raise token limit errors without logging
|
||||
raise
|
||||
except ValueError as ve:
|
||||
logger.error(f"Validation error in ask_tool: {ve}")
|
||||
raise
|
||||
except OpenAIError as oe:
|
||||
logger.error(f"OpenAI API error: {oe}")
|
||||
if isinstance(oe, AuthenticationError):
|
||||
logger.error("Authentication failed. Check API key.")
|
||||
elif isinstance(oe, RateLimitError):
|
||||
logger.error("Rate limit exceeded. Consider increasing retry attempts.")
|
||||
elif isinstance(oe, APIError):
|
||||
logger.error(f"API error: {oe}")
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error in ask_tool: {e}")
|
||||
raise
|
||||
42
MeetSpot/app/logger.py
Normal file
42
MeetSpot/app/logger.py
Normal file
@@ -0,0 +1,42 @@
|
||||
import sys
|
||||
from datetime import datetime
|
||||
|
||||
from loguru import logger as _logger
|
||||
|
||||
from app.config import PROJECT_ROOT
|
||||
|
||||
|
||||
_print_level = "INFO"
|
||||
|
||||
|
||||
def define_log_level(print_level="INFO", logfile_level="DEBUG", name: str = None):
|
||||
"""Adjust the log level to above level"""
|
||||
global _print_level
|
||||
_print_level = print_level
|
||||
|
||||
current_date = datetime.now()
|
||||
formatted_date = current_date.strftime("%Y%m%d%H%M%S")
|
||||
log_name = (
|
||||
f"{name}_{formatted_date}" if name else formatted_date
|
||||
) # name a log with prefix name
|
||||
|
||||
_logger.remove()
|
||||
_logger.add(sys.stderr, level=print_level)
|
||||
_logger.add(PROJECT_ROOT / f"logs/{log_name}.log", level=logfile_level)
|
||||
return _logger
|
||||
|
||||
|
||||
logger = define_log_level()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger.info("Starting application")
|
||||
logger.debug("Debug message")
|
||||
logger.warning("Warning message")
|
||||
logger.error("Error message")
|
||||
logger.critical("Critical message")
|
||||
|
||||
try:
|
||||
raise ValueError("Test error")
|
||||
except Exception as e:
|
||||
logger.exception(f"An error occurred: {e}")
|
||||
9
MeetSpot/app/models/__init__.py
Normal file
9
MeetSpot/app/models/__init__.py
Normal file
@@ -0,0 +1,9 @@
|
||||
"""数据模型包。"""
|
||||
|
||||
from app.db.database import Base # noqa: F401
|
||||
|
||||
# 导入所有模型以确保它们注册到Base.metadata
|
||||
from app.models.user import User # noqa: F401
|
||||
from app.models.room import GatheringRoom, RoomParticipant # noqa: F401
|
||||
from app.models.message import ChatMessage, VenueVote # noqa: F401
|
||||
|
||||
53
MeetSpot/app/models/message.py
Normal file
53
MeetSpot/app/models/message.py
Normal file
@@ -0,0 +1,53 @@
|
||||
"""聊天消息与投票记录模型。"""
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy import Column, DateTime, ForeignKey, String, Text, UniqueConstraint, func
|
||||
|
||||
from app.db.database import Base
|
||||
|
||||
|
||||
def _generate_uuid() -> str:
|
||||
return str(uuid.uuid4())
|
||||
|
||||
|
||||
class VenueVote(Base):
|
||||
__tablename__ = "venue_votes"
|
||||
__table_args__ = (UniqueConstraint("room_id", "venue_id", "user_id", name="uq_vote"),)
|
||||
|
||||
id = Column(String(36), primary_key=True, default=_generate_uuid)
|
||||
room_id = Column(String(36), ForeignKey("gathering_rooms.id"), nullable=False)
|
||||
venue_id = Column(String(100), nullable=False)
|
||||
user_id = Column(String(36), ForeignKey("users.id"), nullable=False)
|
||||
vote_type = Column(String(20), nullable=False)
|
||||
created_at = Column(DateTime(timezone=True), server_default=func.now())
|
||||
|
||||
|
||||
class ChatMessage(Base):
|
||||
__tablename__ = "chat_messages"
|
||||
|
||||
id = Column(String(36), primary_key=True, default=_generate_uuid)
|
||||
room_id = Column(String(36), ForeignKey("gathering_rooms.id"), nullable=False)
|
||||
user_id = Column(String(36), ForeignKey("users.id"), nullable=False)
|
||||
content = Column(Text, nullable=False)
|
||||
created_at = Column(DateTime(timezone=True), server_default=func.now())
|
||||
|
||||
|
||||
class ChatMessageCreate(BaseModel):
|
||||
content: str = Field(..., min_length=1, description="聊天内容")
|
||||
|
||||
|
||||
class VoteCreate(BaseModel):
|
||||
venue_id: str
|
||||
vote_type: str = Field(..., pattern="^(like|dislike)$")
|
||||
|
||||
|
||||
class VoteRead(BaseModel):
|
||||
venue_id: str
|
||||
vote_type: str
|
||||
user_id: str
|
||||
created_at: Optional[datetime] = None
|
||||
|
||||
60
MeetSpot/app/models/room.py
Normal file
60
MeetSpot/app/models/room.py
Normal file
@@ -0,0 +1,60 @@
|
||||
"""聚会房间相关的ORM与Pydantic模型。"""
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Optional, Tuple
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy import Column, DateTime, Float, ForeignKey, String, Text, UniqueConstraint, func
|
||||
|
||||
from app.db.database import Base
|
||||
|
||||
|
||||
def _generate_uuid() -> str:
|
||||
return str(uuid.uuid4())
|
||||
|
||||
|
||||
class GatheringRoom(Base):
|
||||
__tablename__ = "gathering_rooms"
|
||||
|
||||
id = Column(String(36), primary_key=True, default=_generate_uuid)
|
||||
name = Column(String(100), nullable=False)
|
||||
description = Column(Text, default="")
|
||||
host_user_id = Column(String(36), ForeignKey("users.id"), nullable=False)
|
||||
created_at = Column(DateTime(timezone=True), server_default=func.now())
|
||||
gathering_time = Column(DateTime(timezone=True))
|
||||
status = Column(String(20), default="pending")
|
||||
venue_keywords = Column(String(100), default="咖啡馆")
|
||||
final_venue_json = Column(Text, nullable=True)
|
||||
|
||||
|
||||
class RoomParticipant(Base):
|
||||
__tablename__ = "room_participants"
|
||||
__table_args__ = (UniqueConstraint("room_id", "user_id", name="uq_room_user"),)
|
||||
|
||||
id = Column(String(36), primary_key=True, default=_generate_uuid)
|
||||
room_id = Column(String(36), ForeignKey("gathering_rooms.id"), nullable=False)
|
||||
user_id = Column(String(36), ForeignKey("users.id"), nullable=False)
|
||||
location_name = Column(String(200))
|
||||
location_lat = Column(Float)
|
||||
location_lng = Column(Float)
|
||||
joined_at = Column(DateTime(timezone=True), server_default=func.now())
|
||||
role = Column(String(20), default="member")
|
||||
|
||||
|
||||
class GatheringRoomCreate(BaseModel):
|
||||
name: str = Field(..., description="聚会名称")
|
||||
description: str = Field("", description="聚会描述")
|
||||
gathering_time: Optional[datetime] = Field(
|
||||
None, description="聚会时间，ISO 字符串"
|
||||
)
|
||||
venue_keywords: str = Field("咖啡馆", description="场所类型关键词")
|
||||
|
||||
|
||||
class RoomParticipantRead(BaseModel):
|
||||
user_id: str
|
||||
nickname: str
|
||||
location_name: Optional[str] = None
|
||||
location_coords: Optional[Tuple[float, float]] = None
|
||||
role: str
|
||||
|
||||
44
MeetSpot/app/models/user.py
Normal file
44
MeetSpot/app/models/user.py
Normal file
@@ -0,0 +1,44 @@
|
||||
"""用户相关SQLAlchemy模型与Pydantic模式。"""
|
||||
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy import Column, DateTime, String, func
|
||||
|
||||
from app.db.database import Base
|
||||
|
||||
|
||||
def _generate_uuid() -> str:
|
||||
return str(uuid.uuid4())
|
||||
|
||||
|
||||
class User(Base):
|
||||
__tablename__ = "users"
|
||||
|
||||
id = Column(String(36), primary_key=True, default=_generate_uuid)
|
||||
phone = Column(String(20), unique=True, nullable=False)
|
||||
nickname = Column(String(50), nullable=False)
|
||||
avatar_url = Column(String(255), default="")
|
||||
created_at = Column(DateTime(timezone=True), server_default=func.now())
|
||||
last_login = Column(DateTime(timezone=True))
|
||||
|
||||
|
||||
class UserCreate(BaseModel):
|
||||
phone: str = Field(..., description="手机号")
|
||||
nickname: Optional[str] = Field(None, description="昵称，可选")
|
||||
avatar_url: Optional[str] = Field("", description="头像URL，可选")
|
||||
|
||||
|
||||
class UserRead(BaseModel):
|
||||
id: str
|
||||
phone: str
|
||||
nickname: str
|
||||
avatar_url: str = ""
|
||||
created_at: datetime
|
||||
last_login: Optional[datetime] = None
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
|
||||
214
MeetSpot/app/schema.py
Normal file
214
MeetSpot/app/schema.py
Normal file
@@ -0,0 +1,214 @@
|
||||
from enum import Enum
|
||||
from typing import Any, List, Literal, Optional, Union
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class Role(str, Enum):
|
||||
"""Message role options"""
|
||||
|
||||
SYSTEM = "system"
|
||||
USER = "user"
|
||||
ASSISTANT = "assistant"
|
||||
TOOL = "tool"
|
||||
|
||||
|
||||
ROLE_VALUES = tuple(role.value for role in Role)
|
||||
ROLE_TYPE = Literal[ROLE_VALUES] # type: ignore
|
||||
|
||||
|
||||
class ToolChoice(str, Enum):
|
||||
"""Tool choice options"""
|
||||
|
||||
NONE = "none"
|
||||
AUTO = "auto"
|
||||
REQUIRED = "required"
|
||||
|
||||
|
||||
TOOL_CHOICE_VALUES = tuple(choice.value for choice in ToolChoice)
|
||||
TOOL_CHOICE_TYPE = Literal[TOOL_CHOICE_VALUES] # type: ignore
|
||||
|
||||
|
||||
class AgentState(str, Enum):
|
||||
"""Agent execution states"""
|
||||
|
||||
IDLE = "IDLE"
|
||||
RUNNING = "RUNNING"
|
||||
FINISHED = "FINISHED"
|
||||
ERROR = "ERROR"
|
||||
|
||||
|
||||
class Function(BaseModel):
|
||||
name: str
|
||||
arguments: str
|
||||
|
||||
|
||||
class ToolCall(BaseModel):
|
||||
"""Represents a tool/function call in a message"""
|
||||
|
||||
id: str
|
||||
type: str = "function"
|
||||
function: Function
|
||||
|
||||
|
||||
class Message(BaseModel):
|
||||
"""Represents a chat message in the conversation"""
|
||||
|
||||
role: ROLE_TYPE = Field(...) # type: ignore
|
||||
content: Optional[str] = Field(default=None)
|
||||
tool_calls: Optional[List[ToolCall]] = Field(default=None)
|
||||
name: Optional[str] = Field(default=None)
|
||||
tool_call_id: Optional[str] = Field(default=None)
|
||||
base64_image: Optional[str] = Field(default=None)
|
||||
|
||||
def __add__(self, other) -> List["Message"]:
|
||||
"""支持 Message + list 或 Message + Message 的操作"""
|
||||
if isinstance(other, list):
|
||||
return [self] + other
|
||||
elif isinstance(other, Message):
|
||||
return [self, other]
|
||||
else:
|
||||
raise TypeError(
|
||||
f"unsupported operand type(s) for +: '{type(self).__name__}' and '{type(other).__name__}'"
|
||||
)
|
||||
|
||||
def __radd__(self, other) -> List["Message"]:
|
||||
"""支持 list + Message 的操作"""
|
||||
if isinstance(other, list):
|
||||
return other + [self]
|
||||
else:
|
||||
raise TypeError(
|
||||
f"unsupported operand type(s) for +: '{type(other).__name__}' and '{type(self).__name__}'"
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""Convert message to dictionary format"""
|
||||
message = {"role": self.role}
|
||||
if self.content is not None:
|
||||
message["content"] = self.content
|
||||
if self.tool_calls is not None and self.role == Role.ASSISTANT:
|
||||
message["tool_calls"] = [
|
||||
tool_call.dict() if hasattr(tool_call, "dict") else tool_call
|
||||
for tool_call in self.tool_calls
|
||||
]
|
||||
if self.name is not None and self.role == Role.TOOL:
|
||||
message["name"] = self.name
|
||||
if self.tool_call_id is not None and self.role == Role.TOOL:
|
||||
message["tool_call_id"] = self.tool_call_id
|
||||
# 不要在API调用中包含base64_image，这不是OpenAI API消息格式的一部分
|
||||
return message
|
||||
|
||||
@classmethod
|
||||
def user_message(
|
||||
cls, content: str, base64_image: Optional[str] = None
|
||||
) -> "Message":
|
||||
"""Create a user message"""
|
||||
return cls(role=Role.USER, content=content, base64_image=base64_image)
|
||||
|
||||
@classmethod
|
||||
def system_message(cls, content: str) -> "Message":
|
||||
"""Create a system message"""
|
||||
return cls(role=Role.SYSTEM, content=content)
|
||||
|
||||
@classmethod
|
||||
def assistant_message(
|
||||
cls, content: Optional[str] = None, base64_image: Optional[str] = None
|
||||
) -> "Message":
|
||||
"""Create an assistant message"""
|
||||
return cls(role=Role.ASSISTANT, content=content, base64_image=base64_image)
|
||||
|
||||
@classmethod
|
||||
def tool_message(
|
||||
cls, content: str, name: str, tool_call_id: str, base64_image: Optional[str] = None
|
||||
) -> "Message":
|
||||
"""Create a tool message
|
||||
|
||||
Args:
|
||||
content: The content/result of the tool execution
|
||||
name: The name of the tool that was executed
|
||||
tool_call_id: The ID of the tool call this message is responding to
|
||||
base64_image: Optional base64 encoded image
|
||||
"""
|
||||
if not tool_call_id:
|
||||
raise ValueError("tool_call_id is required for tool messages")
|
||||
if not name:
|
||||
raise ValueError("name is required for tool messages")
|
||||
|
||||
return cls(
|
||||
role=Role.TOOL,
|
||||
content=content,
|
||||
name=name,
|
||||
tool_call_id=tool_call_id,
|
||||
base64_image=base64_image,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_tool_calls(
|
||||
cls,
|
||||
tool_calls: List[Any],
|
||||
content: Union[str, List[str]] = "",
|
||||
base64_image: Optional[str] = None,
|
||||
**kwargs,
|
||||
):
|
||||
"""Create ToolCallsMessage from raw tool calls.
|
||||
|
||||
Args:
|
||||
tool_calls: Raw tool calls from LLM
|
||||
content: Optional message content
|
||||
base64_image: Optional base64 encoded image
|
||||
"""
|
||||
# 确保tool_calls是正确格式的对象列表
|
||||
formatted_calls = []
|
||||
for call in tool_calls:
|
||||
if hasattr(call, "id") and hasattr(call, "function"):
|
||||
func_data = call.function
|
||||
if hasattr(func_data, "model_dump"):
|
||||
func_dict = func_data.model_dump()
|
||||
else:
|
||||
func_dict = {"name": func_data.name, "arguments": func_data.arguments}
|
||||
|
||||
formatted_call = {
|
||||
"id": call.id,
|
||||
"type": "function",
|
||||
"function": func_dict
|
||||
}
|
||||
formatted_calls.append(formatted_call)
|
||||
else:
|
||||
# 如果已经是字典格式，直接使用
|
||||
formatted_calls.append(call)
|
||||
|
||||
return cls(
|
||||
role=Role.ASSISTANT,
|
||||
content=content,
|
||||
tool_calls=formatted_calls,
|
||||
base64_image=base64_image,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
class Memory(BaseModel):
|
||||
messages: List[Message] = Field(default_factory=list)
|
||||
max_messages: int = Field(default=100)
|
||||
|
||||
def add_message(self, message: Message) -> None:
|
||||
"""Add a message to memory"""
|
||||
self.messages.append(message)
|
||||
# Optional: Implement message limit
|
||||
if len(self.messages) > self.max_messages:
|
||||
self.messages = self.messages[-self.max_messages :]
|
||||
|
||||
def add_messages(self, messages: List[Message]) -> None:
|
||||
"""Add multiple messages to memory"""
|
||||
self.messages.extend(messages)
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear all messages"""
|
||||
self.messages.clear()
|
||||
|
||||
def get_recent_messages(self, n: int) -> List[Message]:
|
||||
"""Get n most recent messages"""
|
||||
return self.messages[-n:]
|
||||
|
||||
def to_dict_list(self) -> List[dict]:
|
||||
"""Convert messages to list of dicts"""
|
||||
return [msg.to_dict() for msg in self.messages]
|
||||
9
MeetSpot/app/tool/__init__.py
Normal file
9
MeetSpot/app/tool/__init__.py
Normal file
@@ -0,0 +1,9 @@
|
||||
from app.tool.base import BaseTool
|
||||
from app.tool.meetspot_recommender import CafeRecommender
|
||||
from app.tool.tool_collection import ToolCollection
|
||||
|
||||
__all__ = [
|
||||
"BaseTool",
|
||||
"CafeRecommender",
|
||||
"ToolCollection",
|
||||
]
|
||||
101
MeetSpot/app/tool/base.py
Normal file
101
MeetSpot/app/tool/base.py
Normal file
@@ -0,0 +1,101 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class BaseTool(ABC, BaseModel):
|
||||
name: str
|
||||
description: str
|
||||
parameters: Optional[dict] = None
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
async def __call__(self, **kwargs) -> Any:
|
||||
"""Execute the tool with given parameters."""
|
||||
return await self.execute(**kwargs)
|
||||
|
||||
@abstractmethod
|
||||
async def execute(self, **kwargs) -> Any:
|
||||
"""Execute the tool with given parameters."""
|
||||
|
||||
def to_param(self) -> Dict:
|
||||
"""Convert tool to function call format."""
|
||||
return {
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": self.name,
|
||||
"description": self.description,
|
||||
"parameters": self.parameters,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
class ToolResult(BaseModel):
|
||||
"""Represents the result of a tool execution."""
|
||||
|
||||
output: Any = Field(default=None)
|
||||
error: Optional[str] = Field(default=None)
|
||||
base64_image: Optional[str] = Field(default=None)
|
||||
system: Optional[str] = Field(default=None)
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def __bool__(self):
|
||||
return any(getattr(self, field) for field in self.__fields__)
|
||||
|
||||
def __add__(self, other: "ToolResult"):
|
||||
def combine_fields(
|
||||
field: Optional[str], other_field: Optional[str], concatenate: bool = True
|
||||
):
|
||||
if field and other_field:
|
||||
if concatenate:
|
||||
return field + other_field
|
||||
raise ValueError("Cannot combine tool results")
|
||||
return field or other_field
|
||||
|
||||
return ToolResult(
|
||||
output=combine_fields(self.output, other.output),
|
||||
error=combine_fields(self.error, other.error),
|
||||
base64_image=combine_fields(self.base64_image, other.base64_image, False),
|
||||
system=combine_fields(self.system, other.system),
|
||||
)
|
||||
|
||||
def __str__(self):
|
||||
return f"Error: {self.error}" if self.error else self.output
|
||||
|
||||
def replace(self, **kwargs):
|
||||
"""Returns a new ToolResult with the given fields replaced."""
|
||||
# return self.copy(update=kwargs)
|
||||
return type(self)(**{**self.dict(), **kwargs})
|
||||
|
||||
|
||||
class CLIResult(ToolResult):
|
||||
"""A ToolResult that can be rendered as a CLI output."""
|
||||
|
||||
|
||||
class ToolFailure(ToolResult):
|
||||
"""A ToolResult that represents a failure."""
|
||||
|
||||
|
||||
# 为 BaseTool 添加辅助方法
|
||||
def _success_response(data) -> ToolResult:
|
||||
"""创建成功的工具结果"""
|
||||
import json
|
||||
if isinstance(data, str):
|
||||
text = data
|
||||
else:
|
||||
text = json.dumps(data, ensure_ascii=False, indent=2)
|
||||
return ToolResult(output=text)
|
||||
|
||||
|
||||
def _fail_response(msg: str) -> ToolResult:
|
||||
"""创建失败的工具结果"""
|
||||
return ToolResult(error=msg)
|
||||
|
||||
|
||||
# 将辅助方法添加到 BaseTool
|
||||
BaseTool.success_response = staticmethod(_success_response)
|
||||
BaseTool.fail_response = staticmethod(_fail_response)
|
||||
158
MeetSpot/app/tool/file_operators.py
Normal file
158
MeetSpot/app/tool/file_operators.py
Normal file
@@ -0,0 +1,158 @@
|
||||
"""File operation interfaces and implementations for local and sandbox environments."""
|
||||
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
from typing import Optional, Protocol, Tuple, Union, runtime_checkable
|
||||
|
||||
from app.config import SandboxSettings
|
||||
from app.exceptions import ToolError
|
||||
from app.sandbox.client import SANDBOX_CLIENT
|
||||
|
||||
|
||||
PathLike = Union[str, Path]
|
||||
|
||||
|
||||
@runtime_checkable
|
||||
class FileOperator(Protocol):
|
||||
"""Interface for file operations in different environments."""
|
||||
|
||||
async def read_file(self, path: PathLike) -> str:
|
||||
"""Read content from a file."""
|
||||
...
|
||||
|
||||
async def write_file(self, path: PathLike, content: str) -> None:
|
||||
"""Write content to a file."""
|
||||
...
|
||||
|
||||
async def is_directory(self, path: PathLike) -> bool:
|
||||
"""Check if path points to a directory."""
|
||||
...
|
||||
|
||||
async def exists(self, path: PathLike) -> bool:
|
||||
"""Check if path exists."""
|
||||
...
|
||||
|
||||
async def run_command(
|
||||
self, cmd: str, timeout: Optional[float] = 120.0
|
||||
) -> Tuple[int, str, str]:
|
||||
"""Run a shell command and return (return_code, stdout, stderr)."""
|
||||
...
|
||||
|
||||
|
||||
class LocalFileOperator(FileOperator):
|
||||
"""File operations implementation for local filesystem."""
|
||||
|
||||
encoding: str = "utf-8"
|
||||
|
||||
async def read_file(self, path: PathLike) -> str:
|
||||
"""Read content from a local file."""
|
||||
try:
|
||||
return Path(path).read_text(encoding=self.encoding)
|
||||
except Exception as e:
|
||||
raise ToolError(f"Failed to read {path}: {str(e)}") from None
|
||||
|
||||
async def write_file(self, path: PathLike, content: str) -> None:
|
||||
"""Write content to a local file."""
|
||||
try:
|
||||
Path(path).write_text(content, encoding=self.encoding)
|
||||
except Exception as e:
|
||||
raise ToolError(f"Failed to write to {path}: {str(e)}") from None
|
||||
|
||||
async def is_directory(self, path: PathLike) -> bool:
|
||||
"""Check if path points to a directory."""
|
||||
return Path(path).is_dir()
|
||||
|
||||
async def exists(self, path: PathLike) -> bool:
|
||||
"""Check if path exists."""
|
||||
return Path(path).exists()
|
||||
|
||||
async def run_command(
|
||||
self, cmd: str, timeout: Optional[float] = 120.0
|
||||
) -> Tuple[int, str, str]:
|
||||
"""Run a shell command locally."""
|
||||
process = await asyncio.create_subprocess_shell(
|
||||
cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
|
||||
)
|
||||
|
||||
try:
|
||||
stdout, stderr = await asyncio.wait_for(
|
||||
process.communicate(), timeout=timeout
|
||||
)
|
||||
return (
|
||||
process.returncode or 0,
|
||||
stdout.decode(),
|
||||
stderr.decode(),
|
||||
)
|
||||
except asyncio.TimeoutError as exc:
|
||||
try:
|
||||
process.kill()
|
||||
except ProcessLookupError:
|
||||
pass
|
||||
raise TimeoutError(
|
||||
f"Command '{cmd}' timed out after {timeout} seconds"
|
||||
) from exc
|
||||
|
||||
|
||||
class SandboxFileOperator(FileOperator):
|
||||
"""File operations implementation for sandbox environment."""
|
||||
|
||||
def __init__(self):
|
||||
self.sandbox_client = SANDBOX_CLIENT
|
||||
|
||||
async def _ensure_sandbox_initialized(self):
|
||||
"""Ensure sandbox is initialized."""
|
||||
if not self.sandbox_client.sandbox:
|
||||
await self.sandbox_client.create(config=SandboxSettings())
|
||||
|
||||
async def read_file(self, path: PathLike) -> str:
|
||||
"""Read content from a file in sandbox."""
|
||||
await self._ensure_sandbox_initialized()
|
||||
try:
|
||||
return await self.sandbox_client.read_file(str(path))
|
||||
except Exception as e:
|
||||
raise ToolError(f"Failed to read {path} in sandbox: {str(e)}") from None
|
||||
|
||||
async def write_file(self, path: PathLike, content: str) -> None:
|
||||
"""Write content to a file in sandbox."""
|
||||
await self._ensure_sandbox_initialized()
|
||||
try:
|
||||
await self.sandbox_client.write_file(str(path), content)
|
||||
except Exception as e:
|
||||
raise ToolError(f"Failed to write to {path} in sandbox: {str(e)}") from None
|
||||
|
||||
async def is_directory(self, path: PathLike) -> bool:
|
||||
"""Check if path points to a directory in sandbox."""
|
||||
await self._ensure_sandbox_initialized()
|
||||
result = await self.sandbox_client.run_command(
|
||||
f"test -d {path} && echo 'true' || echo 'false'"
|
||||
)
|
||||
return result.strip() == "true"
|
||||
|
||||
async def exists(self, path: PathLike) -> bool:
|
||||
"""Check if path exists in sandbox."""
|
||||
await self._ensure_sandbox_initialized()
|
||||
result = await self.sandbox_client.run_command(
|
||||
f"test -e {path} && echo 'true' || echo 'false'"
|
||||
)
|
||||
return result.strip() == "true"
|
||||
|
||||
async def run_command(
|
||||
self, cmd: str, timeout: Optional[float] = 120.0
|
||||
) -> Tuple[int, str, str]:
|
||||
"""Run a command in sandbox environment."""
|
||||
await self._ensure_sandbox_initialized()
|
||||
try:
|
||||
stdout = await self.sandbox_client.run_command(
|
||||
cmd, timeout=int(timeout) if timeout else None
|
||||
)
|
||||
return (
|
||||
0, # Always return 0 since we don't have explicit return code from sandbox
|
||||
stdout,
|
||||
"", # No stderr capture in the current sandbox implementation
|
||||
)
|
||||
except TimeoutError as exc:
|
||||
raise TimeoutError(
|
||||
f"Command '{cmd}' timed out after {timeout} seconds in sandbox"
|
||||
) from exc
|
||||
except Exception as exc:
|
||||
return 1, "", f"Error executing command in sandbox: {str(exc)}"
|
||||
3615
MeetSpot/app/tool/meetspot_recommender.py
Normal file
3615
MeetSpot/app/tool/meetspot_recommender.py
Normal file
File diff suppressed because it is too large
Load Diff
64
MeetSpot/app/tool/tool_collection.py
Normal file
64
MeetSpot/app/tool/tool_collection.py
Normal file
@@ -0,0 +1,64 @@
|
||||
"""Collection classes for managing multiple tools."""
|
||||
|
||||
import json
|
||||
from typing import Any, Dict, List, Union
|
||||
|
||||
from app.exceptions import ToolError
|
||||
from app.tool.base import BaseTool, ToolFailure, ToolResult
|
||||
|
||||
|
||||
class ToolCollection:
|
||||
"""A collection of defined tools."""
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
def __init__(self, *tools: BaseTool):
|
||||
self.tools = tools
|
||||
self.tool_map = {tool.name: tool for tool in tools}
|
||||
|
||||
def __iter__(self):
|
||||
return iter(self.tools)
|
||||
|
||||
def to_params(self) -> List[Dict[str, Any]]:
|
||||
return [tool.to_param() for tool in self.tools]
|
||||
|
||||
async def execute(self, name: str, tool_input: Union[str, dict]) -> ToolResult:
|
||||
"""Execute a tool by name with given input."""
|
||||
tool = self.get_tool(name)
|
||||
if not tool:
|
||||
return ToolResult(error=f"Tool '{name}' not found")
|
||||
|
||||
# 确保 tool_input 是字典类型
|
||||
if isinstance(tool_input, str):
|
||||
try:
|
||||
tool_input = json.loads(tool_input)
|
||||
except json.JSONDecodeError:
|
||||
return ToolResult(error=f"Invalid tool input format: {tool_input}")
|
||||
|
||||
result = await tool(**tool_input)
|
||||
return result
|
||||
|
||||
async def execute_all(self) -> List[ToolResult]:
|
||||
"""Execute all tools in the collection sequentially."""
|
||||
results = []
|
||||
for tool in self.tools:
|
||||
try:
|
||||
result = await tool()
|
||||
results.append(result)
|
||||
except ToolError as e:
|
||||
results.append(ToolFailure(error=e.message))
|
||||
return results
|
||||
|
||||
def get_tool(self, name: str) -> BaseTool:
|
||||
return self.tool_map.get(name)
|
||||
|
||||
def add_tool(self, tool: BaseTool):
|
||||
self.tools += (tool,)
|
||||
self.tool_map[tool.name] = tool
|
||||
return self
|
||||
|
||||
def add_tools(self, *tools: BaseTool):
|
||||
for tool in tools:
|
||||
self.add_tool(tool)
|
||||
return self
|
||||
101
MeetSpot/app/tool/web_search.py
Normal file
101
MeetSpot/app/tool/web_search.py
Normal file
@@ -0,0 +1,101 @@
|
||||
import asyncio
|
||||
from typing import List
|
||||
|
||||
from tenacity import retry, stop_after_attempt, wait_exponential
|
||||
|
||||
from app.config import config
|
||||
from app.tool.base import BaseTool
|
||||
from app.tool.search import (
|
||||
BaiduSearchEngine,
|
||||
BingSearchEngine,
|
||||
DuckDuckGoSearchEngine,
|
||||
GoogleSearchEngine,
|
||||
WebSearchEngine,
|
||||
)
|
||||
|
||||
|
||||
class WebSearch(BaseTool):
|
||||
name: str = "web_search"
|
||||
description: str = """Perform a web search and return a list of relevant links.
|
||||
This function attempts to use the primary search engine API to get up-to-date results.
|
||||
If an error occurs, it falls back to an alternative search engine."""
|
||||
parameters: dict = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "(required) The search query to submit to the search engine.",
|
||||
},
|
||||
"num_results": {
|
||||
"type": "integer",
|
||||
"description": "(optional) The number of search results to return. Default is 10.",
|
||||
"default": 10,
|
||||
},
|
||||
},
|
||||
"required": ["query"],
|
||||
}
|
||||
_search_engine: dict[str, WebSearchEngine] = {
|
||||
"google": GoogleSearchEngine(),
|
||||
"baidu": BaiduSearchEngine(),
|
||||
"duckduckgo": DuckDuckGoSearchEngine(),
|
||||
"bing": BingSearchEngine(),
|
||||
}
|
||||
|
||||
async def execute(self, query: str, num_results: int = 10) -> List[str]:
|
||||
"""
|
||||
Execute a Web search and return a list of URLs.
|
||||
|
||||
Args:
|
||||
query (str): The search query to submit to the search engine.
|
||||
num_results (int, optional): The number of search results to return. Default is 10.
|
||||
|
||||
Returns:
|
||||
List[str]: A list of URLs matching the search query.
|
||||
"""
|
||||
engine_order = self._get_engine_order()
|
||||
for engine_name in engine_order:
|
||||
engine = self._search_engine[engine_name]
|
||||
try:
|
||||
links = await self._perform_search_with_engine(
|
||||
engine, query, num_results
|
||||
)
|
||||
if links:
|
||||
return links
|
||||
except Exception as e:
|
||||
print(f"Search engine '{engine_name}' failed with error: {e}")
|
||||
return []
|
||||
|
||||
def _get_engine_order(self) -> List[str]:
|
||||
"""
|
||||
Determines the order in which to try search engines.
|
||||
Preferred engine is first (based on configuration), followed by the remaining engines.
|
||||
|
||||
Returns:
|
||||
List[str]: Ordered list of search engine names.
|
||||
"""
|
||||
preferred = "google"
|
||||
if config.search_config and config.search_config.engine:
|
||||
preferred = config.search_config.engine.lower()
|
||||
|
||||
engine_order = []
|
||||
if preferred in self._search_engine:
|
||||
engine_order.append(preferred)
|
||||
for key in self._search_engine:
|
||||
if key not in engine_order:
|
||||
engine_order.append(key)
|
||||
return engine_order
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3),
|
||||
wait=wait_exponential(multiplier=1, min=1, max=10),
|
||||
)
|
||||
async def _perform_search_with_engine(
|
||||
self,
|
||||
engine: WebSearchEngine,
|
||||
query: str,
|
||||
num_results: int,
|
||||
) -> List[str]:
|
||||
loop = asyncio.get_event_loop()
|
||||
return await loop.run_in_executor(
|
||||
None, lambda: list(engine.perform_search(query, num_results=num_results))
|
||||
)
|
||||
2
MeetSpot/config/.gitignore
vendored
Normal file
2
MeetSpot/config/.gitignore
vendored
Normal file
@@ -0,0 +1,2 @@
|
||||
# prevent the local config file from being uploaded to the remote repository
|
||||
config.toml
|
||||
22
MeetSpot/config/config.toml.example
Normal file
22
MeetSpot/config/config.toml.example
Normal file
@@ -0,0 +1,22 @@
|
||||
# MeetSpot配置文件示例
|
||||
# 使用方法: 复制此文件为config.toml并填入您的API密钥
|
||||
|
||||
# 高德地图API配置
|
||||
[amap]
|
||||
api_key = "YOUR_AMAP_API_KEY" # 替换为您的高德Web服务API Key
|
||||
security_js_code = "YOUR_AMAP_SECURITY_JS_CODE" # 替换为您的高德地图JS API安全密钥
|
||||
|
||||
# 如果使用OpenAI或其他LLM服务，也在此配置
|
||||
# [openai]
|
||||
# api_key = "sk-YOUR_OPENAI_API_KEY"
|
||||
# base_url = "YOUR_OPENAI_API_BASE_URL_IF_NEEDED"
|
||||
|
||||
# 日志配置
|
||||
[log]
|
||||
level = "info" # 可选: debug, info, warning, error, critical
|
||||
file = "logs/meetspot.log"
|
||||
|
||||
# 服务器配置
|
||||
[server]
|
||||
host = "0.0.0.0"
|
||||
port = 8000
|
||||
59
MeetSpot/data/address_aliases.json
Normal file
59
MeetSpot/data/address_aliases.json
Normal file
@@ -0,0 +1,59 @@
|
||||
{
|
||||
"_comment": "地址简称映射表 - 只保留简称到全称的映射，POI检索会自动处理全称",
|
||||
"university_aliases": {
|
||||
"北大": "北京大学",
|
||||
"清华": "清华大学",
|
||||
"人大": "中国人民大学",
|
||||
"北师大": "北京师范大学",
|
||||
"北理工": "北京理工大学",
|
||||
"北航": "北京航空航天大学",
|
||||
"中财": "中央财经大学",
|
||||
"对外经贸": "对外经济贸易大学",
|
||||
"央美": "中央美术学院",
|
||||
"北影": "北京电影学院",
|
||||
"中戏": "中央戏剧学院",
|
||||
"中音": "中央音乐学院",
|
||||
"上交": "上海交通大学",
|
||||
"上海交大": "上海交通大学",
|
||||
"复旦": "复旦大学",
|
||||
"同济": "同济大学",
|
||||
"华师大": "华东师范大学",
|
||||
"上戏": "上海戏剧学院",
|
||||
"上音": "上海音乐学院",
|
||||
"中大": "中山大学",
|
||||
"华南理工": "华南理工大学",
|
||||
"华工": "华南理工大学",
|
||||
"暨大": "暨南大学",
|
||||
"浙大": "浙江大学",
|
||||
"南大": "南京大学",
|
||||
"东南": "东南大学",
|
||||
"华科": "华中科技大学",
|
||||
"华中师大": "华中师范大学",
|
||||
"武大": "武汉大学",
|
||||
"西交": "西安交通大学",
|
||||
"西大": "西北大学",
|
||||
"西工大": "西北工业大学",
|
||||
"哈工大": "哈尔滨工业大学",
|
||||
"大连理工": "大连理工大学",
|
||||
"吉大": "吉林大学",
|
||||
"中科大": "中国科学技术大学",
|
||||
"天大": "天津大学",
|
||||
"南开": "南开大学",
|
||||
"厦大": "厦门大学",
|
||||
"山大": "山东大学",
|
||||
"川大": "四川大学",
|
||||
"重大": "重庆大学",
|
||||
"兰大": "兰州大学",
|
||||
"电子科大": "电子科技大学",
|
||||
"中南": "中南大学",
|
||||
"湖大": "湖南大学",
|
||||
"西南": "西南大学"
|
||||
},
|
||||
"landmark_aliases": {
|
||||
"国贸": "国贸CBD",
|
||||
"CBD": "国贸CBD",
|
||||
"外滩": "上海外滩",
|
||||
"陆家嘴": "上海陆家嘴",
|
||||
"南京路": "南京路步行街"
|
||||
}
|
||||
}
|
||||
1104
MeetSpot/data/cities.json
Normal file
1104
MeetSpot/data/cities.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
MeetSpot/docs/AI客服.jpg
Normal file
BIN
MeetSpot/docs/AI客服.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 864 KiB |
BIN
MeetSpot/docs/Wechat.png
Normal file
BIN
MeetSpot/docs/Wechat.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 169 KiB |
BIN
MeetSpot/docs/agent-thinking-complex.jpg
Normal file
BIN
MeetSpot/docs/agent-thinking-complex.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 284 KiB |
BIN
MeetSpot/docs/agent-thinking.jpg
Normal file
BIN
MeetSpot/docs/agent-thinking.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 140 KiB |
BIN
MeetSpot/docs/complex-scenario-input.jpg
Normal file
BIN
MeetSpot/docs/complex-scenario-input.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 237 KiB |
BIN
MeetSpot/docs/finder-filled.jpg
Normal file
BIN
MeetSpot/docs/finder-filled.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 136 KiB |
BIN
MeetSpot/docs/finder-input.jpg
Normal file
BIN
MeetSpot/docs/finder-input.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 241 KiB |
BIN
MeetSpot/docs/homepage.jpg
Normal file
BIN
MeetSpot/docs/homepage.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 546 KiB |
BIN
MeetSpot/docs/logo.jpg
Normal file
BIN
MeetSpot/docs/logo.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1013 KiB |
BIN
MeetSpot/docs/result-ai-process.jpg
Normal file
BIN
MeetSpot/docs/result-ai-process.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 141 KiB |
BIN
MeetSpot/docs/result-map.jpg
Normal file
BIN
MeetSpot/docs/result-map.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 132 KiB |
BIN
MeetSpot/docs/result-recommendations.jpg
Normal file
BIN
MeetSpot/docs/result-recommendations.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 129 KiB |
BIN
MeetSpot/docs/result-summary.jpg
Normal file
BIN
MeetSpot/docs/result-summary.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 141 KiB |
BIN
MeetSpot/docs/result-traffic.jpg
Normal file
BIN
MeetSpot/docs/result-traffic.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 237 KiB |
BIN
MeetSpot/docs/show1.jpg
Normal file
BIN
MeetSpot/docs/show1.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 864 KiB |
BIN
MeetSpot/docs/show2.jpg
Normal file
BIN
MeetSpot/docs/show2.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 622 KiB |
BIN
MeetSpot/docs/show3.jpg
Normal file
BIN
MeetSpot/docs/show3.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 514 KiB |
BIN
MeetSpot/docs/show4.jpg
Normal file
BIN
MeetSpot/docs/show4.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 455 KiB |
BIN
MeetSpot/docs/show5推荐地点.jpg
Normal file
BIN
MeetSpot/docs/show5推荐地点.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 716 KiB |
BIN
MeetSpot/docs/show6停车建议.jpg
Normal file
BIN
MeetSpot/docs/show6停车建议.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 532 KiB |
BIN
MeetSpot/docs/show6地图展示.jpg
Normal file
BIN
MeetSpot/docs/show6地图展示.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 674 KiB |
BIN
MeetSpot/docs/show7高德地图.jpg
Normal file
BIN
MeetSpot/docs/show7高德地图.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 1.0 MiB |
BIN
MeetSpot/docs/多维度智能评分show4.jpg
Normal file
BIN
MeetSpot/docs/多维度智能评分show4.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 448 KiB |
86
MeetSpot/environment-dev.yml
Normal file
86
MeetSpot/environment-dev.yml
Normal file
@@ -0,0 +1,86 @@
|
||||
name: meetspot-dev
|
||||
channels:
|
||||
- conda-forge
|
||||
- defaults
|
||||
|
||||
dependencies:
|
||||
# Python版本
|
||||
- python=3.11
|
||||
|
||||
# 核心Web框架
|
||||
- fastapi=0.116.1
|
||||
- uvicorn=0.35.0
|
||||
- pydantic=2.11.7
|
||||
- pydantic-core=2.33.2
|
||||
|
||||
# HTTP客户端
|
||||
- httpx=0.28.1
|
||||
- aiohttp=3.12.15
|
||||
- aiofiles=24.1.0
|
||||
|
||||
# 模板引擎
|
||||
- jinja2=3.1.6
|
||||
|
||||
# 表单处理
|
||||
- python-multipart=0.0.20
|
||||
|
||||
# 日志
|
||||
- loguru=0.7.3
|
||||
|
||||
# 配置解析
|
||||
- tomli=2.1.0
|
||||
|
||||
# 日期处理
|
||||
- python-dateutil=2.9.0
|
||||
|
||||
# 测试工具
|
||||
- pytest>=7.4.0
|
||||
- pytest-cov>=4.1.0
|
||||
- pytest-asyncio>=0.21.0
|
||||
- pytest-mock>=3.11.0
|
||||
|
||||
# 代码质量工具
|
||||
- black>=23.7.0 # 代码格式化
|
||||
- ruff>=0.1.0 # 快速linter
|
||||
- mypy>=1.5.0 # 类型检查
|
||||
- isort>=5.12.0 # import排序
|
||||
|
||||
# 开发工具
|
||||
- ipython>=8.14.0 # 增强REPL
|
||||
- ipdb>=0.13.13 # 调试器
|
||||
- pre-commit>=3.3.0 # Git hooks
|
||||
|
||||
# 文档生成
|
||||
- sphinx>=7.1.0
|
||||
- sphinx-rtd-theme>=1.3.0
|
||||
|
||||
# Web开发工具
|
||||
- beautifulsoup4>=4.12.0 # HTML解析（SEO验证）
|
||||
- requests>=2.31.0 # HTTP请求（测试）
|
||||
|
||||
# 性能分析
|
||||
- py-spy>=0.3.14 # 性能分析器
|
||||
- memory_profiler>=0.61.0 # 内存分析
|
||||
|
||||
# pip依赖（conda-forge暂无的包）
|
||||
- pip
|
||||
- pip:
|
||||
- jieba==0.42.1
|
||||
- whitenoise==6.6.0
|
||||
- slowapi==0.1.9
|
||||
- markdown2==2.4.12
|
||||
# 开发专用
|
||||
- bandit>=1.7.5 # 安全检查
|
||||
- vulture>=2.9.1 # 死代码检测
|
||||
# 注意：lighthouse-ci通过npm安装，不是Python包
|
||||
|
||||
# 系统工具
|
||||
- git
|
||||
- make
|
||||
- nodejs>=18.0.0 # Lighthouse需要
|
||||
|
||||
# 环境变量
|
||||
variables:
|
||||
PYTHONPATH: "${CONDA_PREFIX}/lib/python3.11/site-packages"
|
||||
AMAP_API_KEY: ""
|
||||
ENVIRONMENT: "development"
|
||||
51
MeetSpot/environment.yml
Normal file
51
MeetSpot/environment.yml
Normal file
@@ -0,0 +1,51 @@
|
||||
name: meetspot
|
||||
channels:
|
||||
- conda-forge
|
||||
- defaults
|
||||
|
||||
dependencies:
|
||||
# Python版本
|
||||
- python=3.11
|
||||
|
||||
# 核心Web框架
|
||||
- fastapi=0.116.1
|
||||
- uvicorn=0.35.0
|
||||
- pydantic=2.11.7
|
||||
- pydantic-core=2.33.2
|
||||
|
||||
# HTTP客户端
|
||||
- httpx=0.28.1
|
||||
- aiohttp=3.12.15
|
||||
- aiofiles=24.1.0
|
||||
|
||||
# 模板引擎
|
||||
- jinja2=3.1.6
|
||||
|
||||
# 表单处理
|
||||
- python-multipart=0.0.20
|
||||
|
||||
# 日志
|
||||
- loguru=0.7.3
|
||||
|
||||
# 配置解析
|
||||
- tomli=2.1.0
|
||||
|
||||
# 日期处理
|
||||
- python-dateutil=2.9.0
|
||||
|
||||
# SEO相关依赖
|
||||
- pip
|
||||
- pip:
|
||||
- jieba==0.42.1 # 中文分词（conda-forge暂无）
|
||||
- whitenoise==6.6.0 # 静态文件服务
|
||||
- slowapi==0.1.9 # API限流
|
||||
- markdown2==2.4.12 # Markdown解析
|
||||
|
||||
# 系统工具（可选，提升开发体验）
|
||||
- git
|
||||
- make
|
||||
|
||||
# 环境变量（可选）
|
||||
variables:
|
||||
PYTHONPATH: "${CONDA_PREFIX}/lib/python3.11/site-packages"
|
||||
AMAP_API_KEY: "" # 在.env或conda activate时设置
|
||||
43
MeetSpot/lighthouserc.json
Normal file
43
MeetSpot/lighthouserc.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
"ci": {
|
||||
"collect": {
|
||||
"url": [
|
||||
"http://localhost:8000/",
|
||||
"http://localhost:8000/about",
|
||||
"http://localhost:8000/faq"
|
||||
],
|
||||
"numberOfRuns": 3
|
||||
},
|
||||
"assert": {
|
||||
"preset": "lighthouse:recommended",
|
||||
"assertions": {
|
||||
"categories:performance": ["error", {"minScore": 0.8}],
|
||||
"categories:accessibility": ["error", {"minScore": 0.85}],
|
||||
"categories:best-practices": ["error", {"minScore": 0.8}],
|
||||
"categories:seo": ["error", {"minScore": 0.92}],
|
||||
|
||||
"color-contrast": "off",
|
||||
"tap-targets": "off",
|
||||
"link-name": "off",
|
||||
"heading-order": "off",
|
||||
"errors-in-console": "warn",
|
||||
"font-display": "off",
|
||||
"csp-xss": "off",
|
||||
|
||||
"installable-manifest": "off",
|
||||
"maskable-icon": "off",
|
||||
"service-worker": "off",
|
||||
"splash-screen": "off",
|
||||
"themed-omnibox": "off",
|
||||
|
||||
"unminified-css": "warn",
|
||||
"unused-css-rules": "warn",
|
||||
"uses-text-compression": "warn",
|
||||
"render-blocking-resources": "warn"
|
||||
}
|
||||
},
|
||||
"upload": {
|
||||
"target": "temporary-public-storage"
|
||||
}
|
||||
}
|
||||
}
|
||||
13
MeetSpot/package.json
Normal file
13
MeetSpot/package.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"name": "meetspot",
|
||||
"version": "1.0.0",
|
||||
"description": "MeetSpot智能会面点推荐系统",
|
||||
"main": "web_server.py",
|
||||
"scripts": {
|
||||
"dev": "python web_server.py",
|
||||
"start": "python web_server.py"
|
||||
},
|
||||
"engines": {
|
||||
"python": "3.11"
|
||||
}
|
||||
}
|
||||
0
MeetSpot/postmortem/.gitkeep
Normal file
0
MeetSpot/postmortem/.gitkeep
Normal file
42
MeetSpot/postmortem/PM-2025-001.yaml
Normal file
42
MeetSpot/postmortem/PM-2025-001.yaml
Normal file
@@ -0,0 +1,42 @@
|
||||
id: PM-2025-001
|
||||
created_at: '2026-01-13T05:56:50.491275Z'
|
||||
source_commit: bb5c8b0
|
||||
severity: high
|
||||
title: 限制缓存大小以防止免费层内存溢出
|
||||
description: 在免费层环境中，因缓存未限制大小导致内存溢出，触发 Render 512MB 内存限制错误，影响服务稳定性。
|
||||
root_cause: Geocode 和 POI 缓存未设置大小限制，导致缓存无限增长，最终耗尽内存。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- CafeRecommender.geocode_cache
|
||||
- CafeRecommender.poi_cache
|
||||
patterns:
|
||||
- geocode_cache
|
||||
- poi_cache
|
||||
- Field\(default_factory=dict\)
|
||||
keywords:
|
||||
- 缓存
|
||||
- cache
|
||||
- memory
|
||||
- OOM
|
||||
- 内存溢出
|
||||
fix_pattern:
|
||||
approach: 为缓存添加大小限制，并在超限时采用 FIFO 策略逐出最旧条目。
|
||||
key_changes:
|
||||
- 添加 GEOCODE_CACHE_MAX 和 POI_CACHE_MAX 常量，分别限制缓存大小为 100 和 50。
|
||||
- 在 geocode_cache 和 poi_cache 写入逻辑中添加 FIFO 驱逐逻辑。
|
||||
verification:
|
||||
- 验证 geocode_cache 和 poi_cache 的大小是否符合限制。
|
||||
- 检查 FIFO 驱逐逻辑是否正确执行。
|
||||
- 在内存受限环境中运行服务，确保不会触发 OOM 错误。
|
||||
- 测试缓存命中率是否符合预期，避免过多 API 调用。
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- geocoding
|
||||
- memory
|
||||
- cache
|
||||
- performance
|
||||
- api
|
||||
35
MeetSpot/postmortem/PM-2025-002.yaml
Normal file
35
MeetSpot/postmortem/PM-2025-002.yaml
Normal file
@@ -0,0 +1,35 @@
|
||||
id: PM-2025-002
|
||||
created_at: '2026-01-13T05:56:55.380581Z'
|
||||
source_commit: 4d12fbe
|
||||
severity: medium
|
||||
title: 推荐系统搜索半径扩大至50公里以解决无结果问题
|
||||
description: 在推荐系统中，当使用fallback策略搜索地点无结果时，搜索半径被扩大至50公里（API最大值），以确保获取更多结果。此变更影响了推荐系统的搜索范围。
|
||||
root_cause: 原有的搜索半径限制导致在某些情况下无法获取足够的推荐结果。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- _search_pois
|
||||
patterns:
|
||||
- radius=10000
|
||||
- radius=50000
|
||||
keywords:
|
||||
- fallback
|
||||
- 搜索半径
|
||||
- API最大
|
||||
fix_pattern:
|
||||
approach: 将搜索半径从10公里扩大到50公里以获取更多结果。
|
||||
key_changes:
|
||||
- radius=10000
|
||||
- radius=50000
|
||||
verification:
|
||||
- 验证在扩大搜索半径后，推荐系统能够返回更多的结果。
|
||||
- 确保API调用在50公里半径下正常工作。
|
||||
- 检查日志信息是否正确反映搜索半径的变化。
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- recommender
|
||||
- api
|
||||
- search radius
|
||||
36
MeetSpot/postmortem/PM-2025-003.yaml
Normal file
36
MeetSpot/postmortem/PM-2025-003.yaml
Normal file
@@ -0,0 +1,36 @@
|
||||
id: PM-2025-003
|
||||
created_at: '2026-01-13T05:56:59.697193Z'
|
||||
source_commit: 3ba4b1d
|
||||
severity: high
|
||||
title: 修复地理编码跨城市歧义问题，提升演示可靠性
|
||||
description: 在地理编码过程中，某些地标名称可能导致跨城市的歧义，影响系统的准确性和演示的可靠性。此次修复通过添加地标映射解决了这一问题。
|
||||
root_cause: 缺乏对常见地标的明确城市映射，导致地理编码结果不准确。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- _get_address
|
||||
patterns:
|
||||
- landmark_mapping\s*=\s*\{
|
||||
keywords:
|
||||
- geocoding
|
||||
- landmark
|
||||
- ambiguity
|
||||
- 城市歧义
|
||||
fix_pattern:
|
||||
approach: 通过添加45个以上的地标映射，确保地标名称能够正确解析到对应的城市。
|
||||
key_changes:
|
||||
- 新增 landmark_mapping 字典
|
||||
- 在 _get_address 函数中增加地标映射逻辑
|
||||
verification:
|
||||
- 验证地标名称是否正确映射到对应城市
|
||||
- 检查新增地标映射是否覆盖主要城市的常见地标
|
||||
- 确保地理编码结果在演示中表现良好
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- geocoding
|
||||
- data-mapping
|
||||
- 城市歧义
|
||||
- demo-reliability
|
||||
45
MeetSpot/postmortem/PM-2025-004.yaml
Normal file
45
MeetSpot/postmortem/PM-2025-004.yaml
Normal file
@@ -0,0 +1,45 @@
|
||||
id: PM-2025-004
|
||||
created_at: '2026-01-13T05:57:03.601645Z'
|
||||
source_commit: a97fad3
|
||||
severity: medium
|
||||
title: 修复非合成动画以通过 Lighthouse 审核
|
||||
description: 移除了使用 width 属性的非 GPU 加速动画，解决了 Lighthouse 中的非合成动画审核失败问题。此问题可能导致页面性能下降，影响用户体验。
|
||||
root_cause: 部分动画使用了 width 和 box-shadow 等非 GPU 加速属性，导致 Lighthouse 审核失败。
|
||||
triggers:
|
||||
files:
|
||||
- docs/*.md
|
||||
- templates/pages/*.html
|
||||
- image*.png
|
||||
- '*.png'
|
||||
functions: []
|
||||
patterns:
|
||||
- .*width.*
|
||||
- .*box-shadow.*
|
||||
keywords:
|
||||
- non-composited animations
|
||||
- Lighthouse
|
||||
- GPU acceleration
|
||||
- transform
|
||||
- opacity
|
||||
fix_pattern:
|
||||
approach: 移除非 GPU 加速的动画属性，确保所有动画仅使用 transform 和 opacity。
|
||||
key_changes:
|
||||
- 移除了 slideProgress 和 typewriter 动画的 keyframes
|
||||
- '将 pulseGlow 动画的 box-shadow 替换为 transform: scale()'
|
||||
verification:
|
||||
- 检查所有动画是否仅使用 transform 和 opacity 属性
|
||||
- 运行 Lighthouse 审核，确保通过非合成动画检查
|
||||
- 验证页面性能是否提升
|
||||
related:
|
||||
files_changed:
|
||||
- '"docs/\347\216\260\345\234\272\345\261\225\346\274\224\350\257\235\346\234\257.md"'
|
||||
- image copy 2.png
|
||||
- image copy.png
|
||||
- image.png
|
||||
- templates/pages/home.html
|
||||
- '"\347\272\277\344\270\213\350\201\232\344\274\232\346\225\260\346\215\256.png"'
|
||||
tags:
|
||||
- ui
|
||||
- performance
|
||||
- animation
|
||||
- Lighthouse
|
||||
43
MeetSpot/postmortem/PM-2025-005.yaml
Normal file
43
MeetSpot/postmortem/PM-2025-005.yaml
Normal file
@@ -0,0 +1,43 @@
|
||||
id: PM-2025-005
|
||||
created_at: '2026-01-13T05:57:06.915651Z'
|
||||
source_commit: b8b64fa
|
||||
severity: high
|
||||
title: 修复 Render 部署环境变量读取问题
|
||||
description: 在 Render 部署时，LLM 的环境变量未正确读取，导致无法生成 LLM 传输提示，影响智能推荐功能。
|
||||
root_cause: 代码未优先读取环境变量 LLM_API_BASE 和 LLM_MODEL，导致配置错误。
|
||||
triggers:
|
||||
files:
|
||||
- app/config.py
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- Config.__init__
|
||||
- _get_llm
|
||||
patterns:
|
||||
- os.getenv\("LLM_API_BASE"
|
||||
- os.getenv\("LLM_MODEL"
|
||||
keywords:
|
||||
- LLM_API_BASE
|
||||
- LLM_MODEL
|
||||
- Render
|
||||
- 环境变量
|
||||
fix_pattern:
|
||||
approach: 优先从环境变量读取 LLM 配置，并在初始化时验证 API Key 的有效性。
|
||||
key_changes:
|
||||
- 在 config.py 中添加对 LLM_API_BASE 和 LLM_MODEL 环境变量的支持。
|
||||
- 调整 _get_llm 函数，增加 API Key 验证逻辑。
|
||||
- 添加 LLM 初始化状态的日志记录。
|
||||
verification:
|
||||
- 确保环境变量 LLM_API_BASE 和 LLM_MODEL 被正确读取。
|
||||
- 验证 _get_llm 函数是否正确初始化 LLM 实例。
|
||||
- 检查日志是否记录 LLM 初始化状态。
|
||||
- 测试在 Render 部署环境下是否生成 LLM 传输提示。
|
||||
related:
|
||||
files_changed:
|
||||
- app/config.py
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- llm
|
||||
- api
|
||||
- environment
|
||||
- render
|
||||
- deployment
|
||||
36
MeetSpot/postmortem/PM-2025-006.yaml
Normal file
36
MeetSpot/postmortem/PM-2025-006.yaml
Normal file
@@ -0,0 +1,36 @@
|
||||
id: PM-2025-006
|
||||
created_at: '2026-01-13T05:57:10.607031Z'
|
||||
source_commit: b6831e5
|
||||
severity: medium
|
||||
title: 地图标记添加位置名称标签修复
|
||||
description: 地图标记未显示位置名称标签，导致用户无法直观识别标记位置。此问题影响用户体验，尤其是在多个标记密集的情况下。
|
||||
root_cause: 地图标记未包含位置名称标签的渲染逻辑。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- CafeRecommender
|
||||
patterns:
|
||||
- 'markerContent = `<div style="background-color: \${{color}};'
|
||||
keywords:
|
||||
- map
|
||||
- marker
|
||||
- label
|
||||
- 位置名称
|
||||
fix_pattern:
|
||||
approach: 为地图标记添加位置名称标签，并调整样式以提高可读性。
|
||||
key_changes:
|
||||
- 为中心点和用户位置标记添加标签显示逻辑
|
||||
- 调整标签样式，包括背景色、阴影和字体
|
||||
verification:
|
||||
- 检查地图标记是否正确显示位置名称标签
|
||||
- 验证标签样式是否符合设计规范
|
||||
- 确保其他标记（如场所标记）未受影响
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- ui
|
||||
- map
|
||||
- label
|
||||
- user experience
|
||||
42
MeetSpot/postmortem/PM-2025-007.yaml
Normal file
42
MeetSpot/postmortem/PM-2025-007.yaml
Normal file
@@ -0,0 +1,42 @@
|
||||
id: PM-2025-007
|
||||
created_at: '2026-01-13T05:57:14.136672Z'
|
||||
source_commit: defe545
|
||||
severity: medium
|
||||
title: 修复重复加载指示器并添加 Agent 品牌标识
|
||||
description: 在加载过程中出现重复的加载指示器（“两个圈”），影响用户体验。问题还涉及品牌标识未正确显示。
|
||||
root_cause: Toast 通知与内联加载指示器同时显示，导致重复加载动画；加载文本未包含品牌标识。
|
||||
triggers:
|
||||
files:
|
||||
- public/meetspot_finder.html
|
||||
functions:
|
||||
- renderLoadingIndicator
|
||||
- updateLoadingMessage
|
||||
patterns:
|
||||
- \bloading\b
|
||||
- \bspinner\b
|
||||
- \btoast\b
|
||||
keywords:
|
||||
- loading indicator
|
||||
- spinner
|
||||
- toast notification
|
||||
- Agent branding
|
||||
fix_pattern:
|
||||
approach: 移除重复的加载指示器，保留内联加载动画，并更新加载文本以动态显示品牌标识和搜索关键词。
|
||||
key_changes:
|
||||
- 移除 toast 通知加载动画
|
||||
- 保留内联加载指示器
|
||||
- 更新加载文本以包含 Agent 品牌标识
|
||||
- 动态显示加载消息中的搜索关键词
|
||||
verification:
|
||||
- 确保加载过程中仅显示一个加载指示器
|
||||
- 检查加载文本是否正确显示 Agent 品牌标识
|
||||
- 验证动态加载消息是否包含搜索关键词
|
||||
- 确认用户界面在加载时无视觉冲突
|
||||
related:
|
||||
files_changed:
|
||||
- public/meetspot_finder.html
|
||||
tags:
|
||||
- ui
|
||||
- branding
|
||||
- loading
|
||||
- ux
|
||||
41
MeetSpot/postmortem/PM-2025-008.yaml
Normal file
41
MeetSpot/postmortem/PM-2025-008.yaml
Normal file
@@ -0,0 +1,41 @@
|
||||
id: PM-2025-008
|
||||
created_at: '2026-01-13T05:57:18.566066Z'
|
||||
source_commit: 0843bfd
|
||||
severity: medium
|
||||
title: LLM交通建议超时保护修复
|
||||
description: 在生成交通与停车建议时，因未设置超时导致请求超时，影响用户体验。修复后添加了15秒超时保护，避免Render 30秒请求超时。
|
||||
root_cause: 缺少超时保护导致请求在30秒后超时。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- _llm_generate_transport_tips
|
||||
- _generate_default_transport_tips
|
||||
patterns:
|
||||
- asyncio.wait_for
|
||||
- asyncio.TimeoutError
|
||||
keywords:
|
||||
- 超时
|
||||
- timeout
|
||||
- LLM
|
||||
- 交通建议
|
||||
- transport tips
|
||||
fix_pattern:
|
||||
approach: 添加15秒超时保护，并在超时时使用默认建议。
|
||||
key_changes:
|
||||
- 使用asyncio.wait_for设置15秒超时
|
||||
- 在超时时捕获asyncio.TimeoutError异常
|
||||
- 记录超时警告日志
|
||||
- 调用_generate_default_transport_tips提供默认建议
|
||||
verification:
|
||||
- 检查是否在15秒内返回交通建议
|
||||
- 验证超时时是否使用默认建议
|
||||
- 确认超时警告日志是否记录
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- timeout
|
||||
- LLM
|
||||
- transport
|
||||
- asyncio
|
||||
36
MeetSpot/postmortem/PM-2025-009.yaml
Normal file
36
MeetSpot/postmortem/PM-2025-009.yaml
Normal file
@@ -0,0 +1,36 @@
|
||||
id: PM-2025-009
|
||||
created_at: '2026-01-13T05:57:21.963815Z'
|
||||
source_commit: cb1784c
|
||||
severity: high
|
||||
title: 修复SEO缓存问题以解决Render冷启动问题
|
||||
description: 由于Render冷启动导致Google Search Console出现“Couldn't fetch”错误，影响了网站的SEO表现。通过扩展sitemap和robots的缓存策略解决了该问题。
|
||||
root_cause: sitemap.xml和robots.txt的缓存时间过短，导致在Render冷启动时无法及时响应搜索引擎爬虫请求。
|
||||
triggers:
|
||||
files:
|
||||
- api/index.py
|
||||
functions:
|
||||
- add_cache_headers
|
||||
patterns:
|
||||
- max-age=3600
|
||||
- max-age=86400, stale-while-revalidate=604800
|
||||
keywords:
|
||||
- sitemap.xml
|
||||
- robots.txt
|
||||
- Cache-Control
|
||||
- cold start
|
||||
fix_pattern:
|
||||
approach: 增加缓存时间并添加stale-while-revalidate策略以确保在冷启动时仍能提供缓存内容。
|
||||
key_changes:
|
||||
- max-age从3600秒增加到86400秒
|
||||
- 添加stale-while-revalidate=604800
|
||||
verification:
|
||||
- 确认sitemap.xml和robots.txt的Cache-Control头包含正确的max-age和stale-while-revalidate值
|
||||
- 使用Google Search Console验证爬虫是否能够正常获取sitemap和robots文件
|
||||
- 模拟Render冷启动环境，确保缓存策略有效
|
||||
related:
|
||||
files_changed:
|
||||
- api/index.py
|
||||
tags:
|
||||
- seo
|
||||
- cache
|
||||
- api
|
||||
49
MeetSpot/postmortem/PM-2025-010.yaml
Normal file
49
MeetSpot/postmortem/PM-2025-010.yaml
Normal file
@@ -0,0 +1,49 @@
|
||||
id: PM-2025-010
|
||||
created_at: '2026-01-13T05:57:29.616214Z'
|
||||
source_commit: 9ebaacf
|
||||
severity: high
|
||||
title: 修复 Google Search Console 抓取超时问题
|
||||
description: 由于 Render 冷启动导致 Google 爬虫超时，出现 GSC 'Couldn't fetch' 错误，影响了网站的 SEO 表现。
|
||||
root_cause: Render 冷启动导致响应时间过长，Google 爬虫无法及时获取 sitemap 和 robots.txt。
|
||||
triggers:
|
||||
files:
|
||||
- api/routers/seo_pages.py
|
||||
- .github/workflows/keep-alive.yml
|
||||
functions:
|
||||
- sitemap
|
||||
- robots_txt
|
||||
patterns:
|
||||
- .*sitemap.*
|
||||
- .*robots\.txt.*
|
||||
- .*Cache-Control.*
|
||||
- .*stale-while-revalidate.*
|
||||
keywords:
|
||||
- sitemap
|
||||
- robots.txt
|
||||
- Cache-Control
|
||||
- stale-while-revalidate
|
||||
- Googlebot
|
||||
- Render cold start
|
||||
fix_pattern:
|
||||
approach: 通过增加缓存策略、提高 keep-alive 频率以及模拟 Googlebot 爬虫访问来解决冷启动问题。
|
||||
key_changes:
|
||||
- 为 sitemap.xml 和 robots.txt 添加长时间缓存策略（max-age=86400, stale-while-revalidate=604800）
|
||||
- 提高 keep-alive 频率从 10 分钟到 5 分钟
|
||||
- 为冷启动响应添加 --max-time 30 超时限制
|
||||
- 模拟 Googlebot 爬虫访问以验证抓取可用性
|
||||
verification:
|
||||
- 检查 sitemap.xml 和 robots.txt 是否正确设置了 Cache-Control 头
|
||||
- 验证 Cloudflare CDN 是否在冷启动期间提供缓存内容
|
||||
- 通过 Google Search Console 验证爬虫抓取是否正常
|
||||
- 模拟 Googlebot 爬虫访问，确保返回有效响应
|
||||
- 检查 keep-alive 机制是否按预期运行
|
||||
related:
|
||||
files_changed:
|
||||
- .github/workflows/keep-alive.yml
|
||||
- api/routers/seo_pages.py
|
||||
tags:
|
||||
- seo
|
||||
- api
|
||||
- performance
|
||||
- googlebot
|
||||
- cache
|
||||
39
MeetSpot/postmortem/PM-2025-011.yaml
Normal file
39
MeetSpot/postmortem/PM-2025-011.yaml
Normal file
@@ -0,0 +1,39 @@
|
||||
id: PM-2025-011
|
||||
created_at: '2026-01-13T05:57:33.139741Z'
|
||||
source_commit: 2456df8
|
||||
severity: high
|
||||
title: 修复SEO问题：添加/sitemap.xml和/robots.txt显式路由
|
||||
description: 由于StaticFiles挂载路径问题，Google Search Console报告“无法读取站点地图”。此问题影响了搜索引擎对网站的抓取和索引。
|
||||
root_cause: sitemap.xml和robots.txt文件路径不正确，导致搜索引擎无法访问。
|
||||
triggers:
|
||||
files:
|
||||
- api/index.py
|
||||
- public/sitemap.xml
|
||||
functions:
|
||||
- sitemap
|
||||
- robots
|
||||
patterns:
|
||||
- '@app.api_route\("/sitemap.xml"'
|
||||
- '@app.api_route\("/robots.txt"'
|
||||
keywords:
|
||||
- sitemap
|
||||
- robots.txt
|
||||
- SEO
|
||||
- StaticFiles
|
||||
fix_pattern:
|
||||
approach: 添加显式路由以正确提供sitemap.xml和robots.txt文件，并设置适当的HTTP头。
|
||||
key_changes:
|
||||
- 在api/index.py中添加/sitemap.xml和/robots.txt路由
|
||||
- 为sitemap.xml和robots.txt设置正确的Content-Type头
|
||||
verification:
|
||||
- 确保/sitemap.xml和/robots.txt路由返回正确的文件内容
|
||||
- 验证Google Search Console中不再出现“无法读取站点地图”错误
|
||||
- 检查sitemap.xml的lastmod日期是否更新
|
||||
related:
|
||||
files_changed:
|
||||
- api/index.py
|
||||
- public/sitemap.xml
|
||||
tags:
|
||||
- SEO
|
||||
- api
|
||||
- routing
|
||||
44
MeetSpot/postmortem/PM-2025-012.yaml
Normal file
44
MeetSpot/postmortem/PM-2025-012.yaml
Normal file
@@ -0,0 +1,44 @@
|
||||
id: PM-2025-012
|
||||
created_at: '2026-01-13T05:57:38.272426Z'
|
||||
source_commit: bc3efc7
|
||||
severity: medium
|
||||
title: 修复UI距离选项和重定向路径问题
|
||||
description: 在应用中，距离选项更新为5/10/20/30/50公里，并修复了重定向和资源路径问题。这些问题可能导致用户体验不佳和资源加载失败。
|
||||
root_cause: 原始代码中距离选项设置不合理，且重定向和资源路径配置错误。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
- public/*.html
|
||||
functions:
|
||||
- CafeRecommender
|
||||
patterns:
|
||||
- href="/css/
|
||||
- href="/public/meetspot_finder.html"
|
||||
- src="/js/
|
||||
keywords:
|
||||
- distance options
|
||||
- redirect
|
||||
- asset paths
|
||||
- window.location.href
|
||||
fix_pattern:
|
||||
approach: 更新距离选项，修正重定向方式和资源路径。
|
||||
key_changes:
|
||||
- 距离选项更新为5/10/20/30/50公里
|
||||
- 大城市默认距离设置为10公里
|
||||
- 使用window.location.href替代window.open
|
||||
- 返回链接指向根目录
|
||||
- 更新CSS和JS资源路径
|
||||
verification:
|
||||
- 验证距离选项是否正确显示
|
||||
- 检查重定向是否正常工作
|
||||
- 确认资源路径是否正确加载
|
||||
- 确保返回链接指向正确页面
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
- public/meetspot_finder.html
|
||||
tags:
|
||||
- ui
|
||||
- redirect
|
||||
- resource paths
|
||||
- distance options
|
||||
38
MeetSpot/postmortem/PM-2025-013.yaml
Normal file
38
MeetSpot/postmortem/PM-2025-013.yaml
Normal file
@@ -0,0 +1,38 @@
|
||||
id: PM-2025-013
|
||||
created_at: '2026-01-13T05:57:42.048224Z'
|
||||
source_commit: 384bf6f
|
||||
severity: high
|
||||
title: 修复环境变量加载问题，确保AI聊天机器人正常运行
|
||||
description: AI聊天机器人显示“配置中”，因为未能加载.env文件中的环境变量，影响了关键配置的使用。
|
||||
root_cause: .env文件中的环境变量未被正确加载，导致配置缺失。
|
||||
triggers:
|
||||
files:
|
||||
- api/*.py
|
||||
- web_server.py
|
||||
functions:
|
||||
- main
|
||||
patterns:
|
||||
- load_dotenv\(\)
|
||||
keywords:
|
||||
- .env
|
||||
- 环境变量
|
||||
- python-dotenv
|
||||
- 配置中
|
||||
fix_pattern:
|
||||
approach: 使用python-dotenv库加载.env文件中的环境变量。
|
||||
key_changes:
|
||||
- 在api/index.py和web_server.py中添加load_dotenv()调用
|
||||
verification:
|
||||
- 确保.env文件中的所有关键环境变量都被正确加载。
|
||||
- 验证AI聊天机器人不再显示“配置中”状态。
|
||||
- 检查python-dotenv库是否在requirements.txt中正确列出。
|
||||
related:
|
||||
files_changed:
|
||||
- api/index.py
|
||||
- requirements.txt
|
||||
- web_server.py
|
||||
tags:
|
||||
- environment
|
||||
- configuration
|
||||
- api
|
||||
- chatbot
|
||||
38
MeetSpot/postmortem/PM-2025-014.yaml
Normal file
38
MeetSpot/postmortem/PM-2025-014.yaml
Normal file
@@ -0,0 +1,38 @@
|
||||
id: PM-2025-014
|
||||
created_at: '2026-01-13T05:57:45.466710Z'
|
||||
source_commit: 5d694dc
|
||||
severity: high
|
||||
title: 修复 sitemap.xml 和 robots.txt 的 HEAD 请求支持
|
||||
description: Google Search Console 报告 sitemap.xml 无法获取，原因是 HEAD 请求返回 405 Method Not
|
||||
Allowed，影响搜索引擎爬虫的文件访问检查。
|
||||
root_cause: sitemap.xml 和 robots.txt 的 API 路由未支持 HEAD 请求，导致爬虫无法验证文件可访问性。
|
||||
triggers:
|
||||
files:
|
||||
- api/routers/seo_pages.py
|
||||
functions:
|
||||
- sitemap
|
||||
- robots_txt
|
||||
patterns:
|
||||
- '@router.api_route\(.*methods=\[.*HEAD.*\]\)'
|
||||
keywords:
|
||||
- HEAD method
|
||||
- sitemap.xml
|
||||
- robots.txt
|
||||
- Google Search Console
|
||||
fix_pattern:
|
||||
approach: 为 sitemap.xml 和 robots.txt 的 API 路由添加 HEAD 请求支持。
|
||||
key_changes:
|
||||
- 将 @router.get 替换为 @router.api_route，并添加 methods=["GET", "HEAD"] 参数。
|
||||
- 确保 HEAD 请求返回正确的 HTTP 状态码和响应头。
|
||||
verification:
|
||||
- 验证 sitemap.xml 和 robots.txt 的 HEAD 请求是否返回 200 状态码。
|
||||
- 检查 HEAD 请求是否正确返回 Content-Type 和其他必要的响应头。
|
||||
- 通过 Google Search Console 确认问题已解决，文件可正常访问。
|
||||
related:
|
||||
files_changed:
|
||||
- api/routers/seo_pages.py
|
||||
tags:
|
||||
- seo
|
||||
- api
|
||||
- crawler
|
||||
- google
|
||||
41
MeetSpot/postmortem/PM-2025-015.yaml
Normal file
41
MeetSpot/postmortem/PM-2025-015.yaml
Normal file
@@ -0,0 +1,41 @@
|
||||
id: PM-2025-015
|
||||
created_at: '2026-01-13T05:57:50.946672Z'
|
||||
source_commit: c13279a
|
||||
severity: high
|
||||
title: 智能城市推断修复简短地名解析错误
|
||||
description: 用户输入简短地名时，地理编码错误地解析到错误城市，导致中心点计算错误和POI搜索失败，前端显示数据处理异常。
|
||||
root_cause: 高德API将简短地名解析到错误城市，未能正确识别用户意图。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
- public/*.html
|
||||
functions:
|
||||
- _geocode
|
||||
- _smart_city_inference
|
||||
patterns:
|
||||
- geocode_result
|
||||
- geocode_results
|
||||
keywords:
|
||||
- 智能城市推断
|
||||
- geocode
|
||||
- 解析错误
|
||||
fix_pattern:
|
||||
approach: 通过智能城市推断功能检测并纠正错误的城市解析，前端处理无结果的成功搜索。
|
||||
key_changes:
|
||||
- 添加智能城市推断功能
|
||||
- 前端处理无结果的成功搜索
|
||||
- 更新文档说明
|
||||
verification:
|
||||
- 验证简短地名能正确解析到预期城市
|
||||
- 检查中心点坐标计算是否正确
|
||||
- 确认前端显示友好的无结果提示
|
||||
related:
|
||||
files_changed:
|
||||
- CLAUDE.md
|
||||
- app/tool/meetspot_recommender.py
|
||||
- public/meetspot_finder.html
|
||||
tags:
|
||||
- geocoding
|
||||
- ui
|
||||
- api
|
||||
- error-handling
|
||||
50
MeetSpot/postmortem/PM-2025-016.yaml
Normal file
50
MeetSpot/postmortem/PM-2025-016.yaml
Normal file
@@ -0,0 +1,50 @@
|
||||
id: PM-2025-016
|
||||
created_at: '2026-01-13T05:57:54.839321Z'
|
||||
source_commit: 9c847c1
|
||||
severity: medium
|
||||
title: 网站内容优化修复，提升用户体验
|
||||
description: 修正了网站内容，使其更具用户友好性，移除了技术性术语并扩展了FAQ页面。影响用户对网站功能的理解和使用体验。
|
||||
root_cause: 网站内容过于技术化，影响用户体验和理解。
|
||||
triggers:
|
||||
files:
|
||||
- api/routers/seo_pages.py
|
||||
- templates/pages/about.html
|
||||
- templates/pages/faq.html
|
||||
- templates/pages/home.html
|
||||
- templates/pages/how_it_works.html
|
||||
functions:
|
||||
- faq_page
|
||||
patterns:
|
||||
- 移除技术性内容
|
||||
- 扩展至10个实用问题
|
||||
- 重写为用户导向内容
|
||||
- 四步骤卡片式布局
|
||||
keywords:
|
||||
- 用户体验
|
||||
- 技术术语
|
||||
- FAQ
|
||||
- 内容优化
|
||||
fix_pattern:
|
||||
approach: 通过参考README优化网站内容，移除技术性术语，增加用户友好描述和实用问题。
|
||||
key_changes:
|
||||
- 首页内容优化
|
||||
- FAQ页面扩展
|
||||
- 关于页面重写
|
||||
- 使用指南布局调整
|
||||
verification:
|
||||
- 检查首页是否移除技术术语
|
||||
- 确认FAQ页面包含10个问题
|
||||
- 确保关于页面内容用户导向
|
||||
- 验证使用指南布局是否为卡片式
|
||||
related:
|
||||
files_changed:
|
||||
- api/routers/seo_pages.py
|
||||
- templates/pages/about.html
|
||||
- templates/pages/faq.html
|
||||
- templates/pages/home.html
|
||||
- templates/pages/how_it_works.html
|
||||
tags:
|
||||
- content
|
||||
- ui
|
||||
- user_experience
|
||||
- documentation
|
||||
34
MeetSpot/postmortem/PM-2025-017.yaml
Normal file
34
MeetSpot/postmortem/PM-2025-017.yaml
Normal file
@@ -0,0 +1,34 @@
|
||||
id: PM-2025-017
|
||||
created_at: '2026-01-13T05:57:58.465498Z'
|
||||
source_commit: 19f9e93
|
||||
severity: medium
|
||||
title: 修复变量命名冲突导致的flake8 F823错误
|
||||
description: 在meetspot_recommender.py中，变量命名冲突导致flake8 F823错误，影响GitHub Actions CI的正常运行。
|
||||
root_cause: 局部变量名与导入模块名冲突，导致变量在赋值前被引用。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- CafeRecommender
|
||||
patterns:
|
||||
- local variable 'html' referenced before assignment
|
||||
keywords:
|
||||
- flake8
|
||||
- F823
|
||||
- 命名冲突
|
||||
- html
|
||||
fix_pattern:
|
||||
approach: 重命名冲突变量以避免与导入模块名重复。
|
||||
key_changes:
|
||||
- 将变量名从`html`改为`html_content`
|
||||
verification:
|
||||
- 确保变量名与导入模块名不冲突
|
||||
- 运行flake8检查确保无F823错误
|
||||
- 验证GitHub Actions CI通过
|
||||
related:
|
||||
files_changed:
|
||||
- app/tool/meetspot_recommender.py
|
||||
tags:
|
||||
- ci
|
||||
- linting
|
||||
- python
|
||||
44
MeetSpot/postmortem/PM-2025-018.yaml
Normal file
44
MeetSpot/postmortem/PM-2025-018.yaml
Normal file
@@ -0,0 +1,44 @@
|
||||
id: PM-2025-018
|
||||
created_at: '2026-01-13T05:58:02.063142Z'
|
||||
source_commit: a34acd4
|
||||
severity: high
|
||||
title: Bing验证文件访问优化 - 支持HEAD请求和防缓存
|
||||
description: Bing站长工具验证失败，HEAD请求返回405错误，导致验证无法通过。同时，CDN缓存问题导致验证文件不可达，robots.txt配置可能阻止验证文件爬取。
|
||||
root_cause: 验证文件路由未正确支持HEAD请求，且未设置防缓存headers，导致验证失败。
|
||||
triggers:
|
||||
files:
|
||||
- api/index.py
|
||||
- public/robots.txt
|
||||
functions:
|
||||
- google_verification
|
||||
- bing_verification
|
||||
patterns:
|
||||
- '@app.api_route'
|
||||
- Cache-Control
|
||||
- robots.txt
|
||||
keywords:
|
||||
- BingSiteAuth.xml
|
||||
- HEAD请求
|
||||
- 防缓存
|
||||
- robots.txt
|
||||
fix_pattern:
|
||||
approach: 优化验证文件路由，支持HEAD请求并添加防缓存headers，同时更新robots.txt明确允许验证文件爬取。
|
||||
key_changes:
|
||||
- 在api/index.py中为Google和Bing验证文件路由添加HEAD请求支持
|
||||
- 为验证文件响应添加防缓存headers
|
||||
- 更新robots.txt，明确Allow验证文件路径
|
||||
verification:
|
||||
- 验证HEAD请求是否返回200 OK
|
||||
- 检查验证文件是否包含防缓存headers
|
||||
- 确认robots.txt中允许验证文件路径
|
||||
- 通过curl命令测试验证文件的GET和HEAD请求是否正常
|
||||
related:
|
||||
files_changed:
|
||||
- api/index.py
|
||||
- public/robots.txt
|
||||
tags:
|
||||
- api
|
||||
- SEO
|
||||
- robots.txt
|
||||
- CDN
|
||||
- validation
|
||||
32
MeetSpot/postmortem/PM-2025-019.yaml
Normal file
32
MeetSpot/postmortem/PM-2025-019.yaml
Normal file
@@ -0,0 +1,32 @@
|
||||
id: PM-2025-019
|
||||
created_at: '2026-01-13T05:58:04.940805Z'
|
||||
source_commit: a27c796
|
||||
severity: medium
|
||||
title: 修复CSS hover语法错误导致的样式问题
|
||||
description: 在 public/index.html 中发现无效的 CSS hover 属性，导致页面样式在某些浏览器中无法正常显示。此问题影响了用户体验。
|
||||
root_cause: 内联样式中使用了无效的 hover 属性，未被浏览器正确解析。
|
||||
triggers:
|
||||
files:
|
||||
- public/index.html
|
||||
functions: []
|
||||
patterns:
|
||||
- hover\s*:\s*[^;]+;
|
||||
keywords:
|
||||
- CSS
|
||||
- hover
|
||||
- 样式错误
|
||||
fix_pattern:
|
||||
approach: 移除无效的内联 hover 样式，确保 CSS 语法正确。
|
||||
key_changes:
|
||||
- 删除无效的 hover 属性
|
||||
verification:
|
||||
- 检查所有 CSS hover 属性的语法是否正确。
|
||||
- 确保页面在不同浏览器中显示一致。
|
||||
- 验证页面 hover 效果是否符合设计预期。
|
||||
related:
|
||||
files_changed:
|
||||
- public/index.html
|
||||
tags:
|
||||
- ui
|
||||
- css
|
||||
- bugfix
|
||||
39
MeetSpot/postmortem/PM-2025-020.yaml
Normal file
39
MeetSpot/postmortem/PM-2025-020.yaml
Normal file
@@ -0,0 +1,39 @@
|
||||
id: PM-2025-020
|
||||
created_at: '2026-01-13T05:58:09.424945Z'
|
||||
source_commit: aba844e
|
||||
severity: medium
|
||||
title: 修复二维码用途和描述错误
|
||||
description: 二维码用途和描述错误，导致用户无法正确识别微信群和支付二维码，影响用户体验。
|
||||
root_cause: 二维码图片和描述信息配置错误，导致用途混淆。
|
||||
triggers:
|
||||
files:
|
||||
- docs/vx_chat.png
|
||||
- public/index.html
|
||||
functions: []
|
||||
patterns:
|
||||
- vx_chat.png
|
||||
- vx_payment.png
|
||||
keywords:
|
||||
- 二维码
|
||||
- 微信
|
||||
- 支付
|
||||
- 交流群
|
||||
fix_pattern:
|
||||
approach: 更新二维码图片及描述信息，确保用途明确。
|
||||
key_changes:
|
||||
- 修改vx_chat.png为微信交流群二维码
|
||||
- 修改vx_payment.png为请喝咖啡支付码
|
||||
- 移除个人微信二维码
|
||||
- 优化支持页面的文案和图标
|
||||
verification:
|
||||
- 确认vx_chat.png显示为微信群二维码
|
||||
- 确认vx_payment.png显示为支付二维码
|
||||
- 检查支持页面文案和图标是否正确
|
||||
related:
|
||||
files_changed:
|
||||
- docs/vx_chat.png
|
||||
- public/index.html
|
||||
tags:
|
||||
- ui
|
||||
- documentation
|
||||
- user-experience
|
||||
33
MeetSpot/postmortem/PM-2025-021.yaml
Normal file
33
MeetSpot/postmortem/PM-2025-021.yaml
Normal file
@@ -0,0 +1,33 @@
|
||||
id: PM-2025-021
|
||||
created_at: '2026-01-13T05:58:12.599589Z'
|
||||
source_commit: 355b89e
|
||||
severity: high
|
||||
title: 修复Google Search Console验证文件访问问题
|
||||
description: 由于Google Search Console验证文件无法从根路径访问，导致验证超时问题，影响网站的搜索引擎优化。
|
||||
root_cause: 缺少专门的路由来处理Google Search Console验证文件的请求。
|
||||
triggers:
|
||||
files:
|
||||
- api/index.py
|
||||
functions:
|
||||
- google_verification
|
||||
patterns:
|
||||
- '@app.get\("/google48ac1a797739b7b0.html"\)'
|
||||
keywords:
|
||||
- Google Search Console
|
||||
- 验证文件
|
||||
- 超时
|
||||
fix_pattern:
|
||||
approach: 添加专门的路由以确保验证文件可从根路径访问。
|
||||
key_changes:
|
||||
- 添加google_verification函数
|
||||
- 定义新的路由
|
||||
verification:
|
||||
- 确认google48ac1a797739b7b0.html文件可以从根路径访问
|
||||
- 检查Google Search Console验证状态是否正常
|
||||
related:
|
||||
files_changed:
|
||||
- api/index.py
|
||||
tags:
|
||||
- seo
|
||||
- api
|
||||
- verification
|
||||
36
MeetSpot/postmortem/PM-2025-022.yaml
Normal file
36
MeetSpot/postmortem/PM-2025-022.yaml
Normal file
@@ -0,0 +1,36 @@
|
||||
id: PM-2025-022
|
||||
created_at: '2026-01-13T05:58:16.981664Z'
|
||||
source_commit: 53c15bb
|
||||
severity: high
|
||||
title: 修复图像响应的令牌计算问题
|
||||
description: 在处理图像响应时，令牌计算存在错误，可能导致不正确的计费或资源分配问题。此问题影响了图像处理模块的准确性。
|
||||
root_cause: 图像响应处理逻辑中缺少对令牌的正确计算。
|
||||
triggers:
|
||||
files:
|
||||
- app/image_processing/*.py
|
||||
functions:
|
||||
- calculate_token_usage
|
||||
- process_image_response
|
||||
patterns:
|
||||
- calculate\s*\(.*\)
|
||||
- token_usage\s*=\s*.*
|
||||
keywords:
|
||||
- token
|
||||
- image response
|
||||
- accounting
|
||||
- fix
|
||||
fix_pattern:
|
||||
approach: 修复了图像响应处理中的令牌计算逻辑，确保令牌使用量的准确性。
|
||||
key_changes:
|
||||
- 修正了 calculate_token_usage 函数中的计算公式
|
||||
- 更新了 process_image_response 函数以正确处理令牌
|
||||
verification:
|
||||
- 检查 calculate_token_usage 函数的计算逻辑是否正确
|
||||
- 验证 process_image_response 函数在不同图像输入下的令牌计算是否准确
|
||||
- 确保所有图像响应的令牌使用量记录在案
|
||||
related:
|
||||
files_changed: []
|
||||
tags:
|
||||
- image processing
|
||||
- token accounting
|
||||
- bug fix
|
||||
40
MeetSpot/postmortem/PM-2025-023.yaml
Normal file
40
MeetSpot/postmortem/PM-2025-023.yaml
Normal file
@@ -0,0 +1,40 @@
|
||||
id: PM-2025-023
|
||||
created_at: '2026-01-13T05:58:20.762479Z'
|
||||
source_commit: a57e614
|
||||
severity: high
|
||||
title: 修复CafeRecommender类缺失execute方法导致的运行时错误
|
||||
description: 在系统运行时，CafeRecommender类缺失execute方法导致服务不可用。此问题影响了推荐功能的正常使用。
|
||||
root_cause: CafeRecommender类中缺失execute方法，导致调用时出现属性错误。
|
||||
triggers:
|
||||
files:
|
||||
- web_server.py
|
||||
- app/__pycache__/*.pyc
|
||||
functions:
|
||||
- CafeRecommender.execute
|
||||
patterns:
|
||||
- def execute\(
|
||||
- from app.tool.meetspot_recommender import CafeRecommender
|
||||
keywords:
|
||||
- CafeRecommender
|
||||
- execute
|
||||
- fallback
|
||||
- MockResult
|
||||
fix_pattern:
|
||||
approach: 在CafeRecommender类中添加execute方法，并实现回退机制。
|
||||
key_changes:
|
||||
- 在CafeRecommender类中添加execute方法
|
||||
- 实现原始推荐器调用失败时的回退机制
|
||||
- 创建fallback_result.html页面
|
||||
verification:
|
||||
- 检查CafeRecommender类中是否存在execute方法
|
||||
- 验证execute方法在原始推荐器不可用时是否正确回退到MockResult
|
||||
- 确保fallback_result.html页面在回退时正确显示
|
||||
related:
|
||||
files_changed:
|
||||
- app/__pycache__/__init__.cpython-312.pyc
|
||||
- app/__pycache__/exceptions.cpython-312.pyc
|
||||
- web_server.py
|
||||
tags:
|
||||
- runtime_error
|
||||
- recommendation_system
|
||||
- fallback_mechanism
|
||||
49
MeetSpot/postmortem/PM-2025-024.yaml
Normal file
49
MeetSpot/postmortem/PM-2025-024.yaml
Normal file
@@ -0,0 +1,49 @@
|
||||
id: PM-2025-024
|
||||
created_at: '2026-01-13T05:58:25.760808Z'
|
||||
source_commit: b481fbc
|
||||
severity: high
|
||||
title: 修复 Python 和 YAML 语法错误，优化工作流文件
|
||||
description: 多个 Python 文件和 YAML 工作流文件存在语法错误，导致部分功能无法正常运行，影响了代码的正常执行和 CI/CD 流程的稳定性。
|
||||
root_cause: 缺少必要的模块导入和工作流文件配置错误，导致语法错误和功能异常。
|
||||
triggers:
|
||||
files:
|
||||
- app/tool/*.py
|
||||
- .github/workflows/*.yml
|
||||
functions:
|
||||
- meetspot_recommender_fixed.py
|
||||
- llm.py
|
||||
patterns:
|
||||
- import .*
|
||||
- f".*"
|
||||
- 'workflow: .*'
|
||||
keywords:
|
||||
- syntax error
|
||||
- datetime
|
||||
- ChatCompletion
|
||||
- workflow
|
||||
- f-string
|
||||
fix_pattern:
|
||||
approach: 添加缺失的模块导入，修复 HTML 模板中的 f-string 冲突，清理并保留有效的工作流文件。
|
||||
key_changes:
|
||||
- 在 meetspot_recommender_fixed.py 中添加 datetime 模块导入
|
||||
- 在 llm.py 中添加 ChatCompletion 模块导入
|
||||
- 修复 HTML 模板中 f-string 与 JavaScript 的冲突
|
||||
- 删除重复或损坏的工作流文件，仅保留 ci.yml、auto-merge.yml 和 update-badges.yml
|
||||
verification:
|
||||
- 确保所有 Python 文件运行时无语法错误
|
||||
- 验证 HTML 模板渲染是否正确
|
||||
- 检查工作流文件是否能正常触发并执行
|
||||
- 确认 CI/CD 流程是否稳定运行
|
||||
related:
|
||||
files_changed:
|
||||
- .github/workflows/auto-merge-clean.yml
|
||||
- .github/workflows/auto-merge-dependabot.yml
|
||||
- .github/workflows/ci-clean.yml
|
||||
- .github/workflows/ci-simple.yml
|
||||
- app/tool/meetspot_recommender_fixed.py
|
||||
tags:
|
||||
- syntax
|
||||
- ci/cd
|
||||
- python
|
||||
- yaml
|
||||
- workflow
|
||||
42
MeetSpot/postmortem/PM-2025-025.yaml
Normal file
42
MeetSpot/postmortem/PM-2025-025.yaml
Normal file
@@ -0,0 +1,42 @@
|
||||
id: PM-2025-025
|
||||
created_at: '2026-01-13T05:58:29.615227Z'
|
||||
source_commit: 810e8f3
|
||||
severity: high
|
||||
title: 修复llm.py和meetspot_recommender_fixed.py中的Python语法错误
|
||||
description: 在llm.py和meetspot_recommender_fixed.py中存在Python语法错误，导致应用程序无法正常运行。这些错误包括缺少导入和f-string语法问题。
|
||||
root_cause: 缺少必要的导入和f-string语法使用不当。
|
||||
triggers:
|
||||
files:
|
||||
- app/llm.py
|
||||
- app/tool/*.py
|
||||
functions:
|
||||
- window.onload
|
||||
patterns:
|
||||
- from openai.types.chat import ChatCompletion
|
||||
- f"{{.*}}"
|
||||
keywords:
|
||||
- ChatCompletion
|
||||
- f-string
|
||||
- JavaScript
|
||||
- HTML
|
||||
fix_pattern:
|
||||
approach: 添加缺失的导入，修复f-string语法错误，并调整注释格式。
|
||||
key_changes:
|
||||
- 添加ChatCompletion导入到llm.py
|
||||
- 将f-string中的单花括号替换为双花括号
|
||||
- 将JavaScript中的//注释替换为/* */注释
|
||||
verification:
|
||||
- 确保所有必要的导入都存在
|
||||
- 检查f-string语法是否正确
|
||||
- 验证JavaScript注释格式是否正确
|
||||
related:
|
||||
files_changed:
|
||||
- GITHUB_ACTIONS_FINAL_REPORT.md
|
||||
- app/llm.py
|
||||
- app/tool/meetspot_recommender_fixed.py
|
||||
tags:
|
||||
- syntax
|
||||
- import
|
||||
- f-string
|
||||
- JavaScript
|
||||
- HTML
|
||||
40
MeetSpot/postmortem/PM-2025-026.yaml
Normal file
40
MeetSpot/postmortem/PM-2025-026.yaml
Normal file
@@ -0,0 +1,40 @@
|
||||
id: PM-2025-026
|
||||
created_at: '2026-01-13T05:58:33.486366Z'
|
||||
source_commit: b601e97
|
||||
severity: high
|
||||
title: 修复 GitHub Actions 工作流语法错误
|
||||
description: 部分 GitHub Actions 工作流文件存在 YAML 语法错误，导致 CI/CD 无法正常运行。问题影响了自动化构建和依赖管理流程。
|
||||
root_cause: 工作流文件中存在不规范的 YAML 语法，导致无法通过验证。
|
||||
triggers:
|
||||
files:
|
||||
- .github/workflows/*.yml
|
||||
functions: []
|
||||
patterns:
|
||||
- ^.*:\s+\[.*\]$
|
||||
- ^.*:\s+\{.*\}$
|
||||
keywords:
|
||||
- syntax error
|
||||
- YAML validation
|
||||
- GitHub Actions
|
||||
fix_pattern:
|
||||
approach: 清理工作流文件，移除语法错误并简化复杂逻辑。
|
||||
key_changes:
|
||||
- 移除有问题的工作流文件，保留 ci-clean.yml 和 auto-merge-clean.yml
|
||||
- 简化 update-badges.yml 文件，避免复杂的 shell 语法
|
||||
- 新增 GITHUB_ACTIONS_CLEANUP_REPORT.md，记录详细清理日志
|
||||
verification:
|
||||
- 确保所有工作流文件通过 YAML 验证
|
||||
- 验证 CI/CD 流程是否正常运行
|
||||
- 检查 update-badges.yml 的 shell 逻辑是否简化且可执行
|
||||
- 确认 GITHUB_ACTIONS_CLEANUP_REPORT.md 包含完整清理记录
|
||||
related:
|
||||
files_changed:
|
||||
- .github/workflows/auto-merge-clean.yml
|
||||
- .github/workflows/ci-clean.yml
|
||||
- .github/workflows/update-badges.yml
|
||||
- GITHUB_ACTIONS_CLEANUP_REPORT.md
|
||||
tags:
|
||||
- ci
|
||||
- github-actions
|
||||
- yaml
|
||||
- automation
|
||||
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Reference in New Issue
Block a user