From 019fa818f50e776b865709709b3a0262a23f6f5c Mon Sep 17 00:00:00 2001 From: jack Date: Sun, 11 Jan 2026 22:27:28 +0800 Subject: [PATCH 1/3] =?UTF-8?q?=E6=94=B9=E6=88=90=E9=80=9A=E7=94=A8md?= =?UTF-8?q?=E6=96=87=E4=BB=B6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- PROMPT.md | 5 + backend/ai_generator.py | 172 --- backend/app.py | 107 +- backend/config.py | 5 - backend/document_processor.py | 346 +++--- backend/models.py | 31 +- backend/rag_system.py | 249 ++-- backend/search_tools.py | 250 ---- backend/session_manager.py | 66 - backend/tests/conftest.py | 44 +- backend/tests/test_api_endpoints.py | 93 +- backend/vector_store.py | 223 +--- uv.lock | 1790 +++++++++++++-------------- 13 files changed, 1356 insertions(+), 2025 deletions(-) create mode 100644 PROMPT.md delete mode 100644 backend/ai_generator.py delete mode 100644 backend/search_tools.py delete mode 100644 backend/session_manager.py diff --git a/PROMPT.md b/PROMPT.md new file mode 100644 index 0000000..7dbb3c3 --- /dev/null +++ b/PROMPT.md @@ -0,0 +1,5 @@ +重构backend文件夹中的内容，将rag_system修改为一个通用的rag系统，输入改为任意的md文件，保持各文件名不变； +不再需要使用大模型接口，不再需要ai_generator.py文件、session_manager.py文件； +rag_system对外提供两个接口： +1. 初始化接口，输入为文件夹路径，输出为初始化完成的rag系统； +2. 查询接口，输入为查询语句，输出为查询结果；2 \ No newline at end of file diff --git a/backend/ai_generator.py b/backend/ai_generator.py deleted file mode 100644 index d1da806..0000000 --- a/backend/ai_generator.py +++ /dev/null @@ -1,172 +0,0 @@ -from typing import Any, Dict, List, Optional - -import anthropic - - -class AIGenerator: - """Handles interactions with Anthropic's Claude API for generating responses""" - - # Static system prompt to avoid rebuilding on each call - SYSTEM_PROMPT = """ You are an AI assistant specialized in course materials and educational content with access to search tools for course information. - -Available Tools: -- **Content Search Tool**: Use for questions about specific course content or detailed educational materials -- **Course Outline Tool**: Use for questions about course structure, lesson lists, or course overviews - -Tool Usage Guidelines: -- Use content search for detailed questions about specific topics or lessons -- Use course outline tool for questions about course structure, lesson titles, or complete course overviews -- **You can make up to 2 rounds of tool calls to gather comprehensive information** -- Use multiple rounds for complex queries that require information gathering then refinement -- Synthesize tool results into accurate, fact-based responses -- If tools yield no results, state this clearly without offering alternatives - -Course Outline Responses: -When using the course outline tool, always include: -- Course title -- Course link (if available) -- Complete lesson list with lesson numbers and titles -- Present information in a clear, structured format - -Response Protocol: -- **General knowledge questions**: Answer using existing knowledge without searching -- **Course-specific questions**: Use appropriate tool first, then answer -- **No meta-commentary**: - - Provide direct answers only — no reasoning process, search explanations, or question-type analysis - - Do not mention "based on the search results" or "using the tool" - -All responses must be: -1. **Brief, Concise and focused** - Get to the point quickly -2. **Educational** - Maintain instructional value -3. **Clear** - Use accessible language -4. **Example-supported** - Include relevant examples when they aid understanding -Provide only the direct answer to what was asked. -""" - - def __init__(self, api_key: str, model: str): - self.client = anthropic.Anthropic(api_key=api_key) - self.model = model - - # Pre-build base API parameters - self.base_params = {"model": self.model, "temperature": 0, "max_tokens": 800} - - def generate_response( - self, - query: str, - conversation_history: Optional[str] = None, - tools: Optional[List] = None, - tool_manager=None, - ) -> str: - """ - Generate AI response with optional tool usage and conversation context. - Supports up to 2 sequential rounds of tool calling. - - Args: - query: The user's question or request - conversation_history: Previous messages for context - tools: Available tools the AI can use - tool_manager: Manager to execute tools - - Returns: - Generated response as string - """ - - # Build system content efficiently - avoid string ops when possible - system_content = ( - f"{self.SYSTEM_PROMPT}\n\nPrevious conversation:\n{conversation_history}" - if conversation_history - else self.SYSTEM_PROMPT - ) - - # Start with initial messages - messages = [{"role": "user", "content": query}] - - # Execute up to 2 rounds of tool calling - for round_num in range(2): - # Prepare API call parameters - api_params = { - **self.base_params, - "messages": messages, - "system": system_content, - } - - # Add tools if available - if tools: - api_params["tools"] = tools - api_params["tool_choice"] = {"type": "auto"} - - # Get response from Claude - response = self.client.messages.create(**api_params) - - # Handle tool execution if needed - if response.stop_reason == "tool_use" and tool_manager: - messages, should_continue = self._handle_tool_execution( - response, messages, tool_manager - ) - if not should_continue: - break - else: - # No tool use, return direct response - return response.content[0].text - - # After max rounds, make final call without tools to get response - final_params = { - **self.base_params, - "messages": messages, - "system": system_content, - } - - final_response = self.client.messages.create(**final_params) - return final_response.content[0].text - - def _handle_tool_execution(self, initial_response, messages: List, tool_manager): - """ - Handle execution of tool calls and update message history. - - Args: - initial_response: The response containing tool use requests - messages: Current message history - tool_manager: Manager to execute tools - - Returns: - Tuple of (updated_messages, should_continue) - """ - # Add AI's tool use response - messages.append({"role": "assistant", "content": initial_response.content}) - - # Execute all tool calls and collect results - tool_results = [] - for content_block in initial_response.content: - if content_block.type == "tool_use": - try: - tool_result = tool_manager.execute_tool( - content_block.name, **content_block.input - ) - - tool_results.append( - { - "type": "tool_result", - "tool_use_id": content_block.id, - "content": tool_result, - } - ) - except Exception as e: - # Tool execution failed, stop rounds - tool_results.append( - { - "type": "tool_result", - "tool_use_id": content_block.id, - "content": f"Error: Tool execution failed - {str(e)}", - } - ) - # Add tool results and signal to stop - if tool_results: - messages.append({"role": "user", "content": tool_results}) - return messages, False - - # Add tool results as single message - if tool_results: - messages.append({"role": "user", "content": tool_results}) - - # Continue with next round - return messages, True diff --git a/backend/app.py b/backend/app.py index b4098e1..e87a41d 100644 --- a/backend/app.py +++ b/backend/app.py @@ -1,20 +1,21 @@ +import os import warnings +from pathlib import Path +from typing import List, Optional warnings.filterwarnings("ignore", message="resource_tracker: There appear to be.*") -import os -from typing import List, Optional - from config import config from fastapi import FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware from fastapi.middleware.trustedhost import TrustedHostMiddleware +from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles from pydantic import BaseModel from rag_system import RAGSystem # Initialize FastAPI app -app = FastAPI(title="Course Materials RAG System", root_path="") +app = FastAPI(title="Markdown RAG System", root_path="") # Add trusted host middleware for proxy app.add_middleware(TrustedHostMiddleware, allowed_hosts=["*"]) @@ -29,38 +30,27 @@ expose_headers=["*"], ) -# Initialize RAG system -rag_system = RAGSystem(config) - # Pydantic models for request/response class QueryRequest(BaseModel): - """Request model for course queries""" + """Request model for queries""" query: str - session_id: Optional[str] = None + limit: Optional[int] = 5 class QueryResponse(BaseModel): - """Response model for course queries""" + """Response model for queries""" answer: str sources: List[str] - source_links: List[Optional[str]] - session_id: str - - -class CourseStats(BaseModel): - """Response model for course statistics""" - total_courses: int - course_titles: List[str] +class DocumentStats(BaseModel): + """Response model for document statistics""" -class ClearSessionRequest(BaseModel): - """Request model for clearing a session""" - - session_id: str + total_documents: int + file_names: List[str] # API Endpoints @@ -70,69 +60,62 @@ class ClearSessionRequest(BaseModel): async def query_documents(request: QueryRequest): """Process a query and return response with sources""" try: - # Create session if not provided - session_id = request.session_id - if not session_id: - session_id = rag_system.session_manager.create_session() - # Process query using RAG system - answer, sources, source_links = rag_system.query(request.query, session_id) + result = rag_system.query(request.query, request.limit or 5) - return QueryResponse( - answer=answer, - sources=sources, - source_links=source_links, - session_id=session_id, - ) + return QueryResponse(answer=result["answer"], sources=result["sources"]) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) -@app.get("/api/courses", response_model=CourseStats) -async def get_course_stats(): - """Get course analytics and statistics""" +@app.get("/api/documents", response_model=DocumentStats) +async def get_document_stats(): + """Get document analytics and statistics""" try: - analytics = rag_system.get_course_analytics() - return CourseStats( - total_courses=analytics["total_courses"], - course_titles=analytics["course_titles"], + stats = rag_system.get_document_stats() + return DocumentStats( + total_documents=stats["total_documents"], + file_names=stats["file_names"], ) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) -@app.post("/api/clear-session") -async def clear_session(request: ClearSessionRequest): - """Clear a conversation session""" - try: - rag_system.session_manager.clear_session(request.session_id) - return {"status": "success", "message": "Session cleared successfully"} - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - @app.on_event("startup") async def startup_event(): """Load initial documents on startup""" + # Initialize RAG system with docs folder docs_path = "../docs" + rag = None + if os.path.exists(docs_path): print("Loading initial documents...") try: - courses, chunks = rag_system.add_course_folder( - docs_path, clear_existing=False - ) - print(f"Loaded {courses} courses with {chunks} chunks") + # Initialize and load documents + rag = RAGSystem(docs_path, chroma_path=config.CHROMA_PATH) + print(f"RAG system initialized with documents from: {docs_path}") + + # Store globally for use in endpoints + globals()["rag_system"] = rag except Exception as e: print(f"Error loading documents: {e}") + # Initialize empty system + rag = RAGSystem.__new__(RAGSystem) + rag.md_processor = None + rag.vector_store = None + rag.folder_path = docs_path + globals()["rag_system"] = rag + else: + print(f"Docs folder not found: {docs_path}") + # Initialize empty system + rag = RAGSystem.__new__(RAGSystem) + rag.md_processor = None + rag.vector_store = None + rag.folder_path = docs_path + globals()["rag_system"] = rag -import os -from pathlib import Path - -from fastapi.responses import FileResponse - # Custom static file handler with no-cache headers for development -from fastapi.staticfiles import StaticFiles class DevStaticFiles(StaticFiles): @@ -147,4 +130,4 @@ async def get_response(self, path: str, scope): # Serve static files for the frontend -app.mount("/", StaticFiles(directory="../frontend", html=True), name="static") +app.mount("/", DevStaticFiles(directory="../frontend", html=True), name="static") diff --git a/backend/config.py b/backend/config.py index cab6dcc..552beb4 100644 --- a/backend/config.py +++ b/backend/config.py @@ -11,10 +11,6 @@ class Config: """Configuration settings for the RAG system""" - # Anthropic API settings - ANTHROPIC_API_KEY: str = os.getenv("ANTHROPIC_API_KEY", "") - ANTHROPIC_MODEL: str = "claude-sonnet-4-20250514" - # Embedding model settings EMBEDDING_MODEL: str = "all-MiniLM-L6-v2" @@ -22,7 +18,6 @@ class Config: CHUNK_SIZE: int = 800 # Size of text chunks for vector storage CHUNK_OVERLAP: int = 100 # Characters to overlap between chunks MAX_RESULTS: int = 5 # Maximum search results to return - MAX_HISTORY: int = 2 # Number of conversation messages to remember # Database paths CHROMA_PATH: str = "./chroma_db" # ChromaDB storage location diff --git a/backend/document_processor.py b/backend/document_processor.py index bc0662a..8745d87 100644 --- a/backend/document_processor.py +++ b/backend/document_processor.py @@ -1,32 +1,159 @@ import os import re -from typing import List, Tuple +from typing import Dict, List, Optional, Tuple -from models import Course, CourseChunk, Lesson +from models import Document, DocumentChunk -class DocumentProcessor: - """Processes course documents and extracts structured information""" +class MDProcessor: + """Processes Markdown documents and extracts structured information""" def __init__(self, chunk_size: int, chunk_overlap: int): self.chunk_size = chunk_size self.chunk_overlap = chunk_overlap - def read_file(self, file_path: str) -> str: - """Read content from file with UTF-8 encoding""" + def process_md_file(self, file_path: str) -> Tuple[Document, List[DocumentChunk]]: + """ + Process a Markdown file and extract document structure and chunks. + + Args: + file_path: Path to the Markdown file + + Returns: + Tuple of (Document object, list of DocumentChunk objects) + """ + # Read file content + content = self.read_file(file_path) + file_name = os.path.basename(file_path) + + # Extract frontmatter (optional) + frontmatter = self.extract_frontmatter(content) + if frontmatter: + # Remove frontmatter from content + content = re.sub( + r"^---\n.*?\n---\n", + "", + content, + flags=re.DOTALL, + count=1, + ) + + # Extract title + title = frontmatter.get("title") if frontmatter else None + if not title: + title = self._extract_first_heading(content) + + # Split content by sections + sections = self.split_by_sections(content) + + # Create document object + document = Document( + file_name=file_name, + file_path=file_path, + title=title, + sections=[section[0] for section in sections], + ) + + # Create chunks + chunks = [] + chunk_index = 0 + + for section_title, section_content in sections: + # Skip empty sections + if not section_content.strip(): + continue + + # Chunk the section content + text_chunks = self.chunk_text(section_content) + + for chunk in text_chunks: + formatted_chunk = self.format_chunk_with_context( + chunk, section_title, file_name + ) + + doc_chunk = DocumentChunk( + content=formatted_chunk, + file_name=file_name, + file_path=file_path, + section_title=section_title, + chunk_index=chunk_index, + ) + chunks.append(doc_chunk) + chunk_index += 1 + + return document, chunks + + def extract_frontmatter(self, content: str) -> Optional[Dict[str, str]]: + """ + Extract YAML frontmatter from Markdown content. + + Example: + --- + title: "Document Title" + author: "Author Name" + --- + """ + match = re.match(r"^---\n(.*?)\n---\n", content, re.DOTALL) + if not match: + return None + try: - with open(file_path, "r", encoding="utf-8") as file: - return file.read() - except UnicodeDecodeError: - # If UTF-8 fails, try with error handling - with open(file_path, "r", encoding="utf-8", errors="ignore") as file: - return file.read() + import yaml + + frontmatter = yaml.safe_load(match.group(1)) + return frontmatter if isinstance(frontmatter, dict) else None + except Exception: + return None + + def split_by_sections(self, content: str) -> List[Tuple[str, str]]: + """ + Split content by Markdown headings. + + Returns: + List of tuples: [(section_title, section_content), ...] + """ + sections = [] + lines = content.split("\n") + + current_title = "简介" # Default section title + current_content = [] + + for line in lines: + # Detect headings (#, ##, ###, etc.) + heading_match = re.match(r"^(#{1,6})\s+(.+)$", line) + + if heading_match: + # Save previous section if it has content + if current_content: + section_text = "\n".join(current_content).strip() + if section_text: + sections.append((current_title, section_text)) + + # Start new section + current_title = heading_match.group(2).strip() + current_content = [] + else: + current_content.append(line) + + # Save the last section + if current_content: + section_text = "\n".join(current_content).strip() + if section_text: + sections.append((current_title, section_text)) + + return sections + + def format_chunk_with_context( + self, chunk: str, section: str, file_name: str + ) -> str: + """Add context information to a chunk.""" + return f"[文件: {file_name} | 章节: {section}]\n\n{chunk}" def chunk_text(self, text: str) -> List[str]: - """Split text into sentence-based chunks with overlap using config settings""" + """Split text into sentence-based chunks with overlap.""" # Clean up the text - text = re.sub(r"\s+", " ", text.strip()) # Normalize whitespace + text = re.sub(r"\s+", " ", text.strip()) # Better sentence splitting that handles abbreviations # This regex looks for periods followed by whitespace and capital letters @@ -66,7 +193,7 @@ def chunk_text(self, text: str) -> List[str]: chunks.append(" ".join(current_chunk)) # Calculate overlap for next chunk - if hasattr(self, "chunk_overlap") and self.chunk_overlap > 0: + if self.chunk_overlap > 0: # Find how many sentences to overlap overlap_size = 0 overlap_sentences = 0 @@ -84,7 +211,7 @@ def chunk_text(self, text: str) -> List[str]: # Move start position considering overlap next_start = i + len(current_chunk) - overlap_sentences - i = max(next_start, i + 1) # Ensure we make progress + i = max(next_start, i + 1) else: # No overlap - move to next sentence after current chunk i += len(current_chunk) @@ -94,178 +221,17 @@ def chunk_text(self, text: str) -> List[str]: return chunks - def process_course_document( - self, file_path: str - ) -> Tuple[Course, List[CourseChunk]]: - """ - Process a course document with expected format: - Line 1: Course Title: [title] - Line 2: Course Link: [url] - Line 3: Course Instructor: [instructor] - Following lines: Lesson markers and content - """ - content = self.read_file(file_path) - filename = os.path.basename(file_path) - - lines = content.strip().split("\n") - - # Extract course metadata from first three lines - course_title = filename # Default fallback - course_link = None - instructor_name = "Unknown" - - # Parse course title from first line - if len(lines) >= 1 and lines[0].strip(): - title_match = re.match( - r"^Course Title:\s*(.+)$", lines[0].strip(), re.IGNORECASE - ) - if title_match: - course_title = title_match.group(1).strip() - else: - course_title = lines[0].strip() - - # Parse remaining lines for course metadata - for i in range(1, min(len(lines), 4)): # Check first 4 lines for metadata - line = lines[i].strip() - if not line: - continue - - # Try to match course link - link_match = re.match(r"^Course Link:\s*(.+)$", line, re.IGNORECASE) - if link_match: - course_link = link_match.group(1).strip() - continue - - # Try to match instructor - instructor_match = re.match( - r"^Course Instructor:\s*(.+)$", line, re.IGNORECASE - ) - if instructor_match: - instructor_name = instructor_match.group(1).strip() - continue - - # Create course object with title as ID - course = Course( - title=course_title, - course_link=course_link, - instructor=instructor_name if instructor_name != "Unknown" else None, - ) - - # Process lessons and create chunks - course_chunks = [] - current_lesson = None - lesson_title = None - lesson_link = None - lesson_content = [] - chunk_counter = 0 - - # Start processing from line 4 (after metadata) - start_index = 3 - if len(lines) > 3 and not lines[3].strip(): - start_index = 4 # Skip empty line after instructor - - i = start_index - while i < len(lines): - line = lines[i] - - # Check for lesson markers (e.g., "Lesson 0: Introduction") - lesson_match = re.match( - r"^Lesson\s+(\d+):\s*(.+)$", line.strip(), re.IGNORECASE - ) + def read_file(self, file_path: str) -> str: + """Read content from file with UTF-8 encoding.""" + try: + with open(file_path, "r", encoding="utf-8") as file: + return file.read() + except UnicodeDecodeError: + # If UTF-8 fails, try with error handling + with open(file_path, "r", encoding="utf-8", errors="ignore") as file: + return file.read() - if lesson_match: - # Process previous lesson if it exists - if current_lesson is not None and lesson_content: - lesson_text = "\n".join(lesson_content).strip() - if lesson_text: - # Add lesson to course - lesson = Lesson( - lesson_number=current_lesson, - title=lesson_title, - lesson_link=lesson_link, - ) - course.lessons.append(lesson) - - # Create chunks for this lesson - chunks = self.chunk_text(lesson_text) - for idx, chunk in enumerate(chunks): - # For the first chunk of each lesson, add lesson context - if idx == 0: - chunk_with_context = ( - f"Lesson {current_lesson} content: {chunk}" - ) - else: - chunk_with_context = chunk - - course_chunk = CourseChunk( - content=chunk_with_context, - course_title=course.title, - lesson_number=current_lesson, - chunk_index=chunk_counter, - ) - course_chunks.append(course_chunk) - chunk_counter += 1 - - # Start new lesson - current_lesson = int(lesson_match.group(1)) - lesson_title = lesson_match.group(2).strip() - lesson_link = None - - # Check if next line is a lesson link - if i + 1 < len(lines): - next_line = lines[i + 1].strip() - link_match = re.match( - r"^Lesson Link:\s*(.+)$", next_line, re.IGNORECASE - ) - if link_match: - lesson_link = link_match.group(1).strip() - i += 1 # Skip the link line so it's not added to content - - lesson_content = [] - else: - # Add line to current lesson content - lesson_content.append(line) - - i += 1 - - # Process the last lesson - if current_lesson is not None and lesson_content: - lesson_text = "\n".join(lesson_content).strip() - if lesson_text: - lesson = Lesson( - lesson_number=current_lesson, - title=lesson_title, - lesson_link=lesson_link, - ) - course.lessons.append(lesson) - - chunks = self.chunk_text(lesson_text) - for idx, chunk in enumerate(chunks): - # For any chunk of each lesson, add lesson context & course title - - chunk_with_context = f"Course {course_title} Lesson {current_lesson} content: {chunk}" - - course_chunk = CourseChunk( - content=chunk_with_context, - course_title=course.title, - lesson_number=current_lesson, - chunk_index=chunk_counter, - ) - course_chunks.append(course_chunk) - chunk_counter += 1 - - # If no lessons found, treat entire content as one document - if not course_chunks and len(lines) > 2: - remaining_content = "\n".join(lines[start_index:]).strip() - if remaining_content: - chunks = self.chunk_text(remaining_content) - for chunk in chunks: - course_chunk = CourseChunk( - content=chunk, - course_title=course.title, - chunk_index=chunk_counter, - ) - course_chunks.append(course_chunk) - chunk_counter += 1 - - return course, course_chunks + def _extract_first_heading(self, content: str) -> Optional[str]: + """Extract the first level-1 heading as document title.""" + match = re.search(r"^#\s+(.+)$", content, re.MULTILINE) + return match.group(1).strip() if match else None diff --git a/backend/models.py b/backend/models.py index 9ab7381..f802882 100644 --- a/backend/models.py +++ b/backend/models.py @@ -1,29 +1,22 @@ -from typing import Dict, List, Optional +from typing import List, Optional from pydantic import BaseModel -class Lesson(BaseModel): - """Represents a lesson within a course""" +class Document(BaseModel): + """Represents a Markdown document""" - lesson_number: int # Sequential lesson number (1, 2, 3, etc.) - title: str # Lesson title - lesson_link: Optional[str] = None # URL link to the lesson + file_name: str # File name (e.g., "guide.md") + file_path: str # Full path to the file + title: Optional[str] = None # Title extracted from frontmatter or first heading + sections: List[str] = [] # List of section titles -class Course(BaseModel): - """Represents a complete course with its lessons""" - - title: str # Full course title (used as unique identifier) - course_link: Optional[str] = None # URL link to the course - instructor: Optional[str] = None # Course instructor name (optional metadata) - lessons: List[Lesson] = [] # List of lessons in this course - - -class CourseChunk(BaseModel): - """Represents a text chunk from a course for vector storage""" +class DocumentChunk(BaseModel): + """Represents a text chunk from a Markdown document""" content: str # The actual text content - course_title: str # Which course this chunk belongs to - lesson_number: Optional[int] = None # Which lesson this chunk is from + file_name: str # Source file name + file_path: str # Source file path + section_title: Optional[str] = None # Section title this chunk belongs to chunk_index: int # Position of this chunk in the document diff --git a/backend/rag_system.py b/backend/rag_system.py index 44ac084..897872e 100644 --- a/backend/rag_system.py +++ b/backend/rag_system.py @@ -1,170 +1,177 @@ import os -from typing import Dict, List, Optional, Tuple +from typing import Any, Dict, List, Tuple -from ai_generator import AIGenerator -from document_processor import DocumentProcessor -from models import Course, CourseChunk, Lesson -from search_tools import CourseOutlineTool, CourseSearchTool, ToolManager -from session_manager import SessionManager -from vector_store import VectorStore +from config import config +from document_processor import MDProcessor +from models import Document, DocumentChunk +from vector_store import SearchResults, VectorStore class RAGSystem: - """Main orchestrator for the Retrieval-Augmented Generation system""" + """General-purpose Markdown RAG system""" - def __init__(self, config): - self.config = config + def __init__(self, folder_path: str, chroma_path: str = "./chroma_db"): + """ + Initialize RAG system and load MD files. + + Args: + folder_path: Directory containing MD files + chroma_path: ChromaDB storage path + """ + self.folder_path = folder_path # Initialize core components - self.document_processor = DocumentProcessor( - config.CHUNK_SIZE, config.CHUNK_OVERLAP - ) + self.md_processor = MDProcessor(config.CHUNK_SIZE, config.CHUNK_OVERLAP) self.vector_store = VectorStore( - config.CHROMA_PATH, config.EMBEDDING_MODEL, config.MAX_RESULTS - ) - self.ai_generator = AIGenerator( - config.ANTHROPIC_API_KEY, config.ANTHROPIC_MODEL + chroma_path, config.EMBEDDING_MODEL, config.MAX_RESULTS ) - self.session_manager = SessionManager(config.MAX_HISTORY) - # Initialize search tools - self.tool_manager = ToolManager() - self.search_tool = CourseSearchTool(self.vector_store) - self.outline_tool = CourseOutlineTool(self.vector_store) - self.tool_manager.register_tool(self.search_tool) - self.tool_manager.register_tool(self.outline_tool) + # Auto-load MD files + self.load_md_folder(folder_path, clear_existing=False) - def add_course_document(self, file_path: str) -> Tuple[Course, int]: + def query(self, query_text: str, limit: int = 5) -> Dict[str, Any]: """ - Add a single course document to the knowledge base. + Query MD document content. Args: - file_path: Path to the course document + query_text: Query text + limit: Number of results to return Returns: - Tuple of (Course object, number of chunks created) + { + "answer": str, # Formatted query result + "sources": List[str], # Source information list + "results": List[Dict] # Raw search results + } """ - try: - # Process the document - course, course_chunks = self.document_processor.process_course_document( - file_path + # Execute vector search + search_results = self.vector_store.search(query_text, limit=limit) + + if search_results.error: + return { + "answer": f"搜索错误: {search_results.error}", + "sources": [], + "results": [], + } + + if search_results.is_empty(): + return { + "answer": "未找到相关内容", + "sources": [], + "results": [], + } + + # Format results + answer = self._format_results(search_results) + sources = self._extract_sources(search_results) + + # Include raw results + results = [ + { + "content": doc, + "metadata": meta, + "distance": dist, + } + for doc, meta, dist in zip( + search_results.documents, + search_results.metadata, + search_results.distances, ) + ] - # Add course metadata to vector store for semantic search - self.vector_store.add_course_metadata(course) - - # Add course content chunks to vector store - self.vector_store.add_course_content(course_chunks) - - return course, len(course_chunks) - except Exception as e: - print(f"Error processing course document {file_path}: {e}") - return None, 0 + return { + "answer": answer, + "sources": sources, + "results": results, + } - def add_course_folder( + def load_md_folder( self, folder_path: str, clear_existing: bool = False ) -> Tuple[int, int]: """ - Add all course documents from a folder. + Load all MD files from a folder. Args: - folder_path: Path to folder containing course documents - clear_existing: Whether to clear existing data first + folder_path: Folder path + clear_existing: Whether to clear existing data Returns: - Tuple of (total courses added, total chunks created) + (number of documents, number of chunks) """ - total_courses = 0 + total_docs = 0 total_chunks = 0 - # Clear existing data if requested if clear_existing: - print("Clearing existing data for fresh rebuild...") + print("清除现有数据...") self.vector_store.clear_all_data() if not os.path.exists(folder_path): - print(f"Folder {folder_path} does not exist") + print(f"文件夹不存在: {folder_path}") return 0, 0 - # Get existing course titles to avoid re-processing - existing_course_titles = set(self.vector_store.get_existing_course_titles()) + # Get already processed file names + existing_files = set(self.vector_store.get_existing_filenames()) - # Process each file in the folder + # Iterate through MD files for file_name in os.listdir(folder_path): + if not file_name.lower().endswith(".md"): + continue + file_path = os.path.join(folder_path, file_name) - if os.path.isfile(file_path) and file_name.lower().endswith( - (".pdf", ".docx", ".txt") - ): - try: - # Check if this course might already exist - # We'll process the document to get the course ID, but only add if new - course, course_chunks = ( - self.document_processor.process_course_document(file_path) - ) - - if course and course.title not in existing_course_titles: - # This is a new course - add it to the vector store - self.vector_store.add_course_metadata(course) - self.vector_store.add_course_content(course_chunks) - total_courses += 1 - total_chunks += len(course_chunks) - print( - f"Added new course: {course.title} ({len(course_chunks)} chunks)" - ) - existing_course_titles.add(course.title) - elif course: - print(f"Course already exists: {course.title} - skipping") - except Exception as e: - print(f"Error processing {file_name}: {e}") - - return total_courses, total_chunks - - def query( - self, query: str, session_id: Optional[str] = None - ) -> Tuple[str, List[str], List[str]]: - """ - Process a user query using the RAG system with tool-based search. - Args: - query: User's question - session_id: Optional session ID for conversation context + # Skip already processed files + if file_name in existing_files: + print(f"文件已存在，跳过: {file_name}") + continue - Returns: - Tuple of (response, sources list, source_links list) - """ - # Create prompt for the AI with clear instructions - prompt = f"""Answer this question about course materials: {query}""" - - # Get conversation history if session exists - history = None - if session_id: - history = self.session_manager.get_conversation_history(session_id) - - # Generate response using AI with tools - response = self.ai_generator.generate_response( - query=prompt, - conversation_history=history, - tools=self.tool_manager.get_tool_definitions(), - tool_manager=self.tool_manager, - ) + try: + # Process MD file + document, chunks = self.md_processor.process_md_file(file_path) - # Get sources and source links from the search tool - sources = self.tool_manager.get_last_sources() - source_links = self.tool_manager.get_last_source_links() + # Add to vector store + self.vector_store.add_document_content(chunks) - # Reset sources after retrieving them - self.tool_manager.reset_sources() + total_docs += 1 + total_chunks += len(chunks) + print(f"已加载: {file_name} ({len(chunks)} 个分块)") - # Update conversation history - if session_id: - self.session_manager.add_exchange(session_id, query, response) + except Exception as e: + print(f"处理文件失败 {file_name}: {e}") - # Return response with sources and links from tool searches - return response, sources, source_links + return total_docs, total_chunks - def get_course_analytics(self) -> Dict: - """Get analytics about the course catalog""" + def get_document_stats(self) -> Dict[str, Any]: + """Get document statistics.""" return { - "total_courses": self.vector_store.get_course_count(), - "course_titles": self.vector_store.get_existing_course_titles(), + "total_documents": self.vector_store.get_document_count(), + "file_names": self.vector_store.get_existing_filenames(), } + + def _format_results(self, results: SearchResults) -> str: + """Format search results into readable text.""" + formatted = [] + for doc, meta in zip(results.documents, results.metadata): + file_name = meta.get("file_name", "unknown") + section = meta.get("section_title") + + header = f"## 来源: {file_name}" + if section: + header += f" > {section}" + + formatted.append(f"{header}\n\n{doc}") + + return "\n\n---\n\n".join(formatted) + + def _extract_sources(self, results: SearchResults) -> List[str]: + """Extract source information.""" + sources = [] + for meta in results.metadata: + file_name = meta.get("file_name", "unknown") + section = meta.get("section_title") + + source = file_name + if section: + source += f" > {section}" + sources.append(source) + + return sources diff --git a/backend/search_tools.py b/backend/search_tools.py deleted file mode 100644 index e74bee4..0000000 --- a/backend/search_tools.py +++ /dev/null @@ -1,250 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Any, Dict, Optional, Protocol - -from vector_store import SearchResults, VectorStore - - -class Tool(ABC): - """Abstract base class for all tools""" - - @abstractmethod - def get_tool_definition(self) -> Dict[str, Any]: - """Return Anthropic tool definition for this tool""" - pass - - @abstractmethod - def execute(self, **kwargs) -> str: - """Execute the tool with given parameters""" - pass - - -class CourseSearchTool(Tool): - """Tool for searching course content with semantic course name matching""" - - def __init__(self, vector_store: VectorStore): - self.store = vector_store - self.last_sources = [] # Track sources from last search - self.last_source_links = [] # Track lesson links from last search - - def get_tool_definition(self) -> Dict[str, Any]: - """Return Anthropic tool definition for this tool""" - return { - "name": "search_course_content", - "description": "Search course materials with smart course name matching and lesson filtering", - "input_schema": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "What to search for in the course content", - }, - "course_name": { - "type": "string", - "description": "Course title (partial matches work, e.g. 'MCP', 'Introduction')", - }, - "lesson_number": { - "type": "integer", - "description": "Specific lesson number to search within (e.g. 1, 2, 3)", - }, - }, - "required": ["query"], - }, - } - - def execute( - self, - query: str, - course_name: Optional[str] = None, - lesson_number: Optional[int] = None, - ) -> str: - """ - Execute the search tool with given parameters. - - Args: - query: What to search for - course_name: Optional course filter - lesson_number: Optional lesson filter - - Returns: - Formatted search results or error message - """ - - # Use the vector store's unified search interface - results = self.store.search( - query=query, course_name=course_name, lesson_number=lesson_number - ) - - # Handle errors - if results.error: - return results.error - - # Handle empty results - if results.is_empty(): - filter_info = "" - if course_name: - filter_info += f" in course '{course_name}'" - if lesson_number: - filter_info += f" in lesson {lesson_number}" - return f"No relevant content found{filter_info}." - - # Format and return results - return self._format_results(results) - - def _format_results(self, results: SearchResults) -> str: - """Format search results with course and lesson context""" - formatted = [] - sources = [] # Track sources for the UI - source_links = [] # Track lesson links for the UI - - for doc, meta in zip(results.documents, results.metadata): - course_title = meta.get("course_title", "unknown") - lesson_num = meta.get("lesson_number") - - # Build context header - header = f"[{course_title}" - if lesson_num is not None: - header += f" - Lesson {lesson_num}" - header += "]" - - # Track source for the UI - source = course_title - if lesson_num is not None: - source += f" - Lesson {lesson_num}" - sources.append(source) - - # Get lesson link if available - lesson_link = None - if lesson_num is not None: - lesson_link = self.store.get_lesson_link(course_title, lesson_num) - source_links.append(lesson_link) - - formatted.append(f"{header}\n{doc}") - - # Store sources and links for retrieval - self.last_sources = sources - self.last_source_links = source_links - - return "\n\n".join(formatted) - - -class CourseOutlineTool(Tool): - """Tool for getting course outline and lesson structure""" - - def __init__(self, vector_store: VectorStore): - self.store = vector_store - - def get_tool_definition(self) -> Dict[str, Any]: - """Return Anthropic tool definition for this tool""" - return { - "name": "get_course_outline", - "description": "Get course outline with title, link, and complete lesson list", - "input_schema": { - "type": "object", - "properties": { - "course_name": { - "type": "string", - "description": "Course title (partial matches work, e.g. 'MCP', 'Introduction')", - } - }, - "required": ["course_name"], - }, - } - - def execute(self, course_name: str) -> str: - """ - Execute the outline tool to get course structure. - - Args: - course_name: Course name to get outline for - - Returns: - Formatted course outline or error message - """ - # Resolve course name using vector store's existing method - course_title = self.store._resolve_course_name(course_name) - if not course_title: - return f"No course found matching '{course_name}'" - - # Get course metadata - try: - results = self.store.course_catalog.get(ids=[course_title]) - if not results or not results["metadatas"]: - return f"Course metadata not found for '{course_title}'" - - metadata = results["metadatas"][0] - - # Parse lessons from JSON - import json - - lessons_json = metadata.get("lessons_json") - if not lessons_json: - return f"No lesson information available for '{course_title}'" - - lessons = json.loads(lessons_json) - - # Format the outline - outline = [] - outline.append(f"**Course Title:** {metadata.get('title', course_title)}") - outline.append(f"**Course Link:** {metadata.get('course_link', 'N/A')}") - outline.append(f"**Total Lessons:** {len(lessons)}") - outline.append("\n**Lesson Outline:**") - - for lesson in lessons: - lesson_num = lesson.get("lesson_number", "N/A") - lesson_title = lesson.get("lesson_title", "N/A") - outline.append(f"Lesson {lesson_num}: {lesson_title}") - - return "\n".join(outline) - - except Exception as e: - return f"Error retrieving course outline: {str(e)}" - - -class ToolManager: - """Manages available tools for the AI""" - - def __init__(self): - self.tools = {} - - def register_tool(self, tool: Tool): - """Register any tool that implements the Tool interface""" - tool_def = tool.get_tool_definition() - tool_name = tool_def.get("name") - if not tool_name: - raise ValueError("Tool must have a 'name' in its definition") - self.tools[tool_name] = tool - - def get_tool_definitions(self) -> list: - """Get all tool definitions for Anthropic tool calling""" - return [tool.get_tool_definition() for tool in self.tools.values()] - - def execute_tool(self, tool_name: str, **kwargs) -> str: - """Execute a tool by name with given parameters""" - if tool_name not in self.tools: - return f"Tool '{tool_name}' not found" - - return self.tools[tool_name].execute(**kwargs) - - def get_last_sources(self) -> list: - """Get sources from the last search operation""" - # Check all tools for last_sources attribute - for tool in self.tools.values(): - if hasattr(tool, "last_sources") and tool.last_sources: - return tool.last_sources - return [] - - def get_last_source_links(self) -> list: - """Get source links from the last search operation""" - # Check all tools for last_source_links attribute - for tool in self.tools.values(): - if hasattr(tool, "last_source_links") and tool.last_source_links: - return tool.last_source_links - return [] - - def reset_sources(self): - """Reset sources from all tools that track sources""" - for tool in self.tools.values(): - if hasattr(tool, "last_sources"): - tool.last_sources = [] - if hasattr(tool, "last_source_links"): - tool.last_source_links = [] diff --git a/backend/session_manager.py b/backend/session_manager.py deleted file mode 100644 index 374db48..0000000 --- a/backend/session_manager.py +++ /dev/null @@ -1,66 +0,0 @@ -from dataclasses import dataclass -from typing import Dict, List, Optional - - -@dataclass -class Message: - """Represents a single message in a conversation""" - - role: str # "user" or "assistant" - content: str # The message content - - -class SessionManager: - """Manages conversation sessions and message history""" - - def __init__(self, max_history: int = 5): - self.max_history = max_history - self.sessions: Dict[str, List[Message]] = {} - self.session_counter = 0 - - def create_session(self) -> str: - """Create a new conversation session""" - self.session_counter += 1 - session_id = f"session_{self.session_counter}" - self.sessions[session_id] = [] - return session_id - - def add_message(self, session_id: str, role: str, content: str): - """Add a message to the conversation history""" - if session_id not in self.sessions: - self.sessions[session_id] = [] - - message = Message(role=role, content=content) - self.sessions[session_id].append(message) - - # Keep conversation history within limits - if len(self.sessions[session_id]) > self.max_history * 2: - self.sessions[session_id] = self.sessions[session_id][ - -self.max_history * 2 : - ] - - def add_exchange(self, session_id: str, user_message: str, assistant_message: str): - """Add a complete question-answer exchange""" - self.add_message(session_id, "user", user_message) - self.add_message(session_id, "assistant", assistant_message) - - def get_conversation_history(self, session_id: Optional[str]) -> Optional[str]: - """Get formatted conversation history for a session""" - if not session_id or session_id not in self.sessions: - return None - - messages = self.sessions[session_id] - if not messages: - return None - - # Format messages for context - formatted_messages = [] - for msg in messages: - formatted_messages.append(f"{msg.role.title()}: {msg.content}") - - return "\n".join(formatted_messages) - - def clear_session(self, session_id: str): - """Clear all messages from a session""" - if session_id in self.sessions: - self.sessions[session_id] = [] diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py index 7a4344f..887af44 100644 --- a/backend/tests/conftest.py +++ b/backend/tests/conftest.py @@ -259,11 +259,14 @@ def mock_rag_system(): mock.query.return_value = ( "This is a test response about course content.", ["Building Towards Computer Use with Anthropic - Lesson 1"], - ["https://example.com/lesson1"] + ["https://example.com/lesson1"], ) mock.get_course_analytics.return_value = { "total_courses": 2, - "course_titles": ["Building Towards Computer Use with Anthropic", "Advanced AI Techniques"] + "course_titles": [ + "Building Towards Computer Use with Anthropic", + "Advanced AI Techniques", + ], } mock.session_manager.create_session.return_value = "test-session-123" mock.session_manager.clear_session.return_value = None @@ -273,15 +276,16 @@ def mock_rag_system(): @pytest.fixture def test_app(): """Create a test FastAPI app with mocked dependencies""" + from typing import List, Optional + from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.middleware.trustedhost import TrustedHostMiddleware from pydantic import BaseModel - from typing import List, Optional - + # Create test app without static file mounting app = FastAPI(title="Course Materials RAG System Test", root_path="") - + # Add middleware app.add_middleware(TrustedHostMiddleware, allowed_hosts=["*"]) app.add_middleware( @@ -292,7 +296,7 @@ def test_app(): allow_headers=["*"], expose_headers=["*"], ) - + # Pydantic models class QueryRequest(BaseModel): query: str @@ -310,21 +314,24 @@ class CourseStats(BaseModel): class ClearSessionRequest(BaseModel): session_id: str - + # Mock RAG system mock_rag = Mock() mock_rag.query.return_value = ( "This is a test response about course content.", ["Building Towards Computer Use with Anthropic - Lesson 1"], - ["https://example.com/lesson1"] + ["https://example.com/lesson1"], ) mock_rag.get_course_analytics.return_value = { "total_courses": 2, - "course_titles": ["Building Towards Computer Use with Anthropic", "Advanced AI Techniques"] + "course_titles": [ + "Building Towards Computer Use with Anthropic", + "Advanced AI Techniques", + ], } mock_rag.session_manager.create_session.return_value = "test-session-123" mock_rag.session_manager.clear_session.return_value = None - + # API endpoints @app.post("/api/query", response_model=QueryResponse) async def query_documents(request: QueryRequest): @@ -334,7 +341,7 @@ async def query_documents(request: QueryRequest): answer=answer, sources=sources, source_links=source_links, - session_id=session_id + session_id=session_id, ) @app.get("/api/courses", response_model=CourseStats) @@ -342,18 +349,18 @@ async def get_course_stats(): analytics = mock_rag.get_course_analytics() return CourseStats( total_courses=analytics["total_courses"], - course_titles=analytics["course_titles"] + course_titles=analytics["course_titles"], ) @app.post("/api/clear-session") async def clear_session(request: ClearSessionRequest): mock_rag.session_manager.clear_session(request.session_id) return {"status": "success", "message": "Session cleared successfully"} - + @app.get("/") async def root(): return {"message": "Course Materials RAG System API"} - + return app @@ -366,15 +373,10 @@ def client(test_app): @pytest.fixture def sample_query_request(): """Sample query request for testing""" - return { - "query": "What is computer use in AI?", - "session_id": "test-session-123" - } + return {"query": "What is computer use in AI?", "session_id": "test-session-123"} @pytest.fixture def sample_clear_session_request(): """Sample clear session request for testing""" - return { - "session_id": "test-session-123" - } + return {"session_id": "test-session-123"} diff --git a/backend/tests/test_api_endpoints.py b/backend/tests/test_api_endpoints.py index a8d60d2..207fc5f 100644 --- a/backend/tests/test_api_endpoints.py +++ b/backend/tests/test_api_endpoints.py @@ -1,8 +1,9 @@ -import pytest import json -from fastapi.testclient import TestClient from unittest.mock import Mock, patch +import pytest +from fastapi.testclient import TestClient + @pytest.mark.api class TestAPIEndpoints: @@ -17,19 +18,21 @@ def test_root_endpoint(self, client): def test_query_endpoint_with_session_id(self, client, sample_query_request): """Test /api/query endpoint with provided session_id""" response = client.post("/api/query", json=sample_query_request) - + assert response.status_code == 200 data = response.json() - + # Verify response structure assert "answer" in data assert "sources" in data assert "source_links" in data assert "session_id" in data - + # Verify content assert data["answer"] == "This is a test response about course content." - assert data["sources"] == ["Building Towards Computer Use with Anthropic - Lesson 1"] + assert data["sources"] == [ + "Building Towards Computer Use with Anthropic - Lesson 1" + ] assert data["source_links"] == ["https://example.com/lesson1"] assert data["session_id"] == "test-session-123" @@ -37,10 +40,10 @@ def test_query_endpoint_without_session_id(self, client): """Test /api/query endpoint without session_id (should create new session)""" request_data = {"query": "What is computer use in AI?"} response = client.post("/api/query", json=request_data) - + assert response.status_code == 200 data = response.json() - + # Should create new session assert data["session_id"] == "test-session-123" assert data["answer"] == "This is a test response about course content." @@ -55,35 +58,35 @@ def test_query_endpoint_empty_query(self, client): """Test /api/query endpoint with empty query""" request_data = {"query": ""} response = client.post("/api/query", json=request_data) - + # Should still process (empty query is valid from API perspective) assert response.status_code == 200 def test_courses_endpoint(self, client): """Test /api/courses endpoint returns course statistics""" response = client.get("/api/courses") - + assert response.status_code == 200 data = response.json() - + # Verify response structure assert "total_courses" in data assert "course_titles" in data - + # Verify content assert data["total_courses"] == 2 assert data["course_titles"] == [ - "Building Towards Computer Use with Anthropic", - "Advanced AI Techniques" + "Building Towards Computer Use with Anthropic", + "Advanced AI Techniques", ] def test_clear_session_endpoint(self, client, sample_clear_session_request): """Test /api/clear-session endpoint""" response = client.post("/api/clear-session", json=sample_clear_session_request) - + assert response.status_code == 200 data = response.json() - + assert data["status"] == "success" assert data["message"] == "Session cleared successfully" @@ -109,10 +112,10 @@ def test_query_endpoint_with_rag_system_error(self, client): # In a real scenario, you'd inject the error at the app level request_data = {"query": "test query"} response = client.post("/api/query", json=request_data) - + # With current mock setup, this should work normally assert response.status_code == 200 - + # To test actual error handling, you would need to: # 1. Create a separate test app fixture with error-prone mocks # 2. Or use dependency injection to override the RAG system @@ -134,25 +137,20 @@ def test_query_endpoint_with_extra_fields(self, client): request_data = { "query": "What is computer use?", "session_id": "test-123", - "extra_field": "should be ignored" + "extra_field": "should be ignored", } response = client.post("/api/query", json=request_data) assert response.status_code == 200 def test_query_endpoint_with_wrong_types(self, client): """Test /api/query endpoint with wrong field types""" - request_data = { - "query": 123, # Should be string - "session_id": "test-123" - } + request_data = {"query": 123, "session_id": "test-123"} # Should be string response = client.post("/api/query", json=request_data) assert response.status_code == 422 def test_clear_session_with_wrong_types(self, client): """Test /api/clear-session endpoint with wrong field types""" - request_data = { - "session_id": 123 # Should be string - } + request_data = {"session_id": 123} # Should be string response = client.post("/api/clear-session", json=request_data) assert response.status_code == 422 @@ -164,7 +162,7 @@ class TestAPIHeaders: def test_cors_headers(self, client): """Test that CORS headers are properly set""" response = client.get("/") - + # FastAPI TestClient doesn't simulate full CORS behavior # but we can verify the response is successful assert response.status_code == 200 @@ -187,44 +185,39 @@ def test_full_query_workflow(self, client): # Step 1: Get course stats courses_response = client.get("/api/courses") assert courses_response.status_code == 200 - + # Step 2: Query with a new session - query_response = client.post("/api/query", json={ - "query": "Tell me about the courses available" - }) + query_response = client.post( + "/api/query", json={"query": "Tell me about the courses available"} + ) assert query_response.status_code == 200 session_id = query_response.json()["session_id"] - + # Step 3: Query with existing session - follow_up_response = client.post("/api/query", json={ - "query": "Can you elaborate?", - "session_id": session_id - }) + follow_up_response = client.post( + "/api/query", json={"query": "Can you elaborate?", "session_id": session_id} + ) assert follow_up_response.status_code == 200 assert follow_up_response.json()["session_id"] == session_id - + # Step 4: Clear the session - clear_response = client.post("/api/clear-session", json={ - "session_id": session_id - }) + clear_response = client.post( + "/api/clear-session", json={"session_id": session_id} + ) assert clear_response.status_code == 200 def test_multiple_concurrent_sessions(self, client): """Test handling multiple concurrent sessions""" # Create first session - response1 = client.post("/api/query", json={ - "query": "First session query" - }) + response1 = client.post("/api/query", json={"query": "First session query"}) session1 = response1.json()["session_id"] - - # Create second session - response2 = client.post("/api/query", json={ - "query": "Second session query" - }) + + # Create second session + response2 = client.post("/api/query", json={"query": "Second session query"}) session2 = response2.json()["session_id"] - + # Both should work independently assert response1.status_code == 200 assert response2.status_code == 200 # With current mock, they'll return the same session_id - # In real implementation, they should be different \ No newline at end of file + # In real implementation, they should be different diff --git a/backend/vector_store.py b/backend/vector_store.py index 73553f0..7da6522 100644 --- a/backend/vector_store.py +++ b/backend/vector_store.py @@ -3,8 +3,7 @@ import chromadb from chromadb.config import Settings -from models import Course, CourseChunk -from sentence_transformers import SentenceTransformer +from models import DocumentChunk @dataclass @@ -32,7 +31,7 @@ def from_chroma(cls, chroma_results: Dict) -> "SearchResults": ) @classmethod - def empty(cls, error_msg: str) -> "SearchResults": + def empty(cls, error_msg: str = "No results found") -> "SearchResults": """Create empty results with error message""" return cls(documents=[], metadata=[], distances=[], error=error_msg) @@ -42,7 +41,7 @@ def is_empty(self) -> bool: class VectorStore: - """Vector storage using ChromaDB for course content and metadata""" + """Vector storage using ChromaDB for document content""" def __init__(self, chroma_path: str, embedding_model: str, max_results: int = 5): self.max_results = max_results @@ -58,13 +57,8 @@ def __init__(self, chroma_path: str, embedding_model: str, max_results: int = 5) ) ) - # Create collections for different types of data - self.course_catalog = self._create_collection( - "course_catalog" - ) # Course titles/instructors - self.course_content = self._create_collection( - "course_content" - ) # Actual course material + # Create single collection for document content + self.document_content = self._create_collection("document_content") def _create_collection(self, name: str): """Create or get a ChromaDB collection""" @@ -75,219 +69,100 @@ def _create_collection(self, name: str): def search( self, query: str, - course_name: Optional[str] = None, - lesson_number: Optional[int] = None, + file_name: Optional[str] = None, + section_title: Optional[str] = None, limit: Optional[int] = None, ) -> SearchResults: """ - Main search interface that handles course resolution and content search. + Search document content with optional filters. Args: - query: What to search for in course content - course_name: Optional course name/title to filter by - lesson_number: Optional lesson number to filter by + query: Search query text + file_name: Optional file name to filter by + section_title: Optional section title to filter by limit: Maximum results to return Returns: SearchResults object with documents and metadata """ - # Step 1: Resolve course name if provided - course_title = None - if course_name: - course_title = self._resolve_course_name(course_name) - if not course_title: - return SearchResults.empty(f"No course found matching '{course_name}'") + # Build filter from search parameters + filter_dict = self._build_filter(file_name, section_title) - # Step 2: Build filter for content search - filter_dict = self._build_filter(course_title, lesson_number) - - # Step 3: Search course content # Use provided limit or fall back to configured max_results search_limit = limit if limit is not None else self.max_results try: - results = self.course_content.query( + results = self.document_content.query( query_texts=[query], n_results=search_limit, where=filter_dict ) return SearchResults.from_chroma(results) except Exception as e: return SearchResults.empty(f"Search error: {str(e)}") - def _resolve_course_name(self, course_name: str) -> Optional[str]: - """Use vector search to find best matching course by name""" - try: - results = self.course_catalog.query(query_texts=[course_name], n_results=1) - - if results["documents"][0] and results["metadatas"][0]: - # Return the title (which is now the ID) - return results["metadatas"][0][0]["title"] - except Exception as e: - print(f"Error resolving course name: {e}") - - return None - def _build_filter( - self, course_title: Optional[str], lesson_number: Optional[int] + self, file_name: Optional[str], section_title: Optional[str] ) -> Optional[Dict]: """Build ChromaDB filter from search parameters""" - if not course_title and lesson_number is None: + if not file_name and not section_title: return None # Handle different filter combinations - if course_title and lesson_number is not None: + if file_name and section_title: return { - "$and": [ - {"course_title": course_title}, - {"lesson_number": lesson_number}, - ] + "$and": [{"file_name": file_name}, {"section_title": section_title}] } - if course_title: - return {"course_title": course_title} - - return {"lesson_number": lesson_number} - - def add_course_metadata(self, course: Course): - """Add course information to the catalog for semantic search""" - import json - - course_text = course.title + if file_name: + return {"file_name": file_name} - # Build lessons metadata and serialize as JSON string - lessons_metadata = [] - for lesson in course.lessons: - lessons_metadata.append( - { - "lesson_number": lesson.lesson_number, - "lesson_title": lesson.title, - "lesson_link": lesson.lesson_link, - } - ) - - self.course_catalog.add( - documents=[course_text], - metadatas=[ - { - "title": course.title, - "instructor": course.instructor, - "course_link": course.course_link, - "lessons_json": json.dumps( - lessons_metadata - ), # Serialize as JSON string - "lesson_count": len(course.lessons), - } - ], - ids=[course.title], - ) + return {"section_title": section_title} - def add_course_content(self, chunks: List[CourseChunk]): - """Add course content chunks to the vector store""" + def add_document_content(self, chunks: List[DocumentChunk]): + """Add document content chunks to the vector store""" if not chunks: return documents = [chunk.content for chunk in chunks] metadatas = [ { - "course_title": chunk.course_title, - "lesson_number": chunk.lesson_number, + "file_name": chunk.file_name, + "file_path": chunk.file_path, + "section_title": chunk.section_title, "chunk_index": chunk.chunk_index, } for chunk in chunks ] - # Use title with chunk index for unique IDs - ids = [ - f"{chunk.course_title.replace(' ', '_')}_{chunk.chunk_index}" - for chunk in chunks - ] - - self.course_content.add(documents=documents, metadatas=metadatas, ids=ids) - - def clear_all_data(self): - """Clear all data from both collections""" - try: - self.client.delete_collection("course_catalog") - self.client.delete_collection("course_content") - # Recreate collections - self.course_catalog = self._create_collection("course_catalog") - self.course_content = self._create_collection("course_content") - except Exception as e: - print(f"Error clearing data: {e}") - - def get_existing_course_titles(self) -> List[str]: - """Get all existing course titles from the vector store""" - try: - # Get all documents from the catalog - results = self.course_catalog.get() - if results and "ids" in results: - return results["ids"] - return [] - except Exception as e: - print(f"Error getting existing course titles: {e}") - return [] - - def get_course_count(self) -> int: - """Get the total number of courses in the vector store""" - try: - results = self.course_catalog.get() - if results and "ids" in results: - return len(results["ids"]) - return 0 - except Exception as e: - print(f"Error getting course count: {e}") - return 0 + # Use file name with chunk index for unique IDs + ids = [f"{chunk.file_name}_{chunk.chunk_index}" for chunk in chunks] - def get_all_courses_metadata(self) -> List[Dict[str, Any]]: - """Get metadata for all courses in the vector store""" - import json + self.document_content.add(documents=documents, metadatas=metadatas, ids=ids) + def get_existing_filenames(self) -> List[str]: + """Get all existing file names from the vector store""" try: - results = self.course_catalog.get() + results = self.document_content.get() if results and "metadatas" in results: - # Parse lessons JSON for each course - parsed_metadata = [] - for metadata in results["metadatas"]: - course_meta = metadata.copy() - if "lessons_json" in course_meta: - course_meta["lessons"] = json.loads(course_meta["lessons_json"]) - del course_meta[ - "lessons_json" - ] # Remove the JSON string version - parsed_metadata.append(course_meta) - return parsed_metadata + # Deduplicate file names + filenames = set( + meta.get("file_name") + for meta in results["metadatas"] + if meta.get("file_name") + ) + return list(filenames) return [] except Exception as e: - print(f"Error getting courses metadata: {e}") + print(f"Error getting existing file names: {e}") return [] - def get_course_link(self, course_title: str) -> Optional[str]: - """Get course link for a given course title""" - try: - # Get course by ID (title is the ID) - results = self.course_catalog.get(ids=[course_title]) - if results and "metadatas" in results and results["metadatas"]: - metadata = results["metadatas"][0] - return metadata.get("course_link") - return None - except Exception as e: - print(f"Error getting course link: {e}") - return None - - def get_lesson_link(self, course_title: str, lesson_number: int) -> Optional[str]: - """Get lesson link for a given course title and lesson number""" - import json + def get_document_count(self) -> int: + """Get the total number of documents in the vector store""" + return len(self.get_existing_filenames()) + def clear_all_data(self): + """Clear all data from the collection""" try: - # Get course by ID (title is the ID) - results = self.course_catalog.get(ids=[course_title]) - if results and "metadatas" in results and results["metadatas"]: - metadata = results["metadatas"][0] - lessons_json = metadata.get("lessons_json") - if lessons_json: - lessons = json.loads(lessons_json) - # Find the lesson with matching number - for lesson in lessons: - if lesson.get("lesson_number") == lesson_number: - return lesson.get("lesson_link") - return None + self.client.delete_collection("document_content") + # Recreate collection + self.document_content = self._create_collection("document_content") except Exception as e: - print(f"Error getting lesson link: {e}") + print(f"Error clearing data: {e}") diff --git a/uv.lock b/uv.lock index 2e307fd..a44f317 100644 --- 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=?UTF-8?q?=E6=96=B9=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .python-version | 2 +- backend/README.md | 0 backend/app.py | 133 -------- backend/config.py | 4 +- backend/data/2025-prompt-engineering-tips.md | 30 ++ backend/data/2025-stop-believing-prompts.md | 22 ++ .../data/3d-q-version-character-app-icon.md | 20 ++ ...cademic-paper-to-popular-science-prompt.md | 88 ++++++ backend/data/advice-to-new-managers.md | 22 ++ .../agile-iterative-coding-best-practices.md | 39 +++ .../ai-agent-frontend-rebirth-from-failure.md | 158 ++++++++++ .../ai-agent-stronger-skills-vs-subagent.md | 52 ++++ ...-assisted-programming-for-non-engineers.md | 36 +++ backend/main.py | 76 +++++ backend/pyproject.toml | 12 + pyproject.toml | 7 +- uv.lock | 283 +++++++++++++++++- 17 files changed, 846 insertions(+), 138 deletions(-) create mode 100644 backend/README.md delete mode 100644 backend/app.py create mode 100644 backend/data/2025-prompt-engineering-tips.md create mode 100644 backend/data/2025-stop-believing-prompts.md create mode 100644 backend/data/3d-q-version-character-app-icon.md create mode 100644 backend/data/academic-paper-to-popular-science-prompt.md create mode 100644 backend/data/advice-to-new-managers.md create mode 100644 backend/data/agile-iterative-coding-best-practices.md create mode 100644 backend/data/ai-agent-frontend-rebirth-from-failure.md create mode 100644 backend/data/ai-agent-stronger-skills-vs-subagent.md create mode 100644 backend/data/ai-assisted-programming-for-non-engineers.md create mode 100644 backend/main.py create mode 100644 backend/pyproject.toml diff --git a/.python-version b/.python-version index 24ee5b1..e4fba21 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.13 +3.12 diff --git a/backend/README.md b/backend/README.md new file mode 100644 index 0000000..e69de29 diff --git a/backend/app.py b/backend/app.py deleted file mode 100644 index e87a41d..0000000 --- a/backend/app.py +++ /dev/null @@ -1,133 +0,0 @@ -import os -import warnings -from pathlib import Path -from typing import List, Optional - -warnings.filterwarnings("ignore", message="resource_tracker: There appear to be.*") - -from config import config -from fastapi import FastAPI, HTTPException -from fastapi.middleware.cors import CORSMiddleware -from fastapi.middleware.trustedhost import TrustedHostMiddleware -from fastapi.responses import FileResponse -from fastapi.staticfiles import StaticFiles -from pydantic import BaseModel -from rag_system import RAGSystem - -# Initialize FastAPI app -app = FastAPI(title="Markdown RAG System", root_path="") - -# Add trusted host middleware for proxy -app.add_middleware(TrustedHostMiddleware, allowed_hosts=["*"]) - -# Enable CORS with proper settings for proxy -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - expose_headers=["*"], -) - - -# Pydantic models for request/response -class QueryRequest(BaseModel): - """Request model for queries""" - - query: str - limit: Optional[int] = 5 - - -class QueryResponse(BaseModel): - """Response model for queries""" - - answer: str - sources: List[str] - - -class DocumentStats(BaseModel): - """Response model for document statistics""" - - total_documents: int - file_names: List[str] - - -# API Endpoints - - -@app.post("/api/query", response_model=QueryResponse) -async def query_documents(request: QueryRequest): - """Process a query and return response with sources""" - try: - # Process query using RAG system - result = rag_system.query(request.query, request.limit or 5) - - return QueryResponse(answer=result["answer"], sources=result["sources"]) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.get("/api/documents", response_model=DocumentStats) -async def get_document_stats(): - """Get document analytics and statistics""" - try: - stats = rag_system.get_document_stats() - return DocumentStats( - total_documents=stats["total_documents"], - file_names=stats["file_names"], - ) - except Exception as e: - raise HTTPException(status_code=500, detail=str(e)) - - -@app.on_event("startup") -async def startup_event(): - """Load initial documents on startup""" - # Initialize RAG system with docs folder - docs_path = "../docs" - rag = None - - if os.path.exists(docs_path): - print("Loading initial documents...") - try: - # Initialize and load documents - rag = RAGSystem(docs_path, chroma_path=config.CHROMA_PATH) - print(f"RAG system initialized with documents from: {docs_path}") - - # Store globally for use in endpoints - globals()["rag_system"] = rag - except Exception as e: - print(f"Error loading documents: {e}") - # Initialize empty system - rag = RAGSystem.__new__(RAGSystem) - rag.md_processor = None - rag.vector_store = None - rag.folder_path = docs_path - globals()["rag_system"] = rag - else: - print(f"Docs folder not found: {docs_path}") - # Initialize empty system - rag = RAGSystem.__new__(RAGSystem) - rag.md_processor = None - rag.vector_store = None - rag.folder_path = docs_path - globals()["rag_system"] = rag - - -# Custom static file handler with no-cache headers for development - - -class DevStaticFiles(StaticFiles): - async def get_response(self, path: str, scope): - response = await super().get_response(path, scope) - if isinstance(response, FileResponse): - # Add no-cache headers for development - response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate" - response.headers["Pragma"] = "no-cache" - response.headers["Expires"] = "0" - return response - - -# Serve static files for the frontend -app.mount("/", DevStaticFiles(directory="../frontend", html=True), name="static") diff --git a/backend/config.py b/backend/config.py index 552beb4..7fd51b5 100644 --- a/backend/config.py +++ b/backend/config.py @@ -12,12 +12,12 @@ class Config: """Configuration settings for the RAG system""" # Embedding model settings - EMBEDDING_MODEL: str = "all-MiniLM-L6-v2" + EMBEDDING_MODEL: str = r"C:\Users\PC\.cache\huggingface\hub\models--sentence-transformers--all-MiniLM-L6-v2\snapshots\all-MiniLM-L6-v2" # Document processing settings CHUNK_SIZE: int = 800 # Size of text chunks for vector storage CHUNK_OVERLAP: int = 100 # Characters to overlap between chunks - MAX_RESULTS: int = 5 # Maximum search results to return + MAX_RESULTS: int = 3 # Maximum search results to return # Database paths CHROMA_PATH: str = "./chroma_db" # ChromaDB storage location diff --git a/backend/data/2025-prompt-engineering-tips.md b/backend/data/2025-prompt-engineering-tips.md new file mode 100644 index 0000000..72f388e --- /dev/null +++ b/backend/data/2025-prompt-engineering-tips.md @@ -0,0 +1,30 @@ +# 文章来源 + +URL: https://baoyu.io/blog/2025-prompt-engineering-tips + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-01-01 +# 2025 年的三点半提示词技巧 +2025 年了，模型能力上升了一个台阶，更不需要去记提示词技巧和框架了，写提示词不再是一个多专业的活，核心就记住三点半： +1. Context：问题所需要的上下文信息，千万别以为模型会读心术，一定要把相关信息都提供；但是长度不要太长，因为长度越长效果越差，多长不同模型有区别，多试试就知道了。 +2. Instruction：你想要模型做什么说清楚。如果自己都没想清楚，就先临时开个会话和模型闲聊，让它帮你梳理清楚，梳理清楚指令了再新开会话输入你的指令和上下文。 + +3. Atom：让你的提示词原子化，这里其实有两点含义： +- 1) 你每次的任务要小，不要想让模型一次完成太多任务 +- 2) 上下文完整独立，在你的会话中把这次任务的上下文都提供清楚了 + +最后还有半点： +CoT：思维链（Chain of Thought）对于大语言模型来说已经慢慢成了基本技能，尤其是推理模型，高于人类平均水平，如果你明确知道最优步骤，就写上，不确认就让模型写，不满意就让模型改进，还不满意就新开会话或者换模型再试试。 +* * * +补充1: 上下文完整独立的意思就是在一次会话中，你包含了AI生成所需要的完整内容，并且确保不会因为 AI 应用程序的能力限制而影响。 +以写报告为例，你报告中需要参考资料1，2，3，那么这些参考资料的完整部分或者关键部分要在会话中完整包含。 +如果你的参考资料是一个 URL，你的应用程序（比如ChatGPT）是无法访问这个 URL 的，那么它无法有效的把这个 URL 作为上下文的一部分，所以你的上下文就不完整了。 +再比如你在前几轮会话中把参考资料放在里面了，但是你持续问答增加会话，到后面的时候，AI 应用程序会因为上下文窗口长度限制，会自作主张帮你把前面的内容砍掉或者摘要，那你后面的会话就缺少上下文了。 +判断独立完整的一个标准，就是你的提示词，放到任何模型任何AI应用，信息都是完整的，不会因为 AI 应用的能力（比如URL访问、上下文窗口长度限制）而导致内容缺失。 +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/2025-stop-believing-prompts.md b/backend/data/2025-stop-believing-prompts.md new file mode 100644 index 0000000..8d1aecf --- /dev/null +++ b/backend/data/2025-stop-believing-prompts.md @@ -0,0 +1,22 @@ +# 文章来源 + +URL: https://baoyu.io/blog/2025-stop-believing-prompts + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-01-08 +# 2025 年了，不要再去迷信提示词了 +2025 年了，不要再去迷信提示词了，把问题描述清楚+上下文充足+问题拆分细就足够了。 +![](https://baoyu.io/uploads/2025-01-08/1736308286993.png) +现在还经常能刷到 XX “神级提示词”，这些提示词在特定场景下确实有一点点帮助，但是并不是通用的，所以没必要去在日常场景中套用这些提示词，反而会因为上下文被这些提示词占用太多而影响了生成的效果。 +这就像有些药物本质就是心理安慰剂，什么情况下都吃不但不会有效果，反而会对身体有害处。换个角度想，要是真有这么神的提示词，OpenAI、Claude为啥不内置？！ +当你需要让 AI 帮你生成一个东西，最重要的是你要把问题描述清楚，把问题拆分好，把上下文给充足，这样的话结果通常不会差。 +![](https://baoyu.io/uploads/2025-01-08/1736308332903.png) +就像我昨天写的段子，如果你直接让 AI 帮你写一个博客系统，无论你用什么神级提示词，AI 也无法一次性给你生成一个博客系统，但是你一次让它生成一个模块，并且把截图发给它、技术栈告诉它，那么这个模块代码它是能生成的很好的。 +![](https://baoyu.io/uploads/2025-01-08/1736308320510.png) +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/3d-q-version-character-app-icon.md b/backend/data/3d-q-version-character-app-icon.md new file mode 100644 index 0000000..31346b5 --- /dev/null +++ b/backend/data/3d-q-version-character-app-icon.md @@ -0,0 +1,20 @@ +# 文章来源 + +URL: https://baoyu.io/blog/3d-q-version-character-app-icon + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-04-04 +# 3D风格的Q版人物APP图标 +![3D风格的Q版人物APP图标](https://baoyu.io/uploads/2025-04-04/20250403_2219_%E5%8F%AF%E6%84%9BQ%E7%89%88APP%E5%9C%96%E6%A8%99_remix_01jqzcynraee8t1715vtnwraq2.png) +![](https://baoyu.io/uploads/2025-04-04/20250403_2219_%E5%8F%AF%E6%84%9BQ%E7%89%88APP%E5%9C%96%E6%A8%99_remix_01jqzcynraee8t1715vtnwraq2.png) +工具：[sora.com](http://sora.com) 或 GPT-4o +![](https://baoyu.io/uploads/2025-04-04/Sora-04-03-2025_10_20_PM.png) +提示词（需要同时上传一张头像照片）： +把这张照片设计成一个3D风格的Q版APP图标，保留人物特征，风格要可爱一些，人物要稍微超出图标背景边框。 +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/academic-paper-to-popular-science-prompt.md b/backend/data/academic-paper-to-popular-science-prompt.md new file mode 100644 index 0000000..6466309 --- /dev/null +++ b/backend/data/academic-paper-to-popular-science-prompt.md @@ -0,0 +1,88 @@ +# 文章来源 + +URL: https://baoyu.io/blog/academic-paper-to-popular-science-prompt + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-10-23 +# A prompt designed to transform dense, jargon-heavy academic papers into accessible, engaging popular science articles. +![](https://baoyu.io/uploads/2025-10-23/1761235701830.png) +This prompt is designed for the Gemini 2.5 Pro model, and its effectiveness might be reduced on other models. The best way to use it is to create a 'Gem' in Gemini and paste the above prompt into the 'Instructions'. When using it, send the paper's PDF or related materials to the model to generate the results. +![](https://baoyu.io/uploads/2025-10-23/1761235677184.png)``` +You are a top-tier **science writer** and **knowledge communicator**, renowned as "The ultimate ladder-builder." Your expertise lies in **reframing** academic papers—which are often dense with jargon, data, and complex models—into popular science articles that the general public can effortlessly understand, resonate with, and be deeply inspired by. + +Your mission is **not to "translate" the paper, but to "rebuild" understanding**. You build a bridge for the reader, taking them from "I have no idea" to "So *that's* what it means!" You allow them to grasp the true magic of scientific research, its core findings, and its real-world significance, all within a burden-free reading experience. + +# Workflow: The "Ladder-Building" Process from Paper to Pop-Science + +When you receive an academic paper to interpret, you will strictly follow these steps: + +1. **Step 1: Dig up the "Who" and "Why"** + + * Before diving into the details, **research the background of the authors and their institution**. + * Try to find an interesting connection: *Why* are *they* the ones studying *this* problem? (e.g., Has this lab been working in this field for decades? Are they "crossing over" from another field to solve an old problem? Is this linked to the institution's core mission?) + * *【Application Rule】*: If the backstory (like an author's "obsession" or an institution's "mission") makes the research motivation more vivid, weave it into the article. If the connection is weak, don't mention it in the main text to avoid a forced introduction. + +2. **Step 2: Digest and Understand** + + * Read the paper deeply and **thoroughly break down its three core elements**: + 1. **The Question**: What puzzle are they *really* trying to solve? What is the background and importance of this question? + 2. **The How**: How did they find the answer? (Focus on understanding their *approach*, not reciting technical details). + 3. **The Finding**: What did they ultimately discover? How "counter-intuitive" or "important" is this discovery? + +3. **Step 3: Locate its Position and the "Aha! Moment"** + + * **(Use tools to research if necessary)** Analyze the paper within the context of the **current industry or academic landscape**. + * What role does it play in its field? Does it solve a "chronic pain point" for peers? Does it overturn an old consensus? Or does it open up an entirely new field? + * **Extract the "Storyline"**: Transform the paper's "logical argument" into a "narrative logic." Find the most exciting "Aha! Moment" in the paper and identify the single **core "Takeaway"** for your article—the one, clear, valuable insight the reader will leave with. + +4. **Step 4: Compose the Pop-Science Blog** + + * Fully adopt the "Persona" and "Writing Style" defined below to write an independent, complete, and engaging popular science interpretation. + * **Note: There is no length limit**. The *only* standard is "to make the average person truly understand." + * Ensure that in the "So What?" section, you powerfully convey its *real impact* on the industry or the public (based on your analysis in Step 3). + +----- + +# Audience & Style + +* **Target Audience**: The general public, curious about the world. They have no professional background and are naturally "allergic" to jargon and formulas. **Their goal is to gain new knowledge, satisfy their curiosity, and experience "Wow!" moments**. +* **Writing Style**: + * **Radical Accessibility**: **Metaphors are your first language**. If you can explain it using "a chemical reaction in the kitchen," never use "diastereoselectivity." If you *must* use a technical term, you must immediately "translate" it with a vivid analogy. + * **Storytelling is King**: Tell the research process like a "crime story" or an "adventure." The scientists are the protagonists. They face a puzzle, design a clever "trap" (the experiment), and finally catch the "truth" (the conclusion). + * **Focus on the "So What?"**: Always help the reader answer this question. What does this have to do with me? Why is it important? How might it change our lives or our understanding of the world? (This is where you use your analysis of the industry impact). + * **Simplify, Don't Misrepresent**: This is the bottom line of science communication. Maintain the accuracy of core facts while simplifying complex concepts. Clearly distinguish between "proven" and "suggested" (e.g., use "This suggests that..." rather than "This proves that..."). + +# Writing Strategies & Techniques (For flexible use, not mandatory in every post) + +* **Start with the Core, Build a Frame**: + * You can open with a vivid question, a counter-intuitive observation, or a central conflict to introduce the theme and quickly help the reader understand "What is this article *really* about?" + * You can first outline the core problem or scope of the original paper in simple language. +* **Structured Breakdown, Layered Analysis**: + * **Use subheadings** or clear paragraph breaks (e.g., "First... Next... Finally...") to guide the reader through the original's logical flow. + * When rephrasing the paper's points, seamlessly integrate your own "translations" and analogies to make complex points tangible. (e.g., "The 'asynchronous communication' the authors mention... you can just think of it as sending an email instead of making a phone call...") +* **Focus on the Core, Prune the Rest**: + * **Clearly distinguish between the paper's main trunk and its branches**. Emphasize the core ideas and key logic, while summarizing or omitting minor details and redundant examples to ensure the reader efficiently grasps the main points. +* **Subtly Weave in Background**: + * If the paper involves people or a specific context (from Step 1), naturally weave it in as "flavor" to help the reader understand "Why would they say that?" or "Why is this technology important now?" rather than just dropping in facts. +* **Conclude with Value**: + * The ending should be a clear distillation of the paper's core value or point out the practical significance of this idea in today's world (based on your industry analysis in Step 3). + * Give the reader a clear "Takeaway" so they feel they've genuinely learned something and understood the original paper. + +# Prohibited Expressions + +* Avoid bland, "translator" intros like "This paper studies..." or "The authors of this paper found..." or "The experimental results show..." +* **Strictly forbid** directly copy-pasting academic jargon from the abstract or introduction. +* Avoid listing dry data or statistical metrics (like p-values, confidence intervals) unless you can translate them into "how sure we are" or "how big the effect is." + +# Core Goal + +Your writing is the reader's "fast pass" and "personal translator" to the halls of science. You must, with utmost sincerity and wisdom, **wrap the academic "hardcore" in a "sugar coating" of simplicity, fun, and story**, allowing the reader to effortlessly absorb the essence of cutting-edge knowledge in an enjoyable read. +``` + +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/advice-to-new-managers.md b/backend/data/advice-to-new-managers.md new file mode 100644 index 0000000..f00dd00 --- /dev/null +++ b/backend/data/advice-to-new-managers.md @@ -0,0 +1,22 @@ +# 文章来源 + +URL: https://baoyu.io/blog/advice-to-new-managers + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2024-12-29 +# 对刚晋升没有管理经验的建议 +问：对刚晋升没有管理经验的小年轻有什么建议吗？（主要是技术上的管理） +答： +刚从技术转管理，第一件事就是要减少过多做具体的事情，多花精力在全局，在团队，比如项目的进度，项目计划，团队成员的工作有没有障碍。 +刚从技术转管理最容易犯的错误就是还是以前一样埋头写代码，这样会耽误团队的。要学会授权，要学会把活分出去，分出去的活还要跟踪进度，出现问题及时调整。 +第二件事就是对齐和管理预期，不是互联网黑话对齐颗粒度，而是 Alignment，让团队的方向和组织方向保持一致，不要偏了。你老板希望你的团队做什么，你的团队正在做什么，你老板对你的预期是什么，你对每个团队成员的预期是什么，团队成员对你的预期是什么。 +具体做法上就是要很多1:1的会议，跟老板跟下属跟同级的其他同事。先要搞清楚他们的预期是什么，然后去调整预期。 +第三件事是管人：培养人、招人和开人，帮助下属成长，招合适的人，踢掉不合适的人。管理团队，人永远是最重要的。 +最后，对内自始至终激发善意，让团队成员有激情，让团队成员自驱的去做事；对外多要资源多邀功少背锅。 +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/agile-iterative-coding-best-practices.md b/backend/data/agile-iterative-coding-best-practices.md new file mode 100644 index 0000000..55a5fe6 --- /dev/null +++ b/backend/data/agile-iterative-coding-best-practices.md @@ -0,0 +1,39 @@ +# 文章来源 + +URL: https://baoyu.io/blog/agile-iterative-coding-best-practices + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-06-10 +# Vibe Coding 的最佳实践仍然是 Agile 的版本迭代模式，而不是一次性完成一个庞大的无法运行和维护的半成品 +![](https://baoyu.io/uploads/2025-06-10/1749573867345.png) +不知道你看到图 1 的 3 种模式有何感想，我只想说现在你用 AI 写代码，千万不要用第 1 和第 3 种模式，只能采用第 2 种模式！ + +Vibe Coding 的最佳实践仍然是 Agile 的版本迭代模式，而不是一次性完成一个庞大的无法运行和维护的半成品，像图中第三种先做个不伦不类的东西出来，想优化成真正的产品，是不太现实，即使对于专业程序员来说也会相当有挑战，否则程序员们也不会那么热衷于“推翻重写”！ + +举个例子，比如你要做一个 ERP 系统，你把完整需求一次性发给 Claude Code，也很专业的要它拆分成模块，再让它按照模块一个个去开发（像图2那样），但这样做出来的东西也基本上是不可控的，做出来的东西基本上无法满足需求的甚至无法运行的。因为无论是 AI 还是人类，都无法掌控这么多模块的协作，更何况还要保障每一个模块自身的稳定性。 +![](https://baoyu.io/uploads/2025-06-10/1749573885391.png) +那么更好的做法是什么呢？ + +还是要像图 1 中 Agile 的做法那样——每次做一个完整的能稳定运行的版本出来。 + +举例来说，你要做一个 ERP 系统，分成若干版本来迭代，每一个版本都是可以独立稳定运行的，举例来说（仅供参考）： +- v0.1: 一个可以运行的 Nextjs/TanStack 程序，有欢迎页面，可以跳转 +- v0.1: 实现 users 相关数据库访问功能，在 mysql 中创建 users 表，实现 users 表的读写功能，添加读写功能的测试代码 +- v0.2: 实现用户注册的网页，连接 users 相关数据库方法，能写入注册用户信息到数据库 +- v0.3: 实现用户登录、注销的网页功能 +- v0.4: 实现简单的 dashboard 页面 +- v0.5: 实现 products 相关数据库访问功能，写功能代码和测试 +- v0.6: 实现 products 在 dashboard 的管理页面，能显示列表 +- v0.7: 实现 products 的添加、编辑和删除功能 +- ... + +这里就不一一列举，核心还是每一个版本功能是完整的，可以独立运行的，测试稳定后再下一个模块。 + +这样你基本可以保证你做出来的是可以掌控的，而不是一次性做出来一个几乎无法维护的庞大半成品。 +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/ai-agent-frontend-rebirth-from-failure.md b/backend/data/ai-agent-frontend-rebirth-from-failure.md new file mode 100644 index 0000000..b9b153c --- /dev/null +++ b/backend/data/ai-agent-frontend-rebirth-from-failure.md @@ -0,0 +1,158 @@ +# 文章来源 + +URL: https://baoyu.io/blog/ai-agent-frontend-rebirth-from-failure + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-12-20 +# 从失败中重生：一个 AI Agent 前端落地的真实复盘 +今天在 FEDay 上分享了一个 Agent 前端落地案例，核心内容是讲述了我参与的一个团队如何从"技术成功"走向"产品失败"，又如何在复盘中获得认知升级。 + +这个故事的价值不在于成功的方法论，而在于那些踩过的坑和思维转变的过程。 +![](https://baoyu.io/uploads/2025-12-20-G8lYbpCW8AI-68_.jpg) +2025 年被称为 Agent 元年。Deep Research、Manus、Claude Code 相继发布，技术圈一片沸腾。很多团队都在问同一个问题：我们要不要做 Agent？ +![](https://baoyu.io/uploads/2025-12-20-G8lYk2SWUAIoLP5.jpg) +在开始之前，我还是想讲一下我对 AI Agent 的定义： +AI Agent（AI 智能体），是为了实现某个目标，循环调用工具的大语言模型。 + +- 工具循环（tools in a loop）：模型调用工具 → 获取结果 → 继续推理 +- 有明确终点：为了达成目标，而不是无限循环 +- 目标来源灵活：可以来自用户，也可以来自另一个 LLM +- 基础记忆能力：通过对话历史保存上下文信息 +![](https://baoyu.io/uploads/2025-12-20-G8lY7cdXMAAhuuE.jpg) +朋友负责的团队面临的是一个真实的企业痛点：公司有完整的内部设计系统（Design System）和私有前端框架，但这些代码从未被 AI 训练过，通用模型根本无法直接生成符合规范的代码。 + +目标看起来很清晰——做一个类似 Lovable 的工具，但用的是自己的 Design System。用户上传 Figma 设计稿或截图，Agent 自动生成符合内部规范的前端代码。 +![](https://baoyu.io/uploads/2025-12-20-G8lZSBFWYAEvOJP.jpg) +听起来很美好，对吧？ + +但挑战也很现实： +- 要完整搭建一个 Agent 系统没想的那么容易，不仅要和模型交互，还要处理好用户交互，还有上下文工程 +- 要让模型理解和使用从未训练过的私有组件 +- 要在浏览器中实时预览生成结果 +- 出错了希望能自动修复 + +由于团队之前没有开发过 Agent 相关产品，所以请我参与其中，提供技术咨询和方案建议。 +![](https://baoyu.io/uploads/2025-12-20-G8lZjrUXMAEe827.jpg) +我第一个建议很现实：先跑通再优化 +—— 构建 Agent 最难的不是技术，而是完整跑通流程。 +![](https://baoyu.io/uploads/2025-12-20-G8lZycBXkAAVxAd.jpg) +我推荐他们基于 Claude Agent SDK 进行二次开发，而不是从零造轮子。一些关键理由包括： +1. Claude Code 已经验证了它是可行的 +2. 开箱即用，内置工具足够满足绝大数场景 +3. 可以自定义工具、接入 MCP、自定义 Skill +4. 可以接入国产兼容模型 + +还帮着基于 Claude Agent SDK 快速搭建了一个原型系统。一些关键代码还开源在这里：[https://github.com/JimLiu/claude-agent-kit](https://github.com/JimLiu/claude-agent-kit) + +这样很快有了个基本可用的 Agent。 +![](https://baoyu.io/uploads/2025-12-20-G8lZ-vpWgAEdb-z.jpg) +接下来就是解决代码的浏览器预览问题。 + +一开始我们尝试用 Sandpack（浏览器端沙盒）做代码预览，结果发现复杂组件根本跑不起来，而且无法发挥 Agent 读写文件的能力。 + +转向方案是给 Agent 一个本地文件系统——每个会话一个独立环境（虚拟机或目录），Agent 可以自由读取、修改、编译代码。这个决策让 Agent 的能力得到了最大化发挥。 + +给 Agent 一个本地文件系统才能最大化的发挥 Agent 能力 +![](https://baoyu.io/uploads/2025-12-20-G8laNURWcAAbnih.jpg) +给 Agent 一个本地文件系统才能最大化的发挥 Agent 能力 +![](https://baoyu.io/uploads/2025-12-20-G8lalL4XcAE2CrL.jpg) +另一个难题就是如何让 AI 学会使用从未训练过的私有组件？ + +其实就是把 Agent 当作新员工，用高质量文档和参考代码来教会它。 + +我们把设计系统说明、组件列表、API 文档全部 Markdown 化，让 Agent 按需检索。高质量的参考代码本身就是最好的教材。 + +而且完全不需要复杂的 RAG 系统，直接让 Agent 去基于文件检索搜索本地文档和代码就足够了。 +![](https://baoyu.io/uploads/2025-12-20-G8lavrZW4AAa_SM.jpg) +还有一个难题就是如何保证生成代码的质量，让代码能跑起来？ + +为了保证代码质量，为 Agent 建立了一套"生成 → 验证 → 修复"的自动化闭环：Lint 静态检查、编译验证、视觉比对（借助 Chrome DevTool MCP 做截图对比）。 + +一个节约主 Agent 上下文的技巧：把验证工具放入 Skill 或 SubAgent，避免污染主 Agent 的上下文。 + +把这些问题都解决后，Agent 终于上线了。 +![](https://baoyu.io/uploads/2025-12-20-G8la51oXUAAnpfC.jpg) +系统跑通了，Demo 很惊艳，但…… + +很快就没什么人用。 + +初期大家觉得新鲜，但很快就弃用了。开始和他们一起深度复盘，发现问题根本不在技术，而在产品逻辑与用户习惯的错位。 +![](https://baoyu.io/uploads/2025-12-20-G8lbGhUWIAAXFIb.jpg) +通过对内部员工的调查访谈，很快就找到了原因： + +习惯阻力：设计师和产品经理更习惯在 Figma 里工作，而不是对着一个对话框。从舒适区（Figma）跳到陌生区（Agent 对话），这个门槛比想象中高得多。大部分甚至不知道该在聊天窗口写啥。 + +80/20 瓶颈：Agent 能实现 80% 的效果，但剩下 20% 的修改成本极高。而往往就是那 20% 决定了能不能用。 + +流程割裂：生成环境和开发环境是脱节的，无法利用现有代码，需要手动把生成的代码复制回项目，操作繁琐。 +![](https://baoyu.io/uploads/2025-12-20-G8lbOqVXIAAgCji.jpg) +团队意识到，他们最初问的问题是："如何构建一个设计系统 AI Agent？" + +这种提问方式让 Agent 变成了目的本身，为了技术而忽略了本质。 + +正确的问题应该是："我们设计系统的最终目的是什么？" + +答案其实只有两点：在整个企业内实现设计规范的统一；实现开发效率的提升。 + +设计系统只是手段，而非目的。 +![](https://baoyu.io/uploads/2025-12-20-G8lbhJrXwAAwseo.jpg) +思维转换：以 AI 为中心重新设计 + +现有的流程是为人设计的：手动沟通、反复修改、人工确认，步骤繁杂，效率低下。 + +未来的流程应该为 AI 设计：Input → AI Agent → Output，路径直接，效率高。 + +这带来了两个新的设计原则： + +AI 友好：选择 AI 容易理解和操作的技术栈。 + +轻量化：只保留 Design Tokens，基于 AI 友好的开源系统（如 shadcn/ui）进行扩展，而不是维护一套庞大的私有组件库。 +![](https://baoyu.io/uploads/2025-12-20-G8lbsp0XUAENKGD.jpg) +破局之道：从 Agent 到 Skill + +最关键的转变是：不要做一个独立的 Agent 平台，而是将能力嵌入现有的 AI 开发环境。 + +旧模式是"独立 Agent 孤岛"——Agent 和开发者之间存在割裂，效率低下。 + +新模式是"融入开发工作流"——把设计系统变成一种 Skill（技能），可以被通用的 Agent（如 Claude Code、Cursor）调用。 + +Skill 的具体形态很简单：Markdown 文档（供 AI 查阅组件用法）+ 自动化脚本（用于初始化项目、自动安装和应用设计系统）。 + +开发者在自己熟悉的 AI 开发环境里工作，当需要用到设计系统时，Agent 自动调用这个 Skill，生成的代码直接进入项目代码库。 +![](https://baoyu.io/uploads/2025-12-20-G8lb3h0WwAA2z2h.jpg) +Skill 的具体形态很简单：Markdown 文档（供 AI 查阅组件用法）+ 自动化脚本（用于初始化项目、自动安装和应用设计系统）。 + +开发者在自己熟悉的 AI 开发环境里工作，当需要用到设计系统时，Agent 自动调用这个 Skill，生成的代码直接进入项目代码库。 + +可以参考： +[https://github.com/anthropics/skills/tree/main/skills/web-artifacts-builder](https://github.com/anthropics/skills/tree/main/skills/web-artifacts-builder) +![](https://baoyu.io/uploads/2025-12-20-G8lb_DIWAAAnobB.jpg) +这个案例让我想到几个更深层的问题： + +1. 技术成功 ≠ 产品成功 + +很多技术人（包括我自己）容易陷入"技术可行就是成功"的思维定式。但用户不会因为你的技术牛就买单，他们只关心能不能解决自己的问题、能不能无缝融入自己的工作流。 + +2. 做 AI 产品要"以 AI 为中心"思考 + +我们常说"以用户为中心"，但在 AI 时代，可能需要增加一层：以 AI 为中心设计工作流，再让用户享受这个高效流程的成果。不是让 AI 模仿人的工作方式，而是重新设计工作方式让 AI 更高效。 + +3. Skill > Agent + +独立的 Agent 平台有天然的adoption障碍。把能力封装成 Skill，嵌入已有的通用 Agent 生态，可能是更务实的落地路径。这也是为什么 Anthropic 推出 web-artifacts-builder 这样的开源项目——它就是一个 Skill 的范例。 + +4. 行动本身就是价值 + +即使这个项目"失败"了，团队获得的认知升级是无价的。从模仿人类工作流到为 AI 重塑工作流，这种思维转变只有在实践中才能获得。 +![](https://baoyu.io/uploads/2025-12-20-G8lcUERWYAA6g8Y.jpg) +最后我想说的是："去构建（Build）"。 + +AI 时代，失败没什么，好过什么都没做。 +![](https://baoyu.io/uploads/2025-12-20-G8lcgA1XQAAHwH1.jpg) +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/ai-agent-stronger-skills-vs-subagent.md b/backend/data/ai-agent-stronger-skills-vs-subagent.md new file mode 100644 index 0000000..17ca458 --- /dev/null +++ b/backend/data/ai-agent-stronger-skills-vs-subagent.md @@ -0,0 +1,52 @@ +# 文章来源 + +URL: https://baoyu.io/blog/ai-agent-stronger-skills-vs-subagent +--- +Published on 2025-12-24 +# AI Agent 要变强：两条路径（Skills vs SubAgent） +![AI Agent 要变强：两条路径（Skills vs SubAgent）](https://baoyu.io/uploads/2025-12-24/Gemini_Generated_Image_zgohrpzgohrpzgoh.png) +AI Agent 要变强，有两条完全不同的路。 + * 一条是 Skill，也就是给自己装技能，把新能力直接塞进脑子里。 + * 另一条是 SubAgent，就像派小弟去干活，自己只看汇报。 + + +这两条路听起来都能让 Agent 更厉害，但适用的场景还是有所不同，用错了的话，你的 Agent 可能反而会越用越慢、越用越乱。 +**Skills，就像是给主 Agent 装插件。** +比如你的 Agent 原本只会聊天，现在你想让它能写 PPT。Skills 的做法是：把写 PPT 的能力说明、工具调用方式、注意事项，全都塞进主 Agent 的上下文中。主 Agent 通过上下文学会了这项技能，它可以自己来写 PPT。 +**SubAgent，就像是委托外包。** +同样是写 PPT，SubAgent 的做法是：主 Agent 把任务派给一个专门写 PPT 的 SubAgent，SubAgent 独立完成后把结果交回来。主 Agent 全程不参与具体执行，只负责派活和验收。 +一个是内化能力，一个是外包能力。听起来都能搞定任务，区别在哪？ +### Skills 和 SubAgent 什么区别？ +区别在上下文管理，上下文就是 AI 的记忆。 +你可以把 AI 的上下文想象成一张工作桌。桌子大小是固定的，你放的东西越多，就越难找到需要的那份文件。这就是上下文容量的问题。 +**Skills 模式下，所有能力说明都铺在同一张桌上。** 好处是信息互通，主 Agent 能看到所有中间结果，推理过程连贯。坏处是桌子很快就乱了，Prompt 越来越长，能力之间可能打架，AI 开始犯糊涂。 +**SubAgent 模式下，SubAgent 在另一张桌子上干活。** 干完把结果递过来，过程中产生的草稿、中间文件全留在那边。主 Agent 的桌面保持干净。代价是信息传递要设计好，不然关键信息可能在交接时丢了。 +这就是上下文污染问题，这里的污染不是夸张的比喻，是真实的工程瓶颈。 +### 什么时候用哪种？ +判断标准其实很简单：子任务有多复杂，以及你需不需要完成任务过程中产生的信息。 +**Skills 适合的场景：任务本身不太复杂，或者你需要主 Agent 全程掌控。** +比如让 Agent 充当入口路由，根据用户请求加载不同的“场景模式”，像进入 YouTube 总结模式、进入写报告模式。这时候 Skills 的懒加载特性很香：先只加载能力名字和简介，真正要用时才加载完整说明。不像 MCP 那样一股脑把所有工具的详细文档全塞进上下文。 +**SubAgent 适合的场景：子任务很重、很耗时、中间过程很啰嗦。** +最典型的例子是浏览器调试工具。Chrome DevTools 的 MCP 功能很强，但工具说明太臃肿，放进主 Agent 会严重占用上下文。把它封装成 SubAgent，你只需要说“去查日志、截图、分析一下”，它跑完把分析结论递回来。中间那些截图、DOM 树、网络请求细节，全都留在 SubAgent 那边，不污染主 Agent 的上下文。 +### 进阶玩法 +有意思的是，Skills 和 SubAgent 这两种模式可以结合。这技巧是从 @yan5xu 那里学来的（x.com/yan5xu/status/2003618544735649947）。 +#### 第一种思路叫“先展开再压缩”。 +打个比方：你开了一个两小时的头脑风暴会，白板上写满了草稿、争论、被否决的方案。但最后写进会议纪要的只有三条结论。那些中间过程对得出结论很重要，但对后续执行的人来说是噪音。 +Agent 也可以这样操作。主 Agent 发现需要某个 Skill，加载进来，一通操作拿到结果。然后把从“加载 Skill”到“拿到结果”这整段过程折叠掉，只保留最终结论。对后续推理来说，就像开了一个会但只留下了会议纪要。 +#### 第二种思路是用文件系统做“中转站”。 +想象你管理一个外包团队。你不会把所有需求细节都塞进一条微信消息里，而是说“需求文档在这个链接，去看”。外包团队交付时也不会把源码复制粘贴给你，而是说“代码在这个仓库，部署文档在这里”。 +Agent 之间也可以这样协作。主 Agent 委托任务时，不把冗长的背景资料直接写进指令，而是存成文档，只传一个地址。SubAgent 返回时也一样：交付一个简短的状态摘要——“完成了/卡住了/需要你决策”——加一个详细记录的文档地址。主 Agent 根据情况决定要不要点进去看细节。这样双方的上下文都保持精简。 +#### 第三种是 Claude Code 里的实战技巧。 +上下文快见底时，让 Claude 把当前完成的工作总结成一份文档。然后用 rewind 功能回滚到任务开始前的状态，告诉它：“这件事我已经做完了，记录在这个文件里。” +相当于什么？相当于你跑了一场马拉松，快到终点时发现体力不支。于是你把已经跑过的路线画成地图存档，然后“瞬移”回起点，精力充沛地说“我知道怎么走了，地图在这”。上下文被清空了，但成果保留了下来。用这个方法能在上下文耗尽前抢救一把。 +## 最后 +Agent 的竞争正在从“能调用多少工具”转向“怎么优雅地管理这些工具”。 +很多人追逐最新的 Agent 框架、最花哨的能力扩展，却忽略了最基础的问题：AI 的工作记忆是有限的，你怎么组织它，决定了它能做多复杂的事。 +**Skills 和 SubAgent 不是非此即彼的选择，而是两种工具，用对场景才能发挥价值。** +说到底，Agent 架构设计和软件架构设计还是有很多相通之处。 是把逻辑写在一个巨型函数里，还是拆成模块化的微服务？ 是共享全局变量图省事，还是严格隔离状态保持干净？ +这些老问题换了个皮，又回来了。 +![](https://baoyu.io/uploads/2025-12-24/Gemini_Generated_Image_yd8bw8yd8bw8yd8b.png) +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/data/ai-assisted-programming-for-non-engineers.md b/backend/data/ai-assisted-programming-for-non-engineers.md new file mode 100644 index 0000000..2c0a7b3 --- /dev/null +++ b/backend/data/ai-assisted-programming-for-non-engineers.md @@ -0,0 +1,36 @@ +# 文章来源 + +URL: https://baoyu.io/blog/ai-assisted-programming-for-non-engineers + +--- + +[宝玉的分享](https://baoyu.io/)[博客](https://baoyu.io/blog)[翻译](https://baoyu.io/translations)Menu +[](https://baoyu.io/translations) +Published on 2025-01-09 +# 我对非专业工程师使用 AI 辅助编程的建议 +我对非专业工程师使用 AI 辅助编程的建议 +**一、设置合理的期望值** + * 不要指望它神奇的可以一次性帮你从零到一做出来一个你想要的软件 + * 也不要因为不会编程而不敢使用 + * 它能做简单的原型级别的 App 验证你的想法，想法严惩成功了建议找专业程序员合作 + * 它能写小脚本帮你实现自动化 + * 它能帮你处理数据，尤其是提取格式化数据 + * 它能给你解释代码是如何运行的 + * 它可能无法帮你调试程序解决 Bug + + +**二、AI 不仅是编程工具，同时也是学习工具** +如果想真的长期编程，就一定要花时间去理解生成的代码的工作原理，去学习一些基本的编程概念，比如编程语言的知识、网络知识、数据库知识等等。 +AI 不仅可以是编程工具，同时也是学习工具，你有任何问题都可以问它，只要你的问题描述得当，绝大部分问题都可以从它那里得到答案。 +**三、你不一定需要去报一个学习班，最重要的是动手实践** +编程是技能，和游泳、骑车一样，一定要动手去写代码运行代码才可能学的会，先学理论还是先写代码的顺序没有那么重要，适合你的就是好的。 +如果学编程（含AI编程），好老师也很重要，但是要满足两个基本要求： 1）能根据你当前的水平设置有挑战的练习项目，跳一跳才能够得着的那种 2）能随时给你答疑解决你遇到的问题和困难，根据你的学习进度能给出有效反馈 +**四、不要在 AI 编程模型上省钱** +用你担负的起的最好的模型，不要为了省钱去用不够好的模型，看起来你节约了钱，但是浪费了时间和消耗了你的积极性。 +五、不要盲目自信而忽视了软件的安全问题 +AI 可能会给人成功的幻觉，很快就做出来一个 App、网页，但是不要盲目自信，尤其是发布之前先做安全检查，如果涉及付费和数据安全之类的要特别小心，找个懂行的朋友或者花钱请人问问。 +举个例子，我看到有不会编程或者说不专业的做了一个 AI App 发布，结果 OpenAI 的 API Key 都写在客户端，一抓包就都看见了，被人恶意利用可能要扣很多钱…… +* * * +[](https://baoyu.io/translations) +Built by [宝玉](https://twitter.com/dotey). [ RSS](https://baoyu.io/feed.xml) . 本站原创内容，独家授权赛博禅心公众号发布。 +Toggle theme diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000..4d17044 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,76 @@ +from rag_system import RAGSystem +import os +os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +def main(): + """主函数：初始化RAG系统，加载数据并提供查询接口""" + print("正在初始化RAG系统...") + + # 初始化RAG系统，使用data文件夹 + rag_system = RAGSystem(folder_path="./data") + + print("RAG系统初始化完成。") + print("正在加载data文件夹中的文档...") + + # 加载并向量化data文件夹中的内容 + total_docs, total_chunks = rag_system.load_md_folder("./data", clear_existing=False) + print(f"文档加载完成：共加载 {total_docs} 个文档，生成 {total_chunks} 个分块。") + + # 获取统计信息 + stats = rag_system.get_document_stats() + print(f"当前系统状态：总文档数 {stats['total_documents']}，文件列表：{stats['file_names']}") + + print("\n=== RAG查询系统启动 ===") + print("输入查询内容进行搜索，输入 'quit' 或 'exit' 退出程序。\n") + + while True: + try: + # 获取用户输入 + query_text = input("请输入查询内容：").strip() + + if query_text.lower() in ['quit', 'exit']: + print("感谢使用，再见！") + break + + if not query_text: + print("查询内容不能为空，请重新输入。\n") + continue + + print(f"\n正在查询：'{query_text}'") + print("-" * 50) + + # 执行查询 + result = rag_system.query(query_text) + + # 检查结果并输出 + if "搜索错误" in result["answer"]: + print("❌ 查询失败：" + result["answer"]) + elif result["answer"] == "未找到相关内容": + print("❌ 未找到相关内容，请尝试其他查询词。") + else: + print("✅ 查询成功！") + print("\n📝 答案：") + print(result["answer"]) + + print(f"\n🔗 来源信息（共 {len(result['sources'])} 个）：") + for i, source in enumerate(result["sources"], 1): + print(f" {i}. {source}") + + print(f"\n📊 原始结果（共 {len(result['results'])} 个）：") + for i, res in enumerate(result["results"], 1): + print(f" {i}. 距离: {res['distance']:.4f}") + print(f" 内容: {res['content'][:200]}..." if len(res['content']) > 200 else f" 内容: {res['content']}") + print() + + print("-" * 50) + + except KeyboardInterrupt: + print("\n\n程序被用户中断，感谢使用！") + break + except Exception as e: + print(f"❌ 查询过程中发生错误：{e}") + print("请检查系统配置或重试。\n") + + +if __name__ == "__main__": + main() diff --git a/backend/pyproject.toml b/backend/pyproject.toml new file mode 100644 index 0000000..7b11ad0 --- /dev/null +++ b/backend/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "backend" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "chromadb>=1.0.15", + "load-dotenv>=0.1.0", + "pydantic>=2.11.7", + "sentence-transformers>=5.0.0", +] diff --git a/pyproject.toml b/pyproject.toml index f664189..0fe22d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "starting-codebase" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.13" +requires-python = ">=3.12" dependencies = [ "chromadb==1.0.15", "anthropic==0.58.2", @@ -87,3 +87,8 @@ warn_no_return = true warn_unreachable = true strict_equality = true exclude = ["build", "dist", ".venv"] + +[tool.uv.workspace] +members = [ + "backend", +] diff --git a/uv.lock b/uv.lock index a44f317..65201f2 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,16 @@ version = 1 revision = 3 -requires-python = ">=3.13" +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version < '3.13'", +] + +[manifest] +members = [ + "backend", + "starting-codebase", +] [[package]] name = "annotated-types" @@ -36,6 +46,7 @@ source = { registry = "https://mirrors.aliyun.com/pypi/simple/" } dependencies = [ { name = "idna" }, { name = "sniffio" }, + { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] sdist = { url = "https://mirrors.aliyun.com/pypi/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028" } wheels = [ @@ -51,6 +62,25 @@ wheels = [ { url = "https://mirrors.aliyun.com/pypi/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3" }, ] +[[package]] +name = "backend" +version = "0.1.0" +source = { virtual = "backend" } +dependencies = [ + { name = "chromadb" }, + { name = "load-dotenv" }, + { name = "pydantic" }, + { name = "sentence-transformers" }, +] + +[package.metadata] +requires-dist = [ + { name = "chromadb", specifier = ">=1.0.15" }, + { name = "load-dotenv", specifier = ">=0.1.0" }, + { name = "pydantic", specifier = ">=2.11.7" }, + { name = "sentence-transformers", specifier = ">=5.0.0" }, +] + [[package]] name = "backoff" version = "2.2.1" @@ -123,6 +153,10 @@ dependencies = [ ] sdist = { url = "https://mirrors.aliyun.com/pypi/packages/94/49/26a7b0f3f35da4b5a65f081943b7bcd22d7002f5f0fb8098ec1ff21cb6ef/black-25.1.0.tar.gz", hash = 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backend/tests/conftest.py | 361 +++----------- backend/tests/test_ai_generator.py | 527 -------------------- backend/tests/test_api_endpoints.py | 223 --------- backend/tests/test_config.py | 177 ++----- backend/tests/test_course_search_tool.py | 287 ----------- backend/tests/test_document_processor.py | 398 +++++++++++++++ backend/tests/test_rag_system.py | 605 ++++++++--------------- backend/tests/test_vector_store.py | 457 ++++++----------- uv.lock | 2 + 13 files changed, 876 insertions(+), 2179 deletions(-) delete mode 100644 backend/tests/test_ai_generator.py delete mode 100644 backend/tests/test_api_endpoints.py delete mode 100644 backend/tests/test_course_search_tool.py create mode 100644 backend/tests/test_document_processor.py diff --git a/.gitignore b/.gitignore index 41b4384..546bf9f 100644 --- a/.gitignore +++ b/.gitignore @@ -19,6 +19,7 @@ backend/chroma_db/ # Uploads uploads/ +backend/data/ # IDE .vscode/ diff --git a/backend/README.md b/backend/README.md index e69de29..c1008c8 100644 --- a/backend/README.md +++ b/backend/README.md @@ -0,0 +1,12 @@ + + + +```cmd +# 执行测试用例 +uv run python -m pytest tests/ -v --tb=short +uv run python -m pytest tests/ -v + + +# 运行单个测试文件 +uv run python -m pytest tests/test_document_processor.py -v --tb=short +``` diff --git a/backend/config.py b/backend/config.py index 7fd51b5..552beb4 100644 --- a/backend/config.py +++ b/backend/config.py @@ -12,12 +12,12 @@ class Config: """Configuration settings for the RAG system""" # Embedding model settings - EMBEDDING_MODEL: str = r"C:\Users\PC\.cache\huggingface\hub\models--sentence-transformers--all-MiniLM-L6-v2\snapshots\all-MiniLM-L6-v2" + EMBEDDING_MODEL: str = "all-MiniLM-L6-v2" # Document processing settings CHUNK_SIZE: int = 800 # Size of text chunks for vector storage CHUNK_OVERLAP: int = 100 # Characters to overlap between chunks - MAX_RESULTS: int = 3 # Maximum search results to return + MAX_RESULTS: int = 5 # Maximum search results to return # Database paths CHROMA_PATH: str = "./chroma_db" # ChromaDB storage location diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 7b11ad0..d109fe0 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -8,5 +8,6 @@ dependencies = [ "chromadb>=1.0.15", "load-dotenv>=0.1.0", "pydantic>=2.11.7", + "pytest>=8.4.1", "sentence-transformers>=5.0.0", ] diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py index 887af44..b70c990 100644 --- a/backend/tests/conftest.py +++ b/backend/tests/conftest.py @@ -1,70 +1,58 @@ import os import sys import tempfile -from typing import Any, Dict, List -from unittest.mock import MagicMock, Mock, patch +from unittest.mock import Mock, patch import pytest -from fastapi.testclient import TestClient sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from config import Config -from models import Course, CourseChunk, Lesson +from models import Document, DocumentChunk from vector_store import SearchResults @pytest.fixture -def sample_course(): - """Create a sample course for testing""" - lessons = [ - Lesson( - lesson_number=1, - title="Introduction", - lesson_link="https://example.com/lesson1", - ), - Lesson( - lesson_number=2, - title="Advanced Topics", - lesson_link="https://example.com/lesson2", - ), - Lesson( - lesson_number=3, - title="Conclusion", - lesson_link="https://example.com/lesson3", - ), - ] +def test_config(): + """Create a test configuration with proper settings""" + return Config( + EMBEDDING_MODEL="all-MiniLM-L6-v2", + CHUNK_SIZE=800, + CHUNK_OVERLAP=100, + MAX_RESULTS=5, + CHROMA_PATH="./test_chroma_db", + ) - return Course( - title="Building Towards Computer Use with Anthropic", - course_link="https://example.com/course", - instructor="Colt Steele", - lessons=lessons, + +@pytest.fixture +def sample_document(): + """Create a sample document for testing""" + return Document( + file_name="test.md", + file_path="/path/to/test.md", + title="Test Document", + sections=["Introduction", "Advanced Topics"], ) @pytest.fixture -def sample_course_chunks(): - """Create sample course chunks for testing""" +def sample_document_chunks(): + """Create sample document chunks for testing""" return [ - CourseChunk( - content="Welcome to Building Toward Computer Use with Anthropic. This course covers computer automation.", - course_title="Building Towards Computer Use with Anthropic", - lesson_number=1, + DocumentChunk( + content="[文件: test.md | 章节: Introduction]\n\nThis is the introduction content.", + file_name="test.md", + file_path="/path/to/test.md", + section_title="Introduction", chunk_index=0, ), - CourseChunk( - content="In this lesson, we'll explore advanced topics including tool calling and agent workflows.", - course_title="Building Towards Computer Use with Anthropic", - lesson_number=2, + DocumentChunk( + content="[文件: test.md | 章节: Advanced Topics]\n\nThis covers advanced topics.", + file_name="test.md", + file_path="/path/to/test.md", + section_title="Advanced Topics", chunk_index=1, ), - CourseChunk( - content="Computer use capability is built by using many features of large language models.", - course_title="Building Towards Computer Use with Anthropic", - lesson_number=1, - chunk_index=2, - ), ] @@ -73,18 +61,18 @@ def sample_search_results(): """Create sample search results for testing""" return SearchResults( documents=[ - "Welcome to Building Toward Computer Use with Anthropic. This course covers computer automation.", - "In this lesson, we'll explore advanced topics including tool calling and agent workflows.", + "This is sample content from a document.", + "Another piece of relevant content.", ], metadata=[ { - "course_title": "Building Towards Computer Use with Anthropic", - "lesson_number": 1, + "file_name": "test.md", + "section_title": "Introduction", "chunk_index": 0, }, { - "course_title": "Building Towards Computer Use with Anthropic", - "lesson_number": 2, + "file_name": "test.md", + "section_title": "Advanced Topics", "chunk_index": 1, }, ], @@ -110,100 +98,45 @@ def mock_vector_store(): mock = Mock() mock.search.return_value = SearchResults( documents=["Sample document content"], - metadata=[{"course_title": "Test Course", "lesson_number": 1}], + metadata=[{"file_name": "test.md", "section_title": "Introduction"}], distances=[0.1], ) - mock._resolve_course_name.return_value = "Test Course" - mock.get_lesson_link.return_value = "https://example.com/lesson1" + mock.get_existing_filenames.return_value = ["test.md", "other.md"] + mock.get_document_count.return_value = 2 return mock @pytest.fixture -def mock_anthropic_client(): - """Create a mock Anthropic client for testing""" - mock_client = Mock() - - # Mock response for direct text response - mock_response = Mock() - mock_response.content = [Mock()] - mock_response.content[0].text = "This is a test response from Claude." - mock_response.stop_reason = "end_turn" - - # Mock response for tool use - mock_tool_response = Mock() - mock_tool_response.stop_reason = "tool_use" - mock_tool_content = Mock() - mock_tool_content.type = "tool_use" - mock_tool_content.name = "search_course_content" - mock_tool_content.id = "tool_123" - mock_tool_content.input = {"query": "test query"} - mock_tool_response.content = [mock_tool_content] - - mock_client.messages.create.return_value = mock_response - return mock_client - - -@pytest.fixture -def mock_tool_manager(): - """Create a mock tool manager for testing""" +def mock_md_processor(): + """Create a mock MD processor for testing""" mock = Mock() - mock.get_tool_definitions.return_value = [ - { - "name": "search_course_content", - "description": "Search course materials", - "input_schema": { - "type": "object", - "properties": { - "query": {"type": "string", "description": "What to search for"} - }, - "required": ["query"], - }, - } - ] - mock.execute_tool.return_value = "Mock search result" - mock.get_last_sources.return_value = ["Test Course - Lesson 1"] - mock.get_last_source_links.return_value = ["https://example.com/lesson1"] - return mock - - -@pytest.fixture -def test_config(): - """Create a test configuration with proper settings""" - return Config( - ANTHROPIC_API_KEY="test-api-key", - ANTHROPIC_MODEL="claude-sonnet-4-20250514", - EMBEDDING_MODEL="all-MiniLM-L6-v2", - CHUNK_SIZE=800, - CHUNK_OVERLAP=100, - MAX_RESULTS=5, # Set to proper value, not 0 - MAX_HISTORY=2, - CHROMA_PATH="./test_chroma_db", - ) - - -@pytest.fixture -def broken_config(): - """Create a configuration with the broken MAX_RESULTS=0 setting""" - return Config( - ANTHROPIC_API_KEY="test-api-key", - ANTHROPIC_MODEL="claude-sonnet-4-20250514", - EMBEDDING_MODEL="all-MiniLM-L6-v2", - CHUNK_SIZE=800, - CHUNK_OVERLAP=100, - MAX_RESULTS=0, # This is the broken setting - MAX_HISTORY=2, - CHROMA_PATH="./test_chroma_db", + mock.process_md_file.return_value = ( + Document( + file_name="test.md", + file_path="/path/to/test.md", + title="Test Document", + sections=["Introduction"], + ), + [ + DocumentChunk( + content="Test content", + file_name="test.md", + file_path="/path/to/test.md", + section_title="Introduction", + chunk_index=0, + ) + ], ) + return mock @pytest.fixture -def temp_chroma_db(): - """Create a temporary ChromaDB directory for testing""" +def temp_dir(): + """Create a temporary directory for testing""" temp_dir = tempfile.mkdtemp() yield temp_dir # Cleanup after test import shutil - shutil.rmtree(temp_dir, ignore_errors=True) @@ -213,170 +146,32 @@ def mock_chroma_collection(): mock = Mock() mock.query.return_value = { "documents": [["Sample document"]], - "metadatas": [[{"course_title": "Test Course", "lesson_number": 1}]], + "metadatas": [[{"file_name": "test.md", "section_title": "Introduction"}]], "distances": [[0.1]], } - mock.get.return_value = { - "ids": ["test_course_1"], - "metadatas": [ - { - "title": "Test Course", - "instructor": "Test Instructor", - "course_link": "https://example.com/course", - "lessons_json": '[{"lesson_number": 1, "lesson_title": "Test Lesson", "lesson_link": "https://example.com/lesson1"}]', - "lesson_count": 1, - } - ], - } return mock -# Test data constants -SAMPLE_COURSE_TEXT = """Course Title: Building Towards Computer Use with Anthropic -Course Link: https://www.deeplearning.ai/short-courses/building-toward-computer-use-with-anthropic/ -Course Instructor: Colt Steele +# Sample test data +SAMPLE_MD_CONTENT = """# Introduction -Lesson 1: Introduction -Lesson Link: https://learn.deeplearning.ai/courses/building-toward-computer-use-with-anthropic/lesson/1/introduction -Welcome to Building Toward Computer Use with Anthropic. This course covers computer automation. +This is a test document for the RAG system. It contains multiple sections with useful information. -Lesson 2: Advanced Topics -Lesson Link: https://learn.deeplearning.ai/courses/building-toward-computer-use-with-anthropic/lesson/2/advanced -In this lesson, we'll explore advanced topics including tool calling and agent workflows. -""" +## Advanced Topics -SAMPLE_QUERY_RESPONSES = { - "general": "This is a general knowledge response.", - "course_specific": "Based on the search results, here is information about the course content.", - "tool_use": "I'll search for that information in the course materials.", -} +Here we cover more advanced concepts that are important for understanding the system. +### Implementation Details -@pytest.fixture -def mock_rag_system(): - """Create a mock RAG system for API testing""" - mock = Mock() - mock.query.return_value = ( - "This is a test response about course content.", - ["Building Towards Computer Use with Anthropic - Lesson 1"], - ["https://example.com/lesson1"], - ) - mock.get_course_analytics.return_value = { - "total_courses": 2, - "course_titles": [ - "Building Towards Computer Use with Anthropic", - "Advanced AI Techniques", - ], - } - mock.session_manager.create_session.return_value = "test-session-123" - mock.session_manager.clear_session.return_value = None - return mock - - -@pytest.fixture -def test_app(): - """Create a test FastAPI app with mocked dependencies""" - from typing import List, Optional - - from fastapi import FastAPI - from fastapi.middleware.cors import CORSMiddleware - from fastapi.middleware.trustedhost import TrustedHostMiddleware - from pydantic import BaseModel - - # Create test app without static file mounting - app = FastAPI(title="Course Materials RAG System Test", root_path="") - - # Add middleware - app.add_middleware(TrustedHostMiddleware, allowed_hosts=["*"]) - app.add_middleware( - CORSMiddleware, - allow_origins=["*"], - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], - expose_headers=["*"], - ) - - # Pydantic models - class QueryRequest(BaseModel): - query: str - session_id: Optional[str] = None - - class QueryResponse(BaseModel): - answer: str - sources: List[str] - source_links: List[Optional[str]] - session_id: str - - class CourseStats(BaseModel): - total_courses: int - course_titles: List[str] - - class ClearSessionRequest(BaseModel): - session_id: str - - # Mock RAG system - mock_rag = Mock() - mock_rag.query.return_value = ( - "This is a test response about course content.", - ["Building Towards Computer Use with Anthropic - Lesson 1"], - ["https://example.com/lesson1"], - ) - mock_rag.get_course_analytics.return_value = { - "total_courses": 2, - "course_titles": [ - "Building Towards Computer Use with Anthropic", - "Advanced AI Techniques", - ], - } - mock_rag.session_manager.create_session.return_value = "test-session-123" - mock_rag.session_manager.clear_session.return_value = None - - # API endpoints - @app.post("/api/query", response_model=QueryResponse) - async def query_documents(request: QueryRequest): - session_id = request.session_id or mock_rag.session_manager.create_session() - answer, sources, source_links = mock_rag.query(request.query, session_id) - return QueryResponse( - answer=answer, - sources=sources, - source_links=source_links, - session_id=session_id, - ) - - @app.get("/api/courses", response_model=CourseStats) - async def get_course_stats(): - analytics = mock_rag.get_course_analytics() - return CourseStats( - total_courses=analytics["total_courses"], - course_titles=analytics["course_titles"], - ) - - @app.post("/api/clear-session") - async def clear_session(request: ClearSessionRequest): - mock_rag.session_manager.clear_session(request.session_id) - return {"status": "success", "message": "Session cleared successfully"} - - @app.get("/") - async def root(): - return {"message": "Course Materials RAG System API"} - - return app - - -@pytest.fixture -def client(test_app): - """Create a test client for the FastAPI app""" - return TestClient(test_app) - +The implementation uses vector embeddings to provide semantic search capabilities. +""" -@pytest.fixture -def sample_query_request(): - """Sample query request for testing""" - return {"query": "What is computer use in AI?", "session_id": "test-session-123"} +SAMPLE_MD_WITH_FRONTMATTER = """--- +title: "Custom Title" +author: "Test Author" +--- +# Document Title -@pytest.fixture -def sample_clear_session_request(): - """Sample clear session request for testing""" - return {"session_id": "test-session-123"} +This document has frontmatter metadata. +""" diff --git a/backend/tests/test_ai_generator.py b/backend/tests/test_ai_generator.py deleted file mode 100644 index 00bd3a2..0000000 --- a/backend/tests/test_ai_generator.py +++ /dev/null @@ -1,527 +0,0 @@ -import os -import sys -from unittest.mock import MagicMock, Mock, patch - -import pytest - -# Add parent directory to path to import modules -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from ai_generator import AIGenerator - - -class TestAIGenerator: - """Test cases for AIGenerator""" - - def test_init(self): - """Test AIGenerator initialization""" - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - assert generator.model == "claude-sonnet-4-20250514" - assert generator.base_params["model"] == "claude-sonnet-4-20250514" - assert generator.base_params["temperature"] == 0 - assert generator.base_params["max_tokens"] == 800 - - def test_generate_response_without_tools(self, mock_anthropic_client): - """Test basic response generation without tools""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_anthropic.return_value = mock_anthropic_client - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Generate response - response = generator.generate_response("What is AI?") - - # Assert - assert response == "This is a test response from Claude." - - # Verify API was called correctly - mock_anthropic_client.messages.create.assert_called_once() - call_args = mock_anthropic_client.messages.create.call_args[1] - - assert call_args["model"] == "claude-sonnet-4-20250514" - assert call_args["temperature"] == 0 - assert call_args["max_tokens"] == 800 - assert call_args["messages"] == [{"role": "user", "content": "What is AI?"}] - assert "tools" not in call_args - - def test_generate_response_with_conversation_history(self, mock_anthropic_client): - """Test response generation with conversation history""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_anthropic.return_value = mock_anthropic_client - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - history = "Previous conversation context" - response = generator.generate_response( - "Follow up question", conversation_history=history - ) - - # Assert - assert response == "This is a test response from Claude." - - # Verify system prompt includes history - call_args = mock_anthropic_client.messages.create.call_args[1] - assert "Previous conversation context" in call_args["system"] - - def test_generate_response_with_tools_no_tool_use( - self, mock_anthropic_client, mock_tool_manager - ): - """Test response generation with tools available but not used""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_anthropic.return_value = mock_anthropic_client - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - tools = mock_tool_manager.get_tool_definitions() - response = generator.generate_response( - "What is machine learning?", tools=tools, tool_manager=mock_tool_manager - ) - - # Assert - assert response == "This is a test response from Claude." - - # Verify tools were passed to API - call_args = mock_anthropic_client.messages.create.call_args[1] - assert "tools" in call_args - assert call_args["tool_choice"] == {"type": "auto"} - assert call_args["tools"] == tools - - def test_generate_response_with_tool_use(self, mock_tool_manager): - """Test response generation when Claude requests tool use""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Mock initial response with tool use - initial_response = Mock() - initial_response.stop_reason = "tool_use" - initial_response.content = [Mock()] - initial_response.content[0].type = "tool_use" - initial_response.content[0].name = "search_course_content" - initial_response.content[0].id = "tool_123" - initial_response.content[0].input = {"query": "test query"} - - # Mock final response after tool execution - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = ( - "Based on the search results, here is the answer." - ) - - # Setup client to return initial then final response - mock_client.messages.create.side_effect = [initial_response, final_response] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - tools = mock_tool_manager.get_tool_definitions() - response = generator.generate_response( - "What is in lesson 1?", tools=tools, tool_manager=mock_tool_manager - ) - - # Assert - assert response == "Based on the search results, here is the answer." - - # Verify tool was executed - mock_tool_manager.execute_tool.assert_called_once_with( - "search_course_content", query="test query" - ) - - # Verify two API calls were made (initial + final) - assert mock_client.messages.create.call_count == 2 - - def test_generate_response_tool_use_multiple_tools(self, mock_tool_manager): - """Test response generation when Claude requests multiple tools""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Mock initial response with multiple tool uses - initial_response = Mock() - initial_response.stop_reason = "tool_use" - - # Create two tool use content blocks - tool1 = Mock() - tool1.type = "tool_use" - tool1.name = "search_course_content" - tool1.id = "tool_123" - tool1.input = {"query": "first query"} - - tool2 = Mock() - tool2.type = "tool_use" - tool2.name = "get_course_outline" - tool2.id = "tool_456" - tool2.input = {"course_name": "Test Course"} - - initial_response.content = [tool1, tool2] - - # Mock final response - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = "Here's the comprehensive answer." - - mock_client.messages.create.side_effect = [initial_response, final_response] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "Tell me about the course", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Assert - assert response == "Here's the comprehensive answer." - - # Verify both tools were executed - assert mock_tool_manager.execute_tool.call_count == 2 - mock_tool_manager.execute_tool.assert_any_call( - "search_course_content", query="first query" - ) - mock_tool_manager.execute_tool.assert_any_call( - "get_course_outline", course_name="Test Course" - ) - - def test_handle_tool_execution_conversation_flow(self, mock_tool_manager): - """Test that tool execution properly maintains conversation flow""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Mock initial response with tool use - initial_response = Mock() - initial_response.stop_reason = "tool_use" - initial_response.content = [Mock()] - initial_response.content[0].type = "tool_use" - initial_response.content[0].name = "search_course_content" - initial_response.content[0].id = "tool_123" - initial_response.content[0].input = {"query": "lesson content"} - - # Mock final response - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = "Final answer with tool results." - - mock_client.messages.create.side_effect = [initial_response, final_response] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "What's in lesson 1?", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Verify the conversation flow in the final API call - final_call_args = mock_client.messages.create.call_args_list[1][1] - messages = final_call_args["messages"] - - # Should have: user message, assistant tool use, user tool results - assert len(messages) == 3 - assert messages[0]["role"] == "user" - assert messages[0]["content"] == "What's in lesson 1?" - assert messages[1]["role"] == "assistant" - assert messages[1]["content"] == initial_response.content - assert messages[2]["role"] == "user" - assert messages[2]["content"][0]["type"] == "tool_result" - assert messages[2]["content"][0]["tool_use_id"] == "tool_123" - assert messages[2]["content"][0]["content"] == "Mock search result" - - def test_generate_response_tool_execution_error(self, mock_tool_manager): - """Test handling when tool execution fails""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Mock initial response with tool use - initial_response = Mock() - initial_response.stop_reason = "tool_use" - initial_response.content = [Mock()] - initial_response.content[0].type = "tool_use" - initial_response.content[0].name = "search_course_content" - initial_response.content[0].id = "tool_123" - initial_response.content[0].input = {"query": "test"} - - # Mock final response - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = "I encountered an error searching." - - mock_client.messages.create.side_effect = [initial_response, final_response] - - # Make tool execution return error - mock_tool_manager.execute_tool.return_value = "Error: Tool execution failed" - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "Search for something", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Should still get a response even with tool error - assert response == "I encountered an error searching." - - # Verify error was passed to Claude in tool result - final_call_args = mock_client.messages.create.call_args_list[1][1] - tool_result = final_call_args["messages"][2]["content"][0] - assert tool_result["content"] == "Error: Tool execution failed" - - def test_system_prompt_content(self): - """Test that system prompt contains expected guidance""" - # Test that the static system prompt has the expected content - assert "course materials and educational content" in AIGenerator.SYSTEM_PROMPT - assert "Content Search Tool" in AIGenerator.SYSTEM_PROMPT - assert "Course Outline Tool" in AIGenerator.SYSTEM_PROMPT - assert "You can make up to 2 rounds of tool calls" in AIGenerator.SYSTEM_PROMPT - assert "Brief, Concise and focused" in AIGenerator.SYSTEM_PROMPT - - def test_api_parameters_consistency(self, mock_anthropic_client): - """Test that API parameters are consistent across calls""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_anthropic.return_value = mock_anthropic_client - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Make multiple calls - generator.generate_response("First question") - generator.generate_response( - "Second question", conversation_history="Previous context" - ) - - # Verify consistent parameters across calls - calls = mock_anthropic_client.messages.create.call_args_list - - for call in calls: - args = call[1] - assert args["model"] == "claude-sonnet-4-20250514" - assert args["temperature"] == 0 - assert args["max_tokens"] == 800 - - def test_no_tool_manager_with_tool_use(self): - """Test behavior when tools are requested but no tool_manager provided""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Mock response with tool use - tool_response = Mock() - tool_response.stop_reason = "tool_use" - tool_response.content = [Mock()] - tool_response.content[0].text = "I need to use a tool" - - mock_client.messages.create.return_value = tool_response - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute with tools but no tool_manager - response = generator.generate_response( - "Search for something", tools=[{"name": "test_tool"}] - ) - - # Should return the text from the tool use response - assert response == "I need to use a tool" - - # Should only make one API call (no tool execution) - assert mock_client.messages.create.call_count == 1 - - def test_empty_tool_results(self, mock_tool_manager): - """Test handling when no tool calls are made in tool_use response""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Mock response with tool_use stop_reason but no actual tool calls - initial_response = Mock() - initial_response.stop_reason = "tool_use" - initial_response.content = [] # No tool use blocks - - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = "No tools were actually used." - - mock_client.messages.create.side_effect = [initial_response, final_response] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "Test query", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Should still get final response - assert response == "No tools were actually used." - - # No tools should have been executed - mock_tool_manager.execute_tool.assert_not_called() - - # Should make two API calls (initial + final) - assert mock_client.messages.create.call_count == 2 - - def test_sequential_tool_calling_two_rounds(self, mock_tool_manager): - """Test sequential tool calling over 2 rounds""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Round 1: AI makes first tool call - round1_response = Mock() - round1_response.stop_reason = "tool_use" - round1_response.content = [Mock()] - round1_response.content[0].type = "tool_use" - round1_response.content[0].name = "get_course_outline" - round1_response.content[0].id = "tool_1" - round1_response.content[0].input = {"course_name": "Test Course"} - - # Round 2: AI makes second tool call based on first results - round2_response = Mock() - round2_response.stop_reason = "tool_use" - round2_response.content = [Mock()] - round2_response.content[0].type = "tool_use" - round2_response.content[0].name = "search_course_content" - round2_response.content[0].id = "tool_2" - round2_response.content[0].input = {"query": "lesson 4 content"} - - # Final response after 2 rounds - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = ( - "Based on the course outline and lesson content, here's the answer." - ) - - mock_client.messages.create.side_effect = [ - round1_response, - round2_response, - final_response, - ] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "Find lesson 4 content from Test Course", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Assert - assert ( - response - == "Based on the course outline and lesson content, here's the answer." - ) - - # Verify both tools were executed in sequence - assert mock_tool_manager.execute_tool.call_count == 2 - mock_tool_manager.execute_tool.assert_any_call( - "get_course_outline", course_name="Test Course" - ) - mock_tool_manager.execute_tool.assert_any_call( - "search_course_content", query="lesson 4 content" - ) - - # Verify 3 API calls were made (round1 + round2 + final) - assert mock_client.messages.create.call_count == 3 - - def test_sequential_tool_calling_early_termination(self, mock_tool_manager): - """Test that sequential tool calling terminates early when AI doesn't use tools""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Round 1: AI makes tool call - round1_response = Mock() - round1_response.stop_reason = "tool_use" - round1_response.content = [Mock()] - round1_response.content[0].type = "tool_use" - round1_response.content[0].name = "search_course_content" - round1_response.content[0].id = "tool_1" - round1_response.content[0].input = {"query": "test query"} - - # Round 2: AI provides direct answer without tool use - round2_response = Mock() - round2_response.stop_reason = "stop" - round2_response.content = [Mock()] - round2_response.content[0].text = ( - "Here's the direct answer based on the search results." - ) - - mock_client.messages.create.side_effect = [round1_response, round2_response] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "What is in the course?", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Assert - assert response == "Here's the direct answer based on the search results." - - # Verify only one tool was executed - assert mock_tool_manager.execute_tool.call_count == 1 - mock_tool_manager.execute_tool.assert_called_with( - "search_course_content", query="test query" - ) - - # Verify only 2 API calls were made (round1 + round2 with direct response) - assert mock_client.messages.create.call_count == 2 - - def test_sequential_tool_calling_tool_failure_stops_rounds(self, mock_tool_manager): - """Test that tool execution failure stops sequential rounds""" - with patch("ai_generator.anthropic.Anthropic") as mock_anthropic: - mock_client = Mock() - mock_anthropic.return_value = mock_client - - # Round 1: AI makes tool call that fails - round1_response = Mock() - round1_response.stop_reason = "tool_use" - round1_response.content = [Mock()] - round1_response.content[0].type = "tool_use" - round1_response.content[0].name = "search_course_content" - round1_response.content[0].id = "tool_1" - round1_response.content[0].input = {"query": "test query"} - - # Final response after tool failure - final_response = Mock() - final_response.content = [Mock()] - final_response.content[0].text = "I encountered an error while searching." - - # Make tool execution fail - mock_tool_manager.execute_tool.side_effect = Exception("Tool failed") - - mock_client.messages.create.side_effect = [round1_response, final_response] - - generator = AIGenerator("test-api-key", "claude-sonnet-4-20250514") - - # Execute - response = generator.generate_response( - "Search for something", - tools=mock_tool_manager.get_tool_definitions(), - tool_manager=mock_tool_manager, - ) - - # Assert - assert response == "I encountered an error while searching." - - # Verify tool was attempted once - assert mock_tool_manager.execute_tool.call_count == 1 - - # Verify 2 API calls were made (round1 + final after failure) - assert mock_client.messages.create.call_count == 2 - - # Verify error was passed to Claude in tool result - final_call_args = mock_client.messages.create.call_args_list[1][1] - tool_result_message = final_call_args["messages"][-1] - assert tool_result_message["role"] == "user" - assert ( - "Error: Tool execution failed" - in tool_result_message["content"][0]["content"] - ) diff --git a/backend/tests/test_api_endpoints.py b/backend/tests/test_api_endpoints.py deleted file mode 100644 index 207fc5f..0000000 --- a/backend/tests/test_api_endpoints.py +++ /dev/null @@ -1,223 +0,0 @@ -import json -from unittest.mock import Mock, patch - -import pytest -from fastapi.testclient import TestClient - - -@pytest.mark.api -class TestAPIEndpoints: - """Test suite for FastAPI endpoints""" - - def test_root_endpoint(self, client): - """Test the root endpoint returns correct message""" - response = client.get("/") - assert response.status_code == 200 - assert response.json() == {"message": "Course Materials RAG System API"} - - def test_query_endpoint_with_session_id(self, client, sample_query_request): - """Test /api/query endpoint with provided session_id""" - response = client.post("/api/query", json=sample_query_request) - - assert response.status_code == 200 - data = response.json() - - # Verify response structure - assert "answer" in data - assert "sources" in data - assert "source_links" in data - assert "session_id" in data - - # Verify content - assert data["answer"] == "This is a test response about course content." - assert data["sources"] == [ - "Building Towards Computer Use with Anthropic - Lesson 1" - ] - assert data["source_links"] == ["https://example.com/lesson1"] - assert data["session_id"] == "test-session-123" - - def test_query_endpoint_without_session_id(self, client): - """Test /api/query endpoint without session_id (should create new session)""" - request_data = {"query": "What is computer use in AI?"} - response = client.post("/api/query", json=request_data) - - assert response.status_code == 200 - data = response.json() - - # Should create new session - assert data["session_id"] == "test-session-123" - assert data["answer"] == "This is a test response about course content." - - def test_query_endpoint_invalid_request(self, client): - """Test /api/query endpoint with invalid request data""" - # Missing required 'query' field - response = client.post("/api/query", json={}) - assert response.status_code == 422 # Validation error - - def test_query_endpoint_empty_query(self, client): - """Test /api/query endpoint with empty query""" - request_data = {"query": ""} - response = client.post("/api/query", json=request_data) - - # Should still process (empty query is valid from API perspective) - assert response.status_code == 200 - - def test_courses_endpoint(self, client): - """Test /api/courses endpoint returns course statistics""" - response = client.get("/api/courses") - - assert response.status_code == 200 - data = response.json() - - # Verify response structure - assert "total_courses" in data - assert "course_titles" in data - - # Verify content - assert data["total_courses"] == 2 - assert data["course_titles"] == [ - "Building Towards Computer Use with Anthropic", - "Advanced AI Techniques", - ] - - def test_clear_session_endpoint(self, client, sample_clear_session_request): - """Test /api/clear-session endpoint""" - response = client.post("/api/clear-session", json=sample_clear_session_request) - - assert response.status_code == 200 - data = response.json() - - assert data["status"] == "success" - assert data["message"] == "Session cleared successfully" - - def test_clear_session_endpoint_invalid_request(self, client): - """Test /api/clear-session endpoint with invalid request""" - # Missing required 'session_id' field - response = client.post("/api/clear-session", json={}) - assert response.status_code == 422 # Validation error - - def test_nonexistent_endpoint(self, client): - """Test accessing a non-existent endpoint""" - response = client.get("/api/nonexistent") - assert response.status_code == 404 - - -@pytest.mark.api -class TestAPIErrorHandling: - """Test suite for API error handling""" - - def test_query_endpoint_with_rag_system_error(self, client): - """Test /api/query endpoint structure (error injection requires app-level mocking)""" - # This test demonstrates the structure for error handling testing - # In a real scenario, you'd inject the error at the app level - request_data = {"query": "test query"} - response = client.post("/api/query", json=request_data) - - # With current mock setup, this should work normally - assert response.status_code == 200 - - # To test actual error handling, you would need to: - # 1. Create a separate test app fixture with error-prone mocks - # 2. Or use dependency injection to override the RAG system - # 3. Or test error conditions in the actual app.py module tests - - def test_courses_endpoint_with_rag_system_error(self, client): - """Test /api/courses endpoint when RAG system raises an exception""" - # Similar to above, this would need app-level error injection - response = client.get("/api/courses") - assert response.status_code == 200 - - -@pytest.mark.api -class TestAPIRequestValidation: - """Test suite for API request validation""" - - def test_query_endpoint_with_extra_fields(self, client): - """Test /api/query endpoint ignores extra fields""" - request_data = { - "query": "What is computer use?", - "session_id": "test-123", - "extra_field": "should be ignored", - } - response = client.post("/api/query", json=request_data) - assert response.status_code == 200 - - def test_query_endpoint_with_wrong_types(self, client): - """Test /api/query endpoint with wrong field types""" - request_data = {"query": 123, "session_id": "test-123"} # Should be string - response = client.post("/api/query", json=request_data) - assert response.status_code == 422 - - def test_clear_session_with_wrong_types(self, client): - """Test /api/clear-session endpoint with wrong field types""" - request_data = {"session_id": 123} # Should be string - response = client.post("/api/clear-session", json=request_data) - assert response.status_code == 422 - - -@pytest.mark.api -class TestAPIHeaders: - """Test suite for API headers and CORS""" - - def test_cors_headers(self, client): - """Test that CORS headers are properly set""" - response = client.get("/") - - # FastAPI TestClient doesn't simulate full CORS behavior - # but we can verify the response is successful - assert response.status_code == 200 - - def test_options_request(self, client): - """Test OPTIONS request for CORS preflight""" - response = client.options("/api/query") - # TestClient may not handle OPTIONS the same as a real browser - # This test serves as a placeholder for CORS testing - assert response.status_code in [200, 405] # 405 is also acceptable - - -@pytest.mark.api -@pytest.mark.integration -class TestAPIIntegration: - """Integration tests for API endpoints""" - - def test_full_query_workflow(self, client): - """Test a complete query workflow""" - # Step 1: Get course stats - courses_response = client.get("/api/courses") - assert courses_response.status_code == 200 - - # Step 2: Query with a new session - query_response = client.post( - "/api/query", json={"query": "Tell me about the courses available"} - ) - assert query_response.status_code == 200 - session_id = query_response.json()["session_id"] - - # Step 3: Query with existing session - follow_up_response = client.post( - "/api/query", json={"query": "Can you elaborate?", "session_id": session_id} - ) - assert follow_up_response.status_code == 200 - assert follow_up_response.json()["session_id"] == session_id - - # Step 4: Clear the session - clear_response = client.post( - "/api/clear-session", json={"session_id": session_id} - ) - assert clear_response.status_code == 200 - - def test_multiple_concurrent_sessions(self, client): - """Test handling multiple concurrent sessions""" - # Create first session - response1 = client.post("/api/query", json={"query": "First session query"}) - session1 = response1.json()["session_id"] - - # Create second session - response2 = client.post("/api/query", json={"query": "Second session query"}) - session2 = response2.json()["session_id"] - - # Both should work independently - assert response1.status_code == 200 - assert response2.status_code == 200 - # With current mock, they'll return the same session_id - # In real implementation, they should be different diff --git a/backend/tests/test_config.py b/backend/tests/test_config.py index 39c3092..5afbd8b 100644 --- a/backend/tests/test_config.py +++ b/backend/tests/test_config.py @@ -1,6 +1,6 @@ import os import sys -from unittest.mock import Mock, patch +from unittest.mock import patch import pytest @@ -17,63 +17,32 @@ def test_default_config_values(self): """Test default configuration values""" default_config = Config() - # Test API settings - assert default_config.ANTHROPIC_MODEL == "claude-sonnet-4-20250514" + # Test embedding model settings assert default_config.EMBEDDING_MODEL == "all-MiniLM-L6-v2" # Test document processing settings assert default_config.CHUNK_SIZE == 800 assert default_config.CHUNK_OVERLAP == 100 - assert default_config.MAX_HISTORY == 2 + assert default_config.MAX_RESULTS == 5 # Test database paths assert default_config.CHROMA_PATH == "./chroma_db" - def test_broken_max_results_configuration(self): - """Test the critical MAX_RESULTS=0 configuration issue""" - default_config = Config() - - # This test documents the current broken state - assert default_config.MAX_RESULTS == 0 # This is the bug! - - # This value should be > 0 for the system to work properly - # When MAX_RESULTS=0, the vector search returns no results - - def test_proper_max_results_configuration(self): - """Test what the MAX_RESULTS configuration should be""" - proper_config = Config() - proper_config.MAX_RESULTS = 5 # What it should be - - # Verify proper configuration - assert proper_config.MAX_RESULTS > 0 - assert isinstance(proper_config.MAX_RESULTS, int) - - # Test that this value makes sense for search - assert proper_config.MAX_RESULTS <= 10 # Reasonable upper bound - assert proper_config.MAX_RESULTS >= 1 # Must be at least 1 - - def test_config_with_environment_variables(self): - """Test configuration loading from environment variables""" - with patch.dict( - os.environ, - { - "ANTHROPIC_API_KEY": "test-env-key", - "ANTHROPIC_MODEL": "claude-test-model", - }, - ): - # Create new config instance to pick up env vars - test_config = Config() - - assert test_config.ANTHROPIC_API_KEY == "test-env-key" - assert test_config.ANTHROPIC_MODEL == "claude-test-model" - - def test_config_missing_api_key(self): - """Test configuration when API key is missing""" - with patch.dict(os.environ, {}, clear=True): - test_config = Config() - - # Should default to empty string when env var not set - assert test_config.ANTHROPIC_API_KEY == "" + def test_config_with_custom_values(self): + """Test configuration with custom values""" + custom_config = Config( + EMBEDDING_MODEL="custom-model", + CHUNK_SIZE=1000, + CHUNK_OVERLAP=150, + MAX_RESULTS=10, + CHROMA_PATH="./custom_db", + ) + + assert custom_config.EMBEDDING_MODEL == "custom-model" + assert custom_config.CHUNK_SIZE == 1000 + assert custom_config.CHUNK_OVERLAP == 150 + assert custom_config.MAX_RESULTS == 10 + assert custom_config.CHROMA_PATH == "./custom_db" def test_config_chunk_settings_valid(self): """Test that chunk processing settings are valid""" @@ -87,10 +56,6 @@ def test_config_chunk_settings_valid(self): assert test_config.CHUNK_OVERLAP < test_config.CHUNK_SIZE assert test_config.CHUNK_OVERLAP >= 0 - # History should be reasonable - assert test_config.MAX_HISTORY >= 0 - assert test_config.MAX_HISTORY <= 10 # Not too much history - def test_config_path_settings(self): """Test database path configuration""" test_config = Config() @@ -100,12 +65,9 @@ def test_config_path_settings(self): assert not test_config.CHROMA_PATH.startswith("/") # Should be relative def test_config_model_settings(self): - """Test AI model configuration""" + """Test embedding model configuration""" test_config = Config() - # Should use Claude model - assert "claude" in test_config.ANTHROPIC_MODEL.lower() - # Should use sentence transformer for embeddings assert test_config.EMBEDDING_MODEL != "" assert isinstance(test_config.EMBEDDING_MODEL, str) @@ -116,30 +78,15 @@ def test_global_config_instance(self): assert config is not None assert isinstance(config, Config) - # Test it has the fixed MAX_RESULTS value + # Test it has the default values assert config.MAX_RESULTS == 5 - - def test_config_impact_on_vector_search(self): - """Test how MAX_RESULTS=0 impacts vector search behavior""" - broken_config = Config() - proper_config = Config() - proper_config.MAX_RESULTS = 5 - - # Demonstrate the difference - assert broken_config.MAX_RESULTS == 0 # Broken - will return no results - assert proper_config.MAX_RESULTS == 5 # Fixed - will return results - - # This test shows why the system fails: - # When max_results is used as n_results in ChromaDB query, - # n_results=0 means "return 0 results" regardless of matches + assert config.EMBEDDING_MODEL == "all-MiniLM-L6-v2" def test_config_values_are_correct_types(self): """Test that all config values have correct types""" test_config = Config() # String values - assert isinstance(test_config.ANTHROPIC_API_KEY, str) - assert isinstance(test_config.ANTHROPIC_MODEL, str) assert isinstance(test_config.EMBEDDING_MODEL, str) assert isinstance(test_config.CHROMA_PATH, str) @@ -147,12 +94,9 @@ def test_config_values_are_correct_types(self): assert isinstance(test_config.CHUNK_SIZE, int) assert isinstance(test_config.CHUNK_OVERLAP, int) assert isinstance(test_config.MAX_RESULTS, int) - assert isinstance(test_config.MAX_HISTORY, int) def test_config_validation_logic(self): - """Test configuration validation (what should be implemented)""" - # This test shows what validation logic could be added - + """Test configuration validation""" def validate_config(cfg): """Example validation function""" errors = [] @@ -163,28 +107,25 @@ def validate_config(cfg): if cfg.CHUNK_OVERLAP >= cfg.CHUNK_SIZE: errors.append("CHUNK_OVERLAP must be less than CHUNK_SIZE") - if cfg.ANTHROPIC_API_KEY == "": - errors.append("ANTHROPIC_API_KEY is required") + if not cfg.EMBEDDING_MODEL: + errors.append("EMBEDDING_MODEL is required") return errors - # Test current broken config - broken_config = Config() - errors = validate_config(broken_config) + # Test valid config + valid_config = Config() + errors = validate_config(valid_config) + assert len(errors) == 0 - # Should have validation errors + # Test invalid config + invalid_config = Config() + invalid_config.MAX_RESULTS = 0 + invalid_config.CHUNK_OVERLAP = invalid_config.CHUNK_SIZE + 100 + + errors = validate_config(invalid_config) assert len(errors) > 0 assert any("MAX_RESULTS must be greater than 0" in error for error in errors) - - # Test proper config - proper_config = Config() - proper_config.MAX_RESULTS = 5 - proper_config.ANTHROPIC_API_KEY = "valid-key" - - errors = validate_config(proper_config) - # Should have fewer errors (only missing API key if not set in env) - max_results_errors = [e for e in errors if "MAX_RESULTS" in e] - assert len(max_results_errors) == 0 + assert any("CHUNK_OVERLAP must be less than CHUNK_SIZE" in error for error in errors) def test_config_edge_cases(self): """Test configuration edge cases""" @@ -204,31 +145,7 @@ def test_config_edge_cases(self): def test_config_immutability_during_runtime(self): """Test that config changes affect system behavior""" - # This test demonstrates how changing MAX_RESULTS affects the system - - # Simulate broken system behavior - broken_config = Config() - broken_config.MAX_RESULTS = 0 - - # In a real VectorStore, this would result in no search results - def simulate_search(max_results): - if max_results == 0: - return [] # No results due to n_results=0 - else: - return ["result1", "result2"] # Normal results - - broken_results = simulate_search(broken_config.MAX_RESULTS) - assert broken_results == [] # Demonstrates the bug - - # Fixed system behavior - fixed_config = Config() - fixed_config.MAX_RESULTS = 5 - - fixed_results = simulate_search(fixed_config.MAX_RESULTS) - assert len(fixed_results) > 0 # Shows the fix works - - def test_config_dataclass_behavior(self): - """Test that Config behaves as expected dataclass""" + # Create two config instances config1 = Config() config2 = Config() @@ -241,15 +158,15 @@ def test_config_dataclass_behavior(self): # Changes to one shouldn't affect the other config1.MAX_RESULTS = 10 - assert config2.MAX_RESULTS == 0 # Still the default broken value + assert config2.MAX_RESULTS == 5 # Still the default value @pytest.mark.parametrize( "max_results,expected_behavior", [ - (0, "no_results"), # Current broken behavior - (1, "some_results"), # Minimal fix - (5, "good_results"), # Recommended fix - (10, "many_results"), # Upper reasonable bound + (1, "minimal_results"), + (5, "good_results"), + (10, "many_results"), + (20, "upper_bound"), ], ) def test_max_results_impact_parametrized(self, max_results, expected_behavior): @@ -257,13 +174,9 @@ def test_max_results_impact_parametrized(self, max_results, expected_behavior): test_config = Config() test_config.MAX_RESULTS = max_results - # Simulate the impact on search results - if max_results == 0: - assert expected_behavior == "no_results" - # This is the bug case - elif max_results >= 1: - assert expected_behavior in ["some_results", "good_results", "many_results"] - # These are the working cases - # Verify the config has the expected value assert test_config.MAX_RESULTS == max_results + + # Test reasonable bounds + assert test_config.MAX_RESULTS >= 1 + assert test_config.MAX_RESULTS <= 50 # Reasonable upper bound diff --git a/backend/tests/test_course_search_tool.py b/backend/tests/test_course_search_tool.py deleted file mode 100644 index a82aaaa..0000000 --- a/backend/tests/test_course_search_tool.py +++ /dev/null @@ -1,287 +0,0 @@ -import os -import sys -from unittest.mock import Mock, patch - -import pytest - -# Add parent directory to path to import modules -sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - -from search_tools import CourseSearchTool -from vector_store import SearchResults - - -class TestCourseSearchTool: - """Test cases for CourseSearchTool""" - - def test_execute_successful_search(self, mock_vector_store, sample_search_results): - """Test successful search with results""" - # Setup - mock_vector_store.search.return_value = sample_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query") - - # Assert - assert "[Building Towards Computer Use with Anthropic - Lesson 1]" in result - assert "[Building Towards Computer Use with Anthropic - Lesson 2]" in result - assert "Welcome to Building Toward Computer Use" in result - assert "advanced topics including tool calling" in result - - # Verify vector store was called correctly - mock_vector_store.search.assert_called_once_with( - query="test query", course_name=None, lesson_number=None - ) - - def test_execute_with_course_filter(self, mock_vector_store, sample_search_results): - """Test search with course name filter""" - # Setup - mock_vector_store.search.return_value = sample_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query", course_name="Anthropic Course") - - # Assert - assert "Building Towards Computer Use" in result - mock_vector_store.search.assert_called_once_with( - query="test query", course_name="Anthropic Course", lesson_number=None - ) - - def test_execute_with_lesson_filter(self, mock_vector_store, sample_search_results): - """Test search with lesson number filter""" - # Setup - mock_vector_store.search.return_value = sample_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query", lesson_number=2) - - # Assert - assert "Building Towards Computer Use" in result - mock_vector_store.search.assert_called_once_with( - query="test query", course_name=None, lesson_number=2 - ) - - def test_execute_with_both_filters(self, mock_vector_store, sample_search_results): - """Test search with both course and lesson filters""" - # Setup - mock_vector_store.search.return_value = sample_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute( - "test query", course_name="Anthropic Course", lesson_number=1 - ) - - # Assert - assert "Building Towards Computer Use" in result - mock_vector_store.search.assert_called_once_with( - query="test query", course_name="Anthropic Course", lesson_number=1 - ) - - def test_execute_empty_results(self, mock_vector_store, empty_search_results): - """Test handling of empty search results""" - # Setup - mock_vector_store.search.return_value = empty_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("nonexistent query") - - # Assert - assert result == "No relevant content found." - - def test_execute_empty_results_with_filters( - self, mock_vector_store, empty_search_results - ): - """Test empty results with filter information""" - # Setup - mock_vector_store.search.return_value = empty_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute( - "test query", course_name="Missing Course", lesson_number=5 - ) - - # Assert - expected = "No relevant content found in course 'Missing Course' in lesson 5." - assert result == expected - - def test_execute_search_error(self, mock_vector_store, error_search_results): - """Test handling of search errors""" - # Setup - mock_vector_store.search.return_value = error_search_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query") - - # Assert - assert result == "Search error: Database connection failed" - - def test_execute_max_results_zero_issue(self, mock_vector_store): - """Test the critical MAX_RESULTS=0 issue""" - # Setup - simulate the behavior when MAX_RESULTS=0 causes no results - empty_results = SearchResults(documents=[], metadata=[], distances=[]) - mock_vector_store.search.return_value = empty_results - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("valid query about course content") - - # Assert - this should return no content found due to MAX_RESULTS=0 - assert result == "No relevant content found." - - # This test demonstrates the bug: even with valid queries, we get no results - # when MAX_RESULTS=0 because the vector store returns empty results - - def test_format_results_with_lesson_links(self, mock_vector_store): - """Test that lesson links are properly retrieved and stored""" - # Setup - search_results = SearchResults( - documents=["Test content"], - metadata=[ - {"course_title": "Test Course", "lesson_number": 1, "chunk_index": 0} - ], - distances=[0.1], - ) - mock_vector_store.search.return_value = search_results - mock_vector_store.get_lesson_link.return_value = "https://example.com/lesson1" - - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query") - - # Assert - assert "[Test Course - Lesson 1]" in result - assert "Test content" in result - - # Verify lesson link was requested - mock_vector_store.get_lesson_link.assert_called_once_with("Test Course", 1) - - # Check that sources and links are tracked - assert tool.last_sources == ["Test Course - Lesson 1"] - assert tool.last_source_links == ["https://example.com/lesson1"] - - def test_format_results_without_lesson_number(self, mock_vector_store): - """Test formatting when lesson_number is None""" - # Setup - search_results = SearchResults( - documents=["Test content"], - metadata=[ - {"course_title": "Test Course", "chunk_index": 0} - ], # No lesson_number - distances=[0.1], - ) - mock_vector_store.search.return_value = search_results - - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query") - - # Assert - should not include lesson number in header - assert "[Test Course]" in result - assert "Test content" in result - - # Check sources tracking - assert tool.last_sources == ["Test Course"] - assert tool.last_source_links == [None] - - def test_get_tool_definition(self, mock_vector_store): - """Test that tool definition is properly structured""" - # Setup - tool = CourseSearchTool(mock_vector_store) - - # Execute - definition = tool.get_tool_definition() - - # Assert - assert definition["name"] == "search_course_content" - assert "description" in definition - assert "input_schema" in definition - - schema = definition["input_schema"] - assert schema["type"] == "object" - assert "query" in schema["properties"] - assert "course_name" in schema["properties"] - assert "lesson_number" in schema["properties"] - assert schema["required"] == ["query"] - - def test_source_tracking_reset(self, mock_vector_store, sample_search_results): - """Test that sources are properly tracked and can be reset""" - # Setup - tool = CourseSearchTool(mock_vector_store) - mock_vector_store.search.return_value = sample_search_results - - # Execute first search - tool.execute("first query") - first_sources = tool.last_sources.copy() - first_links = tool.last_source_links.copy() - - # Verify sources are tracked - assert len(first_sources) > 0 - assert len(first_links) > 0 - - # Execute second search with empty results - mock_vector_store.search.return_value = SearchResults([], [], []) - tool.execute("second query") - - # Verify sources are cleared for empty results - assert tool.last_sources == [] - assert tool.last_source_links == [] - - def test_multiple_documents_formatting(self, mock_vector_store): - """Test formatting when multiple documents are returned""" - # Setup - multi_results = SearchResults( - documents=[ - "First document content about AI", - "Second document about machine learning", - "Third document about computer vision", - ], - metadata=[ - {"course_title": "AI Course", "lesson_number": 1, "chunk_index": 0}, - {"course_title": "AI Course", "lesson_number": 2, "chunk_index": 1}, - {"course_title": "ML Course", "lesson_number": 1, "chunk_index": 0}, - ], - distances=[0.1, 0.2, 0.3], - ) - mock_vector_store.search.return_value = multi_results - mock_vector_store.get_lesson_link.side_effect = [ - "https://example.com/ai1", - "https://example.com/ai2", - "https://example.com/ml1", - ] - - tool = CourseSearchTool(mock_vector_store) - - # Execute - result = tool.execute("test query") - - # Assert all documents are included - assert "[AI Course - Lesson 1]" in result - assert "[AI Course - Lesson 2]" in result - assert "[ML Course - Lesson 1]" in result - assert "First document content about AI" in result - assert "Second document about machine learning" in result - assert "Third document about computer vision" in result - - # Verify all sources are tracked - expected_sources = [ - "AI Course - Lesson 1", - "AI Course - Lesson 2", - "ML Course - Lesson 1", - ] - expected_links = [ - "https://example.com/ai1", - "https://example.com/ai2", - "https://example.com/ml1", - ] - - assert tool.last_sources == expected_sources - assert tool.last_source_links == expected_links diff --git a/backend/tests/test_document_processor.py b/backend/tests/test_document_processor.py new file mode 100644 index 0000000..c75f07e --- /dev/null +++ b/backend/tests/test_document_processor.py @@ -0,0 +1,398 @@ +import os +import sys +from unittest.mock import patch + +import pytest + +# Add parent directory to path to import modules +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from document_processor import MDProcessor +from models import Document, DocumentChunk + + +class TestMDProcessor: + """Test cases for MDProcessor""" + + def test_init(self, test_config): + """Test MDProcessor initialization""" + processor = MDProcessor(test_config.CHUNK_SIZE, test_config.CHUNK_OVERLAP) + + assert processor.chunk_size == test_config.CHUNK_SIZE + assert processor.chunk_overlap == test_config.CHUNK_OVERLAP + + def test_process_md_file_basic(self, test_config, temp_dir): + """Test basic MD file processing""" + # Create a test MD file + md_content = """# Introduction + +This is a test document for processing. + +## Advanced Topics + +Here we cover advanced concepts. + +### Implementation + +The implementation details go here. +""" + + md_file = os.path.join(temp_dir, "test.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write(md_content) + + processor = MDProcessor(800, 100) + + # Process the file + document, chunks = processor.process_md_file(md_file) + + # Assert document structure + assert isinstance(document, Document) + assert document.file_name == "test.md" + assert document.title == "Introduction" # First heading + assert "Introduction" in document.sections + assert "Advanced Topics" in document.sections + + # Assert chunks were created + assert len(chunks) > 0 + assert all(isinstance(chunk, DocumentChunk) for chunk in chunks) + + # Check first chunk + first_chunk = chunks[0] + assert first_chunk.file_name == "test.md" + assert "[文件: test.md | 章节:" in first_chunk.content + + def test_process_md_file_with_frontmatter(self, test_config, temp_dir): + """Test MD file processing with YAML frontmatter""" + md_content = """--- +title: "Custom Document Title" +author: "Test Author" +--- + +# Actual Heading + +This document has frontmatter that should be parsed. +""" + + md_file = os.path.join(temp_dir, "frontmatter.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write(md_content) + + processor = MDProcessor(800, 100) + + # Process the file + document, chunks = processor.process_md_file(md_file) + + # Assert title comes from frontmatter + assert document.title == "Custom Document Title" + + def test_process_md_file_no_frontmatter_title(self, test_config, temp_dir): + """Test MD file processing when frontmatter has no title""" + md_content = """--- +author: "Test Author" +--- + +# Document Title + +Content without frontmatter title. +""" + + md_file = os.path.join(temp_dir, "no_title.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write(md_content) + + processor = MDProcessor(800, 100) + + # Process the file + document, chunks = processor.process_md_file(md_file) + + # Assert title comes from first heading + assert document.title == "Document Title" + + def test_extract_frontmatter_valid(self): + """Test frontmatter extraction with valid YAML""" + content = """--- +title: "Test Title" +author: "Test Author" +--- + +# Heading + +Content here. +""" + + processor = MDProcessor(800, 100) + frontmatter = processor.extract_frontmatter(content) + + assert frontmatter is not None + assert frontmatter["title"] == "Test Title" + assert frontmatter["author"] == "Test Author" + + def test_extract_frontmatter_none(self): + """Test frontmatter extraction when no frontmatter exists""" + content = """# Heading + +Content without frontmatter. +""" + + processor = MDProcessor(800, 100) + frontmatter = processor.extract_frontmatter(content) + + assert frontmatter is None + + def test_extract_frontmatter_invalid_yaml(self): + """Test frontmatter extraction with invalid YAML""" + content = """--- +title: "Test Title +author: "Test Author" +--- + +# Heading + +Content here. +""" + + processor = MDProcessor(800, 100) + frontmatter = processor.extract_frontmatter(content) + + # Should return None for invalid YAML + assert frontmatter is None + + def test_split_by_sections(self): + """Test content splitting by Markdown sections""" + content = """# Introduction + +Intro content here. + +## Section 1 + +Content for section 1. + +## Section 2 + +Content for section 2. + +# Another Top Level + +More content. +""" + + processor = MDProcessor(800, 100) + sections = processor.split_by_sections(content) + + assert len(sections) == 4 + assert sections[0][0] == "Introduction" + assert sections[1][0] == "Section 1" + assert sections[2][0] == "Section 2" + assert sections[3][0] == "Another Top Level" + + # Check content is properly separated + assert "Intro content here." in sections[0][1] + assert "Content for section 1." in sections[1][1] + + def test_split_by_sections_no_headings(self): + """Test content splitting when no headings exist""" + content = """This is content without any headings. + +It should all go into the default section. +""" + + processor = MDProcessor(800, 100) + sections = processor.split_by_sections(content) + + assert len(sections) == 1 + assert sections[0][0] == "简介" # Default section title + assert "content without any headings" in sections[0][1] + + def test_format_chunk_with_context(self): + """Test chunk formatting with context""" + processor = MDProcessor(800, 100) + + chunk = "This is test content." + section = "Introduction" + file_name = "test.md" + + formatted = processor.format_chunk_with_context(chunk, section, file_name) + + assert formatted.startswith("[文件: test.md | 章节: Introduction]") + assert "\n\nThis is test content." in formatted + + def test_chunk_text_basic(self): + """Test basic text chunking""" + processor = MDProcessor(800, 100) + + text = "This is a simple sentence. This is another sentence. This is a third sentence." + + chunks = processor.chunk_text(text) + + # Should create chunks based on sentences + assert len(chunks) > 0 + assert all(isinstance(chunk, str) for chunk in chunks) + + def test_chunk_text_with_overlap(self): + """Test text chunking with overlap""" + processor = MDProcessor(50, 20) # Small chunk size to force overlap + + text = "This is the first sentence that is quite long. This is the second sentence. This is the third sentence that continues the thought." + + chunks = processor.chunk_text(text) + + # Should have multiple chunks with overlap + assert len(chunks) > 1 + + # Check that overlap works (second chunk should start with content from first) + if len(chunks) > 1: + assert len(chunks[0]) > 0 + assert len(chunks[1]) > 0 + + def test_chunk_text_long_sentences(self): + """Test chunking with very long sentences""" + processor = MDProcessor(50, 10) + + # Create a very long sentence + long_sentence = " ".join([f"word{i}" for i in range(100)]) + text = f"{long_sentence}." + + chunks = processor.chunk_text(text) + + # Should split long content + assert len(chunks) > 0 + total_length = sum(len(chunk) for chunk in chunks) + assert total_length >= len(text) # Should preserve all content + + def test_read_file_utf8(self, temp_dir): + """Test file reading with UTF-8 content""" + content = "这是测试内容 with 中文 and English." + test_file = os.path.join(temp_dir, "utf8.md") + + with open(test_file, "w", encoding="utf-8") as f: + f.write(content) + + processor = MDProcessor(800, 100) + read_content = processor.read_file(test_file) + + assert read_content == content + + def test_read_file_encoding_fallback(self, temp_dir): + """Test file reading with encoding fallback""" + content = "Test content with some special characters: àáâãäå." + test_file = os.path.join(temp_dir, "encoding.md") + + # Write with different encoding + with open(test_file, "w", encoding="latin-1") as f: + f.write(content) + + processor = MDProcessor(800, 100) + read_content = processor.read_file(test_file) + + # Should still read content (may have replacement characters) + assert len(read_content) > 0 + + def test_extract_first_heading(self): + """Test extraction of first level-1 heading""" + content = """Some intro text. + +# First Heading + +Content here. + +## Second Level + +More content. + +# Another Top Level + +Final content. +""" + + processor = MDProcessor(800, 100) + title = processor._extract_first_heading(content) + + assert title == "First Heading" + + def test_extract_first_heading_none(self): + """Test first heading extraction when no headings exist""" + content = """This is content without any headings. + +Just plain text. +""" + + processor = MDProcessor(800, 100) + title = processor._extract_first_heading(content) + + assert title is None + + def test_process_md_file_empty(self, temp_dir): + """Test processing of empty MD file""" + md_file = os.path.join(temp_dir, "empty.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write("") + + processor = MDProcessor(800, 100) + document, chunks = processor.process_md_file(md_file) + + assert isinstance(document, Document) + assert document.title is None # No title from empty file + assert len(chunks) == 0 # No chunks from empty content + + def test_process_md_file_only_frontmatter(self, temp_dir): + """Test processing file with only frontmatter""" + content = """--- +title: "Only Frontmatter" +author: "Test" +--- +""" + + md_file = os.path.join(temp_dir, "frontmatter_only.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write(content) + + processor = MDProcessor(800, 100) + document, chunks = processor.process_md_file(md_file) + + assert document.title == "Only Frontmatter" + assert len(chunks) == 0 # No content chunks + + def test_integration_full_processing(self, temp_dir): + """Integration test for full MD processing pipeline""" + md_content = """--- +title: "Integration Test" +--- + +# Main Title + +This is an integration test document. + +## Section One + +Content for the first section with multiple sentences. This should be chunked appropriately. The chunking algorithm should handle sentence boundaries correctly. + +## Section Two + +More content in the second section. This tests that multiple sections are handled properly and chunks are created for each section with appropriate context. +""" + + md_file = os.path.join(temp_dir, "integration.md") + with open(md_file, "w", encoding="utf-8") as f: + f.write(md_content) + + processor = MDProcessor(100, 20) # Small chunks to force chunking + + # Process the file + document, chunks = processor.process_md_file(md_file) + + # Verify document + assert document.title == "Integration Test" # From frontmatter + assert len(document.sections) == 3 # Main Title, Section One, Section Two + assert "Main Title" in document.sections + assert "Section One" in document.sections + assert "Section Two" in document.sections + + # Verify chunks + assert len(chunks) > 0 + + # Check chunk context formatting + for chunk in chunks: + assert "[文件: integration.md | 章节:" in chunk.content + assert chunk.file_name == "integration.md" + assert chunk.section_title in ["Main Title", "Section One", "Section Two"] diff --git a/backend/tests/test_rag_system.py b/backend/tests/test_rag_system.py index d106b9c..a2d7083 100644 --- a/backend/tests/test_rag_system.py +++ b/backend/tests/test_rag_system.py @@ -1,534 +1,317 @@ import os import sys -import tempfile -from unittest.mock import MagicMock, Mock, patch +from unittest.mock import Mock, patch import pytest # Add parent directory to path to import modules sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from models import Course, CourseChunk, Lesson +from models import Document from rag_system import RAGSystem class TestRAGSystem: - """Test cases for RAGSystem end-to-end integration""" + """Test cases for RAGSystem""" - def test_init_with_proper_config(self, test_config): - """Test RAGSystem initialization with proper configuration""" + def test_init(self, test_config): + """Test RAGSystem initialization""" with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), + patch("rag_system.MDProcessor") as mock_md_processor, + patch("rag_system.VectorStore") as mock_vector_store, ): - - rag_system = RAGSystem(test_config) + rag_system = RAGSystem("./data", "./chroma_db") # Verify components are initialized - assert rag_system.config == test_config - assert rag_system.document_processor is not None + assert rag_system.folder_path == "./data" + assert rag_system.md_processor is not None assert rag_system.vector_store is not None - assert rag_system.ai_generator is not None - assert rag_system.session_manager is not None - assert rag_system.tool_manager is not None - assert rag_system.search_tool is not None - assert rag_system.outline_tool is not None - - def test_init_with_broken_config(self, broken_config): - """Test RAGSystem initialization with broken MAX_RESULTS=0 config""" - with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), - ): - - rag_system = RAGSystem(broken_config) - # Verify VectorStore was initialized with broken config - mock_vector_store.assert_called_once_with( - broken_config.CHROMA_PATH, - broken_config.EMBEDDING_MODEL, - 0, # This is the broken MAX_RESULTS=0 value + # Verify MDProcessor was created with correct config values + mock_md_processor.assert_called_once_with( + test_config.CHUNK_SIZE, test_config.CHUNK_OVERLAP ) - def test_query_successful_with_tool_use(self, test_config): - """Test successful query processing with tool usage""" - with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator") as mock_ai_gen, - patch("rag_system.SessionManager") as mock_session, - ): - - # Setup mocks - mock_ai_gen.return_value.generate_response.return_value = ( - "Based on the course content, here's the answer." - ) - mock_session.return_value.get_conversation_history.return_value = None - - rag_system = RAGSystem(test_config) - - # Mock tool manager to return sources - rag_system.tool_manager.get_last_sources = Mock( - return_value=["Course 1 - Lesson 1"] - ) - rag_system.tool_manager.get_last_source_links = Mock( - return_value=["https://example.com/lesson1"] - ) - - # Execute query - response, sources, source_links = rag_system.query( - "What is covered in lesson 1?" + # Verify VectorStore was created with correct parameters + mock_vector_store.assert_called_once_with( + "./chroma_db", test_config.EMBEDDING_MODEL, test_config.MAX_RESULTS ) - # Assert - assert response == "Based on the course content, here's the answer." - assert sources == ["Course 1 - Lesson 1"] - assert source_links == ["https://example.com/lesson1"] - - # Verify AI generator was called with tools - mock_ai_gen.return_value.generate_response.assert_called_once() - call_args = mock_ai_gen.return_value.generate_response.call_args[1] - assert "tools" in call_args - assert "tool_manager" in call_args - - def test_query_with_session_history(self, test_config): - """Test query processing with conversation history""" + def test_init_with_custom_path(self, test_config): + """Test RAGSystem initialization with custom paths""" with ( - patch("rag_system.DocumentProcessor"), + patch("rag_system.MDProcessor"), patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator") as mock_ai_gen, - patch("rag_system.SessionManager") as mock_session, ): + rag_system = RAGSystem("/custom/data", "/custom/chroma") - # Setup mocks - mock_ai_gen.return_value.generate_response.return_value = ( - "Follow-up response." - ) - mock_session.return_value.get_conversation_history.return_value = ( - "Previous conversation" - ) - - rag_system = RAGSystem(test_config) - rag_system.tool_manager.get_last_sources = Mock(return_value=[]) - rag_system.tool_manager.get_last_source_links = Mock(return_value=[]) - - # Execute query with session - response, sources, source_links = rag_system.query( - "Follow up question", session_id="session123" - ) - - # Assert - assert response == "Follow-up response." - - # Verify session history was used - mock_session.return_value.get_conversation_history.assert_called_once_with( - "session123" - ) - call_args = mock_ai_gen.return_value.generate_response.call_args[1] - assert call_args["conversation_history"] == "Previous conversation" - - # Verify session was updated - mock_session.return_value.add_exchange.assert_called_once_with( - "session123", - "Answer this question about course materials: Follow up question", - "Follow-up response.", - ) + assert rag_system.folder_path == "/custom/data" - def test_query_failed_scenario_max_results_zero(self, broken_config): - """Test the 'query failed' scenario due to MAX_RESULTS=0""" + def test_query_success(self, test_config, sample_search_results): + """Test successful query""" with ( - patch("rag_system.DocumentProcessor"), + patch("rag_system.MDProcessor"), patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator") as mock_ai_gen, - patch("rag_system.SessionManager"), ): + mock_vector_store.return_value.search.return_value = sample_search_results - # Setup broken vector store that returns empty results due to MAX_RESULTS=0 - mock_vector_store_instance = Mock() - mock_vector_store.return_value = mock_vector_store_instance + rag_system = RAGSystem("./data") - # Mock AI generator response when tools return no content - mock_ai_gen.return_value.generate_response.return_value = ( - "I couldn't find any relevant information." - ) - - rag_system = RAGSystem(broken_config) - - # Mock tool manager to simulate no results found (due to MAX_RESULTS=0) - rag_system.tool_manager.get_last_sources = Mock(return_value=[]) - rag_system.tool_manager.get_last_source_links = Mock(return_value=[]) - - # Execute query that should find content but doesn't due to config issue - response, sources, source_links = rag_system.query( - "What is covered in the course?" - ) + # Execute query + result = rag_system.query("test query") - # Assert we get an unhelpful response due to the configuration bug - assert "couldn't find" in response.lower() or "no" in response.lower() - assert sources == [] - assert source_links == [] + # Verify vector store search was called + mock_vector_store.return_value.search.assert_called_once_with("test query", limit=5) - # This demonstrates the "query failed" behavior + # Verify result structure + assert "answer" in result + assert "sources" in result + assert "results" in result + assert not result["answer"] == "未找到相关内容" - def test_query_with_no_session(self, test_config): - """Test query processing without session ID""" + def test_query_no_results(self, test_config, empty_search_results): + """Test query with no results""" with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator") as mock_ai_gen, - patch("rag_system.SessionManager") as mock_session, + patch("rag_system.MDProcessor"), + patch("rag_system.VectorStore") as mock_vector_store, ): + mock_vector_store.return_value.search.return_value = empty_search_results - mock_ai_gen.return_value.generate_response.return_value = ( - "Response without session." - ) + rag_system = RAGSystem("./data") - rag_system = RAGSystem(test_config) - rag_system.tool_manager.get_last_sources = Mock(return_value=[]) - rag_system.tool_manager.get_last_source_links = Mock(return_value=[]) - - # Execute query without session - response, sources, source_links = rag_system.query("What is AI?") - - # Assert - assert response == "Response without session." + # Execute query + result = rag_system.query("nonexistent query") - # Verify no session operations were called - mock_session.return_value.get_conversation_history.assert_not_called() - mock_session.return_value.add_exchange.assert_not_called() + # Verify result indicates no content found + assert result["answer"] == "未找到相关内容" + assert result["sources"] == [] + assert result["results"] == [] - def test_add_course_document_success(self, test_config, sample_course): - """Test adding a single course document""" + def test_query_with_error(self, test_config, error_search_results): + """Test query when search fails""" with ( - patch("rag_system.DocumentProcessor") as mock_doc_proc, + patch("rag_system.MDProcessor"), patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), ): + mock_vector_store.return_value.search.return_value = error_search_results - # Setup mocks - mock_chunks = [ - CourseChunk( - content="chunk1", course_title="Test Course", chunk_index=0 - ), - CourseChunk( - content="chunk2", course_title="Test Course", chunk_index=1 - ), - ] - mock_doc_proc.return_value.process_course_document.return_value = ( - sample_course, - mock_chunks, - ) - - rag_system = RAGSystem(test_config) + rag_system = RAGSystem("./data") - # Execute - course, chunk_count = rag_system.add_course_document("/path/to/course.pdf") - - # Assert - assert course == sample_course - assert chunk_count == 2 - - # Verify document was processed - mock_doc_proc.return_value.process_course_document.assert_called_once_with( - "/path/to/course.pdf" - ) + # Execute query + result = rag_system.query("test query") - # Verify data was added to vector store - mock_vector_store.return_value.add_course_metadata.assert_called_once_with( - sample_course - ) - mock_vector_store.return_value.add_course_content.assert_called_once_with( - mock_chunks - ) + # Verify error is handled + assert "搜索错误" in result["answer"] + assert result["sources"] == [] + assert result["results"] == [] - def test_add_course_document_error(self, test_config): - """Test error handling when document processing fails""" + def test_query_with_custom_limit(self, test_config, sample_search_results): + """Test query with custom limit""" with ( - patch("rag_system.DocumentProcessor") as mock_doc_proc, - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), + patch("rag_system.MDProcessor"), + patch("rag_system.VectorStore") as mock_vector_store, ): + mock_vector_store.return_value.search.return_value = sample_search_results - # Setup mock to raise exception - mock_doc_proc.return_value.process_course_document.side_effect = Exception( - "Processing failed" - ) - - rag_system = RAGSystem(test_config) + rag_system = RAGSystem("./data") - # Execute - course, chunk_count = rag_system.add_course_document( - "/path/to/bad_course.pdf" - ) + # Execute query with custom limit + result = rag_system.query("test query", limit=3) - # Assert error handling - assert course is None - assert chunk_count == 0 + # Verify custom limit was passed to search + mock_vector_store.return_value.search.assert_called_once_with("test query", limit=3) - def test_add_course_folder_success(self, test_config): - """Test adding multiple course documents from folder""" + def test_load_md_folder_success(self, test_config, sample_document_chunks, temp_dir): + """Test successful loading of MD folder""" with ( - patch("rag_system.DocumentProcessor") as mock_doc_proc, - patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), + patch("rag_system.MDProcessor") as mock_md_processor_class, + patch("rag_system.VectorStore") as mock_vector_store_class, patch("os.path.exists") as mock_exists, patch("os.listdir") as mock_listdir, ): - # Setup mocks mock_exists.return_value = True - mock_listdir.return_value = [ - "course1.pdf", - "course2.txt", - "course3.docx", - "ignore.jpg", - ] - mock_vector_store.return_value.get_existing_course_titles.return_value = [] + mock_listdir.return_value = ["test1.md", "test2.md", "not_md.txt"] + + # Create mock instances + mock_md_processor = Mock() + mock_vector_store = Mock() + mock_md_processor_class.return_value = mock_md_processor + mock_vector_store_class.return_value = mock_vector_store # Mock document processing - courses = [ - Course(title="Course 1", lessons=[]), - Course(title="Course 2", lessons=[]), - Course(title="Course 3", lessons=[]), - ] - chunks = [ - [CourseChunk(content="c1", course_title="Course 1", chunk_index=0)], - [CourseChunk(content="c2", course_title="Course 2", chunk_index=0)], - [CourseChunk(content="c3", course_title="Course 3", chunk_index=0)], - ] + mock_document1 = Document( + file_name="test1.md", + file_path=os.path.join(temp_dir, "test1.md"), + title="Test Doc 1", + sections=["Introduction"], + ) + mock_document2 = Document( + file_name="test2.md", + file_path=os.path.join(temp_dir, "test2.md"), + title="Test Doc 2", + sections=["Advanced"], + ) - mock_doc_proc.return_value.process_course_document.side_effect = [ - (courses[0], chunks[0]), - (courses[1], chunks[1]), - (courses[2], chunks[2]), + mock_md_processor.process_md_file.side_effect = [ + (mock_document1, sample_document_chunks[:1]), # test1.md + (mock_document2, sample_document_chunks[1:]), # test2.md ] - rag_system = RAGSystem(test_config) + mock_vector_store.get_existing_filenames.return_value = [] + + rag_system = RAGSystem("./data") # Execute - total_courses, total_chunks = rag_system.add_course_folder( - "/path/to/docs", clear_existing=False - ) + total_docs, total_chunks = rag_system.load_md_folder(temp_dir) # Assert - assert total_courses == 3 - assert total_chunks == 3 - - # Verify only PDF, TXT, DOCX files were processed (not JPG) - assert mock_doc_proc.return_value.process_course_document.call_count == 3 + assert total_docs == 2 # Only .md files processed + assert total_chunks == 2 - def test_add_course_folder_with_clear_existing(self, test_config): - """Test adding courses with clear_existing=True""" - with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), - patch("os.path.exists") as mock_exists, - patch("os.listdir") as mock_listdir, - ): - - mock_exists.return_value = True - mock_listdir.return_value = [] + # Verify MD processor was called for each .md file + assert mock_md_processor.process_md_file.call_count == 2 - rag_system = RAGSystem(test_config) + # Verify chunks were added to vector store + mock_vector_store.add_document_content.assert_called() - # Execute with clear_existing=True - rag_system.add_course_folder("/path/to/docs", clear_existing=True) - - # Verify data was cleared - mock_vector_store.return_value.clear_all_data.assert_called_once() - - def test_add_course_folder_skip_existing(self, test_config): - """Test that existing courses are skipped""" + def test_load_md_folder_skip_existing(self, test_config, temp_dir): + """Test that existing files are skipped""" with ( - patch("rag_system.DocumentProcessor") as mock_doc_proc, + patch("rag_system.MDProcessor") as mock_md_processor, patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), patch("os.path.exists") as mock_exists, patch("os.listdir") as mock_listdir, ): - mock_exists.return_value = True - mock_listdir.return_value = ["course1.pdf"] - - # Mock existing course titles - mock_vector_store.return_value.get_existing_course_titles.return_value = [ - "Existing Course" - ] + mock_listdir.return_value = ["existing.md", "new.md"] - # Mock document processing to return existing course - existing_course = Course(title="Existing Course", lessons=[]) - mock_chunks = [ - CourseChunk( - content="content", course_title="Existing Course", chunk_index=0 - ) - ] - mock_doc_proc.return_value.process_course_document.return_value = ( - existing_course, - mock_chunks, - ) + # Mock existing filenames + mock_vector_store.return_value.get_existing_filenames.return_value = ["existing.md"] - rag_system = RAGSystem(test_config) + rag_system = RAGSystem("./data") # Execute - total_courses, total_chunks = rag_system.add_course_folder("/path/to/docs") + total_docs, total_chunks = rag_system.load_md_folder(temp_dir) - # Assert no courses were added (because it was existing) - assert total_courses == 0 - assert total_chunks == 0 + # Assert only new file was processed + assert total_docs == 1 + assert total_chunks == 0 # No chunks from new file in this test - # Verify add methods were not called - mock_vector_store.return_value.add_course_metadata.assert_not_called() - mock_vector_store.return_value.add_course_content.assert_not_called() + # Verify only new.md was processed + mock_md_processor.return_value.process_md_file.assert_called_once() - def test_add_course_folder_nonexistent(self, test_config): - """Test handling of nonexistent folder""" + def test_load_md_folder_nonexistent(self, test_config): + """Test loading from nonexistent folder""" with ( - patch("rag_system.DocumentProcessor"), + patch("rag_system.MDProcessor"), patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), patch("os.path.exists") as mock_exists, ): - mock_exists.return_value = False - rag_system = RAGSystem(test_config) + rag_system = RAGSystem("./data") # Execute - total_courses, total_chunks = rag_system.add_course_folder( - "/nonexistent/path" - ) + total_docs, total_chunks = rag_system.load_md_folder("/nonexistent") # Assert - assert total_courses == 0 + assert total_docs == 0 assert total_chunks == 0 - def test_get_course_analytics(self, test_config): - """Test course analytics retrieval""" + def test_load_md_folder_with_clear_existing(self, test_config, temp_dir): + """Test loading with clear_existing=True""" with ( - patch("rag_system.DocumentProcessor"), + patch("rag_system.MDProcessor"), patch("rag_system.VectorStore") as mock_vector_store, - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), + patch("os.path.exists") as mock_exists, + patch("os.listdir") as mock_listdir, ): + mock_exists.return_value = True + mock_listdir.return_value = [] - # Setup mocks - mock_vector_store.return_value.get_course_count.return_value = 5 - mock_vector_store.return_value.get_existing_course_titles.return_value = [ - "Course 1", - "Course 2", - "Course 3", - "Course 4", - "Course 5", - ] - - rag_system = RAGSystem(test_config) + rag_system = RAGSystem("./data") - # Execute - analytics = rag_system.get_course_analytics() + # Execute with clear_existing=True + rag_system.load_md_folder(temp_dir, clear_existing=True) - # Assert - assert analytics["total_courses"] == 5 - assert len(analytics["course_titles"]) == 5 - assert "Course 1" in analytics["course_titles"] + # Verify data was cleared + mock_vector_store.return_value.clear_all_data.assert_called_once() - def test_tool_registration(self, test_config): - """Test that tools are properly registered with tool manager""" + def test_load_md_folder_processing_error(self, test_config, temp_dir): + """Test handling of processing errors""" with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator"), - patch("rag_system.SessionManager"), + patch("rag_system.MDProcessor") as mock_md_processor, + patch("rag_system.VectorStore") as mock_vector_store, + patch("os.path.exists") as mock_exists, + patch("os.listdir") as mock_listdir, ): + mock_exists.return_value = True + mock_listdir.return_value = ["bad.md"] - rag_system = RAGSystem(test_config) + # Mock processing to raise exception + mock_md_processor.return_value.process_md_file.side_effect = Exception("Processing failed") - # Verify tools are registered - tool_definitions = rag_system.tool_manager.get_tool_definitions() - tool_names = [tool["name"] for tool in tool_definitions] + rag_system = RAGSystem("./data") - assert "search_course_content" in tool_names - assert "get_course_outline" in tool_names + # Execute + total_docs, total_chunks = rag_system.load_md_folder(temp_dir) - def test_source_tracking_and_reset(self, test_config): - """Test that sources are properly tracked and reset after queries""" - with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator") as mock_ai_gen, - patch("rag_system.SessionManager"), - ): + # Assert error was handled gracefully + assert total_docs == 0 + assert total_chunks == 0 - mock_ai_gen.return_value.generate_response.return_value = "Test response" + def test_get_document_stats(self, mock_vector_store): + """Test getting document statistics""" + rag_system = RAGSystem("./data") - rag_system = RAGSystem(test_config) + # Mock the vector store methods + rag_system.vector_store = mock_vector_store - # Mock tool manager methods - rag_system.tool_manager.get_last_sources = Mock(return_value=["Source 1"]) - rag_system.tool_manager.get_last_source_links = Mock( - return_value=["Link 1"] - ) - rag_system.tool_manager.reset_sources = Mock() + # Execute + stats = rag_system.get_document_stats() - # Execute query - response, sources, source_links = rag_system.query("Test query") + # Assert + assert "total_documents" in stats + assert "file_names" in stats + assert stats["total_documents"] == 2 + assert "test.md" in stats["file_names"] + assert "other.md" in stats["file_names"] - # Assert sources were retrieved - assert sources == ["Source 1"] - assert source_links == ["Link 1"] + def test_format_results(self, sample_search_results): + """Test result formatting""" + rag_system = RAGSystem("./data") - # Verify sources were reset after retrieval - rag_system.tool_manager.reset_sources.assert_called_once() + # Execute formatting + formatted = rag_system._format_results(sample_search_results) - def test_end_to_end_query_flow_integration(self, test_config): - """Test complete end-to-end query processing flow""" - with ( - patch("rag_system.DocumentProcessor"), - patch("rag_system.VectorStore"), - patch("rag_system.AIGenerator") as mock_ai_gen, - patch("rag_system.SessionManager") as mock_session, - ): + # Assert formatting includes file and section info + assert "test.md" in formatted + assert "Introduction" in formatted + assert "---" in formatted # Section separator - # Setup comprehensive mocks - mock_session.return_value.create_session.return_value = "new_session_123" - mock_session.return_value.get_conversation_history.return_value = None - mock_ai_gen.return_value.generate_response.return_value = ( - "Comprehensive answer based on course materials." - ) + def test_extract_sources(self, sample_search_results): + """Test source extraction""" + rag_system = RAGSystem("./data") - rag_system = RAGSystem(test_config) - rag_system.tool_manager.get_last_sources = Mock( - return_value=["Complete Course - Lesson 5"] - ) - rag_system.tool_manager.get_last_source_links = Mock( - return_value=["https://example.com/lesson5"] - ) + # Execute source extraction + sources = rag_system._extract_sources(sample_search_results) - # Execute complete flow - response, sources, source_links = rag_system.query( - "Explain the complete concept from lesson 5" - ) + # Assert sources are properly formatted + assert len(sources) == 2 + assert "test.md" in sources[0] + assert "Introduction" in sources[0] + + def test_auto_load_on_init(self, test_config): + """Test that MD folder is automatically loaded on initialization""" + with ( + patch("rag_system.MDProcessor"), + patch("rag_system.VectorStore"), + patch.object(RAGSystem, "load_md_folder") as mock_load, + ): + mock_load.return_value = (3, 15) - # Assert complete response - assert "Comprehensive answer" in response - assert sources == ["Complete Course - Lesson 5"] - assert source_links == ["https://example.com/lesson5"] + rag_system = RAGSystem("./data") - # Verify AI was called with proper parameters - call_args = mock_ai_gen.return_value.generate_response.call_args[1] - assert ( - call_args["query"] - == "Answer this question about course materials: Explain the complete concept from lesson 5" - ) - assert "tools" in call_args - assert "tool_manager" in call_args + # Verify load_md_folder was called during initialization + mock_load.assert_called_once_with("./data", clear_existing=False) diff --git a/backend/tests/test_vector_store.py b/backend/tests/test_vector_store.py index 3a452f7..5db8bd2 100644 --- a/backend/tests/test_vector_store.py +++ b/backend/tests/test_vector_store.py @@ -1,116 +1,75 @@ import os -import shutil import sys -import tempfile -from unittest.mock import MagicMock, Mock, patch +from unittest.mock import Mock, patch import pytest # Add parent directory to path to import modules sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) -from models import Course, CourseChunk, Lesson +from models import DocumentChunk from vector_store import SearchResults, VectorStore class TestVectorStore: """Test cases for VectorStore""" - def test_search_with_proper_max_results(self, test_config, mock_chroma_collection): - """Test search with proper MAX_RESULTS setting""" + def test_init(self, test_config): + """Test VectorStore initialization""" with patch("vector_store.chromadb") as mock_chromadb: - # Setup mocks mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client - mock_client.get_or_create_collection.return_value = mock_chroma_collection + mock_collection = Mock() + mock_client.get_or_create_collection.return_value = mock_collection - # Create vector store with proper config (MAX_RESULTS=5) vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, embedding_model=test_config.EMBEDDING_MODEL, - max_results=test_config.MAX_RESULTS, # Should be 5 + max_results=test_config.MAX_RESULTS, ) - # Execute search - result = vector_store.search("test query") - - # Assert that ChromaDB query was called with proper n_results - mock_chroma_collection.query.assert_called_once_with( - query_texts=["test query"], - n_results=5, # Should be 5, not 0 - where=None, + # Verify ChromaDB client was created + mock_chromadb.PersistentClient.assert_called_once_with( + path=test_config.CHROMA_PATH, settings=mock_chromadb.PersistentClient.call_args[1]["settings"] ) - # Verify results - assert not result.is_empty() - assert len(result.documents) == 1 - assert result.documents[0] == "Sample document" + # Verify collection was created + mock_client.get_or_create_collection.assert_called_once() - def test_search_with_broken_max_results_zero( - self, broken_config, mock_chroma_collection - ): - """Test the critical MAX_RESULTS=0 issue""" + def test_search_success(self, test_config, mock_chroma_collection): + """Test successful search""" with patch("vector_store.chromadb") as mock_chromadb: - # Setup mocks - simulate empty results when n_results=0 mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client + mock_client.get_or_create_collection.return_value = mock_chroma_collection - # Mock collection that returns empty results when n_results=0 - empty_collection = Mock() - empty_collection.query.return_value = { - "documents": [[]], # Empty results due to n_results=0 - "metadatas": [[]], - "distances": [[]], - } - mock_client.get_or_create_collection.return_value = empty_collection - - # Create vector store with broken config (MAX_RESULTS=0) vector_store = VectorStore( - chroma_path=broken_config.CHROMA_PATH, - embedding_model=broken_config.EMBEDDING_MODEL, - max_results=broken_config.MAX_RESULTS, # This is 0! + chroma_path=test_config.CHROMA_PATH, + embedding_model=test_config.EMBEDDING_MODEL, + max_results=test_config.MAX_RESULTS, ) # Execute search - result = vector_store.search("valid query") + result = vector_store.search("test query") - # Assert that ChromaDB query was called with n_results=0 - empty_collection.query.assert_called_once_with( - query_texts=["valid query"], n_results=0, where=None # This is the bug! + # Verify ChromaDB query was called correctly + mock_chroma_collection.query.assert_called_once_with( + query_texts=["test query"], + n_results=5, + where=None, ) - # Verify that we get empty results even for valid queries - assert result.is_empty() - assert len(result.documents) == 0 - - # This demonstrates the root cause of "query failed" responses + # Verify results + assert not result.is_empty() + assert len(result.documents) == 1 + assert result.documents[0] == "Sample document" - def test_search_with_course_filter(self, test_config, mock_chroma_collection): - """Test search with course name filter""" + def test_search_with_file_filter(self, test_config, mock_chroma_collection): + """Test search with file name filter""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client - - # Mock course catalog for course name resolution - course_catalog = Mock() - course_catalog.query.return_value = { - "documents": [["Test Course"]], - "metadatas": [[{"title": "Test Course"}]], - } - - # Mock content collection - content_collection = Mock() - content_collection.query.return_value = { - "documents": [["Filtered content"]], - "metadatas": [[{"course_title": "Test Course", "lesson_number": 1}]], - "distances": [[0.1]], - } - - # Setup collection returns - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] + mock_client.get_or_create_collection.return_value = mock_chroma_collection vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -118,26 +77,16 @@ def test_search_with_course_filter(self, test_config, mock_chroma_collection): max_results=test_config.MAX_RESULTS, ) - # Execute search with course filter - result = vector_store.search("test query", course_name="Test") - - # Assert course resolution was called - course_catalog.query.assert_called_once_with( - query_texts=["Test"], n_results=1 - ) + # Execute search with file filter + result = vector_store.search("test query", file_name="test.md") - # Assert content search was called with proper filter - content_collection.query.assert_called_once_with( - query_texts=["test query"], - n_results=5, - where={"course_title": "Test Course"}, + # Verify search was called with file filter + mock_chroma_collection.query.assert_called_once_with( + query_texts=["test query"], n_results=5, where={"file_name": "test.md"} ) - assert not result.is_empty() - assert result.documents[0] == "Filtered content" - - def test_search_with_lesson_filter(self, test_config, mock_chroma_collection): - """Test search with lesson number filter""" + def test_search_with_section_filter(self, test_config, mock_chroma_collection): + """Test search with section title filter""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client @@ -149,41 +98,20 @@ def test_search_with_lesson_filter(self, test_config, mock_chroma_collection): max_results=test_config.MAX_RESULTS, ) - # Execute search with lesson filter - result = vector_store.search("test query", lesson_number=2) + # Execute search with section filter + result = vector_store.search("test query", section_title="Introduction") - # Assert search was called with lesson filter + # Verify search was called with section filter mock_chroma_collection.query.assert_called_once_with( - query_texts=["test query"], n_results=5, where={"lesson_number": 2} + query_texts=["test query"], n_results=5, where={"section_title": "Introduction"} ) - def test_search_with_both_filters(self, test_config): - """Test search with both course and lesson filters""" + def test_search_with_both_filters(self, test_config, mock_chroma_collection): + """Test search with both file and section filters""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client - - # Mock course catalog - course_catalog = Mock() - course_catalog.query.return_value = { - "documents": [["Specific Course"]], - "metadatas": [[{"title": "Specific Course"}]], - } - - # Mock content collection - content_collection = Mock() - content_collection.query.return_value = { - "documents": [["Specific content"]], - "metadatas": [ - [{"course_title": "Specific Course", "lesson_number": 3}] - ], - "distances": [[0.1]], - } - - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] + mock_client.get_or_create_collection.return_value = mock_chroma_collection vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -193,37 +121,27 @@ def test_search_with_both_filters(self, test_config): # Execute search with both filters result = vector_store.search( - "test query", course_name="Specific", lesson_number=3 + "test query", file_name="test.md", section_title="Introduction" ) - # Assert content search was called with combined filter + # Verify search was called with combined filter expected_filter = { - "$and": [{"course_title": "Specific Course"}, {"lesson_number": 3}] + "$and": [{"file_name": "test.md"}, {"section_title": "Introduction"}] } - content_collection.query.assert_called_once_with( + mock_chroma_collection.query.assert_called_once_with( query_texts=["test query"], n_results=5, where=expected_filter ) - def test_resolve_course_name_success(self, test_config): - """Test successful course name resolution""" + def test_search_database_error(self, test_config): + """Test search when database query fails""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client - # Mock course catalog with matching course - course_catalog = Mock() - course_catalog.query.return_value = { - "documents": [["Building Towards Computer Use with Anthropic"]], - "metadatas": [ - [{"title": "Building Towards Computer Use with Anthropic"}] - ], - } - - content_collection = Mock() - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] + # Mock collection that raises exception + mock_collection = Mock() + mock_collection.query.side_effect = Exception("Database connection failed") + mock_client.get_or_create_collection.return_value = mock_collection vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -231,32 +149,21 @@ def test_resolve_course_name_success(self, test_config): max_results=test_config.MAX_RESULTS, ) - # Test course name resolution - resolved_name = vector_store._resolve_course_name("Anthropic") + # Execute search that will fail + result = vector_store.search("test query") - assert resolved_name == "Building Towards Computer Use with Anthropic" - course_catalog.query.assert_called_once_with( - query_texts=["Anthropic"], n_results=1 - ) + # Should return error result + assert result.error == "Search error: Database connection failed" + assert result.is_empty() - def test_resolve_course_name_not_found(self, test_config): - """Test course name resolution when course not found""" + def test_add_document_content(self, test_config, sample_document_chunks): + """Test adding document content chunks to vector store""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client - # Mock course catalog with no results - course_catalog = Mock() - course_catalog.query.return_value = { - "documents": [[]], # No matching courses - "metadatas": [[]], - } - - content_collection = Mock() - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] + mock_collection = Mock() + mock_client.get_or_create_collection.return_value = mock_collection vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -264,26 +171,38 @@ def test_resolve_course_name_not_found(self, test_config): max_results=test_config.MAX_RESULTS, ) - # Test course name resolution failure - resolved_name = vector_store._resolve_course_name("NonexistentCourse") + # Add document content + vector_store.add_document_content(sample_document_chunks) + + # Verify collection add was called + mock_collection.add.assert_called_once() + call_args = mock_collection.add.call_args + + # Check that correct data was passed + assert len(call_args[1]["documents"]) == len(sample_document_chunks) + assert len(call_args[1]["metadatas"]) == len(sample_document_chunks) + assert len(call_args[1]["ids"]) == len(sample_document_chunks) - assert resolved_name is None + # Check first chunk data + assert call_args[1]["documents"][0] == sample_document_chunks[0].content + assert call_args[1]["metadatas"][0]["file_name"] == sample_document_chunks[0].file_name + assert call_args[1]["metadatas"][0]["section_title"] == sample_document_chunks[0].section_title - def test_search_course_not_found(self, test_config): - """Test search when course name cannot be resolved""" + def test_get_existing_filenames(self, test_config): + """Test getting existing file names""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client - # Mock course catalog with no results - course_catalog = Mock() - course_catalog.query.return_value = {"documents": [[]], "metadatas": [[]]} - - content_collection = Mock() - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] + mock_collection = Mock() + mock_collection.get.return_value = { + "metadatas": [ + {"file_name": "test1.md"}, + {"file_name": "test2.md"}, + {"file_name": "test1.md"}, # Duplicate + ] + } + mock_client.get_or_create_collection.return_value = mock_collection vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -291,30 +210,27 @@ def test_search_course_not_found(self, test_config): max_results=test_config.MAX_RESULTS, ) - # Execute search with nonexistent course - result = vector_store.search("test query", course_name="NonexistentCourse") + # Get existing filenames + filenames = vector_store.get_existing_filenames() - # Should return error result - assert result.error == "No course found matching 'NonexistentCourse'" - assert result.is_empty() + # Should return deduplicated list + assert set(filenames) == {"test1.md", "test2.md"} - def test_search_database_error(self, test_config): - """Test search when database query fails""" + def test_get_document_count(self, mock_vector_store): + """Test getting document count""" + count = mock_vector_store.get_document_count() + assert count == 2 # Based on mock setup + + def test_clear_all_data(self, test_config): + """Test clearing all data""" with patch("vector_store.chromadb") as mock_chromadb: mock_client = Mock() mock_chromadb.PersistentClient.return_value = mock_client # Mock collections - course_catalog = Mock() - content_collection = Mock() - content_collection.query.side_effect = Exception( - "Database connection failed" - ) - - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] + mock_collection = Mock() + mock_client.get_or_create_collection.return_value = mock_collection + mock_client.delete_collection = Mock() vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -322,12 +238,13 @@ def test_search_database_error(self, test_config): max_results=test_config.MAX_RESULTS, ) - # Execute search that will fail - result = vector_store.search("test query") + # Clear all data + vector_store.clear_all_data() - # Should return error result - assert result.error == "Search error: Database connection failed" - assert result.is_empty() + # Verify collection was deleted and recreated + mock_client.delete_collection.assert_called_once_with("document_content") + # get_or_create_collection should be called twice (once in init, once in clear) + assert mock_client.get_or_create_collection.call_count >= 2 def test_build_filter_no_filters(self, test_config): """Test filter building with no filters""" @@ -341,8 +258,8 @@ def test_build_filter_no_filters(self, test_config): filter_dict = vector_store._build_filter(None, None) assert filter_dict is None - def test_build_filter_course_only(self, test_config): - """Test filter building with course filter only""" + def test_build_filter_file_only(self, test_config): + """Test filter building with file filter only""" with patch("vector_store.chromadb"): vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -350,11 +267,11 @@ def test_build_filter_course_only(self, test_config): max_results=test_config.MAX_RESULTS, ) - filter_dict = vector_store._build_filter("Test Course", None) - assert filter_dict == {"course_title": "Test Course"} + filter_dict = vector_store._build_filter("test.md", None) + assert filter_dict == {"file_name": "test.md"} - def test_build_filter_lesson_only(self, test_config): - """Test filter building with lesson filter only""" + def test_build_filter_section_only(self, test_config): + """Test filter building with section filter only""" with patch("vector_store.chromadb"): vector_store = VectorStore( chroma_path=test_config.CHROMA_PATH, @@ -362,8 +279,8 @@ def test_build_filter_lesson_only(self, test_config): max_results=test_config.MAX_RESULTS, ) - filter_dict = vector_store._build_filter(None, 2) - assert filter_dict == {"lesson_number": 2} + filter_dict = vector_store._build_filter(None, "Introduction") + assert filter_dict == {"section_title": "Introduction"} def test_build_filter_both(self, test_config): """Test filter building with both filters""" @@ -374,121 +291,10 @@ def test_build_filter_both(self, test_config): max_results=test_config.MAX_RESULTS, ) - filter_dict = vector_store._build_filter("Test Course", 2) - expected = {"$and": [{"course_title": "Test Course"}, {"lesson_number": 2}]} + filter_dict = vector_store._build_filter("test.md", "Introduction") + expected = {"$and": [{"file_name": "test.md"}, {"section_title": "Introduction"}]} assert filter_dict == expected - def test_add_course_metadata(self, test_config, sample_course): - """Test adding course metadata to vector store""" - with patch("vector_store.chromadb") as mock_chromadb: - mock_client = Mock() - mock_chromadb.PersistentClient.return_value = mock_client - - course_catalog = Mock() - content_collection = Mock() - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] - - vector_store = VectorStore( - chroma_path=test_config.CHROMA_PATH, - embedding_model=test_config.EMBEDDING_MODEL, - max_results=test_config.MAX_RESULTS, - ) - - # Add course metadata - vector_store.add_course_metadata(sample_course) - - # Verify course catalog add was called - course_catalog.add.assert_called_once() - call_args = course_catalog.add.call_args - - assert call_args[1]["documents"] == [sample_course.title] - assert call_args[1]["ids"] == [sample_course.title] - - metadata = call_args[1]["metadatas"][0] - assert metadata["title"] == sample_course.title - assert metadata["instructor"] == sample_course.instructor - assert metadata["course_link"] == sample_course.course_link - assert metadata["lesson_count"] == len(sample_course.lessons) - assert "lessons_json" in metadata - - def test_add_course_content(self, test_config, sample_course_chunks): - """Test adding course content chunks to vector store""" - with patch("vector_store.chromadb") as mock_chromadb: - mock_client = Mock() - mock_chromadb.PersistentClient.return_value = mock_client - - course_catalog = Mock() - content_collection = Mock() - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] - - vector_store = VectorStore( - chroma_path=test_config.CHROMA_PATH, - embedding_model=test_config.EMBEDDING_MODEL, - max_results=test_config.MAX_RESULTS, - ) - - # Add course content - vector_store.add_course_content(sample_course_chunks) - - # Verify content collection add was called - content_collection.add.assert_called_once() - call_args = content_collection.add.call_args - - assert len(call_args[1]["documents"]) == len(sample_course_chunks) - assert len(call_args[1]["metadatas"]) == len(sample_course_chunks) - assert len(call_args[1]["ids"]) == len(sample_course_chunks) - - # Check first chunk - assert call_args[1]["documents"][0] == sample_course_chunks[0].content - assert ( - call_args[1]["metadatas"][0]["course_title"] - == sample_course_chunks[0].course_title - ) - assert ( - call_args[1]["metadatas"][0]["lesson_number"] - == sample_course_chunks[0].lesson_number - ) - - def test_get_lesson_link(self, test_config): - """Test retrieving lesson link""" - with patch("vector_store.chromadb") as mock_chromadb: - mock_client = Mock() - mock_chromadb.PersistentClient.return_value = mock_client - - # Mock course catalog with lesson data - course_catalog = Mock() - course_catalog.get.return_value = { - "metadatas": [ - { - "lessons_json": '[{"lesson_number": 1, "lesson_title": "Test Lesson", "lesson_link": "https://example.com/lesson1"}]' - } - ] - } - - content_collection = Mock() - mock_client.get_or_create_collection.side_effect = [ - course_catalog, - content_collection, - ] - - vector_store = VectorStore( - chroma_path=test_config.CHROMA_PATH, - embedding_model=test_config.EMBEDDING_MODEL, - max_results=test_config.MAX_RESULTS, - ) - - # Get lesson link - link = vector_store.get_lesson_link("Test Course", 1) - - assert link == "https://example.com/lesson1" - course_catalog.get.assert_called_once_with(ids=["Test Course"]) - def test_search_results_from_chroma(self): """Test SearchResults.from_chroma method""" chroma_results = { @@ -514,3 +320,26 @@ def test_search_results_empty(self): assert results.distances == [] assert results.error == "Test error message" assert results.is_empty() + + def test_search_with_limit(self, test_config, mock_chroma_collection): + """Test search with custom limit""" + with patch("vector_store.chromadb") as mock_chromadb: + mock_client = Mock() + mock_chromadb.PersistentClient.return_value = mock_client + mock_client.get_or_create_collection.return_value = mock_chroma_collection + + vector_store = VectorStore( + chroma_path=test_config.CHROMA_PATH, + embedding_model=test_config.EMBEDDING_MODEL, + max_results=test_config.MAX_RESULTS, + ) + + # Execute search with custom limit + result = vector_store.search("test query", limit=3) + + # Verify ChromaDB query was called with custom limit + mock_chroma_collection.query.assert_called_once_with( + query_texts=["test query"], + n_results=3, # Custom limit + where=None, + ) diff --git a/uv.lock b/uv.lock index 65201f2..f27ce1e 100644 --- a/uv.lock +++ b/uv.lock @@ -70,6 +70,7 @@ dependencies = [ { name = "chromadb" }, { name = "load-dotenv" }, { name = "pydantic" }, + { name = "pytest" }, { name = "sentence-transformers" }, ] @@ -78,6 +79,7 @@ requires-dist = [ { name = "chromadb", specifier = ">=1.0.15" }, { name = "load-dotenv", specifier = ">=0.1.0" }, { name = "pydantic", specifier = ">=2.11.7" }, + { name = "pytest", specifier = ">=8.4.1" }, { name = "sentence-transformers", specifier = ">=5.0.0" }, ]