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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