diff --git a/.github/workflows/claude-code-review.yml b/.github/workflows/claude-code-review.yml new file mode 100644 index 000000000..b5e8cfd4d --- /dev/null +++ b/.github/workflows/claude-code-review.yml @@ -0,0 +1,44 @@ +name: Claude Code Review + +on: + pull_request: + types: [opened, synchronize, ready_for_review, reopened] + # Optional: Only run on specific file changes + # paths: + # - "src/**/*.ts" + # - "src/**/*.tsx" + # - "src/**/*.js" + # - "src/**/*.jsx" + +jobs: + claude-review: + # Optional: Filter by PR author + # if: | + # github.event.pull_request.user.login == 'external-contributor' || + # github.event.pull_request.user.login == 'new-developer' || + # github.event.pull_request.author_association == 'FIRST_TIME_CONTRIBUTOR' + + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: read + issues: read + id-token: write + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 1 + + - name: Run Claude Code Review + id: claude-review + uses: anthropics/claude-code-action@v1 + with: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + plugin_marketplaces: 'https://github.com/anthropics/claude-code.git' + plugins: 'code-review@claude-code-plugins' + prompt: '/code-review:code-review ${{ github.repository }}/pull/${{ github.event.pull_request.number }}' + # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md + # or https://code.claude.com/docs/en/cli-reference for available options + diff --git a/.github/workflows/claude.yml b/.github/workflows/claude.yml new file mode 100644 index 000000000..d300267f1 --- /dev/null +++ b/.github/workflows/claude.yml @@ -0,0 +1,50 @@ +name: Claude Code + +on: + issue_comment: + types: [created] + pull_request_review_comment: + types: [created] + issues: + types: [opened, assigned] + pull_request_review: + types: [submitted] + +jobs: + claude: + if: | + (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) || + (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) || + (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) || + (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude'))) + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: read + issues: read + id-token: write + actions: read # Required for Claude to read CI results on PRs + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 1 + + - name: Run Claude Code + id: claude + uses: anthropics/claude-code-action@v1 + with: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + + # This is an optional setting that allows Claude to read CI results on PRs + additional_permissions: | + actions: read + + # Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it. + # prompt: 'Update the pull request description to include a summary of changes.' + + # Optional: Add claude_args to customize behavior and configuration + # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md + # or https://code.claude.com/docs/en/cli-reference for available options + # claude_args: '--allowed-tools Bash(gh pr:*)' + diff --git a/.python-version b/.python-version index 24ee5b1be..2c0733315 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.13 +3.11 diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 000000000..a19da6710 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,94 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Commands + +**Always use `uv` to run Python commands and manage dependencies. Never use `pip` directly.** + +```bash +# Install dependencies +uv sync + +# Install dev dependencies (includes black) +uv sync --extra dev + +# Run the application (starts on http://localhost:8000) +./run.sh +# Or manually: +cd backend && uv run uvicorn app:app --reload --port 8000 + +# Run a single Python file +uv run python backend/.py + +# Code quality commands +./format.sh # Format all Python files with black +./check.sh # Check formatting (exits with error if issues found) +``` + +**Environment Setup**: Copy `.env.example` to `.env` and set `ANTHROPIC_API_KEY`. + +## Architecture + +This is a RAG (Retrieval-Augmented Generation) chatbot for querying course materials. The system uses an agentic pattern where Claude decides when to search. + +### Query Flow + +``` +Frontend (script.js) + → POST /api/query + → RAGSystem.query() + → AIGenerator calls Claude with tools + → Claude invokes search_course_content tool + → CourseSearchTool.execute() + → VectorStore.search() (ChromaDB) + → Results returned to Claude + → Final response sent to frontend +``` + +### Backend Components (`backend/`) + +| File | Purpose | +|------|---------| +| `app.py` | FastAPI endpoints, serves frontend, loads docs on startup | +| `rag_system.py` | Main orchestrator - coordinates all components | +| `ai_generator.py` | Claude API integration with tool execution loop | +| `search_tools.py` | Tool definitions for Claude (CourseSearchTool) | +| `vector_store.py` | ChromaDB wrapper with two collections | +| `document_processor.py` | Parses course files, chunks text with overlap | +| `session_manager.py` | Conversation history per session | +| `config.py` | Settings dataclass (chunk size, model, etc.) | +| `models.py` | Pydantic models: Course, Lesson, CourseChunk | + +### ChromaDB Collections + +- **`course_catalog`**: Course metadata for fuzzy name resolution (title → exact match) +- **`course_content`**: Text chunks with embeddings for semantic search + +### Document Format + +Course files in `/docs` follow this structure: +``` +Course Title: [Name] +Course Link: [URL] +Course Instructor: [Name] + +Lesson 0: [Title] +Lesson Link: [URL] +[Content...] + +Lesson 1: [Title] +[Content...] +``` + +### Key Configuration (`config.py`) + +- `CHUNK_SIZE`: 800 chars, `CHUNK_OVERLAP`: 100 chars +- `MAX_RESULTS`: 5 search results returned +- `MAX_HISTORY`: 2 conversation exchanges kept +- Embedding model: `all-MiniLM-L6-v2` +- LLM: `claude-sonnet-4-20250514` + +### Frontend (`frontend/`) + +Vanilla HTML/CSS/JS with Marked.js for markdown rendering. Communicates via `/api/query` and `/api/courses` endpoints. diff --git a/backend/ai_generator.py b/backend/ai_generator.py index 0363ca90c..ce069e5e4 100644 --- a/backend/ai_generator.py +++ b/backend/ai_generator.py @@ -1,25 +1,39 @@ import anthropic from typing import List, Optional, Dict, Any + 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 a comprehensive search tool for course information. + SYSTEM_PROMPT = """ You are an AI assistant specialized in course materials and educational content with access to tools for course information. + +Available Tools: +1. **search_course_content**: Search for specific content within course materials + - Use for questions about specific topics, concepts, or detailed educational content +2. **get_course_outline**: Get the complete structure of a course + - Use for questions about course structure, lessons list, or course overview + - Returns: course title, course link, and all lessons (number and title for each) -Search Tool Usage: -- Use the search tool **only** for questions about specific course content or detailed educational materials -- **One search per query maximum** -- Synthesize search results into accurate, fact-based responses -- If search yields no results, state this clearly without offering alternatives +Tool Usage Guidelines: +- **Up to 2 sequential tool calls per query** if needed for complex questions +- First search for content, then optionally get more details or search related topics +- Use **get_course_outline** when asked about: + - What lessons are in a course + - Course structure or outline + - Overview of a course + - How many lessons a course has +- Use **search_course_content** for questions about specific topics or content within courses +- Synthesize results into accurate, fact-based responses +- If a tool yields no results, state this clearly without offering alternatives Response Protocol: -- **General knowledge questions**: Answer using existing knowledge without searching -- **Course-specific questions**: Search first, then answer +- **General knowledge questions**: Answer using existing knowledge without using tools +- **Course outline questions**: Use get_course_outline, then present the course title, course link, and lesson list +- **Course content questions**: Use search_course_content, 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" - + - Provide direct answers only — no reasoning process, tool explanations, or question-type analysis + - Do not mention "based on the results" All responses must be: 1. **Brief, Concise and focused** - Get to the point quickly @@ -28,108 +42,166 @@ class AIGenerator: 4. **Example-supported** - Include relevant examples when they aid understanding Provide only the direct answer to what was asked. """ - + + # Maximum sequential tool calling rounds per query + MAX_TOOL_ROUNDS = 2 + 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: + 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. - + Generate AI response with support for sequential tool calls (max 2 rounds). + + Uses a loop-based approach where: + - Each iteration is a complete API call + - Tools remain available until max rounds reached + - Messages accumulate across rounds + + Termination conditions: + - (a) MAX_TOOL_ROUNDS reached + - (b) Claude responds without tool_use + - (c) Tool execution fails + 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 + + # Build system content system_content = ( f"{self.SYSTEM_PROMPT}\n\nPrevious conversation:\n{conversation_history}" - if conversation_history + if conversation_history else self.SYSTEM_PROMPT ) - - # Prepare API call parameters efficiently - api_params = { - **self.base_params, - "messages": [{"role": "user", "content": query}], - "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: - return self._handle_tool_execution(response, api_params, tool_manager) - - # Return direct response - return response.content[0].text - - def _handle_tool_execution(self, initial_response, base_params: Dict[str, Any], tool_manager): + + # Initialize message list - accumulates across rounds + messages = [{"role": "user", "content": query}] + + # Main tool-calling loop + for round_num in range(self.MAX_TOOL_ROUNDS): + # 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"} + + # Make API call + response = self.client.messages.create(**api_params) + + # Termination condition (b): no tool use requested + if response.stop_reason != "tool_use": + return self._extract_text_response(response) + + # Tool use requested but no tool_manager - return any text + if not tool_manager: + return self._extract_text_response(response) + + # Execute tools and check for errors - condition (c) + tool_results, has_error = self._execute_tools(response, tool_manager) + + # Append assistant's tool use response to messages + messages.append({"role": "assistant", "content": response.content}) + + # Append tool results as user message + messages.append({"role": "user", "content": tool_results}) + + # If tool error, make final call without tools + if has_error: + return self._get_final_response(messages, system_content) + + # MAX_TOOL_ROUNDS reached - make final API call without tools + return self._get_final_response(messages, system_content) + + def _extract_text_response(self, response) -> str: """ - Handle execution of tool calls and get follow-up response. - + Extract text content from a Claude response. + + Handles responses that may contain mixed content blocks. + Returns the first text block found. + """ + for content_block in response.content: + if hasattr(content_block, "text"): + return content_block.text + return "" + + def _execute_tools(self, response, tool_manager): + """ + Execute all tool calls from a response and collect results. + Args: - initial_response: The response containing tool use requests - base_params: Base API parameters + response: Anthropic API response containing tool_use blocks tool_manager: Manager to execute tools - + Returns: - Final response text after tool execution + Tuple of (tool_results list for API, has_error boolean) """ - # Start with existing messages - messages = base_params["messages"].copy() - - # 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: + has_error = False + + for content_block in response.content: if content_block.type == "tool_use": - 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 - }) - - # Add tool results as single message - if tool_results: - messages.append({"role": "user", "content": tool_results}) - - # Prepare final API call without tools + 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_results.append( + { + "type": "tool_result", + "tool_use_id": content_block.id, + "content": f"Error executing tool: {str(e)}", + "is_error": True, + } + ) + has_error = True + + return tool_results, has_error + + def _get_final_response(self, messages: List[Dict], system_content: str) -> str: + """ + Make a final API call without tools to get a text response. + + Args: + messages: Accumulated conversation messages + system_content: System prompt with optional history + + Returns: + Final text response from Claude + """ final_params = { **self.base_params, "messages": messages, - "system": base_params["system"] + "system": system_content, } - - # Get final response + final_response = self.client.messages.create(**final_params) - return final_response.content[0].text \ No newline at end of file + return self._extract_text_response(final_response) diff --git a/backend/app.py b/backend/app.py index 5a69d741d..ad7b4b52c 100644 --- a/backend/app.py +++ b/backend/app.py @@ -1,4 +1,5 @@ import warnings + warnings.filterwarnings("ignore", message="resource_tracker: There appear to be.*") from fastapi import FastAPI, HTTPException @@ -16,10 +17,7 @@ app = FastAPI(title="Course Materials RAG System", root_path="") # Add trusted host middleware for proxy -app.add_middleware( - TrustedHostMiddleware, - allowed_hosts=["*"] -) +app.add_middleware(TrustedHostMiddleware, allowed_hosts=["*"]) # Enable CORS with proper settings for proxy app.add_middleware( @@ -34,25 +32,33 @@ # Initialize RAG system rag_system = RAGSystem(config) + # Pydantic models for request/response class QueryRequest(BaseModel): """Request model for course queries""" + query: str session_id: Optional[str] = None + class QueryResponse(BaseModel): """Response model for course queries""" + answer: str - sources: List[str] + sources: List[dict] # {"title": str, "url": str | None} session_id: str + class CourseStats(BaseModel): """Response model for course statistics""" + total_courses: int course_titles: 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""" @@ -61,18 +67,24 @@ async def query_documents(request: QueryRequest): session_id = request.session_id if not session_id: session_id = rag_system.session_manager.create_session() - + # Process query using RAG system answer, sources = rag_system.query(request.query, session_id) - - return QueryResponse( - answer=answer, - sources=sources, - session_id=session_id - ) + + return QueryResponse(answer=answer, sources=sources, session_id=session_id) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) + +@app.post("/api/session/new") +async def new_session(request: Optional[dict] = None): + """Create a new chat session, optionally clearing the old one.""" + if request and request.get("session_id"): + rag_system.session_manager.clear_session(request["session_id"]) + new_session_id = rag_system.session_manager.create_session() + return {"session_id": new_session_id} + + @app.get("/api/courses", response_model=CourseStats) async def get_course_stats(): """Get course analytics and statistics""" @@ -80,11 +92,12 @@ async def get_course_stats(): analytics = rag_system.get_course_analytics() return CourseStats( total_courses=analytics["total_courses"], - course_titles=analytics["course_titles"] + course_titles=analytics["course_titles"], ) 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""" @@ -92,11 +105,14 @@ async def startup_event(): if os.path.exists(docs_path): print("Loading initial documents...") try: - courses, chunks = rag_system.add_course_folder(docs_path, clear_existing=False) + courses, chunks = rag_system.add_course_folder( + docs_path, clear_existing=False + ) print(f"Loaded {courses} courses with {chunks} chunks") except Exception as e: print(f"Error loading documents: {e}") + # Custom static file handler with no-cache headers for development from fastapi.staticfiles import StaticFiles from fastapi.responses import FileResponse @@ -113,7 +129,7 @@ async def get_response(self, path: str, scope): response.headers["Pragma"] = "no-cache" response.headers["Expires"] = "0" return response - - + + # Serve static files for the frontend -app.mount("/", StaticFiles(directory="../frontend", html=True), name="static") \ No newline at end of file +app.mount("/", StaticFiles(directory="../frontend", html=True), name="static") diff --git a/backend/config.py b/backend/config.py index d9f6392ef..7379e7133 100644 --- a/backend/config.py +++ b/backend/config.py @@ -5,25 +5,26 @@ # Load environment variables from .env file load_dotenv() + @dataclass 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" - + # 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_HISTORY: int = 2 # Number of conversation messages to remember - + 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 -config = Config() - +config = Config() diff --git a/backend/document_processor.py b/backend/document_processor.py index 266e85904..32c6648ae 100644 --- a/backend/document_processor.py +++ b/backend/document_processor.py @@ -3,81 +3,84 @@ from typing import List, Tuple from models import Course, Lesson, CourseChunk + class DocumentProcessor: """Processes course 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""" try: - with open(file_path, 'r', encoding='utf-8') as file: + 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: + with open(file_path, "r", encoding="utf-8", errors="ignore") as file: return file.read() - - def chunk_text(self, text: str) -> List[str]: """Split text into sentence-based chunks with overlap using config settings""" - + # Clean up the text - text = re.sub(r'\s+', ' ', text.strip()) # Normalize whitespace - + text = re.sub(r"\s+", " ", text.strip()) # Normalize whitespace + # Better sentence splitting that handles abbreviations # This regex looks for periods followed by whitespace and capital letters # but ignores common abbreviations - sentence_endings = re.compile(r'(? self.chunk_size and current_chunk: break - + current_chunk.append(sentence) current_size += total_addition - + # Add chunk if we have content if current_chunk: - chunks.append(' '.join(current_chunk)) - + chunks.append(" ".join(current_chunk)) + # Calculate overlap for next chunk - if hasattr(self, 'chunk_overlap') and self.chunk_overlap > 0: + if hasattr(self, "chunk_overlap") and self.chunk_overlap > 0: # Find how many sentences to overlap overlap_size = 0 overlap_sentences = 0 - + # Count backwards from end of current chunk for k in range(len(current_chunk) - 1, -1, -1): - sentence_len = len(current_chunk[k]) + (1 if k < len(current_chunk) - 1 else 0) + sentence_len = len(current_chunk[k]) + ( + 1 if k < len(current_chunk) - 1 else 0 + ) if overlap_size + sentence_len <= self.chunk_overlap: overlap_size += sentence_len overlap_sentences += 1 else: break - + # Move start position considering overlap next_start = i + len(current_chunk) - overlap_sentences i = max(next_start, i + 1) # Ensure we make progress @@ -87,14 +90,12 @@ def chunk_text(self, text: str) -> List[str]: else: # No sentences fit, move to next i += 1 - - return chunks - - + return chunks - - def process_course_document(self, file_path: str) -> Tuple[Course, List[CourseChunk]]: + def process_course_document( + self, file_path: str + ) -> Tuple[Course, List[CourseChunk]]: """ Process a course document with expected format: Line 1: Course Title: [title] @@ -104,47 +105,51 @@ def process_course_document(self, file_path: str) -> Tuple[Course, List[CourseCh """ content = self.read_file(file_path) filename = os.path.basename(file_path) - - lines = content.strip().split('\n') - + + 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) + 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) + 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) + instructor_match = re.match( + r"^Course Instructor:\s*(.+)$", line, re.IGNORECASE + ) if instructor_match: instructor_name = instructor_match.group(1).strip() continue - + # Create course object with title as ID course = Course( title=course_title, course_link=course_link, - instructor=instructor_name if instructor_name != "Unknown" else None + instructor=instructor_name if instructor_name != "Unknown" else None, ) - + # Process lessons and create chunks course_chunks = [] current_lesson = None @@ -152,108 +157,114 @@ def process_course_document(self, file_path: str) -> Tuple[Course, List[CourseCh lesson_link = None lesson_content = [] chunk_counter = 0 - + # Start processing from line 4 (after metadata) start_index = 3 if len(lines) > 3 and not lines[3].strip(): start_index = 4 # Skip empty line after instructor - + i = start_index while i < len(lines): line = lines[i] - + # Check for lesson markers (e.g., "Lesson 0: Introduction") - lesson_match = re.match(r'^Lesson\s+(\d+):\s*(.+)$', line.strip(), re.IGNORECASE) - + lesson_match = re.match( + r"^Lesson\s+(\d+):\s*(.+)$", line.strip(), re.IGNORECASE + ) + if lesson_match: # Process previous lesson if it exists if current_lesson is not None and lesson_content: - lesson_text = '\n'.join(lesson_content).strip() + lesson_text = "\n".join(lesson_content).strip() if lesson_text: # Add lesson to course lesson = Lesson( lesson_number=current_lesson, title=lesson_title, - lesson_link=lesson_link + lesson_link=lesson_link, ) course.lessons.append(lesson) - + # Create chunks for this lesson chunks = self.chunk_text(lesson_text) for idx, chunk in enumerate(chunks): # For the first chunk of each lesson, add lesson context if idx == 0: - chunk_with_context = f"Lesson {current_lesson} content: {chunk}" + chunk_with_context = ( + f"Lesson {current_lesson} content: {chunk}" + ) else: chunk_with_context = chunk - + course_chunk = CourseChunk( content=chunk_with_context, course_title=course.title, lesson_number=current_lesson, - chunk_index=chunk_counter + chunk_index=chunk_counter, ) course_chunks.append(course_chunk) chunk_counter += 1 - + # Start new lesson current_lesson = int(lesson_match.group(1)) lesson_title = lesson_match.group(2).strip() lesson_link = None - + # Check if next line is a lesson link if i + 1 < len(lines): next_line = lines[i + 1].strip() - link_match = re.match(r'^Lesson Link:\s*(.+)$', next_line, re.IGNORECASE) + 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() + lesson_text = "\n".join(lesson_content).strip() if lesson_text: lesson = Lesson( lesson_number=current_lesson, title=lesson_title, - lesson_link=lesson_link + 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 + 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() + 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 + chunk_index=chunk_counter, ) course_chunks.append(course_chunk) chunk_counter += 1 - + return course, course_chunks diff --git a/backend/models.py b/backend/models.py index 7f7126fa3..12ae8113e 100644 --- a/backend/models.py +++ b/backend/models.py @@ -1,22 +1,28 @@ from typing import List, Dict, Optional from pydantic import BaseModel + class Lesson(BaseModel): """Represents a lesson within a course""" + lesson_number: int # Sequential lesson number (1, 2, 3, etc.) - title: str # Lesson title + title: str # Lesson title lesson_link: Optional[str] = None # URL link to the lesson + class Course(BaseModel): """Represents a complete course with its lessons""" - title: str # Full course title (used as unique identifier) + + 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 + lessons: List[Lesson] = [] # List of lessons in this course + class CourseChunk(BaseModel): """Represents a text chunk from a course for vector storage""" - 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 - chunk_index: int # Position of this chunk in the document \ No newline at end of file + + 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 + chunk_index: int # Position of this chunk in the document diff --git a/backend/rag_system.py b/backend/rag_system.py index 50d848c8e..ae43567ce 100644 --- a/backend/rag_system.py +++ b/backend/rag_system.py @@ -4,144 +4,165 @@ from vector_store import VectorStore from ai_generator import AIGenerator from session_manager import SessionManager -from search_tools import ToolManager, CourseSearchTool +from search_tools import ToolManager, CourseSearchTool, CourseOutlineTool from models import Course, Lesson, CourseChunk + class RAGSystem: """Main orchestrator for the Retrieval-Augmented Generation system""" - + def __init__(self, config): self.config = config - + # Initialize core components - self.document_processor = DocumentProcessor(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) + self.document_processor = DocumentProcessor( + 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 + ) self.session_manager = SessionManager(config.MAX_HISTORY) - + # Initialize search tools self.tool_manager = ToolManager() self.search_tool = CourseSearchTool(self.vector_store) self.tool_manager.register_tool(self.search_tool) - + self.outline_tool = CourseOutlineTool(self.vector_store) + self.tool_manager.register_tool(self.outline_tool) + def add_course_document(self, file_path: str) -> Tuple[Course, int]: """ Add a single course document to the knowledge base. - + Args: file_path: Path to the course document - + Returns: Tuple of (Course object, number of chunks created) """ try: # Process the document - course, course_chunks = self.document_processor.process_course_document(file_path) - + course, course_chunks = self.document_processor.process_course_document( + file_path + ) + # 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 - - def add_course_folder(self, folder_path: str, clear_existing: bool = False) -> Tuple[int, int]: + + def add_course_folder( + self, folder_path: str, clear_existing: bool = False + ) -> Tuple[int, int]: """ Add all course documents from a folder. - + Args: folder_path: Path to folder containing course documents clear_existing: Whether to clear existing data first - + Returns: Tuple of (total courses added, total chunks created) """ total_courses = 0 total_chunks = 0 - + # Clear existing data if requested if clear_existing: print("Clearing existing data for fresh rebuild...") self.vector_store.clear_all_data() - + if not os.path.exists(folder_path): print(f"Folder {folder_path} does not exist") return 0, 0 - + # Get existing course titles to avoid re-processing existing_course_titles = set(self.vector_store.get_existing_course_titles()) - + # Process each file in the folder for file_name in os.listdir(folder_path): file_path = os.path.join(folder_path, file_name) - if os.path.isfile(file_path) and file_name.lower().endswith(('.pdf', '.docx', '.txt')): + 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) - + 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)") + 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]]: + + def query( + self, query: str, session_id: Optional[str] = None + ) -> Tuple[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 - + Returns: Tuple of (response, sources list - empty for tool-based approach) """ # 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 + tool_manager=self.tool_manager, ) - + # Get sources from the search tool sources = self.tool_manager.get_last_sources() # Reset sources after retrieving them self.tool_manager.reset_sources() - + # Update conversation history if session_id: self.session_manager.add_exchange(session_id, query, response) - + # Return response with sources from tool searches return response, sources - + def get_course_analytics(self) -> Dict: """Get analytics about the course catalog""" return { "total_courses": self.vector_store.get_course_count(), - "course_titles": self.vector_store.get_existing_course_titles() - } \ No newline at end of file + "course_titles": self.vector_store.get_existing_course_titles(), + } diff --git a/backend/search_tools.py b/backend/search_tools.py index adfe82352..68b18c898 100644 --- a/backend/search_tools.py +++ b/backend/search_tools.py @@ -5,12 +5,12 @@ 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""" @@ -19,11 +19,11 @@ def execute(self, **kwargs) -> str: 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 - + def get_tool_definition(self) -> Dict[str, Any]: """Return Anthropic tool definition for this tool""" return { @@ -33,46 +33,49 @@ def get_tool_definition(self) -> Dict[str, Any]: "type": "object", "properties": { "query": { - "type": "string", - "description": "What to search for in the course content" + "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')" + "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)" - } + "description": "Specific lesson number to search within (e.g. 1, 2, 3)", + }, }, - "required": ["query"] - } + "required": ["query"], + }, } - - def execute(self, query: str, course_name: Optional[str] = None, lesson_number: Optional[int] = None) -> str: + + 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 + 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 = "" @@ -81,44 +84,112 @@ def execute(self, query: str, course_name: Optional[str] = None, lesson_number: 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 - + sources = [] # Track sources for the UI (now with URLs) + for doc, meta in zip(results.documents, results.metadata): - course_title = meta.get('course_title', 'unknown') - lesson_num = meta.get('lesson_number') - + 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 + source_title = course_title + if lesson_num is not None: + source_title += f" - Lesson {lesson_num}" + + # Look up lesson link from course catalog + lesson_link = None if lesson_num is not None: - source += f" - Lesson {lesson_num}" - sources.append(source) - + lesson_link = self.store.get_lesson_link(course_title, lesson_num) + + sources.append({"title": source_title, "url": lesson_link}) + formatted.append(f"{header}\n{doc}") - + # Store sources for retrieval self.last_sources = sources - + return "\n\n".join(formatted) + +class CourseOutlineTool(Tool): + """Tool for retrieving course outline including lessons list""" + + 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 the complete outline of a course including its title, link, and all lessons. Use this for questions about course structure, what lessons are available, or course overview.", + "input_schema": { + "type": "object", + "properties": { + "course_name": { + "type": "string", + "description": "Course title to get outline for (partial matches work, e.g. 'MCP', 'Introduction')", + } + }, + "required": ["course_name"], + }, + } + + def execute(self, course_name: str) -> str: + """ + Execute the outline tool with given course name. + + Args: + course_name: Course name/title to look up + + Returns: + Formatted course outline or error message + """ + outline = self.store.get_course_outline(course_name) + + if not outline: + return f"No course found matching '{course_name}'" + + return self._format_outline(outline) + + def _format_outline(self, outline: Dict[str, Any]) -> str: + """Format course outline for display""" + formatted = [] + + # Course title and link + formatted.append(f"Course: {outline['title']}") + if outline.get("course_link"): + formatted.append(f"Course Link: {outline['course_link']}") + + # Lessons list + lessons = outline.get("lessons", []) + if lessons: + formatted.append(f"\nLessons ({len(lessons)} total):") + for lesson in lessons: + lesson_num = lesson.get("lesson_number") + lesson_title = lesson.get("lesson_title", "Untitled") + formatted.append(f" Lesson {lesson_num}: {lesson_title}") + + return "\n".join(formatted) + + 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() @@ -127,28 +198,27 @@ def register_tool(self, tool: Tool): 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: + if hasattr(tool, "last_sources") and tool.last_sources: return tool.last_sources 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 = [] \ No newline at end of file + if hasattr(tool, "last_sources"): + tool.last_sources = [] diff --git a/backend/session_manager.py b/backend/session_manager.py index a5a96b1a1..9e17f346b 100644 --- a/backend/session_manager.py +++ b/backend/session_manager.py @@ -1,61 +1,66 @@ from typing import Dict, List, Optional from dataclasses import dataclass + @dataclass class Message: """Represents a single message in a conversation""" - role: str # "user" or "assistant" + + 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:] - + 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] = [] \ No newline at end of file + self.sessions[session_id] = [] diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py new file mode 100644 index 000000000..9ec264bef --- /dev/null +++ b/backend/tests/conftest.py @@ -0,0 +1,161 @@ +"""Shared fixtures for RAG system API tests.""" + +import pytest +from unittest.mock import Mock, MagicMock +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +from typing import List, Optional +from httpx import AsyncClient, ASGITransport + +import sys +from pathlib import Path + +# Add backend directory to path for imports +backend_dir = Path(__file__).parent.parent +sys.path.insert(0, str(backend_dir)) + + +# --- Pydantic models (duplicated to avoid import issues with static files) --- + +class QueryRequest(BaseModel): + query: str + session_id: Optional[str] = None + + +class QueryResponse(BaseModel): + answer: str + sources: List[dict] + session_id: str + + +class CourseStats(BaseModel): + total_courses: int + course_titles: List[str] + + +# --- Mock fixtures --- + +@pytest.fixture +def mock_session_manager(): + """Mock session manager for testing.""" + manager = Mock() + manager.create_session.return_value = "test_session_123" + manager.get_conversation_history.return_value = None + manager.add_exchange = Mock() + manager.clear_session = Mock() + return manager + + +@pytest.fixture +def mock_rag_system(mock_session_manager): + """Mock RAG system with configurable responses.""" + rag = Mock() + rag.session_manager = mock_session_manager + rag.query.return_value = ( + "This is a test response about the course material.", + [{"title": "Test Course", "url": "https://example.com/course"}] + ) + rag.get_course_analytics.return_value = { + "total_courses": 3, + "course_titles": ["Course A", "Course B", "Course C"] + } + return rag + + +@pytest.fixture +def test_app(mock_rag_system): + """Create a test FastAPI app without static file mounting.""" + from fastapi import HTTPException + + app = FastAPI(title="Test Course Materials RAG System") + + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + # Store mock in app state for access in endpoints + app.state.rag_system = mock_rag_system + + @app.post("/api/query", response_model=QueryResponse) + async def query_documents(request: QueryRequest): + try: + rag = app.state.rag_system + session_id = request.session_id + if not session_id: + session_id = rag.session_manager.create_session() + + answer, sources = rag.query(request.query, session_id) + + return QueryResponse( + answer=answer, + sources=sources, + session_id=session_id + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + @app.post("/api/session/new") + async def new_session(request: Optional[dict] = None): + rag = app.state.rag_system + if request and request.get("session_id"): + rag.session_manager.clear_session(request["session_id"]) + new_session_id = rag.session_manager.create_session() + return {"session_id": new_session_id} + + @app.get("/api/courses", response_model=CourseStats) + async def get_course_stats(): + try: + rag = app.state.rag_system + analytics = rag.get_course_analytics() + return CourseStats( + total_courses=analytics["total_courses"], + course_titles=analytics["course_titles"] + ) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + @app.get("/") + async def root(): + return {"status": "ok", "message": "RAG System API"} + + return app + + +@pytest.fixture +async def client(test_app): + """Async HTTP client for testing API endpoints.""" + transport = ASGITransport(app=test_app) + async with AsyncClient(transport=transport, base_url="http://test") as ac: + yield ac + + +# --- Test data fixtures --- + +@pytest.fixture +def sample_query_request(): + """Sample query request data.""" + return {"query": "What is machine learning?"} + + +@pytest.fixture +def sample_query_with_session(): + """Sample query request with session ID.""" + return { + "query": "Tell me more about neural networks", + "session_id": "existing_session_456" + } + + +@pytest.fixture +def sample_course_titles(): + """Sample course titles for testing.""" + return [ + "Introduction to Machine Learning", + "Deep Learning Fundamentals", + "Natural Language Processing" + ] diff --git a/backend/tests/test_api.py b/backend/tests/test_api.py new file mode 100644 index 000000000..53c60a745 --- /dev/null +++ b/backend/tests/test_api.py @@ -0,0 +1,172 @@ +"""API endpoint tests for the RAG system.""" + +import pytest +from unittest.mock import Mock + + +class TestQueryEndpoint: + """Tests for POST /api/query endpoint.""" + + async def test_query_returns_response(self, client, sample_query_request): + """Test that query endpoint returns a valid response.""" + response = await client.post("/api/query", json=sample_query_request) + + assert response.status_code == 200 + data = response.json() + assert "answer" in data + assert "sources" in data + assert "session_id" in data + + async def test_query_creates_session_when_not_provided( + self, client, sample_query_request, mock_rag_system + ): + """Test that a new session is created when not provided.""" + response = await client.post("/api/query", json=sample_query_request) + + assert response.status_code == 200 + data = response.json() + assert data["session_id"] == "test_session_123" + mock_rag_system.session_manager.create_session.assert_called_once() + + async def test_query_uses_existing_session( + self, client, sample_query_with_session, mock_rag_system + ): + """Test that existing session ID is used when provided.""" + response = await client.post("/api/query", json=sample_query_with_session) + + assert response.status_code == 200 + data = response.json() + assert data["session_id"] == "existing_session_456" + mock_rag_system.query.assert_called_once_with( + "Tell me more about neural networks", + "existing_session_456" + ) + + async def test_query_returns_sources(self, client, sample_query_request): + """Test that query returns sources in response.""" + response = await client.post("/api/query", json=sample_query_request) + + assert response.status_code == 200 + data = response.json() + assert len(data["sources"]) > 0 + assert "title" in data["sources"][0] + assert "url" in data["sources"][0] + + async def test_query_handles_error(self, client, test_app, sample_query_request): + """Test that query endpoint handles errors gracefully.""" + test_app.state.rag_system.query.side_effect = Exception("Test error") + + response = await client.post("/api/query", json=sample_query_request) + + assert response.status_code == 500 + assert "Test error" in response.json()["detail"] + + async def test_query_requires_query_field(self, client): + """Test that query field is required.""" + response = await client.post("/api/query", json={}) + + assert response.status_code == 422 # Validation error + + async def test_query_with_empty_string(self, client): + """Test query with empty string.""" + response = await client.post("/api/query", json={"query": ""}) + + # Empty string is valid, just returns a response + assert response.status_code == 200 + + +class TestCoursesEndpoint: + """Tests for GET /api/courses endpoint.""" + + async def test_courses_returns_stats(self, client): + """Test that courses endpoint returns course statistics.""" + response = await client.get("/api/courses") + + assert response.status_code == 200 + data = response.json() + assert "total_courses" in data + assert "course_titles" in data + assert data["total_courses"] == 3 + assert len(data["course_titles"]) == 3 + + async def test_courses_returns_course_titles(self, client): + """Test that courses endpoint returns list of course titles.""" + response = await client.get("/api/courses") + + assert response.status_code == 200 + data = response.json() + assert "Course A" in data["course_titles"] + assert "Course B" in data["course_titles"] + assert "Course C" in data["course_titles"] + + async def test_courses_handles_error(self, client, test_app): + """Test that courses endpoint handles errors gracefully.""" + test_app.state.rag_system.get_course_analytics.side_effect = Exception( + "Analytics error" + ) + + response = await client.get("/api/courses") + + assert response.status_code == 500 + assert "Analytics error" in response.json()["detail"] + + +class TestSessionEndpoint: + """Tests for POST /api/session/new endpoint.""" + + async def test_new_session_creates_session(self, client, mock_rag_system): + """Test that new session endpoint creates a session.""" + response = await client.post("/api/session/new") + + assert response.status_code == 200 + data = response.json() + assert "session_id" in data + assert data["session_id"] == "test_session_123" + + async def test_new_session_clears_old_session(self, client, mock_rag_system): + """Test that new session clears old session when provided.""" + response = await client.post( + "/api/session/new", + json={"session_id": "old_session_789"} + ) + + assert response.status_code == 200 + mock_rag_system.session_manager.clear_session.assert_called_once_with( + "old_session_789" + ) + + +class TestRootEndpoint: + """Tests for GET / endpoint.""" + + async def test_root_returns_status(self, client): + """Test that root endpoint returns status.""" + response = await client.get("/") + + assert response.status_code == 200 + data = response.json() + assert data["status"] == "ok" + + +class TestRequestValidation: + """Tests for request validation.""" + + async def test_query_invalid_json(self, client): + """Test that invalid JSON returns error.""" + response = await client.post( + "/api/query", + content="not valid json", + headers={"Content-Type": "application/json"} + ) + + assert response.status_code == 422 + + async def test_query_wrong_content_type(self, client): + """Test query with wrong content type.""" + response = await client.post( + "/api/query", + content="query=test", + headers={"Content-Type": "application/x-www-form-urlencoded"} + ) + + assert response.status_code == 422 diff --git a/backend/vector_store.py b/backend/vector_store.py index 390abe71c..df3dad41e 100644 --- a/backend/vector_store.py +++ b/backend/vector_store.py @@ -5,73 +5,88 @@ from models import Course, CourseChunk from sentence_transformers import SentenceTransformer + @dataclass class SearchResults: """Container for search results with metadata""" + documents: List[str] metadata: List[Dict[str, Any]] distances: List[float] error: Optional[str] = None - + @classmethod - def from_chroma(cls, chroma_results: Dict) -> 'SearchResults': + def from_chroma(cls, chroma_results: Dict) -> "SearchResults": """Create SearchResults from ChromaDB query results""" return cls( - documents=chroma_results['documents'][0] if chroma_results['documents'] else [], - metadata=chroma_results['metadatas'][0] if chroma_results['metadatas'] else [], - distances=chroma_results['distances'][0] if chroma_results['distances'] else [] + documents=( + chroma_results["documents"][0] if chroma_results["documents"] else [] + ), + metadata=( + chroma_results["metadatas"][0] if chroma_results["metadatas"] else [] + ), + distances=( + chroma_results["distances"][0] if chroma_results["distances"] else [] + ), ) - + @classmethod - def empty(cls, error_msg: str) -> 'SearchResults': + def empty(cls, error_msg: str) -> "SearchResults": """Create empty results with error message""" return cls(documents=[], metadata=[], distances=[], error=error_msg) - + def is_empty(self) -> bool: """Check if results are empty""" return len(self.documents) == 0 + class VectorStore: """Vector storage using ChromaDB for course content and metadata""" - + def __init__(self, chroma_path: str, embedding_model: str, max_results: int = 5): self.max_results = max_results # Initialize ChromaDB client self.client = chromadb.PersistentClient( - path=chroma_path, - settings=Settings(anonymized_telemetry=False) + path=chroma_path, settings=Settings(anonymized_telemetry=False) ) - + # Set up sentence transformer embedding function - self.embedding_function = chromadb.utils.embedding_functions.SentenceTransformerEmbeddingFunction( - model_name=embedding_model + self.embedding_function = ( + chromadb.utils.embedding_functions.SentenceTransformerEmbeddingFunction( + model_name=embedding_model + ) ) - + # 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 - + self.course_catalog = self._create_collection( + "course_catalog" + ) # Course titles/instructors + self.course_content = self._create_collection( + "course_content" + ) # Actual course material + def _create_collection(self, name: str): """Create or get a ChromaDB collection""" return self.client.get_or_create_collection( - name=name, - embedding_function=self.embedding_function + name=name, embedding_function=self.embedding_function ) - - def search(self, - query: str, - course_name: Optional[str] = None, - lesson_number: Optional[int] = None, - limit: Optional[int] = None) -> SearchResults: + + def search( + self, + query: str, + course_name: Optional[str] = None, + lesson_number: Optional[int] = None, + limit: Optional[int] = None, + ) -> SearchResults: """ Main search interface that handles course resolution and content search. - + 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 limit: Maximum results to return - + Returns: SearchResults object with documents and metadata """ @@ -81,104 +96,111 @@ def search(self, course_title = self._resolve_course_name(course_name) if not course_title: return SearchResults.empty(f"No course found matching '{course_name}'") - + # 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( - query_texts=[query], - n_results=search_limit, - where=filter_dict + 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]: + 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'] + 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]) -> Optional[Dict]: + + def _build_filter( + self, course_title: Optional[str], lesson_number: Optional[int] + ) -> Optional[Dict]: """Build ChromaDB filter from search parameters""" if not course_title and lesson_number is None: return None - + # Handle different filter combinations if course_title and lesson_number is not None: - return {"$and": [ - {"course_title": course_title}, - {"lesson_number": lesson_number} - ]} - + return { + "$and": [ + {"course_title": course_title}, + {"lesson_number": lesson_number}, + ] + } + 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 - + # 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 - }) - + 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] + 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], ) - + def add_course_content(self, chunks: List[CourseChunk]): """Add course 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, - "chunk_index": chunk.chunk_index - } for chunk in chunks] + metadatas = [ + { + "course_title": chunk.course_title, + "lesson_number": chunk.lesson_number, + "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 - ) - + 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: @@ -189,43 +211,46 @@ def clear_all_data(self): 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'] + 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']) + 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 - + def get_all_courses_metadata(self) -> List[Dict[str, Any]]: """Get metadata for all courses in the vector store""" import json + try: results = self.course_catalog.get() - if results and 'metadatas' in results: + if results and "metadatas" in results: # Parse lessons JSON for each course parsed_metadata = [] - for metadata in results['metadatas']: + 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 + 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 return [] @@ -238,30 +263,69 @@ def get_course_link(self, course_title: str) -> Optional[str]: 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') + 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 + 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 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') + if lesson.get("lesson_number") == lesson_number: + return lesson.get("lesson_link") return None except Exception as e: print(f"Error getting lesson link: {e}") - \ No newline at end of file + + def get_course_outline(self, course_name: str) -> Optional[Dict[str, Any]]: + """ + Get course outline by name with semantic matching. + + Args: + course_name: Course name/title (partial matches work) + + Returns: + Dict with title, course_link, and lessons list, or None if not found + """ + import json + + # Resolve course name to exact title using semantic search + course_title = self._resolve_course_name(course_name) + if not course_title: + return None + + try: + # Get course metadata 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] + + # Parse lessons from JSON + lessons = [] + lessons_json = metadata.get("lessons_json") + if lessons_json: + lessons = json.loads(lessons_json) + + return { + "title": metadata.get("title"), + "course_link": metadata.get("course_link"), + "lessons": lessons, + } + return None + except Exception as e: + print(f"Error getting course outline: {e}") + return None diff --git a/check.sh b/check.sh new file mode 100755 index 000000000..5e479088d --- /dev/null +++ b/check.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Run code quality checks +# Using Python 3.11 for torch compatibility +echo "Running code quality checks..." +echo "" + +# Check formatting with black +echo "Checking formatting with black..." +uv run --python 3.11 --extra dev black --check backend/ main.py + +if [ $? -eq 0 ]; then + echo "All checks passed!" +else + echo "" + echo "Formatting issues found. Run ./format.sh to fix them." + exit 1 +fi diff --git a/format.sh b/format.sh new file mode 100755 index 000000000..5f62cf826 --- /dev/null +++ b/format.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +# Format all Python files with black +# Using Python 3.11 for torch compatibility +echo "Formatting Python files with black..." +uv run --python 3.11 --extra dev black backend/ main.py + +echo "Done! All files formatted." diff --git a/frontend-changes.md b/frontend-changes.md new file mode 100644 index 000000000..3e0e8a94a --- /dev/null +++ b/frontend-changes.md @@ -0,0 +1,61 @@ +# Frontend Changes: Dark/Light Theme Toggle + +## Overview +Added a theme toggle button that allows users to switch between dark and light themes with smooth transitions and persistent preferences. + +## Files Modified + +### 1. `frontend/index.html` +- Added a theme toggle button positioned at the top-right of the page +- Button includes sun and moon SVG icons for visual indication +- Includes accessibility attributes (`aria-label`, `title`) for screen readers + +### 2. `frontend/style.css` +- Added light theme CSS variables under `[data-theme="light"]` selector +- Added `--code-bg` variable for code block backgrounds +- Added `.theme-toggle` button styles including: + - Fixed positioning in top-right corner + - Hover, focus, and active states + - Icon toggle visibility based on current theme +- Added smooth transition animations for theme switching on key elements + +### 3. `frontend/script.js` +- Added `themeToggle` DOM element reference +- Added `initTheme()` function to load saved theme preference from localStorage +- Added `toggleTheme()` function to switch between themes +- Added `setTheme(theme)` function to apply theme and persist to localStorage +- Theme toggle event listener added to `setupEventListeners()` + +## Features + +### Toggle Button +- Positioned in top-right corner (fixed position) +- Circular button with sun/moon icons +- Smooth scale animation on hover/click +- Keyboard accessible (focusable, works with Enter/Space) + +### Light Theme Colors +| Variable | Dark Theme | Light Theme | +|----------|-----------|-------------| +| `--background` | `#0f172a` | `#f8fafc` | +| `--surface` | `#1e293b` | `#ffffff` | +| `--surface-hover` | `#334155` | `#f1f5f9` | +| `--text-primary` | `#f1f5f9` | `#1e293b` | +| `--text-secondary` | `#94a3b8` | `#64748b` | +| `--border-color` | `#334155` | `#e2e8f0` | +| `--assistant-message` | `#374151` | `#f1f5f9` | + +### Theme Persistence +- Theme preference is saved to `localStorage` +- Preference persists across page reloads and browser sessions +- Defaults to dark theme if no preference is saved + +### Smooth Transitions +- 0.3s ease transitions on background, border, color, and box-shadow +- Applied to all major UI elements for seamless theme switching + +## Accessibility +- Button has `aria-label="Toggle theme"` for screen readers +- Button has `title` attribute for tooltip on hover +- Supports keyboard navigation (Tab to focus, Enter/Space to activate) +- Color contrast maintained in both themes for readability diff --git a/frontend/index.html b/frontend/index.html index f8e25a62f..67280415f 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -7,18 +7,42 @@ Course Materials Assistant - + + + +
-

Course Materials Assistant

+

Course Material Assistant

Ask questions about courses, instructors, and content

+
+ +
+ +
+
diff --git a/frontend/script.js b/frontend/script.js index 562a8a363..fb6334138 100644 --- a/frontend/script.js +++ b/frontend/script.js @@ -5,7 +5,7 @@ const API_URL = '/api'; let currentSessionId = null; // DOM elements -let chatMessages, chatInput, sendButton, totalCourses, courseTitles; +let chatMessages, chatInput, sendButton, totalCourses, courseTitles, themeToggle; // Initialize document.addEventListener('DOMContentLoaded', () => { @@ -15,8 +15,10 @@ document.addEventListener('DOMContentLoaded', () => { sendButton = document.getElementById('sendButton'); totalCourses = document.getElementById('totalCourses'); courseTitles = document.getElementById('courseTitles'); - + themeToggle = document.getElementById('themeToggle'); + setupEventListeners(); + initTheme(); createNewSession(); loadCourseStats(); }); @@ -28,8 +30,13 @@ function setupEventListeners() { chatInput.addEventListener('keypress', (e) => { if (e.key === 'Enter') sendMessage(); }); - - + + // New chat button + document.getElementById('newChatBtn').addEventListener('click', startNewChat); + + // Theme toggle + themeToggle.addEventListener('click', toggleTheme); + // Suggested questions document.querySelectorAll('.suggested-item').forEach(button => { button.addEventListener('click', (e) => { @@ -122,10 +129,19 @@ function addMessage(content, type, sources = null, isWelcome = false) { let html = `
${displayContent}
`; if (sources && sources.length > 0) { + // Build sources as a clean list, one per line + const sourcesHtml = sources.map(s => { + const displayTitle = escapeHtml(s.title); + if (s.url) { + return `${displayTitle}`; + } + return `${displayTitle}`; + }).join(''); + html += `
Sources -
${sources.join(', ')}
+
${sourcesHtml}
`; } @@ -152,6 +168,31 @@ async function createNewSession() { addMessage('Welcome to the Course Materials Assistant! I can help you with questions about courses, lessons and specific content. What would you like to know?', 'assistant', null, true); } +async function startNewChat() { + try { + // Call backend to create new session (and clear old one) + const response = await fetch(`${API_URL}/session/new`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ session_id: currentSessionId }) + }); + const data = await response.json(); + + // Update frontend state + currentSessionId = data.session_id; + + // Clear chat UI and show welcome message + chatMessages.innerHTML = ''; + addMessage('Welcome to the Course Materials Assistant! I can help you with questions about courses, lessons and specific content. What would you like to know?', 'assistant', null, true); + + // Reset input state + chatInput.value = ''; + chatInput.focus(); + } catch (error) { + console.error('Error starting new chat:', error); + } +} + // Load course statistics async function loadCourseStats() { try { @@ -188,4 +229,26 @@ async function loadCourseStats() { courseTitles.innerHTML = 'Failed to load courses'; } } +} + +// Theme Functions +function initTheme() { + // Check for saved theme preference, default to dark + const savedTheme = localStorage.getItem('theme') || 'dark'; + setTheme(savedTheme); +} + +function toggleTheme() { + const currentTheme = document.documentElement.getAttribute('data-theme') || 'dark'; + const newTheme = currentTheme === 'dark' ? 'light' : 'dark'; + setTheme(newTheme); +} + +function setTheme(theme) { + if (theme === 'light') { + document.documentElement.setAttribute('data-theme', 'light'); + } else { + document.documentElement.removeAttribute('data-theme'); + } + localStorage.setItem('theme', theme); } \ No newline at end of file diff --git a/frontend/style.css b/frontend/style.css index 825d03675..546d8cc1b 100644 --- a/frontend/style.css +++ b/frontend/style.css @@ -5,7 +5,7 @@ padding: 0; } -/* CSS Variables */ +/* CSS Variables - Dark Theme (Default) */ :root { --primary-color: #2563eb; --primary-hover: #1d4ed8; @@ -22,6 +22,26 @@ --focus-ring: rgba(37, 99, 235, 0.2); --welcome-bg: #1e3a5f; --welcome-border: #2563eb; + --code-bg: rgba(0, 0, 0, 0.2); +} + +/* Light Theme */ +[data-theme="light"] { + --primary-color: #2563eb; + --primary-hover: #1d4ed8; + --background: #f8fafc; + --surface: #ffffff; + --surface-hover: #f1f5f9; + --text-primary: #1e293b; + --text-secondary: #64748b; + --border-color: #e2e8f0; + --user-message: #2563eb; + --assistant-message: #f1f5f9; + --shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); + --focus-ring: rgba(37, 99, 235, 0.3); + --welcome-bg: #eff6ff; + --welcome-border: #2563eb; + --code-bg: rgba(0, 0, 0, 0.05); } /* Base Styles */ @@ -111,6 +131,30 @@ header h1 { margin-bottom: 0; } +/* New Chat Button */ +.new-chat-btn { + padding: 0.5rem 0; + background: none; + border: none; + font-size: 0.875rem; + font-weight: 600; + color: var(--text-secondary); + text-transform: uppercase; + letter-spacing: 0.5px; + cursor: pointer; + transition: color 0.2s ease; + text-align: left; +} + +.new-chat-btn:hover { + color: var(--primary-color); +} + +.new-chat-btn:focus { + outline: none; + color: var(--primary-color); +} + /* Main Chat Area */ .chat-main { flex: 1; @@ -240,9 +284,37 @@ header h1 { margin-bottom: 0.25rem; } -.sources-content { - padding: 0 0.5rem 0.25rem 1.5rem; - color: var(--text-secondary); +.sources-list { + padding: 0.5rem 0.5rem 0.5rem 1rem; + display: flex; + flex-direction: column; + gap: 0.4rem; +} + +.source-link { + display: block; + padding: 0.5rem 0.75rem; + background: rgba(99, 102, 241, 0.1); + border-left: 3px solid rgba(99, 102, 241, 0.5); + border-radius: 0 6px 6px 0; + font-size: 0.8rem; + color: var(--text-primary); + text-decoration: none; + transition: all 0.2s ease; +} + +a.source-link:hover { + background: rgba(99, 102, 241, 0.2); + border-left-color: var(--primary-color); + color: #a5b4fc; + transform: translateX(4px); +} + +a.source-link::after { + content: " ↗"; + font-size: 0.7rem; + opacity: 0.6; + margin-left: 0.25rem; } /* Markdown formatting styles */ @@ -277,7 +349,7 @@ header h1 { } .message-content code { - background-color: rgba(0, 0, 0, 0.2); + background-color: var(--code-bg); padding: 0.125rem 0.25rem; border-radius: 3px; font-family: 'Fira Code', 'Consolas', monospace; @@ -285,7 +357,7 @@ header h1 { } .message-content pre { - background-color: rgba(0, 0, 0, 0.2); + background-color: var(--code-bg); padding: 0.75rem; border-radius: 4px; overflow-x: auto; @@ -716,3 +788,82 @@ details[open] .suggested-header::before { width: 280px; } } + +/* Theme Toggle Button */ +.theme-toggle { + position: fixed; + top: 1rem; + right: 1rem; + z-index: 1000; + width: 44px; + height: 44px; + border-radius: 50%; + border: 1px solid var(--border-color); + background: var(--surface); + color: var(--text-primary); + cursor: pointer; + display: flex; + align-items: center; + justify-content: center; + transition: all 0.3s ease; + box-shadow: var(--shadow); +} + +.theme-toggle:hover { + background: var(--surface-hover); + transform: scale(1.05); +} + +.theme-toggle:focus { + outline: none; + box-shadow: 0 0 0 3px var(--focus-ring); +} + +.theme-toggle:active { + transform: scale(0.95); +} + +.theme-toggle svg { + width: 20px; + height: 20px; + transition: transform 0.3s ease, opacity 0.3s ease; +} + +.theme-toggle .sun-icon { + display: none; +} + +.theme-toggle .moon-icon { + display: block; +} + +[data-theme="light"] .theme-toggle .sun-icon { + display: block; +} + +[data-theme="light"] .theme-toggle .moon-icon { + display: none; +} + +/* Theme transition for smooth switching */ +body, +.container, +.main-content, +.sidebar, +.chat-main, +.chat-container, +.chat-messages, +.chat-input-container, +#chatInput, +#sendButton, +.message-content, +.stat-item, +.suggested-item, +.theme-toggle, +.new-chat-btn, +.source-link { + transition: background-color 0.3s ease, + border-color 0.3s ease, + color 0.3s ease, + box-shadow 0.3s ease; +} diff --git a/pyproject.toml b/pyproject.toml index 3f05e2de0..507a5c41f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,13 +3,49 @@ name = "starting-codebase" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.13" +requires-python = ">=3.11,<3.13" dependencies = [ "chromadb==1.0.15", "anthropic==0.58.2", - "sentence-transformers==5.0.0", + "sentence-transformers==2.6.1", + "torch==2.2.2", + 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--reload --port 8000 \ No newline at end of file +# Using Python 3.11 for torch compatibility with Intel Mac +cd backend && uv run --python 3.11 uvicorn app:app --reload --port 8000 \ No newline at end of file diff --git a/uv.lock b/uv.lock index 9ae65c557..09bc1c2ce 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,6 @@ version = 1 -revision = 2 -requires-python = ">=3.13" +revision = 3 +requires-python = ">=3.11, <3.13" [[package]] name = "annotated-types" @@ -36,6 +36,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "idna" }, { name = "sniffio" }, + { name = "typing-extensions" }, ] sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" } wheels = [ @@ -66,20 +67,6 @@ version = "4.3.0" source = { registry = "https://pypi.org/simple" 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