Merge branch 'main' of https://github.com/coleam00/Archon into feature/ui-style-guide

CLAUDE.md

@@ -272,6 +272,8 @@ When connected to Claude/Cursor/Windsurf, the following tools are available:
 - `archon:rag_search_knowledge_base` - Search knowledge base for relevant content
 - `archon:rag_search_code_examples` - Find code snippets in the knowledge base
 - `archon:rag_get_available_sources` - List available knowledge sources
+- `archon:rag_list_pages_for_source` - List all pages for a given source (browse documentation structure)
+- `archon:rag_read_full_page` - Retrieve full page content by page_id or URL
 
 ### Project Management
 

migration/0.1.0/010_add_page_metadata_table.sql

@@ -0,0 +1,74 @@
+-- =====================================================
+-- Add archon_page_metadata table for page-based RAG retrieval
+-- =====================================================
+-- This migration adds support for storing complete documentation pages
+-- alongside chunks for improved agent context retrieval.
+--
+-- Features:
+-- - Full page content storage with metadata
+-- - Support for llms-full.txt section-based pages
+-- - Foreign key relationship from chunks to pages
+-- =====================================================
+
+-- Create archon_page_metadata table
+CREATE TABLE IF NOT EXISTS archon_page_metadata (
+    -- Primary identification
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    source_id TEXT NOT NULL,
+    url TEXT NOT NULL,
+
+    -- Content
+    full_content TEXT NOT NULL,
+
+    -- Section metadata (for llms-full.txt H1 sections)
+    section_title TEXT,
+    section_order INT DEFAULT 0,
+
+    -- Statistics
+    word_count INT NOT NULL,
+    char_count INT NOT NULL,
+    chunk_count INT NOT NULL DEFAULT 0,
+
+    -- Timestamps
+    created_at TIMESTAMPTZ DEFAULT NOW(),
+    updated_at TIMESTAMPTZ DEFAULT NOW(),
+
+    -- Flexible metadata storage
+    metadata JSONB DEFAULT '{}'::jsonb,
+
+    -- Constraints
+    CONSTRAINT archon_page_metadata_url_unique UNIQUE(url),
+    CONSTRAINT archon_page_metadata_source_fk FOREIGN KEY (source_id)
+        REFERENCES archon_sources(source_id) ON DELETE CASCADE
+);
+
+-- Add page_id foreign key to archon_crawled_pages
+-- This links chunks back to their parent page
+-- NULLABLE because existing chunks won't have a page_id yet
+ALTER TABLE archon_crawled_pages
+ADD COLUMN IF NOT EXISTS page_id UUID REFERENCES archon_page_metadata(id) ON DELETE SET NULL;
+
+-- Create indexes for query performance
+CREATE INDEX IF NOT EXISTS idx_archon_page_metadata_source_id ON archon_page_metadata(source_id);
+CREATE INDEX IF NOT EXISTS idx_archon_page_metadata_url ON archon_page_metadata(url);
+CREATE INDEX IF NOT EXISTS idx_archon_page_metadata_section ON archon_page_metadata(source_id, section_title, section_order);
+CREATE INDEX IF NOT EXISTS idx_archon_page_metadata_created_at ON archon_page_metadata(created_at);
+CREATE INDEX IF NOT EXISTS idx_archon_page_metadata_metadata ON archon_page_metadata USING GIN(metadata);
+CREATE INDEX IF NOT EXISTS idx_archon_crawled_pages_page_id ON archon_crawled_pages(page_id);
+
+-- Add comments to document the table structure
+COMMENT ON TABLE archon_page_metadata IS 'Stores complete documentation pages for agent retrieval';
+COMMENT ON COLUMN archon_page_metadata.source_id IS 'References the source this page belongs to';
+COMMENT ON COLUMN archon_page_metadata.url IS 'Unique URL of the page (synthetic for llms-full.txt sections with #anchor)';
+COMMENT ON COLUMN archon_page_metadata.full_content IS 'Complete markdown/text content of the page';
+COMMENT ON COLUMN archon_page_metadata.section_title IS 'H1 section title for llms-full.txt pages';
+COMMENT ON COLUMN archon_page_metadata.section_order IS 'Order of section in llms-full.txt file (0-based)';
+COMMENT ON COLUMN archon_page_metadata.word_count IS 'Number of words in full_content';
+COMMENT ON COLUMN archon_page_metadata.char_count IS 'Number of characters in full_content';
+COMMENT ON COLUMN archon_page_metadata.chunk_count IS 'Number of chunks created from this page';
+COMMENT ON COLUMN archon_page_metadata.metadata IS 'Flexible JSON metadata (page_type, knowledge_type, tags, etc)';
+COMMENT ON COLUMN archon_crawled_pages.page_id IS 'Foreign key linking chunk to parent page';
+
+-- =====================================================
+-- MIGRATION COMPLETE
+-- =====================================================

python/src/mcp_server/features/rag/rag_tools.py

@@ -77,7 +77,11 @@ def register_rag_tools(mcp: FastMCP):
 
     @mcp.tool()
     async def rag_search_knowledge_base(
-        ctx: Context, query: str, source_id: str | None = None, match_count: int = 5
+        ctx: Context,
+        query: str,
+        source_id: str | None = None,
+        match_count: int = 5,
+        return_mode: str = "pages"
     ) -> str:
         """
         Search knowledge base for relevant content using RAG.
@@ -90,20 +94,31 @@ def register_rag_tools(mcp: FastMCP):
                       This is the 'id' field from available sources, NOT a URL or domain name.
                       Example: "src_1234abcd" not "docs.anthropic.com"
             match_count: Max results (default: 5)
+            return_mode: "pages" (default, full pages with metadata) or "chunks" (raw text chunks)
 
         Returns:
             JSON string with structure:
             - success: bool - Operation success status
-            - results: list[dict] - Array of matching documents with content and metadata
+            - results: list[dict] - Array of pages/chunks with content and metadata
+                      Pages include: page_id, url, title, preview, word_count, chunk_matches
+                      Chunks include: content, metadata, similarity
+            - return_mode: str - Mode used ("pages" or "chunks")
             - reranked: bool - Whether results were reranked
             - error: str|null - Error description if success=false
+
+        Note: Use "pages" mode for better context (recommended), or "chunks" for raw granular results.
+        After getting pages, use rag_read_full_page() to retrieve complete page content.
         """
         try:
             api_url = get_api_url()
             timeout = httpx.Timeout(30.0, connect=5.0)
 
             async with httpx.AsyncClient(timeout=timeout) as client:
-                request_data = {"query": query, "match_count": match_count}
+                request_data = {
+                    "query": query,
+                    "match_count": match_count,
+                    "return_mode": return_mode
+                }
                 if source_id:
                     request_data["source"] = source_id
 
@@ -115,6 +130,7 @@ def register_rag_tools(mcp: FastMCP):
                         {
                             "success": True,
                             "results": result.get("results", []),
+                            "return_mode": result.get("return_mode", return_mode),
                             "reranked": result.get("reranked", False),
                             "error": None,
                         },
@@ -198,5 +214,148 @@ def register_rag_tools(mcp: FastMCP):
             logger.error(f"Error searching code examples: {e}")
             return json.dumps({"success": False, "results": [], "error": str(e)}, indent=2)
 
+    @mcp.tool()
+    async def rag_list_pages_for_source(
+        ctx: Context, source_id: str, section: str | None = None
+    ) -> str:
+        """
+        List all pages for a given knowledge source.
+
+        Use this after rag_get_available_sources() to see all pages in a source.
+        Useful for browsing documentation structure or finding specific pages.
+
+        Args:
+            source_id: Source ID from rag_get_available_sources() (e.g., "src_1234abcd")
+            section: Optional filter for llms-full.txt section title (e.g., "# Core Concepts")
+
+        Returns:
+            JSON string with structure:
+            - success: bool - Operation success status
+            - pages: list[dict] - Array of page objects with id, url, section_title, word_count
+            - total: int - Total number of pages
+            - source_id: str - The source ID that was queried
+            - error: str|null - Error description if success=false
+
+        Example workflow:
+            1. Call rag_get_available_sources() to get source_id
+            2. Call rag_list_pages_for_source(source_id) to see all pages
+            3. Call rag_read_full_page(page_id) to read specific pages
+        """
+        try:
+            api_url = get_api_url()
+            timeout = httpx.Timeout(30.0, connect=5.0)
+
+            async with httpx.AsyncClient(timeout=timeout) as client:
+                params = {"source_id": source_id}
+                if section:
+                    params["section"] = section
+
+                response = await client.get(
+                    urljoin(api_url, "/api/pages"),
+                    params=params
+                )
+
+                if response.status_code == 200:
+                    result = response.json()
+                    return json.dumps(
+                        {
+                            "success": True,
+                            "pages": result.get("pages", []),
+                            "total": result.get("total", 0),
+                            "source_id": result.get("source_id", source_id),
+                            "error": None,
+                        },
+                        indent=2,
+                    )
+                else:
+                    error_detail = response.text
+                    return json.dumps(
+                        {
+                            "success": False,
+                            "pages": [],
+                            "total": 0,
+                            "source_id": source_id,
+                            "error": f"HTTP {response.status_code}: {error_detail}",
+                        },
+                        indent=2,
+                    )
+
+        except Exception as e:
+            logger.error(f"Error listing pages for source {source_id}: {e}")
+            return json.dumps(
+                {
+                    "success": False,
+                    "pages": [],
+                    "total": 0,
+                    "source_id": source_id,
+                    "error": str(e)
+                },
+                indent=2
+            )
+
+    @mcp.tool()
+    async def rag_read_full_page(
+        ctx: Context, page_id: str | None = None, url: str | None = None
+    ) -> str:
+        """
+        Retrieve full page content from knowledge base.
+        Use this to get complete page content after RAG search.
+
+        Args:
+            page_id: Page UUID from search results (e.g., "550e8400-e29b-41d4-a716-446655440000")
+            url: Page URL (e.g., "https://docs.example.com/getting-started")
+
+        Note: Provide EITHER page_id OR url, not both.
+
+        Returns:
+            JSON string with structure:
+            - success: bool
+            - page: dict with full_content, title, url, metadata
+            - error: str|null
+        """
+        try:
+            if not page_id and not url:
+                return json.dumps(
+                    {"success": False, "error": "Must provide either page_id or url"},
+                    indent=2
+                )
+
+            api_url = get_api_url()
+            timeout = httpx.Timeout(30.0, connect=5.0)
+
+            async with httpx.AsyncClient(timeout=timeout) as client:
+                if page_id:
+                    response = await client.get(urljoin(api_url, f"/api/pages/{page_id}"))
+                else:
+                    response = await client.get(
+                        urljoin(api_url, "/api/pages/by-url"),
+                        params={"url": url}
+                    )
+
+                if response.status_code == 200:
+                    page_data = response.json()
+                    return json.dumps(
+                        {
+                            "success": True,
+                            "page": page_data,
+                            "error": None,
+                        },
+                        indent=2,
+                    )
+                else:
+                    error_detail = response.text
+                    return json.dumps(
+                        {
+                            "success": False,
+                            "page": None,
+                            "error": f"HTTP {response.status_code}: {error_detail}",
+                        },
+                        indent=2,
+                    )
+
+        except Exception as e:
+            logger.error(f"Error reading page: {e}")
+            return json.dumps({"success": False, "page": None, "error": str(e)}, indent=2)
+
     # Log successful registration
     logger.info("✓ RAG tools registered (HTTP-based version)")

python/src/server/api_routes/knowledge_api.py

@@ -84,39 +84,39 @@ async def _validate_provider_api_key(provider: str = None) -> None:
         safe_provider = provider[:20]  # Limit length
         logger.info(f"🔑 Testing {safe_provider.title()} API key with minimal embedding request...")
 
-        try:
-            # Test API key with minimal embedding request using provider-scoped configuration
-            from ..services.embeddings.embedding_service import create_embedding
-
-            test_result = await create_embedding(text="test", provider=provider)
-
-            if not test_result:
-                logger.error(
-                    f"❌ {provider.title()} API key validation failed - no embedding returned"
-                )
-                raise HTTPException(
-                    status_code=401,
-                    detail={
-                        "error": f"Invalid {provider.title()} API key",
-                        "message": f"Please verify your {provider.title()} API key in Settings.",
-                        "error_type": "authentication_failed",
-                        "provider": provider,
-                    },
-                )
-        except Exception as e:
-            logger.error(
-                f"❌ {provider.title()} API key validation failed: {e}",
-                exc_info=True,
-            )
-            raise HTTPException(
-                status_code=401,
-                detail={
-                    "error": f"Invalid {provider.title()} API key",
-                    "message": f"Please verify your {provider.title()} API key in Settings. Error: {str(e)[:100]}",
-                    "error_type": "authentication_failed",
-                    "provider": provider,
-                },
-            )
+        try:
+            # Test API key with minimal embedding request using provider-scoped configuration
+            from ..services.embeddings.embedding_service import create_embedding
+
+            test_result = await create_embedding(text="test", provider=provider)
+
+            if not test_result:
+                logger.error(
+                    f"❌ {provider.title()} API key validation failed - no embedding returned"
+                )
+                raise HTTPException(
+                    status_code=401,
+                    detail={
+                        "error": f"Invalid {provider.title()} API key",
+                        "message": f"Please verify your {provider.title()} API key in Settings.",
+                        "error_type": "authentication_failed",
+                        "provider": provider,
+                    },
+                )
+        except Exception as e:
+            logger.error(
+                f"❌ {provider.title()} API key validation failed: {e}",
+                exc_info=True,
+            )
+            raise HTTPException(
+                status_code=401,
+                detail={
+                    "error": f"Invalid {provider.title()} API key",
+                    "message": f"Please verify your {provider.title()} API key in Settings. Error: {str(e)[:100]}",
+                    "error_type": "authentication_failed",
+                    "provider": provider,
+                },
+            )
             
         logger.info(f"✅ {provider.title()} API key validation successful")
 
@@ -177,6 +177,7 @@ class RagQueryRequest(BaseModel):
     query: str
     source: str | None = None
     match_count: int = 5
+    return_mode: str = "chunks"  # "chunks" or "pages"
 
 
 @router.get("/crawl-progress/{progress_id}")
@@ -1116,10 +1117,13 @@ async def perform_rag_query(request: RagQueryRequest):
         raise HTTPException(status_code=422, detail="Query cannot be empty")
 
     try:
-        # Use RAGService for RAG query
+        # Use RAGService for unified RAG query with return_mode support
         search_service = RAGService(get_supabase_client())
         success, result = await search_service.perform_rag_query(
-            query=request.query, source=request.source, match_count=request.match_count
+            query=request.query,
+            source=request.source,
+            match_count=request.match_count,
+            return_mode=request.return_mode
         )
 
         if success:
@@ -1288,7 +1292,7 @@ async def stop_crawl_task(progress_id: str):
 
         found = False
         # Step 1: Cancel the orchestration service
-        orchestration = await get_active_orchestration(progress_id)
+        orchestration = await get_active_orchestration(progress_id)
         if orchestration:
             orchestration.cancel()
             found = True
@@ -1306,7 +1310,7 @@ async def stop_crawl_task(progress_id: str):
             found = True
 
         # Step 3: Remove from active orchestrations registry
-        await unregister_orchestration(progress_id)
+        await unregister_orchestration(progress_id)
 
         # Step 4: Update progress tracker to reflect cancellation (only if we found and cancelled something)
         if found:

python/src/server/api_routes/pages_api.py

@@ -0,0 +1,199 @@
+"""
+Pages API Module
+
+This module handles page retrieval operations for RAG:
+- List pages for a source
+- Get page by ID
+- Get page by URL
+"""
+
+from fastapi import APIRouter, HTTPException, Query
+from pydantic import BaseModel
+
+from ..config.logfire_config import get_logger, safe_logfire_error
+from ..utils import get_supabase_client
+
+# Get logger for this module
+logger = get_logger(__name__)
+
+# Create router
+router = APIRouter(prefix="/api", tags=["pages"])
+
+# Maximum character count for returning full page content
+MAX_PAGE_CHARS = 20_000
+
+
+class PageSummary(BaseModel):
+    """Summary model for page listings (no content)"""
+
+    id: str
+    url: str
+    section_title: str | None = None
+    section_order: int = 0
+    word_count: int
+    char_count: int
+    chunk_count: int
+
+
+class PageResponse(BaseModel):
+    """Response model for a single page (with content)"""
+
+    id: str
+    source_id: str
+    url: str
+    full_content: str
+    section_title: str | None = None
+    section_order: int = 0
+    word_count: int
+    char_count: int
+    chunk_count: int
+    metadata: dict
+    created_at: str
+    updated_at: str
+
+
+class PageListResponse(BaseModel):
+    """Response model for page listing"""
+
+    pages: list[PageSummary]
+    total: int
+    source_id: str
+
+
+def _handle_large_page_content(page_data: dict) -> dict:
+    """
+    Replace full_content with a helpful message if page is too large for LLM context.
+
+    Args:
+        page_data: Page data from database
+
+    Returns:
+        Page data with full_content potentially replaced
+    """
+    char_count = page_data.get("char_count", 0)
+
+    if char_count > MAX_PAGE_CHARS:
+        page_data["full_content"] = (
+            f"[Page too large for context - {char_count:,} characters]\n\n"
+            f"This page exceeds the {MAX_PAGE_CHARS:,} character limit for retrieval.\n\n"
+            f"To access content from this page, use a RAG search with return_mode='chunks' instead of 'pages'.\n"
+            f"This will retrieve specific relevant sections rather than the entire page.\n\n"
+            f"Page details:\n"
+            f"- URL: {page_data.get('url', 'N/A')}\n"
+            f"- Section: {page_data.get('section_title', 'N/A')}\n"
+            f"- Word count: {page_data.get('word_count', 0):,}\n"
+            f"- Character count: {char_count:,}\n"
+            f"- Available chunks: {page_data.get('chunk_count', 0)}"
+        )
+
+    return page_data
+
+
+@router.get("/pages")
+async def list_pages(
+    source_id: str = Query(..., description="Source ID to filter pages"),
+    section: str | None = Query(None, description="Filter by section title (for llms-full.txt)"),
+):
+    """
+    List all pages for a given source.
+
+    Args:
+        source_id: The source ID to filter pages
+        section: Optional H1 section title for llms-full.txt sources
+
+    Returns:
+        PageListResponse with list of pages and metadata
+    """
+    try:
+        client = get_supabase_client()
+
+        # Build query - select only summary fields (no full_content)
+        query = client.table("archon_page_metadata").select(
+            "id, url, section_title, section_order, word_count, char_count, chunk_count"
+        ).eq("source_id", source_id)
+
+        # Add section filter if provided
+        if section:
+            query = query.eq("section_title", section)
+
+        # Order by section_order and created_at
+        query = query.order("section_order").order("created_at")
+
+        # Execute query
+        result = query.execute()
+
+        # Use PageSummary (no content handling needed)
+        pages = [PageSummary(**page) for page in result.data]
+
+        return PageListResponse(pages=pages, total=len(pages), source_id=source_id)
+
+    except Exception as e:
+        logger.error(f"Error listing pages for source {source_id}: {e}", exc_info=True)
+        safe_logfire_error(f"Failed to list pages | source_id={source_id} | error={str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to list pages: {str(e)}") from e
+
+
+@router.get("/pages/by-url")
+async def get_page_by_url(url: str = Query(..., description="The URL of the page to retrieve")):
+    """
+    Get a single page by its URL.
+
+    This is useful for retrieving pages from RAG search results which return URLs.
+
+    Args:
+        url: The complete URL of the page (including anchors for llms-full.txt sections)
+
+    Returns:
+        PageResponse with complete page data
+    """
+    try:
+        client = get_supabase_client()
+
+        # Query by URL
+        result = client.table("archon_page_metadata").select("*").eq("url", url).single().execute()
+
+        if not result.data:
+            raise HTTPException(status_code=404, detail=f"Page not found for URL: {url}")
+
+        # Handle large pages
+        page_data = _handle_large_page_content(result.data.copy())
+        return PageResponse(**page_data)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error getting page by URL {url}: {e}", exc_info=True)
+        safe_logfire_error(f"Failed to get page by URL | url={url} | error={str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to get page: {str(e)}") from e
+
+
+@router.get("/pages/{page_id}")
+async def get_page_by_id(page_id: str):
+    """
+    Get a single page by its ID.
+
+    Args:
+        page_id: The UUID of the page
+
+    Returns:
+        PageResponse with complete page data
+    """
+    try:
+        client = get_supabase_client()
+
+        # Query by ID
+        result = client.table("archon_page_metadata").select("*").eq("id", page_id).single().execute()
+
+        if not result.data:
+            raise HTTPException(status_code=404, detail=f"Page not found: {page_id}")
+
+        # Handle large pages
+        page_data = _handle_large_page_content(result.data.copy())
+        return PageResponse(**page_data)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error getting page {page_id}: {e}", exc_info=True)
+        safe_logfire_error(f"Failed to get page | page_id={page_id} | error={str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to get page: {str(e)}") from e

python/src/server/main.py

@@ -25,6 +25,7 @@ from .api_routes.knowledge_api import router as knowledge_router
 from .api_routes.mcp_api import router as mcp_router
 from .api_routes.migration_api import router as migration_router
 from .api_routes.ollama_api import router as ollama_router
+from .api_routes.pages_api import router as pages_router
 from .api_routes.progress_api import router as progress_router
 from .api_routes.projects_api import router as projects_router
 from .api_routes.providers_api import router as providers_router
@@ -183,6 +184,7 @@ app.include_router(settings_router)
 app.include_router(mcp_router)
 # app.include_router(mcp_client_router)  # Removed - not part of new architecture
 app.include_router(knowledge_router)
+app.include_router(pages_router)
 app.include_router(ollama_router)
 app.include_router(projects_router)
 app.include_router(progress_router)

python/src/server/services/crawling/crawling_service.py

@@ -19,6 +19,7 @@ from ..credential_service import credential_service
 # Import strategies
 # Import operations
 from .document_storage_operations import DocumentStorageOperations
+from .page_storage_operations import PageStorageOperations
 from .helpers.site_config import SiteConfig
 
 # Import helpers
@@ -98,6 +99,7 @@ class CrawlingService:
 
         # Initialize operations
         self.doc_storage_ops = DocumentStorageOperations(self.supabase_client)
+        self.page_storage_ops = PageStorageOperations(self.supabase_client)
 
         # Track progress state across all stages to prevent UI resets
         self.progress_state = {"progressId": self.progress_id} if self.progress_id else {}
@@ -431,6 +433,7 @@ class CrawlingService:
                 self._check_cancellation,
                 source_url=url,
                 source_display_name=source_display_name,
+                url_to_page_id=None,  # Will be populated after page storage
             )
 
             # Update progress tracker with source_id now that it's created

python/src/server/services/crawling/document_storage_operations.py

@@ -44,6 +44,7 @@ class DocumentStorageOperations:
         cancellation_check: Callable | None = None,
         source_url: str | None = None,
         source_display_name: str | None = None,
+        url_to_page_id: dict[str, str] | None = None,
     ) -> dict[str, Any]:
         """
         Process crawled documents and store them in the database.
@@ -128,7 +129,7 @@ class DocumentStorageOperations:
                 all_chunk_numbers.append(i)
                 all_contents.append(chunk)
 
-                # Create metadata for each chunk
+                # Create metadata for each chunk (page_id will be set later)
                 word_count = len(chunk.split())
                 metadata = {
                     "url": doc_url,
@@ -136,6 +137,7 @@ class DocumentStorageOperations:
                     "description": doc.get("description", ""),
                     "source_id": source_id,
                     "knowledge_type": request.get("knowledge_type", "documentation"),
+                    "page_id": None,  # Will be set after pages are stored
                     "crawl_type": crawl_type,
                     "word_count": word_count,
                     "char_count": len(chunk),
@@ -155,13 +157,101 @@ class DocumentStorageOperations:
             if doc_index > 0 and doc_index % 5 == 0:
                 await asyncio.sleep(0)
 
-        # Create/update source record FIRST before storing documents
+        # Create/update source record FIRST (required for FK constraints on pages and chunks)
         if all_contents and all_metadatas:
             await self._create_source_records(
                 all_metadatas, all_contents, source_word_counts, request,
                 source_url, source_display_name
             )
 
+        # Store pages AFTER source is created but BEFORE chunks (FK constraint requirement)
+        from .page_storage_operations import PageStorageOperations
+        page_storage_ops = PageStorageOperations(self.supabase_client)
+
+        # Check if this is an llms-full.txt file
+        is_llms_full = crawl_type == "llms-txt" or (
+            len(url_to_full_document) == 1 and
+            next(iter(url_to_full_document.keys())).endswith("llms-full.txt")
+        )
+
+        if is_llms_full and url_to_full_document:
+            # Handle llms-full.txt with section-based pages
+            base_url = next(iter(url_to_full_document.keys()))
+            content = url_to_full_document[base_url]
+
+            # Store section pages
+            url_to_page_id = await page_storage_ops.store_llms_full_sections(
+                base_url,
+                content,
+                original_source_id,
+                request,
+                crawl_type="llms_full",
+            )
+
+            # Parse sections and re-chunk each section
+            from .helpers.llms_full_parser import parse_llms_full_sections
+            sections = parse_llms_full_sections(content, base_url)
+
+            # Clear existing chunks and re-create from sections
+            all_urls.clear()
+            all_chunk_numbers.clear()
+            all_contents.clear()
+            all_metadatas.clear()
+            url_to_full_document.clear()
+
+            # Chunk each section separately
+            for section in sections:
+                # Update url_to_full_document with section content
+                url_to_full_document[section.url] = section.content
+                section_chunks = await storage_service.smart_chunk_text_async(
+                    section.content, chunk_size=5000
+                )
+
+                for i, chunk in enumerate(section_chunks):
+                    all_urls.append(section.url)
+                    all_chunk_numbers.append(i)
+                    all_contents.append(chunk)
+
+                    word_count = len(chunk.split())
+                    metadata = {
+                        "url": section.url,
+                        "title": section.section_title,
+                        "description": "",
+                        "source_id": original_source_id,
+                        "knowledge_type": request.get("knowledge_type", "documentation"),
+                        "page_id": url_to_page_id.get(section.url),
+                        "crawl_type": "llms_full",
+                        "word_count": word_count,
+                        "char_count": len(chunk),
+                        "chunk_index": i,
+                        "tags": request.get("tags", []),
+                    }
+                    all_metadatas.append(metadata)
+        else:
+            # Handle regular pages
+            reconstructed_crawl_results = []
+            for url, markdown in url_to_full_document.items():
+                reconstructed_crawl_results.append({
+                    "url": url,
+                    "markdown": markdown,
+                })
+
+            if reconstructed_crawl_results:
+                url_to_page_id = await page_storage_ops.store_pages(
+                    reconstructed_crawl_results,
+                    original_source_id,
+                    request,
+                    crawl_type,
+                )
+            else:
+                url_to_page_id = {}
+
+            # Update all chunk metadata with correct page_id
+            for metadata in all_metadatas:
+                chunk_url = metadata.get("url")
+                if chunk_url and chunk_url in url_to_page_id:
+                    metadata["page_id"] = url_to_page_id[chunk_url]
+
         safe_logfire_info(f"url_to_full_document keys: {list(url_to_full_document.keys())[:5]}")
 
         # Log chunking results
@@ -183,6 +273,7 @@ class DocumentStorageOperations:
             enable_parallel_batches=True,  # Enable parallel processing
             provider=None,  # Use configured provider
             cancellation_check=cancellation_check,  # Pass cancellation check
+            url_to_page_id=url_to_page_id,  # Link chunks to pages
         )
 
         # Calculate chunk counts

python/src/server/services/crawling/helpers/llms_full_parser.py

@@ -0,0 +1,262 @@
+"""
+LLMs-full.txt Section Parser
+
+Parses llms-full.txt files by splitting on H1 headers (# ) to create separate
+"pages" for each section. Each section gets a synthetic URL with a slug anchor.
+"""
+
+import re
+
+from pydantic import BaseModel
+
+
+class LLMsFullSection(BaseModel):
+    """Parsed section from llms-full.txt file"""
+
+    section_title: str  # Raw H1 text: "# Core Concepts"
+    section_order: int  # Position in document: 0, 1, 2, ...
+    content: str  # Section content (including H1 header)
+    url: str  # Synthetic URL: base.txt#core-concepts
+    word_count: int
+
+
+def create_section_slug(h1_heading: str) -> str:
+    """
+    Generate URL slug from H1 heading.
+
+    Args:
+        h1_heading: H1 text like "# Core Concepts" or "# Getting Started"
+
+    Returns:
+        Slug like "core-concepts" or "getting-started"
+
+    Examples:
+        "# Core Concepts" -> "core-concepts"
+        "# API Reference" -> "api-reference"
+        "# Getting Started!" -> "getting-started"
+    """
+    # Remove "# " prefix if present
+    slug_text = h1_heading.replace("# ", "").strip()
+
+    # Convert to lowercase
+    slug = slug_text.lower()
+
+    # Replace spaces with hyphens
+    slug = slug.replace(" ", "-")
+
+    # Remove special characters (keep only alphanumeric and hyphens)
+    slug = re.sub(r"[^a-z0-9-]", "", slug)
+
+    # Remove consecutive hyphens
+    slug = re.sub(r"-+", "-", slug)
+
+    # Remove leading/trailing hyphens
+    slug = slug.strip("-")
+
+    return slug
+
+
+def create_section_url(base_url: str, h1_heading: str, section_order: int) -> str:
+    """
+    Generate synthetic URL with slug anchor for a section.
+
+    Args:
+        base_url: Base URL like "https://example.com/llms-full.txt"
+        h1_heading: H1 text like "# Core Concepts"
+        section_order: Section position (0-based)
+
+    Returns:
+        Synthetic URL like "https://example.com/llms-full.txt#section-0-core-concepts"
+    """
+    slug = create_section_slug(h1_heading)
+    return f"{base_url}#section-{section_order}-{slug}"
+
+
+def parse_llms_full_sections(content: str, base_url: str) -> list[LLMsFullSection]:
+    """
+    Split llms-full.txt content by H1 headers to create separate sections.
+
+    Each H1 (lines starting with "# " but not "##") marks a new section.
+    Sections are given synthetic URLs with slug anchors.
+
+    Args:
+        content: Full text content of llms-full.txt file
+        base_url: Base URL of the file (e.g., "https://example.com/llms-full.txt")
+
+    Returns:
+        List of LLMsFullSection objects, one per H1 section
+
+    Edge cases:
+        - No H1 headers: Returns single section with entire content
+        - Multiple consecutive H1s: Creates separate sections correctly
+        - Empty sections: Skipped (not included in results)
+
+    Example:
+        Input content:
+        '''
+        # Core Concepts
+        Claude is an AI assistant...
+
+        # Getting Started
+        To get started...
+        '''
+
+        Returns:
+        [
+            LLMsFullSection(
+                section_title="# Core Concepts",
+                section_order=0,
+                content="# Core Concepts\\nClaude is...",
+                url="https://example.com/llms-full.txt#core-concepts",
+                word_count=5
+            ),
+            LLMsFullSection(
+                section_title="# Getting Started",
+                section_order=1,
+                content="# Getting Started\\nTo get started...",
+                url="https://example.com/llms-full.txt#getting-started",
+                word_count=4
+            )
+        ]
+    """
+    lines = content.split("\n")
+
+    # Pre-scan: mark which lines are inside code blocks
+    inside_code_block = set()
+    in_block = False
+    for i, line in enumerate(lines):
+        if line.strip().startswith("```"):
+            in_block = not in_block
+        if in_block:
+            inside_code_block.add(i)
+
+    # Parse sections, ignoring H1 headers inside code blocks
+    sections: list[LLMsFullSection] = []
+    current_h1: str | None = None
+    current_content: list[str] = []
+    section_order = 0
+
+    for i, line in enumerate(lines):
+        # Detect H1 (starts with "# " but not "##") - but ONLY if not in code block
+        is_h1 = line.startswith("# ") and not line.startswith("## ")
+        if is_h1 and i not in inside_code_block:
+            # Save previous section if it exists
+            if current_h1 is not None:
+                section_text = "\n".join(current_content)
+                # Skip empty sections (only whitespace)
+                if section_text.strip():
+                    section_url = create_section_url(base_url, current_h1, section_order)
+                    word_count = len(section_text.split())
+
+                    sections.append(
+                        LLMsFullSection(
+                            section_title=current_h1,
+                            section_order=section_order,
+                            content=section_text,
+                            url=section_url,
+                            word_count=word_count,
+                        )
+                    )
+                    section_order += 1
+
+            # Start new section
+            current_h1 = line
+            current_content = [line]
+        else:
+            # Only accumulate if we've seen an H1
+            if current_h1 is not None:
+                current_content.append(line)
+
+    # Save last section
+    if current_h1 is not None:
+        section_text = "\n".join(current_content)
+        if section_text.strip():
+            section_url = create_section_url(base_url, current_h1, section_order)
+            word_count = len(section_text.split())
+            sections.append(
+                LLMsFullSection(
+                    section_title=current_h1,
+                    section_order=section_order,
+                    content=section_text,
+                    url=section_url,
+                    word_count=word_count,
+                )
+            )
+
+    # Edge case: No H1 headers found, treat entire file as single page
+    if not sections and content.strip():
+        sections.append(
+            LLMsFullSection(
+                section_title="Full Document",
+                section_order=0,
+                content=content,
+                url=base_url,  # No anchor for single-page
+                word_count=len(content.split()),
+            )
+        )
+
+    # Fix sections that were split inside code blocks - merge them with next section
+    if sections:
+        fixed_sections: list[LLMsFullSection] = []
+        i = 0
+        while i < len(sections):
+            current = sections[i]
+
+            # Count ``` at start of lines only (proper code fences)
+            code_fence_count = sum(
+                1 for line in current.content.split('\n')
+                if line.strip().startswith('```')
+            )
+
+            # If odd number, we're inside an unclosed code block - merge with next
+            while code_fence_count % 2 == 1 and i + 1 < len(sections):
+                next_section = sections[i + 1]
+                # Combine content
+                combined_content = current.content + "\n\n" + next_section.content
+                # Update current with combined content
+                current = LLMsFullSection(
+                    section_title=current.section_title,
+                    section_order=current.section_order,
+                    content=combined_content,
+                    url=current.url,
+                    word_count=len(combined_content.split()),
+                )
+                # Move to next section and recount ``` at start of lines
+                i += 1
+                code_fence_count = sum(
+                    1 for line in current.content.split('\n')
+                    if line.strip().startswith('```')
+                )
+
+            fixed_sections.append(current)
+            i += 1
+
+        sections = fixed_sections
+
+    # Combine consecutive small sections (<200 chars) together
+    if sections:
+        combined_sections: list[LLMsFullSection] = []
+        i = 0
+        while i < len(sections):
+            current = sections[i]
+            combined_content = current.content
+
+            # Keep combining while current is small and there are more sections
+            while len(combined_content) < 200 and i + 1 < len(sections):
+                i += 1
+                combined_content = combined_content + "\n\n" + sections[i].content
+
+            # Create combined section with first section's metadata
+            combined = LLMsFullSection(
+                section_title=current.section_title,
+                section_order=current.section_order,
+                content=combined_content,
+                url=current.url,
+                word_count=len(combined_content.split()),
+            )
+            combined_sections.append(combined)
+            i += 1
+
+        sections = combined_sections
+
+    return sections

python/src/server/services/crawling/page_storage_operations.py

@@ -0,0 +1,248 @@
+"""
+Page Storage Operations
+
+Handles the storage of complete documentation pages in the archon_page_metadata table.
+Pages are stored BEFORE chunking to maintain full context for agent retrieval.
+"""
+
+from typing import Any
+
+from postgrest.exceptions import APIError
+
+from ...config.logfire_config import get_logger, safe_logfire_error, safe_logfire_info
+from .helpers.llms_full_parser import parse_llms_full_sections
+
+logger = get_logger(__name__)
+
+
+class PageStorageOperations:
+    """
+    Handles page storage operations for crawled content.
+
+    Pages are stored in the archon_page_metadata table with full content and metadata.
+    This enables agents to retrieve complete documentation pages instead of just chunks.
+    """
+
+    def __init__(self, supabase_client):
+        """
+        Initialize page storage operations.
+
+        Args:
+            supabase_client: The Supabase client for database operations
+        """
+        self.supabase_client = supabase_client
+
+    async def store_pages(
+        self,
+        crawl_results: list[dict],
+        source_id: str,
+        request: dict[str, Any],
+        crawl_type: str,
+    ) -> dict[str, str]:
+        """
+        Store pages in archon_page_metadata table from regular crawl results.
+
+        Args:
+            crawl_results: List of crawled documents with url, markdown, title, etc.
+            source_id: The source ID these pages belong to
+            request: The original crawl request with knowledge_type, tags, etc.
+            crawl_type: Type of crawl performed (sitemap, url, link_collection, etc.)
+
+        Returns:
+            {url: page_id} mapping for FK references in chunks
+        """
+        safe_logfire_info(
+            f"store_pages called | source_id={source_id} | crawl_type={crawl_type} | num_results={len(crawl_results)}"
+        )
+
+        url_to_page_id: dict[str, str] = {}
+        pages_to_insert: list[dict[str, Any]] = []
+
+        for doc in crawl_results:
+            url = doc.get("url", "").strip()
+            markdown = doc.get("markdown", "").strip()
+
+            # Skip documents with empty content or missing URLs
+            if not url or not markdown:
+                continue
+
+            # Prepare page record
+            word_count = len(markdown.split())
+            char_count = len(markdown)
+
+            page_record = {
+                "source_id": source_id,
+                "url": url,
+                "full_content": markdown,
+                "section_title": None,  # Regular page, not a section
+                "section_order": 0,
+                "word_count": word_count,
+                "char_count": char_count,
+                "chunk_count": 0,  # Will be updated after chunking
+                "metadata": {
+                    "knowledge_type": request.get("knowledge_type", "documentation"),
+                    "crawl_type": crawl_type,
+                    "page_type": "documentation",
+                    "tags": request.get("tags", []),
+                },
+            }
+            pages_to_insert.append(page_record)
+
+        # Batch upsert pages
+        if pages_to_insert:
+            try:
+                safe_logfire_info(
+                    f"Upserting {len(pages_to_insert)} pages into archon_page_metadata table"
+                )
+                result = (
+                    self.supabase_client.table("archon_page_metadata")
+                    .upsert(pages_to_insert, on_conflict="url")
+                    .execute()
+                )
+
+                # Build url → page_id mapping
+                for page in result.data:
+                    url_to_page_id[page["url"]] = page["id"]
+
+                safe_logfire_info(
+                    f"Successfully stored {len(url_to_page_id)}/{len(pages_to_insert)} pages in archon_page_metadata"
+                )
+
+            except APIError as e:
+                safe_logfire_error(
+                    f"Database error upserting pages | source_id={source_id} | attempted={len(pages_to_insert)} | error={str(e)}"
+                )
+                logger.error(f"Failed to upsert pages for source {source_id}: {e}", exc_info=True)
+                # Don't raise - allow chunking to continue even if page storage fails
+
+            except Exception as e:
+                safe_logfire_error(
+                    f"Unexpected error upserting pages | source_id={source_id} | attempted={len(pages_to_insert)} | error={str(e)}"
+                )
+                logger.error(f"Unexpected error upserting pages for source {source_id}: {e}", exc_info=True)
+                # Don't raise - allow chunking to continue
+
+        return url_to_page_id
+
+    async def store_llms_full_sections(
+        self,
+        base_url: str,
+        content: str,
+        source_id: str,
+        request: dict[str, Any],
+        crawl_type: str = "llms_full",
+    ) -> dict[str, str]:
+        """
+        Store llms-full.txt sections as separate pages.
+
+        Each H1 section gets its own page record with a synthetic URL.
+
+        Args:
+            base_url: Base URL of the llms-full.txt file
+            content: Full text content of the file
+            source_id: The source ID these sections belong to
+            request: The original crawl request
+            crawl_type: Type of crawl (defaults to "llms_full")
+
+        Returns:
+            {url: page_id} mapping for FK references in chunks
+        """
+        url_to_page_id: dict[str, str] = {}
+
+        # Parse sections from content
+        sections = parse_llms_full_sections(content, base_url)
+
+        if not sections:
+            logger.warning(f"No sections found in llms-full.txt file: {base_url}")
+            return url_to_page_id
+
+        safe_logfire_info(
+            f"Parsed {len(sections)} sections from llms-full.txt file: {base_url}"
+        )
+
+        # Prepare page records for each section
+        pages_to_insert: list[dict[str, Any]] = []
+
+        for section in sections:
+            page_record = {
+                "source_id": source_id,
+                "url": section.url,
+                "full_content": section.content,
+                "section_title": section.section_title,
+                "section_order": section.section_order,
+                "word_count": section.word_count,
+                "char_count": len(section.content),
+                "chunk_count": 0,  # Will be updated after chunking
+                "metadata": {
+                    "knowledge_type": request.get("knowledge_type", "documentation"),
+                    "crawl_type": crawl_type,
+                    "page_type": "llms_full_section",
+                    "tags": request.get("tags", []),
+                    "section_metadata": {
+                        "section_title": section.section_title,
+                        "section_order": section.section_order,
+                        "base_url": base_url,
+                    },
+                },
+            }
+            pages_to_insert.append(page_record)
+
+        # Batch upsert pages
+        if pages_to_insert:
+            try:
+                safe_logfire_info(
+                    f"Upserting {len(pages_to_insert)} section pages into archon_page_metadata"
+                )
+                result = (
+                    self.supabase_client.table("archon_page_metadata")
+                    .upsert(pages_to_insert, on_conflict="url")
+                    .execute()
+                )
+
+                # Build url → page_id mapping
+                for page in result.data:
+                    url_to_page_id[page["url"]] = page["id"]
+
+                safe_logfire_info(
+                    f"Successfully stored {len(url_to_page_id)}/{len(pages_to_insert)} section pages"
+                )
+
+            except APIError as e:
+                safe_logfire_error(
+                    f"Database error upserting sections | base_url={base_url} | attempted={len(pages_to_insert)} | error={str(e)}"
+                )
+                logger.error(f"Failed to upsert sections for {base_url}: {e}", exc_info=True)
+                # Don't raise - allow process to continue
+
+            except Exception as e:
+                safe_logfire_error(
+                    f"Unexpected error upserting sections | base_url={base_url} | attempted={len(pages_to_insert)} | error={str(e)}"
+                )
+                logger.error(f"Unexpected error upserting sections for {base_url}: {e}", exc_info=True)
+                # Don't raise - allow process to continue
+
+        return url_to_page_id
+
+    async def update_page_chunk_count(self, page_id: str, chunk_count: int) -> None:
+        """
+        Update the chunk_count field for a page after chunking is complete.
+
+        Args:
+            page_id: The UUID of the page to update
+            chunk_count: Number of chunks created from this page
+        """
+        try:
+            self.supabase_client.table("archon_page_metadata").update(
+                {"chunk_count": chunk_count}
+            ).eq("id", page_id).execute()
+
+            safe_logfire_info(f"Updated chunk_count={chunk_count} for page_id={page_id}")
+
+        except APIError as e:
+            logger.warning(
+                f"Database error updating chunk_count for page {page_id}: {e}", exc_info=True
+            )
+        except Exception as e:
+            logger.warning(
+                f"Unexpected error updating chunk_count for page {page_id}: {e}", exc_info=True
+            )

python/src/server/services/crawling/strategies/batch.py

@@ -234,10 +234,24 @@ class BatchCrawlStrategy:
                 if result.success and result.markdown and result.markdown.fit_markdown:
                     # Map back to original URL
                     original_url = url_mapping.get(result.url, result.url)
+
+                    # Extract title from HTML <title> tag
+                    title = "Untitled"
+                    if result.html:
+                        import re
+                        title_match = re.search(r'<title[^>]*>(.*?)</title>', result.html, re.IGNORECASE | re.DOTALL)
+                        if title_match:
+                            extracted_title = title_match.group(1).strip()
+                            # Clean up HTML entities
+                            extracted_title = extracted_title.replace('&amp;', '&').replace('&lt;', '<').replace('&gt;', '>').replace('&quot;', '"')
+                            if extracted_title:
+                                title = extracted_title
+
                     successful_results.append({
                         "url": original_url,
                         "markdown": result.markdown.fit_markdown,
                         "html": result.html,  # Use raw HTML
+                        "title": title,
                     })
                 else:
                     logger.warning(

python/src/server/services/crawling/strategies/single_page.py

@@ -179,12 +179,24 @@ class SinglePageCrawlStrategy:
                 if 'getting-started' in url:
                     logger.info(f"Markdown sample for getting-started: {markdown_sample}")
 
+                # Extract title from HTML <title> tag
+                title = "Untitled"
+                if result.html:
+                    import re
+                    title_match = re.search(r'<title[^>]*>(.*?)</title>', result.html, re.IGNORECASE | re.DOTALL)
+                    if title_match:
+                        extracted_title = title_match.group(1).strip()
+                        # Clean up HTML entities
+                        extracted_title = extracted_title.replace('&amp;', '&').replace('&lt;', '<').replace('&gt;', '>').replace('&quot;', '"')
+                        if extracted_title:
+                            title = extracted_title
+
                 return {
                     "success": True,
                     "url": original_url,  # Use original URL for tracking
                     "markdown": result.markdown,
                     "html": result.html,  # Use raw HTML instead of cleaned_html for code extraction
-                    "title": result.title or "Untitled",
+                    "title": title,
                     "links": result.links,
                     "content_length": len(result.markdown)
                 }

python/src/server/services/search/rag_service.py

@@ -172,21 +172,91 @@ class RAGService:
             use_enhancement=True,
         )
 
+    async def _group_chunks_by_pages(
+        self, chunk_results: list[dict[str, Any]], match_count: int
+    ) -> list[dict[str, Any]]:
+        """Group chunk results by page_id (if available) or URL and fetch page metadata."""
+        page_groups: dict[str, dict[str, Any]] = {}
+
+        for result in chunk_results:
+            metadata = result.get("metadata", {})
+            page_id = metadata.get("page_id")
+            url = metadata.get("url")
+
+            # Use page_id as key if available, otherwise URL
+            group_key = page_id if page_id else url
+            if not group_key:
+                continue
+
+            if group_key not in page_groups:
+                page_groups[group_key] = {
+                    "page_id": page_id,
+                    "url": url,
+                    "chunk_matches": 0,
+                    "total_similarity": 0.0,
+                    "best_chunk_content": result.get("content", ""),
+                    "source_id": metadata.get("source_id"),
+                }
+
+            page_groups[group_key]["chunk_matches"] += 1
+            page_groups[group_key]["total_similarity"] += result.get("similarity_score", 0.0)
+
+        page_results = []
+        for group_key, data in page_groups.items():
+            avg_similarity = data["total_similarity"] / data["chunk_matches"]
+            match_boost = min(0.2, data["chunk_matches"] * 0.02)
+            aggregate_score = avg_similarity * (1 + match_boost)
+
+            # Query page by page_id if available, otherwise by URL
+            if data["page_id"]:
+                page_info = (
+                    self.supabase_client.table("archon_page_metadata")
+                    .select("id, url, section_title, word_count")
+                    .eq("id", data["page_id"])
+                    .maybe_single()
+                    .execute()
+                )
+            else:
+                # Regular pages - exact URL match
+                page_info = (
+                    self.supabase_client.table("archon_page_metadata")
+                    .select("id, url, section_title, word_count")
+                    .eq("url", data["url"])
+                    .maybe_single()
+                    .execute()
+                )
+
+            if page_info and page_info.data is not None:
+                page_results.append({
+                    "page_id": page_info.data["id"],
+                    "url": page_info.data["url"],
+                    "section_title": page_info.data.get("section_title"),
+                    "word_count": page_info.data.get("word_count", 0),
+                    "chunk_matches": data["chunk_matches"],
+                    "aggregate_similarity": aggregate_score,
+                    "average_similarity": avg_similarity,
+                    "source_id": data["source_id"],
+                })
+
+        page_results.sort(key=lambda x: x["aggregate_similarity"], reverse=True)
+        return page_results[:match_count]
+
     async def perform_rag_query(
-        self, query: str, source: str = None, match_count: int = 5
+        self, query: str, source: str = None, match_count: int = 5, return_mode: str = "chunks"
     ) -> tuple[bool, dict[str, Any]]:
         """
-        Perform a comprehensive RAG query that combines all enabled strategies.
+        Unified RAG query with all strategies.
 
         Pipeline:
-        1. Start with vector search
-        2. Apply hybrid search if enabled
-        3. Apply reranking if enabled
+        1. Vector/Hybrid Search (based on settings)
+        2. Reranking (if enabled)
+        3. Page Grouping (if return_mode="pages")
 
         Args:
             query: The search query
             source: Optional source domain to filter results
             match_count: Maximum number of results to return
+            return_mode: "chunks" (default) or "pages"
 
         Returns:
             Tuple of (success, result_dict)
@@ -256,6 +326,23 @@ class RAGService:
                         if len(formatted_results) > match_count:
                             formatted_results = formatted_results[:match_count]
 
+                # Step 4: Group by pages if return_mode="pages" AND pages exist
+                actual_return_mode = return_mode
+                if return_mode == "pages":
+                    # Check if any chunks have page_id set
+                    has_page_ids = any(
+                        result.get("metadata", {}).get("page_id") is not None
+                        for result in formatted_results
+                    )
+
+                    if has_page_ids:
+                        # Group by pages when page_ids exist
+                        formatted_results = await self._group_chunks_by_pages(formatted_results, match_count)
+                    else:
+                        # Fall back to chunks when no page_ids (pre-migration data)
+                        actual_return_mode = "chunks"
+                        logger.info("No page_ids found in results, returning chunks instead of pages")
+
                 # Build response
                 response_data = {
                     "results": formatted_results,
@@ -266,13 +353,15 @@ class RAGService:
                     "execution_path": "rag_service_pipeline",
                     "search_mode": "hybrid" if use_hybrid_search else "vector",
                     "reranking_applied": reranking_applied,
+                    "return_mode": actual_return_mode,
                 }
 
                 span.set_attribute("final_results_count", len(formatted_results))
                 span.set_attribute("reranking_applied", reranking_applied)
+                span.set_attribute("return_mode", return_mode)
                 span.set_attribute("success", True)
 
-                logger.info(f"RAG query completed - {len(formatted_results)} results found")
+                logger.info(f"RAG query completed - {len(formatted_results)} {return_mode} found")
                 return True, response_data
 
             except Exception as e:

python/src/server/services/source_management_service.py

@@ -273,26 +273,22 @@ async def update_source_info(
             if original_url:
                 metadata["original_url"] = original_url
 
-            # Update existing source (preserving title)
-            update_data = {
+            # Use upsert to handle race conditions
+            upsert_data = {
+                "source_id": source_id,
+                "title": existing_title,
                 "summary": summary,
                 "total_word_count": word_count,
                 "metadata": metadata,
-                "updated_at": "now()",
             }
 
             # Add new fields if provided
             if source_url:
-                update_data["source_url"] = source_url
+                upsert_data["source_url"] = source_url
             if source_display_name:
-                update_data["source_display_name"] = source_display_name
+                upsert_data["source_display_name"] = source_display_name
 
-            result = (
-                client.table("archon_sources")
-                .update(update_data)
-                .eq("source_id", source_id)
-                .execute()
-            )
+            client.table("archon_sources").upsert(upsert_data).execute()
 
             search_logger.info(
                 f"Updated source {source_id} while preserving title: {existing_title}"

python/src/server/services/storage/base_storage_service.py

@@ -100,6 +100,23 @@ class BaseStorageService(ABC):
             # Move start position for next chunk
             start = end
 
+        # Combine consecutive small chunks (<200 chars) together
+        if chunks:
+            combined_chunks: list[str] = []
+            i = 0
+            while i < len(chunks):
+                current = chunks[i]
+
+                # Keep combining while current is small and there are more chunks
+                while len(current) < 200 and i + 1 < len(chunks):
+                    i += 1
+                    current = current + "\n\n" + chunks[i]
+
+                combined_chunks.append(current)
+                i += 1
+
+            chunks = combined_chunks
+
         return chunks
 
     async def smart_chunk_text_async(

python/src/server/services/storage/document_storage_service.py

@@ -25,6 +25,7 @@ async def add_documents_to_supabase(
     enable_parallel_batches: bool = True,
     provider: str | None = None,
     cancellation_check: Any | None = None,
+    url_to_page_id: dict[str, str] | None = None,
 ) -> dict[str, int]:
     """
     Add documents to Supabase with threading optimizations.
@@ -399,6 +400,9 @@ async def add_documents_to_supabase(
                     search_logger.warning(f"Unsupported embedding dimension {embedding_dim}, using embedding_1536")
                     embedding_column = "embedding_1536"
                 
+                # Get page_id for this URL if available
+                page_id = url_to_page_id.get(batch_urls[j]) if url_to_page_id else None
+
                 data = {
                     "url": batch_urls[j],
                     "chunk_number": batch_chunk_numbers[j],
@@ -409,6 +413,7 @@ async def add_documents_to_supabase(
                     "llm_chat_model": llm_chat_model,  # Add LLM model tracking
                     "embedding_model": embedding_model_name,  # Add embedding model tracking
                     "embedding_dimension": embedding_dim,  # Add dimension tracking
+                    "page_id": page_id,  # Link chunk to page
                 }
                 batch_data.append(data)
 

python/tests/server/services/test_llms_full_parser.py

@@ -0,0 +1,195 @@
+"""
+Tests for LLMs-full.txt Section Parser
+"""
+
+import pytest
+
+from src.server.services.crawling.helpers.llms_full_parser import (
+    create_section_slug,
+    create_section_url,
+    parse_llms_full_sections,
+)
+
+
+def test_create_section_slug():
+    """Test slug generation from H1 headings"""
+    assert create_section_slug("# Core Concepts") == "core-concepts"
+    assert create_section_slug("# Getting Started!") == "getting-started"
+    assert create_section_slug("# API Reference (v2)") == "api-reference-v2"
+    assert create_section_slug("# Hello World") == "hello-world"
+    assert create_section_slug("#   Spaces   ") == "spaces"
+
+
+def test_create_section_url():
+    """Test synthetic URL generation with slug anchor"""
+    base_url = "https://example.com/llms-full.txt"
+    url = create_section_url(base_url, "# Core Concepts", 0)
+    assert url == "https://example.com/llms-full.txt#section-0-core-concepts"
+
+    url = create_section_url(base_url, "# Getting Started", 1)
+    assert url == "https://example.com/llms-full.txt#section-1-getting-started"
+
+
+def test_parse_single_section():
+    """Test parsing a single H1 section"""
+    content = """# Core Concepts
+Claude is an AI assistant built by Anthropic.
+It can help with various tasks.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 1
+    assert sections[0].section_title == "# Core Concepts"
+    assert sections[0].section_order == 0
+    assert sections[0].url == "https://example.com/llms-full.txt#section-0-core-concepts"
+    assert "Claude is an AI assistant" in sections[0].content
+    assert sections[0].word_count > 0
+
+
+def test_parse_multiple_sections():
+    """Test parsing multiple H1 sections"""
+    content = """# Core Concepts
+Claude is an AI assistant built by Anthropic that can help with various tasks.
+It uses advanced language models to understand and respond to queries.
+This section provides an overview of the core concepts and capabilities.
+
+# Getting Started
+To get started with Claude, you'll need to create an account and obtain API credentials.
+Follow the setup instructions and configure your development environment properly.
+This will enable you to make your first API calls and start building applications.
+
+# API Reference
+The API uses REST principles and supports standard HTTP methods like GET, POST, PUT, and DELETE.
+Authentication is handled through API keys that should be kept secure at all times.
+Comprehensive documentation is available for all endpoints and response formats.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 3
+    assert sections[0].section_title == "# Core Concepts"
+    assert sections[1].section_title == "# Getting Started"
+    assert sections[2].section_title == "# API Reference"
+
+    assert sections[0].section_order == 0
+    assert sections[1].section_order == 1
+    assert sections[2].section_order == 2
+
+    assert sections[0].url == "https://example.com/llms-full.txt#section-0-core-concepts"
+    assert sections[1].url == "https://example.com/llms-full.txt#section-1-getting-started"
+    assert sections[2].url == "https://example.com/llms-full.txt#section-2-api-reference"
+
+
+def test_no_h1_headers():
+    """Test handling content with no H1 headers"""
+    content = """This is some documentation.
+It has no H1 headers.
+Just regular content.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 1
+    assert sections[0].section_title == "Full Document"
+    assert sections[0].url == "https://example.com/llms-full.txt"
+    assert "This is some documentation" in sections[0].content
+
+
+def test_h2_not_treated_as_section():
+    """Test that H2 headers (##) are not treated as section boundaries"""
+    content = """# Main Section
+This is the main section.
+
+## Subsection
+This is a subsection.
+
+## Another Subsection
+This is another subsection.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 1
+    assert sections[0].section_title == "# Main Section"
+    assert "## Subsection" in sections[0].content
+    assert "## Another Subsection" in sections[0].content
+
+
+def test_empty_sections_skipped():
+    """Test that empty sections are skipped"""
+    content = """# Section 1
+This is the first section with enough content to prevent automatic combination.
+It contains multiple sentences and provides substantial information for testing purposes.
+The section has several lines to ensure it exceeds the minimum character threshold.
+
+#
+
+# Section 2
+This is the second section with enough content to prevent automatic combination.
+It also contains multiple sentences and provides substantial information for testing.
+The section has several lines to ensure it exceeds the minimum character threshold.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    # Should only have 2 sections (empty one skipped)
+    assert len(sections) == 2
+    assert sections[0].section_title == "# Section 1"
+    assert sections[1].section_title == "# Section 2"
+
+
+def test_consecutive_h1_headers():
+    """Test handling multiple consecutive H1 headers"""
+    content = """# Section 1
+The first section contains enough content to prevent automatic combination with subsequent sections.
+It has multiple sentences and provides substantial information for proper testing functionality.
+This ensures that the section exceeds the minimum character threshold requirement.
+# Section 2
+This section also has enough content to prevent automatic combination with the previous section.
+It contains multiple sentences and provides substantial information for proper testing.
+The content here ensures that the section exceeds the minimum character threshold.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    # Both sections should be parsed
+    assert len(sections) == 2
+    assert sections[0].section_title == "# Section 1"
+    assert sections[1].section_title == "# Section 2"
+    assert "The first section contains enough content" in sections[0].content
+    assert "This section also has enough content" in sections[1].content
+
+
+def test_word_count_calculation():
+    """Test word count calculation for sections"""
+    content = """# Test Section
+This is a test section with exactly ten words here.
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 1
+    # Word count includes the H1 heading
+    assert sections[0].word_count > 10
+
+
+def test_empty_content():
+    """Test handling empty content"""
+    content = ""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 0
+
+
+def test_whitespace_only_content():
+    """Test handling whitespace-only content"""
+    content = """
+
+
+"""
+    base_url = "https://example.com/llms-full.txt"
+    sections = parse_llms_full_sections(content, base_url)
+
+    assert len(sections) == 0

python/tests/test_source_race_condition.py

@@ -119,8 +119,8 @@ class TestSourceRaceCondition:
         assert "upsert" in methods_called, "Should use upsert for new sources"
         assert "insert" not in methods_called, "Should not use insert to avoid race conditions"
 
-    def test_existing_source_uses_update(self):
-        """Test that existing sources still use UPDATE (not affected by change)."""
+    def test_existing_source_uses_upsert(self):
+        """Test that existing sources use UPSERT to handle race conditions."""
         mock_client = Mock()
         
         methods_called = []
@@ -158,9 +158,9 @@ class TestSourceRaceCondition:
         ))
         loop.close()
         
-        # Should use update for existing sources
-        assert "update" in methods_called, "Should use update for existing sources"
-        assert "upsert" not in methods_called, "Should not use upsert for existing sources"
+        # Should use upsert for existing sources to handle race conditions
+        assert "upsert" in methods_called, "Should use upsert for existing sources"
+        assert "update" not in methods_called, "Should not use update (upsert handles race conditions)"
 
     @pytest.mark.asyncio
     async def test_async_concurrent_creation(self):