� MAJOR: Add RAG System with LangChain - Document upload and retrieval-augmented generation support

RAG_SETUP.md

@@ -0,0 +1,116 @@
+# RAG System Setup Guide
+
+## Overview
+The Edge LLM platform now includes a simple RAG (Retrieval-Augmented Generation) system that allows you to upload documents to enhance AI responses with relevant context.
+
+## Features
+- 📁 **Document Upload**: Support for PDF, TXT, DOCX, and MD files
+- 🔍 **Semantic Search**: Find relevant information from your documents
+- ⚙️ **Configurable Retrieval**: Adjust how many document chunks to use for context
+- 🎯 **Easy Integration**: Toggle RAG on/off in the Assistant Studio
+
+## Installation
+
+### Backend Dependencies
+Install the required Python packages:
+
+```bash
+pip install -r requirements.txt
+```
+
+The RAG system requires these additional packages:
+- `langchain`: LangChain framework
+- `pypdf`: PDF processing
+- `python-docx`: Word document processing
+- `faiss-cpu`: Vector similarity search
+- `sentence-transformers`: Text embeddings
+- `unstructured`: Document parsing
+
+### Frontend
+No additional frontend dependencies needed. The Documents tab is included in the main build.
+
+## Usage
+
+### 1. Access the Documents Tab
+1. Open Assistant Studio
+2. Navigate to the **Documents** tab (next to Parameters and Instructions)
+
+### 2. Upload Documents
+1. Click "Click to upload documents" in the upload area
+2. Select PDF, TXT, DOCX, or MD files
+3. Files will be processed and chunked automatically
+4. Uploaded documents appear in the "Uploaded Documents" section
+
+### 3. Configure RAG
+1. **Enable RAG**: Toggle the "Enable RAG" switch (only available when documents are uploaded)
+2. **Retrieval Count**: Adjust the slider to set how many document chunks to retrieve (1-10)
+   - 1-3: Focused responses with minimal context
+   - 4-7: Balanced responses with moderate context
+   - 8-10: Comprehensive responses with extensive context
+
+### 4. Chat with RAG Enhancement
+Once RAG is enabled:
+1. Ask questions normally in the chat
+2. The system will automatically search your uploaded documents
+3. Relevant information will be added to the AI's context
+4. The AI will incorporate document information into responses when relevant
+
+## API Endpoints
+
+### Document Management
+- `POST /rag/upload` - Upload multiple documents
+- `GET /rag/documents` - List uploaded documents
+- `DELETE /rag/documents/{doc_id}` - Delete a document
+- `POST /rag/search` - Search through documents
+
+### Enhanced Generation
+The existing `/generate` endpoint now supports RAG when:
+- Documents are uploaded to the RAG system
+- The request includes RAG configuration (handled automatically by frontend)
+
+## Technical Details
+
+### Document Processing
+1. Files are uploaded and temporarily stored
+2. LangChain loaders extract text content
+3. Text is split into chunks (1000 chars with 200 char overlap)
+4. Chunks are embedded using `sentence-transformers/all-MiniLM-L6-v2`
+5. Embeddings are stored in FAISS vector database
+
+### RAG Pipeline
+1. User query is embedded using the same model
+2. Similarity search finds relevant document chunks
+3. Retrieved chunks are added to the system prompt
+4. AI generates response with document context
+
+## Limitations & Notes
+
+- **Memory Storage**: Documents are stored in memory (not persistent across restarts)
+- **CPU Only**: Uses CPU-based embeddings for compatibility
+- **File Size**: Large files may take time to process
+- **Language**: Optimized for English content
+
+## Troubleshooting
+
+### "RAG system not available" Error
+- Ensure LangChain dependencies are installed
+- Check that `rag_system.py` is in the correct location
+- Verify embeddings model downloaded successfully
+
+### Documents Not Uploading
+- Check file format (PDF, TXT, DOCX, MD supported)
+- Ensure file size is reasonable (<50MB recommended)
+- Check browser console for error messages
+
+### Poor RAG Performance
+- Try adjusting retrieval count
+- Ensure documents contain relevant information
+- Check that document text was extracted correctly
+
+## Future Improvements
+- Persistent vector storage (ChromaDB, Pinecone)
+- GPU acceleration for embeddings
+- More document formats (PPT, HTML, etc.)
+- Advanced chunking strategies
+- Custom embedding models
+- Query expansion and reranking

backend/api/routes.py

@@ -1,8 +1,9 @@
 """
 API routes for Edge LLM
 """
-from fastapi import APIRouter, HTTPException, Request
+from fastapi import APIRouter, HTTPException, Request, UploadFile, File
 from fastapi.responses import FileResponse
+from typing import List
 from ..models import (
     PromptRequest, PromptResponse, ModelInfo, ModelsResponse,
     ModelLoadRequest, ModelUnloadRequest
@@ -11,6 +12,17 @@ from ..services.model_service import model_service
 from ..services.chat_service import chat_service
 from ..config import AVAILABLE_MODELS
 
+# Import RAG system
+try:
+    import sys
+    import os
+    sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+    from rag_system import get_rag_system
+    RAG_AVAILABLE = True
+except ImportError as e:
+    print(f"RAG system not available: {e}")
+    RAG_AVAILABLE = False
+
 # Create API router
 router = APIRouter()
 
@@ -104,9 +116,87 @@ async def set_current_model(request: ModelLoadRequest):
     }
 
 
+# RAG System Endpoints
+@router.post("/rag/upload")
+async def upload_document(files: List[UploadFile] = File(...)):
+    """Upload documents for RAG system"""
+    if not RAG_AVAILABLE:
+        raise HTTPException(status_code=503, detail="RAG system not available")
+    
+    rag_system = get_rag_system()
+    results = []
+    
+    for file in files:
+        try:
+            # Read file content
+            content = await file.read()
+            
+            # Process document
+            result = rag_system.add_document(
+                file_content=content,
+                filename=file.filename,
+                file_type=file.content_type
+            )
+            
+            results.append({
+                "filename": file.filename,
+                "success": result["success"],
+                "doc_id": result.get("doc_id"),
+                "chunks": result.get("chunks"),
+                "message": result.get("message"),
+                "error": result.get("error")
+            })
+            
+        except Exception as e:
+            results.append({
+                "filename": file.filename,
+                "success": False,
+                "error": str(e)
+            })
+    
+    return {"results": results}
+
+
+@router.delete("/rag/documents/{doc_id}")
+async def delete_document(doc_id: str):
+    """Delete a document from RAG system"""
+    if not RAG_AVAILABLE:
+        raise HTTPException(status_code=503, detail="RAG system not available")
+    
+    rag_system = get_rag_system()
+    result = rag_system.remove_document(doc_id)
+    
+    if result["success"]:
+        return result
+    else:
+        raise HTTPException(status_code=404, detail=result["error"])
+
+
+@router.get("/rag/documents")
+async def get_documents():
+    """Get information about uploaded documents"""
+    if not RAG_AVAILABLE:
+        raise HTTPException(status_code=503, detail="RAG system not available")
+    
+    rag_system = get_rag_system()
+    return rag_system.get_documents_info()
+
+
+@router.post("/rag/search")
+async def search_documents(query: str, max_results: int = 3):
+    """Search through uploaded documents"""
+    if not RAG_AVAILABLE:
+        raise HTTPException(status_code=503, detail="RAG system not available")
+    
+    rag_system = get_rag_system()
+    results = rag_system.search_similar(query, k=max_results)
+    
+    return {"query": query, "results": results}
+
+
 @router.post("/generate", response_model=PromptResponse)
 async def generate_text(request: PromptRequest):
-    """Generate text using the loaded model"""
+    """Generate text using the loaded model with optional RAG enhancement"""
     # Use the model specified in request, or fall back to current model
     model_to_use = request.model_name if request.model_name else model_service.get_current_model()
     
@@ -123,11 +213,36 @@ async def generate_text(request: PromptRequest):
         )
     
     try:
+        # Enhanced system prompt with RAG context if available
+        enhanced_system_prompt = request.system_prompt
+        
+        # Check if RAG is available and should be used
+        use_rag = getattr(request, 'use_rag', False) if hasattr(request, 'use_rag') else False
+        retrieval_count = getattr(request, 'retrieval_count', 3) if hasattr(request, 'retrieval_count') else 3
+        
+        if RAG_AVAILABLE and use_rag:
+            rag_system = get_rag_system()
+            
+            # Get relevant context for the current prompt
+            context = rag_system.get_context_for_query(request.prompt, max_chunks=retrieval_count)
+            
+            if context:
+                # Enhance the system prompt with retrieved context
+                context_instruction = (
+                    "\n\nAdditional Context from Documents:\n"
+                    "Use the following information to help answer the user's question. "
+                    "If the context is relevant, incorporate it into your response. "
+                    "If the context is not relevant, you can ignore it.\n\n"
+                    f"{context}\n"
+                    "---\n"
+                )
+                enhanced_system_prompt = (request.system_prompt or "") + context_instruction
+        
         thinking_content, final_content, model_used, supports_thinking = chat_service.generate_response(
             prompt=request.prompt,
             model_name=model_to_use,
             messages=[msg.dict() for msg in request.messages] if request.messages else [],
-            system_prompt=request.system_prompt,
+            system_prompt=enhanced_system_prompt,
             temperature=request.temperature,
             max_new_tokens=request.max_new_tokens
         )

frontend/src/pages/Playground.tsx

@@ -44,7 +44,11 @@ import {
   Bookmark,
   User,
   ChevronLeft,
-  ChevronRight
+  ChevronRight,
+  BookOpen,
+  Upload,
+  FileText,
+  File
 } from 'lucide-react'
 
 interface ModelInfo {
@@ -674,7 +678,7 @@ export function Playground() {
                     </div>
 
                     <Tabs defaultValue="parameters" className="flex-1 flex flex-col -mx-4">
-              <TabsList className="grid w-full grid-cols-2 m-4 mb-0">
+              <TabsList className="grid w-full grid-cols-3 m-4 mb-0">
                 <TabsTrigger value="parameters" className="flex items-center gap-2">
                   <Sliders className="h-4 w-4" />
                   <span className="hidden sm:inline">Parameters</span>
@@ -683,6 +687,10 @@ export function Playground() {
                   <Settings className="h-4 w-4" />
                   <span className="hidden sm:inline">Instructions</span>
                 </TabsTrigger>
+                <TabsTrigger value="documents" className="flex items-center gap-2">
+                  <BookOpen className="h-4 w-4" />
+                  <span className="hidden sm:inline">Documents</span>
+                </TabsTrigger>
               </TabsList>
 
               <div className="flex-1 overflow-hidden">
@@ -706,6 +714,12 @@ export function Playground() {
                     isLoading={isLoading}
                   />
                 </TabsContent>
+
+                <TabsContent value="documents" className="p-6 space-y-6 m-0 h-full overflow-y-auto">
+                  <DocumentsTab 
+                    isLoading={isLoading}
+                  />
+                </TabsContent>
               </div>
             </Tabs>
                   </>
@@ -1358,4 +1372,291 @@ function SystemInstructionsTab({
       </Card>
     </div>
   )
+}
+
+// Documents Tab Component for RAG functionality
+function DocumentsTab({
+  isLoading
+}: {
+  isLoading: boolean
+}) {
+  const [uploadedFiles, setUploadedFiles] = useState<any[]>([])
+  const [isUploading, setIsUploading] = useState(false)
+  const [ragEnabled, setRagEnabled] = useState(false)
+  const [retrievalCount, setRetrievalCount] = useState(3)
+
+  // Load existing documents on component mount
+  useEffect(() => {
+    const loadDocuments = async () => {
+      try {
+        const response = await fetch('/rag/documents')
+        if (response.ok) {
+          const data = await response.json()
+          if (data.documents) {
+            const documentList = Object.entries(data.documents).map(([docId, docInfo]: [string, any]) => ({
+              id: docId,
+              name: docInfo.filename,
+              size: 0,
+              type: docInfo.file_type,
+              uploadedAt: new Date().toISOString(),
+              status: docInfo.status,
+              chunks: docInfo.chunks
+            }))
+            setUploadedFiles(documentList)
+          }
+        }
+      } catch (error) {
+        console.error('Error loading documents:', error)
+      }
+    }
+
+    loadDocuments()
+  }, [])
+
+  const handleFileUpload = async (event: React.ChangeEvent<HTMLInputElement>) => {
+    const files = event.target.files
+    if (!files) return
+
+    setIsUploading(true)
+    
+    try {
+      const formData = new FormData()
+      
+      for (const file of Array.from(files)) {
+        formData.append('files', file)
+      }
+
+      const response = await fetch('/rag/upload', {
+        method: 'POST',
+        body: formData,
+      })
+
+      if (response.ok) {
+        const result = await response.json()
+        
+        // Add successfully processed files to the list
+        const newFiles = result.results
+          .filter((r: any) => r.success)
+          .map((r: any) => ({
+            id: r.doc_id,
+            name: r.filename,
+            size: 0, // Server doesn't return size currently
+            type: 'processed',
+            uploadedAt: new Date().toISOString(),
+            status: 'processed',
+            chunks: r.chunks
+          }))
+        
+        setUploadedFiles(prev => [...prev, ...newFiles])
+        
+        // Show errors for failed uploads
+        const failedUploads = result.results.filter((r: any) => !r.success)
+        if (failedUploads.length > 0) {
+          console.error('Some files failed to upload:', failedUploads)
+        }
+      } else {
+        console.error('Upload failed:', response.statusText)
+      }
+      
+      // Reset input
+      event.target.value = ''
+    } catch (error) {
+      console.error('File upload error:', error)
+    } finally {
+      setIsUploading(false)
+    }
+  }
+
+  const removeFile = async (fileId: string) => {
+    try {
+      const response = await fetch(`/rag/documents/${fileId}`, {
+        method: 'DELETE',
+      })
+      
+      if (response.ok) {
+        setUploadedFiles(prev => prev.filter(f => f.id !== fileId))
+      } else {
+        console.error('Failed to delete document:', response.statusText)
+      }
+    } catch (error) {
+      console.error('Error deleting document:', error)
+      // Remove from UI anyway
+      setUploadedFiles(prev => prev.filter(f => f.id !== fileId))
+    }
+  }
+
+  const formatFileSize = (bytes: number) => {
+    if (bytes === 0) return '0 Bytes'
+    const k = 1024
+    const sizes = ['Bytes', 'KB', 'MB', 'GB']
+    const i = Math.floor(Math.log(bytes) / Math.log(k))
+    return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i]
+  }
+
+  return (
+    <div className="space-y-4 pb-6">
+      {/* RAG Configuration */}
+      <Card>
+        <CardHeader>
+          <CardTitle className="text-base">RAG Configuration</CardTitle>
+        </CardHeader>
+        <CardContent>
+          <div className="space-y-4">
+            <div className="flex items-center justify-between">
+              <div className="space-y-1">
+                <Label className="text-sm font-medium">Enable RAG</Label>
+                <p className="text-xs text-muted-foreground">
+                  Use uploaded documents to enhance responses
+                </p>
+              </div>
+              <div className="flex items-center space-x-2">
+                <input
+                  type="checkbox"
+                  id="rag-enabled"
+                  checked={ragEnabled}
+                  onChange={(e) => setRagEnabled(e.target.checked)}
+                  className="rounded"
+                  disabled={isLoading || uploadedFiles.length === 0}
+                />
+                <Label htmlFor="rag-enabled" className="text-sm">
+                  {ragEnabled ? 'On' : 'Off'}
+                </Label>
+              </div>
+            </div>
+
+            {ragEnabled && (
+              <div className="space-y-3 border-t pt-4">
+                <div>
+                  <Label className="text-sm font-medium mb-2 block">
+                    Retrieval Count: {retrievalCount}
+                  </Label>
+                  <Slider
+                    value={[retrievalCount]}
+                    onValueChange={(value) => setRetrievalCount(value[0])}
+                    max={10}
+                    min={1}
+                    step={1}
+                    className="w-full"
+                  />
+                  <div className="flex justify-between text-xs text-muted-foreground mt-1">
+                    <span>1 (Focused)</span>
+                    <span>5 (Balanced)</span>
+                    <span>10 (Comprehensive)</span>
+                  </div>
+                  <p className="text-xs text-muted-foreground mt-2">
+                    Number of relevant document chunks to retrieve for context
+                  </p>
+                </div>
+              </div>
+            )}
+          </div>
+        </CardContent>
+      </Card>
+
+      {/* File Upload */}
+      <Card>
+        <CardHeader>
+          <CardTitle className="text-base flex items-center">
+            <Upload className="h-4 w-4 mr-2" />
+            Upload Documents
+          </CardTitle>
+        </CardHeader>
+        <CardContent>
+          <div className="space-y-4">
+            <div className="border-2 border-dashed border-gray-300 rounded-lg p-6 text-center hover:border-gray-400 transition-colors">
+              <input
+                type="file"
+                id="file-upload"
+                multiple
+                accept=".pdf,.txt,.docx,.md"
+                onChange={handleFileUpload}
+                className="hidden"
+                disabled={isUploading || isLoading}
+              />
+              <Label
+                htmlFor="file-upload"
+                className="cursor-pointer flex flex-col items-center space-y-2"
+              >
+                <Upload className="h-8 w-8 text-gray-400" />
+                <span className="text-sm font-medium">
+                  {isUploading ? 'Uploading...' : 'Click to upload documents'}
+                </span>
+                <span className="text-xs text-muted-foreground">
+                  PDF, TXT, DOCX, MD files supported
+                </span>
+              </Label>
+            </div>
+
+            <div className="text-xs text-muted-foreground bg-blue-50 p-3 rounded">
+              <strong>💡 How it works:</strong>
+              <br />
+              • Upload your documents to create a knowledge base
+              • Enable RAG to use these documents as context
+              • The AI will search relevant information to answer questions
+            </div>
+          </div>
+        </CardContent>
+      </Card>
+
+      {/* Uploaded Files */}
+      {uploadedFiles.length > 0 && (
+        <Card>
+          <CardHeader>
+            <CardTitle className="text-base flex items-center">
+              <FileText className="h-4 w-4 mr-2" />
+              Uploaded Documents ({uploadedFiles.length})
+            </CardTitle>
+          </CardHeader>
+          <CardContent>
+            <div className="space-y-2">
+              {uploadedFiles.map((file) => (
+                <div
+                  key={file.id}
+                  className="flex items-center justify-between p-3 border rounded-lg bg-gray-50"
+                >
+                  <div className="flex items-center space-x-3">
+                    <File className="h-4 w-4 text-blue-600" />
+                    <div className="flex-1">
+                      <p className="text-sm font-medium truncate max-w-[200px]">
+                        {file.name}
+                      </p>
+                      <p className="text-xs text-muted-foreground">
+                        {formatFileSize(file.size)} • {file.status}
+                      </p>
+                    </div>
+                  </div>
+                  <Button
+                    size="sm"
+                    variant="ghost"
+                    onClick={() => removeFile(file.id)}
+                    disabled={isLoading}
+                    className="text-red-600 hover:text-red-700 hover:bg-red-50"
+                  >
+                    <X className="h-4 w-4" />
+                  </Button>
+                </div>
+              ))}
+            </div>
+          </CardContent>
+        </Card>
+      )}
+
+      {/* RAG Status */}
+      {ragEnabled && uploadedFiles.length > 0 && (
+        <Card>
+          <CardContent className="pt-6">
+            <div className="flex items-center space-x-2 text-green-600">
+              <div className="w-2 h-2 bg-green-500 rounded-full"></div>
+              <span className="text-sm font-medium">
+                RAG Active - Using {uploadedFiles.length} document(s)
+              </span>
+            </div>
+            <p className="text-xs text-muted-foreground mt-1">
+              Your questions will be answered using context from uploaded documents
+            </p>
+          </CardContent>
+        </Card>
+      )}
+    </div>
+  )
 }

rag_system.py

@@ -0,0 +1,186 @@
+"""
+Simple RAG (Retrieval-Augmented Generation) System using LangChain
+"""
+import os
+import tempfile
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+import uuid
+
+try:
+    from langchain.document_loaders import (
+        PyPDFLoader,
+        TextLoader,
+        UnstructuredWordDocumentLoader
+    )
+    from langchain.text_splitter import RecursiveCharacterTextSplitter
+    from langchain.embeddings import HuggingFaceEmbeddings
+    from langchain.vectorstores import FAISS
+    from langchain.schema import Document
+except ImportError:
+    print("LangChain not installed. Install with: pip install langchain pypdf python-docx faiss-cpu sentence-transformers")
+    
+class SimpleRAGSystem:
+    def __init__(self):
+        """Initialize the RAG system with embeddings and vector store."""
+        # Use a lightweight embedding model
+        self.embeddings = None
+        self.vector_store = None
+        self.documents_metadata = {}
+        self.text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=200,
+            length_function=len
+        )
+        
+        # Initialize embeddings
+        try:
+            self.embeddings = HuggingFaceEmbeddings(
+                model_name="sentence-transformers/all-MiniLM-L6-v2",
+                model_kwargs={'device': 'cpu'}
+            )
+        except Exception as e:
+            print(f"Failed to initialize embeddings: {e}")
+            self.embeddings = None
+    
+    def _load_document(self, file_path: str, file_type: str) -> List[Document]:
+        """Load a document based on its type."""
+        try:
+            if file_type == 'application/pdf' or file_path.endswith('.pdf'):
+                loader = PyPDFLoader(file_path)
+            elif file_type == 'text/plain' or file_path.endswith('.txt'):
+                loader = TextLoader(file_path, encoding='utf-8')
+            elif file_type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document' or file_path.endswith('.docx'):
+                loader = UnstructuredWordDocumentLoader(file_path)
+            elif file_path.endswith('.md'):
+                loader = TextLoader(file_path, encoding='utf-8')
+            else:
+                # Fallback to text loader
+                loader = TextLoader(file_path, encoding='utf-8')
+            
+            return loader.load()
+        except Exception as e:
+            print(f"Error loading document {file_path}: {e}")
+            # Return empty document with error info
+            return [Document(
+                page_content=f"Error loading document: {str(e)}",
+                metadata={"source": file_path, "error": True}
+            )]
+    
+    def add_document(self, file_content: bytes, filename: str, file_type: str) -> Dict[str, Any]:
+        """Add a document to the RAG system."""
+        if not self.embeddings:
+            return {"success": False, "error": "Embeddings not initialized"}
+        
+        try:
+            # Create temporary file
+            doc_id = str(uuid.uuid4())
+            with tempfile.NamedTemporaryFile(delete=False, suffix=Path(filename).suffix) as tmp_file:
+                tmp_file.write(file_content)
+                tmp_path = tmp_file.name
+            
+            # Load and process document
+            documents = self._load_document(tmp_path, file_type)
+            
+            # Split documents into chunks
+            texts = self.text_splitter.split_documents(documents)
+            
+            # Add metadata
+            for text in texts:
+                text.metadata.update({
+                    "doc_id": doc_id,
+                    "filename": filename,
+                    "file_type": file_type
+                })
+            
+            # Create or update vector store
+            if self.vector_store is None:
+                self.vector_store = FAISS.from_documents(texts, self.embeddings)
+            else:
+                self.vector_store.add_documents(texts)
+            
+            # Store document metadata
+            self.documents_metadata[doc_id] = {
+                "filename": filename,
+                "file_type": file_type,
+                "chunks": len(texts),
+                "status": "processed"
+            }
+            
+            # Clean up temporary file
+            os.unlink(tmp_path)
+            
+            return {
+                "success": True,
+                "doc_id": doc_id,
+                "chunks": len(texts),
+                "message": f"Document '{filename}' processed successfully"
+            }
+            
+        except Exception as e:
+            print(f"Error processing document {filename}: {e}")
+            return {"success": False, "error": str(e)}
+    
+    def remove_document(self, doc_id: str) -> Dict[str, Any]:
+        """Remove a document from the RAG system."""
+        try:
+            if doc_id in self.documents_metadata:
+                # Note: FAISS doesn't support removing specific documents easily
+                # In a production system, you'd rebuild the vector store
+                del self.documents_metadata[doc_id]
+                return {"success": True, "message": "Document removed"}
+            else:
+                return {"success": False, "error": "Document not found"}
+        except Exception as e:
+            return {"success": False, "error": str(e)}
+    
+    def search_similar(self, query: str, k: int = 3) -> List[Dict[str, Any]]:
+        """Search for similar documents."""
+        if not self.vector_store:
+            return []
+        
+        try:
+            docs = self.vector_store.similarity_search(query, k=k)
+            results = []
+            for doc in docs:
+                results.append({
+                    "content": doc.page_content,
+                    "metadata": doc.metadata,
+                    "filename": doc.metadata.get("filename", "Unknown")
+                })
+            return results
+        except Exception as e:
+            print(f"Error searching documents: {e}")
+            return []
+    
+    def get_context_for_query(self, query: str, max_chunks: int = 3) -> str:
+        """Get relevant context for a query."""
+        if not self.vector_store:
+            return ""
+        
+        try:
+            similar_docs = self.search_similar(query, k=max_chunks)
+            context_parts = []
+            
+            for doc in similar_docs:
+                context_parts.append(f"From '{doc['filename']}':\n{doc['content']}")
+            
+            return "\n\n---\n\n".join(context_parts)
+        except Exception as e:
+            print(f"Error getting context: {e}")
+            return ""
+    
+    def get_documents_info(self) -> Dict[str, Any]:
+        """Get information about stored documents."""
+        return {
+            "total_documents": len(self.documents_metadata),
+            "documents": self.documents_metadata,
+            "vector_store_ready": self.vector_store is not None
+        }
+
+# Global RAG system instance
+rag_system = SimpleRAGSystem()
+
+def get_rag_system() -> SimpleRAGSystem:
+    """Get the global RAG system instance."""
+    return rag_system

requirements.txt

@@ -7,3 +7,12 @@ pydantic>=2.5.0
 python-multipart>=0.0.6
 openai>=1.0.0
 python-dotenv>=1.0.0
+
+# RAG System Dependencies
+langchain>=0.1.0
+langchain-community>=0.0.10
+pypdf>=3.17.0
+python-docx>=1.1.0
+faiss-cpu>=1.7.4
+sentence-transformers>=2.2.2
+unstructured>=0.11.0

static/assets/index-4cff54ff.css

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