From 9a8dceaa6e24b3f675e5e3925bccbe690d8f7a8f Mon Sep 17 00:00:00 2001 From: Hitesh Kumar Date: Sun, 23 Nov 2025 10:57:22 +0530 Subject: [PATCH 1/6] wrok on backend --- .gitignore | 3 + TRANSFORMATION_SUMMARY.md | 200 --------- backend/app/embeddings/generate.py | 123 +++--- backend/app/index_store.py | 8 +- backend/app/ingestion/audio_transcriber.py | 207 ++++++---- backend/app/ingestion/image_processor.py | 55 ++- backend/app/llm/prompts.py | 82 +++- backend/app/main.py | 60 ++- backend/app/rag.py | 8 +- backend/app/vector_store/faiss_store.py | 24 +- backend/requirements.txt | 4 + backend/scripts/test_audio_pipeline.py | 85 ++++ backend/storage/3170716.pdf | Bin 131 -> 0 bytes backend/storage/AzureWebApp_1.docx | Bin 644146 -> 0 bytes backend/storage/AzureWebApp_1.pdf | Bin 131 -> 0 bytes backend/storage/CD_S2023_3170701.pdf | Bin 131 -> 0 bytes backend/storage/faiss.index | Bin 10285 -> 69677 bytes backend/storage/metadata.json | 451 +++++++++++++++++++++ download_whisper.py | 12 - scripts/check_audio_deps.py | 35 ++ test_final_system.py | 429 -------------------- test_image_embed.py | 33 -- test_system.py | 148 ------- test_system_simple.py | 148 ------- test_system_windows.py | 148 ------- test_unified_embeddings.py | 57 --- tests/test_audio.py | 24 ++ 27 files changed, 974 insertions(+), 1370 deletions(-) delete mode 100644 TRANSFORMATION_SUMMARY.md create mode 100644 backend/scripts/test_audio_pipeline.py delete mode 100644 backend/storage/3170716.pdf delete mode 100644 backend/storage/AzureWebApp_1.docx delete mode 100644 backend/storage/AzureWebApp_1.pdf delete mode 100644 backend/storage/CD_S2023_3170701.pdf delete mode 100644 download_whisper.py create mode 100644 scripts/check_audio_deps.py delete mode 100644 test_final_system.py delete mode 100644 test_image_embed.py delete mode 100644 test_system.py delete mode 100644 test_system_simple.py delete mode 100644 test_system_windows.py delete mode 100644 test_unified_embeddings.py create mode 100644 tests/test_audio.py diff --git a/.gitignore b/.gitignore index 7c147f5..ed0cd39 100644 --- a/.gitignore +++ b/.gitignore @@ -162,3 +162,6 @@ docker-compose.override.yml # ------------------------- .DS_Store +# Faiss index and metadata files +backend/storage/faiss.index +backend/storage/metadata.json diff --git a/TRANSFORMATION_SUMMARY.md b/TRANSFORMATION_SUMMARY.md deleted file mode 100644 index 3203d71..0000000 --- a/TRANSFORMATION_SUMMARY.md +++ /dev/null @@ -1,200 +0,0 @@ -# Project Transformation Summary - -## Overview - -Your existing RAG offline chatbot project has been successfully transformed according to the document specifications. The project now follows a modular, production-ready architecture with enhanced functionality and deployment capabilities. - -## What Was Changed - -### 1. Directory Structure Reorganization - -**Before:** - -``` -backend/app/ -├── extractors.py -├── embeddings.py -├── index_store.py -├── rag.py -├── main.py -└── adapters/ -``` - -**After:** - -``` -backend/app/ -├── ingestion/ # Document processing modules -├── embeddings/ # Embedding generation -├── vector_store/ # FAISS wrapper & persistence -├── retriever/ # Retrieval & reranking -├── llm/ # LLM adapters & prompts -├── ui/ # Gradio interface -├── utils/ # Cross-modal linking -└── main.py # Updated FastAPI backend -``` - -### 2. Enhanced Modules - -#### Ingestion (`app/ingestion/`) - -- **`doc_extractor.py`**: Enhanced PDF/DOCX extraction with proper chunking -- **`image_processor.py`**: Image processing with optional OCR -- **`audio_transcriber.py`**: Audio transcription with timestamps -- **`base.py`**: Common chunking utilities and base classes - -#### Embeddings (`app/embeddings/`) - -- **`generate.py`**: Enhanced embedding generation with offline support -- Support for local model paths via environment variables -- Backward compatibility with existing code - -#### Vector Store (`app/vector_store/`) - -- **`faiss_store.py`**: Enhanced FAISS wrapper with metadata persistence -- JSON metadata storage alongside FAISS index -- Better error handling and status reporting - -#### Retriever (`app/retriever/`) - -- **`retriever.py`**: Advanced retrieval with reranking -- Cross-modal search capabilities -- Configurable similarity thresholds - -#### LLM (`app/llm/`) - -- **`adapter.py`**: Enhanced LLM adapters -- **`prompts.py`**: Comprehensive prompt templates -- Better system prompts for multimodal content - -#### UI (`app/ui/`) - -- **`gradio_app.py`**: Complete Gradio web interface -- File upload, query, and status monitoring -- Alternative to the existing React frontend - -#### Utils (`app/utils/`) - -- **`linker.py`**: Cross-modal linking utilities -- **`file_utils.py`**: File handling utilities -- Citation generation and formatting - -### 3. New Scripts and Automation - -#### Build Scripts (`scripts/`) - -- **`build_release.sh`**: Automated release packaging -- **`docker/Dockerfile`**: Production-ready Docker image -- **`ingest_all.py`**: Batch document ingestion - -#### CI/CD (`.github/workflows/`) - -- **`release.yml`**: Automated testing and release -- Docker image building and testing -- Release artifact generation - -### 4. Enhanced Documentation - -- **`README.md`**: Comprehensive project documentation -- **`docs/quickstart.md`**: Step-by-step getting started guide -- **`docs/troubleshooting.md`**: Common issues and solutions -- **`Makefile`**: Easy command execution - -### 5. Testing and Quality - -- **`tests/test_integration.py`**: Comprehensive test suite -- **`test_system.py`**: System verification script -- All modules tested and verified - -## New Features Added - -### 1. Dual Interface Support - -- **Gradio UI**: User-friendly web interface -- **FastAPI Backend**: RESTful API for integration -- **React Frontend**: Preserved existing frontend - -### 2. Enhanced Multimodal Support - -- Cross-modal search and linking -- Better image processing with OCR -- Improved audio transcription -- Citation and source attribution - -### 3. Production-Ready Deployment - -- Docker containerization -- Automated build and release -- Environment variable configuration -- Health checks and monitoring - -### 4. Offline-First Design - -- Local model support -- Environment variables for offline mode -- No internet dependency after setup - -## How to Use - -### Quick Start - -```bash -# Test the system -python test_system.py - -# Run with Gradio UI -python main.py --interface gradio --port 7860 - -# Run with FastAPI backend -python main.py --interface fastapi --port 8000 - -# Build Docker image -make docker-build - -# Run Docker container -make docker-run -``` - -### Development - -```bash -# Install dependencies -make install - -# Run tests -make test - -# Format code -make format - -# Complete setup -make dev-setup -``` - -## Backward Compatibility - -All existing functionality has been preserved: - -- Original FastAPI endpoints still work -- Existing React frontend unchanged -- Configuration files compatible -- Database format maintained - -## Next Steps - -1. **Test the system**: Run `python test_system.py` -2. **Try the Gradio UI**: Run `python main.py --interface gradio` -3. **Build Docker image**: Run `make docker-build` -4. **Deploy**: Use Docker or native installation -5. **Customize**: Modify configuration and prompts as needed - -## Benefits of the New Structure - -1. **Modularity**: Easy to maintain and extend -2. **Scalability**: Better performance and resource management -3. **Deployment**: Production-ready with Docker -4. **Documentation**: Comprehensive guides and troubleshooting -5. **Testing**: Robust test suite and validation -6. **Offline**: True offline operation with local models - -The project is now ready for production use and follows industry best practices for RAG systems. diff --git a/backend/app/embeddings/generate.py b/backend/app/embeddings/generate.py index fb5ee98..9fe5211 100644 --- a/backend/app/embeddings/generate.py +++ b/backend/app/embeddings/generate.py @@ -72,63 +72,61 @@ def _get_clip_model() -> torch.nn.Module: local_path = next((p for p in [env_path, repo_local, alt_repo_local] if p and os.path.exists(p)), None) if local_path: - # Check if it's a TorchScript model or state dict - # TorchScript models may have hardcoded CUDA references, so we need to be careful + # Check file size to detect TorchScript (they're usually small serialized format) + # TorchScript models may have hardcoded CUDA references, so we skip them by default + import stat try: - # Try loading as TorchScript first (always force CPU to avoid CUDA issues) - model = torch.jit.load(local_path, map_location="cpu") - # For TorchScript, we still need preprocess - _, preprocess, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained=False) - # Force model to CPU (TorchScript models may have hardcoded CUDA) - model = model.cpu() - model.eval() - except (Exception, RuntimeError) as e: - error_str = str(e) - # Check if error is CUDA-related - if so, skip this file entirely - is_cuda_error = "CUDA" in error_str or "cuda" in error_str.lower() + file_size = os.path.getsize(local_path) + is_likely_torchscript = file_size < 10 * 1024 * 1024 # Less than 10MB is likely TorchScript - if is_cuda_error: - # TorchScript file has hardcoded CUDA - skip it and create fresh model - print(f"Warning: TorchScript model file has hardcoded CUDA references. Skipping file and creating fresh CPU-compatible model.") + if is_likely_torchscript: + print(f"Warning: Detected TorchScript model file ({file_size} bytes). Skipping to avoid CUDA hardcoding.") model, preprocess, _ = open_clip.create_model_and_transforms( "ViT-B-32", pretrained="openai" ) model = model.to(device) else: - # Fall back to state dict loading or create fresh model - print(f"Loading model from state dict (TorchScript failed: {error_str[:100]})") + # Larger files might be state dicts try: - model, preprocess, _ = open_clip.create_model_and_transforms( - "ViT-B-32", pretrained=False - ) checkpoint = torch.load(local_path, map_location="cpu", weights_only=False) if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: + # State dict checkpoint + model, preprocess, _ = open_clip.create_model_and_transforms( + "ViT-B-32", pretrained=False + ) model.load_state_dict(checkpoint['state_dict']) + model = model.to(device) + print(f"Loaded model from state dict checkpoint") elif isinstance(checkpoint, dict): + # Attempt to load dict as state + model, preprocess, _ = open_clip.create_model_and_transforms( + "ViT-B-32", pretrained=False + ) model.load_state_dict(checkpoint) + model = model.to(device) + print(f"Loaded model from state dict") else: - # If it's not a dict, it might be a TorchScript model object - # Check if it has TorchScript attributes - if hasattr(checkpoint, 'encode_image') and hasattr(checkpoint, 'graph'): - # It's a TorchScript model - skip it - print("Warning: Model file is TorchScript. Creating fresh model for CPU compatibility.") - model, preprocess, _ = open_clip.create_model_and_transforms( - "ViT-B-32", pretrained="openai" - ) - else: - model = checkpoint - _, preprocess, _ = open_clip.create_model_and_transforms("ViT-B-32", pretrained=False) - # Move to device (will be CPU) - model = model.to(device) - except Exception as e2: - # If state dict loading also fails, create a fresh model - print(f"Warning: Could not load model from file. Creating fresh model. Error: {str(e2)[:100]}") + # Unknown format - create fresh model + print(f"Unknown checkpoint format. Creating fresh model.") + model, preprocess, _ = open_clip.create_model_and_transforms( + "ViT-B-32", pretrained="openai" + ) + model = model.to(device) + except Exception as e: + print(f"Warning: Could not load checkpoint. Creating fresh model. Error: {str(e)[:100]}") model, preprocess, _ = open_clip.create_model_and_transforms( "ViT-B-32", pretrained="openai" ) model = model.to(device) + except Exception as e: + print(f"Warning: Error checking local model. Creating fresh model. Error: {str(e)[:100]}") + model, preprocess, _ = open_clip.create_model_and_transforms( + "ViT-B-32", pretrained="openai" + ) + model = model.to(device) else: # Download from openai (requires internet on first run) + print("No local CLIP model found. Downloading from OpenAI...") model, preprocess, _ = open_clip.create_model_and_transforms( "ViT-B-32", pretrained="openai" ) @@ -215,7 +213,11 @@ def embed_text(text: Union[str, List[str]]) -> np.ndarray: def embed_image(path: str) -> np.ndarray: - """Generate embeddings for image with strict CPU-only mode.""" + """Generate embeddings for image with strict CPU-only mode. + + Returns: + numpy array of shape (1, 512) containing normalized CLIP image embedding. + """ global _clip_model, _clip_preprocess # Force CPU-only environment @@ -242,14 +244,22 @@ def embed_image(path: str) -> np.ndarray: try: # Encode image and normalize feats = model.encode_image(batch) + # Normalize: feats shape is (1, 512), normalize per-sample feats = feats / feats.norm(dim=-1, keepdim=True) - return feats.cpu().numpy().astype(np.float32) + result = feats.cpu().numpy().astype(np.float32) + + # Ensure shape is (1, 512) + if result.ndim == 1: + result = result.reshape(1, -1) + + assert result.shape == (1, 512), f"Expected shape (1, 512), got {result.shape}" + return result + except RuntimeError as e: error_msg = str(e) if "CUDA" in error_msg or "cuda" in error_msg.lower(): print("Warning: CUDA operation attempted. Reinitializing in CPU-only mode...") # Reset model to force CPU reinitialization - global _clip_model, _clip_preprocess _clip_model = None _clip_preprocess = None # Retry with fresh CPU model @@ -258,38 +268,15 @@ def embed_image(path: str) -> np.ndarray: batch = torch.stack([image]).cpu() feats = model.encode_image(batch) feats = feats / feats.norm(dim=-1, keepdim=True) - return feats.cpu().numpy().astype(np.float32) + result = feats.cpu().numpy().astype(np.float32) + if result.ndim == 1: + result = result.reshape(1, -1) + return result raise # Re-raise if it's not a CUDA error + except Exception as e: print(f"Error processing image {path}: {str(e)}") raise - - try: - with torch.no_grad(): - feats = model.encode_image(batch) - feats = feats / feats.norm(dim=-1, keepdim=True) - except RuntimeError as e: - if "CUDA" in str(e) or "cuda" in str(e).lower(): - # Model has CUDA hardcoded in TorchScript - need to recreate model - print(f"Warning: TorchScript model has hardcoded CUDA references. Recreating model for CPU compatibility.") - # Reset global model to force recreation - _clip_model = None - _clip_preprocess = None - - # Recreate model (will be CPU-compatible) - model, preprocess = get_clip() - device = torch.device("cpu") - model = model.cpu() - batch = batch.cpu() - - with torch.no_grad(): - feats = model.encode_image(batch) - feats = feats / feats.norm(dim=-1, keepdim=True) - else: - raise - - # Always return CPU numpy array - return feats.cpu().numpy().astype(np.float32) def embed_audio_segment(transcript: str) -> np.ndarray: diff --git a/backend/app/index_store.py b/backend/app/index_store.py index 976cdcb..6e1923e 100644 --- a/backend/app/index_store.py +++ b/backend/app/index_store.py @@ -27,7 +27,7 @@ def connect_db(): CREATE TABLE IF NOT EXISTS vectors ( id INTEGER PRIMARY KEY AUTOINCREMENT, vector_id INTEGER, - content TEXT, + page_content TEXT, file_name TEXT, file_type TEXT, page_number INTEGER, @@ -70,12 +70,12 @@ def add_embeddings_with_metadata(embeddings: np.ndarray, metadatas: List[dict]) for i, meta in enumerate(metadatas): cur.execute( """ - INSERT INTO vectors (vector_id, content, file_name, file_type, page_number, timestamp, filepath, width, height, bbox) + INSERT INTO vectors (vector_id, page_content, file_name, file_type, page_number, timestamp, filepath, width, height, bbox) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( start_id + i, - meta.get("content"), + meta.get("page_content"), meta.get("file_name"), meta.get("file_type"), meta.get("page_number"), @@ -107,7 +107,7 @@ def status() -> dict: def rebuild_from_db(dim: int) -> dict: conn = connect_db() cur = conn.cursor() - cur.execute("SELECT rowid, content FROM vectors ORDER BY rowid") + cur.execute("SELECT rowid, page_content FROM vectors ORDER BY rowid") rows = cur.fetchall() conn.close() if not rows: diff --git a/backend/app/ingestion/audio_transcriber.py b/backend/app/ingestion/audio_transcriber.py index 278d9e9..82ac459 100644 --- a/backend/app/ingestion/audio_transcriber.py +++ b/backend/app/ingestion/audio_transcriber.py @@ -1,108 +1,153 @@ -""" -Audio transcription using Whisper. +"""Audio transcription using local ASR (whisper/faster-whisper if available). + +This module tries to transcribe audio offline using locally installed Whisper +or faster-whisper backends. If neither is available, it will return an empty +transcript and the ingestion pipeline will surface an appropriate message. """ from __future__ import annotations import os -import subprocess -import tempfile from typing import List, Tuple from .base import Chunk, _split_text -def transcribe_audio_with_whisper_cpp(path: str) -> Tuple[str, List[Tuple[float, float, str]]]: - """ - Transcribe audio using Whisper model. - Returns (full_transcript, segments). - segments is a list of tuples (start_ms, end_ms, text). +def _format_timestamp(seconds: float) -> str: + """Format seconds as HH:MM:SS or MM:SS string.""" + try: + seconds = float(seconds) + except Exception: + return "" + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + if hours > 0: + return f"{hours:02d}:{minutes:02d}:{secs:02d}" + return f"{minutes:02d}:{secs:02d}" + + +def transcribe_audio_with_whisper(path: str) -> Tuple[str, List[Tuple[float, float, str]]]: + """Transcribe audio using available local Whisper implementation. + + Returns (full_transcript, segments) where segments is a list of + (start_seconds, end_seconds, text). """ + # Try openai/whisper first try: import whisper - import torch - import numpy as np - - # Force CPU mode for consistency - device = "cpu" - - # Load model with absolute path - model_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "models", "whisper")) - model = whisper.load_model("base", download_root=model_path, device=device) - - # Transcribe audio - result = model.transcribe(path, language=None) - - # Extract transcript and segments + print(f"[Audio] Loading openai-whisper model 'base'...") + model = whisper.load_model("base", device="cpu") + print(f"[Audio] Transcribing {path}...") + result = model.transcribe(path) transcript = result.get("text", "").strip() segments = [] - - # Process segments if available for seg in result.get("segments", []): + start = seg.get("start", 0.0) + end = seg.get("end", 0.0) text = seg.get("text", "").strip() if text: - start = seg.get("start", 0) - end = seg.get("end", 0) - segments.append((start, end, text)) - + segments.append((float(start), float(end), text)) + print(f"[Audio] Transcription complete. Length: {len(transcript)} chars.") return transcript, segments + except ImportError: + print("[Audio] openai-whisper not installed.") + except Exception as e: + print(f"[Audio] openai-whisper failed: {e}") + + # Try faster-whisper if installed (it often works well offline) + try: + from faster_whisper import WhisperModel + print(f"[Audio] Loading faster-whisper model...") + model_size = "small" + model_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "models", "whisper")) + model = WhisperModel(model_dir, device="cpu", compute_type="int8_float16") + segments_iter, info = model.transcribe(path, beam_size=5) + transcript_parts = [] + segments = [] + for segment in segments_iter: + start = float(segment.start) + end = float(segment.end) + text = segment.text.strip() + if text: + transcript_parts.append(text) + segments.append((start, end, text)) + return " ".join(transcript_parts), segments + except ImportError: + print("[Audio] faster-whisper not installed.") except Exception as e: - print(f"Error transcribing audio: {str(e)}") - pass + print(f"[Audio] faster-whisper failed: {e}") + + # If no local ASR available, return empty results (ingestion will handle fallback) + print("[Audio] No ASR backend available or all failed.") return "", [] def transcribe_audio(path: str, file_name: str) -> List[Chunk]: - """Transcribe audio file and create chunks with timestamps.""" - transcript, segments = transcribe_audio_with_whisper_cpp(path) + """Transcribe audio file and return a list of Chunk objects with timestamps. + + Each chunk will have attributes: + - content: transcript text + - file_name, file_type='audio', filepath + - start_ts (float seconds), end_ts (float seconds) + - timestamp (formatted start time string) + """ + transcript, segments = transcribe_audio_with_whisper(path) chunks: List[Chunk] = [] - - if not transcript or transcript.strip() == "": - # If transcription failed, return empty list - return chunks - - # Split transcript into chunks - split_chunks = _split_text(transcript) - - # Map chunks to segments for timestamp assignment - segment_idx = 0 - char_pos = 0 - - for i, ch in enumerate(split_chunks): - # Find corresponding segment for this chunk - chunk_start = char_pos - chunk_end = char_pos + len(ch) - - # Find segment that contains this chunk - timestamp_str = None - for seg_start, seg_end, seg_text in segments: - seg_char_start = sum(len(seg[2]) + 1 for seg in segments[:segments.index((seg_start, seg_end, seg_text))]) - seg_char_end = seg_char_start + len(seg_text) - - if chunk_start >= seg_char_start and chunk_start < seg_char_end: - # Format timestamp as "HH:MM:SS" or "MM:SS" - if seg_start is not None and seg_end is not None: - start_sec = seg_start / 1000.0 - end_sec = seg_end / 1000.0 - hours = int(start_sec // 3600) - minutes = int((start_sec % 3600) // 60) - seconds = int(start_sec % 60) - if hours > 0: - timestamp_str = f"{hours:02d}:{minutes:02d}:{seconds:02d}" - else: - timestamp_str = f"{minutes:02d}:{seconds:02d}" - break - - c = Chunk( - content=ch, - file_name=file_name, - file_type="audio", + + if not segments: + # If ASR failed, create a single placeholder chunk to surface the file + placeholder = Chunk( + content="[Transcription unavailable: please install a local ASR model (whisper or faster-whisper)]", + file_name=file_name, + file_type="audio", filepath=path, - timestamp=timestamp_str # Store timestamp in Chunk + timestamp=None ) - # Also store segments for reference - setattr(c, "segments", segments) - chunks.append(c) - - char_pos = chunk_end - + setattr(placeholder, "start_ts", None) + setattr(placeholder, "end_ts", None) + chunks.append(placeholder) + return chunks + + # For each segment returned by ASR, split further if needed and assign timestamps + for seg_start, seg_end, seg_text in segments: + seg_text = (seg_text or "").strip() + if not seg_text: + continue + + # Split long segment text into subchunks while preserving approximate timestamps + subchunks = _split_text(seg_text, min_size=100, max_size=400) + if not subchunks: + subchunks = [seg_text] + + total_chars = sum(len(s) for s in subchunks) + if total_chars == 0: + continue + + # Distribute timestamp range proportionally across subchunks + seg_duration = max(0.0, float(seg_end) - float(seg_start)) + char_cursor = 0 + for sub in subchunks: + proportion = len(sub) / total_chars + start_offset = (char_cursor / total_chars) * seg_duration + end_offset = ((char_cursor + len(sub)) / total_chars) * seg_duration + start_ts = float(seg_start) + start_offset + end_ts = float(seg_start) + end_offset + + ts_str = _format_timestamp(start_ts) + + c = Chunk( + content=sub, + file_name=file_name, + file_type="audio", + filepath=path, + timestamp=ts_str + ) + setattr(c, "start_ts", start_ts) + setattr(c, "end_ts", end_ts) + setattr(c, "char_start", None) + setattr(c, "char_end", None) + chunks.append(c) + + char_cursor += len(sub) + return chunks diff --git a/backend/app/ingestion/image_processor.py b/backend/app/ingestion/image_processor.py index 1c38353..b08eab6 100644 --- a/backend/app/ingestion/image_processor.py +++ b/backend/app/ingestion/image_processor.py @@ -5,38 +5,77 @@ from __future__ import annotations import os +import logging from typing import List, Optional from PIL import Image from .base import Chunk from ..image_utils import get_image_size +logger = logging.getLogger(__name__) + def detect_text_in_image(path: str) -> str: - """Extract text from image using OCR (optional pytesseract).""" + """Extract text from image using OCR (optional pytesseract). + + Returns: + Extracted text string, or empty string if OCR is not available or fails. + """ try: import pytesseract + logger.debug(f"Attempting OCR on image: {path}") image = Image.open(path) text = pytesseract.image_to_string(image) - return text.strip() + extracted = text.strip() + if extracted: + logger.debug(f"Successfully extracted {len(extracted)} characters via OCR") + return extracted except ImportError: - # pytesseract not available, return empty + # pytesseract not available - use fallback description + logger.debug("pytesseract not available. Using filename-based description.") return "" - except Exception: + except Exception as e: + # Log but don't fail - continue processing without OCR + logger.warning(f"OCR extraction failed for {path}: {e}") return "" def image_to_embedding(path: str, file_name: str) -> List[Chunk]: - """Process image and create chunk for embedding.""" + """Process image and create chunk for embedding with rich content. + + The chunk content includes: + 1. Image filename (always) + 2. OCR text if available (extracted via Tesseract) + 3. A semantic description hint + + This ensures that both visual (via CLIP embedding) and textual (via OCR) + aspects of the image are indexed and retrievable. + """ try: w, h = get_image_size(path) - except Exception: + except Exception as e: + logger.warning(f"Could not get image dimensions for {path}: {e}") w, h = None, None - # Try OCR if available + # Extract OCR text from image (if pytesseract available) ocr_text = detect_text_in_image(path) + # Build rich content description for the image chunk + content_parts = [f"Image: {file_name}"] + + # Add OCR text as primary content if available + if ocr_text: + content_parts.append(f"OCR Text: {ocr_text}") + else: + # Add dimensional hints for retrieval if no OCR + if w and h: + content_parts.append(f"Dimensions: {w}x{h} pixels") + + # Combine content - this will be embedded by CLIP (image embedding) + # and also used for text search in vector store + full_content = " | ".join(content_parts) + ch = Chunk( - content=f"Image: {file_name}" + (f" | OCR: {ocr_text}" if ocr_text else ""), + content=full_content, file_name=file_name, file_type="image", filepath=path, diff --git a/backend/app/llm/prompts.py b/backend/app/llm/prompts.py index 3e2157e..7d20554 100644 --- a/backend/app/llm/prompts.py +++ b/backend/app/llm/prompts.py @@ -26,7 +26,7 @@ def build_prompt(query: str, sources: List[Dict[str, Any]]) -> str: - """Build a comprehensive prompt with context sources.""" + """Build a comprehensive prompt with context sources, optimized for multimodal content.""" if not sources: return f"USER QUESTION: {query}\n\nAnswer: I don't have any relevant information to answer this question." @@ -35,20 +35,29 @@ def build_prompt(query: str, sources: List[Dict[str, Any]]) -> str: # Build source description source_desc = f"[{i}] " - # Add file information + # Add file information and modality indicator file_name = source.get('file_name', 'Unknown') file_type = source.get('file_type', 'unknown') - source_desc += f"{file_name} ({file_type})" + modality = source.get('modality', file_type) + + source_desc += f"{file_name}" + + # Add modality hint for better LLM context + if file_type == 'image': + source_desc += " (Screenshot/Image)" + elif file_type == 'pdf': + source_desc += " (PDF Document)" + elif file_type == 'audio': + source_desc += " (Audio)" # Add location information if file_type == 'pdf' and source.get('page_number'): source_desc += f" - Page {source['page_number']}" elif file_type == 'audio' and source.get('timestamp'): source_desc += f" - {source['timestamp']}" - elif file_type == 'image': - source_desc += " - Image" # Add content snippet + # For images, we want OCR text to be prominent content = source.get('content', '').strip() if content: # Truncate very long content @@ -56,6 +65,12 @@ def build_prompt(query: str, sources: List[Dict[str, Any]]) -> str: content = content[:500] + "..." source_desc += f": {content}" + # If no content but we have OCR text in metadata, show it + # (for backward compatibility, also check for ocr_text field) + ocr_text = source.get('ocr_text', '') + if ocr_text and not content: + source_desc += f": (OCR) {ocr_text}" + context_lines.append(source_desc) context_sources = "\n".join(context_lines) @@ -88,7 +103,7 @@ def build_simple_prompt(query: str, sources: List[Dict[str, Any]]) -> str: def build_multimodal_prompt(query: str, sources: List[Dict[str, Any]]) -> str: - """Build prompt optimized for multimodal content.""" + """Build prompt optimized for multimodal content with proper image handling.""" if not sources: return f"Question: {query}\nAnswer: I don't have any relevant information." @@ -107,31 +122,55 @@ def build_multimodal_prompt(query: str, sources: List[Dict[str, Any]]) -> str: text_sources.append(source) prompt_parts = [] + counter = 1 if text_sources: prompt_parts.append("TEXT SOURCES:") - for i, source in enumerate(text_sources, start=1): + for source in text_sources: content = source.get('content', '').strip() file_name = source.get('file_name', 'Unknown') - prompt_parts.append(f"[{i}] {file_name}: {content}") + if content: + if len(content) > 300: + content = content[:300] + "..." + prompt_parts.append(f"[{counter}] {file_name}: {content}") + counter += 1 if image_sources: - prompt_parts.append("\nIMAGE SOURCES:") - for i, source in enumerate(image_sources, start=len(text_sources) + 1): + prompt_parts.append("\nIMAGE SOURCES (Screenshots/Diagrams):") + for source in image_sources: + # For images, the content field should already contain OCR text (from image_processor.py) content = source.get('content', '').strip() file_name = source.get('file_name', 'Unknown') - prompt_parts.append(f"[{i}] {file_name}: {content}") + + # Fallback to ocr_text field if available and content is just the filename + ocr_text = source.get('ocr_text', '') + if not content or content.startswith('Image:'): + if ocr_text: + content = ocr_text + else: + content = "(Image - describe based on filename and any visible elements)" + + if len(content) > 300: + content = content[:300] + "..." + + # Make image source more distinct for the LLM + prompt_parts.append(f"[{counter}] {file_name} (IMAGE): {content}") + counter += 1 if audio_sources: - prompt_parts.append("\nAUDIO SOURCES:") - for i, source in enumerate(audio_sources, start=len(text_sources) + len(image_sources) + 1): + prompt_parts.append("\nAUDIO SOURCES (Transcripts):") + for source in audio_sources: content = source.get('content', '').strip() file_name = source.get('file_name', 'Unknown') timestamp = source.get('timestamp', '') - if timestamp: - prompt_parts.append(f"[{i}] {file_name} ({timestamp}): {content}") - else: - prompt_parts.append(f"[{i}] {file_name}: {content}") + if content: + if len(content) > 300: + content = content[:300] + "..." + if timestamp: + prompt_parts.append(f"[{counter}] {file_name} ({timestamp}): {content}") + else: + prompt_parts.append(f"[{counter}] {file_name}: {content}") + counter += 1 context = "\n".join(prompt_parts) @@ -141,4 +180,11 @@ def build_multimodal_prompt(query: str, sources: List[Dict[str, Any]]) -> str: Question: {query} -Answer based on the provided sources. Use [1], [2], etc. for citations. If information is missing, say so.""" +Important Instructions: +1. Use ONLY information from the sources [1] through [{counter-1}] +2. For image sources, describe what they show based on the text/OCR content provided +3. Cite sources inline using [1], [2], [3], etc. +4. If information is not in the sources, say "I don't have that information in the provided sources" +5. Be specific and accurate in your answers + +Answer:""" diff --git a/backend/app/main.py b/backend/app/main.py index 35c1289..585fe08 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -116,10 +116,28 @@ async def ingest(file: UploadFile = File(...)): ) if not chunks: - raise HTTPException( - status_code=422, - detail=f"No content could be extracted from {file.filename}. The file may be corrupted or in an unsupported format." - ) + # If the uploaded file is audio, don't fail the whole ingestion when + # transcription cannot be performed (missing ASR/model). Instead, + # create a placeholder audio chunk so the file is still indexed and + # can be surfaced to the user with a message about transcription. + audio_exts = ('.mp3', '.wav', '.m4a', '.flac', '.ogg') + if file.filename.lower().endswith(audio_exts): + from .ingestion.base import Chunk + placeholder = Chunk( + content="[Transcription unavailable: install a local ASR model (whisper or faster-whisper) to enable transcripts]", + file_name=file.filename, + file_type="audio", + filepath=dest_path, + timestamp=None + ) + setattr(placeholder, 'start_ts', None) + setattr(placeholder, 'end_ts', None) + chunks = [placeholder] + else: + raise HTTPException( + status_code=422, + detail=f"No content could be extracted from {file.filename}. The file may be corrupted or in an unsupported format." + ) # Generate embeddings and store using new vector store try: @@ -162,14 +180,16 @@ async def ingest(file: UploadFile = File(...)): 'file_name': chunk.file_name, 'file_type': chunk.file_type, 'page_number': chunk.page_number, - 'timestamp': chunk.timestamp, + 'timestamp': chunk.timestamp, + 'start_ts': getattr(chunk, 'start_ts', None), + 'end_ts': getattr(chunk, 'end_ts', None), 'filepath': chunk.filepath, 'width': getattr(chunk, 'width', None), 'height': getattr(chunk, 'height', None), 'bbox': getattr(chunk, 'bbox', None), 'char_start': getattr(chunk, 'char_start', None), 'char_end': getattr(chunk, 'char_end', None), - 'modality': chunk.file_type + 'modality': chunk.file_type } }) except Exception as e: @@ -193,6 +213,8 @@ async def ingest(file: UploadFile = File(...)): 'file_type': chunk.file_type, 'page_number': chunk.page_number, 'timestamp': chunk.timestamp, + 'start_ts': getattr(chunk, 'start_ts', None), + 'end_ts': getattr(chunk, 'end_ts', None), 'filepath': chunk.filepath, 'width': getattr(chunk, 'width', None), 'height': getattr(chunk, 'height', None), @@ -270,16 +292,34 @@ def query(payload: dict): file_type = r.get("modality", r.get("file_type", "text")) page_num = r.get("page_number") timestamp = r.get("timestamp") - + start_ts = r.get("start_ts") + end_ts = r.get("end_ts") + # Build URL efficiently url = None if file_name: base_path = f"/files/{file_name}" if file_type == "pdf" and page_num is not None: url = f"{base_path}#page={page_num}" - elif file_type == "audio" and timestamp: - # URL-safe timestamp encoding - url = f"{base_path}#t={timestamp.replace(':', '')}" + elif file_type == "audio": + # Prefer numeric start_ts if available, otherwise fall back to timestamp string + ts_str = None + if start_ts is not None: + # format seconds to HH:MM:SS + try: + s = int(start_ts) + h = s // 3600 + m = (s % 3600) // 60 + sec = s % 60 + ts_str = f"{h:02d}:{m:02d}:{sec:02d}" if h > 0 else f"{m:02d}:{sec:02d}" + except Exception: + ts_str = None + if not ts_str and timestamp: + ts_str = timestamp + if ts_str: + url = f"{base_path}#timestamp={ts_str}" + else: + url = base_path elif file_type == "image": url = base_path else: diff --git a/backend/app/rag.py b/backend/app/rag.py index 78b769e..713a45a 100644 --- a/backend/app/rag.py +++ b/backend/app/rag.py @@ -42,7 +42,7 @@ def similarity_search(query: str, k: int) -> List[Dict]: cur = conn.cursor() placeholders = ",".join(["?"] * len(ids)) cur.execute( - f"SELECT vector_id, content, file_name, file_type, page_number, timestamp, filepath FROM vectors WHERE vector_id IN ({placeholders})", + f"SELECT vector_id, page_content, file_name, file_type, page_number, timestamp, filepath FROM vectors WHERE vector_id IN ({placeholders})", ids, ) rows = cur.fetchall() @@ -56,7 +56,7 @@ def similarity_search(query: str, k: int) -> List[Dict]: results.append( { "vector_id": r[0], - "content": r[1], + "page_content": r[1], "file_name": r[2], "file_type": r[3], "page_number": r[4], @@ -77,7 +77,7 @@ def build_prompt(query: str, sources: List[Dict]) -> str: where += f" page {s['page_number']}" if s.get("file_type") == "audio" and s.get("timestamp"): where += f" {s['timestamp']}" - snippet = (s.get("content") or "").replace("\n", " ") + snippet = (s.get("page_content") or "").replace("\n", " ") lines.append(f"Source {marker} {where}: \"{snippet}\"") lines.append("\nAnswer the user query using only the information from sources [1..k]. Provide citations inline like [1], [2]. If the answer is unknown from sources, say you don't know.") lines.append(f"\nUser query: {query}\nAnswer:") @@ -97,7 +97,7 @@ def answer_query(cfg_path: str, query: str) -> dict: { "id": i, "file_name": s.get("file_name"), - "snippet": s.get("content"), + "snippet": s.get("page_content"), "page_number": s.get("page_number"), "timestamp": s.get("timestamp"), "score": s.get("score"), diff --git a/backend/app/vector_store/faiss_store.py b/backend/app/vector_store/faiss_store.py index d2549b3..4157b78 100644 --- a/backend/app/vector_store/faiss_store.py +++ b/backend/app/vector_store/faiss_store.py @@ -121,11 +121,13 @@ def upsert(self, items: List[Dict[str, Any]]) -> int: vector_id = start_id + i self.metadata[str(vector_id)] = { 'vector_id': vector_id, - 'content': metadata.get('content', ''), + 'page_content': metadata.get('page_content', ''), 'file_name': metadata.get('file_name', ''), 'file_type': metadata.get('file_type', ''), 'page_number': metadata.get('page_number'), 'timestamp': metadata.get('timestamp'), + 'start_ts': metadata.get('start_ts'), + 'end_ts': metadata.get('end_ts'), 'filepath': metadata.get('filepath'), 'width': metadata.get('width'), 'height': metadata.get('height'), @@ -217,9 +219,27 @@ def rebuild_from_metadata(self): # This would require re-computing embeddings from original content # For now, just reinitialize with current dimension self.index = faiss.IndexFlatIP(self.dimension) + + # Get all embeddings and IDs from metadata + embeddings = [] + ids = [] + for vector_id, meta in self.metadata.items(): + # Assuming 'page_content' holds the text for embedding + text_to_embed = meta.get('page_content', '') + if text_to_embed: + # Re-create embedding. This requires access to an embedding function. + # This is a placeholder for where you'd call your embedding function. + # from ..embeddings import embed_text + # embedding = embed_text(text_to_embed) + # embeddings.append(embedding) + # ids.append(int(vector_id)) + pass # Placeholder for re-embedding logic + + # if embeddings: + # self.index.add_with_ids(np.array(embeddings), np.array(ids)) + self._persist_index() - # Global store instance for backward compatibility _store_instance: Optional[FAISSStore] = None diff --git a/backend/requirements.txt b/backend/requirements.txt index 367ea45..68bf71c 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -32,3 +32,7 @@ pytest==7.4.3 pytest-asyncio==0.21.1 + +# Audio Transcription +openai-whisper==20231117 +ffmpeg-python==0.2.0 diff --git a/backend/scripts/test_audio_pipeline.py b/backend/scripts/test_audio_pipeline.py new file mode 100644 index 0000000..dee0b73 --- /dev/null +++ b/backend/scripts/test_audio_pipeline.py @@ -0,0 +1,85 @@ +"""End-to-end test script for the audio ingestion pipeline. + +This script is intended to be runnable locally. It: + - Creates a short synthetic WAV file in storage + - Calls the ingestion/transcription pipeline directly + - Embeds transcript segments using the unified embed_text() + - Indexes embeddings in FAISS + - Performs a simple search to verify vectors are searchable + +The script is tolerant of missing ASR backends: if no local ASR is available, +the pipeline will insert a placeholder chunk and indexing will still succeed. +""" + +import os +import wave +import struct +import tempfile +import numpy as np + +from backend.app.ingestion.audio_transcriber import transcribe_audio +from backend.app.embeddings.generate import embed_text +from backend.app.vector_store.faiss_store import get_store, upsert, search + + +def generate_silent_wav(path: str, duration_sec: float = 1.0, rate: int = 16000): + n_samples = int(duration_sec * rate) + amplitude = 0 + with wave.open(path, 'w') as wf: + wf.setnchannels(1) + wf.setsampwidth(2) + wf.setframerate(rate) + frames = 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-
- - {/* Offline Banner */} - {!isOnline && ( -
-

- 🔌 Offline Mode Active - You can query already indexed documents -

-
- )} - +
{/* Mobile Navigation */} -
+
- - - - Upload Documents - -
- -
- - + +
+ + +
+
Chat History
+
+ {chatHistory.map((chat) => ( +
+
{chat.title}
+
{chat.date}
+
+ ))} +
+
- - - - - - - Retrieved Context - -
- +
+
Database Stats
+
+ + {indexedDocs} indexed +
+
+ + {/* User Area */} +
+
+
+ U +
+
User
+
+
-
- {/* Left Panel - File Upload */} - -
- - {/* Toggle Sidebar Button */} - + )} {modalItem && ( setModalItem(null)} /> diff --git a/frontend/src/ChatUI.jsx b/frontend/src/ChatUI.jsx index dd7825e..e306270 100644 --- a/frontend/src/ChatUI.jsx +++ b/frontend/src/ChatUI.jsx @@ -1,10 +1,11 @@ import { useEffect, useRef, useState } from "react"; +import { Mic } from "lucide-react"; export default function ChatUI({ messages, onSend, isTyping = false }) { const [input, setInput] = useState(""); const scrollRef = useRef(null); - // Simplified submit handler for text-only input + // Submit handler const submit = (e) => { e.preventDefault(); if (!input.trim()) return; @@ -12,6 +13,7 @@ export default function ChatUI({ messages, onSend, isTyping = false }) { setInput(""); }; + // Auto-scroll to bottom on new messages useEffect(() => { if (scrollRef.current) { scrollRef.current.scrollTop = scrollRef.current.scrollHeight; @@ -20,112 +22,131 @@ export default function ChatUI({ messages, onSend, isTyping = false }) { const Message = ({ role, content }) => { const isUser = role === "user"; + const isSystem = role === "system"; + + if (isSystem) { + return ( +
+ + {content} + +
+ ); + } + return (
- {!isUser && ( -
- A +
+
+ {isUser ? ( +
+ U +
+ ) : ( +
+ + + + +
+ )}
- )} -
-

{content}

-
- {isUser && ( -
- U + +
+
+

{content}

+
- )} +
); }; const Typing = () => ( -
- - - +
+
+
+ + + + +
+
+ + + +
+
); return ( -
-
- - {/* Set a fixed height and enable scrolling */} +
+ {/* Messages Area */}
{messages.length === 0 && ( -
-
- A +
+
+ + + +
-

Chat with your Documents

-

- Upload files using the box above to get started. -

+

How can I help you today?

+

Ask me anything about your documents

)} - {messages.map((m, idx) => ( - - ))} - - {isTyping && ( -
-
- A -
-
- -
-
- )} +
+ {messages.map((m, idx) => ( + + ))} + {isTyping && } +
-
-
- setInput(e.target.value)} - /> - -
+ {/* Input Area */} +
+
+
+ setInput(e.target.value)} + /> +
+ + +
+
+
+

+ AI can make mistakes. Consider checking important information. +

+
+
); diff --git a/frontend/src/ChatWindow.jsx b/frontend/src/ChatWindow.jsx index ca46797..602df88 100644 --- a/frontend/src/ChatWindow.jsx +++ b/frontend/src/ChatWindow.jsx @@ -1,7 +1,10 @@ -import { useState } from "react"; +import { useState, useEffect, useRef } from "react"; +import { Send, Mic } from "lucide-react"; export default function ChatWindow({ onQuery, messages, isTyping }) { const [input, setInput] = useState(""); + const scrollRef = useRef(null); + const inputRef = useRef(null); const submit = async (e) => { e.preventDefault(); @@ -11,60 +14,166 @@ export default function ChatWindow({ onQuery, messages, isTyping }) { await onQuery(text); }; - return ( -
-
- {messages.map((m, idx) => ( + // Auto-scroll to bottom on new messages + useEffect(() => { + if (scrollRef.current) { + scrollRef.current.scrollTop = scrollRef.current.scrollHeight; + } + }, [messages, isTyping]); + + // Auto-grow textarea + useEffect(() => { + if (inputRef.current) { + inputRef.current.style.height = "auto"; + inputRef.current.style.height = Math.min(inputRef.current.scrollHeight, 150) + "px"; + } + }, [input]); + + const Message = ({ role, content, sources = [], onOpenSource }) => { + const isUser = role === "user"; + const isSystem = role === "system"; + + if (isSystem) { + return ( +
+ + {content} + +
+ ); + } + + return ( +
+ {!isUser && ( +
+ + + + +
+ )} + +
-
- {m.content} +

{content}

+
+ + {sources && sources.length > 0 && ( +
+ {sources.map((s, i) => ( + + ))}
- {m.sources && m.sources.length > 0 && ( -
- {m.sources.map((s, i) => ( - - ))} -
- )} + )} +
+ + {isUser && ( +
+ U
- ))} + )} +
+ ); + }; - {isTyping && ( -
Assistant is typing…
+ const Typing = () => ( +
+
+ + + + +
+
+ + + +
+
+ ); + + return ( +
+ {/* Messages Area */} +
+ {messages.length === 0 && ( +
+
+ + + + +
+

Start a conversation

+

Ask me anything!

+
)} + + {messages.map((m, idx) => ( + + ))} + + {isTyping && }
+ {/* Input Form */}
- setInput(e.target.value)} - /> - +
+

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"width": 4000, + "height": 3000, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "26": { + "vector_id": 26, + "content": "Image: Screenshot 2025-11-07 000409.png | Dimensions: 344x140 pixels", + "file_name": "Screenshot 2025-11-07 000409.png", + "file_type": "image", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\Screenshot 2025-11-07 000409.png", + "width": 344, + "height": 140, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "27": { + "vector_id": 27, + "content": "Image: Screenshot 2025-11-08 020618.png | Dimensions: 649x98 pixels", + "file_name": "Screenshot 2025-11-08 020618.png", + "file_type": "image", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\Screenshot 2025-11-08 020618.png", + "width": 649, + "height": 98, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "28": { + "vector_id": 28, + "content": "Image: Screenshot 2025-11-08 143237.png | Dimensions: 940x217 pixels", + "file_name": "Screenshot 2025-11-08 143237.png", + "file_type": "image", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\Screenshot 2025-11-08 143237.png", + "width": 940, + "height": 217, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "29": { + "vector_id": 29, + "content": "Image: Screenshot 2025-11-15 220712.png | Dimensions: 410x725 pixels", + "file_name": "Screenshot 2025-11-15 220712.png", + "file_type": "image", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\Screenshot 2025-11-15 220712.png", + "width": 410, + "height": 725, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "30": { + "vector_id": 30, + "content": "Image: Screenshot 2025-11-15 235656.png | Dimensions: 987x67 pixels", + "file_name": "Screenshot 2025-11-15 235656.png", + "file_type": "image", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\Screenshot 2025-11-15 235656.png", + "width": 987, + "height": 67, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "31": { + "vector_id": 31, + "content": "Image: Screenshot 2025-11-16 001657.png | Dimensions: 394x729 pixels", + "file_name": "Screenshot 2025-11-16 001657.png", + "file_type": "image", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\Screenshot 2025-11-16 001657.png", + "width": 394, + "height": 729, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "image" + }, + "32": { + "vector_id": 32, + "content": "[Transcription unavailable: please install a local ASR model (whisper or faster-whisper)]", + "file_name": "0_0_0_1_0_0_0_1.wav", + "file_type": "audio", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\0_0_0_1_0_0_0_1.wav", + "width": null, + "height": null, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "audio" + }, + "33": { + "vector_id": 33, + "page_content": "", + "file_name": "0_0_0_1_0_1_1_0.wav", + "file_type": "audio", + "page_number": null, + "timestamp": null, + "start_ts": null, + "end_ts": null, + "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\0_0_0_1_0_1_1_0.wav", + "width": null, + "height": null, + "bbox": null, + "char_start": null, + "char_end": null, + "modality": "audio" } } \ No newline at end of file diff --git a/download_whisper.py b/download_whisper.py deleted file mode 100644 index 788e7c5..0000000 --- a/download_whisper.py +++ /dev/null @@ -1,12 +0,0 @@ -import os -import whisper -from pathlib import Path - -# Set up paths -model_dir = Path("backend/models/whisper") -model_dir.mkdir(parents=True, exist_ok=True) - -# Download the model -print("Downloading Whisper model...") -model = whisper.load_model("base") -print("Model downloaded successfully!") \ No newline at end of file diff --git a/scripts/check_audio_deps.py b/scripts/check_audio_deps.py new file mode 100644 index 0000000..7dedd8b --- /dev/null +++ b/scripts/check_audio_deps.py @@ -0,0 +1,35 @@ +import sys +import subprocess + +def check_whisper(): + print("Checking openai-whisper...") + try: + import whisper + print("✅ openai-whisper is installed.") + except ImportError: + print("❌ openai-whisper is NOT installed.") + return False + return True + +def check_ffmpeg(): + print("Checking ffmpeg...") + try: + # Try running ffmpeg -version + subprocess.run(["ffmpeg", "-version"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + print("✅ ffmpeg is available in PATH.") + except FileNotFoundError: + print("❌ ffmpeg is NOT found in PATH. Whisper requires ffmpeg.") + return False + except Exception as e: + print(f"❌ Error checking ffmpeg: {e}") + return False + return True + +if __name__ == "__main__": + w = check_whisper() + f = check_ffmpeg() + + if w and f: + print("\n🎉 All audio dependencies look good!") + else: + print("\n⚠️ Some dependencies are missing. Audio transcription might not work.") diff --git a/test_final_system.py b/test_final_system.py deleted file mode 100644 index 8e1dbd9..0000000 --- a/test_final_system.py +++ /dev/null @@ -1,429 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive system test for the offline multimodal RAG system. - -Tests all modalities (text, image, audio), citation tracking, retrieval accuracy, -and offline compliance. -""" - -import sys -import os -import json -import numpy as np -from pathlib import Path - -# Add backend to path -backend_dir = Path(__file__).parent / "backend" -sys.path.insert(0, str(backend_dir)) - -from app.embeddings import embed_text, embed_image -from app.vector_store import get_store -from app.retriever import get_retriever -from app.ingestion import extract_any -from app.llm import build_adapter, load_config, generate_answer - - -class Colors: - """ANSI color codes for terminal output.""" - GREEN = '\033[92m' - RED = '\033[91m' - YELLOW = '\033[93m' - BLUE = '\033[94m' - RESET = '\033[0m' - BOLD = '\033[1m' - - -def print_test(name: str): - """Print test header.""" - print(f"\n{Colors.BOLD}{Colors.BLUE}Testing: {name}{Colors.RESET}") - - -def print_pass(msg: str): - """Print pass message.""" - try: - print(f" {Colors.GREEN}[PASS]{Colors.RESET} {msg}") - except UnicodeEncodeError: - print(f" [PASS] {msg}") - - -def print_fail(msg: str): - """Print fail message.""" - try: - print(f" {Colors.RED}[FAIL]{Colors.RESET} {msg}") - except UnicodeEncodeError: - print(f" [FAIL] {msg}") - - -def print_warn(msg: str): - """Print warning message.""" - try: - print(f" {Colors.YELLOW}[WARN]{Colors.RESET} {msg}") - except UnicodeEncodeError: - print(f" [WARN] {msg}") - - -def test_embedding_dimensions(): - """Test 1: Verify all embeddings are 512-dim.""" - print_test("Embedding Dimensions (Unified 512-dim Space)") - - try: - # Test text embedding - text_emb = embed_text("test text") - # Handle both (1, 512) and (512,) shapes - if text_emb.ndim == 1: - assert text_emb.shape[0] == 512, f"Text embedding shape: {text_emb.shape}, expected 512-dim" - print_pass(f"Text embedding: {text_emb.shape} (512-dim)") - else: - assert text_emb.shape[-1] == 512, f"Text embedding shape: {text_emb.shape}, expected 512-dim" - print_pass(f"Text embedding: {text_emb.shape} (512-dim)") - - # Test batch text embedding - text_emb_batch = embed_text(["text1", "text2"]) - # Handle shape variations - if text_emb_batch.ndim == 2: - assert text_emb_batch.shape[0] == 2, f"Batch should have 2 items, got {text_emb_batch.shape[0]}" - assert text_emb_batch.shape[1] == 512, f"Each embedding should be 512-dim, got {text_emb_batch.shape[1]}" - print_pass(f"Batch text embedding: {text_emb_batch.shape}") - else: - print_fail(f"Batch embedding should be 2D, got shape {text_emb_batch.shape}") - return False - - return True - except Exception as e: - print_fail(f"Embedding dimension test failed: {e}") - return False - - -def test_store_configuration(): - """Test 2: Verify store is configured for 512-dim.""" - print_test("FAISS Store Configuration") - - try: - store = get_store() - assert store.dimension == 512, f"Store dimension: {store.dimension}, expected 512" - print_pass(f"Store dimension: {store.dimension}") - - status = store.status() - print_pass(f"Store status: {status['vectors']} vectors, {status['files']} files") - - if status['modalities']: - print_pass(f"Modalities in store: {', '.join(status['modalities'])}") - - return True - except Exception as e: - print_fail(f"Store configuration test failed: {e}") - return False - - -def test_cross_modal_retrieval(): - """Test 3: Test cross-modal retrieval capabilities.""" - print_test("Cross-Modal Retrieval") - - try: - store = get_store() - retriever = get_retriever() - - if store.index.ntotal == 0: - print_warn("No indexed data - skipping cross-modal retrieval test") - print_warn(" (Ingest some documents first to test cross-modal retrieval)") - return True # Not a failure, just no data - - # Test text query - query = "test query" - results = retriever.retrieve(query, top_k=10) - - if not results: - print_warn("No results returned - may need more indexed data") - return True - - print_pass(f"Retrieved {len(results)} results for text query") - - # Check modality distribution - modalities = {} - for r in results: - mod = r.get('modality', r.get('file_type', 'unknown')) - modalities[mod] = modalities.get(mod, 0) + 1 - - print_pass(f"Modalities in results: {dict(modalities)}") - - # Test cross-modal grouping - grouped = retriever.retrieve_cross_modal(query, top_k=5) - if isinstance(grouped, dict): - print_pass(f"Cross-modal grouping: {list(grouped.keys())}") - - return True - except Exception as e: - print_fail(f"Cross-modal retrieval test failed: {e}") - import traceback - traceback.print_exc() - return False - - -def test_citation_tracking(): - """Test 4: Verify citation tracking (file names, pages, timestamps).""" - print_test("Citation Tracking") - - try: - store = get_store() - retriever = get_retriever() - - if store.index.ntotal == 0: - print_warn("No indexed data - skipping citation test") - return True - - query = "test" - results = retriever.retrieve(query, top_k=5) - - if not results: - print_warn("No results to test citations") - return True - - citation_checks = { - 'file_name': 0, - 'page_number': 0, - 'timestamp': 0, - 'filepath': 0, - 'content': 0 - } - - for r in results: - if r.get('file_name'): - citation_checks['file_name'] += 1 - if r.get('page_number') is not None: - citation_checks['page_number'] += 1 - if r.get('timestamp'): - citation_checks['timestamp'] += 1 - if r.get('filepath'): - citation_checks['filepath'] += 1 - if r.get('content'): - citation_checks['content'] += 1 - - total = len(results) - print_pass(f"File names: {citation_checks['file_name']}/{total}") - print_pass(f"Page numbers: {citation_checks['page_number']}/{total} (PDFs)") - print_pass(f"Timestamps: {citation_checks['timestamp']}/{total} (Audio)") - print_pass(f"File paths: {citation_checks['filepath']}/{total}") - print_pass(f"Content: {citation_checks['content']}/{total}") - - # Check citation formatting - from app.utils import create_citations - citations = create_citations(results) - - if citations: - print_pass(f"Citation formatting: {len(citations)} citations created") - # Check first citation structure - first = citations[0] - required_fields = ['id', 'file_name', 'modality', 'snippet', 'score'] - missing = [f for f in required_fields if f not in first] - if not missing: - print_pass("Citation structure: All required fields present") - else: - print_fail(f"Citation structure: Missing fields: {missing}") - - return True - except Exception as e: - print_fail(f"Citation tracking test failed: {e}") - import traceback - traceback.print_exc() - return False - - -def test_offline_compliance(): - """Test 5: Verify offline operation (no external API calls).""" - print_test("Offline Compliance") - - try: - # Check that embeddings can be generated without internet - # (This is a basic check - full offline test would require network isolation) - - # Test embedding generation - text_emb = embed_text("offline test") - assert text_emb is not None, "Text embedding generation failed" - print_pass("Text embedding generation: Works offline") - - # Check model loading (should use local models) - from app.embeddings import get_text_model, get_clip - text_model = get_text_model() - clip_result = get_clip() - - # get_clip returns a tuple (model, preprocess) - if isinstance(clip_result, tuple) and len(clip_result) == 2: - clip_model, _ = clip_result - else: - clip_model = clip_result - - assert text_model is not None, "Text model not loaded" - assert clip_model is not None, "CLIP model not loaded" - print_pass("Model loading: Local models available") - - # Check store (should be local) - store = get_store() - assert store is not None, "Store not initialized" - print_pass("Vector store: Local storage (FAISS + SQLite)") - - return True - except Exception as e: - print_fail(f"Offline compliance test failed: {e}") - return False - - -def test_ingestion_pipeline(): - """Test 6: Test ingestion for different file types.""" - print_test("Ingestion Pipeline") - - try: - # Test that extract_any function exists and works - # (We can't test actual file ingestion without test files) - - # Check that all extractors are importable - from app.ingestion import ( - extract_text_from_pdf, - extract_text_from_docx, - image_to_embedding, - transcribe_audio - ) - print_pass("All ingestion modules importable") - - # Check chunk structure - from app.ingestion.base import Chunk - chunk = Chunk( - content="test", - file_name="test.txt", - file_type="text" - ) - assert chunk.content == "test" - assert chunk.file_name == "test.txt" - print_pass("Chunk structure: Valid") - - return True - except Exception as e: - print_fail(f"Ingestion pipeline test failed: {e}") - return False - - -def test_retrieval_consistency(): - """Test 7: Test retrieval consistency (same query, same results).""" - print_test("Retrieval Consistency") - - try: - store = get_store() - retriever = get_retriever() - - if store.index.ntotal == 0: - print_warn("No indexed data - skipping consistency test") - return True - - query = "test query for consistency" - - # Run query multiple times - results1 = retriever.retrieve(query, top_k=5) - results2 = retriever.retrieve(query, top_k=5) - - if not results1 or not results2: - print_warn("No results - cannot test consistency") - return True - - # Check that results are consistent (same number, same IDs) - if len(results1) == len(results2): - print_pass(f"Result count consistent: {len(results1)} results") - - # Check that top result is the same - if results1 and results2: - id1 = results1[0].get('vector_id') - id2 = results2[0].get('vector_id') - if id1 == id2: - print_pass("Top result consistent across queries") - else: - print_warn("Top result differs (may be due to non-deterministic reranking)") - else: - print_fail(f"Result count inconsistent: {len(results1)} vs {len(results2)}") - return False - - return True - except Exception as e: - print_fail(f"Retrieval consistency test failed: {e}") - return False - - -def test_endpoint_compatibility(): - """Test 8: Verify API endpoint compatibility.""" - print_test("API Endpoint Compatibility") - - try: - # Check that main endpoints can be imported - from app.main import app - print_pass("FastAPI app importable") - - # Check route registration - routes = [r.path for r in app.routes] - required_routes = ['/query', '/ingest', '/status', '/search/similarity'] - - for route in required_routes: - if route in routes: - print_pass(f"Route registered: {route}") - else: - print_fail(f"Route missing: {route}") - return False - - return True - except Exception as e: - print_fail(f"Endpoint compatibility test failed: {e}") - return False - - -def run_all_tests(): - """Run all tests and generate report.""" - print(f"\n{Colors.BOLD}{'='*60}") - print("OFFLINE MULTIMODAL RAG SYSTEM - COMPREHENSIVE TEST SUITE") - print(f"{'='*60}{Colors.RESET}\n") - - tests = [ - ("Embedding Dimensions", test_embedding_dimensions), - ("Store Configuration", test_store_configuration), - ("Cross-Modal Retrieval", test_cross_modal_retrieval), - ("Citation Tracking", test_citation_tracking), - ("Offline Compliance", test_offline_compliance), - ("Ingestion Pipeline", test_ingestion_pipeline), - ("Retrieval Consistency", test_retrieval_consistency), - ("Endpoint Compatibility", test_endpoint_compatibility), - ] - - results = {} - for name, test_func in tests: - try: - results[name] = test_func() - except Exception as e: - print_fail(f"Test '{name}' crashed: {e}") - results[name] = False - - # Summary - print(f"\n{Colors.BOLD}{'='*60}") - print("TEST SUMMARY") - print(f"{'='*60}{Colors.RESET}\n") - - passed = sum(1 for v in results.values() if v) - total = len(results) - - for name, result in results.items(): - status = f"{Colors.GREEN}PASS{Colors.RESET}" if result else f"{Colors.RED}FAIL{Colors.RESET}" - print(f" {status}: {name}") - - print(f"\n{Colors.BOLD}Total: {passed}/{total} tests passed{Colors.RESET}") - - if passed == total: - try: - print(f"\n{Colors.GREEN}{Colors.BOLD}[SUCCESS] All tests passed! System is ready.{Colors.RESET}\n") - except: - print(f"\n[SUCCESS] All tests passed! System is ready.\n") - return 0 - else: - try: - print(f"\n{Colors.YELLOW}{Colors.BOLD}[WARNING] Some tests failed or were skipped. Review above.{Colors.RESET}\n") - except: - print(f"\n[WARNING] Some tests failed or were skipped. Review above.\n") - return 1 - - -if __name__ == "__main__": - sys.exit(run_all_tests()) - diff --git a/test_image_embed.py b/test_image_embed.py deleted file mode 100644 index 5a36777..0000000 --- a/test_image_embed.py +++ /dev/null @@ -1,33 +0,0 @@ -import sys -from pathlib import Path - -# Add the backend directory to Python path -backend_dir = Path(__file__).parent / "backend" -sys.path.insert(0, str(backend_dir)) - -from app.embeddings.generate import embed_image -import numpy as np - -def test_image_embedding(): - """Test image embedding generation with CPU mode.""" - try: - # Try with a test image - test_image = Path(__file__).parent / "backend" / "storage" / "Screenshot 2025-11-06 234029.png" - if not test_image.exists(): - print(f"Test image not found at {test_image}") - return False - - print("Starting image embedding generation...") - embedding = embed_image(str(test_image)) - - # Check embedding shape and type - print(f"Embedding shape: {embedding.shape}") - print(f"Embedding dtype: {embedding.dtype}") - print("✅ Successfully generated image embedding!") - return True - except Exception as e: - print(f"❌ Error generating image embedding: {str(e)}") - return False - -if __name__ == "__main__": - test_image_embedding() \ No newline at end of file diff --git a/test_system.py b/test_system.py deleted file mode 100644 index 5232410..0000000 --- a/test_system.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -""" -Simple test script to verify the system works. -""" - -import sys -import os -from pathlib import Path - -# Add the backend directory to Python path -backend_dir = Path(__file__).parent / "backend" -sys.path.insert(0, str(backend_dir)) - -def test_imports(): - """Test that all modules can be imported.""" - print("Testing imports...") - - try: - from app.ingestion import extract_any, Chunk - print("✓ Ingestion module imported") - except Exception as e: - print(f"✗ Ingestion module failed: {e}") - return False - - try: - from app.embeddings import embed_text, embed_image - print("✓ Embeddings module imported") - except Exception as e: - print(f"✗ Embeddings module failed: {e}") - return False - - try: - from app.vector_store import get_store - print("✓ Vector store module imported") - except Exception as e: - print(f"✗ Vector store module failed: {e}") - return False - - try: - from app.retriever import get_retriever - print("✓ Retriever module imported") - except Exception as e: - print(f"✗ Retriever module failed: {e}") - return False - - try: - from app.llm import build_adapter, load_config - print("✓ LLM module imported") - except Exception as e: - print(f"✗ LLM module failed: {e}") - return False - - try: - from app.utils import create_citations - print("✓ Utils module imported") - except Exception as e: - print(f"✗ Utils module failed: {e}") - return False - - try: - from app.ui import GradioApp - print("✓ UI module imported") - except Exception as e: - print(f"✗ UI module failed: {e}") - return False - - return True - - -def test_basic_functionality(): - """Test basic functionality.""" - print("\nTesting basic functionality...") - - try: - from app.embeddings import embed_text - from app.vector_store import get_store - - # Test text embedding - embedding = embed_text("test text") - print(f"✓ Text embedding generated: {embedding.shape}") - - # Test vector store - store = get_store() - status = store.status() - print(f"✓ Vector store status: {status}") - - return True - - except Exception as e: - print(f"✗ Basic functionality test failed: {e}") - return False - - -def test_config_loading(): - """Test configuration loading.""" - print("\nTesting configuration...") - - try: - from app.llm import load_config - - config_path = Path(__file__).parent / "backend" / "config.yaml" - if config_path.exists(): - config = load_config(str(config_path)) - print(f"✓ Configuration loaded: {config}") - else: - print("⚠ Configuration file not found, using defaults") - config = load_config("") - print(f"✓ Default configuration: {config}") - - return True - - except Exception as e: - print(f"✗ Configuration test failed: {e}") - return False - - -def main(): - """Run all tests.""" - print("Offline Multimodal RAG - System Test") - print("=" * 40) - - tests = [ - test_imports, - test_basic_functionality, - test_config_loading - ] - - passed = 0 - total = len(tests) - - for test in tests: - if test(): - passed += 1 - print() - - print("=" * 40) - print(f"Tests passed: {passed}/{total}") - - if passed == total: - print("✓ All tests passed! System is ready to use.") - return 0 - else: - print("✗ Some tests failed. Check the errors above.") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/test_system_simple.py b/test_system_simple.py deleted file mode 100644 index 5232410..0000000 --- a/test_system_simple.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -""" -Simple test script to verify the system works. -""" - -import sys -import os -from pathlib import Path - -# Add the backend directory to Python path -backend_dir = Path(__file__).parent / "backend" -sys.path.insert(0, str(backend_dir)) - -def test_imports(): - """Test that all modules can be imported.""" - print("Testing imports...") - - try: - from app.ingestion import extract_any, Chunk - print("✓ Ingestion module imported") - except Exception as e: - print(f"✗ Ingestion module failed: {e}") - return False - - try: - from app.embeddings import embed_text, embed_image - print("✓ Embeddings module imported") - except Exception as e: - print(f"✗ Embeddings module failed: {e}") - return False - - try: - from app.vector_store import get_store - print("✓ Vector store module imported") - except Exception as e: - print(f"✗ Vector store module failed: {e}") - return False - - try: - from app.retriever import get_retriever - print("✓ Retriever module imported") - except Exception as e: - print(f"✗ Retriever module failed: {e}") - return False - - try: - from app.llm import build_adapter, load_config - print("✓ LLM module imported") - except Exception as e: - print(f"✗ LLM module failed: {e}") - return False - - try: - from app.utils import create_citations - print("✓ Utils module imported") - except Exception as e: - print(f"✗ Utils module failed: {e}") - return False - - try: - from app.ui import GradioApp - print("✓ UI module imported") - except Exception as e: - print(f"✗ UI module failed: {e}") - return False - - return True - - -def test_basic_functionality(): - """Test basic functionality.""" - print("\nTesting basic functionality...") - - try: - from app.embeddings import embed_text - from app.vector_store import get_store - - # Test text embedding - embedding = embed_text("test text") - print(f"✓ Text embedding generated: {embedding.shape}") - - # Test vector store - store = get_store() - status = store.status() - print(f"✓ Vector store status: {status}") - - return True - - except Exception as e: - print(f"✗ Basic functionality test failed: {e}") - return False - - -def test_config_loading(): - """Test configuration loading.""" - print("\nTesting configuration...") - - try: - from app.llm import load_config - - config_path = Path(__file__).parent / "backend" / "config.yaml" - if config_path.exists(): - config = load_config(str(config_path)) - print(f"✓ Configuration loaded: {config}") - else: - print("⚠ Configuration file not found, using defaults") - config = load_config("") - print(f"✓ Default configuration: {config}") - - return True - - except Exception as e: - print(f"✗ Configuration test failed: {e}") - return False - - -def main(): - """Run all tests.""" - print("Offline Multimodal RAG - System Test") - print("=" * 40) - - tests = [ - test_imports, - test_basic_functionality, - test_config_loading - ] - - passed = 0 - total = len(tests) - - for test in tests: - if test(): - passed += 1 - print() - - print("=" * 40) - print(f"Tests passed: {passed}/{total}") - - if passed == total: - print("✓ All tests passed! System is ready to use.") - return 0 - else: - print("✗ Some tests failed. Check the errors above.") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/test_system_windows.py b/test_system_windows.py deleted file mode 100644 index 754f01c..0000000 --- a/test_system_windows.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -""" -Simple test script to verify the system works. -""" - -import sys -import os -from pathlib import Path - -# Add the backend directory to Python path -backend_dir = Path(__file__).parent / "backend" -sys.path.insert(0, str(backend_dir)) - -def test_imports(): - """Test that all modules can be imported.""" - print("Testing imports...") - - try: - from app.ingestion import extract_any, Chunk - print("[OK] Ingestion module imported") - except Exception as e: - print(f"[FAIL] Ingestion module failed: {e}") - return False - - try: - from app.embeddings import embed_text, embed_image - print("[OK] Embeddings module imported") - except Exception as e: - print(f"[FAIL] Embeddings module failed: {e}") - return False - - try: - from app.vector_store import get_store - print("[OK] Vector store module imported") - except Exception as e: - print(f"[FAIL] Vector store module failed: {e}") - return False - - try: - from app.retriever import get_retriever - print("[OK] Retriever module imported") - except Exception as e: - print(f"[FAIL] Retriever module failed: {e}") - return False - - try: - from app.llm import build_adapter, load_config - print("[OK] LLM module imported") - except Exception as e: - print(f"[FAIL] LLM module failed: {e}") - return False - - try: - from app.utils import create_citations - print("[OK] Utils module imported") - except Exception as e: - print(f"[FAIL] Utils module failed: {e}") - return False - - try: - from app.ui import GradioApp - print("[OK] UI module imported") - except Exception as e: - print(f"[FAIL] UI module failed: {e}") - return False - - return True - - -def test_basic_functionality(): - """Test basic functionality.""" - print("\nTesting basic functionality...") - - try: - from app.embeddings import embed_text - from app.vector_store import get_store - - # Test text embedding - embedding = embed_text("test text") - print(f"[OK] Text embedding generated: {embedding.shape}") - - # Test vector store - store = get_store() - status = store.status() - print(f"[OK] Vector store status: {status}") - - return True - - except Exception as e: - print(f"[FAIL] Basic functionality test failed: {e}") - return False - - -def test_config_loading(): - """Test configuration loading.""" - print("\nTesting configuration...") - - try: - from app.llm import load_config - - config_path = Path(__file__).parent / "backend" / "config.yaml" - if config_path.exists(): - config = load_config(str(config_path)) - print(f"[OK] Configuration loaded: {config}") - else: - print("[WARN] Configuration file not found, using defaults") - config = load_config("") - print(f"[OK] Default configuration: {config}") - - return True - - except Exception as e: - print(f"[FAIL] Configuration test failed: {e}") - return False - - -def main(): - """Run all tests.""" - print("Offline Multimodal RAG - System Test") - print("=" * 40) - - tests = [ - test_imports, - test_basic_functionality, - test_config_loading - ] - - passed = 0 - total = len(tests) - - for test in tests: - if test(): - passed += 1 - print() - - print("=" * 40) - print(f"Tests passed: {passed}/{total}") - - if passed == total: - print("[SUCCESS] All tests passed! System is ready to use.") - return 0 - else: - print("[ERROR] Some tests failed. Check the errors above.") - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/test_unified_embeddings.py b/test_unified_embeddings.py deleted file mode 100644 index 01180b9..0000000 --- a/test_unified_embeddings.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Test unified 512-dim embeddings.""" -import sys -import os -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'backend')) - -from app.embeddings import embed_text, embed_image -from app.vector_store import get_store -import numpy as np - -def test_embeddings(): - """Test unified embedding dimensions.""" - print("Testing unified 512-dim embeddings...") - print("=" * 60) - - # Test 1: Text embedding dimension - print("\n1. Testing text embedding dimension...") - try: - text_emb = embed_text("test text") - assert text_emb.shape == (1, 512), f"Expected (1, 512), got {text_emb.shape}" - print(f" ✓ Text embedding shape: {text_emb.shape} (expected: (1, 512))") - except Exception as e: - print(f" ✗ Error: {e}") - return False - - # Test 2: Store dimension - print("\n2. Testing store dimension...") - try: - store = get_store() - assert store.dimension == 512, f"Expected 512, got {store.dimension}" - print(f" ✓ Store dimension: {store.dimension} (expected: 512)") - except Exception as e: - print(f" ✗ Error: {e}") - return False - - # Test 3: Cross-modal compatibility (if index has data) - print("\n3. Testing cross-modal compatibility...") - try: - store = get_store() - if store.index.ntotal > 0: - results = store.search(text_emb, top_k=5) - print(f" ✓ Cross-modal search returned {len(results)} results") - modalities = set(r.get('modality', r.get('file_type', 'unknown')) for r in results) - print(f" ✓ Retrieved modalities: {modalities}") - else: - print(" ⚠ No indexed data - ingest some files first to test cross-modal retrieval") - except Exception as e: - print(f" ✗ Error: {e}") - return False - - print("\n" + "=" * 60) - print("✅ All tests passed!") - return True - -if __name__ == "__main__": - success = test_embeddings() - sys.exit(0 if success else 1) - diff --git a/tests/test_audio.py b/tests/test_audio.py new file mode 100644 index 0000000..149a60d --- /dev/null +++ b/tests/test_audio.py @@ -0,0 +1,24 @@ +import os +from backend.app.ingestion.audio_transcriber import transcribe_audio + + +def test_transcribe_sanity(): + # Create a small temporary wav file + import wave, struct + tmp = os.path.join(os.path.dirname(__file__), 'tmp_test_silent.wav') + with wave.open(tmp, 'w') as wf: + wf.setnchannels(1) + wf.setsampwidth(2) + wf.setframerate(16000) + frames = (struct.pack('= 1 + # Each chunk should have content and file_type audio + for c in chunks: + assert hasattr(c, 'content') + assert c.file_type == 'audio' + + os.remove(tmp) From 9b357ae4ede35f57f8c41a09585146b6ed7c3ed9 Mon Sep 17 00:00:00 2001 From: Hitesh Kumar Date: Sun, 23 Nov 2025 14:49:53 +0530 Subject: [PATCH 2/6] Ignore storage files --- backend/storage/faiss.index | Bin 69677 -> 0 bytes backend/storage/metadata.db | Bin 393216 -> 0 bytes backend/storage/metadata.json | 3993 --------------------------------- 3 files changed, 3993 deletions(-) delete mode 100644 backend/storage/faiss.index delete mode 100644 backend/storage/metadata.db delete mode 100644 backend/storage/metadata.json diff --git a/backend/storage/faiss.index b/backend/storage/faiss.index deleted file mode 100644 index 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z#2XAa$o7(UfaSuNIQScJUzd~!kn=0dvF|ky)OukH;Pg1BCeLw&P-DyYnhzL&NUeEn z>DUBe#?N>ugm(e%^k>@;k0}K!ijZtgzV^evn^XS$Pkrr&f9uzN=Z>z~Zl$5X5Wm@| zmUSJv2Rr~^|70!Hwn~LssB?lZUGREyjYZMb{rrz}D%R%zZ|~f3n+l>Z3`jI?*a5nw zBa1{akiwl699hD?IDQ}@L=h3=m;`R*M}RT4c~ue?!y5A5{cwu zqC}BmJDU5?dCdI(oHL){fz5;QfW0wJ0XjvL`7r+fR25Dk zHS~Rj`4_7dbS~IM7QMwAT;$y#caa~$QCuvO>D&5AyAnA??$Jy#!tG3>i z=>GU*&l+xUL;6ej9g24+m^ZYX3z^2m989JgVrdqF{vedJx zUh#8Wev&+%PoqlW_c=vB^I@5F z#OUCNpPA3S#bM5pV;`|)yGrh*0on;CqQnej@2vR!%RbgBTy4;4a!5beVYNoF2Q?rj zC~w}VA}za|WGoT2FBTM?25S$qn`ac{mKMhAV(7zPq!-g|a{y3m^SlLqX*zqPfclAi zwNj4%$G~?k9&&odL%tO=hwAFOZUmO|}x~>8XWqfx0lW4GQ zsvwq^d;lc%#+px7}N{$6E8Z6!;Sg zC9gbc^^tqFp1kv_pHM?e0hHY-t5aiT@d?C)!W%V$)YKV-*TynOzEz7PZnm|Gz*$Vn zrS?kTIBmkb84=OHXkua70|W4+juwQxCRam?_Z@Sa7-LDJpi>;qMz$r, <=, >, , <+>, , <;>.\n3. Removing whitespace and comments: Spaces, tabs, and comments are ignored.\n4. Error handling: It detects errors in tokens (like invalid identifiers or unknown symbols).\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 2, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 498, - "char_end": 977, - "modality": "pdf" - }, - "721": { - "vector_id": 721, - "content": "identifiers or unknown symbols).\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.\n2/7", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 2, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 977, - "char_end": 1080, - "modality": "pdf" - }, - "722": { - "vector_id": 722, - "content": "5. Symbol table management: It stores information about identifiers (name, type, etc.).\n6. Communication with parser: Tokens are passed to the syntax analyzer (parser) for further\nprocessing.\nDiagram (text form):\nmathematica\nSource Program → Lexical Analyzer → Tokens → Syntax Analyzer\n👉 In short, the lexical analyzer works like a scanner that converts raw program text into useful tokens.\n✅ This is one complete answer (Q3).", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 3, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 0, - "char_end": 426, - "modality": "pdf" - }, - "723": { - "vector_id": 723, - "content": "ke a scanner that converts raw program text into useful tokens.\n✅ This is one complete answer (Q3).\nWould you like me to continue preparing answers for all the listed questions (Q1 to Q9) one by one, in\nthe same easy GTU exam style?\nQ1 to Q9\nGreat 👍\nNow I will give you answers for Q1 to Q9 from your paper in simple Indian English, exam-ready style.\nEach answer is correct, clear, and written for GTU exams.\n*Q1) Write an algorithm for Thompson’s construction method. Apply", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 3, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 426, - "char_end": 900, - "modality": "pdf" - }, - "724": { - "vector_id": 724, - "content": "clear, and written for GTU exams.\n*Q1) Write an algorithm for Thompson’s construction method. Apply\nthe algorithm to construct NFA for following regular expression: (a |\nb)abb. (7 Marks)\nAlgorithm (Steps of Thompson’s Construction):\n1. For each symbol (like a or b), create an NFA with one transition.\n2. For union (R1 | R2):\nCreate new start and final states.\nAdd ε-transition from new start → R1 start and → R2 start.\nAdd ε-transition from R1 final and R2 final → new final.\n3.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 3, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 900, - "char_end": 1379, - "modality": "pdf" - }, - "725": { - "vector_id": 725, - "content": "rom new start → R1 start and → R2 start.\nAdd ε-transition from R1 final and R2 final → new final.\n3. For concatenation (R1R2):\nConnect final state of R1 to start state of R2 using ε-transition.\n4. For *Kleene star (R)**:\nCreate new start and final states.\nε-transition from new start → R start and → new final.\nε-transition from R final → R start and → new final.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 3, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 1379, - "char_end": 1809, - "modality": "pdf" - }, - "726": { - "vector_id": 726, - "content": "final → R start and → new final.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.\n3/7", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 3, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 1809, - "char_end": 1912, - "modality": "pdf" - }, - "727": { - "vector_id": 727, - "content": "*Apply to (a|b)abb:\nStep 1: (a|b)* → NFA using union + Kleene star.\nStep 2: Append a.\nStep 3: Append b.\nStep 4: Append another b.\nFinal NFA (text diagram):\nrust\n ε ε\n → (q0) -----> (q1) --a--> (q2) --ε--> (q5)\n | (q3) --b--> (q4) --ε--> |\n | |\n --------ε----------------------------\n(q5) --a--> (q6) --b--> (q7) --b--> ((q8 final))\nQ2) What is operator grammar? Generate precedence function\ntable for following grammar. (7 Marks)", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 4, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 0, - "char_end": 496, - "modality": "pdf" - }, - "728": { - "vector_id": 728, - "content": ")\nQ2) What is operator grammar? Generate precedence function\ntable for following grammar. (7 Marks)\nGrammar:\nmathematica\nE → E A E | id\nA → + | *\nDefinition:\nAn operator grammar is a grammar in which no production rule has ε (null) on RHS and no two\nadjacent non-terminals appear in any production.\nIt is mainly used in operator precedence parsing.\nPrecedence function table:\nOperators: + , *\nOperands: id\nMultiplication * has higher precedence than +.\nBoth are left-associative.\nOperator\nPrecedence", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 4, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 496, - "char_end": 995, - "modality": "pdf" - }, - "729": { - "vector_id": 729, - "content": "s: id\nMultiplication * has higher precedence than +.\nBoth are left-associative.\nOperator\nPrecedence\n*\nHigh\n+\nLow\nid\nOperand\n👉 This table is used by the parser to decide which operator to apply first.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 4, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 995, - "char_end": 1261, - "modality": "pdf" - }, - "730": { - "vector_id": 730, - "content": "e which operator to apply first.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.\n4/7", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 4, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 1261, - "char_end": 1364, - "modality": "pdf" - }, - "731": { - "vector_id": 731, - "content": "Q3) Explain role of lexical analyzer. (4 Marks)\n(Already given in my previous answer above ✅)\n*Q4) Construct NFA for (a/b)abb# using Thompson’s notation and\nthen convert it into DFA. (7 Marks)\nStep 1: NFA for (a|b)*\nUse union + Kleene star.\nStep 2: Concatenate with abb#.\nFinal NFA accepts strings of a and b ending with abb#.\nStep 3: Convert NFA → DFA (subset construction):\nStart state = ε-closure of NFA start.\nApply transitions for each input (a, b, #).\nCreate DFA states = set of NFA states.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 5, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 0, - "char_end": 496, - "modality": "pdf" - }, - "732": { - "vector_id": 732, - "content": "re of NFA start.\nApply transitions for each input (a, b, #).\nCreate DFA states = set of NFA states.\nMark accepting states that include final NFA state.\n👉 Final DFA will have deterministic paths for (a|b)*abb#.\n(Draw a neat DFA diagram in exam – circle states, mark final state).\nQ5) Draw transition diagram for relational operators. (4 Marks)\nRelational operators: < , <= , > , >= , == , !=\nTransition diagram (text form):\nrust\nStart --<--> State1(<)\n | \\\n | --> State2(<=)", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 5, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 496, - "char_end": 994, - "modality": "pdf" - }, - "733": { - "vector_id": 733, - "content": "on diagram (text form):\nrust\nStart --<--> State1(<)\n | \\\n | --> State2(<=)\n |\n --->>--> State3(>)\n | \\\n | --> State4(>=)\n |\n --==--> State5(==)\n |\n --!=--> State6(!=)\n👉 Final states = valid relational operators.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 5, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 994, - "char_end": 1352, - "modality": "pdf" - }, - "734": { - "vector_id": 734, - "content": "es = valid relational operators.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.\n5/7", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 5, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 1352, - "char_end": 1455, - "modality": "pdf" - }, - "735": { - "vector_id": 735, - "content": "Q6) Explain subset construction method with example. (7 Marks)\nDefinition:\nSubset construction method is used to convert NFA into DFA.\nIt removes non-determinism by making each DFA state = a set of NFA states.\nSteps:\n1. Take ε-closure of NFA start → DFA start.\n2. For each input symbol, find possible NFA moves, take ε-closure, and form new DFA state.\n3. Repeat until no new DFA states are found.\n4. Mark DFA final states (those containing any NFA final state).\nExample:\nNFA:\ncss", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 6, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 0, - "char_end": 479, - "modality": "pdf" - }, - "736": { - "vector_id": 736, - "content": "tates are found.\n4. Mark DFA final states (those containing any NFA final state).\nExample:\nNFA:\ncss\nState 0 --a--> State 1\nState 0 --b--> State 2\nDFA states:\ncss\n{0} --a--> {1}\n{0} --b--> {2}\n👉 This way, DFA is created from NFA.\n*Q7) Draw DFA for (a|b)a using firstpos(), lastpos(), followpos(). (7\nMarks)\nSteps:\n1. Convert regular expression (a|b)*a into syntax tree.\n2. Compute nullable, firstpos, lastpos, followpos for each node.\n3. Construct DFA:\nStart state = firstpos(root).", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 6, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 479, - "char_end": 960, - "modality": "pdf" - }, - "737": { - "vector_id": 737, - "content": "llable, firstpos, lastpos, followpos for each node.\n3. Construct DFA:\nStart state = firstpos(root).\nFor each input, use followpos to make transitions.\n4. Accepting states = contain position of last symbol a.\n👉 DFA accepts all strings over {a, b} ending with a.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 6, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 960, - "char_end": 1287, - "modality": "pdf" - }, - "738": { - "vector_id": 738, - "content": "rings over {a, b} ending with a.\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.\n6/7", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 6, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 1287, - "char_end": 1390, - "modality": "pdf" - }, - "739": { - "vector_id": 739, - "content": "Q8) Construct DFA for (aε)ab(a|b)# without NFA. Optimize. (7\nMarks)\nSteps:\n1. Write syntax tree for (aε)*ab(a|b)*#.\n2. Compute firstpos, lastpos, followpos.\n3. Construct DFA directly.\n4. Minimize DFA (remove unreachable and equivalent states).\n👉 Optimized DFA will accept strings starting with any number of a, followed by ab, then any\ncombination of a and b, and ending with #.\n*Q9) Construct DFA without NFA for a(a|b)ab. (7 Marks)\nSteps:\n1. Write syntax tree for a(a|b)*ab.\n2.", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 7, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 0, - "char_end": 479, - "modality": "pdf" - }, - "740": { - "vector_id": 740, - "content": "*Q9) Construct DFA without NFA for a(a|b)ab. (7 Marks)\nSteps:\n1. Write syntax tree for a(a|b)*ab.\n2. Compute nullable, firstpos, lastpos, followpos.\n3. Construct DFA states using followpos.\n4. Final DFA accepts strings that:\nStart with a,\nHave zero or more a/b,\nEnd with ab.\n✅ These are exam-ready answers for Q1–Q9.\nDo you want me to also make short-hand diagrams (ASCII/text form) for Q4, Q6, Q7, Q8, Q9 so you\ncan directly draw them in exam?", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 7, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - "height": null, - "bbox": null, - "char_start": 479, - "char_end": 923, - "modality": "pdf" - }, - "741": { - "vector_id": 741, - "content": "short-hand diagrams (ASCII/text form) for Q4, Q6, Q7, Q8, Q9 so you\ncan directly draw them in exam?\nPrinted using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API.\n7/7", - "file_name": "CD-Chapter-2.pdf", - "file_type": "pdf", - "page_number": 7, - "timestamp": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\CD-Chapter-2.pdf", - "width": null, - 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"width": 394, - "height": 729, - "bbox": null, - "char_start": null, - "char_end": null, - "modality": "image" - }, - "32": { - "vector_id": 32, - "content": "[Transcription unavailable: please install a local ASR model (whisper or faster-whisper)]", - "file_name": "0_0_0_1_0_0_0_1.wav", - "file_type": "audio", - "page_number": null, - "timestamp": null, - "start_ts": null, - "end_ts": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\0_0_0_1_0_0_0_1.wav", - "width": null, - "height": null, - "bbox": null, - "char_start": null, - "char_end": null, - "modality": "audio" - }, - "33": { - "vector_id": 33, - "page_content": "", - "file_name": "0_0_0_1_0_1_1_0.wav", - "file_type": "audio", - "page_number": null, - "timestamp": null, - "start_ts": null, - "end_ts": null, - "filepath": "D:\\Portfolio_project\\rag-offline-chatbot\\backend\\storage\\0_0_0_1_0_1_1_0.wav", - "width": null, - "height": null, - "bbox": null, - "char_start": null, - "char_end": null, - "modality": "audio" - } -} \ No newline at end of file From d5c293cc561d95609e051b39392aaaa429e0a426 Mon Sep 17 00:00:00 2001 From: Hitesh Kumar Date: Sun, 23 Nov 2025 20:10:28 +0530 Subject: [PATCH 3/6] remove doc --- docs/quickstart.md | 187 --------------------- docs/troubleshooting.md | 351 ---------------------------------------- 2 files changed, 538 deletions(-) delete mode 100644 docs/quickstart.md delete mode 100644 docs/troubleshooting.md diff --git a/docs/quickstart.md b/docs/quickstart.md deleted file mode 100644 index 4e47758..0000000 --- a/docs/quickstart.md +++ /dev/null @@ -1,187 +0,0 @@ -# Quick Start Guide - -Get up and running with the Offline Multimodal RAG system in minutes. - -## Prerequisites - -- **Docker** (recommended) or **Python 3.11+** -- **8GB RAM** minimum, 16GB recommended -- **20GB disk space** for models and data - -## Option 1: Docker (Easiest) - -### Step 1: Download and Load - -```bash -# Download the latest release -wget https://github.com/your-repo/offline-multimodal-rag/releases/latest/download/offline-multimodal-rag-0.1.0.tar.gz - -# Load the Docker image -docker load < offline-multimodal-rag-0.1.0.tar.gz -``` - -### Step 2: Run the Application - -```bash -# Basic run -docker run -p 7860:7860 offline-multimodal-rag:0.1.0 - -# With GPU support (if available) -docker run --gpus all -p 7860:7860 offline-multimodal-rag:0.1.0 - -# With persistent storage -docker run -p 7860:7860 -v $(pwd)/data:/app/storage offline-multimodal-rag:0.1.0 -``` - -### Step 3: Access the Interface - -Open your browser to: http://localhost:7860 - -## Option 2: Native Installation - -### Step 1: Clone and Install - -```bash -git clone https://github.com/your-repo/offline-multimodal-rag.git -cd offline-multimodal-rag/backend -pip install -r requirements.txt -``` - -### Step 2: Run the Application - -```bash -# Gradio interface (recommended for beginners) -python main.py --interface gradio --port 7860 - -# FastAPI backend only -python main.py --interface fastapi --port 8000 -``` - -### Step 3: Access the Interface - -- **Gradio**: http://localhost:7860 -- **FastAPI**: http://localhost:8000/docs - -## First Steps - -### 1. Upload Documents - -1. Go to the **Upload & Ingest** tab -2. Click **Upload Document** -3. Select your files (PDF, DOCX, images, audio) -4. Click **Ingest Document** -5. Wait for processing to complete - -### 2. Ask Questions - -1. Go to the **Query** tab -2. Type your question in the text box -3. Adjust the number of sources if needed -4. Click **Ask Question** -5. View the answer and sources - -### 3. Check Status - -1. Go to the **Status** tab -2. Click **Refresh Status** -3. View system information and statistics - -## Example Workflow - -### Upload a PDF Document - -```bash -# Using the API -curl -X POST "http://localhost:8000/ingest" \ - -H "Content-Type: multipart/form-data" \ - -F "file=@document.pdf" -``` - -### Ask a Question - -```bash -# Using the API -curl -X POST "http://localhost:8000/query" \ - -H "Content-Type: application/json" \ - -d '{"query": "What is the main topic of the document?"}' -``` - -### Check System Status - -```bash -# Using the API -curl http://localhost:8000/status -``` - -## Configuration - -### Basic Configuration - -Edit `backend/config.yaml`: - -```yaml -model_backend: llama_cpp # or gpt4all, mistral -model_path: ./models/mistral-7b-instruct-v0.2.Q4_K_M.gguf -top_k: 5 -max_tokens: 512 -temperature: 0.2 -``` - -### Environment Variables - -```bash -# For offline mode -export TRANSFORMERS_OFFLINE=1 -export HF_HUB_OFFLINE=1 - -# For custom model paths -export TEXT_MODEL_PATH=/path/to/local/text/model -export CLIP_MODEL_PATH=/path/to/local/clip/model -``` - -## Common Use Cases - -### 1. Document Q&A - -Upload PDFs and ask questions about their content. - -### 2. Image Search - -Upload images and find similar content across your collection. - -### 3. Audio Transcription - -Upload audio files and search through transcripts. - -### 4. Cross-Modal Search - -Find related content across different file types. - -## Troubleshooting - -### Common Issues - -1. **Out of Memory**: Reduce model size or increase RAM -2. **Slow Performance**: Use GPU acceleration or smaller models -3. **Import Errors**: Check Python version and dependencies -4. **File Upload Fails**: Check file size and format support - -### Getting Help - -1. Check the [Troubleshooting Guide](docs/troubleshooting.md) -2. Review the [Full Documentation](README.md) -3. Create an issue on GitHub - -## Next Steps - -- Explore the [API Documentation](http://localhost:8000/docs) -- Try the [Command Line Tools](scripts/) -- Customize the [Configuration](backend/config.yaml) -- Read the [Development Guide](README.md#development) - -## Performance Tips - -- Use GPU acceleration for faster inference -- Pre-process large documents -- Use appropriate chunk sizes for your content -- Monitor system resources during processing diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md deleted file mode 100644 index 325cfa1..0000000 --- a/docs/troubleshooting.md +++ /dev/null @@ -1,351 +0,0 @@ -# Troubleshooting Guide - -This document covers common issues and solutions for the Offline Multimodal RAG system. - -## Installation Issues - -### Docker Installation Problems - -**Problem**: Docker build fails with dependency errors - -``` -Solution: -1. Ensure Docker is up to date -2. Clear Docker cache: docker system prune -a -3. Try building with --no-cache: docker build --no-cache -t offline-multimodal-rag . -``` - -**Problem**: Docker container runs but app doesn't start - -``` -Solution: -1. Check logs: docker logs -2. Verify Python path: docker run --rm python -c "import sys; print(sys.path)" -3. Check file permissions in container -``` - -### Python Installation Problems - -**Problem**: Import errors after pip install - -``` -Solution: -1. Verify Python version: python --version (should be 3.11+) -2. Use virtual environment: python -m venv venv && source venv/bin/activate -3. Reinstall dependencies: pip install --force-reinstall -r requirements.txt -``` - -**Problem**: CUDA/PyTorch installation issues - -``` -Solution: -1. Install CPU-only version: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu -2. For GPU: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 -``` - -## Runtime Issues - -### Memory Problems - -**Problem**: Out of memory errors during inference - -``` -Solutions: -1. Reduce model size in config.yaml -2. Lower max_tokens parameter -3. Use smaller embedding models -4. Increase system RAM or use swap space -``` - -**Problem**: FAISS index too large - -``` -Solutions: -1. Use smaller chunk sizes -2. Implement index compression -3. Use disk-based FAISS indices -4. Split large documents into smaller files -``` - -### Model Loading Issues - -**Problem**: Models fail to load - -``` -Solutions: -1. Check model file paths in config.yaml -2. Verify model file integrity -3. Ensure sufficient disk space -4. Check file permissions -``` - -**Problem**: Embedding models download on every run - -``` -Solutions: -1. Set offline environment variables: - export TRANSFORMERS_OFFLINE=1 - export HF_HUB_OFFLINE=1 -2. Download models manually and set local paths -3. Use local model cache directory -``` - -### Performance Issues - -**Problem**: Slow inference - -``` -Solutions: -1. Use GPU acceleration (add --gpus all to docker run) -2. Reduce model size -3. Use quantization (Q4_K_M, Q8_0) -4. Increase batch sizes for embeddings -``` - -**Problem**: Slow file ingestion - -``` -Solutions: -1. Process files in parallel -2. Use faster storage (SSD) -3. Optimize chunk sizes -4. Use multiprocessing for large files -``` - -## File Processing Issues - -### PDF Processing - -**Problem**: PDF extraction fails - -``` -Solutions: -1. Install PyMuPDF: pip install PyMuPDF -2. Check PDF file integrity -3. Try different PDF libraries (pdfplumber, pymupdf) -4. Convert PDF to images if text extraction fails -``` - -**Problem**: PDF with images not processed - -``` -Solutions: -1. Enable OCR: pip install pytesseract -2. Use image extraction for PDF pages -3. Convert PDF to images first -``` - -### Audio Processing - -**Problem**: Audio transcription fails - -``` -Solutions: -1. Install whisper.cpp: ./scripts/setup_whisper_cpp.sh -2. Check audio file format support -3. Install ffmpeg for audio conversion -4. Use alternative transcription libraries -``` - -**Problem**: Audio files too large - -``` -Solutions: -1. Split audio into smaller segments -2. Use streaming transcription -3. Compress audio files -4. Use lower quality audio -``` - -### Image Processing - -**Problem**: Image embedding fails - -``` -Solutions: -1. Check image file format support -2. Install Pillow: pip install Pillow -3. Verify image file integrity -4. Use alternative image processing libraries -``` - -## API Issues - -### FastAPI Problems - -**Problem**: API endpoints not responding - -``` -Solutions: -1. Check server logs for errors -2. Verify port availability -3. Check CORS configuration -4. Test with curl: curl http://localhost:8000/health -``` - -**Problem**: File upload fails - -``` -Solutions: -1. Check file size limits -2. Verify file permissions -3. Check multipart form handling -4. Test with smaller files first -``` - -### Gradio Problems - -**Problem**: Gradio interface not loading - -``` -Solutions: -1. Check port conflicts -2. Verify Gradio installation -3. Check browser compatibility -4. Try different port: --port 7861 -``` - -## Database Issues - -### FAISS Index Problems - -**Problem**: Index corruption - -``` -Solutions: -1. Rebuild index: POST /index/rebuild -2. Delete corrupted index files -3. Restore from backup -4. Re-ingest all documents -``` - -**Problem**: Dimension mismatch - -``` -Solutions: -1. Check embedding model consistency -2. Rebuild index with correct dimensions -3. Verify model configuration -4. Use consistent embedding models -``` - -### SQLite Database Issues - -**Problem**: Database locked - -``` -Solutions: -1. Check for concurrent access -2. Close all connections properly -3. Use connection pooling -4. Check file permissions -``` - -## Network Issues - -### Offline Mode Problems - -**Problem**: Still trying to access internet - -``` -Solutions: -1. Set environment variables: - export TRANSFORMERS_OFFLINE=1 - export HF_HUB_OFFLINE=1 -2. Use local model paths -3. Disable network interfaces -4. Use air-gapped environment -``` - -## Configuration Issues - -### Config File Problems - -**Problem**: Config not loading - -``` -Solutions: -1. Check YAML syntax -2. Verify file path -3. Check file permissions -4. Use absolute paths -``` - -**Problem**: Model paths incorrect - -``` -Solutions: -1. Use absolute paths -2. Check file existence -3. Verify file permissions -4. Test model loading separately -``` - -## Debugging Tips - -### Enable Debug Logging - -```python -import logging -logging.basicConfig(level=logging.DEBUG) -``` - -### Test Individual Components - -```bash -# Test embeddings -python -c "from app.embeddings import embed_text; print(embed_text('test'))" - -# Test vector store -python -c "from app.vector_store import get_store; print(get_store().status())" - -# Test LLM adapter -python -c "from app.llm import build_adapter; adapter = build_adapter({'model_backend': 'mistral'}); print(adapter.generate('test'))" -``` - -### Check System Resources - -```bash -# Memory usage -free -h - -# Disk space -df -h - -# GPU usage (if available) -nvidia-smi -``` - -## Getting Help - -1. Check this troubleshooting guide first -2. Review the test suite for usage examples -3. Check GitHub issues for similar problems -4. Create a new issue with: - - System information (OS, Python version, etc.) - - Error messages and logs - - Steps to reproduce the issue - - Configuration files (sanitized) - -## Performance Optimization - -### For Large Document Collections - -1. Use smaller chunk sizes (200-300 characters) -2. Implement document filtering -3. Use hierarchical indexing -4. Consider document clustering - -### For Real-time Applications - -1. Pre-compute embeddings -2. Use caching layers -3. Implement connection pooling -4. Use async processing - -### For Resource-Constrained Systems - -1. Use quantized models -2. Implement model pruning -3. Use CPU-only inference -4. Reduce batch sizes From 78b9a8da130e52ea2f18ee05d2e5f58fa72d0c2d Mon Sep 17 00:00:00 2001 From: Hitesh Kumar Date: Sat, 29 Nov 2025 02:24:43 +0530 Subject: [PATCH 4/6] wroking frontend 29-11-2025 --- frontend/src/App.jsx | 291 ++++++++++++------------ frontend/src/ChatUI.jsx | 187 +++++++++------- frontend/src/ChatWindow.jsx | 197 +++++++++++++---- frontend/src/components/ChatUI.jsx | 343 +++++++++++++++++++---------- frontend/src/styles.css | 217 ++++++++++++++++-- 5 files changed, 826 insertions(+), 409 deletions(-) diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx index 9eb4a1d..dc95631 100644 --- a/frontend/src/App.jsx +++ b/frontend/src/App.jsx @@ -1,17 +1,13 @@ import { useEffect, useState } from "react"; import ChatUI from "./components/ChatUI"; -import Uploader from "./Uploader"; import ContextViewer from "./components/ContextViewer"; import SourceModal from "./components/SourceModal"; -import Header from "./components/Header"; import { Sheet, SheetContent, - SheetHeader, - SheetTitle, SheetTrigger, } from "./components/ui/sheet"; -import { Menu, X, FileText, Database } from "lucide-react"; +import { Menu, X, Database } from "lucide-react"; function App() { const [messages, setMessages] = useState([]); @@ -21,11 +17,13 @@ function App() { const [isOnline, setIsOnline] = useState(navigator.onLine); const [showSidebar, setShowSidebar] = useState(true); const [indexedDocs, setIndexedDocs] = useState(0); - const [dark, setDark] = useState(() => { - if (typeof window === "undefined") return false; - const saved = localStorage.getItem("theme-dark"); - return saved ? JSON.parse(saved) : false; - }); + + // Static chat history for demo + const [chatHistory] = useState([ + { id: 1, title: "What is RAG?", date: "Today" }, + { id: 2, title: "Document analysis help", date: "Yesterday" }, + { id: 3, title: "Audio transcription query", date: "Nov 20" } + ]); // Monitor online/offline status useEffect(() => { @@ -35,7 +33,7 @@ function App() { ...prev, { role: "system", - content: "✅ Connection restored. You're back online!", + content: "Connection restored. You're back online!", }, ]); }; @@ -46,7 +44,7 @@ function App() { ...prev, { role: "system", - content: "📴 You're offline. Don't worry, you can still query your uploaded documents!", + content: "You're offline. Don't worry, you can still query your uploaded documents!", }, ]); }; @@ -60,23 +58,7 @@ function App() { }; }, []); - useEffect(() => { - document.documentElement.classList.toggle("dark", dark); - localStorage.setItem("theme-dark", JSON.stringify(dark)); - }, [dark]); - // Welcome message on first load - useEffect(() => { - if (messages.length === 0) { - setMessages([ - { - role: "assistant", - content: - "👋 Welcome to your Offline RAG Assistant!\n\nI can help you:\n• 📄 Query PDF documents\n• 🖼️ Analyze images\n• 🎵 Search audio transcripts\n• 📝 Search through text files\n\nUpload your documents to get started!", - }, - ]); - } - }, []); const handleSend = async (text) => { setMessages((prev) => [...prev, { role: "user", content: text }]); @@ -114,159 +96,176 @@ function App() { { role: "assistant", content: isOnline - ? "❌ Sorry, I couldn't connect to the server. Please check if the backend is running." - : "📴 You're offline. Make sure your documents are already indexed to query them.", + ? "Sorry, I couldn't connect to the server. Please check if the backend is running." + : "You're offline. Make sure your documents are already indexed to query them.", }, ]); } setIsTyping(false); }; - const doSearch = async (query) => { - if (!query || !query.trim()) { - setResults([]); - return; - } - try { - const res = await fetch("http://localhost:8000/search/similarity", { - method: "POST", - headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ query, k: 8 }), - }); - const data = await res.json(); - setResults(data.results || []); - } catch (e) { - console.error("Search failed:", e); - } - }; - const handleUploadComplete = (data) => { setIndexedDocs((prev) => prev + 1); setMessages((prev) => [ ...prev, { role: "system", - content: `✅ Successfully indexed: ${data.file || "document"}\n📊 Vectors indexed: ${data.vectors_indexed || 0}`, + content: `Successfully indexed: ${data.file || "document"}\nVectors indexed: ${data.vectors_indexed || 0}`, }, ]); }; return ( -