🚀 Why FrameRecall?

Transformative Approach

Streamlined System

📦 Installation

Quick Install

pip install framerecall

For PDF Support

pip install framerecall PyPDF2

Recommended Setup (Virtual Environment)

# Create a new project directorymkdir my-framerecall-projectcd my-framerecall-project# Create virtual environmentpython -m venv venv# Activate it# On macOS/Linux:source venv/bin/activate# On Windows:venv\Scripts\activate# Install framerecallpip install framerecall# For PDF support:pip install PyPDF2

🎯 Getting Started Instantly

fromframerecallimportFrameRecallEncoder,FrameRecallChat# Construct memory sequence using textual inputssegments= ["Crucial insight 1","Crucial insight 2","Contextual knowledge snippet"]builder=FrameRecallEncoder()builder.add_chunks(segments)builder.build_video("archive.mp4","archive_index.json")# Interact with stored intelligenceassistant=FrameRecallChat("archive.mp4","archive_index.json")assistant.start_session()output=assistant.chat("Tell me what’s known about past happenings?")print(output)

Constructing Memory from Files

fromframerecallimportFrameRecallEncoderimportos# Prepare input textsassembler=FrameRecallEncoder(chunk_size=512,overlap=50)# Inject content from directoryforfilenameinos.listdir("documents"):withopen(f"documents/{filename}","r")asdocument:assembler.add_text(document.read(),metadata={"source":filename})# Generate compressed video sequenceassembler.build_video("knowledge_base.mp4","knowledge_index.json",fps=30,# More chunks processed per secondframe_size=512# Expanded resolution accommodates extra information)

Intelligent Lookup & Extraction

fromframerecallimportFrameRecallRetriever# Set up fetcherfetcher=FrameRecallRetriever("knowledge_base.mp4","knowledge_index.json")# Contextual discoverymatches=fetcher.search("machine learning algorithms",top_k=5)forfragment,relevanceinmatches:print(f"Score:{relevance:.3f} |{fragment[:100]}...")# Retrieve neighbouring fragmentswindow=fetcher.get_context("explain neural networks",max_tokens=2000)print(window)

Conversational Interface

fromframerecallimportFrameRecallInteractive# Open real-time discussion UIinteractive=FrameRecallInteractive("knowledge_base.mp4","knowledge_index.json")interactive.run()# Web panel opens at http://localhost:7860

Testing with file_chat.py

# Ingest an entire folder of materialspython examples/file_chat.py --input-dir /path/to/documents --provider google# Load chosen documentspython examples/file_chat.py --files doc1.txt doc2.pdf --provider openai# Apply H.265 encoding (Docker required)python examples/file_chat.py --input-dir docs/ --codec h265 --provider google# Adjust chunking for lengthy inputspython examples/file_chat.py --files large.pdf --chunk-size 2048 --overlap 32 --provider google# Resume from previously saved memorypython examples/file_chat.py --load-existing output/my_memory --provider google

Full Demo: Converse with a PDF Book

# 1. Prepare project directory and virtual environmentmkdir book-chat-democd book-chat-demopython -m venv venvsource venv/bin/activate# On Windows: venv\Scripts\activate# 2. Install necessary packagespip install framerecall PyPDF2# 3. Build book_chat.pycat> book_chat.py<< 'EOF'from framerecall import FrameRecallEncoder, chat_with_memoryimport os# Path to your documentbook_pdf = "book.pdf"  # Replace with your PDF filename# Encode video from bookencoder = FrameRecallEncoder()encoder.add_pdf(book_pdf)encoder.build_video("book_memory.mp4", "book_index.json")# Initiate interactive Q&Aapi_key = os.getenv("OPENAI_API_KEY")  # Optional for model outputchat_with_memory("book_memory.mp4", "book_index.json", api_key=api_key)EOF# 4. Launch the assistantexport OPENAI_API_KEY="your-api-key"# Optionalpython book_chat.py

🛠️ Extended Setup

Tailored Embeddings

fromsentence_transformersimportSentenceTransformer# Load alternative semantic modelcustom_model=SentenceTransformer('sentence-transformers/all-mpnet-base-v2')encoder=FrameRecallEncoder(embedding_model=custom_model)

Parallelized Workloads

# Accelerate processing with concurrencyencoder=FrameRecallEncoder(n_workers=8)encoder.add_chunks_parallel(massive_chunk_list)

🐛 Debugging Guide

Frequent Pitfalls

# Confirm the correct Python interpreter is being usedwhich python# Expected to point to your environment# If incorrect, reactivate the virtual setup:source venv/bin/activate# On Windows: venv\Scripts\activate

pip install PyPDF2

# Provide your OpenAI credentials (register at https://platform.openai.com)export OPENAI_API_KEY="sk-..."# macOS/Linux# On Windows:set OPENAI_API_KEY=sk-...

# Reduce segment length for better handlingencoder=FrameRecallEncoder()encoder.add_pdf("large_book.pdf",chunk_size=400,overlap=50)

🤝 Get Involved

# Execute test suitepytest tests/# Execute with coverage reportingpytest --cov=framerecall tests/# Apply code stylingblack framerecall/

🆚 How FrameRecall Compares to Other Technologies

🗺️ What’s Coming Next

📚 Illustrative Use Cases

🔗 Resources

📄 Usage Rights