Memvid.ai Library Structure
Project Root Structure
graphql
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memvid/
│
├── memvid/
│   ├── __init__.py
│   ├── encoder.py         # Handles chunking and QR video creation
│   ├── retriever.py       # Fast semantic search, QR frame extraction, context assembly
│   ├── chat.py            # MemvidChat class: manages conversations, context retrieval, LLM interface
│   ├── index.py           # Index management, embedding, storage, and lookup
│   ├── utils.py           # Shared helper functions (QR encode/decode, video I/O, batching, etc.)
│   └── config.py          # Config defaults, models, parameters
│
├── examples/
│   ├── build_memory.py    # Example: Create video & index from text
│   ├── chat_memory.py     # Example: Interactive conversation using MemvidChat
│
├── tests/
│   ├── test_encoder.py
│   ├── test_retriever.py
│   └── test_chat.py
│
├── requirements.txt
├── setup.py
├── README.md
└── LICENSE
Core Dependencies/Libraries
qrcode & Pillow — QR code generation

opencv-python — Video creation and frame extraction

pyzbar — QR code decoding from images

sentence-transformers — Fast semantic embeddings for context search

numpy — Array and vector operations

openai — (or compatible LLM client, pluggable for Claude, Gemini, etc.)

tqdm — Progress bars (optional, for nice UX)

fastapi or gradio — (optional, for web UI)

Key Classes & Responsibilities
MemvidEncoder

.add_chunks(chunks: List[str])

.build_video(output_file: str, index_file: str)

MemvidRetriever

.search(query: str, top_k=5) -> List[str]

.get_chunk_by_id(chunk_id: int) -> str

MemvidChat

.start_session()

.chat(user_input: str) -> str

Maintains conversation history internally

Handles context window management and smart context selection

IndexManager (internal, used by Encoder/Retriever)

Embedding generation and persistence

Fast nearest-neighbor search (FAISS/Annoy/Chroma)

Metadata management

utils.py

QR code encode/decode helpers

Video frame reading/writing (batch, parallel)

Caching, prefetch, and batching helpers

Example Usage (for End Users)
python
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from memvid import MemvidEncoder, MemvidChat

# Step 1: Build your video memory from data
chunks = [
    "Support: User couldn't login on March 2.",
    "Error 500 on signup page March 4.",
    # ... more data ...
]
encoder = MemvidEncoder()
encoder.add_chunks(chunks)
encoder.build_video(output_file="memory.mp4", index_file="memory_index.json")

# Step 2: Interactive chat with memory
memchat = MemvidChat(
    video_file="memory.mp4",
    index_file="memory_index.json",
    llm_api_key="sk-..."  # or set globally
)
memchat.start_session()

while True:
    user_msg = input("\nYou: ")
    if user_msg.strip().lower() == "exit":
        break
    reply = memchat.chat(user_msg)
    print(f"\nAssistant: {reply}")
Prompt for Your AI Assistant/Developer
Implement a Python library named memvid for QR code video-based AI memory.

Requirements:

Chunk Ingestion & QR Video Encoding:

Provide MemvidEncoder to chunk large text input, generate QR images, assemble into a video (opencv-python), and save chunk metadata and sentence-transformer embeddings into an index file.

Super-Fast Retrieval:

MemvidRetriever must use a disk-based index (embedding + frame mapping) to support lightning-fast semantic search (vector DB: FAISS, Annoy, or Chroma recommended for large scale).

Batch/parallel QR frame extraction and decoding (opencv-python, pyzbar, with thread/process pools) to ensure retrieval is sub-second even for large memories.

Smart caching for hot frames and common queries.

Conversational Interface:

Implement MemvidChat that keeps conversation history, automatically retrieves and summarizes the best context for each user message, and pipes it to an LLM (OpenAI, Anthropic, etc.).

Should support session reset and history export.

Flexible LLM Backend:

Allow for easy API key/config change to support OpenAI, Claude, Gemini, or even local models (plug-and-play).

Robust Indexing:

Build a simple but robust IndexManager that generates, saves, loads, and queries embeddings, frame numbers, and metadata.

Example Scripts:

Provide /examples/build_memory.py and /examples/chat_memory.py for end-to-end workflow (from data to chat).

Documentation & Testing:

Well-documented code, usage docs, and basic unittests in /tests.

Performance Goal:

Retrieval (search + QR decode) should be under 2 seconds for 1M chunks on a modern laptop.

Bonus:

CLI and (optionally) minimal web UI with Gradio/FastAPI.

Preprocessing support (compression, deduplication, etc.).

Summary
memvid will be a professional, modular, developer-friendly library that makes building, storing, and chatting with AI memory via QR code video as simple as using an in-memory DB.

End users need only call MemvidEncoder and MemvidChat, and everything else is handled behind the scenes.

