AIF-BIN: A Binary Encoding Scheme for AI-Native Document Storage with Semantic Retrieval Capabilities

1. Introduction

The proliferation of large language models (LLMs) and embedding-based retrieval systems^[1,10] has created a fundamental tension in document management: traditional file formats optimize for human readability and application compatibility, while AI systems require structured, vectorized representations optimized for similarity computation and context injection.

Current approaches to AI-powered document retrieval typically involve external vector databases (Pinecone, Weaviate, Chroma) that store embeddings separately from source documents^[2]. This architectural pattern introduces several challenges:

• Data synchronization complexity: Maintaining consistency between source documents and their vector representations requires additional infrastructure and monitoring.
• Portability limitations: Vector databases are not designed for file-level portability; migrating a document corpus requires exporting and re-importing both documents and vectors.
• Privacy concerns: Cloud-hosted vector databases introduce data residency and access control considerations that may conflict with enterprise security requirements.
• Operational overhead: Running vector database infrastructure adds cost, latency, and failure modes to document retrieval workflows.

AIF-BIN (also known as AIMF — AI Memory Format) addresses these challenges by encapsulating the complete AI-ready representation of a document—including source content, extracted text, structural metadata, and embedding vectors—within a single, portable file. This "document as database" paradigm enables local-first AI workflows while maintaining compatibility with distributed architectures when required.

1.1 File Extension and Nomenclature

The AIF-BIN specification defines two format versions, each with a distinct file extension:

• .aimf — AI Memory Format (Version 1). JSON-encoded format optimized for human readability and simple tooling. Ideal for debugging, learning, and lightweight deployments.
• .aif-bin — AI Formatted - Binary (Version 2). Binary-encoded format using MessagePack, optimized for performance, storage efficiency, and production AI workloads.

The naming convention reflects the technical reality of each format: v1's JSON encoding serves as a simple "memory format" for basic AI storage, while v2's binary encoding delivers the performance characteristics implied by "AI Formatted - Binary." Tools should use the file extension to determine the appropriate parser.

1.2 Design Principles

The AIF-BIN format adheres to the following design principles:

1. Self-contained completeness: A single .aif-bin file contains all information necessary for AI-powered retrieval without external dependencies.
2. Preservation of provenance: The original source document is preserved byte-for-byte, enabling round-trip extraction and audit trails.
3. Model agnosticism: Embedding vectors are stored with dimensional metadata, supporting any embedding model without format changes.
4. Incremental adoption: The format supports partial population—files can be created without embeddings and enriched later.
5. Efficient access patterns: Binary encoding enables direct offset addressing for O(1) section access.

2. Background and Related Work

2.1 Document Embedding Fundamentals

Modern text embedding models transform variable-length text sequences into fixed-dimensional vector representations that capture semantic meaning^[1]. Given a text sequence T and an embedding function E, the resulting vector v = E(T) exists in a high-dimensional space (typically 384-4096 dimensions) where semantic similarity correlates with geometric proximity.

This property enables semantic search: given a query Q, relevant documents can be identified by computing similarity scores against a corpus of pre-computed embeddings, typically using approximate nearest neighbor (ANN) algorithms for efficiency at scale^[2].

2.2 Chunking Strategies

Transformer-based language models have finite context windows (typically 512-8192 tokens for embedding models, 4096-128000 tokens for generative models). Documents exceeding these limits must be partitioned into chunks that individually fit within model constraints while preserving semantic coherence.

Common chunking strategies include:

AIF-BIN implements overlapping sentence-based chunking as the default strategy, with configurable chunk sizes and overlap ratios. The format supports arbitrary chunking strategies through its typed chunk architecture.

2.3 Related File Formats

Several existing formats address aspects of AI-ready document storage:

• PDF/A: Archival format preserving visual fidelity but lacking structured text extraction or embedding storage.
• EPUB: Structured document format with semantic markup but no native vector support.
• Parquet/Arrow: Columnar formats optimized for tabular data and analytics, not document retrieval.
• SQLite: Embedded database suitable for structured queries but lacking native vector similarity operations.
• ONNX: Model interchange format that inspired AIF-BIN's approach to embedding model metadata.

AIF-BIN distinguishes itself by treating the document as the primary unit of storage while embedding AI-readiness as a first-class concern rather than an afterthought.

3. System Architecture

3.1 Conceptual Model

An AIF-BIN file represents a single source document augmented with AI-derived metadata. The conceptual structure comprises four primary components:

┌─────────────────────────────────────────────────────────┐
│                      AIF-BIN File                       │
├─────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────────────────┐  │
│  │    METADATA     │  │       ORIGINAL RAW          │  │
│  │  - title        │  │  (preserved source bytes)   │  │
│  │  - created_at   │  │                             │  │
│  │  - source_hash  │  │  PDF, DOCX, MD, TXT, etc.   │  │
│  │  - model_info   │  │                             │  │
│  └─────────────────┘  └─────────────────────────────┘  │
│                                                         │
│  ┌─────────────────────────────────────────────────┐   │
│  │                 CONTENT CHUNKS                   │   │
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐           │   │
│  │  │ Chunk 0 │ │ Chunk 1 │ │ Chunk N │  ...      │   │
│  │  │ - type  │ │ - type  │ │ - type  │           │   │
│  │  │ - text  │ │ - text  │ │ - text  │           │   │
│  │  │ - embed │ │ - embed │ │ - embed │           │   │
│  │  └─────────┘ └─────────┘ └─────────┘           │   │
│  └─────────────────────────────────────────────────┘   │
│                                                         │
│  ┌─────────────────────────────────────────────────┐   │
│  │                    FOOTER                        │   │
│  │  - chunk index (offsets)                        │   │
│  │  - checksum (CRC64)                             │   │
│  └─────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────┘
      

3.2 Chunk Type System

AIF-BIN defines a typed chunk system to support heterogeneous document content:

Each chunk type supports type-specific metadata fields while sharing a common embedding interface. This enables unified semantic search across heterogeneous content while preserving type-specific processing capabilities.

4. Version 1: AIMF (AI Memory Format)

4.1 Specification

The v1 format, using the .aimf extension, employs JSON encoding for maximum human readability and tooling compatibility. An AIMF file is a valid JSON document with the following schema:

{
  "version": "1.0.0-lite",
  "format": "json",
  "metadata": {
    "source_file": "document.md",
    "created_at": "2026-02-01T10:00:00Z",
    "content_hash": "sha256:abc123...",
    "chunk_count": 5
  },
  "chunks": [
    {
      "id": 0,
      "type": "text",
      "content": "First paragraph of the document...",
      "embedding": [0.123, -0.456, 0.789, ...]  // optional
    },
    // ... additional chunks
  ],
  "original_raw": "# Original Markdown\n\nFull source content..."
}

4.2 Advantages

• Human readability: Files can be inspected and edited with any text editor.
• Universal parsing: JSON parsers exist in every programming language.
• Schema flexibility: Additional fields can be added without breaking compatibility.
• Debugging simplicity: Content issues can be diagnosed without specialized tools.

4.3 Limitations

The JSON encoding introduces several performance and efficiency constraints:

• Parsing overhead: The entire file must be parsed to access any section, resulting in O(n) time complexity for metadata access where n is file size.
• Size inefficiency: JSON encoding of floating-point embeddings requires 15-20 characters per value versus 4 bytes for binary float32, resulting in 4-5x overhead for embedding storage.
• Memory amplification: Parsed JSON objects consume significantly more memory than raw data due to object overhead and string interning.
• Binary content encoding: Non-text content requires Base64 encoding, adding 33% overhead.

5. Version 2: AIF-BIN (AI Formatted - Binary)

5.1 Design Rationale

The v2 format, using the .aif-bin extension, addresses v1 (AIMF) limitations through a binary encoding scheme optimized for both storage efficiency and access patterns. Key design decisions include:

1. Fixed-offset header: A 64-byte header with known field positions enables O(1) access to section offsets without parsing.
2. MessagePack metadata: Structured metadata uses MessagePack encoding^[4], providing JSON-like flexibility with ~30% smaller representation.
3. Native binary data: Embeddings are stored as contiguous float32 arrays, eliminating encoding overhead.
4. Trailing index: A chunk index at file end enables random access to individual chunks without sequential scanning.
5. Integrity verification: CRC64 checksum enables corruption detection.

5.2 Binary Layout

Offset    Size    Field
──────────────────────────────────────────────────
0x00      6       Magic signature: "AIFBIN"
0x06      2       Format marker: 0x00 0x01
0x08      4       Version (uint32 LE): 2
0x0C      4       Flags (uint32 LE)
0x10      8       Metadata offset (uint64 LE)
0x18      8       Original raw offset (uint64 LE)
0x20      8       Chunks offset (uint64 LE)
0x28      8       Total file size (uint64 LE)
0x30      16      Reserved (zero-padded)
──────────────────────────────────────────────────
0x40      ...     Metadata section (MessagePack)
...       ...     Original raw section (raw bytes)
...       ...     Chunks section (typed chunks)
EOF-16    8       Chunk count (uint64 LE)
EOF-8     8       CRC64 checksum
    

5.3 Chunk Encoding

Each chunk in the v2 format follows a type-length-value (TLV) encoding:

┌──────────────┬──────────────┬──────────────┬──────────────┬──────────────┐
│  Type (u32)  │ Data Len(u64)│ Meta Len(u64)│  Data Bytes  │  Meta (msgp) │
└──────────────┴──────────────┴──────────────┴──────────────┴──────────────┘
    4 bytes        8 bytes        8 bytes       variable       variable
    

This encoding enables:

• Type-based filtering without full chunk parsing
• Direct seeking to chunk data via length fields
• Streaming writes without knowing final chunk count
• Parallel chunk processing with known boundaries

5.4 Embedding Storage

Embeddings are stored within chunk metadata as raw float32 arrays with dimensional metadata:

{
  "embedding": {
    "model": "sentence-transformers/all-MiniLM-L6-v2",
    "dimensions": 384,
    "dtype": "float32",
    "data": <binary blob: 1536 bytes>
  }
}

The binary blob is stored inline within the MessagePack encoding using the bin format type, avoiding Base64 overhead while maintaining schema self-description.

6. Comparative Analysis: AIMF vs AIF-BIN

6.1 Storage Efficiency

6.2 Access Performance

6.3 Benchmark Results

Benchmarks conducted on a standard development machine (AMD Ryzen 7, 32GB RAM, NVMe SSD) with a corpus of 1000 markdown documents:

7. AI Integration Patterns

7.1 Retrieval-Augmented Generation (RAG)

AIF-BIN files integrate naturally with RAG architectures^[3,9]. The standard retrieval workflow:

1. Index construction: Load embedding vectors from AIF-BIN corpus into memory or ANN index.
2. Query embedding: Transform user query using same embedding model as corpus^[5,10].
3. Similarity search: Identify top-k most similar chunks via cosine similarity^[2].
4. Context assembly: Extract chunk text from matched AIF-BIN files.
5. Generation: Prompt LLM with retrieved context and user query.

# Pseudocode: RAG with AIF-BIN
def answer_question(query: str, corpus: List[AifBinFile]) -> str:
    # Load embeddings (v2: O(1) per file, v1: O(n) per file)
    index = build_ann_index([f.get_embeddings() for f in corpus])
    
    # Embed query
    query_vec = embed(query)
    
    # Retrieve relevant chunks
    matches = index.search(query_vec, k=5)
    
    # Extract context
    context = "\n\n".join([
        corpus[m.file_id].get_chunk(m.chunk_id).text 
        for m in matches
    ])
    
    # Generate response
    return llm.generate(f"Context:\n{context}\n\nQuestion: {query}")
    

7.2 Embedding Model Compatibility

AIF-BIN supports embeddings from any model by storing dimensional metadata alongside vectors. Recommended models by use case^[1,6,7,8]:

7.3 Context Window Optimization

AIF-BIN's chunking strategy is designed to maximize context window utilization in LLMs. Given a context window of W tokens and k retrieved chunks of average size c tokens:

The default chunk size of 512 tokens with 50-token overlap allows retrieval of 6-8 relevant chunks within a 4096-token context window while reserving space for the query and system instructions.

7.4 Agentic Workflows

AI agents can leverage AIF-BIN files as persistent memory stores. The format supports:

• Episodic memory: Store conversation summaries as chunks for long-term context.
• Semantic memory: Index knowledge base documents for fact retrieval.
• Procedural memory: Store code examples and tool documentation.
• Working memory: Cache intermediate results with TTL metadata.

"The filesystem becomes the memory system. Each .aif-bin file is a thought that can be recalled by meaning rather than name."

8. Future Directions

8.1 Planned Enhancements

• Compression support: Optional zstd compression for embedding data with minimal decompression overhead.
• Streaming embeddings: Support for quantized embeddings (int8, binary) for memory-constrained deployments.
• Graph relationships: Chunk-level linking for knowledge graph construction.
• Version history: Delta encoding for document version tracking within single files.
• Multi-modal embeddings: Native support for CLIP and other vision-language embeddings.

8.2 Integration Roadmap

Planned integrations with popular AI frameworks and tools:

8.3 Standardization

We are exploring submission of the AIF-BIN specification to relevant standards bodies for broader adoption. The open-source reference implementation (MIT licensed) serves as the canonical specification pending formal standardization.

9. Conclusion

AIF-BIN represents a practical solution to the growing need for AI-native document storage. By encapsulating source content, extracted text, and embedding vectors within a single portable file, the format eliminates the complexity of maintaining separate vector databases while enabling efficient semantic retrieval.

The v2 binary encoding achieves significant improvements over the v1 JSON format:

• 47-52% reduction in file size through binary embedding storage and MessagePack metadata encoding.
• 3.2x faster parsing for full file loads, with up to 9.5x improvement for metadata-only operations.
• 2.7x reduction in memory usage when loading document corpora.
• O(1) random access to metadata and individual chunks via fixed-offset headers and trailing indices.

We believe the "document as database" paradigm embodied by AIF-BIN will become increasingly relevant as AI capabilities expand and organizations seek to balance the power of semantic retrieval with data sovereignty and operational simplicity.

The format specification and reference implementations are available under the MIT License at github.com/Terronex-dev/aifbin-lite.

10. References

1. Reimers, N., and Gurevych, I. (2019). "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks." Proceedings of EMNLP-IJCNLP 2019.
2. Johnson, J., Douze, M., and Jegou, H. (2019). "Billion-scale similarity search with GPUs." IEEE Transactions on Big Data.
3. Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS 2020.
4. Furukawa, S. (2013). "MessagePack: Efficient Binary Serialization Format." msgpack.org.
5. Gao, L., et al. (2023). "Precise Zero-Shot Dense Retrieval without Relevance Labels." ACL 2023.
6. Wang, L., et al. (2022). "Text Embeddings by Weakly-Supervised Contrastive Pre-training." arXiv:2212.03533.
7. Xiao, S., et al. (2023). "BGE: Embedding Models for Text Retrieval." BAAI Technical Report.
8. OpenAI. (2023). "Text Embeddings: New and Improved." OpenAI Blog.
9. Borgeaud, S., et al. (2022). "Improving language models by retrieving from trillions of tokens." ICML 2022.
10. Karpukhin, V., et al. (2020). "Dense Passage Retrieval for Open-Domain Question Answering." EMNLP 2020.