> ## Documentation Index > Fetch the complete documentation index at: https://docs.bindai.dev/llms.txt > Use this file to discover all available pages before exploring further. # 05.2 documents # Documents Documents are the primary source of knowledge in BindAI. They contain the information that agents retrieve during execution to answer questions with accurate, domain-specific context. Documents can represent almost any type of content, from simple Markdown files to large collections of technical documentation. *** # What is a Document? A document is a unit of information that can be indexed and retrieved. Examples include: * Markdown files * PDF documents * Word documents * HTML pages * API documentation * Product manuals * Knowledge base articles * FAQ pages Each document becomes part of the knowledge base available to the agent. *** # Why Documents? Large language models cannot reliably answer questions about information they have never seen. Documents provide that missing information. Instead of asking the model to "remember" everything, BindAI retrieves only the documents relevant to the user's request. *** # Document Lifecycle A typical document passes through several stages. ```text id="9hf1rc" theme={null} Document ↓ Load ↓ Process ↓ Index ↓ Retrieve ↓ Language Model ``` This pipeline prepares documents for efficient retrieval during execution. *** # Example Suppose you have a product manual. ```text id="g6nytw" theme={null} product-manual.pdf ``` A user asks: ```text id="3qcb5v" theme={null} How do I reset the device? ``` BindAI retrieves the section describing the reset procedure and includes it in the prompt sent to the language model. *** # Document Collections Documents are usually grouped into collections. ```text id="m2uk8x" theme={null} Documentation ├── Installation ├── API ├── Tutorials └── FAQ ``` Collections make it easier to organize large knowledge bases. *** # Document Metadata Documents often include metadata. Examples: * title * author * creation date * tags * category * language * version Metadata improves search quality and filtering. Example: ```text id="a4dx8z" theme={null} Title: API Reference Category: Documentation Version: 2.0 ``` *** # Document Chunking Large documents are usually divided into smaller pieces before indexing. Instead of retrieving a 200-page manual: ```text id="n5ty7r" theme={null} Manual ↓ Chunk 1 Chunk 2 Chunk 3 ... Chunk N ``` Only the most relevant chunks are sent to the language model. This improves both speed and accuracy. *** # Supported Formats A knowledge system may support many document types. Common examples include: * Markdown * Plain text * PDF * DOCX * HTML * JSON * CSV Additional formats can be added through custom loaders. *** # Loading Documents Documents are typically imported into a knowledge source before use. Conceptually: ```python id="e7kz2m" theme={null} knowledge.add_document( document, ) ``` The implementation depends on the knowledge provider being used. *** # Updating Documents Knowledge bases evolve over time. Common operations include: * adding new documents * updating existing content * removing obsolete documents * rebuilding indexes Keeping documents current improves response quality. *** # Document Retrieval When a user asks a question: ```text id="v8rm4q" theme={null} Question ↓ Search Documents ↓ Relevant Chunks ↓ Language Model ↓ Answer ``` Only a small subset of the knowledge base is typically retrieved. *** # Documents vs Memory Documents contain reference information. Memory contains conversation history. | Documents | Memory | | ------------------ | --------------------- | | Static information | Dynamic conversation | | Shared knowledge | User-specific context | | Manuals and guides | Previous interactions | | Updated manually | Updated automatically | Both systems complement one another. *** # Best Practices * Keep documents focused on one topic. * Organize documents into logical collections. * Remove outdated versions. * Include useful metadata. * Prefer smaller, well-structured documents over extremely large files. * Use chunking for long documents. * Rebuild indexes after significant updates. *** # Summary Documents are the foundation of BindAI's knowledge system. By organizing, processing, and indexing documents before execution, BindAI can retrieve only the information needed to answer a user's question, producing responses that are more accurate, current, and relevant than relying solely on the language model.