> ## Documentation Index > Fetch the complete documentation index at: https://revolai.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Knowledge Base > Train your agent with documents and content ## Overview The Knowledge Base uses **RAG (Retrieval-Augmented Generation)** to give your agent access to your business content. Documents are split into chunks, converted to vector embeddings, and stored for semantic search. Navigate to the **Knowledge Base** tab in the agent panel. Here you choose which data the agent will use for its responses. Knowledge Base Knowledge Base ## Adding Content ### Documents Upload files in supported formats: * PDF * DOCX * TXT ### URLs Provide web page URLs. Revol will scrape the content and add it to the knowledge base. ### Text Add content directly as text blocks. ## How RAG Works You upload a document or add content. Content is split into manageable chunks. Each chunk is converted to a vector embedding using the selected embedding model. Embeddings are stored in PostgreSQL with pgvector extension. When a user asks a question, the most similar chunks are retrieved using cosine similarity. Retrieved chunks are injected into the LLM prompt as context. ## RAG Settings Open RAG settings via the **gear icon** (⚙) in the Knowledge Base storage panel. All settings are per-company and auto-saved. ### Embedding Model Choose which model converts your text into vector embeddings: | Model | Provider | Dimensions | Price | Best for | | ---------------------- | --------- | ---------- | ---------------- | ------------------------------------- | | text-embedding-3-small | OpenAI | 1536 | \$0.02/1M tokens | General use, English content | | text-embedding-3-large | OpenAI | 1536 | \$0.13/1M tokens | Higher accuracy, English content | | BGE-M3 | DeepInfra | 1024 | \$0.01/1M tokens | Multilingual content (100+ languages) | Changing the embedding model **deletes all existing embeddings** for the company. You must re-train all agents after switching. A confirmation dialog will appear before the change is applied. ### Chunk Limit How many text chunks are returned per RAG search (1–20). Default: **5**. Higher values provide more context to the LLM but increase token usage. ### Character Limit Maximum characters per chunk when splitting documents (500–10,000). Default: **1,500**. Smaller chunks give more precise retrieval. Larger chunks preserve more context per result. ### Chunk Overlap Overlap between consecutive chunks (0–40%). Default: **15%**. Overlap ensures important context at chunk boundaries is not lost. Higher overlap creates more chunks and uses more storage. ### Similarity Threshold Minimum cosine similarity score to include a result (0.1–1.0). Default: **0.35**. Lower values return more results (better recall). Higher values return only highly relevant results (better precision). For multilingual content, use lower thresholds (0.3–0.4). ## Storage Limits | Plan | Knowledge Docs | Embedding Tokens | | ------------ | -------------- | ---------------- | | Free | 10 | 100,000 | | Premium | 100 | 1,000,000 | | Professional | 1,000 | 5,000,000 | *** ## Generate Knowledge Base with Claude Code If you have project documentation (website pages, docs portal, README files, wiki) and want to turn it into a structured knowledge base for your Revol AI agent, you can use **Claude Code** to analyze the documentation and generate ready-to-upload TXT files. ```text theme={null} # Instruction: Generate Knowledge Base Files from Documentation ## Context I use Revol — a platform for building AI sales agents with RAG (Retrieval-Augmented Generation). My agent answers questions using a knowledge base: uploaded files are chunked (configurable size and overlap), embedded via a selectable model (OpenAI text-embedding-3-small/large or BGE-M3), stored in PostgreSQL with pgvector, and searched by cosine similarity at inference time. I need to convert my project documentation into a set of .txt files optimized for this RAG pipeline — so the agent can find and cite accurate answers. ## How RAG Chunking Works (important for file structure) - Each file is split into chunks of ~2000 characters with 20% overlap at sentence boundaries - The file name becomes part of the embedding metadata — use descriptive names so the system can identify the source - Shorter, focused files perform better than one giant file - Each chunk should be self-contained — a reader should understand the chunk without needing the surrounding text - Structured content (lists, tables as text, clear headers) chunks better than long prose paragraphs ## Your Task ### Step 1: Analyze All Documentation Read every documentation file in the project. For each file, identify: 1. The topic it covers 2. Key facts, settings, configurations, and values 3. Step-by-step procedures 4. Feature descriptions with specifics (limits, options, formats) 5. Pricing, plans, and quotas (if applicable) 6. Technical details (APIs, parameters, integrations) ### Step 2: Plan the File Structure Group related content into logical topics. Each file should cover ONE cohesive topic. Target file sizes: - Ideal: 2,000–6,000 characters per file (1–3 chunks) - Maximum: 10,000 characters (5 chunks) - If a topic is larger, split it into sub-topics Naming convention: {Topic-Name}.txt - Use descriptive English names with hyphens - No numbering prefixes (ordering doesn't matter for RAG) - Name should hint at the content for metadata Examples: - Platform-Overview.txt - Getting-Started.txt - Pricing-Plans.txt - API-Authentication.txt - Webhook-Integration.txt ### Step 3: Write the Files For each file, follow these rules: Content rules: - Start with a clear topic heading as plain text - Write in plain text — no Markdown formatting, no HTML, no JSX components - Convert tables to readable text lists or key-value pairs - Convert step-by-step guides to numbered lists - Include specific values: numbers, limits, prices, options, defaults, formats, URLs - Each paragraph or section should be understandable on its own (self-contained chunks) - Remove navigation elements, "Next Steps" links, and cross-references that don't add informational value - Keep the original language of the documentation Optimization for RAG: - Front-load important information — put the answer before the explanation - Use consistent terminology throughout all files - Repeat key terms naturally so they appear in multiple chunks (improves retrieval) - For features with settings: list setting name, type, default value, options, and description - For integrations: list provider, auth method, setup steps, available tools/functions What to exclude: - Screenshots and image references - UI component markup (tabs, accordions, cards, frames) - "See also" and navigation links - Decorative text and marketing fluff - Duplicate content (don't repeat the same info in multiple files) ### Step 4: Create the Output 1. Create a temporary directory (e.g., temp-knowledge-base/) 2. Write all .txt files into it 3. List all files with their sizes and topic descriptions 4. Provide a summary of total files and total size ## Optional: Generate a Workflow Agent Seeder If the project uses Revol and you also need to create an AI agent with a workflow to answer questions about this documentation, generate a Laravel database seeder that: 1. Creates an AiAgent with: - Appropriate system prompt for documentation support - LLM: gpt-4o-mini, temperature: 0.3 (accuracy) - Personality: high clarity (8-9), high formality (6-7), low humor (2-3), low emoji (1-2) 2. Builds a Smart Router workflow: - Start node with keyword-based edges to expert nodes - 3-6 custom "expert" nodes, each covering a topic group - Each expert has a specific conversation_goal describing its domain - Each expert has search_documents + get_company_info tools - One fallback edge to a General Assistant node - All experts connect to a Formatter node - Voice nodes (STT/TTS) inactive by default 3. Keywords on edges should match the natural vocabulary users would use when asking about each topic ## Input My documentation is located at: [PATH OR URL] Project name: [NAME] Project description: [BRIEF DESCRIPTION] Target company_id for seeder: [ID or skip seeder] ``` This prompt works best with comprehensive documentation portals, product docs, API references, and knowledge bases. The generated .txt files are ready to upload directly to Revol's File Manager — just drag and drop, then click **Train**.