Complete Developer Guide - ElizaOS Documentation

The @elizaos/plugin-knowledge package provides Retrieval Augmented Generation (RAG) capabilities for elizaOS agents. It enables agents to store, search, and automatically use knowledge from uploaded documents and text.

Key Features

Multi-format Support: Process PDFs, Word docs, text files, and more
Smart Deduplication: Content-based IDs prevent duplicate entries
Automatic RAG: Knowledge is automatically injected into relevant conversations
Character Knowledge: Load knowledge from character definitions
REST API: Manage documents via HTTP endpoints
Conversation Tracking: Track which knowledge was used in responses

Architecture Overview

Core Components

Knowledge Service

The main service class that handles all knowledge operations:

class KnowledgeService extends Service {
  static readonly serviceType = 'knowledge';
  private knowledgeConfig: KnowledgeConfig;
  private knowledgeProcessingSemaphore: Semaphore;

  constructor(runtime: IAgentRuntime, config?: Partial<KnowledgeConfig>) {
    super(runtime);
    this.knowledgeProcessingSemaphore = new Semaphore(10);

    // Configuration with environment variable support
    this.knowledgeConfig = {
      CTX_KNOWLEDGE_ENABLED: parseBooleanEnv(config?.CTX_KNOWLEDGE_ENABLED),
      LOAD_DOCS_ON_STARTUP: loadDocsOnStartup,
      MAX_INPUT_TOKENS: config?.MAX_INPUT_TOKENS,
      MAX_OUTPUT_TOKENS: config?.MAX_OUTPUT_TOKENS,
      EMBEDDING_PROVIDER: config?.EMBEDDING_PROVIDER,
      TEXT_PROVIDER: config?.TEXT_PROVIDER,
      TEXT_EMBEDDING_MODEL: config?.TEXT_EMBEDDING_MODEL,
    };

    // Auto-load documents on startup if enabled
    if (this.knowledgeConfig.LOAD_DOCS_ON_STARTUP) {
      this.loadInitialDocuments();
    }
  }

  // Main public method for adding knowledge
  async addKnowledge(options: AddKnowledgeOptions): Promise<{
    clientDocumentId: string;
    storedDocumentMemoryId: UUID;
    fragmentCount: number;
  }> {
    // Generate content-based ID for deduplication
    const contentBasedId = generateContentBasedId(options.content, agentId, {
      includeFilename: options.originalFilename,
      contentType: options.contentType,
      maxChars: 2000,
    });

    // Check for duplicates
    const existingDocument = await this.runtime.getMemoryById(contentBasedId);
    if (existingDocument) {
      // Return existing document info
      return { clientDocumentId: contentBasedId, ... };
    }

    // Process new document
    return this.processDocument({ ...options, clientDocumentId: contentBasedId });
  }

  // Semantic search for knowledge
  async getKnowledge(
    message: Memory,
    scope?: { roomId?: UUID; worldId?: UUID; entityId?: UUID }
  ): Promise<KnowledgeItem[]> {
    const embedding = await this.runtime.useModel(ModelType.TEXT_EMBEDDING, {
      text: message.content.text,
    });

    const fragments = await this.runtime.searchMemories({
      tableName: 'knowledge',
      embedding,
      query: message.content.text,
      ...scope,
      count: 20,
      match_threshold: 0.1,
    });

    return fragments.map(fragment => ({
      id: fragment.id,
      content: fragment.content,
      similarity: fragment.similarity,
      metadata: fragment.metadata,
    }));
  }

  // RAG metadata enrichment for conversation tracking
  async enrichConversationMemoryWithRAG(
    memoryId: UUID,
    ragMetadata: {
      retrievedFragments: Array<{
        fragmentId: UUID;
        documentTitle: string;
        similarityScore?: number;
        contentPreview: string;
      }>;
      queryText: string;
      totalFragments: number;
      retrievalTimestamp: number;
    }
  ): Promise<void> {
    // Enriches conversation memories with RAG usage data
  }
}

Document Processing

The service handles different file types with sophisticated processing logic:

private async processDocument(options: AddKnowledgeOptions): Promise<{
  clientDocumentId: string;
  storedDocumentMemoryId: UUID;
  fragmentCount: number;
}> {
  let fileBuffer: Buffer | null = null;
  let extractedText: string;
  let documentContentToStore: string;
  const isPdfFile = contentType === 'application/pdf';

  if (isPdfFile) {
    // PDFs: Store original base64, extract text for fragments
    fileBuffer = Buffer.from(content, 'base64');
    extractedText = await extractTextFromDocument(fileBuffer, contentType, originalFilename);
    documentContentToStore = content; // Keep base64 for PDFs
  } else if (isBinaryContentType(contentType, originalFilename)) {
    // Other binary files: Extract and store as plain text
    fileBuffer = Buffer.from(content, 'base64');
    extractedText = await extractTextFromDocument(fileBuffer, contentType, originalFilename);
    documentContentToStore = extractedText; // Store extracted text
  } else {
    // Text files: Handle both base64 and plain text input
    if (looksLikeBase64(content)) {
      // Decode base64 text files
      const decodedBuffer = Buffer.from(content, 'base64');
      extractedText = decodedBuffer.toString('utf8');
      documentContentToStore = extractedText;
    } else {
      // Already plain text
      extractedText = content;
      documentContentToStore = content;
    }
  }

  // Create document memory with content-based ID
  const documentMemory = createDocumentMemory({
    text: documentContentToStore,
    agentId,
    clientDocumentId,
    originalFilename,
    contentType,
    worldId,
    fileSize: fileBuffer ? fileBuffer.length : extractedText.length,
    documentId: clientDocumentId,
    customMetadata: metadata,
  });

  // Store document and process fragments
  await this.runtime.createMemory(documentMemory, 'documents');
  
  const fragmentCount = await processFragmentsSynchronously({
    runtime: this.runtime,
    documentId: clientDocumentId,
    fullDocumentText: extractedText,
    agentId,
    contentType,
    roomId: roomId || agentId,
    entityId: entityId || agentId,
    worldId: worldId || agentId,
    documentTitle: originalFilename,
  });

  return { clientDocumentId, storedDocumentMemoryId, fragmentCount };
}

Actions

The plugin provides two main actions:

PROCESS_KNOWLEDGE

Adds knowledge from files or text content:

Supports file paths: /path/to/document.pdf
Direct text: “Add this to your knowledge: …”
File types: PDF, DOCX, TXT, MD, CSV, etc.
Automatically splits content into searchable fragments

SEARCH_KNOWLEDGE

Explicitly searches the knowledge base:

Triggered by: “Search your knowledge for…”
Returns top 3 most relevant results
Displays formatted text snippets

Knowledge Provider

Automatically injects relevant knowledge into agent responses:

Dynamic: Runs on every message to find relevant context
Top 5 Results: Retrieves up to 5 most relevant knowledge fragments
RAG Tracking: Enriches conversation memories with knowledge usage metadata
Token Limit: Caps knowledge at ~4000 tokens to prevent context overflow

The provider automatically:

Searches for relevant knowledge based on the user’s message
Formats it with a ”# Knowledge” header
Tracks which knowledge was used in the response
Enriches the conversation memory with RAG metadata

Document Processing Pipeline

1. Document Ingestion

Knowledge can be added through multiple channels:

// File upload (API endpoint sends base64-encoded content)
const result = await knowledgeService.addKnowledge({
  content: base64EncodedContent, // Base64 for binary files, can be plain text
  originalFilename: 'document.pdf',
  contentType: 'application/pdf',
  agentId: agentId, // Optional, defaults to runtime.agentId
  metadata: {
    tags: ['documentation', 'manual']
  }
});

// Direct text addition (internal use)
await knowledgeService._internalAddKnowledge({
  id: generateContentBasedId(content, agentId),
  content: { text: "Important information..." },
  metadata: {
    type: MemoryType.DOCUMENT,
    source: 'direct'
  }
});

// Character knowledge (loaded automatically from character definition)
await knowledgeService.processCharacterKnowledge([
  "Path: knowledge/facts.md\nKey facts about the product...",
  "Another piece of character knowledge..."
]);

2. Text Extraction

Supports multiple file formats:

const supportedFormats = {
  'application/pdf': extractPDF,
  'application/vnd.openxmlformats-officedocument.wordprocessingml.document': extractDOCX,
  'text/plain': (buffer) => buffer.toString('utf-8'),
  'text/markdown': (buffer) => buffer.toString('utf-8'),
  'application/json': (buffer) => JSON.stringify(JSON.parse(buffer.toString('utf-8')), null, 2)
};

3. Content-Based Deduplication

Uses deterministic IDs to prevent duplicates:

// Generate content-based ID combining:
// - Content (first 2KB)
// - Agent ID
// - Filename (if available)
// - Content type
const contentBasedId = generateContentBasedId(content, agentId, {
  includeFilename: options.originalFilename,
  contentType: options.contentType,
  maxChars: 2000
});

// Check if document already exists
const existingDocument = await this.runtime.getMemoryById(contentBasedId);
if (existingDocument) {
  // Return existing document info instead of creating duplicate
  return { clientDocumentId: contentBasedId, ... };
}

4. Intelligent Chunking

Content-aware text splitting:

const defaultChunkOptions = {
  chunkSize: 500,      // tokens
  overlapSize: 100,    // tokens
  separators: ['\n\n', '\n', '. ', ' '],
  keepSeparator: true
};

function chunkText(text: string, options: ChunkOptions): string[] {
  const chunks: string[] = [];
  let currentChunk = '';
  
  // Smart chunking logic that respects:
  // - Token limits
  // - Sentence boundaries
  // - Paragraph structure
  // - Code blocks
  
  return chunks;
}

5. Contextual Enrichment

Optional feature for better retrieval:

// When CTX_KNOWLEDGE_ENABLED=true
async function enrichChunk(chunk: string, document: string): Promise<string> {
  const context = await generateContext(chunk, document);
  return `${context}\n\n${chunk}`;
}

6. Embedding Generation

Create vector embeddings:

async function generateEmbeddings(chunks: string[]): Promise<number[][]> {
  const embeddings = await embedder.embedMany(chunks);
  return embeddings;
}

// Batch processing with rate limiting
const batchSize = 10;
for (let i = 0; i < chunks.length; i += batchSize) {
  const batch = chunks.slice(i, i + batchSize);
  const embeddings = await generateEmbeddings(batch);
  await storeEmbeddings(embeddings);
  
  // Rate limiting
  await sleep(1000);
}

7. Storage

Documents and embeddings are stored separately:

// Document storage
{
  id: "doc_123",
  content: "Full document text",
  metadata: {
    source: "upload",
    filename: "report.pdf",
    createdAt: "2024-01-20T10:00:00Z",
    hash: "sha256_hash"
  }
}

// Vector storage
{
  id: "vec_456",
  documentId: "doc_123",
  chunkIndex: 0,
  embedding: [0.123, -0.456, ...],
  content: "Chunk text",
  metadata: {
    position: { start: 0, end: 500 }
  }
}

Retrieval & RAG

Semantic Search

Find relevant knowledge using vector similarity:

async function searchKnowledge(query: string, limit: number = 10): Promise<KnowledgeItem[]> {
  // Generate query embedding
  const queryEmbedding = await embedder.embed(query);
  
  // Search vector store
  const results = await vectorStore.searchMemories({
    tableName: "knowledge_embeddings",
    agentId: runtime.agentId,
    embedding: queryEmbedding,
    match_threshold: 0.7,
    match_count: limit,
    unique: true
  });
  
  // Enrich with document metadata
  return results.map(result => ({
    id: result.id,
    content: result.content.text,
    score: result.similarity,
    metadata: result.metadata
  }));
}

API Reference

REST Endpoints

Upload Documents

POST /knowledge/upload
Content-Type: multipart/form-data

{
  "file": <binary>,
  "metadata": {
    "tags": ["product", "documentation"]
  }
}

Response: {
  "id": "doc_123",
  "status": "processing",
  "message": "Document uploaded successfully"
}

List Documents

GET /knowledge/documents?page=1&limit=20

Response: {
  "documents": [
    {
      "id": "doc_123",
      "filename": "product-guide.pdf",
      "size": 1024000,
      "createdAt": "2024-01-20T10:00:00Z",
      "chunkCount": 15
    }
  ],
  "total": 45,
  "page": 1,
  "pages": 3
}

Delete Document

DELETE /knowledge/documents/doc_123

Response: {
  "success": true,
  "message": "Document and associated embeddings deleted"
}

Search Knowledge

GET /knowledge/search?q=pricing&limit=5

Response: {
  "results": [
    {
      "id": "chunk_456",
      "content": "Our pricing starts at $99/month...",
      "score": 0.92,
      "metadata": {
        "source": "pricing.pdf",
        "page": 3
      }
    }
  ]
}

TypeScript Interfaces

interface AddKnowledgeOptions {
  agentId?: UUID; // Optional, defaults to runtime.agentId
  worldId: UUID;
  roomId: UUID;
  entityId: UUID;
  clientDocumentId: UUID;
  contentType: string; // MIME type
  originalFilename: string;
  content: string; // Base64 for binary, plain text for text files
  metadata?: Record<string, unknown>;
}

interface KnowledgeConfig {
  CTX_KNOWLEDGE_ENABLED: boolean;
  LOAD_DOCS_ON_STARTUP: boolean;
  MAX_INPUT_TOKENS?: string | number;
  MAX_OUTPUT_TOKENS?: string | number;
  EMBEDDING_PROVIDER?: string;
  TEXT_PROVIDER?: string;
  TEXT_EMBEDDING_MODEL?: string;
}

interface TextGenerationOptions {
  provider?: 'anthropic' | 'openai' | 'openrouter' | 'google';
  modelName?: string;
  maxTokens?: number;
  cacheDocument?: string; // For OpenRouter caching
  cacheOptions?: { type: 'ephemeral' };
  autoCacheContextualRetrieval?: boolean;
}

Advanced Features

Contextual Embeddings

Enable for 50% better retrieval accuracy:

CTX_KNOWLEDGE_ENABLED=true

This feature:

Adds document context to each chunk
Improves semantic understanding
Reduces false positives
Enables better cross-reference retrieval

Document Caching

With OpenRouter, enable caching for 90% cost reduction:

const config = {
  provider: 'openrouter',
  enableCache: true,
  cacheExpiry: 86400 // 24 hours
};

Custom Document Processors

Extend for special formats:

class CustomProcessor extends DocumentProcessor {
  async extractCustomFormat(buffer: Buffer): Promise<string> {
    // Custom extraction logic
    return extractedText;
  }
  
  registerProcessor() {
    this.processors.set('application/custom', this.extractCustomFormat);
  }
}

Performance Optimization

Rate Limiting

const rateLimiter = {
  maxConcurrent: 5,
  requestsPerMinute: 60,
  tokensPerMinute: 40000
};

Batch Processing

async function batchProcess(documents: Document[]) {
  const chunks = [];
  
  for (const batch of chunk(documents, 10)) {
    const results = await Promise.all(
      batch.map(doc => processDocument(doc))
    );
    chunks.push(...results);
    
    await sleep(1000); // Rate limiting
  }
  
  return chunks;
}

Memory Management

// Clear cache periodically
setInterval(() => {
  knowledgeService.clearCache();
}, 3600000); // Every hour

// Stream large files
async function processLargeFile(path: string) {
  const stream = createReadStream(path);
  const chunks = [];
  
  for await (const chunk of stream) {
    chunks.push(await processChunk(chunk));
  }
  
  return chunks;
}

Integration Patterns

Basic Integration

{
  "name": "SupportAgent",
  "plugins": ["@elizaos/plugin-knowledge"],
  "knowledge": [
    "Default knowledge statement 1",
    "Default knowledge statement 2"
  ]
}

Configuration Options

# Enable automatic document loading from agent's docs folder
LOAD_DOCS_ON_STARTUP=true

# Enable contextual embeddings for better retrieval
CTX_KNOWLEDGE_ENABLED=true

# Configure embedding provider (defaults to OpenAI)
EMBEDDING_PROVIDER=openai
TEXT_EMBEDDING_MODEL=text-embedding-3-small

Using the Service

// Get the knowledge service
const knowledgeService = runtime.getService<KnowledgeService>('knowledge');

// Add knowledge programmatically
const result = await knowledgeService.addKnowledge({
  content: documentContent, // Base64 or plain text
  originalFilename: 'guide.pdf',
  contentType: 'application/pdf',
  worldId: runtime.agentId,
  roomId: message.roomId,
  entityId: message.entityId
});

// Search for knowledge
const results = await knowledgeService.getKnowledge(message, {
  roomId: message.roomId,
  worldId: runtime.agentId
});

Best Practices

Use clear, descriptive filenames

Choose names that clearly indicate the content (e.g., product-guide-v2.pdf instead of doc1.pdf)

Group related documents in folders

Create logical folder structures like products/, support/, policies/

Tag documents with metadata

Add categories, dates, and versions to improve searchability

Keep individual documents focused

One topic per document for better retrieval accuracy

Troubleshooting

Common Issues

Documents Not Loading

Check file permissions and paths:

ls -la agent/docs/
# Should show read permissions

Poor Retrieval Quality

Try adjusting chunk size and overlap:

EMBEDDING_CHUNK_SIZE=800
EMBEDDING_OVERLAP_SIZE=200

Rate Limiting Errors

Implement exponential backoff:

async function withRetry(fn, maxRetries = 3) {
  for (let i = 0; i < maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      if (i === maxRetries - 1) throw error;
      await sleep(Math.pow(2, i) * 1000);
    }
  }
}

Debug Logging

Enable verbose logging:

# .env
LOG_LEVEL=debug

Summary

The Knowledge Plugin provides a complete RAG system that:

Processes Documents: Handles PDFs, Word docs, text files, and more with automatic text extraction
Manages Deduplication: Uses content-based IDs to prevent duplicate knowledge entries
Chunks Intelligently: Splits documents into searchable fragments with configurable overlap
Retrieves Semantically: Finds relevant knowledge using vector similarity search
Enhances Conversations: Automatically injects relevant knowledge into agent responses
Tracks Usage: Records which knowledge was used in each conversation

Key features:

Automatic document loading on startup
Character knowledge integration
RAG metadata tracking for conversation history
REST API for document management
Support for contextual embeddings
Provider-agnostic embedding support

The plugin seamlessly integrates with elizaOS agents to provide them with a searchable knowledge base that enhances their ability to provide accurate, contextual responses.

Getting Started

Core Plugins

LLM Providers

Platform Integrations

DeFi Plugins

​Key Features

​Architecture Overview

​Core Components

​Knowledge Service

​Document Processing

​Actions

​PROCESS_KNOWLEDGE

​SEARCH_KNOWLEDGE

​Knowledge Provider

​Document Processing Pipeline

​1. Document Ingestion

​2. Text Extraction

​3. Content-Based Deduplication

​4. Intelligent Chunking

​5. Contextual Enrichment

​6. Embedding Generation

​7. Storage

​Retrieval & RAG

​Semantic Search

​API Reference

​REST Endpoints

​Upload Documents

​List Documents

​Delete Document

​Search Knowledge

​TypeScript Interfaces

​Advanced Features

​Contextual Embeddings

​Document Caching

​Custom Document Processors

​Performance Optimization

​Rate Limiting

​Batch Processing

​Memory Management

​Integration Patterns

​Basic Integration

​Configuration Options

​Using the Service

​Best Practices

​Troubleshooting

​Common Issues

​Documents Not Loading

​Poor Retrieval Quality

​Rate Limiting Errors

​Debug Logging

​Summary