elizaos-plugin-knowledge 2.0.0a4

Knowledge and RAG plugin for elizaOS - provides retrieval augmented generation capabilities

elizaos-plugin-knowledge

Knowledge and RAG (Retrieval Augmented Generation) plugin for elizaOS.

This plugin provides document processing, embedding generation, and semantic search capabilities for elizaOS agents.

Features

• Document Processing: Extract text from PDF, DOCX, Markdown, and plain text files
• Text Chunking: Split documents into semantic chunks with configurable overlap
• Embedding Generation: Generate embeddings using OpenAI, Google, or Anthropic providers
• Semantic Search: Find relevant knowledge based on query similarity
• Contextual Retrieval: Optionally enrich chunks with contextual information

Installation

pip install elizaos-plugin-knowledge

Optional Dependencies

# For OpenAI embedding support
pip install elizaos-plugin-knowledge[openai]

# For Anthropic support
pip install elizaos-plugin-knowledge[anthropic]

# For Google AI support
pip install elizaos-plugin-knowledge[google]

# For PDF processing
pip install elizaos-plugin-knowledge[pdf]

# For DOCX processing
pip install elizaos-plugin-knowledge[docx]

# Install all optional dependencies
pip install elizaos-plugin-knowledge[all]

Usage

Basic Usage

from elizaos_plugin_knowledge import KnowledgeService, KnowledgeConfig

# Create configuration
config = KnowledgeConfig(
    embedding_provider="openai",
    embedding_model="text-embedding-3-small",
    embedding_dimension=1536,
)

# Initialize service
service = KnowledgeService(config)

# Add knowledge from text
await service.add_knowledge(
    content="The capital of France is Paris.",
    content_type="text/plain",
    filename="facts.txt",
)

# Search for knowledge
results = await service.search("What is the capital of France?")
for result in results:
    print(f"Score: {result.similarity:.2f} - {result.content}")

With elizaOS Runtime

from elizaos import Plugin
from elizaos_plugin_knowledge import create_knowledge_plugin

# Create the plugin
plugin = create_knowledge_plugin()

# Register with runtime
runtime.register_plugin(plugin)

Configuration

Parameter	Type	Default	Description
embedding_provider	str	"openai"	Provider for embeddings (openai, google)
embedding_model	str	"text-embedding-3-small"	Model name for embeddings
embedding_dimension	int	1536	Embedding vector dimension
ctx_knowledge_enabled	bool	False	Enable contextual enrichment
text_provider	str	None	Provider for text generation
text_model	str	None	Model for text generation
chunk_size	int	500	Target tokens per chunk
chunk_overlap	int	100	Overlap tokens between chunks

API Reference

KnowledgeService

The main service class for knowledge management.

Methods

• add_knowledge(content, content_type, filename, metadata) - Add a document to the knowledge base
• search(query, count, threshold) - Search for relevant knowledge
• get_knowledge(message) - Get knowledge relevant to a message
• delete_knowledge(knowledge_id) - Delete a knowledge item

KnowledgeProvider

Provider that supplies knowledge context to agent prompts.

License

MIT License - see LICENSE file for details.