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Knowledge Module

Overview

The knowledge module in the AgenticAI Framework manages structured and unstructured knowledge sources for AI agents. It enables retrieval, storage, and querying of domain-specific information to enhance reasoning and contextual understanding.

Key Classes and Functions

  • KnowledgeBase — Core class for storing and retrieving knowledge entries.
  • Document — Represents a single knowledge item with metadata.
  • add_document(document) — Adds a new document to the knowledge base.
  • search(query, kwargs)** — Searches the knowledge base for relevant documents.
  • load_from_source(source) — Loads knowledge from external sources (files, APIs, databases).

Example Usage

from agenticaiframework.knowledge import KnowledgeBase, Document

# Initialize knowledge base
kb = KnowledgeBase()

# Add a document
doc = Document(content="Python is a high-level programming language.", metadata={"topic": "programming"})
kb.add_document(doc)

# Search for information
results = kb.search("What is Python?")
for r in results:
    print(r.content)

Use Cases

  • Enhancing LLM responses with domain-specific facts.
  • Building retrieval-augmented generation (RAG) systems.
  • Creating searchable internal knowledge repositories.
  • Integrating with external data sources for dynamic updates.

Best Practices

  • Keep metadata consistent for better filtering and retrieval.
  • Periodically update the knowledge base to maintain relevance.
  • Use embeddings for semantic search to improve accuracy.
  • Secure sensitive knowledge sources with access controls.