1from agenticx.knowledge import KnowledgeBase
2
3kb = KnowledgeBase(name="my-docs")
4
5# Add documents
6kb.add_file("report.pdf")
7kb.add_url("https://example.com/article")
8kb.add_text("AgenticX is a multi-agent framework...", source="manual")
9
10# Process (chunk, embed, index)
11kb.build()

1# Vector retrieval
2results = kb.search("What are the key features?", top_k=5)
3
4# Hybrid retrieval (vector + BM25)
5results = kb.search("key features", mode="hybrid", top_k=10)
6
7# With reranking
8results = kb.search("key features", mode="hybrid", rerank=True, top_k=5)

1from agenticx.knowledge import GraphKnowledgeBase
2
3gkb = GraphKnowledgeBase(
4    name="research-papers",
5    graph_backend="neo4j",  # or "nebula"
6    neo4j_uri="bolt://localhost:7687"
7)
8
9gkb.add_file("research_paper.pdf")
10gkb.build()  # Extracts entities and relationships
11
12# Graph-aware retrieval
13results = gkb.search("relationship between agent memory and performance")

1from agenticx.tools import KnowledgeBaseTool
2
3kb_tool = KnowledgeBaseTool(knowledge_base=kb)
4
5executor = AgentExecutor(
6    agent=agent,
7    llm=llm,
8    tools=[kb_tool]
9)

1from agenticx.embeddings import OpenAIEmbeddings
2
3embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
4kb = KnowledgeBase(name="my-docs", embeddings=embeddings)