Knowledge Overview

Give your agents access to documents and data

Knowledge Overview

Knowledge allows your agents to answer questions using your own documents – PDFs, text files, web pages, and more.

How It Works

Diagram

Add documents → Chunked and indexed
Ask questions → Agent retrieves relevant context
Get answers → With source citations

Quick Start

python

from praisonaiagents import Agent

agent = Agent(
    name="Research Assistant",
    knowledge=["research.pdf", "docs/"]
)

response = agent.start("What are the key findings?")

That’s it! The agent can now answer questions using your documents.

When to Use

Approach	Best For	Link
`Agent(knowledge=[...])`	Most use cases	Quick Start →
`Knowledge()` class	Custom indexing	Knowledge API →
`RAG()` class	Custom pipelines	RAG Module →

Knowledge vs Memory vs RAG

Feature	Knowledge	Memory	RAG
Purpose	Answer from documents	Remember conversations	Retrieve + Generate
Data Source	Files, URLs	Conversations	Knowledge base
Updates	Manual (re-index)	Automatic	Uses Knowledge
Best For	Q&A, research	Chat continuity	Citations, search

What `Knowledge.search()` returns

Knowledge.search() returns a typed SearchResult from praisonaiagents.knowledge.models.

Field	Type	Description
`results`	`list[SearchResultItem]`	Ranked items
`metadata`	`dict`	Search-level metadata (always dict, never None)
`query`	`str`	Original query string
`total_count`	`int`	Total available (may exceed `len(results)` if paginated)

Warning

A SearchResult is a dataclass, so it is always truthy — even when empty. Check .results instead of the object.

python

# ❌ Wrong — SearchResult is a dataclass, always truthy
if not results:
    print("No results")

# ✅ Right — check .results (or .total_count)
if not results.results:
    print("No results")

python

from praisonaiagents import Agent
from praisonaiagents.knowledge import Knowledge

knowledge = Knowledge()
knowledge.store("Paris is the capital of France.")

results = knowledge.search("capital of France", user_id="alice")

for item in results.results:
    print(f"[{item.score:.2f}] {item.text}  ({item.source or '-'})")

Next Steps

Quick Start

Get started in 5 minutes

Storage Options

Configure vector stores

Chat with PDFs

Build a PDF chat agent

RAG

Advanced retrieval

Knowledge Overview