RAG Quick Start

Build retrieval-augmented agents that answer from your documents and knowledge bases

RAG agents retrieve relevant chunks from your documents before answering, grounding responses in your own knowledge.

from praisonaiagents import Agent

agent = Agent(
    name="Knowledge Agent",
    instructions="Answer from the provided knowledge only.",
    knowledge=["small.pdf"],
)

agent.start("What is KAG in one line?")

The user asks from their documents; the agent retrieves relevant chunks and grounds the reply.

Quick Start

1. Simple Usage

   Pass file paths or directories — the agent indexes on first run, then retrieves on each query:

   from praisonaiagents import Agent
   
   agent = Agent(
       name="Knowledge Agent",
       instructions="Answer from the knowledge base.",
       knowledge=["small.pdf"],
   )
   
   agent.start("What is KAG in one line?")
   
2. With Configuration

   Use KnowledgeConfig for vector store, chunking, and reranking:

   from praisonaiagents import Agent, KnowledgeConfig
   
   agent = Agent(
       name="Knowledge Agent",
       instructions="Answer from the provided knowledge.",
       knowledge=KnowledgeConfig(
           sources=["small.pdf"],
           vector_store={
               "provider": "chroma",
               "config": {"collection_name": "praison", "path": ".praison"},
           },
           retrieval_k=5,
           rerank=True,
       ),
   )
   
   agent.start("What is KAG in one line?")
   

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How It Works

Phase	What happens
1. Index	Documents are chunked and embedded into the vector store on first access
2. Retrieve	User query is embedded; top-k similar chunks are returned
3. Generate	Retrieved context is injected before the LLM prompt; LLM answers from your data

For pre-indexed stores, pass vector config via task context or call agent.retrieve("query") directly.

---

Configuration Options

Option	Type	Default	Description
sources	List[str]	[]	Files, directories, or URLs
embedder	str	"openai"	Embedding provider
chunk_size	int	1000	Chunk size in tokens
chunk_overlap	int	200	Overlap between chunks
retrieval_k	int	5	Chunks retrieved per query
retrieval_threshold	float	0.0	Minimum similarity score
rerank	bool	False	Rerank retrieved chunks
auto_retrieve	bool	True	Inject context automatically
vector_store	dict	None	Backend provider config

Install knowledge extras: pip install "praisonaiagents[knowledge]"

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Best Practices

Index documents before querying

Pass sources via knowledge=["file.pdf"] at agent creation — first run indexes, later runs retrieve without re-indexing.

Use specific questions

Narrow questions retrieve better chunks than broad prompts like “tell me everything”.

Use persistent vector stores in production

Set vector_store with Chroma path, Qdrant, or Pinecone — avoid in-memory stores for production.

Combine RAG with tools for live data

Pair knowledge= with web tools when you need both static documents and real-time data.

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Related

Vector Store

Pluggable embedding storage

Knowledge

Sources and retrieval strategies