from praisonaiagents import Agent

agent = Agent(
    name="KnowledgeAgent",
    instructions="Answer from a large document corpus",
    knowledge={"sources": ["./docs"]},
)
agent.start("What changed in the API this quarter?")

┌─────────────────────────────────────────────────────────────┐
│                     Agent Query                              │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                  Strategy Selection                          │
│  (DIRECT → BASIC → HYBRID → RERANKED → COMPRESSED → HIER)   │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                   Smart Retrieval                            │
│         (Keyword + Semantic + Reranking)                     │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                 Context Compression                          │
│      (Deduplication + Extraction + Truncation)               │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                   Token Budgeting                            │
│           (Enforce limits, reserve response)                 │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                      LLM Response                            │
└─────────────────────────────────────────────────────────────┘

# Index documents
praisonai knowledge index ./docs --user-id myuser

# Get corpus statistics
praisonai knowledge stats ./docs

# Search with options
praisonai knowledge search "query" --strategy hybrid --rerank --compress

# Build hierarchical summaries
praisonai knowledge summarize ./docs --levels 3

# Clear knowledge store
praisonai knowledge clear --yes

from praisonaiagents.rag import RetrievalConfig, RetrievalStrategy

config = RetrievalConfig(
    # Strategy selection
    strategy=RetrievalStrategy.AUTO,
    
    # Retrieval settings
    top_k=10,
    min_score=0.5,
    
    # Token budget
    max_context_tokens=4000,
    reserved_response_tokens=1000,
    
    # Features
    use_hybrid=True,
    use_reranking=True,
    use_compression=True,
    compression_ratio=0.5,
)

agent = Agent(
    name="ConfiguredAgent",
    knowledge={
        "sources": ["./docs"],
        "retrieval_k": 10,
        "retrieval_threshold": 0.5,
        "rerank": True,
    }
)

# Example with unique codes that require retrieval
from praisonaiagents import Agent

agent = Agent(
    name="PolicyExpert",
    instructions="Answer based ONLY on provided knowledge.",
    knowledge={"sources": ["./policies"]},
    memory={"user_id": "demo_user"},
)

# This question requires retrieval - answer cannot be guessed
response = agent.chat("What is the manager approval code?")
# Agent retrieves: "ZEBRA-71" from policy document

from praisonaiagents import Agent
from praisonaiagents.rag import RetrievalConfig, RetrievalStrategy

# For large corpora, use hierarchical strategy
agent = Agent(
    name="LargeCorpusAgent",
    knowledge={
        "sources": ["./large_docs"],  # 100k+ files
        "retrieval_k": 10,
    }
)

# Different users have isolated knowledge
agent_user1 = Agent(
    name="Agent",
    knowledge={"sources": ["./docs"]},
    memory={"user_id": "user1"},
)

agent_user2 = Agent(
    name="Agent",
    knowledge={"sources": ["./docs"]},
    memory={"user_id": "user2"},
)

# Each user's indexed content is isolated