> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Large Context Knowledge Handling
> Complete guide to handling large knowledge bases efficiently
PraisonAI Agents provides a comprehensive system for handling large knowledge bases efficiently, with automatic strategy selection, token budgeting, and intelligent compression.
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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?")
```
The user queries a huge knowledge base; retrieval, budgeting, and compression keep answers within context limits.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
User[👤 User question] --> Agent[🤖 Agent]
Agent --> Budget[📊 Token budget]
Budget --> Retrieve[🔍 Smart retrieval]
Retrieve --> Answer[✅ Grounded answer]
classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff
classDef ok fill:#10B981,stroke:#7C90A0,color:#fff
class Agent agent
class Budget,Retrieve tool
class User,Answer ok
```
## How It Works
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
participant User
participant Agent
participant Feature as Large Context Knowledge Handling
User->>Agent: Request
Agent->>Feature: Process request
Feature-->>Agent: Result Agent-->>User: Response
```
# Large Context Knowledge Handling
## Overview
The large context handling system includes:
| Feature | Description |
| ---------------------------------------------------------- | --------------------------------------------- |
| [Token Budgeting](/docs/features/token-budgeting) | Dynamic budget management for context windows |
| [Incremental Indexing](/docs/features/incremental-indexing) | Efficient file tracking and updates |
| [Retrieval Strategies](/docs/features/retrieval-strategies) | Automatic strategy selection by corpus size |
| [Smart Retrieval](/docs/features/smart-retrieval) | Hybrid search with reranking |
| [Context Compression](/docs/features/context-compression) | Intelligent compression to fit budgets |
| [Hierarchical Summaries](/docs/features/hierarchical-summaries) | Multi-level summaries for large corpora |
## Quick Start
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="KnowledgeAgent",
instructions="Answer questions using the knowledge base.",
knowledge={"sources": ["./docs"]},
memory={"user_id": "my_user"},
)
response = agent.chat("What are the main features?")
```
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai knowledge index ./docs --user-id myuser
praisonai knowledge search "query" --strategy hybrid --compress
```
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────┘
```
## CLI Commands
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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
```
## Configuration
### RetrievalConfig
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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,
}
)
```
## Examples
### Context-Required Q\&A
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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
```
### Large Corpus Handling
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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,
}
)
```
### Scope Isolation
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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
```
## Performance
The system is designed for zero performance impact when not in use:
* **Lazy imports** - Heavy dependencies loaded only when needed
* **Incremental indexing** - Only changed files re-indexed
* **Automatic strategy** - Simpler strategies for smaller corpora
* **Token budgeting** - Prevents context overflow
## Best Practices
Re-index only changed files in large, frequently updated corpora to save time and cost.
Match budgets to your model window so retrieval and history share space safely.
Hybrid search plus reranking improves answer quality on noisy document sets.
For corpora above \~100k tokens, summarise in layers instead of stuffing raw chunks into context.
## Related
Dynamic budget management
Hybrid search with reranking
Compress retrieved context to fit budgets
Multi-level summaries for large corpora