> ## 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.

# Knowledge Module

> Knowledge base management with document loading, storage, and retrieval

# Knowledge Module

The Knowledge module provides a unified interface for building knowledge bases with document loading, vector storage, and intelligent retrieval.

## Import

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.adapters import (
    AutoReader,
    ChromaVectorStore,
    BasicRetriever,
    FusionRetriever,
    LLMReranker
)
```

## Quick Example

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.adapters import AutoReader, ChromaVectorStore, FusionRetriever

# 1. Load documents
reader = AutoReader()
docs = reader.load("./documents/")

# 2. Store in vector database
store = ChromaVectorStore(namespace="knowledge")
store.add(
    texts=[d.content for d in docs],
    embeddings=get_embeddings([d.content for d in docs])
)

# 3. Retrieve relevant documents
retriever = FusionRetriever(
    vector_store=store,
    embedding_fn=get_embedding,
    num_queries=3
)
results = retriever.retrieve("What is Python?", top_k=5)
```

## Features

* Document loading from files, directories, URLs
* Vector storage with ChromaDB and Pinecone
* Multiple retrieval strategies (Basic, Fusion, Recursive, AutoMerge)
* Reranking for improved relevance
* CLI integration for easy management

## Architecture

```
┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│   Readers   │────▶│ Vector Store │────▶│  Retriever  │
│  (Load)     │     │   (Store)    │     │  (Search)   │
└─────────────┘     └──────────────┘     └──────┬──────┘
                                                 │
                                                 ▼
                                         ┌─────────────┐
                                         │  Reranker   │
                                         │  (Refine)   │
                                         └─────────────┘
```

## CLI Commands

### Add Documents

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Add files to knowledge base
praisonai knowledge add document.pdf
praisonai knowledge add ./docs/
praisonai knowledge add "*.md"
praisonai knowledge add https://example.com/page.html
```

### Query Knowledge Base

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Basic query
praisonai knowledge query "What is Python?"

# With options
praisonai knowledge query "Compare Python and Java" \
  --vector-store chroma \
  --retrieval fusion \
  --reranker llm \
  --top-k 5
```

### Manage Knowledge Base

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# List documents
praisonai knowledge list

# Clear knowledge base
praisonai knowledge clear

# Show stats
praisonai knowledge stats
```

## CLI Options

| Option           | Description          | Default     |
| ---------------- | -------------------- | ----------- |
| `--vector-store` | Vector store backend | `chroma`    |
| `--retrieval`    | Retrieval strategy   | `basic`     |
| `--reranker`     | Reranking method     | `none`      |
| `--top-k`        | Number of results    | `10`        |
| `--workspace`    | Workspace directory  | Current dir |

## Example: Building a Documentation Assistant

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.adapters import (
    AutoReader,
    ChromaVectorStore,
    FusionRetriever,
    LLMReranker
)
from praisonaiagents import Agent

# Load documentation
reader = AutoReader()
docs = reader.load("./docs/")

# Create vector store
store = ChromaVectorStore(
    namespace="documentation",
    persist_directory=".praison/docs_kb"
)

# Add documents with embeddings
store.add(
    texts=[d.content for d in docs],
    embeddings=get_embeddings([d.content for d in docs]),
    metadatas=[d.metadata for d in docs]
)

# Create retriever
retriever = FusionRetriever(
    vector_store=store,
    embedding_fn=get_embedding,
    num_queries=3,
    top_k=10
)

# Create reranker
reranker = LLMReranker(model="gpt-4o-mini")

# Query function
def query_docs(question: str) -> str:
    # Retrieve
    results = retriever.retrieve(question, top_k=20)
    
    # Rerank
    reranked = reranker.rerank(
        question,
        [r.text for r in results],
        top_k=5
    )
    
    # Format context
    context = "\n\n".join([r.text for r in reranked])
    
    # Generate answer with agent
    agent = Agent(instructions="Answer based on the context provided")
    return agent.chat(f"Context:\n{context}\n\nQuestion: {question}")

# Use
answer = query_docs("How do I deploy the application?")
print(answer)
```

## Retrieval Strategies

| Strategy     | Use Case          | CLI Flag                 |
| ------------ | ----------------- | ------------------------ |
| `basic`      | Simple queries    | `--retrieval basic`      |
| `fusion`     | Complex questions | `--retrieval fusion`     |
| `recursive`  | Hierarchical docs | `--retrieval recursive`  |
| `auto_merge` | Long documents    | `--retrieval auto_merge` |

## Related

* [Readers Module](/docs/sdk/praisonai/readers) - Document loading
* [Vector Store Module](/docs/sdk/praisonai/vector_store) - Vector storage
* [Retrieval Module](/docs/sdk/praisonai/retrieval) - Retrieval strategies
* [Reranker Module](/docs/sdk/praisonai/reranker) - Result reranking
* [Adapters Module](/docs/sdk/praisonai/adapters) - All adapters overview