> ## 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.

# Adapters Module

> Knowledge base adapters for readers, vector stores, retrievers, and rerankers

# Adapters Module

The Adapters module provides concrete implementations of knowledge base components including readers, vector stores, retrievers, and rerankers.

## Import

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonai.adapters import (
    # Readers
    AutoReader, TextReader, MarkItDownReader, DirectoryReader,
    
    # Vector Stores
    ChromaVectorStore,
    
    # Retrievers
    BasicRetriever, FusionRetriever,
    
    # Rerankers
    LLMReranker
)
```

## Quick Example

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

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

# Store in vector database
store = ChromaVectorStore(namespace="my_docs")
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]
)

# Retrieve
retriever = BasicRetriever(
    vector_store=store,
    embedding_fn=get_embedding
)
results = retriever.retrieve("search query", top_k=5)
```

## Features

* **Readers**: Load documents from files, directories, URLs, and glob patterns
* **Vector Stores**: Store and query document embeddings (ChromaDB, Pinecone)
* **Retrievers**: Find relevant documents (Basic, Fusion, Recursive, AutoMerge)
* **Rerankers**: Improve result relevance (LLM, CrossEncoder, Cohere)

## Module Structure

```
praisonai/adapters/
├── __init__.py          # Lazy loading exports
├── readers.py           # Document readers
├── vector_stores.py     # Vector store adapters
├── retrievers.py        # Retrieval strategies
└── rerankers.py         # Reranking implementations
```

## Available Components

### Readers

| Class              | Description                            |
| ------------------ | -------------------------------------- |
| `AutoReader`       | Automatic source detection and routing |
| `TextReader`       | Plain text files (.txt, .log)          |
| `MarkItDownReader` | Rich documents (PDF, DOCX, etc.)       |
| `DirectoryReader`  | Recursive directory loading            |
| `GlobReader`       | Glob pattern matching                  |
| `URLReader`        | Web page content                       |

### Vector Stores

| Class                 | Description              | Requirements |
| --------------------- | ------------------------ | ------------ |
| `ChromaVectorStore`   | Local persistent storage | `chromadb`   |
| `PineconeVectorStore` | Cloud vector database    | `pinecone`   |

### Retrievers

| Class                | Description              |
| -------------------- | ------------------------ |
| `BasicRetriever`     | Simple vector similarity |
| `FusionRetriever`    | Multi-query with RRF     |
| `RecursiveRetriever` | Depth-limited expansion  |
| `AutoMergeRetriever` | Adjacent chunk merging   |

### Rerankers

| Class                  | Description       | Requirements            |
| ---------------------- | ----------------- | ----------------------- |
| `LLMReranker`          | LLM-based scoring | OpenAI/Anthropic API    |
| `CrossEncoderReranker` | Neural reranking  | `sentence-transformers` |
| `CohereReranker`       | Cohere Rerank API | `cohere`                |

## Lazy Loading

All adapters use lazy loading to minimize import time:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Only loads when accessed
from praisonai.adapters import ChromaVectorStore  # Fast import

# Actual loading happens on first use
store = ChromaVectorStore()  # chromadb loaded here
```

## Example: Full RAG Pipeline

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

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

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

# 3. Create retriever with fusion
agent = Agent(instructions="Query assistant")
retriever = FusionRetriever(
    vector_store=store,
    embedding_fn=get_embedding,
    llm=agent,
    num_queries=3
)

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

# 5. Query pipeline
query = "How to deploy Python apps?"
results = retriever.retrieve(query, top_k=20)
reranked = reranker.rerank(query, [r.text for r in results], top_k=5)

for r in reranked:
    print(f"Score: {r.score:.3f} - {r.text[:100]}...")
```

## CLI Integration

The adapters power the `praisonai knowledge` CLI commands:

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Add documents (uses readers)
praisonai knowledge add ./docs/

# Query (uses vector store + retriever + reranker)
praisonai knowledge query "search query" \
  --vector-store chroma \
  --retrieval fusion \
  --reranker llm
```

## 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) - Document retrieval
* [Reranker Module](/docs/sdk/praisonai/reranker) - Result reranking