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Documentation Index

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Adapters Module

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

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Import

from praisonai.adapters import (
    # Readers
    AutoReader, TextReader, MarkItDownReader, DirectoryReader,
    
    # Vector Stores
    ChromaVectorStore,
    
    # Retrievers
    BasicRetriever, FusionRetriever,
    
    # Rerankers
    LLMReranker
)

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Quick Example

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)

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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)

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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

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Available Components

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Readers

ClassDescription
AutoReaderAutomatic source detection and routing
TextReaderPlain text files (.txt, .log)
MarkItDownReaderRich documents (PDF, DOCX, etc.)
DirectoryReaderRecursive directory loading
GlobReaderGlob pattern matching
URLReaderWeb page content

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Vector Stores

ClassDescriptionRequirements
ChromaVectorStoreLocal persistent storagechromadb
PineconeVectorStoreCloud vector databasepinecone

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Retrievers

ClassDescription
BasicRetrieverSimple vector similarity
FusionRetrieverMulti-query with RRF
RecursiveRetrieverDepth-limited expansion
AutoMergeRetrieverAdjacent chunk merging

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Rerankers

ClassDescriptionRequirements
LLMRerankerLLM-based scoringOpenAI/Anthropic API
CrossEncoderRerankerNeural rerankingsentence-transformers
CohereRerankerCohere Rerank APIcohere

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Lazy Loading

All adapters use lazy loading to minimize import time: 
# Only loads when accessed
from praisonai.adapters import ChromaVectorStore  # Fast import

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

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Example: Full RAG Pipeline

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]}...")

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CLI Integration

The adapters power the praisonai knowledge CLI commands: 
# 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