Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
Retrieval Module
The Retrieval module provides concrete implementations of retrieval strategies for finding relevant documents from vector stores.
Import
from praisonai.adapters import BasicRetriever, FusionRetriever
from praisonai.adapters.retrievers import RecursiveRetriever, AutoMergeRetriever
Quick Example
from praisonai.adapters import BasicRetriever, ChromaVectorStore
# Create vector store and retriever
store = ChromaVectorStore(namespace="docs")
retriever = BasicRetriever(
vector_store=store,
embedding_fn=get_embedding, # Your embedding function
top_k=10
)
# Retrieve documents
results = retriever.retrieve("What is Python?", top_k=5)
for r in results:
print(f"Score: {r.score:.3f} - {r.text[:50]}...")
Features
- Multiple retrieval strategies (Basic, Fusion, Recursive, AutoMerge)
- Reciprocal Rank Fusion for multi-query retrieval
- LLM-powered query expansion
- Recursive depth-limited retrieval
- Adjacent chunk merging for context
Classes
BasicRetriever
Simple vector similarity retrieval.
from praisonai.adapters import BasicRetriever
retriever = BasicRetriever(
vector_store=store,
embedding_fn=get_embedding,
top_k=10
)
results = retriever.retrieve("search query")
| Parameter | Type | Default | Description |
|---|
vector_store | Any | Required | Vector store instance |
embedding_fn | Callable | Required | Function to generate embeddings |
top_k | int | 10 | Default number of results |
FusionRetriever
Multi-query retrieval with Reciprocal Rank Fusion (RRF).
from praisonai.adapters import FusionRetriever
retriever = FusionRetriever(
vector_store=store,
embedding_fn=get_embedding,
llm=agent, # Optional: for query variation
num_queries=3,
top_k=10,
rrf_k=60
)
results = retriever.retrieve("complex question")
| Parameter | Type | Default | Description |
|---|
vector_store | Any | Required | Vector store instance |
embedding_fn | Callable | Required | Embedding function |
llm | Any | None | LLM for query variations |
num_queries | int | 3 | Number of query variations |
top_k | int | 10 | Results per query |
rrf_k | int | 60 | RRF constant |
RecursiveRetriever
Depth-limited recursive retrieval with follow-up queries.
from praisonai.adapters.retrievers import RecursiveRetriever
retriever = RecursiveRetriever(
vector_store=store,
embedding_fn=get_embedding,
llm=agent, # For generating follow-up queries
max_depth=2,
top_k=10
)
results = retriever.retrieve("explain the architecture")
| Parameter | Type | Default | Description |
|---|
vector_store | Any | Required | Vector store instance |
embedding_fn | Callable | Required | Embedding function |
llm | Any | None | LLM for follow-up queries |
max_depth | int | 2 | Maximum recursion depth |
top_k | int | 10 | Results to return |
AutoMergeRetriever
Retrieves and merges adjacent chunks from the same document.
from praisonai.adapters.retrievers import AutoMergeRetriever
retriever = AutoMergeRetriever(
vector_store=store,
embedding_fn=get_embedding,
top_k=10,
max_gap=1 # Max chunk gap for merging
)
results = retriever.retrieve("summarize the document")
| Parameter | Type | Default | Description |
|---|
vector_store | Any | Required | Vector store instance |
embedding_fn | Callable | Required | Embedding function |
top_k | int | 10 | Results to return |
max_gap | int | 1 | Max gap between chunks to merge |
Methods
retrieve(query, top_k=None, filter=None)
Retrieve documents matching the query.
Parameters:
query (str): Search querytop_k (int, optional): Override default result countfilter (dict, optional): Metadata filter
Returns: List[RetrievalResult] - Matching documents with scores
aretrieve(query, top_k=None, filter=None)
Async version of retrieve (calls sync internally).
Example: Fusion Retrieval with LLM
from praisonai.adapters import FusionRetriever, ChromaVectorStore
from praisonaiagents import Agent
# Setup
store = ChromaVectorStore(namespace="knowledge")
agent = Agent(instructions="You help with search queries")
retriever = FusionRetriever(
vector_store=store,
embedding_fn=get_embedding,
llm=agent,
num_queries=3
)
# Query generates variations like:
# - "What is Python?"
# - "What is Python programming language?"
# - "Python definition"
results = retriever.retrieve("What is Python?", top_k=5)
Strategy Selection Guide
| Use Case | Recommended Strategy |
|---|
| Simple factual queries | BasicRetriever |
| Complex multi-part questions | FusionRetriever |
| Hierarchical documents | RecursiveRetriever |
| Long document summarization | AutoMergeRetriever |
CLI Usage
# Basic retrieval
praisonai knowledge query "What is Python?" --retrieval basic
# Fusion retrieval
praisonai knowledge query "Compare Python and Java" --retrieval fusion
# Recursive retrieval
praisonai knowledge query "Explain the architecture" --retrieval recursive
# Auto-merge retrieval
praisonai knowledge query "Summarize the document" --retrieval auto_merge
