LlamaIndex Dewey Retriever
Dewey is a managed document intelligence backend that handles the full RAG pipeline — PDF conversion, section extraction, chunking, embedding, and hybrid retrieval — behind a single REST API.
This package provides a DeweyRetriever that integrates Dewey's hybrid semantic + BM25 search directly into LlamaIndex pipelines.
Installation
pip install llama-index-retrievers-dewey
Usage
from llama_index.retrievers.dewey import DeweyRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.anthropic import Anthropic
retriever = DeweyRetriever(
api_key="dwy_live_...", # Dewey project API key
collection_id="3f7a1b2c-...", # Collection to search
k=8, # Number of chunks to retrieve
)
# Use with any LlamaIndex query engine
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
llm=Anthropic(model="claude-haiku-4-5-20251001"),
)
response = query_engine.query("What are the main findings?")
print(response)
Direct retrieval
nodes = retriever.retrieve("attention mechanism scaling")
for n in nodes:
print(f"[{n.score:.3f}] {n.node.metadata['filename']} › {n.node.metadata['section_title']}")
print(f" {n.node.text[:120]}...")
As part of an agent
from llama_index.core.tools import RetrieverTool
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
tool = RetrieverTool.from_defaults(
retriever=retriever,
description="Search AI research papers for relevant information.",
)
agent = ReActAgent.from_tools([tool], llm=OpenAI(model="gpt-4o-mini"), verbose=True)
agent.chat("Compare the Transformer and RAG architectures.")
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
api_key |
str |
required | Dewey project API key (dwy_live_...) |
collection_id |
str |
required | UUID of the collection to search |
k |
int |
10 |
Max chunks to retrieve (1–50) |
base_url |
str |
https://api.meetdewey.com/v1 |
Override for local dev |
What Dewey returns
Each retrieved node includes:
node.text— chunk contentnode.metadata["filename"]— source document filenamenode.metadata["section_title"]— section headingnode.metadata["section_level"]— heading depth (1 = top-level)node.metadata["document_id"]— document UUIDnode.metadata["section_id"]— section UUIDscore— relevance score from hybrid RRF ranking
Resources
- Dewey documentation
- Free tier signup — no credit card required
- Python SDK (
meetdewey)
Metadata
Release files for llama-index-retrievers-dewey 0.1.0
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