Skip to main content

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 content
  • node.metadata["filename"] — source document filename
  • node.metadata["section_title"] — section heading
  • node.metadata["section_level"] — heading depth (1 = top-level)
  • node.metadata["document_id"] — document UUID
  • node.metadata["section_id"] — section UUID
  • score — relevance score from hybrid RRF ranking

Resources

Metadata

Release files for llama-index-retrievers-dewey 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for llama-index-retrievers-dewey 0.1.0
File Size Uploaded Provenance
llama_index_retrievers_dewey-0.1.0.tar.gz 4.9 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for llama-index-retrievers-dewey 0.1.0
File Interpreter ABI Platform
llama_index_retrievers_dewey-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 9.8 kB

Release files / llama_index_retrievers_dewey-0.1.0.tar.gz

Download URL llama_index_retrievers_dewey-0.1.0.tar.gz
Size 4.9 kB
Tags Source
SHA-256 checksum
How to use checksums
374440573d3c249c77be9b7ba5c8a857e540e8618609bf43fbe891e0ad9b16fb
BLAKE2b-256 checksum
How to use checksums
28d9f7ec26bbce1f105b9973c17c098212e628b167d26ee0bfb95b9c6702e385
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.3

Release files / llama_index_retrievers_dewey-0.1.0-py3-none-any.whl

Download URL llama_index_retrievers_dewey-0.1.0-py3-none-any.whl
Size 4.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
1b99d6b704f27c998c2a3c7f2d4f1044a1b41ddf29e3e1b5350832902f6b111a
BLAKE2b-256 checksum
How to use checksums
acf7571add3d590aac86c31f89b6d9504c3426b4c4892de5e55471c54eb038cc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.3

Security for llama-index-retrievers-dewey 0.1.0

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Release history Release notifications | RSS feed

This release

0.1.0 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page