dewey-haystack

Haystack integration for Dewey — document store, retriever, and research component.

Installation

pip install dewey-haystack

Components

DeweyDocumentStore

Haystack DocumentStore backed by a Dewey collection. Handles document upload and deletion; Dewey manages chunking and embeddings automatically.

from haystack_integrations.document_stores.dewey import DeweyDocumentStore
from haystack.utils import Secret

store = DeweyDocumentStore(
    api_key=Secret.from_env_var("DEWEY_API_KEY"),
    collection_id="3f7a1b2c-...",
)

Upload Haystack Documents:

from haystack import Document

store.write_documents([
    Document(content="Neural networks learn via backpropagation.", meta={"source": "ml.txt"}),
    Document(content="Transformers use self-attention mechanisms."),
])

DeweyRetriever

Drop-in Haystack retriever backed by Dewey's hybrid semantic + BM25 search.

from haystack import Pipeline
from haystack_integrations.document_stores.dewey import DeweyDocumentStore
from haystack_integrations.components.retrievers.dewey import DeweyRetriever
from haystack.utils import Secret

store = DeweyDocumentStore(
    api_key=Secret.from_env_var("DEWEY_API_KEY"),
    collection_id="3f7a1b2c-...",
)

pipeline = Pipeline()
pipeline.add_component("retriever", DeweyRetriever(document_store=store, top_k=8))

result = pipeline.run({"retriever": {"query": "What are the key findings?"}})
for doc in result["retriever"]["documents"]:
    print(f"[{doc.meta['filename']}] {doc.content}")

Each returned Document carries citation metadata:

RAG pipeline with an LLM:

from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator

prompt_template = """
Answer the question using only the provided context.

Context:
{% for doc in documents %}
- {{ doc.content }}
{% endfor %}

Question: {{ query }}
"""

pipeline = Pipeline()
pipeline.add_component("retriever", DeweyRetriever(document_store=store, top_k=5))
pipeline.add_component("prompt", PromptBuilder(template=prompt_template))
pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini"))

pipeline.connect("retriever.documents", "prompt.documents")
pipeline.connect("prompt.prompt", "llm.prompt")

result = pipeline.run({
    "retriever": {"query": "What were the main findings?"},
    "prompt": {"query": "What were the main findings?"},
})
print(result["llm"]["replies"][0])

DeweyResearchComponent

A Haystack component that runs Dewey's full agentic research loop — searching, reading, and synthesising across multiple documents — and returns a grounded Markdown answer with cited sources.

Use this as a drop-in replacement for an LLM generator when you want Dewey to handle both retrieval and generation.

from haystack import Pipeline
from haystack_integrations.components.retrievers.dewey import DeweyResearchComponent
from haystack.utils import Secret

pipeline = Pipeline()
pipeline.add_component(
    "research",
    DeweyResearchComponent(
        api_key=Secret.from_env_var("DEWEY_API_KEY"),
        collection_id="3f7a1b2c-...",
        depth="balanced",
    ),
)

result = pipeline.run({"research": {"query": "What were the key findings across all studies?"}})
print(result["research"]["answer"])

for source in result["research"]["sources"]:
    print(f"  [{source.meta['filename']}] {source.content[:80]}...")

Outputs:

Research depths:

deep and exhaustive require a Dewey Pro plan and a BYOK API key configured on your project.

With a custom model:

DeweyResearchComponent(
    api_key=Secret.from_env_var("DEWEY_API_KEY"),
    collection_id="3f7a1b2c-...",
    depth="deep",
    model="claude-sonnet-4-6",  # requires Anthropic BYOK key on your project
)

Requirements

• Python 3.9+
• meetdewey >= 1.0
• haystack-ai >= 2.0

Development

pip install -e ".[dev]"
pytest