dewey-haystack 0.1.0

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

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:

Field	Description
score	Relevance score (0–1)
document_id	Dewey document ID
filename	Original filename
section_id	Section ID
section_title	Section heading
section_level	Heading depth (1 = top-level)

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:

Key	Type	Description
answer	str	Synthesised Markdown answer
sources	list[Document]	Source chunks cited by the answer

Research depths:

depth	Speed	Tools	Requires BYOK
quick	fast	basic search	no
balanced	fast	basic search	no
deep	slower	full tool suite	yes
exhaustive	slowest	full tool suite	yes

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