Add Dewey integration

Author: lambdabaa

integrations/dewey-haystack.md

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+---
+layout: integration
+name: Dewey
+description: Connect Haystack pipelines to Dewey — a managed document intelligence backend that handles PDF conversion, chunking, embedding, and hybrid retrieval behind a single API.
+authors:
+    - name: Dewey
+      socials:
+        github: meetdewey
+pypi: https://pypi.org/project/dewey-haystack
+repo: https://github.com/meetdewey/dewey-haystack
+type: Document Store
+report_issue: https://github.com/meetdewey/dewey-haystack/issues
+logo: /logos/dewey.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[Dewey](https://meetdewey.com) is a managed document intelligence backend for AI applications. Upload PDFs, Word docs, and other files — Dewey handles conversion, section extraction, chunking, embedding, and hybrid semantic + BM25 retrieval automatically.
+
+This integration provides three Haystack 2.0 components:
+
+- **`DeweyDocumentStore`** — implements the Haystack `DocumentStore` protocol, backed by a Dewey collection
+- **`DeweyRetriever`** — a `@component` that runs hybrid search against a collection and returns ranked `Document` objects
+- **`DeweyResearchComponent`** — a `@component` that runs Dewey's full agentic research loop (multi-step search, synthesis, citations) and returns a grounded Markdown answer
+
+## Installation
+
+```bash
+pip install dewey-haystack
+```
+
+Requires a free Dewey account at [meetdewey.com](https://meetdewey.com). Set your API key:
+
+```bash
+export DEWEY_API_KEY="dwy_live_..."
+```
+
+## Usage
+
+### Components
+
+This integration introduces three components:
+
+- **`DeweyDocumentStore`** (`haystack_integrations.document_stores.dewey`)
+- **`DeweyRetriever`** (`haystack_integrations.components.retrievers.dewey`)
+- **`DeweyResearchComponent`** (`haystack_integrations.components.retrievers.dewey`)
+
+### RAG pipeline with DeweyRetriever
+
+```python
+import os
+from haystack import Pipeline
+from haystack_integrations.document_stores.dewey import DeweyDocumentStore
+from haystack_integrations.components.retrievers.dewey import DeweyRetriever
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from haystack.utils import Secret
+
+store = DeweyDocumentStore(
+    api_key=Secret.from_env_var("DEWEY_API_KEY"),
+    collection_id="3f7a1b2c-...",  # your collection ID
+)
+
+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 are the key findings?"},
+    "prompt": {"query": "What are the key findings?"},
+})
+print(result["llm"]["replies"][0])
+```
+
+### Agentic research with DeweyResearchComponent
+
+`DeweyResearchComponent` is a drop-in replacement for an LLM generator when you want Dewey to handle both retrieval *and* generation. It runs a multi-step research loop internally and returns a grounded answer with cited sources.
+
+```python
+import os
+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",  # "quick" | "balanced" | "deep" | "exhaustive"
+    ),
+)
+
+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]}...")
+```
+
+### Writing documents
+
+Upload content to Dewey directly from a Haystack pipeline using `DeweyDocumentStore.write_documents`:
+
+```python
+from haystack import Document
+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-...",
+)
+
+store.write_documents([
+    Document(content="Neural networks learn via backpropagation.", meta={"source": "ml-intro.txt"}),
+    Document(content="Transformers use self-attention mechanisms.", meta={"source": "transformers.txt"}),
+])
+```
+
+## License
+
+`dewey-haystack` is released under the [MIT License](https://github.com/meetdewey/dewey-haystack/blob/main/LICENSE).