Add Built Simple research API integration

integrations/built-simple.md

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+---
+layout: integration
+name: Built Simple
+description: Search PubMed and ArXiv scientific literature in Haystack pipelines
+authors:
+    - name: Built-Simple
+      socials:
+        github: Built-Simple
+        twitter: BuiltSimpleAI
+        linkedin: https://www.linkedin.com/company/built-simple-ai/
+pypi: https://pypi.org/project/haystack-builtsimple/
+repo: https://github.com/Built-Simple/haystack-builtsimple
+type: Data Ingestion
+report_issue: https://github.com/Built-Simple/haystack-builtsimple/issues
+logo: /logos/built-simple.svg
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+
+## Overview
+
+This integration provides retrievers for [Built-Simple](https://built-simple.ai) research APIs, enabling you to search scientific literature directly in your Haystack pipelines:
+
+- **PubMed Retriever** - Hybrid search over 35M+ biomedical articles
+- **ArXiv Retriever** - Search preprints in physics, math, CS, and more
+- **Combined Retriever** - Search both sources simultaneously
+
+No API key required. The APIs use hybrid semantic + keyword search for high-quality results.
+
+## Installation
+
+```bash
+pip install haystack-builtsimple
+```
+
+## Usage
+
+### Basic Retrieval
+
+```python
+from haystack_builtsimple import BuiltSimplePubMedRetriever, BuiltSimpleArxivRetriever
+
+# Search PubMed
+pubmed = BuiltSimplePubMedRetriever(top_k=5)
+results = pubmed.run(query="CRISPR gene therapy clinical trials")
+for doc in results["documents"]:
+    print(f"[PMID {doc.meta['pmid']}] {doc.meta['title']}")
+
+# Search ArXiv
+arxiv = BuiltSimpleArxivRetriever(top_k=5)
+results = arxiv.run(query="large language models reasoning")
+for doc in results["documents"]:
+    print(f"[{doc.meta['arxiv_id']}] {doc.meta['title']}")
+```
+
+### RAG Pipeline
+
+```python
+from haystack import Pipeline
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from haystack_builtsimple import BuiltSimplePubMedRetriever
+
+pipeline = Pipeline()
+pipeline.add_component("retriever", BuiltSimplePubMedRetriever(top_k=5))
+pipeline.add_component("prompt", PromptBuilder(template="""
+Based on these research papers:
+{% for doc in documents %}
+- {{ doc.meta.title }} (PMID: {{ doc.meta.pmid }})
+  {{ doc.content[:500] }}
+{% endfor %}
+
+Answer: {{ query }}
+"""))
+pipeline.add_component("llm", OpenAIGenerator())
+
+pipeline.connect("retriever.documents", "prompt.documents")
+pipeline.connect("prompt", "llm")
+
+result = pipeline.run({
+    "retriever": {"query": "mRNA vaccine efficacy"},
+    "prompt": {"query": "What factors affect mRNA vaccine efficacy?"}
+})
+print(result["llm"]["replies"][0])
+```
+
+### Combined Search
+
+Search both PubMed and ArXiv simultaneously:
+
+```python
+from haystack_builtsimple import BuiltSimpleCombinedRetriever
+
+retriever = BuiltSimpleCombinedRetriever(
+    top_k=10,
+    merge_strategy="score"  # or "interleave", "pubmed_first", "arxiv_first"
+)
+
+results = retriever.run(query="machine learning drug discovery")
+for doc in results["documents"]:
+    source = doc.meta["source"]  # "pubmed" or "arxiv"
+    print(f"[{source}] {doc.meta['title']}")
+```
+
+### Full Text Retrieval
+
+For PubMed articles, optionally fetch full text:
+
+```python
+pubmed = BuiltSimplePubMedRetriever(
+    top_k=3,
+    fetch_full_text=True
+)
+```