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+---
+layout: integration
+name: AlphaAI
+description: AI-scored financial news and SEC Form 4 insider events as Haystack Documents
+authors:
+    - name: AlphaAI
+      socials:
+        github: makeev
+        twitter: alphai_io
+        linkedin: https://www.linkedin.com/company/alphai-io
+pypi: https://pypi.org/project/alphai-haystack
+repo: https://github.com/makeev/alphai-haystack
+type: Data Ingestion
+report_issue: https://github.com/makeev/alphai-haystack/issues
+logo: /logos/alphai.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [AlphaAINewsFetcher](#alphaainewsfetcher)
+  - [AlphaAIInsiderNewsFetcher](#alphaainsidernewsfetcher)
+  - [In a Pipeline](#in-a-pipeline)
+- [Document shape](#document-shape)
+- [License](#license)
+
+## Overview
+
+[AlphaAI](https://alphai.io) is a financial-news platform built for AI agents: every article is
+enriched at ingest with per-ticker impact analysis, a category, and a 1-10 relevance score, and
+SEC Form 4 insider filings become structured events about 6 minutes after they hit EDGAR.
+
+This integration provides:
+
+- `AlphaAINewsFetcher`: fetches the scored news feed as Haystack `Document` objects, with
+  ticker / category / relevance filters and optional same-story collapsing.
+- `AlphaAIInsiderNewsFetcher`: fetches SEC Form 4 insider events with a structured
+  `meta["insider"]` block (side, shares, average price, total value, who traded, 10b5-1 flag).
+
+An API key is required — the free tier (20 requests/min, 100/day, no card) is enough to try it:
+[alphai.io/developers](https://alphai.io/developers). The components read the key from the
+`ALPHAI_API_KEY` environment variable.
+
+## Installation
+
+```bash
+pip install alphai-haystack
+```
+
+## Usage
+
+### AlphaAINewsFetcher
+
+```python
+from alphai_haystack import AlphaAINewsFetcher
+
+fetcher = AlphaAINewsFetcher(symbol="NVDA", min_relevance=7)
+documents = fetcher.run()["documents"]
+
+for doc in documents:
+    print(doc.meta["relevance_score"], doc.meta["title"])
+```
+
+Filters set in `__init__` (`symbol`, `category`, `min_relevance`, `top_k`) can be overridden
+per `run()` call.
+
+### AlphaAIInsiderNewsFetcher
+
+```python
+from alphai_haystack import AlphaAIInsiderNewsFetcher
+
+fetcher = AlphaAIInsiderNewsFetcher(min_relevance=7)  # higher floor = larger trades only
+for doc in fetcher.run()["documents"]:
+    insider = doc.meta["insider"]
+    print(insider["insider_name"], insider["side"], insider["total_value_usd"], doc.meta["tickers"])
+```
+
+### In a Pipeline
+
+```python
+from haystack import Pipeline
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+
+from alphai_haystack import AlphaAINewsFetcher
+
+template = """Summarize what moved {{ symbol }} today, using only these articles:
+{% for doc in documents %}
+- {{ doc.content }} (relevance {{ doc.meta.relevance_score }}/10)
+{% endfor %}
+"""
+
+pipeline = Pipeline()
+pipeline.add_component("news", AlphaAINewsFetcher(min_relevance=6, collapse_stories=True))
+pipeline.add_component("prompt", PromptBuilder(template=template))
+pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini"))
+pipeline.connect("news.documents", "prompt.documents")
+pipeline.connect("prompt", "llm")
+
+result = pipeline.run({"news": {"symbol": "NVDA"}, "prompt": {"symbol": "NVDA"}})
+print(result["llm"]["replies"][0])
+```
+
+Both components serialize with the rest of the pipeline (`to_dict` / `from_dict`); the API key
+is stored as an environment-variable reference, never as the raw value.
+
+## Document shape
+
+`content` is the article title plus summary. `meta` carries `uid`, `url`, `title`, `source`,
+`source_domain`, `published_at` (ISO 8601), `tickers`, `category`, `relevance_score` (1-10),
+and — on the insider feed — the structured `insider` block.
+
+## License
+
+MIT — see the [repository](https://github.com/makeev/alphai-haystack).
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