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+# Exa and ell Integration: Article Review Generator
+
+This guide demonstrates how to integrate Exa's powerful search capabilities with ell's language model programming to create an article review generator focused on climate change.
+
+## Prerequisites
+
+Before you begin, make sure you have the following:
+
+1. An Exa API key (Get it [here](https://docs.exa.ai/reference/getting-started-with-python))
+2. An OpenAI API key
+3. Python 3.7 or later installed
+
+## Installation
+
+Install the required packages:
+
+```
+pip install exa_py ell openai python-dotenv pydantic
+
+```
+## Setup
+
+1. Create a `.env` file in your project directory with your API keys:
+
+```
+EXA_API_KEY=your_exa_api_key_here
+OPENAI_API_KEY=your_openai_api_key_here
+```
+
+2. Import the necessary libraries and load the environment variables:
+
+```python
+from exa_py import Exa
+import ell
+from openai import OpenAI
+import os
+from pydantic import BaseModel, Field
+from dotenv import load_dotenv
+import json
+
+load_dotenv()
+```
+
+3. Initialize the Exa and OpenAI clients:
+
+```python
+exa = Exa(os.getenv("EXA_API_KEY"))
+openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+```
+
+## Defining the Article Review Structure
+
+Use Pydantic to define the structure of the article review:
+
+```python
+class ArticleReview(BaseModel):
+    title: str = Field(description="The title of the article")
+    summary: str = Field(description="A summary of the article")
+    rating: int = Field(description="A rating of the article from 1 to 10")
+```
+
+## Creating the Article Review Generator
+
+Use ell to create a language model program for generating article reviews:
+
+```python
+@ell.complex(model="gpt-4o", client=openai, response_format=ArticleReview)
+def generate_article_review(article: str, content: str) -> ArticleReview:
+    """You are a article review generator. Given the name of an article, some content,
+    you need to return a structured review"""
+    return f"generate a review for the article {article} with content {content}"
+```
+
+## Implementing Exa Search
+
+Create a function to search for climate change articles and return the results as a JSON object using Exa:
+
+```python
+def exa_search(num_results: int):
+    result = exa.search_and_contents(
+    "newest climate change articles",
+    type="neural",
+    use_autoprompt=True,
+    start_published_date="2024-09-01",
+    num_results=num_results,
+    text=True,
+    )
+    json_data = json.dumps([result.__dict__ for result in result.results])
+    return json.loads(json_data)
+```
+
+## Combining Exa Search and Article Review Generation
+
+Create a RAG (Retrieval-Augmented Generation) function that combines Exa search results with the article review generator:
+
+```python
+def RAG(num_results: int):
+    search_results = exa_search(num_results)
+    for i in range(num_results):
+        result = search_results[i]
+        review = generate_article_review(result["title"], result["text"])
+        print(review)
+```
+
+## Running the Article Review Generator
+
+To generate reviews for the three most recent climate change articles:
+
+```python
+RAG(3)
+```
+
+This will print structured reviews for the three most recent climate change articles found by Exa.
+
+## Conclusion
+
+This integration demonstrates how to combine Exa's powerful search capabilities with ell's language model programming to create an automated article review generator. By leveraging Exa's ability to find recent, relevant articles and ell's structured output generation, you can quickly analyze and summarize the latest information on climate change or any other topic of interest.
+
+For more information on Exa and ell, refer to their respective documentation:
+- [Exa Documentation](https://docs.exa.ai/)
+- [ell Documentation](https://docs.ell.so/)
diff --git a/examples/exa/exa.py b/examples/exa/exa.py
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+from exa_py import Exa
+import ell
+from openai import OpenAI
+import os
+from pydantic import BaseModel, Field
+from dotenv import load_dotenv
+import json
+
+load_dotenv()
+exa = Exa(os.getenv("EXA_API_KEY"))
+
+openai = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+class ArticleReview(BaseModel):
+    title: str = Field(description="The title of the article")
+    summary: str = Field(description="A summary of the article")
+    rating: int = Field(description="A rating of the article from 1 to 10")
+
+@ell.complex(model="gpt-4o", client=openai, response_format=ArticleReview)
+def generate_article_review(article: str, content: str) -> ArticleReview:
+    """You are a article review generator. Given the name of an article, some content,
+    you need to return a structured review"""
+    return f"generate a review for the article {article} with content {content}"
+
+def exa_search(num_results: int):
+    result = exa.search_and_contents(
+    "newest climate change articles",
+    type="neural",
+    use_autoprompt=True,
+    start_published_date="2024-09-01",
+    num_results=num_results,
+    text=True,
+    )
+    json_data = json.dumps([result.__dict__ for result in result.results])
+    return json.loads(json_data)
+
+
+def RAG(num_results: int):
+    search_results = exa_search(num_results)
+    for i in range(num_results):
+        result = search_results[i]
+        review = generate_article_review(result["title"], result["text"])
+        print(review)
+
+RAG(3)
+
+
+
+
+