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Improve your RAG performance with the only
meaning-based search API

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Your AI needs high quality data. Exa's got it.

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Better results, better LLM performance


Exa is the first meaning-based web search powered by embeddings. It unlocks results no other search can, making your AI more relevant, factual, and reducing hallucinations.

Exa is the first meaning-based web search powered by embeddings. It unlocks results no other search can, making your AI more relevant, factual, and reducing hallucinations.

Highly customizable search


You have total control. Powerful filters such as date, category and domain. Choose # of results (1-1000s) and extractions. You name it.

You have total control. Powerful filters such as date, category and domain. Choose # of results (1-1000s) and extractions. You name it.

URL, full content or summaries


Choose if you want to get just links, full parsed text content, key highlights or customizable summaries per URL. For any AI application.

Choose if you want to get just links, full parsed text content, key highlights or customizable summaries per URL. For any AI application.

Performance, Speed, Scalability


High rate limits, low latency and high reliability. Ready to scale with your application.

High rate limits, low latency and high reliability. Ready to scale with your application.

Just a few lines of code,
or integrate with

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pip install exa_pyfrom exa_py import Exa exa = Exa("EXA_API_KEY") results = exa.search( "latest development in machine learning", category="papers" )
{ "results": [ { "score": 0.1836702525615692, "title": "Asymptotically Optimal Regret for Black-Box Predict-then-Optimize", "id": "https://arxiv.org/abs/2406.07866", "url": "https://arxiv.org/abs/2406.07866", "publishedDate": "2024-06-12", "author": "Tan; Samuel; Frazier; Peter I" }, { "score": 0.18168984353542328, "title": "Machine Unlearning for Uplink Interference Cancellation", "id": "https://arxiv.org/abs/2406.05945", "url": "https://arxiv.org/abs/2406.05945", "publishedDate": "2024-06-10", "author": "Guven; Eray; Kurt; Gunes Karabulut" }, { "score": 0.17979080975055695, "title": "Attention as an RNN", "id": "https://arxiv.org/abs/2405.13956", "url": "https://arxiv.org/abs/2405.13956", "publishedDate": "2024-05-22", "author": "Feng; Leo; Tung; Frederick; Hajimirsadeghi; Hossein; Ahmed; Mohamed Osama; Bengio; Yoshua; Mori; Greg" }, { "score": 0.17959752678871155, "title": "FaithFill: Faithful Inpainting for Object Completion Using a Single Reference Image", "id": "https://arxiv.org/abs/2406.07865", "url": "https://arxiv.org/abs/2406.07865", "publishedDate": "2024-06-12", "author": "Mallick; Rupayan; Abdalla; Amr; Bargal; Sarah Adel" }, { "score": 0.17918667197227478, "title": "Talking Heads: Understanding Inter-layer Communication in Transformer Language Models", "id": "https://arxiv.org/abs/2406.09519", "url": "https://arxiv.org/abs/2406.09519", "publishedDate": "2024-06-13", "author": "Merullo; Jack; Eickhoff; Carsten; Pavlick; Ellie" }, { "score": 0.17634162306785583, "title": "FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar", "id": "https://arxiv.org/abs/2406.04546", "url": "https://arxiv.org/abs/2406.04546", "publishedDate": "2024-06-06", "author": "Kahya; Sabri Mustafa; Sivrikaya; Boran Hamdi; Yavuz; Muhammet Sami; Steinbach; Eckehard" }, { "score": 0.17634151875972748, "title": "xLSTM: Extended Long Short-Term Memory", "id": "https://arxiv.org/abs/2405.04517", "url": "https://arxiv.org/abs/2405.04517", "publishedDate": "2024-05-07", "author": "Beck; Maximilian; Pöppel; Korbinian; Spanring; Markus; Auer; Andreas; Prudnikova; Oleksandra; Kopp; Michael; Klambauer; Günter; Brandstetter; Johannes; Hochreiter; Sepp" }, { "score": 0.17621161043643951, "title": "World Models with Hints of Large Language Models for Goal Achieving", "id": "https://arxiv.org/abs/2406.07381", "url": "https://arxiv.org/abs/2406.07381", "publishedDate": "2024-06-11", "author": "Liu; Zeyuan; Huan; Ziyu; Wang; Xiyao; Lyu; Jiafei; Tao; Jian; Li; Xiu; Furong; Xu; Huazhe" }, { "score": 0.17613205313682556, "title": "Deep learning classifier of locally advanced rectal cancer treatment response from endoscopy images", "id": "https://arxiv.org/abs/2405.03762", "url": "https://arxiv.org/abs/2405.03762", "publishedDate": "2024-05-06", "author": "Gomez; Jorge Tapias; Rangnekar; Aneesh; Williams; Hannah; Thompson; Garcia-Aguilar; Julio; Smith; Joshua Jesse; Veeraraghavan; Harini" }, { "score": 0.17593073844909668, "title": "MalleTrain: Deep Neural Network Training on Unfillable Supercomputer Nodes", "id": "https://arxiv.org/abs/2404.15668", "url": "https://arxiv.org/abs/2404.15668", "publishedDate": "2024-04-24", "author": "Ma; Xiaolong; Yan; Feng; Lei; Foster; Ian; Papka; Michael E; Liu; Zhengchun; Kettimuthu; Rajkumar" } ], "requestId": "abb0f98756d6c722ca35e06e0970652b" }
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Thousands of use cases

Get quick, live web results to power your AI applications

Ground your chatbot
Ground your chatbot
Personalize recommendations
Personalize recommendations
Create synthetic data
Create synthetic data
Ground your chatbot
Ground your chatbot
Personalize recommendations
Personalize recommendations
Create synthetic data
Create synthetic data

What you can do with Exa


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