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L1m
Overview :
l1m is a powerful tool that uses large language models (LLMs) via a proxy to extract structured data from unstructured text or images. The importance of this technology lies in its ability to transform complex information into an easily processable format, thereby improving the efficiency and accuracy of data processing. Key advantages of l1m include no complex prompt engineering, support for multiple LLM models, and a built-in caching function. Developed by Inferable, it aims to provide users with a simple, efficient, and flexible data extraction solution. l1m offers a free trial and is suitable for enterprises and developers who need to extract valuable information from large amounts of unstructured data.
Target Users :
l1m is suitable for enterprises and developers who need to extract structured data from large amounts of unstructured text or images. For example, the financial industry can extract key data from financial reports, the healthcare industry can extract patient information from medical records, and developers can use its API to quickly build data processing applications. l1m's flexibility and efficiency make it an ideal choice for handling complex data.
Use Cases
Extract dish names and prices from a restaurant menu image.
Extract the year of key events from historical documents.
Extract temperature and weather conditions from a weather report.
Features
Simple and easy-to-use Schema-first approach: Users can define a JSON Schema to obtain the desired data structure.
No prompt engineering required: No need to write complex prompts or make multiple calls; simply describe the context as a JSON Schema.
Supports multiple LLM models: Users can use any model compatible with OpenAI or Anthropic.
Built-in caching function: By setting the `x-cache-ttl` header, l1m.io can be used as a cache for LLM requests.
Open-source: Users don't need to worry about vendor lock-in; they can choose to use the open-source version or the hosted version.
No data retention: User data is not stored unless the caching function is used.
SDKs for multiple programming languages: SDKs are provided for languages such as Node.js, Python, and Go, making it easy for developers to integrate.
How to Use
1. Define the required JSON Schema, describing the data structure you wish to extract from the text or image.
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2. Prepare the input data, which can be text or the base64 encoding of an image.
3. Send a request using the API, including the input data, Schema, and necessary headers (such as the LLM model and provider URL).
4. Set the cache time (optional) to reduce the overhead of repeated requests.
5. Receive the returned structured JSON data and process it further as needed.
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