Convert natural language to filters & SQL
Custom NLP query parser that can take natural language and convert it into a set of filters that can be to create sql.
ThatNeedle Natural language processing API is the most convenient tool for text analysis of your user search queries. Using it as a text nlp analyzer for better understanding of the natural language queries allows you to deliver better search results (i.e convert natural language to sql query)
Better search results quality will lead to improved click-through rates.
Our deep learning of the retail domain is showing fantastic results. You should give it a try.
Fast analysis of natural language with average time of 1ms.
It's instant nature will enable you to use it as real-time NLP API for your business.
Our natural language search solutions are ideally suited for commercial use as a tool to enhance your ecommerce site.
Enable semantic search on your website without overhauling your databases, schemas, database queries. You can easily convert natural language to filters using our API and then easily create SQL query from there.
Our semantic analysis of user queries will enable semantic information retrieval on traditional non-semantic databases and schemas with query understanding.
You will be able to receive structured information from unstructered text, and you can query your database using traditional SQL queries that you can created from structured information provided by our API.
Integration of the API into your search box is super easy with our cloud NLP api service.
All you need is to make call a url to process natural language queries and you will receive results as json formatted text (nl to json) or as a SQL query (nl to sql) instantly for easy integration with your rpa bi or sql bot.
All language processing is done easily and you don't have to worry about establishing an expensive team of nlp experts or setting up high-end machines.
It is the easy way to convert english to sql query and give a natural language interface to your users.
A lot of text that we see around is unstructured i.e. the text is not organised into fields or assigned to attributes as is done in traditional databases in the form of tables, rows, columns etc.
An example of unstructured text could be an essay on a topic or even a few words query in natural language.
In the real world a small query in natural language like "show me air tickets to chicago for christmas" or "i need a dell laptop with 8 gb ram" would need query parsing software or algorithm to understand the intent of the query.
Behind the scenes such a parsed would be translated into a structured query "select * from table where x=x1;".
A prerequisite for such a conversion of natural language to sql ( structured query language ) would be to understand which value belongs to which field.
This function of natural language query processing needs to be done accurately or else the search results would be wrong. And this has to be done fast so that the end user does have to keep waiting.
When you are using a cloud based Application programming interface / API for natural language processing, you can make a simple HTTP request and receive a JSON formatted string from the server.
This JSON formatted string can be used to generate structured queries which in turn can retrieve information from the database which may be relational database or a NoSql database for your sql chatbot.
Sooner or later, people feel the need for having a custom NLP tool that can perform according to their special needs because the ready made general purpose natural language parsers and NLP tools do not fit the needs. If it is a machine learning enabled system that needs training it will need a lot of data from good datasets to be trained to be effective. More over you have to work to fit a generally trained model and datasets. Many times the economy of training an off the shelf ML ( machine learning ) system can be intimidating. The cost could be in the form of engineers, data scientists, programmers, architects, etc. even if the tool itself is free of cost or open source. Some such tools are mere acedamic and not commercial and may not be suitable for production. Some NLP tools are only cloud based whereas the needs could mean that you keep your data private and use an offline nlp tool. i.e a software that can be installed on your servers in your own data center. Tools like NLTK, StanfordNLP, OpenNLP will give your programming team something to start with but they will be starting right at the parts of speech tag level. Soon enough you might see yourself searching for someone with a PhD in computational linguistics or Machine learning or some one experienced with title of data scientist. Any NL to sql software will take a whole lot of work beyond mere pos tagging before it can be useful to any business in any way. We encourage you to try out options in the market before you come to us so that you can decide what is the best nlp api in the market today. You can either ask us to create an NLP analyzer based component based on our proprietary nlp stack or use one of our entity extraction components to build something useful yourself. It could be providing an easy to use english interface to your application that relies on an English to SQL conversion in the backend. Our api technology can easily be extended to implement a text to sql converter or nl to json that will implement query understanding and interface with your relational database to make it look like a nlp database query and immensely help your application's users by making database queries easy.