LamdAI Playground
A search engine for your documentation using a custom parser.
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- A search engine for your documentation using a custom parser
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- A search engine for your documentation using a custom parser
Overview
LamdAI Playground is a search engine for your documentation that employs a custom parser designed to simulate ChatGPT's functionality, but solely for your specific documentation. It simplifies navigation and complex terms, providing easy access to essential information through semantic search. It allows you to strictly use your provided data, avoiding any "hallucinations".
Access & connections
Where you can use LamdAI Playground.
Platforms
Capabilities
What this tool accepts, produces, and is strongest at.
Highlights
- Custom parser for documentation search
- Semantic search for easy information access
- Avoids hallucinations by using only provided data
- Easy embedding into existing solutions via UI or API
FAQ 8
What is LamdAI Playground?
LamdAI Playground is a search engine for your documentation that employs a custom parser designed to simulate ChatGPT's functionality, but solely for your specific documentation. It simplifies navigation and complex terms, providing easy access to essential information through semantic search. It allows you to strictly use your provided data, avoiding any "hallucinations".
How to use LamdAI Playground?
Upload your documentation (HTML or PDF files) or share the URL of your documentation. LamdAI will handle the integration process for you. You can then embed the UI into your own solution or use the API.
What is a "Search Engine for your Documentation" service?
Lamd.ai's "Search Engine for your Documentation" service employs a custom parser, designed to simulate ChatGPT's functionality, but solely for your specific documentation. LamdAI Playground allows you to strictly use your provided data, avoiding any "hallucinations". This ensures user responses are directly relevant, drawn exclusively from your provided files.
How do I embed the service into my website?
We are developing a user-friendly LamdAI Playground. This will be a plug-and-play service where you can upload your datasets directly, making the process even more convenient and accessible for everyone. At present, you can share your documentation files with us, or direct us to your web-based datasets, and our team at LamdAI will handle the integration process for you.
How does the LamdAI Playground navigate and interpret my documentation?
LamdAI Playground employs a blend of technologies, including AI models from OpenAI, for natural language understanding. Additionally, it utilizes Weaviate, an efficient vector database, for data retrieval. Once your documentation is uploaded, the platform indexes the data for fast and accurate response generation, ensuring responses are always precise and directly derived from your specific documentation.
How does the service handle user privacy and data security?
LamdAI Playground takes user privacy and data security extremely seriously. All data uploaded to the platform is encrypted both in transit and at rest, ensuring it is protected from unauthorized access. Additionally, we adhere to strict privacy policies and comply with all relevant data protection laws. We do not use the data uploaded for any purpose other than to provide the service, and we do not share this data with any third parties.
What sort of analytics or insights can I gain from the service?
As for analytics and insights, the LamdAI Playground offers a wealth of user interaction data. This includes the frequency and nature of queries, user feedback, and Customer Satisfaction (CSAT) scores. These insights can help you understand your users' needs better and continuously improve your services and documentation.
What are the costs associated with the service? Is there a subscription plan?
Currently, we're offering our service at no cost. Our aim is to provide substantial value and demonstrate the positive impact our service can make on your business operations. Once we've proven our worth, we will introduce a fair pricing model.
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