Welcome to the Klu documentation. This site includes resources for using the API and SDK, guides to common Klu platform use cases, and best practices for deploying LLM Apps.
Klu.ai is an LLM App Platform that streamlines designing, deploying, and optimizing LLM-powered applications and features. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context.
Klu helps engineering teams rapidly build and iterate on LLM-powered applications. It provides a unified API access to LLMs like Anthropic Claude 2 and OpenAI GPT-4, allowing developers to quickly test prompt engineering and performance.
Observe & Eval
Engineers use Klu's SDKs to build LLM Apps directly into their applications and gather usage data on LLM performance. This allows efficient A/B testing of different prompts and models to optimize the end-user experience. Klu facilitates LLM evaluation through built-in support for logging, monitoring, and analysis. Developers can easily see how different prompts and models perform with real user input. These observations enable data-driven decisions around model selection, prompt engineering, and fine-tuning.
For storage and retrieval of knolwedge, Klu has built-in support to index and query embeddings, supporting a range of file types, datagbases, and integrations. This enables retrieval augmented generation, reduction in hallucinations, semantic search, and other vector similarity applications out-of-the-box.
By accelerating the build-measure-learn loop, Klu empowers AI Teams to quickly deliver innovative AI capabilities, increasing team productivity and reducing time-to-value. Klu provides the tools for rapid experimentation, while engineering teams bring their domain expertise to build unique solutions using the latest AI.
Klu helps teams establish a defensive moat by making their AI capabilities harder to reproduce. The more an AI Team iterates, the more fine-tuned the AI becomes to their competitive advantage. Klu sustains the flywheel by facilitating this constant experimentation and learning.
|Klu (Azure GPT3.5 & 4)||All Plans|
|Klu (Azure GPT4-32k)||Enterprise|
|Azure OpenAI||Scale +|
|Google Vertex||Scale +|
|AWS Bedrock||Scale +|
|Category||File Type Extensions|
|Audio / Video||.mp3, .mp4|
|Documents||.pdf, .rtf, .txt|
|Email Files||.eml, .msg|
|Markup/Structured Text||.md, .html, .rst, .org, .xml|
|Office Documents||.doc, .docx, .xlsx, .xls, .ppt, .pptx, .odt|
|Structured Data||.csv, .tsv|
|Collaboration||Google, MS Teams, Slack, Zoom|
|Customer Platforms||Intercom, Salesforce, Zendesk|
|Projects||Airtable, Asana, Atlassian, Github, Notion|
|Websites (Crawling)||HTML, Sitemap|
|SQL Database||MySQL, PostgreSQL, SQLite, Oracle, SQL Server|
|Redis||All data types (string, list, set, zset, hash)|
|Elastic||All data types|
|Snowflake||All data types|
|Youtube||All video formats|
Key Klu Concepts
Understand the key concepts of Klu from Actions to Workspace.
Design actions that generate or analyze content.
Design conversational chat agents to interact with your users.
Key LLM concepts when just getting started with GenAI.