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Literal AI

Literal AI Cookbooks and Guides

Welcome to the Literal AI Cookbooks and Guides repository! This repository is dedicated to providing users with comprehensive cookbooks and guides designed to help you understand and implement AI solutions effectively.

Literal AI is an end-to-end observability, evaluation and monitoring platform for building & improving production-grade LLM applications.

For more information, find the full documentation here. Cookbooks from this repo and more guides are presented in the docs with explanations.

Cookbooks

Python

Name Category Description
Context Relevancy with Ragas Evaluation Build a RAG application and evaluate this with RAGAS based on context relevancy.
Evaluate User Satisfaction - Customer Support Conversations Evaluation Retrieve your Customer Support Conversations from Literal AI and evaluate user satisfaction on this conversational data.
LlamaIndex Integration Observability Build a Q&A application with LLamaIndex and monitor it with Literal AI.
Evaluate Agent Runs with Tools Observability (Tools) & Evaluation Build a simple agent which can use two tools. Monitor and evaluate the tool usage.
A/B Testing Client-Side Evaluation Build two prompts, randomly assign to new conversations and A/B test on a metric.
Create a Dataset Evaluation Create a Literal AI Dataset from the SDK
Distributed Tracing Observability Trace a distributed (TS and Py) service
Monitor a Conversational AI agent Observability Monitor a Conversational AI agent, built in FastAPI
Monitor a Multimodal chatbot Observability Monitor a multimodal Conversational AI agent, built with OpenAI
LangGraph example Observability Example with LangGraph : a graph flow with tool use

TypeScript

Name Category Description
Financial Dashboard with AI Copilot Observability Create an interactive financial dashboard with AI-powered assistance and Generative UI
Prompt Iteration with Promptfoo Evaluation Run a simple chat application and evaluate its results with two different prompt templates with Promptfoo.
Image Generation with Dall-E & Literal AI Attachment Observablity Use OpenAI Dall-E to Generate an image with a prompt and then send it to Literal AI.
Chatbot using Next.js, OpenAI and Literal AI Observablity Create a personalized and monitored chatbot with OpenAI, Next.js and Literal AI.
Chatbot using Vercel ai SDK and Literal AI Observablity Create a personalized and monitored chatbot with Vercel ai SDK and Literal AI.
Simple RAG using LanceDB, OpenAI and Literal AI Observability Create Simple RAG on Youtube Transcripts stored using LanceDB
Speech-to-Emoji: Next.js app to summarize audio with OpenAI Whisper, GPT-4o and Literal AI Observability Create a simple web app that transcribes and summarizes audio using OpenAI Whisper, GPT-4 and Literal AI.
Interactive map with a copilot chat bot Observability A world map with a chat bot that is context aware and react to the user position on the map
LangChain and LangGraph examples Observability Three examples with LangChain/LangGraph : a basic RAG, a graph flow with tool use, and a multi-agent flow