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AI LLM Playground

AI LLM Playground is a Python repository of experimental chatbot web applications using OpenAI Assistant APIs, LangChain, embeddings, vector databases and agent patterns. Its examples include PDF question answering, restaurant recommendations and vision-based web-page exploration.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

AI LLM Playground — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as a code reference when exploring document Q&A or data-backed chatbot patterns, or when comparing the repository's newer OpenAI Assistant examples with its older LangChain versions.

Who it is for

  • Developers exploring Python chatbot patterns that connect language models to documents or structured data.
  • Builders comparing assistant-based document chat with older LangChain examples.
  • Developers interested in semantic and location-based search for recommendation chatbots.

Documented uses

  • Ask questions about PDF documents through an assistant interface.
  • Build a restaurant chatbot that searches a database for nearby recommendations.
  • Explore answering questions about web pages using screenshots and a vision API.
  • Study how embeddings, vector search and assistant tools can support conversational applications.
Before you choose

Requirements & limits

  • The repository contains examples and experiments, not one uniform chatbot service.
  • The PDF-chat example uses the OpenAI Assistant API; the multi-document LangChain example is marked outdated.
  • The restaurant advisor uses MongoDB Atlas for semantic search and location filtering, while conversation history in the newer version uses OpenAI Threads rather than Redis.
  • The README describes screenshot text extraction as incomplete and the web-scraper approach as experimental.
  • Setup documentation requires Pipenv and a populated .env file; particular examples also depend on external APIs or databases.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Python project environment

Install the repository dependencies in a Pipenv-managed virtual environment.

Before you start

  • Python project directory
  • Pipenv installed and available on PATH

Steps

  1. Rename .env.template to .env and fill in the required values.

  2. Change to the project directory and run pipenv install to create the virtual environment and install dependencies from Pipfile.lock.

  3. Run pipenv shell from the project directory to enter the environment.

Expected outcome

A project virtual environment with the locked dependencies installed.

Guide limitations

  • This guide covers environment setup only, not running or deploying an application.
  • The README's example PATH adjustment is specific to the author's environment.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
AI LLM PlaygroundAsk questions about PDF documents through an assistant interface.Web
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
Db Per TenantExplore a working application pattern for uploading PDFs and asking questions about their contents.WebThe repository is public and identified as open source. Its README links to a live demo.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
hiper2d/ai-llm-playground · Official homepage title · retained in its source language
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