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RAG & Knowledge Systems

Crawlai Rag

Crawlai Rag is a Python application that crawls website pages, extracts and chunks their text, embeds it in a ChromaDB vector store, and uses retrieval-augmented generation to answer natural-language questions from the indexed content.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Crawlai Rag — 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 when you want natural-language answers based on website content that has first been crawled and indexed.

Who it is for

  • Teams building question-answering access to website documentation or internal reference material.
  • Researchers who want to query information gathered from one or more websites.

Documented uses

  • Index a website’s internal pages as a searchable knowledge base.
  • Ask questions about indexed content, such as what a site covers or which services it lists.
  • Combine multiple websites in a shared vector database for cross-site queries.
Before you choose

Requirements & limits

  • The described answer workflow relies on relevant content being indexed beforehand.
  • The application combines a Streamlit interface with a FastAPI backend and a RAG stack of LangChain, ChromaDB, Sentence-Transformers and Groq.
  • The public repository identifies the project as MIT-licensed.

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

Index a website and ask questions

Create an indexed website collection and query its content through the application.

Before you start

  • A website URL
  • A configured CrawlAI RAG application; the documented stack uses Groq, LangChain, ChromaDB, Sentence-Transformers, FastAPI and Streamlit.

Steps

  1. Enter the website URL in the interface.

  2. Select “Index Website” to crawl, chunk and embed the website content.

  3. Ask a natural-language question about the indexed content.

Expected outcome

The application retrieves relevant indexed content and generates an answer grounded in that material.

Guide limitations

  • Questions are answered from indexed website content; a site must be indexed before querying it.
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
Crawlai RagIndex a website’s internal pages as a searchable knowledge base.Cloud, Self-hosted, Web
ServietteAnswer questions from a maintained folder of documents and cite the sources used.Linux, Self-hosted, ServerServiette is self-hosted and MIT-licensed. Setup requires a free Pathway license key. Provider API keys are needed for the corresponding API-based embedding or generated-answer options.
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.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
AnkitNayak-dev/CrawlAI-RAG · Official homepage title · retained in its source language
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