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Summarai

Summarai is a Streamlit application that scrapes Google search results, extracts content from linked websites, stores and retrieves material with FAISS, and uses a language model with RAG to generate summaries based on a user query.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Summarai — 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 a query-led workflow that gathers Google results and summarizes retrieved page content suits the task, and a local Streamlit setup or Docker container is appropriate.

Who it is for

  • People who want concise summaries of web pages discovered through a Google search query.
  • Developers exploring a Python and Streamlit example of a search-to-summary RAG workflow.

Documented uses

  • Summarize content collected from Google search results.
  • Extract and summarize material from websites linked in search results.
  • Retain generated summaries locally in a Markdown file for later reference.
Before you choose

Requirements & limits

The repository documents local Python and Docker setup paths. The Python path requires a Groq API key; no hosted service or pricing details are specified.

  • The repository version uses FAISS; its Dockerized version uses ANNOY, which the project says builds the vector database faster at the cost of retrieved-document quality.
  • The Python setup requires Python 3.11 or higher, project dependencies and a Groq API key.
  • Summaries are generated from scraped search results and linked-site content, then saved locally as Markdown.
  • The project describes individual summaries for the top three sites as future work, not as a current feature.

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

Run Summarai with Python

Set up and launch the Streamlit application locally for query-based web-content summaries.

Before you start

  • Python 3.11 or higher
  • Dependencies listed in requirements.txt
  • A Groq API key

Steps

  1. Clone the repository and enter its project directory.

  2. Install dependencies with pip install -r requirements.txt.

  3. Add the Groq API key in brain/secrets22.py; the project instructions say not to name the file secrets.py.

  4. Start the interface with streamlit run brain/full_pipe.py.

Expected outcome

The Streamlit interface opens for entering a query and requesting a search-and-summarize workflow.

Guide limitations

  • The documented workflow scrapes Google search results and linked websites; results depend on the content it collects.
  • The guide follows the repository's setup instructions and does not represent an execution test.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
hrishi-008/SummarAI · Official homepage title · retained in its source language
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