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NVIDIA
ChatRTX
Python2024–2024
NVIDIA / ChatRTX · Archived

ChatRTX

A developer reference project for creating Retrieval Augmented Generation (RAG) chatbots on Windows using TensorRT-LLM

2Distinct people
3Mentions
0Explicit recommenders
3.1KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A developer reference project for creating Retrieval Augmented Generation (RAG) chatbots on Windows using TensorRT-LLM

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week3 dated mentions

Observed mentions: 2024-02-13 18:14:58 UTC to 2024-05-10 10:47:12 UTC. Weeks start Monday, UTC.

0122024-02-12 00:00:00 UTC to 2024-02-19 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsFeb 122024-02-19 00:00:00 UTC to 2024-02-26 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-26 00:00:00 UTC to 2024-03-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-04 00:00:00 UTC to 2024-03-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-11 00:00:00 UTC to 2024-03-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-18 00:00:00 UTC to 2024-03-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-25 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMar 252024-04-01 00:00:00 UTC to 2024-04-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-08 00:00:00 UTC to 2024-04-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-15 00:00:00 UTC to 2024-04-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-22 00:00:00 UTC to 2024-04-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-29 00:00:00 UTC to 2024-05-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-05-06 00:00:00 UTC to 2024-05-13 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 6

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 122024-02-12 00:00:00 UTC2024-02-19 00:00:00 UTC202
Feb 192024-02-19 00:00:00 UTC2024-02-26 00:00:00 UTC000
Feb 262024-02-26 00:00:00 UTC2024-03-04 00:00:00 UTC000
Mar 42024-03-04 00:00:00 UTC2024-03-11 00:00:00 UTC000
Mar 112024-03-11 00:00:00 UTC2024-03-18 00:00:00 UTC000
Mar 182024-03-18 00:00:00 UTC2024-03-25 00:00:00 UTC000
Mar 252024-03-25 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 12024-04-01 00:00:00 UTC2024-04-08 00:00:00 UTC000
Apr 82024-04-08 00:00:00 UTC2024-04-15 00:00:00 UTC000
Apr 152024-04-15 00:00:00 UTC2024-04-22 00:00:00 UTC000
Apr 222024-04-22 00:00:00 UTC2024-04-29 00:00:00 UTC000
Apr 292024-04-29 00:00:00 UTC2024-05-06 00:00:00 UTC000
May 62024-05-06 00:00:00 UTC2024-05-13 00:00:00 UTC101

What Hacker News says

3 sources
Having installed this, this is an incredibly then wrapper around the following github repos: https://github.com/NVIDIA/trt-llm-rag-windows https://github.com/NVIDIA/TensorRT-LLM It's quite a thin wrapper around putting both projects into %LocalAppData%, along with a miniconda environment with the correct dependnancies installed. Also for some reason the LLaMA 13b (24.5GB) and Ministral 7b (13.6GB) but only installed Ministral? Ministral 7b runs about as accurate as I remeber, but responses are faster than I can read. This seems at the cost of context and variance/temperature - although it's a chat interface the implementation doesn't seem to take into account previous questions or answers. Asking it the same question also gives the same answer. The RAG (llamaindex) is okay, but a little suspect. The installation comes with a default folde
Read the rest
r dataset, containing text files of nvidia marketing materials. When I tried asking questions about the files, it often cites the wrong file even if it gave the right answer.
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