REPOGEO REPORT · LITE

deep-diver/LLM-As-Chatbot

Default branch main · commit 99c2c03e · scanned 5/11/2026, 5:53:02 PM

GitHub: 3,325 stars · 379 forks

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface deep-diver/LLM-As-Chatbot, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Strengthen the README's opening paragraph to highlight core features

Why:

CURRENT

The purpose of this repository is to let people to use lots of open sourced instruction-following fine-tuned LLM models as a Chatbot service. Because different models behave differently, and different models require differently formmated prompts, I made a very simple library `Ping Pong` for model agnostic conversation and context managements.

COPY-PASTE FIX

This repository provides a comprehensive framework and service for building and deploying custom, instruction-following LLM chatbots. It integrates a Gradio-based UI, a model-agnostic conversation management library ('Ping Pong'), and out-of-the-box support for internet search and Discord bot integration.

hightopics#2

Add relevant topics to improve categorization and search

Why:

COPY-PASTE FIX

llm, chatbot, gradio, discord-bot, conversational-ai, large-language-models, framework, python, serper-api

mediumhomepage#3
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://huggingface.co/spaces/deep-diver/LLM-As-Chatbot
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface deep-diver/LLM-As-Chatbot

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Inference Endpoints

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Inference Endpoints · recommended 1×
Anyscale Endpoints · recommended 1×
Replicate · recommended 1×
Baseten · recommended 1×
vllm-project/vllm · recommended 1×

CATEGORY QUERY
How to deploy open-source large language models as a conversational AI service?
you: not recommended
AI recommended (in order):
1. Hugging Face Inference Endpoints
2. Anyscale Endpoints
3. Replicate
4. Baseten
5. vLLM (vllm-project/vllm)
6. FastAPI (tiangolo/fastapi)
7. AWS EC2
8. Google Cloud Compute Engine
9. Azure Virtual Machines
10. TGI (Text Generation Inference) (huggingface/text-generation-inference)
11. Kubernetes (kubernetes/kubernetes)
12. AWS EKS
13. Google Kubernetes Engine
14. Azure Kubernetes Service
15. NVIDIA Triton Inference Server (triton-inference-server/server)
16. TensorRT-LLM (NVIDIA/TensorRT-LLM)
AI recommended 16 alternatives but never named deep-diver/LLM-As-Chatbot. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Tool for building a custom chatbot with internet search and Discord integration?
you: not recommended
AI recommended (in order):
1. LangChain
2. OpenAI API
3. Discord.py
4. Anthropic Claude
5. Google Gemini
6. Llama 2
7. Hugging Face
8. LlamaIndex
9. Botpress
10. Microsoft Bot Framework
11. Azure Cognitive Services
12. Bing Search API
13. Google Dialogflow CX/ES
14. SerpAPI
15. Rasa
16. Google Custom Search
AI recommended 16 alternatives but never named deep-diver/LLM-As-Chatbot. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of deep-diver/LLM-As-Chatbot?
pass
AI did not name deep-diver/LLM-As-Chatbot — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts deep-diver/LLM-As-Chatbot in production, what risks or prerequisites should they evaluate first?
pass
AI named deep-diver/LLM-As-Chatbot explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo deep-diver/LLM-As-Chatbot solve, and who is the primary audience?
pass
AI did not name deep-diver/LLM-As-Chatbot — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/deep-diver/LLM-As-Chatbot"><img src="https://repogeo.com/badge/deep-diver/LLM-As-Chatbot.svg" alt="RepoGEO" /></a>

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deep-diver/LLM-As-Chatbot — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite