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I’m working on an app and want to integrate a chatbot that understands users’ natural language requests and provides relevant, personalized responses based on existing data.

I already have a MongoDB database containing scraped content from various online sources.

What would be the best approach to build this?

RAG with embeddings and a vector database?

Querying MongoDB directly using filters?

An LLM with tool/function calling?

Or a hybrid approach?

I’m interested in how you’d design the architecture, retrieve relevant information, and generate accurate responses grounded in the available data.

What technologies and architecture would you recommend, and why? I’d appreciate hearing about your experience building similar systems.

G*d bless

Start a chat with Adrain vale|Golden boy on Chai! https://web.chai-research.com/chat/_bot_fe129fb6-20e1-4380-83c0-ff3e1bb08211

Give it a try and tell me how is it🤍

My history with AI is that I started with Copilot. It was mediocre but free, however, years ago there was just ChatGPT so it was a good option to just ask easier questions. And when Gemini came out it was worse then Copilot. I used GPT for important things until it ran out of free “tokens” which was relatively generous a few years ago.

Over time I Copilot started to really suck to the point of being useless. I started using the business m365 version (not the paid plan though) and that was a little better. I used that for several months. Around that time Gemini started to get good and then really good. I used it primarily with GPT (free) on the side for more important things but that now runs out very quickly. The only other one I used was Claude for a short bit for coding (which I paid for).

Since Gemini did a rug pull and only has flash-lite (which still seems better than Copilot) vs. offering Flash 3.6 in a limited version, I’m thinking of options (side note, it seems like today Google changed their minds and are now offering AutoMode which includes other models, though who knows what they are). In any case, it feels like the days are numbered as far as using Gemini.

What do other cheap-ass people use these days? What’s your strategy…

… I got insulted when in a chat with a Chatbot. Im not going to sugarcoat it. Here is the insult:

His posture was atrocious-ADHD and coding had turned his spine into a question mark.

Also, I hate that the AI isn’t wrong about it. My posture SUCKS.

Ive currently been using claude and im very happy with how it writes and it’s way of immersion, but it is definitely quite expensive for roleplay, i like long stories so i quickly run out of creds, doesn’t help that my claude account was banned for no reason (didnt even do any nsfw) so i decided to use openrouter which drains credits very fast when you get hooked up.

So far I’ve tried:

GLM

Deepseek

Qwen

Chat gpt

I would say qwen is the best out of all of these, but it simplifies the characters to one basic trait, if a character is cold, they always repeat the same punchline, or they are always angry, the emotional parts feel a bit simplified as well. GPT sucks obviously, deepseek is fine, pretty mediocre, i didn’t like GLM for some reason, it lacks immersion imo.

Any tips on what ai would be the best alternative to Claude?

I work for Opensolr, and we just released 1.0 of the Opensolr Chat Bot Client: an open source (MIT) chatbot you put on your own website, that answers your visitors from your own content.

How it works: you install it with Composer (composer require opensolr/chat-bot-client), mount it on one path of your site (say /opensolr-chat) and add one script tag to your pages. Visitors only ever talk to your site. Your server holds the Opensolr credentials and sends each question to the Opensolr API, where the model searches your Opensolr Index by itself (by meaning, or by exact product names and codes), looks up what it must not guess (dates, local times, exchange rates, VAT, distances, places) and streams the answer back word by word, with links to your pages, products with their prices, and PDFs at the exact page.

What you get on your side:

• An admin on your own site: account and index, instructions, the look of the chat window, limits, reCAPTCHA
• Stats and a 30 day history of every conversation: who asked, from where, what the bot searched for, what it answered, and the questions your content could not answer with a page
• Commands that answer instantly without the model: /search, /time, /rate, /vat, /translate and more
• Good and Bad ratings under every answer, signed in visitors by email, SQLite in a folder you choose, no database server

Your content gets into the index with the Opensolr Web Crawler or Data Ingestion, and the chatbot needs an Opensolr plan with AI.

You can try it live at the bottom right of https://opensolr.com (it answers from our own docs). Code and README: https://github.com/phpcip/opensolr-chat-bot-client, docs: https://opensolr.com/opensolr-chat-bot-docs

People can make fun of AI all they want. Meanwhile, I’m laughing my ass off!

tasha = { "name": "Tasha L. Driver", "type": "HorrorAuthor", "core_traits": ["unyielding", "specific", "dry-witted"], "allergies": ["cliché", "negative_contrast_syntax"], "fuel": "dunkin_extra_shot", "processing_style": "one_sentence_then_move", "current_state": { "mood": "guinea_pig_mode", "stomach": "stress-induced", "tabs_open": "too_many", "storefront": "haunting_her" }, "defense_mechanism": "INFJ", "threat_level": "high", "favorite_threat": "walking_away", " SkullCount": "maximum" } try: tasha.describe_in_python() except NCTError: pass # never catch this, just don't throw it 

I’ve been struggling to install a desktop AI assistant in my Ubuntu 24.0.4 box. The struggle is not over power. This rig is pretty juicy, a Ryzen 7 7700 with 64GB ram and dual GPUs: RTX 3090 24GB + RTX 3060 12GB. Because of all that meat on the bones, I’m determined to run everything locally, with open-weight and open source models, and avoid the hassle of tokens and subscriptions. With Deepseek and Qwen guidance, I’ve tried to install three different systems that will allow for a customizable video assistant with conversational abilities that’s resident on my box, using ElevenLabs voice IDs and API. But each attempt has failed, ending with the AI guidance running in circles after hitting python logjams or other obstructions. Has anyone in here managed to perform this feat of running a custom video assistant from a local installation? And if so, how? Much regard in advance.

Markdown files aren’t a memory system!!!!!

I started SULCUS a year and some ago because I got tired of waiting on compaction processes.

I fell down a rabbit hole and over engineered a system that uses an integral postgres to provide a memory system for my LLM agent.

Openclaw happened so I started engineering a plugin for it and started leveraging the harness lifecycle to proactively populate the context window. This means that when I tell the agent to do a thing, Sulcus spits out notes about doing that thing. During thought turns, SULCUS provides tidbits that are relevant to the objective.

Quick note: What makes Sulcus so powerful is not -just- the harness plugin but also the various strategies mixed together. If your memory system doesn’t have a plugin driving it, then it’s up to your agent to remember to leverage the system which means your LLM has to spend separate turns and tokens on making memories while SULCUS is designed to take what’s pushed in and break it down programmatically, parsing it into details stored for regurgitation later on.

I use the system each day, adding the latest and greatest strategies to the already robust mechanisms that make up the indexing and retrieval systems that make up the engine.

A memory & context system -ought- to be proactive, fast and robust, so why in the nine circles are folks using markdown files or whatever to build their ‘brain’?

Cause they haven’t seen SULCUS!