Simple Python AI Chatbot

@ASSAMWEB

Greetings, I have read the project description I have been working on a similar project in recent time. I am interested in the work open a chat to discuss requirements in details.

@kend27

Hi, This is a classic RAG (retrieval-augmented generation) setup and a good fit for a clean Python proof-of-concept. My recommendation: command-line interface rather than a web page — keeps the code simpler and easier to follow, which aligns with your goal of understanding the flow clearly. If you want a web UI later it's a small addition. Stack I'd use: — LangChain for the RAG pipeline (document loading, chunking, retrieval) — readable and well-documented — ChromaDB as the local vector store — lightweight, no server needed, just a local folder — OpenAI text-embedding-3-small for embeddings and GPT-4o-mini for answers — cost-effective and reliable — A simple config file to swap between OpenAI and Gemini endpoints without touching the main code The flow in plain terms: your text files get chunked and embedded into ChromaDB on first run, then each question retrieves the most relevant chunks and passes them to the model with a prompt that says "answer using only this context." The response includes which document section was used. Deliverables exactly as listed — source code with inline comments, README covering setup and key config, and clear instructions for switching AI providers. One question: roughly how many files and how large are they? Affects chunking strategy slightly. Can deliver in 2-3 days. Ken

@ahmetTC

Hope you are doing well! I am ready to start immediately. Experience includes building lightweight Python chatbots that accept user input via command line or minimal web interfaces, retrieving context from local knowledge files, and generating accurate AI responses using OpenAI and other LLMs. Previous projects involved implementing vector-based retrieval from multiple text sources, passing relevant context to AI models, and returning responses that reference the correct document sections. Challenges such as handling multiple knowledge files, switching between API endpoints, and ensuring clear, reproducible flows were resolved with modular code and concise in-line comments. Deliverables included runnable Python code, clear README instructions for setup and API keys, and easy swapping between AI endpoints. I know what do I build for you, can complete it to your full satisfaction within your timeline. I am ready for you and waiting here. Thank you.

@indreshpnk

Hello, I understand you need a working proof-of-concept Python AI chatbot that reads user questions, searches through supplied documents, and returns accurate AI-generated answers. The goal is to deliver a clear, lightweight, and reliable solution that demonstrates the full question-to-answer flow. Here’s what I can provide: Command-line or minimal web interface**:** Easy-to-use input for user questions. Context retrieval from local files: Efficiently fetches relevant passages from your text files. AI integration: Uses OpenAI or Gemini to generate accurate responses based on the retrieved context. Clean, well-commented Python code: Easy to follow, extend, and maintain, with instructions for switching endpoints. I bring over 4+ years of experience in Python, AI integrations, and building chatbots, with a strong focus on scalable, readable, and user-friendly applications. I have delivered multiple projects involving local knowledge retrieval and conversational AI, always prioritising performance and clarity. Just to clarify a few things: Do you prefer a command-line interface or a minimal web page as the primary input? How many and what types of documents will you initially supply for the knowledge base? Please come to the chat box to discuss more about your project. Best regards, Indresh Kushwaha

@RajatJSRTech7

As an experienced developer, I have built a plethora of applications fitting the essence of your project. My proficiency extends from constructing custom web applications, SaaS platforms to creating efficient eCommerce pages, like Shopify and WooCommerce. This is particularly important in your case as they reflect I can give you a simplified documentation experience and ease in switching between OpenAI and Gemini endpoints. When it comes to AI-based solutions, my flair for Python, Django, and my knack for data analysis brings out the best results. The ability to optimize the performance is also essential to facilitate testing AI-powered functionalities. You can expect a final product that's runnable, logical, and easy to understand. A comprehensive README outlining setup and instructions for changing knowledge files will be provided, allowing hassle-free customization whenever required! All these skills culminate in a solution that is reliable even in its lightweight proposition, ensuring you test experiment without complications

@AbdulBasitSE

Hi, This sounds like a great, focused POC—and I can build it cleanly and quickly. I’ll create a lightweight Python chatbot that: * Accepts user queries (CLI or simple web UI—your choice) * Searches your local files using a basic retrieval approach (embeddings or keyword-based) * Sends the relevant context to OpenAI or Gemini * Returns a clear, accurate answer with referenced context What you’ll get: * Clean, well-structured Python code with comments * Simple retrieval + generation pipeline (easy to extend later) * README with setup steps, API key config, and how to update knowledge files * Easy switch between OpenAI and Gemini via a config file or flag The focus will be clarity, reliability, and making sure the full flow—from question → retrieval → AI answer—is easy to understand and run. Happy to get started right away. Best Regards.