Get a bespoke, retrieval-augmented AI chatbot built in Python using LangChain, LlamaIndex and your own data — from a simple conversational bot to a fully fine-tuned, source-code-delivered solution.
I Will Build a Custom AI RAG Chatbot With LangChain and Python
A simple, functional AI chatbot with source code included.
- Simple chatbot with core conversational functionality
- Built in Python using LangChain or LlamaIndex
- Choice of LLM (LLaMA 2, Gemini, Claude, Mistral, Phi-2 or OpenAI)
- Full source code delivered
- FastAPI or Express middleware integration
- 2 revisions included
A custom AI chatbot with RAG on your own dataset, extended delivery window.
- Custom AI chatbot tailored to your niche or use case
- RAG pipeline implemented on your custom dataset
- Vector database integration (Pinecone, Chroma, ElasticSearch or similar)
- Local database support (MySQL, MongoDB or similar)
- Full source code delivered
- 2 revisions included
A fully fine-tuned RAG chatbot on your custom dataset with detailed code comments.
- Fine-tuned chatbot aligned to your specific niche and custom dataset
- Full RAG pipeline with advanced LLM integration
- Vector and local database integration
- Local LLM tooling support (Ollama, LM Studio, CPP-Method, GPT4All)
- Full source code with detailed inline code comments
- 1 revision included
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Value Position
Architecture Type
LLM Options
Ownership & Code
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What You'll Receive
Full Description
You need an intelligent chatbot that actually understands your data, your niche and your users — not a generic off-the-shelf widget. That is exactly what this service delivers: a custom-built AI chatbot powered by Retrieval-Augmented Generation (RAG), crafted in Python by a London-based AI development team with deep expertise in LangChain, LlamaIndex and large language models.
Whether you are starting from scratch with a simple conversational interface or you need a sophisticated RAG pipeline ingesting your own proprietary dataset, this service covers the full spectrum. Every deliverable ships with clean, working source code so you own what you paid for.
**What You Can Get Built**
- Simple chatbot with core conversational functionality
- Custom AI chatbot tailored to your use case or niche
- RAG-implemented chatbot trained on your own custom dataset
- Fine-tuned chatbot aligned to a specific domain or audience
**LLMs Available**
Your chatbot can be built on whichever model best suits your needs: LLaMA 2, Gemini, Claude, Mistral, Phi-2, or OpenAI models.
**Technical Stack**
- Language: Python or Node
- Frameworks: LangChain, LlamaIndex
- Middleware: FastAPI or Express
- Vector Databases: Pinecone, Chroma, ElasticSearch or similar
- Local Databases: MySQL, MongoDB or similar
- Local LLM Tools: Ollama, LM Studio, CPP-Method, GPT4All
This breadth of tooling means the architecture is chosen to fit your project — not the other way around.
**How the Process Works**
After you place your order, share your project brief and any datasets, documents or requirements via the order chat. The team will review everything, confirm the scope and get to work. If anything is unclear, you will be contacted directly through the order manager. Delivery includes the full source code, and the Premium tier adds detailed inline code comments so your own developers can maintain and extend the codebase with confidence.
**Who This Is For**
- Founders and product teams wanting to embed AI chat into their platform
- Businesses that need a chatbot trained on internal documentation, FAQs or proprietary knowledge bases
- Developers who want a solid RAG foundation they can build on
- Anyone exploring LLM-powered automation in a specific niche
Have a project idea? Share it via the order chat and the team will work with you to make it a reality.
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Compare Packages
| Feature | Basic | Boost | Premium |
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
| Revisions | 2 | 2 | 1 |
| Simple chatbot with core conversational functionality | ✓ | ✕ | ✕ |
| Built in Python using LangChain or LlamaIndex | ✓ | ✕ | ✕ |
| Choice of LLM (LLaMA 2, Gemini, Claude, Mistral, Phi-2 or OpenAI) | ✓ | ✕ | ✕ |
| Full source code delivered | ✓ | ✓ | ✕ |
| FastAPI or Express middleware integration | ✓ | ✕ | ✕ |
| 2 revisions included | ✓ | ✓ | ✕ |
| Custom AI chatbot tailored to your niche or use case | ✕ | ✓ | ✕ |
| RAG pipeline implemented on your custom dataset | ✕ | ✓ | ✕ |
| Vector database integration (Pinecone, Chroma, ElasticSearch or similar) | ✕ | ✓ | ✕ |
| Local database support (MySQL, MongoDB or similar) | ✕ | ✓ | ✕ |
| Fine-tuned chatbot aligned to your specific niche and custom dataset | ✕ | ✕ | ✓ |
| Full RAG pipeline with advanced LLM integration | ✕ | ✕ | ✓ |
| Vector and local database integration | ✕ | ✕ | ✓ |
| Local LLM tooling support (Ollama, LM Studio, CPP-Method, GPT4All) | ✕ | ✕ | ✓ |
| Full source code with detailed inline code comments | ✕ | ✕ | ✓ |
| 1 revision included | ✕ | ✕ | ✓ |
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Build a Custom AI RAG Chatbot With LangChain and Python
Build a Custom AI RAG Chatbot With LangChain and Python
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Frequently Asked Questions
Please share a clear description of your project requirements, any datasets or documents you want the chatbot trained on, your preferred LLM (if you have one), and any technical constraints such as preferred language or framework. The more detail you provide, the faster work can begin.
The chatbot can be built using LLaMA 2, Gemini, Claude, Mistral, Phi-2 or OpenAI models. If you are unsure which is best for your use case, mention your requirements in the order chat and a recommendation will be made.
Yes — all three tiers include the full source code. The Premium tier additionally includes detailed inline code comments, making it easier for your own developers to maintain and extend the codebase.
Retrieval-Augmented Generation (RAG) allows the chatbot to query your own documents, databases or knowledge bases in real time before generating a response. If you want the chatbot to answer questions based on your specific data rather than general knowledge, you need RAG — the Boost and Premium tiers cover this.
The core frameworks are LangChain and LlamaIndex, with FastAPI or Express as middleware. For vector storage, Pinecone, Chroma or ElasticSearch can be used. Local databases supported include MySQL and MongoDB. Local LLM tooling such as Ollama, LM Studio, CPP-Method and GPT4All is available on the Premium tier.
Basic and Boost tiers include 2 revisions; the Premium tier includes 1 revision. A revision covers adjustments to the delivered build based on feedback — it does not cover new features or a change in core scope. Additional revisions can be purchased as an add-on.
Yes. The chatbot is built with FastAPI or Express middleware, making it straightforward to integrate via API into web applications, internal tools or other platforms. Specific integration requirements should be shared upfront so they can be scoped correctly.
You will receive a message asking for your project details. Once all the necessary information is provided, work begins. Any questions during development will be raised through the order chat. Delivery will be made through the order manager.
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