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I will do rag chatbot langchain rag pinecone ai agent
Hamza Sajid
Über diesen Service
Are generic LLMs hallucinating and failing your business?
Hi, I'm Hamza, a Software Engineer specializing in robust AI architecture. I engineer custom rag chatbot applications designed to retrieve your documents and answer complex questions with pixel-perfect accuracy-zero hallucinations.
Using langchain and state-of-the-art vector embedding, I build production-ready rag systems that connect seamlessly to your internal knowledge bases (PDFs, APIs, PostgreSQL databases).
What You Get:
- Custom rag chatbot tailored to your company's complex datasets.
- Clean backend architecture using Python, LangChain, and vector databases (Pinecone/Chroma).
- Advanced text chunking, embedding, and semantic-search pipelines.
- Integration with leading LLMs (OpenAI, Gemini API, or local models for strict privacy).
- Well-documented, production-ready code.
Every dataset is unique. Please message me before ordering to discuss your technical architecture!
Lerne Hamza Sajid kennen
Hamza Sajid
Software Engineer
- AusPakistan
- Mitglied seitAug. 2022
- ⌀ Antwortzeit1 Stunde
- Letzte Lieferung1 Jahr
Sprachen
Urdu, Hindi, Englisch
Welcome Guys!
Hamza Sajid here! I am a professional software engineer excelling in crafting AI solutions that help business scale their services more efficiently and ultimately generating more profits.
FAQ
Our data is highly sensitive. Can you build a completely local and private rag system?
Absolutely. For enterprise security, I can construct a private rag pipeline utilizing local open-source models (like Llama 3 or Mistral via Ollama) paired with an on-premise vector store so your data never leaves your infrastructure.
How do you prevent the rag chatbot from hallucinating?
I build robust langchain prompt templates that strictly instruct the LLM to ground its responses only within the retrieved context chunks. If the answer doesn't exist in your data, the bot will state it doesn't know, rather than guessing.
What vector databases do you typically work with?
Depending on your scale and architecture, I work with Pinecone, ChromaDB, Weaviate, or PGVector for seamless integration into existing PostgreSQL architectures.
