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$
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I will fix your rag chatbot, openai, and vector retrieval errors
Lukicha.
About this gig
Is your RAG chatbot hallucinating, missing obvious facts, or saying "I don't have information on that" when the answers are clearly in your documentation?
Most RAG failures are not model issuesthey are caused by improper chunk sizes, missing token overlap, embedding dimension mismatches, low similarity thresholds, or overly restrictive system prompts.
I will inspect, debug, and fix your Python, LangChain, OpenAI, and Vector DB (Pinecone / ChromaDB) retrieval pipeline so your chatbot returns grounded, accurate answers.
What I can fix for you: Low retrieval accuracy (tuning chunk size, overlap, and metadata filtering) Chatbot refusing to answer despite source documents being indexed Hallucination reduction via strict prompt grounding and source context injection Python integration bugs across OpenAI API, LangChain, and vector stores
Scope Boundaries: Focused on existing Python and AI backend retrieval logic. Excluded: Full frontend UI development, web hosting deployment, and full-scale database migrations.
Please send me a message before ordering with a brief description of your issue so I can confirm the scope!
Get to know Lukicha.
Lukicha.
Python and AI Developer, RAG, LLM and API Specialist
- FromGeorgia
- Member sinceOct 2026
- Avg. response time1 hour
Languages
English
Hi, I'm Luka! I am a CS student and developer specializing in Python, LLM APIs, and RAG architectures.
I help businesses and developers fix broken AI workflows:
• Debug RAG retrieval (chunking, embeddings, top-k tuning)
• Fix malformed JSON outputs using Pydantic schemas
• Resolve Python API, webhook, and Vector DB errors
Clean, documented code and fast delivery guaranteed. Message me anytime!
FAQ
What do I need to provide before you can start?
Please provide access to your Python script, Jupyter notebook, or GitHub repository, a representative sample of your source documents (PDF, TXT, or Markdown), and 3–5 specific queries where your chatbot currently gives wrong, incomplete, or empty answers.
Which vector databases and frameworks do you support?
I support Pinecone, ChromaDB, and lightweight local vector stores, integrated with Python using LangChain, LlamaIndex, and OpenAI or Gemini APIs.
How do you verify and prove the retrieval issue is fixed?
I test the updated pipeline directly against your failing queries to confirm relevant chunks are retrieved and answers are strictly grounded in your context. Along with the clean code, I provide a clear summary explaining the root cause and how it was fixed.
Do you build the frontend chat widget or handle server deployment?
No. This gig is strictly dedicated to fixing and optimizing the backend AI retrieval pipeline, embedding logic, chunking, and prompt grounding. Frontend UI design, production hosting, and database migrations are excluded to ensure fast delivery and tight scope.
What if my data is confidential?
You do not need to share sensitive proprietary files. You can provide an anonymized sample or dummy documents formatted with the exact same structure (headers, tables, paragraphs) as your original data.
