You will get Document Q&A Chatbot (RAG) | PDF/Docs | LangChain + Python
Top Rated
Project details
I will build a Document Q&A assistant so you can ask questions in plain English and get accurate answers grounded in your PDFs/docs.
It ingests your files, creates a searchable knowledge base, and returns answers with citations (page/section) so you can verify quickly.
You’ll get clean output formatting (bullets/table when needed), “not found” handling, and basic guardrails to reduce hallucinations.
Deliverables include the working pipeline, simple run instructions, and a short test report using your sample questions.
It ingests your files, creates a searchable knowledge base, and returns answers with citations (page/section) so you can verify quickly.
You’ll get clean output formatting (bullets/table when needed), “not found” handling, and basic guardrails to reduce hallucinations.
Deliverables include the working pipeline, simple run instructions, and a short test report using your sample questions.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Localization, Performance Optimization, SecurityWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 14 days |
Number of Revisions | 2 | 3 | 3 |
Number of Pages | 100 | 250 | 500 |
Design Customization | - | ||
Content Upload | |||
Responsive Design | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75 - $180
Additional Revision
+$40
Extra 100 pages
(+ 1 Day)
+$90
Hybrid + Rerank
(+ 2 Days)
+$150Frequently asked questions
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MS
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About Ahmad
AI Python Engineer | RAG Chatbots + Agents | LangChain, FastAPI, SQL
100%
Job Success
Bahawalpur, Pakistan - 12:26 pm local time
Typical work I deliver:
RAG assistants for docs/knowledge bases (embeddings, vector DB, citations, metadata filtering)
AI agents that call tools (APIs, SQL/Postgres, webhooks) to complete multi-step workflows with guardrails + fallbacks
FastAPI backends for AI products (structured JSON responses, background jobs, clean integration)
Data extraction pipelines when needed (OCR + parsing + normalization)
How I build it (so it doesn’t break later):
Start with a small first milestone: one end-to-end flow on your real examples
Add measurable checks: retrieval quality, schema validation, safe SQL/tool execution, and “I don’t know” behavior when context is missing
Keep cost + latency practical: caching where it helps, prompt versioning, and predictable output schemas
Ship with maintainability: clean code, Dockerized setup, and a short runbook your team can operate
Stack I work with: Python, FastAPI, LangChain/LangGraph, OpenAI-compatible LLMs, SQL/PostgreSQL, Docker, and vector databases (FAISS/pgvector/Pinecone-style). I can also support ML/DL (PyTorch) and computer vision when the project needs it, but my day-to-day work is building LLM systems that integrate into real products.
If you share your brief + sample data, I’ll propose a practical first milestone, timeline, and what “done” looks like. I’m comfortable with self-hosted setups (privacy constraints), and can deploy on Docker/cloud as required. I also add basic monitoring hooks so failures surface early (logs, alerts, and simple dashboards). Open to a quick call to align on scope and success metrics.
Steps for completing your project
After purchasing the project, send requirements so Ahmad can start the project.
Delivery time starts when Ahmad receives requirements from you.
Ahmad works on your project following the steps below.
Revisions may occur after the delivery date.
Scope & success criteria
Confirm documents, use case, sample questions, and what “done” looks like.
Document ingestion & cleanup
Extract text, keep page metadata, handle tables, and normalize formatting.