#langchain #ollama #deepseek #refineprompt #pdfchatbot #rag #aichatbots
Dive into a complete step-by-step tutorial on creating a Refine-based RAG Chatbot using LangChain, Ollama, and DeepSeek to answer questions from your PDF data! We’ll explore how the Refine Prompt iteratively improves the chatbot’s responses by revisiting each chunk of text—ensuring higher accuracy and less “hallucination.” Whether you’re developing an internal knowledge-base or an academic research assistant, you’ll see how to integrate a local LLM with OpenAI embeddings, persist embeddings in a Chroma vector store, and handle advanced queries with the Refine Chain.
🎥 In this video, we cover:
✔️ Step by step flowchart of the architecture
✔️ PDF Loading & Chunking: Breaking down a PDF using PyPDFLoader and CharacterTextSplitter.
✔️ Embedding & Vector Store: Generating OpenAI embeddings and storing them in Chroma.
✔️ Refine Prompt Explained: Understanding why we need both a question_prompt and refine_prompt—plus how they work together to produce a polished final answer.
✔️ Live Demo: Querying the PDF in real-time and watching the refine chain update answers as new context is applied.
Link code:
https://github.com/khanhvy31/Rag-with-you-pdf
Tutorial Outline:
Introduction: Why RAG + Refine is critical for reliable PDF Q&A.
Environment Setup: Best practices for isolating dependencies in a Python virtual environment.
Backend Flow: Demonstrating Ollama’s local LLM usage with DeepSeek and LangChain.
Refine Prompt: Step-by-step building of the question and refine prompts to improve accuracy.
Query & Demo: Running the chatbot to see how iterative refinement upgrades the responses.
Troubleshooting: Common issues when using vector databases and how to solve them.
Conclusion: Summing up RAG best practices and next steps
🔥 Why This Tutorial?
Gain practical insights into the Refine Chain process, where answers evolve with each chunk of text. If you’ve struggled with chatbots ignoring crucial data or making things up, refining answers chunk by chunk is the key to higher fidelity responses.
📢 Join the Discussion
Share your experiences building a RAG chatbot with a refine prompt—what worked, what didn’t? Let’s discuss in the comments!
🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬.
https://www.youtube.com/@DS-AIwithKV/?sub_confirmation=1
🔗 Stay Connected With Me.
Twitter (X): https://x.com/khanhvy31
Linkedin: https://www.linkedin.com/in/khanh-vy-nguyen0331/
Github: https://github.com/khanhvy31
Medium: https://medium.com/@khanhvy31
📩 For business inquiries: [email protected]
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3. https://www.youtube.com/watch?v=EKEbI-is7_c
4. https://www.youtube.com/watch?v=73s1_qshsKI
🔎 Related Keywords
Refine Prompt, RAG Chatbot, Ollama, DeepSeek, PDF Q&A, Local LLM, Vector DB, LangChain
#RAG #Ollama #DeepSeek #LangChain #RefinePrompt #Chatbot #PDFChatbot