To build a Generative AI (GenAI) application using LangChain, RAG (Retrieval-Augmented Generation), and OpenAI, you'll need to master several concepts and tools. Below is a step-by-step roadmap, organized into foundational, intermediate, and advanced phases.
Based on my experience, I don't think it's ideal, but all the keywords below will help you get an overview when planning to build a Gen AI app. :)))
Phase 1: Foundations
Step 1: Understand AI/ML Basics
-
Learn the fundamentals of AI/ML:
- Supervised, unsupervised, and reinforcement learning.
- Natural Language Processing (NLP) basics.
- Resources:
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
- Free online courses: Google AI or Andrew Ng's ML course.
Step 2: Programming Proficiency
- Languages: Python and/or TypeScript (preferred for LangChain with Node.js).
- Key skills:
- Handling JSON, APIs, and data formats.
- Writing modular, reusable code.
- Resources:
- FreeCodeCamp, Codecademy, or YouTube tutorials.
Step 3: Cloud Basics
- Learn cloud platforms: AWS, Azure, or GCP.
- Focus on:
- Setting up VMs, databases, and APIs.
- Storing files (e.g., AWS S3, MongoDB).
- Resources:
Phase 2: Intermediate
Step 4: LangChain Fundamentals
- Study LangChain documentation and concepts:
- Chains, Agents, Prompts.
- Memory management.
- Integrations with vector stores (e.g., Pinecone, MongoDB).
- Resources:
- LangChain Documentation.
- Example GitHub projects using LangChain.
Step 5: Learn about RAG (Retrieval-Augmented Generation)
- Understand the RAG pipeline:
- Chunking documents into embeddings.
- Storing and retrieving relevant data from vector stores.
- Using retrieved context to augment prompts for generation.
- Practice tools:
- LangChain for document splitting and embeddings.
- Vector databases like Pinecone, Weaviate, or MongoDB Atlas with vector search.
- Resources:
- Tutorials: LangChain RAG examples.
- Blog posts on OpenAI RAG workflows.
Step 6: OpenAI APIs
- Learn how to use OpenAI APIs:
- Fine-tune GPT models.
- Use Embedding Models (e.g.,
text-embedding-ada-002
) to generate vector representations. - Best practices for prompt engineering.
- Resources:
Step 7: Work with Vector Stores
- Understand how vector stores operate:
- Similarity search and storage.
- Choosing the right store (e.g., Pinecone, Weaviate, MongoDB).
- Learn integrations with LangChain.
- Resources:
Step 8: Integrate NLP Tools
- Preprocessing:
- Tokenization, stopword removal, stemming/lemmatization.
- Tools:
- Spacy, Hugging Face, or Natural.js (for TypeScript).
- Resources:
- NLP in Action by Hobson Lane.
Phase 3: Advanced
Step 9: Build a RAG Workflow
- Set up the RAG pipeline end-to-end:
- Ingest documents.
- Chunk documents (LangChain or custom scripts).
- Generate embeddings (OpenAI or Hugging Face models).
- Store embeddings in a vector database.
- Retrieve context and feed it to a generative model.
- Output meaningful results.
- Resources:
- LangChain RAG demo code.
Step 10: Hugging Face and Transformers
- Learn to use Hugging Face Transformers for custom model workflows:
- Create embeddings locally using models like BERT.
- Fine-tune existing Hugging Face models for specific tasks.
- Resources:
- Hugging Face courses and docs.
- Tutorials on fine-tuning BERT or GPT models.
Step 11: Backend Development with LangChain
- Use LangChain with NestJS or other backend frameworks.
- Set up APIs to expose LangChain pipelines.
- Secure endpoints with authentication (e.g., JWT, OAuth).
- Resources:
- NestJS tutorials.
- LangChain backend integration examples.
Step 12: Optimize Prompt Engineering
- Experiment with various prompt styles.
- Fine-tune models or use OpenAI Playground for better results.
- Resources:
- OpenAI Prompt Engineering Guide.
- LangChain prompt templates.
Step 13: Deploy to Production
- Set up your GenAI app in production:
- Use Docker and Kubernetes for containerization and scaling.
- Optimize cost: Use GPU instances only when necessary.
- Monitor API usage and rate limits.
- Resources:
- Tutorials on deploying AI apps.
- Tools: Docker, AWS/GCP deployment guides.
Step 14: Learn UX/UI for AI Apps
- Build a user-friendly frontend for interaction:
- Use React or Angular for frontend development.
- Integrate LangChain APIs.
- Resources:
- Frontend tutorials (React, Angular).
Step 15: Continuous Learning and Scaling
- Learn:
- Model fine-tuning.
- Dataset preparation for improved accuracy.
- Advanced vector database techniques.
- Explore:
- Distributed systems for scaling AI apps.
- Multi-modal AI (e.g., combining text and images).
Suggested Timeline
Week | Topics |
---|---|
1-2 | AI/ML basics, programming, cloud setup. |
3-4 | LangChain fundamentals, OpenAI API use. |
5-6 | RAG, vector stores, NLP preprocessing. |
7-8 | Build and test RAG workflows. |
9-10 | Hugging Face, backend integration. |
11-12 | Deployment, UI/UX for GenAI apps. |
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