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LangChain and Vector Databases

Harness LangChain and Vector Databases to build intelligent, memory-aware, and retrieval-augmented AI applications with ease.
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Course Duration: 10 Hours
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The next generation of AI applications is built not just on powerful language models but on frameworks and databases that give those models memory, context, and reasoning. This self-paced course on LangChain and Vector Databases is designed for developers, data engineers, AI enthusiasts, and solution architects looking to master tools that bridge large language models (LLMs) with dynamic, queryable, and context-rich data.
 
LangChain is a powerful framework that makes it easy to integrate LLMs (like OpenAI, Claude, or LLaMA) with tools, APIs, databases, and memory stores. It allows developers to build chainable workflows where LLMs can reason, retrieve, act, and store results intelligently. Vector databases, on the other hand, are optimized for semantic search and similarity-based retrieval—critical when working with embeddings and large document corpora.
 
This course walks you through the real-world application of these tools—such as Retrieval-Augmented Generation (RAG), chatbot memory, knowledge graphs, and intelligent agents. You’ll learn to create scalable, efficient, and production-ready AI pipelines that can interact with unstructured data using context-aware queries.
 
What are LangChain and Vector Databases?
  • LangChain: A modular, extensible Python framework for building context-aware applications using LLMs, chains, agents, tools, memory, and retrieval.
  • Vector Databases: Specialized databases (like Pinecone, Chroma, Weaviate, FAISS) designed to store high-dimensional vector embeddings and perform similarity search efficiently.
Together, they enable powerful AI workflows where LLMs retrieve relevant knowledge and respond with context, precision, and personalization.
 
How to Use This Course
  1. Start with Fundamentals – Learn what embeddings, chains, and semantic search are and how LangChain fits into the LLM ecosystem.
  2. Hands-On Projects – Build chatbots, document Q&A systems, and context-aware agents using LangChain and vector stores.
  3. Use Real Tools – Integrate with Pinecone, ChromaDB, FAISS, OpenAI, HuggingFace, and others.
  4. Test, Tune, and Scale – Learn prompt engineering, chunking strategies, metadata filtering, and hybrid search techniques.
  5. Capstone Projects – Deploy and serve full-stack intelligent applications using LangChain with retrieval augmentation and persistent memory.
Whether you’re building customer service bots, internal knowledge agents, or AI copilots, this course gives you the building blocks to bring intelligence to your data using state-of-the-art tools.

Course/Topic 1 - Coming Soon

  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

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Course Objectives Back to Top
By the end of this course, you will:
 
  1. Understand the architecture and use cases of LangChain.
  2. Work with vector databases like FAISS, Pinecone, and ChromaDB.
  3. Generate embeddings from text using OpenAI, HuggingFace, and Cohere.
  4. Implement semantic search and similarity matching.
  5. Build Retrieval-Augmented Generation (RAG) pipelines.
  6. Create chatbots with memory and document-based Q&A systems.
  7. Integrate tools and agents using LangChain agents and tools modules.
  8. Handle chunking, indexing, and metadata filtering in vector stores.
  9. Deploy LangChain apps with Streamlit, FastAPI, or Flask.
  10. Apply LangChain to production use cases like customer support, legal research, and educational assistants.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to LangChain and Vector Databases
  • LLM stack overview
  • What is LangChain?
  • Why vector databases?
Module 2: Fundamentals of Embeddings
  • Word, sentence, and document embeddings
  • Tools: OpenAI, Cohere, HuggingFace
  • Converting text to vector
Module 3: LangChain Basics
  • Chains and memory
  • Prompt templates
  • Simple LLM chain and sequential chains
Module 4: Introduction to Vector Databases
  • FAISS, Chroma, Pinecone, Weaviate
  • Indexing and similarity metrics
  • Creating and querying vector stores
Module 5: Semantic Search and RAG
  • Document ingestion and chunking
  • Similarity search and filtering
  • Retrieval-Augmented Generation with LangChain
Module 6: Building a Document Q&A System
  • Uploading and chunking documents
  • Embedding generation and indexing
  • Contextual question answering pipeline
Module 7: LangChain Agents and Tools
  • Tool integration (Google Search, WolframAlpha, APIs)
  • Creating and managing agents
  • Agent decision making and tool use
Module 8: Chatbots with Context and Memory
  • BufferMemory, ConversationSummaryMemory, VectorStoreMemory
  • Stateful chat systems
  • Context persistence
Module 9: Hybrid Search and Metadata Filtering
  • Combining keyword and semantic search
  • Filtering results based on metadata tags
  • Improving relevance in retrieval pipelines
Module 10: LangChain in Production
  • Serving with FastAPI, Flask, or Streamlit
  • Caching and latency optimization
  • Logging, monitoring, and rate limits
Module 11: Integration with Enterprise Systems
  • APIs, CRMs, knowledge bases
  • Connecting LangChain with Zapier, Notion, Slack, and more
  • Private LLMs vs. hosted models
Module 12: Capstone Project
 
  • Build a full-stack knowledge agent
  • Document upload, RAG, chat interface, tool access
  • Deploy to cloud (Render, Vercel, or AWS)
Certification Back to Top

On successful completion of this course, learners will earn a Certificate of Completion from Uplatz that validates their practical expertise in LangChain and vector databases. This credential affirms your readiness to build, optimize, and deploy AI systems powered by memory, retrieval, and intelligent data access. As AI workflows move from stateless prompts to persistent, context-rich interactions, this certification equips you with the tools and insights to stay ahead. Whether you’re seeking a role in applied AI development, LLMOps, or building next-gen intelligent products, this certification will distinguish your profile. With real-world projects and integration experience, the certificate reflects both architectural understanding and implementation skills using cutting-edge open-source tools.

Career & Jobs Back to Top
The demand for LLM-integrated apps is skyrocketing across industries—from legal and healthcare to edtech and customer support. LangChain and vector database skills are becoming essential in the modern AI developer’s toolkit, especially in roles requiring retrieval-augmented generation (RAG), semantic search, memory-enabled agents, and hybrid intelligence.
 
Job roles that benefit from this course include:
  • AI Application Developer
  • LLM Engineer
  • Vector Search Engineer
  • ML Solutions Architect
  • Conversational AI Developer
  • Semantic Search Specialist
  • Full-Stack AI Engineer
  • Data Engineer (AI-focused)
Organizations are looking for developers who can build LLM-based products with intelligence, personalization, and context. These include chatbots that can access company documents, customer service agents with persistent memory, or AI copilots that retrieve knowledge from CRMs and internal wikis.
 
By completing this course, you'll be able to bridge the gap between static language models and real-world, dynamic data—skills that are highly valued in AI product teams, research labs, and startups. Freelancers and consultants can offer RAG-based systems as client solutions, making them stand out in a competitive market.
 
With the rise of enterprise LLMOps and vector-native applications, this course gives you both the practical know-how and architectural insights to build scalable, intelligent applications.
Interview Questions Back to Top
1. What is LangChain and what problem does it solve?
LangChain is a Python framework for building LLM applications that require memory, context, tools, and data integration. It abstracts LLM workflows and allows chaining and retrieval.
 
2. What is a vector database?
A vector database stores high-dimensional vectors and allows fast similarity search for use cases like semantic search and document retrieval.
 
3. What is Retrieval-Augmented Generation (RAG)?
RAG is a technique where LLMs retrieve relevant documents or context from a vector store before generating responses, improving accuracy and grounding.
 
4. How are embeddings used in vector search?
Embeddings are vector representations of text; they allow comparing semantic similarity between queries and stored content using cosine or dot product.
 
5. What vector databases are supported by LangChain?
LangChain supports FAISS, Chroma, Pinecone, Weaviate, Qdrant, and others.
 
6. What is the role of memory in LangChain?
Memory components store past user inputs, summaries, or embeddings to create contextual and stateful interactions in AI applications.
 
7. How do agents work in LangChain?
Agents use tools and reasoning loops to decide how to fulfill tasks, making them ideal for complex workflows with API calls and document search.
 
8. What is chunking and why is it important?
Chunking splits large documents into smaller parts for better embedding generation and search performance in vector databases.
 
9. How does metadata filtering improve retrieval?
It allows filtering vector search results based on tags like document type, source, or timestamp, improving relevance.
 
10. How can LangChain applications be deployed?
They can be deployed as APIs using FastAPI or Flask, or served via Streamlit/UIs, and containerized for platforms like AWS, GCP, or Vercel.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.
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