LangChain & AI Training

❮ Previous Next ❯ Alternative Vector Databases While FAISS is powerful, there are other vector stores that can also be used with LangChain: Pinecone •  Cloud-native and scalable•  Supports metadata filtering and high-volume workloads•  Ideal for production-ready applications Weaviate •  Open-source with cloud deployment options•  Offers semantic filtering, GraphQL API, and hybrid search ChromaDB •  […]

❮ Previous Next ❯ What is FAISS? FAISS (Facebook AI Similarity Search) is an open-source library that allows fast and scalable similarity search over vector data. It’s widely used for building AI apps that need to match input queries with relevant stored knowledge. Installing FAISS Note: Use faiss-gpu if you’re working with a GPU for

❮ Previous Next ❯ Introduction to Vector Databases Prompt Engineering is the art of designing effective inputs (prompts) to get the most accurate, relevant, and helpful responses from a Large Language Model (LLM). Since LLMs respond based on the text they receive, carefully crafting your prompt directly impacts the quality of the output. What is

❮ Previous Next ❯ Implementing Memory in LangChain LangChain provides easy tools to add memory into your applications, whether for short-term conversation context or long-term user history. Simple conversation memory LangChain includes built-in memory types such as ConversationBufferMemory, which allows your LLM to remember previous messages during a session. Example: This setup enables the model

❮ Previous Next ❯ What is Memory in AI Applications? In the context of AI and chatbots, memory refers to the ability of the system to remember previous interactions and use that information to provide more meaningful and context-aware responses. Without memory, every interaction would be treated as a standalone conversation — the AI wouldn’t

❮ Previous Next ❯ Hands-on: Experimenting with Prompts LangChain allows you to test different prompt strategies with ease. You can dynamically insert variables into templates and observe how changing wording impacts results. Code Sample: Writing and testing different prompt styles Try experimenting with:    •  Open-ended vs. specific prompts   •  Formal vs. conversational tone   •