From the course: Level up LLM applications development with LangChain and OpenAI
Unlock the full course today
Join today to access over 24,800 courses taught by industry experts.
Project setup
From the course: Level up LLM applications development with LangChain and OpenAI
Project setup
- Chatbots are often used for online services and customer support, to offer automated services and answer general inquiries. And chat bots are also used to help customers navigate a website to help them find responses to their queries. And so LangChain has a number of components designed to help build question answering applications by using the RAG architecture. And so to create our custom Q&A chat bots, we're going to go through the different stages, starting with indexing, then retrieval and generation. First, let me walk you through the project setup. So I'm going to invite you to open the README file inside the number three directory. And so inside the README file, you're going to find instructions about installation and configuration of the project, starting with the creation and activation of a virtual environment. So we're going to start with this command, line 18. And I'm going to use this command because I am a Mac user. And you should see then so once it is created, this…
Contents
-
-
-
-
-
(Locked)
RAG: Overview and architecture2m 12s
-
(Locked)
Breaking down the RAG pipeline2m 50s
-
(Locked)
Project setup3m 33s
-
(Locked)
Load and split documents into chunks5m 6s
-
(Locked)
Initialize a vector store (Chroma) and ingest documents5m 6s
-
(Locked)
Create the chain: Prompt + model + parser5m 39s
-
(Locked)
Create the chain: Add context with a retriever4m 48s
-
(Locked)
Pass data with RunnablePassthrough and query data3m 27s
-
Challenge: Create a custom agent with history3m 12s
-
Solution: Add a chain with chat history5m 19s
-
(Locked)
Solution: Context- and history-aware chatbot5m 49s
-
(Locked)
-
-
-
-
-
-
-
-