Build Conversational Agents with Vector DBs - LangChain #9
We've seen in previous chapters how powerful retrieval augmentation (vector databases) and conversational agents (chatbots) can be. They become even more impressive when we begin using them together.
Conversational agents can struggle with data freshness, knowledge about specific domains, or accessing internal documentation. By coupling agents with retrieval augmentation tools, we no longer have these problems.
On the other side, using "naive" retrieval augmentation without the use of an agent means we will retrieve contexts with every query. Again, this isn't always ideal as not every query requires access to external knowledge.
Merging these methods gives us the best of both worlds. In this video, we'll learn how to do using the Pinecone vector database and OpenAI embedding and gpt-3.5-turbo completion models.
📌 Code notebook:
https://github.com/pinecone-io/exampl...
👋🏼 NLP + LLM Consulting:
https://aurelio.ai
🎙️ Support me on Patreon:
/ jamesbriggs
👾 Discord:
/ discord
00:00 LangChain Agents with Vector DBs
01:27 Code Setup and Data Prep
03:14 Vector DB Pipeline Setup
05:35 Indexing with OpenAI and Pinecone
07:53 Querying via LangChain
09:33 Building the Retrieval Augmented Chatbot
13:52 Using the Conversational Agent Chatbot
17:17 Real-world Usage of this Method
#artificialintelligence #langchain #openai #chatgpt #nlp #deeplearning