Previous Index Next

Step 6: Chaining all the components

We’ve come a long way! We’ve extracted valuable content from our website, cleaned and organized it, and stored it in a way our chatbot can access it. Now it’s time to create the core functionality – allowing the chatbot to understand user queries and respond using the knowledge it has gathered.

We add functionalities

The Power of OpenAI: We’ll leverage the capabilities of OpenAI’s large language models (LLMs) like GPT-3. These LLMs are incredibly powerful AI models trained on massive amounts of text data, allowing them to understand and respond to human language in a comprehensive way.
Connecting the Dots with Langchain: Langchain comes back into play! We’ll use it to create a chain of functionalities. This chain will:
1. Take a user query as input.
2. Use the Chroma vector store to retrieve documents relevant to the query based on their content (embeddings, if created).
3. Pass the retrieved documents and the user query to the OpenAI LLM.
4. The LLM will then analyze the documents and the query, allowing it to generate a response tailored to the user’s specific question.

Imagine this script as the brain of the chatbot. It takes user input, retrieves relevant information from the knowledge base, and uses the power of OpenAI’s LLM to craft informative responses.

enter image description here

Project - Building a RAG Chatbot from Your Website Data using OpenAI, Langchain and Vector Database

Step 6: Chaining all the components

XP

Please login to comment

Be the first one to comment!