Chat with your data using OpenAI, Pinecone, Airbyte and Langchain

The article provides a detailed tutorial on how to leverage vector databases and Language Model Machines (LLMs) to make sense of unstructured data using Airbyte and Dagster. The tutorial covers how to extract unstructured data from various sources using Airbyte, how to efficiently load data into a vector database, and how to integrate a vector database into your LLM to ask questions about your proprietary data. The tutorial also explains how to build a real-world use case, such as a bot that can answer questions in plain English about the code base, documentation, and reference previous conversations.

The tutorial also covers how to fetch Github issues, load them into a vector database, create a connection, and set up a chat interface. It also explains how to put the chatbot on Slack and add additional data sources like scraping a documentation website and fetching Slack messages. The tutorial concludes by explaining how to wrap up the process and provides a survey for readers interested in leveraging Airbyte to ship data to their LLM-based applications.

Key takeaways:

The tutorial explains how to use Dagster and Airbyte to power LLM-supported use cases and how to leverage vector databases and LLMs to make sense out of unstructured data.
The tutorial walks through the process of extracting unstructured data from a variety of sources using Airbyte, loading data into a vector database, and integrating a vector database into your LLM to ask questions about your proprietary data.
The tutorial also provides a step-by-step guide on how to build a chat interface that can answer questions in natural language, using Langchain as an orchestration framework.
Finally, the tutorial explains how to extend the chatbot script with a Slack integration, allowing the bot to answer questions in a Slack channel.

Chat with your data using OpenAI, Pinecone, Airbyte and Langchain | Airbyte

Key takeaways:

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