How to load data from GitHub to Weaviate

Learn how to use Airbyte to synchronize your GitHub data into Weaviate within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a GitHub connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Weaviate for your extracted GitHub data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the GitHub to Weaviate in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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How to Sync to Manually

Step 1: Clone GitHub Repository Locally

First, clone the GitHub repository to your local machine. This can be achieved by using Git. Open your terminal and run `git clone `. This will download all the data from the GitHub repository onto your local system.

Set up a local instance of Weaviate. You can do this by using Docker. First, ensure Docker is installed on your machine, then pull the Weaviate Docker image with the command `docker pull semitechnologies/weaviate`. Start the Weaviate instance using `docker run -d -p 8080:8080 semitechnologies/weaviate`.

Examine the cloned repository's data structure. Determine the format and type of data you want to move to Weaviate. Common formats include JSON, CSV, or other structured data types. Understanding the data structure is crucial for mapping it to Weaviate's schema.

Based on the data structure from GitHub, define a schema in Weaviate. This involves specifying class names, properties, and data types that match your data. You can do this by interacting with Weaviate's RESTful API. Use tools like `curl` or Postman to send POST requests to `http://localhost:8080/v1/schema` with the necessary schema JSON.

If your data is not already in JSON format, transform it accordingly. You can write scripts in Python, JavaScript, or any preferred language to convert the data into JSON objects that align with the schema defined in Weaviate. Ensure that each JSON object corresponds to an instance of the defined class in Weaviate.

Use the Weaviate REST API to upload the JSON data. For each JSON object, send a POST request to `http://localhost:8080/v1/objects` with the JSON payload. This will create instances in Weaviate according to your schema. Automate this process by writing a script that iterates through your data and sends the necessary requests.

Finally, verify that the data has been correctly uploaded to Weaviate. Use the REST API to query the data at `http://localhost:8080/v1/objects` and ensure that all expected data is present and accurately reflects the original dataset from GitHub. Adjust any discrepancies by revisiting the schema or data transformation steps.

By following these steps, you can successfully move data from a GitHub repository to a Weaviate instance without relying on third-party connectors or integrations.