Weaviate - Unstructured (original) (raw)

Send processed data from Unstructured to Weaviate.

The requirements are as follows.

For the Unstructured UI or the Unstructured API: only Weaviate Cloud clusters are supported.
For Unstructured Ingest: Weaviate Cloud clusters,Weaviate installed locally, and Embedded Weaviate are supported.
For Weaviate installed locally, you will need the name of the target collection on the local instance.
For Embedded Weaviate, you will need the instance’s connection URL and the name of the target collection on the instance.
For Weaviate Cloud, you will need:
- A Weaviate database instance. The following information assumes that you have a Weaviate Cloud (WCD) account with a Weaviate database cluster in that account.Create a WCD account. Create a database cluster. For other database options, learn more.
- The URL and API key for the database cluster. Get the URL and API key.
- The name of the target collection in the database. Create a collection.
  An existing collection is not required. At runtime, the collection behavior is as follows:
  For the Unstructured UI or the Unstructured API:
  * If an existing collection name is specified, and Unstructured generates embeddings, but the number of dimensions that are generated does not match the existing collection’s embedding settings, the run will fail. You must change your Unstructured embedding settings or your existing collection’s embedding settings to match, and try the run again.
  * If a collection name is not specified, Unstructured creates a new collection in your Weaviate cluster. If Unstructured generates embeddings, the new collection’s name will be U<short-workflow-id>_<short-embedding-model-name>_<number-of-dimensions>. If Unstructured does not generate embeddings, the new collection’s name will be U<short-workflow-id.
For Unstructured Ingest:
* If an existing collection name is specified, and Unstructured generates embeddings, but the number of dimensions that are generated does not match the existing collection’s embedding settings, the run will fail. You must change your Unstructured embedding settings or your existing collection’s embedding settings to match, and try the run again.
* If a collection name is not specified, Unstructured creates a new collection in your Weaviate cluster. The new collection’s name will be Unstructuredautocreated.
If Unstructured creates a new collection and generates embeddings, you will not see an embeddings property in tools such as the Weaviate CloudCollections user interface. To view the generated embeddings, you can run a Weaviate GraphQL query such as the following. In this query, replace <collection-name> with the name of the new collection, and replace <property-name> with the name of each additional available property that you want to return results for, such as text, type, element_id, record_id, and so on. The embeddings will be returned in the vector property.
```
{  
  Get {  
    <collection-name> {  
      _additional {  
        vector  
      }  
      <property-name>  
      <property-name>  
    }  
  }  
}  
```

Weaviate requires an existing collection to have a data schema before you add data. At minimum, this schema must contain the record_id property, as follows:

{
    "class": "Elements",
    "properties": [
        {
            "name": "record_id",
            "dataType": ["text"]
        }
    ]
}

Weaviate generates any additional properties based on the incoming data.

If you have specific schema requirements, you can define the schema manually. Unstructured cannot provide a schema that is guaranteed to work for everyone in all circumstances. This is because these schemas will vary based on your source files’ types; how you want Unstructured to partition, chunk, and generate embeddings; any custom post-processing code that you run; and other factors.

You can adapt the following collection schema example for your own specific schema requirements:

{
    "class": "Elements",
    "properties": [
        {
            "name": "record_id",
            "dataType": ["text"]
        },
        {
            "name": "element_id",
            "dataType": ["text"]
        },
        {
            "name": "text",
            "dataType": ["text"]
        },
        {
            "name": "embeddings",
            "dataType": ["number[]"]
        },
        {
            "name": "metadata",
            "dataType": ["object"],
            "nestedProperties": [
                {
                    "name": "parent_id",
                    "dataType": ["text"]
                },
                {
                    "name": "page_number",
                    "dataType": ["text"]
                },
                {
                    "name": "is_continuation",
                    "dataType": ["boolean"]
                },
                {
                    "name": "orig_elements",
                    "dataType": ["text"]
                },
                {
                    "name": "partitioner_type",
                    "dataType": ["text"]
                }
            ]
        }
    ]
}