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Developing a new Extractor

Custom Extractors allows you to plug in any data processing code you want into an Indexify pipeline. Examples - Custom chunking algorithms for your data, or using your own model in a pipeline.

Concepts

An extractor receives unstructured data in a Content object and transforms the content into one or many Content, each output content can optionally have a Feature added to it during extraction. For example, you could split a PDF content into three content with their text and corresponding embedding or some other metadata such as tabular information encoded as JSON metadata.

The content object has the following properties -

  • data - The unstructured data encoded as raw bytes.
  • content_type - The mime type of the data. For example, text/plain, image/png, etc. This allows you to decode the bytes correctly.
  • Feature - Optional Feature associated with the content, such as embedding or JSON metadata. Embeddings are stored in indexes in Vector Store and JSON metadata are stored in structured store such as Postgres. Features are searchable, if they are embedding you can perform KNN search on the resulting index, if it's JSON you could do JSON path queries on them.

The Content object is defined here

Feature

Feature is some form of extracted information from unstructured data. Embedding, or JSON metadata are the possible features for now. Features extracted are indexed and searchable. Features can be easily constructed from helper methods You can optionally give features a name such as my_custom_text_embedding, we use the names as suffixes of index names.

Install the Extractor SDK

pip install indexify-extractor-sdk

Start from a template

The following command will create a template for a new extractor in the current directory.

curl https://codeload.github.com/tensorlakeai/indexify-extractor-template/tar.gz/main | tar -xz  indexify-extractor-template-main

Implement the Extractor

The template contains a MyExtractor extractor in the custom_extractor.py file. Implement the extract method, which accepts a Content and produces a list of Content. The output content list is usually some form of transformation of the input and some features related to it. The valid output features are Embedding and Metadata.

def extract(self, content: Content) -> List[Content]:
    """
    Extracts features from content.
    """
    output: List[Content] = []
    chunks = content.chunk()
    for chunk in chunks:
        embedding = get_embedding(chunk)
        entities = run_ner_model(chunk)
        embed_chunk = Content.from_text(text=chunk, feature=Feature.embedding(name="text_embedding", values=embedding))
        metadata_chunk = Content.from_text(text=chunk, feature=Feature.metadata(name="metadata", json.dumps(entities))),
        output.append([embed_chunk, metadata_chunk])
    return output

extract - Takes a Content object which have the bytes of unstructured data and the mime-type. You can pass a list of JSON, text, video, audio and documents into the extract method. It should return a list of transformed or derived content, or a list of features. Examples -

  • Text Chunking: Input(Text) -> List(Text)
  • Audio Transcription: Input(Audio) -> List(Text)
  • Speaker Diarization: Input(Audio) -> List(JSON of text and corresponding speaker ids)
  • PDF Extraction: Input(PDF) -> List(Text, Images and JSON representation of tables)
  • PDF to Markdown: Input(PDF) -> List(Markdown)

In this example we iterate over a list of content, chunk each content, run a NER model and an embedding model over each chunk and return them as features along with the chunks of text.

Extractor Dependencies

Use any python or native system dependencies in your extractors because we can package them in a container to deploy them to production.

sample_input: A sample input your extractor can process and a sample input config. This will be run when the extractor starts up to make sure the extractor is functioning properly.

List dependencies

Add a requirements.txt file to the folder if it has any python dependencies.

Extractor Description

Add a name to your extractor, a description of what it does and python and system dependencies. These goes in attributes/properties of your Extractor class -

  • name - The name of the extractor. We use the name of the extractor also to name the container package.
  • description - Long description of the extractor
  • system_dependencies - List of dependencies to be installed when packaging the extractor in a docker container.
  • input_mime_types - The list of input data types the extractor can handle. We use standard mime types as the API. Default is ["text/plain], and you can override or specify which ones your extractor supports from the list here

Test the extractor locally

Extractors are just python modules so you can write a unit test like any any other python module. You should also test the extractor using the indexify binary to make sure it works as expected.

indexify-extractor describe custom_extractor:MyExtractor
indexify-extractor run-local custom_extractor:MyExtractor --text "hello world"

Install the extractor locally

indexify-extractor install-local custom_extractor:MyExtractor 

This makes the extractor available locally to Indexify server

Join with Control Plane

You can join the extractor with the Indexfy server for it to receive streams of content to extract from

indexify-extractor join-server

Package the extractor

Once you have tested the package, package it into a container. From here the extractor is deployable to production using Kubernetes, ECS or other container based deployment platforms.

indexify-extractor package </path/to/extractor>:<ExtractorClass>

If you want to package an extractor in a container that support NVIDIA GPU, you can pass the --gpu flag to the package command.

The packaged extractors should also be visible as a docker container locally.

docker images