Examples | Indexify Extractors | Python Client | TypeScript Client
LLM applications backed by Indexify will never answer outdated information.
Indexify is an open-source engine for building fast data pipelines for unstructured data (video, audio, images and documents) using re-usable extractors for embedding, transformation and feature extraction. Indexify keeps vectordbs, structured databases (Postgres) updated automatically when pipelines produce embedding or structured data.
Applications can query indexes and databases using semantic search and SQL queries.
ETL-based ingestion systems process files in batches, suitable for offline use cases with infrequent data updates. In contrast, Indexify’s pipelines function as live APIs, instantly processing files or text upon ingestion like any online system. Indexify efficiently schedules data processing tasks across thousands of machines, enabling real-time extraction for RAG applications or agents where up-to-date indexes are crucial.
- Incremental Extraction when content is updated. Indexfiy processes only the chunks which have changed when documents, video or audio are updated.
- Extractor SDK allows plugging in any custom model or API in pipelines.
- Many pre-built extractors for embedding and handling PDF, Image and Video.
- Works with any LLM Framework. Built in support for Langchain, DSPy, etc.
- Runs locally for prototyping without any external dependencies.
- Works with many Blob Stores, Vector Stores and Structured Databases
- Automation to deploy to Kubernetes in production.
To get started follow our documentation.
curl https://getindexify.ai | sh
./indexify server -dvirtualenv ve
source ve/bin/activate
pip install indexify indexify-extractor-sdk requestsindexify-extractor download tensorlake/minilm-l6
indexify-extractor download tensorlake/pdfextractor
indexify-extractor download tensorlake/yolo-extractor
indexify-extractor download tensorlake/chunk-extractor
indexify-extractor download tensorlake/summarization
indexify-extractor download tensorlake/whisper-asr
indexify-extractor join-serverThis example shows how to implement RAG on text
from indexify import IndexifyClient, ExtractionGraph
client = IndexifyClient()
extraction_graph_spec = """
name: 'sportsknowledgebase'
extraction_policies:
- extractor: 'tensorlake/minilm-l6'
name: 'minilml6'
"""
extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)
print("indexes", client.indexes())content_ids = client.add_documents("sportsknowledgebase", ["Adam Silver is the NBA Commissioner", "Roger Goodell is the NFL commisioner"])client.wait_for_extraction(content_ids)
context = client.search_index(name="sportsknowledgebase.minilml6.embedding", query="NBA commissioner", top_k=1)The method wait_for_extraction blocks the client until Indexify runs the extraction on the ingested content. In production applications you will most likely won't block your application, and let extraction be asynchronous.
This example shows how to create a pipeline that extracts from PDF documents. More information here - https://docs.getindexify.ai/usecases/pdf_extraction/
from indexify import IndexifyClient, ExtractionGraph
import requests
client = IndexifyClient()
extraction_graph_spec = """
name: 'pdfqa'
extraction_policies:
- extractor: 'tensorlake/pdfextractor'
name: 'docextractor'
"""
extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)with open("sample.pdf", 'wb') as file:
file.write((requests.get("https://extractor-files.diptanu-6d5.workers.dev/scientific-paper-example.pdf")).content)
content_id = client.upload_file("pdfqa", "sample.pdf")client.wait_for_extraction(content_id)
print(client.get_extracted_content(content_id, "pdfqa", "docextractor"))This example shows how to transcribe audio, and create a pipeline that embeds the transcription More details about Audio Use Cases - https://docs.getindexify.ai/usecases/audio_extraction/
from indexify import IndexifyClient, ExtractionGraph
import requests
client = IndexifyClient()
extraction_graph_spec = """
name: 'audiosummary'
extraction_policies:
- extractor: 'tensorlake/whisper-asr'
name: 'transcription'
- extractor: 'tensorlake/summarization'
name: 'summarizer'
input_params:
max_length: 400
min_length: 300
chunk_method: str = 'recursive'
content_source: 'transcription'
- extractor: 'tensorlake/minilm-l6'
name: 'minilml6'
content_source: 'summarizer'
"""
extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)with open("sample.mp3", 'wb') as file:
file.write((requests.get("https://extractor-files.diptanu-6d5.workers.dev/sample-000009.mp3")).content)
content_id = client.upload_file("audiosummary", "sample.mp3")Adding Texts and Files can be a time consuming process and by default we allow asynchronous ingestion for parallel operations. However the following codes might fail until the extraction has been completed. To make it a blocking call, use
client.wait_for_extraction(content_id)after getting the content_id from above.
client.wait_for_extraction(content_id)
print("transcription ----")
print(client.get_extracted_content(content_id, "audiosummary", "transcription"))
print("summary ----")
print(client.get_extracted_content(content_id, "audiosummary", "summarizer"))context = client.search_index(name="audiosummary.minilml6.embedding", query="President of America", top_k=1)This example shows how to create a pipeline that performs object detection on images using the Yolo extractor. More details about Image understanding and retrieval - https://docs.getindexify.ai/usecases/image_retrieval/
from indexify import IndexifyClient, ExtractionGraph
import requests
client = IndexifyClient()
extraction_graph_spec = """
name: 'imageknowledgebase'
extraction_policies:
- extractor: 'tensorlake/yolo-extractor'
name: 'object_detection'
"""
extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)with open("sample.jpg", 'wb') as file:
file.write((requests.get("https://extractor-files.diptanu-6d5.workers.dev/people-standing.jpg")).content)
content_id = client.upload_file("imageknowledgebase", "sample.jpg")client.wait_for_extraction(content_id)
client.get_extracted_content(content_id, "imageknowledgebase", "object_detection")The Yolo extractor adds the objects detected in the image in the database. The table name is same as the extraction graph name
print(client.sql_query("select * from imageknowledgebase where object_name='person';"))Indexify can work with any LLM framework, or with your applications directly. We have an example of a Langchain application here and DSPy here.
We have a ton of other extractors, you can list them and try them out -
indexify-extractor listAny extraction or transformation algorithm can be expressed as an Indexify Extractor. We provide an SDK to write your own. Please follow the docs here for instructions.
Extractors which produce structured data from content, such as bounding boxes and object type, or line items of invoices are stored in structured store. You can query extracted structured data using Indexify's SQL interface.
We have an example here
Please open an issue to discuss new features, or join our Discord group. Contributions are welcome, there are a bunch of open tasks we could use help with!
If you want to contribute on the Rust codebase, please read the developer readme.