
Most common position in a pipeline	Before PreProcessors , or right at the beginning of an indexing pipeline
Mandatory run variables	"sources": File paths
Output variables	"documents": A list of documents
API reference	Converters
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/tika.py

Overview

The TikaDocumentConverter component converts files of different types (pdf, docx, html, and others) into documents. You can use it in an indexing pipeline to index the contents of files into a Document Store. It takes a list of file paths or ByteStream objects as input and outputs the converted result as a list of documents. Optionally, you can attach metadata to the documents through the meta input parameter.

This integration uses Apache Tika to parse the files and requires a running Tika server.

The easiest way to run Tika is by using Docker: docker run -d -p 127.0.0.1:9998:9998 apache/tika:latest.
For more options on running Tika on Docker, see the Tika documentation.

When you initialize the TikaDocumentConverter component, you can specify a custom URL of the Tika server you are using through the parameter tika_url. The default URL is "http://localhost:9998/tika".

Usage

You need to install tika package to use the TikaDocumentConverter component:

pip install tika

On its own

from haystack.components.converters import TikaDocumentConverter

converter = TikaDocumentConverter()

converter.run(sources=[Path("my_file.pdf")])

In a pipeline

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters import TikaDocumentConverter
from haystack.components.preprocessors import DocumentCleaner
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", TikaDocumentConverter())
pipeline.add_component("cleaner", DocumentCleaner())
pipeline.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=5))
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
pipeline.connect("converter", "cleaner")
pipeline.connect("cleaner", "splitter")
pipeline.connect("splitter", "writer")

pipeline.run({"converter": {"sources": file_paths}})