FileToFileContent

FileToFileContent reads local files and converts them into FileContent objects. These are ready for multimodal AI pipelines that need to pass PDFs and other file types to an LLM.

Most common position in a pipeline	Before a ChatPromptBuilder in a query pipeline
Mandatory run variables	sources: A list of file paths or ByteStreams
Output variables	file_contents: A list of FileContent objects
API reference	Converters
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/file_to_file_content.py

Overview

FileToFileContent processes a list of file sources and converts them into FileContent objects that can be embedded into a ChatMessage and passed to a Language Model.

Each source can be:

• A file path (string or Path), or
• A ByteStream object.

Optionally, you can provide extra provider-specific information using the extra parameter. This can be a single dictionary (applied to all files) or a list matching the length of sources.

Support for passing files to LLMs varies by provider. Some providers do not support file inputs, some restrict support to PDF files, and others accept a wider range of file types.

Usage

On its own

python

from haystack.components.converters import FileToFileContent

converter = FileToFileContent()

sources = ["document.pdf", "recording.mp3"]

result = converter.run(sources=sources)
file_contents = result["file_contents"]
print(file_contents)

## [
## FileContent(
##     base64_data='JVBERi0x...', mime_type='application/pdf',
##     filename='document.pdf', extra={}
## ),
## FileContent(
##     base64_data='SUQzBA...', mime_type='audio/mpeg',
##     filename='recording.mp3', extra={}
## )
## ]

In a pipeline

Use FileToFileContent together with a LinkContentFetcher and a ChatPromptBuilder to build a pipeline that fetches a remote file, converts it, and passes it to an LLM.

python

from haystack.components.converters import FileToFileContent
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.generators.chat.openai import OpenAIChatGenerator
from haystack.components.builders import ChatPromptBuilder

from haystack import Pipeline

template = """
{% message role="user"%}
{% for file in files %}
{{ file | templatize_part }}
{% endfor %}
What's the main takeaway of the following document? Just one sentence.
{% endmessage %}
"""

pipeline = Pipeline()
pipeline.add_component("fetcher", LinkContentFetcher())
pipeline.add_component("converter", FileToFileContent())
pipeline.add_component("prompt_builder", ChatPromptBuilder(template=template))
pipeline.add_component("llm", OpenAIChatGenerator(model="gpt-4.1-mini"))

pipeline.connect("fetcher", "converter")
pipeline.connect("converter", "prompt_builder")
pipeline.connect("prompt_builder", "llm")

results = pipeline.run({"fetcher": {"urls": ["https://arxiv.org/pdf/2309.08632"]}})

print(results["llm"]["replies"][0].text)

# The document is a satirical paper humorously claiming that pretraining a
# small language model exclusively on evaluation benchmark test sets can achieve
# perfect performance, highlighting issues of data contamination in model
# evaluation.