> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Multimedia Processing
> Build a knowledge graph from audio and image files and query their summaries
A minimal guide to ingesting non-text files — an audio recording and an image — into cognee. Use it when the knowledge you need lives in recordings, screenshots, charts, or scans rather than in documents.
## Before You Start
* Complete [Quickstart](/getting-started/quickstart) to understand basic operations
* Ensure you have [LLM Providers](/setup-configuration/llm-providers) configured — the audio file needs a provider with a transcription endpoint, and the image needs a vision model
* Read [Loaders](/core-concepts/further-concepts/loaders) for how cognee turns audio and images into the text it then processes
* Have `text_to_speech.mp3` and `example.png` in a `multimedia_audio_image_processing_example_data/` directory next to the script — you can download the [example media files](https://github.com/topoteretes/cognee/tree/dev/examples/guides/multimedia_audio_image_processing_example_data) from the cognee GitHub repository
## Code in Action
```python theme={null}
import asyncio
import os
import pathlib
import cognee
from cognee import SearchType
from cognee.shared.logging_utils import ERROR, setup_logging
async def main():
# Create a clean slate for cognee -- reset data and system state
await cognee.forget(everything=True)
# cognee knowledge graph will be created based on the text
# and description of these files
mp3_file_path = os.path.join(
pathlib.Path(__file__).parent,
"multimedia_audio_image_processing_example_data/text_to_speech.mp3",
)
png_file_path = os.path.join(
pathlib.Path(__file__).parent,
"multimedia_audio_image_processing_example_data/example.png",
)
# Remember the files and create knowledge graph memory
await cognee.remember([mp3_file_path, png_file_path], self_improvement=False)
# Query cognee for summaries of the data in the multimedia files
search_results = await cognee.recall(
query_type=SearchType.SUMMARIES,
query_text="What is in the multimedia files?",
)
# Display search results
for result_text in search_results:
print(result_text)
if __name__ == "__main__":
logger = setup_logging(log_level=ERROR)
asyncio.run(main())
```
## What Just Happened
### Step 1: Locate the Media Files
```python theme={null}
# Create a clean slate for cognee -- reset data and system state
await cognee.forget(everything=True)
# cognee knowledge graph will be created based on the text
# and description of these files
mp3_file_path = os.path.join(
pathlib.Path(__file__).parent,
"multimedia_audio_image_processing_example_data/text_to_speech.mp3",
)
png_file_path = os.path.join(
pathlib.Path(__file__).parent,
"multimedia_audio_image_processing_example_data/example.png",
)
```
Forgetting everything first means the summaries you read at the end come only from this run. The two files are resolved relative to the script itself, inside its `multimedia_audio_image_processing_example_data/` directory — swap in your own paths to ingest different media.
### Step 2: Remember the Audio and the Image
```python theme={null}
# Remember the files and create knowledge graph memory
await cognee.remember([mp3_file_path, png_file_path], self_improvement=False)
```
`remember()` picks a loader per file from its type: the `.mp3` goes to the audio loader for transcription, the `.png` to the image loader for vision transcription. From there both are ordinary text, so the same chunking, extraction, and summarization steps build one graph across the two files.
### Step 3: Recall the Summaries
```python theme={null}
# Query cognee for summaries of the data in the multimedia files
search_results = await cognee.recall(
query_type=SearchType.SUMMARIES,
query_text="What is in the multimedia files?",
)
# Display search results
for result_text in search_results:
print(result_text)
```
`SearchType.SUMMARIES` returns the summaries generated during ingestion rather than asking an LLM to answer the question, which makes it a direct way to see what cognee understood from each file. Everything printed here comes from the transcriptions, so it is also the quickest check that the audio and image were read correctly.
## Advanced Usage
Images are transcribed with an extraction-oriented prompt that asks for entities, their attributes, relationships, and any visible text — set `IMAGE_EXTRACTION_ENABLED="false"` to fall back to a short caption instead. With `IMAGE_OCR_ENABLED="true"` and `pip install cognee[rapidocr]`, a local OCR pass appends the text it recognizes to the transcription, which helps on dense screenshots and scans. See [Loaders](/core-concepts/further-concepts/loaders) for every image setting and its default.
The same call handles the rest of the audio extensions (`.wav`, `.flac`, `.m4a`, and more) and the image formats the vision loader claims. Video files take the video loader, which transcribes the audio track with inline `[HH:MM:SS]` timestamps; `.mp4` and `.webm` work as-is, other containers need `ffmpeg` on your `PATH`.
How each file type is turned into text before ingestion.
The full parameter surface of the ingestion call this guide uses.
Every search mode, including when to prefer SUMMARIES.