> ## 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.
# Multilingual Ingestion
> Translate non-English content before building the knowledge graph
A minimal guide to enabling translation during ingestion. Cognee includes a built-in translation pipeline that detects languages and translates content before graph extraction, so non-English documents are indexed as English knowledge.
**Before you start:**
* Complete [Quickstart](getting-started/quickstart) to understand basic operations
* Ensure you have [LLM Providers](setup-configuration/llm-providers) configured
* Have non-English text or documents to process
## What Translation Does
* Detects language automatically using the `langdetect` library
* Skips chunks already in the target language
* Translates using one of three providers: `llm` (default), `google`, or `azure`
* Stores original text alongside the translation in the knowledge graph
## Configuration
Set these environment variables in your `.env` file:
```dotenv theme={null}
# Provider: "llm" (default), "google", or "azure"
TRANSLATION_PROVIDER=llm
# Target language ISO 639-1 code (default: "en")
TARGET_LANGUAGE=en
# Minimum detection confidence to trigger translation (default: 0.8)
CONFIDENCE_THRESHOLD=0.8
```
The `llm` provider uses your existing [LLM configuration](setup-configuration/llm-providers) — no additional keys needed.
## Using Translation in a Pipeline
Insert `translate_content` as a pipeline task between chunk extraction and graph building:
```python theme={null}
import asyncio
import os
import cognee
from cognee.infrastructure.llm import get_max_chunk_tokens
from cognee.tasks.documents import classify_documents, extract_chunks_from_documents
from cognee.shared.data_models import KnowledgeGraph
from cognee.tasks.translation import translate_content
from cognee.modules.pipelines import Task, run_pipeline
from cognee.tasks.graph import extract_graph_from_data
from cognee.tasks.storage import add_data_points
# the translated text is in data_chunks[].text,
async def drop_translation_metadata(data_chunks):
for chunk in data_chunks:
chunk.contains = None
return data_chunks
async def main():
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
text_fr = "La mémoire artificielle permet aux agents IA de retenir des informations complexes."
tasks = [
Task(classify_documents),
Task(extract_chunks_from_documents, max_chunk_size=get_max_chunk_tokens()),
Task(translate_content, target_language="en", translation_provider="llm"),
Task(drop_translation_metadata),
Task(extract_graph_from_data, graph_model=KnowledgeGraph),
Task(add_data_points),
]
async for _ in run_pipeline(tasks=tasks, datasets=["multilingual"]):
pass
visualize_graph_path = os.path.join(
os.path.dirname(__file__), ".artifacts", "multilingual.html"
)
await cognee.visualize_graph(visualize_graph_path)
asyncio.run(main())
```
`translate_content` mutates chunks in-place: `chunk.text` is replaced with the translation and the original is preserved in a `TranslatedContent` data point attached to the chunk.
## Additional Information
For one-off translation without a pipeline, use `translate_text`:
```python theme={null}
from cognee.tasks.translation import translate_text
result = await translate_text("Bonjour le monde!", target_language="en")
print(result.translated_text) # "Hello world!"
print(result.source_language) # "fr"
```
All three providers translate non-English chunks to your `TARGET_LANGUAGE`. Pick based on cost, setup, and quality trade-offs:
| Provider | Setup | Cost | Best for |
| --------------- | ------------------------------------------------------------------- | --------------------------------------------- | ----------------------------------------------------------------- |
| `llm` (default) | None — reuses your [LLM config](/setup-configuration/llm-providers) | Per-token LLM usage; higher quality, slower | Mixed/long-form documents where context-aware translation matters |
| `google` | Install `google-cloud-translate`, Google Cloud project | Per-character pricing; fast batch translation | High-volume ingestion across many languages |
| `azure` | Azure Cognitive Services key + region | Per-character pricing; fast batch translation | Enterprise deployments already on Azure |
**Supported languages:** detection uses `langdetect` (\~55 languages). The `llm` provider supports any language the underlying model handles. Google Translate and Azure Translator each support 130+ language codes, including locale-specific variants such as `zh-CN` and `zh-TW` — see the [Google Cloud Translation language list](https://cloud.google.com/translate/docs/languages) and [Azure Translator language list](https://learn.microsoft.com/azure/ai-services/translator/language-support) for the full set.
Set `TRANSLATION_PROVIDER` in `.env` to switch — no code changes required.
Uses your existing LLM — no extra configuration needed. Works with any provider configured via `LLM_PROVIDER` and `LLM_API_KEY`.
```dotenv theme={null}
TRANSLATION_PROVIDER=llm
```
Requires the `google-cloud-translate` package and a Google Cloud project.
```bash theme={null}
pip install google-cloud-translate
```
```dotenv theme={null}
TRANSLATION_PROVIDER=google
GOOGLE_TRANSLATE_API_KEY=your_api_key
GOOGLE_PROJECT_ID=your_project_id
```
Requires an Azure Cognitive Services resource.
```dotenv theme={null}
TRANSLATION_PROVIDER=azure
AZURE_TRANSLATOR_KEY=your_key
AZURE_TRANSLATOR_REGION=eastus
# Endpoint defaults to https://api.cognitive.microsofttranslator.com
AZURE_TRANSLATOR_ENDPOINT=https://api.cognitive.microsofttranslator.com
```
| Variable | Default | Description |
| ----------------------------- | ------- | ---------------------------- |
| `TRANSLATION_BATCH_SIZE` | `10` | Chunks per translation batch |
| `TRANSLATION_MAX_RETRIES` | `3` | Retry attempts on failure |
| `TRANSLATION_TIMEOUT_SECONDS` | `30` | Request timeout |
Learn to build custom task pipelines
Configure your LLM provider
Understand knowledge graph fundamentals