> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Embedding

The Unstructured open-source library does not offer built-in support for calling embedding providers to obtain embeddings for pieces of text.

Alternatively, the [Unstructured Ingest CLI](/open-source/ingestion/overview#unstructured-ingest-cli) and the [Unstructured Ingest Python library](/open-source/ingestion/python-ingest)
offer built-in support for calling embedding providers as part of an ingest pipeline. [Learn how](/open-source/how-to/embedding).

Also, you can use common third-party tools and libraries to get embeddings for document elements' text within JSON files that are
produced by calling the Unstructured open-source library. For example, the following sample Python script:

1. Takes an Unstructured open-source library-generated JSON file as input.
2. Reads in the JSON file's contents as a JSON object.
3. Uses the [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
   model on Hugging Face to generate embeddings for each `text` field of each document element in the JSON file.
4. Adds the generated embeddings next to each corresponding `text` field in the original JSON.
5. Saves the results back to the original JSON file.

```python Python theme={null}
# Filename: embeddings.py
# pip install langchain sentence_transformers

import sys
import json
from langchain_huggingface.embeddings import HuggingFaceEmbeddings

if __name__ == "__main__":
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )
    
    # Get the JSON file's path.
    if len(sys.argv) < 2:
        print("Error: Specify the path to the input JSON file.")
        print("For example, 'python embeddings.py myfile.json'")
        sys.exit(1)

    file_path = sys.argv[1]
    
    try:
        # Get the JSON file's contents.
        with open(file_path, 'r') as file:
            file_elements = json.load(file)

        # Process each element in the JSON file.
        for element in file_elements:
            # Get the element's "text" field.
            text = element["text"]
            # Generate the embeddings for that "text" field.
            query_result = embeddings.embed_query(text)
            # Add the embeddings to that element as an "embeddings" field.
            element["embeddings"] = query_result

        # Save the updated JSON back into the original file.
        with open(file_path, 'w') as file:
            json.dump(file_elements, file, indent=2)

        print(f"Done! Updated JSON saved to '{file_path}'.")

    except FileNotFoundError:
        print(f"Error: File '{file_path}' not found.")
    except IOError:
        print(f"Error: Unable to access file '{file_path}'.")
```

## Additional resources

For information about how to use Python scripts to call various embedding providers, see for example:

* [Amazon Bedrock](https://docs.aws.amazon.com/code-library/latest/ug/python_3_bedrock-runtime_code_examples.html#amazon_titan_text_embeddings)
* [Hugging Face](https://huggingface.co/blog/getting-started-with-embeddings)
* [OctoAI](https://octo.ai/blog/introducing-octoais-embedding-api-to-power-your-rag-needs/)
* [OpenAI](https://platform.openai.com/docs/guides/embeddings)
* [together.ai](https://docs.together.ai/docs/embeddings-overview)
* [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings)
* [Voyage AI](https://docs.voyageai.com/docs/embeddings)