wehub-resource-sync
145 lines
6.4 KiB
Markdown
145 lines
6.4 KiB
Markdown
---
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layout: default
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title: Embedding Models
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parent: Components
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nav_order: 6
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description: overview of the major modules and classes of LLMWare
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permalink: /components/embedding_models
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---
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# Embedding Models
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---
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llmware supports 30+ embedding models out of the box in the default ModelCatalog, with easy extensibility to add other
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popular open source embedding models from HuggingFace or Sentence Transformers.
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To get a list of the currently supported embedding models:
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```python
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from llmware.models import ModelCatalog
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embedding_models = ModelCatalog().list_embedding_models()
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for i, models in enumerate(embedding_models):
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print(f"embedding models: {i} - {models}")
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```
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Supported popular models include:
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- Sentence Transformers - `all-MiniLM-L6-v2`, `all-mpnet-base-v2`
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- Jina AI - `jinaai/jina-embeddings-v2-base-en`, `jinaai/jina-embeddings-v2-small-en`
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- Nomic - `nomic-ai/nomic-embed-text-v1`
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- Industry BERT - `industry-bert-insurance`, `industry-bert-contracts`, `industry-bert-asset-management`, `industry-bert-sec`, `industry-bert-loans`
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- OpenAI - `text-embedding-ada-002`, `text-embedding-3-small`, `text-embedding-3-large`
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We also support top embedding models from BAAI, thenlper, llmrails/ember, Google, and Cohere. We are constantly looking to add new innovative open source models to this list
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so please let us know if you are looking for support for a specific embedding model, and usually within 1-2 days, we can test and add to the ModelCatalog.
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# Using an Embedding Model
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Embedding models in llmware can be installed directly by `ModelCatalog().load_model("model_name")`, but in most cases,
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the name of the embedding model will be passed to the `install_new_embedding` handler in the Library class when creating a new
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embedding. Once that is completed, the embedding model is captured in the Library metadata on the LibraryCard as part of the
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embedding record for that library, and as a result, often times, does not need to be used explicitly again, e.g.,
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```python
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from llmware.library import Library
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library = Library().create_new_library("my_library")
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# parses the content from the documents in the file path, text chunks and indexes in a text collection database
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library.add_files(input_folder_path="/local/path/to/my_files", chunk_size=400, max_chunk_size=600, smart_chunking=1)
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# creates embeddings - and keeps synchronized records of which text chunks have been embedded to enable incremental use
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library.install_new_embedding(embedding_model_name="jinaai/jina-embeddings-v2-small-en",
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vector_db="milvus",
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batch_size=100)
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```
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Once the embeddings are installed on the library, you can look up the embedding status to see the updated embeddings, and confirm that
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the model has been correctly captured:
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```python
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from llmware.library import Library
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library = Library().load_library("my_library")
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embedding_record = library.get_embedding_status()
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print("\nupdate: embedding record - ", embedding_record)
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```
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And then you can run semantic retrievals on the Library, using the Query class in the retrievals module, e.g.:
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```python
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from llmware.library import Library
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from llmware.retrieval import Query
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library = Library().load_library("my_library")
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# queries are constructed by creating a Query object, and passing a library as input
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query_results = Query(library).semantic_query("my query", result_count=20)
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for qr in query_results:
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print("my query results: ", qr)
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```
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Need help or have questions?
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============================
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Check out the [llmware videos](https://www.youtube.com/@llmware) and [GitHub repository](https://github.com/llmware-ai/llmware).
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Reach out to us on [GitHub Discussions](https://github.com/llmware-ai/llmware/discussions).
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# About the project
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`llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home).
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## Contributing
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Please first discuss any change you want to make publicly, for example on GitHub via raising an [issue](https://github.com/llmware-ai/llmware/issues) or starting a [new discussion](https://github.com/llmware-ai/llmware/discussions).
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You can also write an email or start a discussion on our Discord channel.
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Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md).
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## Code of conduct
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We welcome everyone into the ``llmware`` community.
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[View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository.
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## ``llmware`` and [AI Bloks](https://www.aibloks.com/home)
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``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``.
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The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service.
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[AI Bloks](https://www.aibloks.com/home) was founded by [Namee Oberst](https://www.linkedin.com/in/nameeoberst/) and [Darren Oberst](https://www.linkedin.com/in/darren-oberst-34a4b54/) in October 2022.
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## License
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`llmware` is distributed by an [Apache-2.0 license](https://github.com/llmware-ai/llmware/blob/main/LICENSE).
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## Thank you to the contributors of ``llmware``!
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<ul class="list-style-none">
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{% for contributor in site.github.contributors %}
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<li class="d-inline-block mr-1">
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<a href="{{ contributor.html_url }}">
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<img src="{{ contributor.avatar_url }}" width="32" height="32" alt="{{ contributor.login }}">
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</a>
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</li>
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{% endfor %}
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</ul>
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---
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<ul class="list-style-none">
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<li class="d-inline-block mr-1">
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<a href="https://discord.gg/MhZn5Nc39h"><span><i class="fa-brands fa-discord"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.youtube.com/@llmware"><span><i class="fa-brands fa-youtube"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://huggingface.co/llmware"><span> <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" class="hugging-face-logo"/> </span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.linkedin.com/company/aibloks/"><span><i class="fa-brands fa-linkedin"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://twitter.com/AiBloks"><span><i class="fa-brands fa-square-x-twitter"></i></span></a>
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</li>
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<li class="d-inline-block mr-1">
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<a href="https://www.instagram.com/aibloks/"><span><i class="fa-brands fa-instagram"></i></span></a>
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</li>
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</ul>
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---
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