Now `encode_kwargs` used for both for documents and queries and this
leads to wrong embeddings. E. g.:
```python
model_kwargs = {"device": "cuda", "trust_remote_code": True}
encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}
model = HuggingFaceEmbeddings(
model_name="dunzhang/stella_en_400M_v5",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
)
query_embedding = np.array(
model.embed_query("What are some ways to reduce stress?",)
)
document_embedding = np.array(
model.embed_documents(
[
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
)
)
print(model._client.similarity(query_embedding, document_embedding)) # output: tensor([[0.8421, 0.3317]], dtype=torch.float64)
```
But from the [model
card](https://huggingface.co/dunzhang/stella_en_400M_v5#sentence-transformers)
expexted like this:
```python
model_kwargs = {"device": "cuda", "trust_remote_code": True}
encode_kwargs = {"normalize_embeddings": False}
query_encode_kwargs = {"normalize_embeddings": False, "prompt_name": "s2p_query"}
model = HuggingFaceEmbeddings(
model_name="dunzhang/stella_en_400M_v5",
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_encode_kwargs=query_encode_kwargs,
)
query_embedding = np.array(
model.embed_query("What are some ways to reduce stress?", )
)
document_embedding = np.array(
model.embed_documents(
[
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
)
)
print(model._client.similarity(query_embedding, document_embedding)) # tensor([[0.8398, 0.2990]], dtype=torch.float64)
```
Backwards compatible change that converts pydantic extras to literals
which is consistent with pydantic 2 usage.
- fireworks
- voyage ai
- mistralai
- mistral ai
- together ai
- huggigng face
- pinecone
First Pr for the langchain_huggingface partner Package
- Moved some of the hugging face related class from `community` to the
new `partner package`
Still needed :
- Documentation
- Tests
- Support for the new apply_chat_template in `ChatHuggingFace`
- Confirm choice of class to support for embeddings witht he
sentence-transformer team.
cc : @efriis
---------
Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
