hf = HuggingFaceInstructEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field encode_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass when calling the encode method of the model.
field model_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass to the model.
field model_name: str = 'hkunlp/instructor-large'#
Model name to use.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.LlamaCppEmbeddings[source]#
Wrapper around llama.cpp embedding models.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: abetlen/llama-cpp-python
Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool = False#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html