embed_documents() — langchain Function Reference
Architecture documentation for the embed_documents() function in huggingface_endpoint.py from the langchain codebase.
Entity Profile
Dependency Diagram
graph TD bcc591c5_13d4_9230_07fe_b338b9fa9f55["embed_documents()"] 3e578245_e691_a97e_a094_2b8fa981436a["HuggingFaceEndpointEmbeddings"] bcc591c5_13d4_9230_07fe_b338b9fa9f55 -->|defined in| 3e578245_e691_a97e_a094_2b8fa981436a 4ef55f39_d22f_e607_6c86_d27f1631763f["embed_query()"] 4ef55f39_d22f_e607_6c86_d27f1631763f -->|calls| bcc591c5_13d4_9230_07fe_b338b9fa9f55 style bcc591c5_13d4_9230_07fe_b338b9fa9f55 fill:#6366f1,stroke:#818cf8,color:#fff
Relationship Graph
Source Code
libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py lines 115–130
def embed_documents(self, texts: list[str]) -> list[list[float]]:
"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
# replace newlines, which can negatively affect performance.
texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
# api doc: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/embed
responses = self.client.feature_extraction(text=texts, **_model_kwargs)
return responses.tolist()Domain
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Called By
Source
Frequently Asked Questions
What does embed_documents() do?
embed_documents() is a function in the langchain codebase, defined in libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py.
Where is embed_documents() defined?
embed_documents() is defined in libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py at line 115.
What calls embed_documents()?
embed_documents() is called by 1 function(s): embed_query.
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