DeterministicFakeEmbedding#
- class langchain_core.embeddings.fake.DeterministicFakeEmbedding[source]#
Bases:
Embeddings,BaseModelDeterministic fake embedding model for unit testing purposes.
This embedding model creates embeddings by sampling from a normal distribution with a seed based on the hash of the text.
Do not use this outside of testing, as it is not a real embedding model.
- Instantiate:
from langchain_core.embeddings import DeterministicFakeEmbedding embed = DeterministicFakeEmbedding(size=100)
- Embed single text:
input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3])
[-0.700234640213188, -0.581266257710429, -1.1328482266445354]
- Embed multiple texts:
input_texts = ["Document 1...", "Document 2..."] vectors = embed.embed_documents(input_texts) print(len(vectors)) # The first 3 coordinates for the first vector print(vectors[0][:3])
2 [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257]
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param size: int [Required]#
The size of the embedding vector.
- async aembed_documents(texts: List[str]) List[List[float]]#
Asynchronous Embed search docs.
- Parameters:
texts (List[str]) – List of text to embed.
- Returns:
List of embeddings.
- Return type:
List[List[float]]
- async aembed_query(text: str) List[float]#
Asynchronous Embed query text.
- Parameters:
text (str) – Text to embed.
- Returns:
Embedding.
- Return type:
List[float]
Examples using DeterministicFakeEmbedding