D_i.SrSSKrSSKrSSKJr SSKJr SSKJr \R"\ 5 SSK r SSS5 "SS\\5r "SS \\5rg!,(df  N'=f) zAModule contains a few fake embedding models for testing purposes.N) BaseModel)override) Embeddingsc\rSrSr%Sr\\S'S\\4Sjr \ S\\ S\\\4Sj5r \ S\ S\\4S j5r S rg ) FakeEmbeddingsaFake embedding model for unit testing purposes. This embedding model creates embeddings by sampling from a normal distribution. !!! danger "Toy model" Do not use this outside of testing, as it is not a real embedding model. Instantiate: ```python from langchain_core.embeddings import FakeEmbeddings embed = FakeEmbeddings(size=100) ``` Embed single text: ```python input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3]) ``` ```python [-0.700234640213188, -0.581266257710429, -1.1328482266445354] ``` Embed multiple texts: ```python 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]) ``` ```python 2 [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257] ``` sizereturnc~[[RR5R UR S95$N)r )listnprandom default_rngnormalr )selfs X/home/james-whalen/.local/lib/python3.13/site-packages/langchain_core/embeddings/fake.py_get_embeddingFakeEmbeddings._get_embedding:s,BII))+22 2BCCtextscLUVs/sHo R5PM sn$s snfNrrr_s rembed_documentsFakeEmbeddings.embed_documents=s!/45u!##%u555s!textc"UR5$rrrrs r embed_queryFakeEmbeddings.embed_queryAs""$$rN)__name__ __module__ __qualname____firstlineno____doc__int__annotations__r floatrrstrrr"__static_attributes__r$rrrrsy$L I+DU D6T#Y64U 3D66%%U %%rrc\rSrSr%Sr\\S'S\S\\4Sjr \ S\ S\4Sj5r \ S \\ S\\\4S j5r\ S\ S\\4S j5rS rg )DeterministicFakeEmbeddingFaDeterministic 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. !!! danger "Toy model" Do not use this outside of testing, as it is not a real embedding model. Instantiate: ```python from langchain_core.embeddings import DeterministicFakeEmbedding embed = DeterministicFakeEmbedding(size=100) ``` Embed single text: ```python input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3]) ``` ```python [-0.700234640213188, -0.581266257710429, -1.1328482266445354] ``` Embed multiple texts: ```python 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]) ``` ```python 2 [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257] ``` r seedr c[RRU5n[UR UR S95$r )rrrr rr )rr2rngs rr)DeterministicFakeEmbedding._get_embeddingqs0ii##D)CJJDIIJ.//rrc[[R"URS55R 5S5S-$)z@Get a seed for the random generator, using the hash of the text.zutf-8ri)r*hashlibsha256encode hexdigest)rs r _get_seed$DeterministicFakeEmbedding._get_seedvs17>>$++g"67AACRH5PPrrchUVs/sH o RURU5S9PM" sn$s snfN)r2rr;rs rr*DeterministicFakeEmbedding.embed_documents{s/EJKU##):#;UKKKs'/c>URURU5S9$r>r?r!s rr"&DeterministicFakeEmbedding.embed_querys""t(<"==rr$N)r%r&r'r(r)r*r+r r,r staticmethodr-r;rrr"r.r$rrr0r0Fs%N I+0304;0 QQQQLT#YL4U 3DLL>>U >>rr0)r) contextlibr7pydanticrtyping_extensionsrlangchain_core.embeddingsrsuppress ImportErrornumpyrrr0r$rrrKsVG&0%&3%Z3%l;>Y;>u&%s A A#