trulens.feedback.embeddings¶

trulens.feedback.embeddings ¶

Classes¶

Embeddings ¶

Bases: WithClassInfo, SerialModel

Embedding related feedback function implementations.

Attributes¶

tru_class_info `instance-attribute` ¶

tru_class_info: Class

Class information of this pydantic object for use in deserialization.

Using this odd key to not pollute attribute names in whatever class we mix this into. Should be the same as CLASS_INFO.

Functions¶

__rich_repr__ ¶

__rich_repr__() -> Result

Requirement for pretty printing using the rich package.

load `staticmethod` ¶

load(obj, *args, **kwargs)

Deserialize/load this object using the class information in tru_class_info to lookup the actual class that will do the deserialization.

model_validate `classmethod` ¶

model_validate(*args, **kwargs) -> Any

Deserialized a jsonized version of the app into the instance of the class it was serialized from.

Note

This process uses extra information stored in the jsonized object and handled by WithClassInfo.

init ¶

__init__(embed_model: BaseEmbedding)

Instantiates embeddings for feedback functions.

Example

Below is just one example. Embedders from llama-index are supported: https://docs.llamaindex.ai/en/latest/module_guides/models/embeddings/

from llama_index.embeddings.openai import OpenAIEmbedding
from trulens.feedback.embeddings import Embeddings

embed_model = OpenAIEmbedding()

f_embed = Embedding(embed_model=embed_model)

PARAMETER	DESCRIPTION
`embed_model`	Supports embedders from llama-index: https://docs.llamaindex.ai/en/latest/module_guides/models/embeddings/ TYPE: `BaseEmbedding`

cosine_distance ¶

cosine_distance(
    query: str, document: str
) -> Union[float, Tuple[float, Dict[str, str]]]

Runs cosine distance on the query and document embeddings

Example

Below is just one example. Embedders from llama-index are supported: https://docs.llamaindex.ai/en/latest/module_guides/models/embeddings/

from llama_index.embeddings.openai import OpenAIEmbedding
from trulens.feedback.embeddings import Embeddings

embed_model = OpenAIEmbedding()

# Create the feedback function
f_embed = feedback.Embeddings(embed_model=embed_model)
f_embed_dist = feedback.Feedback(f_embed.cosine_distance)                .on_input_output()

PARAMETER	DESCRIPTION
`query`	A text prompt to a vector DB. TYPE: `str`
`document`	The document returned from the vector DB. TYPE: `str`

RETURNS	DESCRIPTION
`float`	the embedding vector distance TYPE: `Union[float, Tuple[float, Dict[str, str]]]`

manhattan_distance ¶

manhattan_distance(
    query: str, document: str
) -> Union[float, Tuple[float, Dict[str, str]]]

Runs L1 distance on the query and document embeddings

Example

Below is just one example. Embedders from llama-index are supported: https://docs.llamaindex.ai/en/latest/module_guides/models/embeddings/

from llama_index.embeddings.openai import OpenAIEmbedding
from trulens.feedback.embeddings import Embeddings

embed_model = OpenAIEmbedding()

# Create the feedback function
f_embed = feedback.Embeddings(embed_model=embed_model)
f_embed_dist = feedback.Feedback(f_embed.manhattan_distance)                .on_input_output()

PARAMETER	DESCRIPTION
`query`	A text prompt to a vector DB. TYPE: `str`
`document`	The document returned from the vector DB. TYPE: `str`

RETURNS	DESCRIPTION
`float`	the embedding vector distance TYPE: `Union[float, Tuple[float, Dict[str, str]]]`

euclidean_distance ¶

euclidean_distance(
    query: str, document: str
) -> Union[float, Tuple[float, Dict[str, str]]]

Runs L2 distance on the query and document embeddings

Example

Below is just one example. Embedders from llama-index are supported: https://docs.llamaindex.ai/en/latest/module_guides/models/embeddings/

from llama_index.embeddings.openai import OpenAIEmbedding
from trulens.feedback.embeddings import Embeddings

embed_model = OpenAIEmbedding()

# Create the feedback function
f_embed = feedback.Embeddings(embed_model=embed_model)
f_embed_dist = feedback.Feedback(f_embed.euclidean_distance)                .on_input_output()

PARAMETER	DESCRIPTION
`query`	A text prompt to a vector DB. TYPE: `str`
`document`	The document returned from the vector DB. TYPE: `str`

RETURNS	DESCRIPTION
`float`	the embedding vector distance TYPE: `Union[float, Tuple[float, Dict[str, str]]]`

trulens.feedback.embeddings¶