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from abc import ABC, abstractmethod
from typing import Generic, TypeVar, cast
from llm_engineering.application.networks import EmbeddingModelSingleton
from llm_engineering.domain.chunks import ArticleChunk, Chunk, PostChunk, RepositoryChunk
from llm_engineering.domain.embedded_chunks import (
EmbeddedArticleChunk,
EmbeddedChunk,
EmbeddedPostChunk,
EmbeddedRepositoryChunk,
)
from llm_engineering.domain.queries import EmbeddedQuery, Query
ChunkT = TypeVar("ChunkT", bound=Chunk)
EmbeddedChunkT = TypeVar("EmbeddedChunkT", bound=EmbeddedChunk)
embedding_model = EmbeddingModelSingleton()
class EmbeddingDataHandler(ABC, Generic[ChunkT, EmbeddedChunkT]):
"""
Abstract class for all embedding data handlers.
All data transformations logic for the embedding step is done here
"""
def embed(self, data_model: ChunkT) -> EmbeddedChunkT:
return self.embed_batch([data_model])[0]
def embed_batch(self, data_model: list[ChunkT]) -> list[EmbeddedChunkT]:
embedding_model_input = [data_model.content for data_model in data_model]
embeddings = embedding_model(embedding_model_input, to_list=True)
embedded_chunk = [
self.map_model(data_model, cast(list[float], embedding))
for data_model, embedding in zip(data_model, embeddings, strict=False)
]
return embedded_chunk
@abstractmethod
def map_model(self, data_model: ChunkT, embedding: list[float]) -> EmbeddedChunkT:
pass
class QueryEmbeddingHandler(EmbeddingDataHandler):
def map_model(self, data_model: Query, embedding: list[float]) -> EmbeddedQuery:
return EmbeddedQuery(
id=data_model.id,
author_id=data_model.author_id,
author_full_name=data_model.author_full_name,
content=data_model.content,
embedding=embedding,
metadata={
"embedding_model_id": embedding_model.model_id,
"embedding_size": embedding_model.embedding_size,
"max_input_length": embedding_model.max_input_length,
},
)
class PostEmbeddingHandler(EmbeddingDataHandler):
def map_model(self, data_model: PostChunk, embedding: list[float]) -> EmbeddedPostChunk:
return EmbeddedPostChunk(
id=data_model.id,
content=data_model.content,
embedding=embedding,
platform=data_model.platform,
document_id=data_model.document_id,
author_id=data_model.author_id,
author_full_name=data_model.author_full_name,
metadata={
"embedding_model_id": embedding_model.model_id,
"embedding_size": embedding_model.embedding_size,
"max_input_length": embedding_model.max_input_length,
},
)
class ArticleEmbeddingHandler(EmbeddingDataHandler):
def map_model(self, data_model: ArticleChunk, embedding: list[float]) -> EmbeddedArticleChunk:
return EmbeddedArticleChunk(
id=data_model.id,
content=data_model.content,
embedding=embedding,
platform=data_model.platform,
link=data_model.link,
document_id=data_model.document_id,
author_id=data_model.author_id,
author_full_name=data_model.author_full_name,
metadata={
"embedding_model_id": embedding_model.model_id,
"embedding_size": embedding_model.embedding_size,
"max_input_length": embedding_model.max_input_length,
},
)
class RepositoryEmbeddingHandler(EmbeddingDataHandler):
def map_model(self, data_model: RepositoryChunk, embedding: list[float]) -> EmbeddedRepositoryChunk:
return EmbeddedRepositoryChunk(
id=data_model.id,
content=data_model.content,
embedding=embedding,
platform=data_model.platform,
name=data_model.name,
link=data_model.link,
document_id=data_model.document_id,
author_id=data_model.author_id,
author_full_name=data_model.author_full_name,
metadata={
"embedding_model_id": embedding_model.model_id,
"embedding_size": embedding_model.embedding_size,
"max_input_length": embedding_model.max_input_length,
},
)
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