201 lines
6.8 KiB
Python
201 lines
6.8 KiB
Python
from typing import TYPE_CHECKING, Any
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from pydantic_ai.embeddings import Embedder
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from pydantic_ai.embeddings.openai import OpenAIEmbeddingModel
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from pydantic_ai.providers.ollama import OllamaProvider
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from pydantic_ai.providers.openai import OpenAIProvider
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from haiku.rag.config import AppConfig, Config
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if TYPE_CHECKING:
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from PIL import Image as PILImage
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from haiku.rag.store.models.chunk import Chunk
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ImageInput = "bytes | PILImage.Image"
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class EmbedderWrapper:
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"""Wrapper around pydantic-ai Embedder with explicit query/document methods.
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Subclasses set ``supports_images = True`` and override the image methods
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when the underlying model can encode pictures into the same vector space.
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"""
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supports_images: bool = False
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def __init__(self, embedder: Embedder | None, vector_dim: int):
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self._embedder = embedder
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self._vector_dim = vector_dim
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@property
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def vector_dim(self) -> int:
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return self._vector_dim
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async def embed_query(self, text: str) -> list[float]:
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"""Embed a search query."""
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assert self._embedder is not None
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result = await self._embedder.embed_query(text)
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return list(result.embeddings[0])
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async def embed_documents(self, texts: list[str]) -> list[list[float]]:
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"""Embed documents/chunks for indexing."""
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if not texts:
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return []
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assert self._embedder is not None
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result = await self._embedder.embed_documents(texts)
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return [list(e) for e in result.embeddings]
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async def embed_image(self, image: "Any") -> list[float]:
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"""Embed a single image into the same vector space as text.
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Multimodal providers override this. Picture embedding is single-image:
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vLLM's ``/v1/embeddings`` accepts one image per request via the
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``messages`` superset. Callers loop when they need many.
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"""
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raise NotImplementedError(
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f"{type(self).__name__} does not support image embedding. "
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"Configure a multimodal provider (e.g. provider='vllm')."
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)
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def contextualize(chunks: list["Chunk"]) -> list[str]:
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"""Prepare chunk content for embedding/FTS by adding context.
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Prepends section headings to chunk content for better semantic search.
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Args:
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chunks: List of chunks to contextualize.
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Returns:
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List of contextualized text strings.
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"""
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texts = []
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for chunk in chunks:
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meta = chunk.get_chunk_metadata()
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if meta.headings:
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text = "\n".join(meta.headings) + "\n" + chunk.content
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else:
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text = chunk.content
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texts.append(text)
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return texts
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async def embed_chunks(
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chunks: list["Chunk"], embedder: "EmbedderWrapper", config: AppConfig = Config
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) -> list["Chunk"]:
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"""Generate embeddings for chunks, dispatching text vs picture variants.
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Text chunks are contextualized (headings prepended) and routed through
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``embed_documents``. Picture chunks (those carrying ``_picture_data``)
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are routed through ``embed_images`` and require a multimodal embedder.
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Vectors land in the original chunk order.
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"""
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if not chunks:
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return []
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from haiku.rag.store.models.chunk import Chunk
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text_chunks: list[Chunk] = []
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picture_chunks: list[Chunk] = []
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for chunk in chunks:
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if chunk._picture_data is not None:
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picture_chunks.append(chunk)
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else:
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text_chunks.append(chunk)
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text_embeddings: list[list[float]] = []
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if text_chunks:
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texts = contextualize(text_chunks)
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batch_size = config.embeddings.batch_size
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for i in range(0, len(texts), batch_size):
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batch = texts[i : i + batch_size]
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text_embeddings.extend(await embedder.embed_documents(batch))
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picture_embeddings: list[list[float]] = []
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if picture_chunks:
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if not embedder.supports_images:
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raise ValueError(
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"Picture chunks require a multimodal embedder. Configure "
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"provider='vllm', or omit picture chunks."
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)
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for chunk in picture_chunks:
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picture_embeddings.append(await embedder.embed_image(chunk._picture_data))
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text_iter = iter(text_embeddings)
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picture_iter = iter(picture_embeddings)
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return [
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Chunk(
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id=chunk.id,
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document_id=chunk.document_id,
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content=chunk.content,
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metadata=chunk.metadata,
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order=chunk.order,
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document_uri=chunk.document_uri,
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document_title=chunk.document_title,
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document_meta=chunk.document_meta,
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embedding=(
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next(picture_iter)
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if chunk._picture_data is not None
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else next(text_iter)
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),
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)
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for chunk in chunks
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]
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def get_embedder(config: AppConfig = Config) -> EmbedderWrapper:
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"""Factory function to get the appropriate embedder based on the configuration.
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Args:
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config: Configuration to use. Defaults to global Config.
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Returns:
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An embedder instance configured according to the config.
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"""
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embedding_model = config.embeddings.model
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provider = embedding_model.provider
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model_name = embedding_model.name
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vector_dim = embedding_model.vector_dim
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if provider == "ollama":
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# Use model-level base_url if set, otherwise fall back to providers config
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base_url = embedding_model.base_url or config.providers.ollama.base_url
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if not base_url.rstrip("/").endswith("/v1"):
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base_url = base_url.rstrip("/") + "/v1"
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model = OpenAIEmbeddingModel(
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model_name,
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provider=OllamaProvider(base_url=base_url),
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)
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return EmbedderWrapper(Embedder(model), vector_dim)
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if provider == "openai":
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if embedding_model.base_url:
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model = OpenAIEmbeddingModel(
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model_name,
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provider=OpenAIProvider(base_url=embedding_model.base_url),
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)
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return EmbedderWrapper(Embedder(model), vector_dim)
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return EmbedderWrapper(Embedder(f"openai:{model_name}"), vector_dim)
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if provider == "voyageai":
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return EmbedderWrapper(Embedder(f"voyageai:{model_name}"), vector_dim)
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if provider == "cohere":
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return EmbedderWrapper(Embedder(f"cohere:{model_name}"), vector_dim)
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if provider == "sentence-transformers":
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return EmbedderWrapper(
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Embedder(f"sentence-transformers:{model_name}"), vector_dim
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)
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if provider == "vllm":
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from haiku.rag.embeddings.vllm import VLLMMultimodalEmbedder
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base_url = embedding_model.base_url or "http://localhost:8000/v1"
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if not base_url.rstrip("/").endswith("/v1"):
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base_url = base_url.rstrip("/") + "/v1"
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return VLLMMultimodalEmbedder(model_name, vector_dim, base_url=base_url)
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raise ValueError(f"Unsupported embedding provider: {provider}")
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