From a86f77c4c1aded1a7683fb7b0d561c8dd65cfd66 Mon Sep 17 00:00:00 2001
From: Ravi Theja <ravi03071991@gmail.com>
Date: Wed, 14 Feb 2024 20:47:49 +0530
Subject: [PATCH] Update Nomic Embeddings with v1.5 (#10711)
---
.../llama_index/embeddings/nomic/base.py | 11 ++++++++++-
1 file changed, 10 insertions(+), 1 deletion(-)
diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py
index 1a38b9f1a8..b88aeed10c 100644
--- a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py
+++ b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py
@@ -27,6 +27,7 @@ class NomicEmbedding(BaseEmbedding):
# Instance variables initialized via Pydantic's mechanism
query_task_type: Optional[str] = Field(description="Query Embedding prefix")
document_task_type: Optional[str] = Field(description="Document Embedding prefix")
+ dimensionality: Optional[int] = Field(description="Dimension of the Embedding")
model_name: str = Field(description="Embedding model name")
_model: Any = PrivateAttr()
@@ -38,6 +39,7 @@ class NomicEmbedding(BaseEmbedding):
callback_manager: Optional[CallbackManager] = None,
query_task_type: Optional[str] = "search_query",
document_task_type: Optional[str] = "search_document",
+ dimensionality: Optional[int] = 768,
**kwargs: Any,
) -> None:
if query_task_type not in TASK_TYPES or document_task_type not in TASK_TYPES:
@@ -63,12 +65,14 @@ class NomicEmbedding(BaseEmbedding):
_model=embed,
query_task_type=query_task_type,
document_task_type=document_task_type,
+ dimensionality=dimensionality,
**kwargs,
)
self._model = embed
self.model_name = model_name
self.query_task_type = query_task_type
self.document_task_type = document_task_type
+ self.dimensionality = dimensionality
@classmethod
def class_name(cls) -> str:
@@ -78,7 +82,12 @@ class NomicEmbedding(BaseEmbedding):
self, texts: List[str], task_type: Optional[str] = None
) -> List[List[float]]:
"""Embed sentences using NomicAI."""
- result = self._model.text(texts, model=self.model_name, task_type=task_type)
+ result = self._model.text(
+ texts,
+ model=self.model_name,
+ task_type=task_type,
+ dimensionality=self.dimensionality,
+ )
return result["embeddings"]
def _get_query_embedding(self, query: str) -> List[float]:
--
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