add support for nvidia/llama-3.2-nv-embedqa-1b-v2's dimensions param

libs/ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py

@@ -1,6 +1,4 @@
-"""Embeddings Components Derived from NVEModel/Embeddings"""
-
-from typing import Any, List, Literal, Optional
+from typing import Any, Dict, List, Literal, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.outputs.llm_result import LLMResult
@@ -28,6 +26,8 @@ class NVIDIAEmbeddings(BaseModel, Embeddings):
     - truncate: "NONE", "START", "END", truncate input text if it exceeds the model's
         maximum token length. Default is "NONE", which raises an error if an input is
         too long.
+    - dimensions: int, the number of dimensions for the embeddings. This parameter is
+                  not supported by all models.
     """
 
     model_config = ConfigDict(
@@ -47,6 +47,13 @@ class NVIDIAEmbeddings(BaseModel, Embeddings):
             "Default is 'NONE', which raises an error if an input is too long."
         ),
     )
+    dimensions: Optional[int] = Field(
+        default=None,
+        description=(
+            "The number of dimensions for the embeddings. This parameter is not "
+            "supported by all models."
+        ),
+    )
     max_batch_size: int = Field(default=_DEFAULT_BATCH_SIZE)
 
     def __init__(self, **kwargs: Any):
@@ -67,6 +74,8 @@ def __init__(self, **kwargs: Any):
             trucate (str): "NONE", "START", "END", truncate input text if it exceeds
                             the model's context length. Default is "NONE", which raises
                             an error if an input is too long.
+            dimensions (int): The number of dimensions for the embeddings. This
+                              parameter is not supported by all models.
 
         API Key:
         - The recommended way to provide the API key is through the `NVIDIA_API_KEY`
@@ -125,14 +134,17 @@ def _embed(
         #  user: str                           -- ignored
         #  truncate: "NONE" | "START" | "END"  -- default "NONE", error raised if
         #                                         an input is too long
-        payload = {
+        #  dimensions: int                     -- not supported by all models
+        payload: Dict[str, Any] = {
             "input": texts,
             "model": self.model,
             "encoding_format": "float",
             "input_type": model_type,
         }
         if self.truncate:
             payload["truncate"] = self.truncate
+        if self.dimensions:
+            payload["dimensions"] = self.dimensions
 
         response = self._client.get_req(
             payload=payload,

libs/ai-endpoints/tests/integration_tests/test_embeddings.py

@@ -97,5 +97,81 @@ def test_embed_documents_truncate(
     assert len(output) == count
 
 
+@pytest.mark.parametrize("dimensions", [32, 64, 128, 2048])
+def test_embed_query_with_dimensions(
+    embedding_model: str, mode: dict, dimensions: int
+) -> None:
+    if embedding_model != "nvidia/llama-3.2-nv-embedqa-1b-v2":
+        pytest.skip("Model does not support custom dimensions.")
+    query = "foo bar"
+    embedding = NVIDIAEmbeddings(model=embedding_model, dimensions=dimensions, **mode)
+    assert len(embedding.embed_query(query)) == dimensions
+
+
+@pytest.mark.parametrize("dimensions", [32, 64, 128, 2048])
+def test_embed_documents_with_dimensions(
+    embedding_model: str, mode: dict, dimensions: int
+) -> None:
+    if embedding_model != "nvidia/llama-3.2-nv-embedqa-1b-v2":
+        pytest.skip("Model does not support custom dimensions.")
+    documents = ["foo bar", "bar foo"]
+    embedding = NVIDIAEmbeddings(model=embedding_model, dimensions=dimensions, **mode)
+    output = embedding.embed_documents(documents)
+    assert len(output) == len(documents)
+    assert all(len(doc) == dimensions for doc in output)
+
+
+@pytest.mark.parametrize("dimensions", [102400])
+def test_embed_query_with_large_dimensions(
+    embedding_model: str, mode: dict, dimensions: int
+) -> None:
+    if embedding_model != "nvidia/llama-3.2-nv-embedqa-1b-v2":
+        pytest.skip("Model does not support custom dimensions.")
+    query = "foo bar"
+    embedding = NVIDIAEmbeddings(model=embedding_model, dimensions=dimensions, **mode)
+    assert 2048 <= len(embedding.embed_query(query)) < dimensions
+
+
+@pytest.mark.parametrize("dimensions", [102400])
+def test_embed_documents_with_large_dimensions(
+    embedding_model: str, mode: dict, dimensions: int
+) -> None:
+    if embedding_model != "nvidia/llama-3.2-nv-embedqa-1b-v2":
+        pytest.skip("Model does not support custom dimensions.")
+    documents = ["foo bar", "bar foo"]
+    embedding = NVIDIAEmbeddings(model=embedding_model, dimensions=dimensions, **mode)
+    output = embedding.embed_documents(documents)
+    assert len(output) == len(documents)
+    assert all(2048 <= len(doc) < dimensions for doc in output)
+
+
+@pytest.mark.parametrize("dimensions", [-1])
+def test_embed_query_invalid_dimensions(
+    embedding_model: str, mode: dict, dimensions: int
+) -> None:
+    if embedding_model != "nvidia/llama-3.2-nv-embedqa-1b-v2":
+        pytest.skip("Model does not support custom dimensions.")
+    query = "foo bar"
+    with pytest.raises(Exception) as exc:
+        NVIDIAEmbeddings(
+            model=embedding_model, dimensions=dimensions, **mode
+        ).embed_query(query)
+    assert "400" in str(exc.value)
+
+
+@pytest.mark.parametrize("dimensions", [-1])
+def test_embed_documents_invalid_dimensions(
+    embedding_model: str, mode: dict, dimensions: int
+) -> None:
+    if embedding_model != "nvidia/llama-3.2-nv-embedqa-1b-v2":
+        pytest.skip("Model does not support custom dimensions.")
+    documents = ["foo bar", "bar foo"]
+    with pytest.raises(Exception) as exc:
+        NVIDIAEmbeddings(
+            model=embedding_model, dimensions=dimensions, **mode
+        ).embed_documents(documents)
+    assert "400" in str(exc.value)
+
+
 # todo: test max_length > max length accepted by the model
 # todo: test max_batch_size > max batch size accepted by the model