diff --git a/docs/docs/integrations/text_embedding/nomic.ipynb b/docs/docs/integrations/text_embedding/nomic.ipynb index 6f6b11d4aa..6f731668c3 100644 --- a/docs/docs/integrations/text_embedding/nomic.ipynb +++ b/docs/docs/integrations/text_embedding/nomic.ipynb @@ -58,7 +58,7 @@ "source": [ "from langchain_nomic.embeddings import NomicEmbeddings\n", "\n", - "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1\")" + "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1.5\")" ] }, { @@ -106,6 +106,28 @@ " [\"This is a content of the document\", \"This is another document\"]\n", ")" ] + }, + { + "cell_type": "markdown", + "id": "7a331dc3", + "metadata": {}, + "source": [ + "### Custom Dimensionality\n", + "\n", + "Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka) to enable variable-length embeddings with a single model. This means that you can specify the dimensionality of the embeddings at inference time. The model supports dimensionality from 64 to 768." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "993f65c8", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1.5\", dimensionality=256)\n", + "\n", + "embeddings.embed_query(\"My query to look up\")" + ] } ], "metadata": { diff --git a/libs/partners/nomic/langchain_nomic/embeddings.py b/libs/partners/nomic/langchain_nomic/embeddings.py index 36c476df0d..55152c417f 100644 --- a/libs/partners/nomic/langchain_nomic/embeddings.py +++ b/libs/partners/nomic/langchain_nomic/embeddings.py @@ -3,6 +3,7 @@ from typing import List, Optional import nomic # type: ignore from langchain_core.embeddings import Embeddings +from nomic import embed # type: ignore class NomicEmbeddings(Embeddings): @@ -21,6 +22,7 @@ class NomicEmbeddings(Embeddings): *, model: str, nomic_api_key: Optional[str] = None, + dimensionality: Optional[int] = None, ): """Initialize NomicEmbeddings model. @@ -33,6 +35,7 @@ class NomicEmbeddings(Embeddings): if _api_key: nomic.login(_api_key) self.model = model + self.dimensionality = dimensionality def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]: """Embed texts. @@ -42,10 +45,13 @@ class NomicEmbeddings(Embeddings): task_type: the task type to use when embedding. One of `search_query`, `search_document`, `classification`, `clustering` """ - # TODO: do this via nomic.embed when fixed in nomic sdk - from nomic import embed - output = embed.text(texts=texts, model=self.model, task_type=task_type) + output = embed.text( + texts=texts, + model=self.model, + task_type=task_type, + dimensionality=self.dimensionality, + ) return output["embeddings"] def embed_documents(self, texts: List[str]) -> List[List[float]]: diff --git a/libs/partners/nomic/poetry.lock b/libs/partners/nomic/poetry.lock index 77c5f06419..fa80e5f83f 100644 --- a/libs/partners/nomic/poetry.lock +++ b/libs/partners/nomic/poetry.lock @@ -57,13 +57,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = 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[tool.poetry] name = "langchain-nomic" -version = "0.0.1" +version = "0.0.2" description = "An integration package connecting Nomic and LangChain" authors = [] readme = "README.md" @@ -13,7 +13,7 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" langchain-core = ">=0.0.12" -nomic = "^3.0.7" +nomic = "^3.0.12" [tool.poetry.group.test] optional = true diff --git a/libs/partners/nomic/tests/integration_tests/test_embeddings.py b/libs/partners/nomic/tests/integration_tests/test_embeddings.py index 6800deb839..9fcc7ed9de 100644 --- a/libs/partners/nomic/tests/integration_tests/test_embeddings.py +++ b/libs/partners/nomic/tests/integration_tests/test_embeddings.py @@ -1,9 +1,10 @@ """Test Nomic embeddings.""" + from langchain_nomic.embeddings import NomicEmbeddings def test_langchain_nomic_embedding_documents() -> None: - """Test cohere embeddings.""" + """Test nomic embeddings.""" documents = ["foo bar"] embedding = NomicEmbeddings(model="nomic-embed-text-v1") output = embedding.embed_documents(documents) @@ -12,8 +13,17 @@ def test_langchain_nomic_embedding_documents() -> None: def test_langchain_nomic_embedding_query() -> None: - """Test cohere embeddings.""" + """Test nomic embeddings.""" document = "foo bar" embedding = NomicEmbeddings(model="nomic-embed-text-v1") output = embedding.embed_query(document) assert len(output) > 0 + + +def test_langchain_nomic_embedding_dimensionality() -> None: + """Test nomic embeddings.""" + documents = ["foo bar"] + embedding = NomicEmbeddings(model="nomic-embed-text-v1.5", dimensionality=256) + output = embedding.embed_documents(documents) + assert len(output) == 1 + assert len(output[0]) == 256