diff --git a/libs/community/langchain_community/embeddings/embed_anything.py b/libs/community/langchain_community/embeddings/embed_anything.py
new file mode 100644
index 0000000000000..8ed497d66c202
--- /dev/null
+++ b/libs/community/langchain_community/embeddings/embed_anything.py
@@ -0,0 +1,121 @@
+import importlib
+from langchain_core.embeddings import Embeddings
+from pydantic import BaseModel, ConfigDict
+from typing import Any, Dict
+from langchain_core.utils import pre_init
+
+
+class EmbedAnythingEmbeddings(BaseModel, Embeddings):
+    """EmbedAnything Embeddings model.
+    EmbedAnything is a Python library for embedding generation.
+    See more documentation at:
+    
+    To use this class, you must install the `embed_anything` Python package.
+    
+    `pip install embed_anything`
+
+    For GPU support, you can install the 
+
+    `embed_anything-gpu` package.
+    Example:
+    from langchain_community.embeddings import EmbedAnythingEmbeddings
+    embed_anything = EmbedAnythingEmbeddings()
+    """
+
+    model: str = "jina"
+    """The model to use for embedding generation. Defaults to "jina".
+    The available options are: "bert", "jina", "colbert", "sparse-bert
+    """
+
+    model_id: str = "jinaai/jina-embeddings-v2-base-en"
+    """The model id to use for embedding generation. Defaults to "jinaai/jina-embeddings-v2-base-en".
+    The available options are:
+    * "bert": "bert-base-uncased"
+    * "jina": "jinaai/jina-embeddings-v2-base-en"
+    * "colbert": "castorini/monobert-large-msmarco"
+    * "sparse-bert": "castorini/sparsebert-uncased-msmarco"
+    """
+
+    batch_size: int = 256
+    """Batch size for encoding. Higher values will use more memory, but be faster. Defaults to 256.
+    """
+
+    backend: str = "candle"
+    """The backend to use for embedding generation. Defaults to "candle".
+    The available options are: "candle", "onnx"
+    """
+
+    model_config = ConfigDict(extra="allow", protected_namespaces=())
+    """The model configuration. Defaults to None.
+    """
+
+    embedder: Any = None
+    """The embedder object. Defaults to None.
+    """
+
+    embed_anything: Any = None
+    """The embed_anything module. Defaults to None."""
+
+    config: Any = None
+    """The TextEmbedConfig object. Defaults to None."""
+
+    path_in_repo:str = "model.onnx"
+    """The path to the onnx model in the repository. Defaults to "model.onnx"."""
+
+    @pre_init
+    def validate_environment(cls, values: Dict) -> Dict:
+
+        try:
+            import embed_anything
+            from embed_anything import EmbeddingModel, TextEmbedConfig, WhichModel
+
+            values["embed_anything"] = importlib.import_module("embed_anything")
+        except ImportError:
+            raise ImportError(
+                "You need to install the 'embed_anything' package to use this embedding model. For gpu support, you can install the 'embed_anything-gpu' package."
+            )
+
+        model = values.get("model")
+        model_id = values.get("model_id")
+        backend = values.get("backend")
+        path_in_repo = values.get("path_in_repo")
+
+        if model == "bert":
+            model_type = WhichModel.Bert
+        elif model == "jina":
+            model_type = WhichModel.Jina
+        elif model == "colbert":
+            model_type = WhichModel.ColBert
+        elif model == "sparse-bert":
+            model_type = WhichModel.SparseBert
+
+        if backend == "candle":
+            model = EmbeddingModel.from_pretrained_hf(
+                model=model_type, model_id=model_id
+            )
+
+        elif backend == "onnx":
+            print(path_in_repo)
+
+            model = EmbeddingModel.from_pretrained_onnx(
+                model=model_type, hf_model_id=model_id, path_in_repo=path_in_repo
+            )
+
+        values["config"] = TextEmbedConfig(batch_size=values.get("batch_size"))
+
+        values["embedder"] = model
+        return values
+
+    def embed_documents(self, texts: list[str]):
+
+        embed_data = self.embed_anything.embed_query(
+            texts, self.embedder, config=self.config
+        )
+        return [e.embedding for e in embed_data]
+
+    def embed_query(self, text: str):
+
+        embed_data = self.embed_anything.embed_query(
+            [text], self.embedder, config=self.config
+        )[0]
+        return embed_data.embedding