ollama: init package (#23615)

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/ollama/langchain_ollama/embeddings.py

@@ -0,0 +1,51 @@
+from typing import List
+
+import ollama
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra
+from ollama import AsyncClient
+
+
+class OllamaEmbeddings(BaseModel, Embeddings):
+    """OllamaEmbeddings embedding model.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_ollama import OllamaEmbeddings
+
+            model = OllamaEmbeddings(model="llama3")
+            embedder.embed_query("what is the place that jonathan worked at?")
+    """
+
+    model: str
+    """Model name to use."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        embedded_docs = []
+        for doc in texts:
+            embedded_docs.append(list(ollama.embeddings(self.model, doc)["embedding"]))
+        return embedded_docs
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return self.embed_documents([text])[0]
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        embedded_docs = []
+        for doc in texts:
+            embedded_docs.append(
+                list((await AsyncClient().embeddings(self.model, doc))["embedding"])
+            )
+        return embedded_docs
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        return (await self.aembed_documents([text]))[0]