voyageai: new models (#26907)

Co-authored-by: fzowl <zoltan@voyageai.com>
Co-authored-by: fzowl <160063452+fzowl@users.noreply.github.com>

docs/docs/integrations/document_transformers/voyageai-reranker.ipynb

@@ -87,7 +87,9 @@
     "## Set up the base vector store retriever\n",
     "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs. You can use any of the following Embeddings models: ([source](https://docs.voyageai.com/docs/embeddings)):\n",
     "\n",
-    "- `voyage-large-2` (default)\n",
+    "- `voyage-3`\n",
+    "- `voyage-3-lite` \n",
+    "- `voyage-large-2`\n",
     "- `voyage-code-2`\n",
     "- `voyage-2`\n",
     "- `voyage-law-2`\n",
@@ -341,6 +343,8 @@
     "## Doing reranking with VoyageAIRerank\n",
     "Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll use the Voyage AI reranker to rerank the returned results. You can use any of the following Reranking models: ([source](https://docs.voyageai.com/docs/reranker)):\n",
     "\n",
+    "- `rerank-2`\n",
+    "- `rerank-2-lite`\n",
     "- `rerank-1`\n",
     "- `rerank-lite-1`"
    ]

docs/docs/integrations/text_embedding/voyageai.ipynb

@@ -29,7 +29,9 @@
    "source": [
     "Voyage AI utilizes API keys to monitor usage and manage permissions. To obtain your key, create an account on our [homepage](https://www.voyageai.com). Then, create a VoyageEmbeddings model with your API key. You can use any of the following models: ([source](https://docs.voyageai.com/docs/embeddings)):\n",
     "\n",
-    "- `voyage-large-2` (default)\n",
+    "- `voyage-3`\n",
+    "- `voyage-3-lite`\n",
+    "- `voyage-large-2`\n",
     "- `voyage-code-2`\n",
     "- `voyage-2`\n",
     "- `voyage-law-2`\n",

docs/src/theme/FeatureTables.js

@@ -364,6 +364,12 @@ const FEATURE_TABLES = {
                 package: "langchain-databricks",
                 apiLink: "https://python.langchain.com/api_reference/nomic/embeddings/langchain_databricks.embeddings.DatabricksEmbeddings.html"
             },
+            {
+                name: "VoyageAI",
+                link: "voyageai",
+                package: "langchain-voyageai",
+                apiLink: "https://python.langchain.com/api_reference/voyageai/embeddings/langchain_voyageai.embeddings.VoyageAIEmbeddings.html"
+            },
         ]
     },
     document_retrievers: {