chore(docs): add embedding integration contribution docs

.github/contributing/INTEGRATIONS.md

@@ -152,7 +152,7 @@ Below are links to guides with advice and tips for specific types of integration
 - [Vector stores](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/integrations/VECTOR_STORES.md) (e.g. Pinecone)
 - [Persistent message stores](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/integrations/MESSAGE_STORES.md) (used to persistently store and load raw chat histories, e.g. Redis)
 - [Document loaders](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/integrations/DOCUMENT_LOADERS.md) (used to load documents for later storage into vector stores, e.g. Apify)
-- Embeddings (TODO) (e.g. Cohere)
+- [Embeddings](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/integrations/EMBEDDINGS.md) (used to create embeddings of text documents or strings e.g. Cohere)
 - [Tools](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/integrations/TOOLS.md) (used for agents, e.g. the SERP API tool)
 
 This is a living document, so please make a pull request if we're missing anything useful!

.github/contributing/integrations/EMBEDDINGS.md

@@ -0,0 +1,21 @@
+# Contributing third-party Text Embeddings
+
+This page contains some specific guidelines and examples for contributing integrations with third-party Text Embedding providers.
+
+**Make sure you read the [general guidelines page](https://github.com/langchain-ai/langchainjs/blob/main/.github/contributing/INTEGRATIONS.md) first!**
+
+## Example PR
+
+We'll be referencing this PR adding Gradient Embeddings as an example: https://github.com/langchain-ai/langchainjs/pull/3475
+
+## General ideas
+
+The general idea for adding new third-party Text Embeddings is to subclass the `Embeddings` class and implement the `embedDocuments` and `embedQuery` methods. 
+
+The `embedDocuments` method should take a list of documents and return a list of embeddings for each document. The `embedQuery` method should take a query and return an embedding for that query.
+
+`embedQuery` can typically be implemented by calling `embedDocuments` with a list containing only the query.
+
+## Wrap Text Embeddings requests in this.caller
+
+The base Embeddings class contains an instance property called `caller` that will automatically handle retries, errors, timeouts, and more. You should wrap calls to the LLM in `this.caller.call` [as shown here](https://github.com/langchain-ai/langchainjs/blob/f469ec00d945a3f8421b32f4be78bce3f66a74bb/langchain/src/embeddings/gradient_ai.ts#L72)