Updates to Vectara Implementation (#11366)

Replace this entire comment with:
  - **Description:** updates to documentation and API headers
  - **Tag maintainer:** @baskarya
  - **Twitter handle:** @ofermend

docs/extras/integrations/providers/vectara/index.mdx

@@ -5,7 +5,7 @@
 
 **Vectara Overview:**
 - `Vectara` is developer-first API platform for building GenAI applications
-- To use Vectara - first [sign up](https://console.vectara.com/signup) and create an account. Then create a corpus and an API key for indexing and searching.
+- To use Vectara - first [sign up](https://vectara.com/integrations/langchain) and create an account. Then create a corpus and an API key for indexing and searching.
 - You can use Vectara's [indexing API](https://docs.vectara.com/docs/indexing-apis/indexing) to add documents into Vectara's index
 - You can use Vectara's [Search API](https://docs.vectara.com/docs/search-apis/search) to query Vectara's index (which also supports Hybrid search implicitly).
 - You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.
@@ -13,7 +13,7 @@
 ## Installation and Setup
 
 To use `Vectara` with LangChain no special installation steps are required. 
-To get started, follow our [quickstart](https://docs.vectara.com/docs/quickstart) guide to create an account, a corpus and an API key. 
+To get started, [sign up](https://vectara.com/integrations/langchain) and follow our [quickstart](https://docs.vectara.com/docs/quickstart) guide to create a corpus and an API key. 
 Once you have these, you can provide them as arguments to the Vectara vectorstore, or you can set them as environment variables.
 
 - export `VECTARA_CUSTOMER_ID`="your_customer_id"

libs/langchain/langchain/vectorstores/vectara.py

@@ -74,6 +74,7 @@ class Vectara(VectorStore):
             "x-api-key": self._vectara_api_key,
             "customer-id": self._vectara_customer_id,
             "Content-Type": "application/json",
+            "X-Source": "langchain",
         }
 
     def _delete_doc(self, doc_id: str) -> bool: