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Updates to Vectara Implementation (#11366)
Replace this entire comment with: - **Description:** updates to documentation and API headers - **Tag maintainer:** @baskarya - **Twitter handle:** @ofermend
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**Vectara Overview:**
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- `Vectara` is developer-first API platform for building GenAI applications
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- 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.
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- 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.
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- You can use Vectara's [indexing API](https://docs.vectara.com/docs/indexing-apis/indexing) to add documents into Vectara's index
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- 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).
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- You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.
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## Installation and Setup
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To use `Vectara` with LangChain no special installation steps are required.
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To get started, follow our [quickstart](https://docs.vectara.com/docs/quickstart) guide to create an account, a corpus and an API key.
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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.
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Once you have these, you can provide them as arguments to the Vectara vectorstore, or you can set them as environment variables.
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- export `VECTARA_CUSTOMER_ID`="your_customer_id"
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@ -74,6 +74,7 @@ class Vectara(VectorStore):
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"x-api-key": self._vectara_api_key,
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"customer-id": self._vectara_customer_id,
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"Content-Type": "application/json",
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"X-Source": "langchain",
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}
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def _delete_doc(self, doc_id: str) -> bool:
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