docs:Correcting spelling mistakes (#25612)

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wangda 2024-08-21 20:49:12 +08:00 committed by GitHub
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@ -21,7 +21,7 @@ To run Marqo locally with our docker image, [see our getting started.](https://d
There exists a wrapper around Marqo indexes, allowing you to use them within the vectorstore framework. Marqo lets you select from a range of models for generating embeddings and exposes some preprocessing configurations.
The Marqo vectorstore can also work with existing multimodel indexes where your documents have a mix of images and text, for more information refer to [our documentation](https://docs.marqo.ai/latest/#multi-modal-and-cross-modal-search). Note that instaniating the Marqo vectorstore with an existing multimodal index will disable the ability to add any new documents to it via the langchain vectorstore `add_texts` method.
The Marqo vectorstore can also work with existing multimodal indexes where your documents have a mix of images and text, for more information refer to [our documentation](https://docs.marqo.ai/latest/#multi-modal-and-cross-modal-search). Note that instantiating the Marqo vectorstore with an existing multimodal index will disable the ability to add any new documents to it via the langchain vectorstore `add_texts` method.
To import this vectorstore:
```python

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@ -319,7 +319,7 @@ def multiply(a: int, b: int) -> int:
### Binding tool schemas with our LLM
We will now use the `bind_tools` method to convert our above functions to a "tool" and binding it with the model. This means we are going to pass these tool informations everytime we invoke the model.
We will now use the `bind_tools` method to convert our above functions to a "tool" and binding it with the model. This means we are going to pass these tool information everytime we invoke the model.
```python
tools = [add, multiply]