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docs:Correcting spelling mistakes in readme (#22664)

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Signed-off-by: zhangwangda <zhangwangda94@163.com>
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wangda
2024-06-10 23:33:41 +08:00
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6 changed files with 11 additions and 11 deletions
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templates/rag-gemini-multi-modal/README.md
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@@ -7,7 +7,7 @@ This template create a visual assistant for slide decks, which often contain vis
It uses OpenCLIP embeddings to embed all of the slide images and stores them in Chroma.
Given a question, relevat slides are retrieved and passed to [Google Gemini](https://deepmind.google/technologies/gemini/#introduction) for answer synthesis.
Given a question, relevant slides are retrieved and passed to [Google Gemini](https://deepmind.google/technologies/gemini/#introduction) for answer synthesis.
![Diagram illustrating the process of a visual assistant using multi-modal LLM, from slide deck images to OpenCLIP embedding, retrieval, and synthesis with Google Gemini, resulting in an answer.](https://github.com/langchain-ai/langchain/assets/122662504/b9e69bef-d687-4ecf-a599-937e559d5184 "Workflow Diagram for Visual Assistant Using Multi-modal LLM")
@@ -15,7 +15,7 @@ Given a question, relevat slides are retrieved and passed to [Google Gemini](htt
Supply a slide deck as pdf in the `/docs` directory.
By default, this template has a slide deck about Q3 earnings from DataDog, a public techologyy company.
By default, this template has a slide deck about Q3 earnings from DataDog, a public technology company.
Example questions to ask can be:
```
@@ -37,7 +37,7 @@ You can select different embedding model options (see results [here](https://git
The first time you run the app, it will automatically download the multimodal embedding model.
By default, LangChain will use an embedding model with moderate performance but lower memory requirments, `ViT-H-14`.
By default, LangChain will use an embedding model with moderate performance but lower memory requirements, `ViT-H-14`.
You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/ingest.py`:
```