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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 committed by GitHub
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2
templates/mongo-parent-document-retrieval/README.md
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@ -97,7 +97,7 @@ We will first follow the standard MongoDB Atlas setup instructions [here](https:
2. Create a new project (if not already done)
3. Locate your MongoDB URI.
This can be done by going to the deployement overview page and connecting to you database
This can be done by going to the deployment overview page and connecting to you database
![Screenshot highlighting the 'Connect' button in MongoDB Atlas.](_images/connect.png "MongoDB Atlas Connect Button")

4
templates/rag-chroma-multi-modal-multi-vector/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 GPT-4V to create image summaries for each slide, embeds the summaries, and stores them in Chroma.
Given a question, relevat slides are retrieved and passed to GPT-4V for answer synthesis.
Given a question, relevant slides are retrieved and passed to GPT-4V for answer synthesis.
![Diagram illustrating the multi-modal LLM process with a slide deck, captioning, storage, question input, and answer synthesis with year-over-year growth percentages.](https://github.com/langchain-ai/langchain/assets/122662504/5277ef6b-d637-43c7-8dc1-9b1567470503 "Multi-modal LLM Process Diagram")
@ -15,7 +15,7 @@ Given a question, relevat slides are retrieved and passed to GPT-4V for answer s
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:
```

6
templates/rag-chroma-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 GPT-4V for answer synthesis.
Given a question, relevant slides are retrieved and passed to GPT-4V for answer synthesis.
![Diagram illustrating the workflow of a multi-modal LLM visual assistant using OpenCLIP embeddings and GPT-4V for question-answering based on slide deck images.](https://github.com/langchain-ai/langchain/assets/122662504/b3bc8406-48ae-4707-9edf-d0b3a511b200 "Workflow Diagram for Multi-modal LLM Visual Assistant")
@ -15,7 +15,7 @@ Given a question, relevat slides are retrieved and passed to GPT-4V for answer s
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`:
```

6
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`:
```

2
templates/rag-mongo/README.md
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@ -95,7 +95,7 @@ We will first follow the standard MongoDB Atlas setup instructions [here](https:
2. Create a new project (if not already done)
3. Locate your MongoDB URI.
This can be done by going to the deployement overview page and connecting to you database
This can be done by going to the deployment overview page and connecting to you database
![Screenshot highlighting the 'Connect' button in MongoDB Atlas.](_images/connect.png "MongoDB Atlas Connect Button")

2
templates/rag-multi-modal-mv-local/README.md
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@ -9,7 +9,7 @@ This template demonstrates how to perform private visual search and question-ans
It uses an open source multi-modal LLM of your choice to create image summaries for each photos, embeds the summaries, and stores them in Chroma.
Given a question, relevat photos are retrieved and passed to the multi-modal LLM for answer synthesis.
Given a question, relevant photos are retrieved and passed to the multi-modal LLM for answer synthesis.
![Diagram illustrating the visual search process with food pictures, captioning, a database, a question input, and the synthesis of an answer using a multi-modal LLM.](https://github.com/langchain-ai/langchain/assets/122662504/cd9b3d82-9b06-4a39-8490-7482466baf43 "Visual Search Process Diagram")