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docs: correct typos and grammar (#1839)

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* Update overview.md

* Update web-search.md

docs/source/configuration/models/overview.md CHANGED
@@ -43,7 +43,7 @@ You can change things like the parameters, or customize the preprompt to better
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  When querying the model for a chat response, the `chatPromptTemplate` template is used. `messages` is an array of chat messages, it has the format `[{ content: string }, ...]`. To identify if a message is a user message or an assistant message the `ifUser` and `ifAssistant` block helpers can be used.
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- The following is the default `chatPromptTemplate`, although newlines and indentiation have been added for readability. You can find the prompts used in production for HuggingChat [here](https://github.com/huggingface/chat-ui/blob/main/PROMPTS.md). The templating language used is [Handlebars](https://www.npmjs.com/package/handlebars).
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  ```handlebars
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  {{preprompt}}
 
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  When querying the model for a chat response, the `chatPromptTemplate` template is used. `messages` is an array of chat messages, it has the format `[{ content: string }, ...]`. To identify if a message is a user message or an assistant message the `ifUser` and `ifAssistant` block helpers can be used.
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+ The following is the default `chatPromptTemplate`, although newlines and indentation have been added for readability. You can find the prompts used in production for HuggingChat [here](https://github.com/huggingface/chat-ui/blob/main/PROMPTS.md). The templating language used is [Handlebars](https://www.npmjs.com/package/handlebars).
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  ```handlebars
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  {{preprompt}}
docs/source/configuration/web-search.md CHANGED
@@ -7,7 +7,7 @@ Chat UI features a powerful Web Search feature. A high level overview of how it
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  3. Load each search result into playwright and scrape
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  4. Convert scraped HTML to Markdown tree with headings as parents
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  5. Create embeddings for each Markdown element
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- 6. Find the embedings clossest to the user query using a vector similarity search (inner product)
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  7. Get the corresponding Markdown elements and their parent, up to 8000 characters
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  8. Supply the information as context to the model
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@@ -28,7 +28,7 @@ For locally scraped Google results, put `USE_LOCAL_WEBSEARCH=true` in your `.env
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  > SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled.
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- You may enable support via the `SEARXNG_QUERY_URL` where `<query>` will be replaceed with the query keywords. Please see [the official documentation](https://docs.searxng.org/dev/search_api.html) for more information
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  Example: `https://searxng.yourdomain.com/search?q=<query>&engines=duckduckgo,google&format=json`
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  3. Load each search result into playwright and scrape
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  4. Convert scraped HTML to Markdown tree with headings as parents
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  5. Create embeddings for each Markdown element
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+ 6. Find the embeddings closest to the user query using a vector similarity search (inner product)
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  7. Get the corresponding Markdown elements and their parent, up to 8000 characters
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  8. Supply the information as context to the model
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  > SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled.
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+ You may enable support via the `SEARXNG_QUERY_URL` where `<query>` will be replaced with the query keywords. Please see [the official documentation](https://docs.searxng.org/dev/search_api.html) for more information
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  Example: `https://searxng.yourdomain.com/search?q=<query>&engines=duckduckgo,google&format=json`
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