Fix diff blocks, links, remove footnotes & rearrange sections in docs

Extract performance into separate sectin into shoving it under search
Create page for web interface

docs/search.md

@@ -1,11 +1,11 @@
 ## Khoj Search
-- **Khoj via Obsidian**
+- **Using Obsidian**
   - Click the *Khoj search* icon 🔎 on the [Ribbon](https://help.obsidian.md/User+interface/Workspace/Ribbon) or Search for *Khoj: Search* in the [Command Palette](https://help.obsidian.md/Plugins/Command+palette)
-- **Khoj via Emacs**
+- **Using Emacs**
   - Run `M-x khoj <user-query>`
-- **Khoj via Web**
-  - Open <http://localhost:42110/> directly
-- **Khoj via API**
+- **Using Web**
+  - Open <http://localhost:42110/> in your web browser
+- **Using API**
   - See the Khoj FastAPI [Swagger Docs](http://localhost:42110/docs), [ReDocs](http://localhost:42110/redocs)
 
 ### Query Filters
@@ -27,53 +27,3 @@ Use structured query syntax to filter the natural language search results
     - containing dates from the year *1984*
     - excluding words *"big"* and *"brother"*
     - that best match the natural language query *"what is the meaning of life?"*
-
-## Details
-1. Your query is used to retrieve the most relevant notes, if any, using Khoj search
-2. These notes, the last few messages and associated metadata is passed to ChatGPT along with your query for a response
-
-
-## Performance
-
-### Query performance
-
-- Semantic search using the bi-encoder is fairly fast at \<50 ms
-- Reranking using the cross-encoder is slower at \<2s on 15 results. Tweak `top_k` to tradeoff speed for accuracy of results
-- Filters in query (e.g by file, word or date) usually add \<20ms to query latency
-
-### Indexing performance
-
-- Indexing is more strongly impacted by the size of the source data
-- Indexing 100K+ line corpus of notes takes about 10 minutes
-- Indexing 4000+ images takes about 15 minutes and more than 8Gb of RAM
-- Note: *It should only take this long on the first run* as the index is incrementally updated
-
-### Miscellaneous
-
-- Testing done on a Mac M1 and a \>100K line corpus of notes
-- Search, indexing on a GPU has not been tested yet
-
-## Advanced Usage
-
-### Use OpenAI Models for Search
-#### Setup
-1. Set `encoder-type`, `encoder` and `model-directory` under `asymmetric` and/or `symmetric` `search-type` in your `khoj.yml`[^1]:
-   ```diff
-      asymmetric:
-   -    encoder: "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
-   +    encoder: text-embedding-ada-002
-   +    encoder-type: khoj.utils.models.OpenAI
-        cross-encoder: "cross-encoder/ms-marco-MiniLM-L-6-v2"
-   -    encoder-type: sentence_transformers.SentenceTransformer
-   -    model_directory: "~/.khoj/search/asymmetric/"
-   +    model-directory: null
-   ```
-2. [Setup your OpenAI API key in Khoj](#set-your-openai-api-key-in-khoj)
-3. Restart Khoj server to generate embeddings. It will take longer than with offline models.
-
-#### Warnings
-  This configuration *uses an online model*
-  - It will **send all notes to OpenAI** to generate embeddings
-  - **All queries will be sent to OpenAI** when you search with Khoj
-  - You will be **charged by OpenAI** based on the total tokens processed
-  - It *requires an active internet connection* to search and index