mirror of
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81 lines
5 KiB
Markdown
81 lines
5 KiB
Markdown
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## Advanced Usage
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### Search across Different Languages
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To search for notes in multiple, different languages, you can use a [multi-lingual model](https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models).<br />
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For example, the [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) supports [50+ languages](https://www.sbert.net/docs/pretrained_models.html#:~:text=we%20used%20the%20following%2050%2B%20languages), has good search quality and speed. To use it:
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1. Manually update `search-type > asymmetric > encoder` to `paraphrase-multilingual-MiniLM-L12-v2` in your `~/.khoj/khoj.yml` file for now. See diff of `khoj.yml` below for illustration:
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```diff
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asymmetric:
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- encoder: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
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+ encoder: paraphrase-multilingual-MiniLM-L12-v2
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cross-encoder: cross-encoder/ms-marco-MiniLM-L-6-v2
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model_directory: "~/.khoj/search/asymmetric/"
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```
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2. Regenerate your content index. For example, by opening [\<khoj-url\>/api/update?t=force](http://localhost:42110/api/update?t=force)
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### Access Khoj on Mobile
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1. [Setup Khoj](/#/setup) on your personal server. This can be any always-on machine, i.e an old computer, RaspberryPi(?) etc
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2. [Install](https://tailscale.com/kb/installation/) [Tailscale](tailscale.com/) on your personal server and phone
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3. Open the Khoj web interface of the server from your phone browser.<br /> It should be `http://tailscale-ip-of-server:42110` or `http://name-of-server:42110` if you've setup [MagicDNS](https://tailscale.com/kb/1081/magicdns/)
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4. Click the [Add to Homescreen](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps/Add_to_home_screen) button
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5. Enjoy exploring your notes, documents and images from your phone!
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### Use OpenAI Models for Search
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#### Setup
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1. Set `encoder-type`, `encoder` and `model-directory` under `asymmetric` and/or `symmetric` `search-type` in your `khoj.yml` (at `~/.khoj/khoj.yml`):
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```diff
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asymmetric:
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- encoder: "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
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+ encoder: text-embedding-ada-002
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+ encoder-type: khoj.utils.models.OpenAI
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cross-encoder: "cross-encoder/ms-marco-MiniLM-L-6-v2"
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- encoder-type: sentence_transformers.SentenceTransformer
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- model_directory: "~/.khoj/search/asymmetric/"
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+ model-directory: null
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```
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2. [Setup your OpenAI API key in Khoj](/#/chat?id=setup)
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3. Restart Khoj server to generate embeddings. It will take longer than with the offline search models.
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#### Warnings
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This configuration *uses an online model*
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- It will **send all notes to OpenAI** to generate embeddings
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- **All queries will be sent to OpenAI** when you search with Khoj
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- You will be **charged by OpenAI** based on the total tokens processed
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- It *requires an active internet connection* to search and index
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### Bootstrap Khoj Search for Offline Usage later
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You can bootstrap Khoj pre-emptively to run on machines that do not have internet access. An example use-case would be to run Khoj on an air-gapped machine.
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Note: *Only search can currently run in fully offline mode, not chat.*
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- With Internet
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1. Manually download the [asymmetric text](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1), [symmetric text](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and [image search](https://huggingface.co/sentence-transformers/clip-ViT-B-32) models from HuggingFace
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2. Pip install khoj (and dependencies) in an associated virtualenv. E.g `python -m venv .venv && source .venv/bin/activate && pip install khoj-assistant`
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- Without Internet
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1. Copy each of the search models into their respective folders, `asymmetric`, `symmetric` and `image` under the `~/.khoj/search/` directory on the air-gapped machine
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2. Copy the khoj virtual environment directory onto the air-gapped machine, activate the environment and start and khoj as normal. E.g `source .venv/bin/activate && khoj`
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### Query Filters
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Use structured query syntax to filter entries from your knowledge based used by search results or chat responses.
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- **Word Filter**: Get entries that include/exclude a specified term
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- Entries that contain term_to_include: `+"term_to_include"`
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- Entries that contain term_to_exclude: `-"term_to_exclude"`
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- **Date Filter**: Get entries containing dates in YYYY-MM-DD format from specified date (range)
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- Entries from April 1st 1984: `dt:"1984-04-01"`
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- Entries after March 31st 1984: `dt>="1984-04-01"`
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- Entries before April 2nd 1984 : `dt<="1984-04-01"`
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- **File Filter**: Get entries from a specified file
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- Entries from incoming.org file: `file:"incoming.org"`
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- Combined Example
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- `what is the meaning of life? file:"1984.org" dt>="1984-01-01" dt<="1985-01-01" -"big" -"brother"`
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- Adds all filters to the natural language query. It should return entries
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- from the file *1984.org*
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- containing dates from the year *1984*
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- excluding words *"big"* and *"brother"*
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- that best match the natural language query *"what is the meaning of life?"*
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