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Advanced Usage

Search across Different Languages (Self-Hosting)

To search for notes in multiple, different languages, you can use a multi-lingual model.
For example, the paraphrase-multilingual-MiniLM-L12-v2 supports 50+ languages, has good search quality and speed. To use it:

  1. Manually update the search config in server's admin settings page. Go to the search config. Either create a new one, if none exists, or update the existing one. Set the bi_encoder to sentence-transformers/multi-qa-MiniLM-L6-cos-v1 and the cross_encoder to cross-encoder/ms-marco-MiniLM-L-6-v2.
  2. Regenerate your content index from all the relevant clients. This step is very important, as you'll need to re-encode all your content with the new model.

Query Filters

Use structured query syntax to filter entries from your knowledge based used by search results or chat responses.

  • Word Filter: Get entries that include/exclude a specified term
    • Entries that contain term_to_include: +"term_to_include"
    • Entries that contain term_to_exclude: -"term_to_exclude"
  • Date Filter: Get entries containing dates in YYYY-MM-DD format from specified date (range)
    • Entries from April 1st 1984: dt:"1984-04-01"
    • Entries after March 31st 1984: dt>="1984-04-01"
    • Entries before April 2nd 1984 : dt<="1984-04-01"
  • File Filter: Get entries from a specified file
    • Entries from file: file:""
  • Combined Example
    • what is the meaning of life? file:"" dt>="1984-01-01" dt<="1985-01-01" -"big" -"brother"
    • Adds all filters to the natural language query. It should return entries
      • from the file
      • containing dates from the year 1984
      • excluding words "big" and "brother"
      • that best match the natural language query "what is the meaning of life?"

Use OpenAI compatible LLM API Server (Self Hosting)

Use this if you want to use non-standard, open or commercial, local or hosted LLM models for Khoj chat

  1. Setup your desired chat LLM by installing an OpenAI compatible LLM API Server like LiteLLM, llama-cpp-python
  2. Set environment variable OPENAI_API_BASE="<url-of-your-llm-server>" before starting Khoj
  3. Add ChatModelOptions with model-type OpenAI, and chat-model to anything (e.g gpt-4) during Config
    • (Optional) Set the tokenizer and max-prompt-size relevant to the actual chat model you're using

Sample Setup using LiteLLM and Mistral API

# Install LiteLLM
pip install litellm[proxy]

# Start LiteLLM and use Mistral tiny via Mistral API
litellm --model mistral/mistral-tiny --drop_params

# Set OpenAI API Base to LiteLLM server URL and start Khoj
export OPENAI_API_BASE='http://localhost:8000'
khoj --anonymous-mode