Refer to Model Configs for how to set the environment variables for your particular deployment.

Note: While we support self hosted LLMs, you will get significantly better responses with a more powerful model like GPT-4.

What is Ollama

Ollama provides an easy way to host LLMs locally and to provide a REST API for the model. Refer to the following resources to get started:

Once you start a model with a command like ollama run llama2, you can verify that the API works with a curl request:

curl http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt":"Why is the sky blue?"

Set Danswer to use Ollama

# Model of your choice
# Wherever Ollama is running
# Hint: To point Docker containers to http://localhost:11434, use host.docker.internal instead of localhost

# Let's also make some changes to accommodate the weaker locally hosted LLM
QA_TIMEOUT=120  # Set a longer timeout, running models on CPU can be slow
# Always run search, never skip
# Don't use LLM for reranking, the prompts aren't properly tuned for these models
# Don't try to rephrase the user query, the prompts aren't properly tuned for these models
# Don't use LLM to automatically discover time/source filters
# Uncomment this one if you find that the model is struggling (slow or distracted by too many docs)
# Use only 1 section from the documents and do not require quotes