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Table of Contents

Introduction

Danswer is the AI Assistant that can be connected to many sources like Atlassian Confluence, Sharepoint, Slack, web, files, MS Teams and many more sources. It performs retrieval of documents, generates embeddings and stores them locally. Once a query comes in, a semantic similarity search is performed and the most relevant results are passed to the LLM instead of full documents, by doing this, noise to the model is reduced (System Overview).

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• 8vCPUs.

• 500GB ssd storage.

• 16GB memory reserved.

• 16GB vGPU.

Table of Contents

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\uD83D\uDCD8 Steps

Instructions

Virtual Machine deployment

1. Download

   View file
   name ubuntu-danswer.yaml

   and update the following attributes:

   1. machine.folder: target logical folder. List available folders with vss-cli compute folder ls

   2. metadata.client : your department client.

   3. metadata.inform: email address for automated notifications

2. Deploy your file as follows:

   Code Block
   vss-cli --wait compute vm mk from-file ubuntu-danswer.yaml

3. (Optional) If planning to use Ollama, add a virtual GPU of 16GB, specifically the 16q profile. For more information in the profile used, check the following document How-to Request a Virtual GPU

   Code Block
   vss-cli compute vm set <VM_ID> gpu mk --profile 16q

4. Once the VM has been deployed, a confirmation email will be sent with the assigned IP address and credentials.

5. Power on virtual machine:

   Code Block
   vss-cli compute vm set ubuntu-llm state on

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1. Clone the Danswer repo:

   Code Block
   git clone https://github.com/danswer-ai/danswer.git

2. Go to danswer/deployment/docker_compose:

3. Code Block
   cd danswer/deployment/docker_compose

4. Configure Danswer by creating a file in danswer/deployment/docker_compose/.env with the following contents:

   Code Block

   # Configures basic email/password based login AUTH_TYPE="basic" # Rephrasing the query into different languages to improve search recall MULTILINGUAL_QUERY_EXPANSION="English,Spanish" # Set a cheaper/faster LLM for the flows that are easier (such as translating the query etc.) FAST_GEN_AI_MODEL_VERSION="gpt-3.5-turbo" # Setting more verbose logging LOG_LEVEL="debug" LOG_ALL_MODEL_INTERACTIONS="true" DISABLE_TELEMETRY="true"

   1. More information about configuration settings can be found here https://docs.danswer.dev/configuration_guide

5. Build the containers:

   Code Block
   docker compose -f docker-compose.dev.yml -p danswer-stack up -d --build --force-recreate

6. Danswer will now be running on http://{ip-address}:3000.

7. To stop the stack:

   Code Block
   docker compose -f docker-compose.dev.yml -p danswer-stack down

8. (Optional) If you are using Ollama on the same instance, use the following settings:

   1. Display name: ollama

   2. Provider Name: ollama

   3. [Optional] API Base: http://host.docker.internal:11434

   4. Model Names:

      1. llama3

      2. phi3

   5. Default model: llama3

   6. Fast Model: phi3

9. (Optional) If you are using remote inference like OpenAI or Azure OpenAI, refer to the official danswer.ai docs: https://docs.danswer.dev/quickstart#generative-ai-api-key

10. Create your first connector (https://docs.danswer.dev/connectors/overview )

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