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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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Clone the Danswer repo:
Code Block git clone https://github.com/danswer-ai/danswer.git
Go to
danswer/deployment/docker_compose
:Code Block cd danswer/deployment/docker_compose
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"
More information about configuration settings can be found here https://docs.danswer.dev/configuration_guide
Build the containers:
Code Block docker compose -f docker-compose.dev.yml -p danswer-stack up -d --build --force-recreate
Danswer will now be running on
http://{ip-address}:3000
.To stop the stack:
Code Block docker compose -f docker-compose.dev.yml -p danswer-stack down
(Optional) If you are using Ollama on the same instance, use the following settings:
Display name:
ollama
Provider Name:
ollama
[Optional] API Base:
http://host.docker.internal:11434
Model Names:
llama3
phi3
Default model:
llama3
Fast Model:
phi3
(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
Create your first connector (https://docs.danswer.dev/connectors/overview )
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