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use-local-model

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Mendon Kissling 2025-09-26 16:31:35 -04:00
parent a7d152eca2
commit bc9f181abd
2 changed files with 13 additions and 14 deletions
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3
docs/docs/get-started/install.mdx
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@ -106,7 +106,10 @@ For more information on virtual environments, see [uv](https://docs.astral.sh/uv
<TabItem value="Ollama" label="Ollama">
9. Enter your Ollama server's base URL address.
The default Ollama server address is `http://localhost:11434`.
Since OpenRAG is running in a container, you may need to change `localhost` to access services outside of the container. For example, change `http://localhost:11434` to `http://host.docker.internal:11434` to connect to Ollama.
OpenRAG automatically sends a test connection to your Ollama server to confirm connectivity.
10. Select the **Embedding Model** and **Language Model** your Ollama server is running.
OpenRAG automatically lists the available models from your Ollama server.
11. To load 2 sample PDFs, enable **Sample dataset**.
This is recommended, but not required.
12. Click **Complete**.

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docs/docs/get-started/quickstart.mdx
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@ -11,17 +11,21 @@ Get started with OpenRAG by loading your knowledge, swapping out your language m
## Prerequisites
- Install and start OpenRAG
- [Install and start OpenRAG](/install)
- [Langflow API key](/)
## Find your way around
1. In OpenRAG, click <Icon name="MessageSquare" aria-hidden="true"/> **Chat**.
The chat is powered by the OpenRAG Open Search Agent.
The chat is powered by the OpenRAG OpenSearch Agent.
For more information, see [Langflow Agents](/agents).
2. Ask `What documents are available to you?`
The agent responds with a message summarizing the documents that OpenRAG loads by default, which are PDFs about evaluating data quality when using LLMs in health care.
Knowledge is stored in OpenSearch.
For more information, see Knowledge.
3. To confirm the agent is correct, click <Icon name="Library" aria-hidden="true"/> **Knowledge**.
The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database. Click on a document to display the chunks derived from splitting the default documents into the vector database.
The **Knowledge** page lists the documents OpenRAG has ingested into the OpenSearch vector database.
Click on a document to display the chunks derived from splitting the default documents into the vector database.
## Add your own knowledge
@ -57,20 +61,12 @@ In this example, you'll try a different LLM to demonstrate how the Agent's respo
## Integrate OpenRAG into your application
:::tip
Ensure the `openrag-backend` container has port 8000 exposed in your `docker-compose.yml`:
OpenRAG provides a
```yaml
openrag-backend:
ports:
- "8000:8000"
```
:::
OpenRAG provides a REST API that you can call from Python, TypeScript, or any HTTP client to chat with your documents.
To integrate OpenRAG into your application, use the Langflow API.
You can call from Python, TypeScript, or any HTTP client to chat with your documents.
These example requests are run assuming OpenRAG is in "no-auth" mode.
For complete API documentation, including authentication, request and response parameters, and example requests, see the API documentation.
### Chat with your documents