--- title: How to Retrieve Knowledge Base Content description: Add knowledge base retrieval to DigitalOcean Inference requests so the model can query your private data sources with retrieval-augmented generation … product: Inference url: https://docs.digitalocean.com/products/inference/how-to/use-server-side-tools/retrieve-knowledge-base/ last_updated: "2026-10-16" --- > **For AI agents:** The documentation index is at [https://docs.digitalocean.com/llms.txt](https://docs.digitalocean.com/llms.txt). Markdown versions of pages use the same URL with `index.html.md` in place of the HTML page (for example, append `index.html.md` to the directory path instead of opening the HTML document). # How to Retrieve Knowledge Base Content Inference provides a single control plane for managing inference workflows. It includes a Model Catalog where you can view available foundation models, including both DigitalOcean-hosted and third-party commercial models, compare model capabilities and pricing, use routing to match inference requests to the best-fit model, and run inference using serverless or dedicated deployments. Knowledge base retrieval lets the model query your private data sources during inference using retrieval-augmented generation (RAG). You add the `knowledge_base_retrieval` tool to your API request and the inference API handles retrieval and incorporates the results into the model’s response automatically. To use knowledge base retrieval, send a POST request with your knowledge base ID. You can find the ID in the [DigitalOcean Control Panel](https://cloud.digitalocean.com/knowledge-bases) or by querying the API. For example, use the Chat Completions API with knowledge base retrieval: ### Python ```python import os from pydo.inference import Client client = Client(token=os.environ.get("MODEL_ACCESS_KEY")) resp = client.chat.completions.create( model="openai-gpt-4o", messages=[ {"role": "user", "content": "What are some features of DigitalOcean Inference?"}, ], tools=[ { "type": "knowledge_base_retrieval", "knowledge_base_id": "", } ], tool_choice="auto", stream=False, max_tokens=1024, ) print(resp.choices[0].message.content) ``` ### JavaScript ```javascript import { InferenceClient } from "@digitalocean/dots"; const client = new InferenceClient({ apiKey: process.env.MODEL_ACCESS_KEY, }); const completion = await client.chat.completions.create({ model: "openai-gpt-4o", messages: [ { role: "user", content: "What are some features of DigitalOcean Inference?" }, ], tools: [ { type: "knowledge_base_retrieval", knowledge_base_id: "", }, ], tool_choice: "auto", stream: false, max_tokens: 1024, }); console.log(completion.choices[0].message.content); ``` ### cURL ```shell curl -X POST https://inference.do-ai.run/v1/chat/completions \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "openai-gpt-4o", "messages": [ { "role": "user", "content": "What are some features of DigitalOcean Inference?" } ], "tools": [ { "type": "knowledge_base_retrieval", "knowledge_base_id": "" } ], "tool_choice": "auto", "stream": false, "max_tokens": 1024 }' ``` ### Go ```go package main import ( "context" "encoding/json" "fmt" "os" "github.com/digitalocean/godo" ) func main() { client := godo.NewFromToken(os.Getenv("MODEL_ACCESS_KEY")) ctx := context.Background() completion, _, err := client.Chat.Completions.New(ctx, &godo.ChatCompletionNewParams{ Model: "openai-gpt-4o", Messages: []godo.ChatCompletionMessage{ godo.UserMessage("What are some features of DigitalOcean Inference?"), }, Tools: []godo.ChatCompletionTool{ godo.ChatKnowledgeBaseRetrievalTool(""), }, ToolChoice: json.RawMessage(`"auto"`), MaxTokens: godo.PtrTo(1024), }) if err != nil { panic(err) } if msg := completion.Choices[0].Message.Content; msg != nil { fmt.Println(msg.Text()) } } ``` For the full set of parameters, see the [Serverless Inference API reference](https://docs.digitalocean.com/reference/api/reference/serverless-inference/index.html.md). The response looks like the following: ```text { "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "annotations": [ { "type": "tool_use", "tool_use": { "name": "knowledge_base_retrieval", "call_id": "call_y6aBCI2IrnS8ZgPR6DKjXl9T", "arguments": "{\"query\":\"features of DigitalOcean Inference\"}", "status": "completed", "output": "{\"knowledge_base_id\":\"e7651dee-da73-11ef-bf8f-4e013e2dxxxx\",\"query\":\"features of DigitalOcean Inference\",\"results\":[{\"metadata\":{\"chunk_category\":\"CompositeElement\",\"ingested_timestamp\":\"2026-05-11T18:15:46.532085+00:00\",\"item_name\":\"https://docs.digitalocean.com/\",\"page_number\":null},\"text_content\":\"### 1 May 2026 [ ](https://docs.digitalocean.com/#1-may-2026) * The following DeepSeek model is now available on DigitalOcean Inference for [serverless inference](https://docs.digitalocean.com/products/inference/how-to/use-serverless-inference/), [Agent Development Kit](https://docs.digitalocean.com/products/inference/how-to/build-agents-using-adk/) and [agents](https://docs.digitalocean.com/products/inference/how-to/create-agents/): For more information, see the [Available Models page](https://docs.digitalocean.com/products/inference/details/models/).\"},{\"metadata\":{\"chunk_category\":\"CompositeElement\",\"ingested_timestamp\":\"2026-05-11T18:15:46.532085+00:00\",\"item_name\":\"https://docs.digitalocean.com/\",\"page_number\":null},\"text_content\":\"### 5 May 2026 [ ](https://docs.digitalocean.com/#5-may-2026) ....\"}],\"total_results\":3}" } } ], "content": "DigitalOcean Inference includes several features designed to support serverless inference and facilitate the development and deployment of AI models and agents. ...", "reasoning_content": null, "refusal": null, "role": "assistant" } } ], ... } } ``` Use the Responses API with knowledge base retrieval: ```shell curl -X POST https://inference.do-ai.run/v1/responses \ -H "Authorization: Bearer $MODEL_ACCESS_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek-v4-pro", "input": "What is actions infrastructure? Answer using the knowledge base.", "tools": [ { "type": "knowledge_base_retrieval", "knowledge_base_id": "09d65da1-5225-11f1-b074-4e013e2dxxxx" } ], "stream": false, "max_output_tokens": 1024 }' ``` The response looks similar to the following: ```text { "background": false, "completed_at": 0, "created_at": 1779045957, "error": { "code": "", "message": "" }, "frequency_penalty": 0, "id": "resp_b999e07cebb3dd7a", "incomplete_details": { "reason": "" }, "instructions": { "OfInputItemList": null, "OfString": "" }, "max_output_tokens": 1024, "max_tool_calls": 0, "metadata": null, "model": "deepseek-v4-pro", "object": "response", "output": [ { "arguments": "{\"query\": \"What is actions infrastructure?\"}", "call_id": "chatcmpl-tool-8e9ce86361b0fe64", "name": "knowledge_base_retrieval", "status": "completed", "type": "function_call" }, { "call_id": "chatcmpl-tool-8e9ce86361b0fe64", "output": "{\"knowledge_base_id\":\"09d65da1-5225-11f1-b074-4e013e2dxxxx\",\"query\":\"What is actions infrastructure?\",\"results\":[{\"metadata\":{\"chunk_category\":\"CompositeElement\",\"ingested_timestamp\":\"2026-05-17T19:23:20.695776+00:00\",\"item_name\":\"example-document.pdf\",\"page_number\":45},\"text_content\":\"Example content retrieved from your knowledge base.\"}],\"total_results\":1}", "status": "completed", "type": "function_call_output" } ] } ```