New cookbook - Supply-Chain Copilot with OpenAI Agent SDK and Databricks MCP Servers #1935
+577
−0
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
robtinn
reviewed
Jul 7, 2025
| "- Classical forecasting models for fine-grained demand and lead-time predictions \n", | ||
| "- Graph based route optimizations \n", | ||
| "- Vector-indexed e-mail archives that enable semantic search across unstructured communications for delay signals\n", | ||
| "- Generative-AI workflows that surface insights and mitigation options in plain language\n", |
robtinn
reviewed
Jul 7, 2025
| "\n", | ||
| "The easiest way is to add a profile to `~/.databrickscfg`. The snippet’s `WorkspaceClient(profile=...)` call will pick that up. It tells the SDK which of those stored credentials to load, so your code never needs to embed tokens. Another option would be to create environment variables such as `DATABRICKS_HOST` and `DATABRICKS_TOKEN`, but using `~/.databrickscfg` is recommended.\n", | ||
| "\n", | ||
| "`pip install openai databricks-sdk`" |
There was a problem hiding this comment.
I would have this runnable in a separate cell:
! pip install openai databricks-sdk
There was a problem hiding this comment.
I think there are quite a lot of other dependencies I would add here too getting further along:
! pip install openai databricks-sdk databricks-cli databricks-mcp openai-agents
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary
This walkthrough demonstrates how to build a supply‑chain copilot using the OpenAI Agent SDK and Databricks Managed MCP, highlighting Tracing, Guardrails, and seamless integration with enterprise data sources. It shows how an agent can blend structured inventory tables, forecasting models, graph‑based BOM logic, vector‑indexed e‑mails behind a single conversational interface, then surface the results through FastAPI and a React chat UI. Readers learn to authenticate against Databricks, expose Unity Catalog functions and Vector Search (Databricks vector index) as MCP tools, trace every call in the OpenAI dashboard, and enforce guardrails, all while keeping the underlying data governance intact.
Motivation
We currently don’t have a cookbook that explains how to integrate with Databricks data sources. We lack an example that shows how to connect an Agent to Databricks using Databricks‑managed MCP servers. This guide remedies that by delivering an agent capable of tackling supply‑chain questions such as “Can we meet demand?” and “How much revenue is at risk if we can’t produce the forecasted amount of a certain product?”.
For new content
When contributing new content, read through our contribution guidelines, and mark the following action items as completed:
We will rate each of these areas on a scale from 1 to 4, and will only accept contributions that score 3 or higher on all areas. Refer to our contribution guidelines for more details.