Skip to content

Commit 6fbdff0

Browse files
Update step enumeration for SQL agent tutorial (#1232)
Update step enumeration ## Overview The numbered steps skipped / duplicated numbers ## Type of change **Type:** Fix typo ## Related issues/PRs <!-- Link to related issues, feature PRs, or discussions (if applicable) To automatically close an issue when this PR is merged, use closing keywords: - "closes #123" or "fixes #123" or "resolves #123" For regular references without auto-closing, just use: - "#123" or "See issue #123" Examples: - closes #456 (will auto-close issue #456 when PR is merged) - See #789 for context (will reference but not auto-close issue #789) --> - GitHub issue: - Feature PR: <!-- For LangChain employees, if applicable: --> - Linear issue: - Slack thread: ## Checklist <!-- Put an 'x' in all boxes that apply --> - [x ] I have read the [contributing guidelines](README.md) - [ ] I have tested my changes locally using `docs dev` - [ ] All code examples have been tested and work correctly - [ ] I have used **root relative** paths for internal links - [ ] I have updated navigation in `src/docs.json` if needed - I have gotten approval from the relevant reviewers - (Internal team members only / optional) I have created a preview deployment using the [Create Preview Branch workflow](https://github.com/langchain-ai/docs/actions/workflows/create-preview-branch.yml) ## Additional notes <!-- Any other information that would be helpful for reviewers -->
1 parent 89b70b4 commit 6fbdff0

File tree

1 file changed

+2
-2
lines changed

1 file changed

+2
-2
lines changed

src/oss/langchain/sql-agent.mdx

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -197,7 +197,7 @@ async function getSchema() {
197197
:::
198198

199199
:::python
200-
## 5. Use `create_agent`
200+
## 4. Use `create_agent`
201201

202202
Use @[`create_agent`] to build a [ReAct agent](https://arxiv.org/pdf/2210.03629) with minimal code. The agent will interpret the request and generate a SQL command, which the tools will execute. If the command has an error, the error message is returned to the model. The model can then examine the original request and the new error message and generate a new command. This can continue until the LLM generates the command successfully or reaches an end count. This pattern of providing a model with feedback - error messages in this case - is very powerful.
203203

@@ -244,7 +244,7 @@ agent = create_agent(
244244
)
245245
```
246246

247-
## 6. Run the agent
247+
## 5. Run the agent
248248

249249
Run the agent on a sample query and observe its behavior:
250250

0 commit comments

Comments
 (0)