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Rename callmodel to callLlm in quickstart.mdx (#1158)
## Overview <!-- Brief description of what documentation is being added/updated --> ## Type of change **Type:** [Replace with: New documentation page / Update existing documentation / Fix typo/bug/link/formatting / Remove outdated content / Other] ## Related issues/PRs 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) - [x] I have tested my changes locally using `docs dev` - [x] All code examples have been tested and work correctly - [x] I have used **root relative** paths for internal links - [x] 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 -->
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src/oss/langgraph/quickstart.mdx

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -814,7 +814,7 @@ def call_llm(messages: list[BaseMessage]):
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```typescript
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import { task, entrypoint } from "@langchain/langgraph";
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import { SystemMessage } from "@langchain/core/messages";
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const callmodel = task({ name: "callLlm" }, async (messages: BaseMessage[]) => {
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const callLlm = task({ name: "callLlm" }, async (messages: BaseMessage[]) => {
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return modelWithTools.invoke([
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new SystemMessage(
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"You are a helpful assistant tasked with performing arithmetic on a set of inputs."
@@ -1097,7 +1097,7 @@ const modelWithTools = model.bindTools(tools);
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import { task, entrypoint } from "@langchain/langgraph";
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import { SystemMessage } from "@langchain/core/messages";
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const callmodel = task({ name: "callLlm" }, async (messages: BaseMessage[]) => {
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const callLlm = task({ name: "callLlm" }, async (messages: BaseMessage[]) => {
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return modelWithTools.invoke([
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new SystemMessage(
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"You are a helpful assistant tasked with performing arithmetic on a set of inputs."

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