feat: langchain + local LM Studio

Author: uptonking

.env.example

@@ -0,0 +1,8 @@
+GOOGLE_API_KEY="AIzaSxxxxx"
+GROQ_API_KEY="gsk_U9xxxxxx"
+TAVILY_API_KEY="tvly-dev-Wb8Naxxxxxx"
+
+LANGSMITH_TRACING="true"
+LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+LANGSMITH_API_KEY="lsv2_pt_c3fxxxxxx"
+LANGSMITH_PROJECT="pr-standard-yesxxxxxx"

README.md

@@ -4,15 +4,35 @@
 
 ## features
 
-- llm with groq api
-- tool call: tavily api
-- structured output
+- examples(mostly with langgraph)
+  - llm with groq api
+  - ✨ llm with local llm, tested with LM Studio
+  - tool call: tavily api
+  - structured output
+
+- RAG
+  - simple
+  - generate_query_or_respond
+  - memory: chat history
+  - docs grading
 
 ## quickstart
 
 ```shell
 npm i
 
+# Option 1: use local llm, configure the `baseURL` in code then run
+npx tsx ./langchain/chain-groq1-chat-local-mini.ts
+
+# Option 2: use groq api, configure the `groq_api_key` first
+cp .env.example .env
 npx tsx ./server/chain-groq1-starter.ts
 ```
 
+# roadmap
+- [ ] `graph.stream` not work with local llm
+# notes
+- examples in python: https://github.com/uptonking/langchain-langgraph-play
+
+# license
+MIT

langchain/chain-groq1-chat-local-mini.ts

@@ -0,0 +1,19 @@
+import { HumanMessage } from '@langchain/core/messages';
+import { ChatOpenAI } from '@langchain/openai';
+
+// refers to https://github.com/calcajack3/auto-tool-eval/blob/main/src/utils/utils.ts
+const model = new ChatOpenAI({
+  model: 'qwen/qwen3-4b-2507',
+  configuration: {
+    baseURL: 'http://localhost:1234/v1',
+    apiKey: 'not-needed',
+  },
+  temperature: 0.5,
+});
+
+const messages = [
+  new HumanMessage('give an brief intro to langchain in less than 80 words '),
+];
+
+const res = await model.invoke(messages);
+console.log(res);

langchain/chain-groq1-chat-local-test.ts

@@ -0,0 +1,30 @@
+import '@dotenvx/dotenvx/config';
+
+import { HumanMessage } from '@langchain/core/messages';
+
+import { initChatModel } from 'langchain/chat_models/universal';
+
+// ❌ not working
+const model = await initChatModel('qwen/qwen3-4b-2507', {
+  modelProvider: 'openai',
+  baseUrl: 'http://localhost:1234/v1',
+  apiKey: 'not-needed',
+  temperature: 0,
+});
+
+const messages = [
+  // new SystemMessage('Translate the following from English into Chinese'),
+  // new HumanMessage('what day is it today?'),
+  new HumanMessage('give an brief intro to reactjs in less than 80 words '),
+];
+
+const res = await model.invoke(messages);
+console.log(res);
+
+// const stream = await model.stream(messages);
+
+// const chunks: AIMessageChunk[] = [];
+// for await (const chunk of stream) {
+//   chunks.push(chunk);
+//   console.log(`${chunk.content}`);
+// }

langchain/chain-groq1-chat-starter.ts

@@ -0,0 +1,29 @@
+import '@dotenvx/dotenvx/config';
+
+import {
+  AIMessageChunk,
+  HumanMessage,
+  SystemMessage,
+} from '@langchain/core/messages';
+import { ChatGroq } from '@langchain/groq';
+
+const model = new ChatGroq({
+  model: 'meta-llama/llama-4-scout-17b-16e-instruct',
+  temperature: 0,
+});
+
+const messages = [
+  new SystemMessage('Translate the following from English into Chinese'),
+  new HumanMessage('what day is it today?'),
+];
+
+// const res = await model.invoke(messages);
+// console.log(res);
+
+const stream = await model.stream(messages);
+
+const chunks: AIMessageChunk[] = [];
+for await (const chunk of stream) {
+  chunks.push(chunk);
+  console.log(`${chunk.content}`);
+}

langchain/chain-groq1-init-chat-model.ts

@@ -0,0 +1,27 @@
+import '@dotenvx/dotenvx/config';
+
+import { HumanMessage } from '@langchain/core/messages';
+
+import { initChatModel } from 'langchain/chat_models/universal';
+
+const model = await initChatModel('llama-3.1-8b-instant', {
+  modelProvider: 'groq',
+  temperature: 0,
+});
+
+const messages = [
+  // new SystemMessage('Translate the following from English into Chinese'),
+  // new HumanMessage('what day is it today?'),
+  new HumanMessage('give an brief intro to reactjs in less than 80 words '),
+];
+
+const res = await model.invoke(messages);
+console.log(res);
+
+// const stream = await model.stream(messages);
+
+// const chunks: AIMessageChunk[] = [];
+// for await (const chunk of stream) {
+//   chunks.push(chunk);
+//   console.log(`${chunk.content}`);
+// }

server/chain-groq2-structured-output.ts renamed to langchain/chain-groq2-structured-output.ts

@@ -34,8 +34,8 @@ const structuredLlm = model.withStructuredOutput({
     required: ['briefDescription', 'details'],
   },
 });
-// @ts-expect-error llm-topic
 const res = await structuredLlm.invoke('introduce sort algorithms', {
+  // @ts-expect-error llm-topic
   name: 'computerTopic',
 });