docs(langgraph): add langgraph document to Neurolink BZ-43484 #120
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Important Review skippedAuto incremental reviews are disabled on this repository. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughAdds a new LangGraph documentation page with concepts, workflow, examples, and troubleshooting, and fixes a code snippet in LangChain/LangChainComponentImplementation.md by replacing an incomplete VertexAIEmbeddings call with a concrete embed invocation. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant SG as StateGraph Builder
participant Compiler
participant Runtime as Graph Runtime
participant Node as Node(s)/LLM/Tools
User->>SG: Define state, nodes, edges
SG->>Compiler: Compile()
Compiler-->>SG: Validated graph (START/END, edges)
User->>Runtime: run(initial_state)
loop Flow control (conditional/loops)
Runtime->>Node: Execute node with current state
Node-->>Runtime: State delta / messages
Runtime->>Runtime: Reduce & persist state
alt Human-in-the-loop (optional)
Runtime-->>User: interruptBefore/After hook
User-->>Runtime: Resume/modify
end
end
Runtime-->>User: Final state/result
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Actionable comments posted: 1
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⚠️ Outside diff range comments (5)
LangChain/LangChainComponentImplementation.md (5)
5-9: Fix PromptTemplate usage (typos and object syntax).
- Typo in class name:
PromptTemplatel→PromptTemplate.- Invalid invoke payload: should be
{ topic: "cats" }.- Remove stray character at Line 8.
-import {PromptTemplate} from "@langchain/core/prompts"; -const prompt = PromptTemplatel.fromTemplate("Tell me a joke about {topic}"); -const result = await prompt.invoke({topic} : "cats"); -console.log(result); +import { PromptTemplate } from "@langchain/core/prompts"; +const prompt = PromptTemplate.fromTemplate("Tell me a joke about {topic}"); +const result = await prompt.invoke({ topic: "cats" }); +console.log(result);
30-37: Fix chaining example (template spacing, call syntax).
- Add missing space in template placeholder.
- Fix extra brace/semicolon in
chain2.invoke.- Log remains correct;
res1.content/res2.contentmatches ChatModel output.-const prompt1 = PromptTemplate.fromTemplate("Give me 2 states in{country}"); +const prompt1 = PromptTemplate.fromTemplate("Give me 2 states in {country}"); const prompt2 = PromptTemplate.fromTemplate("Give me 2 places in {state}"); const chain1 = prompt1.pipe(llm); const chain2 = prompt2.pipe(llm); const res1 = await chain1.invoke({country: "India"}); -const res2 = await chain2.invoke({state: res1.content};) -console.log("States: ",res1.content); -console.log("Places: ",res2.content); +const res2 = await chain2.invoke({ state: res1.content }); +console.log("States: ", res1.content); +console.log("Places: ", res2.content);
43-49: Use correct Vertex AI embeddings model id and method name.
- Model id:
"text-embedding-004"(singular), not"text-embeddings-004".- Method: use
embedQuery(text)for a single string. LCJS exposesembedQuery/embedDocuments, notembed. See official docs. (js.langchain.com)import {VertexAIEmbeddings} from "@langchain/google-vertexai"; const embeddings = new VertexAIEmbeddings({ -model : "text-embeddings-004" + model: "text-embedding-004" }); -const vector = await embeddings.embed("I like chocolates"); -console.log(vector.length); +const vector = await embeddings.embedQuery("I like chocolates"); +console.log(vector.length);
54-56: Fix class name and prefer a valid initialization pattern for MemoryVectorStore.
- Typo:
MemoryVectoryStore→MemoryVectorStore.- Initialize with the embeddings instance (constructor) so you can add docs later. Both constructor and
fromDocumentsare supported in LCJS. (v02.api.js.langchain.com)-import {MemoryVectorStore} from "langchain/vectorstores/memory"; -const vectorstore = new MemoryVectoryStore(embeddings); +import { MemoryVectorStore } from "langchain/vectorstores/memory"; +const vectorStore = new MemoryVectorStore(embeddings);
61-71: Correct Document import and construction; fix variable names.
- Import
Document(singular).- Construct with an object literal.
- Use
vectorStoreconsistently; the prior snippet defined it in camelCase.- Consider passing explicit IDs to avoid accidental duplicates. (js.langchain.com)
-import {Documents} from "@langchain/core/documents"; -const document1 = new Document( -pageContent: "I like chocolates."; -metadata:{source: "tweet"}, -) -const document2 = new Document( -pageContent: "The weather forecast for tomorrow is cloudy", -metadata: {source: "news"}, -) -const documents = [document1, document2] -await vectorStore.addDocuments(documents) +import { Document } from "@langchain/core/documents"; +const document1 = new Document({ + pageContent: "I like chocolates.", + metadata: { source: "tweet" }, +}); +const document2 = new Document({ + pageContent: "The weather forecast for tomorrow is cloudy", + metadata: { source: "news" }, +}); +const documents = [document1, document2]; +await vectorStore.addDocuments(documents, { ids: ["doc1", "doc2"] });
🧹 Nitpick comments (6)
LangChain/LangChainComponentImplementation.md (2)
12-19: Clean up ChatPromptTemplate snippet and payload.
- Remove stray control character at Line 12.
- The rest of the snippet is fine; keeping the same message tuple format is acceptable in LCJS v0.2+.
- import {ChatPromptTemplate} from "@langchain/core/prompts"; const chatPrompt = ChatPromptTemplate.fromMessages([ ["system", "You are a funny assistant"], ["human", "Tell me a joke about {topic}."] ]); -const chatResult = await chatPrompt.invoke({topic} : "cats"); +const chatResult = await chatPrompt.invoke({ topic: "cats" }); console.log(chatResult);
75-77: Unify variable name for vector store.
- Earlier you defined
vectorStore(camelCase). Use the same casing here.-const docs = await vectorstore.similaritySearch(query); +const docs = await vectorStore.similaritySearch(query);docs/LangGraph/LangGraph.md (4)
200-243: JS example: align with LangGraph JS API and best practices.
- Imports and
Annotation.Rootusage look correct for JS. ConfirmingStateGraph,Annotation,START,ENDfrom@langchain/langgraph. (langchain-ai.github.io)- For human-in-the-loop later, note that interrupts require a checkpointer; consider adding a comment to set one when needed. (langchain-ai.github.io)
-import { StateGraph, Annotation, START, END } from "@langchain/langgraph"; +import { StateGraph, Annotation, START, END } from "@langchain/langgraph"; // npm i @langchain/langgraph @langchain/core @@ -// Compile and execute +// Compile and execute const graph = builder.compile(); @@ console.log(result);
247-259: Human-in-the-Loop: prefer documented option names and mention checkpointer.
- In JS, compile options are camelCase
interruptBefore/interruptAfter(you used these—good). Consider adding a checkpointer to enable breakpoints in real runs. (langchain-ai.lang.chat, langchain-ai.github.io)-const workflow = graph.compile({ - interruptBefore: ["humanReview"], - interruptAfter: ["analysis"], -}); +const workflow = graph.compile({ + // Requires a checkpointer to persist state during interrupts + // checkpointer: new InMemorySaver(), // import from "@langchain/langgraph" + interruptBefore: ["humanReview"], + interruptAfter: ["analysis"], +});
83-97: Clarify “State is user-defined…” and contrast to LangChain.
- The distinction is correct but consider one line showing that state channels are defined via
Annotation.Root. This ties the prose to the example. (langchain-ai.github.io)-State is user-defined and maintained and passed between nodes during execution. When deciding which node to target next, this is the current state that we look at. +State is user-defined and passed between nodes. In JS, you define state channels with `Annotation.Root({...})`, and LangGraph routes/merges updates via reducers.
160-170: Minor terminology tweak for Messages section.
- “Uses LangChain BaseMessage subclasses” is accurate; consider linking MessagesAnnotation as a common pattern. (langchain-ai.github.io)
-- Uses LangChain BaseMessage subclasses: +- Uses LangChain BaseMessage subclasses (see also `MessagesAnnotation` for a ready-made messages state):
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📒 Files selected for processing (2)
LangChain/LangChainComponentImplementation.md(1 hunks)docs/LangGraph/LangGraph.md(1 hunks)
🧰 Additional context used
🪛 LanguageTool
docs/LangGraph/LangGraph.md
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Context: ...namic routing - Decision-based branching - State-dependent flow control ##### STAR...
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LangChain/LangChainComponentImplementation.md
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docs/LangGraph/LangGraph.md
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Description
This pull request contains comprehensive documentation improvements focusing on LangGraph integration and middleware functionality. The changes enhance the developer experience by providing clearer explanations, working code examples, and better organization of documentation content.
Type of Change
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AI Provider Impact
Component Impact
Testing
Test Environment
Performance Impact
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