Merge branch 'main' into migrate-to-weaviate-v3-client

README.md

@@ -43,7 +43,7 @@ The LangChain libraries themselves are made up of several different packages.
 - **[`@langchain/core`](https://github.com/langchain-ai/langchainjs/blob/main/langchain-core)**: Base abstractions and LangChain Expression Language.
 - **[`@langchain/community`](https://github.com/langchain-ai/langchainjs/blob/main/libs/langchain-community)**: Third party integrations.
 - **[`langchain`](https://github.com/langchain-ai/langchainjs/blob/main/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
-- **[LangGraph.js](https://langchain-ai.github.io/langgraphjs/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
+- **[LangGraph.js](https://langchain-ai.github.io/langgraphjs/)**: LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more. Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
 
 Integrations may also be split into their own compatible packages.
 

docs/core_docs/docs/introduction.mdx

@@ -81,7 +81,7 @@ Trace and evaluate your language model applications and intelligent agents to he
 
 ### [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraphjs/)
 
-Build stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
+Build stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it. LangGraph powers production-grade agents, trusted by Linkedin, Uber, Klarna, GitLab, and many more.
 
 ## Additional resources
 

langchain/package.json

@@ -1,6 +1,6 @@
 {
   "name": "langchain",
-  "version": "0.3.14",
+  "version": "0.3.15",
   "description": "Typescript bindings for langchain",
   "type": "module",
   "engines": {

langchain/src/hub/base.ts

@@ -96,10 +96,6 @@ export function generateModelImportMap(
 ) {
   // eslint-disable-next-line @typescript-eslint/no-explicit-any
   const modelImportMap: Record<string, any> = {};
-  // TODO: Fix in 0.4.0. We can't get lc_id without instantiating the class, so we
-  // must put them inline here. In the future, make this less hacky
-  // This should probably use dynamic imports and have a web-only entrypoint
-  // in a future breaking release
   if (modelClass !== undefined) {
     // eslint-disable-next-line @typescript-eslint/no-explicit-any
     const modelLcName = (modelClass as any)?.lc_name();
@@ -130,3 +126,29 @@ export function generateModelImportMap(
   }
   return modelImportMap;
 }
+
+export function generateOptionalImportMap(
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  modelClass?: new (...args: any[]) => BaseLanguageModel
+) {
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  const optionalImportMap: Record<string, any> = {};
+  if (modelClass !== undefined) {
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const modelLcName = (modelClass as any)?.lc_name();
+    let optionalImportMapKey;
+    if (modelLcName === "ChatGoogleGenerativeAI") {
+      optionalImportMapKey = "langchain_google_genai/chat_models";
+    } else if (modelLcName === "ChatBedrockConverse") {
+      optionalImportMapKey = "langchain_aws/chat_models";
+    } else if (modelLcName === "ChatGroq") {
+      optionalImportMapKey = "langchain_groq/chat_models";
+    }
+    if (optionalImportMapKey !== undefined) {
+      optionalImportMap[optionalImportMapKey] = {
+        [modelLcName]: modelClass,
+      };
+    }
+  }
+  return optionalImportMap;
+}

langchain/src/hub/index.ts

@@ -1,7 +1,12 @@
 import { Runnable } from "@langchain/core/runnables";
 import type { BaseLanguageModel } from "@langchain/core/language_models/base";
 import { load } from "../load/index.js";
-import { basePush, basePull, generateModelImportMap } from "./base.js";
+import {
+  basePush,
+  basePull,
+  generateModelImportMap,
+  generateOptionalImportMap,
+} from "./base.js";
 
 export { basePush as push };
 
@@ -36,7 +41,7 @@ export async function pull<T extends Runnable>(
     const loadedPrompt = await load<T>(
       JSON.stringify(promptObject.manifest),
       undefined,
-      undefined,
+      generateOptionalImportMap(options?.modelClass),
       generateModelImportMap(options?.modelClass)
     );
     return loadedPrompt;

langchain/src/hub/node.ts

@@ -1,5 +1,10 @@
 import { Runnable } from "@langchain/core/runnables";
-import { basePush, basePull, generateModelImportMap } from "./base.js";
+import {
+  basePush,
+  basePull,
+  generateModelImportMap,
+  generateOptionalImportMap,
+} from "./base.js";
 import { load } from "../load/index.js";
 
 // TODO: Make this the default, add web entrypoint in next breaking release
@@ -55,7 +60,7 @@ export async function pull<T extends Runnable>(
   const loadedPrompt = await load<T>(
     JSON.stringify(promptObject.manifest),
     undefined,
-    undefined,
+    generateOptionalImportMap(modelClass),
     generateModelImportMap(modelClass)
   );
   return loadedPrompt;

langchain/src/hub/tests/hub.int.test.ts

@@ -79,12 +79,13 @@ test("Test LangChain Hub while loading model", async () => {
 });
 
 test("Test LangChain Hub while loading model with dynamic imports", async () => {
-  const pulledPrompt = await nodePull("jacob/lahzo-testing", {
+  const pulledPrompt = await nodePull("jacob/groq-test", {
     includeModel: true,
   });
   const res = await pulledPrompt.invoke({
-    agent: { name: "testing" },
-    messages: [new AIMessage("foo")],
+    question:
+      "Who is the current president of the USA as of today? You must use the provided tool for the latest info.",
   });
   expect(res).toBeInstanceOf(AIMessage);
+  expect(res.tool_calls?.length).toEqual(1);
 });

libs/langchain-community/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@langchain/community",
-  "version": "0.3.27",
+  "version": "0.3.28",
   "description": "Third-party integrations for LangChain.js",
   "type": "module",
   "engines": {

libs/langchain-community/src/structured_query/supabase.ts

@@ -12,6 +12,7 @@ import {
   Operator,
   Operators,
   StructuredQuery,
+  isBoolean,
 } from "@langchain/core/structured_query";
 import type {
   SupabaseFilterRPCCall,
@@ -134,6 +135,8 @@ export class SupabaseTranslator<
       column = `metadata->${attr}${includeType ? "::int" : ""}`;
     } else if (isFloat(value)) {
       column = `metadata->${attr}${includeType ? "::float" : ""}`;
+    } else if (isBoolean(value)) {
+      column = `metadata->${attr}${includeType ? "::boolean" : ""}`;
     } else {
       throw new Error("Data type not supported");
     }

libs/langchain-community/src/structured_query/tests/supabase_self_query.int.test.ts

@@ -602,3 +602,105 @@ test("Supabase Store Self Query Retriever Test With Default Filter And Merge Ope
   // console.log(query4);
   expect(query4.length).toEqual(0);
 });
+
+test("Supabase Store Self Query Retriever Test With Boolean Filters", async () => {
+  const docs = [
+    new Document({
+      pageContent: "A fun family movie about toys coming to life",
+      metadata: {
+        type: "movie",
+        year: 1995,
+        genre: "animated",
+        isKidsMovie: true,
+        hasSequels: true,
+      },
+    }),
+    new Document({
+      pageContent: "A dark psychological thriller about dreams",
+      metadata: {
+        type: "movie",
+        year: 2010,
+        director: "Christopher Nolan",
+        rating: 8.2,
+        isKidsMovie: false,
+        hasSequels: false,
+      },
+    }),
+    new Document({
+      pageContent: "A classic dinosaur adventure park goes wrong",
+      metadata: {
+        type: "movie",
+        year: 1993,
+        rating: 7.7,
+        genre: "science fiction",
+        isKidsMovie: false,
+        hasSequels: true,
+      },
+    }),
+  ];
+
+  const attributeInfo: AttributeInfo[] = [
+    {
+      name: "isKidsMovie",
+      description: "Whether the movie is made for children",
+      type: "boolean",
+    },
+    {
+      name: "hasSequels",
+      description: "Whether the movie has sequel movies",
+      type: "boolean",
+    },
+    {
+      name: "year",
+      description: "The year the movie was released",
+      type: "number",
+    },
+  ];
+
+  if (
+    !process.env.SUPABASE_VECTOR_STORE_URL ||
+    !process.env.SUPABASE_VECTOR_STORE_PRIVATE_KEY
+  ) {
+    throw new Error(
+      "Supabase URL or private key not set. Please set it in the .env file"
+    );
+  }
+
+  const embeddings = new OpenAIEmbeddings();
+  const llm = new OpenAI();
+  const documentContents = "Brief summary of a movie";
+  const client = createClient(
+    process.env.SUPABASE_VECTOR_STORE_URL,
+    process.env.SUPABASE_VECTOR_STORE_PRIVATE_KEY
+  );
+  const vectorStore = new SupabaseVectorStore(embeddings, { client });
+  // idempotency
+  const opts = { ids: docs.map((_, idx) => idx) };
+  await vectorStore.addDocuments(docs, opts);
+  const selfQueryRetriever = SelfQueryRetriever.fromLLM({
+    llm,
+    vectorStore,
+    documentContents,
+    attributeInfo,
+    structuredQueryTranslator: new SupabaseTranslator(),
+  });
+
+  const query1 = await selfQueryRetriever.getRelevantDocuments(
+    "Which movies are made for kids?"
+  );
+  expect(query1.length).toEqual(1);
+  expect(query1[0].metadata.isKidsMovie).toBe(true);
+
+  const query2 = await selfQueryRetriever.getRelevantDocuments(
+    "Which movies have sequels?"
+  );
+  expect(query2.length).toEqual(2);
+  expect(query2.every((doc: Document) => doc.metadata.hasSequels)).toBe(true);
+
+  const query3 = await selfQueryRetriever.getRelevantDocuments(
+    "Which movies are not made for kids and have sequels?"
+  );
+  expect(query3.length).toEqual(1);
+  expect(query3[0].metadata.isKidsMovie).toBe(false);
+  expect(query3[0].metadata.hasSequels).toBe(true);
+});