id stringlengths 6 6 | text stringlengths 20 17.2k | title stringclasses 1
value |
|---|---|---|
147787 | test("Test Azure ChatOpenAI token usage reporting for streaming function calls", async () => {
// Running LangChain callbacks in the background will sometimes cause the callbackManager to execute
// after the test/llm call has already finished & returned. Set that environment variable to false
// to prevent that ... | |
147810 | export class PineconeStore extends VectorStore {
declare FilterType: PineconeMetadata;
textKey: string;
namespace?: string;
pineconeIndex: PineconeIndex;
filter?: PineconeMetadata;
caller: AsyncCaller;
_vectorstoreType(): string {
return "pinecone";
}
constructor(embeddings: EmbeddingsInter... | |
147860 | /* eslint-disable no-process-env */
import { test } from "@jest/globals";
import * as fs from "node:fs/promises";
import { fileURLToPath } from "node:url";
import * as path from "node:path";
import {
AIMessage,
AIMessageChunk,
HumanMessage,
SystemMessage,
ToolMessage,
} from "@langchain/core/messages";
impor... | |
147912 | import {
AsyncCaller,
AsyncCallerCallOptions,
AsyncCallerParams,
} from "@langchain/core/utils/async_caller";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import {
MediaBlob,
BlobStore,
BlobStoreOptions,
MediaBlobData,
} from "./utils/media_core.js";
import {
GoogleConnectionParam... | |
147913 | export interface GoogleCloudStorageDownloadConnectionParams<AuthOptions>
extends GoogleCloudStorageConnectionParams,
GoogleConnectionParams<AuthOptions> {
method: GoogleAbstractedClientOpsMethod;
alt: "media" | undefined;
}
export class GoogleCloudStorageDownloadConnection<
ResponseType extends GoogleRespo... | |
147970 | import type { BaseLanguageModelCallOptions } from "@langchain/core/language_models/base";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import { GenerationChunk } from "@langchain/core/outputs";
import type { StringWithAutocomplete } from "@langchain/core/utils/types";
import { LLM, type... | |
147984 | import { test, expect } from "@jest/globals";
import * as fs from "node:fs/promises";
import { fileURLToPath } from "node:url";
import * as path from "node:path";
import { AIMessage, HumanMessage } from "@langchain/core/messages";
import { PromptTemplate } from "@langchain/core/prompts";
import {
BytesOutputParser,
... | |
148029 | > [!IMPORTANT]
> This package is now deprecated in favor of the new Azure integration in the OpenAI SDK. Please use the package [`@langchain/openai`](https://www.npmjs.com/package/@langchain/openai) instead.
> You can find the migration guide [here](https://js.langchain.com/docs/integrations/llms/azure#migration-from-a... | |
148038 | import { Embeddings } from "@langchain/core/embeddings";
import {
type OpenAIClientOptions as AzureOpenAIClientOptions,
OpenAIClient as AzureOpenAIClient,
AzureKeyCredential,
OpenAIKeyCredential,
} from "@azure/openai";
import {
KeyCredential,
TokenCredential,
isTokenCredential,
} from "@azure/core-auth";... | |
148039 | import type {
OpenAIClientOptions,
AzureExtensionsOptions,
ChatRequestMessage,
} from "@azure/openai";
import type { BaseLanguageModelCallOptions } from "@langchain/core/language_models/base";
import type { TiktokenModel } from "js-tiktoken/lite";
import type { EmbeddingsParams } from "@langchain/core/embeddings"... | |
148104 | import { SelfQueryRetriever } from "langchain/retrievers/self_query";
import { OpenAI } from "@langchain/openai";
import { VectaraStore } from "@langchain/community/vectorstores/vectara";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { Document } from "@langchain/core/documents";
import { Vecta... | |
148115 | import { EnsembleRetriever } from "langchain/retrievers/ensemble";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";
import { BaseRetriever, BaseRetrieverInput } from "@langchain/core/retrievers";
import { Document } from "@langchain/core/documents"... | |
148117 | import * as uuid from "uuid";
import { MultiVectorRetriever } from "langchain/retrievers/multi_vector";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { InMemor... | |
148123 | import {
BaseRetriever,
type BaseRetrieverInput,
} from "@langchain/core/retrievers";
import type { CallbackManagerForRetrieverRun } from "@langchain/core/callbacks/manager";
import { Document } from "@langchain/core/documents";
/**
* interface BaseRetrieverInput {
* callbacks?: Callbacks;
* tags?: string[]... | |
148125 | import * as uuid from "uuid";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MultiVectorRetriever } from "langchain/retrievers/multi_vector";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
impo... | |
148153 | import type { ChatPromptTemplate } from "@langchain/core/prompts";
import { pull } from "langchain/hub";
import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
import { SessionsPythonREPLTool } from "@langchain/azure-dynamic-sessions";
import { AzureChatOpenAI } from "@langchain/openai";
const tools... | |
148154 | import { Redis } from "ioredis";
import { OpenAIEmbeddings } from "@langchain/openai";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { Red... | |
148158 | import { OpenAIEmbeddings } from "@langchain/openai";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { InMemoryStore } from "@langchain/core/stores";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { FaissStore } from "@langchain/community/vectorstore... | |
148172 | "use node";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { OpenAIEmbeddings } from "@langchain/openai";
import { ConvexKVStore } from "@langchain/community/storage/convex";
import { RecursiveCharacterTextSplitter } fr... | |
148180 | import { ChatOpenAI } from "@langchain/openai";
import {
BufferMemory,
CombinedMemory,
ConversationSummaryMemory,
} from "langchain/memory";
import { ConversationChain } from "langchain/chains";
import { PromptTemplate } from "@langchain/core/prompts";
// buffer memory
const bufferMemory = new BufferMemory({
m... | |
148182 | import { OpenAI } from "@langchain/openai";
import { ConversationSummaryMemory } from "langchain/memory";
import { LLMChain } from "langchain/chains";
import { PromptTemplate } from "@langchain/core/prompts";
export const run = async () => {
const memory = new ConversationSummaryMemory({
memoryKey: "chat_history... | |
148185 | import { OpenAI } from "@langchain/openai";
import { ConversationTokenBufferMemory } from "langchain/memory";
const model = new OpenAI({});
const memory = new ConversationTokenBufferMemory({
llm: model,
maxTokenLimit: 10,
});
await memory.saveContext({ input: "hi" }, { output: "whats up" });
await memory.saveCont... | |
148195 | import { BufferMemory } from "langchain/memory";
import { UpstashRedisChatMessageHistory } from "@langchain/community/stores/message/upstash_redis";
import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
const memory = new BufferMemory({
chatHistory: new UpstashRedisCha... | |
148201 | import { OpenAI, ChatOpenAI } from "@langchain/openai";
import { ConversationSummaryBufferMemory } from "langchain/memory";
import { ConversationChain } from "langchain/chains";
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
} from "@langchain/core/pro... | |
148202 | import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { VectorStoreRetrieverMemory } from "langchain/memory";
import { LLMChain } from "langchain/chains";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { PromptTemplate } from "@langchain/core/prompts";
const vectorStore = new ... | |
148203 | import { RunnableWithMessageHistory } from "@langchain/core/runnables";
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatOpenAI } from "@langchain/openai";
import { AstraDBChatMessageHistory } from "... | |
148204 | import { ChatOpenAI } from "@langchain/openai";
import { ConversationSummaryMemory } from "langchain/memory";
import { LLMChain } from "langchain/chains";
import { PromptTemplate } from "@langchain/core/prompts";
export const run = async () => {
const memory = new ConversationSummaryMemory({
memoryKey: "chat_his... | |
148206 | /* eslint-disable import/first */
/* eslint-disable import/no-duplicates */
import { BufferMemory } from "langchain/memory";
import { HumanMessage, AIMessage } from "@langchain/core/messages";
const memory = new BufferMemory();
await memory.chatHistory.addMessage(new HumanMessage("Hi!"));
await memory.chatHistory.add... | |
148226 | import { AgentExecutor, createReactAgent } from "langchain/agents";
import { pull } from "langchain/hub";
import type { PromptTemplate } from "@langchain/core/prompts";
import { OpenAI } from "@langchain/openai";
import { SerpAPI } from "@langchain/community/tools/serpapi";
export const run = async () => {
// Defi... | |
148227 | import { AgentExecutor, ChatAgent } from "langchain/agents";
import { ConversationChain, LLMChain } from "langchain/chains";
import { ChatOpenAI } from "@langchain/openai";
import { BufferMemory } from "langchain/memory";
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessag... | |
148228 | import { LLMChain } from "langchain/chains";
import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
export const run = async () => {
const chat = new ChatOpenAI({ temperature: 0 });
const chatPrompt = ChatPromptTemplate.fromMessages([
[
"system",
... | |
148229 | import { ConversationChain } from "langchain/chains";
import { ChatOpenAI } from "@langchain/openai";
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { BufferMemory } from "langchain/memory";
const chat = new ChatOpenAI({ temperature: 0 });
const chatPrompt = ChatPromptT... | |
148230 | import { OpenAI } from "@langchain/openai";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
const llm = new OpenAI({
temperature: 0,
// These tags will be attached to all calls made with this LLM.
tags: ["example", "callbacks", "constructor"],
// This handler will be used for all call... | |
148231 | import { OpenAI } from "@langchain/openai";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
const llm = new OpenAI({
temperature: 0,
});
const response = await llm.invoke("1 + 1 =", {
// These tags will be attached only to this call to the LLM.
tags: ["example", "callbacks", "request"]... | |
148243 | import { LLMChain } from "langchain/chains";
import { OpenAI } from "@langchain/openai";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
import { PromptTemplate } from "@langchain/core/prompts";
export const run = async () => {
const handler = new ConsoleCallbackHandler();
const llm = new... | |
148263 | import { LLMChain } from "langchain/chains";
import { AgentExecutor, ZeroShotAgent } from "langchain/agents";
import { ChatOpenAI } from "@langchain/openai";
import { Calculator } from "@langchain/community/tools/calculator";
import { Serialized } from "@langchain/core/load/serializable";
import { BaseCallbackHandler }... | |
148267 | import { OpenAI } from "@langchain/openai";
import { SqlDatabase } from "langchain/sql_db";
import { createSqlAgent, SqlToolkit } from "langchain/agents/toolkits/sql";
import { DataSource } from "typeorm";
/** This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc.
... | |
148271 | import { ChatOpenAI } from "@langchain/openai";
import { AgentExecutor } from "langchain/agents";
import { Calculator } from "@langchain/community/tools/calculator";
import { pull } from "langchain/hub";
import { BufferMemory } from "langchain/memory";
import { formatLogToString } from "langchain/agents/format_scratchp... | |
148273 | import { ChatOpenAI } from "@langchain/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "@langchain/community/tools/calculator";
import { BufferMemory } from "langchain/memory";
import { MessagesPlaceholder } from "@langchain/core/prompts";
export const run = a... | |
148283 | import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import * as fs from "fs";
import {
VectorStoreToolkit,
createVectorStoreAgent,
VectorStoreInfo,
} from "lang... | |
148294 | import { ChatOpenAI } from "@langchain/openai";
import type { BasePromptTemplate } from "@langchain/core/prompts";
import { Calculator } from "@langchain/community/tools/calculator";
import { pull } from "langchain/hub";
import { AgentExecutor, createReactAgent } from "langchain/agents";
// Define the tools the agent... | |
148303 | import { OpenAI } from "@langchain/openai";
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import type { PromptTemplate } from "@langchain/core/prompts";
import { pull } from "langchain/hub";
import { AgentExecutor, createReactAgent } from "langchain/agents";
// Define the tools the a... | |
148304 | import { ChatOpenAI } from "@langchain/openai";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import { Calculator } from "@langchain/community/tools/calculator";
import { SerpAPI } from "@langchain/community/tools/serpapi";
export const run = async () => {
process.env.LANGCHAIN_TRACING = "tr... | |
148312 | import { OpenAI, ChatOpenAI } from "@langchain/openai";
import process from "process";
import { HumanMessage } from "@langchain/core/messages";
process.env.LANGCHAIN_TRACING_V2 = "true";
const model = new OpenAI({});
const prompts = [
"Say hello to Bob.",
"Say hello to Alice.",
"Say hello to John.",
"Say hel... | |
148319 | import {
SageMakerEndpoint,
SageMakerLLMContentHandler,
} from "@langchain/community/llms/sagemaker_endpoint";
interface ResponseJsonInterface {
generation: {
content: string;
};
}
// Custom for whatever model you'll be using
class LLama213BHandler implements SageMakerLLMContentHandler {
contentType = "... | |
148322 | import { AzureOpenAI } from "@langchain/openai";
const model = new AzureOpenAI({
azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
azureOpenAIApiInstanceName: "<your_instance_name>", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
azureOpenAIApiDeplo... | |
148342 | import { OpenAI } from "@langchain/openai";
// To enable streaming, we pass in `streaming: true` to the LLM constructor.
// Additionally, we pass in a handler for the `handleLLMNewToken` event.
const model = new OpenAI({
maxTokens: 25,
streaming: true,
});
const response = await model.invoke("Tell me a joke.", {
... | |
148350 | import { AzureOpenAIEmbeddings } from "@langchain/openai";
const model = new AzureOpenAIEmbeddings({
azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
azureOpenAIApiInstanceName: "<your_instance_name>", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
... | |
148356 | import { AzureOpenAIEmbeddings } from "@langchain/openai";
const model = new AzureOpenAIEmbeddings({
azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
azureOpenAIApiEmbeddingsDeploymentName: "<your_embedding_deployment_name>", // In Node.js defaults to process.env.AZURE_OP... | |
148361 | import {
DefaultAzureCredential,
getBearerTokenProvider,
} from "@azure/identity";
import { AzureChatOpenAI } from "@langchain/openai";
const credentials = new DefaultAzureCredential();
const azureADTokenProvider = getBearerTokenProvider(
credentials,
"https://cognitiveservices.azure.com/.default"
);
const mo... | |
148365 | import { ChatMistralAI } from "@langchain/mistralai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
const model = new ChatMistralAI({
apiKey: process.env.MISTRAL_API_KEY,
model: "mistral-small",
});
const prompt = ChatPromptTemplate.fromMessages([
["system", "You are a helpful assistant"],
["hu... | |
148373 | import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";
const chat = new ChatOpenAI({
maxTokens: 25,
streaming: true,
});
const response = await chat.invoke([new HumanMessage("Tell me a joke.")], {
callbacks: [
{
handleLLMNewToken(token: string) {
... | |
148384 | import { ChatOpenAI } from "@langchain/openai";
const chatModel = new ChatOpenAI({
model: "gpt-3.5-turbo-0125",
});
const res = await chatModel.invoke("Tell me a joke.");
console.log(res.usage_metadata);
/*
{ input_tokens: 12, output_tokens: 17, total_tokens: 29 }
*/ | |
148392 | import { ChatOpenAI } from "@langchain/openai";
// See https://cookbook.openai.com/examples/using_logprobs for details
const model = new ChatOpenAI({
logprobs: true,
// topLogprobs: 5,
});
const responseMessage = await model.invoke("Hi there!");
console.log(JSON.stringify(responseMessage, null, 2));
/*
{
... | |
148401 | import { LLMChain } from "langchain/chains";
import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
} from "@langchain/core/prompts";
// We can also construct an LLMChain from a ChatPromptTemplate and a chat mo... | |
148407 | import { ChatMistralAI } from "@langchain/mistralai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
const model = new ChatMistralAI({
apiKey: process.env.MISTRAL_API_KEY,
model: "mistral-small",
});
const prompt = ChatPromptTempla... | |
148415 | import { AzureChatOpenAI } from "@langchain/openai";
const model = new AzureChatOpenAI({
temperature: 0.9,
azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
azureOpenAIApiDeploymentName: "<your_deployment_name>", // In Node.js defaults to process.env.AZURE_OPENAI_API_DEP... | |
148419 | import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";
const model = new ChatOpenAI({
temperature: 0.9,
apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
});
// You can also pass tools or functions to the model, learn more here
// https:... | |
148429 | import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({
temperature: 0.9,
configuration: {
baseURL: "https://your_custom_url.com",
},
});
const message = await model.invoke("Hi there!");
console.log(message);
/*
AIMessage {
content: 'Hello! How can I assist you today?',
ad... | |
148442 | import { type LLMResult } from "@langchain/core/outputs";
import { ChatOpenAI } from "@langchain/openai";
import { HumanMessage } from "@langchain/core/messages";
import { Serialized } from "@langchain/core/load/serializable";
// We can pass in a list of CallbackHandlers to the LLM constructor to get callbacks for var... | |
148464 | import { AzureChatOpenAI } from "@langchain/openai";
const model = new AzureChatOpenAI({
temperature: 0.9,
azureOpenAIApiKey: "<your_key>", // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
azureOpenAIApiInstanceName: "<your_instance_name>", // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANC... | |
148475 | import { ChatCohere } from "@langchain/cohere";
import { ChatPromptTemplate } from "@langchain/core/prompts";
const model = new ChatCohere({
apiKey: process.env.COHERE_API_KEY, // Default
});
const prompt = ChatPromptTemplate.fromMessages([
["ai", "You are a helpful assistant"],
["human", "{input}"],
]);
const c... | |
148492 | /* eslint-disable import/first */
import { ChatOpenAI } from "@langchain/openai";
const chatModel = new ChatOpenAI({});
console.log(await chatModel.invoke("what is LangSmith?"));
/*
AIMessage {
content: 'Langsmith can help with testing by generating test cases, automating the testing process, and analyzing tes... | |
148493 | /* eslint-disable import/first */
/* eslint-disable import/no-duplicates */
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
const chatModel = new ChatOpenAI({});
const embeddings = new OpenAIEmbeddings({});
co... | |
148505 | import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { HttpResponseOutputParser } from "langchain/output_parsers";
const TEMPLATE = `You are a pirate named Patchy. All responses must be extremely verbose and in pirate dialect.
{input}`;
const prompt = C... | |
148506 | import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { HttpResponseOutputParser } from "langchain/output_parsers";
const TEMPLATE = `You are a pirate named Patchy. All responses must be extremely verbose and in pirate dialect.
{input}`;
const prompt = C... | |
148514 | import { loadEvaluator } from "langchain/evaluation";
const evaluator = await loadEvaluator("criteria", { criteria: "conciseness" });
const res = await evaluator.evaluateStrings({
input: "What's 2+2?",
prediction:
"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is... | |
148522 | import { ChatOpenAI } from "@langchain/openai";
// Use a model with a shorter context window
const shorterLlm = new ChatOpenAI({
model: "gpt-3.5-turbo",
maxRetries: 0,
});
const longerLlm = new ChatOpenAI({
model: "gpt-3.5-turbo-16k",
});
const modelWithFallback = shorterLlm.withFallbacks([longerLlm]);
const ... | |
148523 | import { z } from "zod";
import { OpenAI, ChatOpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
const prompt = PromptTemplate.fromTemplate(
`Return a JSON object containing the following value wrapped ... | |
148526 | import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import {
createRetrieverTool,
createConv... | |
148529 | import { ChatOpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
const model = new ChatOpenAI({
model: "badmodel",
});
const promptTemplate = PromptTemplate.fromTemplate(
"Tell me a joke about {topic}"
);
const chain = promptTemplate.pipe(model);
const result = await chai... | |
148541 | import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { RunnableSequence } from "@langchain/core/runnables";
import { ChatAnthropic } from "@langchain/anthropic";
const promptTemplate =
ChatPromptTemplate.fromTemplate(`Given the user... | |
148545 | import { ChatOpenAI } from "@langchain/openai";
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import {
RunnableConfig,
RunnableWithMessageHistory,
} from "@langchain/core/runnables";
import { ChatMessageHistory } from "@langchain/community/stores/message/in_memory";
// Ins... | |
148546 | import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { formatDocumentsAsString } from "langchain/util/document";
import { PromptTemplate } from "@langchain/core/prompts";
import {
RunnableSequence,
RunnablePassthrough,
} from "... | |
148555 | import { ZepClient } from "@getzep/zep-cloud";
import {
ChatPromptTemplate,
MessagesPlaceholder,
} from "@langchain/core/prompts";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
import { ChatOpenAI } from "@langchain/openai";
import { RunnableWithMessageHistory } from "@langchain/core/run... | |
148558 | import { ChatOpenAI } from "@langchain/openai";
const model = new ChatOpenAI({});
const promptAsString = "Human: Tell me a short joke about ice cream";
const response = await model.invoke(promptAsString);
console.log(response);
/**
AIMessage {
content: 'Sure, here you go: Why did the ice cream go to school? Because... | |
148559 | import { ChatPromptTemplate } from "@langchain/core/prompts";
const prompt = ChatPromptTemplate.fromMessages([
["human", "Tell me a short joke about {topic}"],
]);
const promptValue = await prompt.invoke({ topic: "ice cream" });
console.log(promptValue);
/**
ChatPromptValue {
messages: [
HumanMessage {
c... | |
148560 | import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { Document } from "@langchain/core/documents";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
RunnableLambda,
RunnableMap,
RunnablePassthrough,
} fr... | |
148564 | import { z } from "zod";
import { ChatOpenAI } from "@langchain/openai";
import { LLMChain } from "langchain/chains";
import { OutputFixingParser } from "langchain/output_parsers";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
const o... | |
148567 | /* eslint-disable @typescript-eslint/no-non-null-assertion */
// Requires a vectorstore that supports maximal marginal relevance search
import { Pinecone } from "@pinecone-database/pinecone";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { PineconeStore } from "@langchain/pinecone";
import {... | |
148568 | import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
PromptTemplate,
SystemMessagePromptTemplate,
} from "@langchain/core/prompts";
export const run = async () => {
const template = "What is a good name for a company that makes {product}?";
const promptA = new PromptTemplate({ template, inputVariables:... | |
148570 | import { OpenAI } from "@langchain/openai";
import { RunnableSequence } from "@langchain/core/runnables";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
const parser = StructuredOutputParser.fromNamesAndDescriptions({
answer: "answer... | |
148571 | import { ChatOpenAI } from "@langchain/openai";
import { HttpResponseOutputParser } from "langchain/output_parsers";
import { JsonOutputFunctionsParser } from "@langchain/core/output_parsers/openai_functions";
const handler = async () => {
const parser = new HttpResponseOutputParser({
contentType: "text/event-st... | |
148572 | import { PromptTemplate } from "@langchain/core/prompts";
export const run = async () => {
// The `partial` method returns a new `PromptTemplate` object that can be used to format the prompt with only some of the input variables.
const promptA = new PromptTemplate({
template: "{foo}{bar}",
inputVariables: ... | |
148584 | import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
PromptTemplate,
SystemMessagePromptTemplate,
} from "@langchain/core/prompts";
export const run = async () => {
// A `PromptTemplate` consists of a template string and a list of input variables.
const template = "What is a good name for a company tha... | |
148585 | import { OpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
// With a `StructuredOutputParser` we can define a schema for the output.
const parser = StructuredOutputParser.fromNamesAndDescriptions({
ans... | |
148586 | import { z } from "zod";
import { OpenAI } from "@langchain/openai";
import { RunnableSequence } from "@langchain/core/runnables";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
// We can use zod to define a schema for the output using... | |
148587 | import { FewShotPromptTemplate, PromptTemplate } from "@langchain/core/prompts";
export const run = async () => {
// First, create a list of few-shot examples.
const examples = [
{ word: "happy", antonym: "sad" },
{ word: "tall", antonym: "short" },
];
// Next, we specify the template to format the ex... | |
148592 | // Ephemeral, in-memory vector store for demo purposes
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { PromptTemplate, FewShotPromptTemplate } from "@langchain/core/prompts";
import { SemanticSimilarityExampleSelector } from "... | |
148595 | import { OpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { CustomListOutputParser } from "@langchain/core/output_parsers";
// With a `CustomListOutputParser`, we can parse a list with a specific length and separator.
const parser = new CustomListOutputParser({ length... | |
148598 | import { z } from "zod";
import { ChatOpenAI } from "@langchain/openai";
import { OutputFixingParser } from "langchain/output_parsers";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
export const run = async () => {
const parser = StructuredOutputParser.fromZodSchema(
z.object({
a... | |
148599 | import { z } from "zod";
import { OpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { StructuredOutputParser } from "@langchain/core/output_parsers";
// We can use zod to define a schema for the output using the `fromZodSchema` method of `StructuredOutputParser`.
const... | |
148604 | import { ChatPromptTemplate } from "@langchain/core/prompts";
const systemTemplate =
"You are a helpful assistant that translates {input_language} to {output_language}.";
const humanTemplate = "{text}";
const chatPrompt = ChatPromptTemplate.fromMessages([
["system", systemTemplate],
["human", humanTemplate],
])... | |
148621 | import { TextLoader } from "langchain/document_loaders/fs/text";
const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load(); | |
148624 | import { ApifyDatasetLoader } from "@langchain/community/document_loaders/web/apify_dataset";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { Document } from "@langchain/core/documents";
import { ChatPromptTemplate } from "@... | |
148638 | import { ApifyDatasetLoader } from "@langchain/community/document_loaders/web/apify_dataset";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { Document } from "@langchain/core/documents";
import { ChatPromptTemplate } from "@... | |
148646 | import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
/* Load all PDFs within the specified directory */
const directoryLoader = new DirectoryLoader(... | |
148648 | import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { TokenTextSplitter } from "@langchain/textsplitters";
import { SearchApiLoader } from "@langchain/community/document_loaders/web/searchapi";
import { ChatPromptTemplate } from "@l... | |
148650 | import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { SerpAPILoader } from "@langchain/community/document_loaders/web/serpapi";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { createStuffDocumentsChain } from ... | |
148675 | import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Chroma } from "@langchain/community/vectorstores/chroma";
import { getDocs } from "./docs.js";
const docs = await getDocs();
const textSplitter = new RecursiveCharacterTextSplitter(... |
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