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4e9727215e95-2500 | Passing specific options here is completely optional, but can be useful if you want to customize the way the response is presented to the end user, or if you have too many documents for the default StuffDocumentsChain.
You can see the API reference of the usable fields here. In case you want to make chat_history avail... |
4e9727215e95-2501 | to let it know which values to store.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter... |
4e9727215e95-2502 | gpt-3.5 or gpt-4) }), questionGeneratorChainOptions: { llm: fasterModel, }, } ); /* Ask it a question */ const question = "What did the president say about Justice Breyer? "; const res = await chain.call({ question }); console.log(res); const followUpRes = await chain.call({ question: "Wa... |
4e9727215e95-2503 | Here's an example:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter } from "langchain/... |
4e9727215e95-2504 | streaming: true, callbacks: [ { handleLLMNewToken(token) { streamedResponse += token; }, }, ], }); const nonStreamingModel = new ChatOpenAI({}); const chain = ConversationalRetrievalQAChain.fromLLM( streamingModel, vectorStore.asRetriever(), { returnSourceDocument... |
4e9727215e95-2505 | a chat_history string or array of HumanMessages and AIMessages directly into the chain.call method:import { OpenAI } from "langchain/llms/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddi... |
4e9727215e95-2506 | ", chat_history: chatHistory,});console.log(followUpRes);API Reference:OpenAI from langchain/llms/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiRecursiveCharacterTextSplitter from langchain/text_splitterPrompt Custo... |
4e9727215e95-2507 | allowing the QA chain to answer meta questions with the additional context:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";i... |
4e9727215e95-2508 | make up an answer.----------------<Relevant chat history excerpt as context here>Standalone question: <Rephrased question here>\`\`\`Your answer:`;const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo", temperature: 0,});const vectorStore = await HNSWLib.fromTexts( [ "Mitochondria are the powerhouse of the cel... |
4e9727215e95-2509 | }*/API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiBufferMemory from langchain/memoryKeep in mind that adding more context to the prompt in this way may distract the ... |
4e9727215e95-2510 | In the below example, we will create one from a vector store, which can be created from embeddings.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langch... |
4e9727215e95-2511 | "; const res = await chain.call({ question }); console.log(res); /* Ask it a follow up question */ const followUpRes = await chain.call({ question: "Was that nice? ", }); console.log(followUpRes);};API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNS... |
4e9727215e95-2512 | Passing specific options here is completely optional, but can be useful if you want to customize the way the response is presented to the end user, or if you have too many documents for the default StuffDocumentsChain.
You can see the API reference of the usable fields here. In case you want to make chat_history avail... |
4e9727215e95-2513 | to let it know which values to store.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter... |
4e9727215e95-2514 | gpt-3.5 or gpt-4) }), questionGeneratorChainOptions: { llm: fasterModel, }, } ); /* Ask it a question */ const question = "What did the president say about Justice Breyer? "; const res = await chain.call({ question }); console.log(res); const followUpRes = await chain.call({ question: "Wa... |
4e9727215e95-2515 | Here's an example:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter } from "langchain/... |
4e9727215e95-2516 | streaming: true, callbacks: [ { handleLLMNewToken(token) { streamedResponse += token; }, }, ], }); const nonStreamingModel = new ChatOpenAI({}); const chain = ConversationalRetrievalQAChain.fromLLM( streamingModel, vectorStore.asRetriever(), { returnSourceDocument... |
4e9727215e95-2517 | a chat_history string or array of HumanMessages and AIMessages directly into the chain.call method:import { OpenAI } from "langchain/llms/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddi... |
4e9727215e95-2518 | ", chat_history: chatHistory,});console.log(followUpRes);API Reference:OpenAI from langchain/llms/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiRecursiveCharacterTextSplitter from langchain/text_splitterPrompt Custo... |
4e9727215e95-2519 | allowing the QA chain to answer meta questions with the additional context:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";i... |
4e9727215e95-2520 | make up an answer.----------------<Relevant chat history excerpt as context here>Standalone question: <Rephrased question here>\`\`\`Your answer:`;const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo", temperature: 0,});const vectorStore = await HNSWLib.fromTexts( [ "Mitochondria are the powerhouse of the cel... |
4e9727215e95-2521 | }*/API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiBufferMemory from langchain/memoryKeep in mind that adding more context to the prompt in this way may distract the ... |
4e9727215e95-2522 | In the below example, we will create one from a vector store, which can be created from embeddings.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langch... |
4e9727215e95-2523 | "; const res = await chain.call({ question }); console.log(res); /* Ask it a follow up question */ const followUpRes = await chain.call({ question: "Was that nice? ", }); console.log(followUpRes);};API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNS... |
4e9727215e95-2524 | Passing specific options here is completely optional, but can be useful if you want to customize the way the response is presented to the end user, or if you have too many documents for the default StuffDocumentsChain.
You can see the API reference of the usable fields here. In case you want to make chat_history avail... |
4e9727215e95-2525 | to let it know which values to store.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter... |
4e9727215e95-2526 | gpt-3.5 or gpt-4) }), questionGeneratorChainOptions: { llm: fasterModel, }, } ); /* Ask it a question */ const question = "What did the president say about Justice Breyer? "; const res = await chain.call({ question }); console.log(res); const followUpRes = await chain.call({ question: "Wa... |
4e9727215e95-2527 | Here's an example:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter } from "langchain/... |
4e9727215e95-2528 | streaming: true, callbacks: [ { handleLLMNewToken(token) { streamedResponse += token; }, }, ], }); const nonStreamingModel = new ChatOpenAI({}); const chain = ConversationalRetrievalQAChain.fromLLM( streamingModel, vectorStore.asRetriever(), { returnSourceDocument... |
4e9727215e95-2529 | a chat_history string or array of HumanMessages and AIMessages directly into the chain.call method:import { OpenAI } from "langchain/llms/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddi... |
4e9727215e95-2530 | ", chat_history: chatHistory,});console.log(followUpRes);API Reference:OpenAI from langchain/llms/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiRecursiveCharacterTextSplitter from langchain/text_splitterPrompt Custo... |
4e9727215e95-2531 | allowing the QA chain to answer meta questions with the additional context:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";i... |
4e9727215e95-2532 | make up an answer.----------------<Relevant chat history excerpt as context here>Standalone question: <Rephrased question here>\`\`\`Your answer:`;const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo", temperature: 0,});const vectorStore = await HNSWLib.fromTexts( [ "Mitochondria are the powerhouse of the cel... |
4e9727215e95-2533 | }*/API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiBufferMemory from langchain/memoryKeep in mind that adding more context to the prompt in this way may distract the ... |
4e9727215e95-2534 | In the below example, we will create one from a vector store, which can be created from embeddings.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langch... |
4e9727215e95-2535 | "; const res = await chain.call({ question }); console.log(res); /* Ask it a follow up question */ const followUpRes = await chain.call({ question: "Was that nice? ", }); console.log(followUpRes);};API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNS... |
4e9727215e95-2536 | Passing specific options here is completely optional, but can be useful if you want to customize the way the response is presented to the end user, or if you have too many documents for the default StuffDocumentsChain.
You can see the API reference of the usable fields here. In case you want to make chat_history avail... |
4e9727215e95-2537 | to let it know which values to store.import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter... |
4e9727215e95-2538 | gpt-3.5 or gpt-4) }), questionGeneratorChainOptions: { llm: fasterModel, }, } ); /* Ask it a question */ const question = "What did the president say about Justice Breyer? "; const res = await chain.call({ question }); console.log(res); const followUpRes = await chain.call({ question: "Wa... |
4e9727215e95-2539 | Here's an example:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter } from "langchain/... |
4e9727215e95-2540 | streaming: true, callbacks: [ { handleLLMNewToken(token) { streamedResponse += token; }, }, ], }); const nonStreamingModel = new ChatOpenAI({}); const chain = ConversationalRetrievalQAChain.fromLLM( streamingModel, vectorStore.asRetriever(), { returnSourceDocument... |
4e9727215e95-2541 | a chat_history string or array of HumanMessages and AIMessages directly into the chain.call method:import { OpenAI } from "langchain/llms/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddi... |
4e9727215e95-2542 | ", chat_history: chatHistory,});console.log(followUpRes);API Reference:OpenAI from langchain/llms/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiRecursiveCharacterTextSplitter from langchain/text_splitterPrompt Custo... |
4e9727215e95-2543 | allowing the QA chain to answer meta questions with the additional context:import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";i... |
4e9727215e95-2544 | make up an answer.----------------<Relevant chat history excerpt as context here>Standalone question: <Rephrased question here>\`\`\`Your answer:`;const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo", temperature: 0,});const vectorStore = await HNSWLib.fromTexts( [ "Mitochondria are the powerhouse of the cel... |
4e9727215e95-2545 | }*/API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiBufferMemory from langchain/memoryKeep in mind that adding more context to the prompt in this way may distract the ... |
4e9727215e95-2546 | import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";imp... |
4e9727215e95-2547 | API Reference:ChatOpenAI from langchain/chat_models/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiRecursiveCharacterTextSplitter from langchain/text_splitterBufferMemory from langchain/memory
In the above code snipp... |
4e9727215e95-2548 | to let it know which values to store.
import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai";import { RecursiveCharacterTextSplitt... |
4e9727215e95-2549 | "; const res = await chain.call({ question }); console.log(res); const followUpRes = await chain.call({ question: "Was that nice?" }); console.log(followUpRes);};
You can also use the above concept of using two different LLMs to stream only the final response from the chain, and not output from the intermediate st... |
4e9727215e95-2550 | true, callbacks: [ { handleLLMNewToken(token) { streamedResponse += token; }, }, ], }); const nonStreamingModel = new ChatOpenAI({}); const chain = ConversationalRetrievalQAChain.fromLLM( streamingModel, vectorStore.asRetriever(), { returnSourceDocuments: true, ... |
4e9727215e95-2551 | a chat_history string or array of HumanMessages and AIMessages directly into the chain.call method:
import { OpenAI } from "langchain/llms/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embed... |
4e9727215e95-2552 | API Reference:OpenAI from langchain/llms/openaiConversationalRetrievalQAChain from langchain/chainsHNSWLib from langchain/vectorstores/hnswlibOpenAIEmbeddings from langchain/embeddings/openaiRecursiveCharacterTextSplitter from langchain/text_splitter
If you want to further change the chain's behavior, you can change t... |
4e9727215e95-2553 | allowing the QA chain to answer meta questions with the additional context:
import { ChatOpenAI } from "langchain/chat_models/openai";import { ConversationalRetrievalQAChain } from "langchain/chains";import { HNSWLib } from "langchain/vectorstores/hnswlib";import { OpenAIEmbeddings } from "langchain/embeddings/openai"... |
4e9727215e95-2554 | chat history excerpt as context here>Standalone question: <Rephrased question here>\`\`\`Your answer:`;const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo", temperature: 0,});const vectorStore = await HNSWLib.fromTexts( [ "Mitochondria are the powerhouse of the cell", "Foo is red", "Bar is red", "Bu... |
4e9727215e95-2555 | Keep in mind that adding more context to the prompt in this way may distract the LLM from other relevant retrieved information.
SQL
Page Title: SQL | 🦜️🔗 Langchain
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Skip to main content🦜️🔗 LangChainDocsUse casesAPILangSmithPython DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/O... |
4e9727215e95-2556 | Postgres, SQLite, Microsoft SQL Server, MySQL, and SAP HANA.Finally follow the instructions on https://database.guide/2-sample-databases-sqlite/ to get the sample database for this example.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";... |
4e9727215e95-2557 | It can also reduce the number of tokens used in the chain.const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource, includesTables: ["Track"],});If desired, you can return the used SQL command when calling the chain.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/opena... |
4e9727215e95-2558 | ', * sql: ' SELECT COUNT(*) FROM "Track";' * } */console.log(res);API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbSAP HanaHere's an example of using the chain with a SAP HANA database:import { DataSource } from "typeorm";import { OpenAI } f... |
4e9727215e95-2559 | ");console.log(res);// There are 3503 tracks.API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbCustom promptYou can also customize the prompt that is used. Here is an example prompting the model to understand that "foobar" is the same as the Em... |
4e9727215e95-2560 | /const datasource = new DataSource({ type: "sqlite", database: "data/Chinook.db",});const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource,});const chain = new SqlDatabaseChain({ llm: new OpenAI({ temperature: 0 }), database: db, sqlOutputKey: "sql", prompt,});const res = await chain.call(... |
4e9727215e95-2561 | Postgres, SQLite, Microsoft SQL Server, MySQL, and SAP HANA.Finally follow the instructions on https://database.guide/2-sample-databases-sqlite/ to get the sample database for this example.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";... |
4e9727215e95-2562 | It can also reduce the number of tokens used in the chain.const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource, includesTables: ["Track"],});If desired, you can return the used SQL command when calling the chain.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/opena... |
4e9727215e95-2563 | ', * sql: ' SELECT COUNT(*) FROM "Track";' * } */console.log(res);API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbSAP HanaHere's an example of using the chain with a SAP HANA database:import { DataSource } from "typeorm";import { OpenAI } f... |
4e9727215e95-2564 | ");console.log(res);// There are 3503 tracks.API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbCustom promptYou can also customize the prompt that is used. Here is an example prompting the model to understand that "foobar" is the same as the Em... |
4e9727215e95-2565 | /const datasource = new DataSource({ type: "sqlite", database: "data/Chinook.db",});const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource,});const chain = new SqlDatabaseChain({ llm: new OpenAI({ temperature: 0 }), database: db, sqlOutputKey: "sql", prompt,});const res = await chain.call(... |
4e9727215e95-2566 | Postgres, SQLite, Microsoft SQL Server, MySQL, and SAP HANA.Finally follow the instructions on https://database.guide/2-sample-databases-sqlite/ to get the sample database for this example.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";... |
4e9727215e95-2567 | It can also reduce the number of tokens used in the chain.const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource, includesTables: ["Track"],});If desired, you can return the used SQL command when calling the chain.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/opena... |
4e9727215e95-2568 | ', * sql: ' SELECT COUNT(*) FROM "Track";' * } */console.log(res);API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbSAP HanaHere's an example of using the chain with a SAP HANA database:import { DataSource } from "typeorm";import { OpenAI } f... |
4e9727215e95-2569 | ");console.log(res);// There are 3503 tracks.API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbCustom promptYou can also customize the prompt that is used. Here is an example prompting the model to understand that "foobar" is the same as the Em... |
4e9727215e95-2570 | /const datasource = new DataSource({ type: "sqlite", database: "data/Chinook.db",});const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource,});const chain = new SqlDatabaseChain({ llm: new OpenAI({ temperature: 0 }), database: db, sqlOutputKey: "sql", prompt,});const res = await chain.call(... |
4e9727215e95-2571 | Postgres, SQLite, Microsoft SQL Server, MySQL, and SAP HANA.Finally follow the instructions on https://database.guide/2-sample-databases-sqlite/ to get the sample database for this example.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";... |
4e9727215e95-2572 | It can also reduce the number of tokens used in the chain.const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource, includesTables: ["Track"],});If desired, you can return the used SQL command when calling the chain.import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/opena... |
4e9727215e95-2573 | ', * sql: ' SELECT COUNT(*) FROM "Track";' * } */console.log(res);API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbSAP HanaHere's an example of using the chain with a SAP HANA database:import { DataSource } from "typeorm";import { OpenAI } f... |
4e9727215e95-2574 | ");console.log(res);// There are 3503 tracks.API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbCustom promptYou can also customize the prompt that is used. Here is an example prompting the model to understand that "foobar" is the same as the Em... |
4e9727215e95-2575 | /const datasource = new DataSource({ type: "sqlite", database: "data/Chinook.db",});const db = await SqlDatabase.fromDataSourceParams({ appDataSource: datasource,});const chain = new SqlDatabaseChain({ llm: new OpenAI({ temperature: 0 }), database: db, sqlOutputKey: "sql", prompt,});const res = await chain.call(... |
4e9727215e95-2576 | import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";import { SqlDatabaseChain } from "langchain/chains/sql_db";/** * This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. * To set it up ... |
4e9727215e95-2577 | If desired, you can return the used SQL command when calling the chain.
import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";import { SqlDatabaseChain } from "langchain/chains/sql_db";/** * This example uses Chinook database, which is a samp... |
4e9727215e95-2578 | Here's an example of using the chain with a SAP HANA database:
import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";import { SqlDatabaseChain } from "langchain/chains/sql_db";/** * This example uses a SAP HANA Cloud database. You can create ... |
4e9727215e95-2579 | import { DataSource } from "typeorm";import { OpenAI } from "langchain/llms/openai";import { SqlDatabase } from "langchain/sql_db";import { SqlDatabaseChain } from "langchain/chains/sql_db";import { PromptTemplate } from "langchain/prompts";const template = `Given an input question, first create a syntactically correct... |
4e9727215e95-2580 | ', sql: ' SELECT COUNT(*) FROM Employee;' }*/
API Reference:OpenAI from langchain/llms/openaiSqlDatabase from langchain/sql_dbSqlDatabaseChain from langchain/chains/sql_dbPromptTemplate from langchain/prompts
Structured Output with OpenAI functions
Page Title: Structured Output with OpenAI functions | 🦜️🔗 Lang... |
4e9727215e95-2581 | library and convert it with the zod-to-json-schema package. To do so, install the following packages:npmYarnpnpmnpm install zod zod-to-json-schemayarn add zod zod-to-json-schemapnpm add zod zod-to-json-schemaFormat Text into Structured Dataimport { z } from "zod";import { zodToJsonSchema } from "zod-to-json-schema";im... |
4e9727215e95-2582 | ), HumanMessagePromptTemplate.fromTemplate("{inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 0 });// Binding "function_call" below makes the model always call the specified function.// If you want to allow the model to call functions sele... |
4e9727215e95-2583 | of using the createStructuredOutputChainFromZod convenience method to return a classic LLMChain:import { z } from "zod";import { ChatOpenAI } from "langchain/chat_models/openai";import { ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,} from "langchain/prompts";import { createStructuredOu... |
4e9727215e95-2584 | ), HumanMessagePromptTemplate.fromTemplate("Additional context: {inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 1 });const chain = createStructuredOutputChainFromZod(zodSchema, { prompt, llm, outputKey: "person",});const response = aw... |
4e9727215e95-2585 | Get startedIntroductionInstallationQuickstartModulesModel I/OData connectionChainsHow toFoundationalDocumentsPopularAPI chainsRetrieval QAConversational Retrieval QASQLStructured Output with OpenAI functionsSummarizationAdditionalMemoryAgentsCallbacksModulesGuidesEcosystemAdditional resourcesCommunity navigatorAPI ref... |
4e9727215e95-2586 | library and convert it with the zod-to-json-schema package. To do so, install the following packages:npmYarnpnpmnpm install zod zod-to-json-schemayarn add zod zod-to-json-schemapnpm add zod zod-to-json-schemaFormat Text into Structured Dataimport { z } from "zod";import { zodToJsonSchema } from "zod-to-json-schema";im... |
4e9727215e95-2587 | ), HumanMessagePromptTemplate.fromTemplate("{inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 0 });// Binding "function_call" below makes the model always call the specified function.// If you want to allow the model to call functions sele... |
4e9727215e95-2588 | of using the createStructuredOutputChainFromZod convenience method to return a classic LLMChain:import { z } from "zod";import { ChatOpenAI } from "langchain/chat_models/openai";import { ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,} from "langchain/prompts";import { createStructuredOu... |
4e9727215e95-2589 | ), HumanMessagePromptTemplate.fromTemplate("Additional context: {inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 1 });const chain = createStructuredOutputChainFromZod(zodSchema, { prompt, llm, outputKey: "person",});const response = aw... |
4e9727215e95-2590 | It converts input schema into an OpenAI function, then forces OpenAI to call that function to return a response in the correct format.You can use it where you would use a chain with a StructuredOutputParser, but it doesn't require any special
instructions stuffed into the prompt. It will also more reliably output stru... |
4e9727215e95-2591 | ), HumanMessagePromptTemplate.fromTemplate("{inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 0 });// Binding "function_call" below makes the model always call the specified function.// If you want to allow the model to call functions sele... |
4e9727215e95-2592 | of using the createStructuredOutputChainFromZod convenience method to return a classic LLMChain:import { z } from "zod";import { ChatOpenAI } from "langchain/chat_models/openai";import { ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,} from "langchain/prompts";import { createStructuredOu... |
4e9727215e95-2593 | ), HumanMessagePromptTemplate.fromTemplate("Additional context: {inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 1 });const chain = createStructuredOutputChainFromZod(zodSchema, { prompt, llm, outputKey: "person",});const response = aw... |
4e9727215e95-2594 | It converts input schema into an OpenAI function, then forces OpenAI to call that function to return a response in the correct format.You can use it where you would use a chain with a StructuredOutputParser, but it doesn't require any special
instructions stuffed into the prompt. It will also more reliably output stru... |
4e9727215e95-2595 | ), HumanMessagePromptTemplate.fromTemplate("{inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 0 });// Binding "function_call" below makes the model always call the specified function.// If you want to allow the model to call functions sele... |
4e9727215e95-2596 | of using the createStructuredOutputChainFromZod convenience method to return a classic LLMChain:import { z } from "zod";import { ChatOpenAI } from "langchain/chat_models/openai";import { ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,} from "langchain/prompts";import { createStructuredOu... |
4e9727215e95-2597 | ), HumanMessagePromptTemplate.fromTemplate("Additional context: {inputText}"), ], inputVariables: ["inputText"],});const llm = new ChatOpenAI({ modelName: "gpt-3.5-turbo-0613", temperature: 1 });const chain = createStructuredOutputChainFromZod(zodSchema, { prompt, llm, outputKey: "person",});const response = aw... |
4e9727215e95-2598 | It converts input schema into an OpenAI function, then forces OpenAI to call that function to return a response in the correct format.
You can use it where you would use a chain with a StructuredOutputParser, but it doesn't require any special
instructions stuffed into the prompt. It will also more reliably output st... |
4e9727215e95-2599 | pnpm add zod zod-to-json-schema
import { z } from "zod";import { zodToJsonSchema } from "zod-to-json-schema";import { ChatOpenAI } from "langchain/chat_models/openai";import { ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate,} from "langchain/prompts";import { JsonOutputFunctionsParser } ... |
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