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-0,0 +1 @@ +"use strict";(self.webpackChunkdocs=self.webpackChunkdocs||[]).push([[1538],{80600:(n,e,o)=>{o.r(e),o.d(e,{assets:()=>l,contentTitle:()=>s,default:()=>u,frontMatter:()=>i,metadata:()=>r,toc:()=>c});var t=o(85893),a=o(11151);const i={},s="Evaluation CLI",r={id:"evaluation/index",title:"Evaluation CLI",description:"The Evaluation CLI documentation is under active development",source:"@site/docs/evaluation/index.md",sourceDirName:"evaluation",slug:"/evaluation/",permalink:"/chatbot/evaluation/",draft:!1,unlisted:!1,editUrl:"https://github.com/mongodb/chatbot/tree/main/docs/docs/evaluation/index.md",tags:[],version:"current",frontMatter:{},sidebar:"main",previous:{title:"Chatbot UI",permalink:"/chatbot/ui"},next:{title:"API Reference",permalink:"/chatbot/reference/"}},l={},c=[{value:"How It Works",id:"how-it-works",level:2},{value:"Install",id:"install",level:2},{value:"Create a Configuration File",id:"create-a-configuration-file",level:2},{value:"Define Configuration Files 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Chatbot Framework comes with an Evaluation CLI that allows you\nto evaluate the performance of your chatbot and its components."}),"\n",(0,t.jsx)(e.p,{children:"Evaluation is important to ensure that the chatbot is providing accurate and relevant responses to user queries.\nIt also helps you measure the effect of changes to the chatbot's components\nas you develop your application."}),"\n",(0,t.jsx)(e.p,{children:"The CLI evaluates the chatbot's performance by comparing the chatbot's responses to a set of questions with the expected answers."}),"\n",(0,t.jsx)(e.h2,{id:"how-it-works",children:"How It Works"}),"\n",(0,t.jsx)(e.p,{children:"The Evaluation CLI works by running a series of commands that generate, evaluate, and report on the performance of your chatbot."}),"\n",(0,t.jsx)(e.h2,{id:"install",children:"Install"}),"\n",(0,t.jsx)(e.p,{children:"To install the Evaluation CLI, run the following 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configuration file for the Evaluation CLI.\nYou can use this configuration file as a starting point for your own configuration."}),"\n",(0,t.jsx)(e.p,{children:"Example configuration file:"}),"\n",(0,t.jsx)(e.pre,{children:(0,t.jsx)(e.code,{className:"language-ts",children:'// eval.config.ts\n\nimport {\n EvalConfig,\n makeMongoDbCommandMetadataStore,\n makeMongoDbGeneratedDataStore,\n makeGenerateConversationData,\n getConversationsTestCasesFromYaml,\n makeEvaluateConversationQuality,\n makeMongoDbEvaluationStore,\n makeMongoDbReportStore,\n mongodbResponseQualityExamples,\n reportStatsForBinaryEvalRun,\n makeEvaluateConversationFaithfulness,\n} from "mongodb-chatbot-evaluation";\nimport { makeMongoDbConversationsService } from "mongodb-chatbot-server";\nimport "dotenv/config";\nimport fs from "fs";\nimport path from "path";\nimport { MongoClient, assertEnvVars } from "mongodb-rag-core";\nimport { envVars } from "./envVars";\n\nexport default async () => {\n const {\n 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makeMongoDbConversationsService(db);\n\n const evalConfig = {\n metadataStore: makeMongoDbCommandMetadataStore(storeDbOptions),\n generatedDataStore: makeMongoDbGeneratedDataStore(storeDbOptions),\n evaluationStore: makeMongoDbEvaluationStore(storeDbOptions),\n reportStore: makeMongoDbReportStore(storeDbOptions),\n\n commands: {\n generate: {\n conversations: {\n type: "conversation",\n testCases: [...miscTestCases, ...faqTestCases],\n generator: makeGenerateConversationData({\n conversations,\n httpHeaders: {\n Origin: "Testing",\n },\n apiBaseUrl: CONVERSATIONS_SERVER_BASE_URL,\n }),\n },\n faqConversations: {\n type: "conversation",\n testCases: faqTestCases,\n generator: makeGenerateConversationData({\n conversations,\n httpHeaders: {\n Origin: "Testing",\n },\n apiBaseUrl: CONVERSATIONS_SERVER_BASE_URL,\n }),\n },\n },\n evaluate: {\n conversationQuality: {\n evaluator: makeEvaluateConversationQuality({\n deploymentName: OPENAI_CHAT_COMPLETION_DEPLOYMENT,\n openAiClient: new OpenAIClient(\n OPENAI_ENDPOINT,\n new AzureKeyCredential(OPENAI_API_KEY)\n ),\n fewShotExamples: mongodbResponseQualityExamples,\n }),\n },\n conversationFaithfulness: {\n evaluator: makeEvaluateConversationFaithfulness({\n llamaIndexLlm: new LlamaIndexOpenAiLlm({\n azure: {\n apiKey: OPENAI_API_KEY,\n endpoint: OPENAI_ENDPOINT,\n deploymentName: OPENAI_GPT_4_CHAT_COMPLETION_DEPLOYMENT,\n },\n }),\n }),\n },\n },\n report: {\n conversationQualityRun: {\n reporter: reportStatsForBinaryEvalRun,\n },\n conversationFaithfulnessRun: {\n reporter: reportStatsForBinaryEvalRun,\n },\n },\n },\n async afterAll() {\n await mongodb.close();\n },\n } satisfies EvalConfig;\n return evalConfig;\n};\n'})}),"\n",(0,t.jsx)(e.h2,{id:"additional-example-configurations",children:"Additional Example Configurations"}),"\n",(0,t.jsx)(e.p,{children:"For additional example configurations, check out the following projects:"}),"\n",(0,t.jsxs)(e.ul,{children:["\n",(0,t.jsxs)(e.li,{children:[(0,t.jsx)(e.a,{href:"https://github.com/mongodb/chatbot/blob/main/packages/chatbot-eval-mongodb-public/src/eval.config.ts",children:"MongoDB AI chatbot evaluation"}),": Evaluations for the MongoDB AI chatbot."]}),"\n"]})]})}function u(n={}){const{wrapper:e}={...(0,a.a)(),...n.components};return e?(0,t.jsx)(e,{...n,children:(0,t.jsx)(d,{...n})}):d(n)}},11151:(n,e,o)=>{o.d(e,{Z:()=>r,a:()=>s});var t=o(67294);const a={},i=t.createContext(a);function s(n){const e=t.useContext(i);return t.useMemo((function(){return"function"==typeof n?n(e):{...e,...n}}),[e,n])}function r(n){let e;return e=n.disableParentContext?"function"==typeof n.components?n.components(a):n.components||a:s(n.components),t.createElement(i.Provider,{value:e},n.children)}}}]); \ No newline at end of file diff --git a/assets/js/19af0c64.e9564986.js b/assets/js/19af0c64.e9564986.js deleted file mode 100644 index 8b76bf943..000000000 --- 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File",id:"create-a-configuration-file",level:2},{value:"Define Configuration Files with TypeScript",id:"define-configuration-files-with-typescript",level:2},{value:"Example Configuration",id:"example-configuration",level:2},{value:"Additional Example Configurations",id:"additional-example-configurations",level:2}];function d(n){const e={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",li:"li",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,a.a)(),...n.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(e.h1,{id:"evaluation-cli",children:"Evaluation CLI"}),"\n",(0,t.jsx)(e.admonition,{title:"\ud83d\udc77\u200d\u2642\ufe0f Work In Progress \ud83d\udc77\u200d\u2642\ufe0f",type:"warning",children:(0,t.jsx)(e.p,{children:"The Evaluation CLI documentation is under active development\nand will be expanded in the near future."})}),"\n",(0,t.jsx)(e.p,{children:"The MongoDB Chatbot Framework comes with an Evaluation CLI that allows you\nto evaluate the performance of your chatbot and its components."}),"\n",(0,t.jsx)(e.p,{children:"Evaluation is important to ensure that the chatbot is providing accurate and relevant responses to user queries.\nIt also helps you measure the effect of changes to the chatbot's components\nas you develop your application."}),"\n",(0,t.jsx)(e.p,{children:"The CLI evaluates the chatbot's performance by comparing the chatbot's responses to a set of questions with the expected answers."}),"\n",(0,t.jsx)(e.h2,{id:"how-it-works",children:"How It Works"}),"\n",(0,t.jsx)(e.p,{children:"The Evaluation CLI works by running a series of commands that generate, evaluate, and report on the performance of your chatbot."}),"\n",(0,t.jsx)(e.h2,{id:"install",children:"Install"}),"\n",(0,t.jsx)(e.p,{children:"To install the Evaluation CLI, run the following command:"}),"\n",(0,t.jsx)(e.pre,{children:(0,t.jsx)(e.code,{className:"language-bash",children:"npm i mongodb-chatbot-evaluation mongodb-chatbot-server\n"})}),"\n",(0,t.jsx)(e.h2,{id:"create-a-configuration-file",children:"Create a Configuration File"}),"\n",(0,t.jsxs)(e.p,{children:["The MongoDB Evaluation CLI uses a ",(0,t.jsx)(e.a,{href:"https://en.wikipedia.org/wiki/CommonJS",children:"CommonJS"}),"\nJavaScript configuration file to determine how to evaluate content."]}),"\n",(0,t.jsxs)(e.p,{children:["Every configuration file must export a ",(0,t.jsx)(e.a,{href:"/chatbot/reference/eval/modules#configconstructor",children:(0,t.jsx)(e.code,{children:"ConfigConstructor"})})," object as its default export."]}),"\n",(0,t.jsx)(e.p,{children:"You must either:"}),"\n",(0,t.jsxs)(e.ul,{children:["\n",(0,t.jsxs)(e.li,{children:["Pass the path to a configuration file to every command with the ",(0,t.jsx)(e.code,{children:"--config"})," flag."]}),"\n",(0,t.jsxs)(e.li,{children:["Put a CommonJS file named ",(0,t.jsx)(e.code,{children:"eval.config.js"})," in the root of your project."]}),"\n"]}),"\n",(0,t.jsxs)(e.p,{children:["For example, to run the ",(0,t.jsx)(e.code,{children:"generate"})," command with a configuration file called ",(0,t.jsx)(e.code,{children:"my-eval.config.js"})," and generation called ",(0,t.jsx)(e.code,{children:"my-conversations"}),", run the following command:"]}),"\n",(0,t.jsx)(e.pre,{children:(0,t.jsx)(e.code,{className:"language-bash",children:"mongodb-chatbot-evaluation generate --config my-eval.config.js --name my-conversations\n"})}),"\n",(0,t.jsx)(e.h2,{id:"define-configuration-files-with-typescript",children:"Define Configuration Files with TypeScript"}),"\n",(0,t.jsx)(e.admonition,{title:"Use TypeScript Configuration Files!",type:"important",children:(0,t.jsxs)(e.p,{children:["We ",(0,t.jsx)(e.strong,{children:"strongly"})," recommend using TypeScript configuration files.\nThe typing system helps you ensure that your configuration is valid."]})}),"\n",(0,t.jsx)(e.p,{children:"You can use TypeScript to make your configuration file. This allows you to use\nthe type system to ensure that your configuration is valid."}),"\n",(0,t.jsxs)(e.p,{children:["You must compile your configuration file to ",(0,t.jsx)(e.strong,{children:"CommmonJS"})," before running the CLI.\nThe CLI only accepts CommonJS configuration files."]}),"\n",(0,t.jsxs)(e.p,{children:["You can build your CommonJS configuration file with ",(0,t.jsx)(e.code,{children:"tsc"}),":"]}),"\n",(0,t.jsx)(e.pre,{children:(0,t.jsx)(e.code,{className:"language-bash",children:"tsc --module commonjs --target es2017 --outDir build eval.config.ts\n"})}),"\n",(0,t.jsx)(e.p,{children:"Then run the Evaluation CLI with the compiled configuration file:"}),"\n",(0,t.jsx)(e.pre,{children:(0,t.jsx)(e.code,{className:"language-bash",children:"mongodb-chatbot-evaluation generate --config build/eval.config.js --name my-conversations\n"})}),"\n",(0,t.jsx)(e.h2,{id:"example-configuration",children:"Example Configuration"}),"\n",(0,t.jsx)(e.p,{children:"Here's a simple example configuration file for the Evaluation CLI.\nYou can use this configuration file as a starting point for your own configuration."}),"\n",(0,t.jsx)(e.p,{children:"Example configuration file:"}),"\n",(0,t.jsx)(e.pre,{children:(0,t.jsx)(e.code,{className:"language-ts",children:'// eval.config.ts\n\nimport {\n EvalConfig,\n makeMongoDbCommandMetadataStore,\n makeMongoDbGeneratedDataStore,\n makeGenerateConversationData,\n getConversationsTestCasesFromYaml,\n makeEvaluateConversationQuality,\n makeMongoDbEvaluationStore,\n makeMongoDbReportStore,\n mongodbResponseQualityExamples,\n reportStatsForBinaryEvalRun,\n makeEvaluateConversationFaithfulness,\n} from "mongodb-chatbot-evaluation";\nimport { makeMongoDbConversationsService } from "mongodb-chatbot-server";\nimport "dotenv/config";\nimport fs from "fs";\nimport path from "path";\nimport { MongoClient, assertEnvVars } from "mongodb-rag-core";\nimport { envVars } from "./envVars";\n\nexport default async () => {\n const {\n MONGODB_DATABASE_NAME,\n MONGODB_CONNECTION_URI,\n CONVERSATIONS_SERVER_BASE_URL,\n OPENAI_CHAT_COMPLETION_DEPLOYMENT,\n OPENAI_ENDPOINT,\n OPENAI_API_KEY,\n OPENAI_GPT_4_CHAT_COMPLETION_DEPLOYMENT,\n } = assertEnvVars(envVars);\n\n const { OpenAIClient, AzureKeyCredential } = await import("@azure/openai");\n const { OpenAI: LlamaIndexOpenAiLlm } = await import("llamaindex");\n const miscTestCases = getConversationsTestCasesFromYaml(\n fs.readFileSync(\n path.resolve(__dirname, "..", "testCases", "conversations.yml"),\n "utf8"\n )\n );\n const faqTestCases = getConversationsTestCasesFromYaml(\n fs.readFileSync(\n path.resolve(__dirname, "..", "testCases", "faq_conversations.yml"),\n "utf8"\n )\n );\n\n const storeDbOptions = {\n connectionUri: MONGODB_CONNECTION_URI,\n databaseName: MONGODB_DATABASE_NAME,\n };\n\n const mongodb = new MongoClient(MONGODB_CONNECTION_URI);\n await mongodb.connect();\n\n const db = mongodb.db(MONGODB_DATABASE_NAME);\n const conversations = makeMongoDbConversationsService(db);\n\n const evalConfig = {\n metadataStore: makeMongoDbCommandMetadataStore(storeDbOptions),\n generatedDataStore: makeMongoDbGeneratedDataStore(storeDbOptions),\n evaluationStore: makeMongoDbEvaluationStore(storeDbOptions),\n reportStore: makeMongoDbReportStore(storeDbOptions),\n\n commands: {\n generate: {\n conversations: {\n type: "conversation",\n testCases: [...miscTestCases, ...faqTestCases],\n generator: makeGenerateConversationData({\n conversations,\n httpHeaders: {\n Origin: "Testing",\n },\n apiBaseUrl: CONVERSATIONS_SERVER_BASE_URL,\n }),\n },\n faqConversations: {\n type: "conversation",\n testCases: faqTestCases,\n generator: makeGenerateConversationData({\n conversations,\n httpHeaders: {\n Origin: "Testing",\n },\n apiBaseUrl: CONVERSATIONS_SERVER_BASE_URL,\n }),\n },\n },\n evaluate: {\n conversationQuality: {\n evaluator: makeEvaluateConversationQuality({\n deploymentName: OPENAI_CHAT_COMPLETION_DEPLOYMENT,\n openAiClient: new OpenAIClient(\n OPENAI_ENDPOINT,\n new AzureKeyCredential(OPENAI_API_KEY)\n ),\n fewShotExamples: mongodbResponseQualityExamples,\n }),\n },\n conversationFaithfulness: {\n evaluator: makeEvaluateConversationFaithfulness({\n llamaIndexLlm: new LlamaIndexOpenAiLlm({\n azure: {\n apiKey: OPENAI_API_KEY,\n endpoint: OPENAI_ENDPOINT,\n deploymentName: OPENAI_GPT_4_CHAT_COMPLETION_DEPLOYMENT,\n },\n }),\n }),\n },\n },\n report: {\n conversationQualityRun: {\n reporter: reportStatsForBinaryEvalRun,\n },\n conversationFaithfulnessRun: {\n reporter: reportStatsForBinaryEvalRun,\n },\n },\n },\n async afterAll() {\n await mongodb.close();\n },\n } satisfies EvalConfig;\n return evalConfig;\n};\n'})}),"\n",(0,t.jsx)(e.h2,{id:"additional-example-configurations",children:"Additional Example Configurations"}),"\n",(0,t.jsx)(e.p,{children:"For additional example configurations, check out the following projects:"}),"\n",(0,t.jsxs)(e.ul,{children:["\n",(0,t.jsxs)(e.li,{children:[(0,t.jsx)(e.a,{href:"https://github.com/mongodb/chatbot/blob/main/packages/chatbot-eval-mongodb-public/src/eval.config.ts",children:"MongoDB AI chatbot evaluation"}),": Evaluations for the MongoDB AI chatbot."]}),"\n"]})]})}function u(n={}){const{wrapper:e}={...(0,a.a)(),...n.components};return e?(0,t.jsx)(e,{...n,children:(0,t.jsx)(d,{...n})}):d(n)}},11151:(n,e,o)=>{o.d(e,{Z:()=>r,a:()=>s});var t=o(67294);const a={},i=t.createContext(a);function s(n){const e=t.useContext(i);return t.useMemo((function(){return"function"==typeof n?n(e):{...e,...n}}),[e,n])}function r(n){let e;return e=n.disableParentContext?"function"==typeof n.components?n.components(a):n.components||a:s(n.components),t.createElement(i.Provider,{value:e},n.children)}}}]); \ No newline at end of file diff --git a/assets/js/217a4326.dc4785a3.js b/assets/js/217a4326.84c74a93.js similarity index 94% rename from assets/js/217a4326.dc4785a3.js 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\ No newline at end of file +"use strict";(self.webpackChunkdocs=self.webpackChunkdocs||[]).push([[4195],{50024:(e,n,r)=>{r.r(n),r.d(n,{assets:()=>c,contentTitle:()=>a,default:()=>h,frontMatter:()=>o,metadata:()=>i,toc:()=>l});var t=r(85893),s=r(11151);const o={id:"ToolCallParams",title:"Interface: ToolCallParams",sidebar_label:"ToolCallParams",sidebar_position:0,custom_edit_url:null},a=void 0,i={id:"reference/server/interfaces/ToolCallParams",title:"Interface: ToolCallParams",description:"Parameters for invoking a tool call.",source:"@site/docs/reference/server/interfaces/ToolCallParams.md",sourceDirName:"reference/server/interfaces",slug:"/reference/server/interfaces/ToolCallParams",permalink:"/chatbot/reference/server/interfaces/ToolCallParams",draft:!1,unlisted:!1,editUrl:null,tags:[],version:"current",sidebarPosition:0,frontMatter:{id:"ToolCallParams",title:"Interface: ToolCallParams",sidebar_label:"ToolCallParams",sidebar_position:0,custom_edit_url:null},sidebar:"main",previous:{title:"Tool",permalink:"/chatbot/reference/server/interfaces/Tool"},next:{title:"Readme",permalink:"/chatbot/reference/core/"}},c={},l=[{value:"Properties",id:"properties",level:2},{value:"conversation",id:"conversation",level:3},{value:"Defined in",id:"defined-in",level:4},{value:"dataStreamer",id:"datastreamer",level:3},{value:"Defined in",id:"defined-in-1",level:4},{value:"functionArgs",id:"functionargs",level:3},{value:"Defined in",id:"defined-in-2",level:4},{value:"request",id:"request",level:3},{value:"Defined in",id:"defined-in-3",level:4}];function d(e){const n={a:"a",code:"code",h2:"h2",h3:"h3",h4:"h4",hr:"hr",p:"p",strong:"strong",...(0,s.a)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(n.p,{children:"Parameters for invoking a tool call."}),"\n",(0,t.jsx)(n.h2,{id:"properties",children:"Properties"}),"\n",(0,t.jsx)(n.h3,{id:"conversation",children:"conversation"}),"\n",(0,t.jsxs)(n.p,{children:["\u2022 ",(0,t.jsx)(n.code,{children:"Optional"})," ",(0,t.jsx)(n.strong,{children:"conversation"}),": ",(0,t.jsx)(n.a,{href:"/chatbot/reference/server/interfaces/Conversation",children:(0,t.jsx)(n.code,{children:"Conversation"})}),"<",(0,t.jsx)(n.a,{href:"/chatbot/reference/server/modules#conversationcustomdata",children:(0,t.jsx)(n.code,{children:"ConversationCustomData"})}),">"]}),"\n",(0,t.jsx)(n.p,{children:"Conversation in the DB. 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n={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",p:"p",pre:"pre",...(0,r.a)(),...e.components};return(0,t.jsxs)(t.Fragment,{children:[(0,t.jsx)(n.h1,{id:"configuration-reference",children:"Configuration Reference"}),"\n",(0,t.jsx)(n.p,{children:"This page contains reference documentation for the configuration options for the MongoDB RAG Ingest CLI."}),"\n",(0,t.jsxs)(n.p,{children:["A Ingest CLI config files is a CommonJS file that exports a ",(0,t.jsx)(n.code,{children:"Config"})," object as its default export."]}),"\n",(0,t.jsxs)(n.p,{children:["For more information on setting up a configuration file, refer to the ",(0,t.jsx)(n.a,{href:"/chatbot/ingest/configure",children:"Configure"})," documentation."]}),"\n",(0,t.jsx)(n.p,{children:"To set up a configuration file, you must first install the following packages:"}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-bash",children:"npm install mongodb-rag-ingest mongodb-rag-core\n"})}),"\n",(0,t.jsx)(n.h2,{id:"api-reference",children:"API Reference"}),"\n",(0,t.jsxs)(n.p,{children:["For a full API reference of all modules exported by ",(0,t.jsx)(n.code,{children:"mongodb-rag-ingest"}),"\nand ",(0,t.jsx)(n.code,{children:"mongodb-rag-core"}),", refer to the ",(0,t.jsx)(n.a,{href:"../reference/",children:"API Reference"})," documentation."]}),"\n",(0,t.jsx)(n.p,{children:"This page links to the key reference documentation for configuring the Ingest CLI."}),"\n",(0,t.jsx)(n.h2,{id:"config",children:(0,t.jsx)(n.code,{children:"Config"})}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.a,{href:"/chatbot/reference/ingest/modules/#config",children:(0,t.jsx)(n.code,{children:"Config"})})," type is the root configuration type for the Ingest CLI."]}),"\n",(0,t.jsx)(n.h2,{id:"ingestmetastore",children:(0,t.jsx)(n.code,{children:"IngestMetaStore"})}),"\n",(0,t.jsxs)(n.p,{children:["The 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This ",(0,t.jsx)(n.code,{children:"PageStore"})," persists data in the ",(0,t.jsx)(n.code,{children:"pages"})," collection in MongoDB."]}),"\n",(0,t.jsxs)(n.p,{children:["To create an ",(0,t.jsx)(n.code,{children:"PageStore"})," with ",(0,t.jsx)(n.code,{children:"makeMongoDbPageStore()"}),":"]}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-ts",children:'import { makeMongoDbPageStore } from "mongodb-rag-core";\n\nconst pageStore = makeMongoDbPageStore({\n connectionUri: MONGODB_CONNECTION_URI,\n databaseName: MONGODB_DATABASE_NAME,\n});\n'})}),"\n",(0,t.jsx)(n.h2,{id:"embeddedcontentstore",children:(0,t.jsx)(n.code,{children:"EmbeddedContentStore"})}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.code,{children:"EmbeddedContentStore"})," is an interface to the stored content and vector\nembeddings used in your RAG app."]}),"\n",(0,t.jsxs)(n.p,{children:["To create an ",(0,t.jsx)(n.code,{children:"EmbeddedContentStore"})," that stores data in MongoDB,\nyou can use the function ",(0,t.jsx)(n.a,{href:"/chatbot/reference/core/modules#makemongodbembeddedcontentstore",children:(0,t.jsx)(n.code,{children:"makeMongoDbEmbeddedContentStore()"})}),".\nThis function returns an ",(0,t.jsx)(n.code,{children:"EmbeddedContentStore"}),". This ",(0,t.jsx)(n.code,{children:"EmbeddedContentStore"})," persists data in the ",(0,t.jsx)(n.code,{children:"embedded_content"})," collection in MongoDB."]}),"\n",(0,t.jsxs)(n.p,{children:["To create an ",(0,t.jsx)(n.code,{children:"EmbeddedContentStore"})," with ",(0,t.jsx)(n.code,{children:"makeMongoDbEmbeddedContentStore()"}),":"]}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-ts",children:'import { makeMongoDbEmbeddedContentStore } from "mongodb-rag-core";\n\nconst embeddedContentStore = makeMongoDbEmbeddedContentStore({\n connectionUri: MONGODB_CONNECTION_URI,\n databaseName: MONGODB_DATABASE_NAME,\n});\n'})}),"\n",(0,t.jsx)(n.admonition,{title:"Set up Atlas Vector Search",type:"important",children:(0,t.jsxs)(n.p,{children:["To use the ",(0,t.jsx)(n.code,{children:"EmbeddedContentStore"})," returned by ",(0,t.jsx)(n.code,{children:"makeMongoDbEmbeddedContentStore()"})," in your RAG app,\nyou must set up Atlas Vector Search on the ",(0,t.jsx)(n.code,{children:"embedded_content"})," collection in MongoDB.\nFor more information on setting up the vector search index on the ",(0,t.jsx)(n.code,{children:"embedded_content"})," collection,\nrefer to the ",(0,t.jsx)(n.a,{href:"/chatbot/mongodb#3-create-atlas-vector-search-index",children:"Create Atlas Vector Search Index"}),"\ndocumentation."]})}),"\n",(0,t.jsx)(n.h2,{id:"datasource",children:(0,t.jsx)(n.code,{children:"DataSource"})}),"\n",(0,t.jsx)(n.p,{children:"Add data sources for the Ingest CLI to pull content from."}),"\n",(0,t.jsxs)(n.p,{children:["Your ",(0,t.jsx)(n.a,{href:"../reference/ingest/modules/sources#datasource",children:(0,t.jsx)(n.code,{children:"DataSource"})})," implementations depend on where the content is coming from.\nTo learn more about creating a ",(0,t.jsx)(n.code,{children:"DataSource"}),", refer to the ",(0,t.jsx)(n.a,{href:"/chatbot/ingest/data-sources",children:"Data Sources"})," documentation."]}),"\n",(0,t.jsx)(n.h2,{id:"embedder",children:(0,t.jsx)(n.code,{children:"Embedder"})}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.a,{href:"/chatbot/reference/core/modules#embedder",children:(0,t.jsx)(n.code,{children:"Embedder"})})," takes in a string and returns a vector embedding for that string."]}),"\n",(0,t.jsxs)(n.p,{children:["To create an ",(0,t.jsx)(n.code,{children:"Embedder"})," that uses the LangChain ",(0,t.jsx)(n.code,{children:"Embeddings"})," class,\nyou can use the function ",(0,t.jsx)(n.a,{href:"/chatbot/reference/core/modules#makelangchainembedder",children:(0,t.jsx)(n.code,{children:"makeLangChainEmbedder()"})}),". To see the various embedding models supported by LangChain, refer to the ",(0,t.jsx)(n.a,{href:"https://js.langchain.com/docs/integrations/text_embedding",children:"LangChain text embedding models"})," documentation."]}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-ts",children:'import { makeLangChainEmbedder } from "mongodb-rag-core";\nimport { OpenAIEmbeddings } from "@langchain/openai";\n\nconst { OPENAI_API_KEY } = process.env;\n\nconst langChainOpenAiEmbeddings = new OpenAIEmbeddings({\n openAIApiKey: OPENAI_API_KEY,\n modelName: "text-embedding-3-large",\n dimensions: 1024,\n});\n\nconst embedder = makeLangChainEmbedder({\n langChainEmbeddings: langChainOpenAiEmbeddings,\n});\n'})}),"\n",(0,t.jsxs)(n.p,{children:["To create an ",(0,t.jsx)(n.code,{children:"Embedder"})," that uses the ",(0,t.jsx)(n.a,{href:"https://platform.openai.com/docs/guides/embeddings",children:"OpenAI Embeddings API"})," directly,\nyou can use the function ",(0,t.jsx)(n.a,{href:"/chatbot/reference/core/modules#makeopenaiembedder",children:(0,t.jsx)(n.code,{children:"makeOpenAiEmbedder()"})}),". This function uses the\n",(0,t.jsx)(n.code,{children:"@azure/openai"})," package to construct the OpenAI client, which supports\nboth the Azure OpenAI Service and the Open API."]}),"\n",(0,t.jsxs)(n.p,{children:["The ",(0,t.jsx)(n.code,{children:"makeOpenAiEmbedder()"})," function also supports configuring exponential backoff\nwith the ",(0,t.jsx)(n.code,{children:"backoffOptions"})," argument. This wraps the ",(0,t.jsx)(n.code,{children:"exponential-backoff"})," package.\nExponential backoff behavior is included because when you are bulk uploading embeddings for content, you\nmay hit the rate limit for the OpenAI Embeddings API. This allows you to\nautomatically retry the embedding request after a delay."]}),"\n",(0,t.jsx)(n.p,{children:"Example usage:"}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-ts",children:'import {\n makeOpenAiEmbedder,\n OpenAIClient,\n AzureKeyCredential,\n} from "mongodb-rag-core";\nconst { OPENAI_ENDPOINT, OPENAI_API_KEY, OPENAI_EMBEDDING_DEPLOYMENT } =\n process.env;\n\nconst embedder = makeOpenAiEmbedder({\n openAiClient: new OpenAIClient(\n OPENAI_ENDPOINT,\n new AzureKeyCredential(OPENAI_API_KEY)\n ),\n deployment: OPENAI_EMBEDDING_DEPLOYMENT,\n backoffOptions: {\n numOfAttempts: 25,\n startingDelay: 1000,\n },\n});\n'})}),"\n",(0,t.jsx)(n.h2,{id:"chunkoptions",children:(0,t.jsx)(n.code,{children:"ChunkOptions"})}),"\n",(0,t.jsxs)(n.p,{children:["Use the ",(0,t.jsx)(n.a,{href:"/chatbot/reference/ingest/modules/embed#chunkoptions",children:(0,t.jsx)(n.code,{children:"ChunkOptions"})}),"\nto configure how the Ingest CLI chunks content when converting ",(0,t.jsx)(n.code,{children:"Page"})," documents\nto ",(0,t.jsx)(n.code,{children:"EmbeddedContent"}),"."]}),"\n",(0,t.jsxs)(n.p,{children:["By default, the Ingest CLI uses the following ",(0,t.jsx)(n.code,{children:"ChunkOptions"}),".\nThese should work for many RAG apps."]}),"\n",(0,t.jsx)(n.pre,{children:(0,t.jsx)(n.code,{className:"language-ts",children:'import GPT3Tokenizer from "gpt3-tokenizer";\n\nconst defaultMdChunkOptions: ChunkOptions = {\n maxChunkSize: 600, // max chunk size of 600 tokens gets avg ~400 tokens/chunk\n minChunkSize: 15, // chunks below this size are discarded, which improves search quality\n chunkOverlap: 0,\n tokenizer: new GPT3Tokenizer({ type: "gpt3" }),\n};\n'})}),"\n",(0,t.jsxs)(n.p,{children:["For more information on optimizing the ",(0,t.jsx)(n.code,{children:"ChunkOptions"}),", refer to ",(0,t.jsx)(n.a,{href:"/chatbot/ingest/optimize#refine-the-chunking-strategy",children:"Refine the Chunking Strategy"})," in the Optimization 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c={},i="Configuration Reference",s={id:"ingest/configuration-reference",title:"Configuration Reference",description:"This page contains reference documentation for the configuration options for the MongoDB RAG Ingest CLI.",source:"@site/docs/ingest/configuration-reference.md",sourceDirName:"ingest",slug:"/ingest/configuration-reference",permalink:"/chatbot/ingest/configuration-reference",draft:!1,unlisted:!1,editUrl:"https://github.com/mongodb/chatbot/tree/main/docs/docs/ingest/configuration-reference.md",tags:[],version:"current",frontMatter:{},sidebar:"main",previous:{title:"Configure the Ingest CLI",permalink:"/chatbot/ingest/configure"},next:{title:"CLI Commands Reference",permalink:"/chatbot/ingest/command-reference"}},d={},a=[{value:"API Reference",id:"api-reference",level:2},{value:"Config",id:"config",level:2},{value:"IngestMetaStore",id:"ingestmetastore",level:2},{value:"PageStore",id:"pagestore",level:2},{value:"EmbeddedContentStore",id:"embeddedcontentstore",level:2},{value:"DataSource",id:"datasource",level:2},{value:"Embedder",id:"embedder",level:2},{value:"ChunkOptions",id:"chunkoptions",level:2}];function h(e){const n={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",p:"p",pre:"pre",...(0,r.a)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(n.h1,{id:"configuration-reference",children:"Configuration Reference"}),"\n",(0,o.jsx)(n.p,{children:"This page contains reference documentation for the configuration options for the MongoDB RAG Ingest CLI."}),"\n",(0,o.jsxs)(n.p,{children:["A Ingest CLI config files is a CommonJS file that exports a ",(0,o.jsx)(n.code,{children:"Config"})," object as its default export."]}),"\n",(0,o.jsxs)(n.p,{children:["For more information on setting up a configuration file, refer to the ",(0,o.jsx)(n.a,{href:"/chatbot/ingest/configure",children:"Configure"})," documentation."]}),"\n",(0,o.jsx)(n.p,{children:"To set up a configuration file, you must first install the following packages:"}),"\n",(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:"npm install mongodb-rag-ingest mongodb-rag-core\n"})}),"\n",(0,o.jsx)(n.h2,{id:"api-reference",children:"API Reference"}),"\n",(0,o.jsxs)(n.p,{children:["For a full API reference of all modules exported by ",(0,o.jsx)(n.code,{children:"mongodb-rag-ingest"}),"\nand ",(0,o.jsx)(n.code,{children:"mongodb-rag-core"}),", refer to the ",(0,o.jsx)(n.a,{href:"../reference/",children:"API Reference"})," documentation."]}),"\n",(0,o.jsx)(n.p,{children:"This page links to the key reference documentation for configuring the Ingest CLI."}),"\n",(0,o.jsx)(n.h2,{id:"config",children:(0,o.jsx)(n.code,{children:"Config"})}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.a,{href:"/chatbot/reference/ingest/modules/#config",children:(0,o.jsx)(n.code,{children:"Config"})})," type is the root configuration type for the Ingest CLI."]}),"\n",(0,o.jsx)(n.h2,{id:"ingestmetastore",children:(0,o.jsx)(n.code,{children:"IngestMetaStore"})}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.a,{href:"/chatbot/reference/ingest/modules#ingestmetastore",children:(0,o.jsx)(n.code,{children:"IngestMetaStore"})})," is an interface to interact with MongoDB collection that tracks metadata associated with the ingest process."]}),"\n",(0,o.jsxs)(n.p,{children:["To create an ",(0,o.jsx)(n.code,{children:"IngestMetaStore"}),", you can use the function ",(0,o.jsx)(n.a,{href:"/chatbot/reference/ingest/modules#makeingestmetastore",children:(0,o.jsx)(n.code,{children:"makeIngestMetaStore()"})}),".\nThis function returns an ",(0,o.jsx)(n.code,{children:"IngestMetaStore"}),".\nThis ",(0,o.jsx)(n.code,{children:"IngestMetaStore"})," persists data in the ",(0,o.jsx)(n.code,{children:"ingest_meta"})," collection in MongoDB."]}),"\n",(0,o.jsxs)(n.p,{children:["To create an ",(0,o.jsx)(n.code,{children:"IngestMetaStore"})," with ",(0,o.jsx)(n.code,{children:"makeIngestMetaStore()"}),":"]}),"\n",(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-ts",children:'import { makeIngestMetaStore } from "mongodb-rag-ingest";\n\nconst ingestMetaStore = makeIngestMetaStore({\n connectionUri: MONGODB_CONNECTION_URI,\n databaseName: MONGODB_DATABASE_NAME,\n entryId: "all",\n});\n'})}),"\n",(0,o.jsx)(n.h2,{id:"pagestore",children:(0,o.jsx)(n.code,{children:"PageStore"})}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.a,{href:"/chatbot/reference/core/modules#pagestore",children:(0,o.jsx)(n.code,{children:"PageStore"})})," is an interface\nto interact with ",(0,o.jsx)(n.a,{href:"/chatbot/reference/core/modules#page",children:(0,o.jsx)(n.code,{children:"Page"})})," data."]}),"\n",(0,o.jsxs)(n.p,{children:["To create a 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