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Config
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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 documentation."]})]})}function l(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,t.jsx)(n,{...e,children:(0,t.jsx)(h,{...e})}):h(e)}},11151:(e,n,o)=>{o.d(n,{Z:()=>d,a:()=>i});var t=o(67294);const r={},c=t.createContext(r);function i(e){const n=t.useContext(c);return t.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function d(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:i(e.components),t.createElement(c.Provider,{value:n},e.children)}}}]);
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deleted file mode 100644
index bdc51315a..000000000
--- a/assets/js/29c2c5fe.c97b09fb.js
+++ /dev/null
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This ",(0,o.jsx)(n.code,{children:"PageStore"})," persists data in the ",(0,o.jsx)(n.code,{children:"pages"})," collection in MongoDB."]}),"\n",(0,o.jsxs)(n.p,{children:["To create an ",(0,o.jsx)(n.code,{children:"PageStore"})," with ",(0,o.jsx)(n.code,{children:"makeMongoDbPageStore()"}),":"]}),"\n",(0,o.jsx)(n.pre,{children:(0,o.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,o.jsx)(n.h2,{id:"embeddedcontentstore",children:(0,o.jsx)(n.code,{children:"EmbeddedContentStore"})}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.code,{children:"EmbeddedContentStore"})," is an interface to the stored content and vector\nembeddings used in your RAG app."]}),"\n",(0,o.jsxs)(n.p,{children:["To create an ",(0,o.jsx)(n.code,{children:"EmbeddedContentStore"})," that stores data in MongoDB,\nyou can use the function ",(0,o.jsx)(n.a,{href:"/chatbot/reference/core/modules#makemongodbembeddedcontentstore",children:(0,o.jsx)(n.code,{children:"makeMongoDbEmbeddedContentStore()"})}),".\nThis function returns an ",(0,o.jsx)(n.code,{children:"EmbeddedContentStore"}),". This ",(0,o.jsx)(n.code,{children:"EmbeddedContentStore"})," persists data in the ",(0,o.jsx)(n.code,{children:"embedded_content"})," collection in MongoDB."]}),"\n",(0,o.jsxs)(n.p,{children:["To create an ",(0,o.jsx)(n.code,{children:"EmbeddedContentStore"})," with ",(0,o.jsx)(n.code,{children:"makeMongoDbEmbeddedContentStore()"}),":"]}),"\n",(0,o.jsx)(n.pre,{children:(0,o.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,o.jsx)(n.admonition,{title:"Set up Atlas Vector Search",type:"important",children:(0,o.jsxs)(n.p,{children:["To use the ",(0,o.jsx)(n.code,{children:"EmbeddedContentStore"})," returned by ",(0,o.jsx)(n.code,{children:"makeMongoDbEmbeddedContentStore()"})," in your RAG app,\nyou must set up Atlas Vector Search on the ",(0,o.jsx)(n.code,{children:"embedded_content"})," collection in MongoDB.\nFor more information on setting up the vector search index on the ",(0,o.jsx)(n.code,{children:"embedded_content"})," collection,\nrefer to the ",(0,o.jsx)(n.a,{href:"/chatbot/mongodb#3-create-atlas-vector-search-index",children:"Create Atlas Vector Search Index"}),"\ndocumentation."]})}),"\n",(0,o.jsx)(n.h2,{id:"datasource",children:(0,o.jsx)(n.code,{children:"DataSource"})}),"\n",(0,o.jsx)(n.p,{children:"Add data sources for the Ingest CLI to pull content from."}),"\n",(0,o.jsxs)(n.p,{children:["Your ",(0,o.jsx)(n.a,{href:"../reference/ingest/modules/sources#datasource",children:(0,o.jsx)(n.code,{children:"DataSource"})})," implementations depend on where the content is coming from.\nTo learn more about creating a ",(0,o.jsx)(n.code,{children:"DataSource"}),", refer to the ",(0,o.jsx)(n.a,{href:"/chatbot/ingest/data-sources",children:"Data Sources"})," documentation."]}),"\n",(0,o.jsx)(n.h2,{id:"embedder",children:(0,o.jsx)(n.code,{children:"Embedder"})}),"\n",(0,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.a,{href:"/chatbot/reference/core/modules#embedder",children:(0,o.jsx)(n.code,{children:"Embedder"})})," takes in a string and returns a vector embedding for that string."]}),"\n",(0,o.jsxs)(n.p,{children:["To create an ",(0,o.jsx)(n.code,{children:"Embedder"})," that uses the ",(0,o.jsx)(n.a,{href:"https://platform.openai.com/docs/guides/embeddings",children:"OpenAI Embeddings API"}),",\nyou can use the function ",(0,o.jsx)(n.a,{href:"/chatbot/reference/core/modules#makeopenaiembedder",children:(0,o.jsx)(n.code,{children:"makeOpenAiEmbedder()"})}),". This function uses the\n",(0,o.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,o.jsxs)(n.p,{children:["The ",(0,o.jsx)(n.code,{children:"makeOpenAiEmbedder()"})," function also supports configuring exponential backoff\nwith the ",(0,o.jsx)(n.code,{children:"backoffOptions"})," argument. This wraps the ",(0,o.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,o.jsx)(n.p,{children:"Example usage:"}),"\n",(0,o.jsx)(n.pre,{children:(0,o.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,o.jsx)(n.h2,{id:"chunkoptions",children:(0,o.jsx)(n.code,{children:"ChunkOptions"})}),"\n",(0,o.jsxs)(n.p,{children:["Use the ",(0,o.jsx)(n.a,{href:"/chatbot/reference/ingest/modules/embed#chunkoptions",children:(0,o.jsx)(n.code,{children:"ChunkOptions"})}),"\nto configure how the Ingest CLI chunks content when converting ",(0,o.jsx)(n.code,{children:"Page"})," documents\nto ",(0,o.jsx)(n.code,{children:"EmbeddedContent"}),"."]}),"\n",(0,o.jsxs)(n.p,{children:["By default, the Ingest CLI uses the following ",(0,o.jsx)(n.code,{children:"ChunkOptions"}),".\nThese should work for many RAG apps."]}),"\n",(0,o.jsx)(n.pre,{children:(0,o.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,o.jsxs)(n.p,{children:["For more information on optimizing the ",(0,o.jsx)(n.code,{children:"ChunkOptions"}),", refer to ",(0,o.jsx)(n.a,{href:"/chatbot/ingest/optimize#refine-the-chunking-strategy",children:"Refine the Chunking Strategy"})," in the Optimization documentation."]})]})}function l(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(h,{...e})}):h(e)}},11151:(e,n,t)=>{t.d(n,{Z:()=>s,a:()=>i});var o=t(67294);const r={},c=o.createContext(r);function i(e){const n=o.useContext(c);return o.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function s(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:i(e.components),o.createElement(c.Provider,{value:n},e.children)}}}]);
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diff --git a/assets/js/2a14c161.3c02a64f.js b/assets/js/2a14c161.63e72a38.js
similarity index 89%
rename from assets/js/2a14c161.3c02a64f.js
rename to assets/js/2a14c161.63e72a38.js
index d70b94012..3a1222108 100644
--- a/assets/js/2a14c161.3c02a64f.js
+++ b/assets/js/2a14c161.63e72a38.js
@@ -1 +1 @@
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diff --git a/assets/js/34ee439d.ac1c79c1.js b/assets/js/34ee439d.ee0207a1.js
similarity index 95%
rename from assets/js/34ee439d.ac1c79c1.js
rename to assets/js/34ee439d.ee0207a1.js
index 77703fd4c..b14c22860 100644
--- a/assets/js/34ee439d.ac1c79c1.js
+++ b/assets/js/34ee439d.ee0207a1.js
@@ -1 +1 @@
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rename from assets/js/37f76691.2d0a8ede.js
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diff --git a/assets/js/47301a5f.0d05c5ff.js b/assets/js/47301a5f.57f6d2a5.js
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diff --git a/assets/js/4c053673.20be928e.js b/assets/js/4c053673.9515590f.js
similarity index 89%
rename from assets/js/4c053673.20be928e.js
rename to assets/js/4c053673.9515590f.js
index eb114ba46..86ea0ce98 100644
--- a/assets/js/4c053673.20be928e.js
+++ b/assets/js/4c053673.9515590f.js
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index 6dbeeb483..9d168f0bc 100644
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diff --git a/assets/js/4fddc917.30f6d16f.js b/assets/js/4fddc917.a24c6ed5.js
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rename from assets/js/4fddc917.30f6d16f.js
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index 608a30ef5..069b2c358 100644
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diff --git a/assets/js/522e53fb.a21a39eb.js b/assets/js/522e53fb.69b73fc2.js
similarity index 97%
rename from assets/js/522e53fb.a21a39eb.js
rename to assets/js/522e53fb.69b73fc2.js
index ae56bead6..235d7c834 100644
--- a/assets/js/522e53fb.a21a39eb.js
+++ b/assets/js/522e53fb.69b73fc2.js
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diff --git a/assets/js/52cccada.c4eaa55c.js b/assets/js/52cccada.28146a12.js
similarity index 95%
rename from assets/js/52cccada.c4eaa55c.js
rename to assets/js/52cccada.28146a12.js
index 4d67343c4..e1d7a0078 100644
--- a/assets/js/52cccada.c4eaa55c.js
+++ b/assets/js/52cccada.28146a12.js
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index 89250463d..9c3300ad1 100644
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diff --git a/assets/js/6c0ba004.a9142b07.js b/assets/js/6c0ba004.1a8d453c.js
similarity index 89%
rename from assets/js/6c0ba004.a9142b07.js
rename to assets/js/6c0ba004.1a8d453c.js
index d5f94f841..278f1276b 100644
--- a/assets/js/6c0ba004.a9142b07.js
+++ b/assets/js/6c0ba004.1a8d453c.js
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diff --git a/assets/js/770044b3.1415a954.js b/assets/js/770044b3.7df59b3e.js
similarity index 84%
rename from assets/js/770044b3.1415a954.js
rename to assets/js/770044b3.7df59b3e.js
index 068e215e6..79086f09e 100644
--- a/assets/js/770044b3.1415a954.js
+++ b/assets/js/770044b3.7df59b3e.js
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