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MongoDB-Atlas-Rivet-Project-Examples.rivet-project
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MongoDB-Atlas-Rivet-Project-Examples.rivet-project
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version: 4
data:
attachedData:
trivet:
testSuites: []
version: 1
graphs:
9s_I6WhlSTWzAaql5XEt7:
metadata:
description: ""
id: 9s_I6WhlSTWzAaql5XEt7
name: 1- MongoDB Vector Store
nodes:
'[6dU3-tA5L4lC4M2U2J15A]:object "Object"':
data:
jsonTemplate: |-
{
"plot" : "Great AI movie with the best Vector Database",
"title" : "My AI Movie",
"genres" : ["Action"]
}
outgoingConnections:
- output->"Extract Object Path" OXi-D6PLgsB0RR-574K07/object
- output->"Store Vector in MongoDB" j6PtA_eIQIUhlyajB95JM/doc
visualData: 459/339/230/null//
'[B71_cSyAccXkpM0IvUoP-]:getEmbedding "Get Embedding"':
data:
integration: openai
useIntegrationInput: false
outgoingConnections:
- embedding->"Store Vector in MongoDB" j6PtA_eIQIUhlyajB95JM/vector
visualData: 1232/206/280/6//
'[OXi-D6PLgsB0RR-574K07]:extractObjectPath "Extract Object Path"':
data:
jsonTemplate: ""
path: $.plot
usePathInput: false
outgoingConnections:
- match->"Get Embedding" B71_cSyAccXkpM0IvUoP-/input
visualData: 832/175/280/5//
'[j6PtA_eIQIUhlyajB95JM]:mongoDBStore "Store Vector in MongoDB"':
data:
collection: embedded_movies
database: sample_mflix
path: plot_embedding
visualData: 1627/315/230/4//
UY796ysc87AHL_LX2G55R:
metadata:
description: ""
id: UY796ysc87AHL_LX2G55R
name: 2- MongoDB Vector Search Retrival
nodes:
'[AG_5XsTQP6g9IdRMF0Uoy]:array "Array"':
data:
flatten: true
flattenDeep: false
visualData: 1665.7411418563056/530.0795635624972/230/null//
'[NFQbYtG8gAUfpOvYYRigD]:getEmbedding "Get Embedding"':
data:
integration: openai
useIntegrationInput: false
outgoingConnections:
- embedding->"Search MongoDB for closest vectors with KNN"
ounFCx4bQGYOpPdItAupq/vector
visualData: 848/369/280/3//
'[TJgowm0LtjmZh7lNS_s3n]:text "Text"':
data:
text: Home Alone
outgoingConnections:
- output->"Get Embedding" NFQbYtG8gAUfpOvYYRigD/input
visualData: 349/498/330/2//
'[oJ0zoSsk-MspGZCzASAzZ]:array "Array"':
data:
flatten: true
flattenDeep: false
visualData: 1665.7411418563056/530.0795635624972/230/null//
'[ounFCx4bQGYOpPdItAupq]:mongoDBVectorKNN "Search MongoDB for closest vectors with KNN"':
data:
collection: embedded_movies
database: sample_mflix
k: 10
path: plot_embedding
outgoingConnections:
- documents->"Array" oJ0zoSsk-MspGZCzASAzZ/input1
visualData: 1217.4670895329114/518.3548244979391/230/1//
puzj17gj_xu56GUlr7164:
metadata:
description: ""
id: puzj17gj_xu56GUlr7164
name: 3- MongoDB AI Shop Graph
nodes:
'[0StxbIFHv-uN5fjfhsmGF]:getEmbedding "Get Embedding"':
data:
integration: openai
useIntegrationInput: false
isSplitRun: true
outgoingConnections:
- embedding->"Object" EoMPFBe-jFb3lse3wwM8M/embedding
splitRunMax: 10
visualData: 2127.165872243633/1227.826010112003/280/304//
'[EIMVNImIY9ilMTKRZ0y1y]:graphOutput "Graph Output"':
data:
dataType: object[]
id: list
visualData: 3808.758116270114/1095.587157999931/330/323//
'[EecRv7ZDLSO4hY7PC9216]:extractJson "Extract JSON"':
outgoingConnections:
- output->"Extract Object Path" Xi8Ftbz5DodT0oMUDvEKp/object
visualData: 841.3324156896467/587.953622792649/352.4861448483714/317//
'[EoMPFBe-jFb3lse3wwM8M]:object "Object"':
data:
jsonTemplate: |-
{
"base": {{baseObject}},
"embeddings": {{embedding}}
}
isSplitRun: true
outgoingConnections:
- output->"Array" hRtRkSGmuGPIN3R814Knp/input1
splitRunMax: 10
visualData: 2427.333616328224/972.9484028950787/605.8378558702143/319//
'[H6x2pi5gIdAe4Izs3YDDi]:graphOutput "Graph Output"':
data:
dataType: string
id: result
visualData: 3808.7444911678117/1311.0210016768099/330/322//
'[JBzVqr_7_xcJXEYfkVVAh]:code "Code"':
data:
code: >-
const shoppingList = inputs.input.value;
const aggregationQuery = [
{
"$search": {
"index": "default",
"knnBeta": {
"vector": shoppingList[0].embeddings,
"path": "embeddings",
"k": 20
}
}
},
{$limit: 3},
{ $addFields: { "searchTerm": shoppingList[0].base.product } },
...shoppingList.slice(1).map((item) => ({
$unionWith: {
coll: "products",
pipeline: [
{
"$search": {
"index": "default",
"knnBeta": {
"vector": item.embeddings,
"path": "embeddings",
"k": 20
}
}
},
{$limit: 3},
{ $addFields: { "searchTerm": item.base.product } }
]
}
})),
{ $group: { _id: "$searchTerm", products: { $push: "$$ROOT" } } },
{ $project: { "_id": 0, "category": "$_id", "products.title": 1, "products.description": 1,"products.emoji" : 1, "products.imageUrl" : 1,"products.price": 1 } }
];
return { output: { value: JSON.stringify(aggregationQuery), type: 'string'} };
inputNames: input
outputNames:
- output
outgoingConnections:
- output->"Graph Output" H6x2pi5gIdAe4Izs3YDDi/value
visualData: 3212.4179266096057/1312.8726507300582/460.4176716828715/321//
'[MTbkPJccFmTxsW8JyT7U2]:prompt "Prompt"':
data:
enableFunctionCall: false
promptText: Build a user grocery list in English as best as possible, if the
input does not fit any of the categories output empty
list.\n{{format_instructions}}\n possible category
{{categories}}\n{{query}}
type: user
useTypeInput: false
outgoingConnections:
- output->"Chat" N6LtISafFOBi17Lb4NTAw/prompt
visualData: 39.87709816790374/614.9105474642867/280/300//
'[N6LtISafFOBi17Lb4NTAw]:chat "Chat"':
data:
cache: false
enableFunctionUse: false
frequencyPenalty: 0
maxTokens: 1024
model: local-model
overrideModel: gpt-4-1106-preview
presencePenalty: 0
stop: ""
temperature: 0
top_p: 1
useAsGraphPartialOutput: true
useFrequencyPenaltyInput: false
useMaxTokensInput: false
useModelInput: false
usePresencePenaltyInput: false
useStop: false
useStopInput: false
useTemperatureInput: false
useTopP: false
useTopPInput: false
useUseTopPInput: false
useUserInput: false
outgoingConnections:
- response->"Extract JSON" EecRv7ZDLSO4hY7PC9216/input
visualData: 481.30979004784757/589.8694388284938/230/301//
'[T2bCGZbE_mC51bnzgYecG]:extractObjectPath "Extract Object Path"':
data:
path: $.product
usePathInput: false
isSplitRun: true
outgoingConnections:
- match->"Get Embedding" 0StxbIFHv-uN5fjfhsmGF/input
splitRunMax: 10
visualData: 1678.0375660006976/1205.9395936938656/280/297//
'[Xi8Ftbz5DodT0oMUDvEKp]:extractObjectPath "Extract Object Path"':
data:
path: $.shopping_list
usePathInput: false
outgoingConnections:
- match->"Extract Object Path" T2bCGZbE_mC51bnzgYecG/object
- match->"Graph Output" EIMVNImIY9ilMTKRZ0y1y/value
- match->"Object" EoMPFBe-jFb3lse3wwM8M/baseObject
visualData: 1298.7619183882202/508.84466247357125/228.56114652962538/318//
'[cbUbrHLW7a3C51T34suUS]:text "Text"':
data:
text: |-
{
"shopping_list":[
{
"product": "THE NAME OF THE PRODUCT",
"quantity": "THE AMOUNT OF THE PRODUCT"),
"unit": "UNIT",
"category": "CATEGORY"}
}]
}
outgoingConnections:
- output->"Prompt" MTbkPJccFmTxsW8JyT7U2/format_instructions
visualData: -539.1186667455959/321.81359535968306/330/299//
'[dvPYtGZvd3M1yHMKofhuW]:graphInput "Graph Input"':
data:
dataType: string
defaultValue: Milk and Banana
id: input
useDefaultValueInput: true
outgoingConnections:
- data->"Prompt" MTbkPJccFmTxsW8JyT7U2/query
visualData: -519.0709966023228/966.4200919523976/330/325//
'[hRtRkSGmuGPIN3R814Knp]:array "Array"':
data:
flatten: true
flattenDeep: false
outgoingConnections:
- output->"Code" JBzVqr_7_xcJXEYfkVVAh/input
visualData: 3040.5847218607637/1616.3790825186936/661.5780313579098/320//
'[jOh-RwT9hW1oJzdJzExDK]:mongoDBCollectionSearch "Search a MongoDB collection and return documents"':
data:
collection: "categories"
database: ai_shop
promptText: ""
outgoingConnections:
- documents->"Prompt" MTbkPJccFmTxsW8JyT7U2/categories
visualData: -521.483500261368/652.4373093084703/314.05463470598704/324//
metadata:
description: A quick project to showcase the MongoDB Connector and a tutorial
for using a complex AI flow.
id: nx2MHt1IaBJxUaAEpTEEx
title: MongoDB-Atlas-Rivet-Project-Examples
plugins:
- id: rivet-plugin-mongodb@latest
package: rivet-plugin-mongodb
tag: latest
type: package