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Copy pathTeachable Agent.rivet-project
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Teachable Agent.rivet-project
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version: 4
data:
attachedData:
trivet:
testSuites: []
version: 1
graphs:
-_004RQ16xMYLJPRxLCcS:
metadata:
description: ""
id: -_004RQ16xMYLJPRxLCcS
name: Subgraphs/storage/get_memos
nodes:
'[1lL9ptCYL6J_Hg9oR_Hnn]:getEmbedding "Get Embedding"':
data:
integration: openai
useIntegrationInput: false
outgoingConnections:
- embedding->"KNN Dataset" mTlPpNu3zFumWlsd9pqUy/embedding
visualData: 834.7408277585336/433.88495359686186/280/180//
'[OXlzUkG0gj_xGY6yT9HDi]:loadDataset "Load Dataset"':
data:
datasetId: ""
useDatasetIdInput: true
outgoingConnections:
- dataset->"Code" rWYNBXisv2S3R6YtvtrEa/output
visualData: 1275.8149437379827/388.19226449747555/280/200//
'[RIgneaBgRmMrnCdkVHZDa]:comment "Comment"':
data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 796.3879224063469
text: "#### Map outputs to inputs"
visualData: 1226.3270713242894/266.4303099667689/378.15421360070536/193//
'[StZeA54b6G5PmEqJGpS4j]:text "Text"':
data:
text: >-
Please create a summary
When I'm summarizing an abstract, I try to make the summary contain just three short bullet points: the title, the innovation, and the key empirical results.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, Chi Wang
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.
visualData: -12.79952684388386/399.10591222753493/330/172//
'[Tstd7Cz0Vhv4EfDoB3cFZ]:subGraph "Subgraph"':
data:
graphId: 6_GsW4EXeLkphMKNqj613
useAsGraphPartialOutput: false
useErrorOutput: false
outgoingConnections:
- dataset_inputs->"KNN Dataset" mTlPpNu3zFumWlsd9pqUy/datasetId
- dataset_outputs->"Load Dataset" OXlzUkG0gj_xGY6yT9HDi/datasetId
visualData: 408.3755428234506/629.6780753797399/330/190/var(--node-color-6)/var(--grey-darkish)
'[Ve7YV6Oz2e-GiZ7MA4Wwm]:graphOutput "Graph Output"':
data:
dataType: object
id: datasets
visualData: 1677.2413766035945/474.4281959469414/330/202/var(--node-color-4)/var(--grey-darkish)
'[WlBmZ50PvaNVQz8BOJhn3]:text "Text"':
data:
text: Hey there! My name is Tim. Please remember that!
outgoingConnections:
- output->"Graph Input" j_Tam5fM63_bWXsK_9aZV/default
visualData: -12.837291538398809/657.309523750739/330/172//
'[j_Tam5fM63_bWXsK_9aZV]:graphInput "Graph Input"':
data:
dataType: string
id: user_query
useDefaultValueInput: true
outgoingConnections:
- data->"Get Embedding" 1lL9ptCYL6J_Hg9oR_Hnn/input
visualData: 413.9069122575419/417.41045568052095/330/3/var(--node-color-3)/var(--grey-darkish)
'[mTlPpNu3zFumWlsd9pqUy]:datasetNearestNeighbors "KNN Dataset"':
data:
datasetId: ""
k: 10
useDatasetIdInput: true
outgoingConnections:
- nearestNeighbors->"Code" rWYNBXisv2S3R6YtvtrEa/input
visualData: 833.4527151152251/616.7969489466558/280/194//
'[rVcbqlNdPwjGo0XhH7-yn]:comment "Comment"':
data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 618.2726494270242
text: "#### Match inputs (e.g. question or task) by vector search"
visualData: 783.2163220261973/269.0065352533857/427.5817780715663/191//
'[rWYNBXisv2S3R6YtvtrEa]:code "Code"':
data:
code: >
// Create a map of the input array for quick lookup
const inputMap = inputs.input.value.reduce((map, item) => {
map[item.id] = item;
return map;
}, {});
// Map over the output array and merge with corresponding input item
const merged = inputs.output.value.map(item => {
const inputItem = inputMap[item.id];
if (inputItem) {
return {
id: item.id,
distance: inputItem.distance,
input: inputItem.data[0],
output: item.data[0]
};
}
return null;
}).filter(item => item !== null); // Filter out any null items
return {
output: {
type: 'object[]',
value: merged
}
};
inputNames:
- input
- output
outputNames:
- output
outgoingConnections:
- output->"Graph Output" Ve7YV6Oz2e-GiZ7MA4Wwm/value
visualData: 1293.580818379832/614.7314307616316/230/201//
0lVj_Q7g0OseLI-Wbw14r:
metadata:
description: ""
id: 0lVj_Q7g0OseLI-Wbw14r
name: Subgraphs/text_analyzer/#1.1 copy_advice
nodes:
'[BO7OP15LyM2XiTpeViY3x]:chat "Chat"':
data:
cache: true
enableFunctionUse: false
frequencyPenalty: 0
headers: []
maxTokens: 1024
model: gpt-3.5-turbo
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->"Graph Output" d2C3oUwiltu0_TZRVrziw/value
visualData: 1118.349006203378/491.3139203240464/230/106//
'[GKXlUbHeRuKe_AK01EhjE]:text "Task (Text)"':
data:
text: Briefly copy any advice from the TEXT that may be useful for a similar but
different task in the future. But if no advice is present, just
respond with 'none'.
outgoingConnections:
- output->"Text" GbbEny_YpcU4HUUFWuYSM/instructions
visualData: 609/275/600.4857099563387/106//
'[GbbEny_YpcU4HUUFWuYSM]:text "Text"':
data:
text: |-
# INSTRUCTIONS
{{instructions}}
# TEXT
{{text}}
# INSTRUCTIONS
{{instructions}}
outgoingConnections:
- output->"Chat" BO7OP15LyM2XiTpeViY3x/prompt
visualData: 661.0927918626575/471.85925816174574/330/106//
'[ShqLHpQz7azUfQYOzL95A]:graphInput "Graph Input"':
data:
dataType: string
id: system_prompt
useDefaultValueInput: false
outgoingConnections:
- data->"Chat" BO7OP15LyM2XiTpeViY3x/systemPrompt
visualData: 247/433/330/106/var(--node-color-3)/var(--grey-darkish)
'[ZeZW02KK4C3t_4LFaElds]:graphInput "Graph Input"':
data:
dataType: string
id: user_input
useDefaultValueInput: false
outgoingConnections:
- data->"Text" GbbEny_YpcU4HUUFWuYSM/text
visualData: 251/598/330/106/var(--node-color-3)/var(--grey-darkish)
'[d2C3oUwiltu0_TZRVrziw]:graphOutput "Graph Output"':
data:
dataType: string
id: output
visualData: 1486/524/330/106/var(--node-color-4)/var(--grey-darkish)
6_GsW4EXeLkphMKNqj613:
metadata:
description: ""
id: 6_GsW4EXeLkphMKNqj613
name: Subgraphs/storage/create_datasets
nodes:
'[3EC1y-ikv8-TY7ff3shbm]:text "Text"':
data:
text: teachable_agent_inputs
outgoingConnections:
- output->"Create Dataset" Uho_l5uHq7WVj9mgzOx_j/datasetId
- output->"Create Dataset" Uho_l5uHq7WVj9mgzOx_j/datasetName
visualData: 591.7709952404231/536.7983155827185/330/24//
'[75ArNIMi4q8zqYQo4hTSa]:graphOutput "Graph Output"':
data:
dataType: string
id: dataset_inputs
visualData: 1469.0112982250769/499.4194414018884/330/56/var(--node-color-3)/var(--grey-darkish)
'[JTLMb5CncJ2IaQmRR5ZnG]:createDataset "Create Dataset"':
outgoingConnections:
- datasetId_out->"Graph Output" kwuAt05ZPfmp3emJmF5pX/value
visualData: 1048.504005004797/707.0587432297854/280/49//
'[LPFPS4nOB6gLePM_7OprJ]:text "Text"':
data:
text: teachable_agent_outputs
outgoingConnections:
- output->"Create Dataset" JTLMb5CncJ2IaQmRR5ZnG/datasetId
- output->"Create Dataset" JTLMb5CncJ2IaQmRR5ZnG/datasetName
visualData: 591.4878113780734/708.7055289851188/330/48//
'[Uho_l5uHq7WVj9mgzOx_j]:createDataset "Create Dataset"':
outgoingConnections:
- datasetId_out->"Graph Output" 75ArNIMi4q8zqYQo4hTSa/value
visualData: 1051.0003423471787/532.3720086226533/280/24//
'[kwuAt05ZPfmp3emJmF5pX]:graphOutput "Graph Output"':
data:
dataType: string
id: dataset_outputs
visualData: 1466.6831946300395/673.8103018564755/330/55/var(--node-color-3)/var(--grey-darkish)
'[y8X98liStSXnZsKccqvi1]:comment "Comment"':
data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 541.8109028899012
text: >-
#### Creates two datasets
1. teachable_agent_inputs is used as a vector database (for searching)
2. teachable_agent_outputs stores the output and will be used like a relational database
Note: Replace internal storage of Rivet by Chroma or something else for more than protoyping
visualData: 563/352/819.7795212047317/25//
BDFMQO7XUR0iQgylPCe9e:
metadata:
description: ""
id: BDFMQO7XUR0iQgylPCe9e
name: Main Graph
nodes:
'[5sQip6QmekFCBfguUIwum]:comment "Comment"':
data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 862.1105217589775
text: "#### Inputs"
visualData: -481.62739386331265/31.652197054474854/412/69//
'[9lPBh1bcnbJWDkFtqynN5]:subGraph "Subgraph"':
data:
graphId: LB-VfWN6qjEupRldUqQKy
useAsGraphPartialOutput: false
useErrorOutput: false
outgoingConnections:
- memos->"Chat" FWnHtA91rxllFrDmPxoGs/systemPrompt
visualData: 765.6554939784504/210.3736666755358/330/100/var(--node-color-6)/var(--grey-darkish)
'[FWnHtA91rxllFrDmPxoGs]:chat "Chat"':
data:
cache: false
enableFunctionUse: false
frequencyPenalty: 0
headers: []
maxTokens: 1024
model: gpt-4-1106-preview
presencePenalty: 0
stop: ""
temperature: 0.5
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:
- all-messages->"Loop Controller" TVwEvIs4MgEu30Cs1uTnB/input2
- response->"Loop Controller" TVwEvIs4MgEu30Cs1uTnB/input1
visualData: 830.5124782154793/401.26397148967936/230/78//
'[LgIqkH-ZMsIPmnNOlZrZ7]:text "Default Message (Text)"':
data:
text: Enter your message...
outgoingConnections:
- output->"Loop Controller" TVwEvIs4MgEu30Cs1uTnB/input1Default
visualData: -444.11403985878417/416.67939380906364/330/96//
'[NuLUWIzS3GK2-juFL50fi]:abortGraph "Abort Graph"':
data:
errorMessage: ""
successfully: true
visualData: 414.3700894240048/-116.21115109191797/230/102/var(--node-color-5)/var(--grey-darkish)
'[TVwEvIs4MgEu30Cs1uTnB]:loopController "Loop Controller"':
data:
maxIterations: 100
outgoingConnections:
- break->"Abort Graph" NuLUWIzS3GK2-juFL50fi/data
- output1->"User Input" ZKQba6NXdVnd5gcVsl4wN/questions
- output2->"Assemble Prompt" qpyygslCPgBLiZ6I9Q6dU/message1
visualData: -3.237030787405697/190.21390090200816/280/10/var(--node-color-2)/var(--grey-darkish)
'[ZKQba6NXdVnd5gcVsl4wN]:userInput "User Input"':
data:
prompt: This is an example question?
useInput: true
outgoingConnections:
- output->"Prompt" lSSyF_m8AodtYxmieQqci/input
- output->"Subgraph" 9lPBh1bcnbJWDkFtqynN5/user_query
- output->"Subgraph" chwpm83mVVlYwvHE77O7d/user_input
visualData: 409.82409067956957/206.49765682538217/280/92//
'[c0z21glEZWFsgYRoP5_4m]:boolean "Teachable (Bool)"':
data:
text: ""
value: true
outgoingConnections:
- value->"Subgraph" chwpm83mVVlYwvHE77O7d/teachable
visualData: -442.42968958904595/201.72422900059462/317.9972956218734/97/var(--node-color-1)/var(--node-color-1)
'[c3DykmfwnANnBzWpvuuni]:comment "Comment"':
data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 236.71056189971222
text: >-
#### Instructions
This project heavily borrows ideas from the "teachable" agent skill in Autogen: https://github.com/microsoft/autogen/
1. Set "Teachable" input to true
2. Explain the AI how you want to solve a certain task
3. Set "Teachable" input to false and ask it to perform the task without offering the how-to
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text: |-
#### Chat Loop
- Input (Data 1): last_response
- Input (Data 2): chat_history
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Cs4fA6m7OIo78iTxRrhDv:
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name: Subgraphs/text_analyzer/#1.1.2 generalize_task
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# INSTRUCTIONS
{{instructions}}
# TEXT
{{text}}
# INSTRUCTIONS
{{instructions}}
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HM41P79DuJHUIicTZ8PXx:
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name: Subgraphs/storage/store_memo
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C9zaUa77QQNqBVBOu60I2/datasetId
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HylV_l0kOds-0wAG66bzs:
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description: ""
id: HylV_l0kOds-0wAG66bzs
name: Subgraphs/text_analyzer/#2. check_if_information
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useFrequencyPenaltyInput: false
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Answer with just one word, yes or no.
outgoingConnections:
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'[PZ3toIMJClhRAdeEWm-6S]:text "Text"':
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# INSTRUCTIONS
{{instructions}}
# TEXT
{{text}}
# INSTRUCTIONS
{{instructions}}
outgoingConnections:
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LB-VfWN6qjEupRldUqQKy:
metadata:
description: ""
id: LB-VfWN6qjEupRldUqQKy
name: Subgraphs/consider_memo_retrieval
nodes:
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data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 603.2495566247363
text: "#### Check if task/advice is included in user_query + retrieval for task"
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'[0VSnADr3pp82Q2LfZmuZ6]:code "Code"':
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code: |
let uniqueArray = [];
let ids = new Set();
inputs.input.value.forEach(item => {
if (!ids.has(item.id)) {
ids.add(item.id);
uniqueArray.push(item);
}
});
return {
output: {
type: 'object[]',
value: uniqueArray
}
};
inputNames:
- input
outputNames:
- output
outgoingConnections:
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flatten: true
flattenDeep: false
outgoingConnections:
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'[0tvjeB9vDnEssebGPJx1H]:comment "Comment"':
data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 1128.9982264989453
text: >-
#### Format/restructure the output
Note: Rivet states KNN seaerch datasets have "distance" attribute, but it is a bug and in reality it returns "similarity"
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data:
backgroundColor: rgba(0,0,0,0.05)
color: rgba(255,255,255,1)
height: 516.7078793499554
text: "#### Merge results and deduplicate"
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'[4EiOwE7XZoX9nrW56CCH3]:text "System prompt (Text)"':
data:
text: >-
You are an expert in text analysis.
The user will give you TEXT to analyze.
The user will give you analysis INSTRUCTIONS copied twice, at both the beginning and the end.
You will follow these INSTRUCTIONS in analyzing the TEXT, then give the results of your expert analysis in the format requested."""
outgoingConnections:
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outgoingConnections:
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'[DltR_oPFFFr_qrg7EqYet]:destructure "Destructure"':
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paths:
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- $.distance
isSplitRun: true
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'[EtH3ha6Z0xQzwHKwMS-Dc]:text "Text"':
data:
text: >-
Please create a summary
When I'm summarizing an abstract, I try to make the summary contain just three short bullet points: the title, the innovation, and the key empirical results.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, Chi Wang
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.
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data:
text: >-
You are a helpful assistant. You can learn new things from the
user (how to do certain tasks) as well as about the user. Always
check "# Memories that might help" before you answer any question
or try to solve a task.
# Memories that might help
{{memories}}
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text: "#### General retrieval for user query"
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text: Hey there! My name is Tim. Please remember that!
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dataType: string
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outgoingConnections:
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'[xH6bD5nDzGs5d9BzryAPU]:text "Text"':
data:
text: |-
## Input
{{input}}
## Output
{{output}}
## Similarity
{{similarity}}
---
isSplitRun: true
outgoingConnections:
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useErrorOutput: false
outgoingConnections:
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R6OHlTA_-RagD-7Ph0EYE:
metadata:
description: ""
id: R6OHlTA_-RagD-7Ph0EYE
name: Subgraphs/text_analyzer/#2.2 extract_information
nodes:
'[92hsKdAWkZtVgrLLHNC2a]:graphInput "Graph Input"':
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dataType: string
id: user_input
useDefaultValueInput: false
outgoingConnections:
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'[BG-g-xFuCuj88ERSlalRX]:text "Task (Text)"':
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no explanation.
outgoingConnections:
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'[JAaZtRHquZ7iOypaZKwHl]:text "Text"':
data:
text: |-
# INSTRUCTIONS
{{instructions}}
# TEXT
{{text}}
# INSTRUCTIONS
{{instructions}}
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'[ikrPOW8BQVWSbG6C8e3Kt]:chat "Chat"':
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frequencyPenalty: 0
headers: []
maxTokens: 1024
model: gpt-3.5-turbo
presencePenalty: 0
stop: ""
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top_p: 1
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useFrequencyPenaltyInput: false
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useUserInput: false
outgoingConnections:
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dataType: string
id: output
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SkELOxEIPxw-kU26Ru_6z:
metadata:
description: ""
id: SkELOxEIPxw-kU26Ru_6z
name: Subgraphs/text_analyzer/#1.1.1 copy_task
nodes:
'[27Dt68Znt5SdgHdngO1Kw]:graphOutput "Graph Output"':
data:
dataType: string
id: output
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'[7CgEeVtWOr3LYwITcVOYN]:text "Text"':
data:
text: |-
# INSTRUCTIONS
{{instructions}}
# TEXT
{{text}}
# INSTRUCTIONS
{{instructions}}
outgoingConnections:
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'[9XjGExDOPcI4Id6q3X399]:text "Task (Text)"':
data:
text: Briefly copy just the task from the TEXT, then stop. Don't solve it, and
don't include any advice.
outgoingConnections:
- output->"Text" 7CgEeVtWOr3LYwITcVOYN/instructions
visualData: 609/275/600.4857099563387/107//
'[UEOC9V3sbVvOhqqPeY8h6]:chat "Chat"':
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headers: []
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useAsGraphPartialOutput: true
useFrequencyPenaltyInput: false
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useModelInput: false
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useTopP: false
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useUseTopPInput: false
useUserInput: false
outgoingConnections:
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visualData: 1118.349006203378/491.3139203240464/230/106//
'[ghX9MDKkBD-s2CoNnQiBP]:graphInput "Graph Input"':
data:
dataType: string
id: system_prompt
useDefaultValueInput: false
outgoingConnections:
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visualData: 247/433/330/106/var(--node-color-3)/var(--grey-darkish)
'[ymNdimMn0ViOb4G3dr2M-]:graphInput "Graph Input"':
data:
dataType: string
id: user_input
useDefaultValueInput: false
outgoingConnections:
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visualData: 251/598/330/106/var(--node-color-3)/var(--grey-darkish)
bJG7IgPHwbetXk4Ojgxfc:
metadata:
description: ""
id: bJG7IgPHwbetXk4Ojgxfc
name: Subgraphs/text_analyzer/#1 check_if_task_or_problem
nodes:
'[87BnqjbHjC0KMb_Hi0MpR]:chat "Chat"':
data:
cache: true
enableFunctionUse: false
frequencyPenalty: 0
headers: []
maxTokens: 1024
model: gpt-3.5-turbo
presencePenalty: 0
stop: ""
temperature: 0
top_p: 1
useAsGraphPartialOutput: true
useFrequencyPenaltyInput: false
useMaxTokensInput: false
useModelInput: false
usePresencePenaltyInput: false
useStop: false
useStopInput: false
useTemperatureInput: false