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rivet.rivet-project
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version: 4
data:
attachedData:
trivet:
testSuites: []
version: 1
graphs:
1C7HNzd5v7ULqf04-5Uob:
metadata:
description: ""
id: 1C7HNzd5v7ULqf04-5Uob
name: RA - Recall Memories
nodes:
'[9HlRtZ5rPSP7q5FiAHjP4]:graphOutput "Graph Output"':
data:
dataType: object[]
id: items
visualData: 1633.1906333726722/443.91188062401466/300/31
'[QD7tiW9UZpo6N30A5foaD]:extractObjectPath "Extract Object Path"':
data:
path: $.topic
usePathInput: false
visualData: 683/375/250/2
'[UqDCzlc17MI37nenyQCWR]:graphInput "Graph Input"':
data:
dataType: string
defaultValue: This is a test topic
id: topic
useDefaultValueInput: false
outgoingConnections:
- data->"Get Embedding" u0PxZtqctJpURQO38Gg9s/input
visualData: 316.85283656184424/219.3939539319971/300/29
'[ZljA5lFqK9KWtpwOGZFSZ]:vectorNearestNeighbors "Vector KNN"':
data:
collectionId: rivet-3ff65ea.svc.us-west1-gcp-free.pinecone.io/test1
integration: pinecone
k: 10
useKInput: true
outgoingConnections:
- results->"Graph Output" 9HlRtZ5rPSP7q5FiAHjP4/value
visualData: 1279.3360881667509/408.90705286005004/200/24
'[u0PxZtqctJpURQO38Gg9s]:getEmbedding "Get Embedding"':
data:
integration: openai
useIntegrationInput: false
outgoingConnections:
- embedding->"Vector KNN" ZljA5lFqK9KWtpwOGZFSZ/vector
visualData: 1033.9087435447254/383.59221170727943/200/23
'[uL9pTJZMx-cuabCpqrtFI]:graphInput "Graph Input"':
data:
dataType: number
defaultValue: "3"
id: n
useDefaultValueInput: false
outgoingConnections:
- data->"Vector KNN" ZljA5lFqK9KWtpwOGZFSZ/k
visualData: 313.1910654160657/406.14428236670517/300/30
3BWUibK-Zr_i2GjKA277_:
metadata:
description: ""
id: 3BWUibK-Zr_i2GjKA277_
name: RA - Extract Commands
nodes:
'[U-sUa3iW4WAjSKQNC8_1e]:graphInput "Graph Input"':
data:
dataType: string
id: input
useDefaultValueInput: false
outgoingConnections:
- data->"Extract YAML" WLtSibdAgyBDWM6gSuUhR/input
visualData: 259/369/300/1
'[WLtSibdAgyBDWM6gSuUhR]:extractYaml "Extract YAML"':
data:
objectPath: $.systemCommands[*]
rootPropertyName: systemCommands
outgoingConnections:
- matches->"Graph Output" g3sxYEM5E-xJYvcuuGZRB/value
visualData: 613/378/250/3
'[g3sxYEM5E-xJYvcuuGZRB]:graphOutput "Graph Output"':
data:
dataType: object[]
id: commands
visualData: 974.8003300330032/379.94224422442244/300/9
49ADNSJiXKFRvgaRMhFvP:
metadata:
description: ""
id: 49ADNSJiXKFRvgaRMhFvP
name: RA - Get Response
nodes:
'[6P8d2awK1kumFWb15ecLK]:subGraph "Subgraph"':
data:
graphId: HRMMlTL5W-Wau-f7nLLEh
outgoingConnections:
- system_prompt->"Chat" 8WoCCKIA_CkgUFUS9Z2ZI/systemPrompt
visualData: 1456.4238678422316/454.96652023842444/300/51
'[8WoCCKIA_CkgUFUS9Z2ZI]:chat "Chat"':
data:
cache: false
enableFunctionUse: false
frequencyPenalty: 0
maxTokens: 4096
model: gpt-4-0613
presencePenalty: 0
stop: ""
temperature: 0.5
top_p: 1
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" kviYXdprAn_78olEtAd-6/value
- response->"Prompt" iJA7SfvwwBOoyE88k97cT/input
- response->"Subgraph" yqSRYu7edOEf3tZQaKswg/input
visualData: 1965.1016252864763/547.5345614232717/200/67
'[9brCG6eTcOIXX31erjqGu]:assemblePrompt "Assemble Prompt"':
outgoingConnections:
- prompt->"Graph Output" DL8bb2mbzNz2rSU2s7KUu/value
visualData: 2872.693974555739/713.594640498702/250/null
'[DL8bb2mbzNz2rSU2s7KUu]:graphOutput "Graph Output"':
data:
dataType: chat-message[]
id: new_messages
visualData: 3297.258302221817/669.5769681505825/300/61
'[DjJurzETlQAbzGr1rHdrc]:graphInput "Graph Input"':
data:
dataType: string
defaultValue: This is a test
id: prompt_message
useDefaultValueInput: false
outgoingConnections:
- data->"Prompt" _ALVrW2X1sanCPkXgVBtx/input
visualData: 544.1815668099302/926.8873241664447/300/63
'[Kc9vHiGqFgor7QZhi1ezN]:trimChatMessages "Trim Chat Messages"':
data:
maxTokenCount: 4096
model: gpt-4
removeFromBeginning: true
outgoingConnections:
- trimmed->"Chat" 8WoCCKIA_CkgUFUS9Z2ZI/prompt
visualData: 1658.6972703882811/613.0651361023686/200/55
'[_ALVrW2X1sanCPkXgVBtx]:prompt "Prompt"':
data:
enableFunctionCall: false
name: SYSTEM INFORMATION
promptText: "{{input}}"
type: user
useTypeInput: false
outgoingConnections:
- output->"Assemble Prompt" uy7Kir4TGhQ-7vrOelzOG/message2
visualData: 949.3527093687692/918.8482141950393/250/65
'[iJA7SfvwwBOoyE88k97cT]:prompt "Prompt"':
data:
enableFunctionCall: false
promptText: "{{input}}"
type: assistant
useTypeInput: false
outgoingConnections:
- output->"Assemble Prompt" 9brCG6eTcOIXX31erjqGu/message2
visualData: 2294.628246648416/401.9702599338506/250/16
'[kviYXdprAn_78olEtAd-6]:graphOutput "Graph Output"':
data:
dataType: string
id: chat_output
visualData: 3321.346081191017/948.6316276694499/300/66
'[raxkQiq1D9gedJiuQYUDd]:graphInput "Graph Input"':
data:
dataType: chat-message[]
id: current_messages
outgoingConnections:
- data->"Assemble Prompt" uy7Kir4TGhQ-7vrOelzOG/message1
visualData: 833.2481831324469/704.4215206372613/300/57
'[s_dZ9oRH1p1IJCuyKuyg3]:subGraph "Subgraph"':
data:
graphId: u6yVHvgJi01zZYY_5f4y3
outgoingConnections:
- commands_output->"Assemble Prompt" 9brCG6eTcOIXX31erjqGu/message3
visualData: 2560.6006996846772/602.3956477259945/196.3548034934497/13
'[uy7Kir4TGhQ-7vrOelzOG]:assemblePrompt "Assemble Prompt"':
outgoingConnections:
- prompt->"Assemble Prompt" 9brCG6eTcOIXX31erjqGu/message1
- prompt->"Trim Chat Messages" Kc9vHiGqFgor7QZhi1ezN/input
visualData: 1313.7972976989524/681.9189138753514/250/58
'[yqSRYu7edOEf3tZQaKswg]:subGraph "Subgraph"':
data:
graphId: 3BWUibK-Zr_i2GjKA277_
outgoingConnections:
- commands->"Subgraph" s_dZ9oRH1p1IJCuyKuyg3/commands
visualData: 2264.247517768967/599.2763797815367/202.5425764192139/11
5S6OjvRiN5DSNiYVUxf2d:
metadata:
description: ""
id: 5S6OjvRiN5DSNiYVUxf2d
name: Graph Maker
nodes:
'[-ELvIeQcb0AtnN6y0Y4w9]:prompt "Prompt"':
data:
enableFunctionCall: false
promptText: >-
You are an AI for creating graphs in Rivet based on a user's
specifications. You know everything you can do with Rivet, and you
create an entire graph in plain english based on the user's
request.
Rivet Specification:
{{rivet}}
# Goal
The user will ask you to create a graph. You must output a series of steps you will take to great a graph like the user has asked for.
For example:
- Add a X node and name it "Foo"
- Add a Y node and name it "Bar"
- Connect the "x" output of Foo to the Y output of Bar
- Etc
Your steps should be extremely thorough, complete, and in plain english.
type: system
useTypeInput: false
outgoingConnections:
- output->"Chat" Sao72Qnzs9LA6eJXT5FX8/systemPrompt
visualData: 1262/546/250/3
'[Sao72Qnzs9LA6eJXT5FX8]:chat "Chat"':
data:
cache: false
enableFunctionUse: false
frequencyPenalty: 0
maxTokens: 1024
model: gpt-3.5-turbo
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
visualData: 1639/620/200/5
'[hn39Ds4GBHK0C8LkBmA5X]:prompt "Prompt"':
data:
enableFunctionCall: false
promptText: Can you give me a graph that would be suitable as a tutorial?
type: user
useTypeInput: false
visualData: 1131/925/250/null
'[qp4n1iV7rXEYXlfWsXUkl]:text "Text"':
data:
text: >-
# Rivet
Rivet is a graph-based tool for creating AI large languagte models chains and AI agents. Rivet works by linking together nodes using their input and output ports. Data flows through the wires to the next node. The graphs start at nodes without inputs, and proceed to nodes without outputs. Graphs are turing-complete due to the variety of nodes available.
## Nodes
Available nodes are:
#### Text Node
Outputs a string of text. It can also interpolate values using interpolation tags. Inputs are dynamic based on the tags (words inside {{}}).
#### Prompt Node
Outputs a Chat Message, which is a string of text with an attached "Assistant", "User", or "System" tag, and optionally a message.
#### Chunk Node
Splits the input text into an array of chunks based on an approximate GPT token count per chunk.
#### Extract With Regex Node
Extracts data from the input text using the configured regular expression. The regular expression can contain capture groups to extract specific parts of the text.
Each capture group corresponds to an output port of the node.
#### Chat Node
Performs a chat with OpenAI. Takes two inputs - a system prompt (optional) and a prompt (which is either a list of strings, chat-messages, or a single string/chat-message). Outputs the response from the AI.
#### Assemble Prompt Node
Takes in a list of strings or chat-messages or arrays of each and outputs a flat list of chat messages for use with a Chat Node.
#### Array Node
Creates an array from the input values. Number of inputs is unlimited. Outputs the array, and its length, and the indexes.
#### Pop Node
Pops the last value off the input array and outputs the new array and the popped value.
#### Number Node
Outputs a configurable number.
#### Extract JSON
Given a string, extracts a JSON object out of the string, parses it, and outputs the object.
#### Extract YAML
Given a string, extracts a YAML object out of the string (must start with `yamlDocument:` by default) and outputs the object.
#### Extract Object Path
Uses jsonpath to extract something out of an object. Outputs the extracted value.
#### Bool Node
Outputs a boolean value.
#### Match Node
Configured with a list of strings. Each string is a regular expression. For the matching regex, the output port with the same index is given the input string. All other output ports are not ran.
#### If Node
Two inputs - if and value. If the `if` value is truthy, the `value` is passed through. Otherwise, the output is not ran.
#### If/Else Node
Three inputs, `if`, `true`, and `false`. If the `if` value is truthy, the `true` value is passed through. Otherwise, the `false` value is passed through.
#### Loop Controller Node
Marks a loop. The `continue` input specifies whether the loop should continue after the first iteration. Contains unlimited pairs of values. For example, `input1` is the value that should be output for `output2`, except for the first iteration, which takes its value from the `input1Default` input. Nodes inside the loop should wrap around to connect to the `inputX` inputs or the `continue` input. The `break` output is executed when `continue` is false, and has the list of values.
#### Coalesce Node
Any number of inputs. Takes the first non-null input and outputs it.
#### Graph Output Node
Each instance of this node represents an individual output of the graph. The value passed into this node becomes part of the overall output of the graph. The `id` data of this specifies the name of the output.
#### Graph Input Node
Defines an input for the graph which can be passed in when the graph is called, or defines one of the input ports when the graph is a subgraph. The `id` data of this specifies the name of the input.
#### User Input Node
Prompts the user for input during the execution of the graph. The user's response becomes the output of this node. Can be configured to either prompt with and input port, or with a static configured text.
#### Subgraph Node
Calls a subgraph as a node. The inputs of the node come from the graph's Graph Input Nodes, and the outputs of the node come from the Graph Output Nodes.
#### External Call Node
Calls a function in the external library that is executing Rivet.
#### Code Node
Executes arbitrary JavaScript code. Takes in one input and has one output.
outgoingConnections:
- output->"Prompt" -ELvIeQcb0AtnN6y0Y4w9/rivet
visualData: 845/412/300/4
7zpW_cdTXlsQbF5nNcgKP:
metadata:
description: ""
id: 7zpW_cdTXlsQbF5nNcgKP
name: "RA - Command: ASK_QUESTION_ABOUT_FILE"
nodes:
'[2338y8qNrzrbfDmzXJvhr]:chunk "Chunk"':
data:
model: gpt-4
numTokensPerChunk: 4096
overlap: 0
useModelInput: false
outgoingConnections:
- chunks->"Text" nELWKzIKfsG5PysjF-cSg/contents
- count->"Text" BiOEV7VKd8ZRQz8vwM9Ng/count
- count->"Text" gpA-l85r9DUJnZux3JRjt/count
- count->"Text" nELWKzIKfsG5PysjF-cSg/count
- indexes->"Text" gpA-l85r9DUJnZux3JRjt/index
- indexes->"Text" nELWKzIKfsG5PysjF-cSg/index
visualData: 1200.8357446452283/346.2666438124874/200/23
'[2_eg1SZ7lUW33gIXYfrVK]:graphInput "Graph Input"':
data:
dataType: object
id: arguments
useDefaultValueInput: false
outgoingConnections:
- data->"Extract Object Path" ei3qRQVhPSrVddaO5bU-U/object
- data->"Extract Object Path" m9kiRPWzz-lraxQy_98B8/object
visualData: 239/357/300/1
'[BiOEV7VKd8ZRQz8vwM9Ng]:text "Text"':
data:
text: >-
A question was asked about the file {{file_name}}. It was chunked
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The results are:
{{results}}
You must now combine these answers into a single answer for this question: {{question}}
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contents of a file of code, and you are given a question about the
code. You directly answer the question given, with as much detail
as possible. However, you do not output code unless you are
explicitly asked to reply with some code. Otherwise, you reply
with plain English only.
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data:
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Chunk {{index}}/{{count}} answer:
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data:
text: >-
Here is chunk {{index}}/{{count}} of a code file:
```
// {{file_name}} ({{index}}/{{count}})
{{contents}}
```
I have this question about the file: {{question}}
Answer to the best of your ability about this particular chunk of the file. If this chunk of the file does not answer the question, then state that this chunk of the file does not have a sufficient answer to the question.
If the file does not exist, instruct the AI calling you that they should use READ_DIRECTORY and then call ASK_QUESTION_ABOUT_FILE again with the correct path.
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outgoingConnections:
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metadata:
description: ""
id: 9cRiigw77WY0G_wWDYp4Z
name: RA - Exec Command
nodes:
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outgoingConnections:
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outgoingConnections:
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'[eEXAsNDCNSOuZyEHA8EOH]:prompt "Prompt"':
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promptText: |-
Results for command {{command_name}}:
"""
{{results}}
"""
type: user
useTypeInput: false
outgoingConnections:
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data:
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$.arguments
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outgoingConnections:
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$.name
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outgoingConnections:
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outgoingConnections:
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'[zAIWmNhfJj_h3xPx0kewP]:text "Text"':
data:
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outgoingConnections:
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visualData: 1501.6714910083379/787.9185236850659/300/31
BCH2-JTaOfU7yrJ1GQRhL:
metadata:
description: ""
id: BCH2-JTaOfU7yrJ1GQRhL
name: Extract List Items
nodes:
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outgoingConnections:
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'[FClS4P3cvRs8mZHieN9VO]:match "Match"':
data:
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cases:
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outgoingConnections:
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outgoingConnections:
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visualData: 288/323/300/8
'[MGGvYxorqqDFpvGmFLWmw]:text "Text"':
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outgoingConnections:
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visualData: 688/648/154/16
'[N-LYZkuLW9qvIFobDpRo5]:graphOutput "Graph Output"':
data:
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id: items
visualData: 1625/402/300/14
'[uscIqNzkwiFK1zWdxydCj]:extractRegex "Extract Regex"':
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outgoingConnections:
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visualData: 1035/383/250/13
DBSqt91CfAs1dewM-IhgT:
metadata:
description: ""
id: DBSqt91CfAs1dewM-IhgT
name: RA - Analyze Until Done
nodes:
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data:
maxIterations: 100
outgoingConnections:
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visualData: 1903.2274878891517/693.1500148714374/250/null
'[2r-NqWWMhUJ2vG07yyGQe]:text "Text"':
data:
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outgoingConnections:
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visualData: 3206.602702252145/1085.4509610314976/300/null
'[A1QAexk0N1yzfWytjwrlK]:extractObjectPath "Extract Object Path"':
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usePathInput: false
outgoingConnections:
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'[EUuTrue5VkIy2ws195G0o]:match "Match"':
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cases:
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outgoingConnections:
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'[Stx1H2iniItB-FoT4f2bS]:extractYaml "Extract YAML"':
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rootPropertyName: systemCommands
outgoingConnections:
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visualData: 2683.4297003524184/613.8913774148516/250/94
'[T5dFxOs_MHn0bLFC9aQDN]:graphOutput "Graph Output"':
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id: chat_output
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'[V50VnTrUvH3Xga1LRYKWS]:ifElse "If/Else"':
outgoingConnections:
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'[Va4d_NrMAW2V8LMdg5WIb]:text "Text"':
data:
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outgoingConnections:
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visualData: 3208.1979218302577/870.0963179862564/300/null
'[Vd9CYlLETL0sAr2X73_lM]:extractObjectPath "Extract Object Path"':
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outgoingConnections:
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visualData: 2247.684285398584/163.36755714024667/250/98
'[_fzwpRZw_bUBIul-X2mMs]:prompt "Prompt"':
data:
enableFunctionCall: false
name: SYSTEM INFORMATION
promptText: Next, talk about the next thing you will be analyzing, and run
RECALL_INFO one or more times to see what you have already
analyzed and remembered. Be as descriptive and verbose as possible
in your topic argument, because the more information you give, the
better the relevant memories can be identified.
type: user
useTypeInput: false
visualData: 1650.9191658546888/358.25772586434255/250/93
'[oW8bIRG1wJNWkXl-NS_qe]:prompt "Prompt"':
data:
enableFunctionCall: false
name: SYSTEM INFORMATION
promptText: >-
What is the next thing you plan on doing? Run RECALL_INFO first
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If you have already run RECALL_INFO for the next thing you plan on doing, and you do not have any memories for the task, you may run a command other than RECALL_INFO to further your memories.
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useTypeInput: false
outgoingConnections:
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'[tSUsLaivP4rtiuLalg8Rn]:graphInput "Graph Input"':
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outgoingConnections:
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'[vLyGV7ovbUYwCpnVv_YTv]:subGraph "Subgraph"':
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outgoingConnections:
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HRMMlTL5W-Wau-f7nLLEh:
metadata:
description: ""
id: HRMMlTL5W-Wau-f7nLLEh
name: RA - System Prompt
nodes:
'[fFj_MIJQSxY4S5DIBtnFk]:text "Text"':
data:
text: >-
# You
You are a source code analyzer. A user will give you a path to a repository. You have a set of system commands that you can then use to interact with the file system and other APIs in order to perform actions. You will go through and interatively analyze the repository, after each command you will update your analysis (either refining it as you have learned new information, or adding to your analysis in some way).
You store your complete analysis using a memory with the ID "analysis".
You frequently check your memory to see if you have already accomplished things.
Before reading or asking questions about any files, you always run RECALL_INFO first to see if you have already done it.
You always reply with a system command to interact with the System. However, you include text before your system command explaining your current thoughts.
There may only be at most one `systemCommands` block in your responses.
{{system_commands}}
outgoingConnections:
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outgoingConnections:
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HXjZhpWO0hluMiDY6pneE:
metadata:
description: ""
id: HXjZhpWO0hluMiDY6pneE
name: Digest File
nodes:
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outgoingConnections:
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id: digest
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'[BykrT115qLE_8iVjmBCAp]:text "Text"':
data:
text: Please tell me what the responsibility for this file is with respect to
other files. Be extremely thorough in your response. Basically, if
another person asked you "what is the purpose of this file? What
does it do?", this would be your detailed response.
outgoingConnections:
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