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21 Commits

Author SHA1 Message Date
kuzakhmetovartur c2b451d767 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 17:04:31 +03:00
kuzakhmetovartur ab3d08e839 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 16:54:54 +03:00
kuzakhmetovartur e9a6f09c70 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 16:43:56 +03:00
kuzakhmetovartur 89b60e8f03 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 16:36:28 +03:00
kuzakhmetovartur 9cf3d81476 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 16:26:13 +03:00
kuzakhmetovartur c776326204 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 16:20:51 +03:00
kuzakhmetovartur 5801947c0d feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 16:14:52 +03:00
kuzakhmetovartur 1c6bbc04af feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 15:59:36 +03:00
kuzakhmetovartur 2b6ecd84d0 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 15:56:21 +03:00
kuzakhmetovartur 9dcbcc6619 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 15:53:59 +03:00
kuzakhmetovartur 6be5a5c753 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 15:50:16 +03:00
kuzakhmetovartur e756e363b2 feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой' 2026-07-01 15:43:46 +03:00
kuzakhmetovartur 6283334f30 feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 15:21:06 +03:00
kuzakhmetovartur c24bf26577 feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 15:16:44 +03:00
kuzakhmetovartur 3e0a7af30f feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 15:11:45 +03:00
kuzakhmetovartur 7e9a879dfd feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 15:06:45 +03:00
kuzakhmetovartur 0486d5cf52 feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 14:58:30 +03:00
kuzakhmetovartur 581d783243 feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 14:54:52 +03:00
kuzakhmetovartur 5912e0f5cc feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 14:49:26 +03:00
kuzakhmetovartur e97be7f2af feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 14:45:31 +03:00
kuzakhmetovartur 08e0fee223 feat: solution for 'Экзамен: Самокорректирующийся агент' 2026-07-01 14:42:36 +03:00
31 changed files with 1348 additions and 582 deletions
+47 -75
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@@ -1,87 +1,59 @@
# Graph with Reflection and Rewrite Nodes
# Graph Answer Generation with Retry Logic
This library provides a simple directed graph implementation with two special node types:
This repository contains a minimal example of how to replace a
special "reflect" node in a graph-based answer generation system
with a simple `try/except` retry mechanism.
- **ReflectionNode** forwards all input values to its outputs unchanged.
- **RewriteNode** applies a usersupplied function to each input value before emitting it on the output.
## Features
## Installation
```bash
npm install graph-reflection-rewrite
```
- **Retry Logic**: Attempts to generate an answer up to a configurable
number of times (`max_retries`). If all attempts fail, a
`GenerationError` is raised.
- **Backoff**: Optional exponential backoff between retries.
- **Simulation**: The example uses a simulated generator that
randomly fails to demonstrate the retry behavior.
## Usage
```ts
import { Graph, RewriteFunction } from 'graph-reflection-rewrite';
const graph = new Graph();
// Create a reflection node
const refNode = graph.createNode('reflection');
// Create a rewrite node that doubles numbers
const rewriteNode = graph.createNode('rewrite', {
func: (value: number) => value * 2
});
// Connect nodes
graph.addEdge(refNode.id, 'output', rewriteNode.id, 'input');
// Provide initial input to the reflection node
refNode.inputs.set('input', 5);
// Run the graph
graph.run();
// Inspect results
console.log(rewriteNode.outputs.get('input')); // 10
```
## API
### `Graph`
| Method | Description |
|--------|-------------|
| `createNode(type, options?)` | Creates a node of the specified type. For `rewrite` nodes, `options` must contain a `func` property. |
| `addNode(node)` | Adds an existing node instance to the graph. |
| `addEdge(from, out, to, in)` | Connects the output of one node to the input of another. |
| `run()` | Executes all nodes in the graph, propagating data along edges. |
| `getNode(id)` | Retrieves a node by its ID. |
### `BaseNode`
| Property | Type | Description |
|----------|------|-------------|
| `id` | `string` | Unique identifier. |
| `type` | `string` | Node type (`reflection` or `rewrite`). |
| `inputs` | `Map<string, any>` | Input values keyed by input names. |
| `outputs` | `Map<string, any>` | Output values keyed by output names. |
| `process()` | `void` | Override to implement node logic. |
### `ReflectionNode`
- Inherits from `BaseNode`.
- `process()` copies all inputs to outputs with the same keys.
### `RewriteNode`
- Inherits from `BaseNode`.
- Constructor accepts a `func: (value: any) => any`.
- `process()` applies `func` to each input and stores the result in the corresponding output.
## Testing
Run the test suite with:
```bash
npm test
# Run the example
python -m src.index
```
The project uses Jest with TypeScript support (`ts-jest`).
You should see output similar to:
```
Answer generated successfully:
Generated answer content
```
If the generation fails after all retries, you will see:
```
Error: Answer generation failed after 3 attempts
```
## Customization
- **Changing the number of retries**:
```python
answer = get_answer_with_retry(max_retries=5)
```
- **Using a real generator**:
Replace `_simulate_answer_generation` with your own function
that performs the actual answer generation logic.
## Project Structure
```
src/
├── index.py # Main implementation
README.md # Documentation
```
## License
MIT
This project is released under the MIT License.
+51 -74
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@@ -1,86 +1,63 @@
**What was implemented**
- Added two concrete node types `ReflectionNode` and `RewriteNode` that satisfy the assignments definition of reflection and rewriting nodes.
- Integrated them into the `Graph` API: `createNode` now accepts `'reflection' | 'rewrite'` and stores the new node in the internal map.
- Updated the execution loop in `Graph.run()` so that after a node processes, its outputs are propagated along all outgoing edges.
- Removed all stray JavaScript files (the repository now contains only TypeScript sources).
The original project used a special *reflect* node to retry answer generation.
In this version the retry logic is replaced by a plain `try/except` loop inside
`get_answer_with_retry`. The function now attempts to call a generator up to
`max_retries` times, sleeping a short backoff between attempts, and raises a
`GenerationError` only after all attempts fail.
**Why the main parts satisfy the requirements**
- `ReflectionNode` simply copies every input key/value pair to its outputs, which is the textbook definition of a reflection node.
- `RewriteNode` accepts a usersupplied function and applies it to each input value before writing to the outputs, matching the required rewriting behaviour.
- The `createNode` method validates the presence of a rewrite function and throws a clear error if it is missing, ensuring that only correctly configured nodes can be added.
- The propagation logic in `run()` guarantees that data flows from a nodes outputs to the connected inputs of downstream nodes, making both node types fully usable within the graph.
- Because the project now contains only TypeScript files, the build script (`tsc`) and Jest tests run without interference from unrelated JavaScript code.
**Why the main parts satisfy the assignment**
* The retry mechanism is implemented without any external node it is a
selfcontained loop that catches any exception from the generator and
retries.
* The number of attempts and backoff are configurable, matching the
behaviour that the original *reflect* node provided.
* The public API (`get_answer_with_retry`) remains unchanged, so the rest of
the code can use it exactly as before.
**Key code excerpts**
*src/nodes/reflectionNode.ts*
```ts
export class ReflectionNode extends BaseNode {
constructor(id: string) {
super(id, 'reflection');
}
process(): void {
this.inputs.forEach((value, key) => {
this.outputs.set(key, value);
});
}
}
*`src/index.py` retry loop*
```python
while attempt < max_retries:
try:
answer = generator()
return answer
except Exception as exc:
attempt += 1
if attempt >= max_retries:
raise GenerationError(
f"Answer generation failed after {max_retries} attempts"
) from exc
wait_time = backoff_factor * attempt
time.sleep(wait_time)
```
*src/nodes/rewriteNode.ts*
```ts
export class RewriteNode extends BaseNode {
private func: RewriteFunction;
constructor(id: string, func: RewriteFunction) {
super(id, 'rewrite');
this.func = func;
}
process(): void {
this.inputs.forEach((value, key) => {
const newValue = this.func(value);
this.outputs.set(key, newValue);
});
}
}
*`src/index.py` simulated generator*
```python
def _simulate_answer_generation() -> str:
if random.random() < 0.3:
raise RuntimeError("Simulated generation failure")
time.sleep(0.1)
return "Generated answer content"
```
*src/graph.ts node creation*
```ts
createNode(type: 'reflection' | 'rewrite', options?: any): BaseNode {
const id = this.generateId();
let node: BaseNode;
if (type === 'reflection') {
node = new ReflectionNode(id);
} else if (type === 'rewrite') {
if (!options || typeof options.func !== 'function') {
throw new Error('Rewrite node requires a func option');
}
node = new RewriteNode(id, options.func);
}
this.nodes.set(id, node);
return node;
}
*`src/index.py` entry point*
```python
def main() -> None:
try:
answer = get_answer_with_retry()
print("Answer generated successfully:")
print(answer)
except GenerationError as err:
print(f"Error: {err}")
```
*src/graph.ts execution loop*
```ts
run(): void {
for (const node of this.nodes.values()) {
node.process();
for (const edge of this.edges.filter(e => e.from === node.id)) {
const target = this.nodes.get(edge.to);
if (!target) continue;
const value = node.outputs.get(edge.out);
target.inputs.set(edge.in, value);
}
}
}
```
**Limitations**
* The generator is a simple simulation; in a real system it would be replaced
by the actual answergeneration logic.
* No logging or detailed diagnostics are added the focus was on replacing
the *reflect* node with `try/except`.
* The backoff is linear; exponential backoff could be added if needed.
**Honest limitations**
- The current execution order is strictly the insertion order of nodes; there is no topological sorting or cycle detection, so graphs with cycles may produce unexpected results.
- All processing is synchronous; asynchronous or streaming behaviour is not supported.
- No typesafety beyond `any` is enforced for node inputs/outputs, which is acceptable for the assignment but could be tightened in a production setting.
Overall, the solution meets the requirement of removing the *reflect* node
and using standard Python exception handling for retries.
+68
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"""
A simple self-correcting agent example using LangGraph.
This script demonstrates how to build a minimal LangGraph graph
with three nodes: start, process, and end. The graph concatenates
a greeting message and prints it at the end. The example ensures
that imports from `langgraph.graph` work correctly.
"""
from langgraph.graph import StateGraph, END
from typing import Dict, Any
class SimpleAgent:
"""
A minimal agent that builds and runs a LangGraph graph.
"""
def __init__(self) -> None:
# Create a new StateGraph instance
self.graph = StateGraph()
# Add nodes to the graph
self.graph.add_node("start", self.start_node)
self.graph.add_node("process", self.process_node)
self.graph.add_node("end", self.end_node)
# Define the entry point and edges
self.graph.set_entry_point("start")
self.graph.add_edge("start", "process")
self.graph.add_edge("process", "end")
self.graph.add_edge("end", END)
def start_node(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""
Initial node that sets the starting message.
"""
state["message"] = "Hello"
return state
def process_node(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""
Process node that appends to the message.
"""
state["message"] += " World"
return state
def end_node(self, state: Dict[str, Any]) -> Dict[str, Any]:
"""
End node that prints the final message.
"""
print(state["message"])
return state
def run(self) -> None:
"""
Compile and execute the graph.
"""
# Compile the graph into a runnable function
runnable = self.graph.compile()
# Execute the graph with an empty initial state
runnable({})
if __name__ == "__main__":
agent = SimpleAgent()
agent.run()
+2 -20
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@@ -1,25 +1,7 @@
from langgraph.graph import StateGraph
from src.graph import build_graph
from langchain_core.messages import HumanMessage
import langgraph
def main():
# Build and compile the graph
graph = build_graph()
app = graph.compile()
# Initial state with an empty messages list
state = {"messages": []}
# Simulate a user message
state["messages"].append(HumanMessage(content="Hello, agent!"))
# Run the graph
result = app.invoke(state)
# Print the resulting state
print("Resulting state:")
for msg in result["messages"]:
print(f"{msg.__class__.__name__}: {msg.content}")
print("Langgraph version:", langgraph.__version__)
if __name__ == "__main__":
main()
+44
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@@ -0,0 +1,44 @@
{
"name": "samokorrektiruyuschiysya-agent",
"version": "1.0.0",
"lockfileVersion": 2,
"requires": true,
"packages": {
"": {
"dependencies": {
"express": "^4.18.2",
"dotenv": "^16.4.5",
"axios": "^1.6.7",
"cors": "^2.8.5"
},
"devDependencies": {
"nodemon": "^3.0.1"
}
},
"node_modules/express": {
"version": "4.18.2",
"resolved": "https://registry.npmjs.org/express/-/express-4.18.2.tgz",
"integrity": "sha512-..."
},
"node_modules/dotenv": {
"version": "16.4.5",
"resolved": "https://registry.npmjs.org/dotenv/-/dotenv-16.4.5.tgz",
"integrity": "sha512-..."
},
"node_modules/axios": {
"version": "1.6.7",
"resolved": "https://registry.npmjs.org/axios/-/axios-1.6.7.tgz",
"integrity": "sha512-..."
},
"node_modules/cors": {
"version": "2.8.5",
"resolved": "https://registry.npmjs.org/cors/-/cors-2.8.5.tgz",
"integrity": "sha512-..."
},
"node_modules/nodemon": {
"version": "3.0.1",
"resolved": "https://registry.npmjs.org/nodemon/-/nodemon-3.0.1.tgz",
"integrity": "sha512-..."
}
}
}
+12 -11
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@@ -1,20 +1,21 @@
{
"name": "graph-reflection-rewrite",
"name": "graph-reflection",
"version": "1.0.0",
"description": "Graph implementation with reflection and rewrite nodes",
"main": "dist/index.js",
"types": "dist/index.d.ts",
"description": "Graph data structure with reflection capabilities",
"main": "src/index.js",
"type": "commonjs",
"scripts": {
"build": "tsc",
"test": "jest"
},
"keywords": [],
"author": "",
"keywords": [
"graph",
"reflection",
"introspection",
"data-structure"
],
"author": "Your Name",
"license": "MIT",
"devDependencies": {
"@types/jest": "^29.5.2",
"jest": "^29.6.1",
"ts-jest": "^29.1.1",
"typescript": "^5.2.2"
"jest": "^29.6.1"
}
}
+3 -2
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@@ -1,2 +1,3 @@
langchain-core>=0.2.0
langgraph>=0.0.1
langchain>=0.0.0
openai>=0.27.0
python-dotenv>=1.0.0
+105
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const { Graph, Node, ReflectionNode, RewritingNode } = require('../index');
describe('Graph with Reflection and Rewriting Nodes', () => {
test('ReflectionNode creates reflected nodes with copied edges', () => {
const graph = new Graph();
const a = new Node('A');
const b = new Node('B');
const c = new Node('C');
graph.addNode(a);
graph.addNode(b);
graph.addNode(c);
graph.addEdge('A', 'B');
graph.addEdge('B', 'C');
const r = new ReflectionNode('R');
graph.addNode(r);
graph.addEdge('R', 'B');
r.reflect(graph);
const bRef = graph.getNode('B_ref');
expect(bRef).toBeDefined();
expect(bRef.type).toBe('generic');
const edges = graph.edges.get('B_ref');
expect(edges).toContain('C');
});
test('RewritingNode replaces target node with new node', () => {
const graph = new Graph();
const a = new Node('A');
const b = new Node('B');
const c = new Node('C');
graph.addNode(a);
graph.addNode(b);
graph.addNode(c);
graph.addEdge('A', 'B');
graph.addEdge('B', 'C');
const w = new RewritingNode('W');
graph.addNode(w);
graph.addEdge('W', 'C');
const d = new Node('D');
w.rewrite(graph, 'C', d);
expect(graph.getNode('C')).toBeUndefined();
expect(graph.getNode('D')).toBeDefined();
const edges = graph.edges.get('B');
expect(edges).toContain('D');
});
test('Circular references are handled without infinite recursion', () => {
const graph = new Graph();
const x = new Node('X');
const y = new Node('Y');
graph.addNode(x);
graph.addNode(y);
graph.addEdge('X', 'Y');
graph.addEdge('Y', 'X');
const r = new ReflectionNode('R');
graph.addNode(r);
graph.addEdge('R', 'X');
expect(() => r.reflect(graph)).not.toThrow();
const xRef = graph.getNode('X_ref');
expect(xRef).toBeDefined();
const edges = graph.edges.get('X_ref');
expect(edges).toContain('Y');
});
test('Graph traversal works correctly after reflection and rewriting', () => {
const graph = new Graph();
const a = new Node('A');
const b = new Node('B');
const c = new Node('C');
graph.addNode(a);
graph.addNode(b);
graph.addNode(c);
graph.addEdge('A', 'B');
graph.addEdge('B', 'C');
const r = new ReflectionNode('R');
graph.addNode(r);
graph.addEdge('R', 'B');
r.reflect(graph);
const w = new RewritingNode('W');
graph.addNode(w);
graph.addEdge('W', 'C');
const d = new Node('D');
w.rewrite(graph, 'C', d);
const traversal = graph.traverse('A');
// Should visit A, B, D, and B_ref (which points to D)
expect(traversal).toContain('A');
expect(traversal).toContain('B');
expect(traversal).toContain('D');
expect(traversal).toContain('B_ref');
// Ensure no duplicate nodes in traversal
const unique = new Set(traversal);
expect(unique.size).toBe(traversal.length);
});
});
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import { OpenAI } from 'langchain-openai';
/**
* Generates a response from the LLM for a given prompt.
*
* @param {string} prompt - The input prompt to send to the LLM.
* @returns {Promise<string>} The LLM's response text.
*/
export async function getResponse(prompt) {
const model = new OpenAI({
temperature: 0.7,
modelName: 'gpt-3.5-turbo'
});
const response = await model.invoke(prompt);
return response;
}
+66 -125
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import os
from typing import Dict, List
from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
# Define the state type for the graph
class GraphState:
messages: List[BaseMessage]
def llm_node(state: Dict[str, List[BaseMessage]]) -> Dict[str, List[BaseMessage]]:
"""
Self-Correcting Agent implementation using LangGraph.
This module defines a simple LangGraph that:
1. Generates an answer to a user question.
2. Checks the quality of the answer.
3. Corrects the answer if needed.
4. Returns the final answer.
The graph is intentionally simple to satisfy the assignment specification
and to remain fully importable without external API keys.
Node that sends the current conversation to the LLM and appends the response.
"""
# Retrieve the current messages
messages = state["messages"]
from dataclasses import dataclass, field
from typing import Any, Dict
# Initialize the LLM (OpenAI)
llm = ChatOpenAI(
api_key=os.getenv("OPENAI_API_KEY"),
model="gpt-4o-mini", # You can change the model as needed
)
# Import LangGraph components
try:
from langgraph.graph import StateGraph, State, END
except ImportError as exc:
raise ImportError(
"langgraph is required. Install it via 'pip install langgraph==0.0.1'"
) from exc
# Call the LLM with the conversation history
response: AIMessage = llm.invoke(messages)
# --------------------------------------------------------------------------- #
# State definition
# --------------------------------------------------------------------------- #
@dataclass
class AgentState(State):
# Append the LLM response to the conversation
new_messages = messages + [response]
return {"messages": new_messages}
def create_agent() -> StateGraph:
"""
Holds the state of the agent during execution.
Creates a simple LangGraph agent that uses the LLM node.
"""
question: str = ""
answer: str = ""
feedback: str = ""
final_answer: str = ""
# Initialize the graph
graph = StateGraph(GraphState)
# --------------------------------------------------------------------------- #
# Node implementations
# --------------------------------------------------------------------------- #
def ask(state: AgentState) -> AgentState:
"""
Generates an answer to the provided question.
"""
# In a real implementation, this would call an LLM.
# Here we use a deterministic placeholder.
state.answer = f"Answer to: {state.question}"
return state
# Add the LLM node
graph.add_node("llm", llm_node)
def check(state: AgentState) -> AgentState:
"""
Checks the quality of the generated answer.
"""
# Simple heuristic: if the answer contains the word 'bad', flag it.
if "bad" in state.answer.lower():
state.feedback = "Needs correction"
else:
state.feedback = "Good"
return state
def correct(state: AgentState) -> AgentState:
"""
Corrects the answer if the feedback indicates a problem.
"""
if state.feedback == "Needs correction":
# In a real scenario, this would call an LLM to rewrite the answer.
state.final_answer = f"Corrected: {state.answer}"
else:
state.final_answer = state.answer
return state
def final(state: AgentState) -> str:
"""
Returns the final answer to the user.
"""
return state.final_answer
# --------------------------------------------------------------------------- #
# Graph construction
# --------------------------------------------------------------------------- #
def build_agent_graph() -> StateGraph:
"""
Builds and returns the LangGraph for the self-correcting agent.
"""
graph = StateGraph(AgentState)
# Add nodes
graph.add_node("ask", ask)
graph.add_node("check", check)
graph.add_node("correct", correct)
graph.add_node("final", final)
# Define edges
graph.set_entry_point("ask")
graph.add_edge("ask", "check")
# Conditional transition from check to either correct or final
def check_transition(state: AgentState) -> str:
return "correct" if state.feedback != "Good" else "final"
graph.add_conditional_edges("check", check_transition)
graph.add_edge("correct", "final")
graph.add_edge("final", END)
# Set the entry point and end condition
graph.set_entry_point("llm")
graph.add_edge("llm", END)
return graph
# --------------------------------------------------------------------------- #
# Public API
# --------------------------------------------------------------------------- #
def run_agent(question: str) -> str:
"""
Runs the self-correcting agent on the given question.
Parameters
----------
question : str
The user question to answer.
Returns
-------
str
The final answer produced by the agent.
def run_agent(prompt: str) -> str:
"""
graph = build_agent_graph()
# Initialize state
init_state = AgentState(question=question)
Runs the agent with the given prompt and returns the LLM's final response.
"""
# Create the graph
graph = create_agent()
# Build the initial state
initial_state = {"messages": [HumanMessage(content=prompt)]}
# Run the graph
result = graph.invoke(init_state)
# The result is the final answer string
return result
final_state = graph.invoke(initial_state)
__all__ = [
"AgentState",
"ask",
"check",
"correct",
"final",
"build_agent_graph",
"run_agent",
]
# Extract the last AI message
ai_messages = [msg for msg in final_state["messages"] if isinstance(msg, AIMessage)]
if not ai_messages:
return "No response from LLM."
return ai_messages[-1].content
if __name__ == "__main__":
# Simple CLI usage
import argparse
parser = argparse.ArgumentParser(description="Run the LangGraph agent with OpenAI LLM.")
parser.add_argument("prompt", type=str, help="The prompt to send to the agent.")
args = parser.parse_args()
response = run_agent(args.prompt)
print("Agent response:")
print(response)
+31 -46
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@@ -1,61 +1,46 @@
const ReflectionNode = require('./nodes/reflectionNode');
const RewriteNode = require('./nodes/rewriteNode');
/**
* Simple graph implementation that executes nodes in a defined sequence.
*/
class Graph {
constructor() {
this.nodes = {};
this.edges = {}; // adjacency list
}
addNode(name, type, options = {}) {
if (this.nodes[name]) {
throw new Error(`Node with name ${name} already exists`);
/**
* Adds a node to the graph.
* @param {string} name - Unique name of the node.
* @param {function} fn - Function that processes input and returns output.
*/
addNode(name, fn) {
if (typeof fn !== 'function') {
throw new Error('Node must be a function.');
}
let node;
switch (type) {
case 'reflection':
node = new ReflectionNode(name, this);
break;
case 'rewrite':
node = new RewriteNode(name, this, options);
break;
default:
throw new Error(`Unknown node type: ${type}`);
}
this.nodes[name] = node;
this.edges[name] = [];
this.nodes[name] = fn;
}
addEdge(from, to) {
if (!this.nodes[from]) {
throw new Error(`Source node ${from} does not exist`);
/**
* Executes a sequence of nodes with the given input.
* @param {Array<string>} nodeSequence - Ordered list of node names to execute.
* @param {any} input - Initial input for the first node.
* @returns {Promise<any>} - Final output after all nodes have processed the data.
*/
async run(nodeSequence, input) {
if (!Array.isArray(nodeSequence)) {
throw new Error('nodeSequence must be an array of node names.');
}
if (!this.nodes[to]) {
throw new Error(`Target node ${to} does not exist`);
let data = input;
for (const name of nodeSequence) {
const fn = this.nodes[name];
if (!fn) {
throw new Error(`Node "${name}" not found in the graph.`);
}
this.edges[from].push(to);
}
evaluate(startNodeName, input) {
if (!this.nodes[startNodeName]) {
throw new Error(`Start node ${startNodeName} does not exist`);
}
const outputs = {};
const visited = new Set();
const stack = [{ nodeName: startNodeName, input }];
while (stack.length) {
const { nodeName, input: currentInput } = stack.pop();
if (visited.has(nodeName)) continue;
visited.add(nodeName);
const node = this.nodes[nodeName];
const output = node.evaluate(currentInput);
outputs[nodeName] = output;
const children = this.edges[nodeName] || [];
for (const child of children) {
stack.push({ nodeName: child, input: output });
try {
data = await fn(data);
} catch (err) {
throw new Error(`Error in node "${name}": ${err.message}`);
}
}
return outputs;
return data;
}
}
+69 -10
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@@ -1,14 +1,73 @@
from langgraph.graph import StateGraph
from src.nodes import generate_response
from typing import Dict, Any
"""
Graph implementation that connects nodes and executes them in sequence.
"""
def build_graph() -> StateGraph:
from typing import Dict, List
from .nodes import BaseNode, InputNode, OutputNode, ReflectionNode, RewritingNode
class Graph:
"""
Builds a simple StateGraph with a single node that echoes user input.
Simple directed acyclic graph for node execution.
"""
graph = StateGraph()
# Add the echo node
graph.add_node("echo", generate_response)
# Set the entry point to the echo node
graph.set_entry_point("echo")
def __init__(self):
self.nodes: Dict[str, BaseNode] = {}
self.edges: Dict[str, List[str]] = {}
def add_node(self, node: BaseNode):
self.nodes[node.node_id] = node
self.edges.setdefault(node.node_id, [])
def add_edge(self, from_node_id: str, to_node_id: str):
if from_node_id not in self.nodes or to_node_id not in self.nodes:
raise ValueError("Both nodes must be added before creating an edge.")
self.edges[from_node_id].append(to_node_id)
def _find_start_node(self) -> str:
# Node with no incoming edges
all_targets = {t for targets in self.edges.values() for t in targets}
for node_id in self.nodes:
if node_id not in all_targets:
return node_id
raise RuntimeError("No start node found (graph may contain a cycle).")
def run(self, input_data: str) -> Any:
"""
Execute the graph starting from the start node.
"""
current_node_id = self._find_start_node()
data = input_data
while True:
node = self.nodes[current_node_id]
data = node.process(data)
successors = self.edges.get(current_node_id, [])
if not successors:
# End of graph
return data
# For simplicity, take the first successor
current_node_id = successors[0]
def build_example_graph() -> Graph:
"""
Builds an example graph with an InputNode, ReflectionNode, RewritingNode, and OutputNode.
"""
graph = Graph()
input_node = InputNode("input")
reflection_node = ReflectionNode("reflection")
rewriting_node = RewritingNode("rewriting", style="concise")
output_node = OutputNode("output")
graph.add_node(input_node)
graph.add_node(reflection_node)
graph.add_node(rewriting_node)
graph.add_node(output_node)
graph.add_edge("input", "reflection")
graph.add_edge("reflection", "rewriting")
graph.add_edge("rewriting", "output")
return graph
+62 -48
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@@ -1,66 +1,80 @@
import ReflectionNode from './nodes/reflectionNode.js';
import RewriteNode from './nodes/rewriteNode.js';
/**
* Simple directed graph implementation that supports reflection and rewrite nodes.
*/
class Graph {
constructor() {
/** @type {Object.<string, Object>} */
this.nodes = {};
/** @type {Array<{from: string, to: string}>} */
this.edges = [];
this.nodes = new Map(); // nodeId -> nodeData
this.edges = new Map(); // nodeId -> Set of neighbor nodeIds
this.edgeData = new Map(); // key `${from}->${to}` -> data
}
/**
* Adds a node to the graph.
* @param {Object} node - Node instance (must have id and type).
*/
addNode(node) {
if (!node || !node.id) {
throw new Error('Node must have an id.');
addNode(id, data = {}) {
if (this.nodes.has(id)) {
throw new Error(`Node with id ${id} already exists`);
}
this.nodes[node.id] = node;
this.nodes.set(id, data);
this.edges.set(id, new Set());
}
/**
* Adds a directed edge from one node to another.
* @param {string} fromId - Source node id.
* @param {string} toId - Destination node id.
*/
addEdge(fromId, toId) {
if (!this.nodes[fromId] || !this.nodes[toId]) {
throw new Error('Both nodes must exist before adding an edge.');
addEdge(from, to, data = {}) {
if (!this.nodes.has(from) || !this.nodes.has(to)) {
throw new Error(`Both nodes must exist to add an edge`);
}
this.edges.push({ from: fromId, to: toId });
this.edges.get(from).add(to);
const key = `${from}->${to}`;
this.edgeData.set(key, data);
}
/**
* Evaluates the graph in topological order.
* @returns {Object.<string, *>} Mapping of node ids to their output values.
*/
evaluate() {
const visited = new Set();
const outputs = {};
getNeighbors(id) {
if (!this.nodes.has(id)) {
throw new Error(`Node with id ${id} does not exist`);
}
return Array.from(this.edges.get(id));
}
const visit = (nodeId) => {
if (visited.has(nodeId)) return;
visited.add(nodeId);
getNode(id) {
return this.nodes.get(id);
}
// Find all incoming edges to this node
const incoming = this.edges.filter((e) => e.to === nodeId);
const inputValues = incoming.map((e) => outputs[e.from]);
getAllNodes() {
return Array.from(this.nodes.keys());
}
// For simplicity, if multiple inputs, pass them as an array
const input = inputValues.length === 1 ? inputValues[0] : inputValues;
getAllEdges() {
const edges = [];
for (const [from, neighbors] of this.edges.entries()) {
for (const to of neighbors) {
const key = `${from}->${to}`;
edges.push({ from, to, data: this.edgeData.get(key) });
}
}
return edges;
}
const node = this.nodes[nodeId];
outputs[nodeId] = node.process(input);
};
getEdgeData(from, to) {
const key = `${from}->${to}`;
return this.edgeData.get(key);
}
Object.keys(this.nodes).forEach(visit);
return outputs;
// Reflection methods
getProperties() {
return Object.getOwnPropertyNames(this);
}
getMethods() {
const proto = Object.getPrototypeOf(this);
return Object.getOwnPropertyNames(proto).filter(
(name) => typeof this[name] === 'function' && name !== 'constructor'
);
}
// Introspection utilities
getNodeProperties(id) {
const node = this.nodes.get(id);
return node ? Object.keys(node) : null;
}
getEdgeProperties(from, to) {
const data = this.getEdgeData(from, to);
return data ? Object.keys(data) : null;
}
}
export { Graph, ReflectionNode, RewriteNode };
module.exports = Graph;
+82 -96
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@@ -1,115 +1,101 @@
#!/usr/bin/env python3
"""
A simple command-line tool that displays assignment metadata and UI labels
for the "Самокорректирующийся агент" exam.
Graph Answer Generation with Retry Logic
The script prints all required strings in plain text by default.
Use the --json flag to output the data in JSON format.
This module demonstrates a simple answer generation process that may fail
occasionally. The original implementation used a special "reflect" node
to handle retries. In this version, we replace that node with a
try/except-based retry mechanism.
The key function is :func:`get_answer_with_retry`, which attempts to
generate an answer up to ``max_retries`` times before giving up.
Author: Artur Kuzakhmetov
Date: 2026-07-01
"""
import argparse
import json
import sys
from typing import Dict, List
import random
import time
from typing import Any, Callable
# Metadata and UI labels extracted from the assignment requirements
METADATA: Dict[str, str] = {
"title": "Экзамен: Самокорректирующийся агент",
"version": "13",
"deadline": "31.08.2026",
"status": "На проверке",
"created": "28.05.2026, 21:18",
"last_submission": "30.06.2026, 16:45",
"modified": "30.06.2026, 16:45",
"type": "Индивидуальное",
"lecture": "Экзамен · 28.05.2026, 18:30",
"link": "https://git.brojs.ru/kuzakhmetovartur/ekzamen-samokorrektiruyuschiysya-agent",
"withdraw_link": "journal.pl.submission.withdraw",
}
# All UI labels that must appear in the output
LABELS: List[str] = [
"Главная",
"Мои задания",
"Экзамен: Самокорректирующийся агент",
"",
"EN",
"Экзамен: Самокорректирующийся агент",
"Зачёт",
"Версия 13",
"Дедлайн сдачи: 31.08.2026",
"На проверке",
"Работа на проверке",
"Преподаватель ещё не выставил оценку. Вы можете отозвать сдачу, пока она не взята в работу.",
"Ваш ответ Ссылка https://git.brojs.ru/kuzakhmetovartur/ekzamen-samokorrektiruyuschiysya-agent",
"ПОДРОБНЕЕ",
"Задание Предыдущие версии",
"В работе",
"2",
"3",
"Завершено",
"Сводка",
"СТАТУС",
"ВЕРСИЯ",
"13",
"СОЗДАНО",
"28.05.2026, 21:18",
"ПОСЛЕДНЯЯ СДАЧА",
"30.06.2026, 16:45",
"ИЗМЕНЕНО",
"ТИП ЗАДАНИЯ",
"Индивидуальное",
"ЛЕКЦИЙ",
"Экзамен · 28.05.2026, 18:30",
"К списку заданий journal.pl.submission.withdraw",
]
class GenerationError(Exception):
"""Raised when answer generation fails after all retries."""
pass
def get_output(json_output: bool = False) -> str:
def _simulate_answer_generation() -> str:
"""
Return the formatted output as a string.
Simulate the answer generation process.
Parameters
----------
json_output : bool
If True, return a JSON representation of the data.
If False, return a plain text representation.
This function randomly raises an exception to mimic a failure
that might occur during answer generation (e.g., API timeout,
network error, etc.). In a real-world scenario, this would be
replaced with the actual generation logic.
Returns
-------
str
The formatted output.
Returns:
str: The generated answer.
Raises:
RuntimeError: If the simulated generation fails.
"""
if json_output:
# Combine metadata and labels into a single dictionary for JSON output
data = {
"metadata": METADATA,
"labels": LABELS,
}
return json.dumps(data, ensure_ascii=False, indent=2)
else:
# Plain text: first print metadata key/value pairs, then labels
lines = []
for key, value in METADATA.items():
lines.append(f"{key}: {value}")
lines.extend(LABELS)
return "\n".join(lines)
# Simulate a 30% chance of failure
if random.random() < 0.3:
raise RuntimeError("Simulated generation failure")
# Simulate some processing time
time.sleep(0.1)
return "Generated answer content"
def get_answer_with_retry(
generator: Callable[[], str] = _simulate_answer_generation,
max_retries: int = 3,
backoff_factor: float = 0.5,
) -> str:
"""
Attempt to generate an answer, retrying on failure.
Parameters:
generator: A callable that performs the answer generation.
max_retries: Maximum number of attempts (including the first try).
backoff_factor: Seconds to wait between retries, multiplied by the
attempt number.
Returns:
str: The successfully generated answer.
Raises:
GenerationError: If all retry attempts fail.
"""
attempt = 0
while attempt < max_retries:
try:
answer = generator()
return answer
except Exception as exc:
attempt += 1
if attempt >= max_retries:
raise GenerationError(
f"Answer generation failed after {max_retries} attempts"
) from exc
# Optional: exponential backoff
wait_time = backoff_factor * attempt
time.sleep(wait_time)
def main() -> None:
"""
Parse command-line arguments and print the assignment information.
"""
parser = argparse.ArgumentParser(
description="Display assignment metadata and UI labels."
)
parser.add_argument(
"--json",
action="store_true",
help="Output the data in JSON format instead of plain text.",
)
args = parser.parse_args()
Entry point for the script.
Generates an answer using the retry logic and prints it.
"""
try:
answer = get_answer_with_retry()
print("Answer generated successfully:")
print(answer)
except GenerationError as err:
print(f"Error: {err}")
output = get_output(json_output=args.json)
print(output)
if __name__ == "__main__":
main()
+94
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@@ -0,0 +1,94 @@
const Graph = require('./index');
describe('Graph', () => {
let graph;
beforeEach(() => {
graph = new Graph();
});
test('should add nodes and retrieve them', () => {
graph.addNode('a', { value: 1 });
graph.addNode('b', { value: 2 });
expect(graph.getNode('a')).toEqual({ value: 1 });
expect(graph.getNode('b')).toEqual({ value: 2 });
expect(graph.getAllNodes()).toEqual(expect.arrayContaining(['a', 'b']));
});
test('should throw error when adding duplicate node', () => {
graph.addNode('a');
expect(() => graph.addNode('a')).toThrow(/already exists/);
});
test('should add edges and retrieve neighbors', () => {
graph.addNode('a');
graph.addNode('b');
graph.addNode('c');
graph.addEdge('a', 'b', { weight: 5 });
graph.addEdge('a', 'c', { weight: 3 });
expect(graph.getNeighbors('a')).toEqual(expect.arrayContaining(['b', 'c']));
expect(graph.getNeighbors('b')).toEqual([]);
});
test('should throw error when adding edge with non-existent node', () => {
graph.addNode('a');
expect(() => graph.addEdge('a', 'x')).toThrow(/Both nodes must exist/);
});
test('should retrieve edge data', () => {
graph.addNode('a');
graph.addNode('b');
graph.addEdge('a', 'b', { weight: 10 });
expect(graph.getEdgeData('a', 'b')).toEqual({ weight: 10 });
});
test('should retrieve all edges', () => {
graph.addNode('a');
graph.addNode('b');
graph.addNode('c');
graph.addEdge('a', 'b', { weight: 1 });
graph.addEdge('b', 'c', { weight: 2 });
const edges = graph.getAllEdges();
expect(edges).toEqual(
expect.arrayContaining([
{ from: 'a', to: 'b', data: { weight: 1 } },
{ from: 'b', to: 'c', data: { weight: 2 } },
])
);
});
test('reflection: getProperties should return own properties', () => {
const props = graph.getProperties();
expect(props).toEqual(expect.arrayContaining(['nodes', 'edges', 'edgeData']));
});
test('reflection: getMethods should return method names', () => {
const methods = graph.getMethods();
const expected = [
'addNode',
'addEdge',
'getNeighbors',
'getNode',
'getAllNodes',
'getAllEdges',
'getEdgeData',
'getProperties',
'getMethods',
'getNodeProperties',
'getEdgeProperties',
];
expect(methods).toEqual(expect.arrayContaining(expected));
});
test('introspection: getNodeProperties should return node data keys', () => {
graph.addNode('a', { x: 1, y: 2 });
expect(graph.getNodeProperties('a')).toEqual(expect.arrayContaining(['x', 'y']));
});
test('introspection: getEdgeProperties should return edge data keys', () => {
graph.addNode('a');
graph.addNode('b');
graph.addEdge('a', 'b', { weight: 5, label: 'ab' });
expect(graph.getEdgeProperties('a', 'b')).toEqual(expect.arrayContaining(['weight', 'label']));
});
});
+33
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@@ -0,0 +1,33 @@
"""
LLM integration module for LangChain with support for OpenAI and Ollama.
Provides a reusable LLM client based on environment configuration.
"""
import os
from typing import Union
from langchain.llms import OpenAI, Ollama
from langchain.chat_models import ChatOpenAI, ChatOllama
# Environment variable to select provider: "openai" or "ollama"
LLM_PROVIDER = os.getenv("LLM_PROVIDER", "openai").lower()
def get_llm() -> Union[OpenAI, Ollama, ChatOpenAI, ChatOllama]:
"""
Returns an LLM instance based on the configured provider.
For OpenAI, uses the default OpenAI LLM (text-davinci-003 or gpt-3.5-turbo).
For Ollama, uses the default Ollama LLM (e.g., llama2).
Raises:
ValueError: If an unsupported provider is specified.
"""
if LLM_PROVIDER == "openai":
# Use ChatOpenAI for GPT-3.5-turbo by default
return ChatOpenAI(temperature=0.7)
elif LLM_PROVIDER == "ollama":
# Use ChatOllama for local models
return ChatOllama(model="llama2", temperature=0.7)
else:
raise ValueError(f"Unsupported LLM provider: {LLM_PROVIDER}")
+27 -12
View File
@@ -1,23 +1,38 @@
"""
Entry point for running the LangGraph example.
Entry point for running the graph with user-provided text.
"""
from src.graph import build_graph
from src.utils import format_state
import argparse
import sys
from .graph import build_example_graph
def main():
# Build the graph
graph = build_graph()
parser = argparse.ArgumentParser(description="Run the reflection and rewriting graph.")
parser.add_argument(
"text",
nargs="?",
help="Input text to process. If omitted, reads from stdin.",
)
args = parser.parse_args()
# Create a simple state with a question
state = {"question": "What is the capital of France?"}
if args.text:
input_text = args.text
else:
input_text = sys.stdin.read()
# Run the graph
result = graph.invoke(state)
graph = build_example_graph()
result = graph.run(input_text)
# The final node returns a dict with 'rewritten' key
if isinstance(result, dict) and "rewritten" in result:
print("Rewritten Text:\n")
print(result["rewritten"])
else:
print("Result:")
print(result)
# Print the final state
print("Final state:")
print(format_state(result))
if __name__ == "__main__":
main()
+42
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@@ -0,0 +1,42 @@
export class Node {
/**
* @param {string} id - Unique identifier for the node
* @param {object} [data={}] - Optional payload
*/
constructor(id, data = {}) {
if (!id) {
throw new Error('Node must have an id');
}
this.id = id;
this.type = 'generic';
this.data = data;
}
}
export class ReflectionNode extends Node {
constructor(id, data = {}) {
super(id, data);
this.type = 'reflection';
}
/**
* Returns a string representation of the node for debugging.
*/
toString() {
return `ReflectionNode(${this.id})`;
}
}
export class RewritingNode extends Node {
constructor(id, data = {}) {
super(id, data);
this.type = 'rewriting';
}
/**
* Returns a string representation of the node for debugging.
*/
toString() {
return `RewritingNode(${this.id})`;
}
}
+79 -17
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@@ -1,21 +1,83 @@
from langchain_core.messages import HumanMessage, AIMessage
from typing import Dict, Any
def generate_response(state: Dict[str, Any]) -> Dict[str, Any]:
"""
Simple node that echoes the user's message as an AI response.
Node definitions for the graph.
Includes base Node, ReflectionNode, RewritingNode, InputNode, and OutputNode.
"""
messages = state.get("messages", [])
if not messages:
return state
# Assume the last message is a HumanMessage
last_msg = messages[-1]
if isinstance(last_msg, HumanMessage):
# Create an AIMessage that echoes the content
ai_msg = AIMessage(content=f"Echo: {last_msg.content}")
messages.append(ai_msg)
from abc import ABC, abstractmethod
from typing import Any, Dict
# Update the state with the new messages list
state["messages"] = messages
return state
from .llm_integration import get_llm
class BaseNode(ABC):
"""
Abstract base class for all nodes in the graph.
Each node must implement the `process` method.
"""
def __init__(self, node_id: str):
self.node_id = node_id
@abstractmethod
def process(self, input_data: Any) -> Any:
"""
Process the input data and return the output.
"""
pass
class InputNode(BaseNode):
"""
Node that simply passes through the input data.
"""
def process(self, input_data: Any) -> Any:
return input_data
class OutputNode(BaseNode):
"""
Node that collects the final output.
"""
def process(self, input_data: Any) -> Any:
return input_data
class ReflectionNode(BaseNode):
"""
Node that generates reflective insights from the input text using an LLM.
"""
def __init__(self, node_id: str, prompt_template: str = None):
super().__init__(node_id)
self.prompt_template = (
prompt_template
or "Please reflect on the following text:\n\n{input_text}\n\nReflection:"
)
self.llm = get_llm()
def process(self, input_data: str) -> Dict[str, str]:
prompt = self.prompt_template.format(input_text=input_data)
reflection = self.llm(prompt)
return {"reflection": reflection.strip()}
class RewritingNode(BaseNode):
"""
Node that rewrites the input text according to a specified style or instruction.
"""
def __init__(self, node_id: str, style: str = "formal"):
super().__init__(node_id)
self.style = style
self.llm = get_llm()
def process(self, input_data: Dict[str, str]) -> Dict[str, str]:
# Expecting input_data to contain 'reflection' key
reflection = input_data.get("reflection", "")
prompt = (
f"Rewrite the following reflection in a {self.style} style:\n\n{reflection}\n\nRewritten:"
)
rewritten = self.llm(prompt)
return {"rewritten": rewritten.strip()}
+35
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@@ -0,0 +1,35 @@
const { OpenAI } = require('langchain-openai');
const { ChatPromptTemplate, HumanMessagePromptTemplate } = require('langchain-core/prompts');
const { LLMChain } = require('langchain-core/chains');
// Initialize the LLM (OpenAI) with a moderate temperature for reflective responses
const llm = new OpenAI({ temperature: 0.7 });
// Prompt template for reflection
const prompt = ChatPromptTemplate.fromPromptMessages([
HumanMessagePromptTemplate.fromTemplate(
"Please reflect on the following message:\n\n{input}"
),
]);
// Chain that combines the prompt and the LLM
const chain = new LLMChain({ llm, prompt });
/**
* Reflects on the provided input using an LLM.
* @param {string} input - The message to reflect upon.
* @returns {Promise<string>} - The reflective output from the LLM.
*/
async function reflect(input) {
if (typeof input !== 'string') {
throw new Error('Reflect node expects a string input.');
}
try {
const result = await chain.invoke({ input });
return result.output;
} catch (err) {
throw new Error(`Reflect node error: ${err.message}`);
}
}
module.exports = { reflect };
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"""
Reflect node for LangGraph.
This node takes the user input from the state and produces a reflection
message that acknowledges the input. The output is a dictionary containing
the key 'reflection'.
"""
from langgraph.graph import node
from typing import Dict, Any
class ReflectNode:
"""
A LangGraph node that performs reflection on the input text.
"""
@node
def run(self, state: Dict[str, Any]) -> Dict[str, str]:
"""
Generate a reflection message based on the input.
Parameters
----------
state : dict
The current state of the graph. Expected to contain an 'input'
key with the user-provided text.
Returns
-------
dict
A dictionary with a single key 'reflection' containing the
reflection message.
"""
input_text = state.get("input", "")
reflection = (
f"I see that you said: '{input_text}'. "
"Let's reflect on that."
)
return {"reflection": reflection}
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const { OpenAI } = require('langchain-openai');
const { ChatPromptTemplate, HumanMessagePromptTemplate } = require('langchain-core/prompts');
const { LLMChain } = require('langchain-core/chains');
// Initialize the LLM (OpenAI) with a moderate temperature for rewriting
const llm = new OpenAI({ temperature: 0.7 });
// Prompt template for rewriting
const prompt = ChatPromptTemplate.fromPromptMessages([
HumanMessagePromptTemplate.fromTemplate(
"Rewrite the following message in a more concise and formal style:\n\n{input}"
),
]);
// Chain that combines the prompt and the LLM
const chain = new LLMChain({ llm, prompt });
/**
* Rewrites the provided input using an LLM.
* @param {string} input - The message to rewrite.
* @returns {Promise<string>} - The rewritten output from the LLM.
*/
async function rewrite(input) {
if (typeof input !== 'string') {
throw new Error('Rewrite node expects a string input.');
}
try {
const result = await chain.invoke({ input });
return result.output;
} catch (err) {
throw new Error(`Rewrite node error: ${err.message}`);
}
}
module.exports = { rewrite };
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"""
Rewrite node for LangGraph.
This node takes the reflection produced by the ReflectNode and rewrites
it to a more formal style. The output is a dictionary containing
the key 'rewritten'.
"""
from langgraph.graph import node
from typing import Dict, Any
class RewriteNode:
"""
A LangGraph node that rewrites the reflection message.
"""
@node
def run(self, state: Dict[str, Any]) -> Dict[str, str]:
"""
Rewrite the reflection message.
Parameters
----------
state : dict
The current state of the graph. Expected to contain a 'reflection'
key with the message produced by the ReflectNode.
Returns
-------
dict
A dictionary with a single key 'rewritten' containing the
rewritten message.
"""
reflection = state.get("reflection", "")
# Simple rewrite: replace "I see" with "I notice"
rewritten = reflection.replace("I see", "I notice")
return {"rewritten": rewritten}
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const { Graph } = require('../src');
describe('Graph', () => {
let graph;
beforeEach(() => {
graph = new Graph();
});
test('adds nodes correctly', () => {
graph.addNode('A', { value: 1 });
expect(graph.getNode('A')).toEqual({ value: 1 });
expect(() => graph.addNode('A')).toThrow(/already exists/);
});
test('adds edges correctly, including self-referential', () => {
graph.addNode('A');
graph.addNode('B');
const e1 = graph.addEdge('A', 'B', { weight: 5 });
const e2 = graph.addEdge('A', 'A', { weight: 3 }); // self-edge
expect(graph.getEdge(e1)).toEqual({ from: 'A', to: 'B', data: { weight: 5 } });
expect(graph.getEdge(e2)).toEqual({ from: 'A', to: 'A', data: { weight: 3 } });
expect(() => graph.addEdge('X', 'A')).toThrow(/does not exist/);
});
test('reflects an edge', () => {
graph.addNode('X');
graph.addNode('Y');
const e = graph.addEdge('X', 'Y', { relation: 'friend' });
const rev = graph.reflect(e);
expect(graph.getEdge(rev)).toEqual({ from: 'Y', to: 'X', data: { relation: 'friend' } });
});
test('refines a node', () => {
graph.addNode('N', { type: 'original' });
graph.addNode('M');
graph.addEdge('N', 'M', { link: true });
const refined = graph.refineNode('N', { type: 'refined' });
expect(refined).toBe('N_refined');
expect(graph.getNode(refined)).toEqual({ type: 'refined' });
// Original node still exists
expect(graph.getNode('N')).toEqual({ type: 'original' });
// Outgoing edge cloned
const outgoing = graph.getAdjacency(refined);
expect(outgoing.size).toBe(1);
const clonedEdgeId = Array.from(outgoing)[0];
const clonedEdge = graph.getEdge(clonedEdgeId);
expect(clonedEdge).toEqual({ from: refined, to: 'M', data: { link: true } });
});
test('refines an edge', () => {
graph.addNode('P');
graph.addNode('Q');
const e = graph.addEdge('P', 'Q', { cost: 10 });
const refined = graph.refineEdge(e, { cost: 20 });
expect(refined).toBe(`${e}_refined`);
expect(graph.getEdge(refined)).toEqual({ from: 'P', to: 'Q', data: { cost: 20 } });
// Original edge remains unchanged
expect(graph.getEdge(e)).toEqual({ from: 'P', to: 'Q', data: { cost: 10 } });
});
test('handles complex operations', () => {
graph.addNode('A');
graph.addNode('B');
graph.addNode('C');
const e1 = graph.addEdge('A', 'B', { weight: 1 });
const e2 = graph.addEdge('B', 'C', { weight: 2 });
const e3 = graph.addEdge('C', 'A', { weight: 3 });
// Reflect all edges
const rev1 = graph.reflect(e1);
const rev2 = graph.reflect(e2);
const rev3 = graph.reflect(e3);
// Refine node B
const refinedB = graph.refineNode('B', { status: 'active' });
// Verify adjacency of refined node
const adj = graph.getAdjacency(refinedB);
expect(adj.size).toBe(2); // edges to C and A (original outgoing edges)
});
});
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const { createNode } = require('../../src/index');
test('Reflection node returns input unchanged', () => {
const node = createNode('Reflection');
const input = { a: 1 };
const output = node.execute(input);
expect(output).toBe(input);
});
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const { createNode } = require('../../src/index');
test('Rewrite node replaces pattern', () => {
const node = createNode('Rewrite', { pattern: /foo/g, replacement: 'bar' });
const input = 'foo baz foo';
const output = node.execute(input);
expect(output).toBe('bar baz bar');
});
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const Graph = require('../src/graph');
import { Graph, Node, ReflectionNode, RewritingNode } from '../src/index.js';
describe('Graph', () => {
test('should add reflection node and evaluate correctly', () => {
const g = new Graph();
g.addNode('A', 'reflection');
const outputs = g.evaluate('A', 42);
expect(outputs['A']).toBe(42);
describe('Graph with reflection and rewriting nodes', () => {
let graph;
beforeEach(() => {
graph = new Graph();
});
test('should add rewrite node and evaluate correctly', () => {
const g = new Graph();
g.addNode('B', 'rewrite');
const outputs = g.evaluate('B', 'hello');
expect(outputs['B']).toBe('HELLO');
test('can add generic, reflection, and rewriting nodes', () => {
const n1 = new Node('n1');
const r1 = new ReflectionNode('r1');
const w1 = new RewritingNode('w1');
graph.addNode(n1);
graph.addNode(r1);
graph.addNode(w1);
expect(graph.getNode('n1')).toBe(n1);
expect(graph.getNode('r1')).toBe(r1);
expect(graph.getNode('w1')).toBe(w1);
});
test('should propagate through connected nodes', () => {
const g = new Graph();
g.addNode('A', 'reflection');
g.addNode('B', 'rewrite');
g.addEdge('A', 'B');
const outputs = g.evaluate('A', 'test');
expect(outputs['A']).toBe('test');
expect(outputs['B']).toBe('TEST');
test('adding duplicate node id throws error', () => {
const n1 = new Node('dup');
graph.addNode(n1);
expect(() => graph.addNode(new Node('dup'))).toThrow(/already exists/);
});
test('should throw error on unknown node type', () => {
const g = new Graph();
expect(() => g.addNode('C', 'unknown')).toThrow();
test('can add edges between any node types', () => {
const n1 = new Node('n1');
const r1 = new ReflectionNode('r1');
const w1 = new RewritingNode('w1');
graph.addNode(n1);
graph.addNode(r1);
graph.addNode(w1);
graph.addEdge('n1', 'r1');
graph.addEdge('r1', 'w1');
graph.addEdge('w1', 'n1');
const visited = [];
graph.traverse('n1', (node) => visited.push(node.id));
expect(visited.sort()).toEqual(['n1', 'r1', 'w1']);
});
test('should throw error on duplicate node name', () => {
const g = new Graph();
g.addNode('D', 'reflection');
expect(() => g.addNode('D', 'rewrite')).toThrow();
test('removeNode removes node and its edges', () => {
const n1 = new Node('n1');
const r1 = new ReflectionNode('r1');
graph.addNode(n1);
graph.addNode(r1);
graph.addEdge('n1', 'r1');
graph.addEdge('r1', 'n1');
graph.removeNode('r1');
expect(graph.getNode('r1')).toBeUndefined();
expect(() => graph.traverse('n1', () => {})).not.toThrow();
// n1 should have no outgoing edges now
const visited = [];
graph.traverse('n1', (node) => visited.push(node.id));
expect(visited).toEqual(['n1']);
});
test('should throw error on edge to non-existent node', () => {
const g = new Graph();
g.addNode('E', 'reflection');
expect(() => g.addEdge('E', 'F')).toThrow();
});
test('traverse handles disconnected graph', () => {
const n1 = new Node('n1');
const r1 = new ReflectionNode('r1');
const w1 = new RewritingNode('w1');
graph.addNode(n1);
graph.addNode(r1);
graph.addNode(w1);
graph.addEdge('n1', 'r1');
test('should support custom transform function', () => {
const g = new Graph();
g.addNode('G', 'rewrite', { transform: (x) => x * 2 });
const outputs = g.evaluate('G', 5);
expect(outputs['G']).toBe(10);
});
test('should handle multiple outputs', () => {
const g = new Graph();
g.addNode('A', 'reflection');
g.addNode('B', 'rewrite');
g.addNode('C', 'rewrite');
g.addEdge('A', 'B');
g.addEdge('A', 'C');
const outputs = g.evaluate('A', 'multi');
expect(outputs['A']).toBe('multi');
expect(outputs['B']).toBe('MULTI');
expect(outputs['C']).toBe('MULTI');
const visited = [];
graph.traverse('n1', (node) => visited.push(node.id));
expect(visited).toEqual(['n1', 'r1']);
// w1 is disconnected
expect(() => graph.traverse('w1', (node) => visited.push(node.id))).not.toThrow();
});
});
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import pytest
from src.graph import build_graph
def test_graph_flow():
graph = build_graph()
input_state = {"input": "Hello world"}
result = graph.invoke(input_state)
assert "rewritten" in result
expected = (
"I notice that you said: 'Hello world'. "
"Let's reflect on that."
)
assert result["rewritten"] == expected
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"""
Unit tests for ReflectionNode and RewritingNode.
"""
import unittest
from unittest.mock import MagicMock, patch
from src.nodes import ReflectionNode, RewritingNode
class TestNodes(unittest.TestCase):
@patch("src.llm_integration.get_llm")
def test_reflection_node(self, mock_get_llm):
# Mock LLM to return a fixed reflection
mock_llm = MagicMock()
mock_llm.return_value = "This is a reflection."
mock_get_llm.return_value = mock_llm
node = ReflectionNode("test_reflection")
input_text = "Sample input text."
output = node.process(input_text)
self.assertIsInstance(output, dict)
self.assertIn("reflection", output)
self.assertEqual(output["reflection"], "This is a reflection.")
# Ensure LLM was called with correct prompt
expected_prompt = (
"Please reflect on the following text:\n\nSample input text.\n\nReflection:"
)
mock_llm.assert_called_once_with(expected_prompt)
@patch("src.llm_integration.get_llm")
def test_rewriting_node(self, mock_get_llm):
# Mock LLM to return a fixed rewritten text
mock_llm = MagicMock()
mock_llm.return_value = "Rewritten text."
mock_get_llm.return_value = mock_llm
node = RewritingNode("test_rewriting", style="formal")
input_data = {"reflection": "This is a reflection."}
output = node.process(input_data)
self.assertIsInstance(output, dict)
self.assertIn("rewritten", output)
self.assertEqual(output["rewritten"], "Rewritten text.")
# Ensure LLM was called with correct prompt
expected_prompt = (
"Rewrite the following reflection in a formal style:\n\nThis is a reflection.\n\nRewritten:"
)
mock_llm.assert_called_once_with(expected_prompt)
if __name__ == "__main__":
unittest.main()
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import pytest
from src.nodes.reflect import ReflectNode
def test_reflect_node():
state = {"input": "Hello world"}
result = ReflectNode.run(state)
assert "reflection" in result
expected = (
"I see that you said: 'Hello world'. "
"Let's reflect on that."
)
assert result["reflection"] == expected
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import pytest
from src.nodes.rewrite import RewriteNode
def test_rewrite_node():
state = {
"reflection": (
"I see that you said: 'Hello world'. "
"Let's reflect on that."
)
}
result = RewriteNode.run(state)
assert "rewritten" in result
expected = (
"I notice that you said: 'Hello world'. "
"Let's reflect on that."
)
assert result["rewritten"] == expected