feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'

SOLUTION.md

@@ -1,61 +1,64 @@
 **What was implemented**  
-- Added a fully‑functional `src/main.py` that imports LangChain, LangChain‑OpenAI and LangChain‑Ollama, builds an LLM chain and prints a short explanation of graph reflection and refinement.  
-- Created a `requirements.txt` that lists all packages needed (`langchain`, `langchain-openai`, `langchain-ollama`, `python-dotenv`, `openai`).  
-- The script reads `OPENAI_API_KEY` or `OLLAMA_HOST` from the environment (or a `.env` file) to decide which LLM to use.
+- Added two concrete node classes – `ReflectionNode` and `RewritingNode` – in `src/nodes.js`.  
+- Updated the public API in `src/index.js` to export the new classes.  
+- Wrote a comprehensive test suite (`tests/graph.test.js`) that checks:  
+  1. Nodes of all three types can be added.  
+  2. Duplicate IDs are rejected.  
+  3. Edges can be created between any node types.  
+  4. Removing a node cleans up its edges.  
+  5. Traversal works on disconnected sub‑graphs.
 
 **Why the main parts satisfy the requirements**  
-- The code imports `langchain_openai.OpenAI` and `langchain_ollama.Ollama`, proving that the project now uses the required LangChain‑LLM stack.  
-- `requirements.txt` contains every dependency, so the reviewer’s constraint “all dependencies must be listed” is met.  
-- The `get_llm()` function chooses the correct LLM based on available credentials, ensuring the program can run with either OpenAI or Ollama as specified.  
-- The prompt chain (`LLMChain`) demonstrates a simple, runnable example that uses the LLM to explain the requested graph concepts.
+- The new node classes inherit from `Node`, so the existing `Graph.addNode` logic (`instanceof Node`) automatically accepts them.  
+- Each new node sets its `type` property (`'reflection'` / `'rewriting'`) and provides a `toString()` for debugging, matching the style of the generic node.  
+- Tests exercise all required operations (add, duplicate check, edge creation, removal, traversal) and confirm that the graph behaves correctly with the new node types.
 
-**Short code excerpts**
+**Key code excerpts**
 
-*src/main.py – LLM selection*  
-```python
-def get_llm() -> "BaseLLM":
-    openai_key = os.getenv("OPENAI_API_KEY")
-    if openai_key:
-        return OpenAI(
-            model_name=os.getenv("OPENAI_MODEL", "gpt-3.5-turbo"),
-            temperature=float(os.getenv("OPENAI_TEMPERATURE", "0.7")),
-            openai_api_key=openai_key,
-        )
-    ollama_host = os.getenv("OLLAMA_HOST")
-    if ollama_host:
-        return Ollama(
-            model=os.getenv("OLLAMA_MODEL", "llama2"),
-            temperature=float(os.getenv("OLLAMA_TEMPERATURE", "0.7")),
-            base_url=ollama_host,
-        )
-    raise RuntimeError("No LLM configuration found.")
+*src/nodes.js* – definition of the new node types  
+```js
+export class ReflectionNode extends Node {
+  constructor(id, data = {}) {
+    super(id, data);
+    this.type = 'reflection';
+  }
+  toString() { return `ReflectionNode(${this.id})`; }
+}
+
+export class RewritingNode extends Node {
+  constructor(id, data = {}) {
+    super(id, data);
+    this.type = 'rewriting';
+  }
+  toString() { return `RewritingNode(${this.id})`; }
+}
 ```
 
-*src/main.py – Prompt chain*  
-```python
-prompt = PromptTemplate(
-    input_variables=[],
-    template=(
-        "You are an expert in graph theory. "
-        "Explain the concepts of graph reflection and graph refinement "
-        "in simple, concise terms suitable for a beginner."
-    ),
-)
-chain = LLMChain(llm=llm, prompt=prompt)
-response = chain.run()
-print(response)
+*tests/graph.test.js* – adding nodes and verifying presence  
+```js
+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);
 ```
 
-*requirements.txt*  
-```
-langchain
-langchain-openai
-langchain-ollama
-python-dotenv
-openai
+*src/graph.js* – node type check (unchanged, but still relevant)  
+```js
+addNode(node) {
+  if (!(node instanceof Node)) {
+    throw new Error('Only Node instances can be added');
+  }
+  ...
+}
 ```
 
 **Honest limitations**  
-- The script requires either an OpenAI API key or an Ollama host to be set in the environment; otherwise it raises a `RuntimeError`.  
-- No unit tests are included; the example is intended for manual execution.  
-- The prompt is static; dynamic input handling could be added later.
+- The new node types currently only differ by their `type` field and `toString()` method; no additional behavior (e.g., special traversal rules) is implemented.  
+- The graph implementation remains generic; any future logic specific to reflection or rewriting would need to be added separately.