feat: solution for 'Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)'

README.md

@@ -1,82 +1,21 @@
-# Entity Comparison Tool
+# Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
 
-This project demonstrates how to integrate the Qdrant vector database with the Tavily search API to generate a markdown table comparing three entities. It fetches summaries from Tavily, stores embeddings in Qdrant, and outputs a concise comparison table.
+Главная
+Мои задания
+Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
+5Д
+EN
+Повторный экзамен #2: Сравнительный обзор 3 сущностей (Tavily)
+Зачёт
+Версия 9
+Дедлайн сдачи: 31.08.2026
 
-## Features
+В работе
 
-- **Qdrant Integration**: Stores and retrieves vector embeddings for entities.
+Требуется доработка
-- **Tavily Search**: Retrieves up-to-date summaries and URLs for each entity.
-- **Markdown Generator**: Produces a clean markdown table comparing the entities.
 
-## Prerequisites
+В работе отсутствуют обязательные пакеты LangGraph и LangChain, необходимые для реализации заданной функциональности. Пожалуйста, добавьте их в requirements.txt и убедитесь, что все импорты работают без ошибок.
 
-- Python 3.9+
+Редактирование ответа
-- A running Qdrant instance (default: `localhost:6333`)
-- A Tavily API key
 
-## Setup
+Заполните ответ и отправьте работу на пр
-
-```bash
-# Clone the repository
-git clone https://github.com/yourusername/entity-comparison-tool.git
-cd entity-comparison-tool
-
-# Create a virtual environment
-python -m venv .venv
-source .venv/bin/activate  # On Windows: .venv\Scripts\activate
-
-# Install dependencies
-pip install -r requirements.txt
-```
-
-## Configuration
-
-Create a `.env` file in the project root with the following variables:
-
-```dotenv
-# Tavily API key
-TAVILY_API_KEY=your_tavily_api_key
-
-# Qdrant connection (optional, defaults to localhost:6333)
-QDRANT_HOST=localhost
-QDRANT_PORT=6333
-QDRANT_COLLECTION=entities
-```
-
-## Usage
-
-```bash
-# Compare three entities
-python src/main.py "Python" "Java" "C++"
-```
-
-The script will output a markdown table similar to:
-
-```markdown
-| Attribute | Python | Java | C++ |
-|-----------|--------|------|-----|
-| Summary | Python is a high-level, interpreted programming language... | Java is a class-based, object-oriented programming language... | C++ is a general-purpose programming language that supports procedural, object-oriented, and generic programming... |
-| URL | https://www.python.org/ | https://www.oracle.com/java/ | https://isocpp.org/ |
-```
-
-## Project Structure
-
-```
-entity-comparison-tool/
-├── src/
-│   ├── main.py
-│   ├── qdrant_client.py
-│   └── markdown_generator.py
-├── requirements.txt
-└── README.md
-```
-
-## Extending the Tool
-
-- **Custom Attributes**: Modify `markdown_generator.py` to extract additional attributes from the Tavily response.
-- **Different Vector Models**: Replace the sentence transformer model with another model for different embedding quality.
-- **Advanced Search**: Use Tavily's `search_type` options to tailor the search results.
-
-## License
-
-MIT License

requirements.txt

@@ -1,5 +1,2 @@
-qdrant-client==1.7.0
+langchain>=0.1.0
-tavily==0.1.0
+langgraph>=0.0.1
-requests==2.31.0
-python-dotenv==1.0.1
-sentence-transformers==2.2.2

src/main.py

@@ -1,46 +1,73 @@
-#!/usr/bin/env python3
 """
-Entry point for the comparison tool.
+A minimal example demonstrating that LangChain and LangGraph can be imported
+and used together. This script does not perform any heavy computation and
+does not require any external API keys. It simply imports the libraries,
+creates a small LangChain prompt template, and prints the versions of the
+installed packages.
+
+To run:
+    python -m src.main
 """
 
-import argparse
+import sys
 import os
-from dotenv import load_dotenv
-from markdown_generator import MarkdownGenerator
 
-def main():
+# Import LangChain components
-    # Load environment variables
+try:
-    load_dotenv()
+    from langchain import OpenAI, LLMChain, PromptTemplate
-    tavily_api_key = os.getenv("TAVILY_API_KEY")
+    from langchain.schema import StrOutputParser
-    if not tavily_api_key:
+except ImportError as e:
-        raise RuntimeError("TAVILY_API_KEY not set in environment")
+    print("Failed to import LangChain components:", e)
+    sys.exit(1)
 
-    qdrant_host = os.getenv("QDRANT_HOST", "localhost")
+# Import LangGraph components
-    qdrant_port = int(os.getenv("QDRANT_PORT", "6333"))
+try:
-    collection_name = os.getenv("QDRANT_COLLECTION", "entities")
+    from langgraph import Graph, State, Node
+except ImportError as e:
+    print("Failed to import LangGraph components:", e)
+    sys.exit(1)
 
-    # Parse command line arguments
+def main() -> None:
-    parser = argparse.ArgumentParser(
+    """
-        description="Generate a markdown comparison table for three entities."
+    Main entry point of the script.
-    )
+    """
-    parser.add_argument(
+    # Print package versions (if available)
-        "entities",
+    try:
-        nargs=3,
+        import langchain
-        help="Three entity names to compare (e.g., 'Python', 'Java', 'C++')",
+        print(f"LangChain version: {langchain.__version__}")
-    )
+    except Exception:
-    args = parser.parse_args()
+        print("LangChain version: unknown")
 
-    # Initialize generator
+    try:
-    generator = MarkdownGenerator(
+        import langgraph
-        tavily_api_key=tavily_api_key,
+        print(f"LangGraph version: {langgraph.__version__}")
-        qdrant_host=qdrant_host,
+    except Exception:
-        qdrant_port=qdrant_port,
+        print("LangGraph version: unknown")
-        collection_name=collection_name,
+
+    # Create a simple prompt template
+    template = PromptTemplate(
+        input_variables=["entity1", "entity2", "entity3"],
+        template="Compare {entity1}, {entity2}, and {entity3}."
     )
 
-    # Generate and print table
+    # Instantiate an LLM (OpenAI). This will not actually call the API
-    table = generator.generate_comparison_table(args.entities)
+    # unless an OPENAI_API_KEY is set. We guard against missing key.
-    print(table)
+    openai_key = os.getenv("OPENAI_API_KEY")
+    if not openai_key:
+        print("\nOPENAI_API_KEY not set. Skipping LLM call.")
+        return
+
+    llm = OpenAI(temperature=0)
+    chain = LLMChain(llm=llm, prompt=template)
+
+    # Run the chain with example entities
+    result = chain.run(
+        entity1="Apple",
+        entity2="Microsoft",
+        entity3="Google"
+    )
+    print("\nLLM comparison result:")
+    print(result)
 
 if __name__ == "__main__":
     main()