ab56691385
- Add VSCodeClient with native VS Code LLM integration - Add VSCodeEmbedder with 1024-dim embeddings and fallbacks - Create graphiti-core[vscodemodels] optional dependency - Add comprehensive documentation and examples - Update README with VS Code models section - Add MCP server VS Code configuration - Include validation tests and troubleshooting guides - Zero external dependencies - works entirely within VS Code Package ready for: pip install 'graphiti-core[vscodemodels]'
88 lines
No EOL
2.8 KiB
Python
88 lines
No EOL
2.8 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
Basic usage example for Graphiti with VS Code Models integration.
|
|
|
|
This example demonstrates how to use Graphiti with VS Code's built-in AI models
|
|
without requiring external API keys.
|
|
|
|
Prerequisites:
|
|
- VS Code with language model extensions (GitHub Copilot, Azure OpenAI, etc.)
|
|
- graphiti-core[vscodemodels] installed
|
|
- Running Neo4j instance
|
|
|
|
Usage:
|
|
python basic_usage.py
|
|
"""
|
|
|
|
import asyncio
|
|
import os
|
|
from datetime import datetime
|
|
from graphiti_core import Graphiti
|
|
from graphiti_core.llm_client.vscode_client import VSCodeClient
|
|
from graphiti_core.embedder.vscode_embedder import VSCodeEmbedder, VSCodeEmbedderConfig
|
|
from graphiti_core.llm_client.config import LLMConfig
|
|
|
|
async def main():
|
|
"""Basic example of using Graphiti with VS Code models."""
|
|
|
|
# Configure VS Code clients
|
|
llm_client = VSCodeClient(
|
|
config=LLMConfig(
|
|
model="gpt-4o-mini", # VS Code model name
|
|
small_model="gpt-4o-mini"
|
|
)
|
|
)
|
|
|
|
embedder = VSCodeEmbedder(
|
|
config=VSCodeEmbedderConfig(
|
|
embedding_model="embedding-001", # VS Code embedding model
|
|
embedding_dim=1024, # 1024-dimensional vectors
|
|
use_fallback=True
|
|
)
|
|
)
|
|
|
|
# Initialize Graphiti
|
|
graphiti = Graphiti(
|
|
uri=os.getenv("NEO4J_URI", "bolt://localhost:7687"),
|
|
user=os.getenv("NEO4J_USER", "neo4j"),
|
|
password=os.getenv("NEO4J_PASSWORD", "password"),
|
|
llm_client=llm_client,
|
|
embedder=embedder
|
|
)
|
|
|
|
# Add some example episodes
|
|
episodes = [
|
|
"John is a software engineer who works at TechCorp. He specializes in Python development.",
|
|
"Sarah is the CTO at TechCorp. She has been leading the engineering team for 5 years.",
|
|
"TechCorp is developing a new AI-powered application using machine learning.",
|
|
"John and Sarah are collaborating on the AI project, with John handling the backend implementation."
|
|
]
|
|
|
|
print("Adding episodes to the knowledge graph...")
|
|
current_time = datetime.now()
|
|
for i, episode in enumerate(episodes):
|
|
await graphiti.add_episode(
|
|
name=f"Episode {i+1}",
|
|
episode_body=episode,
|
|
source_description="Example data",
|
|
reference_time=current_time
|
|
)
|
|
print(f"✓ Added episode {i+1}")
|
|
|
|
# Search for information
|
|
print("\nSearching for information about TechCorp...")
|
|
search_results = await graphiti.search(
|
|
query="Tell me about TechCorp and its employees",
|
|
center_node_uuid=None,
|
|
num_results=5
|
|
)
|
|
|
|
print("Search Results:")
|
|
for i, result in enumerate(search_results):
|
|
print(f"{i+1}. {result.fact[:100]}...")
|
|
|
|
print("\nExample completed successfully!")
|
|
print("VS Code models integration is working properly.")
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |