- Introduced CHUTES_API_TOKEN in .env.example files for configuration. - Updated README.md in quickstart example to include Chutes API setup. - Created quickstart_chutes.py example for demonstrating Chutes integration. - Added ChutesEmbedder and ChutesClient classes for embedding and LLM functionalities. - Updated LLM and embedder provider configurations to support Chutes. - Enhanced factories to include Chutes client and embedder. - Modified schema.py to define Chutes provider configuration. - Updated mcp_server configuration files to integrate Chutes API. - Added necessary dependencies in pyproject.toml for Chutes support.
157 lines
5.6 KiB
Python
157 lines
5.6 KiB
Python
"""
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Copyright 2025, Zep Software, Inc.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import asyncio
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import json
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import logging
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import os
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from datetime import datetime, timezone
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from logging import INFO
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from dotenv import load_dotenv
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from graphiti_core import Graphiti
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from graphiti_core.embedder.chutes import ChutesEmbedder
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from graphiti_core.llm_client.chutes_client import ChutesClient
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from graphiti_core.nodes import EpisodeType
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#################################################
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# CONFIGURATION
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#################################################
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# Set up logging and environment variables for
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# connecting to Neo4j database
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#################################################
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# Configure logging
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logging.basicConfig(
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level=INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S',
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)
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logger = logging.getLogger(__name__)
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load_dotenv()
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# Neo4j connection parameters
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# Make sure Neo4j Desktop is running with a local DBMS started
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neo4j_uri = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
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neo4j_user = os.environ.get('NEO4J_USER', 'neo4j')
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neo4j_password = os.environ.get('NEO4J_PASSWORD', 'password')
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chutes_api_key = os.environ.get('CHUTES_API_TOKEN')
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if not neo4j_uri or not neo4j_user or not neo4j_password:
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raise ValueError('NEO4J_URI, NEO4J_USER, and NEO4J_PASSWORD must be set')
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if not chutes_api_key:
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raise ValueError('CHUTES_API_TOKEN must be set')
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async def main():
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#################################################
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# INITIALIZATION
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#################################################
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# Connect to Neo4j and set up Graphiti indices
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# This is required before using other Graphiti
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# functionality
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#################################################
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# Initialize Graphiti with Neo4j connection and Chutes provider
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graphiti = Graphiti(
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neo4j_uri,
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neo4j_user,
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neo4j_password,
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llm_client=ChutesClient(),
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embedder=ChutesEmbedder(),
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)
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try:
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#################################################
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# ADDING EPISODES
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#################################################
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# Episodes are the primary units of information
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# in Graphiti. They can be text or structured JSON
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# and are automatically processed to extract entities
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# and relationships.
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#################################################
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# Example: Add Episodes
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# Episodes list containing both text and JSON episodes
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episodes = [
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{
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'content': 'Kamala Harris is the Attorney General of California. She was previously '
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'the district attorney for San Francisco.',
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'type': EpisodeType.text,
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'description': 'podcast transcript',
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},
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{
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'content': 'As AG, Harris was in office from January 3, 2011 – January 3, 2017',
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'type': EpisodeType.text,
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'description': 'podcast transcript',
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},
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]
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# Add episodes to the graph
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for i, episode in enumerate(episodes):
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await graphiti.add_episode(
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name=f'Freakonomics Radio {i}',
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episode_body=episode['content']
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if isinstance(episode['content'], str)
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else json.dumps(episode['content']),
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source=episode['type'],
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source_description=episode['description'],
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reference_time=datetime.now(timezone.utc),
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)
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print(f'Added episode: Freakonomics Radio {i} ({episode["type"].value})')
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#################################################
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# BASIC SEARCH
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#################################################
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# The simplest way to retrieve relationships (edges)
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# from Graphiti is using the search method, which
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# performs a hybrid search combining semantic
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# similarity and BM25 text retrieval.
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#################################################
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# Perform a hybrid search combining semantic similarity and BM25 retrieval
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print("\nSearching for: 'Who was the California Attorney General?'")
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results = await graphiti.search('Who was the California Attorney General?')
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# Print search results
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print('\nSearch Results:')
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for result in results:
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print(f'UUID: {result.uuid}')
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print(f'Fact: {result.fact}')
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if hasattr(result, 'valid_at') and result.valid_at:
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print(f'Valid from: {result.valid_at}')
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if hasattr(result, 'invalid_at') and result.invalid_at:
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print(f'Valid until: {result.invalid_at}')
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print('---')
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finally:
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#################################################
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# CLEANUP
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#################################################
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# Always close the connection to Neo4j when
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# finished to properly release resources
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#################################################
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# Close the connection
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await graphiti.close()
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print('\nConnection closed')
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if __name__ == '__main__':
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asyncio.run(main())
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