mirror of
https://github.com/getzep/graphiti.git
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enable use of Azure OpenAI with mcp server (#368)
* add azure open ai dependency * update readme * Create support for azure open ai endpoints * fix: formatting --------- Co-authored-by: paulpaliychuk <pavlo.paliychuk.ca@gmail.com>
This commit is contained in:
Diego Colombo
GitHub
parent
daaa0beab9
commit
a193476abe
@ -24,3 +24,12 @@ MODEL_NAME=gpt-4.1-mini
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# NEO4J_server_memory_heap_initial__size=512m
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# NEO4J_server_memory_heap_max__size=1G
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# NEO4J_server_memory_pagecache_size=512m
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# Azure OpenAI configuration
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# Optional: Only needed for Azure OpenAI endpoints
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# AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint_here
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# AZURE_OPENAI_API_VERSION=2025-01-01-preview
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# AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-gpt-4o-mini-deployment
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# AZURE_OPENAI_EMBEDDING_API_VERSION=2023-05-15
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# AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=text-embedding-3-large-deployment
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# AZURE_OPENAI_USE_MANAGED_IDENTITY=false
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@ -17,6 +17,8 @@ RUN uv sync
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# Copy application code
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COPY graphiti_mcp_server.py .
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EXPOSE 8000
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# Set environment variables
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ENV PYTHONUNBUFFERED=1
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@ -52,6 +52,12 @@ The server uses the following environment variables:
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- `OPENAI_API_KEY`: OpenAI API key (required for LLM operations)
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- `OPENAI_BASE_URL`: Optional base URL for OpenAI API
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- `MODEL_NAME`: Optional model name to use for LLM inference
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- `AZURE_OPENAI_ENDPOINT`: Optional Azure OpenAI endpoint URL
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- `AZURE_OPENAI_DEPLOYMENT_NAME`: Optional Azure OpenAI deployment name
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- `AZURE_OPENAI_API_VERSION`: Optional Azure OpenAI API version
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- `AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME`: Optional Azure OpenAI embedding deployment name
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- `AZURE_OPENAI_EMBEDDING_API_VERSION`: Optional Azure OpenAI API version
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- `AZURE_OPENAI_USE_MANAGED_IDENTITY`: Optional use Azure Managed Identities for authentication
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You can set these variables in a `.env` file in the project directory.
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@ -9,15 +9,22 @@ import logging
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import os
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import sys
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import uuid
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from collections.abc import Callable
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from datetime import datetime, timezone
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from typing import Any, TypedDict, cast
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from azure.identity import DefaultAzureCredential, get_bearer_token_provider
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from dotenv import load_dotenv
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from mcp.server.fastmcp import FastMCP
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from openai import AsyncAzureOpenAI
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from pydantic import BaseModel, Field
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from graphiti_core import Graphiti
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from graphiti_core.cross_encoder.client import CrossEncoderClient
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from graphiti_core.cross_encoder.openai_reranker_client import OpenAIRerankerClient
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from graphiti_core.edges import EntityEdge
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from graphiti_core.embedder.client import EmbedderClient
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from graphiti_core.embedder.openai import OpenAIEmbedder, OpenAIEmbedderConfig
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from graphiti_core.llm_client import LLMClient
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from graphiti_core.llm_client.config import LLMConfig
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from graphiti_core.llm_client.openai_client import OpenAIClient
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@ -32,6 +39,7 @@ from graphiti_core.utils.maintenance.graph_data_operations import clear_data
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load_dotenv()
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DEFAULT_LLM_MODEL = 'gpt-4.1-mini'
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DEFAULT_EMBEDDER_MODEL = 'text-embedding-3-small'
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class Requirement(BaseModel):
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@ -153,10 +161,19 @@ class StatusResponse(TypedDict):
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message: str
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def create_azure_credential_token_provider() -> Callable[[], str]:
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credential = DefaultAzureCredential()
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token_provider = get_bearer_token_provider(
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credential, 'https://cognitiveservices.azure.com/.default'
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)
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return token_provider
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# Server configuration classes
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# The configuration system has a hierarchy:
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# - GraphitiConfig is the top-level configuration
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# - LLMConfig handles all OpenAI/LLM related settings
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# - EmbedderConfig manages embedding settings
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# - Neo4jConfig manages database connection details
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# - Various other settings like group_id and feature flags
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# Configuration values are loaded from:
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@ -172,6 +189,10 @@ class GraphitiLLMConfig(BaseModel):
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api_key: str | None = None
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model: str = DEFAULT_LLM_MODEL
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temperature: float = 0.0
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azure_openai_endpoint: str | None = None
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azure_openai_deployment_name: str | None = None
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azure_openai_api_version: str | None = None
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azure_openai_use_managed_identity: bool = False
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@classmethod
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def from_env(cls) -> 'GraphitiLLMConfig':
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@ -180,22 +201,53 @@ class GraphitiLLMConfig(BaseModel):
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model_env = os.environ.get('MODEL_NAME', '')
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model = model_env if model_env.strip() else DEFAULT_LLM_MODEL
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# Log if empty model was provided
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if model_env == '':
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logger.debug(
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f'MODEL_NAME environment variable not set, using default: {DEFAULT_LLM_MODEL}'
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)
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elif not model_env.strip():
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logger.warning(
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f'Empty MODEL_NAME environment variable, using default: {DEFAULT_LLM_MODEL}'
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)
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return cls(
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api_key=os.environ.get('OPENAI_API_KEY'),
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model=model,
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temperature=float(os.environ.get('LLM_TEMPERATURE', '0.0')),
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azure_openai_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT', None)
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azure_openai_api_version = os.environ.get('AZURE_OPENAI_API_VERSION', None)
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azure_openai_deployment_name = os.environ.get('AZURE_OPENAI_DEPLOYMENT_NAME', None)
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azure_openai_use_managed_identity = (
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os.environ.get('AZURE_OPENAI_USE_MANAGED_IDENTITY', 'false').lower() == 'true'
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)
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if azure_openai_endpoint is None:
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# Setup for OpenAI API
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# Log if empty model was provided
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if model_env == '':
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logger.debug(
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f'MODEL_NAME environment variable not set, using default: {DEFAULT_LLM_MODEL}'
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)
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elif not model_env.strip():
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logger.warning(
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f'Empty MODEL_NAME environment variable, using default: {DEFAULT_LLM_MODEL}'
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)
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return cls(
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api_key=os.environ.get('OPENAI_API_KEY'),
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model=model,
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temperature=float(os.environ.get('LLM_TEMPERATURE', '0.0')),
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)
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else:
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# Setup for Azure OpenAI API
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# Log if empty deployment name was provided
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if azure_openai_deployment_name is None:
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logger.error('AZURE_OPENAI_DEPLOYMENT_NAME environment variable not set')
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raise ValueError('AZURE_OPENAI_DEPLOYMENT_NAME environment variable not set')
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if not azure_openai_use_managed_identity:
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# api key
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api_key = os.environ.get('OPENAI_API_KEY', None)
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else:
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# Managed identity
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api_key = None
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return cls(
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azure_openai_use_managed_identity=azure_openai_use_managed_identity,
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azure_openai_endpoint=azure_openai_endpoint,
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api_key=api_key,
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azure_openai_api_version=azure_openai_api_version,
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azure_openai_deployment_name=azure_openai_deployment_name,
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temperature=float(os.environ.get('LLM_TEMPERATURE', '0.0')),
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)
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@classmethod
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def from_cli_and_env(cls, args: argparse.Namespace) -> 'GraphitiLLMConfig':
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"""Create LLM configuration from CLI arguments, falling back to environment variables."""
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@ -220,8 +272,32 @@ class GraphitiLLMConfig(BaseModel):
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"""Create an LLM client based on this configuration.
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Returns:
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LLMClient instance if API key is available, None otherwise
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LLMClient instance if able, None otherwise
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"""
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if self.azure_openai_endpoint is not None:
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# Azure OpenAI API setup
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if self.azure_openai_use_managed_identity:
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# Use managed identity for authentication
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token_provider = create_azure_credential_token_provider()
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return AsyncAzureOpenAI(
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azure_endpoint=self.azure_openai_endpoint,
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azure_deployment=self.azure_openai_deployment_name,
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api_version=self.azure_openai_api_version,
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azure_ad_token_provider=token_provider,
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)
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elif self.api_key:
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# Use API key for authentication
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return AsyncAzureOpenAI(
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azure_endpoint=self.azure_openai_endpoint,
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azure_deployment=self.azure_openai_deployment_name,
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api_version=self.azure_openai_api_version,
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api_key=self.api_key,
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)
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else:
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logger.error('OPENAI_API_KEY must be set when using Azure OpenAI API')
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return None
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if not self.api_key:
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return None
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@ -232,6 +308,110 @@ class GraphitiLLMConfig(BaseModel):
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return OpenAIClient(config=llm_client_config)
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def create_cross_encoder_client(self) -> CrossEncoderClient | None:
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"""Create a cross-encoder client based on this configuration."""
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if self.azure_openai_endpoint is not None:
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client = self.create_client()
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return OpenAIRerankerClient(client=client)
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else:
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llm_client_config = LLMConfig(api_key=self.api_key, model=self.model)
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return OpenAIRerankerClient(config=llm_client_config)
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class GraphitiEmbedderConfig(BaseModel):
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"""Configuration for the embedder client.
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Centralizes all embedding-related configuration parameters.
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"""
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model: str = DEFAULT_EMBEDDER_MODEL
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api_key: str | None = None
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azure_openai_endpoint: str | None = None
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azure_openai_deployment_name: str | None = None
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azure_openai_api_version: str | None = None
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azure_openai_use_managed_identity: bool = False
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@classmethod
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def from_env(cls) -> 'GraphitiEmbedderConfig':
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"""Create embedder configuration from environment variables."""
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# Get model from environment, or use default if not set or empty
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model_env = os.environ.get('EMBEDDER_MODEL_NAME', '')
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model = model_env if model_env.strip() else DEFAULT_EMBEDDER_MODEL
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azure_openai_endpoint = os.environ.get('AZURE_OPENAI_ENDPOINT', None)
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azure_openai_api_version = os.environ.get('AZURE_OPENAI_EMBEDDING_API_VERSION', None)
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azure_openai_deployment_name = os.environ.get(
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'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME', None
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)
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azure_openai_use_managed_identity = (
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os.environ.get('AZURE_OPENAI_USE_MANAGED_IDENTITY', 'false').lower() == 'true'
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)
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if azure_openai_endpoint is not None:
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# Setup for Azure OpenAI API
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# Log if empty deployment name was provided
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azure_openai_deployment_name = os.environ.get(
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'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME', None
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)
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if azure_openai_deployment_name is None:
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logger.error('AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable not set')
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raise ValueError(
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'AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME environment variable not set'
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)
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if not azure_openai_use_managed_identity:
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# api key
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api_key = os.environ.get('OPENAI_API_KEY', None)
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else:
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# Managed identity
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api_key = None
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return cls(
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azure_openai_use_managed_identity=azure_openai_use_managed_identity,
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azure_openai_endpoint=azure_openai_endpoint,
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api_key=api_key,
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azure_openai_api_version=azure_openai_api_version,
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azure_openai_deployment_name=azure_openai_deployment_name,
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)
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else:
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return cls(
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model=model,
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api_key=os.environ.get('OPENAI_API_KEY'),
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)
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def create_client(self) -> EmbedderClient | None:
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if self.azure_openai_endpoint is not None:
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# Azure OpenAI API setup
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if self.azure_openai_use_managed_identity:
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# Use managed identity for authentication
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token_provider = create_azure_credential_token_provider()
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return AsyncAzureOpenAI(
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azure_endpoint=self.azure_openai_endpoint,
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azure_deployment=self.azure_openai_deployment_name,
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api_version=self.azure_openai_api_version,
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azure_ad_token_provider=token_provider,
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)
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elif self.api_key:
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# Use API key for authentication
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return AsyncAzureOpenAI(
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azure_endpoint=self.azure_openai_endpoint,
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azure_deployment=self.azure_openai_deployment_name,
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api_version=self.azure_openai_api_version,
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api_key=self.api_key,
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)
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else:
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logger.error('OPENAI_API_KEY must be set when using Azure OpenAI API')
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return None
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else:
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# OpenAI API setup
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if not self.api_key:
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return None
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embedder_config = OpenAIEmbedderConfig(api_key=self.api_key, model=self.model)
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return OpenAIEmbedder(config=embedder_config)
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class Neo4jConfig(BaseModel):
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"""Configuration for Neo4j database connection."""
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@ -257,6 +437,7 @@ class GraphitiConfig(BaseModel):
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"""
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llm: GraphitiLLMConfig = Field(default_factory=GraphitiLLMConfig)
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embedder: GraphitiEmbedderConfig = Field(default_factory=GraphitiEmbedderConfig)
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neo4j: Neo4jConfig = Field(default_factory=Neo4jConfig)
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group_id: str | None = None
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use_custom_entities: bool = False
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@ -267,6 +448,7 @@ class GraphitiConfig(BaseModel):
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"""Create a configuration instance from environment variables."""
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return cls(
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llm=GraphitiLLMConfig.from_env(),
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embedder=GraphitiEmbedderConfig.from_env(),
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neo4j=Neo4jConfig.from_env(),
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)
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@ -369,12 +551,17 @@ async def initialize_graphiti():
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if not config.neo4j.uri or not config.neo4j.user or not config.neo4j.password:
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raise ValueError('NEO4J_URI, NEO4J_USER, and NEO4J_PASSWORD must be set')
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embedder_client = config.embedder.create_client()
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cross_encoder_client = config.llm.create_cross_encoder_client()
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# Initialize Graphiti client
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graphiti_client = Graphiti(
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uri=config.neo4j.uri,
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user=config.neo4j.user,
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password=config.neo4j.password,
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llm_client=llm_client,
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embedder=embedder_client,
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cross_encoder=cross_encoder_client,
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)
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# Destroy graph if requested
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@ -8,4 +8,5 @@ dependencies = [
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"mcp>=1.5.0",
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"openai>=1.68.2",
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"graphiti-core>=0.8.2",
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"azure-identity>=1.21.0",
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]
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282
mcp_server/uv.lock
generated
282
mcp_server/uv.lock
generated
@ -1,4 +1,5 @@
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version = 1
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revision = 1
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requires-python = ">=3.10"
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[[package]]
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@ -25,6 +26,36 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
|
||||
]
|
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|
||||
[[package]]
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||||
name = "azure-core"
|
||||
version = "1.33.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "requests" },
|
||||
{ name = "six" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/75/aa/7c9db8edd626f1a7d99d09ef7926f6f4fb34d5f9fa00dc394afdfe8e2a80/azure_core-1.33.0.tar.gz", hash = "sha256:f367aa07b5e3005fec2c1e184b882b0b039910733907d001c20fb08ebb8c0eb9", size = 295633 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/07/b7/76b7e144aa53bd206bf1ce34fa75350472c3f69bf30e5c8c18bc9881035d/azure_core-1.33.0-py3-none-any.whl", hash = "sha256:9b5b6d0223a1d38c37500e6971118c1e0f13f54951e6893968b38910bc9cda8f", size = 207071 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "azure-identity"
|
||||
version = "1.21.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "azure-core" },
|
||||
{ name = "cryptography" },
|
||||
{ name = "msal" },
|
||||
{ name = "msal-extensions" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b5/a1/f1a683672e7a88ea0e3119f57b6c7843ed52650fdcac8bfa66ed84e86e40/azure_identity-1.21.0.tar.gz", hash = "sha256:ea22ce6e6b0f429bc1b8d9212d5b9f9877bd4c82f1724bfa910760612c07a9a6", size = 266445 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/3d/9f/1f9f3ef4f49729ee207a712a5971a9ca747f2ca47d9cbf13cf6953e3478a/azure_identity-1.21.0-py3-none-any.whl", hash = "sha256:258ea6325537352440f71b35c3dffe9d240eae4a5126c1b7ce5efd5766bd9fd9", size = 189190 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
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[[package]]
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name = "requests"
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version = "2.32.3"
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source = { registry = "https://pypi.org/simple" }
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||||
dependencies = [
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{ name = "certifi" },
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{ name = "charset-normalizer" },
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{ name = "idna" },
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{ name = "urllib3" },
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]
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wheels = [
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[[package]]
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name = "six"
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version = "1.17.0"
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source = { registry = "https://pypi.org/simple" }
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wheels = [
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[[package]]
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version = "1.3.1"
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{ name = "colorama", marker = "sys_platform == 'win32'" },
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wheels = [
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[[package]]
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wheels = [
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|
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[[package]]
|
||||
name = "uvicorn"
|
||||
version = "0.34.0"
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user