conductor-checkpoint-msg_01WRZxPMQYjNEjcFNTMzWYeL
This commit is contained in:
Daniel Chalef
parent
ca0092d510
commit
58bad9b542
10 changed files with 335 additions and 87 deletions
|
|
@ -23,7 +23,7 @@ The Graphiti MCP server provides comprehensive knowledge graph capabilities:
|
|||
- **Graph Database Support**: Multiple backend options including Kuzu (default), Neo4j, and FalkorDB
|
||||
- **Multiple LLM Providers**: Support for OpenAI, Anthropic, Gemini, Groq, and Azure OpenAI
|
||||
- **Multiple Embedding Providers**: Support for OpenAI, Voyage, Sentence Transformers, and Gemini embeddings
|
||||
- **Custom Entity Types**: Define and use custom entity types for domain-specific knowledge extraction
|
||||
- **Rich Entity Types**: Built-in entity types including Preferences, Requirements, Procedures, Locations, Events, Organizations, Documents, and more for structured knowledge extraction
|
||||
- **HTTP Transport**: Default HTTP transport with MCP endpoint at `/mcp/` for broad client compatibility
|
||||
- **Queue-based Processing**: Asynchronous episode processing with configurable concurrency limits
|
||||
|
||||
|
|
@ -187,6 +187,36 @@ embedder:
|
|||
|
||||
Make sure Ollama is running locally with: `ollama serve`
|
||||
|
||||
### Entity Types
|
||||
|
||||
Graphiti MCP Server includes built-in entity types for structured knowledge extraction. These entity types are always enabled and configured via the `entity_types` section in your `config.yaml`:
|
||||
|
||||
**Available Entity Types:**
|
||||
|
||||
- **Preference**: User preferences, choices, opinions, or selections (prioritized for user-specific information)
|
||||
- **Requirement**: Specific needs, features, or functionality that must be fulfilled
|
||||
- **Procedure**: Standard operating procedures and sequential instructions
|
||||
- **Location**: Physical or virtual places where activities occur
|
||||
- **Event**: Time-bound activities, occurrences, or experiences
|
||||
- **Organization**: Companies, institutions, groups, or formal entities
|
||||
- **Document**: Information content in various forms (books, articles, reports, videos, etc.)
|
||||
- **Topic**: Subject of conversation, interest, or knowledge domain (used as a fallback)
|
||||
- **Object**: Physical items, tools, devices, or possessions (used as a fallback)
|
||||
|
||||
These entity types are defined in `config.yaml` and can be customized by modifying the descriptions:
|
||||
|
||||
```yaml
|
||||
graphiti:
|
||||
entity_types:
|
||||
- name: "Preference"
|
||||
description: "User preferences, choices, opinions, or selections"
|
||||
- name: "Requirement"
|
||||
description: "Specific needs, features, or functionality"
|
||||
# ... additional entity types
|
||||
```
|
||||
|
||||
The MCP server automatically uses these entity types during episode ingestion to extract and structure information from conversations and documents.
|
||||
|
||||
### Environment Variables
|
||||
|
||||
The `config.yaml` file supports environment variable expansion using `${VAR_NAME}` or `${VAR_NAME:default}` syntax. Key variables:
|
||||
|
|
@ -292,7 +322,6 @@ uv run graphiti_mcp_server.py --config config/config-docker-falkordb.yaml
|
|||
- `--transport`: Choose the transport method (http or stdio, default: http)
|
||||
- `--group-id`: Set a namespace for the graph (optional). If not provided, defaults to "main"
|
||||
- `--destroy-graph`: If set, destroys all Graphiti graphs on startup
|
||||
- `--use-custom-entities`: Enable entity extraction using the predefined ENTITY_TYPES
|
||||
|
||||
### Concurrency and LLM Provider 429 Rate Limit Errors
|
||||
|
||||
|
|
@ -505,7 +534,7 @@ To integrate the Graphiti MCP Server with the Cursor IDE, follow these steps:
|
|||
1. Run the Graphiti MCP server using the default HTTP transport:
|
||||
|
||||
```bash
|
||||
uv run graphiti_mcp_server.py --use-custom-entities --group-id <your_group_id>
|
||||
uv run graphiti_mcp_server.py --group-id <your_group_id>
|
||||
```
|
||||
|
||||
Hint: specify a `group_id` to namespace graph data. If you do not specify a `group_id`, the server will use "main" as the group_id.
|
||||
|
|
|
|||
|
|
@ -81,9 +81,21 @@ graphiti:
|
|||
episode_id_prefix: ${EPISODE_ID_PREFIX:}
|
||||
user_id: ${USER_ID:mcp_user}
|
||||
entity_types:
|
||||
- name: "Requirement"
|
||||
description: "Represents a requirement"
|
||||
- name: "Preference"
|
||||
description: "User preferences and settings"
|
||||
description: "User preferences, choices, opinions, or selections (PRIORITIZE over most other types except User/Assistant)"
|
||||
- name: "Requirement"
|
||||
description: "Specific needs, features, or functionality that must be fulfilled"
|
||||
- name: "Procedure"
|
||||
description: "Standard operating procedures"
|
||||
description: "Standard operating procedures and sequential instructions"
|
||||
- name: "Location"
|
||||
description: "Physical or virtual places where activities occur"
|
||||
- name: "Event"
|
||||
description: "Time-bound activities, occurrences, or experiences"
|
||||
- name: "Organization"
|
||||
description: "Companies, institutions, groups, or formal entities"
|
||||
- name: "Document"
|
||||
description: "Information content in various forms (books, articles, reports, etc.)"
|
||||
- name: "Topic"
|
||||
description: "Subject of conversation, interest, or knowledge domain (use as last resort)"
|
||||
- name: "Object"
|
||||
description: "Physical items, tools, devices, or possessions (use as last resort)"
|
||||
|
|
@ -81,9 +81,21 @@ graphiti:
|
|||
episode_id_prefix: ${EPISODE_ID_PREFIX:}
|
||||
user_id: ${USER_ID:mcp_user}
|
||||
entity_types:
|
||||
- name: "Requirement"
|
||||
description: "Represents a requirement"
|
||||
- name: "Preference"
|
||||
description: "User preferences and settings"
|
||||
description: "User preferences, choices, opinions, or selections (PRIORITIZE over most other types except User/Assistant)"
|
||||
- name: "Requirement"
|
||||
description: "Specific needs, features, or functionality that must be fulfilled"
|
||||
- name: "Procedure"
|
||||
description: "Standard operating procedures"
|
||||
description: "Standard operating procedures and sequential instructions"
|
||||
- name: "Location"
|
||||
description: "Physical or virtual places where activities occur"
|
||||
- name: "Event"
|
||||
description: "Time-bound activities, occurrences, or experiences"
|
||||
- name: "Organization"
|
||||
description: "Companies, institutions, groups, or formal entities"
|
||||
- name: "Document"
|
||||
description: "Information content in various forms (books, articles, reports, etc.)"
|
||||
- name: "Topic"
|
||||
description: "Subject of conversation, interest, or knowledge domain (use as last resort)"
|
||||
- name: "Object"
|
||||
description: "Physical items, tools, devices, or possessions (use as last resort)"
|
||||
|
|
@ -83,9 +83,21 @@ graphiti:
|
|||
episode_id_prefix: ${EPISODE_ID_PREFIX:}
|
||||
user_id: ${USER_ID:mcp_user}
|
||||
entity_types:
|
||||
- name: "Requirement"
|
||||
description: "Represents a requirement"
|
||||
- name: "Preference"
|
||||
description: "User preferences and settings"
|
||||
description: "User preferences, choices, opinions, or selections (PRIORITIZE over most other types except User/Assistant)"
|
||||
- name: "Requirement"
|
||||
description: "Specific needs, features, or functionality that must be fulfilled"
|
||||
- name: "Procedure"
|
||||
description: "Standard operating procedures"
|
||||
description: "Standard operating procedures and sequential instructions"
|
||||
- name: "Location"
|
||||
description: "Physical or virtual places where activities occur"
|
||||
- name: "Event"
|
||||
description: "Time-bound activities, occurrences, or experiences"
|
||||
- name: "Organization"
|
||||
description: "Companies, institutions, groups, or formal entities"
|
||||
- name: "Document"
|
||||
description: "Information content in various forms (books, articles, reports, etc.)"
|
||||
- name: "Topic"
|
||||
description: "Subject of conversation, interest, or knowledge domain (use as last resort)"
|
||||
- name: "Object"
|
||||
description: "Physical items, tools, devices, or possessions (use as last resort)"
|
||||
|
|
@ -91,9 +91,21 @@ graphiti:
|
|||
episode_id_prefix: ${EPISODE_ID_PREFIX:}
|
||||
user_id: ${USER_ID:mcp_user}
|
||||
entity_types:
|
||||
- name: "Requirement"
|
||||
description: "Represents a requirement"
|
||||
- name: "Preference"
|
||||
description: "User preferences and settings"
|
||||
description: "User preferences, choices, opinions, or selections (PRIORITIZE over most other types except User/Assistant)"
|
||||
- name: "Requirement"
|
||||
description: "Specific needs, features, or functionality that must be fulfilled"
|
||||
- name: "Procedure"
|
||||
description: "Standard operating procedures"
|
||||
description: "Standard operating procedures and sequential instructions"
|
||||
- name: "Location"
|
||||
description: "Physical or virtual places where activities occur"
|
||||
- name: "Event"
|
||||
description: "Time-bound activities, occurrences, or experiences"
|
||||
- name: "Organization"
|
||||
description: "Companies, institutions, groups, or formal entities"
|
||||
- name: "Document"
|
||||
description: "Information content in various forms (books, articles, reports, etc.)"
|
||||
- name: "Topic"
|
||||
description: "Subject of conversation, interest, or knowledge domain (use as last resort)"
|
||||
- name: "Object"
|
||||
description: "Physical items, tools, devices, or possessions (use as last resort)"
|
||||
|
|
@ -151,7 +151,9 @@ class LLMConfig(BaseModel):
|
|||
|
||||
provider: str = Field(default='openai', description='LLM provider')
|
||||
model: str = Field(default='gpt-4.1', description='Model name')
|
||||
temperature: float | None = Field(default=None, description='Temperature (optional, defaults to None for reasoning models)')
|
||||
temperature: float | None = Field(
|
||||
default=None, description='Temperature (optional, defaults to None for reasoning models)'
|
||||
)
|
||||
max_tokens: int = Field(default=4096, description='Max tokens')
|
||||
providers: LLMProvidersConfig = Field(default_factory=LLMProvidersConfig)
|
||||
|
||||
|
|
@ -240,7 +242,6 @@ class GraphitiConfig(BaseSettings):
|
|||
graphiti: GraphitiAppConfig = Field(default_factory=GraphitiAppConfig)
|
||||
|
||||
# Additional server options
|
||||
use_custom_entities: bool = Field(default=False, description='Enable custom entity types')
|
||||
destroy_graph: bool = Field(default=False, description='Clear graph on startup')
|
||||
|
||||
model_config = SettingsConfigDict(
|
||||
|
|
|
|||
|
|
@ -21,7 +21,6 @@ from mcp.server.fastmcp import FastMCP
|
|||
from pydantic import BaseModel
|
||||
|
||||
from config.schema import GraphitiConfig, ServerConfig
|
||||
from models.entity_types import ENTITY_TYPES
|
||||
from models.response_types import (
|
||||
EpisodeSearchResponse,
|
||||
ErrorResponse,
|
||||
|
|
@ -82,6 +81,7 @@ def configure_uvicorn_logging():
|
|||
uvicorn_logger.addHandler(handler)
|
||||
uvicorn_logger.propagate = False
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Create global config instance - will be properly initialized later
|
||||
|
|
@ -165,7 +165,7 @@ class GraphitiService:
|
|||
# Get database configuration
|
||||
db_config = DatabaseDriverFactory.create_config(self.config.database)
|
||||
|
||||
# Build custom entity types if configured
|
||||
# Build entity types from configuration
|
||||
custom_types = None
|
||||
if self.config.graphiti.entity_types:
|
||||
custom_types = {}
|
||||
|
|
@ -180,13 +180,6 @@ class GraphitiService:
|
|||
},
|
||||
)
|
||||
custom_types[entity_type.name] = entity_model
|
||||
# Also support the existing ENTITY_TYPES if use_custom_entities is set
|
||||
elif hasattr(self.config, 'use_custom_entities') and self.config.use_custom_entities:
|
||||
# Convert ENTITY_TYPES list to dict if needed
|
||||
if isinstance(ENTITY_TYPES, list):
|
||||
custom_types = {et.__name__: et for et in ENTITY_TYPES}
|
||||
else:
|
||||
custom_types = ENTITY_TYPES
|
||||
|
||||
# Store entity types for later use
|
||||
self.entity_types = custom_types
|
||||
|
|
@ -678,7 +671,8 @@ async def get_status() -> StatusResponse:
|
|||
if config.database.provider.lower() == 'kuzu':
|
||||
provider_info = f'{config.database.provider} database (in-memory)'
|
||||
return StatusResponse(
|
||||
status='ok', message=f'Graphiti MCP server is running and connected to {provider_info}'
|
||||
status='ok',
|
||||
message=f'Graphiti MCP server is running and connected to {provider_info}',
|
||||
)
|
||||
|
||||
# For Neo4j and FalkorDB, test connection with a simple query
|
||||
|
|
@ -776,11 +770,6 @@ async def initialize_server() -> ServerConfig:
|
|||
action='store_true',
|
||||
help='Destroy all Graphiti graphs on startup',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--use-custom-entities',
|
||||
action='store_true',
|
||||
help='Enable entity extraction using the predefined ENTITY_TYPES',
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
|
|
@ -795,8 +784,6 @@ async def initialize_server() -> ServerConfig:
|
|||
config.apply_cli_overrides(args)
|
||||
|
||||
# Also apply legacy CLI args for backward compatibility
|
||||
if hasattr(args, 'use_custom_entities'):
|
||||
config.use_custom_entities = args.use_custom_entities
|
||||
if hasattr(args, 'destroy_graph'):
|
||||
config.destroy_graph = args.destroy_graph
|
||||
|
||||
|
|
@ -811,6 +798,7 @@ async def initialize_server() -> ServerConfig:
|
|||
# Log graphiti-core version
|
||||
try:
|
||||
import graphiti_core
|
||||
|
||||
graphiti_version = getattr(graphiti_core, '__version__', 'unknown')
|
||||
logger.info(f' - Graphiti Core: {graphiti_version}')
|
||||
except Exception:
|
||||
|
|
|
|||
|
|
@ -76,8 +76,162 @@ class Procedure(BaseModel):
|
|||
)
|
||||
|
||||
|
||||
class Location(BaseModel):
|
||||
"""A Location represents a physical or virtual place where activities occur or entities exist.
|
||||
|
||||
IMPORTANT: Before using this classification, first check if the entity is a:
|
||||
User, Assistant, Preference, Organization, Document, Event - if so, use those instead.
|
||||
|
||||
Instructions for identifying and extracting locations:
|
||||
1. Look for mentions of physical places (cities, buildings, rooms, addresses)
|
||||
2. Identify virtual locations (websites, online platforms, virtual meeting rooms)
|
||||
3. Extract specific location names rather than generic references
|
||||
4. Include relevant context about the location's purpose or significance
|
||||
5. Pay attention to location hierarchies (e.g., "conference room in Building A")
|
||||
6. Capture both permanent locations and temporary venues
|
||||
7. Note any significant activities or events associated with the location
|
||||
"""
|
||||
|
||||
name: str = Field(
|
||||
...,
|
||||
description='The name or identifier of the location',
|
||||
)
|
||||
description: str = Field(
|
||||
...,
|
||||
description='Brief description of the location and its significance. Only use information mentioned in the context.',
|
||||
)
|
||||
|
||||
|
||||
class Event(BaseModel):
|
||||
"""An Event represents a time-bound activity, occurrence, or experience.
|
||||
|
||||
Instructions for identifying and extracting events:
|
||||
1. Look for activities with specific time frames (meetings, appointments, deadlines)
|
||||
2. Identify planned or scheduled occurrences (vacations, projects, celebrations)
|
||||
3. Extract unplanned occurrences (accidents, interruptions, discoveries)
|
||||
4. Capture the purpose or nature of the event
|
||||
5. Include temporal information when available (past, present, future, duration)
|
||||
6. Note participants or stakeholders involved in the event
|
||||
7. Identify outcomes or consequences of the event when mentioned
|
||||
8. Extract both recurring events and one-time occurrences
|
||||
"""
|
||||
|
||||
name: str = Field(
|
||||
...,
|
||||
description='The name or title of the event',
|
||||
)
|
||||
description: str = Field(
|
||||
...,
|
||||
description='Brief description of the event. Only use information mentioned in the context.',
|
||||
)
|
||||
|
||||
|
||||
class Object(BaseModel):
|
||||
"""An Object represents a physical item, tool, device, or possession.
|
||||
|
||||
IMPORTANT: Use this classification ONLY as a last resort. First check if entity fits into:
|
||||
User, Assistant, Preference, Organization, Document, Event, Location, Topic - if so, use those instead.
|
||||
|
||||
Instructions for identifying and extracting objects:
|
||||
1. Look for mentions of physical items or possessions (car, phone, equipment)
|
||||
2. Identify tools or devices used for specific purposes
|
||||
3. Extract items that are owned, used, or maintained by entities
|
||||
4. Include relevant attributes (brand, model, condition) when mentioned
|
||||
5. Note the object's purpose or function when specified
|
||||
6. Capture relationships between objects and their owners or users
|
||||
7. Avoid extracting objects that are better classified as Documents or other types
|
||||
"""
|
||||
|
||||
name: str = Field(
|
||||
...,
|
||||
description='The name or identifier of the object',
|
||||
)
|
||||
description: str = Field(
|
||||
...,
|
||||
description='Brief description of the object. Only use information mentioned in the context.',
|
||||
)
|
||||
|
||||
|
||||
class Topic(BaseModel):
|
||||
"""A Topic represents a subject of conversation, interest, or knowledge domain.
|
||||
|
||||
IMPORTANT: Use this classification ONLY as a last resort. First check if entity fits into:
|
||||
User, Assistant, Preference, Organization, Document, Event, Location - if so, use those instead.
|
||||
|
||||
Instructions for identifying and extracting topics:
|
||||
1. Look for subjects being discussed or areas of interest (health, technology, sports)
|
||||
2. Identify knowledge domains or fields of study
|
||||
3. Extract themes that span multiple conversations or contexts
|
||||
4. Include specific subtopics when mentioned (e.g., "machine learning" rather than just "AI")
|
||||
5. Capture topics associated with projects, work, or hobbies
|
||||
6. Note the context in which the topic appears
|
||||
7. Avoid extracting topics that are better classified as Events, Documents, or Organizations
|
||||
"""
|
||||
|
||||
name: str = Field(
|
||||
...,
|
||||
description='The name or identifier of the topic',
|
||||
)
|
||||
description: str = Field(
|
||||
...,
|
||||
description='Brief description of the topic and its context. Only use information mentioned in the context.',
|
||||
)
|
||||
|
||||
|
||||
class Organization(BaseModel):
|
||||
"""An Organization represents a company, institution, group, or formal entity.
|
||||
|
||||
Instructions for identifying and extracting organizations:
|
||||
1. Look for company names, employers, and business entities
|
||||
2. Identify institutions (schools, hospitals, government agencies)
|
||||
3. Extract formal groups (clubs, teams, associations)
|
||||
4. Include organizational type when mentioned (company, nonprofit, agency)
|
||||
5. Capture relationships between people and organizations (employer, member)
|
||||
6. Note the organization's industry or domain when specified
|
||||
7. Extract both large entities and small groups if formally organized
|
||||
"""
|
||||
|
||||
name: str = Field(
|
||||
...,
|
||||
description='The name of the organization',
|
||||
)
|
||||
description: str = Field(
|
||||
...,
|
||||
description='Brief description of the organization. Only use information mentioned in the context.',
|
||||
)
|
||||
|
||||
|
||||
class Document(BaseModel):
|
||||
"""A Document represents information content in various forms.
|
||||
|
||||
Instructions for identifying and extracting documents:
|
||||
1. Look for references to written or recorded content (books, articles, reports)
|
||||
2. Identify digital content (emails, videos, podcasts, presentations)
|
||||
3. Extract specific document titles or identifiers when available
|
||||
4. Include document type (report, article, video) when mentioned
|
||||
5. Capture the document's purpose or subject matter
|
||||
6. Note relationships to authors, creators, or sources
|
||||
7. Include document status (draft, published, archived) when mentioned
|
||||
"""
|
||||
|
||||
title: str = Field(
|
||||
...,
|
||||
description='The title or identifier of the document',
|
||||
)
|
||||
description: str = Field(
|
||||
...,
|
||||
description='Brief description of the document and its content. Only use information mentioned in the context.',
|
||||
)
|
||||
|
||||
|
||||
ENTITY_TYPES: dict[str, BaseModel] = {
|
||||
'Requirement': Requirement, # type: ignore
|
||||
'Preference': Preference, # type: ignore
|
||||
'Procedure': Procedure, # type: ignore
|
||||
'Location': Location, # type: ignore
|
||||
'Event': Event, # type: ignore
|
||||
'Object': Object, # type: ignore
|
||||
'Topic': Topic, # type: ignore
|
||||
'Organization': Organization, # type: ignore
|
||||
'Document': Document, # type: ignore
|
||||
}
|
||||
|
|
|
|||
|
|
@ -94,7 +94,9 @@ def _validate_and_mask_api_key(provider_name: str, api_key: str | None, logger)
|
|||
ValueError: If API key is None or empty
|
||||
"""
|
||||
if not api_key:
|
||||
raise ValueError(f'{provider_name} API key is not configured. Please set the appropriate environment variable.')
|
||||
raise ValueError(
|
||||
f'{provider_name} API key is not configured. Please set the appropriate environment variable.'
|
||||
)
|
||||
|
||||
# Log masked API key for debugging
|
||||
masked_key = f'{api_key[:7]}...{api_key[-4:]}' if len(api_key) > 11 else '***'
|
||||
|
|
@ -110,6 +112,7 @@ class LLMClientFactory:
|
|||
def create(config: LLMConfig) -> LLMClient:
|
||||
"""Create an LLM client based on the configured provider."""
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
provider = config.provider.lower()
|
||||
|
|
@ -125,16 +128,14 @@ class LLMClientFactory:
|
|||
from graphiti_core.llm_client.config import LLMConfig as CoreLLMConfig
|
||||
|
||||
# Determine appropriate small model based on main model type
|
||||
small_model = None
|
||||
is_reasoning_model = (
|
||||
config.model.startswith('gpt-5')
|
||||
or config.model.startswith('o1')
|
||||
or config.model.startswith('o3')
|
||||
)
|
||||
if is_reasoning_model:
|
||||
small_model = 'gpt-5-nano' # Use reasoning model for small tasks too
|
||||
else:
|
||||
small_model = 'gpt-4.1-mini' # Use non-reasoning model for small tasks
|
||||
small_model = (
|
||||
'gpt-5-nano' if is_reasoning_model else 'gpt-4.1-mini'
|
||||
) # Use reasoning model for small tasks if main model is reasoning
|
||||
|
||||
llm_config = CoreLLMConfig(
|
||||
api_key=api_key,
|
||||
|
|
@ -152,18 +153,10 @@ class LLMClientFactory:
|
|||
)
|
||||
|
||||
if is_reasoning_model:
|
||||
return OpenAIClient(
|
||||
config=llm_config,
|
||||
reasoning='minimal',
|
||||
verbosity='low'
|
||||
)
|
||||
return OpenAIClient(config=llm_config, reasoning='minimal', verbosity='low')
|
||||
else:
|
||||
# For non-reasoning models, explicitly pass None to disable these parameters
|
||||
return OpenAIClient(
|
||||
config=llm_config,
|
||||
reasoning=None,
|
||||
verbosity=None
|
||||
)
|
||||
return OpenAIClient(config=llm_config, reasoning=None, verbosity=None)
|
||||
|
||||
case 'azure_openai':
|
||||
if not HAS_AZURE_LLM:
|
||||
|
|
@ -278,6 +271,7 @@ class EmbedderFactory:
|
|||
def create(config: EmbedderConfig) -> EmbedderClient:
|
||||
"""Create an Embedder client based on the configured provider."""
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
provider = config.provider.lower()
|
||||
|
|
@ -314,7 +308,9 @@ class EmbedderFactory:
|
|||
api_key: str | None = None
|
||||
azure_ad_token_provider = None
|
||||
if azure_config.use_azure_ad:
|
||||
logger.info('Creating Azure OpenAI Embedder client with Azure AD authentication')
|
||||
logger.info(
|
||||
'Creating Azure OpenAI Embedder client with Azure AD authentication'
|
||||
)
|
||||
azure_ad_token_provider = create_azure_credential_token_provider()
|
||||
else:
|
||||
api_key = azure_config.api_key
|
||||
|
|
|
|||
96
mcp_server/uv.lock
generated
96
mcp_server/uv.lock
generated
|
|
@ -488,6 +488,19 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/8e/98/2c050dec90e295a524c9b65c4cb9e7c302386a296b2938710448cbd267d5/faker-37.12.0-py3-none-any.whl", hash = "sha256:afe7ccc038da92f2fbae30d8e16d19d91e92e242f8401ce9caf44de892bab4c4", size = 1975461, upload-time = "2025-10-24T15:19:55.739Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "falkordb"
|
||||
version = "1.2.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "python-dateutil" },
|
||||
{ name = "redis" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/9e/3b/2d846e877aec104841c523e99e1c584d4aa2423a0e31afee5b8369e3c8c5/falkordb-1.2.0.tar.gz", hash = "sha256:ce57365b86722d538e75aa5d438de67ecd8eb9478da612506d9812cd7f182d0b", size = 28976, upload-time = "2025-06-30T18:03:37.829Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/39/81/1265e623c989bb8c12fe566645bad390d04e66fa62b84ca656e5bafce1f0/falkordb-1.2.0-py3-none-any.whl", hash = "sha256:7572d9cc377735d22efc52fe6fe73c7a435422c827b6ea3ca223a850a77be12e", size = 34737, upload-time = "2025-06-30T18:03:36.582Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "filelock"
|
||||
version = "3.19.1"
|
||||
|
|
@ -648,6 +661,14 @@ dependencies = [
|
|||
{ name = "tenacity" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
falkordb = [
|
||||
{ name = "falkordb" },
|
||||
]
|
||||
kuzu = [
|
||||
{ name = "kuzu" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.49.0" },
|
||||
|
|
@ -930,36 +951,36 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "kuzu"
|
||||
version = "0.11.2"
|
||||
version = "0.11.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/66/fd/adbd05ccf81e6ad2674fcd3849d5d6ffeaf2141a9b8d1c1c4e282e923e1f/kuzu-0.11.2.tar.gz", hash = "sha256:9f224ec218ab165a18acaea903695779780d70335baf402d9b7f59ba389db0bd", size = 4902887, upload-time = "2025-08-21T05:17:00.152Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/96/0c/f141a81485729a072dc527b474e7580d5632309c68ad1a5aa6ed9ac45387/kuzu-0.11.3.tar.gz", hash = "sha256:e7bea3ca30c4bb462792eedcaa7f2125c800b243bb4a872e1eedc16917c1967a", size = 19430620, upload-time = "2025-10-10T13:36:54.984Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0e/91/bed837f5f49220a9f869da8a078b34a3484f210f7b57b267177821545c03/kuzu-0.11.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cf4b25174cdb721aae47896ed62842d3859679607b493a9a6bbbcd9fb7fb3707", size = 3702618, upload-time = "2025-08-21T05:15:53.726Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/72/8a/fd5e053b0055718afe00b6a99393a835c6254354128fbb7f66a35fd76089/kuzu-0.11.2-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:9a8567c53bfe282f4727782471ff718842ffead8c48c1762c1df9197408fc986", size = 4101371, upload-time = "2025-08-21T05:15:55.889Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ad/4b/e45cadc85bdc5079f432675bbe8d557600f0d4ab46fe24ef218374419902/kuzu-0.11.2-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d793bb5a0a14ada730a697eccac2a4c68b434b82692d985942900ef2003e099e", size = 6211974, upload-time = "2025-08-21T05:15:57.505Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/10/ca/92d6a1e6452fcf06bfc423ce2cde819ace6b6e47921921cc8fae87c27780/kuzu-0.11.2-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c1be4e9b6c93ca8591b1fb165f9b9a27d70a56af061831afcdfe7aebb89ee6ff", size = 6992196, upload-time = "2025-08-21T05:15:59.006Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/49/6c/983fc6265dfc1169c87c4a0722f36ee665c5688e1166faeb4cd85e6af078/kuzu-0.11.2-cp310-cp310-win_amd64.whl", hash = "sha256:e0ec7a304c746a2a98ecfd7e7c3f6fe92c4dfee2e2565c0b7cb4cffd0c2e374a", size = 4303517, upload-time = "2025-08-21T05:16:00.814Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b5/14/8ae2c52657b93715052ecf47d70232f2c8d9ffe2d1ec3527c8e9c3cb2df5/kuzu-0.11.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:bf53b4f321a4c05882b14cef96d39a1e90fa993bab88a1554fb1565367553b8c", size = 3704177, upload-time = "2025-08-21T05:16:02.354Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/2d/7a/bce7bb755e16f9ca855f76a3acc6cfa9fae88c4d6af9df3784c50b2120a5/kuzu-0.11.2-cp311-cp311-macosx_11_0_x86_64.whl", hash = "sha256:2d749883b74f5da5ff4a4b0635a98f6cc5165743995828924321d2ca797317cb", size = 4102372, upload-time = "2025-08-21T05:16:04.249Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c8/12/f5b1d51fcb78a86c078fb85cc53184ce962a3e86852d47d30e287a932e3f/kuzu-0.11.2-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:632507e5982928ed24fbb5e70ad143d7970bc4059046e77e0522707efbad303b", size = 6212492, upload-time = "2025-08-21T05:16:05.99Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/81/96/d6e57af6ccf9e0697812ad3039c80b87b768cf2674833b0b23d317ea3427/kuzu-0.11.2-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5211884601f8f08ae97ba25006d0facde24077c5333411d944282b8a2068ab4", size = 6992888, upload-time = "2025-08-21T05:16:07.896Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/40/ee/1f275ac5679a3f615ce0d9cf8c79001fdb535ccc8bc344e49b14484c7cd7/kuzu-0.11.2-cp311-cp311-win_amd64.whl", hash = "sha256:82a6c8bfe1278dc1010790e398bf772683797ef5c16052fa0f6f78bacbc59aa3", size = 4304064, upload-time = "2025-08-21T05:16:10.163Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/73/ba/9f20d9e83681a0ddae8ec13046b116c34745fa0e66862d4e2d8414734ce0/kuzu-0.11.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:aed88ffa695d07289a3d8557bd8f9e743298a4f4349208a60bbb06f4ebf15c26", size = 3703781, upload-time = "2025-08-21T05:16:12.232Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/53/a0/bb815c0490f3d4d30389156369b9fe641e154f0d4b1e8340f09a76021922/kuzu-0.11.2-cp312-cp312-macosx_11_0_x86_64.whl", hash = "sha256:595824b03248af928e3faee57f6825d3a46920f2d3b9bd0c0bb7fc3fa097fce9", size = 4103990, upload-time = "2025-08-21T05:16:14.139Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a5/6f/97b647c0547a634a669055ff4cfd21a92ea3999aedc6a7fe9004f03f25e3/kuzu-0.11.2-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5674c6d9d26f5caa0c7ce6f34c02e4411894879aa5b2ce174fad576fa898523", size = 6211947, upload-time = "2025-08-21T05:16:16.48Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/42/74/c7f1a1cfb08c05c91c5a94483be387e80fafab8923c4243a22e9cced5c1b/kuzu-0.11.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c61daf02da35b671f4c6f3c17105725c399a5e14b7349b00eafbcd24ac90034a", size = 6991879, upload-time = "2025-08-21T05:16:18.402Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/54/9e/50d67d7bc08faed95ede6de1a6aa0d81079c98028ca99e32d09c2ab1aead/kuzu-0.11.2-cp312-cp312-win_amd64.whl", hash = "sha256:682096cd87dcbb8257f933ea4172d9dc5617a8d0a5bdd19cd66cf05b68881afd", size = 4305706, upload-time = "2025-08-21T05:16:20.244Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/65/f0/5649a01af37def50293cd7c194afc19f09b343fd2b7f2b28e021a207f8ce/kuzu-0.11.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:17a11b67652e8b331c85cd1a1a30b32ee6783750084473abbab2aa1963ee2a3b", size = 3703740, upload-time = "2025-08-21T05:16:21.896Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/24/e2/e0beb9080911fc1689899a42da0f83534949f43169fb80197def3ec1223f/kuzu-0.11.2-cp313-cp313-macosx_11_0_x86_64.whl", hash = "sha256:bdded35426210faeca8da11e8b4a54e60ccc0c1a832660d76587b5be133b0f55", size = 4104073, upload-time = "2025-08-21T05:16:23.819Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f2/4c/7a831c9c6e609692953db677f54788bd1dde4c9d34e6ba91f1e153d2e7fe/kuzu-0.11.2-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6116b609aac153f3523130b31295643d34a6c9509914c0fa9d804b26b23eee73", size = 6212263, upload-time = "2025-08-21T05:16:25.351Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/47/95/615ef10b46b22ec1d33fdbba795e6e79733d9a244aabdeeb910f267ab36c/kuzu-0.11.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:09da5b8cb24dc6b281a6e4ac0f7f24226eb9909803b187e02d014da13ba57bcf", size = 6992492, upload-time = "2025-08-21T05:16:27.518Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a7/dd/2c905575913c743e6c67a5ca89a6b4ea9d9737238966d85d7e710f0d3e60/kuzu-0.11.2-cp313-cp313-win_amd64.whl", hash = "sha256:c663fb84682f8ebffbe7447a4e552a0e03bd29097d319084a2c53c2e032a780e", size = 4305267, upload-time = "2025-08-21T05:16:29.307Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/89/05/44fbfc9055dba3f472ea4aaa8110635864d3441eede987526ef401680765/kuzu-0.11.2-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5c03fb95ffb9185c1519333f8ee92b7a9695aa7aa9a179e868a7d7bd13d10a16", size = 6216795, upload-time = "2025-08-21T05:16:30.944Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/4f/ca/16c81dc68cc1e8918f8481e7ee89c28aa665c5cb36be7ad0fc1d0d295760/kuzu-0.11.2-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d857f0efddf26d5e2dc189facb84bf04a997e395972486669b418a470cc76034", size = 6996333, upload-time = "2025-08-21T05:16:32.568Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/48/d8/9275c7e6312bd76dc670e8e2da68639757c22cf2c366e96527595a1d881c/kuzu-0.11.2-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fb9e4641867c35b98ceaa604aa79832c0eeed41f5fd1b6da22b1c217b2f1b8ea", size = 6212202, upload-time = "2025-08-21T05:16:34.571Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/88/89/67a977493c60bca3610845df13020711f357a5d80bf91549e4b48d877c2f/kuzu-0.11.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:553408d76a0b4fdecc1338b69b71d7bde42f6936d3b99d9852b30d33bda15978", size = 6992264, upload-time = "2025-08-21T05:16:36.316Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b6/49/869ceceb1d8a5ea032a35c734e55cfee919340889973623096da7eb94f6b/kuzu-0.11.2-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:989a87fa13ffa39ab7773d968fe739ac4f8faf9ddb5dad72ced2eeef12180293", size = 6216814, upload-time = "2025-08-21T05:16:38.348Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bc/cd/933b34a246edb882a042eb402747167719222c05149b73b48ba7d310d554/kuzu-0.11.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e67420d04a9643fd6376a23b17b398a3e32bb0c2bd8abbf8d1e4697056596c7e", size = 6996343, upload-time = "2025-08-21T05:16:39.973Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f5/3d/830489670618ceb6094b6ddc322e3e6457f5cf4fd6916526b7b8cd6e2c1f/kuzu-0.11.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3d93131f3dc7b647da7a5124c975dc2cc207afc38a9fbb83badc23d2e25dbfec", size = 4093610, upload-time = "2025-10-10T13:35:47.691Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/82/1c/7cf246a66a287d466c6cc2f01d5f4e12bd6d23350217c592dc6b51988ed5/kuzu-0.11.3-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:4f021a6b4e31867a0b49e369431c8f05ef4244cea1cb337c76114649975f56e3", size = 4517385, upload-time = "2025-10-10T13:35:49.471Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d6/35/ed37f146225167ed7a8573a09e2ee4ebafd0111b815cb0532dc823069729/kuzu-0.11.3-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4ac7d2a81d7370dc12400431a3331c45954340b96439fcf2b730794cf670684a", size = 6795559, upload-time = "2025-10-10T13:35:50.977Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/03/9f/2c8e3cf777aba73d515f73c9491ba65fbebb7852f41abedfc70a26bec229/kuzu-0.11.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8621bb470390f95810c7e79e36a670e9a2f27e189ef429f3df892626de63a652", size = 7616362, upload-time = "2025-10-10T13:35:52.717Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/fb/02/22789dc00abb34206e6d96b0ae0eb1884b524fb7fdffd5121fc3178a9ed6/kuzu-0.11.3-cp310-cp310-win_amd64.whl", hash = "sha256:126efba7f8a504614f5b307bcb94ac0e6d9f87f7e34b4334ca769ad6cfe2215e", size = 4712323, upload-time = "2025-10-10T13:35:54.44Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/46/1b/65d3974551f10d100ca4682b1e4beff23a9c5b7555c6ea552a3855555cc0/kuzu-0.11.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:836739dced9f61912a80bb7ad1df2159cef456c5b5cfe92f15394b9c51a785cb", size = 4094223, upload-time = "2025-10-10T13:35:56.023Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a9/e8/0efbc4812796468ca47273fc53c21c63706bc5f7bc4fa3459918d323ced8/kuzu-0.11.3-cp311-cp311-macosx_11_0_x86_64.whl", hash = "sha256:8d4f0e4085d3a85b0e7a8e337082bec6a3cf8c92c9a35209ffe53b2ed212ab08", size = 4519024, upload-time = "2025-10-10T13:35:57.665Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/dd/b2/07e81d9f1858a592d1ddc1f02a483718cdfac3315bbca019b13b2ddd8c3e/kuzu-0.11.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1419ada227d15107c2b2536c66ae715c59876585d434b1918c17598956dcd5f7", size = 6796096, upload-time = "2025-10-10T13:35:59.174Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b1/e0/8ea0d289ef6840fbd00e642657ed07d03690a97a01676e2b79d5c3e9ddf8/kuzu-0.11.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ef094001d319804fcc8eb72775a184f1119d84af1bace29581a003bd806c36cd", size = 7616892, upload-time = "2025-10-10T13:36:00.866Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a3/d6/9ea65a74c9140e13d7f68dd9d8f95f42b55b9d7750e7a20df3d9b2f09734/kuzu-0.11.3-cp311-cp311-win_amd64.whl", hash = "sha256:eb0858ec8084b10badeae37e730fbe0c3b2846dfe3508001d123087de262efbb", size = 4712696, upload-time = "2025-10-10T13:36:02.607Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/64/88/ed193fd0ddfdbdde6c79e96b96df3b760fe48b2626e7151d81a1ed90fd9f/kuzu-0.11.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d865ca31506867cf1ccf50c094c44de96de94bc77ffb350bfcaca0e4c5e469da", size = 4093637, upload-time = "2025-10-10T13:36:04.206Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d6/6d/06e02828b78297d6d99ff3dfb0ab7b5ec5d075053aae33b53189437bbb66/kuzu-0.11.3-cp312-cp312-macosx_11_0_x86_64.whl", hash = "sha256:109372bc16ce6724f88e0312bc686e34145e330d69b163b22ba92f4d3d96b48f", size = 4520482, upload-time = "2025-10-10T13:36:06.302Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/72/d5/0939a953860a8b373bef7b8a66a4571b27ff9faeb22672d2cd2cf3b6ba15/kuzu-0.11.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d6274da6c470c001b7d332ec78a076395b009c2f267914640884fd6fa78bf47d", size = 6795398, upload-time = "2025-10-10T13:36:08Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7d/5d/8e3dfb89aa3f70f63aa283c523f2dd2ac90a1b3ed990643e3a89909236f9/kuzu-0.11.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1bb3b833ca2d1d919423cb3e0150592c2587562ab85259277f622e6f06e0b487", size = 7615389, upload-time = "2025-10-10T13:36:09.809Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6c/19/c8e93185d6142f01b2e6daec4ad537dfd32afd1f69894889769b725b08c1/kuzu-0.11.3-cp312-cp312-win_amd64.whl", hash = "sha256:605909f744763775b8647014a03526d7f928a7b5a62a8b8c1d1e7bbdaf9dbb6c", size = 4714355, upload-time = "2025-10-10T13:36:11.527Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c9/e4/2c0e222a9b0605745234fec2774a25dd2e472699931f683f15d28ab8c076/kuzu-0.11.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e20ab3e3b20ccf75219872feb86582f959e313eeb59f51131adf4c91ebfabe30", size = 4093664, upload-time = "2025-10-10T13:36:13.116Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/88/05/3020ed9a0a7b492597f211f805233b77ef37266a23c27efc40bb7cb37402/kuzu-0.11.3-cp313-cp313-macosx_11_0_x86_64.whl", hash = "sha256:054479d3ce71410b8af2f5fa6aa37883db7fea5b25606af8d3bd7cf717aa5395", size = 4520498, upload-time = "2025-10-10T13:36:14.933Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/0f/66/1a502700a7f2863f8f60621a412a7074d7eda9e92f18fd1d8d86905aa4d3/kuzu-0.11.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:143a37f1ae38b6b4337ccfcf42fa4f779a897223fff9c6c29f1a5a5a86911300", size = 6795804, upload-time = "2025-10-10T13:36:16.55Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/79/c2/1ea8cdc05946cb5906a7ebb451d7268e501ebb51ebecc0437969f8c07450/kuzu-0.11.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:548bfb3045d89bce1fbe89f4a890d636789671abfa80cbde2054c671e6069133", size = 7615668, upload-time = "2025-10-10T13:36:18.882Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d1/c3/336d6181f8f50126cf3d7186b3c5479f9f49d973145f79bed45cf87a9bb7/kuzu-0.11.3-cp313-cp313-win_amd64.whl", hash = "sha256:87bf6c369f182a59e5b8a38b3ca288b90fab2827577d9b0d2170a202c42bc8f5", size = 4714372, upload-time = "2025-10-10T13:36:20.877Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/94/db/e7e6cada6dc924eb8939bd35c5f724f5de4fc430a64d6d9e71b75cd0c271/kuzu-0.11.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eb5c165bc5838059e498e8325939fc6bac075e1941157e8df6ebdd710135d43b", size = 6798556, upload-time = "2025-10-10T13:36:22.472Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/cf/68/a0fa02134cb255c80b5ed5bb5f6130fbbc75a8ae8be4fd6ea6eb6bc8014b/kuzu-0.11.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:88c73dfd3d6a1fb374031050b725236fa9dd9a95424b09b20086a3d274bed51f", size = 7620378, upload-time = "2025-10-10T13:36:24.453Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d6/b7/2a4569984995f09476dbf1ef2e0a7298aa9fdb8896f2e8195d80e11786f4/kuzu-0.11.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9d2752a9e37adda6aef3bf041932ae3a1cf74ca7e893bbbacdd5e62b3ac6f8c2", size = 6795649, upload-time = "2025-10-10T13:36:26.15Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/77/13/df6e06a7d7506743c3a6cfbe50ee3f9d3fc58228e2a2fcbe7e74e7c17b00/kuzu-0.11.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:86be7d113e4e2c6761b1701079af8aeffc04c6981517f2d6aa393e883cc46036", size = 7615882, upload-time = "2025-10-10T13:36:28.215Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/32/85/c52c3b167edcc67da3b8788a20a2fb5b4f045060cbe1aed6121ce3ce83d3/kuzu-0.11.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d72ebd88e231b562e7a60ff88d200825d53e78a681bddd7f8d77b78126a5060c", size = 6798657, upload-time = "2025-10-10T13:36:29.935Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/06/be/5b4ff168718165c2ff5848ab79e22ecce72ad00522afee6820d390cb0753/kuzu-0.11.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:64c7ec822906bdee154eb38d93e64f184d8f94b30bbeaceaa252725f2b9efab3", size = 7620394, upload-time = "2025-10-10T13:36:31.69Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -1094,8 +1115,7 @@ version = "1.0.0rc0"
|
|||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "azure-identity" },
|
||||
{ name = "graphiti-core" },
|
||||
{ name = "kuzu" },
|
||||
{ name = "graphiti-core", extra = ["falkordb", "kuzu"] },
|
||||
{ name = "mcp" },
|
||||
{ name = "openai" },
|
||||
{ name = "pydantic-settings" },
|
||||
|
|
@ -1134,11 +1154,10 @@ requires-dist = [
|
|||
{ name = "anthropic", marker = "extra == 'providers'", specifier = ">=0.49.0" },
|
||||
{ name = "azure-identity", specifier = ">=1.21.0" },
|
||||
{ name = "google-genai", marker = "extra == 'providers'", specifier = ">=1.8.0" },
|
||||
{ name = "graphiti-core", editable = "../" },
|
||||
{ name = "graphiti-core", marker = "extra == 'dev'", editable = "../" },
|
||||
{ name = "graphiti-core", extras = ["kuzu", "falkordb"], editable = "../" },
|
||||
{ name = "groq", marker = "extra == 'providers'", specifier = ">=0.2.0" },
|
||||
{ name = "httpx", marker = "extra == 'dev'", specifier = ">=0.28.1" },
|
||||
{ name = "kuzu", specifier = ">=0.11.2" },
|
||||
{ name = "mcp", specifier = ">=1.9.4" },
|
||||
{ name = "mcp", marker = "extra == 'dev'", specifier = ">=1.9.4" },
|
||||
{ name = "openai", specifier = ">=1.91.0" },
|
||||
|
|
@ -2186,6 +2205,19 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/fa/de/02b54f42487e3d3c6efb3f89428677074ca7bf43aae402517bc7cca949f3/PyYAML-6.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563", size = 156446, upload-time = "2024-08-06T20:33:04.33Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "redis"
|
||||
version = "5.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "async-timeout", marker = "python_full_version < '3.11.3'" },
|
||||
{ name = "pyjwt" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6a/cf/128b1b6d7086200c9f387bd4be9b2572a30b90745ef078bd8b235042dc9f/redis-5.3.1.tar.gz", hash = "sha256:ca49577a531ea64039b5a36db3d6cd1a0c7a60c34124d46924a45b956e8cf14c", size = 4626200, upload-time = "2025-07-25T08:06:27.778Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/26/5c5fa0e83c3621db835cfc1f1d789b37e7fa99ed54423b5f519beb931aa7/redis-5.3.1-py3-none-any.whl", hash = "sha256:dc1909bd24669cc31b5f67a039700b16ec30571096c5f1f0d9d2324bff31af97", size = 272833, upload-time = "2025-07-25T08:06:26.317Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "regex"
|
||||
version = "2025.7.34"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue