mcp server
This commit is contained in:
Nicolò Boschi
parent
fb45440d99
commit
e0a33f5fa1
12 changed files with 929 additions and 26 deletions
29
.github/workflows/release.yml
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29
.github/workflows/release.yml
vendored
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@ -195,10 +195,35 @@ jobs:
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id: get_version
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run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT
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- name: Download all artifacts
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- name: Download Python package
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uses: actions/download-artifact@v4
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with:
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path: ./artifacts
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name: python-memora-dist
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path: ./artifacts/python-memora-dist
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- name: Download Rust CLI (Linux)
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uses: actions/download-artifact@v4
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with:
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name: rust-cli-memora-linux-amd64
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path: ./artifacts/rust-cli-memora-linux-amd64
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- name: Download Rust CLI (macOS Intel)
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uses: actions/download-artifact@v4
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with:
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name: rust-cli-memora-darwin-amd64
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path: ./artifacts/rust-cli-memora-darwin-amd64
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- name: Download Rust CLI (macOS ARM)
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uses: actions/download-artifact@v4
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with:
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name: rust-cli-memora-darwin-arm64
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path: ./artifacts/rust-cli-memora-darwin-arm64
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- name: Download Helm chart
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uses: actions/download-artifact@v4
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with:
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name: helm-chart
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path: ./artifacts/helm-chart
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- name: Prepare release assets
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run: |
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2
.github/workflows/test.yml
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2
.github/workflows/test.yml
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@ -45,7 +45,7 @@ jobs:
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python-version-file: ".python-version"
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- name: Install dependencies
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run: uv sync --all-extras --dev
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run: uv sync --extra test
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- name: Run migrations
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working-directory: ./memora
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@ -1,20 +1,41 @@
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FROM python:3.11-slim
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FROM python:3.11-slim AS builder
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# Install system dependencies
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# Install build dependencies
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RUN apt-get update && apt-get install -y \
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curl \
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build-essential \
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&& rm -rf /var/lib/apt/lists/*
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# Set working directory
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WORKDIR /app
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# Copy project files
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COPY memora /app/memora
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# Copy only dependency files first for better caching
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COPY memora/pyproject.toml memora/README.md /app/memora/
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COPY memora/memora /app/memora/memora
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# Install Python dependencies
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# Install uv for faster dependency installation
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RUN pip install --no-cache-dir uv
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# Install Python dependencies to a virtual environment
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WORKDIR /app/memora
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RUN uv venv /opt/venv && \
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. /opt/venv/bin/activate && \
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uv pip install --no-cache -e .
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# Production stage
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FROM python:3.11-slim
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# Install only runtime dependencies
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RUN apt-get update && apt-get install -y \
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curl \
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libgomp1 \
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&& rm -rf /var/lib/apt/lists/*
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# Copy virtual environment from builder
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COPY --from=builder /opt/venv /opt/venv
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COPY --from=builder /app/memora /app/memora
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# Set working directory
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WORKDIR /app/memora
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RUN pip install --no-cache-dir -e .
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# Expose API port
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EXPOSE 8080
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@ -22,6 +43,8 @@ EXPOSE 8080
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# Set environment variables
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ENV PYTHONUNBUFFERED=1
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ENV DATABASE_URL=postgresql://memora:memora_dev@postgres:5432/memora
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ENV PATH="/opt/venv/bin:$PATH"
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ENV PYTHONPATH=/app/memora
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# Run the API server
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CMD ["python", "-m", "memora.web.server", "--host", "0.0.0.0", "--port", "8080"]
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44
memora-mcp-server/README.md
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44
memora-mcp-server/README.md
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@ -0,0 +1,44 @@
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# Memora MCP Server
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Remote MCP server for integrating Memora memory capabilities with Claude Desktop and other MCP clients.
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## Configuration
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Required environment variables:
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- `MEMORA_AGENT_ID`: The agent ID to use for all operations
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- `MEMORA_API_URL`: Memora API endpoint (default: http://localhost:8080)
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- `MEMORA_API_KEY`: API key for authentication (optional)
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## Usage
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### Start the HTTP/SSE Server
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```bash
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export MEMORA_AGENT_ID=your-agent-id
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export MEMORA_API_URL=http://localhost:8080
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export PORT=8765 # optional, default is 8765
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export HOST=127.0.0.1 # optional, default is 127.0.0.1
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uv run memora-mcp-server
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```
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The server will start on `http://127.0.0.1:8765`
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### Claude Desktop Integration
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Add to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):
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```json
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{
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"mcpServers": {
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"memora": {
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"url": "http://127.0.0.1:8765/sse"
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}
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}
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}
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```
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Make sure the Memora MCP server is running before starting Claude Desktop.
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## Available Tools
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- `memora_put`: Store facts/memories with required context
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- `memora_search`: Search through memories using semantic search
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3
memora-mcp-server/__init__.py
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3
memora-mcp-server/__init__.py
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@ -0,0 +1,3 @@
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"""Memora MCP Server - Remote MCP server for Memora memory system."""
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__version__ = "0.0.1"
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62
memora-mcp-server/client.py
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62
memora-mcp-server/client.py
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@ -0,0 +1,62 @@
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"""Memora API client wrapper."""
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import httpx
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from typing import Any
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class MemoraClient:
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"""Client for interacting with Memora API."""
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def __init__(self, api_url: str, agent_id: str, api_key: str | None = None):
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self.api_url = api_url.rstrip("/")
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self.agent_id = agent_id
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self.headers = {}
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if api_key:
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self.headers["Authorization"] = f"Bearer {api_key}"
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async def remember(
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self, content: str, context: str
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) -> dict[str, Any]:
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"""Store a memory using batch endpoint."""
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async with httpx.AsyncClient() as client:
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payload = {
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"agent_id": self.agent_id,
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"items": [
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{
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"content": content,
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"context": context,
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}
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]
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}
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response = await client.post(
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f"{self.api_url}/api/memories/batch",
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json=payload,
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headers=self.headers,
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timeout=30.0,
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)
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response.raise_for_status()
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return response.json()
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async def search(
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self, query: str, max_tokens: int = 4096
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) -> dict[str, Any]:
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"""Search memories using search endpoint."""
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async with httpx.AsyncClient() as client:
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payload = {
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"agent_id": self.agent_id,
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"query": query,
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"thinking_budget": 100,
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"max_tokens": max_tokens,
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"reranker": "heuristic",
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"trace": False,
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}
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response = await client.post(
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f"{self.api_url}/api/search",
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json=payload,
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headers=self.headers,
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timeout=30.0,
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)
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response.raise_for_status()
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return response.json()
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26
memora-mcp-server/config.py
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26
memora-mcp-server/config.py
Normal file
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"""Configuration management for Memora MCP Server."""
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import os
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from dataclasses import dataclass
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@dataclass
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class Config:
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"""MCP Server configuration."""
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agent_id: str
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api_url: str = "http://localhost:8080"
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api_key: str | None = None
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@classmethod
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def from_env(cls) -> "Config":
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"""Load configuration from environment variables."""
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agent_id = os.getenv("MEMORA_AGENT_ID")
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if not agent_id:
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raise ValueError("MEMORA_AGENT_ID environment variable is required")
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return cls(
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agent_id=agent_id,
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api_url=os.getenv("MEMORA_API_URL", "http://localhost:8080"),
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api_key=os.getenv("MEMORA_API_KEY"),
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)
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20
memora-mcp-server/pyproject.toml
Normal file
20
memora-mcp-server/pyproject.toml
Normal file
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@ -0,0 +1,20 @@
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[project]
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name = "memora-mcp-server"
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version = "0.0.1"
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description = "Remote MCP server for Memora memory system"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"fastmcp>=0.7.0",
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"httpx>=0.28.1",
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]
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[project.scripts]
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memora-mcp-server = "server:main"
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[tool.hatch.build.targets.wheel]
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packages = ["."]
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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105
memora-mcp-server/server.py
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105
memora-mcp-server/server.py
Normal file
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@ -0,0 +1,105 @@
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"""Memora MCP Server implementation using FastMCP."""
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import json
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import logging
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import os
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from fastmcp import FastMCP
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from config import Config
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from client import MemoraClient
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Load config
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config = Config.from_env()
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client = MemoraClient(
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api_url=config.api_url,
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agent_id=config.agent_id,
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api_key=config.api_key,
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)
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# Create FastMCP server
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mcp = FastMCP("memora-mcp-server")
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@mcp.tool()
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async def memora_put(content: str, context: str) -> str:
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"""
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**CRITICAL: Store important user information to long-term memory.**
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Use this tool PROACTIVELY whenever the user shares:
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- Personal facts, preferences, or interests (e.g., "I love hiking", "I'm a vegetarian")
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- Important events or milestones (e.g., "I got promoted", "My birthday is June 15")
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- User history, experiences, or background (e.g., "I used to work at Google", "I studied CS at MIT")
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- Decisions, opinions, or stated preferences (e.g., "I prefer Python over JavaScript")
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- Goals, plans, or future intentions (e.g., "I'm planning to visit Japan next year")
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- Relationships or people mentioned (e.g., "My manager Sarah", "My wife Alice")
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- Work context, projects, or responsibilities
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- Any other information the user would want remembered for future conversations
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**When to use**: Immediately after user shares personal information. Don't ask permission - just store it naturally.
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**Context guidelines**: Use descriptive contexts like "personal_preferences", "work_history", "family", "hobbies",
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"career_goals", "project_details", etc. This helps organize and retrieve related memories later.
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Args:
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content: The fact/memory to store (be specific and include relevant details)
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context: Categorize the memory (e.g., 'personal_preferences', 'work_history', 'hobbies', 'family')
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"""
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try:
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result = await client.remember(content=content, context=context)
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return f"Fact stored successfully: {result.get('message', 'Success')}"
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except Exception as e:
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logger.error(f"Error storing fact: {e}", exc_info=True)
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return f"Error: {str(e)}"
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@mcp.tool()
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async def memora_search(query: str, max_tokens: int = 4096) -> str:
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"""
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**CRITICAL: Search user's memory to provide personalized, context-aware responses.**
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Use this tool PROACTIVELY at the start of conversations or when making recommendations to:
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- Check user's preferences before making suggestions (e.g., "what foods does the user like?")
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- Recall user's history to provide continuity (e.g., "what projects has the user worked on?")
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- Remember user's goals and context (e.g., "what is the user trying to accomplish?")
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- Avoid repeating information or asking questions you should already know
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- Personalize responses based on user's background, interests, and past interactions
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- Reference past conversations or events the user mentioned
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**When to use**:
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- Start of conversation: Search for relevant context about the user
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- Before recommendations: Check user preferences and past experiences
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- When user asks about something they may have mentioned before
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- To provide continuity across conversations
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**Search tips**: Use natural language queries like "user's programming language preferences",
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"user's work experience", "user's dietary restrictions", "what does the user know about X?"
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Args:
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query: Natural language search query to find relevant memories
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max_tokens: Maximum tokens for search context (default: 4096)
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"""
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try:
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result = await client.search(query=query, max_tokens=max_tokens)
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return json.dumps(result, indent=2)
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except Exception as e:
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logger.error(f"Error searching: {e}", exc_info=True)
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return f"Error: {str(e)}"
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def main():
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"""Main entry point."""
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port = int(os.getenv("PORT", "8765"))
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host = os.getenv("HOST", "127.0.0.1")
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logger.info(f"Starting Memora MCP Server for agent: {config.agent_id}")
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logger.info(f"MCP server starting on http://{host}:{port}")
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mcp.run(transport="sse", host=host, port=port)
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if __name__ == "__main__":
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main()
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@ -15,8 +15,6 @@ dependencies = [
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"pydantic>=2.0.0",
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"rich>=13.0.0",
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"sentence-transformers>=2.2.0",
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"pytest>=7.0.0",
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"pytest-asyncio>=0.21.0",
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"langchain-text-splitters>=0.3.0",
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"fastapi[standard]>=0.120.3",
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"uvicorn>=0.38.0",
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@ -25,12 +23,18 @@ dependencies = [
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"pgvector>=0.4.1",
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"greenlet>=3.2.4",
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"psycopg2-binary>=2.9.11",
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"pytest-timeout>=2.4.0",
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"dateparser>=1.2.0",
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"tiktoken>=0.12.0",
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"httpx>=0.27.0",
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]
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[project.optional-dependencies]
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test = [
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"pytest>=7.0.0",
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"pytest-asyncio>=0.21.0",
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"pytest-timeout>=2.4.0",
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]
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[tool.hatch.build.targets.wheel]
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packages = ["memora"]
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|
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@ -1,5 +1,5 @@
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[tool.uv.workspace]
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members = ["memora", "memora-dev", "memora-dev/benchmarks"]
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members = ["memora", "memora-dev", "memora-dev/benchmarks", "memora-mcp-server"]
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[tool.uv]
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dev-dependencies = []
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|
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615
uv.lock
615
uv.lock
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|
@ -11,6 +11,7 @@ members = [
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"benchmarks",
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"memora",
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"memora-dev",
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||||
"memora-mcp-server",
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||||
]
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||||
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||||
[[package]]
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||||
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|
@ -202,6 +203,36 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615 },
|
||||
]
|
||||
|
||||
[[package]]
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||||
name = "authlib"
|
||||
version = "1.6.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "cryptography" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/cd/3f/1d3bbd0bf23bdd99276d4def22f29c27a914067b4cf66f753ff9b8bbd0f3/authlib-1.6.5.tar.gz", hash = "sha256:6aaf9c79b7cc96c900f0b284061691c5d4e61221640a948fe690b556a6d6d10b", size = 164553 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/f8/aa/5082412d1ee302e9e7d80b6949bc4d2a8fa1149aaab610c5fc24709605d6/authlib-1.6.5-py2.py3-none-any.whl", hash = "sha256:3e0e0507807f842b02175507bdee8957a1d5707fd4afb17c32fb43fee90b6e3a", size = 243608 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "backports-tarfile"
|
||||
version = "1.2.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/86/72/cd9b395f25e290e633655a100af28cb253e4393396264a98bd5f5951d50f/backports_tarfile-1.2.0.tar.gz", hash = "sha256:d75e02c268746e1b8144c278978b6e98e85de6ad16f8e4b0844a154557eca991", size = 86406 }
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||||
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||||
sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zstandard"
|
||||
version = "0.25.0"
|
||||
|
|
|
|||
Loading…
Reference in a new issue