# Install uv if you don't have it already
pip install -U uv

# Create a virtual environment with Python 3.10 or newer
uv venv --python=3.10  # or 3.11, 3.12, etc.

# Activate the virtual environment (bash/zsh)
source .venv/bin/activate
# For fish shell
# source .venv/bin/activate.fish

# Install from PyPI
uv pip install kb-mcp-server

# Create a new conda environment (optional)
conda create -n embedding-mcp python=3.10
conda activate embedding-mcp

# Install from PyPI
pip install kb-mcp-server

# Create a new conda environment
conda create -n embedding-mcp python=3.10
conda activate embedding-mcp

# Clone the repository
git clone https://github.com/Geeksfino/kb-mcp-server.git.git
cd kb-mcp-server

# Install dependencies
pip install -e .

# Install uv if not already installed
pip install uv

# Create a new virtual environment
uv venv
source .venv/bin/activate

# Option 1: Install from PyPI
uv pip install kb-mcp-server

# Option 2: Install from source (for development)
uv pip install -e .

# Run the MCP server
uvx --from kb-mcp-server@0.3.0 kb-mcp-server --embeddings /path/to/knowledge_base

# Build a knowledge base
uvx --from kb-mcp-server@0.3.0 kb-build --input /path/to/documents --config config.yml

# Search a knowledge base
uvx --from kb-mcp-server@0.3.0 kb-search /path/to/knowledge_base "Your search query"

# Build a knowledge base from documents
kb-build --input /path/to/documents --config config.yml

# Update an existing knowledge base with new documents
kb-build --input /path/to/new_documents --update

# Export a knowledge base for portability
kb-build --input /path/to/documents --export my_knowledge_base.tar.gz

# Search a knowledge base
kb-search /path/to/knowledge_base "What is machine learning?"

# Search with graph enhancement
kb-search /path/to/knowledge_base "What is machine learning?" --graph --limit 10

# Build a knowledge base from documents
uvx --from kb-mcp-server@0.3.0 kb-build --input /path/to/documents --config config.yml

# Update an existing knowledge base with new documents
uvx --from kb-mcp-server@0.3.0 kb-build --input /path/to/new_documents --update

# Export a knowledge base for portability
uvx --from kb-mcp-server@0.3.0 kb-build --input /path/to/documents --export my_knowledge_base.tar.gz

# Search a knowledge base
uvx --from kb-mcp-server@0.3.0 kb-search /path/to/knowledge_base "What is machine learning?"

# Search with graph enhancement
uvx --from kb-mcp-server@0.3.0 kb-search /path/to/knowledge_base "What is machine learning?" --graph --limit 10

# Build a knowledge base from documents
python -m kb_builder build --input /path/to/documents --config config.yml

# Update an existing knowledge base with new documents
python -m kb_builder build --input /path/to/new_documents --update

# Export a knowledge base for portability
python -m kb_builder build --input /path/to/documents --export my_knowledge_base.tar.gz

# Build a knowledge base using a template configuration
./scripts/kb_build.sh /path/to/documents technical_docs

# Build using a custom configuration file
./scripts/kb_build.sh /path/to/documents /path/to/my_config.yml

# Update an existing knowledge base
./scripts/kb_build.sh /path/to/documents technical_docs --update

# Search a knowledge base
./scripts/kb_search.sh /path/to/knowledge_base "What is machine learning?"

# Search with graph enhancement
./scripts/kb_search.sh /path/to/knowledge_base "What is machine learning?" --graph

# Start with a specific knowledge base folder
kb-mcp-server --embeddings /path/to/knowledge_base_folder

# Start with a given knowledge base archive
kb-mcp-server --embeddings /path/to/knowledge_base.tar.gz

# Start with a specific knowledge base folder
uvx kb-mcp-server@0.2.6 --embeddings /path/to/knowledge_base_folder

# Start with a given knowledge base archive
uvx kb-mcp-server@0.2.6 --embeddings /path/to/knowledge_base.tar.gz

# Start with a specific knowledge base folder
python -m txtai_mcp_server --embeddings /path/to/knowledge_base_folder

# Start with a given knowledge base archive
python -m txtai_mcp_server --embeddings /path/to/knowledge_base.tar.gz

# Start the server with command-line arguments
kb-mcp-server --embeddings /path/to/knowledge_base --host 0.0.0.0 --port 8000

# Or using uvx (no installation required)
uvx kb-mcp-server@0.2.6 --embeddings /path/to/knowledge_base --host 0.0.0.0 --port 8000

# Or using the Python module
python -m txtai_mcp_server --embeddings /path/to/knowledge_base --host 0.0.0.0 --port 8000

# Or use environment variables
export TXTAI_EMBEDDINGS=/path/to/knowledge_base
export MCP_SSE_HOST=0.0.0.0
export MCP_SSE_PORT=8000
python -m txtai_mcp_server

{
  "mcpServers": {
    "kb-server": {
      "command": "/your/home/project/.venv/bin/kb-mcp-server",
      "args": [
        "--embeddings", 
        "/path/to/knowledge_base.tar.gz"
      ],
      "cwd": "/path/to/working/directory"
    }
  }
}

{
    "rag-server": {
      "command": "python3",
      "args": [
        "-m",
        "txtai_mcp_server",
        "--embeddings",
        "/path/to/knowledge_base.tar.gz",
        "--enable-causal-boost"
      ],
      "cwd": "/path/to/working/directory"
    }
}

# Create a symlink to /usr/local/bin (which is typically in the system PATH)
sudo ln -s /Users/cliang/.local/bin/uvx /usr/local/bin/uvx

# Create a plist file to set environment variables for all GUI applications
sudo nano /Library/LaunchAgents/environment.plist

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
  <key>Label</key>
  <string>my.startup</string>
  <key>ProgramArguments</key>
  <array>
    <string>sh</string>
    <string>-c</string>
    <string>launchctl setenv PATH $PATH:/Users/cliang/.local/bin</string>
  </array>
  <key>RunAtLoad</key>
  <true/>
</dict>
</plist>

sudo launchctl load -w /Library/LaunchAgents/environment.plist

{
  "mcpServers": {
    "kb-server": {
      "command": "uvx",
      "args": [
        "kb-mcp-server@0.2.6",
        "--embeddings", "/path/to/knowledge_base",
        "--host", "localhost",
        "--port", "8000"
      ],
      "cwd": "/path/to/working/directory"
    }
  }
}

# Path to save/load embeddings index
path: ~/.txtai/embeddings
writable: true

# Content storage in SQLite
content:
  path: sqlite:///~/.txtai/content.db

# Embeddings configuration
embeddings:
  # Model settings
  path: sentence-transformers/nli-mpnet-base-v2
  backend: faiss
  gpu: true
  batch: 32
  normalize: true
  
  # Scoring settings
  scoring: hybrid
  hybridalpha: 0.75

# Pipeline configuration
pipeline:
  workers: 2
  queue: 100
  timeout: 300

# Question-answering pipeline
extractor:
  path: distilbert-base-cased-distilled-squad
  maxlength: 512
  minscore: 0.3

# Graph configuration
graph:
  backend: sqlite
  path: ~/.txtai/graph.db
  similarity: 0.75  # Threshold for creating graph connections
  limit: 10  # Maximum connections per node

python -m kb_builder build --input /path/to/documents --config src/kb_builder/configs/technical_docs.yml

# Or use a storage-specific configuration
python -m kb_builder build --input /path/to/documents --config src/kb_builder/configs/postgres-pgvector.yml