chunksilo 2.1.2

Local RAG-based semantic document search with MCP server interface

ChunkSilo MCP Server

ChunkSilo is like a local Google for your documents. It uses semantic search — matching by meaning rather than exact keywords — so your LLM can find relevant information across all your files even when the wording differs from your query. Point it at your PDFs, Word docs, Markdown, and text files, and it builds a fully searchable index locally on your machine.

• Runs entirely on your machine — no servers, no infrastructure
• Semantic search + keyword filename matching across PDF, DOCX, DOC, Markdown, and TXT
• Incremental indexing — only reprocesses new or changed files
• Heading-aware results with source links back to the original file
• Date filtering and recency boosting
• Optional Confluence integration

Example search_docs output

{
  "matched_files": [
    { "uri": "file:///docs/database-configuration.docx", "score": 0.8432 }
  ],
  "num_matched_files": 1,
  "chunks": [
    {
      "text": "To configure the database connection, set the DATABASE_URL environment variable...",
      "score": 0.912,
      "location": {
        "uri": "file:///docs/setup-guide.pdf",
        "page": 12,
        "line": null,
        "heading_path": ["Getting Started", "Configuration", "Database"]
      }
    }
  ],
  "num_chunks": 1,
  "query": "how to configure the database",
  "retrieval_time": "0.42s"
}

Installation

Option A: Install from PyPI (Recommended)

Requires Python 3.11 or later. Models are downloaded automatically on first run (~250MB). The first run may appear to pause while models download — this is normal.

pip install chunksilo

# Or with Confluence support:
pip install chunksilo[confluence]

# Or with Jira support:
pip install chunksilo[jira]

# Or with both Confluence and Jira:
pip install chunksilo[confluence,jira]

Then:

1. Create a config file at ~/.config/chunksilo/config.yaml (see Configuration)
2. Build the index: chunksilo --build-index
3. Configure your MCP client (see MCP Client Configuration)

Option B: Offline Bundle

A self-contained package with pre-downloaded models, ideal for air-gapped environments or systems without Python installed.

Download from the Releases page:

1. Download the chunksilo-vX.Y.Z-manylinux_2_34_x86_64.tar.gz file
2. Extract and install:

tar -xzf chunksilo-vX.Y.Z-manylinux_2_34_x86_64.tar.gz
cd chunksilo
./setup.sh

3. Edit config.yaml to set your document directories
4. Build the index: ./venv/bin/chunksilo --build-index
5. Configure your MCP client (see MCP Client Configuration)

Configuration

ChunkSilo uses a single configuration file: config.yaml

Configuration File

Edit config.yaml to configure your settings:

# Indexing settings - used by chunksilo --build-index
indexing:
  directories:
    - "./data"
    - "/mnt/nfs/shared-docs"
    - path: "/mnt/samba/engineering"
      include: ["**/*.pdf", "**/*.md"]
      exclude: ["**/archive/**"]
  chunk_size: 1600
  chunk_overlap: 200

# Retrieval settings - used when searching
retrieval:
  embed_top_k: 20
  rerank_top_k: 5
  score_threshold: 0.1

# Confluence integration (optional)
confluence:
  url: "https://confluence.example.com"
  username: "your-username"
  api_token: "your-api-token"

# Storage paths (usually don't need to change)
storage:
  storage_dir: "./storage"
  model_cache_dir: "./models"

All settings are optional and have sensible defaults.

Configuration Reference

Indexing Settings

Setting	Default	Description
indexing.directories	["./data"]	List of directories to index (strings or objects)
indexing.chunk_size	1600	Maximum size of text chunks
indexing.chunk_overlap	200	Overlap between adjacent chunks

Per-directory options (when using object format):

Option	Default	Description
path	(required)	Directory path to index
include	["**/*.pdf", "**/*.md", "**/*.txt", "**/*.docx", "**/*.doc"]	Glob patterns for files to include
exclude	[]	Glob patterns for files to exclude
recursive	true	Whether to recurse into subdirectories
enabled	true	Whether to index this directory

Retrieval Settings

Setting	Default	Description
retrieval.embed_model_name	BAAI/bge-small-en-v1.5	Embedding model for vector search
retrieval.embed_top_k	20	Candidates from vector search before reranking
retrieval.rerank_model_name	ms-marco-MiniLM-L-12-v2	Reranker model
retrieval.rerank_top_k	5	Final results after reranking
retrieval.rerank_candidates	100	Maximum candidates sent to reranker
retrieval.score_threshold	0.1	Minimum score (0.0-1.0) for results
retrieval.recency_boost	0.3	Recency boost weight (0.0-1.0)
retrieval.recency_half_life_days	365	Days until recency boost halves
retrieval.bm25_similarity_top_k	10	Files returned by BM25 filename search
retrieval.offline	false	Prevent ML library network requests

Confluence Settings (optional)

Note: Confluence integration requires the optional dependency. Install with: pip install chunksilo[confluence]

Setting	Default	Description
confluence.url	""	Confluence base URL (empty = disabled)
confluence.username	""	Confluence username
confluence.api_token	""	Confluence API token
confluence.timeout	10.0	Request timeout in seconds
confluence.max_results	30	Maximum results per search

Jira Settings (optional)

Note: Jira integration requires the optional dependency. Install with: pip install chunksilo[jira]

Setting	Default	Description
jira.url	""	Jira base URL (empty = disabled)
jira.username	""	Jira username/email
jira.api_token	""	Jira API token
jira.timeout	10.0	Request timeout in seconds
jira.max_results	30	Maximum results per search
jira.projects	[]	Project keys to search (empty = all)
jira.include_comments	true	Include issue comments in search
jira.include_custom_fields	true	Include custom fields in search

Creating a Jira API Token:

1. Log into Jira
2. Go to Account Settings > Security > API Tokens
3. Click "Create API Token"
4. Copy the token and add it to your config

SSL Settings (optional)

Setting	Default	Description
ssl.ca_bundle_path	""	Path to custom CA bundle file

Storage Settings

Setting	Default	Description
storage.storage_dir	./storage	Directory for vector index and state
storage.model_cache_dir	./models	Directory for model cache

CLI Usage

The chunksilo command provides indexing, searching, and model management:

# Build or update the search index
chunksilo --build-index

# Search for documents
chunksilo "your search query"

# Search with date filtering
chunksilo "quarterly report" --date-from 2024-01-01 --date-to 2024-03-31

# Output results as JSON
chunksilo "search query" --json

# Show verbose output (model loading, search stats)
chunksilo "search query" --verbose

# Pre-download ML models (useful before going offline)
chunksilo --download-models

# Use a custom config file
chunksilo --build-index --config /path/to/config.yaml

CLI Options

Option	Description
query	Search query text (positional argument)
--build-index	Build or update the search index, then exit
--download-models	Download required ML models, then exit
--date-from	Start date filter (YYYY-MM-DD format, inclusive)
--date-to	End date filter (YYYY-MM-DD format, inclusive)
--json	Output results as JSON instead of formatted text
-v, --verbose	Show diagnostic messages (model loading, search stats)
--config	Path to config.yaml (overrides auto-discovery)

MCP Client Configuration

Configure your MCP client to run ChunkSilo. Below are examples for common clients.

Note: For PyPI installs, use chunksilo-mcp directly. For offline bundles, use the full path /path/to/chunksilo/venv/bin/chunksilo-mcp. You can find the PyPI-installed binary location with which chunksilo-mcp.

Claude Code

Add chunksilo as an MCP server using the CLI:

PyPI install:

claude mcp add chunksilo --scope user -- chunksilo-mcp --config ~/.config/chunksilo/config.yaml

Offline bundle:

claude mcp add chunksilo --scope user -- /path/to/chunksilo/venv/bin/chunksilo-mcp --config /path/to/chunksilo/config.yaml

Verify it's connected:

claude mcp list

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

PyPI install:

{
  "mcpServers": {
    "chunksilo": {
      "command": "chunksilo-mcp",
      "args": ["--config", "/path/to/config.yaml"]
    }
  }
}

Offline bundle:

{
  "mcpServers": {
    "chunksilo": {
      "command": "/path/to/chunksilo/venv/bin/chunksilo-mcp",
      "args": ["--config", "/path/to/chunksilo/config.yaml"]
    }
  }
}

Cline (VS Code Extension)

Add to cline_mcp_settings.json (typically in ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/):

PyPI install:

{
  "mcpServers": {
    "chunksilo": {
      "command": "chunksilo-mcp",
      "args": ["--config", "/path/to/config.yaml"],
      "disabled": false,
      "autoApprove": []
    }
  }
}

Offline bundle:

{
  "mcpServers": {
    "chunksilo": {
      "command": "/path/to/chunksilo/venv/bin/chunksilo-mcp",
      "args": ["--config", "/path/to/chunksilo/config.yaml"],
      "disabled": false,
      "autoApprove": []
    }
  }
}

Roo Code (VS Code Extension)

Add to mcp_settings.json (typically in ~/.config/Code/User/globalStorage/rooveterinaryinc.roo-cline/settings/):

PyPI install:

{
  "mcpServers": {
    "chunksilo": {
      "command": "chunksilo-mcp",
      "args": ["--config", "/path/to/config.yaml"]
    }
  }
}

Offline bundle:

{
  "mcpServers": {
    "chunksilo": {
      "command": "/path/to/chunksilo/venv/bin/chunksilo-mcp",
      "args": ["--config", "/path/to/chunksilo/config.yaml"]
    }
  }
}

Troubleshooting

• Index missing: Run chunksilo --build-index (PyPI install) or ./venv/bin/chunksilo --build-index (offline bundle).
• Retrieval errors: Check paths in your MCP client configuration.
• Offline mode: PyPI installs default to offline: false (models auto-download). The offline bundle includes pre-downloaded models and sets offline: true. Set retrieval.offline: true in config.yaml to prevent network calls after initial model download.
• Confluence Integration: Install with pip install chunksilo[confluence], then set confluence.url, confluence.username, and confluence.api_token in config.yaml.
• Jira Integration: Install with pip install chunksilo[jira], then set jira.url, jira.username, and jira.api_token in config.yaml. Optionally configure jira.projects to restrict search to specific project keys.
• Custom CA Bundle: Set ssl.ca_bundle_path in config.yaml for custom certificates.
• Network mounts: Unavailable directories are skipped with a warning; indexing continues with available directories.
• Legacy .doc files: Requires LibreOffice to be installed for automatic conversion to .docx. If LibreOffice is not found, .doc files are skipped with a warning. Full heading extraction is supported.

License

Apache-2.0. See LICENSE for details.