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PDF Search for Zed

PDF Search for Zed

Local
github.com/freespiritFiles & Storage3Updated 78w ago
Repository

About PDF Search for Zed

PDF Search for Zed is a Zed extension that semantically searches PDF content and adds relevant passages to Zed's AI Assistant context. It uses an MCP server and currently requires an OpenAI API key for embeddings, with manual index creation before use.

Key features

  1. 01Semantic search across PDFs
  2. 02Adds passages to AI context
  3. 03Supports multiple PDFs
  4. 04Supports additional file formats

Use cases

  1. 01Find relevant sections in a PDF
  2. 02Supply document passages to Zed's AI Assistant

MCP support

The extension includes a Python-based MCP server that searches indexed documents and supplies matching passages for Zed's AI Assistant. An index must be built manually before use.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

PDF Search for Zed

A document search extension for Zed that lets you semantically search through a PDF document and use the results in Zed's AI Assistant.

Prerequisites

This extension currently requires:

  1. An OpenAI API key (to generate embeddings)
  2. uv installed on your system

Note: While the current setup requires an OpenAI API key for generating embeddings, we plan to implement a self-contained alternative in future versions. Community feedback will help prioritize these improvements.

Quick Start

  1. Clone the repository
git clone https://github.com/freespirit/pdfsearch-zed.git
  1. Set up the Python environment for the MCP server:
cd pdfsearch-zed/pdf_rag
uv venv
uv sync

  1. Install Dev Extension in Zed

  2. Build the search db

cd /path/to/pdfsearch-zed/pdf_rag

echo "OPENAI_API_KEY=sk-..." > src/pdf_rag/.env

# This may take a couple of minutes, depending on the documents' size
# You can provide multiple files and directories as arguments.
#  - files would be chunked.
#  - a directory would be considered as if its files contains chunks.
#    E.g. they won't be further split.
uv run src/pdf_rag/rag.py build "file1.pdf" "dir1" "file2.md" ...
  1. Configure Zed
"context_servers": {
    "pdfsearch-context-server": {
        "settings": {
            "extension_path": "/path/to/pdfsearch-zed"
        }
    }
}

Usage

  1. Open Zed's AI Assistant panel
  2. Type /pdfsearch followed by your search query
  3. The extension will search the PDF and add relevant sections to the AI Assistant's context

Future Improvements

  • Self-contained vector store
  • Self-contained embeddings
  • Automated index building on first run
  • Configurable result size
  • Support for multiple PDFs
  • Optional: Additional file formats beyond PDF

Project Structure

  • pdf_rag/: Python-based MCP server implementation
  • src/: Zed extension code
  • extension.toml and Cargo.toml: Zed extension configuration files

Known Limitations

  • Manual index building is required before first use
  • Requires external services (OpenAI)

Comments

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