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https://github.com/daradib/openwebui-plugins

Custom tools for Open WebUI
https://github.com/daradib/openwebui-plugins

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Custom tools for Open WebUI

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# openwebui-plugins



A collection of powerful tools to enhance [Open WebUI](https://github.com/open-webui/open-webui) with agentic search and retrieval capabilities for multi-step reasoning and ReAct (Reasoning and Acting) workflows.



## Table of Contents



- [Overview](#overview)

- [Tools](#tools)

  - [Document Search](#document-search)

  - [Linkup Web Search](#linkup-web-search)

  - [Perplexity Web Search (OpenRouter)](#perplexity-web-search-openrouter)

- [License](#license)



## Overview



While Open WebUI has built-in document and web search functionality, these tools provide **native tool access** that enables models to use search capabilities in agentic workflows. This allows models to:



- **Decompose complex questions** into focused sub-queries

- **Use search tools iteratively**, refining queries based on results

- **Reason through multi-step problems** using ReAct (Reasoning and Acting) patterns

- **Chain multiple searches** to build comprehensive understanding



This repository provides three specialized tools for Open WebUI:



- **Document Search**: Search document collections with hybrid semantic and keyword matching

- **Linkup Web Search**: Access current web search results with flexible filtering

- **Perplexity Web Search (OpenRouter)**: Access search summaries through OpenRouter



Each tool includes automatic citation generation and is designed for seamless integration into agentic reasoning workflows.



## Tools



### Document Search



**Purpose**: Search through document collections with agentic workflow support.



This tool enables models to iteratively explore knowledge bases through multi-step reasoning. Models can split and chain searches, using results to inform follow-up queries. This approach provides more accurate results, making it perfect for agentic Retrieval-Augmented Generation (RAG).



#### Key Features



- Hybrid semantic and keyword search for better accuracy

- Optionally specify result count (default: 5 results)

- Optionally specify file name to filter results (default: None)

- High-performance vector storage (Qdrant)

- Configurable embedding models (Ollama, DeepInfra, or HuggingFace)

- Automatic citation generation with sequential indices for inline references

- Build utility which supports multiple document extractors (LlamaIndex, PyMuPDF4LLM, or Docling) and efficient incremental updates to the vector store (new, modified, or deleted files)



#### Quick Start



1. **Prepare documents** in a folder

2. **Build the document store** using the [build utility][build_document_store.py]

3. **Import the tool** into Open WebUI

4. **Configure the connection** to the document store

5. **Create a model** in the workspace with access to the tool and a custom prompt



#### Tool Parameters



- **Required**: `query` - search query

- **Optional**: `top_k` - number of results (default: 5)

- **Optional**: `file_name` - filter results by filename (default: None)



#### Tips for Better Accuracy



- Parse PDF files into Markdown or JSON format instead of unstructured plain text.

- Use a larger embedding model like the Qwen3-Embedding model series (0.6B, 4B, or 8B). For some suggestions from the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), choose English or Multilingual as appropriate, filter the column "Max Tokens" >= 1024 (chunk size), and sort by the "Retrieval" column.

- Set the appropriate text instruction in the [build utility][build_document_store.py] arguments if needed, e.g., not needed for the Qwen3-Embedding model series.

- Set the appropriate query instruction in the tool configuration if needed, e.g., for the Qwen3-Embedding model series the query instruction is: `Given a web search query, retrieve relevant passages that answer the query\nQuery:`

- Use a reranker model like Qwen3-Reranker-8B so that more search candidates can be retrieved.

- Provide context on the documents in the Open WebUI system prompt (see [prompt example](#example-system-prompt)).



#### Building the Document Store



Download the [build utility][build_document_store.py].



**Basic setup** (fast, good for testing):

```bash

python utils/build_document_store.py /path/to/documents

```



**Recommended setup - example #1** (slower, higher quality):

```bash

python utils/build_document_store.py \

  --embedding_model Qwen/Qwen3-Embedding-0.6B \

  --output_format markdown \

  /path/to/documents

```



**Recommended setup - example #2** (slowest, highest quality):

```bash

python utils/build_document_store.py \

  --embedding_model Qwen/Qwen3-Embedding-8B \

  --output_format json \

  /path/to/documents

```



You can run the [build utility][build_document_store.py] with all dependencies pre-installed using Docker/Podman.



Docker/Podman build:



```bash

# CPU variant (default)

docker build --build-arg VARIANT=cpu -t build-document-store utils



# Nvidia CUDA variant

docker build --build-arg VARIANT=cuda -t build-document-store utils



# AMD ROCm variant

docker build --build-arg VARIANT=rocm -t build-document-store utils

```



Docker/Podman run:



```bash

docker run --rm \

  --net host \

  -v ./cache:/root/.cache \

  -v ./documents:/data:ro \

  build-document-store \

  --qdrant-url http://localhost:6333 \

  /data

```



The [build utility][build_document_store.py] supports several options:



```

usage: build_document_store.py [-h] [--qdrant-url QDRANT_URL]

                               [--qdrant-collection QDRANT_COLLECTION]

                               [--qdrant-api-key QDRANT_API_KEY]

                               [--embedding-model EMBEDDING_MODEL]

                               [--embedding-text-instruction EMBEDDING_TEXT_INSTRUCTION]

                               [--ollama-base-url OLLAMA_BASE_URL | --deepinfra-api-key DEEPINFRA_API_KEY]

                               [--format {plain,markdown,json}]

                               [--workers WORKERS] [--dry-run]

                               input_dir



Build a document store using LlamaIndex and Qdrant



positional arguments:

  input_dir             Directory containing input documents



options:

  -h, --help            show this help message and exit

  --qdrant-url QDRANT_URL

                        Path to a local Qdrant directory or remote Qdrant

                        instance (default: ./qdrant_db)

  --qdrant-collection QDRANT_COLLECTION

                        Qdrant collection to build (default: llamacollection)

  --qdrant-api-key QDRANT_API_KEY

                        API key for remote Qdrant instance (default: None)

  --embedding-model EMBEDDING_MODEL

                        Model for dense vector embeddings (default: sentence-

                        transformers/all-MiniLM-L6-v2)

  --embedding-text-instruction EMBEDDING_TEXT_INSTRUCTION

                        Instruction to prepend to text before embedding, e.g.,

                        'passage:'. Escape sequences like \n are interpreted.

                        (default: None)

  --ollama-base-url OLLAMA_BASE_URL

                        Base URL for Ollama API. When set, uses Ollama instead

                        of downloading the embedding model from HuggingFace.

                        (default: None)

  --deepinfra-api-key DEEPINFRA_API_KEY

                        API key for DeepInfra. When set, uses DeepInfra

                        instead of downloading the embedding model from

                        HuggingFace. (default: None)

  --format {plain,markdown,json}

                        Format to parse PDF files into (default: plain)

  --workers WORKERS     Number of workers to use for parsing documents,

                        generating embeddings, and saving to the vector store

                        (default: None)

  --dry-run             Compare files between input directory and Qdrant

                        collection without actually adding or deleting

                        documents (default: False)

```



There are also utilities to [copy from Milvus to Qdrant][copy_milvus_to_qdrant.py] if you're looking to migrate from Milvus as well as to [copy Qdrant collections between servers][copy_qdrant_to_qdrant.py].



[build_document_store.py]: https://github.com/daradib/openwebui-plugins/blob/main/utils/build_document_store.py

[copy_milvus_to_qdrant.py]: https://github.com/daradib/openwebui-plugins/blob/main/utils/copy_milvus_to_qdrant.py

[copy_qdrant_to_qdrant.py]: https://github.com/daradib/openwebui-plugins/blob/main/utils/copy_qdrant_to_qdrant.py



#### Example System Prompt



> You are Danesh, a highly specialized AI assistant and expert query engine for a knowledge base of documents.

>

> SEARCH STRATEGY:

> - Decompose complex questions into focused sub-queries

> - Use the search tool iteratively, refining queries based on results

> - Gather comprehensive context before synthesizing your final answer

>

> RESPONSE REQUIREMENTS:

> - Base answers STRICTLY and EXCLUSIVELY on search result information

> - If insufficient information is found, clearly state this limitation

> - Include inline citations as [1][2][3] when ID numbers are available in search results

> - Provide factual, accurate, and comprehensive responses

>

> SCOPE:

> - Focus on document search results

> - For tangentially related queries, acknowledge the connection but redirect to document-specific aspects



### Linkup Web Search



**Purpose**: Enable agentic web search with real-time information gathering.



This tool empowers models to conduct web research through iterative search strategies. Models can split and chain searches to build comprehensive understanding of topics.



#### Key Features



- Access web search results or AI-generated answers

- Date range filtering

- Domain inclusion/exclusion

- Automatic citation generation with sequential indices for inline references



#### Setup



1. **Import the tool** into Open WebUI (Workspace → Tools)

2. **Get a Linkup API key** from [Linkup](https://linkup.so/)

3. **Configure the tool** by clicking the valves settings icon and entering the API key



#### Tool Parameters



- **Required**: `query` - search query

- **Optional**: `from_date` - search results from this date

- **Optional**: `to_date` - search results until this date

- **Optional**: `exclude_domains` - domains to exclude from results

- **Optional**: `include_domains` - only include results from these domains



#### Output Modes



- **searchResults** (default): returns raw search results with full content and individual citations

- **sourcedAnswer**: returns an AI-generated answer with source list and citations



Choose `searchResults` for more accurate model grounding or `sourcedAnswer` to reduce token usage.



### Perplexity Web Search (OpenRouter)



**Purpose**: Access search summaries for multi-step reasoning workflows.



This tool enables models to leverage Perplexity's search summaries. It is adapted from the [Perplexity Web Search Tool](https://openwebui.com/t/abhiactually/perplexity) to support OpenRouter.



#### Key Features



- Access AI-generated answers from web search

- Configurable model selection

- Automatic citation generation



#### Setup



1. **Import the tool** into Open WebUI (Workspace → Tools)

2. **Get an OpenRouter API key** from [OpenRouter](https://openrouter.ai/)

3. **Configure the tool** by clicking the valves settings icon and entering the API key



The tool defaults to Perplexity Sonar Pro but can be configured to use other compatible models.



## License



Copyright © 2025 Dara Adib



This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.



This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.



You should have received a copy of the GNU Affero General Public License along with this program. If not, see .