> ## Documentation Index
> Fetch the complete documentation index at: https://ultrarag.openbmb.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# Custom

## Function

The Custom Server is used to store custom tool functions that cannot be classified into standard modules (such as Retriever, Generation, Evaluation, etc.).
It provides developers with a flexible extension space for implementing various logical components that cooperate with core RAG modules, such as:

* Data cleaning and preprocessing
* Keyword extraction or feature construction
* Specific task logic (such as answer extraction, formatting, filtering, etc.)

<Note>The Custom Server is your free toolbox — any functional logic that does not belong to core Servers can be defined and reused here.</Note>

## Implementation Example

The following takes a common example `output_extract_from_boxed` to show how to customize and register a Tool.

```python servers/custom/src/custom.py icon="python" theme={null}
@app.tool(output="ans_ls->pred_ls")
def output_extract_from_boxed(ans_ls: List[str]) -> Dict[str, List[str]]:
    def extract(ans: str) -> str:
        start = ans.rfind(r"\boxed{")
        if start == -1:
            content = ans.strip()
        else:
            i = start + len(r"\boxed{")
            brace_level = 1
            end = i
            while end < len(ans) and brace_level > 0:
                if ans[end] == "{":
                    brace_level += 1
                elif ans[end] == "}":
                    brace_level -= 1
                end += 1
            content = ans[i : end - 1].strip()
            content = re.sub(r"^\$+|\$+$", "", content).strip()
            content = re.sub(r"^\\\(|\\\)$", "", content).strip()
            if content.startswith(r"\text{") and content.endswith("}"):
                content = content[len(r"\text{") : -1].strip()
            content = content.strip("()").strip()

        content = content.replace("\\", " ")
        content = content.replace("  ", " ")
        return content

    return {"pred_ls": [extract(ans) for ans in ans_ls]}
```

The function of this tool is to extract the final answer text in `\boxed{...}` format from the model output string,
and the output result will be mapped to the variable `pred_ls` for use by downstream evaluation or post-processing modules.

## Usage Example

After defining the custom tool, you only need to register the custom module in the Pipeline and call the corresponding Tool:

```yaml examples/rag_full.yaml icon="https://mintcdn.com/ultrarag/T7GffHzZitf6TThi/images/yaml.svg?fit=max&auto=format&n=T7GffHzZitf6TThi&q=85&s=69b41e79144bc908039c2ee3abbb1c3b" highlight="8,20" theme={null}
# MCP Server
servers:
  benchmark: servers/benchmark
  retriever: servers/retriever
  prompt: servers/prompt
  generation: servers/generation
  evaluation: servers/evaluation
  custom: servers/custom

# MCP Client Pipeline
pipeline:
- benchmark.get_data
- retriever.retriever_init
- retriever.retriever_embed
- retriever.retriever_index
- retriever.retriever_search
- generation.generation_init
- prompt.qa_rag_boxed
- generation.generate
- custom.output_extract_from_boxed
- evaluation.evaluate
```

In this example, `custom.output_extract_from_boxed` is used to extract the standardized answer from the model output,
and then it is passed to `evaluation.evaluate` for evaluation.