> ## 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.

# Code Integration

Through two methods, **ToolCall** and **PipelineCall**, you can directly call UltraRAG's capabilities in your local code.

## ToolCall

When you only need a specific function of UltraRAG (such as data loading, encoding, retrieval, etc.) and do not need to run the full Pipeline, you can call it as a function via `ToolCall`.

<Tip>`ToolCall` requires using `initialize` to specify the Servers to be enabled first.</Tip>
<Tip>`server_root` recommends using an absolute path, e.g., `/home/user/project/UltraRAG/servers`.</Tip>

```python script/api_usage_example.py theme={null}
from ultrarag.api import initialize, ToolCall


initialize(["benchmark"], server_root="servers") 

benchmark_param_dict = {
    "key_map":{
      "gt_ls": "golden_answers",
      "q_ls": "question"
    },
    "limit": -1,
    "seed": 42,
    "name": "nq",
    "path": "data/sample_nq_10.jsonl",
    
}
benchmark = ToolCall.benchmark.get_data(benchmark_param_dict)

```

<Note>The usage method is consistent with ordinary Python functions; just pass in the corresponding parameters as needed.</Note>

```python script/api_usage_example.py theme={null}
from ultrarag.api import initialize, ToolCall


initialize(["benchmark", "retriever"], server_root="servers") 

benchmark_param_dict = {
    "key_map":{
      "gt_ls": "golden_answers",
      "q_ls": "question"
    },
    "limit": -1,
    "seed": 42,
    "name": "nq",
    "path": "data/sample_nq_10.jsonl",
    
}
benchmark = ToolCall.benchmark.get_data(benchmark_param_dict)

query_list = benchmark['q_ls']


retriever_init_param_dict = {
    "model_name_or_path": "Qwen/Qwen3-Embedding-0.6B",
}

ToolCall.retriever.retriever_init(
    **retriever_init_param_dict
)

result = ToolCall.retriever.retriever_search(
    query_list=query_list,
    top_k=5,
)

retrieve_passages = result['ret_psg']

```

<Note>Only pass the parameters you wish to modify; other parameters will be automatically completed from the Server's default parameter file.</Note>

## PipelineCall

If you wish to run an entire UltraRAG Pipeline directly locally and obtain the execution results of all steps, you can use `PipelineCall`.

<Note>You need to first generate the `pipeline_parameter.yaml` and parameter files for the corresponding Pipeline via UltraRAG's `build` function.</Note>

```python script/api_usage_example.py theme={null}
from ultrarag.api import PipelineCall

result = PipelineCall(
    pipeline_file="examples/rag_deploy.yaml",
    parameter_file="examples/parameter/rag_deploy_parameter.yaml",
)

final_step_result = result['final_result']
all_steps_result = result['all_results']

```

<Note>`final_result` is the running result of the last step of the Pipeline, and `all_steps_result` contains the running results of all steps.</Note>