> For the complete documentation index, see [llms.txt](https://nomadamas.gitbook.io/ragchain-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nomadamas.gitbook.io/ragchain-docs/pipeline/visconde-run-pipeline.md).

# ViscondeRunPipeline

## Overview

The `ViscondeRunPipeline` class is implementation of Visconde pipeline. You can check out Visconde paper at [here](https://arxiv.org/abs/2212.09656).

Visconde pipeline perform three task: decompose, retrieve, and aggregate. It uses [Query Decomposition](/ragchain-docs/utils/query-decomposition.md) for answering multi-hop questions. So, it is effective to answer real-world questions that need to check out multiple passages.

## Usage

#### Initialize

To create an instance of `ViscondeRunPipeline`, you need to provide an instance of a [`Retrieval`](https://github.com/NomaDamas/RAGchain-docs/blob/main/retrieval/README.md) class, and llm module to generate answer. Optionally, you can specify the instance of [query decomposition module](/ragchain-docs/utils/query-decomposition.md), a custom prompt, and other options for retrieval and use passage count for generation. FYI, you can't use chat model for this pipeline. It has a default prompt for strategyQA style multi-hop questions. You need to change prompt using PromptTemplate if you want to use another few-shot prompts.

```python
from RAGchain.pipeline import ViscondeRunPipeline
from RAGchain.retrieval import BM25Retrieval
from langchain.llms.openai import OpenAI

retrieval = BM25Retrieval(save_path="path/to/your/bm25/save_path")
pipeline = ViscondeRunPipeline(retrieval, OpenAI(model_name="babbage-002"))
```

#### Ask

You can ask a question to the LLM model and get an answer using `invoke` method. Also, you can use another LCEL's method like stream or batch as well.

```python
question = "Is reranker and retriever have same role?"
answer = pipeline.run.invoke({"question": question})
print(answer)
```

If you want to get used passages or relevance scores of retrieved passages, you can use `get_passages_and_run` method.

```python
answers, passages, scores = pipeline.get_passages_and_run([question])
```