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+---
+id: integrate_with_haystack.md
+summary: This page goes over how to search for the best answer to questions using Milvus as the Vector Database and Haystack as the LLM framework.
+---
+
+# Build Retrieval Augmented Generative System with Milvus and Haystack
+
+[Haystack](https://github.com/deepset-ai/haystack) is an open source LLM framework in Python by [deepset](https://www.deepset.ai/) for building customizable, production-ready LLM applications. It is an end-to-end framework that assists the orchestration of complete NLP applications by providing tooling for each step of the application-building life cycle.
+
+This guide demonstrates how to build an LLM-driven question-answering application **on the Milvus documentation** with the [Milvus integration of Haystack](https://haystack.deepset.ai/integrations/milvus-document-store). In this example, **Haystack** and **Milvus** work together first to ingest documentation pages and store them in a `MilvusDocumentStore`, and then to use `gpt-4` to answer questions using retrieval-augmentation.
+
+🚀 See the full application that uses the `MilvusDocumentStore` to do Milvus documentation QA [here](https://github.com/TuanaCelik/milvus-documentation-qa/tree/main).
+
+## Installation
+
+Install Haystack and the Milvus integration:
+
+```bash
+pip install milvus-haystack
+```
+
+## Usage
+
+First, start a Milvus service following the '[Start Milvus](https://milvus.io/docs/install_standalone-docker.md#Start-Milvus)' instructions in the documentation.
+
+Once you have Milvus running locally on `localhost:19530`, you can start using Milvus with Haystack by initializing a `MilvusDocumentStore`: 
+
+```python
+from milvus_haystack import MilvusDocumentStore
+
+document_store = MilvusDocumentStore()
+```
+
+## Writing Documents to MilvusDocumentStore
+
+To write documents to your `MilvusDocumentStore`, create an indexing pipeline or use the `write_documents()` function.
+For this step, you may make use of the available [FileConverters](https://docs.haystack.deepset.ai/docs/file_converters) and [PreProcessors](https://docs.haystack.deepset.ai/docs/preprocessor), as well as other [Haystack Integrations](https://haystack.deepset.ai/integrations) that might help you fetch data from other resources and preprocess them. 
+
+Below is the example indexing pipeline used in the Milvus Documentation QA demo. In this indexing pipeline, we use:
+- [Crawler](https://docs.haystack.deepset.ai/docs/crawler) to fetch documentation pages in `https://milvus.io/docs/`
+- [PreProcessors](https://docs.haystack.deepset.ai/docs/preprocessor) to split fetched pages into smaller chunks
+- [EmbeddingRetriever](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended) to create embeddings for each chunk using `sentence-transformers/multi-qa-mpnet-base-dot-v1` model and index them into `MilvusDocumentStore`
+
+```python
+from haystack import Pipeline
+from haystack.nodes import Crawler, PreProcessor, EmbeddingRetriever
+from milvus_haystack import MilvusDocumentStore
+
+document_store = MilvusDocumentStore(recreate_index=True, return_embedding=True, similarity="cosine")
+crawler = Crawler(urls=["https://milvus.io/docs/"], crawler_depth=1, overwrite_existing_files=True, output_dir="crawled_files")
+preprocessor = PreProcessor(
+    clean_empty_lines=True,
+    clean_whitespace=False,
+    clean_header_footer=True,
+    split_by="word",
+    split_length=500,
+    split_respect_sentence_boundary=True,
+)
+retriever = EmbeddingRetriever(document_store=document_store, embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_node(component=crawler, name="crawler", inputs=['File'])
+indexing_pipeline.add_node(component=preprocessor, name="preprocessor", inputs=['crawler'])
+indexing_pipeline.add_node(component=retriever, name="retriever", inputs=['preprocessor'])
+indexing_pipeline.add_node(component=document_store, name="document_store", inputs=['retriever'])
+
+indexing_pipeline.run()
+```
+
+## Creating a Retrieval Augmented Generative (RAG) Pipeline
+
+Once you have documents in your `MilvusDocumentStore`, they can be used in any Haystack pipeline. 
+
+Below is a RAG pipeline that makes use of GPT-4 model from OpenAI with the ["deepset/question-answering"](https://prompthub.deepset.ai/?prompt=deepset%2Fquestion-answering) prompt that is designed to generate answers for the retrieved documents:
+
+```python
+from haystack import Pipeline
+from haystack.nodes import EmbeddingRetriever, PromptNode, PromptTemplate, AnswerParser
+from milvus_haystack import MilvusDocumentStore
+
+document_store = MilvusDocumentStore()
+
+retriever = EmbeddingRetriever(document_store=document_store, embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1")
+template = PromptTemplate(prompt="deepset/question-answering", output_parser=AnswerParser())
+prompt_node = PromptNode(model_name_or_path="gpt-4", default_prompt_template=template, api_key=YOUR_OPENAI_API_KEY, max_length=200)
+
+query_pipeline = Pipeline()
+query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
+query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])
+```
+
+## Ask your question
+
+
+Once the query pipeline is ready, you can go ahead and run it with a query. The pipeline will then extract relevant sections from the Milvus documentation and generate a response based on the information retrieved.
+
+```python
+response = query_pipeline.run(query="How can I start Milvus with Docker?")
+
+print(response)
+```