This repo includes en example application that makes use of a Retrieval Augmented Generative architecture built with Haystack to do search on the Milvus documentation.
Screen.Recording.2023-08-28.at.13.51.59.mov
pip install -r requirements.txtAn indexing pipeline is used to write documents to a database. In this example, we use the MilvusDocumentStore as our database for the RAG pipeline. So, we need to write the Milvus documentation into our Milvus database. For demonstration purposes, we use the Crawler component to crawl everything under https://milvus.io/docs/
Once you have Milvus running locally on localhost:19530, you can use the indexing pipeline as follows:
python scripts/index_files.pyThe RAG pipeline that we use is the following:
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=OPENAI_API_KEY, max_length=500)
query_pipeline = Pipeline()
query_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
query_pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"])streamlit run app.pypython scripts/documentation_qa.py