eqa-search

Enhanced QA Search

Description

Enhanced question-answering is the process of creating the knowledge base and generating answers with LLMs(large language model), thus preventing illusions. It involves inserting data as knowledge base and querying questions, and eqa-search is used to query questions from knowledge base.

Code Example

Create pipeline and set the configuration

More parameters refer to the Configuration.

from towhee import AutoPipes, AutoConfig

config = AutoConfig.load_config('eqa-search')
config.collection_name = 'chatbot'

p = AutoPipes.pipeline('eqa-search', config=config)
res = p('https://raw.githubusercontent.com/towhee-io/towhee/main/README.md')

Enhanced QA Search Config

Configuration for Sentence Embedding

model (str):

The model name in the sentence embedding pipeline, defaults to 'all-MiniLM-L6-v2'. You can refer to the above Model(s) list to set the model, some of these models are from HuggingFace (open source), and some are from OpenAI (not open, required API key).

openai_api_key (str):

The api key of openai, default to None. This key is required if the model is from OpenAI, you can check the model provider in the above Model(s) list.

embedding_device (int):

The number of devices, defaults to -1, which means using the CPU. If the setting is not -1, the specified GPU device will be used.

Configuration for Milvus

host (str):

Host of Milvus vector database, default is '127.0.0.1'.

port (str):

Port of Milvus vector database, default is '19530'.

top_k (int):

The number of nearest search results, defaults to 5.

collection_name (str):

The collection name for Milvus vector database.

user (str):

The user name for Cloud user, defaults to None.

password (str):

The user password for Cloud user, defaults to None.

Interface

Query a question from Milvus knowledge base.

Parameters:

• question (str): The question to query.

• history (List[str]): The chat history to provide background information.

Returns:

• Answer (str): The answer to the question.