# 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.


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## Code Example

### **Create pipeline and set the configuration**

> More parameters refer to the Configuration.

```python
from towhee import AutoPipes, AutoConfig

config = AutoConfig.load_config('eqa-search')
config.openai_api_key = [your-openai-api-key]
config.collection_name = 'chatbot'
# The llm model source, openai or dolly
config.llm_src = 'openai'
# The llm model name
config.llm_model = 'gpt-3.5-turbo'
# The threshold to filter milvus search result
config.threshold = 0.5


p = AutoPipes.pipeline('eqa-search', config=config)
res = p('https://github.com/towhee-io/towhee/blob/main/README.md', [])
```


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## 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 ](https://towhee.io/tasks/detail/operator?field_name=Natural-Language-Processing&task_name=Sentence-Embedding)to set the model, some of these models are from [HuggingFace](https://huggingface.co/) (open source), and some are from [OpenAI](https://openai.com/) (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](https://towhee.io/sentence-embedding/openai).

***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](https://towhee.io/ann-search/milvus-client)

***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](https://zilliz.com/cloud), defaults to `None`.

***password (str):***

The user password for [Cloud user](https://zilliz.com/cloud), defaults to `None`.

### Configuration for Similarity Evaluation

***threshold (Union[float, int]):***

The threshold to filter the milvus search result.


### Configuration for LLM

***llm_src (str):***

The llm model source, `openai` or `dolly`, defaults to `openai`.

***llm_model (str):***

The llm model name, defaults to `gpt-3.5-turbo` for `openai`, `databricks/dolly-v2-12b` for `dolly`.


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## 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.