# Enhanced QA Insert

## 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-insert** is used to insert document data for knowledge base.

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## Code Example

### **Create Milvus collection**

Before running the pipeline, please [create Milvus collection](https://milvus.io/docs/v2.0.x/create_collection.md) first.

> The `dim` is the dimensionality of the feature vector generated by the configured `model` in the `eqa-insert` pipeline.

```python
from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility

collection_name = 'chatbot'
dim = 384

connections.connect(host='127.0.0.1', port='19530')

fields = [
   FieldSchema(name='id', dtype=DataType.INT64, descrition='ids', is_primary=True, auto_id=True),
   FieldSchema(name='text_id', dtype=DataType.VARCHAR, descrition='text', max_length=500),
   FieldSchema(name='text', dtype=DataType.VARCHAR, descrition='text', max_length=1000),
   FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, descrition='embedding vectors', dim=dim)
]
schema = CollectionSchema(fields=fields, description='enhanced qa')
collection = Collection(name=collection_name, schema=schema)

index_params = {
    'metric_type':"IP",
    'index_type':"IVF_FLAT",
    'params':{"nlist":2048}
}
collection.create_index(field_name="embedding", index_params=index_params)
```

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

> More parameters refer to the Configuration.

```python
from towhee import AutoPipes, AutoConfig

config = AutoConfig.load_config('eqa-insert')
config.model = 'all-MiniLM-L6-v2'
config.host = '127.0.0.1'
config.port = '19530'
config.collection_name = collection_name

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

Then you can run `collection.flush() ` and `collection.num_entities` to check the number of the data in Milvus as a knowledge base.

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## Configuration 

### **EnhancedQAInsertConfig**

#### **Configuration for [Text Spliter](https://towhee.io/towhee/text-spliter):**

***chunk_size: int***
The size of each chunk, defaults to 300.

#### **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).

***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-insert/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'`. 

***collection_name: str***
The collection name for Milvus vector database, is required when inserting data into Milvus.

***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`.

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## Interface

Insert documentation into Milvus as a knowledge base.

**Parameters:**

 ***doc***: str

Path or url of the document to be loaded.

**Returns:** MutationResult

A MutationResult after inserting Milvus.