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# eqa-insert
# 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.
<br />
## Code Example
- Create Milvus Collection
Before running the pipeline, please create Milvus collection 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='172.16.70.4', 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 image embedding 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://raw.githubusercontent.com/towhee-io/towhee/main/README.md')
```
Then you can run `collection.num_entities` to check the number of the data in Milvus as a knowledge base.
<br />
## Configuration
**EnhancedQAInsertConfig**
- Configuration for [Text Loader](https://towhee.io/towhee/text-loader)
***chunk_size: int***
The size of each chunk, defaults to 300.
***source_type: str***
The type of the soure, defaults to `'file'`, you can also set to `'url'` for you url of your documentation.
- 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).
***customize_embedding_op: str***
The name of the customize embedding operator, defaults to `None`.
***normalize_vec: bool***
Whether to normalize the embedding vectors, defaults to `True`.
***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`.
<br />
## 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.

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