eqa-insert/README.md

# 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, description='ids', is_primary=True, auto_id=True),
   FieldSchema(name='text_id', dtype=DataType.VARCHAR, description='text', max_length=500),
   FieldSchema(name='text', dtype=DataType.VARCHAR, description='text', max_length=1000),
   FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, description='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.embedding_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.

<br />




## Configuration 

### **EnhancedQAInsertConfig**

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

***type***: str

The type of splitter, defaults to 'RecursiveCharacter'. You can set this parameter in ['[RecursiveCharacter](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/recursive_text_splitter.html)', '[Markdown](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/markdown.html)', '[PythonCode](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/python.html)', '[Character](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html#)', '[NLTK](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/nltk.html)', '[Spacy](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/spacy.html)', '[Tiktoken](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/tiktoken_splitter.html)', '[HuggingFace](https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/huggingface_length_function.html)'].

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

***splitter_kwargs***: dict

The kwargs for the splitter, defaults to {}.

#### **Configuration for Sentence Embedding:**

***embedding_model***: str
The model name for sentence embedding, 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 device, 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.


# More Resources

- [Enhancing RAG with Knowledge Graphs - Zilliz blog](https://zilliz.com/blog/enhance-rag-with-knowledge-graphs): Knowledge Graphs (KGs) store and link data based on their relationships. KG-enhanced RAG can significantly improve retrieval capabilities and answer quality.
- [Metrics-Driven Development of RAGs - Zilliz blog](https://zilliz.com/blog/metrics-driven-development-of-rags): Evaluating and improving Retrieval-Augmented Generation (RAG) systems is a nuanced but essential task in the realm of AI-driven information retrieval. By leveraging a metrics-driven approach, as demonstrated by Jithin James and Shahul Es, you can systematically refine your RAG systems to ensure they deliver accurate, relevant, and trustworthy information.
- [How to Evaluate RAG Applications - Zilliz blog](https://zilliz.com/learn/How-To-Evaluate-RAG-Applications): Effective Evaluation strategies for your RAG Application
- [Building an Intelligent QA System with NLP and Milvus - Zilliz blog](https://zilliz.com/blog/building-intelligent-chatbot-with-nlp-and-milvus): The Next-Gen QA Bot is here
- [Using Voyage AI's embedding models in Zilliz Cloud Pipelines - Zilliz blog](https://zilliz.com/blog/craft-superior-rag-for-code-intensive-texts-with-zcp-and-voyage): Assess the effectiveness of a RAG system implemented with various embedding models for code-related tasks.
- [How to Build Retrieval Augmented Generation (RAG) with Milvus Lite, Llama3 and LlamaIndex  - Zilliz blog](https://zilliz.com/learn/build-rag-with-milvus-lite-llama3-and-llamaindex): Retrieval Augmented Generation (RAG) is a method for mitigating LLM hallucinations. Learn how to build a chatbot RAG with Milvus, Llama3, and LlamaIndex.
- [Safeguarding Data Integrity: On-Prem RAG Deployment](https://zilliz.com/event/ai-bloks-safeguarding-data-integrity/success): Register for a free webinar exploring how you can deploy RAG applications on-prem using open source tools such as LLMWare and Milvus.
- [Safeguarding Data Integrity: On-Prem RAG Deployment](https://zilliz.com/event/ai-bloks-safeguarding-data-integrity): Register for a free webinar exploring how you can deploy RAG applications on-prem using open source tools such as LLMWare and Milvus.