# IndexPrompt

> `IndexPrompt` is the class for retrieving from an index and concat the retrieved context information with the query input, to get the result from LLM. The class is implemented based on `llama_index`.

> NOTE: the class only supports `SimpleVectorIndex` and `KGIndex` right now.

#### **Constructor**

```python
__init__(self, index_name="simple_vector_index", index_path=None)
```

**Parameters**

* `index_name` (str): The type of index you want to build or load, should be in one of \["simple\_vector\_index", "kg\_index" ].
* `index_path` (str): The path to your saved index file, default to be None. If provided, the index will be loaded during initializaion.

**Example**

```python
from easyinstruct.prompts import IndexPrompt
simple_index = IndexPrompt("simple_vector_index")
```

#### **build\_index**

```python
build_index(
    self, 
    data_path, 
    llm_model_name="text-davinci-002", 
    chunk_size_limit=512, 
    max_triplets_per_chunk=5
)
```

**Description**

Build a index on your custom data.

**Parameters**

* `data_path` (str): The document data path.
* `llm_model_name` (str): Large language model type to predict triplets from raw text. If not provided, will use the default setting "text-dacinci-002" for prediction.
* `chunk_size_limit` (int): Chunk size limit. Default is 512 (4096 max input size).
* `max_triplets_per_chunk` (int): Triplets number limit. Default is 5.

**Returns**

* `List[Document]`: A list of documents. `Document` is a class from llama\_index.

#### **load\_from\_disk**

```python
load_from_disk(self, index_path)
```

Load index from saved path

**Parameters**

* `index_path` (str): The path to your saved index.

#### **save\_to\_disk**

```python
save_to_disk(self, save_path)
```

**Description**

Save index to local path

**Parameters**

* `save_path` (str): The path to save your index.

#### **query**

```python
query(self, prompt)
```

**Description**

Query for ChatGPT/GPT3. Retrieve from built index, and concat the retrieved knowledge with the input prompt.

**Parameters**

* `prompt` (str): your input question.

**Returns**

* `dict` : A response dict from LLM.

**Examples**

```python
from easyinstruct.prompts import IndexPrompt
from easyinstruct.utils import set_openai_key

# set your own API-KEY
set_openai_key("YOUR-KEY")

# example for building a simple_vector_index
simple_index = IndexPrompt("simple_vector_index")
_ = simple_index.build_index("./data", chunk_size_limit=500) # return the documents
response = simple_index.query("Where is A.E Dimitra Efxeinoupolis club?")
print(response)
simple_index.save_to_disk("./index/simple_index.json")

# example for building a kg_index
kg_index = IndexPrompt("kg_index")
_ = kg_index.build_index("./data", llm_model_name="text-davinci-002", max_triplets_per_chunk=5, chunk_size_limit=512)
response = kg_index.query("Where is A.E Dimitra Efxeinoupolis club?")
kg_index.save_to_disk("./index/kg_index.json")
print(response)

# example for loading a kg_index from local file
kg_load_index = IndexPrompt("kg_index", "./index/kg_index.json")
response = kg_load_index.query("Where is A.E Dimitra Efxeinoupolis club?")
print(response)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://zjunlp.gitbook.io/easyinstruct/documentations/prompts/indexprompt.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.