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

__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

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

build_index

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

load_from_disk(self, index_path)

Load index from saved path

Parameters

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

save_to_disk

save_to_disk(self, save_path)

Description

Save index to local path

Parameters

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

query

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

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)
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