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To set the
chunk_size and
overlap_size when using
GPTPineconeIndex, you need to create a custom
PromptHelper and pass it to a
ServiceContext. Here's an example based on the extracted information:
from gpt_index import GPTPineconeIndex, SimpleDirectoryReader, LLMPredictor, PromptHelper, ServiceContext
from langchain import OpenAI
# Load documents
documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()
# Define LLM
llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003"))
# Define PromptHelper with custom chunk_size and overlap_size
chunk_size = 256
overlap_size = 20
prompt_helper = PromptHelper(chunk_size, num_output=chunk_size, max_chunk_overlap=overlap_size)
# Create ServiceContext with custom PromptHelper
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
# Build the GPTPineconeIndex
index = GPTPineconeIndex.from_documents(documents, pinecone_index=index, service_context=service_context)To save the built index for future use, you can use the
save_to_disk and
load_from_disk methods. Make sure to pass the same
ServiceContext when loading the index:
# Save the index to disk
index.save_to_disk('index.json')
# Load the index from disk
index = GPTPineconeIndex.load_from_disk('index.json', service_context=service_context)Keep in mind that the code snippets provided are based on the information extracted from the sources. You may need to adapt them to your specific use case and import the necessary libraries.
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