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**Description** Simple fix, percentage value is expected to be float in range 0..1 **Notes for Reviewers** **[Signed commits](../CONTRIBUTING.md#signing-off-on-commits-developer-certificate-of-origin)** - [x] Yes, I signed my commits. <!-- Thank you for contributing to LocalAI! Contributing Conventions: 1. Include descriptive PR titles with [<component-name>] prepended. 2. Build and test your changes before submitting a PR. 3. Sign your commits By following the community's contribution conventions upfront, the review process will be accelerated and your PR merged more quickly. -->
28 lines
1.2 KiB
Python
28 lines
1.2 KiB
Python
import os
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# Uncomment to specify your OpenAI API key here (local testing only, not in production!), or add corresponding environment variable (recommended)
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# os.environ['OPENAI_API_KEY']= ""
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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, LLMPredictor, PromptHelper, ServiceContext
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from langchain.llms.openai import OpenAI
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from llama_index import StorageContext, load_index_from_storage
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base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
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# This example uses text-davinci-003 by default; feel free to change if desired
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llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", openai_api_base=base_path))
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# Configure prompt parameters and initialise helper
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max_input_size = 400
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num_output = 400
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max_chunk_overlap = 0.3
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prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
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# Load documents from the 'data' directory
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documents = SimpleDirectoryReader('data').load_data()
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service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit = 400)
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index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
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index.storage_context.persist(persist_dir="./storage")
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