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28 lines
994 B
28 lines
994 B
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import os
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from langchain.vectorstores import Chroma
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter,TokenTextSplitter,CharacterTextSplitter
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from langchain.llms import OpenAI
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from langchain.chains import VectorDBQA
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from langchain.document_loaders import TextLoader
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base_path = os.environ.get('OPENAI_API_BASE', 'http://localhost:8080/v1')
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# Load and process the text
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loader = TextLoader('state_of_the_union.txt')
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documents = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=70)
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#text_splitter = TokenTextSplitter()
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texts = text_splitter.split_documents(documents)
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# Embed and store the texts
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# Supplying a persist_directory will store the embeddings on disk
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persist_directory = 'db'
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embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
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vectordb = Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory)
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vectordb.persist()
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vectordb = None |