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42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
import openai
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import numpy as np
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# this function compare similarity between two vectors.
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# The higher value the dot product have, the more alike between these vectors
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def similarity(v1, v2):
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return np.dot(v1, v2)
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# return a list of vectors
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def embedding(content, engine='text-embedding-ada-002'):
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response = openai.Embedding.create(input=content, engine=engine)
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vector = response['data'][0]['embedding']
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return vector
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def search_chunks(text, data, count=1):
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vector = embedding(text)
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points = []
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for item in data:
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# compare search terms with brain-data
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point = similarity(vector, item['vector'])
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points.append({
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'content': item['content'],
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'point': point
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})
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# sort points base on decendent order
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ordered = sorted(points, key=lambda d: d['point'], reverse=True)
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return ordered[0:count]
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def gpt3(prompt, model, temp, max_tokens, top_p, freq_penl, pres_penl):
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response = openai.Completion.create(
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model= model,
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prompt=prompt,
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temperature=temp,
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max_tokens=max_tokens,
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top_p=top_p,
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frequency_penalty=freq_penl,
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presence_penalty=pres_penl
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)
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text = response['choices'][0]['text'].strip()
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return text |