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Major dev 1.0.0
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,3 @@ | ||
| from GPT import query | ||
| from GPT import toolkit | ||
| from GPT import gpt_tools | ||
| from GPT import model |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| import openai | ||
| import numpy as np | ||
| import requests | ||
| import sseclient | ||
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| # this function compare similarity between two vectors. | ||
| # The higher value the dot product have, the more alike between these vectors | ||
| def similarity(v1, v2): | ||
| return np.dot(v1, v2) | ||
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| # return a list of vectors | ||
| def embedding(content, engine='text-embedding-ada-002'): | ||
| response = openai.Embedding.create(input=content, engine=engine) | ||
| vector = response['data'][0]['embedding'] | ||
| return vector | ||
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| def search_chunks(query, data, count=1): | ||
| vector = embedding(query) | ||
| points = [] | ||
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| for item in data: | ||
| # compare search terms with brain-data | ||
| point = similarity(vector, item['vector']) | ||
| points.append({ | ||
| 'content': item['content'], | ||
| 'point': point | ||
| }) | ||
| # sort points base on descendant order | ||
| 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, params): | ||
| response = openai.Completion.create( | ||
| model=model, | ||
| prompt=prompt, | ||
| temperature=params.temp, | ||
| max_tokens=params.max_tokens, | ||
| top_p=params.top_p, | ||
| frequency_penalty=params.frequency_penalty, | ||
| presence_penalty=params.present_penalty | ||
| ) | ||
| text = response['choices'][0]['text'].strip() | ||
| return text | ||
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| def gpt35(prompt, params, system_role_content: str = 'You are a helpful assistant.'): | ||
| completions = openai.ChatCompletion.create( | ||
| model="gpt-3.5-turbo", | ||
| max_tokens=params.max_tokens, | ||
| temperature=params.temp, | ||
| top_p=params.top_p, | ||
| frequency_penalty=params.frequency_penalty, | ||
| presence_penalty=params.present_penalty, | ||
| messages=[ | ||
| {"role": "system", "content": system_role_content}, | ||
| {"role": "user", "content": prompt} | ||
| ]) | ||
| text = completions['choices'][0]['message']['content'] | ||
| return text | ||
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| def gpt3_stream(prompt, model, params): | ||
| response = openai.Completion.create( | ||
| model=model, | ||
| stream=True, | ||
| prompt=prompt, | ||
| temperature=params.temp, | ||
| max_tokens=params.max_tokens, | ||
| top_p=params.top_p, | ||
| frequency_penalty=params.frequency_penalty, | ||
| presence_penalty=params.present_penalty | ||
| ) | ||
| return response | ||
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| def gpt35_stream(prompt, params, system_role_content: str = 'You are a helpful assistant.'): | ||
| completions = openai.ChatCompletion.create( | ||
| model="gpt-3.5-turbo", | ||
| max_tokens=params.max_tokens, | ||
| temperature=params.temp, | ||
| top_p=params.top_p, | ||
| frequency_penalty=params.frequency_penalty, | ||
| presence_penalty=params.present_penalty, | ||
| stream=True, | ||
| messages=[ | ||
| {"role": "system", "content": system_role_content}, | ||
| {"role": "user", "content": prompt} | ||
| ]) | ||
| return completions |
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