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guidance-example.py(fichier créé)
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| 1 | + | import guidance | |
| 2 | + | gpt35 = guidance.models.OpenAI("gpt-3.5-turbo") | |
| 3 | + | ||
| 4 | + | import re | |
| 5 | + | from guidance import gen, select, system, user, assistant | |
| 6 | + | ||
| 7 | + | @guidance | |
| 8 | + | def plan_for_goal(lm, goal: str): | |
| 9 | + | ||
| 10 | + | # This is a helper function which we will use below | |
| 11 | + | def parse_best(prosandcons, options): | |
| 12 | + | best = re.search(r'Best=(\d+)', prosandcons) | |
| 13 | + | if not best: | |
| 14 | + | best = re.search(r'Best.*?(\d+)', 'Best= option is 3') | |
| 15 | + | if best: | |
| 16 | + | best = int(best.group(1)) | |
| 17 | + | else: | |
| 18 | + | best = 0 | |
| 19 | + | return options[best] | |
| 20 | + | ||
| 21 | + | # Some general instruction to the model | |
| 22 | + | with system(): | |
| 23 | + | lm += "You are a helpful assistant." | |
| 24 | + | ||
| 25 | + | # Simulate a simple request from the user | |
| 26 | + | # Note that we switch to using 'lm2' here, because these are intermediate steps (so we don't want to overwrite the current lm object) | |
| 27 | + | with user(): | |
| 28 | + | lm2 = lm + f"""\ | |
| 29 | + | I want to {goal} | |
| 30 | + | Can you please generate one option for how to accomplish this? | |
| 31 | + | Please make the option very short, at most one line.""" | |
| 32 | + | ||
| 33 | + | # Generate several options. Note that this means several sequential generation requests | |
| 34 | + | n_options = 5 | |
| 35 | + | with assistant(): | |
| 36 | + | options = [] | |
| 37 | + | for i in range(n_options): | |
| 38 | + | options.append((lm2 + gen(name='option', temperature=1.0, max_tokens=50))["option"]) | |
| 39 | + | ||
| 40 | + | # Have the user request pros and cons | |
| 41 | + | with user(): | |
| 42 | + | lm2 += f"""\ | |
| 43 | + | I want to {goal} | |
| 44 | + | Can you please comment on the pros and cons of each of the following options, and then pick the best option? | |
| 45 | + | --- | |
| 46 | + | """ | |
| 47 | + | for i, opt in enumerate(options): | |
| 48 | + | lm2 += f"Option {i}: {opt}\n" | |
| 49 | + | lm2 += f"""\ | |
| 50 | + | --- | |
| 51 | + | Please discuss each option very briefly (one line for pros, one for cons), and end by saying Best=X, where X is the number of the best option.""" | |
| 52 | + | ||
| 53 | + | # Get the pros and cons from the model | |
| 54 | + | with assistant(): | |
| 55 | + | lm2 += gen(name='prosandcons', temperature=0.0, max_tokens=600, stop="Best=") + "Best=" + gen("best", regex="[0-9]+") | |
| 56 | + | ||
| 57 | + | # The user now extracts the one selected as the best, and asks for a full plan | |
| 58 | + | # We switch back to 'lm' because this is the final result we want | |
| 59 | + | with user(): | |
| 60 | + | lm += f"""\ | |
| 61 | + | I want to {goal} | |
| 62 | + | Here is my plan: {options[int(lm2["best"])]} | |
| 63 | + | Please elaborate on this plan, and tell me how to best accomplish it.""" | |
| 64 | + | ||
| 65 | + | # The plan is generated | |
| 66 | + | with assistant(): | |
| 67 | + | lm += gen(name='plan', max_tokens=500) | |
| 68 | + | ||
| 69 | + | return lm | |
| 70 | + | ||
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