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  1. __pycache__/qa.cpython-310.pyc +0 -0
  2. base.sh~ +1 -0
  3. bash.log +10 -8
  4. bash.pmpt.tpl~ +0 -0
  5. bash.py~ +21 -0
  6. chat.log +16 -14
  7. chat.py +0 -1
  8. chat.py~ +67 -0
  9. chatgpt.pmpt.tpl~ +15 -0
  10. chatgpt.py~ +9 -0
  11. color.py~ +26 -0
  12. examples.py~ +2 -0
  13. gatsby.log +6 -4
  14. gatsby.pmpt.tpl~ +5 -0
  15. gatsby.py~ +42 -0
  16. math.log +12 -10
  17. math.pmpt.tpl~ +26 -0
  18. math.py +38 -0
  19. math.py~ +14 -0
  20. ner.log +14 -12
  21. ner.pmpt.tpl~ +7 -0
  22. ner.py~ +5 -0
  23. pal.log +8 -6
  24. pal.pmpt.tpl~ +148 -0
  25. pal.py~ +64 -0
  26. parallel.py~ +6 -0
  27. qa.log +4 -18
  28. qa.pmpt.tpl~ +12 -0
  29. qa.py +15 -9
  30. qa.py~ +0 -0
  31. show.py~ +3 -0
  32. sixers.txt~ +7 -0
  33. stats.log +2 -2
  34. story.py~ +6 -0
  35. summary.py~ +16 -0
  36. temp.py~ +6 -0
  37. tmp.tpl~ +1 -0
__pycache__/qa.cpython-310.pyc ADDED
Binary file (1.26 kB). View file
 
base.sh~ ADDED
@@ -0,0 +1 @@
 
 
1
+ export OPENAI_KEY="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"
bash.log CHANGED
@@ -1,8 +1,10 @@
1
- {"action_status": "started", "timestamp": 1678759606.621539, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [1]}
2
- {"action_status": "succeeded", "timestamp": 1678759606.6216574, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [2]}
3
- {"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
4
- {"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
5
- {"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
6
- {"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
7
- {"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
8
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
 
 
1
+ {"action_status": "started", "timestamp": 1678759967.2243814, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.2245207, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.2472994, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.2474098, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [2]}
5
+ {"action_status": "started", "timestamp": 1678759967.4239852, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [1]}
6
+ {"action_status": "succeeded", "timestamp": 1678759967.4241192, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [2]}
7
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
8
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
9
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
10
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
bash.pmpt.tpl~ ADDED
File without changes
bash.py~ ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ class Bash(JinjaPrompt[]):
3
+ prompt_template = """If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:
4
+
5
+ Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"
6
+
7
+ I need to take the following actions:
8
+ - List all files in the directory
9
+ - Create a new directory
10
+ - Copy the files from the first directory into the second directory
11
+ ```bash
12
+ ls
13
+ mkdir myNewDirectory
14
+ cp -r target/* myNewDirectory
15
+ ```
16
+
17
+ That is the format. Begin!
18
+
19
+ Question: {question}"""
20
+
21
+
chat.log CHANGED
@@ -1,14 +1,16 @@
1
- {"action_status": "started", "timestamp": 1678759606.424852, "task_uuid": "0b226ec0-566b-4e36-af45-25c0881e5228", "action_type": "chat", "task_level": [1]}
2
- {"action_status": "succeeded", "timestamp": 1678759606.4249172, "task_uuid": "0b226ec0-566b-4e36-af45-25c0881e5228", "action_type": "chat", "task_level": [2]}
3
- {"action_status": "started", "timestamp": 1678759606.5748045, "task_uuid": "a2181bdc-7e7a-47a7-9a58-6abf2c2e10cd", "action_type": "ner", "task_level": [1]}
4
- {"action_status": "succeeded", "timestamp": 1678759606.5748866, "task_uuid": "a2181bdc-7e7a-47a7-9a58-6abf2c2e10cd", "action_type": "ner", "task_level": [2]}
5
- {"action_status": "started", "timestamp": 1678759606.5984528, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [1]}
6
- {"action_status": "succeeded", "timestamp": 1678759606.5985456, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [2]}
7
- {"action_status": "started", "timestamp": 1678759606.621539, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [1]}
8
- {"action_status": "succeeded", "timestamp": 1678759606.6216574, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [2]}
9
- {"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
10
- {"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
11
- {"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
12
- {"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
13
- {"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
14
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
 
 
1
+ {"action_status": "started", "timestamp": 1678759967.0213578, "task_uuid": "2e616b5e-e097-431b-91b7-b700c35e48b9", "action_type": "chat", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.021416, "task_uuid": "2e616b5e-e097-431b-91b7-b700c35e48b9", "action_type": "chat", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.1781576, "task_uuid": "9836e1af-a597-4ef0-9216-4f9a72f30046", "action_type": "ner", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.1782339, "task_uuid": "9836e1af-a597-4ef0-9216-4f9a72f30046", "action_type": "ner", "task_level": [2]}
5
+ {"action_status": "started", "timestamp": 1678759967.2008536, "task_uuid": "81655907-1b95-4737-a624-b80c13bf67c6", "action_type": "math", "task_level": [1]}
6
+ {"action_status": "succeeded", "timestamp": 1678759967.2009714, "task_uuid": "81655907-1b95-4737-a624-b80c13bf67c6", "action_type": "math", "task_level": [2]}
7
+ {"action_status": "started", "timestamp": 1678759967.2243814, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [1]}
8
+ {"action_status": "succeeded", "timestamp": 1678759967.2245207, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [2]}
9
+ {"action_status": "started", "timestamp": 1678759967.2472994, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [1]}
10
+ {"action_status": "succeeded", "timestamp": 1678759967.2474098, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [2]}
11
+ {"action_status": "started", "timestamp": 1678759967.4239852, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [1]}
12
+ {"action_status": "succeeded", "timestamp": 1678759967.4241192, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [2]}
13
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
14
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
15
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
16
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
chat.py CHANGED
@@ -3,7 +3,6 @@
3
  import warnings
4
  from dataclasses import dataclass
5
  from typing import List, Tuple
6
- from IPython.display import Markdown, display
7
  import minichain
8
 
9
  # + tags=["hide_inp"]
 
3
  import warnings
4
  from dataclasses import dataclass
5
  from typing import List, Tuple
 
6
  import minichain
7
 
8
  # + tags=["hide_inp"]
chat.py~ ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+ import warnings
4
+ from dataclasses import dataclass
5
+ from typing import List, Tuple
6
+ from IPython.display import Markdown, display
7
+ import minichain
8
+
9
+ # + tags=["hide_inp"]
10
+ warnings.filterwarnings("ignore")
11
+ # -
12
+
13
+
14
+ # Generic stateful Memory
15
+
16
+ MEMORY = 2
17
+
18
+ @dataclass
19
+ class State:
20
+ memory: List[Tuple[str, str]]
21
+ human_input: str = ""
22
+
23
+ def push(self, response: str) -> "State":
24
+ memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]
25
+ return State(memory + [(self.human_input, response)])
26
+
27
+ # Chat prompt with memory
28
+
29
+ class ChatPrompt(minichain.TemplatePrompt):
30
+ template_file = "chatgpt.pmpt.tpl"
31
+ def parse(self, out: str, inp: State) -> State:
32
+ result = out.split("Assistant:")[-1]
33
+ return inp.push(result)
34
+
35
+ class Human(minichain.Prompt):
36
+ def parse(self, out: str, inp: State) -> State:
37
+ return inp.human_input = out
38
+
39
+
40
+ with minichain.start_chain("chat") as backend:
41
+ prompt = ChatPrompt(backend.OpenAI())
42
+ state = State([])
43
+
44
+ gradio = prompt.to_gradio(fields= ["question"],
45
+ state= {"state", state},
46
+ examples=['Go up one directory, and then into the minichain directory,'
47
+ 'and list the files in the directory'],
48
+ out_type="markdown"
49
+ )
50
+ if __name__ == "__main__":
51
+ gradio.launch()
52
+
53
+ # for i in range(len(fake_human)):
54
+ # human.chain(prompt)
55
+
56
+
57
+ fake_human = [
58
+ "I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.",
59
+ "ls ~",
60
+ "cd ~",
61
+ "{Please make a file jokes.txt inside and put some jokes inside}",
62
+ """echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py && python3 run.py""",
63
+ """echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""",
64
+ """echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04\nCOPY entrypoint.sh entrypoint.sh\nENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image""",
65
+ "nvidia-smi"
66
+ ]
67
+
chatgpt.pmpt.tpl~ ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Assistant is a large language model trained by OpenAI.
2
+
3
+ Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
4
+
5
+ Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
6
+
7
+ Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
8
+
9
+ {% for d in memory %}
10
+ Human: {{d.human}}
11
+ AI: {{d.ai}}
12
+ {% endfor %}
13
+
14
+ Human: {{human_input}}
15
+ Assistant:
chatgpt.py~ ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+
2
+
3
+
4
+
5
+
6
+
7
+
8
+
9
+ output = chatgpt_chain.predict(human_input="I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.")
color.py~ ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Answer a math problem with code.
2
+ # Adapted from Dust [maths-generate-code](https://dust.tt/spolu/a/d12ac33169)
3
+
4
+ from minichain import Backend, JinjaPrompt, Prompt, start_chain, SimplePrompt, show_log
5
+
6
+
7
+ # Prompt that asks LLM for code from math.
8
+
9
+ class ColorPrompt(Prompt[str, bool]):
10
+ def parse(inp: str) -> str:
11
+ return f"Answer 'Yes' if this is a color, {inp}. Answer:"
12
+
13
+ def parse(out: str, inp) -> bool:
14
+ # Encode the parsing logic
15
+ return out.strip() == "Yes"
16
+ ColorPrompt().show({"inp": "dog"}, "No")
17
+
18
+
19
+ with start_chain("math") as backend:
20
+ question = 'What is the sum of the powers of 3 (3^i) that are smaller than 100?'
21
+ prompt = MathPrompt(backend.OpenAI()).chain(SimplePrompt(backend.Python()))
22
+ result = prompt({"question": question})
23
+ print(result)
24
+
25
+
26
+ show_log("math.log")
examples.py~ ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ from ner import gradio as ner
2
+ from math import gradio as math
gatsby.log CHANGED
@@ -1,4 +1,6 @@
1
- {"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
2
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3
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4
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
 
 
1
+ {"action_status": "started", "timestamp": 1678759967.4239852, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.4241192, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
5
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
6
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
gatsby.pmpt.tpl~ ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ Context information is below.
2
+ ---------------------
3
+ {context_str}
4
+ ---------------------
5
+ Given the context information and not prior knowledge, answer the question: {query_str}
gatsby.py~ ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Questions answering with embeddings. Adapted from [OpenAI
2
+ # Notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).
3
+
4
+ import datasets
5
+ import numpy as np
6
+
7
+ from minichain import Prompt, TemplatePrompt, show_log, start_chain
8
+
9
+ # Load data with embeddings (computed beforehand)
10
+
11
+ olympics = datasets.load_from_disk("olympics.data")
12
+ olympics.add_faiss_index("embeddings")
13
+
14
+ # Fast KNN retieval prompt
15
+
16
+
17
+ class KNNPrompt(Prompt):
18
+ def find(self, out, inp):
19
+ res = olympics.get_nearest_examples("embeddings", np.array(out), 3)
20
+ return {"question": inp, "docs": res.examples["content"]}
21
+
22
+
23
+ # QA prompt to ask question with examples
24
+
25
+
26
+ class QAPrompt(TemplatePrompt):
27
+ template_file = "qa.pmpt.tpl"
28
+
29
+
30
+ with start_chain("qa") as backend:
31
+ question = "Who won the 2020 Summer Olympics men's high jump?"
32
+ prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
33
+ result = prompt(question)
34
+ print(result)
35
+
36
+ # + tags=["hide_inp"]
37
+ QAPrompt().show(
38
+ {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
39
+ )
40
+ # -
41
+
42
+ show_log("qa.log")
math.log CHANGED
@@ -1,10 +1,12 @@
1
- {"action_status": "started", "timestamp": 1678759606.5984528, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [1]}
2
- {"action_status": "succeeded", "timestamp": 1678759606.5985456, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [2]}
3
- {"action_status": "started", "timestamp": 1678759606.621539, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [1]}
4
- {"action_status": "succeeded", "timestamp": 1678759606.6216574, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [2]}
5
- {"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
6
- {"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
7
- {"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
8
- {"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
9
- {"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
10
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
 
 
1
+ {"action_status": "started", "timestamp": 1678759967.2008536, "task_uuid": "81655907-1b95-4737-a624-b80c13bf67c6", "action_type": "math", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.2009714, "task_uuid": "81655907-1b95-4737-a624-b80c13bf67c6", "action_type": "math", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.2243814, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.2245207, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [2]}
5
+ {"action_status": "started", "timestamp": 1678759967.2472994, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [1]}
6
+ {"action_status": "succeeded", "timestamp": 1678759967.2474098, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [2]}
7
+ {"action_status": "started", "timestamp": 1678759967.4239852, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [1]}
8
+ {"action_status": "succeeded", "timestamp": 1678759967.4241192, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [2]}
9
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
10
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
11
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
12
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
math.pmpt.tpl~ ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ question:
2
+ What is 37593 * 67?
3
+ code:
4
+ 37593 * 67
5
+ 1:
6
+ { question, code }
7
+ question:
8
+ Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?
9
+ code:
10
+ (16-3-4)*2
11
+ 2:
12
+ { question, code }
13
+ question:
14
+ How many of the integers between 0 and 99 inclusive are divisible by 8?
15
+ code:
16
+ let count = 0;
17
+ for (let i = 0; i <= 99; i++) {
18
+ if (i % 8 === 0) { count+=1 }
19
+ };
20
+ count
21
+ 3:
22
+ { question, code }
23
+ question:
24
+ A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?
25
+ code:
26
+ 2 + 2/2
math.py ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Notebook to answer a math problem with code.
2
+ # Adapted from Dust [maths-generate-code](https://dust.tt/spolu/a/d12ac33169)
3
+
4
+ import minichain
5
+
6
+ # Prompt that asks LLM for code from math.
7
+
8
+ class MathPrompt(minichain.TemplatePrompt[str]):
9
+ template_file = "math.pmpt.tpl"
10
+
11
+
12
+ # Ask a question and run it as python code.
13
+
14
+ with minichain.start_chain("math") as backend:
15
+ math_prompt = MathPrompt(backend.OpenAI())
16
+ code_prompt = minichain.SimplePrompt(backend.Python())
17
+ prompt = math_prompt.chain(code_prompt)
18
+ # result = prompt({"question": question})
19
+ # print(result)
20
+
21
+ gradio = prompt.to_gradio(fields =["question"],
22
+ examples=["What is the sum of the powers of 3 (3^i) that are smaller than 100?"],
23
+ out_type="markdown"
24
+
25
+ )
26
+ if __name__ == "__main__":
27
+ gradio.launch()
28
+
29
+
30
+ # View the prompt
31
+
32
+ # + tags=["hide_inp"]
33
+ # MathPrompt().show({"question": "What is 10 + 12?"}, "10 + 12")
34
+ # # -
35
+
36
+ # # View the log
37
+
38
+ # minichain.show_log("math.log")
math.py~ ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List
2
+ from minichain import Backend, JinjaPrompt, Prompt, start_chain
3
+
4
+
5
+ # Prompt that asks LLM to produce a bash command.
6
+
7
+ class CLIPrompt(JinjaPrompt[List[str]]):
8
+ template_file = "bash.pmpt.tpl"
9
+ def parse(self, result: str) -> str:
10
+ return result
11
+
12
+ CLIPrompt().show(
13
+ {"question": "list the files in the directory"},
14
+ """```bash\nls\n```""")
ner.log CHANGED
@@ -1,12 +1,14 @@
1
- {"action_status": "started", "timestamp": 1678759606.5748045, "task_uuid": "a2181bdc-7e7a-47a7-9a58-6abf2c2e10cd", "action_type": "ner", "task_level": [1]}
2
- {"action_status": "succeeded", "timestamp": 1678759606.5748866, "task_uuid": "a2181bdc-7e7a-47a7-9a58-6abf2c2e10cd", "action_type": "ner", "task_level": [2]}
3
- {"action_status": "started", "timestamp": 1678759606.5984528, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [1]}
4
- {"action_status": "succeeded", "timestamp": 1678759606.5985456, "task_uuid": "694c3be6-2b7a-47b3-8d6a-0408b8dc6a26", "action_type": "math", "task_level": [2]}
5
- {"action_status": "started", "timestamp": 1678759606.621539, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [1]}
6
- {"action_status": "succeeded", "timestamp": 1678759606.6216574, "task_uuid": "b1f606f3-d7b6-48bb-a3a5-77e51fda3fa6", "action_type": "bash", "task_level": [2]}
7
- {"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
8
- {"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
9
- {"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
10
- {"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
11
- {"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
12
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
 
 
1
+ {"action_status": "started", "timestamp": 1678759967.1781576, "task_uuid": "9836e1af-a597-4ef0-9216-4f9a72f30046", "action_type": "ner", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.1782339, "task_uuid": "9836e1af-a597-4ef0-9216-4f9a72f30046", "action_type": "ner", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.2008536, "task_uuid": "81655907-1b95-4737-a624-b80c13bf67c6", "action_type": "math", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.2009714, "task_uuid": "81655907-1b95-4737-a624-b80c13bf67c6", "action_type": "math", "task_level": [2]}
5
+ {"action_status": "started", "timestamp": 1678759967.2243814, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [1]}
6
+ {"action_status": "succeeded", "timestamp": 1678759967.2245207, "task_uuid": "f1fd39ca-381d-4b9e-9a0d-5812a8c20c21", "action_type": "bash", "task_level": [2]}
7
+ {"action_status": "started", "timestamp": 1678759967.2472994, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [1]}
8
+ {"action_status": "succeeded", "timestamp": 1678759967.2474098, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [2]}
9
+ {"action_status": "started", "timestamp": 1678759967.4239852, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [1]}
10
+ {"action_status": "succeeded", "timestamp": 1678759967.4241192, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [2]}
11
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
12
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
13
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
14
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
ner.pmpt.tpl~ ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ You are a highly intelligent and accurate {{ domain }} domain Named-entity recognition(NER) system. You take Passage as input and your task is to recognize and extract specific types of {{ domain }} domain named entities in that given passage and classify into a set of following predefined entity types:
2
+ {{ labels }}
3
+
4
+ Your output format is only {{ output_format|default("[{{'T': type of entity from predefined entity types, 'E': entity in the input text}},...,{{'branch' : Appropriate branch of the passage ,'group': Appropriate Group of the passage}}]") }} form, no other form.
5
+
6
+ Input: {{ text_input }}
7
+ Output:
ner.py~ ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ # Notebook implementation of named entity recognition.
2
+ # Adapted from [promptify](https://github.com/promptslab/Promptify/blob/main/promptify/prompts/nlp/templates/ner.jinja)
3
+
4
+
5
+ import minichain
pal.log CHANGED
@@ -1,6 +1,8 @@
1
- {"action_status": "started", "timestamp": 1678759606.6456344, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [1]}
2
- {"action_status": "succeeded", "timestamp": 1678759606.645799, "task_uuid": "147ddfba-ef61-4a52-9f0c-1107c3fbaa6a", "action_type": "pal", "task_level": [2]}
3
- {"action_status": "started", "timestamp": 1678759606.9333436, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [1]}
4
- {"action_status": "succeeded", "timestamp": 1678759606.9335442, "task_uuid": "3246fe37-fa91-436b-96be-67648cd1ef76", "action_type": "gatsby", "task_level": [2]}
5
- {"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
6
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
 
 
1
+ {"action_status": "started", "timestamp": 1678759967.2472994, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.2474098, "task_uuid": "d8d39770-770f-47b8-9220-ad86e3d3caab", "action_type": "pal", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.4239852, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.4241192, "task_uuid": "20b25aab-148d-4f7e-8d5a-e4f812df7390", "action_type": "gatsby", "task_level": [2]}
5
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
6
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
7
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
8
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
pal.pmpt.tpl~ ADDED
@@ -0,0 +1,148 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?
2
+
3
+ # solution in Python:
4
+
5
+
6
+ def solution():
7
+ """Olivia has $23. She bought five bagels for $3 each. How much money does she have left?"""
8
+ money_initial = 23
9
+ bagels = 5
10
+ bagel_cost = 3
11
+ money_spent = bagels * bagel_cost
12
+ money_left = money_initial - money_spent
13
+ result = money_left
14
+ return result
15
+
16
+
17
+
18
+
19
+
20
+ Q: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?
21
+
22
+ # solution in Python:
23
+
24
+
25
+ def solution():
26
+ """Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""
27
+ golf_balls_initial = 58
28
+ golf_balls_lost_tuesday = 23
29
+ golf_balls_lost_wednesday = 2
30
+ golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday
31
+ result = golf_balls_left
32
+ return result
33
+
34
+
35
+
36
+
37
+
38
+ Q: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?
39
+
40
+ # solution in Python:
41
+
42
+
43
+ def solution():
44
+ """There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""
45
+ computers_initial = 9
46
+ computers_per_day = 5
47
+ num_days = 4 # 4 days between monday and thursday
48
+ computers_added = computers_per_day * num_days
49
+ computers_total = computers_initial + computers_added
50
+ result = computers_total
51
+ return result
52
+
53
+
54
+
55
+
56
+
57
+ Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?
58
+
59
+ # solution in Python:
60
+
61
+
62
+ def solution():
63
+ """Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""
64
+ toys_initial = 5
65
+ mom_toys = 2
66
+ dad_toys = 2
67
+ total_received = mom_toys + dad_toys
68
+ total_toys = toys_initial + total_received
69
+ result = total_toys
70
+ return result
71
+
72
+
73
+
74
+
75
+
76
+ Q: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?
77
+
78
+ # solution in Python:
79
+
80
+
81
+ def solution():
82
+ """Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?"""
83
+ jason_lollipops_initial = 20
84
+ jason_lollipops_after = 12
85
+ denny_lollipops = jason_lollipops_initial - jason_lollipops_after
86
+ result = denny_lollipops
87
+ return result
88
+
89
+
90
+
91
+
92
+
93
+ Q: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?
94
+
95
+ # solution in Python:
96
+
97
+
98
+ def solution():
99
+ """Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?"""
100
+ leah_chocolates = 32
101
+ sister_chocolates = 42
102
+ total_chocolates = leah_chocolates + sister_chocolates
103
+ chocolates_eaten = 35
104
+ chocolates_left = total_chocolates - chocolates_eaten
105
+ result = chocolates_left
106
+ return result
107
+
108
+
109
+
110
+
111
+
112
+ Q: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?
113
+
114
+ # solution in Python:
115
+
116
+
117
+ def solution():
118
+ """If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""
119
+ cars_initial = 3
120
+ cars_arrived = 2
121
+ total_cars = cars_initial + cars_arrived
122
+ result = total_cars
123
+ return result
124
+
125
+
126
+
127
+
128
+
129
+ Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
130
+
131
+ # solution in Python:
132
+
133
+
134
+ def solution():
135
+ """There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""
136
+ trees_initial = 15
137
+ trees_after = 21
138
+ trees_added = trees_after - trees_initial
139
+ result = trees_added
140
+ return result
141
+
142
+
143
+
144
+
145
+
146
+ Q: {question}
147
+
148
+ # solution in Python:
pal.py~ ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Prompt from ...
2
+ #
3
+ from dataclasses import dataclass
4
+
5
+ from parsita import TextParsers, lit, reg
6
+
7
+ from minichain import Backend, JinjaPrompt, SimplePrompt, start_chain
8
+
9
+
10
+ # Define the states of the bot.
11
+ @dataclass
12
+ class IntermediateState:
13
+ s: str
14
+ @dataclass
15
+ class FinalState:
16
+ s: str
17
+ States = FinalState | IntermediateState
18
+
19
+
20
+ class Pal(JinjaPrompt[States]):
21
+ template_file = "pal.pmpt.tpl"
22
+
23
+
24
+ def parse(self, inp: str) -> States:
25
+ return self.SelfAskParser.response.parse(inp).or_die() # type: ignore
26
+
27
+ SelfAsk().show({"input": "What is the zip code of the city where George Washington was born?",
28
+ "agent_scratchpad":True},
29
+ "Follow up: Where was George Washington born?")
30
+
31
+
32
+
33
+ def selfask(inp: str, openai: Backend, google: Backend) -> str:
34
+ prompt1 = SelfAsk(openai)
35
+ prompt2 = SimplePrompt(google)
36
+ suffix = ""
37
+ for i in range(3):
38
+ r1 = prompt1(
39
+ dict(input=inp, suffix=suffix, agent_scratchpad=True), name=f"Chat {i}"
40
+ )
41
+ if isinstance(r1.val, FinalState):
42
+ break
43
+ out = prompt2(r1.val.s, name=f"Google{i}")
44
+ suffix += "\nIntermediate answer:" + out.echo
45
+ return r1.val.s
46
+
47
+
48
+ with start_chain("pal") as backend:
49
+ result = pal(
50
+ "What is the zip code of the city where George Washington was born?",
51
+ backend.Mock(
52
+ [
53
+ "Follow up: Where was George Washington born?",
54
+ "Follow up: What is the zip code of Virginia?",
55
+ "So the final answer is: 12312",
56
+ ]
57
+ ),
58
+ # OpenAI("")
59
+ backend.Mock(["Virginia", "12312"]),
60
+ )
61
+ print(result)
62
+
63
+ !eliot-tree -l 0 selfask.log | grep -v "succeeded" | grep -v "started"
64
+
parallel.py~ ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ from minichain import Prompt, OpenAI, Google
2
+ import trio
3
+
4
+ async def promptee():
5
+ async with trio.open_nursery() as nursery:
6
+ pass
qa.log CHANGED
@@ -1,18 +1,4 @@
1
- {"action_status": "started", "timestamp": 1677511183.6399937, "task_uuid": "8f704116-b88c-4e34-a26a-196b09fbe252", "action_type": "qa", "task_level": [1]}
2
- {"action_status": "started", "timestamp": 1677511183.6462893, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "<class '__main__.KNNPrompt'>", "task_level": [1]}
3
- {"input": "Who won the 2020 Summer Olympics men's high jump?", "action_status": "started", "timestamp": 1677511183.64635, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "Input Function", "task_level": [2, 1]}
4
- {"action_status": "succeeded", "timestamp": 1677511183.6463842, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "Input Function", "task_level": [2, 2]}
5
- {"prompt": "Who won the 2020 Summer Olympics men's high jump?", "action_status": "started", "timestamp": 1677511183.6464071, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "Prompted", "task_level": [3, 1]}
6
- {"action_status": "succeeded", "timestamp": 1677511184.2114058, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "Prompted", "task_level": [3, 2]}
7
- {"result": [0.011016231030225754, -0.0031887420918792486, 0.014024118892848492, -0.022197451442480087, 0.007824316620826721, 0.027616726234555244, -0.02050948143005371, -0.026829853653907776, -0.011498508043587208, -0.005558881442993879, 0.015267887152731419, 0.006593239493668079, 0.002844484755769372, -0.018999191001057625, 0.028073621913790703, -0.031906455755233765, 0.015661323443055153, -0.0171843059360981, 0.01038800086826086, -0.02090291678905487, -0.028454367071390152, -0.00035179281258024275, -0.0003016216796822846, 0.010077059268951416, 0.02153749391436577, 0.0013873407151550055, 0.039749812334775925, -0.018148859962821007, 0.023758508265018463, -0.03817606344819069, 0.015889771282672882, 0.022425899282097816, -0.019253021106123924, -0.009359989315271378, -0.027439044788479805, -0.010000910609960556, -0.026931384578347206, -0.002877800026908517, 0.031043434515595436, -0.02509111538529396, 0.0162958987057209, 0.011980785988271236, -0.011828487738966942, -0.007957576774060726, 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8
- {"action_status": "succeeded", "timestamp": 1677511184.216066, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "Result", "task_level": [4, 2]}
9
- {"action_status": "succeeded", "timestamp": 1677511184.2161038, "task_uuid": "6483e094-0601-4a93-8394-c753301f1a18", "action_type": "<class '__main__.KNNPrompt'>", "task_level": [5]}
10
- {"action_status": "started", "timestamp": 1677511184.2161624, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "<class '__main__.QAPrompt'>", "task_level": [1]}
11
- {"input": {"question": "Who won the 2020 Summer Olympics men's high jump?", "docs": ["The men's high jump event at the 2020 Summer Olympics took place between 30 July and 1 August 2021 at the Olympic Stadium. 33 athletes from 24 nations competed; the total possible number depended on how many nations would use universality places to enter athletes in addition to the 32 qualifying through mark or ranking (no universality places were used in 2021). Italian athlete Gianmarco Tamberi along with Qatari athlete Mutaz Essa Barshim emerged as joint winners of the event following a tie between both of them as they cleared 2.37m. Both Tamberi and Barshim agreed to share the gold medal in a rare instance where the athletes of different nations had agreed to share the same medal in the history of Olympics. Barshim in particular was heard to ask a competition official \"Can we have two golds?\" in response to being offered a 'jump off'. Maksim Nedasekau of Belarus took bronze. The medals were the first ever in the men's high jump for Italy and Belarus, the first gold in the men's high jump for Italy and Qatar, and the third consecutive medal in the men's high jump for Qatar (all by Barshim). Barshim became only the second man to earn three medals in high jump, joining Patrik Sj\u00f6berg of Sweden (1984 to 1992).", "The men's long jump event at the 2020 Summer Olympics took place between 31 July and 2 August 2021 at the Japan National Stadium. Approximately 35 athletes were expected to compete; the exact number was dependent on how many nations use universality places to enter athletes in addition to the 32 qualifying through time or ranking (1 universality place was used in 2016). 31 athletes from 20 nations competed. Miltiadis Tentoglou won the gold medal, Greece's first medal in the men's long jump. Cuban athletes Juan Miguel Echevarr\u00eda and Maykel Mass\u00f3 earned silver and bronze, respectively, the nation's first medals in the event since 2008.", "The men's pole vault event at the 2020 Summer Olympics took place between 31 July and 3 August 2021 at the Japan National Stadium. 29 athletes from 18 nations competed. Armand Duplantis of Sweden won gold, with Christopher Nilsen of the United States earning silver and Thiago Braz of Brazil taking bronze. It was Sweden's first victory in the event and first medal of any color in the men's pole vault since 1952. Braz, who had won in 2016, became the ninth man to earn multiple medals in the pole vault."]}, "action_status": "started", "timestamp": 1677511184.216189, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "Input Function", "task_level": [2, 1]}
12
- {"action_status": "succeeded", "timestamp": 1677511184.2190657, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "Input Function", "task_level": [2, 2]}
13
- {"prompt": "Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say \"I don't know.\"\n\nContext:\n\n\n* The men's high jump event at the 2020 Summer Olympics took place between 30 July and 1 August 2021 at the Olympic Stadium. 33 athletes from 24 nations competed; the total possible number depended on how many nations would use universality places to enter athletes in addition to the 32 qualifying through mark or ranking (no universality places were used in 2021). Italian athlete Gianmarco Tamberi along with Qatari athlete Mutaz Essa Barshim emerged as joint winners of the event following a tie between both of them as they cleared 2.37m. Both Tamberi and Barshim agreed to share the gold medal in a rare instance where the athletes of different nations had agreed to share the same medal in the history of Olympics. Barshim in particular was heard to ask a competition official \"Can we have two golds?\" in response to being offered a 'jump off'. Maksim Nedasekau of Belarus took bronze. The medals were the first ever in the men's high jump for Italy and Belarus, the first gold in the men's high jump for Italy and Qatar, and the third consecutive medal in the men's high jump for Qatar (all by Barshim). Barshim became only the second man to earn three medals in high jump, joining Patrik Sj\u00f6berg of Sweden (1984 to 1992).\n\n* The men's long jump event at the 2020 Summer Olympics took place between 31 July and 2 August 2021 at the Japan National Stadium. Approximately 35 athletes were expected to compete; the exact number was dependent on how many nations use universality places to enter athletes in addition to the 32 qualifying through time or ranking (1 universality place was used in 2016). 31 athletes from 20 nations competed. Miltiadis Tentoglou won the gold medal, Greece's first medal in the men's long jump. Cuban athletes Juan Miguel Echevarr\u00eda and Maykel Mass\u00f3 earned silver and bronze, respectively, the nation's first medals in the event since 2008.\n\n* The men's pole vault event at the 2020 Summer Olympics took place between 31 July and 3 August 2021 at the Japan National Stadium. 29 athletes from 18 nations competed. Armand Duplantis of Sweden won gold, with Christopher Nilsen of the United States earning silver and Thiago Braz of Brazil taking bronze. It was Sweden's first victory in the event and first medal of any color in the men's pole vault since 1952. Braz, who had won in 2016, became the ninth man to earn multiple medals in the pole vault.\n\n\n Q: Who won the 2020 Summer Olympics men's high jump?\n\n A: \n", "action_status": "started", "timestamp": 1677511184.2191062, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "Prompted", "task_level": [3, 1]}
14
- {"action_status": "succeeded", "timestamp": 1677511188.0845268, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "Prompted", "task_level": [3, 2]}
15
- {"result": "Italian athlete Gianmarco Tamberi and Qatari athlete Mutaz Essa Barshim emerged as joint winners of the event following a tie between both of them as they cleared 2.37m. Both Tamberi and Barshim agreed to share the gold medal.", "action_status": "started", "timestamp": 1677511188.084622, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "Result", "task_level": [4, 1]}
16
- {"action_status": "succeeded", "timestamp": 1677511188.0846646, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "Result", "task_level": [4, 2]}
17
- {"action_status": "succeeded", "timestamp": 1677511188.0846934, "task_uuid": "16863091-c5ac-408a-8ccc-46724b433a5d", "action_type": "<class '__main__.QAPrompt'>", "task_level": [5]}
18
- {"action_status": "succeeded", "timestamp": 1677511188.0849996, "task_uuid": "8f704116-b88c-4e34-a26a-196b09fbe252", "action_type": "qa", "task_level": [2]}
 
1
+ {"action_status": "started", "timestamp": 1678759967.5648448, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.5656788, "task_uuid": "166d72a3-608f-4a3b-ad1f-53067e284187", "action_type": "qa", "task_level": [2]}
3
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
4
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
qa.pmpt.tpl~ ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."
2
+
3
+ Context:
4
+
5
+ {% for doc in docs %}
6
+ * {{doc}}
7
+ {% endfor %}
8
+
9
+ Q: {{question}}
10
+
11
+ A:
12
+
qa.py CHANGED
@@ -32,15 +32,21 @@ class QAPrompt(TemplatePrompt):
32
 
33
 
34
  with start_chain("qa") as backend:
35
- question = "Who won the 2020 Summer Olympics men's high jump?"
36
  prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
37
- result = prompt(question)
38
- print(result)
39
 
40
- # + tags=["hide_inp"]
41
- QAPrompt().show(
42
- {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
43
- )
44
- # -
45
 
46
- show_log("qa.log")
 
 
 
 
 
 
 
 
 
32
 
33
 
34
  with start_chain("qa") as backend:
 
35
  prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
36
+
37
+ question = "Who won the 2020 Summer Olympics men's high jump?"
38
 
39
+ gradio = prompt.to_gradio(fields=["query"],
40
+ examples=[question])
41
+ if __name__ == "__main__":
42
+ gradio.launch()
 
43
 
44
+
45
+
46
+ # # + tags=["hide_inp"]
47
+ # QAPrompt().show(
48
+ # {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
49
+ # )
50
+ # # -
51
+
52
+ # show_log("qa.log")
qa.py~ ADDED
File without changes
show.py~ ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ import json, sys
2
+ from eliottree import tasks_from_iterable, render_tasks
3
+ render_tasks(sys.stdout.write, tasks_from_iterable([json.parse(l) for l in open("bash")]), colorize=True)
sixers.txt~ ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ The Philadelphia 76ers entered their Christmas Day matinee at Madison Square Garden against the New York Knicks having won seven straight games, all at home. They can now add an eighth victory to that list. Led by a dominant fourth quarter, the 76ers (20-12) defeated the Knicks (18-16), 119-112.
2
+
3
+ Joel Embiid led the way with 35 points and eight rebounds, while James Harden added 29 points and 13 assists in another dazzling performance. They got a boost off the bench from Georges Niang, who nailed four 3s in the fourth quarter to finish with 16 points.
4
+
5
+ The Knicks have now lost three straight games following an eight-game unbeaten run. Julius Randle matched Embiid with 35 points, but only four of those came in the fourth quarter.
6
+
7
+ Follow here for updates and analysis from The Athletic's staff.
stats.log CHANGED
@@ -1,2 +1,2 @@
1
- {"action_status": "started", "timestamp": 1678759606.9647467, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [1]}
2
- {"action_status": "succeeded", "timestamp": 1678759606.964883, "task_uuid": "86f09cf7-ec3b-41eb-aac0-144426f1a6f4", "action_type": "stats", "task_level": [2]}
 
1
+ {"action_status": "started", "timestamp": 1678759967.5982351, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [1]}
2
+ {"action_status": "succeeded", "timestamp": 1678759967.598474, "task_uuid": "d9a2055f-ef19-4094-a058-38024869062f", "action_type": "stats", "task_level": [2]}
story.py~ ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+
2
+ @chain
3
+ def story(bot):
4
+ out = bot(outline)
5
+ character = bot(characters)
6
+ return character
summary.py~ ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List
2
+ from minichain import Backend, JinjaPrompt, Prompt, start_chain
3
+
4
+
5
+
6
+ # Prompt that asks LLM to produce a bash command.
7
+
8
+ class SummaryPrompt(JinjaPrompt[List[str]]):
9
+ template_file = "summary.pmpt.tpl"
10
+ def parse(self, result: str) -> List[str]:
11
+ assert result.startswith("```bash")
12
+ return result.split("\n")[1:-1]
13
+
14
+ CLIPrompt().show(
15
+ {"question": "list the files in the directory"},
16
+ """```bash\nls\n```""")
temp.py~ ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ from dataclasses import fields, dataclass
2
+
3
+ @dataclass
4
+ class B:
5
+ a: int
6
+ b: str
tmp.tpl~ ADDED
@@ -0,0 +1 @@
 
 
1
+ {{typ}}