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Callbacks Refactor [base] (#3256)

Browse Source 
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Ankush Gola
2023-04-30 11:14:09 -07:00
committed by GitHub
parent 18ec22fe56
commit d3ec00b566
208 changed files with 6394 additions and 3353 deletions
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30
docs/modules/chains/examples/llm_bash.ipynb
View File

@@ -10,7 +10,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -37,7 +37,7 @@
"'Hello World\\n'"
]
},
"execution_count": 1,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -50,7 +50,7 @@
"\n",
"text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
"\n",
"bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
"bash_chain = LLMBashChain.from_llm(llm, verbose=True)\n",
"\n",
"bash_chain.run(text)"
]
@@ -65,11 +65,12 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.prompt import PromptTemplate\n",
"from langchain.chains.llm_bash.prompt import BashOutputParser\n",
"\n",
"_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:\n",
"Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
@@ -88,12 +89,12 @@
"That is the format. Begin!\n",
"Question: {question}\"\"\"\n",
"\n",
"PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
"PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -120,13 +121,13 @@
"'Hello World\\n'"
]
},
"execution_count": 3,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)\n",
"bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
"\n",
"text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
"\n",
@@ -134,7 +135,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -145,7 +145,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -177,7 +177,7 @@
"'api.ipynb\\t\\t\\tllm_summarization_checker.ipynb\\r\\nconstitutional_chain.ipynb\\tmoderation.ipynb\\r\\nllm_bash.ipynb\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.ipynb\\t\\topenapi.ipynb\\r\\nllm_math.ipynb\\t\\t\\tpal.ipynb\\r\\nllm_requests.ipynb\\t\\tsqlite.ipynb'"
]
},
"execution_count": 4,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -187,7 +187,7 @@
"\n",
"\n",
"persistent_process = BashProcess(persistent=True)\n",
"bash_chain = LLMBashChain.from_bash_process(llm=llm, bash_process=persistent_process, verbose=True)\n",
"bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
"\n",
"text = \"List the current directory then move up a level.\"\n",
"\n",
@@ -196,7 +196,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -224,7 +224,7 @@
"'examples\\t\\tgetting_started.ipynb\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
]
},
"execution_count": 5,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -258,7 +258,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
"version": "3.9.1"
}
},
"nbformat": 4,

22
docs/modules/chains/examples/llm_checker.ipynb
View File

@@ -23,28 +23,16 @@
"\n",
"\n",
"\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
"\u001b[1mChain 0\u001b[0m:\n",
"{'statement': '\\nNone. Mammals do not lay eggs.'}\n",
"\n",
"\u001b[1mChain 1\u001b[0m:\n",
"{'assertions': '\\n• Mammals reproduce using live birth\\n• Mammals do not lay eggs\\n• Animals that lay eggs are not mammals'}\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"\u001b[1mChain 2\u001b[0m:\n",
"{'checked_assertions': '\\n1. True\\n\\n2. True\\n\\n3. False - Mammals are a class of animals that includes animals that lay eggs, such as monotremes (platypus and echidna).'}\n",
"\n",
"\u001b[1mChain 3\u001b[0m:\n",
"{'revised_statement': ' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'}\n",
"\n",
"\n",
"\u001b[1m> Finished SequentialChain chain.\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMCheckerChain chain.\u001b[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'"
"' No mammal lays the biggest eggs. The Elephant Bird, which was a species of giant bird, laid the largest eggs of any bird.'"
]
},
"execution_count": 1,
@@ -60,7 +48,7 @@
"\n",
"text = \"What type of mammal lays the biggest eggs?\"\n",
"\n",
"checker_chain = LLMCheckerChain(llm=llm, verbose=True)\n",
"checker_chain = LLMCheckerChain.from_llm(llm, verbose=True)\n",
"\n",
"checker_chain.run(text)"
]
@@ -89,7 +77,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

116
docs/modules/chains/examples/llm_math.ipynb
View File

@@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"id": "44e9ba31",
"metadata": {},
"outputs": [
@@ -24,23 +24,22 @@
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
"```python\n",
"import math\n",
"print(math.pow(13, .3432))\n",
"```text\n",
"13 ** .3432\n",
"```\n",
"...numexpr.evaluate(\"13 ** .3432\")...\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Answer: 2.4116004626599237\\n'"
"'Answer: 2.4116004626599237'"
]
},
"execution_count": 1,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -49,102 +48,7 @@
"from langchain import OpenAI, LLMMathChain\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"llm_math = LLMMathChain(llm=llm, verbose=True)\n",
"\n",
"llm_math.run(\"What is 13 raised to the .3432 power?\")"
]
},
{
"cell_type": "markdown",
"id": "2bdd5fc6",
"metadata": {},
"source": [
"## Customize Prompt\n",
"You can also customize the prompt that is used. Here is an example prompting it to use numpy"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "76be17b0",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts.prompt import PromptTemplate\n",
"\n",
"_PROMPT_TEMPLATE = \"\"\"You are GPT-3, and you can't do math.\n",
"\n",
"You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers.\n",
"\n",
"So we hooked you up to a Python 3 kernel, and now you can execute code. If you execute code, you must print out the final answer using the print function. You MUST use the python package numpy to answer your question. You must import numpy as np.\n",
"\n",
"\n",
"Question: ${{Question with hard calculation.}}\n",
"```python\n",
"${{Code that prints what you need to know}}\n",
"print(${{code}})\n",
"```\n",
"```output\n",
"${{Output of your code}}\n",
"```\n",
"Answer: ${{Answer}}\n",
"\n",
"Begin.\n",
"\n",
"Question: What is 37593 * 67?\n",
"\n",
"```python\n",
"import numpy as np\n",
"print(np.multiply(37593, 67))\n",
"```\n",
"```output\n",
"2518731\n",
"```\n",
"Answer: 2518731\n",
"\n",
"Question: {question}\"\"\"\n",
"\n",
"PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "0c42faa0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
"\n",
"```python\n",
"import numpy as np\n",
"print(np.power(13, .3432))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
"\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Answer: 2.4116004626599237\\n'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm_math = LLMMathChain(llm=llm, prompt=PROMPT, verbose=True)\n",
"llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",
"\n",
"llm_math.run(\"What is 13 raised to the .3432 power?\")"
]
@@ -152,7 +56,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "0c62951b",
"id": "e978bb8e",
"metadata": {},
"outputs": [],
"source": []
@@ -174,7 +78,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

61
docs/modules/chains/examples/llm_summarization_checker.ipynb
View File

@@ -221,11 +221,11 @@
"\n",
"• The light from these galaxies has been traveling for over 13 billion years to reach us. - True \n",
"\n",
"• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 1995. \n",
"• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004. \n",
"\n",
"• Exoplanets were first discovered in 1992. - True \n",
"\n",
"• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.\n",
"• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.\n",
"\"\"\"\n",
"\n",
"Original Summary:\n",
@@ -296,11 +296,11 @@
"\n",
"• The light from these galaxies has been traveling for over 13 billion years to reach us. - True \n",
"\n",
"• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 1995. \n",
"• JWST has provided us with the first images of exoplanets, which are planets outside of our own solar system. - False. The first exoplanet was discovered in 1992, but the first images of exoplanets were taken by the Hubble Space Telescope in 2004. \n",
"\n",
"• Exoplanets were first discovered in 1992. - True \n",
"\n",
"• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.\n",
"• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. The JWST has not yet been launched, so it is not yet known how much detail it will be able to provide.\n",
"\"\"\"\n",
"Result:\u001b[0m\n",
"\n",
@@ -312,7 +312,7 @@
"Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n",
"• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\n",
"• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\n",
"• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail than ever before.\n",
"• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\n",
"These discoveries can spark a child's imagination about the infinite wonders of the universe.\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
@@ -321,7 +321,7 @@
{
"data": {
"text/plain": [
"'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail than ever before.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'"
"'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\\n• In 2023, The JWST will spot a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\\n• The telescope will capture images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion years to reach us.\\n• Exoplanets, which are planets outside of our own solar system, were first discovered in 1992. The JWST will allow us to see them in greater detail when it is launched in 2023.\\nThese discoveries can spark a child\\'s imagination about the infinite wonders of the universe.'"
]
},
"execution_count": 1,
@@ -334,7 +334,7 @@
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"checker_chain = LLMSummarizationCheckerChain(llm=llm, verbose=True, max_checks=2)\n",
"checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)\n",
"text = \"\"\"\n",
"Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n",
"• In 2023, The JWST spotted a number of galaxies nicknamed \"green peas.\" They were given this name because they are small, round, and green, like peas.\n",
@@ -407,7 +407,8 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.\n",
"\n",
"Checked Assertions:\"\"\"\n",
"Checked Assertions:\n",
"\"\"\"\n",
"\n",
"- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
"\n",
@@ -428,7 +429,8 @@
"- It is considered the northern branch of the Norwegian Sea. True\n",
"\"\"\"\n",
"\n",
"Original Summary:\"\"\"\n",
"Original Summary:\n",
"\"\"\"\n",
"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\n",
"\"\"\"\n",
"\n",
@@ -443,7 +445,7 @@
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
"\n",
"If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
"\n",
@@ -555,7 +557,8 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.\n",
"\n",
"Checked Assertions:\"\"\"\n",
"Checked Assertions:\n",
"\"\"\"\n",
"\n",
"- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
"\n",
@@ -574,7 +577,8 @@
"- It is considered the northern branch of the Norwegian Sea. False - It is considered the northern branch of the Atlantic Ocean.\n",
"\"\"\"\n",
"\n",
"Original Summary:\"\"\"\n",
"Original Summary:\n",
"\"\"\"\n",
"\n",
"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\n",
"\"\"\"\n",
@@ -583,14 +587,20 @@
"\n",
"The output should have the same structure and formatting as the original summary.\n",
"\n",
"Summary:\u001b[0m\n",
"Summary:\u001b[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
"\n",
"If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
"\n",
@@ -701,7 +711,8 @@
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.\n",
"\n",
"Checked Assertions:\"\"\"\n",
"Checked Assertions:\n",
"\"\"\"\n",
"\n",
"- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True\n",
"\n",
@@ -718,7 +729,8 @@
"- It is considered the northern branch of the Atlantic Ocean. False - The Greenland Sea is considered part of the Arctic Ocean, not the Atlantic Ocean.\n",
"\"\"\"\n",
"\n",
"Original Summary:\"\"\"\n",
"Original Summary:\n",
"\"\"\"\n",
"\n",
"\n",
"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the country of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Atlantic Ocean.\n",
@@ -735,7 +747,7 @@
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true of false.\n",
"\u001b[32;1m\u001b[1;3mBelow are some assertions that have been fact checked and are labeled as true or false.\n",
"\n",
"If all of the assertions are true, return \"True\". If any of the assertions are false, return \"False\".\n",
"\n",
@@ -813,14 +825,14 @@
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"checker_chain = LLMSummarizationCheckerChain(llm=llm, verbose=True, max_checks=3)\n",
"checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)\n",
"text = \"The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, Indian Ocean, and the Southern Ocean. It is the smallest of the five oceans and is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. The sea is named after the island of Greenland, and is the Arctic Ocean's main outlet to the Atlantic. It is often frozen over so navigation is limited, and is considered the northern branch of the Norwegian Sea.\"\n",
"checker_chain.run(text)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -1077,7 +1089,7 @@
"'Birds are not mammals, but they are a class of their own. They lay eggs, unlike mammals which give birth to live young.'"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -1087,17 +1099,10 @@
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"checker_chain = LLMSummarizationCheckerChain(llm=llm, max_checks=3, verbose=True)\n",
"checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)\n",
"text = \"Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.\"\n",
"checker_chain.run(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

32
docs/modules/chains/examples/pal.ipynb
View File

@@ -28,7 +28,7 @@
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)"
"llm = OpenAI(temperature=0, max_tokens=512)"
]
},
{
@@ -63,7 +63,9 @@
"cell_type": "code",
"execution_count": 4,
"id": "3ef64b27",
"metadata": {},
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
@@ -71,17 +73,17 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3mdef solution():\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
" cindy_pets = 4\n",
" marcia_pets = cindy_pets + 2\n",
" jan_pets = marcia_pets * 3\n",
" total_pets = cindy_pets + marcia_pets + jan_pets\n",
" result = total_pets\n",
" return result\u001B[0m\n",
" return result\u001b[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
@@ -139,8 +141,8 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
@@ -151,9 +153,9 @@
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001B[0m\n",
"answer = num_purple\u001b[0m\n",
"\n",
"\u001B[1m> Finished PALChain chain.\u001B[0m\n"
"\u001b[1m> Finished PALChain chain.\u001b[0m\n"
]
},
{
@@ -212,8 +214,8 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
@@ -224,9 +226,9 @@
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001B[0m\n",
"answer = num_purple\u001b[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
}
],
@@ -280,7 +282,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

14
docs/modules/chains/examples/sqlite.ipynb
View File

@@ -73,7 +73,7 @@
"metadata": {},
"outputs": [],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
"db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
]
},
{
@@ -175,7 +175,7 @@
"metadata": {},
"outputs": [],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True)"
"db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True)"
]
},
{
@@ -230,7 +230,7 @@
"metadata": {},
"outputs": [],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
"db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)"
]
},
{
@@ -285,7 +285,7 @@
"metadata": {},
"outputs": [],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3)"
"db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, top_k=3)"
]
},
{
@@ -407,7 +407,7 @@
"metadata": {},
"outputs": [],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
"db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
]
},
{
@@ -569,7 +569,7 @@
}
],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
"db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)\n",
"db_chain.run(\"What are some example tracks by Bach?\")"
]
},
@@ -681,7 +681,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
"version": "3.9.1"
}
},
"nbformat": 4,

199
docs/modules/chains/generic/custom_chain.ipynb Normal file
View File

@@ -0,0 +1,199 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "593f7553-7038-498e-96d4-8255e5ce34f0",
"metadata": {},
"source": [
"# Creating a custom Chain\n",
"\n",
"To implement your own custom chain you can subclass `Chain` and implement the following methods:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c19c736e-ca74-4726-bb77-0a849bcc2960",
"metadata": {
"tags": [],
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"from __future__ import annotations\n",
"\n",
"from typing import Any, Dict, List, Optional\n",
"\n",
"from pydantic import Extra\n",
"\n",
"from langchain.base_language import BaseLanguageModel\n",
"from langchain.callbacks.manager import (\n",
" AsyncCallbackManagerForChainRun,\n",
" CallbackManagerForChainRun,\n",
")\n",
"from langchain.chains.base import Chain\n",
"from langchain.prompts.base import BasePromptTemplate\n",
"\n",
"\n",
"class MyCustomChain(Chain):\n",
" \"\"\"\n",
" An example of a custom chain.\n",
" \"\"\"\n",
"\n",
" prompt: BasePromptTemplate\n",
" \"\"\"Prompt object to use.\"\"\"\n",
" llm: BaseLanguageModel\n",
" output_key: str = \"text\" #: :meta private:\n",
"\n",
" class Config:\n",
" \"\"\"Configuration for this pydantic object.\"\"\"\n",
"\n",
" extra = Extra.forbid\n",
" arbitrary_types_allowed = True\n",
"\n",
" @property\n",
" def input_keys(self) -> List[str]:\n",
" \"\"\"Will be whatever keys the prompt expects.\n",
"\n",
" :meta private:\n",
" \"\"\"\n",
" return self.prompt.input_variables\n",
"\n",
" @property\n",
" def output_keys(self) -> List[str]:\n",
" \"\"\"Will always return text key.\n",
"\n",
" :meta private:\n",
" \"\"\"\n",
" return [self.output_key]\n",
"\n",
" def _call(\n",
" self,\n",
" inputs: Dict[str, Any],\n",
" run_manager: Optional[CallbackManagerForChainRun] = None,\n",
" ) -> Dict[str, str]:\n",
" # Your custom chain logic goes here\n",
" # This is just an example that mimics LLMChain\n",
" prompt_value = self.prompt.format_prompt(**inputs)\n",
" \n",
" # Whenever you call a language model, or another chain, you should pass\n",
" # a callback manager to it. This allows the inner run to be tracked by\n",
" # any callbacks that are registered on the outer run.\n",
" # You can always obtain a callback manager for this by calling\n",
" # `run_manager.get_child()` as shown below.\n",
" response = self.llm.generate_prompt(\n",
" [prompt_value],\n",
" callbacks=run_manager.get_child() if run_manager else None\n",
" )\n",
"\n",
" # If you want to log something about this run, you can do so by calling\n",
" # methods on the `run_manager`, as shown below. This will trigger any\n",
" # callbacks that are registered for that event.\n",
" if run_manager:\n",
" run_manager.on_text(\"Log something about this run\")\n",
" \n",
" return {self.output_key: response.generations[0][0].text}\n",
"\n",
" async def _acall(\n",
" self,\n",
" inputs: Dict[str, Any],\n",
" run_manager: Optional[AsyncCallbackManagerForChainRun] = None,\n",
" ) -> Dict[str, str]:\n",
" # Your custom chain logic goes here\n",
" # This is just an example that mimics LLMChain\n",
" prompt_value = self.prompt.format_prompt(**inputs)\n",
" \n",
" # Whenever you call a language model, or another chain, you should pass\n",
" # a callback manager to it. This allows the inner run to be tracked by\n",
" # any callbacks that are registered on the outer run.\n",
" # You can always obtain a callback manager for this by calling\n",
" # `run_manager.get_child()` as shown below.\n",
" response = await self.llm.agenerate_prompt(\n",
" [prompt_value],\n",
" callbacks=run_manager.get_child() if run_manager else None\n",
" )\n",
"\n",
" # If you want to log something about this run, you can do so by calling\n",
" # methods on the `run_manager`, as shown below. This will trigger any\n",
" # callbacks that are registered for that event.\n",
" if run_manager:\n",
" await run_manager.on_text(\"Log something about this run\")\n",
" \n",
" return {self.output_key: response.generations[0][0].text}\n",
"\n",
" @property\n",
" def _chain_type(self) -> str:\n",
" return \"my_custom_chain\"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "18361f89",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new MyCustomChain chain...\u001b[0m\n",
"Log something about this run\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Why did the callback function feel lonely? Because it was always waiting for someone to call it back!'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.callbacks.stdout import StdOutCallbackHandler\n",
"from langchain.chat_models.openai import ChatOpenAI\n",
"from langchain.prompts.prompt import PromptTemplate\n",
"\n",
"\n",
"chain = MyCustomChain(\n",
" prompt=PromptTemplate.from_template('tell us a joke about {topic}'),\n",
" llm=ChatOpenAI()\n",
")\n",
"\n",
"chain.run({'topic': 'callbacks'}, callbacks=[StdOutCallbackHandler()])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

3
docs/modules/chains/index_examples/chat_vector_db.ipynb
View File

@@ -589,7 +589,6 @@
"outputs": [],
"source": [
"from langchain.chains.llm import LLMChain\n",
"from langchain.callbacks.base import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT\n",
"from langchain.chains.question_answering import load_qa_chain\n",
@@ -597,7 +596,7 @@
"# Construct a ConversationalRetrievalChain with a streaming llm for combine docs\n",
"# and a separate, non-streaming llm for question generation\n",
"llm = OpenAI(temperature=0)\n",
"streaming_llm = OpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
"streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)\n",
"\n",
"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
"doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=QA_PROMPT)\n",