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big docs refactor (#1978)

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Co-authored-by: Ankush Gola <ankush.gola@gmail.com>
This commit is contained in:
Harrison Chase
2023-03-26 19:49:46 -07:00
committed by GitHub
parent b83e826510
commit 705431aecc
306 changed files with 5696 additions and 7036 deletions
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17
docs/modules/agents/agent_executors.rst Normal file
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@@ -0,0 +1,17 @@
Agent Executors
===============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent-executor>`_
Agent executors take an agent and tools and use the agent to decide which tools to call and in what order.
In this part of the documentation we cover other related functionality to agent executors
.. toctree::
:maxdepth: 1
:glob:
./agent_executors/examples/*

24
docs/modules/agents/examples/agent_vectorstore.ipynb → docs/modules/agents/agent_executors/examples/agent_vectorstore.ipynb
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@@ -5,7 +5,7 @@
"id": "68b24990",
"metadata": {},
"source": [
"# Agents and Vectorstores\n",
"# How to combine agents and vectorstores\n",
"\n",
"This notebook covers how to combine agents and vectorstores. The use case for this is that you've ingested your data into a vectorstore and want to interact with it in an agentic manner.\n",
"\n",
@@ -22,7 +22,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 16,
"id": "2e87c10a",
"metadata": {},
"outputs": [],
@@ -37,7 +37,23 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 17,
"id": "0b7b772b",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"relevant_parts = []\n",
"for p in Path(\".\").absolute().parts:\n",
" relevant_parts.append(p)\n",
" if relevant_parts[-3:] == [\"langchain\", \"docs\", \"modules\"]:\n",
" break\n",
"doc_path = str(Path(*relevant_parts) / \"state_of_the_union.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f2675861",
"metadata": {},
"outputs": [
@@ -52,7 +68,7 @@
],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../../state_of_the_union.txt')\n",
"loader = TextLoader(doc_path)\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",

4
docs/modules/agents/examples/async_agent.ipynb → docs/modules/agents/agent_executors/examples/async_agent.ipynb
View File

@@ -5,7 +5,7 @@
"id": "6fb92deb-d89e-439b-855d-c7f2607d794b",
"metadata": {},
"source": [
"# Async API for Agent\n",
"# How to use the async API for Agents\n",
"\n",
"LangChain provides async support for Agents by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
"\n",
@@ -403,7 +403,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

968
docs/modules/agents/agent_executors/examples/chatgpt_clone.ipynb Normal file
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@@ -0,0 +1,968 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "b253f4d5",
"metadata": {},
"source": [
"# How to create ChatGPT Clone\n",
"\n",
"This chain replicates ChatGPT by combining (1) a specific prompt, and (2) the concept of memory.\n",
"\n",
"Shows off the example as in https://www.engraved.blog/building-a-virtual-machine-inside/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a99acd89",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"\n",
"Human: 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.\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"```\n",
"/home/user\n",
"```\n"
]
}
],
"source": [
"from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate\n",
"from langchain.memory import ConversationBufferWindowMemory\n",
"\n",
"\n",
"template = \"\"\"Assistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"{history}\n",
"Human: {human_input}\n",
"Assistant:\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"history\", \"human_input\"], \n",
" template=template\n",
")\n",
"\n",
"\n",
"chatgpt_chain = LLMChain(\n",
" llm=OpenAI(temperature=0), \n",
" prompt=prompt, \n",
" verbose=True, \n",
" memory=ConversationBufferWindowMemory(k=2),\n",
")\n",
"\n",
"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.\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4ef711d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: 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.\n",
"AI: \n",
"```\n",
"$ pwd\n",
"/\n",
"```\n",
"Human: ls ~\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"```\n",
"$ ls ~\n",
"Desktop Documents Downloads Music Pictures Public Templates Videos\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"ls ~\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a5d6dac2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: 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.\n",
"AI: \n",
"```\n",
"$ pwd\n",
"/\n",
"```\n",
"Human: ls ~\n",
"AI: \n",
"```\n",
"$ ls ~\n",
"Desktop Documents Downloads Music Pictures Public Templates Videos\n",
"```\n",
"Human: cd ~\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
" \n",
"```\n",
"$ cd ~\n",
"$ pwd\n",
"/home/user\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"cd ~\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b9283077",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: ls ~\n",
"AI: \n",
"```\n",
"$ ls ~\n",
"Desktop Documents Downloads Music Pictures Public Templates Videos\n",
"```\n",
"Human: cd ~\n",
"AI: \n",
"```\n",
"$ cd ~\n",
"$ pwd\n",
"/home/user\n",
"```\n",
"Human: {Please make a file jokes.txt inside and put some jokes inside}\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ touch jokes.txt\n",
"$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n",
"$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n",
"$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"{Please make a file jokes.txt inside and put some jokes inside}\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "570e785e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: cd ~\n",
"AI: \n",
"```\n",
"$ cd ~\n",
"$ pwd\n",
"/home/user\n",
"```\n",
"Human: {Please make a file jokes.txt inside and put some jokes inside}\n",
"AI: \n",
"\n",
"```\n",
"$ touch jokes.txt\n",
"$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n",
"$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n",
"$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n",
"```\n",
"Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n",
"$ python3 run.py\n",
"Result: 33\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cd0a23d9",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: {Please make a file jokes.txt inside and put some jokes inside}\n",
"AI: \n",
"\n",
"```\n",
"$ touch jokes.txt\n",
"$ echo \"Why did the chicken cross the road? To get to the other side!\" >> jokes.txt\n",
"$ echo \"What did the fish say when it hit the wall? Dam!\" >> jokes.txt\n",
"$ echo \"Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!\" >> jokes.txt\n",
"```\n",
"Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\n",
"AI: \n",
"\n",
"```\n",
"$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n",
"$ python3 run.py\n",
"Result: 33\n",
"```\n",
"Human: 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\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n",
"$ python3 run.py\n",
"[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"\"\"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\"\"\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "90db6eb2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\n",
"AI: \n",
"\n",
"```\n",
"$ echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py\n",
"$ python3 run.py\n",
"Result: 33\n",
"```\n",
"Human: 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\n",
"AI: \n",
"\n",
"```\n",
"$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n",
"$ python3 run.py\n",
"[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
"```\n",
"Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\n",
"COPY entrypoint.sh entrypoint.sh\n",
"ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ echo -e \"echo 'Hello from Docker\" > entrypoint.sh\n",
"$ echo -e \"FROM ubuntu:20.04\n",
"COPY entrypoint.sh entrypoint.sh\n",
"ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\n",
"$ docker build . -t my_docker_image\n",
"$ docker run -t my_docker_image\n",
"Hello from Docker\n",
"```\n"
]
}
],
"source": [
"docker_input = \"\"\"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\"\"\"\n",
"output = chatgpt_chain.predict(human_input=docker_input)\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c3806f89",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: 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\n",
"AI: \n",
"\n",
"```\n",
"$ echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py\n",
"$ python3 run.py\n",
"[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\n",
"```\n",
"Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\n",
"COPY entrypoint.sh entrypoint.sh\n",
"ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\n",
"AI: \n",
"\n",
"```\n",
"$ echo -e \"echo 'Hello from Docker\" > entrypoint.sh\n",
"$ echo -e \"FROM ubuntu:20.04\n",
"COPY entrypoint.sh entrypoint.sh\n",
"ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\n",
"$ docker build . -t my_docker_image\n",
"$ docker run -t my_docker_image\n",
"Hello from Docker\n",
"```\n",
"Human: nvidia-smi\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ nvidia-smi\n",
"Sat May 15 21:45:02 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"|===============================+======================+======================|\n",
"| 0 GeForce GTX 108... Off | 00000000:01:00.0 Off | N/A |\n",
"| N/A 45C P0 N/A / N/A | 511MiB / 10206MiB | 0% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: GPU Memory |\n",
"| GPU PID Type Process name Usage |\n",
"|=============================================================================|\n",
"\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"nvidia-smi\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f508f597",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\n",
"COPY entrypoint.sh entrypoint.sh\n",
"ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\n",
"AI: \n",
"\n",
"```\n",
"$ echo -e \"echo 'Hello from Docker\" > entrypoint.sh\n",
"$ echo -e \"FROM ubuntu:20.04\n",
"COPY entrypoint.sh entrypoint.sh\n",
"ENTRYPOINT [\"/bin/sh\",\"entrypoint.sh\"]\">Dockerfile\n",
"$ docker build . -t my_docker_image\n",
"$ docker run -t my_docker_image\n",
"Hello from Docker\n",
"```\n",
"Human: nvidia-smi\n",
"AI: \n",
"\n",
"```\n",
"$ nvidia-smi\n",
"Sat May 15 21:45:02 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"|===============================+======================+======================|\n",
"| 0 GeForce GTX 108... Off | 00000000:01:00.0 Off | N/A |\n",
"| N/A 45C P0 N/A / N/A | 511MiB / 10206MiB | 0% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: GPU Memory |\n",
"| GPU PID Type Process name Usage |\n",
"|=============================================================================|\n",
"\n",
"Human: ping bbc.com\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ ping bbc.com\n",
"PING bbc.com (151.101.65.81): 56 data bytes\n",
"64 bytes from 151.101.65.81: icmp_seq=0 ttl=53 time=14.945 ms\n",
"64 bytes from 151.101.65.81: icmp_seq=1 ttl=53 time=14.945 ms\n",
"64 bytes from 151.101.65.81: icmp_seq=2 ttl=53 time=14.945 ms\n",
"\n",
"--- bbc.com ping statistics ---\n",
"3 packets transmitted, 3 packets received, 0.0% packet loss\n",
"round-trip min/avg/max/stddev = 14.945/14.945/14.945/0.000 ms\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"ping bbc.com\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cbd607f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: nvidia-smi\n",
"AI: \n",
"\n",
"```\n",
"$ nvidia-smi\n",
"Sat May 15 21:45:02 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"|===============================+======================+======================|\n",
"| 0 GeForce GTX 108... Off | 00000000:01:00.0 Off | N/A |\n",
"| N/A 45C P0 N/A / N/A | 511MiB / 10206MiB | 0% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: GPU Memory |\n",
"| GPU PID Type Process name Usage |\n",
"|=============================================================================|\n",
"\n",
"Human: ping bbc.com\n",
"AI: \n",
"\n",
"```\n",
"$ ping bbc.com\n",
"PING bbc.com (151.101.65.81): 56 data bytes\n",
"64 bytes from 151.101.65.81: icmp_seq=0 ttl=53 time=14.945 ms\n",
"64 bytes from 151.101.65.81: icmp_seq=1 ttl=53 time=14.945 ms\n",
"64 bytes from 151.101.65.81: icmp_seq=2 ttl=53 time=14.945 ms\n",
"\n",
"--- bbc.com ping statistics ---\n",
"3 packets transmitted, 3 packets received, 0.0% packet loss\n",
"round-trip min/avg/max/stddev = 14.945/14.945/14.945/0.000 ms\n",
"```\n",
"Human: curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
"1.8.1\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"\"\"curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\"\"\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d33e0e28",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: ping bbc.com\n",
"AI: \n",
"\n",
"```\n",
"$ ping bbc.com\n",
"PING bbc.com (151.101.65.81): 56 data bytes\n",
"64 bytes from 151.101.65.81: icmp_seq=0 ttl=53 time=14.945 ms\n",
"64 bytes from 151.101.65.81: icmp_seq=1 ttl=53 time=14.945 ms\n",
"64 bytes from 151.101.65.81: icmp_seq=2 ttl=53 time=14.945 ms\n",
"\n",
"--- bbc.com ping statistics ---\n",
"3 packets transmitted, 3 packets received, 0.0% packet loss\n",
"round-trip min/avg/max/stddev = 14.945/14.945/14.945/0.000 ms\n",
"```\n",
"Human: curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
"AI: \n",
"\n",
"```\n",
"$ curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
"1.8.1\n",
"```\n",
"Human: lynx https://www.deepmind.com/careers\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ lynx https://www.deepmind.com/careers\n",
"DeepMind Careers\n",
"\n",
"Welcome to DeepMind Careers. We are a world-leading artificial intelligence research and development company, and we are looking for talented people to join our team.\n",
"\n",
"We offer a range of exciting opportunities in research, engineering, product, and operations. Our mission is to solve intelligence and make it useful, and we are looking for people who share our passion for pushing the boundaries of AI.\n",
"\n",
"Explore our current openings and apply today. We look forward to hearing from you.\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"lynx https://www.deepmind.com/careers\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "57c2f113",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
"AI: \n",
"\n",
"```\n",
"$ curl -fsSL \"https://api.github.com/repos/pytorch/pytorch/releases/latest\" | jq -r '.tag_name' | sed 's/[^0-9\\.\\-]*//g'\n",
"1.8.1\n",
"```\n",
"Human: lynx https://www.deepmind.com/careers\n",
"AI: \n",
"\n",
"```\n",
"$ lynx https://www.deepmind.com/careers\n",
"DeepMind Careers\n",
"\n",
"Welcome to DeepMind Careers. We are a world-leading artificial intelligence research and development company, and we are looking for talented people to join our team.\n",
"\n",
"We offer a range of exciting opportunities in research, engineering, product, and operations. Our mission is to solve intelligence and make it useful, and we are looking for people who share our passion for pushing the boundaries of AI.\n",
"\n",
"Explore our current openings and apply today. We look forward to hearing from you.\n",
"```\n",
"Human: curl https://chat.openai.com/chat\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
" \n",
"\n",
"```\n",
"$ curl https://chat.openai.com/chat\n",
"<html>\n",
" <head>\n",
" <title>OpenAI Chat</title>\n",
" </head>\n",
" <body>\n",
" <h1>Welcome to OpenAI Chat!</h1>\n",
" <p>\n",
" OpenAI Chat is a natural language processing platform that allows you to interact with OpenAI's AI models in a conversational way.\n",
" </p>\n",
" <p>\n",
" To get started, type a message in the box below and press enter.\n",
" </p>\n",
" </body>\n",
"</html>\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"curl https://chat.openai.com/chat\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "babadc78",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: lynx https://www.deepmind.com/careers\n",
"AI: \n",
"\n",
"```\n",
"$ lynx https://www.deepmind.com/careers\n",
"DeepMind Careers\n",
"\n",
"Welcome to DeepMind Careers. We are a world-leading artificial intelligence research and development company, and we are looking for talented people to join our team.\n",
"\n",
"We offer a range of exciting opportunities in research, engineering, product, and operations. Our mission is to solve intelligence and make it useful, and we are looking for people who share our passion for pushing the boundaries of AI.\n",
"\n",
"Explore our current openings and apply today. We look forward to hearing from you.\n",
"```\n",
"Human: curl https://chat.openai.com/chat\n",
"AI: \n",
"\n",
"```\n",
"$ curl https://chat.openai.com/chat\n",
"<html>\n",
" <head>\n",
" <title>OpenAI Chat</title>\n",
" </head>\n",
" <body>\n",
" <h1>Welcome to OpenAI Chat!</h1>\n",
" <p>\n",
" OpenAI Chat is a natural language processing platform that allows you to interact with OpenAI's AI models in a conversational way.\n",
" </p>\n",
" <p>\n",
" To get started, type a message in the box below and press enter.\n",
" </p>\n",
" </body>\n",
"</html>\n",
"```\n",
"Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
"\n",
"\n",
"```\n",
"$ curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\n",
"\n",
"{\n",
" \"response\": \"Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction. AI is used to develop computer systems that can think and act like humans.\"\n",
"}\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\"\"\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0954792a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mAssistant is a large language model trained by OpenAI.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"Human: curl https://chat.openai.com/chat\n",
"AI: \n",
"\n",
"```\n",
"$ curl https://chat.openai.com/chat\n",
"<html>\n",
" <head>\n",
" <title>OpenAI Chat</title>\n",
" </head>\n",
" <body>\n",
" <h1>Welcome to OpenAI Chat!</h1>\n",
" <p>\n",
" OpenAI Chat is a natural language processing platform that allows you to interact with OpenAI's AI models in a conversational way.\n",
" </p>\n",
" <p>\n",
" To get started, type a message in the box below and press enter.\n",
" </p>\n",
" </body>\n",
"</html>\n",
"```\n",
"Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\n",
"AI: \n",
"\n",
"```\n",
"$ curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"What is artificial intelligence?\"}' https://chat.openai.com/chat\n",
"\n",
"{\n",
" \"response\": \"Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction. AI is used to develop computer systems that can think and act like humans.\"\n",
"}\n",
"```\n",
"Human: curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"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.\"}' https://chat.openai.com/chat\n",
"Assistant:\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n",
" \n",
"\n",
"```\n",
"$ curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"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.\"}' https://chat.openai.com/chat\n",
"\n",
"{\n",
" \"response\": \"```\\n/current/working/directory\\n```\"\n",
"}\n",
"```\n"
]
}
],
"source": [
"output = chatgpt_chain.predict(human_input=\"\"\"curl --header \"Content-Type:application/json\" --request POST --data '{\"message\": \"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.\"}' https://chat.openai.com/chat\"\"\")\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e68a087e",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

2
docs/modules/agents/examples/intermediate_steps.ipynb → docs/modules/agents/agent_executors/examples/intermediate_steps.ipynb
View File

@@ -5,7 +5,7 @@
"id": "5436020b",
"metadata": {},
"source": [
"# Intermediate Steps\n",
"# How to access intermediate steps\n",
"\n",
"In order to get more visibility into what an agent is doing, we can also return intermediate steps. This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples."
]

2
docs/modules/agents/examples/max_iterations.ipynb → docs/modules/agents/agent_executors/examples/max_iterations.ipynb
View File

@@ -5,7 +5,7 @@
"id": "75c041b7",
"metadata": {},
"source": [
"# Max Iterations\n",
"# How to cap the max number of iterations\n",
"\n",
"This notebook walks through how to cap an agent at taking a certain number of steps. This can be useful to ensure that they do not go haywire and take too many steps."
]

8
docs/modules/agents/examples/sharedmemory_for_tools.ipynb → docs/modules/agents/agent_executors/examples/sharedmemory_for_tools.ipynb
View File

@@ -1,12 +1,11 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "fa6802ac",
"metadata": {},
"source": [
"# Adding SharedMemory to an Agent and its Tools\n",
"# How to add SharedMemory to an Agent and its Tools\n",
"\n",
"This notebook goes over adding memory to **both** of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n",
"\n",
@@ -260,7 +259,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4ebd8326",
"metadata": {},
@@ -292,7 +290,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cc3d0aa4",
"metadata": {},
@@ -493,7 +490,6 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d07415da",
"metadata": {},
@@ -544,7 +540,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

35
docs/modules/agents/agents.rst Normal file
View File

@@ -0,0 +1,35 @@
Agents
=============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent>`_
In this part of the documentation we cover the different types of agents, disregarding which specific tools they are used with.
For a high level overview of the different types of agents, see the below documentation.
.. toctree::
:maxdepth: 1
:glob:
./agents/agent_types.md
For documentation on how to create a custom agent, see the below.
We also have documentation for an in-depth dive into each agent type.
.. toctree::
:maxdepth: 1
:glob:
./agents/custom_agent.ipynb
We also have documentation for an in-depth dive into each agent type.
.. toctree::
:maxdepth: 1
:glob:
./agents/examples/*

5
docs/modules/agents/agents.md → docs/modules/agents/agents/agent_types.md
View File

@@ -1,12 +1,9 @@
# Agents
# Agent Types
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
For a list of easily loadable tools, see [here](tools.md).
Here are the agents available in LangChain.
For a tutorial on how to load agents, see [here](getting_started.ipynb).
## `zero-shot-react-description`
This agent uses the ReAct framework to determine which tool to use

0
docs/modules/agents/examples/custom_agent.ipynb → docs/modules/agents/agents/custom_agent.ipynb
View File

0
docs/modules/agents/examples/chat_conversation_agent.ipynb → docs/modules/agents/agents/examples/chat_conversation_agent.ipynb
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280
docs/modules/agents/agents/examples/conversational_agent.ipynb Normal file
View File

@@ -0,0 +1,280 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4658d71a",
"metadata": {},
"source": [
"# Conversation Agent\n",
"\n",
"This notebook walks through using an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.\n",
"\n",
"This is accomplished with a specific type of agent (`conversational-react-description`) which expects to be used with a memory component."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f65308ab",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import Tool\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain import OpenAI\n",
"from langchain.utilities import GoogleSearchAPIWrapper\n",
"from langchain.agents import initialize_agent"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5fb14d6d",
"metadata": {},
"outputs": [],
"source": [
"search = GoogleSearchAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Current Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events or the current state of the world\"\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "dddc34c4",
"metadata": {},
"outputs": [],
"source": [
"memory = ConversationBufferMemory(memory_key=\"chat_history\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cafe9bc1",
"metadata": {},
"outputs": [],
"source": [
"llm=OpenAI(temperature=0)\n",
"agent_chain = initialize_agent(tools, llm, agent=\"conversational-react-description\", verbose=True, memory=memory)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dc70b454",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Thought: Do I need to use a tool? No\n",
"AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Hi Bob, nice to meet you! How can I help you today?'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"hi, i am bob\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3dcf7953",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Thought: Do I need to use a tool? No\n",
"AI: Your name is Bob!\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Your name is Bob!'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"what's my name?\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "aa05f566",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Thought: Do I need to use a tool? No\n",
"AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c5d8b7ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Thought: Do I need to use a tool? Yes\n",
"Action: Current Search\n",
"Action Input: Who won the World Cup in 1978\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mThe Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final defeat at the Estadio ... The match was won by the Argentine squad in extra time by a score of 3–1. Mario Kempes, who finished as the tournament's top scorer, was named the man of the ... May 21, 2022 ... Argentina won the World Cup for the first time in their history, beating Netherlands 3-1 in the final. This edition of the World Cup was full of ... The adidas Golden Ball is presented to the best player at each FIFA World Cup finals. Those who finish as runners-up in the vote receive the adidas Silver ... Holders West Germany failed to beat Holland and Italy and were eliminated when Berti Vogts' own goal gave Austria a 3-2 victory. Holland thrashed the Austrians ... Jun 14, 2018 ... On a clear afternoon on 1 June 1978 at the revamped El Monumental stadium in Buenos Aires' Belgrano barrio, several hundred children in white ... Dec 15, 2022 ... The tournament couldn't have gone better for the ruling junta. Argentina went on to win the championship, defeating the Netherlands, 3-1, in the ... Nov 9, 2022 ... Host: Argentina Teams: 16. Format: Group stage, second round, third-place playoff, final. Matches: 38. Goals: 102. Winner: Argentina Feb 19, 2009 ... Argentina sealed their first World Cup win on home soil when they defeated the Netherlands in an exciting final that went to extra-time. For the ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
"AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f608889b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Thought: Do I need to use a tool? Yes\n",
"Action: Current Search\n",
"Action Input: Current temperature in Pomfret\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mA mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Pomfret Center Weather Forecasts. ... Pomfret Center, CT Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be COOLER than today. It is 46 degrees fahrenheit, or 8 degrees celsius and feels like 46 degrees fahrenheit. The barometric pressure is 29.78 - measured by inch of mercury units - ... Pomfret Weather Forecasts. ... Pomfret, MD Weather Conditionsstar_ratehome ... Tomorrow's temperature is forecast to be MUCH COOLER than today. Additional Headlines. En Español · Share |. Current conditions at ... Pomfret CT. Tonight ... Past Weather Information · Interactive Forecast Map. Pomfret MD detailed current weather report for 20675 in Charles county, Maryland. ... Pomfret, MD weather condition is Mostly Cloudy and 43°F. Mostly Cloudy. Hazardous Weather Conditions. Hazardous Weather Outlook · En Español · Share |. Current conditions at ... South Pomfret VT. Tonight. Pomfret Center, CT Weather. Current Report for Thu Jan 5 2023. As of 2:00 PM EST. 5-Day Forecast | Road Conditions. 45°F 7°c. Feels Like 44°F. Pomfret Center CT. Today. Today: Areas of fog before 9am. Otherwise, cloudy, with a ... Otherwise, cloudy, with a temperature falling to around 33 by 5pm.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Do I need to use a tool? No\n",
"AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"whats the current temperature in pomfret?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0084efd6",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

0
docs/modules/agents/implementations/mrkl.ipynb → docs/modules/agents/agents/examples/mrkl.ipynb
View File

0
docs/modules/agents/implementations/mrkl_chat.ipynb → docs/modules/agents/agents/examples/mrkl_chat.ipynb
View File

0
docs/modules/agents/implementations/react.ipynb → docs/modules/agents/agents/examples/react.ipynb
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0
docs/modules/agents/implementations/self_ask_with_search.ipynb → docs/modules/agents/agents/examples/self_ask_with_search.ipynb
View File

131
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View File

@@ -1,131 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "991b1cc1",
"metadata": {},
"source": [
"# Loading from LangChainHub\n",
"\n",
"This notebook covers how to load agents from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bd4450a2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No `_type` key found, defaulting to `prompt`.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3m Yes.\n",
"Follow up: Who is the reigning men's U.S. Open champion?\u001B[0m\n",
"Intermediate answer: \u001B[36;1m\u001B[1;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001B[0m\n",
"\u001B[32;1m\u001B[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
"Follow up: What is Rafael Nadal's hometown?\u001B[0m\n",
"Intermediate answer: \u001B[36;1m\u001B[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001B[0m\n",
"\u001B[32;1m\u001B[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001B[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
]
},
{
"data": {
"text/plain": [
"'Manacor, Mallorca, Spain.'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import OpenAI, SerpAPIWrapper\n",
"from langchain.agents import initialize_agent, Tool\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name=\"Intermediate Answer\",\n",
" func=search.run,\n",
" description=\"useful for when you need to ask with search\"\n",
" )\n",
"]\n",
"\n",
"self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc://agents/self-ask-with-search/agent.json\", verbose=True)\n",
"self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
]
},
{
"cell_type": "markdown",
"id": "3aede965",
"metadata": {},
"source": [
"# Pinning Dependencies\n",
"\n",
"Specific versions of LangChainHub agents can be pinned with the `lc@<ref>://` syntax."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e679f7b6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No `_type` key found, defaulting to `prompt`.\n"
]
}
],
"source": [
"self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d3d6697",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

154
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@@ -1,154 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "bfe18e28",
"metadata": {},
"source": [
"# Serialization\n",
"\n",
"This notebook goes over how to serialize agents. For this notebook, it is important to understand the distinction we draw between `agents` and `tools`. An agent is the LLM powered decision maker that decides which actions to take and in which order. Tools are various instruments (functions) an agent has access to, through which an agent can interact with the outside world. When people generally use agents, they primarily talk about using an agent WITH tools. However, when we talk about serialization of agents, we are talking about the agent by itself. We plan to add support for serializing an agent WITH tools sometime in the future.\n",
"\n",
"Let's start by creating an agent with tools as we normally do:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "eb729f16",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import load_tools\n",
"from langchain.agents import initialize_agent\n",
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "0578f566",
"metadata": {},
"source": [
"Let's now serialize the agent. To be explicit that we are serializing ONLY the agent, we will call the `save_agent` method."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dc544de6",
"metadata": {},
"outputs": [],
"source": [
"agent.save_agent('agent.json')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "62dd45bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
" \"llm_chain\": {\r\n",
" \"memory\": null,\r\n",
" \"verbose\": false,\r\n",
" \"prompt\": {\r\n",
" \"input_variables\": [\r\n",
" \"input\",\r\n",
" \"agent_scratchpad\"\r\n",
" ],\r\n",
" \"output_parser\": null,\r\n",
" \"template\": \"Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\r\n",
" \"template_format\": \"f-string\",\r\n",
" \"validate_template\": true,\r\n",
" \"_type\": \"prompt\"\r\n",
" },\r\n",
" \"llm\": {\r\n",
" \"model_name\": \"text-davinci-003\",\r\n",
" \"temperature\": 0.0,\r\n",
" \"max_tokens\": 256,\r\n",
" \"top_p\": 1,\r\n",
" \"frequency_penalty\": 0,\r\n",
" \"presence_penalty\": 0,\r\n",
" \"n\": 1,\r\n",
" \"best_of\": 1,\r\n",
" \"request_timeout\": null,\r\n",
" \"logit_bias\": {},\r\n",
" \"_type\": \"openai\"\r\n",
" },\r\n",
" \"output_key\": \"text\",\r\n",
" \"_type\": \"llm_chain\"\r\n",
" },\r\n",
" \"allowed_tools\": [\r\n",
" \"Search\",\r\n",
" \"Calculator\"\r\n",
" ],\r\n",
" \"return_values\": [\r\n",
" \"output\"\r\n",
" ],\r\n",
" \"_type\": \"zero-shot-react-description\"\r\n",
"}"
]
}
],
"source": [
"!cat agent.json"
]
},
{
"cell_type": "markdown",
"id": "0eb72510",
"metadata": {},
"source": [
"We can now load the agent back in"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "eb660b76",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(tools, llm, agent_path=\"agent.json\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa624ea5",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

87
docs/modules/agents/implementations/natbot.py
View File

@@ -1,87 +0,0 @@
"""Run NatBot."""
import time
from langchain.chains.natbot.base import NatBotChain
from langchain.chains.natbot.crawler import Crawler
def run_cmd(cmd: str, _crawler: Crawler) -> None:
"""Run command."""
cmd = cmd.split("\n")[0]
if cmd.startswith("SCROLL UP"):
_crawler.scroll("up")
elif cmd.startswith("SCROLL DOWN"):
_crawler.scroll("down")
elif cmd.startswith("CLICK"):
commasplit = cmd.split(",")
id = commasplit[0].split(" ")[1]
_crawler.click(id)
elif cmd.startswith("TYPE"):
spacesplit = cmd.split(" ")
id = spacesplit[1]
text_pieces = spacesplit[2:]
text = " ".join(text_pieces)
# Strip leading and trailing double quotes
text = text[1:-1]
if cmd.startswith("TYPESUBMIT"):
text += "\n"
_crawler.type(id, text)
time.sleep(2)
if __name__ == "__main__":
objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
print("\nWelcome to natbot! What is your objective?")
i = input()
if len(i) > 0:
objective = i
quiet = False
nat_bot_chain = NatBotChain.from_default(objective)
_crawler = Crawler()
_crawler.go_to_page("google.com")
try:
while True:
browser_content = "\n".join(_crawler.crawl())
llm_command = nat_bot_chain.execute(_crawler.page.url, browser_content)
if not quiet:
print("URL: " + _crawler.page.url)
print("Objective: " + objective)
print("----------------\n" + browser_content + "\n----------------\n")
if len(llm_command) > 0:
print("Suggested command: " + llm_command)
command = input()
if command == "r" or command == "":
run_cmd(llm_command, _crawler)
elif command == "g":
url = input("URL:")
_crawler.go_to_page(url)
elif command == "u":
_crawler.scroll("up")
time.sleep(1)
elif command == "d":
_crawler.scroll("down")
time.sleep(1)
elif command == "c":
id = input("id:")
_crawler.click(id)
time.sleep(1)
elif command == "t":
id = input("id:")
text = input("text:")
_crawler.type(id, text)
time.sleep(1)
elif command == "o":
objective = input("Objective:")
else:
print(
"(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click"
"\n(t) to type\n(h) to view commands again"
"\n(r/enter) to run suggested command\n(o) change objective"
)
except KeyboardInterrupt:
print("\n[!] Ctrl+C detected, exiting gracefully.")
exit(0)

16
docs/modules/agents/key_concepts.md
View File

@@ -1,16 +0,0 @@
# Key Concepts
## Agents
Agents use an LLM to determine which actions to take and in what order.
For more detailed information on agents, and different types of agents in LangChain, see [this documentation](agents.md).
## Tools
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
For more detailed information on tools, and different types of tools in LangChain, see [this documentation](tools.md).
## ToolKits
Toolkits are groups of tools that are best used together.
They allow you to logically group and initialize a set of tools that share a particular resource (such as a database connection or json object).
They can be used to construct an agent for a specific use-case.
For more detailed information on toolkits and their use cases, see [this documentation](how_to_guides.rst#agent-toolkits) (the "Agent Toolkits" section).

18
docs/modules/agents/toolkits.rst Normal file
View File

@@ -0,0 +1,18 @@
Toolkits
==============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/toolkit>`_
This section of documentation covers agents with toolkits - eg an agent applied to a particular use case.
See below for a full list of agent toolkits
.. toctree::
:maxdepth: 1
:glob:
./toolkits/examples/*

0
docs/modules/agents/agent_toolkits/csv.ipynb → docs/modules/agents/toolkits/examples/csv.ipynb
View File

0
docs/modules/agents/agent_toolkits/json.ipynb → docs/modules/agents/toolkits/examples/json.ipynb
View File

0
docs/modules/agents/agent_toolkits/openai_openapi.yml → docs/modules/agents/toolkits/examples/openai_openapi.yml
View File

0
docs/modules/agents/agent_toolkits/openapi.ipynb → docs/modules/agents/toolkits/examples/openapi.ipynb
View File

0
docs/modules/agents/agent_toolkits/pandas.ipynb → docs/modules/agents/toolkits/examples/pandas.ipynb
View File

2
docs/modules/agents/agent_toolkits/python.ipynb → docs/modules/agents/toolkits/examples/python.ipynb
View File

@@ -5,7 +5,7 @@
"id": "82a4c2cc-20ea-4b20-a565-63e905dee8ff",
"metadata": {},
"source": [
"## Python Agent\n",
"# Python Agent\n",
"\n",
"This notebook showcases an agent designed to write and execute python code to answer a question."
]

0
docs/modules/agents/agent_toolkits/sql_database.ipynb → docs/modules/agents/toolkits/examples/sql_database.ipynb
View File

0
docs/modules/agents/agent_toolkits/titanic.csv → docs/modules/agents/toolkits/examples/titanic.csv
View File

6
docs/modules/agents/agent_toolkits/vectorstore.ipynb → docs/modules/agents/toolkits/examples/vectorstore.ipynb
View File

@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "345bb078-4ec1-4e3a-827b-cd238c49054d",
"metadata": {
"tags": []
@@ -53,7 +53,7 @@
],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../../state_of_the_union.txt')\n",
"loader = TextLoader('../../../state_of_the_union.txt')\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
@@ -409,7 +409,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.1"
}
},
"nbformat": 4,

38
docs/modules/agents/tools.rst Normal file
View File

@@ -0,0 +1,38 @@
Tools
=============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/tool>`_
Tools are ways that an agent can use to interact with the outside world.
For an overview of what a tool is, how to use them, and a full list of examples, please see the getting started documentation
.. toctree::
:maxdepth: 1
:glob:
./tools/getting_started.md
Next, we have some examples of customizing and generically working with tools
.. toctree::
:maxdepth: 1
:glob:
./tools/custom_tools.ipynb
./tools/multi_input_tool.ipynb
In this documentation we cover generic tooling functionality (eg how to create your own)
as well as examples of tools and how to use them.
.. toctree::
:maxdepth: 1
:glob:
./tools/examples/*

0
docs/modules/agents/examples/custom_tools.ipynb → docs/modules/agents/tools/custom_tools.ipynb
View File

85
docs/modules/agents/tools/examples/bash.ipynb Normal file
View File

@@ -0,0 +1,85 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "8f210ec3",
"metadata": {},
"source": [
"# Bash\n",
"It can often be useful to have an LLM generate bash commands, and then run them. A common use case for this is letting the LLM interact with your local file system. We provide an easy util to execute bash commands."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f7b3767b",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import BashProcess"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cf1c92f0",
"metadata": {},
"outputs": [],
"source": [
"bash = BashProcess()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2fa952fc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bash.ipynb\n",
"google_search.ipynb\n",
"python.ipynb\n",
"requests.ipynb\n",
"serpapi.ipynb\n",
"\n"
]
}
],
"source": [
"print(bash.run(\"ls\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "851fee9f",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}

192
docs/modules/agents/tools/examples/bing_search.ipynb Normal file
View File

@@ -0,0 +1,192 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bing Search"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook goes over how to use the bing search component.\n",
"\n",
"First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found [here](https://levelup.gitconnected.com/api-tutorial-how-to-use-bing-web-search-api-in-python-4165d5592a7e).\n",
"\n",
"Then we will need to set some environment variables."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"BING_SUBSCRIPTION_KEY\"] = \"\"\n",
"os.environ[\"BING_SEARCH_URL\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import BingSearchAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"search = BingSearchAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor. <b>Python</b> releases by version number: Release version Release date Click for more. <b>Python</b> 3.11.1 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.10.9 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.9.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.8.16 Dec. 6, 2022 Download Release Notes. <b>Python</b> 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in <b>Python</b> and see how it works with several simple examples.. The operator ‘+=’ is a shorthand for the addition assignment operator.It adds two values and assigns the sum to a variable (left operand). W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, <b>Python</b>, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the <b>Python</b> language and system. It helps to have a <b>Python</b> interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It&#39;s a great first language because <b>Python</b> code is concise and easy to read. Whatever you want to do, <b>python</b> can do it. From web development to machine learning to data science, <b>Python</b> is the language for you. To install <b>Python</b> using the Microsoft Store: Go to your Start menu (lower left Windows icon), type &quot;Microsoft Store&quot;, select the link to open the store. Once the store is open, select Search from the upper-right menu and enter &quot;<b>Python</b>&quot;. Select which version of <b>Python</b> you would like to use from the results under Apps. Under the “<b>Python</b> Releases for Mac OS X” heading, click the link for the Latest <b>Python</b> 3 Release - <b>Python</b> 3.x.x. As of this writing, the latest version was <b>Python</b> 3.8.4. Scroll to the bottom and click macOS 64-bit installer to start the download. When the installer is finished downloading, move on to the next step. Step 2: Run the Installer'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"python\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of results\n",
"You can use the `k` parameter to set the number of results"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"search = BingSearchAPIWrapper(k=1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Thanks to the flexibility of <b>Python</b> and the powerful ecosystem of packages, the Azure CLI supports features such as autocompletion (in shells that support it), persistent credentials, JMESPath result parsing, lazy initialization, network-less unit tests, and more. Building an open-source and cross-platform Azure CLI with <b>Python</b> by Dan Taylor.'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"python\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Metadata Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run query through BingSearch and return snippet, title, and link metadata.\n",
"\n",
"- Snippet: The description of the result.\n",
"- Title: The title of the result.\n",
"- Link: The link to the result."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"search = BingSearchAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'snippet': 'Lady Alice. Pink Lady <b>apples</b> aren’t the only lady in the apple family. Lady Alice <b>apples</b> were discovered growing, thanks to bees pollinating, in Washington. They are smaller and slightly more stout in appearance than other varieties. Their skin color appears to have red and yellow stripes running from stem to butt.',\n",
" 'title': '25 Types of Apples - Jessica Gavin',\n",
" 'link': 'https://www.jessicagavin.com/types-of-apples/'},\n",
" {'snippet': '<b>Apples</b> can do a lot for you, thanks to plant chemicals called flavonoids. And they have pectin, a fiber that breaks down in your gut. If you take off the apple’s skin before eating it, you won ...',\n",
" 'title': 'Apples: Nutrition &amp; Health Benefits - WebMD',\n",
" 'link': 'https://www.webmd.com/food-recipes/benefits-apples'},\n",
" {'snippet': '<b>Apples</b> boast many vitamins and minerals, though not in high amounts. However, <b>apples</b> are usually a good source of vitamin C. Vitamin C. Also called ascorbic acid, this vitamin is a common ...',\n",
" 'title': 'Apples 101: Nutrition Facts and Health Benefits',\n",
" 'link': 'https://www.healthline.com/nutrition/foods/apples'},\n",
" {'snippet': 'Weight management. The fibers in <b>apples</b> can slow digestion, helping one to feel greater satisfaction after eating. After following three large prospective cohorts of 133,468 men and women for 24 years, researchers found that higher intakes of fiber-rich fruits with a low glycemic load, particularly <b>apples</b> and pears, were associated with the least amount of weight gain over time.',\n",
" 'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health',\n",
" 'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.results(\"apples\", 5)"
]
}
],
"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"
},
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

19
docs/modules/agents/examples/chatgpt_plugins.ipynb → docs/modules/agents/tools/examples/chatgpt_plugins.ipynb
View File

@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"id": "d41405b5",
"metadata": {},
"outputs": [],
@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 2,
"id": "d9e61df5",
"metadata": {},
"outputs": [],
@@ -38,9 +38,11 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"id": "edc0ea0e",
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
@@ -58,8 +60,8 @@
"Thought:\u001b[32;1m\u001b[1;3mI need to use the Klarna Shopping API to search for t shirts.\n",
"Action: requests_get\n",
"Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Lacoste Men's Pack of Plain T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?source=openai\",\"price\":\"$28.99\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\"]},{\"name\":\"Hanes Men's Ultimate 6pk. Crewneck T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?source=openai\",\"price\":\"$13.40\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White\"]},{\"name\":\"Nike Boy's Jordan Stretch T-shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Color:White,Green\",\"Model:Boy\",\"Pattern:Solid Color\",\"Size (Small-Large):S,XL,L,M\"]},{\"name\":\"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?source=openai\",\"price\":\"$29.95\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Blue,Black\"]},{\"name\":\"adidas Comfort T-shirts Men's 3-pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\",\"Pattern:Solid Color\"]}]}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe available t shirts on Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\n",
"Observation: \u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Lacoste Men's Pack of Plain T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?source=openai\",\"price\":\"$28.02\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\"]},{\"name\":\"Hanes Men's Ultimate 6pk. Crewneck T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?source=openai\",\"price\":\"$13.82\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White\"]},{\"name\":\"Nike Boy's Jordan Stretch T-shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Color:White,Green\",\"Model:Boy\",\"Pattern:Solid Color\",\"Size (Small-Large):S,XL,L,M\"]},{\"name\":\"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?source=openai\",\"price\":\"$29.95\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Blue,Black\"]},{\"name\":\"adidas Comfort T-shirts Men's 3-pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\",\"Pattern:Solid Color\",\"Neckline:Round\"]}]}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThese are the available t shirts on Klarna: Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\n",
"Final Answer: The available t shirts on Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
@@ -71,18 +73,17 @@
"\"The available t shirts on Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\""
]
},
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm = ChatOpenAI(temperature=0)\n",
"llm = ChatOpenAI(temperature=0,)\n",
"tools = load_tools([\"requests\"] )\n",
"tools += [tool]\n",
"\n",
"agent_chain = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
"\n",
"agent_chain.run(\"what t shirts are available in klarna?\")"
]
},

210
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{
"cells": [
{
"cell_type": "markdown",
"id": "245a954a",
"metadata": {},
"source": [
"# Google Search\n",
"\n",
"This notebook goes over how to use the google search component.\n",
"\n",
"First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found [here](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search).\n",
"\n",
"Then we will need to set some environment variables."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "34bb5968",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"GOOGLE_CSE_ID\"] = \"\"\n",
"os.environ[\"GOOGLE_API_KEY\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ac4910f8",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import GoogleSearchAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "84b8f773",
"metadata": {},
"outputs": [],
"source": [
"search = GoogleSearchAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "068991a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1 Child\\'s First Name. 2. 6. 7d. Street Address. 71. (Type or print). BARACK. Sex. 3. This Birth. 4. If Twin or Triplet,. Was Child Born. Barack Hussein Obama II is an American retired politician who served as the 44th president of the United States from 2009 to 2017. His full name is Barack Hussein Obama II. Since the “II” is simply because he was named for his father, his last name is Obama. Feb 9, 2015 ... Michael Jordan misspelled Barack Obama\\'s first name on 50th-birthday gift ... Knowing Obama is a Chicagoan and huge basketball fan,\\xa0... Aug 18, 2017 ... It took him several seconds and multiple clues to remember former President Barack Obama\\'s first name. Miller knew that every answer had to end\\xa0... First Lady Michelle LaVaughn Robinson Obama is a lawyer, writer, and the wife of the 44th President, Barack Obama. She is the first African-American First\\xa0... Barack Obama, in full Barack Hussein Obama II, (born August 4, 1961, Honolulu, Hawaii, U.S.), 44th president of the United States (2009–17) and the first\\xa0... When Barack Obama was elected president in 2008, he became the first African American to hold ... The Middle East remained a key foreign policy challenge. Feb 27, 2020 ... President Barack Obama was born Barack Hussein Obama, II, as shown here on his birth certificate here . As reported by Reuters here , his\\xa0... Jan 16, 2007 ... 4, 1961, in Honolulu. His first name means \"one who is blessed\" in Swahili. While Obama\\'s father, Barack Hussein Obama Sr., was from Kenya, his\\xa0...'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"Obama's first name?\")"
]
},
{
"cell_type": "markdown",
"id": "074b7f07",
"metadata": {},
"source": [
"## Number of Results\n",
"You can use the `k` parameter to set the number of results"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5083fbdd",
"metadata": {},
"outputs": [],
"source": [
"search = GoogleSearchAPIWrapper(k=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "77aaa857",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'The official home of the Python Programming Language.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"python\")"
]
},
{
"cell_type": "markdown",
"id": "11c8d94f",
"metadata": {},
"source": [
"'The official home of the Python Programming Language.'"
]
},
{
"cell_type": "markdown",
"id": "73473110",
"metadata": {},
"source": [
"## Metadata Results"
]
},
{
"cell_type": "markdown",
"id": "109fe796",
"metadata": {},
"source": [
"Run query through GoogleSearch and return snippet, title, and link metadata.\n",
"\n",
"- Snippet: The description of the result.\n",
"- Title: The title of the result.\n",
"- Link: The link to the result."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "028f4cba",
"metadata": {},
"outputs": [],
"source": [
"search = GoogleSearchAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4d8f734f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'snippet': 'Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment,\\xa0...',\n",
" 'title': 'Apple',\n",
" 'link': 'https://www.apple.com/'},\n",
" {'snippet': \"Jul 10, 2022 ... Whether or not you're up on your apple trivia, no doubt you know how delicious this popular fruit is, and how nutritious. Apples are rich in\\xa0...\",\n",
" 'title': '25 Types of Apples and What to Make With Them - Parade ...',\n",
" 'link': 'https://parade.com/1330308/bethlipton/types-of-apples/'},\n",
" {'snippet': 'An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the\\xa0...',\n",
" 'title': 'Apple - Wikipedia',\n",
" 'link': 'https://en.wikipedia.org/wiki/Apple'},\n",
" {'snippet': 'Apples are a popular fruit. They contain antioxidants, vitamins, dietary fiber, and a range of other nutrients. Due to their varied nutrient content,\\xa0...',\n",
" 'title': 'Apples: Benefits, nutrition, and tips',\n",
" 'link': 'https://www.medicalnewstoday.com/articles/267290'},\n",
" {'snippet': \"An apple is a crunchy, bright-colored fruit, one of the most popular in the United States. You've probably heard the age-old saying, “An apple a day keeps\\xa0...\",\n",
" 'title': 'Apples: Nutrition & Health Benefits',\n",
" 'link': 'https://www.webmd.com/food-recipes/benefits-apples'}]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.results(\"apples\", 5)"
]
}
],
"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"
},
"vscode": {
"interpreter": {
"hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "dc23c48e",
"metadata": {},
"source": [
"# Google Serper API\n",
"\n",
"This notebook goes over how to use the Google Serper component to search the web. First you need to sign up for a free account at [serper.dev](https://serper.dev) and get your api key."
]
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"import os\n",
"os.environ[\"SERPER_API_KEY\"] = \"\""
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 2,
"id": "54bf5afd",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import GoogleSerperAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "31f8f382",
"metadata": {},
"outputs": [],
"source": [
"search = GoogleSerperAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "25ce0225",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "'Barack Hussein Obama II'"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"Obama's first name?\")"
]
},
{
"cell_type": "markdown",
"source": [
"## As part of a Self Ask With Search Chain"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"os.environ['OPENAI_API_KEY'] = \"\""
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001B[1m> Entering new AgentExecutor chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3m Yes.\n",
"Follow up: Who is the reigning men's U.S. Open champion?\u001B[0m\n",
"Intermediate answer: \u001B[36;1m\u001B[1;3mCurrent champions Carlos Alcaraz, 2022 men's singles champion.\u001B[0m\n",
"\u001B[32;1m\u001B[1;3mFollow up: Where is Carlos Alcaraz from?\u001B[0m\n",
"Intermediate answer: \u001B[36;1m\u001B[1;3mEl Palmar, Spain\u001B[0m\n",
"\u001B[32;1m\u001B[1;3mSo the final answer is: El Palmar, Spain\u001B[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
]
},
{
"data": {
"text/plain": "'El Palmar, Spain'"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.utilities import GoogleSerperAPIWrapper\n",
"from langchain.llms.openai import OpenAI\n",
"from langchain.agents import initialize_agent, Tool\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"search = GoogleSerperAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name=\"Intermediate Answer\",\n",
" func=search.run,\n",
" description=\"useful for when you need to ask with search\"\n",
" )\n",
"]\n",
"\n",
"self_ask_with_search = initialize_agent(tools, llm, agent=\"self-ask-with-search\", verbose=True)\n",
"self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
],
"metadata": {
"collapsed": false
}
}
],
"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
}

0
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{
"cells": [
{
"cell_type": "markdown",
"id": "16763ed3",
"metadata": {},
"source": [
"# IFTTT WebHooks\n",
"\n",
"This notebook shows how to use IFTTT Webhooks.\n",
"\n",
"From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.\n",
"\n",
"## Creating a webhook\n",
"- Go to https://ifttt.com/create\n",
"\n",
"## Configuring the \"If This\"\n",
"- Click on the \"If This\" button in the IFTTT interface.\n",
"- Search for \"Webhooks\" in the search bar.\n",
"- Choose the first option for \"Receive a web request with a JSON payload.\"\n",
"- Choose an Event Name that is specific to the service you plan to connect to.\n",
"This will make it easier for you to manage the webhook URL.\n",
"For example, if you're connecting to Spotify, you could use \"Spotify\" as your\n",
"Event Name.\n",
"- Click the \"Create Trigger\" button to save your settings and create your webhook.\n",
"\n",
"## Configuring the \"Then That\"\n",
"- Tap on the \"Then That\" button in the IFTTT interface.\n",
"- Search for the service you want to connect, such as Spotify.\n",
"- Choose an action from the service, such as \"Add track to a playlist\".\n",
"- Configure the action by specifying the necessary details, such as the playlist name,\n",
"e.g., \"Songs from AI\".\n",
"- Reference the JSON Payload received by the Webhook in your action. For the Spotify\n",
"scenario, choose \"{{JsonPayload}}\" as your search query.\n",
"- Tap the \"Create Action\" button to save your action settings.\n",
"- Once you have finished configuring your action, click the \"Finish\" button to\n",
"complete the setup.\n",
"- Congratulations! You have successfully connected the Webhook to the desired\n",
"service, and you're ready to start receiving data and triggering actions 🎉\n",
"\n",
"## Finishing up\n",
"- To get your webhook URL go to https://ifttt.com/maker_webhooks/settings\n",
"- Copy the IFTTT key value from there. The URL is of the form\n",
"https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "10a46e7e",
"metadata": {},
"outputs": [],
"source": [
"from langchain.tools.ifttt import IFTTTWebhook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "12003d72",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"key = os.environ[\"IFTTTKey\"]\n",
"url = f\"https://maker.ifttt.com/trigger/spotify/json/with/key/{key}\"\n",
"tool = IFTTTWebhook(name=\"Spotify\", description=\"Add a song to spotify playlist\", url=url)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6e68f846",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Congratulations! You've fired the spotify JSON event\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tool.run(\"taylor swift\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7e599c9",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "984a8fca",
"metadata": {},
"source": [
"# Python REPL\n",
"\n",
"Sometimes, for complex calculations, rather than have an LLM generate the answer directly, it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. In order to easily do that, we provide a simple Python REPL to execute commands in.\n",
"\n",
"This interface will only return things that are printed - therefor, if you want to use it to calculate an answer, make sure to have it print out the answer."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f6593089",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import PythonREPL"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6f21f0a4",
"metadata": {},
"outputs": [],
"source": [
"python_repl = PythonREPL()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7ebbbaea",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2\\n'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"python_repl.run(\"print(1+1)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54fc1f03",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "DUXgyWySl5"
},
"source": [
"# SearxNG Search API\n",
"\n",
"This notebook goes over how to use a self hosted SearxNG search API to search the web.\n",
"\n",
"You can [check this link](https://docs.searxng.org/dev/search_api.html) for more informations about Searx API parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jukit_cell_id": "OIHXztO2UT"
},
"outputs": [],
"source": [
"import pprint\n",
"from langchain.utilities import SearxSearchWrapper"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jukit_cell_id": "4SzT9eDMjt"
},
"outputs": [],
"source": [
"search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "jCSkIlQDUK"
},
"source": [
"For some engines, if a direct `answer` is available the warpper will print the answer instead of the full list of search results. You can use the `results` method of the wrapper if you want to obtain all the results."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"jukit_cell_id": "gGM9PVQX6m"
},
"outputs": [
{
"data": {
"text/plain": [
"'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"What is the capital of France\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "OHyurqUPbS"
},
"source": [
"## Custom Parameters\n",
"\n",
"SearxNG supports up to [139 search engines](https://docs.searxng.org/admin/engines/configured_engines.html#configured-engines). You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api."
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "n1B2AyLKi4"
},
"source": [
"In this example we will be using the `engines` parameters to query wikipedia"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jukit_cell_id": "UTEdJ03LqA"
},
"outputs": [],
"source": [
"search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\", k=5) # k is for max number of items"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"jukit_cell_id": "3FyQ6yHI8K",
"tags": [
"scroll-output"
]
},
"outputs": [
{
"data": {
"text/plain": [
"'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.\\n\\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.\\n\\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.\\n\\nAll of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...\\n\\nLarge language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"large language model \", engines=['wiki'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "SYz8nFkt81"
},
"source": [
"Passing other Searx parameters for searx like `language`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"jukit_cell_id": "32rDh0Mvbx"
},
"outputs": [
{
"data": {
"text/plain": [
"'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\", k=1)\n",
"search.run(\"deep learning\", language='es', engines=['wiki'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "d0x164ssV1"
},
"source": [
"## Obtaining results with metadata"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "pF6rs8XcDH"
},
"source": [
"In this example we will be looking for scientific paper using the `categories` parameter and limiting the results to a `time_range` (not all engines support the time range option).\n",
"\n",
"We also would like to obtain the results in a structured way including metadata. For this we will be using the `results` method of the wrapper."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"jukit_cell_id": "BFgpPH0sxF"
},
"outputs": [],
"source": [
"search = SearxSearchWrapper(searx_host=\"http://127.0.0.1:8888\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"jukit_cell_id": "r7qUtvKNOh",
"tags": [
"scroll-output"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'snippet': '… on natural language instructions, large language models (… the '\n",
" 'prompt used to steer the model, and most effective prompts … to '\n",
" 'prompt engineering, we propose Automatic Prompt …',\n",
" 'title': 'Large language models are human-level prompt engineers',\n",
" 'link': 'https://arxiv.org/abs/2211.01910',\n",
" 'engines': ['google scholar'],\n",
" 'category': 'science'},\n",
" {'snippet': '… Large language models (LLMs) have introduced new possibilities '\n",
" 'for prototyping with AI [18]. Pre-trained on a large amount of '\n",
" 'text data, models … language instructions called prompts. …',\n",
" 'title': 'Promptchainer: Chaining large language model prompts through '\n",
" 'visual programming',\n",
" 'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729',\n",
" 'engines': ['google scholar'],\n",
" 'category': 'science'},\n",
" {'snippet': '… can introspect the large prompt model. We derive the view '\n",
" 'ϕ0(X) and the model h0 from T01. However, instead of fully '\n",
" 'fine-tuning T0 during co-training, we focus on soft prompt '\n",
" 'tuning, …',\n",
" 'title': 'Co-training improves prompt-based learning for large language '\n",
" 'models',\n",
" 'link': 'https://proceedings.mlr.press/v162/lang22a.html',\n",
" 'engines': ['google scholar'],\n",
" 'category': 'science'},\n",
" {'snippet': '… With the success of large language models (LLMs) of code and '\n",
" 'their use as … prompt design process become important. In this '\n",
" 'work, we propose a framework called Repo-Level Prompt …',\n",
" 'title': 'Repository-level prompt generation for large language models of '\n",
" 'code',\n",
" 'link': 'https://arxiv.org/abs/2206.12839',\n",
" 'engines': ['google scholar'],\n",
" 'category': 'science'},\n",
" {'snippet': '… Figure 2 | The benefits of different components of a prompt '\n",
" 'for the largest language model (Gopher), as estimated from '\n",
" 'hierarchical logistic regression. Each point estimates the '\n",
" 'unique …',\n",
" 'title': 'Can language models learn from explanations in context?',\n",
" 'link': 'https://arxiv.org/abs/2204.02329',\n",
" 'engines': ['google scholar'],\n",
" 'category': 'science'}]\n"
]
}
],
"source": [
"results = search.results(\"Large Language Model prompt\", num_results=5, categories='science', time_range='year')\n",
"pprint.pp(results)"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "2seI78pR8T"
},
"source": [
"Get papers from arxiv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"jukit_cell_id": "JyNgoFm0vo",
"tags": [
"scroll-output"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'snippet': 'Thanks to the advanced improvement of large pre-trained language '\n",
" 'models, prompt-based fine-tuning is shown to be effective on a '\n",
" 'variety of downstream tasks. Though many prompting methods have '\n",
" 'been investigated, it remains unknown which type of prompts are '\n",
" 'the most effective among three types of prompts (i.e., '\n",
" 'human-designed prompts, schema prompts and null prompts). In '\n",
" 'this work, we empirically compare the three types of prompts '\n",
" 'under both few-shot and fully-supervised settings. Our '\n",
" 'experimental results show that schema prompts are the most '\n",
" 'effective in general. Besides, the performance gaps tend to '\n",
" 'diminish when the scale of training data grows large.',\n",
" 'title': 'Do Prompts Solve NLP Tasks Using Natural Language?',\n",
" 'link': 'http://arxiv.org/abs/2203.00902v1',\n",
" 'engines': ['arxiv'],\n",
" 'category': 'science'},\n",
" {'snippet': 'Cross-prompt automated essay scoring (AES) requires the system '\n",
" 'to use non target-prompt essays to award scores to a '\n",
" 'target-prompt essay. Since obtaining a large quantity of '\n",
" 'pre-graded essays to a particular prompt is often difficult and '\n",
" 'unrealistic, the task of cross-prompt AES is vital for the '\n",
" 'development of real-world AES systems, yet it remains an '\n",
" 'under-explored area of research. Models designed for '\n",
" 'prompt-specific AES rely heavily on prompt-specific knowledge '\n",
" 'and perform poorly in the cross-prompt setting, whereas current '\n",
" 'approaches to cross-prompt AES either require a certain quantity '\n",
" 'of labelled target-prompt essays or require a large quantity of '\n",
" 'unlabelled target-prompt essays to perform transfer learning in '\n",
" 'a multi-step manner. To address these issues, we introduce '\n",
" 'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our '\n",
" 'method requires no access to labelled or unlabelled '\n",
" 'target-prompt data during training and is a single-stage '\n",
" 'approach. PAES is easy to apply in practice and achieves '\n",
" 'state-of-the-art performance on the Automated Student Assessment '\n",
" 'Prize (ASAP) dataset.',\n",
" 'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to '\n",
" 'Cross-prompt Automated Essay Scoring',\n",
" 'link': 'http://arxiv.org/abs/2008.01441v1',\n",
" 'engines': ['arxiv'],\n",
" 'category': 'science'},\n",
" {'snippet': 'Research on prompting has shown excellent performance with '\n",
" 'little or even no supervised training across many tasks. '\n",
" 'However, prompting for machine translation is still '\n",
" 'under-explored in the literature. We fill this gap by offering a '\n",
" 'systematic study on prompting strategies for translation, '\n",
" 'examining various factors for prompt template and demonstration '\n",
" 'example selection. We further explore the use of monolingual '\n",
" 'data and the feasibility of cross-lingual, cross-domain, and '\n",
" 'sentence-to-document transfer learning in prompting. Extensive '\n",
" 'experiments with GLM-130B (Zeng et al., 2022) as the testbed '\n",
" 'show that 1) the number and the quality of prompt examples '\n",
" 'matter, where using suboptimal examples degenerates translation; '\n",
" '2) several features of prompt examples, such as semantic '\n",
" 'similarity, show significant Spearman correlation with their '\n",
" 'prompting performance; yet, none of the correlations are strong '\n",
" 'enough; 3) using pseudo parallel prompt examples constructed '\n",
" 'from monolingual data via zero-shot prompting could improve '\n",
" 'translation; and 4) improved performance is achievable by '\n",
" 'transferring knowledge from prompt examples selected in other '\n",
" 'settings. We finally provide an analysis on the model outputs '\n",
" 'and discuss several problems that prompting still suffers from.',\n",
" 'title': 'Prompting Large Language Model for Machine Translation: A Case '\n",
" 'Study',\n",
" 'link': 'http://arxiv.org/abs/2301.07069v2',\n",
" 'engines': ['arxiv'],\n",
" 'category': 'science'},\n",
" {'snippet': 'Large language models can perform new tasks in a zero-shot '\n",
" 'fashion, given natural language prompts that specify the desired '\n",
" 'behavior. Such prompts are typically hand engineered, but can '\n",
" 'also be learned with gradient-based methods from labeled data. '\n",
" 'However, it is underexplored what factors make the prompts '\n",
" 'effective, especially when the prompts are natural language. In '\n",
" 'this paper, we investigate common attributes shared by effective '\n",
" 'prompts. We first propose a human readable prompt tuning method '\n",
" '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a '\n",
" 'fluency constraint to find a diverse distribution of effective '\n",
" 'and fluent prompts. Our analysis reveals that effective prompts '\n",
" 'are topically related to the task domain and calibrate the prior '\n",
" 'probability of label words. Based on these findings, we also '\n",
" 'propose a method for generating prompts using only unlabeled '\n",
" 'data, outperforming strong baselines by an average of 7.0% '\n",
" 'accuracy across three tasks.',\n",
" 'title': \"Toward Human Readable Prompt Tuning: Kubrick's The Shining is a \"\n",
" 'good movie, and a good prompt too?',\n",
" 'link': 'http://arxiv.org/abs/2212.10539v1',\n",
" 'engines': ['arxiv'],\n",
" 'category': 'science'},\n",
" {'snippet': 'Prevailing methods for mapping large generative language models '\n",
" \"to supervised tasks may fail to sufficiently probe models' novel \"\n",
" 'capabilities. Using GPT-3 as a case study, we show that 0-shot '\n",
" 'prompts can significantly outperform few-shot prompts. We '\n",
" 'suggest that the function of few-shot examples in these cases is '\n",
" 'better described as locating an already learned task rather than '\n",
" 'meta-learning. This analysis motivates rethinking the role of '\n",
" 'prompts in controlling and evaluating powerful language models. '\n",
" 'In this work, we discuss methods of prompt programming, '\n",
" 'emphasizing the usefulness of considering prompts through the '\n",
" 'lens of natural language. We explore techniques for exploiting '\n",
" 'the capacity of narratives and cultural anchors to encode '\n",
" 'nuanced intentions and techniques for encouraging deconstruction '\n",
" 'of a problem into components before producing a verdict. '\n",
" 'Informed by this more encompassing theory of prompt programming, '\n",
" 'we also introduce the idea of a metaprompt that seeds the model '\n",
" 'to generate its own natural language prompts for a range of '\n",
" 'tasks. Finally, we discuss how these more general methods of '\n",
" 'interacting with language models can be incorporated into '\n",
" 'existing and future benchmarks and practical applications.',\n",
" 'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot '\n",
" 'Paradigm',\n",
" 'link': 'http://arxiv.org/abs/2102.07350v1',\n",
" 'engines': ['arxiv'],\n",
" 'category': 'science'}]\n"
]
}
],
"source": [
"results = search.results(\"Large Language Model prompt\", num_results=5, engines=['arxiv'])\n",
"pprint.pp(results)"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "LhEisLFcZM"
},
"source": [
"In this example we query for `large language models` under the `it` category. We then filter the results that come from github."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"jukit_cell_id": "aATPfXzGzx"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'snippet': 'Guide to using pre-trained large language models of source code',\n",
" 'title': 'Code-LMs',\n",
" 'link': 'https://github.com/VHellendoorn/Code-LMs',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Dramatron uses large language models to generate coherent '\n",
" 'scripts and screenplays.',\n",
" 'title': 'dramatron',\n",
" 'link': 'https://github.com/deepmind/dramatron',\n",
" 'engines': ['github'],\n",
" 'category': 'it'}]\n"
]
}
],
"source": [
"results = search.results(\"large language model\", num_results = 20, categories='it')\n",
"pprint.pp(list(filter(lambda r: r['engines'][0] == 'github', results)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"jukit_cell_id": "zDo2YjafuU"
},
"source": [
"We could also directly query for results from `github` and other source forges."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"jukit_cell_id": "5NrlredKxM",
"tags": [
"scroll-output"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'snippet': \"Implementation of 'A Watermark for Large Language Models' paper \"\n",
" 'by Kirchenbauer & Geiping et. al.',\n",
" 'title': 'Peutlefaire / LMWatermark',\n",
" 'link': 'https://gitlab.com/BrianPulfer/LMWatermark',\n",
" 'engines': ['gitlab'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Guide to using pre-trained large language models of source code',\n",
" 'title': 'Code-LMs',\n",
" 'link': 'https://github.com/VHellendoorn/Code-LMs',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': '',\n",
" 'title': 'Simen Burud / Large-scale Language Models for Conversational '\n",
" 'Speech Recognition',\n",
" 'link': 'https://gitlab.com/BrianPulfer',\n",
" 'engines': ['gitlab'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Dramatron uses large language models to generate coherent '\n",
" 'scripts and screenplays.',\n",
" 'title': 'dramatron',\n",
" 'link': 'https://github.com/deepmind/dramatron',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Code for loralib, an implementation of \"LoRA: Low-Rank '\n",
" 'Adaptation of Large Language Models\"',\n",
" 'title': 'LoRA',\n",
" 'link': 'https://github.com/microsoft/LoRA',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Code for the paper \"Evaluating Large Language Models Trained on '\n",
" 'Code\"',\n",
" 'title': 'human-eval',\n",
" 'link': 'https://github.com/openai/human-eval',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'A trend starts from \"Chain of Thought Prompting Elicits '\n",
" 'Reasoning in Large Language Models\".',\n",
" 'title': 'Chain-of-ThoughtsPapers',\n",
" 'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent '\n",
" 'and accessible large-scale language model training, built with '\n",
" 'Hugging Face 🤗 Transformers.',\n",
" 'title': 'mistral',\n",
" 'link': 'https://github.com/stanford-crfm/mistral',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'A prize for finding tasks that cause large language models to '\n",
" 'show inverse scaling',\n",
" 'title': 'prize',\n",
" 'link': 'https://github.com/inverse-scaling/prize',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Optimus: the first large-scale pre-trained VAE language model',\n",
" 'title': 'Optimus',\n",
" 'link': 'https://github.com/ChunyuanLI/Optimus',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel '\n",
" 'Hill, Fall 2022)',\n",
" 'title': 'llm-seminar',\n",
" 'link': 'https://github.com/craffel/llm-seminar',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'A central, open resource for data and tools related to '\n",
" 'chain-of-thought reasoning in large language models. Developed @ '\n",
" 'Samwald research group: https://samwald.info/',\n",
" 'title': 'ThoughtSource',\n",
" 'link': 'https://github.com/OpenBioLink/ThoughtSource',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'A comprehensive list of papers using large language/multi-modal '\n",
" 'models for Robotics/RL, including papers, codes, and related '\n",
" 'websites',\n",
" 'title': 'Awesome-LLM-Robotics',\n",
" 'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Tools for curating biomedical training data for large-scale '\n",
" 'language modeling',\n",
" 'title': 'biomedical',\n",
" 'link': 'https://github.com/bigscience-workshop/biomedical',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, '\n",
" 'written by ChatGPT',\n",
" 'title': 'ChatGPT-at-Home',\n",
" 'link': 'https://github.com/Sentdex/ChatGPT-at-Home',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Design and Deploy Large Language Model Apps',\n",
" 'title': 'dust',\n",
" 'link': 'https://github.com/dust-tt/dust',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in '\n",
" 'Multi-languages',\n",
" 'title': 'polyglot',\n",
" 'link': 'https://github.com/EleutherAI/polyglot',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'Code release for \"Learning Video Representations from Large '\n",
" 'Language Models\"',\n",
" 'title': 'LaViLa',\n",
" 'link': 'https://github.com/facebookresearch/LaViLa',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization '\n",
" 'for Large Language Models',\n",
" 'title': 'smoothquant',\n",
" 'link': 'https://github.com/mit-han-lab/smoothquant',\n",
" 'engines': ['github'],\n",
" 'category': 'it'},\n",
" {'snippet': 'This repository contains the code, data, and models of the paper '\n",
" 'titled \"XL-Sum: Large-Scale Multilingual Abstractive '\n",
" 'Summarization for 44 Languages\" published in Findings of the '\n",
" 'Association for Computational Linguistics: ACL-IJCNLP 2021.',\n",
" 'title': 'xl-sum',\n",
" 'link': 'https://github.com/csebuetnlp/xl-sum',\n",
" 'engines': ['github'],\n",
" 'category': 'it'}]\n"
]
}
],
"source": [
"results = search.results(\"large language model\", num_results = 20, engines=['github', 'gitlab'])\n",
"pprint.pp(results)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "dc23c48e",
"metadata": {},
"source": [
"# SerpAPI\n",
"\n",
"This notebook goes over how to use the SerpAPI component to search the web."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "54bf5afd",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import SerpAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "31f8f382",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "25ce0225",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Barack Hussein Obama II'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"Obama's first name?\")"
]
},
{
"cell_type": "markdown",
"id": "fe3ee213",
"metadata": {},
"source": [
"## Custom Parameters\n",
"You can also customize the SerpAPI wrapper with arbitrary parameters. For example, in the below example we will use `bing` instead of `google`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "deffcc8b",
"metadata": {},
"outputs": [],
"source": [
"params = {\n",
" \"engine\": \"bing\",\n",
" \"gl\": \"us\",\n",
" \"hl\": \"en\",\n",
"}\n",
"search = SerpAPIWrapper(params=params)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2c752d08",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Barack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American presi…New content will be added above the current area of focus upon selectionBarack Hussein Obama II is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004, and previously worked as a civil rights lawyer before entering politics.Wikipediabarackobama.com'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search.run(\"Obama's first name?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e0a1dc1c",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}

124
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@@ -0,0 +1,124 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "245a954a",
"metadata": {},
"source": [
"# Wolfram Alpha\n",
"\n",
"This notebook goes over how to use the wolfram alpha component.\n",
"\n",
"First, you need to set up your Wolfram Alpha developer account and get your APP ID:\n",
"\n",
"1. Go to wolfram alpha and sign up for a developer account [here](https://developer.wolframalpha.com/)\n",
"2. Create an app and get your APP ID\n",
"3. pip install wolframalpha\n",
"\n",
"Then we will need to set some environment variables:\n",
"1. Save your APP ID into WOLFRAM_ALPHA_APPID env variable"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "961b3689",
"metadata": {
"vscode": {
"languageId": "shellscript"
}
},
"outputs": [],
"source": [
"pip install wolframalpha"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "34bb5968",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"WOLFRAM_ALPHA_APPID\"] = \"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ac4910f8",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "84b8f773",
"metadata": {},
"outputs": [],
"source": [
"wolfram = WolframAlphaAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "068991a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'x = 2/5'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wolfram.run(\"What is 2x+5 = -3x + 7?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "028f4cba",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.9.7"
},
"vscode": {
"interpreter": {
"hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

326
docs/modules/agents/tools/examples/zapier.ipynb Normal file
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@@ -0,0 +1,326 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "16763ed3",
"metadata": {},
"source": [
"# Zapier Natural Language Actions API\n",
"\\\n",
"Full docs here: https://nla.zapier.com/api/v1/docs\n",
"\n",
"**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.\n",
"\n",
"NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/apps\n",
"\n",
"Zapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.\n",
"\n",
"NLA offers both API Key and OAuth for signing NLA API requests.\n",
"\n",
"1. Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer's Zapier account (and will use the developer's connected accounts on Zapier.com)\n",
"\n",
"2. User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com\n",
"\n",
"This quick start will focus on the server-side use case for brevity. Review [full docs](https://nla.zapier.com/api/v1/docs) or reach out to nla@zapier.com for user-facing oauth developer support.\n",
"\n",
"This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
"In code, below:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a363309c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5cf33377",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# get from https://platform.openai.com/\n",
"os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"\")\n",
"\n",
"# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in): \n",
"os.environ[\"ZAPIER_NLA_API_KEY\"] = os.environ.get(\"ZAPIER_NLA_API_KEY\", \"\")"
]
},
{
"cell_type": "markdown",
"id": "4881b484-1b97-478f-b206-aec407ceff66",
"metadata": {},
"source": [
"## Example with Agent\n",
"Zapier tools can be used with an agent. See the example below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b2044b17-c941-4ffb-8a03-027a35e2df81",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.agents import initialize_agent\n",
"from langchain.agents.agent_toolkits import ZapierToolkit\n",
"from langchain.utilities.zapier import ZapierNLAWrapper"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7b505eeb",
"metadata": {},
"outputs": [],
"source": [
"## step 0. expose gmail 'find email' and slack 'send channel message' actions\n",
"\n",
"# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields \"Have AI guess\"\n",
"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cab18227-c232-4214-9256-bb8dd352266c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
"zapier = ZapierNLAWrapper()\n",
"toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
"agent = initialize_agent(toolkit.get_tools(), llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f94713de-b64d-465f-a087-00288b5f80ec",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find the email and summarize it.\n",
"Action: Gmail: Find Email\n",
"Action Input: Find the latest email from Silicon Valley Bank\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to summarize the email and send it to the #test-zapier channel in Slack.\n",
"Action: Slack: Send Channel Message\n",
"Action Input: Send a slack message to the #test-zapier channel with the text \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m{\"message__text\": \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\", \"message__permalink\": \"https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259\", \"channel\": \"C04TSGU0RA7\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:58:52Z\", \"message__bot_profile__icons__image_36\": \"https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png\", \"message__blocks[]block_id\": \"kdZZ\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\")"
]
},
{
"cell_type": "markdown",
"id": "bcdea831",
"metadata": {},
"source": [
"# Example with SimpleSequentialChain\n",
"If you need more explicit control, use a chain, like below."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "10a46e7e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.chains import LLMChain, TransformChain, SimpleSequentialChain\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.tools.zapier.tool import ZapierNLARunAction\n",
"from langchain.utilities.zapier import ZapierNLAWrapper"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b9358048",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## step 0. expose gmail 'find email' and slack 'send direct message' actions\n",
"\n",
"# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields \"Have AI guess\"\n",
"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first\n",
"\n",
"actions = ZapierNLAWrapper().list()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4e80f461",
"metadata": {},
"outputs": [],
"source": [
"## step 1. gmail find email\n",
"\n",
"GMAIL_SEARCH_INSTRUCTIONS = \"Grab the latest email from Silicon Valley Bank\"\n",
"\n",
"def nla_gmail(inputs):\n",
" action = next((a for a in actions if a[\"description\"].startswith(\"Gmail: Find Email\")), None)\n",
" return {\"email_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(inputs[\"instructions\"])}\n",
"gmail_chain = TransformChain(input_variables=[\"instructions\"], output_variables=[\"email_data\"], transform=nla_gmail)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "46893233",
"metadata": {},
"outputs": [],
"source": [
"## step 2. generate draft reply\n",
"\n",
"template = \"\"\"You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).\n",
"\n",
"Incoming email:\n",
"{email_data}\n",
"\n",
"Draft email reply:\"\"\"\n",
"\n",
"prompt_template = PromptTemplate(input_variables=[\"email_data\"], template=template)\n",
"reply_chain = LLMChain(llm=OpenAI(temperature=.7), prompt=prompt_template)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "cd85c4f8",
"metadata": {},
"outputs": [],
"source": [
"## step 3. send draft reply via a slack direct message\n",
"\n",
"SLACK_HANDLE = \"@Ankush Gola\"\n",
"\n",
"def nla_slack(inputs):\n",
" action = next((a for a in actions if a[\"description\"].startswith(\"Slack: Send Direct Message\")), None)\n",
" instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs[\"draft_reply\"]}'\n",
" return {\"slack_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(instructions)}\n",
"slack_chain = TransformChain(input_variables=[\"draft_reply\"], output_variables=[\"slack_data\"], transform=nla_slack)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4829cab4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m\n",
"Dear Silicon Valley Bridge Bank, \n",
"\n",
"Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n",
"\n",
"Best regards, \n",
"[Your Name]\u001b[0m\n",
"\u001b[38;5;200m\u001b[1;3m{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[['text']]\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\\\n\\\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\\\n\\\\nBest regards, \\\\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[[\\'text\\']]\", \"message__blocks[]elements[]type\": \"[\\'rich_text_section\\']\"}'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## finally, execute\n",
"\n",
"overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)\n",
"overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09ff954e-45f2-4595-92ea-91627abde4a0",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

6
docs/modules/agents/tools.md → docs/modules/agents/tools/getting_started.md
View File

@@ -1,4 +1,4 @@
# Tools
# Getting Started
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
@@ -118,7 +118,7 @@ Below is a list of all supported tools and relevant information:
- Notes: Uses the Google Custom Search API
- Requires LLM: No
- Extra Parameters: `google_api_key`, `google_cse_id`
- For more information on this, see [this page](../../ecosystem/google_search.md)
- For more information on this, see [this page](../../../ecosystem/google_search.md)
**searx-search**
@@ -135,7 +135,7 @@ Below is a list of all supported tools and relevant information:
- Notes: Calls the [serper.dev](https://serper.dev) Google Search API and then parses results.
- Requires LLM: No
- Extra Parameters: `serper_api_key`
- For more information on this, see [this page](../../ecosystem/google_serper.md)
- For more information on this, see [this page](../../../ecosystem/google_serper.md)
**wikipedia**

0
docs/modules/agents/examples/multi_input_tool.ipynb → docs/modules/agents/tools/multi_input_tool.ipynb
View File