id stringlengths 14 16 | source stringlengths 49 117 | text stringlengths 16 2.73k |
|---|---|---|
90a0cca0ac7e-21 | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html | 'longitude': -73.96473379999999,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPDGchokDvppoLfmVEo6X_bWd3Fz0HyxIHTEe9V=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 276,
'category': 'Italian'},
{'position': 4,
'title': 'Luna Rossa... |
90a0cca0ac7e-22 | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html | 'latitude': 40.772124999999996,
'longitude': -73.965012,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNrX19G0NVdtDyMovCQ-M-m0c_gLmIxrWDQAAbz=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 176,
'category': 'Italian'},
{'position':... |
90a0cca0ac7e-23 | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html | 'title': 'Pinocchio Restaurant',
'address': '300 E 92nd St',
'latitude': 40.781453299999995,
'longitude': -73.9486788,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNtxlIyEEJHtDtFtTR9nB38S8A2VyMu-mVVz72A=w92-h92-n-k-no',
'rating': 4.5,
... |
89efac01e0d0-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/wolfram_alpha.html | .ipynb
.pdf
Wolfram Alpha
Wolfram Alpha#
This notebook goes over how to use the wolfram alpha component.
First, you need to set up your Wolfram Alpha developer account and get your APP ID:
Go to wolfram alpha and sign up for a developer account here
Create an app and get your APP ID
pip install wolframalpha
Then we wil... |
2717a60edcfe-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html | .ipynb
.pdf
Bing Search
Contents
Number of results
Metadata Results
Bing Search#
This notebook goes over how to use the bing search component.
First, you need to set up the proper API keys and environment variables. To set it up, follow the instructions found here.
Then we will need to set some environment variables.... |
2717a60edcfe-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html | '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 Azur... |
2717a60edcfe-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html | and modules, see The <b>Python</b> Standard ... <b>Python</b> is a general-purpose, versatile, and powerful programming language. It'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 sci... |
2717a60edcfe-3 | https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html | Number of results#
You can use the k parameter to set the number of results
search = BingSearchAPIWrapper(k=1)
search.run("python")
'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 credentia... |
2717a60edcfe-4 | https://python.langchain.com/en/latest/modules/agents/tools/examples/bing_search.html | {'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 ...',
'title': 'Apples 101: Nutrition Facts and Health Benefits',
'link': 'https://www.healthline.com... |
df327ff5c32c-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/gradio_tools.html | .ipynb
.pdf
Gradio Tools
Contents
Using a tool
Using within an agent
Gradio Tools#
There are many 1000s of Gradio apps on Hugging Face Spaces. This library puts them at the tips of your LLM’s fingers 🦾
Specifically, gradio-tools is a Python library for converting Gradio apps into tools that can be leveraged by a lar... |
df327ff5c32c-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/gradio_tools.html | from gradio_tools.tools import (StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool,
TextToVideoTool)
from langchain.memory import ConversationBufferMemory
llm = OpenAI(temperature=0)
memory = ConversationBufferMemory(memory_key="chat_history")
tools = [StableDif... |
df327ff5c32c-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/gradio_tools.html | Action Input: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha
Job Status: Status.STARTING eta: None
Job Status: Status.PROCESSING eta: None
Observation: /Users/harrisonchase/workplace/langchain/docs/modules/age... |
df327ff5c32c-3 | https://python.langchain.com/en/latest/modules/agents/tools/examples/gradio_tools.html | Observation: /var/folders/bm/ylzhm36n075cslb9fvvbgq640000gn/T/tmp5snj_nmzf20_cb3m.mp4
Thought: Do I need to use a tool? No
AI: Here is a video of a painting of a dog sitting on a skateboard.
> Finished chain.
previous
Google Serper API
next
GraphQL tool
Contents
Using a tool
Using within an agent
By Harrison Chase
... |
69045b8961ee-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/youtube.html | .ipynb
.pdf
YouTubeSearchTool
YouTubeSearchTool#
This notebook shows how to use a tool to search YouTube
Adapted from venuv/langchain_yt_tools
#! pip install youtube_search
from langchain.tools import YouTubeSearchTool
tool = YouTubeSearchTool()
tool.run("lex friedman")
"['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu',... |
5a54175b6a7a-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/sceneXplain.html | .ipynb
.pdf
SceneXplain
Contents
Usage in an Agent
SceneXplain#
SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.
To use this tool, you’ll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.
import os
os.environ["SCENEX_API_KEY"] = "<Y... |
5a54175b6a7a-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/sceneXplain.html | Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto t... |
5a54175b6a7a-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/sceneXplain.html | Last updated on Jun 04, 2023. |
f315fc84fc8e-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/openweathermap.html | .ipynb
.pdf
OpenWeatherMap API
Contents
Use the wrapper
Use the tool
OpenWeatherMap API#
This notebook goes over how to use the OpenWeatherMap component to fetch weather information.
First, you need to sign up for an OpenWeatherMap API key:
Go to OpenWeatherMap and sign up for an API key here
pip install pyowm
Then w... |
f315fc84fc8e-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/openweathermap.html | agent_chain.run("What's the weather like in London?")
> Entering new AgentExecutor chain...
I need to find out the current weather in London.
Action: OpenWeatherMap
Action Input: London,GB
Observation: In London,GB, the current weather is as follows:
Detailed status: broken clouds
Wind speed: 2.57 m/s, direction: 240°... |
f0b3e582178c-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/ifttt.html | .ipynb
.pdf
IFTTT WebHooks
Contents
Creating a webhook
Configuring the “If This”
Configuring the “Then That”
Finishing up
IFTTT WebHooks#
This notebook shows how to use IFTTT Webhooks.
From https://github.com/SidU/teams-langchain-js/wiki/Connecting-IFTTT-Services.
Creating a webhook#
Go to https://ifttt.com/create
Co... |
f0b3e582178c-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/ifttt.html | Finishing up#
To get your webhook URL go to https://ifttt.com/maker_webhooks/settings
Copy the IFTTT key value from there. The URL is of the form
https://maker.ifttt.com/use/YOUR_IFTTT_KEY. Grab the YOUR_IFTTT_KEY value.
from langchain.tools.ifttt import IFTTTWebhook
import os
key = os.environ["IFTTTKey"]
url = f"https... |
ea532d04ee70-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | .ipynb
.pdf
Metaphor Search
Contents
Metaphor Search
Call the API
Use Metaphor as a tool
Metaphor Search#
This notebook goes over how to use Metaphor search.
First, you need to set up the proper API keys and environment variables. Request an API key [here](Sign up for early access here).
Then enter your API key as an... |
ea532d04ee70-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | {'results': [{'url': 'https://www.anthropic.com/index/core-views-on-ai-safety', 'title': 'Core Views on AI Safety: When, Why, What, and How', 'dateCreated': '2023-03-08', 'author': None, 'score': 0.1998831331729889}, {'url': 'https://aisafety.wordpress.com/', 'title': 'Extinction Risk from Artificial Intelligence', 'da... |
ea532d04ee70-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | '2023-02-24', 'author': 'Authors', 'score': 0.18665121495723724}, {'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html', 'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why', 'dateCreated': '2015-01-22', 'author': 'Tim Urban', 'score': 0.18604731559753418}, {'url': 'http... |
ea532d04ee70-3 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | [{'title': 'Core Views on AI Safety: When, Why, What, and How',
'url': 'https://www.anthropic.com/index/core-views-on-ai-safety',
'author': None,
'date_created': '2023-03-08'},
{'title': 'Extinction Risk from Artificial Intelligence',
'url': 'https://aisafety.wordpress.com/',
'author': None,
'date_created'... |
ea532d04ee70-4 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | {'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why',
'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html',
'author': 'Tim Urban',
'date_created': '2015-01-22'},
{'title': 'Anthropic: Core Views on AI Safety: When, Why, What, and How - EA Forum',
'url': 'https:... |
ea532d04ee70-5 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | tools = toolkit.get_tools()
tools_by_name = {tool.name: tool for tool in tools}
print(tools_by_name.keys())
navigate_tool = tools_by_name["navigate_browser"]
extract_text = tools_by_name["extract_text"]
from langchain.agents import initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
from langchain.... |
ea532d04ee70-6 | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html | 'I need to navigate to the URL provided in the search results to find the tweet.'
previous
IFTTT WebHooks
next
OpenWeatherMap API
Contents
Metaphor Search
Call the API
Use Metaphor as a tool
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
48d01c26c3ce-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/serpapi.html | .ipynb
.pdf
SerpAPI
Contents
Custom Parameters
SerpAPI#
This notebook goes over how to use the SerpAPI component to search the web.
from langchain.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
search.run("Obama's first name?")
'Barack Hussein Obama II'
Custom Parameters#
You can also customize the SerpAPI... |
48d01c26c3ce-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/serpapi.html | description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
func=search.run,
)
previous
SearxNG Search API
next
Twilio
Contents
Custom Parameters
By Harrison Chase
© Co... |
a61809ec04a1-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html | .ipynb
.pdf
Human as a tool
Contents
Configuring the Input Function
Human as a tool#
Human are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent
... |
a61809ec04a1-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html | print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.")
contents = []
while True:
try:
line = input()
except EOFError:
break
if line == "q":
break
contents.append(line)
return "\n".join(contents)
# You can modify... |
a61809ec04a1-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html | Action: DuckDuckGo Search
Action Input: "Who said 'Veni, vidi, vici'?" |
a61809ec04a1-3 | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html | Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounc... |
a61809ec04a1-4 | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html | the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have been used by Caesa... |
a61809ec04a1-5 | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html | Thought:I now know the final answer
Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".
> Finished chain.
'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".'
previous
HuggingFace Tools
next
IFTTT WebHooks
Contents
Configurin... |
eb6cc50b52c7-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/huggingface_tools.html | .ipynb
.pdf
HuggingFace Tools
HuggingFace Tools#
Huggingface Tools supporting text I/O can be
loaded directly using the load_huggingface_tool function.
# Requires transformers>=4.29.0 and huggingface_hub>=0.14.1
!pip install --upgrade transformers huggingface_hub > /dev/null
from langchain.agents import load_huggingfac... |
7f6c15c95b56-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html | .ipynb
.pdf
ArXiv API Tool
Contents
The ArXiv API Wrapper
ArXiv API Tool#
This notebook goes over how to use the arxiv component.
First, you need to install arxiv python package.
!pip install arxiv
from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
llm = ... |
7f6c15c95b56-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html | Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.
> Finished chain.
'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'
The ArXiv API Wrapper#
The tool wraps the API Wrapper. Below, we can explore some of t... |
7f6c15c95b56-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html | 'Published: 2017-10-10\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\nAuthors: Caprice Stanley, Seth Sullivant\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\ninteger sequence $\\{ G_n \\}_{n \\geq 1}$ generated by a linear recurrence\nrela... |
7f6c15c95b56-3 | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html | Now, we are trying to find information about non-existing article. In this case, the response is “No good Arxiv Result was found”
docs = arxiv.run("1605.08386WWW")
docs
'No good Arxiv Result was found'
previous
Apify
next
AWS Lambda API
Contents
The ArXiv API Wrapper
By Harrison Chase
© Copyright 2023, H... |
e559d7e2f081-0 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | .ipynb
.pdf
ChatGPT Plugins
ChatGPT Plugins#
This example shows how to use ChatGPT Plugins within LangChain abstractions.
Note 1: This currently only works for plugins with no auth.
Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!
from lang... |
e559d7e2f081-1 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get':... |
e559d7e2f081-2 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}} |
e559d7e2f081-3 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | Thought:I need to use the Klarna Shopping API to search for t shirts.
Action: requests_get
Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts |
e559d7e2f081-4 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | Observation: {"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/?utm_source=openai","price":"$26.60","attributes":["Material:Cotton","Target Group:Man","Color:White,Black"]},{"name":"Hanes Men's Ultima... |
e559d7e2f081-5 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | 3-pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Target Group:Man","Color:White,Black","Neckline:Round"]}]} |
e559d7e2f081-6 | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html | Thought:The available t shirts in 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.
Final Answer: The available t shirts in Klarna are Lacoste Men's P... |
feff7a739661-0 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | .ipynb
.pdf
Custom LLM Agent
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Adding Memory
Custom LLM Agent#
This notebook goes through how to create your own custom LLM agent.
An LLM agent consists of three parts:
PromptTemplate... |
feff7a739661-1 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | from langchain import OpenAI, SerpAPIWrapper, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
Set up tool#
Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent knows to use these tools).
# Define which tools the... |
feff7a739661-2 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | # Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a p... |
feff7a739661-3 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | # Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_... |
feff7a739661-4 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
Use the Agent#
Now we can use it!
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in ... |
feff7a739661-5 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | ... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Previous conversation history:
{history}
New question: {input}
{... |
feff7a739661-6 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_agent.html | Final Answer: Arrr, there be 38,658,314 people livin' in Canada as of 2023!
> Finished chain.
"Arrr, there be 38,658,314 people livin' in Canada as of 2023!"
agent_executor.run("how about in mexico?")
> Entering new AgentExecutor chain...
Thought: I need to find out how many people live in Mexico.
Action: Search
Action... |
290a3eb9a134-0 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html | .ipynb
.pdf
Custom LLM Agent (with a ChatModel)
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Custom LLM Agent (with a ChatModel)#
This notebook goes through how to create your own custom agent based on a chat model.
An LLM cha... |
290a3eb9a134-1 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html | from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import BaseChatPromptTemplate
from langchain import SerpAPIWrapper, LLMChain
from langchain.chat_models import ChatOpenAI
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish,... |
290a3eb9a134-2 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html | ... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
These were previous tasks you completed:
Begin!
Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(BaseChatP... |
290a3eb9a134-3 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html | The output parser is responsible for parsing the LLM output into AgentAction and AgentFinish. This usually depends heavily on the prompt used.
This is where you can change the parsing to do retries, handle whitespace, etc
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[Ag... |
290a3eb9a134-4 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html | This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an Observation (otherwise, the LLM may hallucinate an observation for you).
Set up the Agent#
We can now combine everything to set up our agent
# LLM chain consisting of... |
290a3eb9a134-5 | https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 04, 2023. |
a112f241bce3-0 | https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html | .ipynb
.pdf
Custom MRKL Agent
Contents
Custom LLMChain
Multiple inputs
Custom MRKL Agent#
This notebook goes through how to create your own custom MRKL agent.
A MRKL agent consists of three parts:
- Tools: The tools the agent has available to use.
- LLMChain: The LLMChain that produces the text that is parsed in a ce... |
a112f241bce3-1 | https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html | For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate.
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLMChain
search = SerpAPIWrapper()
tools = [
Tool(
name = "Sea... |
a112f241bce3-2 | https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html | Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
Question: {input}
{agent_scratchpad}
Note that we are able to feed agents a self-defined prompt template, i.e. not restricted to the prompt generated by the create_prompt function, assuming it meets the agent’s requirements.
For exam... |
a112f241bce3-3 | https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html | suffix = """When answering, you MUST speak in the following language: {language}.
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "language", "agent_scratchpad"]
)
llm_chain = LLMChain(llm=OpenAI(temperature=... |
a112f241bce3-4 | https://python.langchain.com/en/latest/modules/agents/agents/custom_mrkl_agent.html | 'La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023, dopo un record di crescita demografica di 1.050.110 persone dal 1° gennaio 2022 al 1° gennaio 2023.'
previous
Custom LLM Agent (with a ChatModel)
next
Custom MultiAction Agent
Contents
Custom LLMChain
Multiple inputs
By Harrison Chase
... |
09f9d091aff1-0 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent.html | .ipynb
.pdf
Custom Agent
Custom Agent#
This notebook goes through how to create your own custom agent.
An agent consists of two parts:
- Tools: The tools the agent has available to use.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a custom agent.
from langc... |
09f9d091aff1-1 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent.html | along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
return AgentAction(tool="Search", tool_input=kwargs["input"], log="")
agent = FakeAgent()
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=... |
8a4644394d37-0 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | .ipynb
.pdf
Custom Agent with Tool Retrieval
Contents
Set up environment
Set up tools
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
Custom Agent with Tool Retrieval#
This notebook builds off of this notebook and assumes familiarity with how agents work.
The novel ... |
8a4644394d37-1 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | name=f"foo-{i}",
func=fake_func,
description=f"a silly function that you can use to get more information about the number {i}"
)
for i in range(99)
]
ALL_TOOLS = [search_tool] + fake_tools
Tool Retriever#
We will use a vectorstore to create embeddings for each tool description. Then, for an i... |
8a4644394d37-2 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | Tool(name='foo-95', description='a silly function that you can use to get more information about the number 95', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-12', ... |
8a4644394d37-3 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | Tool(name='foo-14', description='a silly function that you can use to get more information about the number 14', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, coroutine=None),
Tool(name='foo-11', ... |
8a4644394d37-4 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | # Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
############## NEW ######################
# The list of tools available
tools_getter: Callable
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentActio... |
8a4644394d37-5 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | # Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_... |
8a4644394d37-6 | https://python.langchain.com/en/latest/modules/agents/agents/custom_agent_with_tool_retrieval.html | agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("What's the weather in SF?")
> Entering new AgentExecutor chain...
Thought: I need to find out what the weather is in SF
Action: Search
Action Input: Weather in SF
Observation:Mostly cloudy skies early, then p... |
f9356a09366a-0 | https://python.langchain.com/en/latest/modules/agents/agents/custom_multi_action_agent.html | .ipynb
.pdf
Custom MultiAction Agent
Custom MultiAction Agent#
This notebook goes through how to create your own custom agent.
An agent consists of two parts:
- Tools: The tools the agent has available to use.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a ... |
f9356a09366a-1 | https://python.langchain.com/en/latest/modules/agents/agents/custom_multi_action_agent.html | AgentAction(tool="Search", tool_input=kwargs["input"], log=""),
AgentAction(tool="RandomWord", tool_input=kwargs["input"], log=""),
]
else:
return AgentFinish(return_values={"output": "bar"}, log="")
async def aplan(
self, intermediate_steps: List[Tuple[AgentA... |
0b6e50eb9bbc-0 | https://python.langchain.com/en/latest/modules/agents/agents/agent_types.html | .md
.pdf
Agent Types
Contents
zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
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.
Here a... |
0b6e50eb9bbc-1 | https://python.langchain.com/en/latest/modules/agents/agents/agent_types.html | Last updated on Jun 04, 2023. |
0fc7bfd99b75-0 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | .ipynb
.pdf
Structured Tool Chat Agent
Contents
Initialize Tools
Adding in memory
Structured Tool Chat Agent#
This notebook walks through using a chat agent capable of using multi-input tools.
Older agents are configured to specify an action input as a single string, but this agent can use the provided tools’ args_sc... |
0fc7bfd99b75-1 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | "action": "Final Answer",
"action_input": "Hello Erica, how can I assist you today?"
}
```
> Finished chain.
Hello Erica, how can I assist you today?
response = await agent_chain.arun(input="Don't need help really just chatting.")
print(response)
> Entering new AgentExecutor chain...
> Finished chain.
I'm here to cha... |
0fc7bfd99b75-2 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | We recently open-sourced an auto-evaluator tool for grading LLM question-answer chains. We are now releasing an open source, free to use hosted app and API to expand usability. Below we discuss a few opportunities to further improve May 1, 2023 5 min read Callbacks Improvements TL;DR: We're announcing improvements to o... |
0fc7bfd99b75-3 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | and is moderately technical. |
0fc7bfd99b75-4 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | 💡 TL;DR: We’ve introduced a new abstraction and a new document Retriever to facilitate the post-processing of retrieved documents. Specifically, the new abstraction makes it easy to take a set of retrieved documents and extract from them Apr 20, 2023 3 min read Autonomous Agents & Agent Simulations Over the past two w... |
0fc7bfd99b75-5 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | Originally we designed LangChain.js to run in Node.js, which is the Apr 11, 2023 3 min read LangChain x Supabase Supabase is holding an AI Hackathon this week. Here at LangChain we are big fans of both Supabase and hackathons, so we thought this would be a perfect time to highlight the multiple ways you can use LangCha... |
0fc7bfd99b75-6 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | The reason we like Supabase so much is that Apr 8, 2023 2 min read Announcing our $10M seed round led by Benchmark It was only six months ago that we released the first version of LangChain, but it seems like several years. When we launched, generative AI was starting to go mainstream: stable diffusion had just been re... |
0fc7bfd99b75-7 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | OpenAI/evals - focused on evaluating OpenAI models. Mar 14, 2023 3 min read LLMs and SQL Francisco Ingham and Jon Luo are two of the community members leading the change on the SQL integrations. We’re really excited to write this blog post with them going over all the tips and tricks they’ve learned doing so. We’re eve... |
0fc7bfd99b75-8 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | Authors: Parth Asawa (pgasawa@), Ayushi Batwara (ayushi.batwara@), Jason Mar 8, 2023 4 min read Prompt Selectors One common complaint we've heard is that the default prompt templates do not work equally well for all models. This became especially pronounced this past week when OpenAI released a ChatGPT API. This new AP... |
0fc7bfd99b75-9 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | What does this mean? It means that all your favorite prompts, chains, and agents are all recreatable in TypeScript natively. Both the Python version and TypeScript version utilize the same serializable format, meaning that artifacts can seamlessly be shared between languages. As an Feb 17, 2023 2 min read Streaming Sup... |
0fc7bfd99b75-10 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | }
```
Observation: Navigating to https://xkcd.com/ returned status code 200
Thought:I can extract the latest comic title and alt text using CSS selectors.
Action:
```
{
"action": "get_elements",
"action_input": {
"selector": "#ctitle, #comic img",
"attributes": ["alt", "src"]
}
}
```
Observation: [{"alt"... |
0fc7bfd99b75-11 | https://python.langchain.com/en/latest/modules/agents/agents/examples/structured_chat.html | Hi Erica! How can I assist you today?
response = await agent_chain.arun(input="whats my name?")
print(response)
> Entering new AgentExecutor chain...
Your name is Erica.
> Finished chain.
Your name is Erica.
previous
Self Ask With Search
next
Toolkits
Contents
Initialize Tools
Adding in memory
By Harrison Chase
... |
d2958e41e08b-0 | https://python.langchain.com/en/latest/modules/agents/agents/examples/self_ask_with_search.html | .ipynb
.pdf
Self Ask With Search
Self Ask With Search#
This notebook showcases the Self Ask With Search chain.
from langchain import OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
tools = [
Tool(... |
7626c405342e-0 | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html | .ipynb
.pdf
MRKL Chat
MRKL Chat#
This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models.
This uses the example Chinook database.
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at t... |
7626c405342e-1 | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html | mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain...
Thought: The first question requires a search, while the second question requires a calculator.
Action:
```
{
"action": "Search",
"action_input": "Leo DiCaprio girlfriend"
}
```
Obse... |
7626c405342e-2 | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html | mrkl.run("What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?")
> Entering new AgentExecutor chain...
Question: What is the full name of the artist who recently released an alb... |
7626c405342e-3 | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl_chat.html | SELECT "Title" FROM "Album" WHERE "ArtistId" IN (SELECT "ArtistId" FROM "Artist" WHERE "Name" = 'Alanis Morissette') LIMIT 5;
SQLResult: [('Jagged Little Pill',)]
Answer: Alanis Morissette has the album Jagged Little Pill in the database.
> Finished chain.
Observation: Alanis Morissette has the album Jagged Little Pil... |
8b36b792a09c-0 | https://python.langchain.com/en/latest/modules/agents/agents/examples/react.html | .ipynb
.pdf
ReAct
ReAct#
This notebook showcases using an agent to implement the ReAct logic.
from langchain import OpenAI, Wikipedia
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.react.base import DocstoreExplorer
docstore=DocstoreExplorer(Wikipedia())... |
8b36b792a09c-1 | https://python.langchain.com/en/latest/modules/agents/agents/examples/react.html | Observation: David Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth Good and Felix Zandman. He ha... |
209ac4e27a65-0 | https://python.langchain.com/en/latest/modules/agents/agents/examples/chat_conversation_agent.html | .ipynb
.pdf
Conversation Agent (for Chat Models)
Conversation Agent (for Chat Models)#
This notebook walks through using an agent optimized for conversation, using ChatModels. 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 w... |
209ac4e27a65-1 | https://python.langchain.com/en/latest/modules/agents/agents/examples/chat_conversation_agent.html | "action_input": "Hello Bob! How can I assist you today?"
}
> Finished chain.
'Hello Bob! How can I assist you today?'
agent_chain.run(input="what's my name?")
> Entering new AgentExecutor chain...
{
"action": "Final Answer",
"action_input": "Your name is Bob."
}
> Finished chain.
'Your name is Bob.'
agent_chain... |
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