Ela279/langchain_docs huggingface dataset:
.ipynb
.pdf
GraphQL tool
GraphQL tool#
This Jupyter Notebook demonstrates how to use the BaseGraphQLTool component with an Agent.
GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
By including a BaseGraphQLTool in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.
In this example, we’ll be using the public Star Wars GraphQL API available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.
First, you need to install httpx and gql Python packages.
pip install httpx gql > /dev/null
Now, let’s create a BaseGraphQLTool instance with the specified Star Wars API endpoint and initialize an Agent with the tool.
from langchain import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
from langchain.utilities import GraphQLAPIWrapper
llm = OpenAI(temperature=0)
tools = load_tools(["graphql"], graphql_endpoint="https://swapi-graphql.netlify.app/.netlify/functions/index", llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
Now, we can use the Agent to run queries against the Star Wars GraphQL API. Let’s ask the Agent to list all the Star Wars films and their release dates.
graphql_fields = """allFilms {
    films {
      title
      director
      releaseDate
      speciesConnection {
        species {
          name
          classification
          homeworld {
            name
          }
species {
          name
          classification
          homeworld {
            name
          }
        }
      }
    }
  }
"""
suffix = "Search for the titles of all the stawars films stored in the graphql database that has this schema "
agent.run(suffix + graphql_fields)
> Entering new AgentExecutor chain...
 I need to query the graphql database to get the titles of all the star wars films
Action: query_graphql
Action Input: query { allFilms { films { title } } }
Observation: "{\n  \"allFilms\": {\n    \"films\": [\n      {\n        \"title\": \"A New Hope\"\n      },\n      {\n        \"title\": \"The Empire Strikes Back\"\n      },\n      {\n        \"title\": \"Return of the Jedi\"\n      },\n      {\n        \"title\": \"The Phantom Menace\"\n      },\n      {\n        \"title\": \"Attack of the Clones\"\n      },\n      {\n        \"title\": \"Revenge of the Sith\"\n      }\n    ]\n  }\n}"
Thought: I now know the titles of all the star wars films
Final Answer: The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.
> Finished chain.
'The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.'

Gradio Tools

HuggingFace Tools
By Harrison Chase
By Harrison Chase
.ipynb
.pdf
Apify
Apify#
This notebook shows how to use the Apify integration for LangChain.
Apify is a cloud platform for web scraping and data extraction,
which provides an ecosystem of more than a thousand
ready-made apps called Actors for various web scraping, crawling, and data extraction use cases.
For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.
In this example, we’ll use the Website Content Crawler Actor,
which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs,
and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.
#!pip install apify-client
First, import ApifyWrapper into your source code:
from langchain.document_loaders.base import Document
from langchain.indexes import VectorstoreIndexCreator
from langchain.utilities import ApifyWrapper
Initialize it using your Apify API token and for the purpose of this example, also with your OpenAI API key:
import os
os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
os.environ["APIFY_API_TOKEN"] = "Your Apify API token"
apify = ApifyWrapper()
Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.
Note that if you already have some results in an Apify dataset, you can load them directly using ApifyDatasetLoader, as shown in this notebook. In that notebook, you’ll also find the explanation of the dataset_mapping_function, which is used to map fields from the Apify dataset records to LangChain Document fields.
loader = apify.call_actor(
    actor_id="apify/website-content-crawler",
loader = apify.call_actor(
    actor_id="apify/website-content-crawler",
    run_input={"startUrls": [{"url": "https://python.langchain.com/en/latest/"}]},
    dataset_mapping_function=lambda item: Document(
        page_content=item["text"] or "", metadata={"source": item["url"]}
    ),
)
Initialize the vector index from the crawled documents:
index = VectorstoreIndexCreator().from_loaders([loader])
And finally, query the vector index:
query = "What is LangChain?"
result = index.query_with_sources(query)
print(result["answer"])
print(result["sources"])
 LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities.
https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.html

Tool Input Schema

ArXiv API Tool
By Harrison Chase
.ipynb
.pdf
DuckDuckGo Search
DuckDuckGo Search#
This notebook goes over how to use the duck-duck-go search component.
# !pip install duckduckgo-search
from langchain.tools import DuckDuckGoSearchRun
search = DuckDuckGoSearchRun()
search.run("Obama's first name?")
'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 African American to hold the office. Before winning the presidency, Obama represented Illinois in the U.S. Senate (2005-08). Barack Hussein Obama II (/ b ə ˈ r ɑː k h uː ˈ s eɪ n oʊ ˈ b ɑː m ə / bə-RAHK hoo-SAYN oh-BAH-mə; born August 4, 1961) is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president of the United States. Obama previously served as a U.S. senator representing ... Barack Obama was the first African American president of the United States (2009-17). He oversaw the recovery of the U.S. economy (from the Great Recession of 2008-09) and the enactment of landmark health care reform (the Patient Protection and Affordable Care Act ). In 2009 he was awarded the Nobel Peace Prize. His birth certificate lists his first name as Barack: That\'s how Obama has spelled his name throughout his life. His name derives from a Hebrew name which means "lightning.". The Hebrew word has been transliterated into English in various spellings, including Barak, Buraq, Burack, and Barack. Most common names of U.S. presidents 1789-2021. Published by. Aaron O\'Neill , Jun 21, 2022. The most common first name for a U.S. president is James, followed by John and then William. Six U.S ...'

ChatGPT Plugins
ChatGPT Plugins

File System Tools
By Harrison Chase
.ipynb
.pdf
AWS Lambda API
AWS Lambda API#
This notebook goes over how to use the AWS Lambda Tool component.
AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.
By including a awslambda in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.
When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.
First, you need to install boto3 python package.
!pip install boto3 > /dev/null
In order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function’s logic.
You must also provide the name of your function.
Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run aws configure in order to make use of the tool. For more detail, see here
from langchain import OpenAI
from langchain.agents import load_tools, AgentType
llm = OpenAI(temperature=0)
tools = load_tools(
    ["awslambda"],
    awslambda_tool_name="email-sender",
    awslambda_tool_description="sends an email with the specified content to test@testing123.com",
    function_name="testFunction1"
)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Send an email to test@testing123.com saying hello world.")

ArXiv API Tool

Shell Tool
By Harrison Chase
.ipynb
.pdf
Google Places
Google Places#
This notebook goes through how to use Google Places API
#!pip install googlemaps
import os
os.environ["GPLACES_API_KEY"] = ""
from langchain.tools import GooglePlacesTool
places = GooglePlacesTool()
places.run("al fornos")
"1. Delfina Restaurant\nAddress: 3621 18th St, San Francisco, CA 94110, USA\nPhone: (415) 552-4055\nWebsite: https://www.delfinasf.com/\n\n\n2. Piccolo Forno\nAddress: 725 Columbus Ave, San Francisco, CA 94133, USA\nPhone: (415) 757-0087\nWebsite: https://piccolo-forno-sf.com/\n\n\n3. L'Osteria del Forno\nAddress: 519 Columbus Ave, San Francisco, CA 94133, USA\nPhone: (415) 982-1124\nWebsite: Unknown\n\n\n4. Il Fornaio\nAddress: 1265 Battery St, San Francisco, CA 94111, USA\nPhone: (415) 986-0100\nWebsite: https://www.ilfornaio.com/\n\n"

File System Tools

Google Search
By Harrison Chase
.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 will need to set some environment variables:
Save your APP ID into WOLFRAM_ALPHA_APPID env variable
pip install wolframalpha
import os
os.environ["WOLFRAM_ALPHA_APPID"] = ""
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
wolfram = WolframAlphaAPIWrapper()
wolfram.run("What is 2x+5 = -3x + 7?")
'x = 2/5'

Wikipedia

YouTubeSearchTool
By Harrison Chase
.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', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu']"
You can also specify the number of results that are returned
tool.run("lex friedman,5")
"['/watch?v=VcVfceTsD0A&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=YVJ8gTnDC4Y&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=Udh22kuLebg&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=gPfriiHBBek&pp=ygUMbGV4IGZyaWVkbWFu', '/watch?v=L_Guz73e6fw&pp=ygUMbGV4IGZyaWVkbWFu']"

Wolfram Alpha

Zapier Natural Language Actions API
By Harrison Chase
.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"] = "<YOUR_API_KEY>"
from langchain.agents import load_tools
tools = load_tools(["sceneXplain"])
Or directly instantiate the tool.
from langchain.tools import SceneXplainTool
tool = SceneXplainTool()
Usage in an Agent#
The tool can be used in any LangChain agent as follows:
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.memory import ConversationBufferMemory
llm = OpenAI(temperature=0)
memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
    tools, llm, memory=memory, agent="conversational-react-description", verbose=True
)
output = agent.run(
    input=(
        "What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. "
        "Is it movie or a game? If it is a movie, what is the name of the movie?"
    )
)
print(output)
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Image Explainer
Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png
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 the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight.
Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm.
In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image.
Thought: Do I need to use a tool? No
AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.
> Finished chain.
This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.

Requests

Search Tools
 Contents
  
Usage in an Agent
By Harrison Chase
By Harrison Chase
.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 we will need to set some environment variables:
Save your API KEY into OPENWEATHERMAP_API_KEY env variable
Use the wrapper#
from langchain.utilities import OpenWeatherMapAPIWrapper
import os
os.environ["OPENWEATHERMAP_API_KEY"] = ""
weather = OpenWeatherMapAPIWrapper()
weather_data = weather.run("London,GB")
print(weather_data)
In London,GB, the current weather is as follows:
Detailed status: broken clouds
Wind speed: 2.57 m/s, direction: 240°
Humidity: 55%
Temperature: 
  - Current: 20.12°C
  - High: 21.75°C
  - Low: 18.68°C
  - Feels like: 19.62°C
Rain: {}
Heat index: None
Cloud cover: 75%
Use the tool#
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
import os
os.environ["OPENAI_API_KEY"] = ""
os.environ["OPENWEATHERMAP_API_KEY"] = ""
llm = OpenAI(temperature=0)
tools = load_tools(["openweathermap-api"], llm)
agent_chain = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)
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°
Humidity: 56%
Temperature: 
  - Current: 20.11°C
  - High: 21.75°C
  - Low: 18.68°C
  - Feels like: 19.64°C
Rain: {}
Heat index: None
Cloud cover: 75%
Thought: I now know the current weather in London.
Final Answer: The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None.
> Finished chain.
'The current weather in London is broken clouds, with a wind speed of 2.57 m/s, direction 240°, humidity of 56%, temperature of 20.11°C, high of 21.75°C, low of 18.68°C, and a heat index of None.'

Metaphor Search

Python REPL
 Contents
  
Use the wrapper
Use the tool
By Harrison Chase
.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
Configuring the “If This”#
Click on the “If This” button in the IFTTT interface.
Search for “Webhooks” in the search bar.
Choose the first option for “Receive a web request with a JSON payload.”
Choose an Event Name that is specific to the service you plan to connect to.
This will make it easier for you to manage the webhook URL.
For example, if you’re connecting to Spotify, you could use “Spotify” as your
Event Name.
Click the “Create Trigger” button to save your settings and create your webhook.
Configuring the “Then That”#
Tap on the “Then That” button in the IFTTT interface.
Search for the service you want to connect, such as Spotify.
Choose an action from the service, such as “Add track to a playlist”.
Configure the action by specifying the necessary details, such as the playlist name,
e.g., “Songs from AI”.
Reference the JSON Payload received by the Webhook in your action. For the Spotify
scenario, choose “{{JsonPayload}}” as your search query.
Tap the “Create Action” button to save your action settings.
Once you have finished configuring your action, click the “Finish” button to
complete the setup.
Congratulations! You have successfully connected the Webhook to the desired
service, and you’re ready to start receiving data and triggering actions 🎉
service, and you’re ready to start receiving data and triggering actions 🎉
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://maker.ifttt.com/trigger/spotify/json/with/key/{key}"
tool = IFTTTWebhook(name="Spotify", description="Add a song to spotify playlist", url=url)
tool.run("taylor swift")
"Congratulations! You've fired the spotify JSON event"

Human as a tool

Metaphor Search
 Contents
  
Creating a webhook
Configuring the “If This”
Configuring the “Then That”
Finishing up
By Harrison Chase
.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 wrapper with arbitrary parameters. For example, in the below example we will use bing instead of google.
params = {
    "engine": "bing",
    "gl": "us",
    "hl": "en",
}
search = SerpAPIWrapper(params=params)
search.run("Obama's first name?")
'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'
from langchain.agents import Tool
# You can create the tool to pass to an agent
repl_tool = Tool(
    name="python_repl",
repl_tool = Tool(
    name="python_repl",
    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,
)

SearxNG Search API

Wikipedia
 Contents
  
Custom Parameters
By Harrison Chase
.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_huggingface_tool
tool = load_huggingface_tool("lysandre/hf-model-downloads")
print(f"{tool.name}: {tool.description}")
model_download_counter: This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. It takes the name of the category (such as text-classification, depth-estimation, etc), and returns the name of the checkpoint
tool.run("text-classification")
'facebook/bart-large-mnli'

GraphQL tool

Human as a tool
By Harrison Chase