Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter
Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter
Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter
Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter
Unsupported APIPropertyLocation "header" for parameter Accept. Valid values are ['path', 'query'] Ignoring optional parameter
Unsupported APIPropertyLocation "header" for parameter Content-Type. Valid values are ['path', 'query'] Ignoring optional parameter
natural_language_api_tools = (speak_toolkit.get_tools()
                              + klarna_toolkit.get_tools()
                              + spoonacular_toolkit.get_tools()[:30]
                             )
print(f"{len(natural_language_api_tools)} tools loaded.")
34 tools loaded.
# Create an agent with the new tools
mrkl = initialize_agent(natural_language_api_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                        verbose=True, agent_kwargs={"format_instructions":openapi_format_instructions})
# Make the query more complex!
user_input = (
    "I'm learning Italian, and my language class is having an end of year party... "
    " Could you help me find an Italian outfit to wear and"
    " an appropriate recipe to prepare so I can present for the class in Italian?"
)
mrkl.run(user_input)
> Entering new AgentExecutor chain...
 I need to find a recipe and an outfit that is Italian-themed.
Action: spoonacular_API.searchRecipes
Action Input: Italian


Action: spoonacular_API.searchRecipes
Action Input: Italian
Observation: The API response contains 10 Italian recipes, including Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, and Pappa Al Pomodoro.
Thought: I need to find an Italian-themed outfit.
Action: Open_AI_Klarna_product_Api.productsUsingGET
Action Input: Italian
Observation: I found 10 products related to 'Italian' in the API response. These products include Italian Gold Sparkle Perfectina Necklace - Gold, Italian Design Miami Cuban Link Chain Necklace - Gold, Italian Gold Miami Cuban Link Chain Necklace - Gold, Italian Gold Herringbone Necklace - Gold, Italian Gold Claddagh Ring - Gold, Italian Gold Herringbone Chain Necklace - Gold, Garmin QuickFit 22mm Italian Vacchetta Leather Band, Macy's Italian Horn Charm - Gold, Dolce & Gabbana Light Blue Italian Love Pour Homme EdT 1.7 fl oz.
Thought: I now know the final answer.
Final Answer: To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.
> Finished chain.


> Finished chain.
'To present for your Italian language class, you could wear an Italian Gold Sparkle Perfectina Necklace - Gold, an Italian Design Miami Cuban Link Chain Necklace - Gold, or an Italian Gold Miami Cuban Link Chain Necklace - Gold. For a recipe, you could make Turkey Tomato Cheese Pizza, Broccolini Quinoa Pilaf, Bruschetta Style Pork & Pasta, Salmon Quinoa Risotto, Italian Tuna Pasta, Roasted Brussels Sprouts With Garlic, Asparagus Lemon Risotto, Italian Steamed Artichokes, Crispy Italian Cauliflower Poppers Appetizer, or Pappa Al Pomodoro.'
Thank you!#
natural_language_api_tools[1].run("Tell the LangChain audience to 'enjoy the meal' in Italian, please!")
"In Italian, you can say 'Buon appetito' to someone to wish them to enjoy their meal. This phrase is commonly used in Italy when someone is about to eat, often at the beginning of a meal. It's similar to saying 'Bon appétit' in French or 'Guten Appetit' in German."
previous
OpenAPI agents
next
Pandas Dataframe Agent
 Contents

First, import dependencies and load the LLM
Next, load the Natural Language API Toolkits
Create the Agent
Using Auth + Adding more Endpoints
Thank you!
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Pandas Dataframe Agent
Pandas Dataframe Agent#
This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.
NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.
from langchain.agents import create_pandas_dataframe_agent
from langchain.llms import OpenAI
import pandas as pd
df = pd.read_csv('titanic.csv')
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)
agent.run("how many rows are there?")
> Entering new AgentExecutor chain...
Thought: I need to count the number of rows
Action: python_repl_ast
Action Input: len(df)
Observation: 891
Thought: I now know the final answer
Final Answer: There are 891 rows in the dataframe.
> Finished chain.
'There are 891 rows in the dataframe.'
agent.run("how many people have more than 3 sibligngs")
> Entering new AgentExecutor chain...
Thought: I need to count the number of people with more than 3 siblings
Action: python_repl_ast
Action Input: df[df['SibSp'] > 3].shape[0]
Observation: 30
Thought: I now know the final answer
Final Answer: 30 people have more than 3 siblings.
> Finished chain.
'30 people have more than 3 siblings.'
agent.run("whats the square root of the average age?")
> Entering new AgentExecutor chain...
Thought: I need to calculate the average age first
Action: python_repl_ast
Action Input: df['Age'].mean()


Action: python_repl_ast
Action Input: df['Age'].mean()
Observation: 29.69911764705882
Thought: I can now calculate the square root
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: name 'math' is not defined
Thought: I need to import the math library
Action: python_repl_ast
Action Input: import math
Observation: None
Thought: I can now calculate the square root
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: 5.449689683556195
Thought: I now know the final answer
Final Answer: 5.449689683556195
> Finished chain.
'5.449689683556195'
previous
Natural Language APIs
next
PowerBI Dataset Agent
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
PowerBI Dataset Agent
 Contents
Some notes
Initialization
Example: describing a table
Example: simple query on a table
Example: running queries
Example: add your own few-shot prompts
PowerBI Dataset Agent#
This notebook showcases an agent designed to interact with a Power BI Dataset. The agent is designed to answer more general questions about a dataset, as well as recover from errors.
Note that, as this agent is in active development, all answers might not be correct. It runs against the executequery endpoint, which does not allow deletes.
Some notes#
It relies on authentication with the azure.identity package, which can be installed with pip install azure-identity. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.
You can also supply a username to impersonate for use with datasets that have RLS enabled.
The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.
Testing was done mostly with a text-davinci-003 model, codex models did not seem to perform ver well.
Initialization#
from langchain.agents.agent_toolkits import create_pbi_agent
from langchain.agents.agent_toolkits import PowerBIToolkit
from langchain.utilities.powerbi import PowerBIDataset
from langchain.llms.openai import AzureOpenAI
from langchain.agents import AgentExecutor
from azure.identity import DefaultAzureCredential
fast_llm = AzureOpenAI(temperature=0.5, max_tokens=1000, deployment_name="gpt-35-turbo", verbose=True)
smart_llm = AzureOpenAI(temperature=0, max_tokens=100, deployment_name="gpt-4", verbose=True)
toolkit = PowerBIToolkit(


toolkit = PowerBIToolkit(
    powerbi=PowerBIDataset(dataset_id="<dataset_id>", table_names=['table1', 'table2'], credential=DefaultAzureCredential()),
    llm=smart_llm
)
agent_executor = create_pbi_agent(
    llm=fast_llm,
    toolkit=toolkit,
    verbose=True,
)
Example: describing a table#
agent_executor.run("Describe table1")
Example: simple query on a table#
In this example, the agent actually figures out the correct query to get a row count of the table.
agent_executor.run("How many records are in table1?")
Example: running queries#
agent_executor.run("How many records are there by dimension1 in table2?")
agent_executor.run("What unique values are there for dimensions2 in table2")
Example: add your own few-shot prompts#
#fictional example
few_shots = """
Question: How many rows are in the table revenue?
DAX: EVALUATE ROW("Number of rows", COUNTROWS(revenue_details))
----
Question: How many rows are in the table revenue where year is not empty?
DAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(revenue_details, revenue_details[year] <> "")))
----
Question: What was the average of value in revenue in dollars?
DAX: EVALUATE ROW("Average", AVERAGE(revenue_details[dollar_value]))
----
"""
toolkit = PowerBIToolkit(
    powerbi=PowerBIDataset(dataset_id="<dataset_id>", table_names=['table1', 'table2'], credential=DefaultAzureCredential()),
    llm=smart_llm,
    examples=few_shots,
)
agent_executor = create_pbi_agent(


examples=few_shots,
)
agent_executor = create_pbi_agent(
    llm=fast_llm,
    toolkit=toolkit,
    verbose=True,
)
agent_executor.run("What was the maximum of value in revenue in dollars in 2022?")
previous
Pandas Dataframe Agent
next
Python Agent
 Contents

Some notes
Initialization
Example: describing a table
Example: simple query on a table
Example: running queries
Example: add your own few-shot prompts
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Python Agent
 Contents
Fibonacci Example
Training neural net
Python Agent#
This notebook showcases an agent designed to write and execute python code to answer a question.
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
from langchain.llms.openai import OpenAI
agent_executor = create_python_agent(
    llm=OpenAI(temperature=0, max_tokens=1000),
    tool=PythonREPLTool(),
    verbose=True
)
Fibonacci Example#
This example was created by John Wiseman.
agent_executor.run("What is the 10th fibonacci number?")
> Entering new AgentExecutor chain...
 I need to calculate the 10th fibonacci number
Action: Python REPL
Action Input: def fibonacci(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)
Observation:
Thought: I need to call the function with 10 as the argument
Action: Python REPL
Action Input: fibonacci(10)
Observation:
Thought: I now know the final answer
Final Answer: 55
> Finished chain.
'55'
Training neural net#
This example was created by Samee Ur Rehman.
agent_executor.run("""Understand, write a single neuron neural network in PyTorch.
Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.
Return prediction for x = 5""")
> Entering new AgentExecutor chain...
 I need to write a neural network in PyTorch and train it on the given data.


I need to write a neural network in PyTorch and train it on the given data.
Action: Python REPL
Action Input:
import torch
# Define the model
model = torch.nn.Sequential(
    torch.nn.Linear(1, 1)
)
# Define the loss
loss_fn = torch.nn.MSELoss()
# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# Define the data
x_data = torch.tensor([[1.0], [2.0], [3.0], [4.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0], [8.0]])
# Train the model
for epoch in range(1000):
    # Forward pass
    y_pred = model(x_data)
    # Compute and print loss
    loss = loss_fn(y_pred, y_data)
    if (epoch+1) % 100 == 0:
        print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')
    # Zero the gradients
    optimizer.zero_grad()
    # Backward pass
    loss.backward()
    # Update the weights
    optimizer.step()
Observation: Epoch 100: loss = 0.0013
Epoch 200: loss = 0.0007
Epoch 300: loss = 0.0004
Epoch 400: loss = 0.0002
Epoch 500: loss = 0.0001
Epoch 600: loss = 0.0001
Epoch 700: loss = 0.0000
Epoch 800: loss = 0.0000
Epoch 900: loss = 0.0000
Epoch 1000: loss = 0.0000


Epoch 1000: loss = 0.0000
Thought: I now know the final answer
Final Answer: The prediction for x = 5 is 10.0.
> Finished chain.
'The prediction for x = 5 is 10.0.'
previous
PowerBI Dataset Agent
next
SQL Database Agent
 Contents

Fibonacci Example
Training neural net
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
SQL Database Agent
 Contents
Initialization
Example: describing a table
Example: describing a table, recovering from an error
Example: running queries
Recovering from an error
SQL Database Agent#
This notebook showcases an agent designed to interact with a sql databases. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors.
Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won’t perform DML statements on your database given certain questions. Be careful running it on sensitive data!
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 the root of this repository.
Initialization#
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain.agents import AgentExecutor
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
toolkit = SQLDatabaseToolkit(db=db)
agent_executor = create_sql_agent(
    llm=OpenAI(temperature=0),
    toolkit=toolkit,
    verbose=True
)
Example: describing a table#
agent_executor.run("Describe the playlisttrack table")
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: Artist, Invoice, Playlist, Genre, Album, PlaylistTrack, Track, InvoiceLine, MediaType, Employee, Customer
Thought: I should look at the schema of the playlisttrack table
Action: schema_sql_db
Action Input: "PlaylistTrack"
Observation:


Action: schema_sql_db
Action Input: "PlaylistTrack"
Observation:
CREATE TABLE "PlaylistTrack" (
	"PlaylistId" INTEGER NOT NULL,
	"TrackId" INTEGER NOT NULL,
	PRIMARY KEY ("PlaylistId", "TrackId"),
	FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
	FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")
)
SELECT * FROM 'PlaylistTrack' LIMIT 3;
PlaylistId TrackId
1 3402
1 3389
1 3390
Thought: I now know the final answer
Final Answer: The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.
> Finished chain.
'The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.'
Example: describing a table, recovering from an error#
In this example, the agent tries to search for a table that doesn’t exist, but finds the next best result
agent_executor.run("Describe the playlistsong table")
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: Genre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist
Thought: I should look at the schema of the PlaylistSong table
Action: schema_sql_db
Action Input: "PlaylistSong"
Observation: Error: table_names {'PlaylistSong'} not found in database
Thought: I should check the spelling of the table
Action: list_tables_sql_db
Action Input: ""
Observation: Genre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist
Thought: The table is called PlaylistTrack


Thought: The table is called PlaylistTrack
Action: schema_sql_db
Action Input: "PlaylistTrack"
Observation:
CREATE TABLE "PlaylistTrack" (
	"PlaylistId" INTEGER NOT NULL,
	"TrackId" INTEGER NOT NULL,
	PRIMARY KEY ("PlaylistId", "TrackId"),
	FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
	FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")
)
SELECT * FROM 'PlaylistTrack' LIMIT 3;
PlaylistId TrackId
1 3402
1 3389
1 3390
Thought: I now know the final answer
Final Answer: The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.
> Finished chain.
'The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.'
Example: running queries#
agent_executor.run("List the total sales per country. Which country's customers spent the most?")
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: Invoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer
Thought: I should look at the schema of the relevant tables to see what columns I can use.
Action: schema_sql_db
Action Input: "Invoice, Customer"
Observation:
CREATE TABLE "Customer" (
	"CustomerId" INTEGER NOT NULL,
	"FirstName" NVARCHAR(40) NOT NULL,
	"LastName" NVARCHAR(20) NOT NULL,
	"Company" NVARCHAR(80),
	"Address" NVARCHAR(70),


"Address" NVARCHAR(70),
	"City" NVARCHAR(40),
	"State" NVARCHAR(40),
	"Country" NVARCHAR(40),
	"PostalCode" NVARCHAR(10),
	"Phone" NVARCHAR(24),
	"Fax" NVARCHAR(24),
	"Email" NVARCHAR(60) NOT NULL,
	"SupportRepId" INTEGER,
	PRIMARY KEY ("CustomerId"),
	FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("EmployeeId")
)
SELECT * FROM 'Customer' LIMIT 3;
CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId
1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 luisg@embraer.com.br 3
2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 +49 0711 2842222 None leonekohler@surfeu.de 5
3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None ftremblay@gmail.com 3
CREATE TABLE "Invoice" (
	"InvoiceId" INTEGER NOT NULL,
	"CustomerId" INTEGER NOT NULL,
	"InvoiceDate" DATETIME NOT NULL,
	"BillingAddress" NVARCHAR(70),
	"BillingCity" NVARCHAR(40),


"BillingCity" NVARCHAR(40),
	"BillingState" NVARCHAR(40),
	"BillingCountry" NVARCHAR(40),
	"BillingPostalCode" NVARCHAR(10),
	"Total" NUMERIC(10, 2) NOT NULL,
	PRIMARY KEY ("InvoiceId"),
	FOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")
)
SELECT * FROM 'Invoice' LIMIT 3;
InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total
1 2 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98
2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96
3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94
Thought: I should query the Invoice and Customer tables to get the total sales per country.
Action: query_sql_db
Action Input: SELECT c.Country, SUM(i.Total) AS TotalSales FROM Invoice i INNER JOIN Customer c ON i.CustomerId = c.CustomerId GROUP BY c.Country ORDER BY TotalSales DESC LIMIT 10


Observation: [('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)]
Thought: I now know the final answer
Final Answer: The customers from the USA spent the most, with a total of $523.06.
> Finished chain.
'The customers from the USA spent the most, with a total of $523.06.'
agent_executor.run("Show the total number of tracks in each playlist. The Playlist name should be included in the result.")
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: Invoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer
Thought: I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use.
Action: schema_sql_db
Action Input: "Playlist, PlaylistTrack"
Observation:
CREATE TABLE "Playlist" (
	"PlaylistId" INTEGER NOT NULL,
	"Name" NVARCHAR(120),
	PRIMARY KEY ("PlaylistId")
)
SELECT * FROM 'Playlist' LIMIT 3;
PlaylistId Name
1 Music
2 Movies
3 TV Shows
CREATE TABLE "PlaylistTrack" (
	"PlaylistId" INTEGER NOT NULL,
	"TrackId" INTEGER NOT NULL,


"TrackId" INTEGER NOT NULL,
	PRIMARY KEY ("PlaylistId", "TrackId"),
	FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
	FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId")
)
SELECT * FROM 'PlaylistTrack' LIMIT 3;
PlaylistId TrackId
1 3402
1 3389
1 3390
Thought: I can use a SELECT statement to get the total number of tracks in each playlist.
Action: query_checker_sql_db
Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name
Observation:
SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name
Thought: The query looks correct, I can now execute it.
Action: query_sql_db
Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name LIMIT 10
Observation: [('90’s Music', 1477), ('Brazilian Music', 39), ('Classical', 75), ('Classical 101 - Deep Cuts', 25), ('Classical 101 - Next Steps', 25), ('Classical 101 - The Basics', 25), ('Grunge', 15), ('Heavy Metal Classic', 26), ('Music', 6580), ('Music Videos', 1)]
Thought: I now know the final answer.


Thought: I now know the final answer.
Final Answer: The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).
> Finished chain.
"The total number of tracks in each playlist are: '90’s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1)."
Recovering from an error#
In this example, the agent is able to recover from an error after initially trying to access an attribute (Track.ArtistId) which doesn’t exist.
agent_executor.run("Who are the top 3 best selling artists?")
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: MediaType, Track, Invoice, Album, Playlist, Customer, Employee, InvoiceLine, PlaylistTrack, Genre, Artist
Thought: I should look at the schema of the Artist, InvoiceLine, and Track tables to see what columns I can use.
Action: schema_sql_db
Action Input: "Artist, InvoiceLine, Track"
Observation:
CREATE TABLE "Artist" (
	"ArtistId" INTEGER NOT NULL,


CREATE TABLE "Artist" (
	"ArtistId" INTEGER NOT NULL,
	"Name" NVARCHAR(120),
	PRIMARY KEY ("ArtistId")
)
SELECT * FROM 'Artist' LIMIT 3;
ArtistId Name
1 AC/DC
2 Accept
3 Aerosmith
CREATE TABLE "Track" (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"AlbumId" INTEGER,
	"MediaTypeId" INTEGER NOT NULL,
	"GenreId" INTEGER,
	"Composer" NVARCHAR(220),
	"Milliseconds" INTEGER NOT NULL,
	"Bytes" INTEGER,
	"UnitPrice" NUMERIC(10, 2) NOT NULL,
	PRIMARY KEY ("TrackId"),
	FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"),
	FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),
	FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")
)
SELECT * FROM 'Track' LIMIT 3;
TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice
1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99
2 Balls to the Wall 2 2 1 None 342562 5510424 0.99
3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99
CREATE TABLE "InvoiceLine" (
	"InvoiceLineId" INTEGER NOT NULL,
	"InvoiceId" INTEGER NOT NULL,


"InvoiceId" INTEGER NOT NULL,
	"TrackId" INTEGER NOT NULL,
	"UnitPrice" NUMERIC(10, 2) NOT NULL,
	"Quantity" INTEGER NOT NULL,
	PRIMARY KEY ("InvoiceLineId"),
	FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
	FOREIGN KEY("InvoiceId") REFERENCES "Invoice" ("InvoiceId")
)
SELECT * FROM 'InvoiceLine' LIMIT 3;
InvoiceLineId InvoiceId TrackId UnitPrice Quantity
1 1 2 0.99 1
2 1 4 0.99 1
3 2 6 0.99 1
Thought: I should query the database to get the top 3 best selling artists.
Action: query_sql_db
Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3
Observation: Error: (sqlite3.OperationalError) no such column: Track.ArtistId
[SQL: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3]
(Background on this error at: https://sqlalche.me/e/14/e3q8)
Thought: I should double check my query before executing it.
Action: query_checker_sql_db


Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3
Observation:
SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity
FROM Artist
INNER JOIN Track ON Artist.ArtistId = Track.ArtistId
INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId
GROUP BY Artist.Name
ORDER BY TotalQuantity DESC
LIMIT 3;
Thought: I now know the final answer.
Action: query_sql_db
Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Album ON Artist.ArtistId = Album.ArtistId INNER JOIN Track ON Album.AlbumId = Track.AlbumId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3
Observation: [('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]
Thought: I now know the final answer.
Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica.
> Finished chain.
'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'
previous
Python Agent
next
Vectorstore Agent
 Contents

Initialization
Example: describing a table
Example: describing a table, recovering from an error
Example: running queries
Recovering from an error
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Vectorstore Agent
 Contents
Create the Vectorstores
Initialize Toolkit and Agent
Examples
Multiple Vectorstores
Examples
Vectorstore Agent#
This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.
Create the Vectorstores#
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA
llm = OpenAI(temperature=0)
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
state_of_union_store = Chroma.from_documents(texts, embeddings, collection_name="state-of-union")
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://beta.ruff.rs/docs/faq/")
docs = loader.load()
ruff_texts = text_splitter.split_documents(docs)
ruff_store = Chroma.from_documents(ruff_texts, embeddings, collection_name="ruff")
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
Initialize Toolkit and Agent#
First, we’ll create an agent with a single vectorstore.
from langchain.agents.agent_toolkits import (
    create_vectorstore_agent,
    VectorStoreToolkit,
    VectorStoreInfo,
)
vectorstore_info = VectorStoreInfo(
    name="state_of_union_address",


)
vectorstore_info = VectorStoreInfo(
    name="state_of_union_address",
    description="the most recent state of the Union adress",
    vectorstore=state_of_union_store
)
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)
agent_executor = create_vectorstore_agent(
    llm=llm,
    toolkit=toolkit,
    verbose=True
)
Examples#
agent_executor.run("What did biden say about ketanji brown jackson is the state of the union address?")
> Entering new AgentExecutor chain...
 I need to find the answer in the state of the union address
Action: state_of_union_address
Action Input: What did biden say about ketanji brown jackson
Observation:  Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.
Thought: I now know the final answer
Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.
> Finished chain.
"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence."
agent_executor.run("What did biden say about ketanji brown jackson is the state of the union address? List the source.")
> Entering new AgentExecutor chain...
 I need to use the state_of_union_address_with_sources tool to answer this question.
Action: state_of_union_address_with_sources
Action Input: What did biden say about ketanji brown jackson


Action Input: What did biden say about ketanji brown jackson
Observation: {"answer": " Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\n", "sources": "../../state_of_the_union.txt"}
Thought: I now know the final answer
Final Answer: Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt
> Finished chain.
"Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt"
Multiple Vectorstores#
We can also easily use this initialize an agent with multiple vectorstores and use the agent to route between them. To do this. This agent is optimized for routing, so it is a different toolkit and initializer.
from langchain.agents.agent_toolkits import (
    create_vectorstore_router_agent,
    VectorStoreRouterToolkit,
    VectorStoreInfo,
)
ruff_vectorstore_info = VectorStoreInfo(
    name="ruff",
    description="Information about the Ruff python linting library",
    vectorstore=ruff_store
)
router_toolkit = VectorStoreRouterToolkit(
    vectorstores=[vectorstore_info, ruff_vectorstore_info],
    llm=llm
)
agent_executor = create_vectorstore_router_agent(
    llm=llm,
    toolkit=router_toolkit,
    verbose=True
)


toolkit=router_toolkit,
    verbose=True
)
Examples#
agent_executor.run("What did biden say about ketanji brown jackson is the state of the union address?")
> Entering new AgentExecutor chain...
 I need to use the state_of_union_address tool to answer this question.
Action: state_of_union_address
Action Input: What did biden say about ketanji brown jackson
Observation:  Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.
Thought: I now know the final answer
Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.
> Finished chain.
"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence."
agent_executor.run("What tool does ruff use to run over Jupyter Notebooks?")
> Entering new AgentExecutor chain...
 I need to find out what tool ruff uses to run over Jupyter Notebooks
Action: ruff
Action Input: What tool does ruff use to run over Jupyter Notebooks?
Observation:  Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb
Thought: I now know the final answer


Thought: I now know the final answer
Final Answer: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb
> Finished chain.
'Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb'
agent_executor.run("What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?")
> Entering new AgentExecutor chain...
 I need to find out what tool ruff uses and if the president mentioned it in the state of the union.
Action: ruff
Action Input: What tool does ruff use to run over Jupyter Notebooks?
Observation:  Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb
Thought: I need to find out if the president mentioned nbQA in the state of the union.
Action: state_of_union_address
Action Input: Did the president mention nbQA in the state of the union?
Observation:  No, the president did not mention nbQA in the state of the union.
Thought: I now know the final answer.
Final Answer: No, the president did not mention nbQA in the state of the union.
> Finished chain.
'No, the president did not mention nbQA in the state of the union.'
previous
SQL Database Agent
next


previous
SQL Database Agent
next
Agent Executors
 Contents

Create the Vectorstores
Initialize Toolkit and Agent
Examples
Multiple Vectorstores
Examples
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Defining Custom Tools
 Contents
Completely New Tools
Tool dataclass
Subclassing the BaseTool class
Using the tool decorator
Modify existing tools
Defining the priorities among Tools
Using tools to return directly
Multi-argument tools
Defining Custom Tools#
When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:
name (str), is required and must be unique within a set of tools provided to an agent
description (str), is optional but recommended, as it is used by an agent to determine tool use
return_direct (bool), defaults to False
args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information or validation for expected parameters.
The function that should be called when the tool is selected should return a single string.
There are two ways to define a tool, we will cover both in the example below.
# Import things that are needed generically
from langchain import LLMMathChain, SerpAPIWrapper
from langchain.agents import AgentType, Tool, initialize_agent, tool
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool
Initialize the LLM to use for the agent.
llm = ChatOpenAI(temperature=0)
Completely New Tools#
First, we show how to create completely new tools from scratch.
There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class.
Tool dataclass#
# Load the tool configs that are needed.
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
tools = [
    Tool(
        name = "Search",


tools = [
    Tool(
        name = "Search",
        func=search.run,
        description="useful for when you need to answer questions about current events"
    ),
]
# You can also define an args_schema to provide more information about inputs
from pydantic import BaseModel, Field
class CalculatorInput(BaseModel):
    question: str = Field()

tools.append(
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for when you need to answer questions about math",
        args_schema=CalculatorInput
    )
)
# Construct the agent. We will use the default agent type here.
# See documentation for a full list of options.
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain...
I need to find out Leo DiCaprio's girlfriend's name and her age
Action: Search
Action Input: "Leo DiCaprio girlfriend"DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.I need to find out Camila Morrone's current age
Action: Calculator
Action Input: 25^(0.43)
> Entering new LLMMathChain chain...
25^(0.43)```text
25**(0.43)
```
...numexpr.evaluate("25**(0.43)")...
Answer: 3.991298452658078
> Finished chain.
Answer: 3.991298452658078I now know the final answer
Final Answer: 3.991298452658078


Final Answer: 3.991298452658078
> Finished chain.
'3.991298452658078'
Subclassing the BaseTool class#
from typing import Type
class CustomSearchTool(BaseTool):
    name = "Search"
    description = "useful for when you need to answer questions about current events"
    def _run(self, query: str) -> str:
        """Use the tool."""
        return search.run(query)

    async def _arun(self, query: str) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("BingSearchRun does not support async")

class CustomCalculatorTool(BaseTool):
    name = "Calculator"
    description = "useful for when you need to answer questions about math"
    args_schema: Type[BaseModel] = CalculatorInput
    def _run(self, query: str) -> str:
        """Use the tool."""
        return llm_math_chain.run(query)

    async def _arun(self, query: str) -> str:
        """Use the tool asynchronously."""
        raise NotImplementedError("BingSearchRun does not support async")
tools = [CustomSearchTool(), CustomCalculatorTool()]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain...
I need to find out Leo DiCaprio's girlfriend's name and her age
Action: Search


Action: Search
Action Input: "Leo DiCaprio girlfriend"DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.I need to find out Camila Morrone's current age
Action: Calculator
Action Input: 25^(0.43)
> Entering new LLMMathChain chain...
25^(0.43)```text
25**(0.43)
```
...numexpr.evaluate("25**(0.43)")...
Answer: 3.991298452658078
> Finished chain.
Answer: 3.991298452658078I now know the final answer
Final Answer: 3.991298452658078
> Finished chain.
'3.991298452658078'
Using the tool decorator#
To make it easier to define custom tools, a @tool decorator is provided. This decorator can be used to quickly create a Tool from a simple function. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function’s docstring as the tool’s description.
from langchain.agents import tool
@tool
def search_api(query: str) -> str:
    """Searches the API for the query."""
    return f"Results for query {query}"
search_api
Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd664c0>, coroutine=None)


You can also provide arguments like the tool name and whether to return directly.
@tool("search", return_direct=True)
def search_api(query: str) -> str:
    """Searches the API for the query."""
    return "Results"
search_api
Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)
You can also provide args_schema to provide more information about the argument
class SearchInput(BaseModel):
    query: str = Field(description="should be a search query")

@tool("search", return_direct=True, args_schema=SearchInput)
def search_api(query: str) -> str:
    """Searches the API for the query."""
    return "Results"
search_api
Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)
Modify existing tools#
Now, we show how to load existing tools and just modify them. In the example below, we do something really simple and change the Search tool to have the name Google Search.
from langchain.agents import load_tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)
tools[0].name = "Google Search"


tools[0].name = "Google Search"
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain...
I need to find out Leo DiCaprio's girlfriend's name and her age.
Action: Google Search
Action Input: "Leo DiCaprio girlfriend"I draw the lime at going to get a Mohawk, though." DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.Now I need to find out Camila Morrone's current age.
Action: Calculator
Action Input: 25^0.43Answer: 3.991298452658078I now know the final answer.
Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.
> Finished chain.
"Camila Morrone's current age raised to the 0.43 power is approximately 3.99."
Defining the priorities among Tools#
When you made a Custom tool, you may want the Agent to use the custom tool more than normal tools.
For example, you made a custom tool, which gets information on music from your database. When a user wants information on songs, You want the Agent to use  the custom tool more than the normal Search tool. But the Agent might prioritize a normal Search tool.
This can be accomplished by adding a statement such as Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?' to the description.


An example is below.
# Import things that are needed generically
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.llms import OpenAI
from langchain import LLMMathChain, SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
    Tool(
        name = "Search",
        func=search.run,
        description="useful for when you need to answer questions about current events"
    ),
    Tool(
        name="Music Search",
        func=lambda x: "'All I Want For Christmas Is You' by Mariah Carey.", #Mock Function
        description="A Music search engine. Use this more than the normal search if the question is about Music, like 'who is the singer of yesterday?' or 'what is the most popular song in 2022?'",
    )
]
agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("what is the most famous song of christmas")
> Entering new AgentExecutor chain...
 I should use a music search engine to find the answer
Action: Music Search
Action Input: most famous song of christmas'All I Want For Christmas Is You' by Mariah Carey. I now know the final answer
Final Answer: 'All I Want For Christmas Is You' by Mariah Carey.
> Finished chain.
"'All I Want For Christmas Is You' by Mariah Carey."
Using tools to return directly#
Often, it can be desirable to have a tool output returned directly to the user, if it’s called. You can do this easily with LangChain by setting the return_direct flag for a tool to be True.
llm_math_chain = LLMMathChain(llm=llm)
tools = [


llm_math_chain = LLMMathChain(llm=llm)
tools = [
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for when you need to answer questions about math",
        return_direct=True
    )
]
llm = OpenAI(temperature=0)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("whats 2**.12")
> Entering new AgentExecutor chain...
 I need to calculate this
Action: Calculator
Action Input: 2**.12Answer: 1.086734862526058
> Finished chain.
'Answer: 1.086734862526058'
Multi-argument tools#
Many functions expect structured inputs. These can also be supported using the Tool decorator or by directly subclassing BaseTool! We have to modify the LLM’s OutputParser to map its string output to a dictionary to pass to the action, however.
from typing import Optional, Union
@tool
def custom_search(k: int, query: str, other_arg: Optional[str] = None):
    """The custom search function."""
    return f"Here are the results for the custom search: k={k}, query={query}, other_arg={other_arg}"
import re
from langchain.schema import (
    AgentAction,
    AgentFinish,
)
from langchain.agents import AgentOutputParser
# We will add a custom parser to map the arguments to a dictionary
class CustomOutputParser(AgentOutputParser):

    def parse_tool_input(self, action_input: str) -> dict:
        # Regex pattern to match arguments and their values


# Regex pattern to match arguments and their values
        pattern = r"(\w+)\s*=\s*(None|\"[^\"]*\"|\d+)"
        matches = re.findall(pattern, action_input)

        if not matches:
            raise ValueError(f"Could not parse action input: `{action_input}`")
        # Create a dictionary with the parsed arguments and their values
        parsed_input = {}
        for arg, value in matches:
            if value == "None":
                parsed_value = None
            elif value.isdigit():
                parsed_value = int(value)
            else:
                parsed_value = value.strip('"')
            parsed_input[arg] = parsed_value
        return parsed_input

    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        # 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_output.split("Final Answer:")[-1].strip()},
                log=llm_output,
            )
        # Parse out the action and action input
        regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
        match = re.search(regex, llm_output, re.DOTALL)
        if not match:
            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
        action = match.group(1).strip()
        action_input = match.group(2)
        tool_input = self.parse_tool_input(action_input)
        # Return the action and action


# Return the action and action
        return AgentAction(tool=action, tool_input=tool_input, log=llm_output)
llm = OpenAI(temperature=0)
agent = initialize_agent([custom_search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, agent_kwargs={"output_parser": CustomOutputParser()})
agent.run("Search for me and tell me whatever it says")
> Entering new AgentExecutor chain...
 I need to use a search function to find the answer
Action: custom_search
Action Input: k=1, query="me"Here are the results for the custom search: k=1, query=me, other_arg=None I now know the final answer
Final Answer: The results of the custom search for k=1, query=me, other_arg=None.
> Finished chain.
'The results of the custom search for k=1, query=me, other_arg=None.'
previous
Getting Started
next
Multi-Input Tools
 Contents

Completely New Tools
Tool dataclass
Subclassing the BaseTool class
Using the tool decorator
Modify existing tools
Defining the priorities among Tools
Using tools to return directly
Multi-argument tools
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.md
.pdf
Getting Started
 Contents
List of 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.
Currently, tools can be loaded with the following snippet:
from langchain.agents import load_tools
tool_names = [...]
tools = load_tools(tool_names)
Some tools (e.g. chains, agents) may require a base LLM to use to initialize them.
In that case, you can pass in an LLM as well:
from langchain.agents import load_tools
tool_names = [...]
llm = ...
tools = load_tools(tool_names, llm=llm)
Below is a list of all supported tools and relevant information:
Tool Name: The name the LLM refers to the tool by.
Tool Description: The description of the tool that is passed to the LLM.
Notes: Notes about the tool that are NOT passed to the LLM.
Requires LLM: Whether this tool requires an LLM to be initialized.
(Optional) Extra Parameters: What extra parameters are required to initialize this tool.
List of Tools#
python_repl
Tool Name: Python REPL
Tool Description: A Python shell. Use this to execute python commands. Input should be a valid python command. If you expect output it should be printed out.
Notes: Maintains state.
Requires LLM: No
serpapi
Tool Name: Search
Tool Description: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.
Notes: Calls the Serp API and then parses results.
Requires LLM: No
wolfram-alpha
Tool Name: Wolfram Alpha


Requires LLM: No
wolfram-alpha
Tool Name: Wolfram Alpha
Tool Description: A wolfram alpha search engine. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.
Notes: Calls the Wolfram Alpha API and then parses results.
Requires LLM: No
Extra Parameters: wolfram_alpha_appid: The Wolfram Alpha app id.
requests
Tool Name: Requests
Tool Description: A portal to the internet. Use this when you need to get specific content from a site. Input should be a specific url, and the output will be all the text on that page.
Notes: Uses the Python requests module.
Requires LLM: No
terminal
Tool Name: Terminal
Tool Description: Executes commands in a terminal. Input should be valid commands, and the output will be any output from running that command.
Notes: Executes commands with subprocess.
Requires LLM: No
pal-math
Tool Name: PAL-MATH
Tool Description: A language model that is excellent at solving complex word math problems. Input should be a fully worded hard word math problem.
Notes: Based on this paper.
Requires LLM: Yes
pal-colored-objects
Tool Name: PAL-COLOR-OBJ
Tool Description: A language model that is wonderful at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning problem. Make sure to include all information about the objects AND the final question you want to answer.
Notes: Based on this paper.
Requires LLM: Yes
llm-math
Tool Name: Calculator
Tool Description: Useful for when you need to answer questions about math.
Notes: An instance of the LLMMath chain.
Requires LLM: Yes
open-meteo-api
Tool Name: Open Meteo API


Requires LLM: Yes
open-meteo-api
Tool Name: Open Meteo API
Tool Description: Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.
Notes: A natural language connection to the Open Meteo API (https://api.open-meteo.com/), specifically the /v1/forecast endpoint.
Requires LLM: Yes
news-api
Tool Name: News API
Tool Description: Use this when you want to get information about the top headlines of current news stories. The input should be a question in natural language that this API can answer.
Notes: A natural language connection to the News API (https://newsapi.org), specifically the /v2/top-headlines endpoint.
Requires LLM: Yes
Extra Parameters: news_api_key (your API key to access this endpoint)
tmdb-api
Tool Name: TMDB API
Tool Description: Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.
Notes: A natural language connection to the TMDB API (https://api.themoviedb.org/3), specifically the /search/movie endpoint.
Requires LLM: Yes
Extra Parameters: tmdb_bearer_token (your Bearer Token to access this endpoint - note that this is different from the API key)
google-search
Tool Name: Search
Tool Description: A wrapper around Google Search. Useful for when you need to answer questions about current events. Input should be a search query.
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
searx-search
Tool Name: Search


For more information on this, see this page
searx-search
Tool Name: Search
Tool Description: A wrapper around SearxNG meta search engine. Input should be a search query.
Notes: SearxNG is easy to deploy self-hosted. It is a good privacy friendly alternative to Google Search. Uses the SearxNG API.
Requires LLM: No
Extra Parameters: searx_host
google-serper
Tool Name: Search
Tool Description: A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.
Notes: Calls the 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
wikipedia
Tool Name: Wikipedia
Tool Description: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
Notes: Uses the wikipedia Python package to call the MediaWiki API and then parses results.
Requires LLM: No
Extra Parameters: top_k_results
podcast-api
Tool Name: Podcast API
Tool Description: Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.
Notes: A natural language connection to the Listen Notes Podcast API (https://www.PodcastAPI.com), specifically the /search/ endpoint.
Requires LLM: Yes
Extra Parameters: listen_api_key (your api key to access this endpoint)
openweathermap-api
Tool Name: OpenWeatherMap
Tool Description: A wrapper around OpenWeatherMap API. Useful for fetching current weather information for a specified location. Input should be a location string (e.g. ‘London,GB’).


Notes: A connection to the OpenWeatherMap API (https://api.openweathermap.org), specifically the /data/2.5/weather endpoint.
Requires LLM: No
Extra Parameters: openweathermap_api_key (your API key to access this endpoint)
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Tools
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Defining Custom Tools
 Contents

List of Tools
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Multi-Input Tools
Multi-Input Tools#
This notebook shows how to use a tool that requires multiple inputs with an agent.
The difficulty in doing so comes from the fact that an agent decides its next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be parsed from that. Therefore, the currently supported way to do this is to write a smaller wrapper function that parses a string into multiple inputs.
For a concrete example, let’s work on giving an agent access to a multiplication function, which takes as input two integers. In order to use this, we will tell the agent to generate the “Action Input” as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function.
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
Here is the multiplication function, as well as a wrapper to parse a string as input.
def multiplier(a, b):
    return a * b
def parsing_multiplier(string):
    a, b = string.split(",")
    return multiplier(int(a), int(b))
llm = OpenAI(temperature=0)
tools = [
    Tool(
        name = "Multiplier",
        func=parsing_multiplier,
        description="useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2."
    )
]


)
]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
mrkl.run("What is 3 times 4")
> Entering new AgentExecutor chain...
 I need to multiply two numbers
Action: Multiplier
Action Input: 3,4
Observation: 12
Thought: I now know the final answer
Final Answer: 3 times 4 is 12
> Finished chain.
'3 times 4 is 12'
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Defining Custom Tools
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Tool Input Schema
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Tool Input Schema
Tool Input Schema#
By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic.
from typing import Any, Dict
from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper
from pydantic import BaseModel, Field, root_validator
llm = OpenAI(temperature=0)
!pip install tldextract > /dev/null
[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: pip install --upgrade pip
import tldextract
_APPROVED_DOMAINS = {
    "langchain",
    "wikipedia",
}
class ToolInputSchema(BaseModel):
    url: str = Field(...)

    @root_validator
    def validate_query(cls, values: Dict[str, Any]) -> Dict:
        url = values["url"]
        domain = tldextract.extract(url).domain
        if domain not in _APPROVED_DOMAINS:
            raise ValueError(f"Domain {domain} is not on the approved list:"
                             f" {sorted(_APPROVED_DOMAINS)}")
        return values

tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())
agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)
# This will succeed, since there aren't any arguments that will be triggered during validation
answer = agent.run("What's the main title on langchain.com?")
print(answer)


print(answer)
The main title of langchain.com is "LANG CHAIN 🦜🔗 Official Home Page"
agent.run("What's the main title on google.com?")
---------------------------------------------------------------------------
ValidationError                           Traceback (most recent call last)
Cell In[7], line 1
----> 1 agent.run("What's the main title on google.com?")
File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)
    211     if len(args) != 1:
    212         raise ValueError("`run` supports only one positional argument.")
--> 213     return self(args[0])[self.output_keys[0]]
    215 if kwargs and not args:
    216     return self(kwargs)[self.output_keys[0]]
File ~/code/lc/lckg/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
    114 except (KeyboardInterrupt, Exception) as e:
    115     self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116     raise e
    117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
    118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File ~/code/lc/lckg/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
    107 self.callback_manager.on_chain_start(
    108     {"name": self.__class__.__name__},
    109     inputs,
    110     verbose=self.verbose,
    111 )
    112 try:
--> 113     outputs = self._call(inputs)
    114 except (KeyboardInterrupt, Exception) as e:


114 except (KeyboardInterrupt, Exception) as e:
    115     self.callback_manager.on_chain_error(e, verbose=self.verbose)
File ~/code/lc/lckg/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)
    790 # We now enter the agent loop (until it returns something).
    791 while self._should_continue(iterations, time_elapsed):
--> 792     next_step_output = self._take_next_step(
    793         name_to_tool_map, color_mapping, inputs, intermediate_steps
    794     )
    795     if isinstance(next_step_output, AgentFinish):
    796         return self._return(next_step_output, intermediate_steps)
File ~/code/lc/lckg/langchain/agents/agent.py:695, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)
    693         tool_run_kwargs["llm_prefix"] = ""
    694     # We then call the tool on the tool input to get an observation
--> 695     observation = tool.run(
    696         agent_action.tool_input,
    697         verbose=self.verbose,
    698         color=color,
    699         **tool_run_kwargs,
    700     )
    701 else:
    702     tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File ~/code/lc/lckg/langchain/tools/base.py:110, in BaseTool.run(self, tool_input, verbose, start_color, color, **kwargs)
    101 def run(
    102     self,
    103     tool_input: Union[str, Dict],
   (...)
    107     **kwargs: Any,
    108 ) -> str:


107     **kwargs: Any,
    108 ) -> str:
    109     """Run the tool."""
--> 110     run_input = self._parse_input(tool_input)
    111     if not self.verbose and verbose is not None:
    112         verbose_ = verbose
File ~/code/lc/lckg/langchain/tools/base.py:71, in BaseTool._parse_input(self, tool_input)
     69 if issubclass(input_args, BaseModel):
     70     key_ = next(iter(input_args.__fields__.keys()))
---> 71     input_args.parse_obj({key_: tool_input})
     72 # Passing as a positional argument is more straightforward for
     73 # backwards compatability
     74 return tool_input
File ~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526, in pydantic.main.BaseModel.parse_obj()
File ~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for ToolInputSchema
__root__
  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)
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Multi-Input Tools
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Apify
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.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.
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",


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
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Tool Input Schema
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Arxiv API
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Arxiv API
Arxiv API#
This notebook goes over how to use the arxiv component.
First, you need to install arxiv python package.
!pip install arxiv
from langchain.utilities import ArxivAPIWrapper
arxiv = ArxivAPIWrapper()
docs = arxiv.run("1605.08386")
docs
'Published: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'
docs = arxiv.run("Caprice Stanley")
docs


docs = arxiv.run("Caprice Stanley")
docs
'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\nrelation. Fourier analysis provides explicit formulas to compute the\neigenvalues of the transition matrices and we use this to bound the mixing time\nof the random walks.\n\nPublished: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.\n\nPublished: 2003-03-18\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\nAuthors: V. Plyaskin\nSummary: The results on the fluxes of charged particles and neutrinos from a\n3-dimensional (3D) simulation of atmospheric showers are presented. An\nagreement of calculated fluxes with data on charged particles from the AMS and\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\nexperimental sites are compared with results from other calculations.'
docs = arxiv.run("1605.08386WWW")
docs


docs = arxiv.run("1605.08386WWW")
docs
'No good Arxiv Result was found'
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Apify
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Bash
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Bash
Bash#
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.
from langchain.utilities import BashProcess
bash = BashProcess()
print(bash.run("ls"))
bash.ipynb
google_search.ipynb
python.ipynb
requests.ipynb
serpapi.ipynb
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Arxiv API
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Bing Search
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.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.
import os
os.environ["BING_SUBSCRIPTION_KEY"] = ""
os.environ["BING_SEARCH_URL"] = ""
from langchain.utilities import BingSearchAPIWrapper
search = BingSearchAPIWrapper()
search.run("python")


'Thanks to the flexibility of Python 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 Python by Dan Taylor. Python releases by version number: Release version Release date Click for more. Python 3.11.1 Dec. 6, 2022 Download Release Notes. Python 3.10.9 Dec. 6, 2022 Download Release Notes. Python 3.9.16 Dec. 6, 2022 Download Release Notes. Python 3.8.16 Dec. 6, 2022 Download Release Notes. Python 3.7.16 Dec. 6, 2022 Download Release Notes. In this lesson, we will look at the += operator in Python 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, Python, SQL, Java, and many, many more. This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python 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


self-contained, so the tutorial can be read off-line as well. For a description of standard objects and modules, see The Python Standard ... Python is a general-purpose, versatile, and powerful programming language. It's a great first language because Python code is concise and easy to read. Whatever you want to do, python can do it. From web development to machine learning to data science, Python is the language for you. To install Python using the Microsoft Store: Go to your Start menu (lower left Windows icon), type "Microsoft Store", select the link to open the store. Once the store is open, select Search from the upper-right menu and enter "Python". Select which version of Python you would like to use from the results under Apps. Under the “Python Releases for Mac OS X” heading, click the link for the Latest Python 3 Release - Python 3.x.x. As of this writing, the latest version was Python 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'


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 Python 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 Python by Dan Taylor.'
Metadata Results#
Run query through BingSearch and return snippet, title, and link metadata.
Snippet: The description of the result.
Title: The title of the result.
Link: The link to the result.
search = BingSearchAPIWrapper()
search.results("apples", 5)
[{'snippet': 'Lady Alice. Pink Lady apples aren’t the only lady in the apple family. Lady Alice apples 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.',
  'title': '25 Types of Apples - Jessica Gavin',
  'link': 'https://www.jessicagavin.com/types-of-apples/'},
 {'snippet': 'Apples 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 ...',
  'title': 'Apples: Nutrition & Health Benefits - WebMD',
  'link': 'https://www.webmd.com/food-recipes/benefits-apples'},


{'snippet': 'Apples boast many vitamins and minerals, though not in high amounts. However, apples 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/nutrition/foods/apples'},
 {'snippet': 'Weight management. The fibers in apples 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 apples and pears, were associated with the least amount of weight gain over time.',
  'title': 'Apples | The Nutrition Source | Harvard T.H. Chan School of Public Health',
  'link': 'https://www.hsph.harvard.edu/nutritionsource/food-features/apples/'}]
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Bash
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ChatGPT Plugins
 Contents

Number of results
Metadata Results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.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 langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools import AIPluginTool
tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")
llm = ChatOpenAI(temperature=0)
tools = load_tools(["requests_all"] )
tools += [tool]
agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_chain.run("what t shirts are available in klarna?")
> Entering new AgentExecutor chain...
I need to check the Klarna Shopping API to see if it has information on available t shirts.
Action: KlarnaProducts
Action Input: None
Observation: Usage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user.


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': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties':


'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}}


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


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 Ultimate 6pk. Crewneck T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?utm_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/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Color:White,Green","Model:Boy","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/?utm_source=openai","price":"$29.95","attributes":["Material:Cotton","Target Group:Man","Color:White,Blue,Black"]},{"name":"adidas Comfort T-shirts Men's


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 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.
> Finished chain.
"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."
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Bing Search
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DuckDuckGo Search
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.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 DuckDuckGoSearchTool
search = DuckDuckGoSearchTool()
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 ...'
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ChatGPT Plugins
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Google Places
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.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"
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DuckDuckGo Search
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Google Search
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Google Search
 Contents
Number of Results
Metadata Results
Google Search#
This notebook goes over how to use the google search component.
First, you need to set up the proper API keys and environment variables. To set it up, create the GOOGLE_API_KEY in the Google Cloud credential console (https://console.cloud.google.com/apis/credentials) and a GOOGLE_CSE_ID using the Programmable Search Enginge (https://programmablesearchengine.google.com/controlpanel/create). Next, it is good to follow the instructions found here.
Then we will need to set some environment variables.
import os
os.environ["GOOGLE_CSE_ID"] = ""
os.environ["GOOGLE_API_KEY"] = ""
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
search.run("Obama's first name?")


'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...'


Number of Results#
You can use the k parameter to set the number of results
search = GoogleSearchAPIWrapper(k=1)
search.run("python")
'The official home of the Python Programming Language.'
‘The official home of the Python Programming Language.’
Metadata Results#
Run query through GoogleSearch and return snippet, title, and link metadata.
Snippet: The description of the result.
Title: The title of the result.
Link: The link to the result.
search = GoogleSearchAPIWrapper()
search.results("apples", 5)
[{'snippet': 'Discover the innovative world of Apple and shop everything iPhone, iPad, Apple Watch, Mac, and Apple TV, plus explore accessories, entertainment,\xa0...',
  'title': 'Apple',
  'link': 'https://www.apple.com/'},
 {'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...",
  'title': '25 Types of Apples and What to Make With Them - Parade ...',
  'link': 'https://parade.com/1330308/bethlipton/types-of-apples/'},
 {'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...',
  'title': 'Apple - Wikipedia',
  'link': 'https://en.wikipedia.org/wiki/Apple'},
 {'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...',
  'title': 'Apples: Benefits, nutrition, and tips',


'title': 'Apples: Benefits, nutrition, and tips',
  'link': 'https://www.medicalnewstoday.com/articles/267290'},
 {'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...",
  'title': 'Apples: Nutrition & Health Benefits',
  'link': 'https://www.webmd.com/food-recipes/benefits-apples'}]
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Google Places
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Google Serper API
 Contents

Number of Results
Metadata Results
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Google Serper API
 Contents
As part of a Self Ask With Search Chain
Google Serper API#
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 and get your api key.
import os
os.environ["SERPER_API_KEY"] = ""
from langchain.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("Obama's first name?")
'Barack Hussein Obama II'
As part of a Self Ask With Search Chain#
os.environ['OPENAI_API_KEY'] = ""
from langchain.utilities import GoogleSerperAPIWrapper
from langchain.llms.openai import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = GoogleSerperAPIWrapper()
tools = [
    Tool(
        name="Intermediate Answer",
        func=search.run,
        description="useful for when you need to ask with search"
    )
]
self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
> Entering new AgentExecutor chain...
 Yes.
Follow up: Who is the reigning men's U.S. Open champion?
Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
Follow up: Where is Carlos Alcaraz from?
Intermediate answer: El Palmar, Spain
So the final answer is: El Palmar, Spain
> Finished chain.
'El Palmar, Spain'
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Google Search
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Gradio Tools


'El Palmar, Spain'
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Google Search
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Gradio Tools
 Contents

As part of a Self Ask With Search Chain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.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 large language model (LLM)-based agent to complete its task. For example, an LLM could use a Gradio tool to transcribe a voice recording it finds online and then summarize it for you. Or it could use a different Gradio tool to apply OCR to a document on your Google Drive and then answer questions about it.
It’s very easy to create you own tool if you want to use a space that’s not one of the pre-built tools. Please see this section of the gradio-tools documentation for information on how to do that. All contributions are welcome!
# !pip install gradio_tools
Using a tool#
from gradio_tools.tools import StableDiffusionTool
StableDiffusionTool().langchain.run("Please create a photo of a dog riding a skateboard")
Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔
Job Status: Status.STARTING eta: None
'/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/examples/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg'
from PIL import Image
im = Image.open("/Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/examples/b61c1dd9-47e2-46f1-a47c-20d27640993d/tmp4ap48vnm.jpg")
display(im)
Using within an agent#


display(im)
Using within an agent#
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
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 = [StableDiffusionTool().langchain, ImageCaptioningTool().langchain,
         StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain]
agent = initialize_agent(tools, llm, memory=memory, agent="conversational-react-description", verbose=True)
output = agent.run(input=("Please create a photo of a dog riding a skateboard "
                          "but improve my prompt prior to using an image generator."
                          "Please caption the generated image and create a video for it using the improved prompt."))
Loaded as API: https://gradio-client-demos-stable-diffusion.hf.space ✔
Loaded as API: https://taesiri-blip-2.hf.space ✔
Loaded as API: https://microsoft-promptist.hf.space ✔
Loaded as API: https://damo-vilab-modelscope-text-to-video-synthesis.hf.space ✔
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: StableDiffusionPromptGenerator
Action Input: A dog riding a skateboard
Job Status: Status.STARTING eta: None
Observation: A dog riding a skateboard, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha
Thought: Do I need to use a tool? Yes
Action: StableDiffusion


Thought: Do I need to use a tool? Yes
Action: StableDiffusion
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/agents/tools/examples/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg
Thought: Do I need to use a tool? Yes
Action: ImageCaptioner
Action Input: /Users/harrisonchase/workplace/langchain/docs/modules/agents/tools/examples/2e280ce4-4974-4420-8680-450825c31601/tmpfmiz2g1c.jpg
Job Status: Status.STARTING eta: None
Observation: a painting of a dog sitting on a skateboard
Thought: Do I need to use a tool? Yes
Action: TextToVideo
Action Input: a painting of a dog sitting on a skateboard
Job Status: Status.STARTING eta: None
Due to heavy traffic on this app, the prediction will take approximately 73 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)
Job Status: Status.IN_QUEUE eta: 73.89824726581574
Due to heavy traffic on this app, the prediction will take approximately 42 seconds.For faster predictions without waiting in queue, you may duplicate the space using: Client.duplicate(damo-vilab/modelscope-text-to-video-synthesis)
Job Status: Status.IN_QUEUE eta: 42.49370198879602


Job Status: Status.IN_QUEUE eta: 42.49370198879602
Job Status: Status.IN_QUEUE eta: 21.314297944849187
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.
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Google Serper API
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Human as a tool
 Contents

Using a tool
Using within an agent
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.


.ipynb
.pdf
Human as a tool
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.
import sys
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
llm = ChatOpenAI(temperature=0.0)
math_llm = OpenAI(temperature=0.0)
tools = load_tools(
    ["human", "llm-math"],
    llm=math_llm,
)
agent_chain = initialize_agent(
    tools,
    llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True,
)
In the above code you can see the tool takes input directly from command line.
You can customize prompt_func and input_func according to your need.
agent_chain.run("What is Eric Zhu's birthday?")
# Answer with "last week"
> Entering new AgentExecutor chain...
I don't know Eric Zhu, so I should ask a human for guidance.
Action: Human
Action Input: "Do you know when Eric Zhu's birthday is?"
Do you know when Eric Zhu's birthday is?
last week
Observation: last week
Thought:That's not very helpful. I should ask for more information.
Action: Human
Action Input: "Do you know the specific date of Eric Zhu's birthday?"
Do you know the specific date of Eric Zhu's birthday?
august 1st
Observation: august 1st
Thought:Now that I have the date, I can check if it's a leap year or not.
Action: Calculator
Action Input: "Is 2021 a leap year?"


Action: Calculator
Action Input: "Is 2021 a leap year?"
Observation: Answer: False
Thought:I have all the information I need to answer the original question.
Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.
> Finished chain.
"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021."
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Gradio Tools
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IFTTT WebHooks
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 25, 2023.