id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
482cbafcf0bc-14 | ```
{
"action": "get_elements",
"action_input": {
"selector": "h1, h2, h3, h4, h5, h6"
}
}
```
Observation: []
Thought: Thought: I need to navigate to langchain.com to see the headers
Action:
```
{
"action": "navigate_browser",
"action_input": "https://langchain.com/"
}
```
Observation: Navigating to http... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/playwright.html |
d97538810e0e-0 | .ipynb
.pdf
JSON Agent
Contents
Initialization
Example: getting the required POST parameters for a request
JSON Agent#
This notebook showcases an agent designed to interact with large JSON/dict objects. This is useful when you want to answer questions about a JSON blob that’s too large to fit in the context window of... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/json.html |
d97538810e0e-1 | Thought: I should look at the paths key to see what endpoints exist
Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/json.html |
d97538810e0e-2 | Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]
Observation: ['application/json']
Thought: I should look at the application/json key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/json.html |
d97538810e0e-3 | Initialization
Example: getting the required POST parameters for a request
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/json.html |
f038243e71ff-0 | .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 langchai... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/vectorstore.html |
f038243e71ff-1 | )
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,
ve... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/vectorstore.html |
f038243e71ff-2 | 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", "s... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/vectorstore.html |
f038243e71ff-3 | 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... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/vectorstore.html |
f038243e71ff-4 | 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... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/vectorstore.html |
f038243e71ff-5 | 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 May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/vectorstore.html |
fc445f5e4b24-0 | .ipynb
.pdf
Azure Cognitive Services Toolkit
Contents
Create the Toolkit
Use within an Agent
Azure Cognitive Services Toolkit#
This toolkit is used to interact with the Azure Cognitive Services API to achieve some multimodal capabilities.
Currently There are four tools bundled in this toolkit:
AzureCogsImageAnalysisT... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/azure_cognitive_services.html |
fc445f5e4b24-1 | toolkit = AzureCognitiveServicesToolkit()
[tool.name for tool in toolkit.get_tools()]
['Azure Cognitive Services Image Analysis',
'Azure Cognitive Services Form Recognizer',
'Azure Cognitive Services Speech2Text',
'Azure Cognitive Services Text2Speech']
Use within an Agent#
from langchain import OpenAI
from langchai... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/azure_cognitive_services.html |
fc445f5e4b24-2 | 'You can make pancakes, omelettes, or quiches with these ingredients!'
audio_file = agent.run("Tell me a joke and read it out for me.")
> Entering new AgentExecutor chain...
Action:
```
{
"action": "Azure Cognitive Services Text2Speech",
"action_input": "Why did the chicken cross the playground? To get to the other... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/azure_cognitive_services.html |
f91c4e2e4100-0 | .ipynb
.pdf
Spark Dataframe Agent
Contents
Spark Connect Example
Spark Dataframe Agent#
This notebook shows how to use agents to interact with a Spark dataframe and Spark Connect. It is mostly optimized for question answering.
NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-1 | +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+
| 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S|
| 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-2 | | 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S|
| 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S|
| 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-3 | | 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S|
| 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S|
| 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-4 | only showing top 20 rows
agent = create_spark_dataframe_agent(llm=OpenAI(temperature=0), df=df, verbose=True)
agent.run("how many rows are there?")
> Entering new AgentExecutor chain...
Thought: I need to find out how many rows are in the dataframe
Action: python_repl_ast
Action Input: df.count()
Observation: 891
Thoug... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-5 | Thought: I now have the math library imported, I can get the square root
Action: python_repl_ast
Action Input: math.sqrt(29.69911764705882)
Observation: 5.449689683556195
Thought: I now know the final answer
Final Answer: 5.449689683556195
> Finished chain.
'5.449689683556195'
spark.stop()
Spark Connect Example#
# in a... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-6 | df = spark.read.csv(csv_file_path, header=True, inferSchema=True)
df.show()
+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+
|PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked|
+-----------+-... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-7 | | 6| 0| 3| Moran, Mr. James| male|null| 0| 0| 330877| 8.4583| null| Q|
| 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S|
| 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-8 | | 13| 0| 3|Saundercock, Mr. ...| male|20.0| 0| 0| A/5. 2151| 8.05| null| S|
| 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S|
| 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
f91c4e2e4100-9 | | 20| 1| 3|Masselmani, Mrs. ...|female|null| 0| 0| 2649| 7.225| null| C|
+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+
only showing top 20 rows
from langchain.agents import create_spark_dataframe_agent
from la... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html |
13cbfe5817ad-0 | .ipynb
.pdf
Pandas Dataframe Agent
Contents
Multi DataFrame Example
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 ... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/pandas.html |
13cbfe5817ad-1 | Action: python_repl_ast
Action Input: df['Age'].mean()
Observation: 29.69911764705882
Thought: I now need to calculate the square root of the average age
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: NameError("name 'math' is not defined")
Thought: I need to import the math library
Acti... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/pandas.html |
13cbfe5817ad-2 | '177 rows in the age column are different.'
previous
Natural Language APIs
next
PlayWright Browser Toolkit
Contents
Multi DataFrame Example
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/pandas.html |
2c4afcc92bdf-0 | .ipynb
.pdf
Spark SQL Agent
Contents
Initialization
Example: describing a table
Example: running queries
Spark SQL Agent#
This notebook shows how to use agents to interact with a Spark SQL. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and facilitate error recovery.
NOTE: ... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-1 | +-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+
| 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S|
| 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-2 | | 7| 0| 1|McCarthy, Mr. Tim...| male|54.0| 0| 0| 17463|51.8625| E46| S|
| 8| 0| 3|Palsson, Master. ...| male| 2.0| 3| 1| 349909| 21.075| null| S|
| 9| 1| 3|Johnson, Mrs. Osc...|female|27.0| 0| 2| 347742... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-3 | | 14| 0| 3|Andersson, Mr. An...| male|39.0| 1| 5| 347082| 31.275| null| S|
| 15| 0| 3|Vestrom, Miss. Hu...|female|14.0| 0| 0| 350406| 7.8542| null| S|
| 16| 1| 2|Hewlett, Mrs. (Ma...|female|55.0| 0| 0| 248706... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-4 | only showing top 20 rows
# Note, you can also connect to Spark via Spark connect. For example:
# db = SparkSQL.from_uri("sc://localhost:15002", schema=schema)
spark_sql = SparkSQL(schema=schema)
llm = ChatOpenAI(temperature=0)
toolkit = SparkSQLToolkit(db=spark_sql, llm=llm)
agent_executor = create_spark_sql_agent(
... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-5 | 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S
*/
Thought:I now know the schema and sample rows for the titanic table.
Final Answer: The titanic table has the following columns: PassengerId (INT), Survived (INT), Pclass (INT), Name (STRING), Sex (STRING), Age (DOUBLE), SibSp (INT), Parch (IN... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-6 | > Finished chain.
'The titanic table has the following columns: PassengerId (INT), Survived (INT), Pclass (INT), Name (STRING), Sex (STRING), Age (DOUBLE), SibSp (INT), Parch (INT), Ticket (STRING), Fare (DOUBLE), Cabin (STRING), and Embarked (STRING). Here are some sample rows from the table: \n\n1. PassengerId: 1, Su... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-7 | Action: list_tables_sql_db
Action Input:
Observation: titanic
Thought:I should check the schema of the titanic table to see if there is an age column.
Action: schema_sql_db
Action Input: titanic
Observation: CREATE TABLE langchain_example.titanic (
PassengerId INT,
Survived INT,
Pclass INT,
Name STRING,
Sex ... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-8 | SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic
Thought:The query is correct, so I can execute it to find the square root of the average age.
Action: query_sql_db
Action Input: SELECT SQRT(AVG(Age)) as square_root_of_avg_age FROM titanic
Observation: [('5.449689683556195',)]
Thought:I now know the final an... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
2c4afcc92bdf-9 | 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C
3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 None S
*/
Thought:I can use the titanic table to find the oldest survived passenger. I will query the Name and Age columns, filtering by Survived and order... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark_sql.html |
86cae1c1cdb7-0 | .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.p... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/python.html |
86cae1c1cdb7-1 | 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... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/python.html |
86cae1c1cdb7-2 | 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
Spark Dataframe Agent
Contents
Fibonacci Example
Training neural net
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/python.html |
80dde9d622a1-0 | .ipynb
.pdf
Natural Language APIs
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!
Natural Language APIs#
Natural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine ... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
80dde9d622a1-1 | Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Create the Agent#
# Slightly twe... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
80dde9d622a1-2 | Action: Open_AI_Klarna_product_Api.productsUsingGET
Action Input: Italian clothes
Observation: The API response contains two products from the Alé brand in Italian Blue. The first is the Alé Colour Block Short Sleeve Jersey Men - Italian Blue, which costs $86.49, and the second is the Alé Dolid Flash Jersey Men - Itali... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
80dde9d622a1-3 | llm,
"https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json",
requests=requests,
max_text_length=1800, # If you want to truncate the response text
)
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for be... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
80dde9d622a1-4 | 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.... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
80dde9d622a1-5 | 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, I... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
80dde9d622a1-6 | > 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 Quino... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi_nla.html |
48b9c0374177-0 | .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 SQLDatabase... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-1 | 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"... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-2 | 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("Playl... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-3 | "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" ("Emplo... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-4 | "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 Invoice... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-5 | 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)]
Th... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-6 | "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... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-7 | 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' (... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-8 | 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... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-9 | "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;... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
48b9c0374177-10 | 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... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html |
ae896a11d06a-0 | .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 ... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html |
ae896a11d06a-1 | 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("Desc... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html |
ae896a11d06a-2 | 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
PlayWright Browser Toolkit
next
Python Agent
Contents
Some notes
Initialization
Example: describing a table
... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html |
11908b797739-0 | .ipynb
.pdf
OpenAPI agents
Contents
1st example: hierarchical planning agent
To start, let’s collect some OpenAPI specs.
How big is this spec?
Let’s see some examples!
Try another API.
2nd example: “json explorer” agent
OpenAPI agents#
We can construct agents to consume arbitrary APIs, here APIs conformant to the Ope... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-1 | !mv openapi.yaml spotify_openapi.yaml
--2023-03-31 15:45:56-- https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercont... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-2 | --2023-03-31 15:45:57-- https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yaml
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|18... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-3 | You’ll have to set up an application in the Spotify developer console, documented here, to get credentials: CLIENT_ID, CLIENT_SECRET, and REDIRECT_URI.
To get an access tokens (and keep them fresh), you can implement the oauth flows, or you can use spotipy. If you’ve set your Spotify creedentials as environment variabl... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-4 | from langchain.agents.agent_toolkits.openapi import planner
llm = OpenAI(model_name="gpt-4", temperature=0.0)
/Users/jeremywelborn/src/langchain/langchain/llms/openai.py:169: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_mo... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-5 | Thought:I have the plan, now I need to execute the API calls.
Action: api_controller
Action Input: 1. GET /search to search for the album "Kind of Blue"
2. GET /albums/{id}/tracks to get the tracks from the "Kind of Blue" album
3. GET /me to get the current user's information
4. POST /users/{user_id}/playlists to creat... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-6 | Thought:Action: requests_post
Action Input: {"url": "https://api.spotify.com/v1/users/22rhrz4m4kvpxlsb5hezokzwi/playlists", "data": {"name": "Machine Blues"}, "output_instructions": "Extract the id of the created playlist"}
Observation: 7lzoEi44WOISnFYlrAIqyX
Thought:Action: requests_post
Action Input: {"url": "https:/... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-7 | user_query = "give me a song I'd like, make it blues-ey"
spotify_agent.run(user_query)
> Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to get a blues song recommendation for the user
Observation: 1. GET /me to get the current user's information
2. GET /recommen... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-8 | Observation: acoustic, afrobeat, alt-rock, alternative, ambient, anime, black-metal, bluegrass, blues, bossanova, brazil, breakbeat, british, cantopop, chicago-house, children, chill, classical, club, comedy, country, dance, dancehall, death-metal, deep-house, detroit-techno, disco, disney, drum-and-bass, dub, dubstep,... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-9 | Observation: [
{
id: '03lXHmokj9qsXspNsPoirR',
name: 'Get Away Jordan'
}
]
Thought:I am finished executing the plan.
Final Answer: The recommended blues song for user Jeremy Welborn (ID: 22rhrz4m4kvpxlsb5hezokzwi) is "Get Away Jordan" with the track ID: 03lXHmokj9qsXspNsPoirR.
> Finished chain.
Observation:... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-10 | > Entering new AgentExecutor chain...
Action: api_planner
Action Input: I need to find the right API calls to generate a short piece of advice
Observation: 1. GET /engines to retrieve the list of available engines
2. POST /completions with the selected engine and a prompt for generating a short piece of advice
Thought:... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-11 | Action: requests_post
Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"engine": "davinci", "prompt": "Give me a short piece of advice on how to be more productive."}, "output_instructions": "Extract the text from the first choice"}
Observation: "you must provide a model parameter"
Thought:!! Cou... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-12 | Observation: babbage, davinci, text-davinci-edit-001, babbage-code-search-code, text-similarity-babbage-001, code-davinci-edit-001, text-davinci-edit-001, ada
Thought:Action: requests_post
Action Input: {"url": "https://api.openai.com/v1/completions", "data": {"model": "davinci", "prompt": "Give me a short piece of adv... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-13 | Action: api_controller
Action Input: 1. GET /models to retrieve the list of available models
2. Choose a suitable model for generating text (e.g., text-davinci-002)
3. POST /completions with the chosen model and a prompt related to improving communication skills to generate a short piece of advice
> Entering new AgentE... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-14 | Takes awhile to get there!
2nd example: “json explorer” agent#
Here’s an agent that’s not particularly practical, but neat! The agent has access to 2 toolkits. One comprises tools to interact with json: one tool to list the keys of a json object and another tool to get the value for a given key. The other toolkit compr... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-15 | Thought: I should look at the servers key to see what the base url is
Action: json_spec_list_keys
Action Input: data["servers"][0]
Observation: ValueError('Value at path `data["servers"][0]` is not a dict, get the value directly.')
Thought: I should get the value of the servers key
Action: json_spec_get_value
Action In... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-16 | Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/chat/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/audio/transcriptions', '/audio/translations', '/engines/{engine_id}/search', '/files', '... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-17 | Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/chat/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/audio/transcriptions', '/audio/translations', '/engines/{engine_id}/search', '/files', '... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-18 | Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]
Observation: ['schema']
Thought: I should look at the schema key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-19 | > Finished chain.
Observation: The required parameters for a POST request to the /completions endpoint are 'model'.
Thought: I now know the parameters needed to make the request.
Action: requests_post
Action Input: { "url": "https://api.openai.com/v1/completions", "data": { "model": "davinci", "prompt": "tell me a joke... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
11908b797739-20 | > Finished chain.
'The response of the POST request is {"id":"cmpl-70Ivzip3dazrIXU8DSVJGzFJj2rdv","object":"text_completion","created":1680307139,"model":"davinci","choices":[{"text":" with mummy not there”\\n\\nYou dig deep and come up with,","index":0,"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/openapi.html |
6094b001d8a1-0 | .ipynb
.pdf
Gmail Toolkit
Contents
Create the Toolkit
Customizing Authentication
Use within an Agent
Gmail Toolkit#
This notebook walks through connecting a LangChain email to the Gmail API.
To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you’ve downloaded the crede... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/gmail.html |
6094b001d8a1-1 | toolkit = GmailToolkit(api_resource=api_resource)
tools = toolkit.get_tools()
tools
[GmailCreateDraft(name='create_gmail_draft', description='Use this tool to create a draft email with the provided message fields.', args_schema=<class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>, return_direct=False, verbose... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/gmail.html |
6094b001d8a1-2 | GmailGetThread(name='get_gmail_thread', description=('Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.',), args_schema=<class 'langchain.tools.gmail.get_thread.GetThreadSchema'>, return_direct=False, verbose=False, callbacks=None, callback_manager... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/gmail.html |
6094b001d8a1-3 | WARNING:root:Failed to persist run: {"detail":"Not Found"}
"The latest email in your drafts is from hopefulparrot@gmail.com with the subject 'Collaboration Opportunity'. The body of the email reads: 'Dear [Friend], I hope this letter finds you well. I am writing to you in the hopes of rekindling our friendship and to d... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/gmail.html |
1a8affa8b2d9-0 | .ipynb
.pdf
CSV Agent
Contents
Multi CSV Example
CSV Agent#
This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.
NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - th... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/csv.html |
1a8affa8b2d9-1 | Observation: 29.69911764705882
Thought: I now need to calculate the square root of the average age
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: NameError("name 'math' is not defined")
Thought: I need to import the math library
Action: python_repl_ast
Action Input: import math
Observati... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/csv.html |
975d9bc25f40-0 | .ipynb
.pdf
Jira
Jira#
This notebook goes over how to use the Jira tool.
The Jira tool allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jir... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/jira.html |
975d9bc25f40-1 | Observation: None
Thought: I now know the final answer
Final Answer: A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice".
> Finished chain.
'A new issue has been created in project PW with the summary "Make more fried rice" and descriptio... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/jira.html |
6c81de2bc637-0 | .ipynb
.pdf
How to use the async API for Agents
Contents
Serial vs. Concurrent Execution
How to use the async API for Agents#
LangChain provides async support for Agents by leveraging the asyncio library.
Async methods are currently supported for the following Tools: GoogleSerperAPIWrapper, SerpAPIWrapper and LLMMath... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-1 | ]
llm = OpenAI(temperature=0)
tools = load_tools(["google-serper", "llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
s = time.perf_counter()
for q in questions:
agent.run(q)
elapsed = time.perf_counter() - s
print(f"Serial executed in {elapse... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-2 | Observation: Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ... Draw: 128 (16 Q / 8 WC). Champion: Rafael Nadal. Runner-up: Daniil Medvedev. Score: 7–5, 6–3, 5–7, 4–6, 6–4. Bianca Andreescu won the women's singl... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-3 | Daniil Medvedev in the men's singles final of the U.S. Open on Sunday. Rafael Nadal survived. The 33-year-old defeated Daniil Medvedev in the final of the 2019 U.S. Open to earn his 19th Grand Slam title Sunday ... NEW YORK -- Rafael Nadal defeated Daniil Medvedev in an epic five-set match, 7-5, 6-3, 5-7, 4-6, 6-4 to w... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-4 | Thought: I now know that Rafael Nadal won the US Open men's final in 2019 and he is 33 years old.
Action: Calculator
Action Input: 33^0.334
Observation: Answer: 3.215019829667466
Thought: I now know the final answer.
Final Answer: Rafael Nadal won the US Open men's final in 2019 and his age raised to the 0.334 power is... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-5 | Action: Google Serper
Action Input: "who won the most recent formula 1 grand prix"
Observation: Max Verstappen won his first Formula 1 world title on Sunday after the championship was decided by a last-lap overtake of his rival Lewis Hamilton in the Abu Dhabi Grand Prix. Dec 12, 2021
Thought: I need to find out Max Ver... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-6 | Observation: Answer: 2.7212987634680084
Thought: I now know the final answer.
Final Answer: Nineteen-year-old Canadian Bianca Andreescu won the US Open women's final in 2019 and her age raised to the 0.34 power is 2.7212987634680084.
> Finished chain.
> Entering new AgentExecutor chain...
I need to find out who Beyonc... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-7 | await asyncio.gather(*tasks)
elapsed = time.perf_counter() - s
print(f"Concurrent executed in {elapsed:0.2f} seconds.")
> Entering new AgentExecutor chain...
> Entering new AgentExecutor chain...
> Entering new AgentExecutor chain...
> Entering new AgentExecutor chain...
> Entering new AgentExecutor chain...
I need to... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-8 | Thought:
Observation: Jay-Z
Thought: | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
6c81de2bc637-9 | Observation: Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ... Draw: 128 (16 Q / 8 WC). Champion: Rafael Nadal. Runner-up: Daniil Medvedev. Score: 7–5, 6–3, 5–7, 4–6, 6–4. Bianca Andreescu won the women's singl... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
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