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"""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)
Custom Structured Tools#
If your functions require more structured arguments, you can use the StructuredTool class directly, or still subclass the BaseTool class.
StructuredTool dataclass#
To dynamically generate a structured tool from a given function, the fastest way to get started is with StructuredTool.from_function().
import requests
from langchain.tools import StructuredTool
def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:
"""Sends a POST request to the given url with the given body and parameters."""
result = requests.post(url, json=body, params=parameters)
return f"Status: {result.status_code} - {result.text}"
tool = StructuredTool.from_function(post_message)
Subclassing the BaseTool#
The BaseTool automatically infers the schema from the _run method’s signature.
from typing import Optional, Type
from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun
class CustomSearchTool(BaseTool):
name = "custom_search"
description = "useful for when you need to answer questions about current events"
def _run(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
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"""Use the tool."""
search_wrapper = SerpAPIWrapper(params={"engine": engine, "gl": gl, "hl": hl})
return search_wrapper.run(query)
async def _arun(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
# You can provide a custom args schema to add descriptions or custom validation
class SearchSchema(BaseModel):
query: str = Field(description="should be a search query")
engine: str = Field(description="should be a search engine")
gl: str = Field(description="should be a country code")
hl: str = Field(description="should be a language code")
class CustomSearchTool(BaseTool):
name = "custom_search"
description = "useful for when you need to answer questions about current events"
args_schema: Type[SearchSchema] = SearchSchema
def _run(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
"""Use the tool."""
search_wrapper = SerpAPIWrapper(params={"engine": engine, "gl": gl, "hl": hl})
return search_wrapper.run(query)
async def _arun(self, query: str, engine: str = "google", gl: str = "us", hl: str = "en", run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
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"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
Using the decorator#
The tool decorator creates a structured tool automatically if the signature has multiple arguments.
import requests
from langchain.tools import tool
@tool
def post_message(url: str, body: dict, parameters: Optional[dict] = None) -> str:
"""Sends a POST request to the given url with the given body and parameters."""
result = requests.post(url, json=body, params=parameters)
return f"Status: {result.status_code} - {result.text}"
Modify existing tools#
Now, we show how to load existing tools and modify them directly. 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"
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"
Observation: After rumours of a romance with Gigi Hadid, the Oscar winner has seemingly moved on. First being linked to the television personality in September 2022, it appears as if his "age bracket" has moved up. This follows his rumoured relationship with mere 19-year-old Eden Polani.
Thought:I still need to find out his current girlfriend's name and her age.
Action: Google Search
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Action: Google Search
Action Input: "Leo DiCaprio current girlfriend age"
Observation: Leonardo DiCaprio has been linked with 19-year-old model Eden Polani, continuing the rumour that he doesn't date any women over the age of ...
Thought:I need to find out the age of Eden Polani.
Action: Calculator
Action Input: 19^(0.43)
Observation: Answer: 3.547023357958959
Thought:I now know the final answer.
Final Answer: The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55.
> Finished chain.
"The age of Leo DiCaprio's girlfriend raised to the 0.43 power is approximately 3.55."
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,
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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 = [
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)
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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'
previous
Getting Started
next
Multi-Input Tools
Contents
Completely New Tools - String Input and Output
Tool dataclass
Subclassing the BaseTool class
Using the tool decorator
Custom Structured Tools
StructuredTool dataclass
Subclassing the BaseTool
Using the decorator
Modify existing tools
Defining the priorities among Tools
Using tools to return directly
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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.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
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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
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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
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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).
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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)
previous
Tools
next
Defining Custom Tools
Contents
List of Tools
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
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.pdf
Zapier Natural Language Actions API
Contents
Zapier Natural Language Actions API
Example with Agent
Example with SimpleSequentialChain
Zapier Natural Language Actions API#
Full docs here: https://nla.zapier.com/api/v1/docs
Zapier Natural Language Actions gives you access to the 5k+ apps, 20k+ actions on Zapier’s platform through a natural language API interface.
NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/apps
Zapier NLA handles ALL the underlying API auth and translation from natural language –> underlying API call –> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.
NLA offers both API Key and OAuth for signing NLA API requests.
Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer’s Zapier account (and will use the developer’s connected accounts on Zapier.com)
User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user’s exposed actions and connected accounts on Zapier.com
This quick start will focus on the server-side use case for brevity. Review full docs or reach out to [email protected] for user-facing oauth developer support.
This example goes over how to use the Zapier integration with a SimpleSequentialChain, then an Agent.
In code, below:
import os
# get from https://platform.openai.com/
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
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os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "")
# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in):
os.environ["ZAPIER_NLA_API_KEY"] = os.environ.get("ZAPIER_NLA_API_KEY", "")
Example with Agent#
Zapier tools can be used with an agent. See the example below.
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import ZapierToolkit
from langchain.agents import AgentType
from langchain.utilities.zapier import ZapierNLAWrapper
## step 0. expose gmail 'find email' and slack 'send channel message' actions
# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first
llm = OpenAI(temperature=0)
zapier = ZapierNLAWrapper()
toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
agent = initialize_agent(toolkit.get_tools(), llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.")
> Entering new AgentExecutor chain...
I need to find the email and summarize it.
Action: Gmail: Find Email
Action Input: Find the latest email from Silicon Valley Bank
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Action: Gmail: Find Email
Action Input: Find the latest email from Silicon Valley Bank
Observation: {"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "[email protected]", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "[email protected]", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "[email protected]", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"}
Thought: I need to summarize the email and send it to the #test-zapier channel in Slack.
Action: Slack: Send Channel Message
Action Input: Send a slack message to the #test-zapier channel with the text "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild."
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Observation: {"message__text": "Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.", "message__permalink": "https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259", "channel": "C04TSGU0RA7", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:58:52Z", "message__bot_profile__icons__image_36": "https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png", "message__blocks[]block_id": "kdZZ", "message__blocks[]elements[]type": "['rich_text_section']"}
Thought: I now know the final answer.
Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.
> Finished chain.
'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'
Example with SimpleSequentialChain#
If you need more explicit control, use a chain, like below.
from langchain.llms import OpenAI
from langchain.chains import LLMChain, TransformChain, SimpleSequentialChain
from langchain.prompts import PromptTemplate
from langchain.tools.zapier.tool import ZapierNLARunAction
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from langchain.tools.zapier.tool import ZapierNLARunAction
from langchain.utilities.zapier import ZapierNLAWrapper
## step 0. expose gmail 'find email' and slack 'send direct message' actions
# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields "Have AI guess"
# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first
actions = ZapierNLAWrapper().list()
## step 1. gmail find email
GMAIL_SEARCH_INSTRUCTIONS = "Grab the latest email from Silicon Valley Bank"
def nla_gmail(inputs):
action = next((a for a in actions if a["description"].startswith("Gmail: Find Email")), None)
return {"email_data": ZapierNLARunAction(action_id=action["id"], zapier_description=action["description"], params_schema=action["params"]).run(inputs["instructions"])}
gmail_chain = TransformChain(input_variables=["instructions"], output_variables=["email_data"], transform=nla_gmail)
## step 2. generate draft reply
template = """You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).
Incoming email:
{email_data}
Draft email reply:"""
prompt_template = PromptTemplate(input_variables=["email_data"], template=template)
reply_chain = LLMChain(llm=OpenAI(temperature=.7), prompt=prompt_template)
## step 3. send draft reply via a slack direct message
SLACK_HANDLE = "@Ankush Gola"
def nla_slack(inputs):
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SLACK_HANDLE = "@Ankush Gola"
def nla_slack(inputs):
action = next((a for a in actions if a["description"].startswith("Slack: Send Direct Message")), None)
instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs["draft_reply"]}'
return {"slack_data": ZapierNLARunAction(action_id=action["id"], zapier_description=action["description"], params_schema=action["params"]).run(instructions)}
slack_chain = TransformChain(input_variables=["draft_reply"], output_variables=["slack_data"], transform=nla_slack)
## finally, execute
overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)
overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)
> Entering new SimpleSequentialChain chain...
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overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)
> Entering new SimpleSequentialChain chain...
{"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "[email protected]", "body_plain": "Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG", "reply_to__email": "[email protected]", "subject": "Meet the new CEO Tim Mayopoulos", "date": "Tue, 14 Mar 2023 23:42:29 -0500 (CDT)", "message_url": "https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a", "attachment_count": "0", "to__emails": "[email protected]", "message_id": "186e393b13cfdf0a", "labels": "IMPORTANT, CATEGORY_UPDATES, INBOX"}
Dear Silicon Valley Bridge Bank,
Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you.
Best regards,
[Your Name]
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Best regards,
[Your Name]
{"message__text": "Dear Silicon Valley Bridge Bank, \n\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n\nBest regards, \n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[['text']]", "message__blocks[]elements[]type": "['rich_text_section']"}
> Finished chain.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html
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ffce77cc65b8-8
|
> Finished chain.
'{"message__text": "Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]", "message__permalink": "https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629", "channel": "D04TKF5BBHU", "message__bot_profile__name": "Zapier", "message__team": "T04F8K3FZB5", "message__bot_id": "B04TRV4R74K", "message__bot_profile__deleted": "false", "message__bot_profile__app_id": "A024R9PQM", "ts_time": "2023-03-15T05:59:28Z", "message__blocks[]block_id": "p7i", "message__blocks[]elements[]elements[]type": "[[\'text\']]", "message__blocks[]elements[]type": "[\'rich_text_section\']"}'
previous
YouTubeSearchTool
next
Agents
Contents
Zapier Natural Language Actions API
Example with Agent
Example with SimpleSequentialChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html
|
54699311d2f5-0
|
.ipynb
.pdf
File System Tools
Contents
The FileManagementToolkit
Selecting File System Tools
File System Tools#
LangChain provides tools for interacting with a local file system out of the box. This notebook walks through some of them.
Note: these tools are not recommended for use outside a sandboxed environment!
First, we’ll import the tools.
from langchain.tools.file_management import (
ReadFileTool,
CopyFileTool,
DeleteFileTool,
MoveFileTool,
WriteFileTool,
ListDirectoryTool,
)
from langchain.agents.agent_toolkits import FileManagementToolkit
from tempfile import TemporaryDirectory
# We'll make a temporary directory to avoid clutter
working_directory = TemporaryDirectory()
The FileManagementToolkit#
If you want to provide all the file tooling to your agent, it’s easy to do so with the toolkit. We’ll pass the temporary directory in as a root directory as a workspace for the LLM.
It’s recommended to always pass in a root directory, since without one, it’s easy for the LLM to pollute the working directory, and without one, there isn’t any validation against
straightforward prompt injection.
toolkit = FileManagementToolkit(root_dir=str(working_directory.name)) # If you don't provide a root_dir, operations will default to the current working directory
toolkit.get_tools()
|
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toolkit.get_tools()
[CopyFileTool(name='copy_file', description='Create a copy of a file in a specified location', args_schema=<class 'langchain.tools.file_management.copy.FileCopyInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
DeleteFileTool(name='file_delete', description='Delete a file', args_schema=<class 'langchain.tools.file_management.delete.FileDeleteInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
FileSearchTool(name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=<class 'langchain.tools.file_management.file_search.FileSearchInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/filesystem.html
|
54699311d2f5-2
|
MoveFileTool(name='move_file', description='Move or rename a file from one location to another', args_schema=<class 'langchain.tools.file_management.move.FileMoveInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]
|
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|
54699311d2f5-3
|
Selecting File System Tools#
If you only want to select certain tools, you can pass them in as arguments when initializing the toolkit, or you can individually initialize the desired tools.
tools = FileManagementToolkit(root_dir=str(working_directory.name), selected_tools=["read_file", "write_file", "list_directory"]).get_tools()
tools
[ReadFileTool(name='read_file', description='Read file from disk', args_schema=<class 'langchain.tools.file_management.read.ReadFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
WriteFileTool(name='write_file', description='Write file to disk', args_schema=<class 'langchain.tools.file_management.write.WriteFileInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),
ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=<class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1156f4350>, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]
read_tool, write_tool, list_tool = tools
write_tool.run({"file_path": "example.txt", "text": "Hello World!"})
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/filesystem.html
|
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|
write_tool.run({"file_path": "example.txt", "text": "Hello World!"})
'File written successfully to example.txt.'
# List files in the working directory
list_tool.run({})
'example.txt'
previous
DuckDuckGo Search
next
Google Places
Contents
The FileManagementToolkit
Selecting File System Tools
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/filesystem.html
|
4960f86e3f06-0
|
.ipynb
.pdf
Python REPL
Python REPL#
Sometimes, for complex calculations, rather than have an LLM generate the answer directly, it can be better to have the LLM generate code to calculate the answer, and then run that code to get the answer. In order to easily do that, we provide a simple Python REPL to execute commands in.
This interface will only return things that are printed - therefore, if you want to use it to calculate an answer, make sure to have it print out the answer.
from langchain.agents import Tool
from langchain.utilities import PythonREPL
python_repl = PythonREPL()
python_repl.run("print(1+1)")
'2\n'
# You can create the tool to pass to an agent
repl_tool = Tool(
name="python_repl",
description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
func=python_repl.run
)
previous
OpenWeatherMap API
next
Requests
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/python.html
|
1f072cdd524a-0
|
.ipynb
.pdf
Shell Tool
Contents
Use with Agents
Shell Tool#
Giving agents access to the shell is powerful (though risky outside a sandboxed environment).
The LLM can use it to execute any shell commands. A common use case for this is letting the LLM interact with your local file system.
from langchain.tools import ShellTool
shell_tool = ShellTool()
print(shell_tool.run({"commands": ["echo 'Hello World!'", "time"]}))
Hello World!
real 0m0.000s
user 0m0.000s
sys 0m0.000s
/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.
warnings.warn(
Use with Agents#
As with all tools, these can be given to an agent to accomplish more complex tasks. Let’s have the agent fetch some links from a web page.
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.agents import AgentType
llm = ChatOpenAI(temperature=0)
shell_tool.description = shell_tool.description + f"args {shell_tool.args}".replace("{", "{{").replace("}", "}}")
self_ask_with_search = initialize_agent([shell_tool], llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
self_ask_with_search.run("Download the langchain.com webpage and grep for all urls. Return only a sorted list of them. Be sure to use double quotes.")
> Entering new AgentExecutor chain...
Question: What is the task?
Thought: We need to download the langchain.com webpage and extract all the URLs from it. Then we need to sort the URLs and return them.
Action:
```
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/bash.html
|
1f072cdd524a-1
|
Action:
```
{
"action": "shell",
"action_input": {
"commands": [
"curl -s https://langchain.com | grep -o 'http[s]*://[^\" ]*' | sort"
]
}
}
```
/Users/wfh/code/lc/lckg/langchain/tools/shell/tool.py:34: UserWarning: The shell tool has no safeguards by default. Use at your own risk.
warnings.warn(
Observation: https://blog.langchain.dev/
https://discord.gg/6adMQxSpJS
https://docs.langchain.com/docs/
https://github.com/hwchase17/chat-langchain
https://github.com/hwchase17/langchain
https://github.com/hwchase17/langchainjs
https://github.com/sullivan-sean/chat-langchainjs
https://js.langchain.com/docs/
https://python.langchain.com/en/latest/
https://twitter.com/langchainai
Thought:The URLs have been successfully extracted and sorted. We can return the list of URLs as the final answer.
Final Answer: ["https://blog.langchain.dev/", "https://discord.gg/6adMQxSpJS", "https://docs.langchain.com/docs/", "https://github.com/hwchase17/chat-langchain", "https://github.com/hwchase17/langchain", "https://github.com/hwchase17/langchainjs", "https://github.com/sullivan-sean/chat-langchainjs", "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", "https://twitter.com/langchainai"]
> Finished chain.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/bash.html
|
1f072cdd524a-2
|
> Finished chain.
'["https://blog.langchain.dev/", "https://discord.gg/6adMQxSpJS", "https://docs.langchain.com/docs/", "https://github.com/hwchase17/chat-langchain", "https://github.com/hwchase17/langchain", "https://github.com/hwchase17/langchainjs", "https://github.com/sullivan-sean/chat-langchainjs", "https://js.langchain.com/docs/", "https://python.langchain.com/en/latest/", "https://twitter.com/langchainai"]'
previous
AWS Lambda API
next
Bing Search
Contents
Use with Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/bash.html
|
f064ee142c41-0
|
.ipynb
.pdf
Search Tools
Contents
Google Serper API Wrapper
SerpAPI
GoogleSearchAPIWrapper
SearxNG Meta Search Engine
Search Tools#
This notebook shows off usage of various search tools.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
Google Serper API Wrapper#
First, let’s try to use the Google Serper API tool.
tools = load_tools(["google-serper"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is the weather in Pomfret?")
> Entering new AgentExecutor chain...
I should look up the current weather conditions.
Action: Search
Action Input: "weather in Pomfret"
Observation: 37°F
Thought: I now know the current temperature in Pomfret.
Final Answer: The current temperature in Pomfret is 37°F.
> Finished chain.
'The current temperature in Pomfret is 37°F.'
SerpAPI#
Now, let’s use the SerpAPI tool.
tools = load_tools(["serpapi"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is the weather in Pomfret?")
> Entering new AgentExecutor chain...
I need to find out what the current weather is in Pomfret.
Action: Search
Action Input: "weather in Pomfret"
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/search_tools.html
|
f064ee142c41-1
|
Action: Search
Action Input: "weather in Pomfret"
Observation: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 ...
Thought: I now know the current weather in Pomfret.
Final Answer: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.
> Finished chain.
'Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.'
GoogleSearchAPIWrapper#
Now, let’s use the official Google Search API Wrapper.
tools = load_tools(["google-search"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is the weather in Pomfret?")
> Entering new AgentExecutor chain...
I should look up the current weather conditions.
Action: Google Search
Action Input: "weather in Pomfret"
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/search_tools.html
|
f064ee142c41-2
|
Action: Google Search
Action Input: "weather in Pomfret"
Observation: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next 3 days. Hourly Weather-Pomfret, CT. As of 12:52 am EST. Special Weather Statement +2 ... Hazardous Weather Conditions. Special Weather Statement ... Pomfret CT. Tonight ... National Digital Forecast Database Maximum Temperature Forecast. Pomfret Center Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for ... Pomfret, CT 12 hour by hour weather forecast includes precipitation, temperatures, sky conditions, rain chance, dew-point, relative humidity, wind direction ... North Pomfret Weather Forecasts. Weather Underground provides local & long-range weather forecasts, weatherreports, maps & tropical weather conditions for ... Today's Weather - Pomfret, CT. Dec 31, 2022 4:00 PM. Putnam MS. --. Weather forecast icon. Feels like --. Hi --. Lo --. Pomfret, CT temperature trend for the next 14 Days. Find daytime highs and nighttime lows from TheWeatherNetwork.com. Pomfret, MD Weather Forecast Date: 332 PM EST Wed Dec 28 2022. The area/counties/county of: Charles, including the cites of: St. Charles and Waldorf.
Thought: I now know the current weather conditions in Pomfret.
Final Answer: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/search_tools.html
|
f064ee142c41-3
|
> Finished AgentExecutor chain.
'Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.'
SearxNG Meta Search Engine#
Here we will be using a self hosted SearxNG meta search engine.
tools = load_tools(["searx-search"], searx_host="http://localhost:8888", llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("What is the weather in Pomfret")
> Entering new AgentExecutor chain...
I should look up the current weather
Action: SearX Search
Action Input: "weather in Pomfret"
Observation: Mainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch.
10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49°/ 41° 52% Mon 27 | Day 49° 52% SE 14 mph Cloudy with occasional rain showers. High 49F. Winds SE at 10 to 20 mph. Chance of rain 50%....
10 Day Weather - Pomfret, VT As of 3:51 am EST Special Weather Statement Today 39°/ 32° 37% Wed 01 | Day 39° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F....
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/search_tools.html
|
f064ee142c41-4
|
Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo.
Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Here’s Why We Get More Colds and Flu When It’s Cold Coast-To-Coast...
Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35° F RealFeel® 36° RealFeel Shade™ 36° Air Quality Excellent Wind E 3 mph Wind Gusts 5 mph Cloudy More Details WinterCast...
Pomfret, VT Weather Forecast | AccuWeather Current Weather 11:21 AM 23° F RealFeel® 27° RealFeel Shade™ 25° Air Quality Fair Wind ESE 3 mph Wind Gusts 7 mph Cloudy More Details WinterCast...
Pomfret Center, CT Weather Forecast | AccuWeather Daily Current Weather 6:50 PM 39° F RealFeel® 36° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast...
12:00 pm · Feels Like36° · WindN 5 mph · Humidity43% · UV Index3 of 10 · Cloud Cover65% · Rain Amount0 in ...
Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 °F Clear Manhattan, NY 37 °F Fair Schiller Park, IL (60176) warning39 °F Mostly Cloudy...
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/search_tools.html
|
f064ee142c41-5
|
Thought: I now know the final answer
Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.
> Finished chain.
'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'
previous
SceneXplain
next
SearxNG Search API
Contents
Google Serper API Wrapper
SerpAPI
GoogleSearchAPIWrapper
SearxNG Meta Search Engine
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/search_tools.html
|
d1822393c6b6-0
|
.ipynb
.pdf
GraphQL tool
GraphQL tool#
This Jupyter Notebook demonstrates how to use the BaseGraphQLTool component with an Agent.
GraphQL is a query language for APIs and a runtime for executing those queries against your data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
By including a BaseGraphQLTool in the list of tools provided to an Agent, you can grant your Agent the ability to query data from GraphQL APIs for any purposes you need.
In this example, we’ll be using the public Star Wars GraphQL API available at the following endpoint: https://swapi-graphql.netlify.app/.netlify/functions/index.
First, you need to install httpx and gql Python packages.
pip install httpx gql > /dev/null
Now, let’s create a BaseGraphQLTool instance with the specified Star Wars API endpoint and initialize an Agent with the tool.
from langchain import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
from langchain.utilities import GraphQLAPIWrapper
llm = OpenAI(temperature=0)
tools = load_tools(["graphql"], graphql_endpoint="https://swapi-graphql.netlify.app/.netlify/functions/index", llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
Now, we can use the Agent to run queries against the Star Wars GraphQL API. Let’s ask the Agent to list all the Star Wars films and their release dates.
graphql_fields = """allFilms {
films {
title
director
releaseDate
speciesConnection {
species {
name
classification
homeworld {
name
}
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/graphql.html
|
d1822393c6b6-1
|
species {
name
classification
homeworld {
name
}
}
}
}
}
"""
suffix = "Search for the titles of all the stawars films stored in the graphql database that has this schema "
agent.run(suffix + graphql_fields)
> Entering new AgentExecutor chain...
I need to query the graphql database to get the titles of all the star wars films
Action: query_graphql
Action Input: query { allFilms { films { title } } }
Observation: "{\n \"allFilms\": {\n \"films\": [\n {\n \"title\": \"A New Hope\"\n },\n {\n \"title\": \"The Empire Strikes Back\"\n },\n {\n \"title\": \"Return of the Jedi\"\n },\n {\n \"title\": \"The Phantom Menace\"\n },\n {\n \"title\": \"Attack of the Clones\"\n },\n {\n \"title\": \"Revenge of the Sith\"\n }\n ]\n }\n}"
Thought: I now know the titles of all the star wars films
Final Answer: The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.
> Finished chain.
'The titles of all the star wars films are: A New Hope, The Empire Strikes Back, Return of the Jedi, The Phantom Menace, Attack of the Clones, and Revenge of the Sith.'
previous
Gradio Tools
next
HuggingFace Tools
By Harrison Chase
© Copyright 2023, Harrison Chase.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/graphql.html
|
d1822393c6b6-2
|
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/graphql.html
|
94325153a569-0
|
.ipynb
.pdf
Apify
Apify#
This notebook shows how to use the Apify integration for LangChain.
Apify is a cloud platform for web scraping and data extraction,
which provides an ecosystem of more than a thousand
ready-made apps called Actors for various web scraping, crawling, and data extraction use cases.
For example, you can use it to extract Google Search results, Instagram and Facebook profiles, products from Amazon or Shopify, Google Maps reviews, etc. etc.
In this example, we’ll use the Website Content Crawler Actor,
which can deeply crawl websites such as documentation, knowledge bases, help centers, or blogs,
and extract text content from the web pages. Then we feed the documents into a vector index and answer questions from it.
#!pip install apify-client
First, import ApifyWrapper into your source code:
from langchain.document_loaders.base import Document
from langchain.indexes import VectorstoreIndexCreator
from langchain.utilities import ApifyWrapper
Initialize it using your Apify API token and for the purpose of this example, also with your OpenAI API key:
import os
os.environ["OPENAI_API_KEY"] = "Your OpenAI API key"
os.environ["APIFY_API_TOKEN"] = "Your Apify API token"
apify = ApifyWrapper()
Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.
Note that if you already have some results in an Apify dataset, you can load them directly using ApifyDatasetLoader, as shown in this notebook. In that notebook, you’ll also find the explanation of the dataset_mapping_function, which is used to map fields from the Apify dataset records to LangChain Document fields.
loader = apify.call_actor(
actor_id="apify/website-content-crawler",
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loader = apify.call_actor(
actor_id="apify/website-content-crawler",
run_input={"startUrls": [{"url": "https://python.langchain.com/en/latest/"}]},
dataset_mapping_function=lambda item: Document(
page_content=item["text"] or "", metadata={"source": item["url"]}
),
)
Initialize the vector index from the crawled documents:
index = VectorstoreIndexCreator().from_loaders([loader])
And finally, query the vector index:
query = "What is LangChain?"
result = index.query_with_sources(query)
print(result["answer"])
print(result["sources"])
LangChain is a standard interface through which you can interact with a variety of large language models (LLMs). It provides modules that can be used to build language model applications, and it also provides chains and agents with memory capabilities.
https://python.langchain.com/en/latest/modules/models/llms.html, https://python.langchain.com/en/latest/getting_started/getting_started.html
previous
Tool Input Schema
next
ArXiv API Tool
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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.pdf
Twilio
Contents
Setup
Sending a message
Twilio#
This notebook goes over how to use the Twilio API wrapper to send a text message.
Setup#
To use this tool you need to install the Python Twilio package twilio
# !pip install twilio
You’ll also need to set up a Twilio account and get your credentials. You’ll need your Account String Identifier (SID) and your Auth Token. You’ll also need a number to send messages from.
You can either pass these in to the TwilioAPIWrapper as named parameters account_sid, auth_token, from_number, or you can set the environment variables TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_FROM_NUMBER.
Sending a message#
from langchain.utilities.twilio import TwilioAPIWrapper
twilio = TwilioAPIWrapper(
# account_sid="foo",
# auth_token="bar",
# from_number="baz,"
)
twilio.run("hello world", "+16162904619")
previous
SerpAPI
next
Wikipedia
Contents
Setup
Sending a message
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/agents/tools/examples/twilio.html
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.pdf
SearxNG Search API
Contents
Custom Parameters
Obtaining results with metadata
SearxNG Search API#
This notebook goes over how to use a self hosted SearxNG search API to search the web.
You can check this link for more informations about Searx API parameters.
import pprint
from langchain.utilities import SearxSearchWrapper
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")
For some engines, if a direct answer is available the warpper will print the answer instead of the full list of search results. You can use the results method of the wrapper if you want to obtain all the results.
search.run("What is the capital of France")
'Paris is the capital of France, the largest country of Europe with 550 000 km2 (65 millions inhabitants). Paris has 2.234 million inhabitants end 2011. She is the core of Ile de France region (12 million people).'
Custom Parameters#
SearxNG supports up to 139 search engines. You can also customize the Searx wrapper with arbitrary named parameters that will be passed to the Searx search API . In the below example we will making a more interesting use of custom search parameters from searx search api.
In this example we will be using the engines parameters to query wikipedia
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=5) # k is for max number of items
search.run("large language model ", engines=['wiki'])
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search.run("large language model ", engines=['wiki'])
'Large language models (LLMs) represent a major advancement in AI, with the promise of transforming domains through learned knowledge. LLM sizes have been increasing 10X every year for the last few years, and as these models grow in complexity and size, so do their capabilities.\n\nGPT-3 can translate language, write essays, generate computer code, and more — all with limited to no supervision. In July 2020, OpenAI unveiled GPT-3, a language model that was easily the largest known at the time. Put simply, GPT-3 is trained to predict the next word in a sentence, much like how a text message autocomplete feature works.\n\nA large language model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets. Large language models are among the most successful applications of transformer models.\n\nAll of today’s well-known language models—e.g., GPT-3 from OpenAI, PaLM or LaMDA from Google, Galactica or OPT from Meta, Megatron-Turing from Nvidia/Microsoft, Jurassic-1 from AI21 Labs—are...\n\nLarge language models (LLMs) such as GPT-3are increasingly being used to generate text. These tools should be used with care, since they can generate content that is biased, non-verifiable, constitutes original research, or violates copyrights.'
Passing other Searx parameters for searx like language
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888", k=1)
search.run("deep learning", language='es', engines=['wiki'])
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search.run("deep learning", language='es', engines=['wiki'])
'Aprendizaje profundo (en inglés, deep learning) es un conjunto de algoritmos de aprendizaje automático (en inglés, machine learning) que intenta modelar abstracciones de alto nivel en datos usando arquitecturas computacionales que admiten transformaciones no lineales múltiples e iterativas de datos expresados en forma matricial o tensorial. 1'
Obtaining results with metadata#
In this example we will be looking for scientific paper using the categories parameter and limiting the results to a time_range (not all engines support the time range option).
We also would like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.
search = SearxSearchWrapper(searx_host="http://127.0.0.1:8888")
results = search.results("Large Language Model prompt", num_results=5, categories='science', time_range='year')
pprint.pp(results)
[{'snippet': '… on natural language instructions, large language models (… the '
'prompt used to steer the model, and most effective prompts … to '
'prompt engineering, we propose Automatic Prompt …',
'title': 'Large language models are human-level prompt engineers',
'link': 'https://arxiv.org/abs/2211.01910',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… Large language models (LLMs) have introduced new possibilities '
'for prototyping with AI [18]. Pre-trained on a large amount of '
'text data, models … language instructions called prompts. …',
'title': 'Promptchainer: Chaining large language model prompts through '
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'title': 'Promptchainer: Chaining large language model prompts through '
'visual programming',
'link': 'https://dl.acm.org/doi/abs/10.1145/3491101.3519729',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… can introspect the large prompt model. We derive the view '
'ϕ0(X) and the model h0 from T01. However, instead of fully '
'fine-tuning T0 during co-training, we focus on soft prompt '
'tuning, …',
'title': 'Co-training improves prompt-based learning for large language '
'models',
'link': 'https://proceedings.mlr.press/v162/lang22a.html',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… With the success of large language models (LLMs) of code and '
'their use as … prompt design process become important. In this '
'work, we propose a framework called Repo-Level Prompt …',
'title': 'Repository-level prompt generation for large language models of '
'code',
'link': 'https://arxiv.org/abs/2206.12839',
'engines': ['google scholar'],
'category': 'science'},
{'snippet': '… Figure 2 | The benefits of different components of a prompt '
'for the largest language model (Gopher), as estimated from '
'hierarchical logistic regression. Each point estimates the '
'unique …',
'title': 'Can language models learn from explanations in context?',
'link': 'https://arxiv.org/abs/2204.02329',
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'link': 'https://arxiv.org/abs/2204.02329',
'engines': ['google scholar'],
'category': 'science'}]
Get papers from arxiv
results = search.results("Large Language Model prompt", num_results=5, engines=['arxiv'])
pprint.pp(results)
[{'snippet': 'Thanks to the advanced improvement of large pre-trained language '
'models, prompt-based fine-tuning is shown to be effective on a '
'variety of downstream tasks. Though many prompting methods have '
'been investigated, it remains unknown which type of prompts are '
'the most effective among three types of prompts (i.e., '
'human-designed prompts, schema prompts and null prompts). In '
'this work, we empirically compare the three types of prompts '
'under both few-shot and fully-supervised settings. Our '
'experimental results show that schema prompts are the most '
'effective in general. Besides, the performance gaps tend to '
'diminish when the scale of training data grows large.',
'title': 'Do Prompts Solve NLP Tasks Using Natural Language?',
'link': 'http://arxiv.org/abs/2203.00902v1',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Cross-prompt automated essay scoring (AES) requires the system '
'to use non target-prompt essays to award scores to a '
'target-prompt essay. Since obtaining a large quantity of '
'pre-graded essays to a particular prompt is often difficult and '
'unrealistic, the task of cross-prompt AES is vital for the '
'development of real-world AES systems, yet it remains an '
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'development of real-world AES systems, yet it remains an '
'under-explored area of research. Models designed for '
'prompt-specific AES rely heavily on prompt-specific knowledge '
'and perform poorly in the cross-prompt setting, whereas current '
'approaches to cross-prompt AES either require a certain quantity '
'of labelled target-prompt essays or require a large quantity of '
'unlabelled target-prompt essays to perform transfer learning in '
'a multi-step manner. To address these issues, we introduce '
'Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our '
'method requires no access to labelled or unlabelled '
'target-prompt data during training and is a single-stage '
'approach. PAES is easy to apply in practice and achieves '
'state-of-the-art performance on the Automated Student Assessment '
'Prize (ASAP) dataset.',
'title': 'Prompt Agnostic Essay Scorer: A Domain Generalization Approach to '
'Cross-prompt Automated Essay Scoring',
'link': 'http://arxiv.org/abs/2008.01441v1',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Research on prompting has shown excellent performance with '
'little or even no supervised training across many tasks. '
'However, prompting for machine translation is still '
'under-explored in the literature. We fill this gap by offering a '
'systematic study on prompting strategies for translation, '
'examining various factors for prompt template and demonstration '
'example selection. We further explore the use of monolingual '
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'example selection. We further explore the use of monolingual '
'data and the feasibility of cross-lingual, cross-domain, and '
'sentence-to-document transfer learning in prompting. Extensive '
'experiments with GLM-130B (Zeng et al., 2022) as the testbed '
'show that 1) the number and the quality of prompt examples '
'matter, where using suboptimal examples degenerates translation; '
'2) several features of prompt examples, such as semantic '
'similarity, show significant Spearman correlation with their '
'prompting performance; yet, none of the correlations are strong '
'enough; 3) using pseudo parallel prompt examples constructed '
'from monolingual data via zero-shot prompting could improve '
'translation; and 4) improved performance is achievable by '
'transferring knowledge from prompt examples selected in other '
'settings. We finally provide an analysis on the model outputs '
'and discuss several problems that prompting still suffers from.',
'title': 'Prompting Large Language Model for Machine Translation: A Case '
'Study',
'link': 'http://arxiv.org/abs/2301.07069v2',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Large language models can perform new tasks in a zero-shot '
'fashion, given natural language prompts that specify the desired '
'behavior. Such prompts are typically hand engineered, but can '
'also be learned with gradient-based methods from labeled data. '
'However, it is underexplored what factors make the prompts '
'effective, especially when the prompts are natural language. In '
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'effective, especially when the prompts are natural language. In '
'this paper, we investigate common attributes shared by effective '
'prompts. We first propose a human readable prompt tuning method '
'(F LUENT P ROMPT) based on Langevin dynamics that incorporates a '
'fluency constraint to find a diverse distribution of effective '
'and fluent prompts. Our analysis reveals that effective prompts '
'are topically related to the task domain and calibrate the prior '
'probability of label words. Based on these findings, we also '
'propose a method for generating prompts using only unlabeled '
'data, outperforming strong baselines by an average of 7.0% '
'accuracy across three tasks.',
'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a "
'good movie, and a good prompt too?',
'link': 'http://arxiv.org/abs/2212.10539v1',
'engines': ['arxiv'],
'category': 'science'},
{'snippet': 'Prevailing methods for mapping large generative language models '
"to supervised tasks may fail to sufficiently probe models' novel "
'capabilities. Using GPT-3 as a case study, we show that 0-shot '
'prompts can significantly outperform few-shot prompts. We '
'suggest that the function of few-shot examples in these cases is '
'better described as locating an already learned task rather than '
'meta-learning. This analysis motivates rethinking the role of '
'prompts in controlling and evaluating powerful language models. '
'In this work, we discuss methods of prompt programming, '
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'In this work, we discuss methods of prompt programming, '
'emphasizing the usefulness of considering prompts through the '
'lens of natural language. We explore techniques for exploiting '
'the capacity of narratives and cultural anchors to encode '
'nuanced intentions and techniques for encouraging deconstruction '
'of a problem into components before producing a verdict. '
'Informed by this more encompassing theory of prompt programming, '
'we also introduce the idea of a metaprompt that seeds the model '
'to generate its own natural language prompts for a range of '
'tasks. Finally, we discuss how these more general methods of '
'interacting with language models can be incorporated into '
'existing and future benchmarks and practical applications.',
'title': 'Prompt Programming for Large Language Models: Beyond the Few-Shot '
'Paradigm',
'link': 'http://arxiv.org/abs/2102.07350v1',
'engines': ['arxiv'],
'category': 'science'}]
In this example we query for large language models under the it category. We then filter the results that come from github.
results = search.results("large language model", num_results = 20, categories='it')
pprint.pp(list(filter(lambda r: r['engines'][0] == 'github', results)))
[{'snippet': 'Guide to using pre-trained large language models of source code',
'title': 'Code-LMs',
'link': 'https://github.com/VHellendoorn/Code-LMs',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Dramatron uses large language models to generate coherent '
'scripts and screenplays.',
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'scripts and screenplays.',
'title': 'dramatron',
'link': 'https://github.com/deepmind/dramatron',
'engines': ['github'],
'category': 'it'}]
We could also directly query for results from github and other source forges.
results = search.results("large language model", num_results = 20, engines=['github', 'gitlab'])
pprint.pp(results)
[{'snippet': "Implementation of 'A Watermark for Large Language Models' paper "
'by Kirchenbauer & Geiping et. al.',
'title': 'Peutlefaire / LMWatermark',
'link': 'https://gitlab.com/BrianPulfer/LMWatermark',
'engines': ['gitlab'],
'category': 'it'},
{'snippet': 'Guide to using pre-trained large language models of source code',
'title': 'Code-LMs',
'link': 'https://github.com/VHellendoorn/Code-LMs',
'engines': ['github'],
'category': 'it'},
{'snippet': '',
'title': 'Simen Burud / Large-scale Language Models for Conversational '
'Speech Recognition',
'link': 'https://gitlab.com/BrianPulfer',
'engines': ['gitlab'],
'category': 'it'},
{'snippet': 'Dramatron uses large language models to generate coherent '
'scripts and screenplays.',
'title': 'dramatron',
'link': 'https://github.com/deepmind/dramatron',
'engines': ['github'],
'category': 'it'},
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'engines': ['github'],
'category': 'it'},
{'snippet': 'Code for loralib, an implementation of "LoRA: Low-Rank '
'Adaptation of Large Language Models"',
'title': 'LoRA',
'link': 'https://github.com/microsoft/LoRA',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Code for the paper "Evaluating Large Language Models Trained on '
'Code"',
'title': 'human-eval',
'link': 'https://github.com/openai/human-eval',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A trend starts from "Chain of Thought Prompting Elicits '
'Reasoning in Large Language Models".',
'title': 'Chain-of-ThoughtsPapers',
'link': 'https://github.com/Timothyxxx/Chain-of-ThoughtsPapers',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Mistral: A strong, northwesterly wind: Framework for transparent '
'and accessible large-scale language model training, built with '
'Hugging Face 🤗 Transformers.',
'title': 'mistral',
'link': 'https://github.com/stanford-crfm/mistral',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A prize for finding tasks that cause large language models to '
'show inverse scaling',
'title': 'prize',
'link': 'https://github.com/inverse-scaling/prize',
'engines': ['github'],
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'engines': ['github'],
'category': 'it'},
{'snippet': 'Optimus: the first large-scale pre-trained VAE language model',
'title': 'Optimus',
'link': 'https://github.com/ChunyuanLI/Optimus',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Seminar on Large Language Models (COMP790-101 at UNC Chapel '
'Hill, Fall 2022)',
'title': 'llm-seminar',
'link': 'https://github.com/craffel/llm-seminar',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A central, open resource for data and tools related to '
'chain-of-thought reasoning in large language models. Developed @ '
'Samwald research group: https://samwald.info/',
'title': 'ThoughtSource',
'link': 'https://github.com/OpenBioLink/ThoughtSource',
'engines': ['github'],
'category': 'it'},
{'snippet': 'A comprehensive list of papers using large language/multi-modal '
'models for Robotics/RL, including papers, codes, and related '
'websites',
'title': 'Awesome-LLM-Robotics',
'link': 'https://github.com/GT-RIPL/Awesome-LLM-Robotics',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Tools for curating biomedical training data for large-scale '
'language modeling',
'title': 'biomedical',
'link': 'https://github.com/bigscience-workshop/biomedical',
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'link': 'https://github.com/bigscience-workshop/biomedical',
'engines': ['github'],
'category': 'it'},
{'snippet': 'ChatGPT @ Home: Large Language Model (LLM) chatbot application, '
'written by ChatGPT',
'title': 'ChatGPT-at-Home',
'link': 'https://github.com/Sentdex/ChatGPT-at-Home',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Design and Deploy Large Language Model Apps',
'title': 'dust',
'link': 'https://github.com/dust-tt/dust',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Polyglot: Large Language Models of Well-balanced Competence in '
'Multi-languages',
'title': 'polyglot',
'link': 'https://github.com/EleutherAI/polyglot',
'engines': ['github'],
'category': 'it'},
{'snippet': 'Code release for "Learning Video Representations from Large '
'Language Models"',
'title': 'LaViLa',
'link': 'https://github.com/facebookresearch/LaViLa',
'engines': ['github'],
'category': 'it'},
{'snippet': 'SmoothQuant: Accurate and Efficient Post-Training Quantization '
'for Large Language Models',
'title': 'smoothquant',
'link': 'https://github.com/mit-han-lab/smoothquant',
'engines': ['github'],
'category': 'it'},
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'engines': ['github'],
'category': 'it'},
{'snippet': 'This repository contains the code, data, and models of the paper '
'titled "XL-Sum: Large-Scale Multilingual Abstractive '
'Summarization for 44 Languages" published in Findings of the '
'Association for Computational Linguistics: ACL-IJCNLP 2021.',
'title': 'xl-sum',
'link': 'https://github.com/csebuetnlp/xl-sum',
'engines': ['github'],
'category': 'it'}]
previous
Search Tools
next
SerpAPI
Contents
Custom Parameters
Obtaining results with metadata
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/agents/tools/examples/searx_search.html
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25df4d83289a-0
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.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.tools import Tool
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tool = Tool(
name = "Google Search",
description="Search Google for recent results.",
func=search.run
)
tool.run("Obama's first name?")
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tool.run("Obama's first name?")
"STATE OF HAWAII. 1 Child's First Name. (Type or print). 2. Sex. BARACK. 3. This Birth. CERTIFICATE OF LIVE BIRTH. FILE. NUMBER 151 le. lb. Middle Name. Barack Hussein Obama II is an American former politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic\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. Jan 19, 2017 ... Jordan Barack Treasure, New York City, born in 2008 ... Jordan Barack Treasure made national news when he was the focus of a New York newspaper\xa0... Portrait of George Washington, the 1st President of the United States ... Portrait of Barack Obama, the 44th President of the United States\xa0... 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. Mar 22, 2008 ... Barry Obama decided that he didn't like his nickname. A few of his friends at Occidental College had already begun to call him Barack (his\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\xa0... 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... 4 days ago ... 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\xa0..."
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Number of Results#
You can use the k parameter to set the number of results
search = GoogleSearchAPIWrapper(k=1)
tool = Tool(
name = "I'm Feeling Lucky",
description="Search Google and return the first result.",
func=search.run
)
tool.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()
def top5_results(query):
return search.results(query, 5)
tool = Tool(
name = "Google Search Snippets",
description="Search Google for recent results.",
func=top5_results
)
previous
Google Places
next
Google Serper API
Contents
Number of Results
Metadata Results
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/agents/tools/examples/google_search.html
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951c5fd0314a-0
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.ipynb
.pdf
DuckDuckGo Search
DuckDuckGo Search#
This notebook goes over how to use the duck-duck-go search component.
# !pip install duckduckgo-search
from langchain.tools import DuckDuckGoSearchRun
search = DuckDuckGoSearchRun()
search.run("Obama's first name?")
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https://python.langchain.com/en/latest/modules/agents/tools/examples/ddg.html
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951c5fd0314a-1
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'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 ...'
previous
ChatGPT Plugins
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https://python.langchain.com/en/latest/modules/agents/tools/examples/ddg.html
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951c5fd0314a-2
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previous
ChatGPT Plugins
next
File System Tools
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
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https://python.langchain.com/en/latest/modules/agents/tools/examples/ddg.html
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e93b142b3050-0
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.ipynb
.pdf
Wikipedia
Wikipedia#
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.
First, you need to install wikipedia python package.
!pip install wikipedia
from langchain.utilities import WikipediaAPIWrapper
wikipedia = WikipediaAPIWrapper()
wikipedia.run('HUNTER X HUNTER')
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-1
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'Page: Hunter × Hunter\nSummary: Hunter × Hunter (stylized as HUNTER×HUNTER and pronounced "hunter hunter") is a Japanese manga series written and illustrated by Yoshihiro Togashi. It has been serialized in Shueisha\'s shōnen manga magazine Weekly Shōnen Jump since March 1998, although the manga has frequently gone on extended hiatuses since 2006. Its chapters have been collected in 37 tankōbon volumes as of November 2022. The story focuses on a young boy named Gon Freecss who discovers that his father, who left him at a young age, is actually a world-renowned Hunter, a licensed professional who specializes in fantastical pursuits such as locating rare or unidentified animal species, treasure hunting, surveying unexplored enclaves, or hunting down lawless individuals. Gon departs on a journey to become a Hunter and eventually find his father. Along the way, Gon meets various other Hunters and encounters the paranormal.\nHunter × Hunter was adapted into a 62-episode
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-2
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× Hunter was adapted into a 62-episode anime television series produced by Nippon Animation and directed by Kazuhiro Furuhashi, which ran on Fuji Television from October 1999 to March 2001. Three separate original video animations (OVAs) totaling 30 episodes were subsequently produced by Nippon Animation and released in Japan from 2002 to 2004. A second anime television series by Madhouse aired on Nippon Television from October 2011 to September 2014, totaling 148 episodes, with two animated theatrical films released in 2013. There are also numerous audio albums, video games, musicals, and other media based on Hunter × Hunter.\nThe manga has been translated into English and released in North America by Viz Media since April 2005. Both television series have been also licensed by Viz Media, with the first series having aired on the Funimation Channel in 2009 and the second series broadcast on Adult Swim\'s Toonami programming block from April 2016 to June 2019.\nHunter × Hunter has been a huge critical and financial success
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-3
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× Hunter has been a huge critical and financial success and has become one of the best-selling manga series of all time, having over 84 million copies in circulation by July 2022.\n\nPage: Hunter × Hunter (2011 TV series)\nSummary: Hunter × Hunter is an anime television series that aired from 2011 to 2014 based on Yoshihiro Togashi\'s manga series Hunter × Hunter. The story begins with a young boy named Gon Freecss, who one day discovers that the father who he thought was dead, is in fact alive and well. He learns that his father, Ging, is a legendary "Hunter", an individual who has proven themselves an elite member of humanity. Despite the fact that Ging left his son with his relatives in order to pursue his own dreams, Gon becomes determined to follow in his father\'s footsteps, pass the rigorous "Hunter Examination", and eventually find his father to become a Hunter in his own right.\nThis new Hunter × Hunter anime was announced on July
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-4
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new Hunter × Hunter anime was announced on July 24, 2011. It is a complete reboot of the anime adaptation starting from the beginning of the manga, with no connections to the first anime from 1999. Produced by Nippon TV, VAP, Shueisha and Madhouse, the series is directed by Hiroshi Kōjina, with Atsushi Maekawa and Tsutomu Kamishiro handling series composition, Takahiro Yoshimatsu designing the characters and Yoshihisa Hirano composing the music. Instead of having the old cast reprise their roles for the new adaptation, the series features an entirely new cast to voice the characters. The new series premiered airing weekly on Nippon TV and the nationwide Nippon News Network from October 2, 2011. The series started to be collected in both DVD and Blu-ray format on January 25, 2012. Viz Media has licensed the anime for a DVD/Blu-ray release in North America with an English dub. On television, the series began airing on Adult
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-5
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On television, the series began airing on Adult Swim\'s Toonami programming block on April 17, 2016, and ended on June 23, 2019.The anime series\' opening theme is alternated between the song "Departure!" and an alternate version titled "Departure! -Second Version-" both sung by Galneryus\' vocalist Masatoshi Ono. Five pieces of music were used as the ending theme; "Just Awake" by the Japanese band Fear, and Loathing in Las Vegas in episodes 1 to 26, "Hunting for Your Dream" by Galneryus in episodes 27 to 58, "Reason" sung by Japanese duo Yuzu in episodes 59 to 75, "Nagareboshi Kirari" also sung by Yuzu from episode 76 to 98, which was originally from the anime film adaptation, Hunter × Hunter: Phantom Rouge, and "Hyōri Ittai" by Yuzu featuring Hyadain from episode 99 to 146, which was also used in the film Hunter × Hunter: The Last Mission. The background music and soundtrack for the series was composed
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-6
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The background music and soundtrack for the series was composed by Yoshihisa Hirano.\n\n\n\nPage: List of Hunter × Hunter characters\nSummary: The Hunter × Hunter manga series, created by Yoshihiro Togashi, features an extensive cast of characters. It takes place in a fictional universe where licensed specialists known as Hunters travel the world taking on special jobs ranging from treasure hunting to assassination. The story initially focuses on Gon Freecss and his quest to become a Hunter in order to find his father, Ging, who is himself a famous Hunter. On the way, Gon meets and becomes close friends with Killua Zoldyck, Kurapika and Leorio Paradinight.\nAlthough most characters are human, most possess superhuman strength and/or supernatural abilities due to Nen, the ability to control one\'s own life energy or aura. The world of the series also includes fantastical beasts such as the Chimera Ants or the Five great calamities.'
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https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
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e93b142b3050-7
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previous
Twilio
next
Wolfram Alpha
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html
|
952837c3297f-0
|
.ipynb
.pdf
AWS Lambda API
AWS Lambda API#
This notebook goes over how to use the AWS Lambda Tool component.
AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications.
By including a awslambda in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need.
When an Agent uses the awslambda tool, it will provide an argument of type string which will in turn be passed into the Lambda function via the event parameter.
First, you need to install boto3 python package.
!pip install boto3 > /dev/null
In order for an agent to use the tool, you must provide it with the name and description that match the functionality of you lambda function’s logic.
You must also provide the name of your function.
Note that because this tool is effectively just a wrapper around the boto3 library, you will need to run aws configure in order to make use of the tool. For more detail, see here
from langchain import OpenAI
from langchain.agents import load_tools, AgentType
llm = OpenAI(temperature=0)
tools = load_tools(
["awslambda"],
awslambda_tool_name="email-sender",
awslambda_tool_description="sends an email with the specified content to [email protected]",
function_name="testFunction1"
)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/awslambda.html
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952837c3297f-1
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agent.run("Send an email to [email protected] saying hello world.")
previous
ArXiv API Tool
next
Shell Tool
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/awslambda.html
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239e755e3fe1-0
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.ipynb
.pdf
Requests
Contents
Inside the tool
Requests#
The web contains a lot of information that LLMs do not have access to. In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL.
from langchain.agents import load_tools
requests_tools = load_tools(["requests_all"])
requests_tools
[RequestsGetTool(name='requests_get', description='A portal to the internet. Use this when you need to get specific content from a website. Input should be a url (i.e. https://www.google.com). The output will be the text response of the GET request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),
RequestsPostTool(name='requests_post', description='Use this when you want to POST to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to POST to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the POST request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),
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https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-1
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RequestsPatchTool(name='requests_patch', description='Use this when you want to PATCH to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PATCH to the url.\n Be careful to always use double quotes for strings in the json string\n The output will be the text response of the PATCH request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),
RequestsPutTool(name='requests_put', description='Use this when you want to PUT to a website.\n Input should be a json string with two keys: "url" and "data".\n The value of "url" should be a string, and the value of "data" should be a dictionary of \n key-value pairs you want to PUT to the url.\n Be careful to always use double quotes for strings in the json string.\n The output will be the text response of the PUT request.\n ', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None)),
RequestsDeleteTool(name='requests_delete', description='A portal to the internet. Use this when you need to make a DELETE request to a URL. Input should be a specific url, and the output will be the text response of the DELETE request.', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, requests_wrapper=TextRequestsWrapper(headers=None, aiosession=None))]
Inside the tool#
Each requests tool contains a requests wrapper. You can work with these wrappers directly below
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-2
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Each requests tool contains a requests wrapper. You can work with these wrappers directly below
# Each tool wrapps a requests wrapper
requests_tools[0].requests_wrapper
TextRequestsWrapper(headers=None, aiosession=None)
from langchain.utilities import TextRequestsWrapper
requests = TextRequestsWrapper()
requests.get("https://www.google.com")
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-3
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'<!doctype html><html itemscope="" itemtype="http://schema.org/WebPage" lang="en"><head><meta content="Search the world\'s information, including webpages, images, videos and more. Google has many special features to help you find exactly what you\'re looking for." name="description"><meta content="noodp" name="robots"><meta content="text/html; charset=UTF-8" http-equiv="Content-Type"><meta content="/images/branding/googleg/1x/googleg_standard_color_128dp.png" itemprop="image"><title>Google</title><script
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-4
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nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google={kEI:\'TA9QZOa5EdTakPIPuIad-Ac\',kEXPI:\'0,1359409,6059,206,4804,2316,383,246,5,1129120,1197768,626,380097,16111,28687,22431,1361,12319,17581,4997,13228,37471,7692,2891,3926,213,7615,606,50058,8228,17728,432,3,346,1244,1,16920,2648,4,1528,2304,29062,9871,3194,13658,2980,1457,16786,5803,2554,4094,7596,1,42154,2,14022,2373,342,23024,6699,31123
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https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-5
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342,23024,6699,31123,4568,6258,23418,1252,5835,14967,4333,4239,3245,445,2,2,1,26632,239,7916,7321,60,2,3,15965,872,7830,1796,10008,7,1922,9779,36154,6305,2007,17765,427,20136,14,82,2730,184,13600,3692,109,2412,1548,4308,3785,15175,3888,1515,3030,5628,478,4,9706,1804,7734,2738,1853,1032,9480,2995,576,1041,5648,3722,2058,3048,2130,2365,662,476,958,87,111,5807,2,975,1167,891,3580,1439,1128,7343,426,2
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https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-6
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1439,1128,7343,426,249,517,95,1102,14,696,1270,750,400,2208,274,2776,164,89,119,204,139,129,1710,2505,320,3,631,439,2,300,1645,172,1783,784,169,642,329,401,50,479,614,238,757,535,717,102,2,739,738,44,232,22,442,961,45,214,383,567,500,487,151,120,256,253,179,673,2,102,2,10,535,123,135,1685,5206695,190,2,20,50,198,5994221,2804424,3311,141,795,19735,1,1,346,5008,7,13,10,24,31,2,39,1,5,1,16,7,2,41,247
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-7
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,1,5,1,16,7,2,41,247,4,9,7,9,15,4,4,121,24,23944834,4042142,1964,16672,2894,6250,15739,1726,647,409,837,1411438,146986,23612960,7,84,93,33,101,816,57,532,163,1,441,86,1,951,73,31,2,345,178,243,472,2,148,962,455,167,178,29,702,1856,288,292,805,93,137,68,416,177,292,399,55,95,2566\',kBL:\'hw1A\',kOPI:89978449};google.sn=\'webhp\';google.kHL=\'en\';})();(function(){\nvar
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https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
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239e755e3fe1-8
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h=this||self;function l(){return void 0!==window.google&&void 0!==window.google.kOPI&&0!==window.google.kOPI?window.google.kOPI:null};var m,n=[];function p(a){for(var b;a&&(!a.getAttribute||!(b=a.getAttribute("eid")));)a=a.parentNode;return b||m}function q(a){for(var b=null;a&&(!a.getAttribute||!(b=a.getAttribute("leid")));)a=a.parentNode;return b}function r(a){/^http:/i.test(a)&&"https:"===window.location.protocol&&(google.ml&&google.ml(Error("a"),!1,{src:a,glmm:1}),a="");return a}\nfunction t(a,b,c,d,k){var e="";-1===b.search("&ei=")&&(e="&ei="+p(d),-1===b.search("&lei=")&&(d=q(d))&&(e+="&lei="+d));d="";var g=-1===b.search("&cshid=")&&"slh"!==a,f=[];f.push(["zx",Date.now().toString()]);h._cshid&&g&&f.push(["cshid",h._cshid]);c=c();null!=c&&f.push(["opi",c.toString()]);for(c=0;c<f.length;c++){if(0===c||0<c)d+="&";d+=f[c][0]+"="+f[c][1]}return"/"+(k||"gen_204")+"?atyp=i&ct="+String(a)+"&cad="+(b+e+d)};m=google.kEI;google.getEI=p;google.getLEI=q;google.ml=function(){return null};google.log=function(a,b,c,d,k,e){e=void
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239e755e3fe1-9
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null};google.log=function(a,b,c,d,k,e){e=void 0===e?l:e;c||(c=t(a,b,e,d,k));if(c=r(c)){a=new Image;var g=n.length;n[g]=a;a.onerror=a.onload=a.onabort=function(){delete n[g]};a.src=c}};google.logUrl=function(a,b){b=void 0===b?l:b;return t("",a,b)};}).call(this);(function(){google.y={};google.sy=[];google.x=function(a,b){if(a)var c=a.id;else{do c=Math.random();while(google.y[c])}google.y[c]=[a,b];return!1};google.sx=function(a){google.sy.push(a)};google.lm=[];google.plm=function(a){google.lm.push.apply(google.lm,a)};google.lq=[];google.load=function(a,b,c){google.lq.push([[a],b,c])};google.loadAll=function(a,b){google.lq.push([a,b])};google.bx=!1;google.lx=function(){};}).call(this);google.f={};(function(){\ndocument.documentElement.addEventListener("submit",function(b){var a;if(a=b.target){var c=a.getAttribute("data-submitfalse");a="1"===c||"q"===c&&!a.elements.q.value?!0:!1}else a=!1;a&&(b.preventDefault(),b.stopPropagation())},!0);document.documentElement.addEventListener("click",function(b){var a;a:{for(a=b.target;a&&a!==document.documentElement;a=a.parentElement)if("A"===a.tagName){a="1"===a.getAttribute("data-nohref");break
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a}a=!1}a&&b.preventDefault()},!0);}).call(this);</script><style>#gbar,#guser{font-size:13px;padding-top:1px !important;}#gbar{height:22px}#guser{padding-bottom:7px !important;text-align:right}.gbh,.gbd{border-top:1px solid #c9d7f1;font-size:1px}.gbh{height:0;position:absolute;top:24px;width:100%}@media all{.gb1{height:22px;margin-right:.5em;vertical-align:top}#gbar{float:left}}a.gb1,a.gb4{text-decoration:underline !important}a.gb1,a.gb4{color:#00c !important}.gbi .gb4{color:#dd8e27 !important}.gbf .gb4{color:#900 !important}\n</style><style>body,td,a,p,.h{font-family:arial,sans-serif}body{margin:0;overflow-y:scroll}#gog{padding:3px 8px 0}td{line-height:.8em}.gac_m td{line-height:17px}form{margin-bottom:20px}.h{color:#1558d6}em{font-weight:bold;font-style:normal}.lst{height:25px;width:496px}.gsfi,.lst{font:18px arial,sans-serif}.gsfs{font:17px arial,sans-serif}.ds{display:inline-box;display:inline-block;margin:3px 0 4px;margin-left:4px}input{font-family:inherit}body{background:#fff;color:#000}a{color:#4b11a8;text-decoration:none}a:hover,a:active{text-decoration:underline}.fl
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a{color:#1558d6}a:visited{color:#4b11a8}.sblc{padding-top:5px}.sblc a{display:block;margin:2px 0;margin-left:13px;font-size:11px}.lsbb{background:#f8f9fa;border:solid 1px;border-color:#dadce0 #70757a #70757a #dadce0;height:30px}.lsbb{display:block}#WqQANb a{display:inline-block;margin:0 12px}.lsb{background:url(/images/nav_logo229.png) 0 -261px repeat-x;border:none;color:#000;cursor:pointer;height:30px;margin:0;outline:0;font:15px arial,sans-serif;vertical-align:top}.lsb:active{background:#dadce0}.lst:focus{outline:none}</style><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google.erd={jsr:1,bv:1785,de:true};\nvar h=this||self;var k,l=null!=(k=h.mei)?k:1,n,p=null!=(n=h.sdo)?n:!0,q=0,r,t=google.erd,v=t.jsr;google.ml=function(a,b,d,m,e){e=void 0===e?2:e;b&&(r=a&&a.message);if(google.dl)return google.dl(a,e,d),null;if(0>v){window.console&&console.error(a,d);if(-2===v)throw a;b=!1}else b=!a||!a.message||"Error loading script"===a.message||q>=l&&!m?!1:!0;if(!b)return null;q++;d=d||{};b=encodeURIComponent;var
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null;q++;d=d||{};b=encodeURIComponent;var c="/gen_204?atyp=i&ei="+b(google.kEI);google.kEXPI&&(c+="&jexpid="+b(google.kEXPI));c+="&srcpg="+b(google.sn)+"&jsr="+b(t.jsr)+"&bver="+b(t.bv);var f=a.lineNumber;void 0!==f&&(c+="&line="+f);var g=\na.fileName;g&&(0<g.indexOf("-extension:/")&&(e=3),c+="&script="+b(g),f&&g===window.location.href&&(f=document.documentElement.outerHTML.split("\\n")[f],c+="&cad="+b(f?f.substring(0,300):"No script found.")));c+="&jsel="+e;for(var u in d)c+="&",c+=b(u),c+="=",c+=b(d[u]);c=c+"&emsg="+b(a.name+": "+a.message);c=c+"&jsst="+b(a.stack||"N/A");12288<=c.length&&(c=c.substr(0,12288));a=c;m||google.log(0,"",a);return a};window.onerror=function(a,b,d,m,e){r!==a&&(a=e instanceof Error?e:Error(a),void 0===d||"lineNumber"in a||(a.lineNumber=d),void 0===b||"fileName"in a||(a.fileName=b),google.ml(a,!1,void 0,!1,"SyntaxError"===a.name||"SyntaxError"===a.message.substring(0,11)||-1!==a.message.indexOf("Script error")?3:0));r=null;p&&q>=l&&(window.onerror=null)};})();</script></head><body bgcolor="#fff"><script
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bgcolor="#fff"><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var src=\'/images/nav_logo229.png\';var iesg=false;document.body.onload = function(){window.n && window.n();if (document.images){new Image().src=src;}\nif (!iesg){document.f&&document.f.q.focus();document.gbqf&&document.gbqf.q.focus();}\n}\n})();</script><div id="mngb"><div id=gbar><nobr><b class=gb1>Search</b> <a class=gb1 href="https://www.google.com/imghp?hl=en&tab=wi">Images</a> <a class=gb1 href="https://maps.google.com/maps?hl=en&tab=wl">Maps</a> <a class=gb1 href="https://play.google.com/?hl=en&tab=w8">Play</a> <a class=gb1 href="https://www.youtube.com/?tab=w1">YouTube</a> <a class=gb1 href="https://news.google.com/?tab=wn">News</a> <a class=gb1 href="https://mail.google.com/mail/?tab=wm">Gmail</a> <a class=gb1 href="https://drive.google.com/?tab=wo">Drive</a> <a class=gb1 style="text-decoration:none" href="https://www.google.com/intl/en/about/products?tab=wh"><u>More</u> »</a></nobr></div><div id=guser width=100%><nobr><span id=gbn class=gbi></span><span id=gbf
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id=gbn class=gbi></span><span id=gbf class=gbf></span><span id=gbe></span><a href="http://www.google.com/history/optout?hl=en" class=gb4>Web History</a> | <a href="/preferences?hl=en" class=gb4>Settings</a> | <a target=_top id=gb_70 href="https://accounts.google.com/ServiceLogin?hl=en&passive=true&continue=https://www.google.com/&ec=GAZAAQ" class=gb4>Sign in</a></nobr></div><div class=gbh style=left:0></div><div class=gbh style=right:0></div></div><center><br clear="all" id="lgpd"><div id="lga"><img alt="Google" height="92" src="/images/branding/googlelogo/1x/googlelogo_white_background_color_272x92dp.png" style="padding:28px 0 14px" width="272" id="hplogo"><br><br></div><form action="/search" name="f"><table cellpadding="0" cellspacing="0"><tr valign="top"><td width="25%"> </td><td align="center" nowrap=""><input name="ie" value="ISO-8859-1" type="hidden"><input value="en" name="hl" type="hidden"><input name="source" type="hidden" value="hp"><input name="biw" type="hidden"><input name="bih" type="hidden"><div class="ds"
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name="bih" type="hidden"><div class="ds" style="height:32px;margin:4px 0"><input class="lst" style="margin:0;padding:5px 8px 0 6px;vertical-align:top;color:#000" autocomplete="off" value="" title="Google Search" maxlength="2048" name="q" size="57"></div><br style="line-height:0"><span class="ds"><span class="lsbb"><input class="lsb" value="Google Search" name="btnG" type="submit"></span></span><span class="ds"><span class="lsbb"><input class="lsb" id="tsuid_1" value="I\'m Feeling Lucky" name="btnI" type="submit"><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var id=\'tsuid_1\';document.getElementById(id).onclick = function(){if (this.form.q.value){this.checked = 1;if (this.form.iflsig)this.form.iflsig.disabled = false;}\nelse top.location=\'/doodles/\';};})();</script><input value="AOEireoAAAAAZFAdXGKCXWBK5dlWxPhh8hNPQz1s9YT6" name="iflsig" type="hidden"></span></span></td><td class="fl sblc" align="left" nowrap="" width="25%"><a href="/advanced_search?hl=en&authuser=0">Advanced search</a></td></tr></table><input id="gbv"
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239e755e3fe1-16
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search</a></td></tr></table><input id="gbv" name="gbv" type="hidden" value="1"><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var a,b="1";if(document&&document.getElementById)if("undefined"!=typeof XMLHttpRequest)b="2";else if("undefined"!=typeof ActiveXObject){var c,d,e=["MSXML2.XMLHTTP.6.0","MSXML2.XMLHTTP.3.0","MSXML2.XMLHTTP","Microsoft.XMLHTTP"];for(c=0;d=e[c++];)try{new ActiveXObject(d),b="2"}catch(h){}}a=b;if("2"==a&&-1==location.search.indexOf("&gbv=2")){var f=google.gbvu,g=document.getElementById("gbv");g&&(g.value=a);f&&window.setTimeout(function(){location.href=f},0)};}).call(this);</script></form><div id="gac_scont"></div><div style="font-size:83%;min-height:3.5em"><br><div id="prm"><style>.szppmdbYutt__middle-slot-promo{font-size:small;margin-bottom:32px}.szppmdbYutt__middle-slot-promo a.ZIeIlb{display:inline-block;text-decoration:none}.szppmdbYutt__middle-slot-promo img{border:none;margin-right:5px;vertical-align:middle}</style><div class="szppmdbYutt__middle-slot-promo" data-ved="0ahUKEwjmj7fr6dT-AhVULUQIHThDB38QnIcBCAQ"><a class="NKcBbd"
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class="NKcBbd" href="https://www.google.com/url?q=https://blog.google/outreach-initiatives/diversity/asian-pacific-american-heritage-month-2023/%3Futm_source%3Dhpp%26utm_medium%3Downed%26utm_campaign%3Dapahm&source=hpp&id=19035152&ct=3&usg=AOvVaw1zrN82vzhoWl4hz1zZ4gLp&sa=X&ved=0ahUKEwjmj7fr6dT-AhVULUQIHThDB38Q8IcBCAU" rel="nofollow">Celebrate Asian Pacific American Heritage Month with Google</a></div></div></div><span id="footer"><div style="font-size:10pt"><div style="margin:19px auto;text-align:center" id="WqQANb"><a href="/intl/en/ads/">Advertising</a><a href="/services/">Business Solutions</a><a href="/intl/en/about.html">About Google</a></div></div><p style="font-size:8pt;color:#70757a">© 2023 - <a href="/intl/en/policies/privacy/">Privacy</a> - <a href="/intl/en/policies/terms/">Terms</a></p></span></center><script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){window.google.cdo={height:757,width:1440};(function(){var a=window.innerWidth,b=window.innerHeight;if(!a||!b){var
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239e755e3fe1-18
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a=window.innerWidth,b=window.innerHeight;if(!a||!b){var c=window.document,d="CSS1Compat"==c.compatMode?c.documentElement:c.body;a=d.clientWidth;b=d.clientHeight}a&&b&&(a!=google.cdo.width||b!=google.cdo.height)&&google.log("","","/client_204?&atyp=i&biw="+a+"&bih="+b+"&ei="+google.kEI);}).call(this);})();</script> <script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){google.xjs={ck:\'xjs.hp.vUsZk7fd8do.L.X.O\',cs:\'ACT90oF8ktm8JGoaZ23megDhHoJku7YaGw\',excm:[]};})();</script> <script nonce="MXrF0nnIBPkxBza4okrgPA">(function(){var u=\'/xjs/_/js/k\\x3dxjs.hp.en.q0lHXBfs9JY.O/am\\x3dAAAA6AQAUABgAQ/d\\x3d1/ed\\x3d1/rs\\x3dACT90oE3ek6-fjkab6CsTH0wUEUUPhnExg/m\\x3dsb_he,d\';var amd=0;\nvar e=this||self,f=function(c){return c};var h;var n=function(c,g){this.g=g===l?c:""};n.prototype.toString=function(){return this.g+""};var l={};\nfunction p(){var c=u,g=function(){};google.lx=google.stvsc?g:function(){google.timers&&google.timers.load&&google.tick&&google.tick("load","xjsls");var
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a=document;var b="SCRIPT";"application/xhtml+xml"===a.contentType&&(b=b.toLowerCase());b=a.createElement(b);a=null===c?"null":void 0===c?"undefined":c;if(void 0===h){var d=null;var m=e.trustedTypes;if(m&&m.createPolicy){try{d=m.createPolicy("goog#html",{createHTML:f,createScript:f,createScriptURL:f})}catch(r){e.console&&e.console.error(r.message)}h=\nd}else h=d}a=(d=h)?d.createScriptURL(a):a;a=new n(a,l);b.src=a instanceof n&&a.constructor===n?a.g:"type_error:TrustedResourceUrl";var k,q;(k=(a=null==(q=(k=(b.ownerDocument&&b.ownerDocument.defaultView||window).document).querySelector)?void 0:q.call(k,"script[nonce]"))?a.nonce||a.getAttribute("nonce")||"":"")&&b.setAttribute("nonce",k);document.body.appendChild(b);google.psa=!0;google.lx=g};google.bx||google.lx()};google.xjsu=u;e._F_jsUrl=u;setTimeout(function(){0<amd?google.caft(function(){return p()},amd):p()},0);})();window._ = window._ || {};window._DumpException = _._DumpException = function(e){throw e;};window._s = window._s || {};_s._DumpException = _._DumpException;window._qs = window._qs || {};_qs._DumpException = _._DumpException;function
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|| {};_qs._DumpException = _._DumpException;function _F_installCss(c){}\n(function(){google.jl={blt:\'none\',chnk:0,dw:false,dwu:true,emtn:0,end:0,ico:false,ikb:0,ine:false,injs:\'none\',injt:0,injth:0,injv2:false,lls:\'default\',pdt:0,rep:0,snet:true,strt:0,ubm:false,uwp:true};})();(function(){var pmc=\'{\\x22d\\x22:{},\\x22sb_he\\x22:{\\x22agen\\x22:true,\\x22cgen\\x22:true,\\x22client\\x22:\\x22heirloom-hp\\x22,\\x22dh\\x22:true,\\x22ds\\x22:\\x22\\x22,\\x22fl\\x22:true,\\x22host\\x22:\\x22google.com\\x22,\\x22jsonp\\x22:true,\\x22msgs\\x22:{\\x22cibl\\x22:\\x22Clear Search\\x22,\\x22dym\\x22:\\x22Did you mean:\\x22,\\x22lcky\\x22:\\x22I\\\\u0026#39;m Feeling Lucky\\x22,\\x22lml\\x22:\\x22Learn more\\x22,\\x22psrc\\x22:\\x22This search was removed from your \\\\u003Ca href\\x3d\\\\\\x22/history\\\\\\x22\\\\u003EWeb
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href\\x3d\\\\\\x22/history\\\\\\x22\\\\u003EWeb History\\\\u003C/a\\\\u003E\\x22,\\x22psrl\\x22:\\x22Remove\\x22,\\x22sbit\\x22:\\x22Search by image\\x22,\\x22srch\\x22:\\x22Google Search\\x22},\\x22ovr\\x22:{},\\x22pq\\x22:\\x22\\x22,\\x22rfs\\x22:[],\\x22sbas\\x22:\\x220 3px 8px 0 rgba(0,0,0,0.2),0 0 0 1px rgba(0,0,0,0.08)\\x22,\\x22stok\\x22:\\x22C3TIBpTor6RHJfEIn2nbidnhv50\\x22}}\';google.pmc=JSON.parse(pmc);})();</script> </body></html>'
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239e755e3fe1-22
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previous
Python REPL
next
SceneXplain
Contents
Inside the tool
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/requests.html
|
ef6839188684-0
|
.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"
previous
File System Tools
next
Google Search
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023.
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/google_places.html
|
24a4737753c1-0
|
.ipynb
.pdf
Google Serper API
Contents
As part of a Self Ask With Search Chain
Obtaining results with metadata
Searching for Google Images
Searching for Google News
Searching for Google Places
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
import pprint
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
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html
|
24a4737753c1-1
|
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'
Obtaining results with metadata#
If you would also like to obtain the results in a structured way including metadata. For this we will be using the results method of the wrapper.
search = GoogleSerperAPIWrapper()
results = search.results("Apple Inc.")
pprint.pp(results)
{'searchParameters': {'q': 'Apple Inc.',
'gl': 'us',
'hl': 'en',
'num': 10,
'type': 'search'},
'knowledgeGraph': {'title': 'Apple',
'type': 'Technology company',
'website': 'http://www.apple.com/',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQwGQRv5TjjkycpctY66mOg_e2-npacrmjAb6_jAWhzlzkFE3OTjxyzbA&s=0',
'description': 'Apple Inc. is an American multinational '
'technology company headquartered in '
'Cupertino, California. Apple is the '
"world's largest technology company by "
'revenue, with US$394.3 billion in 2022 '
'revenue. As of March 2023, Apple is the '
"world's biggest...",
'descriptionSource': 'Wikipedia',
'descriptionLink': 'https://en.wikipedia.org/wiki/Apple_Inc.',
'attributes': {'Customer service': '1 (800) 275-2273',
'CEO': 'Tim Cook (Aug 24, 2011–)',
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html
|
24a4737753c1-2
|
'CEO': 'Tim Cook (Aug 24, 2011–)',
'Headquarters': 'Cupertino, CA',
'Founded': 'April 1, 1976, Los Altos, CA',
'Founders': 'Steve Jobs, Steve Wozniak, '
'Ronald Wayne, and more',
'Products': 'iPhone, iPad, Apple TV, and '
'more'}},
'organic': [{'title': 'Apple',
'link': 'https://www.apple.com/',
'snippet': 'Discover the innovative world of Apple and shop '
'everything iPhone, iPad, Apple Watch, Mac, and Apple '
'TV, plus explore accessories, entertainment, ...',
'sitelinks': [{'title': 'Support',
'link': 'https://support.apple.com/'},
{'title': 'iPhone',
'link': 'https://www.apple.com/iphone/'},
{'title': 'Site Map',
'link': 'https://www.apple.com/sitemap/'},
{'title': 'Business',
'link': 'https://www.apple.com/business/'},
{'title': 'Mac',
'link': 'https://www.apple.com/mac/'},
{'title': 'Watch',
'link': 'https://www.apple.com/watch/'}],
'position': 1},
{'title': 'Apple Inc. - Wikipedia',
'link': 'https://en.wikipedia.org/wiki/Apple_Inc.',
'snippet': 'Apple Inc. is an American multinational technology '
'company headquartered in Cupertino, California. '
"Apple is the world's largest technology company by "
'revenue, ...',
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html
|
24a4737753c1-3
|
"Apple is the world's largest technology company by "
'revenue, ...',
'attributes': {'Products': 'AirPods; Apple Watch; iPad; iPhone; '
'Mac; Full list',
'Founders': 'Steve Jobs; Steve Wozniak; Ronald '
'Wayne; Mike Markkula'},
'sitelinks': [{'title': 'History',
'link': 'https://en.wikipedia.org/wiki/History_of_Apple_Inc.'},
{'title': 'Timeline of Apple Inc. products',
'link': 'https://en.wikipedia.org/wiki/Timeline_of_Apple_Inc._products'},
{'title': 'Litigation involving Apple Inc.',
'link': 'https://en.wikipedia.org/wiki/Litigation_involving_Apple_Inc.'},
{'title': 'Apple Store',
'link': 'https://en.wikipedia.org/wiki/Apple_Store'}],
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRvmB5fT1LjqpZx02UM7IJq0Buoqt0DZs_y0dqwxwSWyP4PIN9FaxuTea0&s',
'position': 2},
{'title': 'Apple Inc. | History, Products, Headquarters, & Facts '
'| Britannica',
'link': 'https://www.britannica.com/topic/Apple-Inc',
'snippet': 'Apple Inc., formerly Apple Computer, Inc., American '
'manufacturer of personal computers, smartphones, '
'tablet computers, computer peripherals, and computer '
'...',
'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony '
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html
|
24a4737753c1-4
|
'attributes': {'Related People': 'Steve Jobs Steve Wozniak Jony '
'Ive Tim Cook Angela Ahrendts',
'Date': '1976 - present'},
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3liELlhrMz3Wpsox29U8jJ3L8qETR0hBWHXbFnwjwQc34zwZvFELst2E&s',
'position': 3},
{'title': 'AAPL: Apple Inc Stock Price Quote - NASDAQ GS - '
'Bloomberg.com',
'link': 'https://www.bloomberg.com/quote/AAPL:US',
'snippet': 'AAPL:USNASDAQ GS. Apple Inc. COMPANY INFO ; Open. '
'170.09 ; Prev Close. 169.59 ; Volume. 48,425,696 ; '
'Market Cap. 2.667T ; Day Range. 167.54170.35.',
'position': 4},
{'title': 'Apple Inc. (AAPL) Company Profile & Facts - Yahoo '
'Finance',
'link': 'https://finance.yahoo.com/quote/AAPL/profile/',
'snippet': 'Apple Inc. designs, manufactures, and markets '
'smartphones, personal computers, tablets, wearables, '
'and accessories worldwide. The company offers '
'iPhone, a line ...',
'position': 5},
{'title': 'Apple Inc. (AAPL) Stock Price, News, Quote & History - '
'Yahoo Finance',
'link': 'https://finance.yahoo.com/quote/AAPL',
|
https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html
|
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