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422e1aad04d4-0 | .ipynb
.pdf
MRKL
MRKL#
This notebook showcases using an agent to replicate the MRKL chain.
This uses the example Chinook database.
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository.
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
db = SQLDatabase.from_uri("sqlite:///../../../../../notebooks/Chinook.db")
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events. You should ask targeted questions"
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
),
Tool(
name="FooBar DB",
func=db_chain.run,
description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context"
)
]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain... | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html |
422e1aad04d4-1 | > Entering new AgentExecutor chain...
I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.
Action: Search
Action Input: "Who is Leo DiCaprio's girlfriend?"
Observation: DiCaprio met actor Camila Morrone in December 2017, when she was 20 and he was 43. They were spotted at Coachella and went on multiple vacations together. Some reports suggested that DiCaprio was ready to ask Morrone to marry him. The couple made their red carpet debut at the 2020 Academy Awards.
Thought: I need to calculate Camila Morrone's age raised to the 0.43 power.
Action: Calculator
Action Input: 21^0.43
> Entering new LLMMathChain chain...
21^0.43
```text
21**0.43
```
...numexpr.evaluate("21**0.43")...
Answer: 3.7030049853137306
> Finished chain.
Observation: Answer: 3.7030049853137306
Thought: I now know the final answer.
Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.7030049853137306.
> Finished chain.
"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.7030049853137306."
mrkl.run("What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?")
> Entering new AgentExecutor chain... | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html |
422e1aad04d4-2 | > Entering new AgentExecutor chain...
I need to find out the artist's full name and then search the FooBar database for their albums.
Action: Search
Action Input: "The Storm Before the Calm" artist
Observation: The Storm Before the Calm (stylized in all lowercase) is the tenth (and eighth international) studio album by Canadian-American singer-songwriter Alanis Morissette, released June 17, 2022, via Epiphany Music and Thirty Tigers, as well as by RCA Records in Europe.
Thought: I now need to search the FooBar database for Alanis Morissette's albums.
Action: FooBar DB
Action Input: What albums by Alanis Morissette are in the FooBar database?
> Entering new SQLDatabaseChain chain...
What albums by Alanis Morissette are in the FooBar database?
SQLQuery:
/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:191: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.
sample_rows = connection.execute(command)
SELECT "Title" FROM "Album" INNER JOIN "Artist" ON "Album"."ArtistId" = "Artist"."ArtistId" WHERE "Name" = 'Alanis Morissette' LIMIT 5;
SQLResult: [('Jagged Little Pill',)]
Answer: The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.
> Finished chain.
Observation: The albums by Alanis Morissette in the FooBar database are Jagged Little Pill.
Thought: I now know the final answer. | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html |
422e1aad04d4-3 | Thought: I now know the final answer.
Final Answer: The artist who released the album 'The Storm Before the Calm' is Alanis Morissette and the albums of hers in the FooBar database are Jagged Little Pill.
> Finished chain.
"The artist who released the album 'The Storm Before the Calm' is Alanis Morissette and the albums of hers in the FooBar database are Jagged Little Pill."
previous
Conversation Agent
next
MRKL Chat
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/agents/examples/mrkl.html |
d4e8a596bb5b-0 | .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 | https://python.langchain.com/en/latest/modules/agents/tools/getting_started.html |
d4e8a596bb5b-1 | 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 | https://python.langchain.com/en/latest/modules/agents/tools/getting_started.html |
d4e8a596bb5b-2 | 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 | https://python.langchain.com/en/latest/modules/agents/tools/getting_started.html |
d4e8a596bb5b-3 | 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). | https://python.langchain.com/en/latest/modules/agents/tools/getting_started.html |
d4e8a596bb5b-4 | 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. | https://python.langchain.com/en/latest/modules/agents/tools/getting_started.html |
e6531256ba05-0 | .ipynb
.pdf
Multi-Input Tools
Contents
Multi-Input Tools with a string format
Multi-Input Tools#
This notebook shows how to use a tool that requires multiple inputs with an agent. The recommended way to do so is with the StructuredTool class.
import os
os.environ["LANGCHAIN_TRACING"] = "true"
from langchain import OpenAI
from langchain.agents import initialize_agent, AgentType
llm = OpenAI(temperature=0)
from langchain.tools import StructuredTool
def multiplier(a: float, b: float) -> float:
"""Multiply the provided floats."""
return a * b
tool = StructuredTool.from_function(multiplier)
# Structured tools are compatible with the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent type.
agent_executor = initialize_agent([tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_executor.run("What is 3 times 4")
> Entering new AgentExecutor chain...
Thought: I need to multiply 3 and 4
Action:
```
{
"action": "multiplier",
"action_input": {"a": 3, "b": 4}
}
```
Observation: 12
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "3 times 4 is 12"
}
```
> Finished chain.
'3 times 4 is 12'
Multi-Input Tools with a string format# | https://python.langchain.com/en/latest/modules/agents/tools/multi_input_tool.html |
e6531256ba05-1 | '3 times 4 is 12'
Multi-Input Tools with a string format#
An alternative to the structured tool would be to use the regular Tool class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can’t reliabl generate structured schema.
Let’s take the multiplication function as an example. In order to use this, we will tell the agent to generate the “Action Input” as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function.
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
Here is the multiplication function, as well as a wrapper to parse a string as input.
def multiplier(a, b):
return a * b
def parsing_multiplier(string):
a, b = string.split(",")
return multiplier(int(a), int(b))
llm = OpenAI(temperature=0)
tools = [
Tool(
name = "Multiplier",
func=parsing_multiplier,
description="useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2."
)
]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
mrkl.run("What is 3 times 4")
> Entering new AgentExecutor chain... | https://python.langchain.com/en/latest/modules/agents/tools/multi_input_tool.html |
e6531256ba05-2 | > Entering new AgentExecutor chain...
I need to multiply two numbers
Action: Multiplier
Action Input: 3,4
Observation: 12
Thought: I now know the final answer
Final Answer: 3 times 4 is 12
> Finished chain.
'3 times 4 is 12'
previous
Defining Custom Tools
next
Tool Input Schema
Contents
Multi-Input Tools with a string format
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/multi_input_tool.html |
d7d02dc557f7-0 | .ipynb
.pdf
Tool Input Schema
Tool Input Schema#
By default, tools infer the argument schema by inspecting the function signature. For more strict requirements, custom input schema can be specified, along with custom validation logic.
from typing import Any, Dict
from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper
from pydantic import BaseModel, Field, root_validator
llm = OpenAI(temperature=0)
!pip install tldextract > /dev/null
[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: pip install --upgrade pip
import tldextract
_APPROVED_DOMAINS = {
"langchain",
"wikipedia",
}
class ToolInputSchema(BaseModel):
url: str = Field(...)
@root_validator
def validate_query(cls, values: Dict[str, Any]) -> Dict:
url = values["url"]
domain = tldextract.extract(url).domain
if domain not in _APPROVED_DOMAINS:
raise ValueError(f"Domain {domain} is not on the approved list:"
f" {sorted(_APPROVED_DOMAINS)}")
return values
tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())
agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)
# This will succeed, since there aren't any arguments that will be triggered during validation
answer = agent.run("What's the main title on langchain.com?")
print(answer) | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
d7d02dc557f7-1 | answer = agent.run("What's the main title on langchain.com?")
print(answer)
The main title of langchain.com is "LANG CHAIN 🦜️🔗 Official Home Page"
agent.run("What's the main title on google.com?")
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[7], line 1
----> 1 agent.run("What's the main title on google.com?")
File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)
211 if len(args) != 1:
212 raise ValueError("`run` supports only one positional argument.")
--> 213 return self(args[0])[self.output_keys[0]]
215 if kwargs and not args:
216 return self(kwargs)[self.output_keys[0]]
File ~/code/lc/lckg/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116 raise e
117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
118 return self.prep_outputs(inputs, outputs, return_only_outputs)
File ~/code/lc/lckg/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
107 self.callback_manager.on_chain_start(
108 {"name": self.__class__.__name__},
109 inputs,
110 verbose=self.verbose,
111 )
112 try:
--> 113 outputs = self._call(inputs) | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
d7d02dc557f7-2 | 112 try:
--> 113 outputs = self._call(inputs)
114 except (KeyboardInterrupt, Exception) as e:
115 self.callback_manager.on_chain_error(e, verbose=self.verbose)
File ~/code/lc/lckg/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)
790 # We now enter the agent loop (until it returns something).
791 while self._should_continue(iterations, time_elapsed):
--> 792 next_step_output = self._take_next_step(
793 name_to_tool_map, color_mapping, inputs, intermediate_steps
794 )
795 if isinstance(next_step_output, AgentFinish):
796 return self._return(next_step_output, intermediate_steps)
File ~/code/lc/lckg/langchain/agents/agent.py:695, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)
693 tool_run_kwargs["llm_prefix"] = ""
694 # We then call the tool on the tool input to get an observation
--> 695 observation = tool.run(
696 agent_action.tool_input,
697 verbose=self.verbose,
698 color=color,
699 **tool_run_kwargs,
700 )
701 else:
702 tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File ~/code/lc/lckg/langchain/tools/base.py:110, in BaseTool.run(self, tool_input, verbose, start_color, color, **kwargs)
101 def run(
102 self,
103 tool_input: Union[str, Dict],
(...) | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
d7d02dc557f7-3 | 103 tool_input: Union[str, Dict],
(...)
107 **kwargs: Any,
108 ) -> str:
109 """Run the tool."""
--> 110 run_input = self._parse_input(tool_input)
111 if not self.verbose and verbose is not None:
112 verbose_ = verbose
File ~/code/lc/lckg/langchain/tools/base.py:71, in BaseTool._parse_input(self, tool_input)
69 if issubclass(input_args, BaseModel):
70 key_ = next(iter(input_args.__fields__.keys()))
---> 71 input_args.parse_obj({key_: tool_input})
72 # Passing as a positional argument is more straightforward for
73 # backwards compatability
74 return tool_input
File ~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526, in pydantic.main.BaseModel.parse_obj()
File ~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for ToolInputSchema
__root__
Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)
previous
Multi-Input Tools
next
Apify
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
2ebff0101125-0 | .ipynb
.pdf
Defining Custom 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
Defining Custom Tools#
When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:
name (str), is required and must be unique within a set of tools provided to an agent
description (str), is optional but recommended, as it is used by an agent to determine tool use
return_direct (bool), defaults to False
args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.
There are two main ways to define a tool, we will cover both in the example below.
# Import things that are needed generically
from langchain import LLMMathChain, SerpAPIWrapper
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
Initialize the LLM to use for the agent.
llm = ChatOpenAI(temperature=0)
Completely New Tools - String Input and Output#
The simplest tools accept a single query string and return a string output. If your tool function requires multiple arguments, you might want to skip down to the StructuredTool section below.
There are two ways to do this: either by using the Tool dataclass, or by subclassing the BaseTool class.
Tool dataclass# | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-1 | Tool dataclass#
The ‘Tool’ dataclass wraps functions that accept a single string input and returns a string output.
# Load the tool configs that are needed.
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
tools = [
Tool.from_function(
func=search.run,
name = "Search",
description="useful for when you need to answer questions about current events"
# coroutine= ... <- you can specify an async method if desired as well
),
]
/Users/wfh/code/lc/lckg/langchain/chains/llm_math/base.py:50: UserWarning: Directly instantiating an LLMMathChain with an llm is deprecated. Please instantiate with llm_chain argument or using the from_llm class method.
warnings.warn(
You can also define a custom `args_schema`` to provide more information about inputs.
from pydantic import BaseModel, Field
class CalculatorInput(BaseModel):
question: str = Field()
tools.append(
Tool.from_function(
func=llm_math_chain.run,
name="Calculator",
description="useful for when you need to answer questions about math",
args_schema=CalculatorInput
# coroutine= ... <- you can specify an async method if desired as well
)
)
# Construct the agent. We will use the default agent type here.
# See documentation for a full list of options.
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-2 | > Entering new AgentExecutor chain...
I need to find out Leo DiCaprio's girlfriend's name and her age
Action: Search
Action Input: "Leo DiCaprio girlfriend"
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 age
Action: Search
Action Input: "Leo DiCaprio current girlfriend"
Observation: Just Jared on Instagram: “Leonardo DiCaprio & girlfriend Camila Morrone couple up for a lunch date!
Thought:Now that I know his girlfriend's name is Camila Morrone, I need to find her current age
Action: Search
Action Input: "Camila Morrone age"
Observation: 25 years
Thought:Now that I have her age, I need to calculate her age raised to the 0.43 power
Action: Calculator
Action Input: 25^(0.43)
> Entering new LLMMathChain chain...
25^(0.43)```text
25**(0.43)
```
...numexpr.evaluate("25**(0.43)")...
Answer: 3.991298452658078
> Finished chain.
Observation: Answer: 3.991298452658078
Thought:I now know the final answer
Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.
> Finished chain.
"Camila Morrone's current age raised to the 0.43 power is approximately 3.99."
Subclassing the BaseTool class# | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-3 | Subclassing the BaseTool class#
You can also directly subclass BaseTool. This is useful if you want more control over the instance variables or if you want to propagate callbacks to nested chains or other tools.
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, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
"""Use the tool."""
return search.run(query)
async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
class CustomCalculatorTool(BaseTool):
name = "Calculator"
description = "useful for when you need to answer questions about math"
args_schema: Type[BaseModel] = CalculatorInput
def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
"""Use the tool."""
return llm_math_chain.run(query)
async def _arun(self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("Calculator does not support async")
tools = [CustomSearchTool(), CustomCalculatorTool()]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True) | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-4 | 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 use custom_search to find out who Leo DiCaprio's girlfriend is, and then use the Calculator to raise her age to the 0.43 power.
Action: custom_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 need to find out the current age of Eden Polani.
Action: custom_search
Action Input: "Eden Polani age"
Observation: 19 years old
Thought:Now I can use the Calculator to raise her age to the 0.43 power.
Action: Calculator
Action Input: 19 ^ 0.43
> Entering new LLMMathChain chain...
19 ^ 0.43```text
19 ** 0.43
```
...numexpr.evaluate("19 ** 0.43")...
Answer: 3.547023357958959
> Finished chain.
Observation: Answer: 3.547023357958959
Thought:I now know the final answer.
Final Answer: 3.547023357958959
> Finished chain.
'3.547023357958959'
Using the tool decorator# | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-5 | > Finished chain.
'3.547023357958959'
Using the tool decorator#
To make it easier to define custom tools, a @tool decorator is provided. This decorator can be used to quickly create a Tool from a simple function. The decorator uses the function name as the tool name by default, but this can be overridden by passing a string as the first argument. Additionally, the decorator will use the function’s docstring as the tool’s description.
from langchain.tools import tool
@tool
def search_api(query: str) -> str:
"""Searches the API for the query."""
return f"Results for query {query}"
search_api
You can also provide arguments like the tool name and whether to return directly.
@tool("search", return_direct=True)
def search_api(query: str) -> str:
"""Searches the API for the query."""
return "Results"
search_api
Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)
You can also provide args_schema to provide more information about the argument
class SearchInput(BaseModel):
query: str = Field(description="should be a search query")
@tool("search", return_direct=True, args_schema=SearchInput)
def search_api(query: str) -> str:
"""Searches the API for the query."""
return "Results"
search_api | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-6 | """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: | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-7 | """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.""" | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-8 | """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 | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-9 | 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, | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-10 | 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) | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
2ebff0101125-11 | 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. | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
5d51269a1462-0 | .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", | https://python.langchain.com/en/latest/modules/agents/tools/examples/apify.html |
5d51269a1462-1 | 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. | https://python.langchain.com/en/latest/modules/agents/tools/examples/apify.html |
90dc3b0df7d7-0 | .ipynb
.pdf
Metaphor Search
Contents
Metaphor Search
Call the API
Use Metaphor as a tool
Metaphor Search#
This notebook goes over how to use Metaphor search.
First, you need to set up the proper API keys and environment variables. Request an API key [here](Sign up for early access here).
Then enter your API key as an environment variable.
import os
os.environ["METAPHOR_API_KEY"] = ""
from langchain.utilities import MetaphorSearchAPIWrapper
search = MetaphorSearchAPIWrapper()
Call the API#
results takes in a Metaphor-optimized search query and a number of results (up to 500). It returns a list of results with title, url, author, and creation date.
search.results("The best blog post about AI safety is definitely this: ", 10) | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-1 | {'results': [{'url': 'https://www.anthropic.com/index/core-views-on-ai-safety', 'title': 'Core Views on AI Safety: When, Why, What, and How', 'dateCreated': '2023-03-08', 'author': None, 'score': 0.1998831331729889}, {'url': 'https://aisafety.wordpress.com/', 'title': 'Extinction Risk from Artificial Intelligence', 'dateCreated': '2013-10-08', 'author': None, 'score': 0.19801370799541473}, {'url': 'https://www.lesswrong.com/posts/WhNxG4r774bK32GcH/the-simple-picture-on-ai-safety', 'title': 'The simple picture on AI safety - LessWrong', 'dateCreated': '2018-05-27', 'author': 'Alex Flint', 'score': 0.19735534489154816}, {'url': 'https://slatestarcodex.com/2015/05/29/no-time-like-the-present-for-ai-safety-work/', 'title': 'No Time Like The Present For AI Safety Work', 'dateCreated': '2015-05-29', 'author': None, 'score': 0.19408763945102692}, {'url': 'https://www.lesswrong.com/posts/5BJvusxdwNXYQ4L9L/so-you-want-to-save-the-world', 'title': 'So You Want to Save the World | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-2 | 'title': 'So You Want to Save the World - LessWrong', 'dateCreated': '2012-01-01', 'author': 'Lukeprog', 'score': 0.18853715062141418}, {'url': 'https://openai.com/blog/planning-for-agi-and-beyond', 'title': 'Planning for AGI and beyond', 'dateCreated': '2023-02-24', 'author': 'Authors', 'score': 0.18665121495723724}, {'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html', 'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why', 'dateCreated': '2015-01-22', 'author': 'Tim Urban', 'score': 0.18604731559753418}, {'url': 'https://forum.effectivealtruism.org/posts/uGDCaPFaPkuxAowmH/anthropic-core-views-on-ai-safety-when-why-what-and-how', 'title': 'Anthropic: Core Views on AI Safety: When, Why, What, and How - EA Forum', 'dateCreated': '2023-03-09', 'author': 'Jonmenaster', 'score': 0.18415069580078125}, {'url': 'https://www.lesswrong.com/posts/xBrpph9knzWdtMWeQ/the-proof-of-doom', 'title': 'The Proof of Doom - | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-3 | 'title': 'The Proof of Doom - LessWrong', 'dateCreated': '2022-03-09', 'author': 'Johnlawrenceaspden', 'score': 0.18159329891204834}, {'url': 'https://intelligence.org/why-ai-safety/', 'title': 'Why AI Safety? - Machine Intelligence Research Institute', 'dateCreated': '2017-03-01', 'author': None, 'score': 0.1814115345478058}]} | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-4 | [{'title': 'Core Views on AI Safety: When, Why, What, and How',
'url': 'https://www.anthropic.com/index/core-views-on-ai-safety',
'author': None,
'date_created': '2023-03-08'},
{'title': 'Extinction Risk from Artificial Intelligence',
'url': 'https://aisafety.wordpress.com/',
'author': None,
'date_created': '2013-10-08'},
{'title': 'The simple picture on AI safety - LessWrong',
'url': 'https://www.lesswrong.com/posts/WhNxG4r774bK32GcH/the-simple-picture-on-ai-safety',
'author': 'Alex Flint',
'date_created': '2018-05-27'},
{'title': 'No Time Like The Present For AI Safety Work',
'url': 'https://slatestarcodex.com/2015/05/29/no-time-like-the-present-for-ai-safety-work/',
'author': None,
'date_created': '2015-05-29'},
{'title': 'So You Want to Save the World - LessWrong',
'url': 'https://www.lesswrong.com/posts/5BJvusxdwNXYQ4L9L/so-you-want-to-save-the-world',
'author': 'Lukeprog',
'date_created': '2012-01-01'},
{'title': 'Planning for AGI and beyond',
'url': 'https://openai.com/blog/planning-for-agi-and-beyond',
'author': 'Authors',
'date_created': '2023-02-24'}, | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-5 | 'date_created': '2023-02-24'},
{'title': 'The Artificial Intelligence Revolution: Part 1 - Wait But Why',
'url': 'https://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html',
'author': 'Tim Urban',
'date_created': '2015-01-22'},
{'title': 'Anthropic: Core Views on AI Safety: When, Why, What, and How - EA Forum',
'url': 'https://forum.effectivealtruism.org/posts/uGDCaPFaPkuxAowmH/anthropic-core-views-on-ai-safety-when-why-what-and-how',
'author': 'Jonmenaster',
'date_created': '2023-03-09'},
{'title': 'The Proof of Doom - LessWrong',
'url': 'https://www.lesswrong.com/posts/xBrpph9knzWdtMWeQ/the-proof-of-doom',
'author': 'Johnlawrenceaspden',
'date_created': '2022-03-09'},
{'title': 'Why AI Safety? - Machine Intelligence Research Institute',
'url': 'https://intelligence.org/why-ai-safety/',
'author': None,
'date_created': '2017-03-01'}]
Use Metaphor as a tool#
Metaphor can be used as a tool that gets URLs that other tools such as browsing tools.
from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit
from langchain.tools.playwright.utils import (
create_async_playwright_browser,# A synchronous browser is available, though it isn't compatible with jupyter.
)
async_browser = create_async_playwright_browser() | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-6 | )
async_browser = create_async_playwright_browser()
toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)
tools = toolkit.get_tools()
tools_by_name = {tool.name: tool for tool in tools}
print(tools_by_name.keys())
navigate_tool = tools_by_name["navigate_browser"]
extract_text = tools_by_name["extract_text"]
from langchain.agents import initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
from langchain.tools import MetaphorSearchResults
llm = ChatOpenAI(model_name="gpt-4", temperature=0.7)
metaphor_tool = MetaphorSearchResults(api_wrapper=search)
agent_chain = initialize_agent([metaphor_tool, extract_text, navigate_tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_chain.run("find me an interesting tweet about AI safety using Metaphor, then tell me the first sentence in the post. Do not finish until able to retrieve the first sentence.")
> Entering new AgentExecutor chain...
Thought: I need to find a tweet about AI safety using Metaphor Search.
Action:
```
{
"action": "Metaphor Search Results JSON",
"action_input": {
"query": "interesting tweet AI safety",
"num_results": 1
}
}
```
{'results': [{'url': 'https://safe.ai/', 'title': 'Center for AI Safety', 'dateCreated': '2022-01-01', 'author': None, 'score': 0.18083244562149048}]}
Observation: [{'title': 'Center for AI Safety', 'url': 'https://safe.ai/', 'author': None, 'date_created': '2022-01-01'}] | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
90dc3b0df7d7-7 | Thought:I need to navigate to the URL provided in the search results to find the tweet.
> Finished chain.
'I need to navigate to the URL provided in the search results to find the tweet.'
previous
IFTTT WebHooks
next
OpenWeatherMap API
Contents
Metaphor Search
Call the API
Use Metaphor as a tool
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/examples/metaphor_search.html |
55f919e871db-0 | .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?") | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_search.html |
55f919e871db-1 | 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..." | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_search.html |
55f919e871db-2 | 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. | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_search.html |
d299b3cbf12e-0 | .ipynb
.pdf
SceneXplain
Contents
Usage in an Agent
SceneXplain#
SceneXplain is an ImageCaptioning service accessible through the SceneXplain Tool.
To use this tool, you’ll need to make an account and fetch your API Token from the website. Then you can instantiate the tool.
import os
os.environ["SCENEX_API_KEY"] = "<YOUR_API_KEY>"
from langchain.agents import load_tools
tools = load_tools(["sceneXplain"])
Or directly instantiate the tool.
from langchain.tools import SceneXplainTool
tool = SceneXplainTool()
Usage in an Agent#
The tool can be used in any LangChain agent as follows:
from langchain.llms import OpenAI
from langchain.agents import initialize_agent
from langchain.memory import ConversationBufferMemory
llm = OpenAI(temperature=0)
memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
tools, llm, memory=memory, agent="conversational-react-description", verbose=True
)
output = agent.run(
input=(
"What is in this image https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png. "
"Is it movie or a game? If it is a movie, what is the name of the movie?"
)
)
print(output)
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Image Explainer
Action Input: https://storage.googleapis.com/causal-diffusion.appspot.com/imagePrompts%2F0rw369i5h9t%2Foriginal.png | https://python.langchain.com/en/latest/modules/agents/tools/examples/sceneXplain.html |
d299b3cbf12e-1 | Observation: In a charmingly whimsical scene, a young girl is seen braving the rain alongside her furry companion, the lovable Totoro. The two are depicted standing on a bustling street corner, where they are sheltered from the rain by a bright yellow umbrella. The girl, dressed in a cheerful yellow frock, holds onto the umbrella with both hands while gazing up at Totoro with an expression of wonder and delight.
Totoro, meanwhile, stands tall and proud beside his young friend, holding his own umbrella aloft to protect them both from the downpour. His furry body is rendered in rich shades of grey and white, while his large ears and wide eyes lend him an endearing charm.
In the background of the scene, a street sign can be seen jutting out from the pavement amidst a flurry of raindrops. A sign with Chinese characters adorns its surface, adding to the sense of cultural diversity and intrigue. Despite the dreary weather, there is an undeniable sense of joy and camaraderie in this heartwarming image.
Thought: Do I need to use a tool? No
AI: This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.
> Finished chain.
This image appears to be a still from the 1988 Japanese animated fantasy film My Neighbor Totoro. The film follows two young girls, Satsuki and Mei, as they explore the countryside and befriend the magical forest spirits, including the titular character Totoro.
previous
Requests
next
Search Tools
Contents
Usage in an Agent
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/modules/agents/tools/examples/sceneXplain.html |
d299b3cbf12e-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/examples/sceneXplain.html |
0e8924b050a0-0 | .ipynb
.pdf
Human as a tool
Contents
Configuring the Input Function
Human as a tool#
Human are AGI so they can certainly be used as a tool to help out AI agent
when it is confused.
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
llm = ChatOpenAI(temperature=0.0)
math_llm = OpenAI(temperature=0.0)
tools = load_tools(
["human", "llm-math"],
llm=math_llm,
)
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
In the above code you can see the tool takes input directly from command line.
You can customize prompt_func and input_func according to your need (as shown below).
agent_chain.run("What's my friend Eric's surname?")
# Answer with 'Zhu'
> Entering new AgentExecutor chain...
I don't know Eric's surname, so I should ask a human for guidance.
Action: Human
Action Input: "What is Eric's surname?"
What is Eric's surname?
Zhu
Observation: Zhu
Thought:I now know Eric's surname is Zhu.
Final Answer: Eric's surname is Zhu.
> Finished chain.
"Eric's surname is Zhu."
Configuring the Input Function#
By default, the HumanInputRun tool uses the python input function to get input from the user.
You can customize the input_func to be anything you’d like.
For instance, if you want to accept multi-line input, you could do the following:
def get_input() -> str: | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
0e8924b050a0-1 | def get_input() -> str:
print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.")
contents = []
while True:
try:
line = input()
except EOFError:
break
if line == "q":
break
contents.append(line)
return "\n".join(contents)
# You can modify the tool when loading
tools = load_tools(
["human", "ddg-search"],
llm=math_llm,
input_func=get_input
)
# Or you can directly instantiate the tool
from langchain.tools import HumanInputRun
tool = HumanInputRun(input_func=get_input)
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
agent_chain.run("I need help attributing a quote")
> Entering new AgentExecutor chain...
I should ask a human for guidance
Action: Human
Action Input: "Can you help me attribute a quote?"
Can you help me attribute a quote?
Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.
vini
vidi
vici
q
Observation: vini
vidi
vici
Thought:I need to provide more context about the quote
Action: Human
Action Input: "The quote is 'Veni, vidi, vici'"
The quote is 'Veni, vidi, vici'
Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.
oh who said it
q
Observation: oh who said it | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
0e8924b050a0-2 | oh who said it
q
Observation: oh who said it
Thought:I can use DuckDuckGo Search to find out who said the quote
Action: DuckDuckGo Search
Action Input: "Who said 'Veni, vidi, vici'?" | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
0e8924b050a0-3 | Observation: Updated on September 06, 2019. "Veni, vidi, vici" is a famous phrase said to have been spoken by the Roman Emperor Julius Caesar (100-44 BCE) in a bit of stylish bragging that impressed many of the writers of his day and beyond. The phrase means roughly "I came, I saw, I conquered" and it could be pronounced approximately Vehnee, Veedee ... Veni, vidi, vici (Classical Latin: [weːniː wiːdiː wiːkiː], Ecclesiastical Latin: [ˈveni ˈvidi ˈvitʃi]; "I came; I saw; I conquered") is a Latin phrase used to refer to a swift, conclusive victory.The phrase is popularly attributed to Julius Caesar who, according to Appian, used the phrase in a letter to the Roman Senate around 47 BC after he had achieved a quick victory in his short ... veni, vidi, vici Latin quotation from Julius Caesar ve· ni, vi· di, vi· ci | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
0e8924b050a0-4 | Caesar ve· ni, vi· di, vi· ci ˌwā-nē ˌwē-dē ˈwē-kē ˌvā-nē ˌvē-dē ˈvē-chē : I came, I saw, I conquered Articles Related to veni, vidi, vici 'In Vino Veritas' and Other Latin... Dictionary Entries Near veni, vidi, vici Venite veni, vidi, vici Venizélos See More Nearby Entries Cite this Entry Style The simplest explanation for why veni, vidi, vici is a popular saying is that it comes from Julius Caesar, one of history's most famous figures, and has a simple, strong meaning: I'm powerful and fast. But it's not just the meaning that makes the phrase so powerful. Caesar was a gifted writer, and the phrase makes use of Latin grammar to ... One of the best known and most frequently quoted Latin expression, veni, vidi, vici may be found hundreds of times throughout the centuries used as an expression of triumph. The words are said to have | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
0e8924b050a0-5 | expression of triumph. The words are said to have been used by Caesar as he was enjoying a triumph. | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
0e8924b050a0-6 | Thought:I now know the final answer
Final Answer: Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".
> Finished chain.
'Julius Caesar said the quote "Veni, vidi, vici" which means "I came, I saw, I conquered".'
previous
HuggingFace Tools
next
IFTTT WebHooks
Contents
Configuring the Input Function
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/examples/human_tools.html |
cdb1eda32e2c-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 |
cdb1eda32e2c-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 |
cdb1eda32e2c-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 |
cdb1eda32e2c-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 |
cdb1eda32e2c-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 |
cdb1eda32e2c-5 | 'link': 'https://finance.yahoo.com/quote/AAPL',
'snippet': 'Find the latest Apple Inc. (AAPL) stock quote, '
'history, news and other vital information to help '
'you with your stock trading and investing.',
'position': 6}],
'peopleAlsoAsk': [{'question': 'What does Apple Inc do?',
'snippet': 'Apple Inc. (Apple) designs, manufactures and '
'markets smartphones, personal\n'
'computers, tablets, wearables and accessories '
'and sells a range of related\n'
'services.',
'title': 'AAPL.O - | Stock Price & Latest News - Reuters',
'link': 'https://www.reuters.com/markets/companies/AAPL.O/'},
{'question': 'What is the full form of Apple Inc?',
'snippet': '(formerly Apple Computer Inc.) is an American '
'computer and consumer electronics\n'
'company famous for creating the iPhone, iPad '
'and Macintosh computers.',
'title': 'What is Apple? An products and history overview '
'- TechTarget',
'link': 'https://www.techtarget.com/whatis/definition/Apple'},
{'question': 'What is Apple Inc iPhone?',
'snippet': 'Apple Inc (Apple) designs, manufactures, and '
'markets smartphones, tablets,\n'
'personal computers, and wearable devices. The '
'company also offers software\n'
'applications and related services, '
'accessories, and third-party digital content.\n'
"Apple's product portfolio includes iPhone, "
'iPad, Mac, iPod, Apple Watch, and\n'
'Apple TV.', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-6 | 'iPad, Mac, iPod, Apple Watch, and\n'
'Apple TV.',
'title': 'Apple Inc Company Profile - Apple Inc Overview - '
'GlobalData',
'link': 'https://www.globaldata.com/company-profile/apple-inc/'},
{'question': 'Who runs Apple Inc?',
'snippet': 'Timothy Donald Cook (born November 1, 1960) is '
'an American business executive\n'
'who has been the chief executive officer of '
'Apple Inc. since 2011. Cook\n'
"previously served as the company's chief "
'operating officer under its co-founder\n'
'Steve Jobs. He is the first CEO of any Fortune '
'500 company who is openly gay.',
'title': 'Tim Cook - Wikipedia',
'link': 'https://en.wikipedia.org/wiki/Tim_Cook'}],
'relatedSearches': [{'query': 'Who invented the iPhone'},
{'query': 'Apple iPhone'},
{'query': 'History of Apple company PDF'},
{'query': 'Apple company history'},
{'query': 'Apple company introduction'},
{'query': 'Apple India'},
{'query': 'What does Apple Inc own'},
{'query': 'Apple Inc After Steve'},
{'query': 'Apple Watch'},
{'query': 'Apple App Store'}]}
Searching for Google Images#
We can also query Google Images using this wrapper. For example:
search = GoogleSerperAPIWrapper(type="images")
results = search.results("Lion")
pprint.pp(results)
{'searchParameters': {'q': 'Lion',
'gl': 'us',
'hl': 'en',
'num': 10, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-7 | 'hl': 'en',
'num': 10,
'type': 'images'},
'images': [{'title': 'Lion - Wikipedia',
'imageUrl': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/73/Lion_waiting_in_Namibia.jpg/1200px-Lion_waiting_in_Namibia.jpg',
'imageWidth': 1200,
'imageHeight': 900,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRye79ROKwjfb6017jr0iu8Bz2E1KKuHg-A4qINJaspyxkZrkw&s',
'thumbnailWidth': 259,
'thumbnailHeight': 194,
'source': 'Wikipedia',
'domain': 'en.wikipedia.org',
'link': 'https://en.wikipedia.org/wiki/Lion',
'position': 1},
{'title': 'Lion | Characteristics, Habitat, & Facts | Britannica',
'imageUrl': 'https://cdn.britannica.com/55/2155-050-604F5A4A/lion.jpg',
'imageWidth': 754,
'imageHeight': 752,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS3fnDub1GSojI0hJ-ZGS8Tv-hkNNloXh98DOwXZoZ_nUs3GWSd&s',
'thumbnailWidth': 225,
'thumbnailHeight': 224,
'source': 'Encyclopedia Britannica',
'domain': 'www.britannica.com', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-8 | 'domain': 'www.britannica.com',
'link': 'https://www.britannica.com/animal/lion',
'position': 2},
{'title': 'African lion, facts and photos',
'imageUrl': 'https://i.natgeofe.com/n/487a0d69-8202-406f-a6a0-939ed3704693/african-lion.JPG',
'imageWidth': 3072,
'imageHeight': 2043,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTPlTarrtDbyTiEm-VI_PML9VtOTVPuDXJ5ybDf_lN11H2mShk&s',
'thumbnailWidth': 275,
'thumbnailHeight': 183,
'source': 'National Geographic',
'domain': 'www.nationalgeographic.com',
'link': 'https://www.nationalgeographic.com/animals/mammals/facts/african-lion',
'position': 3},
{'title': 'Saint Louis Zoo | African Lion',
'imageUrl': 'https://optimise2.assets-servd.host/maniacal-finch/production/animals/african-lion-01-01.jpg?w=1200&auto=compress%2Cformat&fit=crop&dm=1658933674&s=4b63f926a0f524f2087a8e0613282bdb',
'imageWidth': 1200,
'imageHeight': 1200, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-9 | 'imageWidth': 1200,
'imageHeight': 1200,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTlewcJ5SwC7yKup6ByaOjTnAFDeoOiMxyJTQaph2W_I3dnks4&s',
'thumbnailWidth': 225,
'thumbnailHeight': 225,
'source': 'St. Louis Zoo',
'domain': 'stlzoo.org',
'link': 'https://stlzoo.org/animals/mammals/carnivores/lion',
'position': 4},
{'title': 'How to Draw a Realistic Lion like an Artist - Studio '
'Wildlife',
'imageUrl': 'https://studiowildlife.com/wp-content/uploads/2021/10/245528858_183911853822648_6669060845725210519_n.jpg',
'imageWidth': 1431,
'imageHeight': 2048,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTmn5HayVj3wqoBDQacnUtzaDPZzYHSLKUlIEcni6VB8w0mVeA&s',
'thumbnailWidth': 188,
'thumbnailHeight': 269,
'source': 'Studio Wildlife',
'domain': 'studiowildlife.com',
'link': 'https://studiowildlife.com/how-to-draw-a-realistic-lion-like-an-artist/',
'position': 5},
{'title': 'Lion | Characteristics, Habitat, & Facts | Britannica', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-10 | {'title': 'Lion | Characteristics, Habitat, & Facts | Britannica',
'imageUrl': 'https://cdn.britannica.com/29/150929-050-547070A1/lion-Kenya-Masai-Mara-National-Reserve.jpg',
'imageWidth': 1600,
'imageHeight': 1085,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSCqaKY_THr0IBZN8c-2VApnnbuvKmnsWjfrwKoWHFR9w3eN5o&s',
'thumbnailWidth': 273,
'thumbnailHeight': 185,
'source': 'Encyclopedia Britannica',
'domain': 'www.britannica.com',
'link': 'https://www.britannica.com/animal/lion',
'position': 6},
{'title': "Where do lions live? Facts about lions' habitats and "
'other cool facts',
'imageUrl': 'https://www.gannett-cdn.com/-mm-/b2b05a4ab25f4fca0316459e1c7404c537a89702/c=0-0-1365-768/local/-/media/2022/03/16/USATODAY/usatsports/imageForEntry5-ODq.jpg?width=1365&height=768&fit=crop&format=pjpg&auto=webp',
'imageWidth': 1365,
'imageHeight': 768, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-11 | 'imageWidth': 1365,
'imageHeight': 768,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTc_4vCHscgvFvYy3PSrtIOE81kNLAfhDK8F3mfOuotL0kUkbs&s',
'thumbnailWidth': 299,
'thumbnailHeight': 168,
'source': 'USA Today',
'domain': 'www.usatoday.com',
'link': 'https://www.usatoday.com/story/news/2023/01/08/where-do-lions-live-habitat/10927718002/',
'position': 7},
{'title': 'Lion',
'imageUrl': 'https://i.natgeofe.com/k/1d33938b-3d02-4773-91e3-70b113c3b8c7/lion-male-roar_square.jpg',
'imageWidth': 3072,
'imageHeight': 3072,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQqLfnBrBLcTiyTZynHH3FGbBtX2bd1ScwpcuOLnksTyS9-4GM&s',
'thumbnailWidth': 225,
'thumbnailHeight': 225,
'source': 'National Geographic Kids',
'domain': 'kids.nationalgeographic.com',
'link': 'https://kids.nationalgeographic.com/animals/mammals/facts/lion',
'position': 8},
{'title': "Lion | Smithsonian's National Zoo", | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-12 | {'title': "Lion | Smithsonian's National Zoo",
'imageUrl': 'https://nationalzoo.si.edu/sites/default/files/styles/1400_scale/public/animals/exhibit/africanlion-005.jpg?itok=6wA745g_',
'imageWidth': 1400,
'imageHeight': 845,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSgB3z_D4dMEOWJ7lajJk4XaQSL4DdUvIRj4UXZ0YoE5fGuWuo&s',
'thumbnailWidth': 289,
'thumbnailHeight': 174,
'source': "Smithsonian's National Zoo",
'domain': 'nationalzoo.si.edu',
'link': 'https://nationalzoo.si.edu/animals/lion',
'position': 9},
{'title': "Zoo's New Male Lion Explores Habitat for the First Time "
'- Virginia Zoo',
'imageUrl': 'https://virginiazoo.org/wp-content/uploads/2022/04/ZOO_0056-scaled.jpg',
'imageWidth': 2560,
'imageHeight': 2141,
'thumbnailUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTDCG7XvXRCwpe_-Vy5mpvrQpVl5q2qwgnDklQhrJpQzObQGz4&s',
'thumbnailWidth': 246,
'thumbnailHeight': 205,
'source': 'Virginia Zoo',
'domain': 'virginiazoo.org', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-13 | 'source': 'Virginia Zoo',
'domain': 'virginiazoo.org',
'link': 'https://virginiazoo.org/zoos-new-male-lion-explores-habitat-for-thefirst-time/',
'position': 10}]}
Searching for Google News#
We can also query Google News using this wrapper. For example:
search = GoogleSerperAPIWrapper(type="news")
results = search.results("Tesla Inc.")
pprint.pp(results)
{'searchParameters': {'q': 'Tesla Inc.',
'gl': 'us',
'hl': 'en',
'num': 10,
'type': 'news'},
'news': [{'title': 'ISS recommends Tesla investors vote against re-election '
'of Robyn Denholm',
'link': 'https://www.reuters.com/business/autos-transportation/iss-recommends-tesla-investors-vote-against-re-election-robyn-denholm-2023-05-04/',
'snippet': 'Proxy advisory firm ISS on Wednesday recommended Tesla '
'investors vote against re-election of board chair Robyn '
'Denholm, citing "concerns on...',
'date': '5 mins ago',
'source': 'Reuters',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcROdETe_GUyp1e8RHNhaRM8Z_vfxCvdfinZwzL1bT1ZGSYaGTeOojIdBoLevA&s',
'position': 1},
{'title': 'Global companies by market cap: Tesla fell most in April', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-14 | {'title': 'Global companies by market cap: Tesla fell most in April',
'link': 'https://www.reuters.com/markets/global-companies-by-market-cap-tesla-fell-most-april-2023-05-02/',
'snippet': 'Tesla Inc was the biggest loser among top companies by '
'market capitalisation in April, hit by disappointing '
'quarterly earnings after it...',
'date': '1 day ago',
'source': 'Reuters',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ4u4CP8aOdGyRFH6o4PkXi-_eZDeY96vLSag5gDjhKMYf98YBER2cZPbkStQ&s',
'position': 2},
{'title': 'Tesla Wanted an EV Price War. Ford Showed Up.',
'link': 'https://www.bloomberg.com/opinion/articles/2023-05-03/tesla-wanted-an-ev-price-war-ford-showed-up',
'snippet': 'The legacy automaker is paring back the cost of its '
'Mustang Mach-E model after Tesla discounted its '
'competing EVs, portending tighter...',
'date': '6 hours ago',
'source': 'Bloomberg.com',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS_3Eo4VI0H-nTeIbYc5DaQn5ep7YrWnmhx6pv8XddFgNF5zRC9gEpHfDq8yQ&s',
'position': 3}, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-15 | 'position': 3},
{'title': 'Joby Aviation to get investment from Tesla shareholder '
'Baillie Gifford',
'link': 'https://finance.yahoo.com/news/joby-aviation-investment-tesla-shareholder-204450712.html',
'snippet': 'This comes days after Joby clinched a $55 million '
'contract extension to deliver up to nine air taxis to '
'the U.S. Air Force,...',
'date': '4 hours ago',
'source': 'Yahoo Finance',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQO0uVn297LI-xryrPNqJ-apUOulj4ohM-xkN4OfmvMOYh1CPdUEBbYx6hviw&s',
'position': 4},
{'title': 'Tesla resumes U.S. orders for a Model 3 version at lower '
'price, range',
'link': 'https://finance.yahoo.com/news/tesla-resumes-us-orders-model-045736115.html',
'snippet': '(Reuters) -Tesla Inc has resumed taking orders for its '
'Model 3 long-range vehicle in the United States, the '
"company's website showed late on...",
'date': '19 hours ago',
'source': 'Yahoo Finance',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTIZetJ62sQefPfbQ9KKDt6iH7Mc0ylT5t_hpgeeuUkHhJuAx2FOJ4ZTRVDFg&s',
'position': 5}, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-16 | 'position': 5},
{'title': 'The Tesla Model 3 Long Range AWD Is Now Available in the '
'U.S. With 325 Miles of Range',
'link': 'https://www.notateslaapp.com/news/1393/tesla-reopens-orders-for-model-3-long-range-after-months-of-unavailability',
'snippet': 'Tesla has reopened orders for the Model 3 Long Range '
'RWD, which has been unavailable for months due to high '
'demand.',
'date': '7 hours ago',
'source': 'Not a Tesla App',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSecrgxZpRj18xIJY-nDHljyP-A4ejEkswa9eq77qhMNrScnVIqe34uql5U4w&s',
'position': 6},
{'title': 'Tesla Cybertruck alpha prototype spotted at the Fremont '
'factory in new pics and videos',
'link': 'https://www.teslaoracle.com/2023/05/03/tesla-cybertruck-alpha-prototype-interior-and-exterior-spotted-at-the-fremont-factory-in-new-pics-and-videos/',
'snippet': 'A Tesla Cybertruck alpha prototype goes to Fremont, '
'California for another round of testing before going to '
'production later this year (pics...',
'date': '14 hours ago',
'source': 'Tesla Oracle', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-17 | 'date': '14 hours ago',
'source': 'Tesla Oracle',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRO7M5ZLQE-Zo4-_5dv9hNAQZ3wSqfvYCuKqzxHG-M6CgLpwPMMG_ssebdcMg&s',
'position': 7},
{'title': 'Tesla putting facility in new part of country - Austin '
'Business Journal',
'link': 'https://www.bizjournals.com/austin/news/2023/05/02/tesla-leases-building-seattle-area.html',
'snippet': 'Check out what Puget Sound Business Journal has to '
"report about the Austin-based company's real estate "
'footprint in the Pacific Northwest.',
'date': '22 hours ago',
'source': 'The Business Journals',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR9kIEHWz1FcHKDUtGQBS0AjmkqtyuBkQvD8kyIY3kpaPrgYaN7I_H2zoOJsA&s',
'position': 8},
{'title': 'Tesla (TSLA) Resumes Orders for Model 3 Long Range After '
'Backlog',
'link': 'https://www.bloomberg.com/news/articles/2023-05-03/tesla-resumes-orders-for-popular-model-3-long-range-at-47-240',
'snippet': 'Tesla Inc. has resumed taking orders for its Model 3 '
'Long Range edition with a starting price of $47240, ' | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-18 | 'Long Range edition with a starting price of $47240, '
'according to its website.',
'date': '5 hours ago',
'source': 'Bloomberg.com',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTWWIC4VpMTfRvSyqiomODOoLg0xhoBf-Tc1qweKnSuaiTk-Y1wMJZM3jct0w&s',
'position': 9}]}
If you want to only receive news articles published in the last hour, you can do the following:
search = GoogleSerperAPIWrapper(type="news", tbs="qdr:h")
results = search.results("Tesla Inc.")
pprint.pp(results)
{'searchParameters': {'q': 'Tesla Inc.',
'gl': 'us',
'hl': 'en',
'num': 10,
'type': 'news',
'tbs': 'qdr:h'},
'news': [{'title': 'Oklahoma Gov. Stitt sees growing foreign interest in '
'investments in ...',
'link': 'https://www.reuters.com/world/us/oklahoma-gov-stitt-sees-growing-foreign-interest-investments-state-2023-05-04/',
'snippet': 'T)), a battery supplier to electric vehicle maker Tesla '
'Inc (TSLA.O), said on Sunday it is considering building '
'a battery plant in Oklahoma, its third in...',
'date': '53 mins ago',
'source': 'Reuters', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-19 | 'date': '53 mins ago',
'source': 'Reuters',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSSTcsXeenqmEKdiekvUgAmqIPR4nlAmgjTkBqLpza-lLfjX1CwB84MoNVj0Q&s',
'position': 1},
{'title': 'Ryder lanza solución llave en mano para vehículos '
'eléctricos en EU',
'link': 'https://www.tyt.com.mx/nota/ryder-lanza-solucion-llave-en-mano-para-vehiculos-electricos-en-eu',
'snippet': 'Ryder System Inc. presentó RyderElectric+ TM como su '
'nueva solución llave en mano ... Ryder también tiene '
'reservados los semirremolques Tesla y continúa...',
'date': '56 mins ago',
'source': 'Revista Transportes y Turismo',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQJhXTQQtjSUZf9YPM235WQhFU5_d7lEA76zB8DGwZfixcgf1_dhPJyKA1Nbw&s',
'position': 2},
{'title': '"I think people can get by with $999 million," Bernie '
'Sanders tells American Billionaires.', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-20 | 'Sanders tells American Billionaires.',
'link': 'https://thebharatexpressnews.com/i-think-people-can-get-by-with-999-million-bernie-sanders-tells-american-billionaires-heres-how-the-ultra-rich-can-pay-less-income-tax-than-you-legally/',
'snippet': 'The report noted that in 2007 and 2011, Amazon.com Inc. '
'founder Jeff Bezos “did not pay a dime in federal ... '
'If you want to bet on Musk, check out Tesla.',
'date': '11 mins ago',
'source': 'THE BHARAT EXPRESS NEWS',
'imageUrl': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcR_X9qqSwVFBBdos2CK5ky5IWIE3aJPCQeRYR9O1Jz4t-MjaEYBuwK7AU3AJQ&s',
'position': 3}]}
Some examples of the tbs parameter:
qdr:h (past hour)
qdr:d (past day)
qdr:w (past week)
qdr:m (past month)
qdr:y (past year)
You can specify intermediate time periods by adding a number:
qdr:h12 (past 12 hours)
qdr:d3 (past 3 days)
qdr:w2 (past 2 weeks)
qdr:m6 (past 6 months)
qdr:m2 (past 2 years)
For all supported filters simply go to Google Search, search for something, click on “Tools”, add your date filter and check the URL for “tbs=”.
Searching for Google Places#
We can also query Google Places using this wrapper. For example: | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-21 | Searching for Google Places#
We can also query Google Places using this wrapper. For example:
search = GoogleSerperAPIWrapper(type="places")
results = search.results("Italian restaurants in Upper East Side")
pprint.pp(results)
{'searchParameters': {'q': 'Italian restaurants in Upper East Side',
'gl': 'us',
'hl': 'en',
'num': 10,
'type': 'places'},
'places': [{'position': 1,
'title': "L'Osteria",
'address': '1219 Lexington Ave',
'latitude': 40.777154599999996,
'longitude': -73.9571363,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNjU7BWEq_aYQANBCbX52Kb0lDpd_lFIx5onw40=w92-h92-n-k-no',
'rating': 4.7,
'ratingCount': 91,
'category': 'Italian'},
{'position': 2,
'title': "Tony's Di Napoli",
'address': '1081 3rd Ave',
'latitude': 40.7643567,
'longitude': -73.9642373,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNbNv6jZkJ9nyVi60__8c1DQbe_eEbugRAhIYye=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 2265,
'category': 'Italian'},
{'position': 3,
'title': 'Caravaggio', | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-22 | {'position': 3,
'title': 'Caravaggio',
'address': '23 E 74th St',
'latitude': 40.773412799999996,
'longitude': -73.96473379999999,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPDGchokDvppoLfmVEo6X_bWd3Fz0HyxIHTEe9V=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 276,
'category': 'Italian'},
{'position': 4,
'title': 'Luna Rossa',
'address': '347 E 85th St',
'latitude': 40.776593999999996,
'longitude': -73.950351,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNPCpCPuqPAb1Mv6_fOP7cjb8Wu1rbqbk2sMBlh=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 140,
'category': 'Italian'},
{'position': 5,
'title': "Paola's",
'address': '1361 Lexington Ave',
'latitude': 40.7822019,
'longitude': -73.9534096,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPJr2Vcx-B6K-GNQa4koOTffggTePz8TKRTnWi3=w92-h92-n-k-no',
'rating': 4.5, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-23 | 'rating': 4.5,
'ratingCount': 344,
'category': 'Italian'},
{'position': 6,
'title': 'Come Prima',
'address': '903 Madison Ave',
'latitude': 40.772124999999996,
'longitude': -73.965012,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNrX19G0NVdtDyMovCQ-M-m0c_gLmIxrWDQAAbz=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 176,
'category': 'Italian'},
{'position': 7,
'title': 'Botte UES',
'address': '1606 1st Ave.',
'latitude': 40.7750785,
'longitude': -73.9504801,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPPN5GXxfH3NDacBc0Pt3uGAInd9OChS5isz9RF=w92-h92-n-k-no',
'rating': 4.4,
'ratingCount': 152,
'category': 'Italian'},
{'position': 8,
'title': 'Piccola Cucina Uptown',
'address': '106 E 60th St',
'latitude': 40.7632468,
'longitude': -73.9689825, | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-24 | 'longitude': -73.9689825,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipPifIgzOCD5SjgzzqBzGkdZCBp0MQsK5k7M7znn=w92-h92-n-k-no',
'rating': 4.6,
'ratingCount': 941,
'category': 'Italian'},
{'position': 9,
'title': 'Pinocchio Restaurant',
'address': '300 E 92nd St',
'latitude': 40.781453299999995,
'longitude': -73.9486788,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipNtxlIyEEJHtDtFtTR9nB38S8A2VyMu-mVVz72A=w92-h92-n-k-no',
'rating': 4.5,
'ratingCount': 113,
'category': 'Italian'},
{'position': 10,
'title': 'Barbaresco',
'address': '843 Lexington Ave #1',
'latitude': 40.7654332,
'longitude': -73.9656873,
'thumbnailUrl': 'https://lh5.googleusercontent.com/p/AF1QipMb9FbPuXF_r9g5QseOHmReejxSHgSahPMPJ9-8=w92-h92-n-k-no',
'rating': 4.3,
'ratingCount': 122,
'locationHint': 'In The Touraine',
'category': 'Italian'}]}
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Google Search
next
Gradio Tools
Contents | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
cdb1eda32e2c-25 | previous
Google Search
next
Gradio Tools
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
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/examples/google_serper.html |
41bb065829e7-0 | .ipynb
.pdf
ChatGPT Plugins
ChatGPT Plugins#
This example shows how to use ChatGPT Plugins within LangChain abstractions.
Note 1: This currently only works for plugins with no auth.
Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!
from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools import AIPluginTool
tool = AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json")
llm = ChatOpenAI(temperature=0)
tools = load_tools(["requests_all"] )
tools += [tool]
agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_chain.run("what t shirts are available in klarna?")
> Entering new AgentExecutor chain...
I need to check the Klarna Shopping API to see if it has information on available t shirts.
Action: KlarnaProducts
Action Input: None
Observation: Usage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user. | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
41bb065829e7-1 | OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
41bb065829e7-2 | {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}} | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
41bb065829e7-3 | Thought:I need to use the Klarna Shopping API to search for t shirts.
Action: requests_get
Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
41bb065829e7-4 | Observation: {"products":[{"name":"Lacoste Men's Pack of Plain T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai","price":"$26.60","attributes":["Material:Cotton","Target Group:Man","Color:White,Black"]},{"name":"Hanes Men's Ultimate 6pk. Crewneck T-Shirts","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?utm_source=openai","price":"$13.82","attributes":["Material:Cotton","Target Group:Man","Color:White"]},{"name":"Nike Boy's Jordan Stretch T-shirts","url":"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Color:White,Green","Model:Boy","Size (Small-Large):S,XL,L,M"]},{"name":"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?utm_source=openai","price":"$29.95","attributes":["Material:Cotton","Target Group:Man","Color:White,Blue,Black"]},{"name":"adidas Comfort T-shirts Men's | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
41bb065829e7-5 | Comfort T-shirts Men's 3-pack","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?utm_source=openai","price":"$14.99","attributes":["Material:Cotton","Target Group:Man","Color:White,Black","Neckline:Round"]}]} | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
41bb065829e7-6 | Thought:The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.
Final Answer: The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.
> Finished chain.
"The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack."
previous
Bing Search
next
DuckDuckGo Search
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/examples/chatgpt_plugins.html |
b40e113f63c7-0 | .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 test@testing123.com",
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 |
b40e113f63c7-1 | agent.run("Send an email to test@testing123.com 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 |
40cb057d1304-0 | .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 |
6512b113544f-0 | .ipynb
.pdf
ArXiv API Tool
Contents
The ArXiv API Wrapper
ArXiv API Tool#
This notebook goes over how to use the arxiv component.
First, you need to install arxiv python package.
!pip install arxiv
from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
llm = ChatOpenAI(temperature=0.0)
tools = load_tools(
["arxiv"],
)
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
agent_chain.run(
"What's the paper 1605.08386 about?",
)
> Entering new AgentExecutor chain...
I need to use Arxiv to search for the paper.
Action: Arxiv
Action Input: "1605.08386"
Observation: Published: 2016-05-26
Title: Heat-bath random walks with Markov bases
Authors: Caprice Stanley, Tobias Windisch
Summary: Graphs on lattice points are studied whose edges come from a finite set of
allowed moves of arbitrary length. We show that the diameter of these graphs on
fibers of a fixed integer matrix can be bounded from above by a constant. We
then study the mixing behaviour of heat-bath random walks on these graphs. We
also state explicit conditions on the set of moves so that the heat-bath random
walk, a generalization of the Glauber dynamics, is an expander in fixed
dimension.
Thought:The paper is about heat-bath random walks with Markov bases on graphs of lattice points. | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html |
6512b113544f-1 | Thought:The paper is about heat-bath random walks with Markov bases on graphs of lattice points.
Final Answer: The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.
> Finished chain.
'The paper 1605.08386 is about heat-bath random walks with Markov bases on graphs of lattice points.'
The ArXiv API Wrapper#
The tool wraps the API Wrapper. Below, we can explore some of the features it provides.
from langchain.utilities import ArxivAPIWrapper
Run a query to get information about some scientific article/articles. The query text is limited to 300 characters.
It returns these article fields:
Publishing date
Title
Authors
Summary
Next query returns information about one article with arxiv Id equal “1605.08386”.
arxiv = ArxivAPIWrapper()
docs = arxiv.run("1605.08386")
docs
'Published: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.'
Now, we want to get information about one author, Caprice Stanley.
This query returns information about three articles. By default, the query returns information only about three top articles.
docs = arxiv.run("Caprice Stanley")
docs | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html |
6512b113544f-2 | docs = arxiv.run("Caprice Stanley")
docs
'Published: 2017-10-10\nTitle: On Mixing Behavior of a Family of Random Walks Determined by a Linear Recurrence\nAuthors: Caprice Stanley, Seth Sullivant\nSummary: We study random walks on the integers mod $G_n$ that are determined by an\ninteger sequence $\\{ G_n \\}_{n \\geq 1}$ generated by a linear recurrence\nrelation. Fourier analysis provides explicit formulas to compute the\neigenvalues of the transition matrices and we use this to bound the mixing time\nof the random walks.\n\nPublished: 2016-05-26\nTitle: Heat-bath random walks with Markov bases\nAuthors: Caprice Stanley, Tobias Windisch\nSummary: Graphs on lattice points are studied whose edges come from a finite set of\nallowed moves of arbitrary length. We show that the diameter of these graphs on\nfibers of a fixed integer matrix can be bounded from above by a constant. We\nthen study the mixing behaviour of heat-bath random walks on these graphs. We\nalso state explicit conditions on the set of moves so that the heat-bath random\nwalk, a generalization of the Glauber dynamics, is an expander in fixed\ndimension.\n\nPublished: 2003-03-18\nTitle: Calculation of fluxes of charged particles and neutrinos from atmospheric showers\nAuthors: V. Plyaskin\nSummary: The results on the fluxes of charged particles and neutrinos from a\n3-dimensional (3D) simulation of atmospheric showers are presented. An\nagreement of calculated fluxes with data on charged particles from the AMS and\nCAPRICE detectors is demonstrated. Predictions on neutrino fluxes at different\nexperimental sites are compared with results from other calculations.' | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html |
6512b113544f-3 | Now, we are trying to find information about non-existing article. In this case, the response is “No good Arxiv Result was found”
docs = arxiv.run("1605.08386WWW")
docs
'No good Arxiv Result was found'
previous
Apify
next
AWS Lambda API
Contents
The ArXiv API Wrapper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/modules/agents/tools/examples/arxiv.html |
cec7bb3b0cad-0 | .ipynb
.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. | https://python.langchain.com/en/latest/modules/agents/tools/examples/twilio.html |
9f838025c411-0 | .ipynb
.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 nla@zapier.com 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", "") | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-1 | 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 | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-2 | 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": "sreply@svb.com", "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": "sreply@svb.com", "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": "ankush@langchain.dev", "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." | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-3 | 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 | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-4 | 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): | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-5 | 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... | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-6 | overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)
> Entering new SimpleSequentialChain chain...
{"from__name": "Silicon Valley Bridge Bank, N.A.", "from__email": "sreply@svb.com", "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": "sreply@svb.com", "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": "ankush@langchain.dev", "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] | https://python.langchain.com/en/latest/modules/agents/tools/examples/zapier.html |
9f838025c411-7 | 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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