id stringlengths 14 16 | text stringlengths 45 2.05k | source stringlengths 53 111 |
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a1cb1f3c2793-0 | .ipynb
.pdf
Search Tools
Contents
Google Serper API Wrapper
SerpAPI
GoogleSearchAPIWrapper
SearxNG Meta Search Engine
Search Tools#
This notebook shows off usage of various search tools.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
llm = OpenA... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/search_tools.html |
a1cb1f3c2793-1 | Action: Search
Action Input: "weather in Pomfret"
Observation: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 ...
Thought: I now know the current weather in Pomfret.
Final Answer: Partly cloudy skies during... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/search_tools.html |
a1cb1f3c2793-2 | Action: Google Search
Action Input: "weather in Pomfret"
Observation: Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect fo... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/search_tools.html |
a1cb1f3c2793-3 | > Finished AgentExecutor chain.
'Showers early becoming a steady light rain later in the day. Near record high temperatures. High around 60F. Winds SW at 10 to 15 mph. Chance of rain 60%.'
SearxNG Meta Search Engine#
Here we will be using a self hosted SearxNG meta search engine.
tools = load_tools(["searx-search"], se... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/search_tools.html |
a1cb1f3c2793-4 | Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo.
Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Here’s Why We Get More Colds and Flu Wh... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/search_tools.html |
a1cb1f3c2793-5 | Thought: I now know the final answer
Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.
> Finished chain.
'The current weather in Pomfret is mainly cloudy with snow showers around in t... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/search_tools.html |
241d6728892e-0 | .ipynb
.pdf
Adding SharedMemory to an Agent and its Tools
Adding SharedMemory to an Agent and its Tools#
This notebook goes over adding memory to both of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:
Adding memory to a... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-1 | Tool(
name = "Summary",
func=summry_chain.run,
description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary."
)
]
prefix = """Have a conversation with a human, answering the following questions as best you can. ... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-2 | Action: Search
Action Input: "ChatGPT"
Observation: Nov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built ... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-3 | Thought: I now know the final answer.
Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capab... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-4 | Action Input: Who developed ChatGPT
Observation: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. Th... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-5 | Thought: I now know the final answer
Final Answer: ChatGPT was developed by OpenAI.
> Finished chain.
'ChatGPT was developed by OpenAI.'
agent_chain.run(input="Thanks. Summarize the conversation, for my daughter 5 years old.")
> Entering new AgentExecutor chain...
Thought: I need to simplify the conversation for a 5 ye... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-6 | print(agent_chain.memory.buffer)
Human: What is ChatGPT?
AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is a... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-7 | Tool(
name = "Summary",
func=summry_chain.run,
description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary."
)
]
prefix = """Have a conversation with a human, answering the following questions as best you can. ... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-8 | Action: Search
Action Input: "ChatGPT"
Observation: Nov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built ... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-9 | Thought: I now know the final answer.
Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capab... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-10 | Action Input: Who developed ChatGPT
Observation: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. Th... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-11 | Thought: I now know the final answer
Final Answer: ChatGPT was developed by OpenAI.
> Finished chain.
'ChatGPT was developed by OpenAI.'
agent_chain.run(input="Thanks. Summarize the conversation, for my daughter 5 years old.")
> Entering new AgentExecutor chain...
Thought: I need to simplify the conversation for a 5 ye... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
241d6728892e-12 | print(agent_chain.memory.buffer)
Human: What is ChatGPT?
AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is a... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/sharedmemory_for_tools.html |
b5b0408ee424-0 | .ipynb
.pdf
Defining Custom Tools
Contents
Completely New Tools
Tool dataclass
Subclassing the BaseTool class
Using the tool decorator
Modify existing tools
Defining the priorities among Tools
Using tools to return directly
Defining Custom Tools#
When constructing your own agent, you will need to provide it with a li... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-1 | Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
)
]
# 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="zero-shot-re... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-2 | def _run(self, query: str) -> str:
"""Use the tool."""
return search.run(query)
async def _arun(self, query: str) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("BingSearchRun does not support async")
class CustomCalculatorTool(BaseTool):
name = "C... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-3 | > Finished chain.
Observation: Answer: 3.777824273683966
Thought: I now know the final answer
Final Answer: Camila Morrone's age raised to the 0.43 power is 3.777824273683966.
> Finished chain.
"Camila Morrone's age raised to the 0.43 power is 3.777824273683966."
Using the tool decorator#
To make it easier to define cu... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-4 | """Searches the API for the query."""
return "Results"
search_api
Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x1184e0cd0>, func=<function search_api at ... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-5 | 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 3.991298452658078.
> Finished chain.
"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078."
Defining the priorities amo... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-6 | )
]
agent = initialize_agent(tools, OpenAI(temperature=0), agent="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 christm... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
b5b0408ee424-7 | Contents
Completely New Tools
Tool dataclass
Subclassing the BaseTool class
Using the tool decorator
Modify existing tools
Defining the priorities among Tools
Using tools to return directly
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_tools.html |
df831441e784-0 | .ipynb
.pdf
Custom Agent
Contents
Custom LLMChain
Multiple inputs
Custom Agent Class
Custom Agent#
This notebook goes through how to create your own custom agent.
An agent consists of three parts:
- Tools: The tools the agent has available to use.
- LLMChain: The LLMChain that produces the text that is parsed in a ce... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html |
df831441e784-1 | prefix: String to put before the list of tools.
suffix: String to put after the list of tools.
input_variables: List of input variables the final prompt will expect.
For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate.
from langchain.age... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html |
df831441e784-2 | Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lot... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html |
df831441e784-3 | Thought: I now know the final answer
Final Answer: Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!
> Finished chain.
"Arrr, Canada be havin' 38,610,447 scallywags livin' there as of 2023!"
Multiple inputs#
Agents can also work with prompts that require multiple inputs.
prefix = """Answer the follo... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html |
df831441e784-4 | Thought: I now know the final answer.
Final Answer: La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.
> Finished chain.
'La popolazione del Canada nel 2023 è stimata in 38.610.447 persone.'
Custom Agent Class#
Coming soon.
previous
Conversation Agent (for Chat Models)
next
Defining Custom Tools
Conte... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/custom_agent.html |
34a694f827e1-0 | .ipynb
.pdf
Async API for Agent
Contents
Serial vs. Concurrent Execution
Using Tracing with Asynchronous Agents
Async API for Agent#
LangChain provides async support for Agents by leveraging the asyncio library.
Async methods are currently supported for the following Tools: SerpAPIWrapper and LLMMathChain. Async supp... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-1 | ]
def generate_serially():
for q in questions:
llm = OpenAI(temperature=0)
tools = load_tools(["llm-math", "serpapi"], llm=llm)
agent = initialize_agent(
tools, llm, agent="zero-shot-react-description", verbose=True
)
agent.run(q)
s = time.perf_counter()
generate_... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-2 | Action Input: "Olivia Wilde boyfriend"
Observation: Jason Sudeikis
Thought: I need to find out Jason Sudeikis' age
Action: Search
Action Input: "Jason Sudeikis age"
Observation: 47 years
Thought: I need to calculate 47 raised to the 0.23 power
Action: Calculator
Action Input: 47^0.23
Observation: Answer: 2.424278485567... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-3 | Action: Search
Action Input: "US Open women's final 2019 winner"
Observation: Bianca Andreescu defeated Serena Williams in the final, 6–3, 7–5 to win the women's singles tennis title at the 2019 US Open. It was her first major title, and she became the first Canadian, as well as the first player born in the 2000s, to w... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-4 | Thought: I now know the final answer
Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.
> Finished chain.
Serial executed in 65.11 seconds.
async def generate_concurrently():
agents = []
# To make async requests in Tools more efficient, you can pass in your own ai... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-5 | Action: Search
Action Input: "Olivia Wilde boyfriend" I need to find out who Beyonce's husband is and then calculate his age raised to the 0.19 power.
Action: Search
Action Input: "Who is Beyonce's husband?"
Observation: Jay-Z
Thought: I need to find out who won the grand prix and then calculate their age raised to the... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-6 | Action: Search
Action Input: "US Open men's final 2019 winner"
Observation: Rafael Nadal defeated Daniil Medvedev in the final, 7–5, 6–3, 5–7, 4–6, 6–4 to win the men's singles tennis title at the 2019 US Open. It was his fourth US ...
Thought:
Observation: 47 years
Thought: I need to find out Max Verstappen's age
Acti... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-7 | Action: Calculator
Action Input: 36^0.334
Observation: Answer: 2.8603798598506933
Thought: I now know the final answer
Final Answer: Jay-Z is Beyonce's husband and his age raised to the 0.19 power is 2.12624064206896.
> Finished chain.
I now know the final answer
Final Answer: Max Verstappen, 25 years old, raised to t... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-8 | # but you must manually close the client session at the end of your program/event loop
aiosession = ClientSession()
tracer = LangChainTracer()
tracer.load_default_session()
manager = CallbackManager([StdOutCallbackHandler(), tracer])
# Pass the manager into the llm if you want llm calls traced.
llm = OpenAI(temperature... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
34a694f827e1-9 | next
Conversation Agent (for Chat Models)
Contents
Serial vs. Concurrent Execution
Using Tracing with Asynchronous Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/examples/async_agent.html |
17b8a3d5faac-0 | .ipynb
.pdf
Agents and Vectorstores
Contents
Create the Vectorstore
Create the Agent
Use the Agent solely as a router
Multi-Hop vectorstore reasoning
Agents and Vectorstores#
This notebook covers how to combine agents and vectorstores. The use case for this is that you’ve ingested your data into a vectorstore and wan... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-1 | from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://beta.ruff.rs/docs/faq/")
docs = loader.load()
ruff_texts = text_splitter.split_documents(docs)
ruff_db = Chroma.from_documents(ruff_texts, embeddings, collection_name="ruff")
ruff = VectorDBQA.from_chain_type(llm=llm, chain_type="stuff... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-2 | Action: State of Union QA System
Action Input: What did Biden say about Ketanji Brown Jackson in the State of the Union address?
Observation: Biden said that Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.
Thought: I now know the final answer
Final Answe... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-3 | Thought: I now know the final answer
Final Answer: Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quality tools natively, including... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-4 | Tool(
name = "Ruff QA System",
func=ruff.run,
description="useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question.",
return_direct=True
),
]
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=T... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-5 | Action Input: What are the advantages of using ruff over flake8?
Observation: Ruff can be used as a drop-in replacement for Flake8 when used (1) without or with a small number of plugins, (2) alongside Black, and (3) on Python 3 code. It also re-implements some of the most popular Flake8 plugins and related code quali... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-6 | Tool(
name = "Ruff QA System",
func=ruff.run,
description="useful for when you need to answer questions about ruff (a python linter). Input should be a fully formed question, not referencing any obscure pronouns from the conversation before."
),
]
# Construct the agent. We will use the defau... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
17b8a3d5faac-7 | previous
How-To Guides
next
Async API for Agent
Contents
Create the Vectorstore
Create the Agent
Use the Agent solely as a router
Multi-Hop vectorstore reasoning
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/examples/agent_vectorstore.html |
a3e65075799d-0 | .ipynb
.pdf
Intermediate Steps
Intermediate Steps#
In order to get more visibility into what an agent is doing, we can also return intermediate steps. This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples.
from langchain.agents import load_tools
from langchain.agent... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/intermediate_steps.html |
a3e65075799d-1 | # The actual return type is a NamedTuple for the agent action, and then an observation
print(response["intermediate_steps"])
[(AgentAction(tool='Search', tool_input='Leo DiCaprio girlfriend', log=' I should look up who Leo DiCaprio is dating\nAction: Search\nAction Input: "Leo DiCaprio girlfriend"'), 'Camila Morrone'),... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/intermediate_steps.html |
a3e65075799d-2 | ],
"Answer: 3.991298452658078\n"
]
]
previous
Defining Custom Tools
next
Loading from LangChainHub
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/examples/intermediate_steps.html |
6fc992f342a4-0 | .ipynb
.pdf
Conversation Agent (for Chat Models)
Conversation Agent (for Chat Models)#
This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may w... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/chat_conversation_agent.html |
6fc992f342a4-1 | > Entering new AgentExecutor chain...
{
"action": "Final Answer",
"action_input": "Your name is Bob."
}
> Finished chain.
'Your name is Bob.'
agent_chain.run("what are some good dinners to make this week, if i like thai food?")
> Entering new AgentExecutor chain...
{
"action": "Current Search",
"action_... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/chat_conversation_agent.html |
6fc992f342a4-2 | "action_input": "who won the world cup in 1978"
}
```
Observation: The Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world cham... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/chat_conversation_agent.html |
6fc992f342a4-3 | previous
Async API for Agent
next
Custom Agent
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/examples/chat_conversation_agent.html |
16acd0d0d753-0 | .ipynb
.pdf
Loading from LangChainHub
Contents
Loading from LangChainHub
Pinning Dependencies
Loading from LangChainHub#
This notebook covers how to load agents from LangChainHub.
from langchain import OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
llm = OpenAI(temperature=0)
search = Serp... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/load_from_hub.html |
16acd0d0d753-1 | Specific versions of LangChainHub agents can be pinned with the lc@<ref>:// syntax.
self_ask_with_search = initialize_agent(tools, llm, agent_path="lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json", verbose=True)
No `_type` key found, defaulting to `prompt`.
previous
Intermediate Ste... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/load_from_hub.html |
70154fce5887-0 | .ipynb
.pdf
Max Iterations
Max Iterations#
This notebook walks through how to cap an agent at taking a certain number of steps. This can be useful to ensure that they do not go haywire and take too many steps.
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms impo... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/max_iterations.html |
70154fce5887-1 | I should try Jester again
Action: Jester
Action Input: foo
Observation: foo is not a valid tool, try another one.
> Finished chain.
'Agent stopped due to max iterations.'
By default, the early stopping uses method force which just returns that constant string. Alternatively, you could specify method generate which then... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/max_iterations.html |
fddb143421dc-0 | .ipynb
.pdf
Self Ask With Search
Self Ask With Search#
This notebook showcases the Self Ask With Search chain.
from langchain import OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
tools = [
Tool(
name="Intermediate Answer",
... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/self_ask_with_search.html |
587c9e0b66a3-0 | .ipynb
.pdf
MRKL Chat
MRKL Chat#
This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models.
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 t... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl_chat.html |
587c9e0b66a3-1 | mrkl.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
> Entering new AgentExecutor chain...
Thought: The first question requires a search, while the second question requires a calculator.
Action:
```
{
"action": "Search",
"action_input": "Who is Leo DiCaprio's girlfriend?"
... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl_chat.html |
587c9e0b66a3-2 | Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.
Action:
```
{
"action": "Search",
"action_input": "Who recently released an album called 'The Storm Before the Calm'"
}
```
Observation: A... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl_chat.html |
587c9e0b66a3-3 | Thought:I have found the answer to both parts of the question.
Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.
> Finished chain.
"The artist who recently released an album called 'The Storm Before ... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl_chat.html |
562debe2a166-0 | .ipynb
.pdf
ReAct
ReAct#
This notebook showcases using an agent to implement the ReAct logic.
from langchain import OpenAI, Wikipedia
from langchain.agents import initialize_agent, Tool
from langchain.agents.react.base import DocstoreExplorer
docstore=DocstoreExplorer(Wikipedia())
tools = [
Tool(
name="Sear... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/react.html |
562debe2a166-1 | Thought 2: The U.S. Navy admiral David Chanoff collaborated with is William J. Crowe.
Action 2: Search[William J. Crowe]
Observation 2: William James Crowe Jr. (January 2, 1925 – October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/react.html |
d87ba273c9f9-0 | .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 L... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl.html |
d87ba273c9f9-1 | > 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: Camila Morrone
Thought: I need to find out Camila Morrone's age
Action: Search
Action Input: "How ... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl.html |
d87ba273c9f9-2 | 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 ...
Thought: I now need to search the FooBar database for Alanis Morissette's albums
Actio... | https://langchain.readthedocs.io/en/latest/modules/agents/implementations/mrkl.html |
d4eebcb52bb8-0 | .ipynb
.pdf
Vectorstore Agent
Contents
Create the Vectorstores
Initialize Toolkit and Agent
Examples
Multiple Vectorstores
Examples
Vectorstore Agent#
This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources.
Create the Vectorstores#
from langchai... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html |
d4eebcb52bb8-1 | )
vectorstore_info = VectorStoreInfo(
name="state_of_union_address",
description="the most recent state of the Union adress",
vectorstore=state_of_union_store
)
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)
agent_executor = create_vectorstore_agent(
llm=llm,
toolkit=toolkit,
ve... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html |
d4eebcb52bb8-2 | Action Input: What did biden say about ketanji brown jackson
Observation: {"answer": " Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\n", "s... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html |
d4eebcb52bb8-3 | toolkit=router_toolkit,
verbose=True
)
Examples#
agent_executor.run("What did biden say about ketanji brown jackson is the state of the union address?")
> Entering new AgentExecutor chain...
I need to use the state_of_union_address tool to answer this question.
Action: state_of_union_address
Action Input: What did... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html |
d4eebcb52bb8-4 | Thought: I now know the final answer
Final Answer: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb
> Finished chain.
'Ruff is integrated into nbQA, a tool for running... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html |
d4eebcb52bb8-5 | previous
SQL Database Agent
next
MRKL
Contents
Create the Vectorstores
Initialize Toolkit and Agent
Examples
Multiple Vectorstores
Examples
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/vectorstore.html |
238550d9f924-0 | .ipynb
.pdf
OpenAPI Agent
Contents
Initialization
Example: agent capable of analyzing OpenAPI spec and making requests
OpenAPI Agent#
This notebook showcases an agent designed to interact with an OpenAPI spec and make a correct API request based on the information it has gathered from the spec.
In the below example, ... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/openapi.html |
238550d9f924-1 | > Entering new AgentExecutor chain...
Action: json_spec_list_keys
Action Input: data
Observation: ['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']
Thought: I should look at the servers key to see what the base url is
Action: json_spec_list_keys
Action Input: data["servers"][0]
Observation: Val... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/openapi.html |
238550d9f924-2 | Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifi... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/openapi.html |
238550d9f924-3 | Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifi... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/openapi.html |
238550d9f924-4 | Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]["application/json"]
Observation: ['schema']
Thought: I should look at the schema key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/openapi.html |
238550d9f924-5 | > Finished chain.
Observation: The required parameters for a POST request to the /completions endpoint are 'model'.
Thought: I now know the parameters needed to make the request.
Action: requests_post
Action Input: { "url": "https://api.openai.com/v1/completions", "data": { "model": "davinci", "prompt": "tell me a joke... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/openapi.html |
424c3964a7b8-0 | .ipynb
.pdf
Python Agent
Contents
Python Agent
Fibonacci Example
Training neural net
Python Agent#
This notebook showcases an agent designed to write and execute python code to answer a question.
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
fro... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/python.html |
424c3964a7b8-1 | I need to write a neural network in PyTorch and train it on the given data.
Action: Python REPL
Action Input:
import torch
# Define the model
model = torch.nn.Sequential(
torch.nn.Linear(1, 1)
)
# Define the loss
loss_fn = torch.nn.MSELoss()
# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/python.html |
424c3964a7b8-2 | Thought: I now know the final answer
Final Answer: The prediction for x = 5 is 10.0.
> Finished chain.
'The prediction for x = 5 is 10.0.'
previous
Pandas Dataframe Agent
next
SQL Database Agent
Contents
Python Agent
Fibonacci Example
Training neural net
By Harrison Chase
© Copyright 2023, Harrison Chase... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/python.html |
f5da6cbc114f-0 | .ipynb
.pdf
JSON Agent
Contents
Initialization
Example: getting the required POST parameters for a request
JSON Agent#
This notebook showcases an agent designed to interact with large JSON/dict objects. This is useful when you want to answer questions about a JSON blob that’s too large to fit in the context window of... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/json.html |
f5da6cbc114f-1 | Thought: I should look at the paths key to see what endpoints exist
Action: json_spec_list_keys
Action Input: data["paths"]
Observation: ['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/json.html |
f5da6cbc114f-2 | Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"]["content"]
Observation: ['application/json']
Thought: I should look at the application/json key to see what parameters are required
Action: json_spec_list_keys
Action Input: data["paths"]["/completions"]["post"]["requestBody"... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/json.html |
f5da6cbc114f-3 | Initialization
Example: getting the required POST parameters for a request
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/json.html |
3ca9f3fd24b6-0 | .ipynb
.pdf
Pandas Dataframe Agent
Pandas Dataframe Agent#
This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.
NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Pyt... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/pandas.html |
3ca9f3fd24b6-1 | Action: python_repl_ast
Action Input: df['Age'].mean()
Observation: 29.69911764705882
Thought: I can now calculate the square root
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: name 'math' is not defined
Thought: I need to import the math library
Action: python_repl_ast
Action Input: im... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/pandas.html |
49e72c90359a-0 | .ipynb
.pdf
CSV Agent
CSV Agent#
This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.
NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM gene... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/csv.html |
49e72c90359a-1 | Observation: 29.69911764705882
Thought: I can now calculate the square root
Action: python_repl_ast
Action Input: math.sqrt(df['Age'].mean())
Observation: name 'math' is not defined
Thought: I need to import the math library
Action: python_repl_ast
Action Input: import math
Observation: None
Thought: I can now calculat... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/csv.html |
dd6daa9d75f6-0 | .ipynb
.pdf
SQL Database Agent
Contents
Initialization
Example: describing a table
Example: describing a table, recovering from an error
Example: running queries
Recovering from an error
SQL Database Agent#
This notebook showcases an agent designed to interact with a sql databases. The agent builds off of SQLDatabase... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/sql_database.html |
dd6daa9d75f6-1 | Action: schema_sql_db
Action Input: "PlaylistTrack"
Observation:
CREATE TABLE "PlaylistTrack" (
"PlaylistId" INTEGER NOT NULL,
"TrackId" INTEGER NOT NULL,
PRIMARY KEY ("PlaylistId", "TrackId"),
FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
FOREIGN KEY("PlaylistId") REFERENCES "Playlist" ("PlaylistId"... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/sql_database.html |
dd6daa9d75f6-2 | Thought: The table is called PlaylistTrack
Action: schema_sql_db
Action Input: "PlaylistTrack"
Observation:
CREATE TABLE "PlaylistTrack" (
"PlaylistId" INTEGER NOT NULL,
"TrackId" INTEGER NOT NULL,
PRIMARY KEY ("PlaylistId", "TrackId"),
FOREIGN KEY("TrackId") REFERENCES "Track" ("TrackId"),
FOREIGN KEY("Playl... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/sql_database.html |
dd6daa9d75f6-3 | "Address" NVARCHAR(70),
"City" NVARCHAR(40),
"State" NVARCHAR(40),
"Country" NVARCHAR(40),
"PostalCode" NVARCHAR(10),
"Phone" NVARCHAR(24),
"Fax" NVARCHAR(24),
"Email" NVARCHAR(60) NOT NULL,
"SupportRepId" INTEGER,
PRIMARY KEY ("CustomerId"),
FOREIGN KEY("SupportRepId") REFERENCES "Employee" ("Emplo... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/sql_database.html |
dd6daa9d75f6-4 | "BillingCity" NVARCHAR(40),
"BillingState" NVARCHAR(40),
"BillingCountry" NVARCHAR(40),
"BillingPostalCode" NVARCHAR(10),
"Total" NUMERIC(10, 2) NOT NULL,
PRIMARY KEY ("InvoiceId"),
FOREIGN KEY("CustomerId") REFERENCES "Customer" ("CustomerId")
)
SELECT * FROM 'Invoice' LIMIT 3;
InvoiceId CustomerId Invoice... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/sql_database.html |
dd6daa9d75f6-5 | Observation: [('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)]
Th... | https://langchain.readthedocs.io/en/latest/modules/agents/agent_toolkits/sql_database.html |
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