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18c25b58ddd0-0 | .md
.pdf
Weaviate
Contents
Installation and Setup
Wrappers
VectorStore
Weaviate#
This page covers how to use the Weaviate ecosystem within LangChain.
What is Weaviate?
Weaviate in a nutshell:
Weaviate is an open-source database of the type vector search engine.
Weaviate allows you to store JSON documents in a class... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\weaviate.html |
18c25b58ddd0-1 | To import this vectorstore:
from langchain.vectorstores import Weaviate
For a more detailed walkthrough of the Weaviate wrapper, see this notebook
previous
Weights & Biases
next
Wolfram Alpha Wrapper
Contents
Installation and Setup
Wrappers
VectorStore
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\weaviate.html |
309ed9406af0-0 | .md
.pdf
Wolfram Alpha Wrapper
Contents
Installation and Setup
Wrappers
Utility
Tool
Wolfram Alpha Wrapper#
This page covers how to use the Wolfram Alpha API within LangChain.
It is broken into two parts: installation and setup, and then references to specific Wolfram Alpha wrappers.
Installation and Setup#
Install r... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\wolfram_alpha.html |
0c4b349dcab5-0 | .md
.pdf
Writer
Contents
Installation and Setup
Wrappers
LLM
Writer#
This page covers how to use the Writer ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
Installation and Setup#
Get an Writer api key and set it as an environment varia... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\writer.html |
ebdd10ef60df-0 | .md
.pdf
Yeager.ai
Contents
What is Yeager.ai?
yAgents
How to use?
Creating and Executing Tools with yAgents
Yeager.ai#
This page covers how to use Yeager.ai to generate LangChain tools and agents.
What is Yeager.ai?#
Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools.
It featu... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\yeagerai.html |
ebdd10ef60df-1 | create a tool that returns the n-th prime number
Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example:
load the tool that you just created it into your toolkit
Execute the tool: To run a tool or agent, simply provide a command to yAgents that include... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\yeagerai.html |
6b2895e82441-0 | .md
.pdf
Zilliz
Contents
Installation and Setup
Wrappers
VectorStore
Zilliz#
This page covers how to use the Zilliz Cloud ecosystem within LangChain.
Zilliz uses the Milvus integration.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
Installation and Setup#
Instal... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\ecosystem\zilliz.html |
fc8e3872b86e-0 | .md
.pdf
Quickstart Guide
Contents
Installation
Environment Setup
Building a Language Model Application: LLMs
LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add S... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-1 | The most basic building block of LangChain is calling an LLM on some input.
Let’s walk through a simple example of how to do this.
For this purpose, let’s pretend we are building a service that generates a company name based on what the company makes.
In order to do this, we first need to import the LLM wrapper.
from l... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-2 | template="What is a good name for a company that makes {product}?",
)
Let’s now see how this works! We can call the .format method to format it.
print(prompt.format(product="colorful socks"))
What is a good name for a company that makes colorful socks?
For more details, check out the getting started guide for prompts.
... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-3 | There we go! There’s the first chain - an LLM Chain.
This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains.
For more details, check out the getting started guide for chains.
Agents: Dynamically Call Chains Based on User Input#
So far the cha... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-4 | Now we can get started!
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
llm = OpenAI(temperature=0)
# Next, let's load some tools... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-5 | > Finished chain.
Memory: Add State to Chains and Agents#
So far, all the chains and agents we’ve gone through have been stateless. But often, you may want a chain or agent to have some concept of “memory” so that it may remember information about its previous interactions. The clearest and simple example of this is wh... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-6 | print(output)
> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Huma... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-7 | chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
You can also pass in multiple messages for OpenAI’s gpt-3.5-turbo and gpt-4 models.
messages = [
SystemMessage(content="You are a helpful assistant t... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-8 | result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
Chat Prompt Templates#
Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s f... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-9 | ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-10 | # Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
> Entering new AgentExecutor chain...
Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
"action": "Search",
"a... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-11 | ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
prompt = ChatPromptTemplate.from_messages([
SystemMessag... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
fc8e3872b86e-12 | Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add State to Chains and Agents
Building a Language Model Application: Chat Models
Get Message Completions from a Chat Model
Chat Prompt Templates
Chains with Chat Models
Agents with Chat Models
Memory: A... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\getting_started\getting_started.html |
995c46011f89-0 | .rst
.pdf
Agents
Contents
Go Deeper
Agents#
Note
Conceptual Guide
Some applications will require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user’s input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents.html |
7efb230e3e3b-0 | .rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\chains.html |
727d35daf162-0 | .rst
.pdf
Indexes
Contents
Go Deeper
Indexes#
Note
Conceptual Guide
Indexes refer to ways to structure documents so that LLMs can best interact with them.
This module contains utility functions for working with documents, different types of indexes, and then examples for using those indexes in chains.
The most common... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\indexes.html |
727d35daf162-1 | previous
Structured Output Parser
next
Getting Started
Contents
Go Deeper
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\indexes.html |
d6bdd54a4627-0 | .rst
.pdf
Memory
Memory#
Note
Conceptual Guide
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a sh... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\memory.html |
36e19ff4f7d2-0 | .rst
.pdf
Models
Contents
Go Deeper
Models#
Note
Conceptual Guide
This section of the documentation deals with different types of models that are used in LangChain.
On this page we will go over the model types at a high level,
but we have individual pages for each model type.
The pages contain more detailed “how-to” ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\models.html |
2101c0723b7d-0 | .rst
.pdf
Prompts
Contents
Go Deeper
Prompts#
Note
Conceptual Guide
The new way of programming models is through prompts.
A “prompt” refers to the input to the model.
This input is rarely hard coded, but rather is often constructed from multiple components.
A PromptTemplate is responsible for the construction of this... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\prompts.html |
cd3b30864bd3-0 | .rst
.pdf
Agents
Agents#
Note
Conceptual Guide
In this part of the documentation we cover the different types of agents, disregarding which specific tools they are used with.
For a high level overview of the different types of agents, see the below documentation.
Agent Types
For documentation on how to create a custom ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents.html |
85b22f1e66ba-0 | .rst
.pdf
Agent Executors
Agent Executors#
Note
Conceptual Guide
Agent executors take an agent and tools and use the agent to decide which tools to call and in what order.
In this part of the documentation we cover other related functionality to agent executors
How to combine agents and vectorstores
How to use the asyn... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors.html |
8e00f242ac1b-0 | .ipynb
.pdf
Getting Started
Getting Started#
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning to the user.
When used correctly agents can be extremely powerful. The purpose of this notebook is to show you how to easily us... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\getting_started.html |
8e00f242ac1b-1 | agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
Now let’s test it out!
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 who Leo DiCaprio's girlfriend is and then calc... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\getting_started.html |
379e7c088b32-0 | .rst
.pdf
Toolkits
Toolkits#
Note
Conceptual Guide
This section of documentation covers agents with toolkits - eg an agent applied to a particular use case.
See below for a full list of agent toolkits
CSV Agent
Jira
JSON Agent
OpenAPI agents
Natural Language APIs
Pandas Dataframe Agent
PowerBI Dataset Agent
Python Agen... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\toolkits.html |
52819952003b-0 | .rst
.pdf
Tools
Tools#
Note
Conceptual Guide
Tools are ways that an agent can use to interact with the outside world.
For an overview of what a tool is, how to use them, and a full list of examples, please see the getting started documentation
Getting Started
Next, we have some examples of customizing and generically w... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\tools.html |
276e1e245e53-0 | .md
.pdf
Agent Types
Contents
zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
Agent Types#
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
Here a... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\agent_types.html |
276e1e245e53-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\agent_types.html |
efeb7992d618-0 | .ipynb
.pdf
Custom Agent
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.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create a custom agent.
from lan... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent.html |
efeb7992d618-1 | Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
return AgentAction(tool="Search", tool_input=kwargs["input"], log="")
agent = FakeAgent()... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent.html |
b3a81c490fcf-0 | .ipynb
.pdf
Custom Agent with Tool Retrieval
Contents
Set up environment
Set up tools
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
Custom Agent with Tool Retrieval#
This notebook builds off of this notebook and assumes familiarity with how agents work.
The novel ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-1 | return "foo"
fake_tools = [
Tool(
name=f"foo-{i}",
func=fake_func,
description=f"a silly function that you can use to get more information about the number {i}"
)
for i in range(99)
]
ALL_TOOLS = [search_tool] + fake_tools
Tool Retriever#
We will use a vectorstore to create embedd... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-2 | get_tools("whats the weather?")
[Tool(name='Search', description='useful for when you need to answer questions about current events', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(sea... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-3 | get_tools("whats the number 13?")
[Tool(name='foo-13', description='a silly function that you can use to get more information about the number 13', return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x114b28a90>, func=<function fake_func at 0x15e5bd1f0>, cor... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-4 | {tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can r... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-5 | # Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools_getter=get_tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-6 | action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
output_parser = CustomOutputParser()
Set up LLM, stop sequence, and the agent#
Also the same as the previous notebook
llm = OpenAI(temperature... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
b3a81c490fcf-7 | > Finished chain.
"'Arg, 'tis mostly cloudy skies early, then partly cloudy in the afternoon. High near 60F. ENE winds shiftin' to W at 10 to 15 mph. Humidity71%. UV Index6 of 10."
previous
Custom MultiAction Agent
next
Conversation Agent (for Chat Models)
Contents
Set up environment
Set up tools
Tool Retriever
Pro... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_agent_with_tool_retrieval.html |
1385c4d17401-0 | .ipynb
.pdf
Custom LLM Agent
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Adding Memory
Custom LLM Agent#
This notebook goes through how to create your own custom LLM agent.
An LLM agent consists of three parts:
PromptTemplate... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
1385c4d17401-1 | from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, SerpAPIWrapper, LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish
import re
Set up tool#
Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
1385c4d17401-2 | Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observat... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
1385c4d17401-3 | class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `outp... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
1385c4d17401-4 | Set up the Agent#
We can now combine everything to set up our agent
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
1385c4d17401-5 | {tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can r... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
1385c4d17401-6 | Thought: I need to find out the population of Canada in 2023
Action: Search
Action Input: Population of Canada in 2023
Observation:The current population of Canada is 38,658,314 as of Wednesday, April 12, 2023, based on Worldometer elaboration of the latest United Nations data. I now know the final answer
Final Answer:... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_agent.html |
eaca291c8c02-0 | .ipynb
.pdf
Custom LLM Agent (with a ChatModel)
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up the Agent
Use the Agent
Custom LLM Agent (with a ChatModel)#
This notebook goes through how to create your own custom agent based on a chat model.
An LLM cha... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_chat_agent.html |
eaca291c8c02-1 | Set up environment#
Do necessary imports, etc.
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import BaseChatPromptTemplate
from langchain import SerpAPIWrapper, LLMChain
from langchain.chat_models import ChatOpenAI
from typing import List, Union
from la... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_chat_agent.html |
eaca291c8c02-2 | ... (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 lots of "Arg"s
Question: {input}
{agent_scratchpad}"""
# Set up a prompt templa... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_chat_agent.html |
eaca291c8c02-3 | input_variables=["input", "intermediate_steps"]
)
Output Parser#
The output parser is responsible for parsing the LLM output into AgentAction and AgentFinish. This usually depends heavily on the prompt used.
This is where you can change the parsing to do retries, handle whitespace, etc
class CustomOutputParser(AgentOut... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_chat_agent.html |
eaca291c8c02-4 | This is important because it tells the LLM when to stop generation.
This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an Observation (otherwise, the LLM may hallucinate an observation for you).
Set up the Agent#
We can ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_chat_agent.html |
eaca291c8c02-5 | Final Answer: Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.
> Finished chain.
'Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.'
previous
Custom LLM Agent
next
Custom MRKL Agent
Contents
Set up environment
Set up tool
Prompt Template
Output Parser
Set up LLM
Define the stop sequence
Set up th... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_llm_chat_agent.html |
f5c9eef120e4-0 | .ipynb
.pdf
Custom MRKL Agent
Contents
Custom LLMChain
Multiple inputs
Custom MRKL Agent#
This notebook goes through how to create your own custom MRKL agent.
A MRKL 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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_mrkl_agent.html |
f5c9eef120e4-1 | 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.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, SerpAPIWrapper, LLM... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_mrkl_agent.html |
f5c9eef120e4-2 | 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 lots of "Args"
Question: {input}
{agent_scratchpad}
Note that we are able to feed agents a self-defined prompt template, i.e. not restricted to the p... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_mrkl_agent.html |
f5c9eef120e4-3 | Multiple inputs#
Agents can also work with prompts that require multiple inputs.
prefix = """Answer the following questions as best you can. You have access to the following tools:"""
suffix = """When answering, you MUST speak in the following language: {language}.
Question: {input}
{agent_scratchpad}"""
prompt = ZeroS... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_mrkl_agent.html |
f5c9eef120e4-4 | Thought: I now know the final answer.
Final Answer: La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023, dopo un record di crescita demografica di 1.050.110 persone dal 1° gennaio 2022 al 1° gennaio 2023.
> Finished chain.
'La popolazione del Canada è stata stimata a 39.566.248 il 1° gennaio 2023,... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_mrkl_agent.html |
05fa29a56eee-0 | .ipynb
.pdf
Custom MultiAction Agent
Custom MultiAction 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.
- The agent class itself: this decides which action to take.
In this notebook we walk through how to create ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_multi_action_agent.html |
05fa29a56eee-1 | """
if len(intermediate_steps) == 0:
return [
AgentAction(tool="Search", tool_input="foo", log=""),
AgentAction(tool="RandomWord", tool_input="foo", log=""),
]
else:
return AgentFinish(return_values={"output": "bar"}, log="")
async ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_multi_action_agent.html |
05fa29a56eee-2 | foo
> Finished chain.
'bar'
previous
Custom MRKL Agent
next
Custom Agent with Tool Retrieval
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\custom_multi_action_agent.html |
3b50419d0eb8-0 | .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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\chat_conversation_agent.html |
3b50419d0eb8-1 | agent_chain.run(input="hi, i am bob")
> Entering new AgentExecutor chain...
WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fab40d0>: Failed to establish a new ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\chat_conversation_agent.html |
3b50419d0eb8-2 | Thought:
WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x13fae8be0>: Failed to establish a new connection: [Errno 61] Connection refused'))
{
"action": "Final... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\chat_conversation_agent.html |
3b50419d0eb8-3 | }
```
> Finished chain.
"The last letter in your name is 'b', and the winner of the 1978 World Cup was the Argentina national football team."
agent_chain.run(input="whats the weather like in pomfret?")
> Entering new AgentExecutor chain...
{
"action": "Current Search",
"action_input": "weather in pomfret"
}
Obs... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\chat_conversation_agent.html |
3b50419d0eb8-4 | }
> Finished chain.
'The weather in Pomfret, CT for the next 10 days is as follows: Sun 16. 64° · 50°. 24% · NE 7 mph ; Mon 17. 58° · 45°. 70% · ESE 8 mph ; Tue 18. 57° · 37°. 8% · WSW 15 mph.'
previous
Custom Agent with Tool Retrieval
next
Conversation Agent
By Harrison Chase
© Copyright 2023, Harrison Chas... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\chat_conversation_agent.html |
7a10200590a5-0 | .ipynb
.pdf
Conversation Agent
Conversation Agent#
This notebook walks through using an agent optimized for conversation. 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 want the agent to be able to chat with the user as well... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\conversational_agent.html |
7a10200590a5-1 | AI: 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...
Thought: Do I need to use a tool? Yes
Action: Current Search
Action Input: Thai food dinner recipes
Observation: 59 easy Thai recipes fo... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\conversational_agent.html |
7a10200590a5-2 | > Finished chain.
'The last letter in your name is "b" and the winner of the 1978 World Cup was the Argentina national football team.'
agent_chain.run(input="whats the current temperature in pomfret?")
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Current Search
Action Input: Curre... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\conversational_agent.html |
c1548d86aaed-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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl.html |
c1548d86aaed-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 spott... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl.html |
c1548d86aaed-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 b... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl.html |
c1548d86aaed-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 album... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl.html |
532a184a8860-0 | .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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl_chat.html |
532a184a8860-1 | 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": "Leo DiCaprio girlfriend"
}
```
Obse... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl_chat.html |
532a184a8860-2 | 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...
Question: What is the full name of the artist who recently released an alb... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl_chat.html |
532a184a8860-3 | sample_rows = connection.execute(command)
SELECT "Title" FROM "Album" WHERE "ArtistId" IN (SELECT "ArtistId" FROM "Artist" WHERE "Name" = 'Alanis Morissette') LIMIT 5;
SQLResult: [('Jagged Little Pill',)]
Answer: Alanis Morissette has the album Jagged Little Pill in the database.
> Finished chain.
Observation: Alanis... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\mrkl_chat.html |
da3c9f94f5d8-0 | .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 import AgentType
from langchain.agents.react.base import DocstoreExplorer
docstore=DocstoreExplorer(Wikipedia())... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\react.html |
da3c9f94f5d8-1 | Action: Search[David Chanoff]
Observation: David Chanoff is a noted author of non-fiction work. His work has typically involved collaborations with the principal protagonist of the work concerned. His collaborators have included; Augustus A. White, Joycelyn Elders, Đoàn Văn Toại, William J. Crowe, Ariel Sharon, Kenneth... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\react.html |
1dd0160413f8-0 | .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
from langchain.agents import AgentType
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
tools = [
Tool(... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agents\examples\self_ask_with_search.html |
c1a7943a7dc3-0 | .ipynb
.pdf
How to combine agents and vectorstores
Contents
Create the Vectorstore
Create the Agent
Use the Agent solely as a router
Multi-Hop vectorstore reasoning
How to combine agents and vectorstores#
This notebook covers how to combine agents and vectorstores. The use case for this is that you’ve ingested your d... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-1 | texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings, collection_name="state-of-union")
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
state_of_union = RetrievalQA.from_chain_type(llm=llm... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-2 | ),
]
# 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("What did biden say about ketanji brown jackson is the state of the union address?")
> Entering n... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-3 | 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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-4 | Notice that in the above examples the agent did some extra work after querying the RetrievalQAChain. You can avoid that and just return the result directly.
tools = [
Tool(
name = "State of Union QA System",
func=state_of_union.run,
description="useful for when you need to answer questions a... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-5 | Action: Ruff QA System
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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-6 | 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 default agent type ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
c1a7943a7dc3-7 | previous
Agent Executors
next
How to use the async API for Agents
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 Apr 25, 2023. | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\agent_vectorstore.html |
b27349a0cc1d-0 | .ipynb
.pdf
How to use the async API for Agents
Contents
Serial vs. Concurrent Execution
Using Tracing with Asynchronous Agents
How to use the async API for Agents#
LangChain provides async support for Agents by leveraging the asyncio library.
Async methods are currently supported for the following Tools: SerpAPIWrap... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
b27349a0cc1d-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=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run(q)
s = time.perf_counter()
g... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
b27349a0cc1d-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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
b27349a0cc1d-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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
b27349a0cc1d-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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
b27349a0cc1d-5 | > Entering new AgentExecutor chain...
> Entering new AgentExecutor chain...
I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.
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 ... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
b27349a0cc1d-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... | rtdocs\python.langchain.com\langchain.readthedocs.io\en\latest\modules\agents\agent_executors\examples\async_agent.html |
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