id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
85fc73b1b01f-1 | Current conversation:
Human: Hi there!
AI:
> Finished chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation betw... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
85fc73b1b01f-2 | Human: Tell me about yourself.
AI:
> Finished chain.
" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
And tha... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
19c4fa357ff4-0 | .ipynb
.pdf
ConversationSummaryMemory
Contents
Initializing with messages
Using in a chain
ConversationSummaryMemory#
Now let’s take a look at using a slightly more complex type of memory - ConversationSummaryMemory. This type of memory creates a summary of the conversation over time. This can be useful for condensin... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
19c4fa357ff4-1 | history.add_user_message("hi")
history.add_ai_message("hi there!")
memory = ConversationSummaryMemory.from_messages(llm=OpenAI(temperature=0), chat_memory=history, return_messages=True)
memory.buffer
'\nThe human greets the AI, to which the AI responds with a friendly greeting.'
Using in a chain#
Let’s walk through an ... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
19c4fa357ff4-2 | Human: Tell me more about it!
AI:
> Finished chain.
" Sure! The customer is having trouble with their computer not connecting to the internet. I'm helping them troubleshoot the issue and figure out what the problem is. So far, we've tried resetting the router and checking the network settings, but the issue still persi... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
dbf1a3957575-0 | .ipynb
.pdf
Momento
Momento#
This notebook goes over how to use Momento Cache to store chat message history using the MomentoChatMessageHistory class. See the Momento docs for more detail on how to get set up with Momento.
Note that, by default we will create a cache if one with the given name doesn’t already exist.
Yo... | https://python.langchain.com/en/latest/modules/memory/examples/momento_chat_message_history.html |
da3eaf1989ba-0 | .ipynb
.pdf
Zep Memory
Contents
REACT Agent Chat Message History Example
Initialize the Zep Chat Message History Class and initialize the Agent
Add some history data
Run the agent
Inspect the Zep memory
Vector search over the Zep memory
Zep Memory#
REACT Agent Chat Message History Example#
This notebook demonstrates ... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-1 | session_id = str(uuid4()) # This is a unique identifier for the user
# Load your OpenAI key from a .env file
from dotenv import load_dotenv
load_dotenv()
True
Initialize the Zep Chat Message History Class and initialize the Agent#
ddg = DuckDuckGoSearchRun()
tools = [ddg]
# Set up Zep Chat History
zep_chat_history = Z... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-2 | "The most well-known adaptation of Octavia Butler's work is the FX series"
" Kindred, based on her novel of the same name."
),
},
{"role": "human", "content": "Who were her contemporaries?"},
{
"role": "ai",
"content": (
"Octavia Butler's contemporaries includ... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-3 | ),
},
]
for msg in test_history:
zep_chat_history.append(
HumanMessage(content=msg["content"])
if msg["role"] == "human"
else AIMessage(content=msg["content"])
)
Run the agent#
Doing so will automatically add the input and response to the Zep memory.
agent_chain.run(
input="WWhat... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-4 | Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.
{'role': 'human', 'content': 'What awards did she win?', 'uuid': '9fa75c3c-edae-41e3-b9bc-9fcf16b523c9', 'created_at': '2023-05-25T15:09:41.91662Z', 'token_count': 8}
{'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur F... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-5 | {'role': 'human', 'content': "Write a short synopsis of Butler's book, Parable of the Sower. What is it about?", 'uuid': '5678d056-7f05-4e70-b8e5-f85efa56db01', 'created_at': '2023-05-25T15:09:41.938974Z', 'token_count': 23}
{'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, p... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-6 | {'role': 'ai', 'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-7 | {'uuid': '52cfe3e8-b800-4dd8-a7dd-8e9e4764dfc8', 'created_at': '2023-05-25T15:09:41.913856Z', 'role': 'ai', 'content': "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.", 'token_count': 27} 0.852352466457884
{'uuid': 'd40da612-0867-4a43-92ec-778b86490a39', 'created_at': '20... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-8 | {'uuid': '862107de-8f6f-43c0-91fa-4441f01b2b3a', 'created_at': '2023-05-25T15:09:41.898149Z', 'role': 'human', 'content': 'Which books of hers were made into movies?', 'token_count': 11} 0.7954322970428519
{'uuid': '97164506-90fe-4c71-9539-69ebcd1d90a2', 'created_at': '2023-05-25T15:09:41.90887Z', 'role': 'human', 'con... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
da3eaf1989ba-9 | previous
Redis Chat Message History
next
Indexes
Contents
REACT Agent Chat Message History Example
Initialize the Zep Chat Message History Class and initialize the Agent
Add some history data
Run the agent
Inspect the Zep memory
Vector search over the Zep memory
By Harrison Chase
© Copyright 2023, Harris... | https://python.langchain.com/en/latest/modules/memory/examples/zep_memory.html |
e5cc100057d4-0 | .ipynb
.pdf
How to add Memory to an Agent
How to add Memory to an Agent#
This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:
Adding memory to an LLM Chain
Custom Agents
In order to add a memory to a... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-1 | )
memory = ConversationBufferMemory(memory_key="chat_history")
We can now construct the LLMChain, with the Memory object, and then create the agent.
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-2 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-3 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered corr... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-4 | Action: Search
Action Input: National Anthem of Canada
Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-5 | Thought: I now know the final answer.
Final Answer: The national anthem of Canada is called "O Canada".
> Finished AgentExecutor chain.
'The national anthem of Canada is called "O Canada".'
We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name o... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-6 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-7 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
agent_without_memory.run("what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I should look up the an... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-8 | Action: Search
Action Input: national anthem of [country]
Observation: Most nation states have an anthem, defined as "a song, as of praise, devotion, or patriotism"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language o... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
e5cc100057d4-9 | Thought: I now know the final answer
Final Answer: The national anthem of [country] is [name of anthem].
> Finished AgentExecutor chain.
'The national anthem of [country] is [name of anthem].'
previous
How to add memory to a Multi-Input Chain
next
Adding Message Memory backed by a database to an Agent
By Harrison Chase... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory.html |
ad8c515acdf3-0 | .ipynb
.pdf
Postgres Chat Message History
Postgres Chat Message History#
This notebook goes over how to use Postgres to store chat message history.
from langchain.memory import PostgresChatMessageHistory
history = PostgresChatMessageHistory(connection_string="postgresql://postgres:mypassword@localhost/chat_history", se... | https://python.langchain.com/en/latest/modules/memory/examples/postgres_chat_message_history.html |
94682ae5082a-0 | .ipynb
.pdf
Cassandra Chat Message History
Cassandra Chat Message History#
This notebook goes over how to use Cassandra to store chat message history.
Cassandra is a distributed database that is well suited for storing large amounts of data.
It is a good choice for storing chat message history because it is easy to sca... | https://python.langchain.com/en/latest/modules/memory/examples/cassandra_chat_message_history.html |
0d37ecf6ca83-0 | .ipynb
.pdf
How to create a custom Memory class
How to create a custom Memory class#
Although there are a few predefined types of memory in LangChain, it is highly possible you will want to add your own type of memory that is optimal for your application. This notebook covers how to do that.
For this notebook, we will ... | https://python.langchain.com/en/latest/modules/memory/examples/custom_memory.html |
0d37ecf6ca83-1 | """Define the variables we are providing to the prompt."""
return [self.memory_key]
def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
"""Load the memory variables, in this case the entity key."""
# Get the input text and run through spacy
doc = nlp(inputs[lis... | https://python.langchain.com/en/latest/modules/memory/examples/custom_memory.html |
0d37ecf6ca83-2 | {entities}
Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(
input_variables=["entities", "input"], template=template
)
And now we put it all together!
llm = OpenAI(temperature=0)
conversation = ConversationChain(llm=llm, prompt=prompt, verbose=True, memory=SpacyEntityMemory())
In the first example, with... | https://python.langchain.com/en/latest/modules/memory/examples/custom_memory.html |
0d37ecf6ca83-3 | Relevant entity information:
Harrison likes machine learning
Conversation:
Human: What do you think Harrison's favorite subject in college was?
AI:
> Finished ConversationChain chain.
' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in... | https://python.langchain.com/en/latest/modules/memory/examples/custom_memory.html |
94b96bb1b101-0 | .ipynb
.pdf
How to use multiple memory classes in the same chain
How to use multiple memory classes in the same chain#
It is also possible to use multiple memory classes in the same chain. To combine multiple memory classes, we can initialize the CombinedMemory class, and then use that.
from langchain.llms import OpenA... | https://python.langchain.com/en/latest/modules/memory/examples/multiple_memory.html |
94b96bb1b101-1 | Summary of conversation:
Current conversation:
Human: Hi!
AI:
> Finished chain.
' Hi there! How can I help you?'
conversation.run("Can you tell me a joke?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and prov... | https://python.langchain.com/en/latest/modules/memory/examples/multiple_memory.html |
39bf7d3b2771-0 | .ipynb
.pdf
Motörhead Memory
Contents
Setup
Motörhead Memory#
Motörhead is a memory server implemented in Rust. It automatically handles incremental summarization in the background and allows for stateless applications.
Setup#
See instructions at Motörhead for running the server locally.
from langchain.memory.motorhe... | https://python.langchain.com/en/latest/modules/memory/examples/motorhead_memory.html |
39bf7d3b2771-1 | Human: whats my name?
AI:
> Finished chain.
' You said your name is Bob. Is that correct?'
llm_chain.run("whats for dinner?")
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.
Human: hi im bob
AI: Hi Bob, nice to meet you! How are you doing today?
Human: wh... | https://python.langchain.com/en/latest/modules/memory/examples/motorhead_memory.html |
d0c696d7e20f-0 | .ipynb
.pdf
Redis Chat Message History
Redis Chat Message History#
This notebook goes over how to use Redis to store chat message history.
from langchain.memory import RedisChatMessageHistory
history = RedisChatMessageHistory("foo")
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
[A... | https://python.langchain.com/en/latest/modules/memory/examples/redis_chat_message_history.html |
f8a11dae8630-0 | .ipynb
.pdf
Mongodb Chat Message History
Mongodb Chat Message History#
This notebook goes over how to use Mongodb to store chat message history.
MongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas... | https://python.langchain.com/en/latest/modules/memory/examples/mongodb_chat_message_history.html |
a8b7059dcbb3-0 | .ipynb
.pdf
How to add Memory to an LLMChain
How to add Memory to an LLMChain#
This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add the ConversationBufferMemory class, although this can be any memory class.
from langchain.memory import ConversationBuff... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory.html |
a8b7059dcbb3-1 | Human: Hi there my friend
AI: Hi there, how are you doing today?
Human: Not to bad - how are you?
Chatbot:
> Finished LLMChain chain.
" I'm doing great, thank you for asking!"
previous
VectorStore-Backed Memory
next
How to add memory to a Multi-Input Chain
By Harrison Chase
© Copyright 2023, Harrison Chase.... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory.html |
cf793851075f-0 | .ipynb
.pdf
How to add memory to a Multi-Input Chain
How to add memory to a Multi-Input Chain#
Most memory objects assume a single input. In this notebook, we go over how to add memory to a chain that has multiple inputs. As an example of such a chain, we will add memory to a question/answering chain. This chain takes ... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
cf793851075f-1 | {context}
{chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"],
template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
495201855317-0 | .ipynb
.pdf
Adding Message Memory backed by a database to an Agent
Adding Message Memory backed by a database to an Agent#
This notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-1 | {chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"]
)
Now we can create the ChatMessageHistory backed by the database.
message_history = RedisChatMessageHistory(... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-2 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-3 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered corr... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-4 | Action: Search
Action Input: National Anthem of Canada
Observation: Jun 7, 2010 ... https://twitter.com/CanadaImmigrantCanadian National Anthem O Canada in HQ - complete with lyrics, captions, vocals & music.LYRICS:O Canada! Nov 23, 2022 ... After 100 years of tradition, O Canada was proclaimed Canada's national anthem... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-5 | Thought: I now know the final answer.
Final Answer: The national anthem of Canada is called "O Canada".
> Finished AgentExecutor chain.
'The national anthem of Canada is called "O Canada".'
We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name o... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-6 | Action: Search
Action Input: Population of Canada
Observation: The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada ... Additional information related to Canadian population trends can be found on Statistics Canada... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-7 | > Finished AgentExecutor chain.
'The current population of Canada is 38,566,192 as of Saturday, December 31, 2022, based on Worldometer elaboration of the latest United Nations data.'
agent_without_memory.run("what is their national anthem called?")
> Entering new AgentExecutor chain...
Thought: I should look up the an... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-8 | Action: Search
Action Input: national anthem of [country]
Observation: Most nation states have an anthem, defined as "a song, as of praise, devotion, or patriotism"; most anthems are either marches or hymns in style. List of all countries around the world with its national anthem. ... Title and lyrics in the language o... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
495201855317-9 | Thought: I now know the final answer
Final Answer: The national anthem of [country] is [name of anthem].
> Finished AgentExecutor chain.
'The national anthem of [country] is [name of anthem].'
previous
How to add Memory to an Agent
next
Cassandra Chat Message History
By Harrison Chase
© Copyright 2023, Harri... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
64460fee5317-0 | .ipynb
.pdf
How to customize conversational memory
Contents
AI Prefix
Human Prefix
How to customize conversational memory#
This notebook walks through a few ways to customize conversational memory.
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import Conversati... | https://python.langchain.com/en/latest/modules/memory/examples/conversational_customization.html |
64460fee5317-1 | Current conversation:
Human: Hi there!
AI: Hi there! It's nice to meet you. How can I help you today?
Human: What's the weather?
AI:
> Finished ConversationChain chain.
' The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the next few days is sunny with temperatures in ... | https://python.langchain.com/en/latest/modules/memory/examples/conversational_customization.html |
64460fee5317-2 | > Entering new ConversationChain 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:
... | https://python.langchain.com/en/latest/modules/memory/examples/conversational_customization.html |
64460fee5317-3 | verbose=True,
memory=ConversationBufferMemory(human_prefix="Friend")
)
conversation.predict(input="Hi there!")
> Entering new ConversationChain 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 co... | https://python.langchain.com/en/latest/modules/memory/examples/conversational_customization.html |
d2336f83f44f-0 | .ipynb
.pdf
Dynamodb Chat Message History
Contents
DynamoDBChatMessageHistory
Agent with DynamoDB Memory
Dynamodb Chat Message History#
This notebook goes over how to use Dynamodb to store chat message history.
First make sure you have correctly configured the AWS CLI. Then make sure you have installed boto3.
Next, c... | https://python.langchain.com/en/latest/modules/memory/examples/dynamodb_chat_message_history.html |
d2336f83f44f-1 | from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.utilities import PythonREPL
from getpass import getpass
message_history = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="1")
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=mes... | https://python.langchain.com/en/latest/modules/memory/examples/dynamodb_chat_message_history.html |
d2336f83f44f-2 | }
Observation: X Corp. (2023–present)Twitter, Inc. (2006–2023)
Thought:{
"action": "Final Answer",
"action_input": "X Corp. (2023–present)Twitter, Inc. (2006–2023)"
}
> Finished chain.
'X Corp. (2023–present)Twitter, Inc. (2006–2023)'
agent_chain.run(input="My name is Bob.")
> Entering new AgentExecutor chain..... | https://python.langchain.com/en/latest/modules/memory/examples/dynamodb_chat_message_history.html |
f83b97ad51cd-0 | .ipynb
.pdf
Callbacks
Contents
Callbacks
How to use callbacks
When do you want to use each of these?
Using an existing handler
Creating a custom handler
Async Callbacks
Using multiple handlers, passing in handlers
Tracing and Token Counting
Tracing
Token Counting
Callbacks#
LangChain provides a callbacks system that ... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-1 | CallbackHandlers are objects that implement the CallbackHandler interface, which has a method for each event that can be subscribed to. The CallbackManager will call the appropriate method on each handler when the event is triggered.
class BaseCallbackHandler:
"""Base callback handler that can be used to handle cal... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-2 | def on_tool_end(self, output: str, **kwargs: Any) -> Any:
"""Run when tool ends running."""
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> Any:
"""Run when tool errors."""
def on_text(self, text: str, **kwargs: Any) -> Any:
"""Run on a... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-3 | The verbose argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) as a constructor argument, eg. LLMChain(verbose=True), and it is equivalent to passing a ConsoleCallbackHandler to the callbacks argument of that object and all child objects. This is useful for debugging, as it w... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-4 | # First, let's explicitly set the StdOutCallbackHandler in `callbacks`
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])
chain.run(number=2)
# Then, let's use the `verbose` flag to achieve the same result
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
chain.run(number=2)
# Finally, let's use the req... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-5 | chat([HumanMessage(content="Tell me a joke")])
My custom handler, token:
My custom handler, token: Why
My custom handler, token: did
My custom handler, token: the
My custom handler, token: tomato
My custom handler, token: turn
My custom handler, token: red
My custom handler, token: ?
My custom handler, token: Be... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-6 | self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when chain starts running."""
print("zzzz....")
await asyncio.sleep(0.3)
class_name = serialized["name"]
print("Hi! I just woke up. Your llm is starting")
async def on_llm_end(self, resp... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-7 | Sync handler being called in a `thread_pool_executor`: token: they
Sync handler being called in a `thread_pool_executor`: token: make
Sync handler being called in a `thread_pool_executor`: token: up
Sync handler being called in a `thread_pool_executor`: token: everything
Sync handler being called in a `thread_pool_... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-8 | from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import tracing_enabled
from langchain.llms import OpenAI
# First, define custom callback handler implementations
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], pr... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-9 | handler1 = MyCustomHandlerOne()
handler2 = MyCustomHandlerTwo()
# Setup the agent. Only the `llm` will issue callbacks for handler2
llm = OpenAI(temperature=0, streaming=True, callbacks=[handler2])
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRI... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-10 | on_chain_start LLMChain
on_llm_start OpenAI
on_llm_start (I'm the second handler!!) OpenAI
on_new_token
on_new_token ```text
on_new_token
on_new_token 2
on_new_token **
on_new_token 0
on_new_token .
on_new_token 235
on_new_token
on_new_token ```
on_new_token ...
on_new_token num
on_new_token expr
on_new_token .
on_n... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-11 | Using a context manager with tracing_enabled() to trace a particular block of code.
Note if the environment variable is set, all code will be traced, regardless of whether or not it’s within the context manager.
import os
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks impo... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-12 | 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: I need to find out the age of the winner
Action: Search
Action Input: "Rafa... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-13 | Action: Calculator
Action Input: 29^0.23
Observation: Answer: 2.169459462491557
Thought: I now know the final answer.
Final Answer: Harry Styles is Olivia Wilde's boyfriend and his current age raised to the 0.23 power is 2.169459462491557.
> Finished chain.
# Now, we unset the environment variable and use a context man... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-14 | Thought: I now know the final answer
Final Answer: Rafael Nadal, aged 36, won the US Open men's final in 2019 and his age raised to the 0.334 power is 3.3098250249682484.
> Finished chain.
> Entering new AgentExecutor chain...
I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the ... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-15 | task = asyncio.create_task(agent.arun(questions[0])) # this should not be traced
with tracing_enabled() as session:
assert session
tasks = [agent.arun(q) for q in questions[1:3]] # these should be traced
await asyncio.gather(*tasks)
await task
> Entering new AgentExecutor chain...
> Entering new AgentExec... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-16 | Action: Search
Action Input: "Rafael Nadal age"36 years I need to find out Harry Styles' age.
Action: Search
Action Input: "Harry Styles age" I need to find out Lewis Hamilton's age
Action: Search
Action Input: "Lewis Hamilton Age"29 years I need to calculate the age raised to the 0.334 power
Action: Calculator
Action ... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
f83b97ad51cd-17 | with get_openai_callback() as cb:
await asyncio.gather(
*[llm.agenerate(["What is the square root of 4?"]) for _ in range(3)]
)
assert cb.total_tokens == total_tokens * 3
# The context manager is concurrency safe
task = asyncio.create_task(llm.agenerate(["What is the square root of 4?"]))
with get_opena... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
8c4999df65d0-0 | .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... | https://python.langchain.com/en/latest/modules/agents/agent_executors.html |
c65d63464450-0 | .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... | https://python.langchain.com/en/latest/modules/agents/getting_started.html |
c65d63464450-1 | 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... | https://python.langchain.com/en/latest/modules/agents/getting_started.html |
f746ce34a33a-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 ... | https://python.langchain.com/en/latest/modules/agents/agents.html |
7bf9181c1008-0 | .ipynb
.pdf
Plan and Execute
Contents
Plan and Execute
Imports
Tools
Planner, Executor, and Agent
Run Example
Plan and Execute#
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by BabyAGI and then the “Plan-and-Solve” paper.
The ... | https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html |
7bf9181c1008-1 | > Entering new PlanAndExecute chain...
steps=[Step(value="Search for Leo DiCaprio's girlfriend on the internet."), Step(value='Find her current age.'), Step(value='Raise her current age to the 0.43 power using a calculator or programming language.'), Step(value='Output the result.'), Step(value="Given the above steps t... | https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html |
7bf9181c1008-2 | Current objective: value='Find her current age.'
Action:
```
{
"action": "Search",
"action_input": "What is Gigi Hadid's current age?"
}
```
Observation: 28 years
Thought:Previous steps: steps=[(Step(value="Search for Leo DiCaprio's girlfriend on the internet."), StepResponse(response='Leo DiCaprio is currently lin... | https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html |
7bf9181c1008-3 | Step: Raise her current age to the 0.43 power using a calculator or programming language.
Response: Gigi Hadid's current age raised to the 0.43 power is approximately 4.19.
> Entering new AgentExecutor chain...
Action:
```
{
"action": "Final Answer",
"action_input": "The result is approximately 4.19."
}
```
> Finis... | https://python.langchain.com/en/latest/modules/agents/plan_and_execute.html |
e81c19074ad5-0 | .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
Azure Cognitive Services Toolkit
CSV Agent
Gmail Toolkit
Jira
JSON Agent
OpenAPI agents
Natural Language APIs
Pandas Da... | https://python.langchain.com/en/latest/modules/agents/toolkits.html |
636ffff0d0d1-0 | .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... | https://python.langchain.com/en/latest/modules/agents/tools.html |
7d38fd850b92-0 | .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... | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
7d38fd850b92-1 | 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 Tra... | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
7d38fd850b92-2 | 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 ... | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
7d38fd850b92-3 | 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... | https://python.langchain.com/en/latest/modules/agents/tools/tool_input_validation.html |
a1f308947045-0 | .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 Op... | https://python.langchain.com/en/latest/modules/agents/tools/multi_input_tool.html |
a1f308947045-1 | '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... | https://python.langchain.com/en/latest/modules/agents/tools/multi_input_tool.html |
a1f308947045-2 | > 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 st... | https://python.langchain.com/en/latest/modules/agents/tools/multi_input_tool.html |
a85d300b2eb9-0 | .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... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
a85d300b2eb9-1 | 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",
... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
a85d300b2eb9-2 | > 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 202... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
a85d300b2eb9-3 | 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, Ca... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
a85d300b2eb9-4 | 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 DiCapr... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
a85d300b2eb9-5 | > 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 s... | https://python.langchain.com/en/latest/modules/agents/tools/custom_tools.html |
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