id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 115 |
|---|---|---|
8c9a3f326c95-1 | toolkit = PowerBIToolkit(
powerbi=PowerBIDataset(dataset_id="<dataset_id>", table_names=['table1', 'table2'], credential=DefaultAzureCredential()),
llm=smart_llm
)
agent_executor = create_pbi_agent(
llm=fast_llm,
toolkit=toolkit,
verbose=True,
)
Example: describing a table#
agent_executor.run("Desc... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html |
8c9a3f326c95-2 | examples=few_shots,
)
agent_executor = create_pbi_agent(
llm=fast_llm,
toolkit=toolkit,
verbose=True,
)
agent_executor.run("What was the maximum of value in revenue in dollars in 2022?")
previous
Pandas Dataframe Agent
next
Python Agent
Contents
Some notes
Initialization
Example: describing a table
Exam... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/powerbi.html |
28d6c1430b7e-0 | .ipynb
.pdf
Jira
Jira#
This notebook goes over how to use the Jira tool.
The Jira tool allows agents to interact with a given Jira instance, performing actions such as searching for issues and creating issues, the tool wraps the atlassian-python-api library, for more see: https://atlassian-python-api.readthedocs.io/jir... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/jira.html |
28d6c1430b7e-1 | Observation: None
Thought: I now know the final answer
Final Answer: A new issue has been created in project PW with the summary "Make more fried rice" and description "Reminder to make more fried rice".
> Finished chain.
'A new issue has been created in project PW with the summary "Make more fried rice" and descriptio... | https://python.langchain.com/en/latest/modules/agents/toolkits/examples/jira.html |
38e8fc1cd9dd-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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
38e8fc1cd9dd-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 28, 2023. | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/agent_vectorstore.html |
166fcd4fc304-0 | .ipynb
.pdf
How to use a timeout for the agent
How to use a timeout for the agent#
This notebook walks through how to cap an agent executor after a certain amount of time. This can be useful for safeguarding against long running agent runs.
from langchain.agents import load_tools
from langchain.agents import initialize... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/max_time_limit.html |
166fcd4fc304-1 | Final Answer: foo
> Finished chain.
'foo'
Now let’s try it again with the max_execution_time=1 keyword argument. It now stops nicely after 1 second (only one iteration usually)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_execution_time=1)
agent.run(adversarial_pro... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/max_time_limit.html |
07bd751f6138-0 | .ipynb
.pdf
How to cap the max number of iterations
How to cap the max number of 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.agent... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/max_iterations.html |
07bd751f6138-1 | Final Answer: foo
> Finished chain.
'foo'
Now let’s try it again with the max_iterations=2 keyword argument. It now stops nicely after a certain amount of iterations!
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, max_iterations=2)
agent.run(adversarial_prompt)
> Enterin... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/max_iterations.html |
07bd751f6138-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/max_iterations.html |
f746923a32c0-0 | .ipynb
.pdf
How to add SharedMemory to an Agent and its Tools
How to add 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 mem... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-1 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-2 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-3 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-4 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-5 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-6 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-7 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-8 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-9 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-10 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-11 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
f746923a32c0-12 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/sharedmemory_for_tools.html |
539a71bc3ac0-0 | .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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-1 | ]
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... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-2 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-3 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-4 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-5 | > 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 ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-6 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-7 | 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-8 | # 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://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
539a71bc3ac0-9 | next
How to create ChatGPT Clone
Contents
Serial vs. Concurrent Execution
Using Tracing with Asynchronous Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/async_agent.html |
e2e8ac27abf9-0 | .ipynb
.pdf
How to access intermediate steps
How to access 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 loa... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/intermediate_steps.html |
e2e8ac27abf9-1 | > Finished chain.
# 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"'), ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/intermediate_steps.html |
e2e8ac27abf9-2 | ],
"Answer: 3.991298452658078\n"
]
]
previous
How to create ChatGPT Clone
next
How to cap the max number of iterations
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/intermediate_steps.html |
b8fd92acc541-0 | .ipynb
.pdf
How to create ChatGPT Clone
How to create ChatGPT Clone#
This chain replicates ChatGPT by combining (1) a specific prompt, and (2) the concept of memory.
Shows off the example as in https://www.engraved.blog/building-a-virtual-machine-inside/
from langchain import OpenAI, ConversationChain, LLMChain, Prompt... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-1 | llm=OpenAI(temperature=0),
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(k=2),
)
output = chatgpt_chain.predict(human_input="I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal o... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-2 | Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-3 | Human: I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-4 | Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
Human: I want you to act as a ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-5 | Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-6 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-7 | Assistant:
> Finished LLMChain chain.
```
$ echo -e "x=lambda y:y*5+3;print('Result:' + str(x(6)))" > run.py
$ python3 run.py
Result: 33
```
output = chatgpt_chain.predict(human_input="""echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py && python3 run.py""")
print(output... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-8 | AI:
```
$ touch jokes.txt
$ echo "Why did the chicken cross the road? To get to the other side!" >> jokes.txt
$ echo "What did the fish say when it hit the wall? Dam!" >> jokes.txt
$ echo "Why did the scarecrow win the Nobel Prize? Because he was outstanding in his field!" >> jokes.txt
```
Human: echo -e "x=lambda y:y... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-9 | print(output)
> Entering new LLMChain chain...
Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a languag... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-10 | AI:
```
$ echo -e "print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])" > run.py
$ python3 run.py
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
```
Human: echo -e "echo 'Hello from Docker" > entrypoint.sh && echo -e "FROM ubuntu:20.04
COPY entrypoint.sh entrypoint.sh
ENTRYPOINT ["/bin/sh","entrypoin... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-11 | Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-12 | ENTRYPOINT ["/bin/sh","entrypoint.sh"]">Dockerfile
$ docker build . -t my_docker_image
$ docker run -t my_docker_image
Hello from Docker
```
Human: nvidia-smi
Assistant:
> Finished LLMChain chain.
```
$ nvidia-smi
Sat May 15 21:45:02 2021
+-------------------------------------------------------------------------... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-13 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-14 | Hello from Docker
```
Human: nvidia-smi
AI:
```
$ nvidia-smi
Sat May 15 21:45:02 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-15 | --- bbc.com ping statistics ---
3 packets transmitted, 3 packets received, 0.0% packet loss
round-trip min/avg/max/stddev = 14.945/14.945/14.945/0.000 ms
```
output = chatgpt_chain.predict(human_input="""curl -fsSL "https://api.github.com/repos/pytorch/pytorch/releases/latest" | jq -r '.tag_name' | sed 's/[^0-9\.\-]*//... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-16 | Sat May 15 21:45:02 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id ... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-17 | ```
Human: curl -fsSL "https://api.github.com/repos/pytorch/pytorch/releases/latest" | jq -r '.tag_name' | sed 's/[^0-9\.\-]*//g'
Assistant:
> Finished LLMChain chain.
```
$ curl -fsSL "https://api.github.com/repos/pytorch/pytorch/releases/latest" | jq -r '.tag_name' | sed 's/[^0-9\.\-]*//g'
1.8.1
```
output = chatgpt_... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-18 | Human: ping bbc.com
AI:
```
$ ping bbc.com
PING bbc.com (151.101.65.81): 56 data bytes
64 bytes from 151.101.65.81: icmp_seq=0 ttl=53 time=14.945 ms
64 bytes from 151.101.65.81: icmp_seq=1 ttl=53 time=14.945 ms
64 bytes from 151.101.65.81: icmp_seq=2 ttl=53 time=14.945 ms
--- bbc.com ping statistics ---
3 packets tran... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-19 | Explore our current openings and apply today. We look forward to hearing from you.
```
output = chatgpt_chain.predict(human_input="curl https://chat.openai.com/chat")
print(output)
> Entering new LLMChain chain...
Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to b... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-20 | ```
Human: lynx https://www.deepmind.com/careers
AI:
```
$ lynx https://www.deepmind.com/careers
DeepMind Careers
Welcome to DeepMind Careers. We are a world-leading artificial intelligence research and development company, and we are looking for talented people to join our team.
We offer a range of exciting opportuni... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-21 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-li... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-22 | ```
$ curl https://chat.openai.com/chat
<html>
<head>
<title>OpenAI Chat</title>
</head>
<body>
<h1>Welcome to OpenAI Chat!</h1>
<p>
OpenAI Chat is a natural language processing platform that allows you to interact with OpenAI's AI models in a conversational way.
</p>
<p>
To get st... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-23 | }
```
output = chatgpt_chain.predict(human_input="""curl --header "Content-Type:application/json" --request POST --data '{"message": "I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique cod... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-24 | Human: curl https://chat.openai.com/chat
AI:
```
$ curl https://chat.openai.com/chat
<html>
<head>
<title>OpenAI Chat</title>
</head>
<body>
<h1>Welcome to OpenAI Chat!</h1>
<p>
OpenAI Chat is a natural language processing platform that allows you to interact with OpenAI's AI models in a conve... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
b8fd92acc541-25 | }
```
Human: curl --header "Content-Type:application/json" --request POST --data '{"message": "I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write... | https://python.langchain.com/en/latest/modules/agents/agent_executors/examples/chatgpt_clone.html |
aea3450e6400-0 | .rst
.pdf
How-To Guides
Contents
Types
Usage
How-To Guides#
Types#
The first set of examples all highlight different types of memory.
ConversationBufferMemory
ConversationBufferWindowMemory
Entity Memory
Conversation Knowledge Graph Memory
ConversationSummaryMemory
ConversationSummaryBufferMemory
ConversationTokenBuf... | https://python.langchain.com/en/latest/modules/memory/how_to_guides.html |
c9afcfaa2b4e-0 | .ipynb
.pdf
Getting Started
Contents
ChatMessageHistory
ConversationBufferMemory
Using in a chain
Saving Message History
Getting Started#
This notebook walks through how LangChain thinks about memory.
Memory involves keeping a concept of state around throughout a user’s interactions with an language model. A user’s i... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
c9afcfaa2b4e-1 | history.messages
[HumanMessage(content='hi!', additional_kwargs={}),
AIMessage(content='whats up?', additional_kwargs={})]
ConversationBufferMemory#
We now show how to use this simple concept in a chain. We first showcase ConversationBufferMemory which is just a wrapper around ChatMessageHistory that extracts the mess... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
c9afcfaa2b4e-2 | 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/getting_started.html |
c9afcfaa2b4e-3 | 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."
Saving ... | https://python.langchain.com/en/latest/modules/memory/getting_started.html |
76e560005a67-0 | .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 |
76e560005a67-1 | {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 |
76e560005a67-2 | 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 |
76e560005a67-3 | > 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 |
76e560005a67-4 | 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 |
76e560005a67-5 | 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 |
76e560005a67-6 | 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 |
76e560005a67-7 | > 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 |
76e560005a67-8 | 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 |
76e560005a67-9 | 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
How to customize conversational memory
By Harrison Chase
© Copyright 202... | https://python.langchain.com/en/latest/modules/memory/examples/agent_with_memory_in_db.html |
f7b588663baa-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 output. In this notebook, we go over how to add memory to a chain that has multiple outputs. As an example of such a chain, we will add memory to a question/answering chain. This chain take... | https://python.langchain.com/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
f7b588663baa-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 |
bb37ea2b83ee-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 |
bb37ea2b83ee-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 |
ee8655453ce8-0 | .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 |
be086683a48d-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 |
2b3128458c7c-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 |
2b3128458c7c-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 |
2b3128458c7c-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 |
2b3128458c7c-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
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