id stringlengths 14 16 | text stringlengths 45 2.05k | source stringlengths 53 111 |
|---|---|---|
a6e3bd7602ef-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://langchain.readthedocs.io/en/latest/modules/memory/types/buffer.html |
305c1d8195e9-0 | .ipynb
.pdf
ConversationSummaryMemory
Contents
ConversationSummaryMemory
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 condensing... | https://langchain.readthedocs.io/en/latest/modules/memory/types/summary.html |
305c1d8195e9-1 | conversation_with_summary = ConversationChain(
llm=llm,
memory=ConversationSummaryMemory(llm=OpenAI()),
verbose=True
)
conversation_with_summary.predict(input="Hi, what's up?")
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an... | https://langchain.readthedocs.io/en/latest/modules/memory/types/summary.html |
305c1d8195e9-2 | > 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://langchain.readthedocs.io/en/latest/modules/memory/types/summary.html |
75c5303201ba-0 | .ipynb
.pdf
Adding Memory to an Agent
Adding 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 an agent ... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
75c5303201ba-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
Adding Memory to a Multi-Input Chain
next
ChatGPT Clone
By Harrison Chase
© Copyright 2023, Harrison Chase.... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/agent_with_memory.html |
70ace9f62dd0-0 | .ipynb
.pdf
Custom Memory
Custom Memory#
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 add a custom memory type to ConversationChai... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/custom_memory.html |
70ace9f62dd0-1 | 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[list(inputs.keys())[0]])
# Extract known information about entities, if they exist.
... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/custom_memory.html |
70ace9f62dd0-2 | 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 no prior knowledge about Harrison, the “Releva... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/custom_memory.html |
70ace9f62dd0-3 | 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 the subject and has mentioned it often.'
Again, please note that this implementation is pretty simple and brittle and probably not useful in a ... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/custom_memory.html |
d054fc7eb627-0 | .ipynb
.pdf
Multiple Memory
Multiple Memory#
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 OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains impor... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/multiple_memory.html |
d054fc7eb627-1 | 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 provides lots of specific det... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/multiple_memory.html |
0a513bb92aed-0 | .ipynb
.pdf
ChatGPT Clone
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, PromptTemplate
from langchain.memo... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-1 | 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 output inside one unique code blo... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
0a513bb92aed-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://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html |
89aa7cef2a36-0 | .ipynb
.pdf
Adding Memory To an LLMChain
Adding 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 ConversationBufferMemory... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/adding_memory.html |
89aa7cef2a36-1 | 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
ConversationTokenBufferMemory
next
Adding Memory to a Multi-Input Chain
By Harrison Chase
© Copyright 2023, Harrison Chase.... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/adding_memory.html |
f47aba27d9a7-0 | .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... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_agent.html |
f47aba27d9a7-1 | > Entering new AgentExecutor chain...
Thought: Do I need to use a tool? No
AI: If you like Thai food, some great dinner options this week could include Thai green curry, Pad Thai, or a Thai-style stir-fry. You could also try making a Thai-style soup or salad. Enjoy!
> Finished chain.
'If you like Thai food, some great ... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_agent.html |
f47aba27d9a7-2 | Observation: The Cup was won by the host nation, Argentina, who defeated the Netherlands 3–1 in the final, after extra time. The final was held at River Plate's home stadium ... Amid Argentina's celebrations, there was sympathy for the Netherlands, runners-up for the second tournament running, following a 3-1 final def... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_agent.html |
f47aba27d9a7-3 | Thought: Do I need to use a tool? No
AI: The last letter in your name is 'b'. Argentina won the World Cup in 1978.
> Finished chain.
"The last letter in your name is 'b'. Argentina won the World Cup in 1978."
agent_chain.run(input="whats the current temperature in pomfret?")
> Entering new AgentExecutor chain...
Though... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_agent.html |
f47aba27d9a7-4 | Action: Current Search
Action Input: Current temperature in Pomfret
Observation: A mixture of rain and snow showers. High 39F. Winds NNW at 5 to 10 mph. Chance of precip 50%. Snow accumulations less than one inch. Pomfret, CT Weather Forecast, with current conditions, wind, air quality, and what to expect for the next ... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_agent.html |
f47aba27d9a7-5 | Thought: Do I need to use a tool? No
AI: The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.
> Finished chain.
'The current temperature in Pomfret is 45°F (7°C) and it feels like 44°F.'
previous
ChatGPT Clone
next
Conversational Memory Customization
By Harrison Chase
© Copyright 2023, Ha... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_agent.html |
97115a2f2c8f-0 | .ipynb
.pdf
Adding Memory to a Multi-Input Chain
Adding 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 takes as inp... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
97115a2f2c8f-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://langchain.readthedocs.io/en/latest/modules/memory/examples/adding_memory_chain_multiple_inputs.html |
1e4dd7b85201-0 | .ipynb
.pdf
Conversational Memory Customization
Contents
AI Prefix
Human Prefix
Conversational Memory Customization#
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 ConversationBuff... | https://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_customization.html |
1e4dd7b85201-1 | 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://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_customization.html |
1e4dd7b85201-2 | > 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://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_customization.html |
1e4dd7b85201-3 | 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://langchain.readthedocs.io/en/latest/modules/memory/examples/conversational_customization.html |
53ccae536d9f-0 | .md
.pdf
Key Concepts
Contents
ChatMessage
HumanMessage
AIMessage
SystemMessage
ChatMessage
ChatGeneration
Chat Model
Key Concepts#
ChatMessage#
A chat message is what we refer to as the modular unit of information.
At the moment, this consists of “content”, which refers to the content of the chat message.
At the mom... | https://langchain.readthedocs.io/en/latest/modules/chat/key_concepts.html |
c23a0eb9770a-0 | .rst
.pdf
How-To Guides
How-To Guides#
The examples here all address certain “how-to” guides for working with chat models.
Agent
Chat Vector DB
Few Shot Examples
Memory
PromptLayer ChatOpenAI
Streaming
Vector DB Question/Answering
VectorDB Question Answering with Sources
previous
Key Concepts
next
Agent
By Harrison Cha... | https://langchain.readthedocs.io/en/latest/modules/chat/how_to_guides.html |
857254c73d0c-0 | .ipynb
.pdf
Getting Started
Contents
PromptTemplates
LLMChain
Streaming
Getting Started#
This notebook covers how to get started with chat models. The interface is based around messages rather than raw text.
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.pro... | https://langchain.readthedocs.io/en/latest/modules/chat/getting_started.html |
857254c73d0c-1 | [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
Hum... | https://langchain.readthedocs.io/en/latest/modules/chat/getting_started.html |
857254c73d0c-2 | system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted mes... | https://langchain.readthedocs.io/en/latest/modules/chat/getting_started.html |
857254c73d0c-3 | A taste that's sure to excite
Chorus:
Sparkling water, oh so fine
A drink that's always on my mind
With every sip, I feel alive
Sparkling water, you're my vibe
Verse 2:
No sugar, no calories, just pure bliss
A drink that's hard to resist
It's the perfect way to quench my thirst
A drink that always comes first
Chorus:
S... | https://langchain.readthedocs.io/en/latest/modules/chat/getting_started.html |
d545e71fde10-0 | .ipynb
.pdf
Streaming
Streaming#
This notebook goes over how to use streaming with a chat model.
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
)
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHa... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/streaming.html |
d545e71fde10-1 | Sparkling
previous
PromptLayer ChatOpenAI
next
Vector DB Question/Answering
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/chat/examples/streaming.html |
06f3030032f9-0 | .ipynb
.pdf
VectorDB Question Answering with Sources
VectorDB Question Answering with Sources#
This notebook goes over how to do question-answering with sources with a chat model over a vector database. It does this by using the VectorDBQAWithSourcesChain, which does the lookup of the documents from a vector database.
... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/vector_db_qa_with_sources.html |
06f3030032f9-1 | )
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
system_template="""Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
ALWAYS return a "SOURCES" part in your answer.
The "SOUR... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/vector_db_qa_with_sources.html |
3d54a4de45b7-0 | .ipynb
.pdf
Chat Vector DB
Contents
Chat Vector DB with streaming to stdout
Chat Vector DB#
This notebook goes over how to set up a chat model to chat with a vector database.
This notebook is very similar to the example of using an LLM in the ChatVectorDBChain. The only differences here are (1) using a ChatModel, and... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/chat_vector_db.html |
3d54a4de45b7-1 | AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
system_template="""Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/chat_vector_db.html |
3d54a4de45b7-2 | Chat Vector DB with streaming to stdout#
Output from the chain will be streamed to stdout token by token in this example.
from langchain.chains.llm import LLMChain
from langchain.llms import OpenAI
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallb... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/chat_vector_db.html |
3d54a4de45b7-3 | query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})
The context does not provide information on who Ketanji Brown Jackson succeeded on the United States Supreme Court.
previous
Agent
next
Few Shot Examples
Contents
Chat Vector DB with streaming to stdout
By Harri... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/chat_vector_db.html |
7b06ab875ad5-0 | .ipynb
.pdf
Few Shot Examples
Contents
Alternating Human/AI messages
System Messages
Few Shot Examples#
This notebook covers how to use few shot examples in chat models.
There does not appear to be solid consensus on how best to do few shot prompting. As a result, we are not solidifying any abstractions around this y... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/few_shot_examples.html |
7b06ab875ad5-1 | template="You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = SystemMessagePromptTemplate.from_template("Hi", additional_kwargs={"name": "example_user"})
example_ai = SystemMessagePromptTemplate.from_template("Argh m... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/few_shot_examples.html |
9801fe3b9432-0 | .ipynb
.pdf
PromptLayer ChatOpenAI
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
PromptLayer ChatOpenAI#
This example showcases how to connect to PromptLayer to start recording your ChatOpenAI requests.
Install PromptLayer#
The promp... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/promptlayer_chatopenai.html |
9801fe3b9432-1 | The above request should now appear on your PromptLayer dashboard.
Using PromptLayer Track#
If you would like to use any of the PromptLayer tracking features, you need to pass the argument return_pl_id when instantializing the PromptLayer LLM to get the request id.
chat = PromptLayerChatOpenAI(return_pl_id=True)
chat_r... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/promptlayer_chatopenai.html |
e4315ca9cf5f-0 | .ipynb
.pdf
Agent
Agent#
This notebook covers how to create a custom agent for a chat model. It will utilize chat specific prompts.
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain.chains import LLMChain
from langchain.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
tools = [
... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/agent.html |
e4315ca9cf5f-1 | tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
> Entering new AgentExecutor chain...
Arrr, ye be i... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/agent.html |
4d869befd7f2-0 | .ipynb
.pdf
Memory
Memory#
This notebook goes over how to use Memory with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
from langchain.prompts import (
ChatPromptTempla... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/memory.html |
4d869befd7f2-1 | conversation.predict(input="Tell me about yourself.")
"Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/memory.html |
5bbf22eb47c2-0 | .ipynb
.pdf
Vector DB Question/Answering
Vector DB Question/Answering#
This example showcases using a chat model to do question answering over a vector database.
This notebook is very similar to the example of using an LLM in the ChatVectorDBChain. The only differences here are (1) using a ChatModel, and (2) passing in... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/vector_db_qa.html |
5bbf22eb47c2-1 | HumanMessagePromptTemplate.from_template("{question}")
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
qa = VectorDBQA.from_chain_type(llm=ChatOpenAI(), chain_type="stuff", vectorstore=docsearch, chain_type_kwargs=chain_type_kwargs)
query = "What did the president say about ... | https://langchain.readthedocs.io/en/latest/modules/chat/examples/vector_db_qa.html |
7ef7f346ccc5-0 | .md
.pdf
Key Concepts
Contents
Agents
Tools
ToolKits
Key Concepts#
Agents#
Agents use an LLM to determine which actions to take and in what order.
For more detailed information on agents, and different types of agents in LangChain, see this documentation.
Tools#
Tools are functions that agents can use to interact wit... | https://langchain.readthedocs.io/en/latest/modules/agents/key_concepts.html |
218017928180-0 | .rst
.pdf
How-To Guides
Contents
Agent Overview
Agent Toolkits
Agent Types
How-To Guides#
There are three types of examples in this section:
Agent Overview: how-to-guides for generic agent functionality
Agent Toolkits: how-to-guides for specific agent toolkits (agents optimized for interacting with a certain resource... | https://langchain.readthedocs.io/en/latest/modules/agents/how_to_guides.html |
218017928180-1 | VectorStore Agent: This notebook covers how to interact with VectorStores using an agent.
Python Agent: This notebook covers how to produce and execute python code using an agent.
Pandas DataFrame Agent: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls ... | https://langchain.readthedocs.io/en/latest/modules/agents/how_to_guides.html |
608b7d07a00b-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://langchain.readthedocs.io/en/latest/modules/agents/getting_started.html |
608b7d07a00b-1 | agent = initialize_agent(tools, llm, agent="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 calculate he... | https://langchain.readthedocs.io/en/latest/modules/agents/getting_started.html |
2a6b67dcbf12-0 | .md
.pdf
Agents
Contents
zero-shot-react-description
react-docstore
self-ask-with-search
conversational-react-description
Agents#
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.
For a list of ea... | https://langchain.readthedocs.io/en/latest/modules/agents/agents.html |
2a6b67dcbf12-1 | self-ask-with-search
conversational-react-description
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/agents/agents.html |
cd91184a0589-0 | .md
.pdf
Tools
Contents
List of Tools
Tools#
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
Currently, tools can be loaded with the following snippet:
from langchain.agents import load_tools
tool_names = [...]... | https://langchain.readthedocs.io/en/latest/modules/agents/tools.html |
cd91184a0589-1 | Requires LLM: No
wolfram-alpha
Tool Name: Wolfram Alpha
Tool Description: A wolfram alpha search engine. Useful for when you need to answer questions about Math, Science, Technology, Culture, Society and Everyday Life. Input should be a search query.
Notes: Calls the Wolfram Alpha API and then parses results.
Requires ... | https://langchain.readthedocs.io/en/latest/modules/agents/tools.html |
cd91184a0589-2 | Requires LLM: Yes
open-meteo-api
Tool Name: Open Meteo API
Tool Description: Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.
Notes: A natural language connection to the Open Meteo API (https://api.open-meteo.com/), ... | https://langchain.readthedocs.io/en/latest/modules/agents/tools.html |
cd91184a0589-3 | For more information on this, see this page
searx-search
Tool Name: Search
Tool Description: A wrapper around SearxNG meta search engine. Input should be a search query.
Notes: SearxNG is easy to deploy self-hosted. It is a good privacy friendly alternative to Google Search. Uses the SearxNG API.
Requires LLM: No
Extra... | https://langchain.readthedocs.io/en/latest/modules/agents/tools.html |
1f342b81eed0-0 | .ipynb
.pdf
Serialization
Serialization#
This notebook goes over how to serialize agents. For this notebook, it is important to understand the distinction we draw between agents and tools. An agent is the LLM powered decision maker that decides which actions to take and in which order. Tools are various instruments (fu... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/serialization.html |
1f342b81eed0-1 | "agent_scratchpad"
],
"output_parser": null,
"template": "Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/serialization.html |
1f342b81eed0-2 | "Calculator"
],
"return_values": [
"output"
],
"_type": "zero-shot-react-description"
}
We can now load the agent back in
agent = initialize_agent(tools, llm, agent_path="agent.json", verbose=True)
previous
Search Tools
next
Adding SharedMemory to an Agent and its Tools
By Harrison Chase
... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/serialization.html |
4097e9fbf0e2-0 | .ipynb
.pdf
Multi Input Tools
Multi Input Tools#
This notebook shows how to use a tool that requires multiple inputs with an agent.
The difficulty in doing so comes from the fact that an agent decides it’s next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/multi_input_tool.html |
4097e9fbf0e2-1 | mrkl.run("What is 3 times 4")
> 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
Max Iterations
next
Search Tools
By Harrison Chase
... | https://langchain.readthedocs.io/en/latest/modules/agents/examples/multi_input_tool.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.