id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 115 |
|---|---|---|
6717b8584a7a-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
6717b8584a7a-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 |
fd95a47ec0b3-0 | .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 |
fd95a47ec0b3-1 | 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 |
6c8adef64c95-0 | .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 |
6c8adef64c95-1 | 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 |
6c8adef64c95-2 | > 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 |
6c8adef64c95-3 | 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 |
c21e8b8aa563-0 | .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 |
c21e8b8aa563-1 | 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 |
12a5f02cfc23-0 | .ipynb
.pdf
ConversationTokenBufferMemory
Contents
Using in a chain
ConversationTokenBufferMemory#
ConversationTokenBufferMemory keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.
Let’s first walk through how to use the ut... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
12a5f02cfc23-1 | > 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/types/token_buffer.html |
12a5f02cfc23-2 | AI: Sounds like a productive day! What kind of documentation are you writing?
Human: For LangChain! Have you heard of it?
AI:
> Finished chain.
" Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're wr... | https://python.langchain.com/en/latest/modules/memory/types/token_buffer.html |
c0ff2755cb94-0 | .ipynb
.pdf
ConversationBufferWindowMemory
Contents
Using in a chain
ConversationBufferWindowMemory#
ConversationBufferWindowMemory keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so ... | https://python.langchain.com/en/latest/modules/memory/types/buffer_window.html |
c0ff2755cb94-1 | memory=ConversationBufferWindowMemory(k=2),
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 AI. The AI is talkative and provides lots of specific details from ... | https://python.langchain.com/en/latest/modules/memory/types/buffer_window.html |
c0ff2755cb94-2 | Current conversation:
Human: Hi, what's up?
AI: Hi there! I'm doing great. I'm currently helping a customer with a technical issue. How about you?
Human: What's their issues?
AI: The customer is having trouble connecting to their Wi-Fi network. I'm helping them troubleshoot the issue and get them connected.
Human: Is... | https://python.langchain.com/en/latest/modules/memory/types/buffer_window.html |
9d80491eb7e2-0 | .ipynb
.pdf
ConversationBufferMemory
Contents
Using in a chain
ConversationBufferMemory#
This notebook shows how to use ConversationBufferMemory. This memory allows for storing of messages and then extracts the messages in a variable.
We can first extract it as a string.
from langchain.memory import ConversationBuffe... | https://python.langchain.com/en/latest/modules/memory/types/buffer.html |
9d80491eb7e2-1 | 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 |
9d80491eb7e2-2 | 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 |
9e6bd5316b3e-0 | .ipynb
.pdf
ConversationSummaryBufferMemory
Contents
Using in a chain
ConversationSummaryBufferMemory#
ConversationSummaryBufferMemory combines the last two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses bot... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
9e6bd5316b3e-1 | from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(
llm=llm,
# We set a very low max_token_limit for the purposes of testing.
memory=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),
verbose=True
)
conversation_with_summary.predict(input="Hi, w... | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
9e6bd5316b3e-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/types/summary_buffer.html |
9e6bd5316b3e-3 | AI:
> Finished chain.
' Oh, okay. What is LangChain?'
previous
ConversationSummaryMemory
next
ConversationTokenBufferMemory
Contents
Using in a chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/memory/types/summary_buffer.html |
c239f96e5fc8-0 | .ipynb
.pdf
Conversation Knowledge Graph Memory
Contents
Using in a chain
Conversation Knowledge Graph Memory#
This type of memory uses a knowledge graph to recreate memory.
Let’s first walk through how to use the utilities
from langchain.memory import ConversationKGMemory
from langchain.llms import OpenAI
llm = Open... | https://python.langchain.com/en/latest/modules/memory/types/kg.html |
c239f96e5fc8-1 | Let’s now use this in a chain!
llm = OpenAI(temperature=0)
from langchain.prompts.prompt import PromptTemplate
from langchain.chains import ConversationChain
template = """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 ... | https://python.langchain.com/en/latest/modules/memory/types/kg.html |
c239f96e5fc8-2 | > 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. The AI ONLY uses info... | https://python.langchain.com/en/latest/modules/memory/types/kg.html |
8eef769049e1-0 | .ipynb
.pdf
Entity Memory
Contents
Using in a chain
Inspecting the memory store
Entity Memory#
This notebook shows how to work with a memory module that remembers things about specific entities. It extracts information on entities (using LLMs) and builds up its knowledge about that entity over time (also using LLMs).... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-1 | AIMessage(content=' That sounds like a great project! What kind of project are they working on?', additional_kwargs={})],
'entities': {'Sam': 'Sam is working on a hackathon project with Deven.'}}
Using in a chain#
Let’s now use it in a chain!
from langchain.chains import ConversationChain
from langchain.memory import ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-2 | Overall, you are 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 the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.
Context:
{'Deven': 'Deven is wor... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-3 | You are constantly learning and improving, and your capabilities are constantly evolving. You are 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. You have access to some personalized information provided by the ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-4 | You are an assistant to a human, powered by a large language model trained by OpenAI.
You are 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, you are able to generate human-like t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-5 | AI: That sounds like a great project! What kind of project are they working on?
Human: They are trying to add more complex memory structures to Langchain
AI: That sounds like an interesting project! What kind of memory structures are they trying to add?
Last line:
Human: They are adding in a key-value store for entit... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-6 | Context:
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation.', 'Sam': 'Sam is working on a hackathon project with Deven, t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-7 | {'Daimon': 'Daimon is a company founded by Sam, a successful entrepreneur.',
'Deven': 'Deven is working on a hackathon project with Sam, which they are '
'entering into a hackathon. They are trying to add more complex '
'memory structures to Langchain, including a key-value store for '
'e... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-8 | You are an assistant to a human, powered by a large language model trained by OpenAI.
You are 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, you are able to generate human-like t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-9 | Human: What do you know about Deven & Sam?
AI: Deven and Sam are working on a hackathon project together, trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation. They seem to be working hard on this project and have a great idea for how ... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-10 | 'memory structures, including a key-value store for entities '
'mentioned so far in the conversation.',
'Sam': 'Sam is working on a hackathon project with Deven, trying to add more '
'complex memory structures to Langchain, including a key-value store '
'for entities mentioned so far in t... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-11 | Context:
{'Deven': 'Deven is working on a hackathon project with Sam, which they are entering into a hackathon. They are trying to add more complex memory structures to Langchain, including a key-value store for entities mentioned so far in the conversation, and seem to be working hard on this project with a great idea... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
8eef769049e1-12 | Last line:
Human: What do you know about Sam?
You:
> Finished chain.
' Sam is the founder of a successful company called Daimon. He is also working on a hackathon project with Deven to add more complex memory structures to Langchain. They seem to have a great idea for how the key-value store can help.'
previous
Convers... | https://python.langchain.com/en/latest/modules/memory/types/entity_summary_memory.html |
ac201c3ed3fd-0 | .ipynb
.pdf
ConversationSummaryMemory
Contents
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 information from the conv... | https://python.langchain.com/en/latest/modules/memory/types/summary.html |
ac201c3ed3fd-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://python.langchain.com/en/latest/modules/memory/types/summary.html |
ac201c3ed3fd-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/types/summary.html |
0ac70326622f-0 | .ipynb
.pdf
VectorStore-Backed Memory
Contents
Initialize your VectorStore
Create your the VectorStoreRetrieverMemory
Using in a chain
VectorStore-Backed Memory#
VectorStoreRetrieverMemory stores memories in a VectorDB and queries the top-K most “salient” docs every time it is called.
This differs from most of the ot... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
0ac70326622f-1 | memory = VectorStoreRetrieverMemory(retriever=retriever)
# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"},... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
0ac70326622f-2 | memory=memory,
verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, what's up?")
> 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.... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
0ac70326622f-3 | conversation_with_summary.predict(input="Whats my favorite food")
> 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 que... | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
0ac70326622f-4 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/memory/types/vectorstore_retriever_memory.html |
ec8d0d6cb07d-0 | .rst
.pdf
Output Parsers
Output Parsers#
Note
Conceptual Guide
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an out... | https://python.langchain.com/en/latest/modules/prompts/output_parsers.html |
d3c68f25e01d-0 | .rst
.pdf
Example Selectors
Example Selectors#
Note
Conceptual Guide
If you have a large number of examples, you may need to select which ones to include in the prompt. The ExampleSelector is the class responsible for doing so.
The base interface is defined as below:
class BaseExampleSelector(ABC):
"""Interface for... | https://python.langchain.com/en/latest/modules/prompts/example_selectors.html |
edaa5d5b6866-0 | .ipynb
.pdf
Chat Prompt Template
Chat Prompt Template#
Chat Models takes a list of chat messages as input - this list commonly referred to as a prompt.
Typically this is not simply a hardcoded list of messages but rather a combination of a template, some examples, and user input.
LangChain provides several classes and ... | https://python.langchain.com/en/latest/modules/prompts/chat_prompt_template.html |
edaa5d5b6866-1 | HumanMessage(content='I love programming.', additional_kwargs={})]
If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:
prompt=PromptTemplate(
template="You are a helpful assistant that translates {input_language} to {output_language}... | https://python.langchain.com/en/latest/modules/prompts/chat_prompt_template.html |
5bd4b1821f7d-0 | .rst
.pdf
Prompt Templates
Prompt Templates#
Note
Conceptual Guide
Language models take text as input - that text is commonly referred to as a prompt.
Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
LangChain provides several classes and functions t... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates.html |
e6c48917cd81-0 | .ipynb
.pdf
Similarity ExampleSelector
Similarity ExampleSelector#
The SemanticSimilarityExampleSelector selects examples based on which examples are most similar to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.
from langchain.prompts.exam... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/similarity.html |
e6c48917cd81-1 | example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}\nOutput:",
input_variables=["adjective"],
)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
# Input is a feeling, so should select the happy/sad example
pr... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/similarity.html |
a403c0685b77-0 | .md
.pdf
How to create a custom example selector
Contents
Implement custom example selector
Use custom example selector
How to create a custom example selector#
In this tutorial, we’ll create a custom example selector that selects every alternate example from a given list of examples.
An ExampleSelector must implemen... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
a403c0685b77-1 | # Add new example to the set of examples
example_selector.add_example({"foo": "4"})
example_selector.examples
# -> [{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]
# Select examples
example_selector.select_examples({"foo": "foo"})
# -> array([{'foo': '1'}, {'foo': '4'}], dtype=object)
previous
Example Selectors... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/custom_example_selector.html |
2dc60b71ad79-0 | .ipynb
.pdf
NGram Overlap ExampleSelector
NGram Overlap ExampleSelector#
The NGramOverlapExampleSelector selects and orders examples based on which examples are most similar to the input, according to an ngram overlap score. The ngram overlap score is a float between 0.0 and 1.0, inclusive.
The selector allows for a th... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
2dc60b71ad79-1 | {"input": "Spot can run.", "output": "Spot puede correr."},
]
example_prompt = PromptTemplate(
input_variables=["input", "output"],
template="Input: {input}\nOutput: {output}",
)
example_selector = NGramOverlapExampleSelector(
# These are the examples it has available to choose from.
examples=examples, ... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
2dc60b71ad79-2 | Output: Ver correr a Spot.
Input: My dog barks.
Output: Mi perro ladra.
Input: Spot can run fast.
Output:
# You can add examples to NGramOverlapExampleSelector as well.
new_example = {"input": "Spot plays fetch.", "output": "Spot juega a buscar."}
example_selector.add_example(new_example)
print(dynamic_prompt.format(se... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
2dc60b71ad79-3 | Input: Spot plays fetch.
Output: Spot juega a buscar.
Input: Spot can play fetch.
Output:
# Setting threshold greater than 1.0
example_selector.threshold=1.0+1e-9
print(dynamic_prompt.format(sentence="Spot can play fetch."))
Give the Spanish translation of every input
Input: Spot can play fetch.
Output:
previous
Maxima... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
3d9ae5768fa7-0 | .ipynb
.pdf
Maximal Marginal Relevance ExampleSelector
Maximal Marginal Relevance ExampleSelector#
The MaxMarginalRelevanceExampleSelector selects examples based on a combination of which examples are most similar to the inputs, while also optimizing for diversity. It does this by finding the examples with the embeddin... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/mmr.html |
3d9ae5768fa7-1 | k=2
)
mmr_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Input: {adjective}\nOutput:",
input_variables=["adjective"],
)
# Input is a feeling,... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/mmr.html |
d1d19344d5fa-0 | .ipynb
.pdf
LengthBased ExampleSelector
LengthBased ExampleSelector#
This ExampleSelector selects which examples to use based on length. This is useful when you are worried about constructing a prompt that will go over the length of the context window. For longer inputs, it will select fewer examples to include, while ... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/length_based.html |
d1d19344d5fa-1 | # it is provided as a default value if none is specified.
# get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
)
dynamic_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
pref... | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/length_based.html |
d1d19344d5fa-2 | Input: sunny
Output: gloomy
Input: windy
Output: calm
Input: big
Output: small
Input: enthusiastic
Output:
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How to create a custom example selector
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Maximal Marginal Relevance ExampleSelector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/prompts/example_selectors/examples/length_based.html |
208249242201-0 | .rst
.pdf
How-To Guides
How-To Guides#
If you’re new to the library, you may want to start with the Quickstart.
The user guide here shows more advanced workflows and how to use the library in different ways.
Connecting to a Feature Store
How to create a custom prompt template
How to create a prompt template that uses f... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/how_to_guides.html |
4ca09f2ece3c-0 | .md
.pdf
Getting Started
Contents
What is a prompt template?
Create a prompt template
Template formats
Validate template
Serialize prompt template
Pass few shot examples to a prompt template
Select examples for a prompt template
Getting Started#
In this tutorial, we will learn about:
what a prompt template is, and wh... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4ca09f2ece3c-1 | no_input_prompt.format()
# -> "Tell me a joke."
# An example prompt with one input variable
one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
one_input_prompt.format(adjective="funny")
# -> "Tell me a funny joke."
# An example prompt with multiple input variables
m... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4ca09f2ece3c-2 | # -> Tell me a funny joke about chickens.
Currently, PromptTemplate only supports jinja2 and f-string templating format. If there is any other templating format that you would like to use, feel free to open an issue in the Github page.
Validate template#
By default, PromptTemplate will validate the template string by c... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4ca09f2ece3c-3 | To generate a prompt with few shot examples, you can use the FewShotPromptTemplate. This class takes in a PromptTemplate and a list of few shot examples. It then formats the prompt template with the few shot examples.
In this example, we’ll create a prompt to generate word antonyms.
from langchain import PromptTemplate... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4ca09f2ece3c-4 | input_variables=["input"],
# The example_separator is the string we will use to join the prefix, examples, and suffix together with.
example_separator="\n\n",
)
# We can now generate a prompt using the `format` method.
print(few_shot_prompt.format(input="big"))
# -> Give the antonym of every input
# ->
# -> Wo... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4ca09f2ece3c-5 | {"word": "windy", "antonym": "calm"},
]
# We'll use the `LengthBasedExampleSelector` to select the examples.
example_selector = LengthBasedExampleSelector(
# These are the examples is has available to choose from.
examples=examples,
# This is the PromptTemplate being used to format the examples.
exampl... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
4ca09f2ece3c-6 | long_string = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"
print(dynamic_prompt.format(input=long_string))
# -> Give the antonym of every input
# -> Word: happy
# -> Antonym: sad
# ->
# -> Word: big and huge and massive and large and gigantic and ta... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/getting_started.html |
8273e5c37b85-0 | .ipynb
.pdf
How to create a prompt template that uses few shot examples
Contents
Use Case
Using an example set
Create the example set
Create a formatter for the few shot examples
Feed examples and formatter to FewShotPromptTemplate
Using an example selector
Feed examples into ExampleSelector
Feed example selector int... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
8273e5c37b85-1 | "answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
"""
},
... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
8273e5c37b85-2 | print(example_prompt.format(**examples[0]))
Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
8273e5c37b85-3 | Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
8273e5c37b85-4 | # This is the list of examples available to select from.
examples,
# This is the embedding class used to produce embeddings which are used to measure semantic similarity.
OpenAIEmbeddings(),
# This is the VectorStore class that is used to store the embeddings and do a similarity search over.
Chroma,... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
8273e5c37b85-5 | suffix="Question: {input}",
input_variables=["input"]
)
print(prompt.format(input="Who was the father of Mary Ball Washington?"))
Question: Who was the maternal grandfather of George Washington?
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The m... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
bcd94d9ed185-0 | .ipynb
.pdf
How to create a custom prompt template
Contents
Why are custom prompt templates needed?
Creating a Custom Prompt Template
Use the custom prompt template
How to create a custom prompt template#
Let’s suppose we want the LLM to generate English language explanations of a function given its name. To achieve ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
bcd94d9ed185-1 | import inspect
def get_source_code(function_name):
# Get the source code of the function
return inspect.getsource(function_name)
Next, we’ll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function.
from langchain.prompt... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
bcd94d9ed185-2 | prompt = fn_explainer.format(function_name=get_source_code)
print(prompt)
Given the function name and source code, generate an English language explanation of the function.
Function Name: get_source_code
Source Code:
def get_source_code(function_name):
# Get the source code of the fu... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
9c6a6d011621-0 | .ipynb
.pdf
How to serialize prompts
Contents
PromptTemplate
Loading from YAML
Loading from JSON
Loading Template from a File
FewShotPromptTemplate
Examples
Loading from YAML
Loading from JSON
Examples in the Config
Example Prompt from a File
How to serialize prompts#
It is often preferrable to store prompts not as p... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
9c6a6d011621-1 | prompt = load_prompt("simple_prompt.yaml")
print(prompt.format(adjective="funny", content="chickens"))
Tell me a funny joke about chickens.
Loading from JSON#
This shows an example of loading a PromptTemplate from JSON.
!cat simple_prompt.json
{
"_type": "prompt",
"input_variables": ["adjective", "content"],
... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
9c6a6d011621-2 | output: sad
- input: tall
output: short
Loading from YAML#
This shows an example of loading a few shot example from YAML.
!cat few_shot_prompt.yaml
_type: few_shot
input_variables:
["adjective"]
prefix:
Write antonyms for the following words.
example_prompt:
_type: prompt
input_variables:
["i... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
9c6a6d011621-3 | !cat few_shot_prompt.json
{
"_type": "few_shot",
"input_variables": ["adjective"],
"prefix": "Write antonyms for the following words.",
"example_prompt": {
"_type": "prompt",
"input_variables": ["input", "output"],
"template": "Input: {input}\nOutput: {output}"
},
"exampl... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
9c6a6d011621-4 | Output: short
Input: funny
Output:
Example Prompt from a File#
This shows an example of loading the PromptTemplate that is used to format the examples from a separate file. Note that the key changes from example_prompt to example_prompt_path.
!cat example_prompt.json
{
"_type": "prompt",
"input_variables": ["in... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
1887b888a4ac-0 | .ipynb
.pdf
How to work with partial Prompt Templates
Contents
Partial With Strings
Partial With Functions
How to work with partial Prompt Templates#
A prompt template is a class with a .format method which takes in a key-value map and returns a string (a prompt) to pass to the language model. Like other methods, it ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
1887b888a4ac-1 | print(prompt.format(bar="baz"))
foobaz
Partial With Functions#
The other common use is to partial with a function. The use case for this is when you have a variable you know that you always want to fetch in a common way. A prime example of this is with date or time. Imagine you have a prompt which you always want to ha... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
1887b888a4ac-2 | Contents
Partial With Strings
Partial With Functions
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 28, 2023. | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/partial.html |
1de099c2ba4c-0 | .ipynb
.pdf
Connecting to a Feature Store
Contents
Feast
Load Feast Store
Prompts
Use in a chain
Tecton
Prerequisites
Define and Load Features
Prompts
Use in a chain
Connecting to a Feature Store#
Feature stores are a concept from traditional machine learning that make sure data fed into models is up-to-date and rele... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
1de099c2ba4c-1 | Note that the input to this prompt template is just driver_id, since that is the only user defined piece (all other variables are looked up inside the prompt template).
from langchain.prompts import PromptTemplate, StringPromptTemplate
template = """Given the driver's up to date stats, write them note relaying those st... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
1de099c2ba4c-2 | Here are the drivers stats:
Conversation rate: 0.4745151400566101
Acceptance rate: 0.055561766028404236
Average Daily Trips: 936
Your response:
Use in a chain#
We can now use this in a chain, successfully creating a chain that achieves personalization backed by a feature store
from langchain.chat_models import ChatOpen... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
1de099c2ba4c-3 | user_transaction_metrics = FeatureService(
name = "user_transaction_metrics",
features = [user_transaction_counts]
)
The above Feature Service is expected to be applied to a live workspace. For this example, we will be using the “prod” workspace.
import tecton
workspace = tecton.get_workspace("prod")
feature_se... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
1de099c2ba4c-4 | kwargs["transaction_count_30d"] = feature_vector["user_transaction_counts.transaction_count_30d_1d"]
return prompt.format(**kwargs)
prompt_template = TectonPromptTemplate(input_variables=["user_id"])
print(prompt_template.format(user_id="user_469998441571"))
Given the vendor's up to date transaction stats, writ... | https://python.langchain.com/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
946c6539e331-0 | .ipynb
.pdf
Output Parsers
Output Parsers#
Language models output text. But many times you may want to get more structured information than just text back. This is where output parsers come in.
Output parsers are classes that help structure language model responses. There are two main methods an output parser must impl... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/getting_started.html |
946c6539e331-1 | punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator('setup')
def question_ends_with_question_mark(cls, field):
if field[-1] != '?':
raise ValueError("Badly formed question!")
return field
# S... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/getting_started.html |
4f4c2329cf9c-0 | .ipynb
.pdf
CommaSeparatedListOutputParser
CommaSeparatedListOutputParser#
Here’s another parser strictly less powerful than Pydantic/JSON parsing.
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langch... | https://python.langchain.com/en/latest/modules/prompts/output_parsers/examples/comma_separated.html |
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