id stringlengths 14 15 | text stringlengths 101 5.26k | source stringlengths 57 120 |
|---|---|---|
9ed49f9f780f-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://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
9ed49f9f780f-1 | # it is excluded.
example_selector.threshold=0.0
print(dynamic_prompt.format(sentence="Spot can run fast."))
Give the Spanish translation of every input
Input: Spot can run.
Output: Spot puede correr.
Input: See Spot run.
Output: Ver correr a Spot.
Input: Spot plays fetch.
Output: Spot juega a buscar.
Input: Spot can r... | https://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/ngram_overlap.html |
49045e7c8e58-0 | .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://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/length_based.html |
83d1e17d693a-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://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/similarity.html |
589c461060dd-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://langchain.readthedocs.io/en/latest/modules/prompts/example_selectors/examples/mmr.html |
fe5cfec8036e-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://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
fe5cfec8036e-1 | from langchain.prompts import load_prompt
loaded_prompt = load_prompt("awesome_prompt.json")
assert prompt_template == loaded_prompt
langchain also supports loading prompt template from LangChainHub, which contains a collection of useful prompts you can use in your project. You can read more about LangChainHub and the ... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
fe5cfec8036e-2 | # This is the PromptTemplate being used to format the examples.
example_prompt=example_prompt,
# This is the maximum length that the formatted examples should be.
# Length is measured by the get_text_length function below.
max_length=25
# This is the function used to get the length of a string, whi... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/getting_started.html |
0bec5795a7de-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://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/how_to_guides.html |
cb73239fa79a-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://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/custom_prompt_template.html |
cb19152b8063-0 | .ipynb
.pdf
Prompt Composition
Prompt Composition#
This notebook goes over how to compose multiple prompts together. This can be useful when you want to reuse parts of prompts. This can be done with a PipelinePrompt. A PipelinePrompt consists of two main parts:
final_prompt: This is the final prompt that is returned
pi... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_composition.html |
4f42ee7b736a-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://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/partial.html |
2d3b70e088b8-0 | .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
Featureform
Initialize Featureform
Prompts
Use in a chain
Connecting to a Feature Store#
Feature stores are a concept from traditional machine learning ... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
2d3b70e088b8-1 | chain.run(1001)
"Hi there! I wanted to update you on your current stats. Your acceptance rate is 0.055561766028404236 and your average daily trips are 936. While your conversation rate is currently 0.4745151400566101, I have no doubt that with a little extra effort, you'll be able to exceed that .5 mark! Keep up the gr... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
2d3b70e088b8-2 | Featureform#
Finally, we will use Featureform an open-source and enterprise-grade feature store to run the same example. Featureform allows you to work with your infrastructure like Spark or locally to define your feature transformations.
Initialize Featureform#
You can follow in the instructions in the README to initi... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/connecting_to_a_feature_store.html |
f6c05cba93d4-0 | .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://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
f6c05cba93d4-1 | 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 answer: Alan Turing was 41 years old when he died.
So the final answer is: Muhammad Ali
Question: Whe... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
f6c05cba93d4-2 | 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 is: Joseph Ball
Question: Who was the father of Mary Ball Washington?
previous
How to create ... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/few_shot_examples.html |
1a1fe5222c39-0 | .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
PromptTempalte with OutputParser
How to serialize prompts#
It is often pref... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
1a1fe5222c39-1 | _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}"
examples:
examples.yaml
suffix:
"Input: {adjective}\nOutput:"
prompt... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
1a1fe5222c39-2 | }
prompt = load_prompt("prompt_with_output_parser.json")
prompt.output_parser.parse("George Washington was born in 1732 and died in 1799.\nScore: 1/2")
{'answer': 'George Washington was born in 1732 and died in 1799.',
'score': '1/2'}
previous
Prompt Composition
next
Prompts
Contents
PromptTemplate
Loading from YA... | https://langchain.readthedocs.io/en/latest/modules/prompts/prompt_templates/examples/prompt_serialization.html |
9f4c2d26ae5b-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://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/getting_started.html |
a8939f07e56d-0 | .ipynb
.pdf
Structured Output Parser
Structured Output Parser#
While the Pydantic/JSON parser is more powerful, we initially experimented data structures having text fields only.
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate, ChatPromptTemplate,... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/structured.html |
04258ed25ad1-0 | .ipynb
.pdf
PydanticOutputParser
PydanticOutputParser#
This output parser allows users to specify an arbitrary JSON schema and query LLMs for JSON outputs that conform to that schema.
Keep in mind that large language models are leaky abstractions! You’ll have to use an LLM with sufficient capacity to generate well-form... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/pydantic.html |
8632c7babfac-0 | .ipynb
.pdf
Enum Output Parser
Enum Output Parser#
This notebook shows how to use an Enum output parser
from langchain.output_parsers.enum import EnumOutputParser
from enum import Enum
class Colors(Enum):
RED = "red"
GREEN = "green"
BLUE = "blue"
parser = EnumOutputParser(enum=Colors)
parser.parse("red")
<C... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/enum.html |
204a1b14411b-0 | .ipynb
.pdf
RetryOutputParser
RetryOutputParser#
While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can’t. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example.
from langchain.prompts... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/retry.html |
a7787a172610-0 | .ipynb
.pdf
Datetime
Datetime#
This OutputParser shows out to parse LLM output into datetime format.
from langchain.prompts import PromptTemplate
from langchain.output_parsers import DatetimeOutputParser
from langchain.chains import LLMChain
from langchain.llms import OpenAI
output_parser = DatetimeOutputParser()
templ... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/datetime.html |
a75840c8a3a0-0 | .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://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/comma_separated.html |
cbf1f18c47da-0 | .ipynb
.pdf
OutputFixingParser
OutputFixingParser#
This output parser wraps another output parser and tries to fix any mistakes
The Pydantic guardrail simply tries to parse the LLM response. If it does not parse correctly, then it errors.
But we can do other things besides throw errors. Specifically, we can pass the mi... | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
cbf1f18c47da-1 | previous
Enum Output Parser
next
PydanticOutputParser
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/prompts/output_parsers/examples/output_fixing_parser.html |
e7f50ecba501-0 | .rst
.pdf
Welcome to LangChain
Contents
Getting Started
Modules
Use Cases
Reference Docs
Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a l... | https://langchain.readthedocs.io/en/latest/langchain/index.html |
e7f50ecba501-1 | Tracing: A guide on using tracing in LangChain to visualize the execution of chains and agents.
Model Laboratory: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
Discord: Join us on our Discord to discuss all... | https://langchain.readthedocs.io/en/latest/langchain/index.html |
3911e74c4326-0 | .md
.pdf
Deployments
Contents
Anyscale
Streamlit
Gradio (on Hugging Face)
Chainlit
Beam
Vercel
FastAPI + Vercel
Kinsta
Fly.io
Digitalocean App Platform
Google Cloud Run
SteamShip
Langchain-serve
BentoML
Databutton
Deployments#
So, you’ve created a really cool chain - now what? How do you deploy it and make it easily ... | https://langchain.readthedocs.io/en/latest/ecosystem/deployments.html |
3911e74c4326-1 | Beam
Vercel
FastAPI + Vercel
Kinsta
Fly.io
Digitalocean App Platform
Google Cloud Run
SteamShip
Langchain-serve
BentoML
Databutton
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/ecosystem/deployments.html |
cf5217c4d644-0 | .md
.pdf
Tracing
Contents
Tracing Walkthrough
Changing Sessions
Tracing#
By enabling tracing in your LangChain runs, you’ll be able to more effectively visualize, step through, and debug your chains and agents.
First, you should install tracing and set up your environment properly.
You can use either a locally hosted... | https://langchain.readthedocs.io/en/latest/additional_resources/tracing.html |
cd8629718a26-0 | .rst
.pdf
Deploying LLMs in Production
Contents
Outline
Designing a Robust LLM Application Service
Monitoring
Fault tolerance
Zero down time upgrade
Load balancing
Maintaining Cost-Efficiency and Scalability
Self-hosting models
Resource Management and Auto-Scaling
Utilizing Spot Instances
Independent Scaling
Batching... | https://langchain.readthedocs.io/en/latest/additional_resources/deploy_llms.html |
cd8629718a26-1 | There are several strategies for load balancing. For example, one common method is the Round Robin strategy, where each request is sent to the next server in line, cycling back to the first when all servers have received a request. This works well when all servers are equally capable. However, if some servers are more ... | https://langchain.readthedocs.io/en/latest/additional_resources/deploy_llms.html |
cd8629718a26-2 | Infrastructure as Code (IaC)#
Rapid iteration also involves the ability to recreate your infrastructure quickly and reliably. This is where Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Kubernetes YAML files come into play. They allow you to define your infrastructure in code files, which can be... | https://langchain.readthedocs.io/en/latest/additional_resources/deploy_llms.html |
a2f0049aa52d-0 | .ipynb
.pdf
Model Comparison
Model Comparison#
Constructing your language model application will likely involved choosing between many different options of prompts, models, and even chains to use. When doing so, you will want to compare these different options on different inputs in an easy, flexible, and intuitive way... | https://langchain.readthedocs.io/en/latest/additional_resources/model_laboratory.html |
a2f0049aa52d-1 | > Finished chain.
So the final answer is: El Palmar, Spain
Cohere
Params: {'model': 'command-xlarge-20221108', 'max_tokens': 256, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}
> Entering new chain...
What is the hometown of the reigning men's U.S. Open champion?
Are follow up questi... | https://langchain.readthedocs.io/en/latest/additional_resources/model_laboratory.html |
93d8bb37c16b-0 | .md
.pdf
YouTube
Contents
⛓️Official LangChain YouTube channel⛓️
Introduction to LangChain with Harrison Chase, creator of LangChain
Videos (sorted by views)
YouTube#
This is a collection of LangChain videos on YouTube.
⛓️Official LangChain YouTube channel⛓️#
Introduction to LangChain with Harrison Chase, creator of ... | https://langchain.readthedocs.io/en/latest/additional_resources/youtube.html |
93d8bb37c16b-1 | ⛓️ LangChain Tutorial - ChatGPT mit eigenen Daten by Coding Crashkurse
⛓️ Chat with a CSV | LangChain Agents Tutorial (Beginners) by GoDataProf
⛓️ Introdução ao Langchain - #Cortes - Live DataHackers by Prof. João Gabriel Lima
⛓️ LangChain: Level up ChatGPT !? | LangChain Tutorial Part 1 by Code Affinity
⛓️ KI schreibt... | https://langchain.readthedocs.io/en/latest/additional_resources/youtube.html |
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