id stringlengths 14 16 | text stringlengths 45 2.05k | source stringlengths 53 111 |
|---|---|---|
343660a0e518-1 | objects = []
objects += [('booklet', 'blue')] * 2
objects += [('booklet', 'purple')] * 2
objects += [('sunglasses', 'yellow')] * 2
# Remove all pairs of sunglasses
objects = [object for object in objects if object[0] != 'sunglasses']
# Count number of purple objects
num_purple = len([object for object in objects if obj... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/pal.html |
343660a0e518-2 | answer = num_purple
> Finished chain.
result['intermediate_steps']
"# Put objects into a list to record ordering\nobjects = []\nobjects += [('booklet', 'blue')] * 2\nobjects += [('booklet', 'purple')] * 2\nobjects += [('sunglasses', 'yellow')] * 2\n\n# Remove all pairs of sunglasses\nobjects = [object for object in obj... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/pal.html |
d78e22b0d39c-0 | .ipynb
.pdf
Moderation
Contents
How to use the moderation chain
How to append a Moderation chain to an LLMChain
Moderation#
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
d78e22b0d39c-1 | 'This is okay'
moderation_chain.run("I will kill you")
"Text was found that violates OpenAI's content policy."
Here’s an example of using the moderation chain to throw an error.
moderation_chain_error = OpenAIModerationChain(error=True)
moderation_chain_error.run("This is okay")
'This is okay'
moderation_chain_error.ru... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
d78e22b0d39c-2 | 79 text = inputs[self.input_key]
80 results = self.client.create(text)
---> 81 output = self._moderate(text, results["results"][0])
82 return {self.output_key: output}
File ~/workplace/langchain/langchain/chains/moderation.py:73, in OpenAIModerationChain._moderate(self, text, results)
71 error_str = "Tex... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
d78e22b0d39c-3 | prompt = PromptTemplate(template="{text}", input_variables=["text"])
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
text = """We are playing a game of repeat after me.
Person 1: Hi
Person 2: Hi
Person 1: How's your day
Person 2: How's your day
Person 1: I will kill you
Per... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
d78e22b0d39c-4 | chain(inputs, return_only_outputs=True)
{'sanitized_text': "Text was found that violates OpenAI's content policy."}
previous
LLMSummarizationCheckerChain
next
PAL
Contents
How to use the moderation chain
How to append a Moderation chain to an LLMChain
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/moderation.html |
8fb0e3510d09-0 | .ipynb
.pdf
LLMSummarizationCheckerChain
LLMSummarizationCheckerChain#
This notebook shows some examples of LLMSummarizationCheckerChain in use with different types of texts. It has a few distinct differences from the LLMCheckerChain, in that it doesn’t have any assumtions to the format of the input text (or summary).... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-1 | • JWST took the very first pictures of a planet outside of our own solar system. These distant worlds are called "exoplanets." Exo means "from outside."
These discoveries can spark a child's imagination about the infinite wonders of the universe."""
checker_chain.run(text)
> Entering new LLMSummarizationCheckerChain ch... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-2 | • JWST took the very first pictures of a planet outside of our own solar system.
• These distant worlds are called "exoplanets."
"""
For each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".
If the fact is false, expl... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-3 | These discoveries can spark a child's imagination about the infinite wonders of the universe.
"""
Using these checked assertions, rewrite the original summary to be completely true.
The output should have the same structure and formatting as the original summary.
Summary:
> Finished chain.
> Entering new LLMChain chain... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-4 | • In 2023, The JWST spotted a number of galaxies nicknamed "green peas." They were given this name because they are small, round, and green, like peas.
• The telescope captured images of galaxies that are over 13 billion years old. This means that the light from these galaxies has been traveling for over 13 billion yea... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-5 | > Finished chain.
> Entering new LLMChain chain...
Prompt after formatting:
You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.
Here is a bullet point list of facts:
"""
• The James Webb Space Telescope (JWST) spotted a number of galaxies nicknamed "gre... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-6 | • Exoplanets were first discovered in 1992. - True
• The JWST has allowed us to see exoplanets in greater detail. - Undetermined. It is too early to tell as the JWST has not been launched yet.
"""
Original Summary:
"""
Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):
•... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-7 | Checked Assertions: """
- The sky is blue: True
- Water is wet: True
- The sun is a star: True
"""
Result: True
===
Checked Assertions: """
- The sky is blue - True
- Water is made of lava- False
- The sun is a star - True
"""
Result: False
===
Checked Assertions:"""
• The James Webb Space Telescope (JWST) spotted a nu... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-8 | These discoveries can spark a child's imagination about the infinite wonders of the universe.
> Finished chain.
'Your 9-year old might like these recent discoveries made by The James Webb Space Telescope (JWST):\n• In 2023, The JWST will spot a number of galaxies nicknamed "green peas." They were given this name becaus... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-9 | checker_chain.run(text)
> Entering new LLMSummarizationCheckerChain chain...
> Entering new SequentialChain chain...
> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.
Format your output as a bulleted list.
Text:
"""
The Greenland Sea is an outlying portion... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-10 | - The sea is named after the island of Greenland.
- It is the Arctic Ocean's main outlet to the Atlantic.
- It is often frozen over so navigation is limited.
- It is considered the northern branch of the Norwegian Sea.
"""
For each fact, determine whether it is true or false about the subject. If you are unable to dete... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-11 | - It is considered the northern branch of the Norwegian Sea. True
"""
Original Summary:"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is one of five oceans in the world, alongside the Pa... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-12 | """
Result: False
===
Checked Assertions:"""
- The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. True
- It has an area of 465,000 square miles. True
- It is one of five oceans in the world, alongside the Pacific Ocean, Atlantic Ocean, I... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-13 | Format your output as a bulleted list.
Text:
"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-14 | > Finished chain.
> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false. If the answer is false, a suggestion is given for a correction.
Checked Assertions:"""
- The Greenland Sea is an outlying portion of the Arctic Ocean locat... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-15 | > Finished chain.
> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are labeled as true of false.
If all of the assertions are true, return "True". If any of the assertions are false, return "False".
Here are some examples:
===
Checked Assertions: """
- ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-16 | """
Result:
> Finished chain.
> Finished chain.
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-17 | - It has an area of 465,000 square miles.
- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs.
- The sea is named after the country of Greenland.
- It is the Arctic Ocean's main outlet to the Atlantic.
- It is often frozen over so navigation is limited.
- It is conside... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-18 | """
Original Summary:"""
The Greenland Sea is an outlying portion of the Arctic Ocean located between Iceland, Norway, the Svalbard archipelago and Greenland. It has an area of 465,000 square miles and is an arm of the Arctic Ocean. It is covered almost entirely by water, some of which is frozen in the form of glaciers... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-19 | - It has an area of 465,000 square miles. True
- It is covered almost entirely by water, some of which is frozen in the form of glaciers and icebergs. True
- The sea is named after the country of Greenland. True
- It is the Arctic Ocean's main outlet to the Atlantic. False - The Arctic Ocean's main outlet to the Atlant... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-20 | from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
checker_chain = LLMSummarizationCheckerChain(llm=llm, max_checks=3, verbose=True)
text = "Mammals can lay eggs, birds can lay eggs, therefore birds are mammals."
checker_chain.run(text)
> Entering new LLMSummarizationCheckerChain chain...
> Entering new Sequ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-21 | - Birds can lay eggs: True. Birds are capable of laying eggs.
- Birds are mammals: False. Birds are not mammals, they are a class of their own.
"""
Original Summary:
"""
Mammals can lay eggs, birds can lay eggs, therefore birds are mammals.
"""
Using these checked assertions, rewrite the original summary to be complete... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-22 | > Entering new SequentialChain chain...
> Entering new LLMChain chain...
Prompt after formatting:
Given some text, extract a list of facts from the text.
Format your output as a bulleted list.
Text:
"""
Birds and mammals are both capable of laying eggs, however birds are not mammals, they are a class of their own.
"""... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8fb0e3510d09-23 | """
Using these checked assertions, rewrite the original summary to be completely true.
The output should have the same structure and formatting as the original summary.
Summary:
> Finished chain.
> Entering new LLMChain chain...
Prompt after formatting:
Below are some assertions that have been fact checked and are lab... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_summarization_checker.html |
8a75497a9e82-0 | .ipynb
.pdf
API Chains
Contents
OpenMeteo Example
TMDB Example
Listen API Example
API Chains#
This notebook showcases using LLMs to interact with APIs to retrieve relevant information.
from langchain.chains.api.prompt import API_RESPONSE_PROMPT
from langchain.chains import APIChain
from langchain.prompts.prompt impor... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-1 | from langchain.chains.api import tmdb_docs
headers = {"Authorization": f"Bearer {os.environ['TMDB_BEARER_TOKEN']}"}
chain = APIChain.from_llm_and_api_docs(llm, tmdb_docs.TMDB_DOCS, headers=headers, verbose=True)
chain.run("Search for 'Avatar'")
> Entering new APIChain chain...
https://api.themoviedb.org/3/search/movie... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-2 | {"page":1,"results":[{"adult":false,"backdrop_path":"/o0s4XsEDfDlvit5pDRKjzXR4pp2.jpg","genre_ids":[28,12,14,878],"id":19995,"original_language":"en","original_title":"Avatar","overview":"In the 22nd century, a paraplegic Marine is dispatched to the moon Pandora on a unique mission, but becomes torn between following o... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-3 | they fight to stay alive, and the tragedies they endure.","popularity":3948.296,"poster_path":"/t6HIqrRAclMCA60NsSmeqe9RmNV.jpg","release_date":"2022-12-14","title":"Avatar: The Way of Water","video":false,"vote_average":7.7,"vote_count":4219},{"adult":false,"backdrop_path":"/uEwGFGtao9YG2JolmdvtHLLVbA9.jpg","genre_ids... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-4 | Scene Deconstruction","video":false,"vote_average":7.8,"vote_count":12},{"adult":false,"backdrop_path":null,"genre_ids":[28,18,878,12,14],"id":83533,"original_language":"en","original_title":"Avatar 3","overview":"","popularity":172.488,"poster_path":"/4rXqTMlkEaMiJjiG0Z2BX6F6Dkm.jpg","release_date":"2024-12-18","title... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-5 | Avatar is a feature length behind-the-scenes documentary about the making of Avatar. It uses footage from the film's development, as well as stock footage from as far back as the production of Titanic in 1995. Also included are numerous interviews with cast, artists, and other crew members. The documentary was released... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-6 | The Deep Dive - A Special Edition of 20/20","video":false,"vote_average":6.5,"vote_count":5},{"adult":false,"backdrop_path":null,"genre_ids":[99],"id":278698,"original_language":"en","original_title":"Avatar Spirits","overview":"Bryan Konietzko and Michael Dante DiMartino, co-creators of the hit television series, Avat... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-7 | the scenes look at the new James Cameron blockbuster “Avatar”, which stars Aussie Sam Worthington. Hastily produced by Australia’s Nine Network following the film’s release.","popularity":30.903,"poster_path":"/9MHY9pYAgs91Ef7YFGWEbP4WJqC.jpg","release_date":"2009-12-05","title":"Avatar: Enter The World","video":false,... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-8 | Agni Kai","video":false,"vote_average":7,"vote_count":1},{"adult":false,"backdrop_path":"/e8mmDO7fKK93T4lnxl4Z2zjxXZV.jpg","genre_ids":[],"id":668297,"original_language":"en","original_title":"The Last Avatar","overview":"The Last Avatar is a mystical adventure film, a story of a young man who leaves Hollywood to find ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-9 | awaken and create a world of truth, harmony and possibility.","popularity":8.786,"poster_path":"/XWz5SS5g5mrNEZjv3FiGhqCMOQ.jpg","release_date":"2014-12-06","title":"The Last Avatar","video":false,"vote_average":4.5,"vote_count":2},{"adult":false,"backdrop_path":null,"genre_ids":[],"id":424768,"original_language":"en",... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-10 | 2018","overview":"Live At Graspop Festival Belgium 2018","popularity":9.855,"poster_path":null,"release_date":"","title":"Avatar - Live At Graspop 2018","video":false,"vote_average":9,"vote_count":1},{"adult":false,"backdrop_path":null,"genre_ids":[10402],"id":874770,"original_language":"en","original_title":"Avatar Ag... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-11 | Ages: Madness","video":false,"vote_average":8,"vote_count":1},{"adult":false,"backdrop_path":"/dj8g4jrYMfK6tQ26ra3IaqOx5Ho.jpg","genre_ids":[10402],"id":874700,"original_language":"en","original_title":"Avatar Ages: Dreams","overview":"On the night of dreams Avatar performed Hunter Gatherer in its entirety, plus a sele... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
8a75497a9e82-12 | > Finished chain.
' This response contains 57 movies related to the search query "Avatar". The first movie in the list is the 2009 movie "Avatar" starring Sam Worthington. Other movies in the list include sequels to Avatar, documentaries, and live performances.'
Listen API Example#
import os
from langchain.llms import ... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/api.html |
7a4924f64f74-0 | .ipynb
.pdf
LLMCheckerChain
LLMCheckerChain#
This notebook showcases how to use LLMCheckerChain.
from langchain.chains import LLMCheckerChain
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.7)
text = "What type of mammal lays the biggest eggs?"
checker_chain = LLMCheckerChain(llm=llm, verbose=True)
checker... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_checker.html |
7aafa7264e8a-0 | .ipynb
.pdf
BashChain
Contents
Customize Prompt
BashChain#
This notebook showcases using LLMs and a bash process to do perform simple filesystem commands.
from langchain.chains import LLMBashChain
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
text = "Please write a bash script that prints 'Hello World... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_bash.html |
7aafa7264e8a-1 | That is the format. Begin!
Question: {question}"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)
bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)
text = "Please write a bash script that prints 'Hello World' to the console."
bash_chain.run(text)
> Entering new LLMBashCha... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/llm_bash.html |
e71078e9e52d-0 | .ipynb
.pdf
Self-Critique Chain with Constitutional AI
Self-Critique Chain with Constitutional AI#
This notebook showcases how to use the ConstitutionalChain.
Sometimes LLMs can produce harmful, toxic, or otherwise undesirable outputs. This chain allows you to apply a set of constitutional principles to the output of a... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/constitutional_chain.html |
e71078e9e52d-1 | > Entering new ConstitutionalChain chain...
Initial response: Break into a pet store at night and take as many kittens as you can carry.
Applying Ethical Principle...
Critique: The model's response is unethical and illegal, as it encourages stealing kittens.
Updated response: It is illegal and unethical to steal kitte... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/constitutional_chain.html |
e71078e9e52d-2 | Applying Master Yoda Principle...
Critique: The model's response does not use the wise and cryptic language of Master Yoda. It is a straightforward answer that does not use any of the characteristic Yoda-isms such as inverted syntax, rhyming, or alliteration.
Updated response: Stealing kittens is not the path of wisdom... | https://langchain.readthedocs.io/en/latest/modules/chains/examples/constitutional_chain.html |
8cb86c0c5032-0 | .ipynb
.pdf
Serialization
Contents
Saving a chain to disk
Loading a chain from disk
Saving components separately
Serialization#
This notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow t... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/serialization.html |
8cb86c0c5032-1 | "best_of": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
},
"output_key": "text",
"_type": "llm_chain"
}
Loading a chain from disk#
We can load a chain from disk by using the load_chain method.
from langchain.chains import load_chain
chain = load_chain("llm_chain.js... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/serialization.html |
8cb86c0c5032-2 | "top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"n": 1,
"best_of": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
}
config = {
"memory": None,
"verbose": True,
"prompt_path": "prompt.json",
"llm_path": "llm.json",
"output_key": "text",
"_ty... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/serialization.html |
e8cad34e8972-0 | .ipynb
.pdf
Transformation Chain
Transformation Chain#
This notebook showcases using a generic transformation chain.
As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those.
from langc... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/transformation.html |
7c8115ab1556-0 | .ipynb
.pdf
Loading from LangChainHub
Loading from LangChainHub#
This notebook covers how to load chains from LangChainHub.
from langchain.chains import load_chain
chain = load_chain("lc://chains/llm-math/chain.json")
chain.run("whats 2 raised to .12")
> Entering new LLMMathChain chain...
whats 2 raised to .12
Answer: ... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/from_hub.html |
7c8115ab1556-1 | query = "What did the president say about Ketanji Brown Jackson"
chain.run(query)
" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support ... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/from_hub.html |
3cbcd9d058a5-0 | .ipynb
.pdf
Sequential Chains
Contents
SimpleSequentialChain
Sequential Chain
Memory in Sequential Chains
Sequential Chains#
The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
3cbcd9d058a5-1 | prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)
# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of pl... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
3cbcd9d058a5-2 | The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succu... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
3cbcd9d058a5-3 | The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.
Sequential... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
3cbcd9d058a5-4 | Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="review")
# This is the overall chain where we run these two chains in sequence.
... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
3cbcd9d058a5-5 | from langchain.memory import SimpleMemory
llm = OpenAI(temperature=.7)
template = """You are a social media manager for a theater company. Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for tha... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
3cbcd9d058a5-6 | 'location': 'Theater in the Park',
'social_post_text': "\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love i... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/sequential_chains.html |
d1df381273c8-0 | .ipynb
.pdf
LLM Chain
Contents
Single Input
Multiple Inputs
From string
LLM Chain#
This notebook showcases a simple LLM chain.
from langchain import PromptTemplate, OpenAI, LLMChain
Single Input#
First, lets go over an example using a single input
template = """Question: {question}
Answer: Let's think step by step.""... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/llm_chain.html |
d1df381273c8-1 | Prompt after formatting:
Write a sad poem about ducks.
> Finished LLMChain chain.
"\n\nThe ducks swim in the pond,\nTheir feathers so soft and warm,\nBut they can't help but feel so forlorn.\n\nTheir quacks echo in the air,\nBut no one is there to hear,\nFor they have no one to share.\n\nThe ducks paddle around in circ... | https://langchain.readthedocs.io/en/latest/modules/chains/generic/llm_chain.html |
d1df381273c8-2 | previous
Loading from LangChainHub
next
Sequential Chains
Contents
Single Input
Multiple Inputs
From string
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/chains/generic/llm_chain.html |
dee892cac239-0 | .md
.pdf
Key Concepts
Contents
Text Splitter
Embeddings
Vectorstores
CombineDocuments Chains
Key Concepts#
Text Splitter#
This class is responsible for splitting long pieces of text into smaller components.
It contains different ways for splitting text (on characters, using Spacy, etc)
as well as different ways for m... | https://langchain.readthedocs.io/en/latest/modules/indexes/key_concepts.html |
d3353c768902-0 | .rst
.pdf
How To Guides
Contents
Utils
Vectorstores
Chains
How To Guides#
Utils#
There are a lot of different utilities that LangChain provides integrations for
These guides go over how to use them.
The utilities here are all utilities that make it easier to work with documents.
Text Splitters: A walkthrough of how t... | https://langchain.readthedocs.io/en/latest/modules/indexes/how_to_guides.html |
d3353c768902-1 | The examples here are all end-to-end chains that use indexes or utils covered above.
Question Answering: A walkthrough of how to use LangChain for question answering over specific documents.
Question Answering with Sources: A walkthrough of how to use LangChain for question answering (with sources) over specific docume... | https://langchain.readthedocs.io/en/latest/modules/indexes/how_to_guides.html |
1795792d35ec-0 | .ipynb
.pdf
Getting Started
Contents
One Line Index Creation
Walkthrough
Getting Started#
By default, LangChain uses Chroma as the vectorstore to index and search embeddings. To walk through this tutorial, we’ll first need to install chromadb.
pip install chromadb
This example showcases question answering over docume... | https://langchain.readthedocs.io/en/latest/modules/indexes/getting_started.html |
1795792d35ec-1 | query = "What did the president say about Ketanji Brown Jackson"
index.query(query)
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He a... | https://langchain.readthedocs.io/en/latest/modules/indexes/getting_started.html |
1795792d35ec-2 | documents = loader.load()
Next, we will split the documents into chunks.
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
We will then select which embeddings we want to use.
from langchain.... | https://langchain.readthedocs.io/en/latest/modules/indexes/getting_started.html |
1795792d35ec-3 | )
Hopefully this highlights what is going on under the hood of VectorstoreIndexCreator. While we think it’s important to have a simple way to create indexes, we also think it’s important to understand what’s going on under the hood.
previous
Indexes
next
Key Concepts
Contents
One Line Index Creation
Walkthrough
By ... | https://langchain.readthedocs.io/en/latest/modules/indexes/getting_started.html |
3b75d7ce3247-0 | .md
.pdf
CombineDocuments Chains
Contents
Stuffing
Map Reduce
Refine
Map-Rerank
CombineDocuments Chains#
CombineDocuments chains are useful for when you need to run a language over multiple documents.
Common use cases for this include question answering, question answering with sources, summarization, and more.
For m... | https://langchain.readthedocs.io/en/latest/modules/indexes/combine_docs.html |
3b75d7ce3247-1 | This method involves running an initial prompt on each chunk of data (for summarization tasks, this
could be a summary of that chunk; for question-answering tasks, it could be an answer based solely on that chunk).
Then a different prompt is run to combine all the initial outputs. This is implemented in the LangChain a... | https://langchain.readthedocs.io/en/latest/modules/indexes/combine_docs.html |
3b75d7ce3247-2 | Stuffing
Map Reduce
Refine
Map-Rerank
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/indexes/combine_docs.html |
7573557c0053-0 | .ipynb
.pdf
FAISS
Contents
Similarity Search with score
Saving and loading
Merging
FAISS#
This notebook shows how to use functionality related to the FAISS vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores im... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/faiss.html |
7573557c0053-1 | Similarity Search with score#
There are some FAISS specific methods. One of them is similarity_search_with_score, which allows you to return not only the documents but also the similarity score of the query to them.
docs_and_scores = db.similarity_search_with_score(query)
docs_and_scores[0]
(Document(page_content='In s... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/faiss.html |
7573557c0053-2 | db.save_local("faiss_index")
new_db = FAISS.load_local("faiss_index", embeddings)
docs = new_db.similarity_search(query)
docs[0]
Document(page_content='In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n\nWe cannot let this happen. \n\nTonight. I call on t... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/faiss.html |
7573557c0053-3 | db2.docstore._dict
{'bdc50ae3-a1bb-4678-9260-1b0979578f40': Document(page_content='bar', lookup_str='', metadata={}, lookup_index=0)}
db1.merge_from(db2)
db1.docstore._dict
{'e0b74348-6c93-4893-8764-943139ec1d17': Document(page_content='foo', lookup_str='', metadata={}, lookup_index=0),
'd5211050-c777-493d-8825-4800e7... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/faiss.html |
f6b12f046b77-0 | .ipynb
.pdf
AtlasDB
AtlasDB#
This notebook shows you how to use functionality related to the AtlasDB
import time
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import SpacyTextSplitter
from langchain.vectorstores import AtlasDB
from langchain.document_loaders import TextLoader
!py... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/atlas.html |
f6b12f046b77-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/atlas.html |
ed72051b4880-0 | .ipynb
.pdf
PGVector
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
PGVector#
This notebook shows how to use functionality related to the Postgres vector database (PGVector).
## Loading Environment Variables
from typing import List, Tuple
from dotenv import load_dotenv
load_... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/pgvector.html |
ed72051b4880-1 | # permission to create a table.
db = PGVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name="state_of_the_union",
connection_string=CONNECTION_STRING,
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] = db.similarity_... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/pgvector.html |
ed72051b4880-2 | Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President h... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/pgvector.html |
ed72051b4880-3 | Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President h... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/pgvector.html |
c9135c148f0e-0 | .ipynb
.pdf
Redis
Redis#
This notebook shows how to use functionality related to the Redis database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.redis import Redis
from langchain.document_loaders import TextLoader
loader ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/redis.html |
c9135c148f0e-1 | And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
print(rds.add_texts(["Ankush went to Princeton"]))
['doc:333eadf75bd74be393acafa8bca48669']
query = "Princeton"
results = rds.si... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/redis.html |
dc04d859880b-0 | .ipynb
.pdf
Qdrant
Qdrant#
This notebook shows how to use functionality related to the Qdrant vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.document_loaders import TextLoader
fr... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/qdrant.html |
c17384b63945-0 | .ipynb
.pdf
Milvus
Milvus#
This notebook shows how to use functionality related to the Milvus vector database.
To run, you should have a Milvus instance up and running: https://milvus.io/docs/install_standalone-docker.md
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import Charac... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/milvus.html |
004d0bb8a134-0 | .ipynb
.pdf
Chroma
Contents
Similarity search with score
Persistance
Initialize PeristedChromaDB
Persist the Database
Load the Database from disk, and create the chain
Chroma#
This notebook shows how to use functionality related to the Chroma vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
f... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/chroma.html |
004d0bb8a134-1 | One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/chroma.html |
004d0bb8a134-2 | # Embed and store the texts
# Supplying a persist_directory will store the embeddings on disk
persist_directory = 'db'
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents(documents=docs, embedding=embedding, persist_directory=persist_directory)
Running Chroma using direct local API.
No existing DB found in ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/chroma.html |
44183cbbeefb-0 | .ipynb
.pdf
Deep Lake
Contents
Deep Lake datasets on cloud or local
Deep Lake#
This notebook showcases basic functionality related to Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming ... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/deeplake.html |
44183cbbeefb-1 | Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President h... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/deeplake.html |
44183cbbeefb-2 | We cannot let this happen.
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justi... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/deeplake.html |
0d94ef8821b0-0 | .ipynb
.pdf
Weaviate
Weaviate#
This notebook shows how to use functionality related to the Weaviate vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Weaviate
from langchain.document_loaders import TextL... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/weaviate.html |
0d94ef8821b0-1 | },
],
},
]
}
client.schema.create(schema)
vectorstore = Weaviate(client, "Paragraph", "content")
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(query)
print(docs[0].page_content)
previous
Redis
next
Analyze Document
By Harrison Chase
... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/weaviate.html |
af0e26b20a3d-0 | .ipynb
.pdf
OpenSearch
Contents
similarity_search using Approximate k-NN Search with Custom Parameters
similarity_search using Script Scoring with Custom Parameters
similarity_search using Painless Scripting with Custom Parameters
OpenSearch#
This notebook shows how to use functionality related to the OpenSearch data... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/opensearch.html |
af0e26b20a3d-1 | query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
similarity_search using Script Scoring with Custom Parameters#
docsearch = OpenSearchVectorSearch.from_texts(texts, embeddings, opensearch_url="http://localhost:9200", is_appx_search=Fa... | https://langchain.readthedocs.io/en/latest/modules/indexes/vectorstore_examples/opensearch.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.