id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 50 116 |
|---|---|---|
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
3e42b75c7a2a-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:///python.langchain.com/en/latest/modules/chains/examples/api.html |
b945a12eb084-0 | .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.from_llm(llm, verbose=True)
ch... | https:///python.langchain.com/en/latest/modules/chains/examples/llm_checker.html |
42534b5afc24-0 | .ipynb
.pdf
BashChain
Contents
Customize Prompt
Persistent Terminal
BashChain#
This notebook showcases using LLMs and a bash process to 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 pr... | https:///python.langchain.com/en/latest/modules/chains/examples/llm_bash.html |
42534b5afc24-1 | Do not use 'echo' when writing the script.
That is the format. Begin!
Question: {question}"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())
bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)
text = "Please write a bash script that pr... | https:///python.langchain.com/en/latest/modules/chains/examples/llm_bash.html |
42534b5afc24-2 | llm_requests.ipynb sqlite.ipynb
> Finished chain.
'api.ipynb\t\t\tllm_summarization_checker.ipynb\r\nconstitutional_chain.ipynb\tmoderation.ipynb\r\nllm_bash.ipynb\t\t\topenai_openapi.yaml\r\nllm_checker.ipynb\t\topenapi.ipynb\r\nllm_math.ipynb\t\t\tpal.ipynb\r\nllm_requests.ipynb\t\tsqlite.ipynb'
# Run the same comma... | https:///python.langchain.com/en/latest/modules/chains/examples/llm_bash.html |
2caa87dd8a11-0 | .ipynb
.pdf
Self-Critique Chain with Constitutional AI
Contents
Intermediate Steps
No revision necessary
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... | https:///python.langchain.com/en/latest/modules/chains/examples/constitutional_chain.html |
2caa87dd8a11-1 | > 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, and should not be condoned.
Updated response: It is illegal and unethical to steal kittens. If ... | https:///python.langchain.com/en/latest/modules/chains/examples/constitutional_chain.html |
2caa87dd8a11-2 | 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:///python.langchain.com/en/latest/modules/chains/examples/constitutional_chain.html |
2caa87dd8a11-3 | 'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.')]}
No revision necessary#
We can also see that the chain recognizes when no revision is necessary.
good_qa_prompt = PromptTemplate(
template="""You are good and must only give... | https:///python.langchain.com/en/latest/modules/chains/examples/constitutional_chain.html |
2caa87dd8a11-4 | 'critiques_and_revisions': [('No critique needed.', '')]}
previous
API Chains
next
BashChain
Contents
Intermediate Steps
No revision necessary
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/chains/examples/constitutional_chain.html |
bd52ec04807e-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:///python.langchain.com/en/latest/modules/chains/generic/serialization.html |
bd52ec04807e-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:///python.langchain.com/en/latest/modules/chains/generic/serialization.html |
bd52ec04807e-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:///python.langchain.com/en/latest/modules/chains/generic/serialization.html |
853c429e1e92-0 | .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:///python.langchain.com/en/latest/modules/chains/generic/transformation.html |
b1ab7f9978c2-0 | .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:///python.langchain.com/en/latest/modules/chains/generic/from_hub.html |
b1ab7f9978c2-1 | 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:///python.langchain.com/en/latest/modules/chains/generic/from_hub.html |
932d3ce7f9bc-0 | .ipynb
.pdf
Async API for Chain
Async API for Chain#
LangChain provides async support for Chains by leveraging the asyncio library.
Async methods are currently supported in LLMChain (through arun, apredict, acall) and LLMMathChain (through arun and acall), ChatVectorDBChain, and QA chains. Async support for other chain... | https:///python.langchain.com/en/latest/modules/chains/generic/async_chain.html |
932d3ce7f9bc-1 | await generate_concurrently()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Concurrent executed in {elapsed:0.2f} seconds." + '\033[0m')
s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Serial executed in {elapsed:0.2f} seconds." + '\033[0m')
BrightSmile Toothpas... | https:///python.langchain.com/en/latest/modules/chains/generic/async_chain.html |
983e13481560-0 | .ipynb
.pdf
Creating a custom Chain
Creating a custom Chain#
To implement your own custom chain you can subclass Chain and implement the following methods:
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
fro... | https:///python.langchain.com/en/latest/modules/chains/generic/custom_chain.html |
983e13481560-1 | # Whenever you call a language model, or another chain, you should pass
# a callback manager to it. This allows the inner run to be tracked by
# any callbacks that are registered on the outer run.
# You can always obtain a callback manager for this by calling
# `run_manager.get_child()` ... | https:///python.langchain.com/en/latest/modules/chains/generic/custom_chain.html |
983e13481560-2 | callbacks=run_manager.get_child() if run_manager else None
)
# If you want to log something about this run, you can do so by calling
# methods on the `run_manager`, as shown below. This will trigger any
# callbacks that are registered for that event.
if run_manager:
a... | https:///python.langchain.com/en/latest/modules/chains/generic/custom_chain.html |
0012b5a47672-0 | .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:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-1 | 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 play, it is your job to write a review for that play.
Play Synopsis:
{synopsis}
R... | https:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-2 | 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:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-3 | 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:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-4 | 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:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-5 | 'era': 'Victorian England',
'synopsis': "\n\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\n\nOn th... | https:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-6 | 'review': "\n\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and st... | https:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-7 | 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:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
0012b5a47672-8 | '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:///python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
c85ca926fefb-0 | .ipynb
.pdf
LLM Chain
Contents
LLM Chain
Additional ways of running LLM Chain
Parsing the outputs
Initialize from string
LLM Chain#
LLMChain is perhaps one of the most popular ways of querying an LLM object. It formats the prompt template using the input key values provided (and also memory key values, if available),... | https:///python.langchain.com/en/latest/modules/chains/generic/llm_chain.html |
c85ca926fefb-1 | llm_chain.generate(input_list)
LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'... | https:///python.langchain.com/en/latest/modules/chains/generic/llm_chain.html |
c85ca926fefb-2 | template = """List all the colors in a rainbow"""
prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)
llm_chain = LLMChain(prompt=prompt, llm=llm)
llm_chain.predict()
'\n\nRed, orange, yellow, green, blue, indigo, violet'
With predict_and_parser:
llm_chain.predict_and_parse()
['R... | https:///python.langchain.com/en/latest/modules/chains/generic/llm_chain.html |
8c347d8a1ea1-0 | .rst
.pdf
Vectorstores
Vectorstores#
Note
Conceptual Guide
Vectorstores are one of the most important components of building indexes.
For an introduction to vectorstores and generic functionality see:
Getting Started
We also have documentation for all the types of vectorstores that are supported.
Please see below for t... | https:///python.langchain.com/en/latest/modules/indexes/vectorstores.html |
388bd535f56b-0 | .rst
.pdf
Retrievers
Retrievers#
Note
Conceptual Guide
The retriever interface is a generic interface that makes it easy to combine documents with
language models. This interface exposes a get_relevant_documents method which takes in a query
(a string) and returns a list of documents.
Please see below for a list of all... | https:///python.langchain.com/en/latest/modules/indexes/retrievers.html |
7106a199cfe7-0 | .ipynb
.pdf
Getting Started
Contents
One Line Index Creation
Walkthrough
Getting Started#
LangChain primary focuses on constructing indexes with the goal of using them as a Retriever. In order to best understand what this means, it’s worth highlighting what the base Retriever interface is. The BaseRetriever class in ... | https:///python.langchain.com/en/latest/modules/indexes/getting_started.html |
7106a199cfe7-1 | Create a Retriever from that index
Create a question answering chain
Ask questions!
Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on.
First, let’s imp... | https:///python.langchain.com/en/latest/modules/indexes/getting_started.html |
7106a199cfe7-2 | index.query_with_sources(query)
{'question': 'What did the president say about Ketanji Brown Jackson',
'answer': " The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, one of the nation's top legal minds, to continue Justice Breyer's legacy of excellence, and that she has received... | https:///python.langchain.com/en/latest/modules/indexes/getting_started.html |
7106a199cfe7-3 | We will then select which embeddings we want to use.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
We now create the vectorstore to use as the index.
from langchain.vectorstores import Chroma
db = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API.
Using D... | https:///python.langchain.com/en/latest/modules/indexes/getting_started.html |
7106a199cfe7-4 | )
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
Document Loaders
Contents
One Line Index Creation
Walkthrough... | https:///python.langchain.com/en/latest/modules/indexes/getting_started.html |
e9043c921ea8-0 | .rst
.pdf
Text Splitters
Text Splitters#
Note
Conceptual Guide
When you want to deal with long pieces of text, it is necessary to split up that text into chunks.
As simple as this sounds, there is a lot of potential complexity here. Ideally, you want to keep the semantically related pieces of text together. What “seman... | https:///python.langchain.com/en/latest/modules/indexes/text_splitters.html |
6f3d6b6772ac-0 | .rst
.pdf
Document Loaders
Document Loaders#
Note
Conceptual Guide
Combining language models with your own text data is a powerful way to differentiate them.
The first step in doing this is to load the data into “documents” - a fancy way of say some pieces of text.
This module is aimed at making this easy.
A primary dr... | https:///python.langchain.com/en/latest/modules/indexes/document_loaders.html |
6f3d6b6772ac-1 | YouTube
previous
Getting Started
next
CoNLL-U
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/document_loaders.html |
e6812baf0a46-0 | .ipynb
.pdf
Pinecone Hybrid Search
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
Pinecone Hybrid Search#
This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.
The logic of this retriever is taken from this... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
e6812baf0a46-1 | index = pinecone.Index(index_name)
Get embeddings and sparse encoders#
Embeddings are used for the dense vectors, tokenizer is used for the sparse vector
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
To encode the text to sparse values you can either choose SPLADE or BM25. For out of... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
e6812baf0a46-2 | Use Retriever#
We can now use the retriever!
result = retriever.get_relevant_documents("foo")
result[0]
Document(page_content='foo', metadata={})
previous
Metal
next
Self-querying retriever
Contents
Setup Pinecone
Get embeddings and sparse encoders
Load Retriever
Add texts (if necessary)
Use Retriever
By Harrison C... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/pinecone_hybrid_search.html |
08939e85fd05-0 | .ipynb
.pdf
Vespa retriever
Vespa retriever#
This notebook shows how to use Vespa.ai as a LangChain retriever.
Vespa.ai is a platform for highly efficient structured text and vector search.
Please refer to Vespa.ai for more information.
In order to create a retriever, we use pyvespa to
create a connection a Vespa servi... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/vespa_retriever.html |
08939e85fd05-1 | previous
VectorStore Retriever
next
Weaviate Hybrid Search
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/vespa_retriever.html |
ccf3bf0ca18b-0 | .ipynb
.pdf
ChatGPT Plugin Retriever
Contents
Create
Using the ChatGPT Retriever Plugin
ChatGPT Plugin Retriever#
This notebook shows how to use the ChatGPT Retriever Plugin within LangChain.
Create#
First, let’s go over how to create the ChatGPT Retriever Plugin.
To set up the ChatGPT Retriever Plugin, please follow... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/chatgpt-plugin-retriever.html |
ccf3bf0ca18b-1 | The below code walks through how to do that.
from langchain.retrievers import ChatGPTPluginRetriever
retriever = ChatGPTPluginRetriever(url="http://0.0.0.0:8000", bearer_token="foo")
retriever.get_relevant_documents("alice's phone number")
[Document(page_content="This is Alice's phone number: 123-456-7890", lookup_str=... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/chatgpt-plugin-retriever.html |
ccf3bf0ca18b-2 | Document(page_content='Team: Angels "Payroll (millions)": 154.49 "Wins": 89', lookup_str='', metadata={'id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631_0', 'metadata': {'source': None, 'source_id': None, 'url': None, 'created_at': None, 'author': None, 'document_id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631'}, 'embedding': Non... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/chatgpt-plugin-retriever.html |
f2d4df128fbe-0 | .ipynb
.pdf
ElasticSearch BM25
Contents
Create New Retriever
Add texts (if necessary)
Use Retriever
ElasticSearch BM25#
This notebook goes over how to use a retriever that under the hood uses ElasticSearcha and BM25.
For more information on the details of BM25 see this blog post.
from langchain.retrievers import Elas... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html |
f2d4df128fbe-1 | result = retriever.get_relevant_documents("foo")
result
[Document(page_content='foo', metadata={}),
Document(page_content='foo bar', metadata={})]
previous
Databerry
next
Metal
Contents
Create New Retriever
Add texts (if necessary)
Use Retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/elastic_search_bm25.html |
e637043b361c-0 | .ipynb
.pdf
TF-IDF Retriever
Contents
Create New Retriever with Texts
Use Retriever
TF-IDF Retriever#
This notebook goes over how to use a retriever that under the hood uses TF-IDF using scikit-learn.
For more information on the details of TF-IDF see this blog post.
from langchain.retrievers import TFIDFRetriever
# !... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/tf_idf_retriever.html |
6bdc704cc234-0 | .ipynb
.pdf
SVM Retriever
Contents
Create New Retriever with Texts
Use Retriever
SVM Retriever#
This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn.
Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb
from langchain.retrievers import SVMRet... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/svm_retriever.html |
25696bbf30de-0 | .ipynb
.pdf
Databerry
Contents
Query
Databerry#
This notebook shows how to use Databerry’s retriever.
First, you will need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url
Query#
Now that our index is set up, we can set up a retriever and start querying it.
from lang... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/databerry.html |
25696bbf30de-1 | Document(page_content="✨ Made with DaftpageOpen main menuPricingTemplatesLoginSearchHelpGetting StartedFeaturesAffiliate ProgramHelp CenterWelcome to Daftpage’s help center—the one-stop shop for learning everything about building websites with Daftpage.Daftpage is the simplest way to create websites for all purposes in... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/databerry.html |
25696bbf30de-2 | Document(page_content=" is the simplest way to create websites for all purposes in seconds. Without knowing how to code, and for free!Get StartedDaftpage is a new type of website builder that works like a doc.It makes website building easy, fun and offers tons of powerful features for free. Just type / in your page to ... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/databerry.html |
3921e9c69bad-0 | .ipynb
.pdf
Self-querying retriever
Contents
Self-querying retriever
Creating a Pinecone index
Creating our self-querying retriever
Testing it out
Self-querying retriever#
In the notebook we’ll demo the SelfQueryRetriever, which, as the name suggests, has the ability to query itself. Specifically, given any natural l... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html |
3921e9c69bad-1 | from langchain.vectorstores import Pinecone
embeddings = OpenAIEmbeddings()
# create new index
pinecone.create_index("langchain-self-retriever-demo", dimension=1536)
docs = [
Document(page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": [... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html |
3921e9c69bad-2 | )
Creating our self-querying retriever#
Now we can instantiate our retriever. To do this we’ll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.
from langchain.llms import OpenAI
from langchain.retrievers.self_query.base impor... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html |
3921e9c69bad-3 | Document(page_content='Toys come alive and have a blast doing so', metadata={'genre': 'animated', 'year': 1995.0}),
Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'director': 'Satoshi Kon', 'rating': 8.6, 'year': 2... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html |
3921e9c69bad-4 | [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'director': 'Greta Gerwig', 'rating': 8.3, 'year': 2019.0})]
# This example specifies a composite filter
retriever.get_relevant_documents("What's a highly rated (above 8.5) science fiction film?")
quer... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html |
3921e9c69bad-5 | next
SVM Retriever
Contents
Self-querying retriever
Creating a Pinecone index
Creating our self-querying retriever
Testing it out
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html |
3ad9ddd48fda-0 | .ipynb
.pdf
Weaviate Hybrid Search
Weaviate Hybrid Search#
This notebook shows how to use Weaviate hybrid search as a LangChain retriever.
import weaviate
import os
WEAVIATE_URL = "..."
client = weaviate.Client(
url=WEAVIATE_URL,
)
from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetrieve... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate-hybrid.html |
d27df9aa5c62-0 | .ipynb
.pdf
Contextual Compression Retriever
Contents
Contextual Compression Retriever
Using a vanilla vector store retriever
Adding contextual compression with an LLMChainExtractor
More built-in compressors: filters
LLMChainFilter
EmbeddingsFilter
Stringing compressors and document transformers together
Contextual C... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-1 | texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()
docs = retriever.get_relevant_documents("What did the president say about Ketanji Brown Jackson")
pretty_print_docs(docs)
Document 1:
Tonight. I call on the Senate to: Pass the Freedom to Vote Act... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-2 | We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
----------------------------------------------------------------------------------------------------
Document 3:
And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-3 | Let’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.
Adding contextual compression with an LLMChainExtractor#
Now let’s wrap our base retriever with a ContextualCompressionRetriever. We’l... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-4 | More built-in compressors: filters#
LLMChainFilter#
The LLMChainFilter is slightly simpler but more robust compressor that uses an LLM chain to decide which of the initially retrieved documents to filter out and which ones to return, without manipulating the document contents.
from langchain.retrievers.document_compres... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-5 | from langchain.retrievers.document_compressors import EmbeddingsFilter
embeddings = OpenAIEmbeddings()
embeddings_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.76)
compression_retriever = ContextualCompressionRetriever(base_compressor=embeddings_filter, base_retriever=retriever)
compressed_doc... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-6 | We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have th... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-7 | Below we create a compressor pipeline by first splitting our docs into smaller chunks, then removing redundant documents, and then filtering based on relevance to the query.
from langchain.document_transformers import EmbeddingsRedundantFilter
from langchain.retrievers.document_compressors import DocumentCompressorPipe... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
d27df9aa5c62-8 | previous
Cohere Reranker
next
Databerry
Contents
Contextual Compression Retriever
Using a vanilla vector store retriever
Adding contextual compression with an LLMChainExtractor
More built-in compressors: filters
LLMChainFilter
EmbeddingsFilter
Stringing compressors and document transformers together
By Harrison Cha... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/contextual-compression.html |
f16297b90ac2-0 | .ipynb
.pdf
Cohere Reranker
Contents
Set up the base vector store retriever
Doing reranking with CohereRerank
Cohere Reranker#
This notebook shows how to use Cohere’s rerank endpoint in a retriever. This builds on top of ideas in the ContextualCompressionRetriever.
# Helper function for printing docs
def pretty_print... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-1 | 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.
----------------------------------------------------------------------------------------------------
Document 2:
As I said last ... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-2 | I’ve worked on these issues a long time.
I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.
So let’s not abandon our streets. Or choose between safety and equal justice.
-------------------------------... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-3 | ----------------------------------------------------------------------------------------------------
Document 9:
All told, we created 369,000 new manufacturing jobs in America just last year.
Powered by people I’ve met like JoJo Burgess, from generations of union steelworkers from Pittsburgh, who’s here with us tonigh... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-4 | Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans.
With a duty to one another to the American people to the Constitution.
And with an unwavering resolve that freedom will always triumph over tyranny.
--------------------------------------------------------------------------... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-5 | Our troops in Iraq and Afghanistan faced many dangers.
----------------------------------------------------------------------------------------------------
Document 16:
When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long ... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-6 | Because people were hurting. We needed to act, and we did.
Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.
----------------------------------------------------------------------------------------------------
Document 20:
So let’s not abandon our streets. Or choose... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-7 | ----------------------------------------------------------------------------------------------------
Document 2:
I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.
I’ve worked on these i... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
f16297b90ac2-8 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/cohere-reranker.html |
c777cec2bc33-0 | .ipynb
.pdf
Metal
Contents
Ingest Documents
Query
Metal#
This notebook shows how to use Metal’s retriever.
First, you will need to sign up for Metal and get an API key. You can do so here
# !pip install metal_sdk
from metal_sdk.metal import Metal
API_KEY = ""
CLIENT_ID = ""
INDEX_ID = ""
metal = Metal(API_KEY, CLIENT... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/metal.html |
c777cec2bc33-1 | previous
ElasticSearch BM25
next
Pinecone Hybrid Search
Contents
Ingest Documents
Query
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/metal.html |
e0c815dba11f-0 | .ipynb
.pdf
Time Weighted VectorStore Retriever
Contents
Low Decay Rate
High Decay Rate
Time Weighted VectorStore Retriever#
This retriever uses a combination of semantic similarity and recency.
The algorithm for scoring them is:
semantic_similarity + (1.0 - decay_rate) ** hours_passed
Notably, hours_passed refers to... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
e0c815dba11f-1 | retriever.add_documents([Document(page_content="hello foo")])
['5c9f7c06-c9eb-45f2-aea5-efce5fb9f2bd']
# "Hello World" is returned first because it is most salient, and the decay rate is close to 0., meaning it's still recent enough
retriever.get_relevant_documents("hello world")
[Document(page_content='hello world', m... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
e0c815dba11f-2 | # "Hello Foo" is returned first because "hello world" is mostly forgotten
retriever.get_relevant_documents("hello world")
[Document(page_content='hello foo', metadata={'last_accessed_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 494798), 'created_at': datetime.datetime(2023, 4, 16, 22, 9, 2, 178722), 'buffer_idx': 1})]... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/time_weighted_vectorstore.html |
672a37e35f96-0 | .ipynb
.pdf
VectorStore Retriever
VectorStore Retriever#
The index - and therefore the retriever - that LangChain has the most support for is a VectorStoreRetriever. As the name suggests, this retriever is backed heavily by a VectorStore.
Once you construct a VectorStore, its very easy to construct a retriever. Let’s w... | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/vectorstore-retriever.html |
672a37e35f96-1 | next
Vespa retriever
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/retrievers/examples/vectorstore-retriever.html |
1f72f115a043-0 | .ipynb
.pdf
Getting Started
Getting Started#
The default recommended text splitter is the RecursiveCharacterTextSplitter. This text splitter takes a list of characters. It tries to create chunks based on splitting on the first character, but if any chunks are too large it then moves onto the next character, and so fort... | https:///python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html |
1f72f115a043-1 | previous
Text Splitters
next
Character Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/modules/indexes/text_splitters/getting_started.html |
a584cf30b9c8-0 | .ipynb
.pdf
Python Code Text Splitter
Python Code Text Splitter#
PythonCodeTextSplitter splits text along python class and method definitions. It’s implemented as a simple subclass of RecursiveCharacterSplitter with Python-specific separators. See the source code to see the Python syntax expected by default.
How the te... | https:///python.langchain.com/en/latest/modules/indexes/text_splitters/examples/python.html |
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