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a3704047d63c-14 | 'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione. Ha anche menzionato le nuove tecnologie come scanner all'avanguardia per rilevare meglio il traffico di droga, le pattuglie congiunte con Messico e Guatemala per catturare più trafficanti di esseri umani, l'istituzione di giudici di immigrazione dedicati per far sì che le famiglie che fuggono da per"}
The map-rerank Chain#
This sections shows results of using the map-rerank Chain to do question answering with sources.
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_rerank", metadata_keys=['source'], return_intermediate_steps=True)
query = "What did the president say about Justice Breyer"
result = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
result["output_text"]
' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.'
result["intermediate_steps"]
[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',
'score': '100'}, | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\qa_with_sources.html |
a3704047d63c-15 | 'score': '100'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'}]
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
from langchain.output_parsers import RegexParser
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
Question: [question here]
Helpful Answer In Italian: [answer here]
Score: [score between 0 and 100]
Begin!
Context:
---------
{context}
---------
Question: {question}
Helpful Answer In Italian:"""
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
output_parser=output_parser,
)
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_rerank", metadata_keys=['source'], return_intermediate_steps=True, prompt=PROMPT)
query = "What did the president say about Justice Breyer"
result = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
result
{'source': 30, | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\qa_with_sources.html |
a3704047d63c-16 | result
{'source': 30,
'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',
'score': '100'},
{'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
'score': '100'},
{'answer': ' Non so.', 'score': '0'},
{'answer': ' Il presidente non ha detto nulla sulla giustizia Breyer.',
'score': '100'}],
'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.'}
previous
Graph QA
next
Question Answering
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\qa_with_sources.html |
c8ff00a66815-0 | .ipynb
.pdf
Question Answering
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering#
This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: stuff, map_reduce, refine, map_rerank. For a more in depth explanation of what these chain types are, see here.
Prepare Data#
First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.indexes.vectorstore import VectorstoreIndexCreator
index_creator = VectorstoreIndexCreator()
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
docsearch = index_creator.from_loaders([loader])
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
query = "What did the president say about Justice Breyer"
docs = docsearch.similarity_search(query)
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
Quickstart#
If you just want to get started as quickly as possible, this is the recommended way to do it:
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain.run(input_documents=docs, question=query) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-1 | chain.run(input_documents=docs, question=query)
' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'
If you want more control and understanding over what is happening, please see the information below.
The stuff Chain#
This sections shows results of using the stuff Chain to do question answering.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': ' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Answer in Italian:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera come giudice della Corte Suprema degli Stati Uniti.'} | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-2 | The map_reduce Chain#
This sections shows results of using the map_reduce Chain to do question answering.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': ' The president said, "Justice Breyer, thank you for your service."'}
Intermediate Steps
We can also return the intermediate steps for map_reduce chains, should we want to inspect them. This is done with the return_map_steps variable.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': [' "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."',
' None',
' None',
' None'],
'output_text': ' The president said, "Justice Breyer, thank you for your service."'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
question_prompt_template = """Use the following portion of a long document to see if any of the text is relevant to answer the question.
Return any relevant text translated into italian.
{context}
Question: {question}
Relevant text, if any, in Italian:"""
QUESTION_PROMPT = PromptTemplate(
template=question_prompt_template, input_variables=["context", "question"]
) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-3 | template=question_prompt_template, input_variables=["context", "question"]
)
combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer italian.
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
QUESTION: {question}
=========
{summaries}
=========
Answer in Italian:"""
COMBINE_PROMPT = PromptTemplate(
template=combine_prompt_template, input_variables=["summaries", "question"]
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.",
'\nNessun testo pertinente.',
"\nCome ho detto l'anno scorso, soprattutto ai nostri giovani americani transgender, avrò sempre il tuo sostegno come tuo Presidente, in modo che tu possa essere te stesso e raggiungere il tuo potenziale donato da Dio.", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-4 | '\nNella mia amministrazione, i guardiani sono stati accolti di nuovo. Stiamo andando dietro ai criminali che hanno rubato miliardi di dollari di aiuti di emergenza destinati alle piccole imprese e a milioni di americani. E stasera, annuncio che il Dipartimento di Giustizia nominerà un procuratore capo per la frode pandemica.'],
'output_text': ' Non conosco la risposta alla tua domanda su cosa abbia detto il Presidente riguardo al Giustizia Breyer.'}
Batch Size
When using the map_reduce chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:
llm = OpenAI(batch_size=5, temperature=0)
The refine Chain#
This sections shows results of using the refine Chain to do question answering.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine")
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-5 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}
Intermediate Steps
We can also return the intermediate steps for refine chains, should we want to inspect them. This is done with the return_refine_steps variable.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ['\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',
'\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',
'\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act.', | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-6 | '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'],
'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
refine_prompt_template = (
"The original question is as follows: {question}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer"
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer. Reply in Italian."
)
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"], | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-7 | input_variables=["question", "existing_answer", "context_str"],
template=refine_prompt_template,
)
initial_qa_template = (
"Context information is below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {question}\nYour answer should be in Italian.\n"
)
initial_qa_prompt = PromptTemplate(
input_variables=["context_str", "question"], template=initial_qa_template
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True,
question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ['\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito.', | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-8 | "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani.", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-9 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-10 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic"], | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-11 | 'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic"}
The map-rerank Chain#
This sections shows results of using the map-rerank Chain to do question answering with sources.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True)
query = "What did the president say about Justice Breyer"
results = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
results["output_text"]
' The president thanked Justice Breyer for his service and honored him for dedicating his life to serving the country. '
results["intermediate_steps"]
[{'answer': ' The president thanked Justice Breyer for his service and honored him for dedicating his life to serving the country. ',
'score': '100'}, | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-12 | 'score': '100'},
{'answer': " The president said that Justice Breyer is a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that since she's been nominated, she's received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and that she is a consensus builder.",
'score': '100'},
{'answer': ' The president did not mention Justice Breyer in this context.',
'score': '0'},
{'answer': ' The president did not mention Justice Breyer in the given context. ',
'score': '0'}]
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
from langchain.output_parsers import RegexParser
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:
Question: [question here]
Helpful Answer In Italian: [answer here]
Score: [score between 0 and 100]
Begin!
Context:
---------
{context}
---------
Question: {question}
Helpful Answer In Italian:"""
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question"],
output_parser=output_parser,
) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
c8ff00a66815-13 | input_variables=["context", "question"],
output_parser=output_parser,
)
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_rerank", return_intermediate_steps=True, prompt=PROMPT)
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',
'score': '100'},
{'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
'score': '100'},
{'answer': ' Non so.', 'score': '0'},
{'answer': ' Il presidente non ha detto nulla sulla giustizia Breyer.',
'score': '100'}],
'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.'}
previous
Question Answering with Sources
next
Summarization
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\question_answering.html |
39075dc87dde-0 | .ipynb
.pdf
Summarization
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
Summarization#
This notebook walks through how to use LangChain for summarization over a list of documents. It covers three different chain types: stuff, map_reduce, and refine. For a more in depth explanation of what these chain types are, see here.
Prepare Data#
First we prepare the data. For this example we create multiple documents from one long one, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents).
from langchain import OpenAI, PromptTemplate, LLMChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.prompts import PromptTemplate
llm = OpenAI(temperature=0)
text_splitter = CharacterTextSplitter()
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
texts = text_splitter.split_text(state_of_the_union)
from langchain.docstore.document import Document
docs = [Document(page_content=t) for t in texts[:3]]
from langchain.chains.summarize import load_summarize_chain
Quickstart#
If you just want to get started as quickly as possible, this is the recommended way to do it:
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-1 | chain.run(docs)
' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and create jobs. The US remains unified and determined to protect Ukraine and the free world.'
If you want more control and understanding over what is happening, please see the information below.
The stuff Chain#
This sections shows results of using the stuff Chain to do summarization.
chain = load_summarize_chain(llm, chain_type="stuff")
chain.run(docs)
' In his speech, President Biden addressed the crisis in Ukraine, the American Rescue Plan, and the Bipartisan Infrastructure Law. He discussed the need to invest in America, educate Americans, and build the economy from the bottom up. He also announced the release of 60 million barrels of oil from reserves around the world, and the creation of a dedicated task force to go after the crimes of Russian oligarchs. He concluded by emphasizing the need to Buy American and use taxpayer dollars to rebuild America.'
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
prompt_template = """Write a concise summary of the following:
{text}
CONCISE SUMMARY IN ITALIAN:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(llm, chain_type="stuff", prompt=PROMPT)
chain.run(docs) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-2 | chain.run(docs)
"\n\nIn questa serata, il Presidente degli Stati Uniti ha annunciato una serie di misure per affrontare la crisi in Ucraina, causata dall'aggressione di Putin. Ha anche annunciato l'invio di aiuti economici, militari e umanitari all'Ucraina. Ha anche annunciato che gli Stati Uniti e i loro alleati stanno imponendo sanzioni economiche a Putin e stanno rilasciando 60 milioni di barili di petrolio dalle riserve di tutto il mondo. Inoltre, ha annunciato che il Dipartimento di Giustizia degli Stati Uniti sta creando una task force dedicata ai crimini degli oligarchi russi. Il Presidente ha anche annunciato l'approvazione della legge bipartitica sull'infrastruttura, che prevede investimenti per la ricostruzione dell'America. Questo porterà a creare posti"
The map_reduce Chain#
This sections shows results of using the map_reduce Chain to do summarization.
chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)
" In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and releasing oil from its Strategic Petroleum Reserve. President Biden and Vice President Harris have passed legislation to help struggling families and rebuild America's infrastructure."
Intermediate Steps
We can also return the intermediate steps for map_reduce chains, should we want to inspect them. This is done with the return_map_steps variable. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-3 | chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True)
chain({"input_documents": docs}, return_only_outputs=True)
{'map_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.",
' The United States and its European allies are taking action to punish Russia for its invasion of Ukraine, including seizing assets, closing off airspace, and providing economic and military assistance to Ukraine. The US is also mobilizing forces to protect NATO countries and has released 30 million barrels of oil from its Strategic Petroleum Reserve to help blunt gas prices. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens.',
" President Biden and Vice President Harris ran for office with a new economic vision for America, and have since passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and rebuild America's infrastructure. This includes creating jobs, modernizing roads, airports, ports, and waterways, replacing lead pipes, providing affordable high-speed internet, and investing in American products to support American jobs."],
'output_text': " In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and passing legislation to help struggling families and rebuild America's infrastructure. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens."}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-4 | prompt_template = """Write a concise summary of the following:
{text}
CONCISE SUMMARY IN ITALIAN:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
chain({"input_documents": docs}, return_only_outputs=True)
{'intermediate_steps': ["\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Gli Stati Uniti e i loro alleati stanno ora imponendo sanzioni economiche a Putin e stanno tagliando l'accesso della Russia alla tecnologia. Il Dipartimento di Giustizia degli Stati Uniti sta anche creando una task force dedicata per andare dopo i crimini degli oligarchi russi.", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-5 | "\n\nStiamo unendo le nostre forze con quelle dei nostri alleati europei per sequestrare yacht, appartamenti di lusso e jet privati di Putin. Abbiamo chiuso lo spazio aereo americano ai voli russi e stiamo fornendo più di un miliardo di dollari in assistenza all'Ucraina. Abbiamo anche mobilitato le nostre forze terrestri, aeree e navali per proteggere i paesi della NATO. Abbiamo anche rilasciato 60 milioni di barili di petrolio dalle riserve di tutto il mondo, di cui 30 milioni dalla nostra riserva strategica di petrolio. Stiamo affrontando una prova reale e ci vorrà del tempo, ma alla fine Putin non riuscirà a spegnere l'amore dei popoli per la libertà.", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-6 | "\n\nIl Presidente Biden ha lottato per passare l'American Rescue Plan per aiutare le persone che soffrivano a causa della pandemia. Il piano ha fornito sollievo economico immediato a milioni di americani, ha aiutato a mettere cibo sulla loro tavola, a mantenere un tetto sopra le loro teste e a ridurre il costo dell'assicurazione sanitaria. Il piano ha anche creato più di 6,5 milioni di nuovi posti di lavoro, il più alto numero di posti di lavoro creati in un anno nella storia degli Stati Uniti. Il Presidente Biden ha anche firmato la legge bipartitica sull'infrastruttura, la più ampia iniziativa di ricostruzione della storia degli Stati Uniti. Il piano prevede di modernizzare le strade, gli aeroporti, i porti e le vie navigabili in"],
'output_text': "\n\nIl Presidente Biden sta lavorando per aiutare le persone che soffrono a causa della pandemia attraverso l'American Rescue Plan e la legge bipartitica sull'infrastruttura. Gli Stati Uniti e i loro alleati stanno anche imponendo sanzioni economiche a Putin e tagliando l'accesso della Russia alla tecnologia. Stanno anche sequestrando yacht, appartamenti di lusso e jet privati di Putin e fornendo più di un miliardo di dollari in assistenza all'Ucraina. Alla fine, Putin non riuscirà a spegnere l'amore dei popoli per la libertà."}
The refine Chain#
This sections shows results of using the refine Chain to do summarization. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-7 | The refine Chain#
This sections shows results of using the refine Chain to do summarization.
chain = load_summarize_chain(llm, chain_type="refine")
chain.run(docs)
"\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This investment will"
Intermediate Steps
We can also return the intermediate steps for refine chains, should we want to inspect them. This is done with the return_refine_steps variable.
chain = load_summarize_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True)
chain({"input_documents": docs}, return_only_outputs=True) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-8 | chain({"input_documents": docs}, return_only_outputs=True)
{'refine_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.",
"\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. Putin's war on Ukraine has left Russia weaker and the rest of the world stronger, with the world uniting in support of democracy and peace.", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-9 | "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing"], | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-10 | 'output_text': "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing"}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
prompt_template = """Write a concise summary of the following:
{text}
CONCISE SUMMARY IN ITALIAN:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
refine_template = (
"Your job is to produce a final summary\n"
"We have provided an existing summary up to a certain point: {existing_answer}\n"
"We have the opportunity to refine the existing summary"
"(only if needed) with some more context below.\n"
"------------\n" | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-11 | "------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary in Italian"
"If the context isn't useful, return the original summary."
)
refine_prompt = PromptTemplate(
input_variables=["existing_answer", "text"],
template=refine_template,
)
chain = load_summarize_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True, question_prompt=PROMPT, refine_prompt=refine_prompt)
chain({"input_documents": docs}, return_only_outputs=True)
{'intermediate_steps': ["\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia e bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi.", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-12 | "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare,", | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-13 | "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare."], | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
39075dc87dde-14 | 'output_text': "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare."}
previous
Question Answering
next
Retrieval Question/Answering
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\summarize.html |
2b5c045dde84-0 | .ipynb
.pdf
Retrieval Question/Answering
Contents
Chain Type
Custom Prompts
Return Source Documents
Retrieval Question/Answering#
This example showcases question answering over an index.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" The president said that she 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 also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
Chain Type#
You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see this notebook. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
2b5c045dde84-1 | There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=docsearch.as_retriever())
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" The president said that Judge Ketanji Brown Jackson is one of our nation's top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQA chain with the combine_documents_chain parameter. For example:
from langchain.chains.question_answering import load_qa_chain
qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
2b5c045dde84-2 | query = "What did the president say about Ketanji Brown Jackson"
qa.run(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 also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
Custom Prompts#
You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chain
from langchain.prompts import PromptTemplate
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Answer in Italian:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" Il presidente ha detto che Ketanji Brown Jackson è una delle menti legali più importanti del paese, che continuerà l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."
Return Source Documents# | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
2b5c045dde84-3 | Return Source Documents#
Additionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), return_source_documents=True)
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"query": query})
result["result"]
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."
result["source_documents"]
[Document(page_content='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. \n\nTonight, 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. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
2b5c045dde84-4 | Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
2b5c045dde84-5 | Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
2b5c045dde84-6 | Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’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.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]
previous
Summarization
next
Retrieval Question Answering with Sources
Contents
Chain Type
Custom Prompts
Return Source Documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa.html |
0836fb20f5f6-0 | .ipynb
.pdf
Retrieval Question Answering with Sources
Contents
Chain Type
Retrieval Question Answering with Sources#
This notebook goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings.cohere import CohereEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
from langchain.vectorstores import Chroma
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))])
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
from langchain.chains import RetrievalQAWithSourcesChain
from langchain import OpenAI
chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())
chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
{'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
'sources': '31-pl'}
Chain Type# | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa_with_sources.html |
0836fb20f5f6-1 | 'sources': '31-pl'}
Chain Type#
You can easily specify different chain types to load and use in the RetrievalQAWithSourcesChain chain. For a more detailed walkthrough of these types, please see this notebook.
There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.
chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="map_reduce", retriever=docsearch.as_retriever())
chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
{'answer': ' The president said "Justice Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service."\n',
'sources': '31-pl'}
The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQAWithSourcesChain chain with the combine_documents_chain parameter. For example:
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
qa = RetrievalQAWithSourcesChain(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())
qa({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa_with_sources.html |
0836fb20f5f6-2 | {'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
'sources': '31-pl'}
previous
Retrieval Question/Answering
next
Vector DB Text Generation
Contents
Chain Type
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_qa_with_sources.html |
af1738ba15ef-0 | .ipynb
.pdf
Vector DB Text Generation
Contents
Prepare Data
Set Up Vector DB
Set Up LLM Chain with Custom Prompt
Generate Text
Vector DB Text Generation#
This notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a large body of custom text, for example, generating blog posts that have an understanding of previous blog posts written, or product tutorials that can refer to product documentation.
Prepare Data#
First, we prepare the data. For this example, we fetch a documentation site that consists of markdown files hosted on Github and split them into small enough Documents.
from langchain.llms import OpenAI
from langchain.docstore.document import Document
import requests
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.prompts import PromptTemplate
import pathlib
import subprocess
import tempfile
def get_github_docs(repo_owner, repo_name):
with tempfile.TemporaryDirectory() as d:
subprocess.check_call(
f"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .",
cwd=d,
shell=True,
)
git_sha = (
subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d)
.decode("utf-8")
.strip()
)
repo_path = pathlib.Path(d)
markdown_files = list(repo_path.glob("*/*.md")) + list(
repo_path.glob("*/*.mdx")
)
for markdown_file in markdown_files:
with open(markdown_file, "r") as f:
relative_path = markdown_file.relative_to(repo_path) | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-1 | relative_path = markdown_file.relative_to(repo_path)
github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}"
yield Document(page_content=f.read(), metadata={"source": github_url})
sources = get_github_docs("yirenlu92", "deno-manual-forked")
source_chunks = []
splitter = CharacterTextSplitter(separator=" ", chunk_size=1024, chunk_overlap=0)
for source in sources:
for chunk in splitter.split_text(source.page_content):
source_chunks.append(Document(page_content=chunk, metadata=source.metadata))
Cloning into '.'...
Set Up Vector DB#
Now that we have the documentation content in chunks, let’s put all this information in a vector index for easy retrieval.
search_index = Chroma.from_documents(source_chunks, OpenAIEmbeddings())
Set Up LLM Chain with Custom Prompt#
Next, let’s set up a simple LLM chain but give it a custom prompt for blog post generation. Note that the custom prompt is parameterized and takes two inputs: context, which will be the documents fetched from the vector search, and topic, which is given by the user.
from langchain.chains import LLMChain
prompt_template = """Use the context below to write a 400 word blog post about the topic below:
Context: {context}
Topic: {topic}
Blog post:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "topic"]
)
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=PROMPT)
Generate Text# | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-2 | chain = LLMChain(llm=llm, prompt=PROMPT)
Generate Text#
Finally, we write a function to apply our inputs to the chain. The function takes an input parameter topic. We find the documents in the vector index that correspond to that topic, and use them as additional context in our simple LLM chain.
def generate_blog_post(topic):
docs = search_index.similarity_search(topic, k=4)
inputs = [{"context": doc.page_content, "topic": topic} for doc in docs]
print(chain.apply(inputs))
generate_blog_post("environment variables") | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-3 | [{'text': '\n\nEnvironment variables are a great way to store and access sensitive information in your Deno applications. Deno offers built-in support for environment variables with `Deno.env`, and you can also use a `.env` file to store and access environment variables.\n\nUsing `Deno.env` is simple. It has getter and setter methods, so you can easily set and retrieve environment variables. For example, you can set the `FIREBASE_API_KEY` and `FIREBASE_AUTH_DOMAIN` environment variables like this:\n\n```ts\nDeno.env.set("FIREBASE_API_KEY", "examplekey123");\nDeno.env.set("FIREBASE_AUTH_DOMAIN", "firebasedomain.com");\n\nconsole.log(Deno.env.get("FIREBASE_API_KEY")); // examplekey123\nconsole.log(Deno.env.get("FIREBASE_AUTH_DOMAIN")); // firebasedomain.com\n```\n\nYou can also store environment variables in a `.env` file. This is a great'}, {'text': '\n\nEnvironment variables are a powerful tool for managing configuration settings in a program. They allow us to set values that can be used by the program, without having to hard-code them into the code. This makes it easier to change | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-4 | into the code. This makes it easier to change settings without having to modify the code.\n\nIn Deno, environment variables can be set in a few different ways. The most common way is to use the `VAR=value` syntax. This will set the environment variable `VAR` to the value `value`. This can be used to set any number of environment variables before running a command. For example, if we wanted to set the environment variable `VAR` to `hello` before running a Deno command, we could do so like this:\n\n```\nVAR=hello deno run main.ts\n```\n\nThis will set the environment variable `VAR` to `hello` before running the command. We can then access this variable in our code using the `Deno.env.get()` function. For example, if we ran the following command:\n\n```\nVAR=hello && deno eval "console.log(\'Deno: \' + Deno.env.get(\'VAR'}, {'text': '\n\nEnvironment variables are a powerful tool for developers, allowing them to store and access data without having to hard-code it into their applications. In Deno, | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-5 | to hard-code it into their applications. In Deno, you can access environment variables using the `Deno.env.get()` function.\n\nFor example, if you wanted to access the `HOME` environment variable, you could do so like this:\n\n```js\n// env.js\nDeno.env.get("HOME");\n```\n\nWhen running this code, you\'ll need to grant the Deno process access to environment variables. This can be done by passing the `--allow-env` flag to the `deno run` command. You can also specify which environment variables you want to grant access to, like this:\n\n```shell\n# Allow access to only the HOME env var\ndeno run --allow-env=HOME env.js\n```\n\nIt\'s important to note that environment variables are case insensitive on Windows, so Deno also matches them case insensitively (on Windows only).\n\nAnother thing to be aware of when using environment variables is subprocess permissions. Subprocesses are powerful and can access system resources regardless of the permissions you granted to the Den'}, {'text': '\n\nEnvironment variables are an important part of any programming language, | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-6 | variables are an important part of any programming language, and Deno is no exception. Deno is a secure JavaScript and TypeScript runtime built on the V8 JavaScript engine, and it recently added support for environment variables. This feature was added in Deno version 1.6.0, and it is now available for use in Deno applications.\n\nEnvironment variables are used to store information that can be used by programs. They are typically used to store configuration information, such as the location of a database or the name of a user. In Deno, environment variables are stored in the `Deno.env` object. This object is similar to the `process.env` object in Node.js, and it allows you to access and set environment variables.\n\nThe `Deno.env` object is a read-only object, meaning that you cannot directly modify the environment variables. Instead, you must use the `Deno.env.set()` function to set environment variables. This function takes two arguments: the name of the environment variable and the value to set it to. For example, if you wanted to set the `FOO` | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-7 | example, if you wanted to set the `FOO` environment variable to `bar`, you would use the following code:\n\n```'}] | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
af1738ba15ef-8 | previous
Retrieval Question Answering with Sources
next
Chains
Contents
Prepare Data
Set Up Vector DB
Set Up LLM Chain with Custom Prompt
Generate Text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\chain_examples\vector_db_text_generation.html |
030105f605e6-0 | .ipynb
.pdf
Embeddings
Contents
OpenAI
AzureOpenAI
Cohere
Hugging Face Hub
TensorflowHub
InstructEmbeddings
Self Hosted Embeddings
Fake Embeddings
SageMaker Endpoint Embeddings
Embeddings#
This notebook goes over how to use the Embedding class in LangChain.
The Embedding class is a class designed for interfacing with embeddings. There are lots of Embedding providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them.
Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space.
The base Embedding class in LangChain exposes two methods: embed_documents and embed_query. The largest difference is that these two methods have different interfaces: one works over multiple documents, while the other works over a single document. Besides this, another reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself).
OpenAI#
Let’s load the OpenAI Embedding class.
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
Let’s load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see here
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model_name="ada")
text = "This is a test document." | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\embeddings.html |
030105f605e6-1 | text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
AzureOpenAI#
Let’s load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints.
# set the environment variables needed for openai package to know to reach out to azure
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
embeddings = OpenAIEmbeddings(model="your-embeddings-deployment-name")
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
Cohere#
Let’s load the Cohere Embedding class.
from langchain.embeddings import CohereEmbeddings
embeddings = CohereEmbeddings(cohere_api_key=cohere_api_key)
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
Hugging Face Hub#
Let’s load the Hugging Face Embedding class.
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
doc_result = embeddings.embed_documents([text])
TensorflowHub#
Let’s load the TensorflowHub Embedding class.
from langchain.embeddings import TensorflowHubEmbeddings
embeddings = TensorflowHubEmbeddings() | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\embeddings.html |
030105f605e6-2 | embeddings = TensorflowHubEmbeddings()
2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
text = "This is a test document."
query_result = embeddings.embed_query(text)
InstructEmbeddings#
Let’s load the HuggingFace instruct Embeddings class.
from langchain.embeddings import HuggingFaceInstructEmbeddings
embeddings = HuggingFaceInstructEmbeddings(
query_instruction="Represent the query for retrieval: "
)
load INSTRUCTOR_Transformer
max_seq_length 512
text = "This is a test document."
query_result = embeddings.embed_query(text)
Self Hosted Embeddings#
Let’s load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes.
from langchain.embeddings import (
SelfHostedEmbeddings,
SelfHostedHuggingFaceEmbeddings,
SelfHostedHuggingFaceInstructEmbeddings,
)
import runhouse as rh
# For an on-demand A100 with GCP, Azure, or Lambda | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\embeddings.html |
030105f605e6-3 | # For an on-demand A100 with GCP, Azure, or Lambda
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
# For an on-demand A10G with AWS (no single A100s on AWS)
# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')
# For an existing cluster
# gpu = rh.cluster(ips=['<ip of the cluster>'],
# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},
# name='my-cluster')
embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
text = "This is a test document."
query_result = embeddings.embed_query(text)
And similarly for SelfHostedHuggingFaceInstructEmbeddings:
embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
Now let’s load an embedding model with a custom load function:
def get_pipeline():
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
) # Must be inside the function in notebooks
model_id = "facebook/bart-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
def inference_fn(pipeline, prompt):
# Return last hidden state of the model
if isinstance(prompt, list):
return [emb[0][-1] for emb in pipeline(prompt)]
return pipeline(prompt)[0][-1]
embeddings = SelfHostedEmbeddings(
model_load_fn=get_pipeline,
hardware=gpu, | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\embeddings.html |
030105f605e6-4 | model_load_fn=get_pipeline,
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
inference_fn=inference_fn,
)
query_result = embeddings.embed_query(text)
Fake Embeddings#
LangChain also provides a fake embedding class. You can use this to test your pipelines.
from langchain.embeddings import FakeEmbeddings
embeddings = FakeEmbeddings(size=1352)
query_result = embeddings.embed_query("foo")
doc_results = embeddings.embed_documents(["foo"])
SageMaker Endpoint Embeddings#
Let’s load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.
For instrucstions on how to do this, please see here
!pip3 install langchain boto3
from typing import Dict
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
import json
class ContentHandler(ContentHandlerBase):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps({"inputs": prompt, **model_kwargs})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["embeddings"]
content_handler = ContentHandler()
embeddings = SagemakerEndpointEmbeddings(
# endpoint_name="endpoint-name",
# credentials_profile_name="credentials-profile-name",
endpoint_name="huggingface-pytorch-inference-2023-03-21-16-14-03-834", | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\embeddings.html |
030105f605e6-5 | region_name="us-east-1",
content_handler=content_handler
)
query_result = embeddings.embed_query("foo")
doc_results = embeddings.embed_documents(["foo"])
doc_results
previous
How To Guides
next
Hypothetical Document Embeddings
Contents
OpenAI
AzureOpenAI
Cohere
Hugging Face Hub
TensorflowHub
InstructEmbeddings
Self Hosted Embeddings
Fake Embeddings
SageMaker Endpoint Embeddings
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\embeddings.html |
a75f47a08acb-0 | .ipynb
.pdf
Hypothetical Document Embeddings
Contents
Multiple generations
Using our own prompts
Using HyDE
Hypothetical Document Embeddings#
This notebook goes over how to use Hypothetical Document Embeddings (HyDE), as described in this paper.
At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then embeds that generated document and uses that as the final example.
In order to use HyDE, we therefore need to provide a base embedding model, as well as an LLMChain that can be used to generate those documents. By default, the HyDE class comes with some default prompts to use (see the paper for more details on them), but we can also create our own.
from langchain.llms import OpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import LLMChain, HypotheticalDocumentEmbedder
from langchain.prompts import PromptTemplate
base_embeddings = OpenAIEmbeddings()
llm = OpenAI()
# Load with `web_search` prompt
embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, "web_search")
# Now we can use it as any embedding class!
result = embeddings.embed_query("Where is the Taj Mahal?")
Multiple generations#
We can also generate multiple documents and then combine the embeddings for those. By default, we combine those by taking the average. We can do this by changing the LLM we use to generate documents to return multiple things.
multi_llm = OpenAI(n=4, best_of=4)
embeddings = HypotheticalDocumentEmbedder.from_llm(multi_llm, base_embeddings, "web_search")
result = embeddings.embed_query("Where is the Taj Mahal?")
Using our own prompts# | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\hyde.html |
a75f47a08acb-1 | result = embeddings.embed_query("Where is the Taj Mahal?")
Using our own prompts#
Besides using preconfigured prompts, we can also easily construct our own prompts and use those in the LLMChain that is generating the documents. This can be useful if we know the domain our queries will be in, as we can condition the prompt to generate text more similar to that.
In the example below, let’s condition it to generate text about a state of the union address (because we will use that in the next example).
prompt_template = """Please answer the user's question about the most recent state of the union address
Question: {question}
Answer:"""
prompt = PromptTemplate(input_variables=["question"], template=prompt_template)
llm_chain = LLMChain(llm=llm, prompt=prompt)
embeddings = HypotheticalDocumentEmbedder(llm_chain=llm_chain, base_embeddings=base_embeddings)
result = embeddings.embed_query("What did the president say about Ketanji Brown Jackson")
Using HyDE#
Now that we have HyDE, we can use it as we would any other embedding class! Here is using it to find similar passages in the state of the union example.
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
docsearch = Chroma.from_texts(texts, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\hyde.html |
a75f47a08acb-2 | Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
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: 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 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 excellence.
previous
Embeddings
next
Text Splitter
Contents
Multiple generations
Using our own prompts
Using HyDE
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\hyde.html |
e69adc12e844-0 | .ipynb
.pdf
Text Splitter
Contents
Generic Recursive Text Splitting
Markdown Text Splitter
Latex Text Splitter
Python Code Text Splitter
Character Text Splitting
HuggingFace Length Function
tiktoken (OpenAI) Length Function
NLTK Text Splitter
Spacy Text Splitter
Token Text Splitter
Text Splitter#
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 “semantically related” means could depend on the type of text.
This notebook showcases several ways to do that.
At a high level, text splitters work as following:
Split the text up into small, semantically meaningful chunks (often sentences).
Start combining these small chunks into a larger chunk until you reach a certain size (as measured by some function).
Once you reach that size, make that chunk its own piece of text and then start creating a new chunk of text with some overlap (to keep context between chunks).
That means there two different axes along which you can customize your text splitter:
How the text is split
How the chunk size is measured
For all the examples below, we will highlight both of these attributes
# This is a long document we can split up.
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
Generic Recursive Text Splitting# | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-1 | state_of_the_union = f.read()
Generic Recursive Text Splitting#
This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is ["\n\n", "\n", " ", ""]. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.
How the text is split: by list of characters
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
# Set a really small chunk size, just to show.
chunk_size = 100,
chunk_overlap = 20,
length_function = len,
)
texts = text_splitter.create_documents([state_of_the_union])
print(texts[0])
print(texts[1])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet.' lookup_str='' metadata={} lookup_index=0
page_content='and the Cabinet. Justices of the Supreme Court. My fellow Americans.' lookup_str='' metadata={} lookup_index=0
Markdown Text Splitter#
MarkdownTextSplitter splits text along Markdown headings, code blocks, or horizontal rules. It’s implemented as a simple subclass of RecursiveCharacterSplitter with Markdown-specific separators. See the source code to see the Markdown syntax expected by default.
How the text is split: by list of markdown specific characters
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import MarkdownTextSplitter | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-2 | from langchain.text_splitter import MarkdownTextSplitter
markdown_text = """
# 🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
## Quick Install
```bash
# Hopefully this code block isn't split
pip install langchain
```
As an open source project in a rapidly developing field, we are extremely open to contributions.
"""
markdown_splitter = MarkdownTextSplitter(chunk_size=100, chunk_overlap=0)
docs = markdown_splitter.create_documents([markdown_text])
docs
[Document(page_content='# 🦜️🔗 LangChain\n\n⚡ Building applications with LLMs through composability ⚡', lookup_str='', metadata={}, lookup_index=0),
Document(page_content="Quick Install\n\n```bash\n# Hopefully this code block isn't split\npip install langchain", lookup_str='', metadata={}, lookup_index=0),
Document(page_content='As an open source project in a rapidly developing field, we are extremely open to contributions.', lookup_str='', metadata={}, lookup_index=0)]
Latex Text Splitter#
LatexTextSplitter splits text along Latex headings, headlines, enumerations and more. It’s implemented as a simple subclass of RecursiveCharacterSplitter with Latex-specific separators. See the source code to see the Latex syntax expected by default.
How the text is split: by list of latex specific tags
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import LatexTextSplitter
latex_text = """
\documentclass{article}
\begin{document}
\maketitle
\section{Introduction} | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-3 | \begin{document}
\maketitle
\section{Introduction}
Large language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.
\subsection{History of LLMs}
The earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.
\subsection{Applications of LLMs}
LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.
\end{document}
"""
latex_splitter = LatexTextSplitter(chunk_size=400, chunk_overlap=0)
docs = latex_splitter.create_documents([latex_text])
docs
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 text is split: by list of python specific characters
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import PythonCodeTextSplitter
python_text = """
class Foo:
def bar():
def foo():
def testing_func():
def bar():
""" | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-4 | def foo():
def testing_func():
def bar():
"""
python_splitter = PythonCodeTextSplitter(chunk_size=30, chunk_overlap=0)
docs = python_splitter.create_documents([python_text])
docs
[Document(page_content='Foo:\n\n def bar():', lookup_str='', metadata={}, lookup_index=0),
Document(page_content='foo():\n\ndef testing_func():', lookup_str='', metadata={}, lookup_index=0),
Document(page_content='bar():', lookup_str='', metadata={}, lookup_index=0)]
Character Text Splitting#
This is a more simple method. This splits based on characters (by default “\n\n”) and measure chunk length by number of characters.
How the text is split: by single character
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(
separator = "\n\n",
chunk_size = 1000,
chunk_overlap = 200,
length_function = len,
)
texts = text_splitter.create_documents([state_of_the_union])
print(texts[0]) | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-5 | texts = text_splitter.create_documents([state_of_the_union])
print(texts[0])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.' lookup_str='' metadata={} lookup_index=0
Here’s an example of passing metadata along with the documents, notice that it is split along with the documents.
metadatas = [{"document": 1}, {"document": 2}]
documents = text_splitter.create_documents([state_of_the_union, state_of_the_union], metadatas=metadatas)
print(documents[0]) | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-6 | print(documents[0])
page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.' lookup_str='' metadata={'document': 1} lookup_index=0
HuggingFace Length Function#
Most LLMs are constrained by the number of tokens that you can pass in, which is not the same as the number of characters. In order to get a more accurate estimate, we can use HuggingFace tokenizers to count the text length.
How the text is split: by character passed in
How the chunk size is measured: by Hugging Face tokenizer
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(tokenizer, chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0]) | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-7 | texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Last year COVID-19 kept us apart. This year we are finally together again.
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.
tiktoken (OpenAI) Length Function#
You can also use tiktoken, a open source tokenizer package from OpenAI to estimate tokens used. Will probably be more accurate for their models.
How the text is split: by character passed in
How the chunk size is measured: by tiktoken tokenizer
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.
Last year COVID-19 kept us apart. This year we are finally together again.
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.
NLTK Text Splitter#
Rather than just splitting on “\n\n”, we can use NLTK to split based on tokenizers.
How the text is split: by NLTK
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import NLTKTextSplitter
text_splitter = NLTKTextSplitter(chunk_size=1000) | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-8 | text_splitter = NLTKTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.
Members of Congress and the Cabinet.
Justices of the Supreme Court.
My fellow Americans.
Last year COVID-19 kept us apart.
This year we are finally together again.
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.
Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.
But he badly miscalculated.
He thought he could roll into Ukraine and the world would roll over.
Instead he met a wall of strength he never imagined.
He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
Groups of citizens blocking tanks with their bodies.
Spacy Text Splitter#
Another alternative to NLTK is to use Spacy.
How the text is split: by Spacy
How the chunk size is measured: by length function passed in (defaults to number of characters)
from langchain.text_splitter import SpacyTextSplitter
text_splitter = SpacyTextSplitter(chunk_size=1000)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.
Members of Congress and the Cabinet.
Justices of the Supreme Court.
My fellow Americans.
Last year COVID-19 kept us apart. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
e69adc12e844-9 | My fellow Americans.
Last year COVID-19 kept us apart.
This year we are finally together again.
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.
Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.
But he badly miscalculated.
He thought he could roll into Ukraine and the world would roll over.
Instead he met a wall of strength he never imagined.
He met the Ukrainian people.
From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.
Groups of citizens blocking tanks with their bodies.
Token Text Splitter#
How the text is split: by tiktoken tokens
How the chunk size is measured: by tiktoken tokens
from langchain.text_splitter import TokenTextSplitter
text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
print(texts[0])
Madam Speaker, Madam Vice President, our
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Hypothetical Document Embeddings
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VectorStores
Contents
Generic Recursive Text Splitting
Markdown Text Splitter
Latex Text Splitter
Python Code Text Splitter
Character Text Splitting
HuggingFace Length Function
tiktoken (OpenAI) Length Function
NLTK Text Splitter
Spacy Text Splitter
Token Text Splitter
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\textsplitter.html |
7d4109e38a72-0 | .ipynb
.pdf
VectorStores
Contents
Add texts
From Documents
VectorStores#
This notebook showcases basic functionality related to VectorStores. A key part of working with vectorstores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the embedding notebook before diving into this.
This covers generic high level functionality related to all vector stores. For guides on specific vectorstores, please see the how-to guides here
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
with open('../../state_of_the_union.txt') as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
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. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\vectorstores.html |
7d4109e38a72-1 | 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 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 excellence.
Add texts#
You can easily add text to a vectorstore with the add_texts method. It will return a list of document IDs (in case you need to use them downstream).
docsearch.add_texts(["Ankush went to Princeton"])
['a05e3d0c-ab40-11ed-a853-e65801318981']
query = "Where did Ankush go to college?"
docs = docsearch.similarity_search(query)
docs[0]
Document(page_content='Ankush went to Princeton', lookup_str='', metadata={}, lookup_index=0)
From Documents#
We can also initialize a vectorstore from documents directly. This is useful when we use the method on the text splitter to get documents directly (handy when the original documents have associated metadata).
documents = text_splitter.create_documents([state_of_the_union], metadatas=[{"source": "State of the Union"}])
docsearch = Chroma.from_documents(documents, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content) | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\vectorstores.html |
7d4109e38a72-2 | print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
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: 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 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 excellence.
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Text Splitter
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AtlasDB
Contents
Add texts
From Documents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\examples\vectorstores.html |
9def69394f9b-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 instructions here.
You can also create the ChatGPT Retriever Plugin from LangChain document loaders. The below code walks through how to do that.
# STEP 1: Load
# Load documents using LangChain's DocumentLoaders
# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='../../document_loaders/examples/example_data/mlb_teams_2012.csv')
data = loader.load()
# STEP 2: Convert
# Convert Document to format expected by https://github.com/openai/chatgpt-retrieval-plugin
from typing import List
from langchain.docstore.document import Document
import json
def write_json(path: str, documents: List[Document])-> None:
results = [{"text": doc.page_content} for doc in documents]
with open(path, "w") as f:
json.dump(results, f, indent=2)
write_json("foo.json", data)
# STEP 3: Use
# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_json
Using the ChatGPT Retriever Plugin#
Okay, so we’ve created the ChatGPT Retriever Plugin, but how do we actually use it?
The below code walks through how to do that. | https://langchain.readthedocs.io\en\latest\modules\indexes\retriever_examples\chatgpt-plugin-retriever.html |
9def69394f9b-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='', metadata={'id': '456_0', 'metadata': {'source': 'email', 'source_id': '567', 'url': None, 'created_at': '1609592400.0', 'author': 'Alice', 'document_id': '456'}, 'embedding': None, 'score': 0.925571561}, lookup_index=0),
Document(page_content='This is a document about something', lookup_str='', metadata={'id': '123_0', 'metadata': {'source': 'file', 'source_id': 'https://example.com/doc1', 'url': 'https://example.com/doc1', 'created_at': '1609502400.0', 'author': 'Alice', 'document_id': '123'}, 'embedding': None, 'score': 0.6987589}, lookup_index=0), | https://langchain.readthedocs.io\en\latest\modules\indexes\retriever_examples\chatgpt-plugin-retriever.html |
9def69394f9b-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': None, 'score': 0.697888613}, lookup_index=0)]
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Weaviate
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VectorStore Retriever
Contents
Create
Using the ChatGPT Retriever Plugin
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\retriever_examples\chatgpt-plugin-retriever.html |
0fba9298f555-0 | .ipynb
.pdf
VectorStore Retriever
VectorStore Retriever#
The index - and therefor 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 walk through an example.
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
retriever = db.as_retriever()
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ChatGPT Plugin Retriever
next
Analyze Document
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\retriever_examples\vectorstore-retriever.html |
1f3f3d848115-0 | .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
!python -m spacy download en_core_web_sm
ATLAS_TEST_API_KEY = '7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6'
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = SpacyTextSplitter(separator='|')
texts = []
for doc in text_splitter.split_documents(documents):
texts.extend(doc.page_content.split('|'))
texts = [e.strip() for e in texts]
db = AtlasDB.from_texts(texts=texts,
name='test_index_'+str(time.time()), # unique name for your vector store
description='test_index', #a description for your vector store
api_key=ATLAS_TEST_API_KEY,
index_kwargs={'build_topic_model': True})
db.project.wait_for_project_lock()
db.project
test_index_1677255228.136989
A description for your project 508 datums inserted.
1 index built.
Projections
test_index_1677255228.136989_index. Status Completed. view online
Projection ID: db996d77-8981-48a0-897a-ff2c22bbf541
Hide embedded project
Explore on atlas.nomic.ai
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VectorStores
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Chroma
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\atlas.html |
1f3f3d848115-1 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\atlas.html |
ad0773984e25-0 | .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
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
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: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\chroma.html |
ad0773984e25-1 | 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 excellence.
Similarity search with score#
docs = db.similarity_search_with_score(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 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. \n\nTonight, 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. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
0.3913410007953644)
Persistance#
The below steps cover how to persist a ChromaDB instance
Initialize PeristedChromaDB#
Create embeddings for each chunk and insert into the Chroma vector database. The persist_directory argument tells ChromaDB where to store the database when it’s persisted. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\chroma.html |
ad0773984e25-2 | # 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 db, skipping load
No existing DB found in db, skipping load
Persist the Database#
In a notebook, we should call persist() to ensure the embeddings are written to disk. This isn’t necessary in a script - the database will be automatically persisted when the client object is destroyed.
vectordb.persist()
vectordb = None
Persisting DB to disk, putting it in the save folder db
PersistentDuckDB del, about to run persist
Persisting DB to disk, putting it in the save folder db
Load the Database from disk, and create the chain#
Be sure to pass the same persist_directory and embedding_function as you did when you instantiated the database. Initialize the chain we will use for question answering.
# Now we can load the persisted database from disk, and use it as normal.
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
Running Chroma using direct local API.
loaded in 4 embeddings
loaded in 1 collections
previous
AtlasDB
next
Deep Lake
Contents
Similarity search with score
Persistance
Initialize PeristedChromaDB
Persist the Database
Load the Database from disk, and create the chain
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\chroma.html |
3707fc6fe4b6-0 | .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 dataloader to deep learning frameworks.
For more information, please see the Deep Lake documentation or api reference
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import DeepLake
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = DeepLake.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
Evaluating ingest: 100%|██████████| 41/41 [00:00<00:00
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
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. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\deeplake.html |
3707fc6fe4b6-1 | 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 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 excellence.
Deep Lake datasets on cloud or local#
By default deep lake datasets are stored in memory, in case you want to persist locally or to any object storage you can simply provide path to the dataset. You can retrieve token from app.activeloop.ai
!activeloop login -t <token>
/bin/bash: -c: line 0: syntax error near unexpected token `newline'
/bin/bash: -c: line 0: `activeloop login -t <token>'
# Embed and store the texts
dataset_path = "hub://{username}/{dataset_name}" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://, etc.
embedding = OpenAIEmbeddings()
vectordb = DeepLake.from_documents(documents=docs, embedding=embedding, dataset_path=dataset_path)
Evaluating ingest: 100%|██████████| 4/4 [00:00<00:00
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
We cannot let this happen. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\deeplake.html |
3707fc6fe4b6-2 | 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: 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 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 excellence.
vectordb.ds.summary()
Dataset(path='./local/path', tensors=['embedding', 'ids', 'metadata', 'text'])
tensor htype shape dtype compression
------- ------- ------- ------- -------
embedding generic (4, 1536) None None
ids text (4, 1) str None
metadata json (4, 1) str None
text text (4, 1) str None
embeddings = vectordb.ds.embedding.numpy()
previous
Chroma
next
ElasticSearch
Contents
Deep Lake datasets on cloud or local
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\deeplake.html |
eb38d5422064-0 | .ipynb
.pdf
ElasticSearch
ElasticSearch#
This notebook shows how to use functionality related to the ElasticSearch database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = ElasticVectorSearch.from_documents(docs, embeddings, elasticsearch_url="http://localhost:9200"
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
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: 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 has is nominating someone to serve on the United States Supreme Court. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\elasticsearch.html |
eb38d5422064-1 | 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.
previous
Deep Lake
next
FAISS
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\elasticsearch.html |
1e9cc9f11241-0 | .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 import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
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: 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 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 excellence.
Similarity Search with score# | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\faiss.html |
1e9cc9f11241-1 | 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 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 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. \n\nTonight, 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. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
0.3914415)
It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.
embedding_vector = embeddings.embed_query(query)
docs_and_scores = db.similarity_search_by_vector(embedding_vector)
Saving and loading#
You can also save and load a FAISS index. This is useful so you don’t have to recreate it everytime you use it. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\faiss.html |
1e9cc9f11241-2 | 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 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. \n\nTonight, 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. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)
Merging#
You can also merge two FAISS vectorstores
db1 = FAISS.from_texts(["foo"], embeddings)
db2 = FAISS.from_texts(["bar"], embeddings)
db1.docstore._dict
{'e0b74348-6c93-4893-8764-943139ec1d17': Document(page_content='foo', lookup_str='', metadata={}, lookup_index=0)}
db2.docstore._dict | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\faiss.html |
1e9cc9f11241-3 | 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-8825-4800e74cfdb6': Document(page_content='bar', lookup_str='', metadata={}, lookup_index=0)}
previous
ElasticSearch
next
Milvus
Contents
Similarity Search with score
Saving and loading
Merging
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\faiss.html |
965add0a9f2d-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 CharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vector_db = Milvus.from_documents(
docs,
embeddings,
connection_args={"host": "127.0.0.1", "port": "19530"},
)
docs = vector_db.similarity_search(query)
docs[0]
previous
FAISS
next
OpenSearch
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\milvus.html |
fcc18c1a468c-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 database.
To run, you should have the opensearch instance up and running: here
similarity_search by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for
large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.
Check this for more details.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = OpenSearchVectorSearch.from_texts(texts, embeddings, opensearch_url="http://localhost:9200")
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
similarity_search using Approximate k-NN Search with Custom Parameters#
docsearch = OpenSearchVectorSearch.from_texts(texts, embeddings, opensearch_url="http://localhost:9200", engine="faiss", space_type="innerproduct", ef_construction=256, m=48) | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\opensearch.html |
fcc18c1a468c-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=False)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring")
print(docs[0].page_content)
similarity_search using Painless Scripting with Custom Parameters#
docsearch = OpenSearchVectorSearch.from_texts(texts, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)
filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}}
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search("What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter)
print(docs[0].page_content)
previous
Milvus
next
PGVector
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
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\opensearch.html |
8e3c9c345ce0-0 | .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_dotenv()
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.pgvector import PGVector
from langchain.document_loaders import TextLoader
from langchain.docstore.document import Document
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
## PGVector needs the connection string to the database.
## We will load it from the environment variables.
import os
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host=os.environ.get("PGVECTOR_HOST", "localhost"),
port=int(os.environ.get("PGVECTOR_PORT", "5432")),
database=os.environ.get("PGVECTOR_DATABASE", "postgres"),
user=os.environ.get("PGVECTOR_USER", "postgres"),
password=os.environ.get("PGVECTOR_PASSWORD", "postgres"),
)
## Example
# postgresql+psycopg2://username:password@localhost:5432/database_name
Similarity search with score#
Similarity Search with Euclidean Distance (Default)#
# The PGVector Module will try to create a table with the name of the collection. So, make sure that the collection name is unique and the user has the
# permission to create a table. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\pgvector.html |
8e3c9c345ce0-1 | # 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_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.6076628081132506
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: 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 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 excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6076628081132506
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. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\pgvector.html |
8e3c9c345ce0-2 | 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 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 excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6076804780049968
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: 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 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 excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.6076804780049968
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. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\pgvector.html |
8e3c9c345ce0-3 | 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 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 excellence.
--------------------------------------------------------------------------------
previous
OpenSearch
next
Pinecone
Contents
Similarity search with score
Similarity Search with Euclidean Distance (Default)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\pgvector.html |
72c0d8f329e3-0 | .ipynb
.pdf
Pinecone
Pinecone#
This notebook shows how to use functionality related to the Pinecone vector database.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
import pinecone
# initialize pinecone
pinecone.init(
api_key="YOUR_API_KEY", # find at app.pinecone.io
environment="YOUR_ENV" # next to api key in console
)
index_name = "langchain-demo"
docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
previous
PGVector
next
Qdrant
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\pinecone.html |
a478cb7e291e-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
from langchain.document_loaders import TextLoader
loader = TextLoader('../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
host = "<---host name here --->"
api_key = "<---api key here--->"
qdrant = Qdrant.from_documents(docs, embeddings, host=host, prefer_grpc=True, api_key=api_key)
query = "What did the president say about Ketanji Brown Jackson"
docs = qdrant.similarity_search(query)
docs[0]
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By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 24, 2023. | https://langchain.readthedocs.io\en\latest\modules\indexes\vectorstore_examples\qdrant.html |