'\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 support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'],
 '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 support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice Breyer for his role in helping to pass the Bipartisan Infrastructure Law, which is the most sweeping investment to rebuild America in history.'}
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"],
    template=refine_prompt_template,
)
initial_qa_template = (
    "Context information is below. \n"


)
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 reso omaggio al suo servizio.',
  "\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere e la risoluzione del sistema di immigrazione.",


"\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei.",
  "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"],


'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha reso omaggio al suo servizio e ha sostenuto la nomina di una top litigatrice in pratica privata, un ex difensore pubblico federale e una famiglia di insegnanti e agenti di polizia delle scuole pubbliche. Ha anche sottolineato l'importanza di avanzare la libertà e la giustizia attraverso la sicurezza delle frontiere, la risoluzione del sistema di immigrazione, la protezione degli americani LGBTQ+ e l'approvazione dell'Equality Act. Ha inoltre sottolineato l'importanza di lavorare insieme per sconfiggere l'epidemia di oppiacei e per investire in America, educare gli americani, far crescere la forza lavoro e costruire l'economia dal"}
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 serve the country.'
results["intermediate_steps"]
[{'answer': ' The President thanked Justice Breyer for his service and honored him for dedicating his life to serve the country.',
  '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'},
 {'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_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.',
   'score': '100'},


'score': '100'},
  {'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
   'score': '100'},
  {'answer': ' Non so.', 'score': '0'},
  {'answer': ' Non so.', 'score': '0'}],
 'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese.'}
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 Apr 26, 2023.


.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#


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.


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
Graph QA
next
Question Answering with Sources
 Contents

Multiple generations
Using our own prompts
Using HyDE
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 26, 2023.


.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)


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)


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.


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.


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.",


"\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.",


"\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.


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)


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.",


"\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"],


'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"


"------------\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.",


"\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,",


"\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."],


'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 Apr 26, 2023.


.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.


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)


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#


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),


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),


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),


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 Apr 26, 2023.


.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)


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#


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")


[{'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


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,


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,


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`


example, if you wanted to set the `FOO` environment variable to `bar`, you would use the following code:\n\n```'}]


previous
Retrieval Question Answering with Sources
next
API 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 Apr 26, 2023.


.ipynb
.pdf
OpenAPI Chain
 Contents
Load the spec
Select the Operation
Construct the chain
Return raw response
Example POST message
OpenAPI Chain#
This notebook shows an example of using an OpenAPI chain to call an endpoint in natural language, and get back a response in natural language
from langchain.tools import OpenAPISpec, APIOperation
from langchain.chains import OpenAPIEndpointChain
from langchain.requests import Requests
from langchain.llms import OpenAI
Load the spec#
Load a wrapper of the spec (so we can work with it more easily). You can load from a url or from a local file.
spec = OpenAPISpec.from_url("https://www.klarna.com/us/shopping/public/openai/v0/api-docs/")
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
# Alternative loading from file
# spec = OpenAPISpec.from_file("openai_openapi.yaml")
Select the Operation#
In order to provide a focused on modular chain, we create a chain specifically only for one of the endpoints. Here we get an API operation from a specified endpoint and method.
operation = APIOperation.from_openapi_spec(spec, '/public/openai/v0/products', "get")
Construct the chain#
We can now construct a chain to interact with it. In order to construct such a chain, we will pass in:
The operation endpoint
A requests wrapper (can be used to handle authentication, etc)
The LLM to use to interact with it
llm = OpenAI() # Load a Language Model
chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=True,


llm,
    requests=Requests(),
    verbose=True,
    return_intermediate_steps=True # Return request and response text
)
output = chain("whats the most expensive shirt?")
> Entering new OpenAPIEndpointChain chain...
> Entering new APIRequesterChain chain...
Prompt after formatting:
You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.
API_SCHEMA: ```typescript
/* API for fetching Klarna product information */
type productsUsingGET = (_: {
/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */
		q: string,
/* number of products returned */
		size?: number,
/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		min_price?: number,
/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		max_price?: number,
}) => any;
```
USER_INSTRUCTIONS: "whats the most expensive shirt?"
Your arguments must be plain json provided in a markdown block:
ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example


ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example
-----
ARGS: ```json
{"foo": "bar", "baz": {"qux": "quux"}}
```
The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.
You MUST strictly comply to the types indicated by the provided schema, including all required args.
If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:
Message: ```text
Concise response requesting the additional information that would make calling the function successful.
```
Begin
-----
ARGS:
> Finished chain.
{"q": "shirt", "size": 1, "max_price": null}
{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]}]}
> Entering new APIResponderChain chain...
Prompt after formatting:
You are a helpful AI assistant trained to answer user queries from API responses.
You attempted to call an API, which resulted in:


You attempted to call an API, which resulted in:
API_RESPONSE: {"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]}]}
USER_COMMENT: "whats the most expensive shirt?"
If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:
Response: ```json
{"response": "Human-understandable synthesis of the API_RESPONSE"}
```
Otherwise respond with the following markdown json block:
Response Error: ```json
{"response": "What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion."}
```
You MUST respond as a markdown json code block. The person you are responding to CANNOT see the API_RESPONSE, so if there is any relevant information there you must include it in your response.
Begin:
---
> Finished chain.
The most expensive shirt in the API response is the Burberry Check Poplin Shirt, which costs $360.00.
> Finished chain.
# View intermediate steps
output["intermediate_steps"]
{'request_args': '{"q": "shirt", "size": 1, "max_price": null}',


'response_text': '{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]}]}'}
Return raw response#
We can also run this chain without synthesizing the response. This will have the effect of just returning the raw API output.
chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=True,
    return_intermediate_steps=True, # Return request and response text
    raw_response=True # Return raw response
)
output = chain("whats the most expensive shirt?")
> Entering new OpenAPIEndpointChain chain...
> Entering new APIRequesterChain chain...
Prompt after formatting:
You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.
API_SCHEMA: ```typescript
/* API for fetching Klarna product information */
type productsUsingGET = (_: {
/* A precise query that matches one very small category or product that needs to be searched for to find the products the user is looking for. If the user explicitly stated what they want, use that as a query. The query is as specific as possible to the product name or category mentioned by the user in its singular form, and don't contain any clarifiers like latest, newest, cheapest, budget, premium, expensive or similar. The query is always taken from the latest topic, if there is a new topic a new query is started. */
		q: string,


q: string,
/* number of products returned */
		size?: number,
/* (Optional) Minimum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		min_price?: number,
/* (Optional) Maximum price in local currency for the product searched for. Either explicitly stated by the user or implicitly inferred from a combination of the user's request and the kind of product searched for. */
		max_price?: number,
}) => any;
```
USER_INSTRUCTIONS: "whats the most expensive shirt?"
Your arguments must be plain json provided in a markdown block:
ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example
-----
ARGS: ```json
{"foo": "bar", "baz": {"qux": "quux"}}
```
The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.
You MUST strictly comply to the types indicated by the provided schema, including all required args.
If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:
Message: ```text
Concise response requesting the additional information that would make calling the function successful.
```
Begin
-----
ARGS:
> Finished chain.
{"q": "shirt", "max_price": null}


{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]},{"name":"Burberry Vintage Check Cotton Shirt - Beige","url":"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin","price":"$229.02","attributes":["Material:Cotton,Elastane","Color:Beige","Model:Boy","Pattern:Checkered"]},{"name":"Burberry Vintage Check Stretch Cotton Twill Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$309.99","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Woman","Color:Beige","Properties:Stretch","Pattern:Checkered"]},{"name":"Burberry Somerton Check Shirt -


Somerton Check Shirt - Camel","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin","price":"$450.00","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Man","Color:Beige"]},{"name":"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$19.99","attributes":["Material:Polyester,Nylon","Target Group:Man","Color:Red,Pink,White,Blue,Purple,Beige,Black,Green","Properties:Pockets","Pattern:Solid Color"]}]}


> Finished chain.
output
{'instructions': 'whats the most expensive shirt?',


'output': '{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]},{"name":"Burberry Vintage Check Cotton Shirt - Beige","url":"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin","price":"$229.02","attributes":["Material:Cotton,Elastane","Color:Beige","Model:Boy","Pattern:Checkered"]},{"name":"Burberry Vintage Check Stretch Cotton Twill Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$309.99","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Woman","Color:Beige","Properties:Stretch","Pattern:Checkered"]},{"name":"Burberry Somerton Check Shirt -


Somerton Check Shirt - Camel","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin","price":"$450.00","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Man","Color:Beige"]},{"name":"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$19.99","attributes":["Material:Polyester,Nylon","Target Group:Man","Color:Red,Pink,White,Blue,Purple,Beige,Black,Green","Properties:Pockets","Pattern:Solid Color"]}]}',


'intermediate_steps': {'request_args': '{"q": "shirt", "max_price": null}',


'response_text': '{"products":[{"name":"Burberry Check Poplin Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201810981/Clothing/Burberry-Check-Poplin-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$360.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray,Blue,Beige","Properties:Pockets","Pattern:Checkered"]},{"name":"Burberry Vintage Check Cotton Shirt - Beige","url":"https://www.klarna.com/us/shopping/pl/cl359/3200280807/Children-s-Clothing/Burberry-Vintage-Check-Cotton-Shirt-Beige/?utm_source=openai&ref-site=openai_plugin","price":"$229.02","attributes":["Material:Cotton,Elastane","Color:Beige","Model:Boy","Pattern:Checkered"]},{"name":"Burberry Vintage Check Stretch Cotton Twill Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3202342515/Clothing/Burberry-Vintage-Check-Stretch-Cotton-Twill-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$309.99","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Woman","Color:Beige","Properties:Stretch","Pattern:Checkered"]},{"name":"Burberry Somerton Check Shirt -


Somerton Check Shirt - Camel","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201112728/Clothing/Burberry-Somerton-Check-Shirt-Camel/?utm_source=openai&ref-site=openai_plugin","price":"$450.00","attributes":["Material:Elastane/Lycra/Spandex,Cotton","Target Group:Man","Color:Beige"]},{"name":"Magellan Outdoors Laguna Madre Solid Short Sleeve Fishing Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203102142/Clothing/Magellan-Outdoors-Laguna-Madre-Solid-Short-Sleeve-Fishing-Shirt/?utm_source=openai&ref-site=openai_plugin","price":"$19.99","attributes":["Material:Polyester,Nylon","Target Group:Man","Color:Red,Pink,White,Blue,Purple,Beige,Black,Green","Properties:Pockets","Pattern:Solid Color"]}]}'}}


Example POST message#
For this demo, we will interact with the speak API.
spec = OpenAPISpec.from_url("https://api.speak.com/openapi.yaml")
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.1 spec.  This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
operation = APIOperation.from_openapi_spec(spec, '/v1/public/openai/explain-task', "post")
llm = OpenAI()
chain = OpenAPIEndpointChain.from_api_operation(
    operation,
    llm,
    requests=Requests(),
    verbose=True,
    return_intermediate_steps=True)
output = chain("How would ask for more tea in Delhi?")
> Entering new OpenAPIEndpointChain chain...
> Entering new APIRequesterChain chain...
Prompt after formatting:
You are a helpful AI Assistant. Please provide JSON arguments to agentFunc() based on the user's instructions.
API_SCHEMA: ```typescript
type explainTask = (_: {
/* Description of the task that the user wants to accomplish or do. For example, "tell the waiter they messed up my order" or "compliment someone on their shirt" */
  task_description?: string,
/* The foreign language that the user is learning and asking about. The value can be inferred from question - for example, if the user asks "how do i ask a girl out in mexico city", the value should be "Spanish" because of Mexico City. Always use the full name of the language (e.g. Spanish, French). */
  learning_language?: string,


learning_language?: string,
/* The user's native language. Infer this value from the language the user asked their question in. Always use the full name of the language (e.g. Spanish, French). */
  native_language?: string,
/* A description of any additional context in the user's question that could affect the explanation - e.g. setting, scenario, situation, tone, speaking style and formality, usage notes, or any other qualifiers. */
  additional_context?: string,
/* Full text of the user's question. */
  full_query?: string,
}) => any;
```
USER_INSTRUCTIONS: "How would ask for more tea in Delhi?"
Your arguments must be plain json provided in a markdown block:
ARGS: ```json
{valid json conforming to API_SCHEMA}
```
Example
-----
ARGS: ```json
{"foo": "bar", "baz": {"qux": "quux"}}
```
The block must be no more than 1 line long, and all arguments must be valid JSON. All string arguments must be wrapped in double quotes.
You MUST strictly comply to the types indicated by the provided schema, including all required args.
If you don't have sufficient information to call the function due to things like requiring specific uuid's, you can reply with the following message:
Message: ```text
Concise response requesting the additional information that would make calling the function successful.
```
Begin
-----
ARGS:
> Finished chain.
{"task_description": "ask for more tea", "learning_language": "Hindi", "native_language": "English", "full_query": "How would I ask for more tea in Delhi?"}


{"explanation":"<what-to-say language=\"Hindi\" context=\"None\">\nऔर चाय लाओ। (Aur chai lao.) \n</what-to-say>\n\n<alternatives context=\"None\">\n1. \"चाय थोड़ी ज्यादा मिल सकती है?\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\n2. \"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\n3. \"क्या मुझे or cup में milk/tea powder मिल सकता है?\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of milk or


tone, asking for an extra serving of milk or tea powder)*\n</alternatives>\n\n<usage-notes>\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\n</usage-notes>\n\n<example-convo language=\"Hindi\">\n<context>At home during breakfast.</context>\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as well.)\n</example-convo>\n\n*[Report an issue or leave


an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*","extra_response_instructions":"Use all information in the API response and fully render all Markdown.\nAlways end your response with a link to report an issue or leave feedback on the plugin."}


> Entering new APIResponderChain chain...
Prompt after formatting:
You are a helpful AI assistant trained to answer user queries from API responses.
You attempted to call an API, which resulted in:


API_RESPONSE: {"explanation":"<what-to-say language=\"Hindi\" context=\"None\">\nऔर चाय लाओ। (Aur chai lao.) \n</what-to-say>\n\n<alternatives context=\"None\">\n1. \"चाय थोड़ी ज्यादा मिल सकती है?\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\n2. \"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\n3. \"क्या मुझे or cup में milk/tea powder मिल सकता है?\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an extra serving of


USER_COMMENT: "How would ask for more tea in Delhi?"
If the API_RESPONSE can answer the USER_COMMENT respond with the following markdown json block:
Response: ```json
{"response": "Concise response to USER_COMMENT based on API_RESPONSE."}
```
Otherwise respond with the following markdown json block:
Response Error: ```json
{"response": "What you did and a concise statement of the resulting error. If it can be easily fixed, provide a suggestion."}
```
You MUST respond as a markdown json code block.
Begin:
---
> Finished chain.
In Delhi you can ask for more tea by saying 'Chai thodi zyada mil sakti hai?'
> Finished chain.
# Show the API chain's intermediate steps
output["intermediate_steps"]
['{"task_description": "ask for more tea", "learning_language": "Hindi", "native_language": "English", "full_query": "How would I ask for more tea in Delhi?"}',


'{"explanation":"<what-to-say language=\\"Hindi\\" context=\\"None\\">\\nऔर चाय लाओ। (Aur chai lao.) \\n</what-to-say>\\n\\n<alternatives context=\\"None\\">\\n1. \\"चाय थोड़ी ज्यादा मिल सकती है?\\" *(Chai thodi zyada mil sakti hai? - Polite, asking if more tea is available)*\\n2. \\"मुझे महसूस हो रहा है कि मुझे कुछ अन्य प्रकार की चाय पीनी चाहिए।\\" *(Mujhe mehsoos ho raha hai ki mujhe kuch anya prakar ki chai peeni chahiye. - Formal, indicating a desire for a different type of tea)*\\n3. \\"क्या मुझे or cup में milk/tea powder मिल सकता है?\\" *(Kya mujhe aur cup mein milk/tea powder mil sakta hai? - Very informal/casual tone, asking for an


- Very informal/casual tone, asking for an extra serving of milk or tea powder)*\\n</alternatives>\\n\\n<usage-notes>\\nIn India and Indian culture, serving guests with food and beverages holds great importance in hospitality. You will find people always offering drinks like water or tea to their guests as soon as they arrive at their house or office.\\n</usage-notes>\\n\\n<example-convo language=\\"Hindi\\">\\n<context>At home during breakfast.</context>\\nPreeti: सर, क्या main aur cups chai lekar aaun? (Sir,kya main aur cups chai lekar aaun? - Sir, should I get more tea cups?)\\nRahul: हां,बिल्कुल। और चाय की मात्रा में भी थोड़ा सा इजाफा करना। (Haan,bilkul. Aur chai ki matra mein bhi thoda sa eejafa karna. - Yes, please. And add a little extra in the quantity of tea as


add a little extra in the quantity of tea as well.)\\n</example-convo>\\n\\n*[Report an issue or leave feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*","extra_response_instructions":"Use all information in the API response and fully render all Markdown.\\nAlways end your response with a link to report an issue or leave feedback on the plugin."}']


previous
Moderation
next
PAL
 Contents

Load the spec
Select the Operation
Construct the chain
Return raw response
Example POST message
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 26, 2023.


.ipynb
.pdf
LLMRequestsChain
LLMRequestsChain#
Using the request library to get HTML results from a URL and then an LLM to parse results
from langchain.llms import OpenAI
from langchain.chains import LLMRequestsChain, LLMChain
from langchain.prompts import PromptTemplate
template = """Between >>> and <<< are the raw search result text from google.
Extract the answer to the question '{query}' or say "not found" if the information is not contained.
Use the format
Extracted:<answer or "not found">
>>> {requests_result} <<<
Extracted:"""
PROMPT = PromptTemplate(
    input_variables=["query", "requests_result"],
    template=template,
)
chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))
question = "What are the Three (3) biggest countries, and their respective sizes?"
inputs = {
    "query": question,
    "url": "https://www.google.com/search?q=" + question.replace(" ", "+")
}
chain(inputs)
{'query': 'What are the Three (3) biggest countries, and their respective sizes?',
 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',
 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}
previous
LLM Math
next
LLMSummarizationCheckerChain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 26, 2023.


.ipynb
.pdf
LLM Math
 Contents
Customize Prompt
LLM Math#
This notebook showcases using LLMs and Python REPLs to do complex word math problems.
from langchain import OpenAI, LLMMathChain
llm = OpenAI(temperature=0)
llm_math = LLMMathChain(llm=llm, verbose=True)
llm_math.run("What is 13 raised to the .3432 power?")
> Entering new LLMMathChain chain...
What is 13 raised to the .3432 power?
```python
import math
print(math.pow(13, .3432))
```
Answer: 2.4116004626599237
> Finished chain.
'Answer: 2.4116004626599237\n'
Customize Prompt#
You can also customize the prompt that is used. Here is an example prompting it to use numpy
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers.
So we hooked you up to a Python 3 kernel, and now you can execute code. If you execute code, you must print out the final answer using the print function. You MUST use the python package numpy to answer your question. You must import numpy as np.
Question: ${{Question with hard calculation.}}
```python
${{Code that prints what you need to know}}
print(${{code}})
```
```output
${{Output of your code}}
```
Answer: ${{Answer}}
Begin.


${{Output of your code}}
```
Answer: ${{Answer}}
Begin.
Question: What is 37593 * 67?
```python
import numpy as np
print(np.multiply(37593, 67))
```
```output
2518731
```
Answer: 2518731
Question: {question}"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)
llm_math = LLMMathChain(llm=llm, prompt=PROMPT, verbose=True)
llm_math.run("What is 13 raised to the .3432 power?")
> Entering new LLMMathChain chain...
What is 13 raised to the .3432 power?
```python
import numpy as np
print(np.power(13, .3432))
```
Answer: 2.4116004626599237
> Finished chain.
'Answer: 2.4116004626599237\n'
previous
LLMCheckerChain
next
LLMRequestsChain
 Contents

Customize Prompt
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 26, 2023.


.ipynb
.pdf
SQL Chain example
 Contents
Customize Prompt
Return Intermediate Steps
Choosing how to limit the number of rows returned
Adding example rows from each table
Custom Table Info
SQLDatabaseSequentialChain
SQL Chain example#
This example demonstrates the use of the SQLDatabaseChain for answering questions over a database.
Under the hood, LangChain uses SQLAlchemy to connect to SQL databases. The SQLDatabaseChain can therefore be used with any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, and SQLite. Please refer to the SQLAlchemy documentation for more information about requirements for connecting to your database. For example, a connection to MySQL requires an appropriate connector such as PyMySQL. A URI for a MySQL connection might look like: mysql+pymysql://user:pass@some_mysql_db_address/db_name
This demonstration uses SQLite and the example Chinook database.
To set it up, follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository.
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
llm = OpenAI(temperature=0)
NOTE: For data-sensitive projects, you can specify return_direct=True in the SQLDatabaseChain initialization to directly return the output of the SQL query without any additional formatting. This prevents the LLM from seeing any contents within the database. Note, however, the LLM still has access to the database scheme (i.e. dialect, table and key names) by default.
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
db_chain.run("How many employees are there?")
> Entering new SQLDatabaseChain chain...
How many employees are there?
SQLQuery:


How many employees are there?
SQLQuery:
/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:120: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.
  sample_rows = connection.execute(command)
 SELECT COUNT(*) FROM Employee;
SQLResult: [(8,)]
Answer: There are 8 employees.
> Finished chain.
' There are 8 employees.'
Customize Prompt#
You can also customize the prompt that is used. Here is an example prompting it to understand that foobar is the same as the Employee table
from langchain.prompts.prompt import PromptTemplate
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
If someone asks for the table foobar, they really mean the employee table.
Question: {input}"""
PROMPT = PromptTemplate(
    input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE
)
db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True)
db_chain.run("How many employees are there in the foobar table?")
> Entering new SQLDatabaseChain chain...
How many employees are there in the foobar table?
SQLQuery: SELECT COUNT(*) FROM Employee;
SQLResult: [(8,)]


SQLQuery: SELECT COUNT(*) FROM Employee;
SQLResult: [(8,)]
Answer: There are 8 employees in the foobar table.
> Finished chain.
' There are 8 employees in the foobar table.'
Return Intermediate Steps#
You can also return the intermediate steps of the SQLDatabaseChain. This allows you to access the SQL statement that was generated, as well as the result of running that against the SQL Database.
db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True)
result = db_chain("How many employees are there in the foobar table?")
result["intermediate_steps"]
> Entering new SQLDatabaseChain chain...
How many employees are there in the foobar table?
SQLQuery: SELECT COUNT(*) FROM Employee;
SQLResult: [(8,)]
Answer: There are 8 employees in the foobar table.
> Finished chain.
[' SELECT COUNT(*) FROM Employee;', '[(8,)]']
Choosing how to limit the number of rows returned#
If you are querying for several rows of a table you can select the maximum number of results you want to get by using the ‘top_k’ parameter (default is 10). This is useful for avoiding query results that exceed the prompt max length or consume tokens unnecessarily.
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3)
db_chain.run("What are some example tracks by composer Johann Sebastian Bach?")
> Entering new SQLDatabaseChain chain...
What are some example tracks by composer Johann Sebastian Bach?
SQLQuery: SELECT Name, Composer FROM Track WHERE Composer LIKE '%Johann Sebastian Bach%' LIMIT 3;


SQLResult: [('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach')]
Answer: Some example tracks by composer Johann Sebastian Bach are 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', and 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude'.
> Finished chain.
' Some example tracks by composer Johann Sebastian Bach are \'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\', \'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria\', and \'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\'.'
Adding example rows from each table#
Sometimes, the format of the data is not obvious and it is optimal to include a sample of rows from the tables in the prompt to allow the LLM to understand the data before providing a final query. Here we will use this feature to let the LLM know that artists are saved with their full names by providing two rows from the Track table.
db = SQLDatabase.from_uri(
    "sqlite:///../../../../notebooks/Chinook.db",
    include_tables=['Track'], # we include only one table to save tokens in the prompt :)


include_tables=['Track'], # we include only one table to save tokens in the prompt :)
    sample_rows_in_table_info=2)
The sample rows are added to the prompt after each corresponding table’s column information:
print(db.table_info)
CREATE TABLE "Track" (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"AlbumId" INTEGER,
	"MediaTypeId" INTEGER NOT NULL,
	"GenreId" INTEGER,
	"Composer" NVARCHAR(220),
	"Milliseconds" INTEGER NOT NULL,
	"Bytes" INTEGER,
	"UnitPrice" NUMERIC(10, 2) NOT NULL,
	PRIMARY KEY ("TrackId"),
	FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"),
	FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),
	FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")
)
/*
2 rows from Track table:
TrackId	Name	AlbumId	MediaTypeId	GenreId	Composer	Milliseconds	Bytes	UnitPrice
1	For Those About To Rock (We Salute You)	1	1	1	Angus Young, Malcolm Young, Brian Johnson	343719	11170334	0.99
2	Balls to the Wall	2	2	1	None	342562	5510424	0.99
*/
/home/jon/projects/langchain/langchain/sql_database.py:135: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.
  sample_rows = connection.execute(command)


sample_rows = connection.execute(command)
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
db_chain.run("What are some example tracks by Bach?")
> Entering new SQLDatabaseChain chain...
What are some example tracks by Bach?
SQLQuery: SELECT Name FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;
SQLResult: [('American Woman',), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace',), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria',), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude',), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata',)]
Answer: Some example tracks by Bach are 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.
> Finished chain.


> Finished chain.
' Some example tracks by Bach are \'American Woman\', \'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\', \'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria\', \'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\', and \'Toccata and Fugue in D Minor, BWV 565: I. Toccata\'.'
Custom Table Info#
In some cases, it can be useful to provide custom table information instead of using the automatically generated table definitions and the first sample_rows_in_table_info sample rows. For example, if you know that the first few rows of a table are uninformative, it could help to manually provide example rows that are more diverse or provide more information to the model. It is also possible to limit the columns that will be visible to the model if there are unnecessary columns.
This information can be provided as a dictionary with table names as the keys and table information as the values. For example, let’s provide a custom definition and sample rows for the Track table with only a few columns:
custom_table_info = {
    "Track": """CREATE TABLE Track (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"Composer" NVARCHAR(220),
	PRIMARY KEY ("TrackId")
)
/*
3 rows from Track table:
TrackId	Name	Composer
1	For Those About To Rock (We Salute You)	Angus Young, Malcolm Young, Brian Johnson
2	Balls to the Wall	None
3	My favorite song ever	The coolest composer of all time
*/"""
}
db = SQLDatabase.from_uri(


*/"""
}
db = SQLDatabase.from_uri(
    "sqlite:///../../../../notebooks/Chinook.db",
    include_tables=['Track', 'Playlist'],
    sample_rows_in_table_info=2,
    custom_table_info=custom_table_info)
print(db.table_info)
CREATE TABLE "Playlist" (
	"PlaylistId" INTEGER NOT NULL,
	"Name" NVARCHAR(120),
	PRIMARY KEY ("PlaylistId")
)
/*
2 rows from Playlist table:
PlaylistId	Name
1	Music
2	Movies
*/
CREATE TABLE Track (
	"TrackId" INTEGER NOT NULL,
	"Name" NVARCHAR(200) NOT NULL,
	"Composer" NVARCHAR(220),
	PRIMARY KEY ("TrackId")
)
/*
3 rows from Track table:
TrackId	Name	Composer
1	For Those About To Rock (We Salute You)	Angus Young, Malcolm Young, Brian Johnson
2	Balls to the Wall	None
3	My favorite song ever	The coolest composer of all time
*/
Note how our custom table definition and sample rows for Track overrides the sample_rows_in_table_info parameter. Tables that are not overridden by custom_table_info, in this example Playlist, will have their table info gathered automatically as usual.
db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
db_chain.run("What are some example tracks by Bach?")
> Entering new SQLDatabaseChain chain...
What are some example tracks by Bach?
SQLQuery: SELECT Name, Composer FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;


SQLQuery: SELECT Name, Composer FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;
SQLResult: [('American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach'), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata', 'Johann Sebastian Bach')]
Answer: Some example tracks by Bach are 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.
> Finished chain.
' Some example tracks by Bach are \'American Woman\', \'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\', \'Aria Mit 30 Veränderungen, BWV 988 "Goldberg Variations": Aria\', \'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\', and \'Toccata and Fugue in D Minor, BWV 565: I. Toccata\'.'
SQLDatabaseSequentialChain#


SQLDatabaseSequentialChain#
Chain for querying SQL database that is a sequential chain.
The chain is as follows:
1. Based on the query, determine which tables to use.
2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables in the database is large.
from langchain.chains import SQLDatabaseSequentialChain
db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db")
chain = SQLDatabaseSequentialChain.from_llm(llm, db, verbose=True)
chain.run("How many employees are also customers?")
> Entering new SQLDatabaseSequentialChain chain...
Table names to use:
['Customer', 'Employee']
> Entering new SQLDatabaseChain chain...
How many employees are also customers?
SQLQuery: SELECT COUNT(*) FROM Employee INNER JOIN Customer ON Employee.EmployeeId = Customer.SupportRepId;
SQLResult: [(59,)]
Answer: 59 employees are also customers.
> Finished chain.
> Finished chain.
' 59 employees are also customers.'
previous
PAL
next
Chains
 Contents

Customize Prompt
Return Intermediate Steps
Choosing how to limit the number of rows returned
Adding example rows from each table
Custom Table Info
SQLDatabaseSequentialChain
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 26, 2023.


.ipynb
.pdf
PAL
 Contents
Math Prompt
Colored Objects
Intermediate Steps
PAL#
Implements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.
from langchain.chains import PALChain
from langchain import OpenAI
llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)
Math Prompt#
pal_chain = PALChain.from_math_prompt(llm, verbose=True)
question = "Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?"
pal_chain.run(question)
> Entering new PALChain chain...
def solution():
    """Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?"""
    cindy_pets = 4
    marcia_pets = cindy_pets + 2
    jan_pets = marcia_pets * 3
    total_pets = cindy_pets + marcia_pets + jan_pets
    result = total_pets
    return result
> Finished chain.
'28'
Colored Objects#
pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)
question = "On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?"
pal_chain.run(question)
> Entering new PALChain chain...
# Put objects into a list to record ordering
objects = []
objects += [('booklet', 'blue')] * 2


objects = []
objects += [('booklet', 'blue')] * 2
objects += [('booklet', 'purple')] * 2
objects += [('sunglasses', 'yellow')] * 2
# Remove all pairs of sunglasses
objects = [object for object in objects if object[0] != 'sunglasses']
# Count number of purple objects
num_purple = len([object for object in objects if object[1] == 'purple'])
answer = num_purple
> Finished PALChain chain.
'2'
Intermediate Steps#
You can also use the intermediate steps flag to return the code executed that generates the answer.
pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True, return_intermediate_steps=True)
question = "On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses. If I remove all the pairs of sunglasses from the desk, how many purple items remain on it?"
result = pal_chain({"question": question})
> Entering new PALChain chain...
# Put objects into a list to record ordering
objects = []
objects += [('booklet', 'blue')] * 2
objects += [('booklet', 'purple')] * 2
objects += [('sunglasses', 'yellow')] * 2
# Remove all pairs of sunglasses
objects = [object for object in objects if object[0] != 'sunglasses']
# Count number of purple objects
num_purple = len([object for object in objects if object[1] == 'purple'])
answer = num_purple
> Finished chain.
result['intermediate_steps']


answer = num_purple
> Finished chain.
result['intermediate_steps']
"# Put objects into a list to record ordering\nobjects = []\nobjects += [('booklet', 'blue')] * 2\nobjects += [('booklet', 'purple')] * 2\nobjects += [('sunglasses', 'yellow')] * 2\n\n# Remove all pairs of sunglasses\nobjects = [object for object in objects if object[0] != 'sunglasses']\n\n# Count number of purple objects\nnum_purple = len([object for object in objects if object[1] == 'purple'])\nanswer = num_purple"
previous
OpenAPI Chain
next
SQL Chain example
 Contents

Math Prompt
Colored Objects
Intermediate Steps
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 26, 2023.


.ipynb
.pdf
Moderation
 Contents
How to use the moderation chain
How to append a Moderation chain to an LLMChain
Moderation#
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, specifically prohibit you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.
If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this notebook.
In this notebook, we will show:
How to run any piece of text through a moderation chain.
How to append a Moderation chain to an LLMChain.
from langchain.llms import OpenAI
from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain
from langchain.prompts import PromptTemplate
How to use the moderation chain#
Here’s an example of using the moderation chain with default settings (will return a string explaining stuff was flagged).
moderation_chain = OpenAIModerationChain()
moderation_chain.run("This is okay")
'This is okay'
moderation_chain.run("I will kill you")


'This is okay'
moderation_chain.run("I will kill you")
"Text was found that violates OpenAI's content policy."
Here’s an example of using the moderation chain to throw an error.
moderation_chain_error = OpenAIModerationChain(error=True)
moderation_chain_error.run("This is okay")
'This is okay'
moderation_chain_error.run("I will kill you")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[7], line 1
----> 1 moderation_chain_error.run("I will kill you")
File ~/workplace/langchain/langchain/chains/base.py:138, in Chain.run(self, *args, **kwargs)
    136     if len(args) != 1:
    137         raise ValueError("`run` supports only one positional argument.")
--> 138     return self(args[0])[self.output_keys[0]]
    140 if kwargs and not args:
    141     return self(kwargs)[self.output_keys[0]]
File ~/workplace/langchain/langchain/chains/base.py:112, in Chain.__call__(self, inputs, return_only_outputs)
    108 if self.verbose:
    109     print(
    110         f"\n\n\033[1m> Entering new {self.__class__.__name__} chain...\033[0m"
    111     )
--> 112 outputs = self._call(inputs)
    113 if self.verbose:
    114     print(f"\n\033[1m> Finished {self.__class__.__name__} chain.\033[0m")
File ~/workplace/langchain/langchain/chains/moderation.py:81, in OpenAIModerationChain._call(self, inputs)
     79 text = inputs[self.input_key]


79 text = inputs[self.input_key]
     80 results = self.client.create(text)
---> 81 output = self._moderate(text, results["results"][0])
     82 return {self.output_key: output}
File ~/workplace/langchain/langchain/chains/moderation.py:73, in OpenAIModerationChain._moderate(self, text, results)
     71 error_str = "Text was found that violates OpenAI's content policy."
     72 if self.error:
---> 73     raise ValueError(error_str)
     74 else:
     75     return error_str
ValueError: Text was found that violates OpenAI's content policy.
Here’s an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI’s moderation endpoint results (see docs here).
class CustomModeration(OpenAIModerationChain):

    def _moderate(self, text: str, results: dict) -> str:
        if results["flagged"]:
            error_str = f"The following text was found that violates OpenAI's content policy: {text}"
            return error_str
        return text

custom_moderation = CustomModeration()
custom_moderation.run("This is okay")
'This is okay'
custom_moderation.run("I will kill you")
"The following text was found that violates OpenAI's content policy: I will kill you"
How to append a Moderation chain to an LLMChain#
To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.
Let’s start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful.
prompt = PromptTemplate(template="{text}", input_variables=["text"])