id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
d564fca063c0-5 | "\n\nStiamo unendo le nostre forze con quelle dei nostri alleati europei per sequestrare yacht, appartamenti di lusso e jet privati di Putin. Abbiamo chiuso lo spazio aereo americano ai voli russi e stiamo fornendo più di un miliardo di dollari in assistenza all'Ucraina. Abbiamo anche mobilitato le nostre forze terrest... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-6 | "\n\nIl Presidente Biden ha lottato per passare l'American Rescue Plan per aiutare le persone che soffrivano a causa della pandemia. Il piano ha fornito sollievo economico immediato a milioni di americani, ha aiutato a mettere cibo sulla loro tavola, a mantenere un tetto sopra le loro teste e a ridurre il costo dell'as... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-7 | The refine Chain#
This sections shows results of using the refine Chain to do summarization.
chain = load_summarize_chain(llm, chain_type="refine")
chain.run(docs)
"\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Put... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-8 | chain({"input_documents": docs}, return_only_outputs=True)
{'refine_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-9 | "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are ... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-10 | 'output_text': "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-11 | "------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary in Italian"
"If the context isn't useful, return the original summary."
)
refine_prompt = PromptTemplate(
input_variables=["existing_answer", "text"],
template=refine_template,
)
chain = load_summari... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-12 | "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso ... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-13 | "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso ... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
d564fca063c0-14 | 'output_text': "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagli... | https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
94ee795cf875-0 | .ipynb
.pdf
Question Answering with Sources
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering with Sources#
This notebook walks through how to use LangChain for question answering with sources over a list of documents. It covers four differe... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-1 | from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import OpenAI
Quickstart#
If you just want to get started as quickly as possible, this is the recommended way to do it:
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the presiden... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-2 | PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"])
chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT)
query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'out... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-3 | ' None',
' None',
' None'],
'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Italian.
question_prompt_template = """Use the following portion of a long document to see if an... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-4 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema d... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-5 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': "\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked him for his service and praised his c... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-6 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ['\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service.... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-7 | '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-8 | '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal public defender, and... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-9 | 'output_text': '\n\nThe president said that he was honoring Justice Breyer for his dedication to serving the country and that he was a retiring Justice of the United States Supreme Court. He also thanked Justice Breyer for his service, noting his background as a top litigator in private practice, a former federal publi... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-10 | "answer the question (in Italian)"
"If you do update it, please update the sources as well. "
"If the context isn't useful, return the original answer."
)
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=refine_template,
)
question_template = (
... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-11 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-12 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-13 | "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sottolineato l'impor... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-14 | 'output_text': "\n\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese, ha onorato la sua carriera e ha contribuito a costruire un consenso. Ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Inoltre, ha sotto... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-15 | 'score': '100'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'},
{'answer': ' This document does not answer the question', 'score': '0'}]
Custom Prompts
You can also use your own prompts with this chain. In this example... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
94ee795cf875-16 | result
{'source': 30,
'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',
'score': '100'},
{'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',
'score': '100'},
{'answer': ' Non so.', '... | https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
96bc3995775f-0 | .ipynb
.pdf
Hypothetical Document Embeddings
Contents
Multiple generations
Using our own prompts
Using HyDE
Hypothetical Document Embeddings#
This notebook goes over how to use Hypothetical Document Embeddings (HyDE), as described in this paper.
At a high level, HyDE is an embedding technique that takes queries, gene... | https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
96bc3995775f-1 | result = embeddings.embed_query("Where is the Taj Mahal?")
Using our own prompts#
Besides using preconfigured prompts, we can also easily construct our own prompts and use those in the LLMChain that is generating the documents. This can be useful if we know the domain our queries will be in, as we can condition the pro... | https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
96bc3995775f-2 | Using DuckDB in-memory for database. Data will be transient.
print(docs[0].page_content)
In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.
We cannot let this happen.
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Votin... | https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
4c7bb8cdf212-0 | .ipynb
.pdf
Chat Over Documents with Chat History
Contents
Pass in chat history
Return Source Documents
ConversationalRetrievalChain with search_distance
ConversationalRetrievalChain with map_reduce
ConversationalRetrievalChain with Question Answering with sources
ConversationalRetrievalChain with streaming to stdout... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-1 | Using embedded DuckDB without persistence: data will be transient
We can now create a memory object, which is neccessary to track the inputs/outputs and hold a conversation.
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
We now in... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-2 | result = qa({"question": query, "chat_history": chat_history})
result["answer"]
" 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 ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-3 | result['source_documents'][0]
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 ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-4 | from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce")
chain = Convers... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-5 | combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-6 | chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
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 ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
4c7bb8cdf212-7 | result = qa({"question": query, "chat_history": chat_history})
result['answer']
" 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 ... | https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
782f2811ec8e-0 | .ipynb
.pdf
Question Answering
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
The map-rerank Chain
Question Answering#
This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chains: stuff, map_reduce, ... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-1 | from langchain.llms import OpenAI
Quickstart#
If you just want to get started as quickly as possible, this is the recommended way to do it:
chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What did the president say about Justice Breyer"
chain.run(input_documents=docs, question=query)
' The pre... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-2 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha ricevuto una vasta gamma di supporto.'}
The map_reduce Chain#
This sections shows results of using the map_reduce Chain to do ques... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-3 | ' None',
' None'],
'output_text': ' The president said that Justice Breyer is an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, and thanked him for his service.'}
Custom Prompts
You can also use your own prompts with this chain. In this example, we will respond in Ital... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-4 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema d... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-5 | chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equalit... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-6 | '\n\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his support of the Equality Act and his commitment to protecting the rights of LGBTQ+ Americans. He also praised Justice... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-7 | )
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... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-8 | "\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 sottol... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-9 | '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... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-10 | {'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 ... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
782f2811ec8e-11 | '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 ... | https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
7e37be0ae203-0 | .ipynb
.pdf
Analyze Document
Contents
Summarize
Question Answering
Analyze Document#
The AnalyzeDocumentChain is more of an end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain. This can be used as more of an end-to-end chain.
with open("../../state_of_th... | https://python.langchain.com/en/latest/modules/chains/index_examples/analyze_document.html |
7e37be0ae203-1 | qa_chain = load_qa_chain(llm, chain_type="map_reduce")
qa_document_chain = AnalyzeDocumentChain(combine_docs_chain=qa_chain)
qa_document_chain.run(input_document=state_of_the_union, question="what did the president say about justice breyer?")
' The president thanked Justice Breyer for his service.'
previous
Transformat... | https://python.langchain.com/en/latest/modules/chains/index_examples/analyze_document.html |
975f2344fbb4-0 | .ipynb
.pdf
Retrieval Question/Answering
Contents
Chain Type
Custom Prompts
Return Source Documents
Retrieval Question/Answering#
This example showcases question answering over an index.
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter imp... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
975f2344fbb4-1 | There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_ty... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
975f2344fbb4-2 | query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also s... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
975f2344fbb4-3 | Return Source Documents#
Additionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), return_source_documents=True)
query = "What did th... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
975f2344fbb4-4 | Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
975f2344fbb4-5 | Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
975f2344fbb4-6 | Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards ... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
4e847effc67a-0 | .ipynb
.pdf
Graph QA
Contents
Create the graph
Querying the graph
Save the graph
Graph QA#
This notebook goes over how to do question answering over a graph data structure.
Create the graph#
In this section, we construct an example graph. At the moment, this works best for small pieces of text.
from langchain.indexes... | https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
4e847effc67a-1 | 'is the ground on which')]
Querying the graph#
We can now use the graph QA chain to ask question of the graph
from langchain.chains import GraphQAChain
chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)
chain.run("what is Intel going to build?")
> Entering new GraphQAChain chain...
Entities... | https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
4e847effc67a-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
3049636f7def-0 | .ipynb
.pdf
Retrieval Question Answering with Sources
Contents
Chain Type
Retrieval Question Answering with Sources#
This notebook goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index.
from langch... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
3049636f7def-1 | 'sources': '31-pl'}
Chain Type#
You can easily specify different chain types to load and use in the RetrievalQAWithSourcesChain chain. For a more detailed walkthrough of these types, please see this notebook.
There are two ways to load different chain types. First, you can specify the chain type argument in the from_ch... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
3049636f7def-2 | {'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
'sources': '31-pl'}
previous
Retrieval Question/Answering
next
Vector DB Text Generation
Contents
Chain Type
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, ... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
fd42d371f50d-0 | .ipynb
.pdf
Vector DB Text Generation
Contents
Prepare Data
Set Up Vector DB
Set Up LLM Chain with Custom Prompt
Generate Text
Vector DB Text Generation#
This notebook walks through how to use LangChain for text generation over a vector index. This is useful if we want to generate text that is able to draw from a lar... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-1 | relative_path = markdown_file.relative_to(repo_path)
github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}"
yield Document(page_content=f.read(), metadata={"source": github_url})
sources = get_github_docs("yirenlu92", "deno-manual-forked")
source_chunk... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-2 | chain = LLMChain(llm=llm, prompt=PROMPT)
Generate Text#
Finally, we write a function to apply our inputs to the chain. The function takes an input parameter topic. We find the documents in the vector index that correspond to that topic, and use them as additional context in our simple LLM chain.
def generate_blog_post(... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-3 | [{'text': '\n\nEnvironment variables are a great way to store and access sensitive information in your Deno applications. Deno offers built-in support for environment variables with `Deno.env`, and you can also use a `.env` file to store and access environment variables.\n\nUsing `Deno.env` is simple. It has getter and... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-4 | into the code. This makes it easier to change settings without having to modify the code.\n\nIn Deno, environment variables can be set in a few different ways. The most common way is to use the `VAR=value` syntax. This will set the environment variable `VAR` to the value `value`. This can be used to set any number of e... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-5 | to hard-code it into their applications. In Deno, you can access environment variables using the `Deno.env.get()` function.\n\nFor example, if you wanted to access the `HOME` environment variable, you could do so like this:\n\n```js\n// env.js\nDeno.env.get("HOME");\n```\n\nWhen running this code, you\'ll need to grant... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-6 | variables are an important part of any programming language, and Deno is no exception. Deno is a secure JavaScript and TypeScript runtime built on the V8 JavaScript engine, and it recently added support for environment variables. This feature was added in Deno version 1.6.0, and it is now available for use in Deno appl... | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-7 | example, if you wanted to set the `FOO` environment variable to `bar`, you would use the following code:\n\n```'}] | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
fd42d371f50d-8 | 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 May 02, 2023. | https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
90f3c81b2521-0 | .ipynb
.pdf
LLM Chain
Contents
LLM Chain
Additional ways of running LLM Chain
Parsing the outputs
Initialize from string
LLM Chain#
LLMChain is perhaps one of the most popular ways of querying an LLM object. It formats the prompt template using the input key values provided (and also memory key values, if available),... | https://python.langchain.com/en/latest/modules/chains/generic/llm_chain.html |
90f3c81b2521-1 | llm_chain.generate(input_list)
LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'... | https://python.langchain.com/en/latest/modules/chains/generic/llm_chain.html |
90f3c81b2521-2 | template = """List all the colors in a rainbow"""
prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)
llm_chain = LLMChain(prompt=prompt, llm=llm)
llm_chain.predict()
'\n\nRed, orange, yellow, green, blue, indigo, violet'
With predict_and_parser:
llm_chain.predict_and_parse()
['R... | https://python.langchain.com/en/latest/modules/chains/generic/llm_chain.html |
58bfe67cbf1f-0 | .ipynb
.pdf
Serialization
Contents
Saving a chain to disk
Loading a chain from disk
Saving components separately
Serialization#
This notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow t... | https://python.langchain.com/en/latest/modules/chains/generic/serialization.html |
58bfe67cbf1f-1 | "best_of": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
},
"output_key": "text",
"_type": "llm_chain"
}
Loading a chain from disk#
We can load a chain from disk by using the load_chain method.
from langchain.chains import load_chain
chain = load_chain("llm_chain.js... | https://python.langchain.com/en/latest/modules/chains/generic/serialization.html |
58bfe67cbf1f-2 | "top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"n": 1,
"best_of": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
}
config = {
"memory": None,
"verbose": True,
"prompt_path": "prompt.json",
"llm_path": "llm.json",
"output_key": "text",
"_ty... | https://python.langchain.com/en/latest/modules/chains/generic/serialization.html |
82f4a2b7a655-0 | .ipynb
.pdf
Creating a custom Chain
Creating a custom Chain#
To implement your own custom chain you can subclass Chain and implement the following methods:
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.base_language import BaseLanguageModel
fro... | https://python.langchain.com/en/latest/modules/chains/generic/custom_chain.html |
82f4a2b7a655-1 | # Whenever you call a language model, or another chain, you should pass
# a callback manager to it. This allows the inner run to be tracked by
# any callbacks that are registered on the outer run.
# You can always obtain a callback manager for this by calling
# `run_manager.get_child()` ... | https://python.langchain.com/en/latest/modules/chains/generic/custom_chain.html |
82f4a2b7a655-2 | callbacks=run_manager.get_child() if run_manager else None
)
# If you want to log something about this run, you can do so by calling
# methods on the `run_manager`, as shown below. This will trigger any
# callbacks that are registered for that event.
if run_manager:
a... | https://python.langchain.com/en/latest/modules/chains/generic/custom_chain.html |
2602798001b0-0 | .ipynb
.pdf
Async API for Chain
Async API for Chain#
LangChain provides async support for Chains by leveraging the asyncio library.
Async methods are currently supported in LLMChain (through arun, apredict, acall) and LLMMathChain (through arun and acall), ChatVectorDBChain, and QA chains. Async support for other chain... | https://python.langchain.com/en/latest/modules/chains/generic/async_chain.html |
2602798001b0-1 | await generate_concurrently()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Concurrent executed in {elapsed:0.2f} seconds." + '\033[0m')
s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print('\033[1m' + f"Serial executed in {elapsed:0.2f} seconds." + '\033[0m')
BrightSmile Toothpas... | https://python.langchain.com/en/latest/modules/chains/generic/async_chain.html |
5c8fab61d0f7-0 | .ipynb
.pdf
Loading from LangChainHub
Loading from LangChainHub#
This notebook covers how to load chains from LangChainHub.
from langchain.chains import load_chain
chain = load_chain("lc://chains/llm-math/chain.json")
chain.run("whats 2 raised to .12")
> Entering new LLMMathChain chain...
whats 2 raised to .12
Answer: ... | https://python.langchain.com/en/latest/modules/chains/generic/from_hub.html |
5c8fab61d0f7-1 | query = "What did the president say about Ketanji Brown Jackson"
chain.run(query)
" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support ... | https://python.langchain.com/en/latest/modules/chains/generic/from_hub.html |
371ee8ee9333-0 | .ipynb
.pdf
Transformation Chain
Transformation Chain#
This notebook showcases using a generic transformation chain.
As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those.
from langc... | https://python.langchain.com/en/latest/modules/chains/generic/transformation.html |
bd16129d4e81-0 | .ipynb
.pdf
Sequential Chains
Contents
SimpleSequentialChain
Sequential Chain
Memory in Sequential Chains
Sequential Chains#
The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-1 | synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)
# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.
Play Synopsis:
{synopsis}
R... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-2 | The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succu... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-3 | The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.
Sequential... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-4 | Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="review")
# This is the overall chain where we run these two chains in sequence.
... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-5 | 'era': 'Victorian England',
'synopsis': "\n\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\n\nOn th... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-6 | 'review': "\n\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and st... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-7 | from langchain.memory import SimpleMemory
llm = OpenAI(temperature=.7)
template = """You are a social media manager for a theater company. Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for tha... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
bd16129d4e81-8 | 'location': 'Theater in the Park',
'social_post_text': "\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love i... | https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html |
d04b74d6fb51-0 | .ipynb
.pdf
Callbacks
Contents
Callbacks
How to use callbacks
When do you want to use each of these?
Using an existing handler
Creating a custom handler
Async Callbacks
Using multiple handlers, passing in handlers
Tracing and Token Counting
Tracing
Token Counting
Callbacks#
LangChain provides a callbacks system that ... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
d04b74d6fb51-1 | CallbackHandlers are objects that implement the CallbackHandler interface, which has a method for each event that can be subscribed to. The CallbackManager will call the appropriate method on each handler when the event is triggered.
class BaseCallbackHandler:
"""Base callback handler that can be used to handle cal... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
d04b74d6fb51-2 | def on_tool_end(self, output: str, **kwargs: Any) -> Any:
"""Run when tool ends running."""
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> Any:
"""Run when tool errors."""
def on_text(self, text: str, **kwargs: Any) -> Any:
"""Run on a... | https://python.langchain.com/en/latest/modules/callbacks/getting_started.html |
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