id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
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ec70b958996e-23 | > 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: | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
ec70b958996e-24 | 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... | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
ec70b958996e-25 | 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 feed... | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
ec70b958996e-26 | 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: ```... | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
ec70b958996e-27 | '{"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 अन... | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
ec70b958996e-28 | 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 feedback](https://speak.com/chatgpt?rid=d4mcapbkopo164pqpbk321oc})*","extra_... | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
ec70b958996e-29 | 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. | /content/https://python.langchain.com/en/latest/modules/chains/examples/openapi.html |
432276d2e816-0 | .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#
... | /content/https://python.langchain.com/en/latest/modules/chains/examples/pal.html |
432276d2e816-1 | 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 = []
obje... | /content/https://python.langchain.com/en/latest/modules/chains/examples/pal.html |
432276d2e816-2 | # 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_purp... | /content/https://python.langchain.com/en/latest/modules/chains/examples/pal.html |
c3d9055b594c-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
c3d9055b594c-1 | ('Intel', 'state-of-the-art factories', 'is building'),
('Intel', '10,000 new good-paying jobs', 'is creating'),
('Intel', 'Silicon Valley', 'is helping build'),
('Field of dreams',
"America's future will be built",
'is the ground on which')]
Querying the graph#
We can now use the graph QA chain to ask question ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
c3d9055b594c-2 | ('Intel', 'state-of-the-art factories', 'is building'),
('Intel', '10,000 new good-paying jobs', 'is creating'),
('Intel', 'Silicon Valley', 'is helping build'),
('Field of dreams',
"America's future will be built",
'is the ground on which')]
previous
Chat Over Documents with Chat History
next
Hypothetical Docum... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/graph_qa.html |
9f6f89335c6b-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-1 | embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))])
Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
query = "What did the president say about Justice Breyer"
docs = docsearch.simil... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-2 | {'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.
template = """Given the following extracted parts of a long document and a question, create a final answer with referen... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-3 | query = "What did the president say about Justice Breyer"
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}
Intermediate Steps
We can also return the intermediate steps for map_reduce chains, should we ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-4 | Return any relevant text in Italian.
{context}
Question: {question}
Relevant text, if any, in Italian:"""
QUESTION_PROMPT = PromptTemplate(
template=question_prompt_template, input_variables=["context", "question"]
)
combine_prompt_template = """Given the following extracted parts of a long document and a question,... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-5 | {'intermediate_steps': ["\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.",
' Non pert... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-6 | {'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 career as a top litigator in private practice, a former federal public defender... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-7 | chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True)
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 tha... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-9 | '\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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-10 | '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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-11 | "------------\n"
"{context_str}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"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 = PromptTemp... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-14 | chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="map_rerank", metadata_keys=['source'], return_intermediate_steps=True)
query = "What did the president say about Justice Breyer"
result = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
result["output_text"]
' The Presid... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-15 | 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. Th... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
9f6f89335c6b-16 | 'score': '100'},
{'answer': ' Non so.', 'score': '0'},
{'answer': ' Il presidente non ha detto nulla sulla giustizia Breyer.',
'score': '100'}],
'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.'}
previous
Hypothetical Documen... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/qa_with_sources.html |
95b907f9b305-0 | .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, ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-1 | Running Chroma using direct local API.
Using DuckDB in-memory for database. Data will be transient.
query = "What did the president say about Justice Breyer"
docs = docsearch.get_relevant_documents(query)
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
Quickstart#
If you ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-2 | 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", ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-3 | chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce", return_map_steps=True)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
{'intermediate_steps': [' "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army vetera... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-4 | QUESTION_PROMPT = PromptTemplate(
template=question_prompt_template, input_variables=["context", "question"]
)
combine_prompt_template = """Given the following extracted parts of a long document and a question, create a final answer italian.
If you don't know the answer, just say that you don't know. Don't try to ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-5 | " Non c'è testo pertinente."],
'output_text': ' Non ha detto nulla riguardo a Justice Breyer.'}
Batch Size
When using the map_reduce chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-6 | Intermediate Steps
We can also return the intermediate steps for refine chains, should we want to inspect them. This is done with the return_refine_steps variable.
chain = load_qa_chain(OpenAI(temperature=0), chain_type="refine", return_refine_steps=True)
chain({"input_documents": docs, "question": query}, return_only_... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-7 | '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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-8 | "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", ret... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-9 | "\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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-10 | '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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-11 | '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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
95b907f9b305-12 | 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': '... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/question_answering.html |
74fd853d76ab-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-1 | .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(re... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-2 | 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.c... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-4 | 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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-5 | 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\... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-6 | 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` envi... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
74fd853d76ab-7 | 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. | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_text_generation.html |
5a470061f072-0 | .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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
5a470061f072-1 | from langchain import OpenAI
chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())
chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)
{'answer': ' The president honored Justice Breyer for his servi... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
5a470061f072-2 | 'sources': '31-pl'}
The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQAWithS... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa_with_sources.html |
f32bc5d16be6-0 | .ipynb
.pdf
Summarization
Contents
Prepare Data
Quickstart
The stuff Chain
The map_reduce Chain
The refine Chain
Summarization#
This notebook walks through how to use LangChain for summarization over a list of documents. It covers three different chain types: stuff, map_reduce, and refine. For a more in depth explana... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-1 | chain.run(docs)
' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-2 | chain = load_summarize_chain(llm, chain_type="stuff", prompt=PROMPT)
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'Uc... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-3 | chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True)
chain({"input_documents": docs}, return_only_outputs=True)
{'map_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sancti... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-4 | '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 strug... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-5 | {'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 sanzion... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-6 | "\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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-7 | 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-gotte... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-8 | 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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-9 | "\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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-10 | "\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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-11 | '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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-12 | {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 s... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-13 | {'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 economic... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-14 | "\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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
f32bc5d16be6-15 | 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. | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/summarize.html |
55a9fd051f0d-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/analyze_document.html |
55a9fd051f0d-1 | summarize_document_chain.run(state_of_the_union)
" In this speech, President Biden addresses the American people and the world, discussing the recent aggression of Russia's Vladimir Putin in Ukraine and the US response. He outlines economic sanctions and other measures taken to hold Putin accountable, and announces the... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/analyze_document.html |
3d743cc15bfd-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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-1 | text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
Using embedded DuckDB without persistence: data will be transient
We can now create a memory object, whi... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-2 | result['answer']
' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'
Pass in chat history#
In the above example, we used a Memory object to track chat history. We can also just pass it in explicitly. In order to do this, we need to initialize a chain without any memory object.
... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-3 | Return Source Documents#
You can also easily return source documents from the ConversationalRetrievalChain. This is useful for when you want to inspect what documents were returned.
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)
chat_history =... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-4 | ConversationalRetrievalChain with search_distance#
If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter.
vectordbkwargs = {"search_distance": 0.9}
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_do... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-5 | 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 practice, a former federal public defender, from a... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-6 | 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, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fra... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-7 | doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)
qa = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa(... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
3d743cc15bfd-8 | return "\n".join(res)
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})
result['answer']
" The ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html |
b6da74138738-0 | .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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-1 | 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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-2 | 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_d... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-3 | 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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-4 | 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 Ord... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-5 | 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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-6 | 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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
b6da74138738-7 | 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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html |
e4b19ca000c0-0 | .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... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
e4b19ca000c0-1 | 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#
Besides using preconfigured prompts, we can also easily construct our own prompts and use those in the LLMChai... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
e4b19ca000c0-2 | 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 ... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
e4b19ca000c0-3 | 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 Har... | /content/https://python.langchain.com/en/latest/modules/chains/index_examples/hyde.html |
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