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Docs refactor (#480)

Browse Source 
Big docs refactor! Motivation is to make it easier for people to find
resources they are looking for. To accomplish this, there are now three
main sections:

- Getting Started: steps for getting started, walking through most core
functionality
- Modules: these are different modules of functionality that langchain
provides. Each part here has a "getting started", "how to", "key
concepts" and "reference" section (except in a few select cases where it
didnt easily fit).
- Use Cases: this is to separate use cases (like summarization, question
answering, evaluation, etc) from the modules, and provide a different
entry point to the code base.

There is also a full reference section, as well as extra resources
(glossary, gallery, etc)

Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
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Harrison Chase
2023-01-02 08:24:09 -08:00
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41
docs/modules/chains/combine_docs.md Normal file
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# CombineDocuments Chains
CombineDocuments chains are useful for when you need to run a language over multiple documents.
Common use cases for this include question answering, question answering with sources, summarization, and more.
For more information on specific use cases as well as different methods for **fetching** these documents, please see
[this overview](/use_cases/combine_docs.md).
This documentation now picks up from after you've fetched your documents - now what?
How do you pass them to the language model in a format it can understand?
There are a few different methods, or chains, for doing so. LangChain supports three of the more common ones - and
we are actively looking to include more, so if you have any ideas please reach out! Note that there is not
one best method - the decision of which one to use is often very context specific. In order from simplest to
most complex:
## Stuffing
Stuffing is the simplest method, whereby you simply stuff all the related data into the prompt as context
to pass to the language model. This is implemented in LangChain as the `StuffDocumentsChain`.
**Pros:** Only makes a single call to the LLM. When generating text, the LLM has access to all the data at once.
**Cons:** Most LLMs have a context length, and for large documents (or many documents) this will not work as it will result in a prompt larger than the context length.
The main downside of this method is that it only works one smaller pieces of data. Once you are working
with many pieces of data, this approach is no longer feasible. The next two approaches are designed to help deal with that.
## Map Reduce
This method involves an initial prompt on each chunk of data (for summarization tasks, this
could be a summary of that chunk; for question-answering tasks, it could be an answer based solely on that chunk).
Then a different prompt is run to combine all the initial outputs. This is implemented in the LangChain as the `MapReduceDocumentsChain`.
**Pros:** Can scale to larger documents (and more documents) than `StuffDocumentsChain`. The calls to the LLM on individual documents are independent and can therefore be parallelized.
**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. Loses some information during the final combining call.
## Refine
This method involves an initial prompt on the first chunk of data, generating some output.
For the remaining documents, that output is passed in, along with the next document,
asking the LLM to refine the output based on the new document.
**Pros:** Can pull in more relevant context, and may be less lossy than `MapReduceDocumentsChain`.
**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. The calls are also NOT independent, meaning they cannot be paralleled like `MapReduceDocumentsChain`. There is also some potential dependencies on the ordering of the documents.

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{
"cells": [
{
"cell_type": "markdown",
"id": "74148cee",
"metadata": {},
"source": [
"# Question Answering with Sources\n",
"\n",
"This notebook walks through how to use LangChain for question answering with sources 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](../combine_docs.md)."
]
},
{
"cell_type": "markdown",
"id": "ca2f0efc",
"metadata": {},
"source": [
"## Prepare Data\n",
"First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "78f28130",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.embeddings.cohere import CohereEmbeddings\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
"from langchain.vectorstores.faiss import FAISS\n",
"from langchain.docstore.document import Document"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4da195a3",
"metadata": {},
"outputs": [],
"source": [
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_text(state_of_the_union)\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5ec2b55b",
"metadata": {},
"outputs": [],
"source": [
"docsearch = FAISS.from_texts(texts, embeddings, metadatas=[{\"source\": i} for i in range(len(texts))])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5286f58f",
"metadata": {},
"outputs": [],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"docs = docsearch.similarity_search(query)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "005a47e9",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "markdown",
"id": "d82f899a",
"metadata": {},
"source": [
"## The `stuff` Chain\n",
"\n",
"This sections shows results of using the `stuff` Chain to do question answering with sources."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fc1a5ed6",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7d766417",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': ' The president thanked Justice Breyer for his service.\\nSOURCES: 30-pl'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "c5dbb304",
"metadata": {},
"source": [
"## The `map_reduce` Chain\n",
"\n",
"This sections shows results of using the `map_reduce` Chain to do question answering with sources."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "921db0a4",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e417926a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': ' The president thanked Justice Breyer for his service.\\nSOURCES: 30-pl'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "ae2f6d97",
"metadata": {},
"source": [
"**Intermediate Steps**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "15af265f",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "21b136e5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'map_steps': [' \"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"',\n",
" ' None',\n",
" ' None',\n",
" ' None'],\n",
" 'output_text': ' The president thanked Justice Breyer for his service.\\nSOURCES: 30-pl'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "5bf0e1ab",
"metadata": {},
"source": [
"## The `refine` Chain\n",
"\n",
"This sections shows results of using the `refine` Chain to do question answering with sources."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "904835c8",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f60875c6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'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, and a family of public school educators and police officers. He noted Justice Breyer's reputation as a consensus builder and the broad range of support he has received from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also highlighted the importance of securing the border and fixing the immigration system in order to advance liberty and justice, and mentioned the new technology, joint patrols, dedicated immigration judges, and commitments to support partners in South and Central America that have been put in place. He also expressed his commitment to the LGBTQ+ community, noting the need for the bipartisan Equality Act and the importance of protecting transgender Americans from state laws targeting them. He also highlighted his commitment to bipartisanship, noting the 80 bipartisan bills he signed into law last year, and his plans to strengthen the Violence Against Women Act. Additionally, he announced that the Justice Department will name a chief prosecutor for pandemic fraud and his plan to lower the deficit by more than one trillion dollars in a\"}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "ac357530",
"metadata": {},
"source": [
"**Intermediate Steps**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3396a773",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "be5739ef",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'refine_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.',\n",
" \"\\n\\nThe president said that he was honoring Justice Breyer for his dedication to serving the country, his career as a top litigator in private practice, a former federal public defender, and his family of public school educators and police officers. He also noted Justice Breyer's consensus-building skills and the broad range of support he has received from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also highlighted the importance of advancing liberty and justice by securing the border and fixing the immigration system, noting the new technology and joint patrols with Mexico and Guatemala to catch more human traffickers, as well as the dedicated immigration judges and commitments to support partners in South and Central America to host more refugees and secure their own borders. \\nSource: 31\",\n",
" \"\\n\\nThe president said that he was honoring Justice Breyer for his dedication to serving the country, his career as a top litigator in private practice, a former federal public defender, and his family of public school educators and police officers. He also noted Justice Breyer's consensus-building skills and the broad range of support he has received from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also highlighted the importance of advancing liberty and justice by securing the border and fixing the immigration system, noting the new technology and joint patrols with Mexico and Guatemala to catch more human traffickers, as well as the dedicated immigration judges and commitments to support partners in South and Central America to host more refugees and secure their own borders. Additionally, he mentioned the need for the bipartisan Equality Act to be passed and signed into law, and the importance of strengthening the Violence Against Women Act. He also offered a Unity Agenda for the Nation, which includes beating the opioid epidemic. \\nSource: 31, 33\",\n",
" \"\\n\\nThe president said that he was honoring Justice Breyer for his dedication to serving the country, his career as a top litigator in private practice, a former federal public defender, and his family of public school educators and police officers. He also noted Justice Breyer's consensus-building skills and the broad range of support he has received from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also highlighted the importance of advancing liberty and justice by securing the border and fixing the immigration system, noting the new technology and joint patrols with Mexico and Guatemala to catch more human traffickers, as well as the dedicated immigration judges and commitments to support partners in South and Central America to host more refugees and secure their own borders. Additionally, he mentioned the need for the bipartisan Equality Act to be passed and signed into law, and the importance of strengthening the Violence Against Women Act. He also offered a Unity Agenda for the Nation, which includes beating the opioid epidemic, and announced that the Justice Department will name a chief prosecutor for pandemic fraud. Source: 31, 33, 20\"],\n",
" 'output_text': \"\\n\\nThe president said that he was honoring Justice Breyer for his dedication to serving the country, his career as a top litigator in private practice, a former federal public defender, and his family of public school educators and police officers. He also noted Justice Breyer's consensus-building skills and the broad range of support he has received from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. He also highlighted the importance of advancing liberty and justice by securing the border and fixing the immigration system, noting the new technology and joint patrols with Mexico and Guatemala to catch more human traffickers, as well as the dedicated immigration judges and commitments to support partners in South and Central America to host more refugees and secure their own borders. Additionally, he mentioned the need for the bipartisan Equality Act to be passed and signed into law, and the importance of strengthening the Violence Against Women Act. He also offered a Unity Agenda for the Nation, which includes beating the opioid epidemic, and announced that the Justice Department will name a chief prosecutor for pandemic fraud. Source: 31, 33, 20\"}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"vscode": {
"interpreter": {
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "05859721",
"metadata": {},
"source": [
"# Question Answering\n",
"\n",
"This notebook walks through how to use LangChain for question answering over a list of documents. It covers three different types of chaings: `stuff`, `map_reduce`, and `refine`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
]
},
{
"cell_type": "markdown",
"id": "726f4996",
"metadata": {},
"source": [
"## Prepare Data\n",
"First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "17fcbc0f",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores.faiss import FAISS\n",
"from langchain.docstore.document import Document"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "291f0117",
"metadata": {},
"outputs": [],
"source": [
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_text(state_of_the_union)\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fd9666a9",
"metadata": {},
"outputs": [],
"source": [
"docsearch = FAISS.from_texts(texts, embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d1eaf6e6",
"metadata": {},
"outputs": [],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"docs = docsearch.similarity_search(query)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a16e3453",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.question_answering import load_qa_chain\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "markdown",
"id": "f78787a0",
"metadata": {},
"source": [
"## The `stuff` Chain\n",
"\n",
"This sections shows results of using the `stuff` Chain to do question answering."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "180fd4c1",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "77fdf1aa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': ' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "91522e29",
"metadata": {},
"source": [
"## The `map_reduce` Chain\n",
"\n",
"This sections shows results of using the `map_reduce` Chain to do question answering."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b0060f51",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "fbdb9137",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': ' The president said, \"Justice Breyer, thank you for your service.\"'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "31478d32",
"metadata": {},
"source": [
"**Intermediate Steps**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "452c8680",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "90b47a75",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'map_steps': [' \"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"',\n",
" ' None',\n",
" ' None',\n",
" ' None'],\n",
" 'output_text': ' The president said, \"Justice Breyer, thank you for your service.\"'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "6ea50ad0",
"metadata": {},
"source": [
"## The `refine` Chain\n",
"\n",
"This sections shows results of using the `refine` Chain to do question answering."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "fb167057",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d8b5286e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Justice Breyer\"\n",
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "f95dfb2e",
"metadata": {},
"source": [
"**Intermediate Steps**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a5c64200",
"metadata": {},
"outputs": [],
"source": [
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "817546ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'refine_steps': ['\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',\n",
" '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',\n",
" '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act.',\n",
" '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'],\n",
" 'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"vscode": {
"interpreter": {
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

294
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{
"cells": [
{
"cell_type": "markdown",
"id": "d9a0131f",
"metadata": {},
"source": [
"# Summarization\n",
"\n",
"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](../combine_docs.md)."
]
},
{
"cell_type": "markdown",
"id": "0b5660bf",
"metadata": {},
"source": [
"## Prepare Data\n",
"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)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e9db25f3",
"metadata": {},
"outputs": [],
"source": [
"from langchain import OpenAI, PromptTemplate, LLMChain\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.chains.mapreduce import MapReduceChain\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"\n",
"text_splitter = CharacterTextSplitter()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "99bbe19b",
"metadata": {},
"outputs": [],
"source": [
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()\n",
"texts = text_splitter.split_text(state_of_the_union)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "baa6e808",
"metadata": {},
"outputs": [],
"source": [
"from langchain.docstore.document import Document\n",
"\n",
"docs = [Document(page_content=t) for t in texts[:3]]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "27989fc4",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains.summarize import load_summarize_chain"
]
},
{
"cell_type": "markdown",
"id": "ea2d5c99",
"metadata": {},
"source": [
"## The `stuff` Chain\n",
"\n",
"This sections shows results of using the `stuff` Chain to do summarization."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f01f3196",
"metadata": {},
"outputs": [],
"source": [
"chain = load_summarize_chain(llm, chain_type=\"stuff\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "da4d9801",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' 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.'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(docs)"
]
},
{
"cell_type": "markdown",
"id": "9c868e86",
"metadata": {},
"source": [
"## The `map_reduce` Chain\n",
"\n",
"This sections shows results of using the `map_reduce` Chain to do summarization."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ef28e1d4",
"metadata": {},
"outputs": [],
"source": [
"chain = load_summarize_chain(llm, chain_type=\"map_reduce\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f82c5f9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\" 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.\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(docs)"
]
},
{
"cell_type": "markdown",
"id": "d0c2a6d3",
"metadata": {},
"source": [
"**Intermediate Steps**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d9cfc24e",
"metadata": {},
"outputs": [],
"source": [
"chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c7dff5e8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'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.\",\n",
" ' 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.',\n",
" \" 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.\"],\n",
" '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.\"}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain({\"input_documents\": docs}, return_only_outputs=True)"
]
},
{
"cell_type": "markdown",
"id": "f61350f9",
"metadata": {},
"source": [
"## The `refine` Chain\n",
"\n",
"This sections shows results of using the `refine` Chain to do summarization."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3bcbe31e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\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\""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = load_summarize_chain(llm, chain_type=\"refine\")\n",
"\n",
"chain.run(docs)"
]
},
{
"cell_type": "markdown",
"id": "84e9567e",
"metadata": {},
"source": [
"**Intermediate Steps**\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "cd49ac4d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'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",
" \"\\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",
" \"\\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\"],\n",
" '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\"}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = load_summarize_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)\n",
"\n",
"chain({\"input_documents\": docs}, return_only_outputs=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.0 64-bit ('llm-env')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
},
"vscode": {
"interpreter": {
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "07c1e3b9",
"metadata": {},
"source": [
"# Vector DB Question/Answering\n",
"\n",
"This example showcases question answering over a vector database."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "82525493",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores.faiss import FAISS\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain import OpenAI, VectorDBQA"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5c7049db",
"metadata": {},
"outputs": [],
"source": [
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_text(state_of_the_union)\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"docsearch = FAISS.from_texts(texts, embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3018f865",
"metadata": {},
"outputs": [],
"source": [
"qa = VectorDBQA.from_llm(llm=OpenAI(), vectorstore=docsearch)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "032a47f8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"qa.run(query)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.0 64-bit ('llm-env')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
},
"vscode": {
"interpreter": {
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "efc5be67",
"metadata": {},
"source": [
"# VectorDB Question Ansering with Sources\n",
"\n",
"This notebook goes over how to do question-answering with sources over a vector database. It does this by using the `VectorDBQAWithSourcesChain`, which does the lookup of the documents from a vector database. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1c613960",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.embeddings.cohere import CohereEmbeddings\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
"from langchain.vectorstores.faiss import FAISS"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "17d1306e",
"metadata": {},
"outputs": [],
"source": [
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_text(state_of_the_union)\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0e745d99",
"metadata": {},
"outputs": [],
"source": [
"docsearch = FAISS.from_texts(texts, embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f42d79dc",
"metadata": {},
"outputs": [],
"source": [
"# Add in a fake source information\n",
"for i, d in enumerate(docsearch.docstore._dict.values()):\n",
" d.metadata = {'source': f\"{i}-pl\"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8aa571ae",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import VectorDBQAWithSourcesChain"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "aa859d4c",
"metadata": {},
"outputs": [],
"source": [
"from langchain import OpenAI\n",
"\n",
"chain = VectorDBQAWithSourcesChain.from_llm(OpenAI(temperature=0), vectorstore=docsearch)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8ba36fa7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'answer': ' The president thanked Justice Breyer for his service.',\n",
" 'sources': '30-pl'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.0 64-bit ('llm-env')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
},
"vscode": {
"interpreter": {
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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CombineDocuments Chains
-----------------------
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all end-to-end chains for working with documents.
`Question Answering <combine_docs_examples/question_answering.html>`_: A walkthrough of how to use LangChain for question answering over specific documents.
`Question Answering with Sources <combine_docs_examples/qa_with_sources.html>`_: A walkthrough of how to use LangChain for question answering (with sources) over specific documents.
`Summarization <combine_docs_examples/summarize.html>`_: A walkthrough of how to use LangChain for summarization over specific documents.
`Vector DB Question Answering <combine_docs_examples/vector_db_qa.html>`_: A walkthrough of how to use LangChain for question answering over a vector database.
`Vector DB Question Answering with Sources <combine_docs_examples/vector_db_qa_with_sources.html>`_: A walkthrough of how to use LangChain for question answering (with sources) over a vector database.
.. toctree::
:maxdepth: 1
:glob:
:caption: CombineDocument Chains
:name: combine_docs
:hidden:
combine_docs_examples/*

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BashChain\n",
"This notebook showcases using LLMs and a bash process to do perform simple filesystem commands."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
"Please write a bash script that prints 'Hello World' to the console.\u001b[32;1m\u001b[1;3m\n",
"\n",
"```bash\n",
"echo \"Hello World\"\n",
"```\u001b[0m['```bash', 'echo \"Hello World\"', '```']\n",
"\n",
"Answer: \u001b[33;1m\u001b[1;3mHello World\n",
"\u001b[0m\n",
"\u001b[1m> Finished LLMBashChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Hello World\\n'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains import LLMBashChain\n",
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"\n",
"text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
"\n",
"bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
"\n",
"bash_chain.run(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

97
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LLMCheckerChain\n",
"This notebook showcases how to use LLMCheckerChain."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMCheckerChain chain...\u001b[0m\n",
"\n",
"\n",
"\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
"\u001b[1mChain 0\u001b[0m:\n",
"{'statement': '\\nNone. Mammals do not lay eggs.'}\n",
"\n",
"\u001b[1mChain 1\u001b[0m:\n",
"{'assertions': '\\n• Mammals reproduce using live birth\\n• Mammals do not lay eggs\\n• Animals that lay eggs are not mammals'}\n",
"\n",
"\u001b[1mChain 2\u001b[0m:\n",
"{'checked_assertions': '\\n1. True\\n\\n2. True\\n\\n3. False - Mammals are a class of animals that includes animals that lay eggs, such as monotremes (platypus and echidna).'}\n",
"\n",
"\u001b[1mChain 3\u001b[0m:\n",
"{'revised_statement': ' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'}\n",
"\n",
"\n",
"\u001b[1m> Finished SequentialChain chain.\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMCheckerChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Monotremes, such as the platypus and echidna, lay the biggest eggs of any mammal.'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains import LLMCheckerChain\n",
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0.7)\n",
"\n",
"text = \"What type of mammal lays the biggest eggs?\"\n",
"\n",
"checker_chain = LLMCheckerChain(llm=llm, verbose=True)\n",
"\n",
"checker_chain.run(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "e71e720f",
"metadata": {},
"source": [
"# LLM Math\n",
"\n",
"This notebook showcases using LLMs and Python REPLs to do complex word math problems."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "44e9ba31",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
"```python\n",
"import math\n",
"print(math.pow(13, .3432))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\n",
"\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Answer: 2.4116004626599237\\n'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import OpenAI, LLMMathChain\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"llm_math = LLMMathChain(llm=llm, verbose=True)\n",
"\n",
"llm_math.run(\"What is 13 raised to the .3432 power?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f62f0c75",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "dd7ec7af",
"metadata": {},
"source": [
"# LLMRequestsChain\n",
"\n",
"Using the request library to get HTML results from a URL and then an LLM to parse results"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "dd8eae75",
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.chains import LLMRequestsChain, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "65bf324e",
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"\n",
"template = \"\"\"Between >>> and <<< are the raw search result text from google.\n",
"Extract the answer to the question '{query}' or say \"not found\" if the information is not contained.\n",
"Use the format\n",
"Extracted:<answer or \"not found\">\n",
">>> {requests_result} <<<\n",
"Extracted:\"\"\"\n",
"\n",
"PROMPT = PromptTemplate(\n",
" input_variables=[\"query\", \"requests_result\"],\n",
" template=template,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f36ae0d8",
"metadata": {},
"outputs": [],
"source": [
"chain = LLMRequestsChain(llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=PROMPT))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b5d22d9d",
"metadata": {},
"outputs": [],
"source": [
"question = \"What are the Three (3) biggest countries, and their respective sizes?\"\n",
"inputs = {\n",
" \"query\": question,\n",
" \"url\": \"https://www.google.com/search?q=\" + question.replace(\" \", \"+\")\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2ea81168",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'query': 'What are the Three (3) biggest countries, and their respective sizes?',\n",
" 'url': 'https://www.google.com/search?q=What+are+the+Three+(3)+biggest+countries,+and+their+respective+sizes?',\n",
" 'output': ' Russia (17,098,242 km²), Canada (9,984,670 km²), United States (9,826,675 km²)'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain(inputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db8f2b6d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "b83e61ed",
"metadata": {},
"source": [
"# Moderation\n",
"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](https://beta.openai.com/docs/usage-policies/use-case-policy) 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.\n",
"\n",
"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.\n",
"\n",
"In this notebook, we will show:\n",
"\n",
"1. How to run any piece of text through a moderation chain.\n",
"2. How to append a Moderation chain to a LLMChain."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b7aa1ff2",
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain\n",
"from langchain.prompts import PromptTemplate"
]
},
{
"cell_type": "markdown",
"id": "c26d5be6",
"metadata": {},
"source": [
"## How to use the moderation chain\n",
"\n",
"Here's an example of using the moderation chain with default settings (will return a string explaining stuff was flagged)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fd0fc85c",
"metadata": {},
"outputs": [],
"source": [
"moderation_chain = OpenAIModerationChain()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3fa47dd7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'This is okay'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"moderation_chain.run(\"This is okay\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "37bfad73",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Text was found that violates OpenAI's content policy.\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"moderation_chain.run(\"I will kill you\")"
]
},
{
"cell_type": "markdown",
"id": "196820ab",
"metadata": {},
"source": [
"Here's an example of using the moderation chain to throw an error."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b29c1150",
"metadata": {},
"outputs": [],
"source": [
"moderation_chain_error = OpenAIModerationChain(error=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f9ab64d9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'This is okay'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"moderation_chain_error.run(\"This is okay\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "954f3da2",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Text was found that violates OpenAI's content policy.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmoderation_chain_error\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mI will kill you\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/workplace/langchain/langchain/chains/base.py:138\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs)[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n",
"File \u001b[0;32m~/workplace/langchain/langchain/chains/base.py:112\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m 109\u001b[0m \u001b[38;5;28mprint\u001b[39m(\n\u001b[1;32m 110\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Entering new \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain...\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 111\u001b[0m )\n\u001b[0;32m--> 112\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose:\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[1m> Finished \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m chain.\u001b[39m\u001b[38;5;130;01m\\033\u001b[39;00m\u001b[38;5;124m[0m\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/workplace/langchain/langchain/chains/moderation.py:81\u001b[0m, in \u001b[0;36mOpenAIModerationChain._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 79\u001b[0m text \u001b[38;5;241m=\u001b[39m inputs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_key]\n\u001b[1;32m 80\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient\u001b[38;5;241m.\u001b[39mcreate(text)\n\u001b[0;32m---> 81\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_moderate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresults\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_key: output}\n",
"File \u001b[0;32m~/workplace/langchain/langchain/chains/moderation.py:73\u001b[0m, in \u001b[0;36mOpenAIModerationChain._moderate\u001b[0;34m(self, text, results)\u001b[0m\n\u001b[1;32m 71\u001b[0m error_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mText was found that violates OpenAI\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms content policy.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror:\n\u001b[0;32m---> 73\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_str)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m error_str\n",
"\u001b[0;31mValueError\u001b[0m: Text was found that violates OpenAI's content policy."
]
}
],
"source": [
"moderation_chain_error.run(\"I will kill you\")"
]
},
{
"cell_type": "markdown",
"id": "8de5dcbb",
"metadata": {},
"source": [
"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](https://beta.openai.com/docs/api-reference/moderations))."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3960e985",
"metadata": {},
"outputs": [],
"source": [
"class CustomModeration(OpenAIModerationChain):\n",
" \n",
" def _moderate(self, text: str, results: dict) -> str:\n",
" if results[\"flagged\"]:\n",
" error_str = f\"The following text was found that violates OpenAI's content policy: {text}\"\n",
" return error_str\n",
" return text\n",
" \n",
"custom_moderation = CustomModeration()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1152ec11",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'This is okay'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_moderation.run(\"This is okay\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "973257bf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"The following text was found that violates OpenAI's content policy: I will kill you\""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"custom_moderation.run(\"I will kill you\")"
]
},
{
"cell_type": "markdown",
"id": "8718111f",
"metadata": {},
"source": [
"## How to append a Moderation chain to an LLMChain\n",
"\n",
"To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0d129333",
"metadata": {},
"outputs": [],
"source": [
"prompt = PromptTemplate(template=\"{text}\", input_variables=[\"text\"])\n",
"llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a557c531",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' I will kill you'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = \"\"\"We are playing a game of repeat after me.\n",
"\n",
"Person 1: Hi\n",
"Person 2: Hi\n",
"\n",
"Person 1: How's your day\n",
"Person 2: How's your day\n",
"\n",
"Person 1: I will kill you\n",
"Person 2:\"\"\"\n",
"llm_chain.run(text)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d4d10f1c",
"metadata": {},
"outputs": [],
"source": [
"chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "02f37985",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Text was found that violates OpenAI's content policy.\""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(text)"
]
},
{
"cell_type": "markdown",
"id": "72643128",
"metadata": {},
"source": [
"Now let's walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can't use the SimpleSequentialChain)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7118ec36",
"metadata": {},
"outputs": [],
"source": [
"prompt = PromptTemplate(template=\"{setup}{new_input}Person2:\", input_variables=[\"setup\", \"new_input\"])\n",
"llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\"), prompt=prompt)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "003bdfce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': ' I will kill you'}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"setup = \"\"\"We are playing a game of repeat after me.\n",
"\n",
"Person 1: Hi\n",
"Person 2: Hi\n",
"\n",
"Person 1: How's your day\n",
"Person 2: How's your day\n",
"\n",
"Person 1:\"\"\"\n",
"new_input = \"I will kill you\"\n",
"inputs = {\"setup\": setup, \"new_input\": new_input}\n",
"llm_chain(inputs, return_only_outputs=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "77b64228",
"metadata": {},
"outputs": [],
"source": [
"# Setting the input/output keys so it lines up\n",
"moderation_chain.input_key = \"text\"\n",
"moderation_chain.output_key = \"sanitized_text\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "998a95be",
"metadata": {},
"outputs": [],
"source": [
"chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=[\"setup\", \"new_input\"])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "9c97a136",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sanitized_text': \"Text was found that violates OpenAI's content policy.\"}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain(inputs, return_only_outputs=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ddc90e15",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "32e022a2",
"metadata": {},
"source": [
"# PAL\n",
"\n",
"Implements Program-Aided Language Models, as in https://arxiv.org/pdf/2211.10435.pdf.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1370e40f",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import PALChain\n",
"from langchain import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "beddcac7",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
"pal_chain = PALChain.from_math_prompt(llm, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e2eab9d4",
"metadata": {},
"outputs": [],
"source": [
"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?\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3ef64b27",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"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?\"\"\"\n",
" cindy_pets = 4\n",
" marcia_pets = cindy_pets + 2\n",
" jan_pets = marcia_pets * 3\n",
" total_pets = cindy_pets + marcia_pets + jan_pets\n",
" result = total_pets\n",
" return result\u001b[0m\n",
"\n",
"\u001b[1m> Finished PALChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'28'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e524f81f",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(model_name='code-davinci-002', temperature=0, max_tokens=512)\n",
"pal_chain = PALChain.from_colored_object_prompt(llm, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "03a237b8",
"metadata": {},
"outputs": [],
"source": [
"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?\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a84a4352",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
"objects += [('sunglasses', 'yellow')] * 2\n",
"\n",
"# Remove all pairs of sunglasses\n",
"objects = [object for object in objects if object[0] != 'sunglasses']\n",
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001b[0m\n",
"\n",
"\u001b[1m> Finished PALChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'2'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pal_chain.run(question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ab20fec",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

129
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{
"cells": [
{
"cell_type": "markdown",
"id": "0ed6aab1",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# SQLite example\n",
"\n",
"This example showcases hooking up an LLM to answer questions over a database."
]
},
{
"cell_type": "markdown",
"id": "b2f66479",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"This uses the example Chinook database.\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d0e27d88",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "72ede462",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
"llm = OpenAI(temperature=0)\n",
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "15ff81df",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
"How many employees are there? \n",
"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT COUNT(*) FROM Employee;\u001b[0m\n",
"SQLResult: \u001b[33;1m\u001b[1;3m[(9,)]\u001b[0m\n",
"Answer:\u001b[32;1m\u001b[1;3m There are 9 employees.\u001b[0m\n",
"\u001b[1m> Finished SQLDatabaseChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' There are 9 employees.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db_chain.run(\"How many employees are there?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61d91b85",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

154
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{
"cells": [
{
"cell_type": "markdown",
"id": "d8a5c5d4",
"metadata": {},
"source": [
"# LLM Chain\n",
"\n",
"This notebook showcases a simple LLM chain."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "835e6978",
"metadata": {},
"outputs": [],
"source": [
"from langchain import PromptTemplate, OpenAI, LLMChain"
]
},
{
"cell_type": "markdown",
"id": "06bcb078",
"metadata": {},
"source": [
"## Single Input\n",
"\n",
"First, lets go over an example using a single input"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "51a54c4d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
"\n",
"Answer: Let's think step by step.\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Justin Bieber was born in 1994, so the NFL team that won the Super Bowl in 1994 was the Dallas Cowboys.'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
"\n",
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.predict(question=question)"
]
},
{
"cell_type": "markdown",
"id": "79c3ec4d",
"metadata": {},
"source": [
"## Multiple Inputs\n",
"Now lets go over an example using multiple inputs."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "03dd6918",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mWrite a sad poem about ducks.\u001b[0m\n",
"\n",
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"\\n\\nThe ducks swim in the pond,\\nTheir feathers so soft and warm,\\nBut they can't help but feel so forlorn.\\n\\nTheir quacks echo in the air,\\nBut no one is there to hear,\\nFor they have no one to share.\\n\\nThe ducks paddle around in circles,\\nTheir heads hung low in despair,\\nFor they have no one to care.\\n\\nThe ducks look up to the sky,\\nBut no one is there to see,\\nFor they have no one to be.\\n\\nThe ducks drift away in the night,\\nTheir hearts filled with sorrow and pain,\\nFor they have no one to gain.\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
"\n",
"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8310cdaa",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

279
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{
"cells": [
{
"cell_type": "markdown",
"id": "4f73605d",
"metadata": {},
"source": [
"# Sequential Chains"
]
},
{
"cell_type": "markdown",
"id": "3b235f7a",
"metadata": {},
"source": [
"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 another.\n",
"\n",
"In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains are defined as a series of chains, called in deterministic order. There are two types of sequential chains:\n",
"\n",
"- `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.\n",
"- `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs."
]
},
{
"cell_type": "markdown",
"id": "5162794e",
"metadata": {},
"source": [
"## SimpleSequentialChain\n",
"\n",
"In this series of chains, each individual chain has a single input and a single output, and the output of one step is used as input to the next.\n",
"\n",
"Let's walk through a toy example of doing this, where the first chain takes in the title of an imaginary play and then generates a synopsis for that title, and the second chain takes in the synopsis of that play and generates an imaginary review for that play."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3f2f9b8c",
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.chains import LLMChain\n",
"from langchain.prompts import PromptTemplate"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b8237d1a",
"metadata": {},
"outputs": [],
"source": [
"# This is an LLMChain to write a synopsis given a title of a play.\n",
"llm = OpenAI(temperature=.7)\n",
"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
"\n",
"Title: {title}\n",
"Playwright: This is a synopsis for the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4a391730",
"metadata": {},
"outputs": [],
"source": [
"# This is an LLMChain to write a review of a play given a synopsis.\n",
"llm = OpenAI(temperature=.7)\n",
"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.\n",
"\n",
"Play Synopsis:\n",
"{synopsis}\n",
"Review from a New York Times play critic of the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)\n",
"review_chain = LLMChain(llm=llm, prompt=prompt_template)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9368bd63",
"metadata": {},
"outputs": [],
"source": [
"# This is the overall chain where we run these two chains in sequence.\n",
"from langchain.chains import SimpleSequentialChain\n",
"overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d39e15f5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3m\n",
"\n",
"Tragedy at Sunset on the Beach follows the story of a young couple, Jack and Annie, who have just started to explore the possibility of a relationship together. After a day spent in the sun and sand, they decide to take a romantic stroll down the beach as the sun sets. \n",
"\n",
"However, their romantic evening quickly turns tragic when they stumble upon a body lying in the sand. As they approach to investigate, they are shocked to discover that it is Jack's long-lost brother, who has been missing for several years. \n",
"\n",
"The story follows Jack and Annie as they navigate their way through the tragedy and their newfound relationship. With the help of their friends, family, and the beach's inhabitants, Jack and Annie must come to terms with their deep-seated emotions and the reality of the situation. \n",
"\n",
"Ultimately, the play explores themes of family, love, and loss, as Jack and Annie's story unfolds against the beautiful backdrop of the beach at sunset.\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m\n",
"\n",
"Tragedy at Sunset on the Beach is an emotionally complex tale of family, love, and loss. Told against the beautiful backdrop of a beach at sunset, the story follows Jack and Annie, a young couple just beginning to explore a relationship together. When they stumble upon the body of Jack's long-lost brother on the beach, they must face the reality of the tragedy and come to terms with their deep-seated emotions. \n",
"\n",
"The playwright has crafted a heartfelt and thought-provoking story, one that probes into the depths of the human experience. The cast of characters is well-rounded and fully realized, and the dialogue is natural and emotional. The direction and choreography are top-notch, and the scenic design is breathtaking. \n",
"\n",
"Overall, Tragedy at Sunset on the Beach is a powerful and moving story about the fragility of life and the strength of love. It is sure to tug at your heartstrings and leave you with a newfound appreciation of life's precious moments. Highly recommended.\u001b[0m\n",
"\n",
"\u001b[1m> Finished SimpleSequentialChain chain.\u001b[0m\n"
]
}
],
"source": [
"review = overall_chain.run(\"Tragedy at sunset on the beach\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c6649a01",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Tragedy at Sunset on the Beach is an emotionally complex tale of family, love, and loss. Told against the beautiful backdrop of a beach at sunset, the story follows Jack and Annie, a young couple just beginning to explore a relationship together. When they stumble upon the body of Jack's long-lost brother on the beach, they must face the reality of the tragedy and come to terms with their deep-seated emotions. \n",
"\n",
"The playwright has crafted a heartfelt and thought-provoking story, one that probes into the depths of the human experience. The cast of characters is well-rounded and fully realized, and the dialogue is natural and emotional. The direction and choreography are top-notch, and the scenic design is breathtaking. \n",
"\n",
"Overall, Tragedy at Sunset on the Beach is a powerful and moving story about the fragility of life and the strength of love. It is sure to tug at your heartstrings and leave you with a newfound appreciation of life's precious moments. Highly recommended.\n"
]
}
],
"source": [
"print(review)"
]
},
{
"cell_type": "markdown",
"id": "c3f1549a",
"metadata": {},
"source": [
"## Sequential Chain\n",
"Of course, not all sequential chains will be as simple as passing a single string as an argument and getting a single string as output for all steps in the chain. In this next example, we will experiment with more complex chains that involve multiple inputs, and where there also multiple final outputs. \n",
"\n",
"Of particular importance is how we name the input/output variable names. In the above example we didn't have to think about that because we were just passing the output of one chain directly as input to the next, but here we do have worry about that because we have multiple inputs."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "02016a51",
"metadata": {},
"outputs": [],
"source": [
"# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.\n",
"llm = OpenAI(temperature=.7)\n",
"template = \"\"\"You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.\n",
"\n",
"Title: {title}\n",
"Era: {era}\n",
"Playwright: This is a synopsis for the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"title\", 'era'], template=template)\n",
"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"synopsis\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8bd38cc2",
"metadata": {},
"outputs": [],
"source": [
"# This is an LLMChain to write a review of a play given a synopsis.\n",
"llm = OpenAI(temperature=.7)\n",
"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.\n",
"\n",
"Play Synopsis:\n",
"{synopsis}\n",
"Review from a New York Times play critic of the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)\n",
"review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"review\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "524523af",
"metadata": {},
"outputs": [],
"source": [
"# This is the overall chain where we run these two chains in sequence.\n",
"from langchain.chains import SequentialChain\n",
"overall_chain = SequentialChain(\n",
" chains=[synopsis_chain, review_chain],\n",
" input_variables=[\"era\", \"title\"],\n",
" # Here we return multiple variables\n",
" output_variables=[\"synopsis\", \"review\"],\n",
" verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3fd3a7be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
"\u001b[1mChain 0\u001b[0m:\n",
"{'synopsis': \" \\n\\nTragedy at Sunset on the Beach is a dark and gripping drama set in Victorian England. The play follows the story of two lovers, Emma and Edward, whose passionate relationship is threatened by the strict rules and regulations of the time.\\n\\nThe two are deeply in love, but Edward is from a wealthy family and Emma is from a lower class background. Despite the obstacles, the two are determined to be together and decide to elope.\\n\\nOn the night of their planned escape, Emma and Edward meet at the beach at sunset to declare their love for one another and begin a new life together. However, their plans are disrupted when Emma's father discovers their plan and appears on the beach with a gun.\\n\\nIn a heartbreaking scene, Emma's father orders Edward to leave, but Edward refuses and fights for their love. In a fit of rage, Emma's father shoots Edward, killing him instantly. \\n\\nThe tragedy of the play lies in the fact that Emma and Edward are denied their chance at a happy ending due to the rigid social conventions of Victorian England. The audience is left with a heavy heart as the play ends with Emma standing alone on the beach, mourning the loss of her beloved.\"}\n",
"\n",
"\u001b[1mChain 1\u001b[0m:\n",
"{'review': \"\\n\\nTragedy at Sunset on the Beach is an emotionally charged production that will leave audiences heartsick. The play follows the ill-fated love story of Emma and Edward, two star-crossed lovers whose passionate relationship is tragically thwarted by Victorian England's societal conventions. The performance is captivating from start to finish, as the audience is taken on an emotional rollercoaster of love, loss, and heartbreak.\\n\\nThe acting is powerful and sincere, and the performances of the two leads are particularly stirring. Emma and Edward are both portrayed with such tenderness and emotion that it's hard not to feel their pain as they fight for their forbidden love. The climactic scene, in which Edward is shot by Emma's father, is especially heartbreaking and will leave audience members on the edge of their seats.\\n\\nOverall, Tragedy at Sunset on the Beach is a powerful and moving work of theatre. It is a tragedy of impossible love, and a vivid reminder of the devastating consequences of social injustice. The play is sure to leave a lasting impression on anyone who experiences it.\"}\n",
"\n",
"\n",
"\u001b[1m> Finished SequentialChain chain.\u001b[0m\n"
]
}
],
"source": [
"review = overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6be70d27",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "872bb8b5",
"metadata": {},
"source": [
"# Transformation Chain\n",
"\n",
"This notebook showcases using a generic transformation chain.\n",
"\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bbbb4330",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain\n",
"from langchain.llms import OpenAI\n",
"from langchain.prompts import PromptTemplate"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8ae5937c",
"metadata": {},
"outputs": [],
"source": [
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "98739592",
"metadata": {},
"outputs": [],
"source": [
"def transform_func(inputs: dict) -> dict:\n",
" text = inputs[\"text\"]\n",
" shortened_text = \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])\n",
" return {\"output_text\": shortened_text}\n",
"\n",
"transform_chain = TransformChain(input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e9397934",
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"Summarize this text:\n",
"\n",
"{output_text}\n",
"\n",
"Summary:\"\"\"\n",
"prompt = PromptTemplate(input_variables=[\"output_text\"], template=template)\n",
"llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "06f51f17",
"metadata": {},
"outputs": [],
"source": [
"sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f7caa1ee",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sequential_chain.run(state_of_the_union)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3ca6409",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

33
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Generic Chains
--------------
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all generic end-to-end chains that are meant to be used to construct other chains rather than serving a specific purpose.
**LLMChain**
- **Links Used**: PromptTemplate, LLM
- **Notes**: This chain is the simplest chain, and is widely used by almost every other chain. This chain takes arbitrary user input, creates a prompt with it from the PromptTemplate, passes that to the LLM, and then returns the output of the LLM as the final output.
- `Example Notebook <generic/llm_chain.html>`_
**Transformation Chain**
- **Links Used**: TransformationChain
- **Notes**: This notebook shows how to use the Transformation Chain, which takes an arbitrary python function and applies it to inputs/outputs of other chains.
- `Example Notebook <generic/transformation.html>`_
**Sequential Chain**
- **Links Used**: Sequential
- **Notes**: This notebook shows how to combine calling multiple other chains in sequence.
- `Example Notebook <generic/sequential_chains.html>`_
.. toctree::
:maxdepth: 1
:glob:
:caption: Generic Chains
:name: generic
:hidden:
generic/*

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting Started\n",
"\n",
"In this tutorial, we will learn about creating simple chains in LangChain. We will learn how to create a chain, add components to it, and run it.\n",
"\n",
"In this tutorial, we will cover:\n",
"- Using the simple LLM chain\n",
"- Creating sequential chains\n",
"- Creating a custom chain\n",
"\n",
"## Why do we need chains?\n",
"\n",
"Chains allow us to combine multiple components together to create a single, coherent application. For example, we can create a chain that takes user input, format it with a PromptTemplate, and then passes the formatted response to an LLM. We can build more complex chains by combining multiple chains together, or by combining chains with other components.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Query an LLM with the `LLMChain`\n",
"\n",
"The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input and returns the response from an LLM.\n",
"\n",
"To use the `LLMChain`, first create a prompt template."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0.9)\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"product\"],\n",
" template=\"What is a good name for a company that makes {product}?\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now create a very simple chain that will take user input, format the prompt with it, and then send it to the LLM."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Vibrancy Socks.\n"
]
}
],
"source": [
"from langchain.chains import LLMChain\n",
"chain = LLMChain(llm=llm, prompt=prompt)\n",
"\n",
"# Run the chain only specifying the input variable.\n",
"print(chain.run(\"colorful socks\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Combine chains with the `SequentialChain`\n",
"\n",
"The next step after calling a language model is make a series of calls to a language model. We can do this using sequential chains, which are chains that execute their links in a predefined order. Specifically, we will use the `SimpleSequentialChain`. This is the simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.\n",
"\n",
"In this tutorial, our sequential chain will:\n",
"1. First, create a company name for a product. We will reuse the `LLMChain` we'd previously initialized to create this company name.\n",
"2. Then, create a catchphrase for the product. We will initialize a new `LLMChain` to create this catchphrase, as shown below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"second_prompt = PromptTemplate(\n",
" input_variables=[\"company_name\"],\n",
" template=\"Write a catchphrase for the following company: {company_name}\",\n",
")\n",
"chain_two = LLMChain(llm=llm, prompt=second_prompt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can combine the two LLMChains, so that we can create a company name and a catchphrase in a single step."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3m\n",
"\n",
"Cheerful Toes.\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m\n",
"\n",
"\"Spread smiles from your toes!\"\u001b[0m\n",
"\n",
"\u001b[1m> Finished SimpleSequentialChain chain.\u001b[0m\n",
"\n",
"\n",
"\"Spread smiles from your toes!\"\n"
]
}
],
"source": [
"from langchain.chains import SimpleSequentialChain\n",
"overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)\n",
"\n",
"# Run the chain specifying only the input variable for the first chain.\n",
"catchphrase = overall_chain.run(\"colorful socks\")\n",
"print(catchphrase)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a custom chain with the `Chain` class\n",
"\n",
"LangChain provides many chains out of the box, but sometimes you may want to create a custom chains for your specific use case. For this example, we will create a custom chain that concatenates the outputs of 2 `LLMChain`s.\n",
"\n",
"In order to create a custom chain:\n",
"1. Start by subclassing the `Chain` class,\n",
"2. Fill out the `input_keys` and `output_keys` properties,\n",
"3. Add the `_call` method that shows how to execute the chain.\n",
"\n",
"These steps are demonstrated in the example below:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import LLMChain\n",
"from langchain.chains.base import Chain\n",
"\n",
"from typing import Dict, List\n",
"\n",
"\n",
"class ConcatenateChain(Chain):\n",
" chain_1: LLMChain\n",
" chain_2: LLMChain\n",
"\n",
" @property\n",
" def input_keys(self) -> List[str]:\n",
" # Union of the input keys of the two chains.\n",
" all_input_vars = set(self.chain_1.input_keys).union(set(self.chain_2.input_keys))\n",
" return list(all_input_vars)\n",
"\n",
" @property\n",
" def output_keys(self) -> List[str]:\n",
" return ['concat_output']\n",
"\n",
" def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:\n",
" output_1 = self.chain_1.run(inputs)\n",
" output_2 = self.chain_2.run(inputs)\n",
" return {'concat_output': output_1 + output_2}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can try running the chain that we called."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Concatenated output:\n",
"\n",
"\n",
"Rainbow Socks Co.\n",
"\n",
"\"Step Into Colorful Comfort!\"\n"
]
}
],
"source": [
"prompt_1 = PromptTemplate(\n",
" input_variables=[\"product\"],\n",
" template=\"What is a good name for a company that makes {product}?\",\n",
")\n",
"chain_1 = LLMChain(llm=llm, prompt=prompt_1)\n",
"\n",
"prompt_2 = PromptTemplate(\n",
" input_variables=[\"product\"],\n",
" template=\"What is a good slogan for a company that makes {product}?\",\n",
")\n",
"chain_2 = LLMChain(llm=llm, prompt=prompt_2)\n",
"\n",
"concat_chain = ConcatenateChain(chain_1=chain_1, chain_2=chain_2)\n",
"concat_output = concat_chain.run(\"colorful socks\")\n",
"print(f\"Concatenated output:\\n{concat_output}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it! For more details about how to do cool things with Chains, check out the [how-to guide](how_to_guides.rst) for chains."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"vscode": {
"interpreter": {
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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How-To Guides
=============
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all end-to-end chains for specific applications.
They are broken up into three categories:
1. `Generic Chains <generic_how_to.html>`_: Generic chains, that are meant to help build other chains rather than serve a particular purpose.
2. `CombineDocuments Chains <combine_docs_how_to.html>`_: Chains aimed at making it easy to work with documents (question answering, summarization, etc).
3. `Utility Chains <utility_how_to.html>`_: Chains consisting of an LLMChain interacting with a specific util.
.. toctree::
:maxdepth: 1
:glob:
:hidden:
generic_how_to.rst
combine_docs_how_to.rst
utility_how_to.rst

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# Key Concepts
## Chains
A chain is made up of links, which can be either primitives or other chains.
They vary greatly in complexity and are combination of generic, highly configurable pipelines and more narrow (but usually more complex) pipelines.
## Sequential Chain
This is a specific type of chain where multiple other chains are run in sequence, with the outputs being added as inputs
to the next. A subtype of this type of chain is the `SimpleSequentialChain`, where all subchains have only one input and one output,
and the output of one is therefor used as sole input to the next chain.
## CombineDocuments Chains
These are a subset of chains designed to work with documents. There are two pieces to consider:
1. The underlying chain method (eg, how the documents are combined)
2. Use cases for these types of chains.
For the first, please see [this documentation](combine_docs.md) for more detailed information on the types of chains LangChain supports.
For the second, please see the Use Cases section for more information on [question answering](/use_cases/question_answering.md),
[question answering with sources](/use_cases/qa_with_sources.md), and [summarization](/use_cases/summarization.md).

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Utility Chains
--------------
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all end-to-end chains for specific applications, focused on interacting an LLMChain with a specific utility.
**LLMMath**
- **Links Used**: Python REPL, LLMChain
- **Notes**: This chain takes user input (a math question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
- `Example Notebook <examples/llm_math.html>`_
**PAL**
- **Links Used**: Python REPL, LLMChain
- **Notes**: This chain takes user input (a reasoning question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
- `Paper <https://arxiv.org/abs/2211.10435>`_
- `Example Notebook <examples/pal.html>`_
**SQLDatabase Chain**
- **Links Used**: SQLDatabase, LLMChain
- **Notes**: This chain takes user input (a question), uses a first LLM chain to construct a SQL query to run against the SQL database, and then uses another LLMChain to take the results of that query and use it to answer the original question.
- `Example Notebook <examples/sqlite.html>`_
**LLMBash Chain**
- **Links Used**: BashProcess, LLMChain
- **Notes**: This chain takes user input (a question), uses an LLM chain to convert it to a bash command to run in the terminal, and then returns that as the result.
- `Example Notebook <examples/llm_bash.html>`_
**LLMChecker Chain**
- **Links Used**: LLMChain
- **Notes**: This chain takes user input (a question), uses an LLM chain to answer that question, and then uses other LLMChains to self-check that answer.
- `Example Notebook <examples/llm_checker.html>`_
**LLMRequests Chain**
- **Links Used**: Requests, LLMChain
- **Notes**: This chain takes a URL and other inputs, uses Requests to get the data at that URL, and then passes that along with the other inputs into an LLMChain to generate a response. The example included shows how to ask a question to Google - it firsts constructs a Google url, then fetches the data there, then passes that data + the original question into an LLMChain to get an answer.
- `Example Notebook <examples/llm_requests.html>`_
**Moderation Chain**
- **Links Used**: LLMChain, ModerationChain
- **Notes**: This chain shows how to use OpenAI's content moderation endpoint to screen output, and shows how to connect this to an LLMChain.
- `Example Notebook <examples/moderation.html>`_
.. toctree::
:maxdepth: 1
:glob:
:caption: Generic Chains
:name: generic
:hidden:
examples/*