Loading from LangChainHub#
This notebook covers how to load chains from LangChainHub.
from langchain.chains import load_chain chain = load_chain("lc://chains/llm-math/chain.json")
chain.run("whats 2 raised to .12")
> Entering new LLMMathChain chain... whats 2 raised to .12 Answer: 1.0791812460476249 > Finished chain.
Sometimes chains will require extra arguments that were not serialized with the chain. For example, a chain that does question answering over a vector database will require a vector database.
from langchain.embeddings.openai import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import CharacterTextSplitter from langchain import OpenAI, VectorDBQA
from langchain.document_loaders import TextLoader loader = TextLoader('../../state_of_the_union.txt') documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(texts, embeddings)
Running Chroma using direct local API. Using DuckDB in-memory for database. Data will be transient.
chain = load_chain("lc://chains/vector-db-qa/stuff/chain.json", vectorstore=vectorstore)
query = "What did the president say about Ketanji Brown Jackson" chain.run(query)
" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence."