the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.'While this does contain relevant information about Ketanji Brown Jackson, we don't have a concise, human-readable answer. We'll tackle that in the next section.Use an LLM to generate fluent answers‚ÄãWith the data indexed in MVI, we can integrate with any chain that leverages vector similarity search. Here we use the RetrievalQA chain to demonstrate how to answer questions from the indexed data.First we load the relevant modules:from langchain.chat_models import ChatOpenAIfrom langchain.chains import RetrievalQAThen we instantiate the retrieval QA chain:llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)qa_chain = RetrievalQA.from_chain_type(llm, retriever=vector_db.as_retriever())qa_chain({"query": "What did the president say about Ketanji Brown Jackson?"})    {'query': 'What did the president say about Ketanji Brown Jackson?',     'result': "The President said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds and mentioned that she has received broad support from various groups, including the Fraternal Order of Police and former judges appointed by Democrats and Republicans."}Next StepsThat's it! You've now indexed your data and can query it using the Momento Vector Index. You can use the same index to query your data from any chain that supports vector similarity search.With Momento you can not only index your vector data, but also cache your API calls and store your chat message history. Check out the other Momento
MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs.
MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. ->: the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.'While this does contain relevant information about Ketanji Brown Jackson, we don't have a concise, human-readable answer. We'll tackle that in the next section.Use an LLM to generate fluent answers‚ÄãWith the data indexed in MVI, we can integrate with any chain that leverages vector similarity search. Here we use the RetrievalQA chain to demonstrate how to answer questions from the indexed data.First we load the relevant modules:from langchain.chat_models import ChatOpenAIfrom langchain.chains import RetrievalQAThen we instantiate the retrieval QA chain:llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)qa_chain = RetrievalQA.from_chain_type(llm, retriever=vector_db.as_retriever())qa_chain({"query": "What did the president say about Ketanji Brown Jackson?"})    {'query': 'What did the president say about Ketanji Brown Jackson?',     'result': "The President said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds and mentioned that she has received broad support from various groups, including the Fraternal Order of Police and former judges appointed by Democrats and Republicans."}Next StepsThat's it! You've now indexed your data and can query it using the Momento Vector Index. You can use the same index to query your data from any chain that supports vector similarity search.With Momento you can not only index your vector data, but also cache your API calls and store your chat message history. Check out the other Momento