This object is used to specify the embedding model that will be used to embed the documents during index construction and any queries you make using the query engine later on. Here is an example of how you can do this: from llama_index import ServiceContext from llama_index.embeddings....
@dosu-bot, I think I may have found a way to do this: documents = [index.docstore.get_document(document_id) for document_id in index.index_struct.nodes_dict.values()] docs_embeddings = [index.vector_store.get(vector_id) for vector_id in index.index_struct.nodes_dict] MarouaneMaatouk...
pip install llama-index-embeddings-openai !pip install pymongo !pip install datasets !pip install pandas 2. Data Loading and OpenAI Key SetUp The command below assigns an OpenAI API key to the environment variable OPENAI_API_KEY. This is required to ensure LlamaIndex creates an OpenAI client ...
32 print(f"Finished inserting embeddings for the {model} model") The above code does the following: Creates a PyMongo client (client) to connect to a MongoDB Atlas cluster Specifies the database (DB_NAME) to connect to — we are calling the database ragas_evals; if the database doesn’...
To complete the actions presented below, you must have: A Scaleway account logged into the console Owner status or IAM permissions allowing you to perform actions in the intended Organization The default support plan for an Organization is the Basic plan, but it can be upgraded on the Support ...
A vector database is an organized collection of vector embeddings that can be created, read, updated, and deleted at any point in time.
VectorStore serves as a storage facility for these embeddings, allowing efficient search based on semantic similarity. Indexes and retrievers Indexes act as databases storing details and metadata about the model's training data, while retrievers swiftly search this index for specific information. This ...
database and so on, and all of them also have vector embeddings, so when I basically look at one of these questions, then you see here you have the question body and here you have the embedding data directly attached to your entity, which is then also stored ...
The CSV file does not have the 'T' joining the date and time values but has a space between them instead. You used the replace() function to change the space to the character 'T' and get the string into the expected format. Then, you wrapped the datetime() function around that to ...
32 print(f"Finished inserting embeddings for the {model} model") The above code does the following: Creates a PyMongo client (client) to connect to a MongoDB Atlas cluster Specifies the database (DB_NAME) to connect to — we are calling the database ragas_evals; if the database doesn’...