from_embeddings

Construct Lantern wrapper from raw documents and pre- generated embeddings.

Postgres connection string is required "Either pass it as connection_string parameter or set the LANTERN_CONNECTION_STRING environment variable.

Order of elements for lists ids, text_embeddings, metadatas should match, so each row will be associated with correct values.

• connection_string is fully populated connection string for postgres database
• text_embeddings is array with tuples (text, embedding) to insert into collection.
• embedding is :class:Embeddings that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used.
• metadatas row metadata to insert into collection.
• collection_name is the name of the collection to use. (default: langchain)
  • NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables.
• ids row ids to insert into collection.
• pre_delete_collection if True, will delete the collection if it exists. (default: False)
  • Useful for testing.
• distance_strategy is the distance strategy to use. (default: EUCLIDEAN)
  • EUCLIDEAN is the euclidean distance.
  • COSINE is the cosine distance.
  • HAMMING is the hamming distance.