Lantern

Return connection string from database parameters.

Create HNSW index on collection.

Delete vectors by ids or uuids.

Initialize Lantern vectorstore from list of texts. The embeddings will be generated using embedding class provided.

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

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

• connection_string is fully populated connection string for postgres database
• texts texts 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.
• 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.
• ids row ids to insert into collection.
• pre_delete_collection if True, will delete the collection if it exists. (default: False)
  • Useful for testing.

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.

Get instance of an existing Lantern store.This method will return the instance of the store without inserting any new embeddings

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

• connection_string is a postgres connection string.
• 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.
• 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.

Initialize a vector store with a set of documents.

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

• connection_string is a postgres connection string.
• documents is list of :class:Document to initialize the vector store with
• 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.
• 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.
• 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.
• ids row ids to insert into collection.
• pre_delete_collection if True, will delete the collection if it exists. (default: False)
  • Useful for testing.

Return docs selected using the maximal marginal relevance with score to embedding vector.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance with score.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Return docs selected using the maximal marginal relevance to embedding vector.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.