LangChain

LangChain is a framework designed for building applications with large language models (LLMs) by chaining together various components. It supports a range of functionalities including memory, agents, and chat models, enabling developers to create context-aware applications.

LangChain streamlines these stages (in figure above) by providing pre-built components and tools for integration, memory management, and deployment, allowing developers to focus on application logic rather than underlying complexities.

Integration of Langchain with LanceDB enables applications to retrieve the most relevant data by comparing query vectors against stored vectors, facilitating effective information retrieval. It results in better and context aware replies and actions by the LLMs.

Quick Start

You can load your document data using langchain’s loaders, for this example we are using TextLoader and OpenAIEmbeddings as the embedding model.

Documentation

In the above example LanceDB vector store class object is created using from_documents() method which is a classmethod and returns the initialized class object.

You can also use LanceDB.from_texts(texts: List[str],embedding: Embeddings) class method.

The exhaustive list of parameters for LanceDB vector store are :

Name	type	Purpose	default
connection	(Optional) Any	lancedb.db.LanceDBConnection connection object to use. If not provided, a new connection will be created.	None
embedding	(Optional) Embeddings	Langchain embedding model.	Provided by user.
uri	(Optional) str	It specifies the directory location of LanceDB database and establishes a connection that can be used to interact with the database.	/tmp/lancedb
vector_key	(Optional) str	Column name to use for vector’s in the table.	'vector'
id_key	(Optional) str	Column name to use for id’s in the table.	'id'
text_key	(Optional) str	Column name to use for text in the table.	'text'
table_name	(Optional) str	Name of your table in the database.	'vectorstore'
api_key	(Optional str)	API key to use for LanceDB Enterprise deployment.	None
region	(Optional) str	Region to use for LanceDB Enterprise deployment.	Only for LanceDB Enterprise : None.
mode	(Optional) str	Mode to use for adding data to the table. Valid values are “append” and “overwrite”.	'overwrite'
table	(Optional) Any	You can connect to an existing table of LanceDB, created outside of langchain, and utilize it.	None
distance	(Optional) str	The choice of distance metric used to calculate the similarity between vectors.	'l2'
reranker	(Optional) Any	The reranker to use for LanceDB.	None
relevance_score_fn	(Optional) Callable[[float], float]	Langchain relevance score function to be used.	None
limit	int	Set the maximum number of results to return.	DEFAULT_K (it is 4)

Methods

add_texts()

This method turn texts into embedding and add it to the database.

Name	Purpose	defaults
texts	Iterable of strings to add to the vectorstore.	Provided by user
metadatas	Optional list[dict()] of metadatas associated with the texts.	None
ids	Optional list of ids to associate with the texts.	None
kwargs	Other keyworded arguments provided by the user.	-

It returns list of ids of the added texts.

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create_index()

This method creates a scalar(for non-vector cols) or a vector index on a table.

Name	type	Purpose	defaults
vector_col	Optional[str]	Provide if you want to create index on a vector column.	None
col_name	Optional[str]	Provide if you want to create index on a non-vector column.	None
metric	Optional[str]	Provide the metric to use for vector index. choice of metrics: ‘l2’, ‘dot’, ‘cosine’.	l2
num_partitions	Optional[int]	Number of partitions to use for the index.	256
num_sub_vectors	Optional[int]	Number of sub-vectors to use for the index.	96
index_cache_size	Optional[int]	Size of the index cache.	None
name	Optional[str]	Name of the table to create index on.	None

For index creation make sure your table has enough data in it. An ANN index is usually not needed for datasets ~100K vectors. For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index.

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similarity_search()

This method performs similarity search based on text query.

Name	Type	Purpose	Default
query	str	A str representing the text query that you want to search for in the vector store.	N/A
k	Optional[int]	It specifies the number of documents to return.	None
filter	Optional[Dict[str, str]]	It is used to filter the search results by specific metadata criteria.	None
fts	Optional[bool]	It indicates whether to perform a full-text search (FTS).	False
name	Optional[str]	It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance.	None
kwargs	Any	Other keyworded arguments provided by the user.	N/A

Return documents most similar to the query without relevance scores.

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similarity_search_by_vector()

The method returns documents that are most similar to the specified embedding (query) vector.

Name	Type	Purpose	Default
embedding	List[float]	The embedding vector you want to use to search for similar documents in the vector store.	N/A
k	Optional[int]	It specifies the number of documents to return.	None
filter	Optional[Dict[str, str]]	It is used to filter the search results by specific metadata criteria.	None
name	Optional[str]	It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance.	None
kwargs	Any	Other keyworded arguments provided by the user.	N/A

It does not provide relevance scores.

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similarity_search_with_score()

Returns documents most similar to the query string along with their relevance scores.

Name	Type	Purpose	Default
query	str	A str representing the text query you want to search for in the vector store. This query will be converted into an embedding using the specified embedding function.	N/A
k	Optional[int]	It specifies the number of documents to return.	None
filter	Optional[Dict[str, str]]	It is used to filter the search results by specific metadata criteria. This allows you to narrow down the search results based on certain metadata attributes associated with the documents.	None
kwargs	Any	Other keyworded arguments provided by the user.	N/A

It gets called by base class’s similarity_search_with_relevance_scores which selects relevance score based on our _select_relevance_score_fn.

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similarity_search_by_vector_with_relevance_scores()

Similarity search using query vector.

Name	Type	Purpose	Default
embedding	List[float]	The embedding vector you want to use to search for similar documents in the vector store.	N/A
k	Optional[int]	It specifies the number of documents to return.	None
filter	Optional[Dict[str, str]]	It is used to filter the search results by specific metadata criteria.	None
name	Optional[str]	It is used for specifying the name of the table to query.	None
kwargs	Any	Other keyworded arguments provided by the user.	N/A

The method returns documents most similar to the specified embedding (query) vector, along with their relevance scores.

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max_marginal_relevance_search()

This method returns docs selected using the maximal marginal relevance(MMR). Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Name	Type	Purpose	Default
query	str	Text to look up documents similar to.	N/A
k	Optional[int]	Number of Documents to return.	4
fetch_k	Optional[int]	Number of Documents to fetch to pass to MMR algorithm.	None
lambda_mult	float	Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity.	0.5
filter	Optional[Dict[str, str]]	Filter by metadata.	None
kwargs	Other keyworded arguments provided by the user.	-

Similarly, max_marginal_relevance_search_by_vector() function returns docs most similar to the embedding passed to the function using MMR. instead of a string query you need to pass the embedding to be searched for.

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add_images()

This method adds images by automatically creating their embeddings and adds them to the vectorstore.

Name	Type	Purpose	Default
uris	List[str]	File path to the image	N/A
metadatas	Optional[List[dict]]	Optional list of metadatas	None
ids	Optional[List[str]]	Optional list of IDs	None

It returns list of IDs of the added images.