Tony20100/langchain_full huggingface dataset:
- “python” - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNING: using this
option with big datasets is discouraged due to potential
memory issues.
”compute_engine” - Performant C++ implementation of the DeepLake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be used
with in-memory or local datasets.
”tensor_db” - Performant, fully-hosted Managed Tensor Database.Responsible for storage and query execution. Only available for
data stored in the Deep Lake Managed Database. To store datasets
in this database, specify runtime = {“db_engine”: True}
during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improvingsearch results. Defaults to False if deep_memory is not specified
in the Vector Store initialization. If True, the distance metric
is set to “deepmemory_distance”, which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to “COS” or
whatever distance metric user specifies.
Returns
List of documents most similar to the querytext with distance in float.
Return type
List[Tuple[Document, float]]
Examples using DeepLake¶
Activeloop Deep Lake
Activeloop Deep Memory
Deep Lake
langchain_postgres.vectorstores.PGVector¶
class langchain_postgres.vectorstores.PGVector(embeddings: Embeddings, *, connection: Optional[Union[Engine, str]] = None, embedding_length: Optional[int] = None, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, engine_args: Optional[dict[str, Any]] = None, use_jsonb: bool = True, create_extension: bool = True)[source]¶
Vectorstore implementation using Postgres as the backend.
Currently, there is no mechanism for supporting data migration.
So breaking changes in the vectorstore schema will require the user to recreate
the tables and re-add the documents.
If this is a concern, please use a different vectorstore. If
not, this implementation should be fine for your use case.
To use this vectorstore you need to have the vector extension installed.
The vector extension is a Postgres extension that provides vector
similarity search capabilities.
```sh
docker run –name pgvector-container -e POSTGRES_PASSWORD=…
-d pgvector/pgvector:pg16
```
Example
from langchain_postgres.vectorstores import PGVector
from langchain_openai.embeddings import OpenAIEmbeddings
connection_string = "postgresql+psycopg://..."
collection_name = "state_of_the_union_test"
embeddings = OpenAIEmbeddings()
vectorstore = PGVector.from_documents(
    embedding=embeddings,
    documents=docs,
    connection=connection_string,
    collection_name=collection_name,
    use_jsonb=True,
)
This code has been ported over from langchain_community with minimal changes
)
This code has been ported over from langchain_community with minimal changes
to allow users to easily transition from langchain_community to langchain_postgres.
Some changes had to be made to address issues with the community implementation:
* langchain_postgres now works with psycopg3. Please update your
connection strings from postgresql+psycopg2://… to
postgresql+psycopg://langchain:langchain@…
(yes, the driver name is psycopg not psycopg3)
The schema of the embedding store and collection have been changed to make
add_documents work correctly with user specified ids, specifically
when overwriting existing documents.
You will need to recreate the tables if you are using an existing database.
A Connection object has to be provided explicitly. Connections will not be
picked up automatically based on env variables.
Initialize the PGVector store.
Parameters
connection (Optional[Connection]) – Postgres connection string.
embeddings (Embeddings) – Any embedding function implementing
langchain.embeddings.base.Embeddings interface.
embedding_length (Optional[int]) – The length of the embedding vector. (default: None)
NOTE: This is not mandatory. Defining it will prevent vectors of
any other size to be added to the embeddings table but, without it,
the embeddings can’t be indexed.
collection_name (str) – The name of the collection to use. (default: langchain)
NOTE: This is not the name of the table, but the name of the collection.
The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy (DistanceStrategy) – The distance strategy to use. (default: COSINE)
pre_delete_collection (bool) – If True, will delete the collection if it exists.
(default: False). Useful for testing.
(default: False). Useful for testing.
engine_args (Optional[dict[str, Any]]) – SQLAlchemy’s create engine arguments.
use_jsonb (bool) – Use JSONB instead of JSON for metadata. (default: True)
Strongly discouraged from using JSON as it’s not as efficient
for querying.
It’s provided here for backwards compatibility with older versions,
and will be removed in the future.
create_extension (bool) – If True, will create the vector extension if it
doesn’t exist. disabling creation is useful when using ReadOnly
Databases.
collection_metadata (Optional[dict]) – 
logger (Optional[logging.Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
Attributes
distance_strategy
embeddings
Access the query embedding object if available.
Methods
__init__(embeddings, *[, connection, ...])
Initialize the PGVector store.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
connection_string_from_db_params(driver, ...)
Return connection string from database parameters.
create_collection()
create_tables_if_not_exists()
create_vector_extension()
delete([ids, collection_only])
Delete vectors by ids or uuids.
delete_collection()
drop_tables()
from_documents(documents, embedding, *[, ...])
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding, *)
Construct PGVector wrapper from raw documents and embeddings.
from_existing_index(embedding, *[, ...])
Get instance of an existing PGVector store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from documents and embeddings.
get_collection(session)
get_connection_string(kwargs)
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance
max_marginal_relevance_search_with_score(query)
Return docs selected using the maximal marginal relevance with score.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance with score
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with PGVector with distance.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(embeddings: Embeddings, *, connection: Optional[Union[Engine, str]] = None, embedding_length: Optional[int] = None, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, engine_args: Optional[dict[str, Any]] = None, use_jsonb: bool = True, create_extension: bool = True) → None[source]¶
Initialize the PGVector store.
Parameters
connection (Optional[Union[Engine, str]]) – Postgres connection string.
embeddings (Embeddings) – Any embedding function implementing
langchain.embeddings.base.Embeddings interface.
langchain.embeddings.base.Embeddings interface.
embedding_length (Optional[int]) – The length of the embedding vector. (default: None)
NOTE: This is not mandatory. Defining it will prevent vectors of
any other size to be added to the embeddings table but, without it,
the embeddings can’t be indexed.
collection_name (str) – The name of the collection to use. (default: langchain)
NOTE: This is not the name of the table, but the name of the collection.
The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy (DistanceStrategy) – The distance strategy to use. (default: COSINE)
pre_delete_collection (bool) – If True, will delete the collection if it exists.
(default: False). Useful for testing.
engine_args (Optional[dict[str, Any]]) – SQLAlchemy’s create engine arguments.
use_jsonb (bool) – Use JSONB instead of JSON for metadata. (default: True)
Strongly discouraged from using JSON as it’s not as efficient
for querying.
It’s provided here for backwards compatibility with older versions,
and will be removed in the future.
create_extension (bool) – If True, will create the vector extension if it
doesn’t exist. disabling creation is useful when using ReadOnly
Databases.
collection_metadata (Optional[dict]) – 
logger (Optional[Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
Return type
None
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) –
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add embeddings to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
embeddings (List[List[float]]) – List of list of embedding vectors.
metadatas (Optional[List[dict]]) – List of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
ids (Optional[List[str]]) – 
Return type
List[str]
ids (Optional[List[str]]) – 
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
ids (Optional[List[str]]) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
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.
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
filter (Optional[Dict[str, str]]) – 
kwargs (Any) –
filter (Optional[Dict[str, str]]) – 
kwargs (Any) – 
Return type
List[Document]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
kwargs (Any) – 
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
List of Documents most similar to the query vector.
Return type
List[Document]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
Parameters
driver (str) – 
host (str) – 
port (int) – 
database (str) – 
user (str) – 
password (str) – 
Return type
str
create_collection() → None[source]¶
Return type
None
create_tables_if_not_exists() → None[source]¶
Return type
None
create_vector_extension() → None[source]¶
Return type
None
Return type
None
create_vector_extension() → None[source]¶
Return type
None
delete(ids: Optional[List[str]] = None, collection_only: bool = False, **kwargs: Any) → None[source]¶
Delete vectors by ids or uuids.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
collection_only (bool) – Only delete ids in the collection.
kwargs (Any) – 
Return type
None
delete_collection() → None[source]¶
Return type
None
drop_tables() → None[source]¶
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, *, connection: Optional[Union[Engine, str]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, use_jsonb: bool = True, **kwargs: Any) → PGVector[source]¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
connection (Optional[Union[Engine, str]]) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
use_jsonb (bool) – 
kwargs (Any) – 
Return type
PGVector
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, *, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGVector[source]¶
Construct PGVector wrapper from raw documents and embeddings.
Parameters
text_embeddings (List[Tuple[str, List[float]]]) – List of tuples of text and embeddings.
embedding (Embeddings) – Embeddings object.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
collection_name (str) – Name of the collection.
distance_strategy (DistanceStrategy) – Distance strategy to use.
ids (Optional[List[str]]) – Optional list of ids for the documents.
pre_delete_collection (bool) – If True, will delete the collection if it exists.
Attention: This will delete all the documents in the existing
collection.
kwargs (Any) – Additional arguments.
Returns
PGVector instance.
Return type
PGVector
Example
from langchain_postgres.vectorstores import PGVector
from langchain_openai.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
vectorstore = PGVector.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_existing_index(embedding: Embeddings, *, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, connection: Optional[Union[Engine, str]] = None, **kwargs: Any) → PGVector[source]¶
Get instance of an existing PGVector store.This method will
return the instance of the store without inserting any new
embeddings
Parameters
embedding (Embeddings) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
pre_delete_collection (bool) – 
connection (Optional[Union[Engine, str]]) – 
kwargs (Any) – 
Return type
PGVector