Tony20100/langchain_full huggingface dataset:
vectorstore = VDMS(
    client=VDMS_Client("localhost", 55555),
    embedding=HuggingFaceEmbeddings(),
    collection_name="langchain-demo",
    distance_strategy="L2",
    engine="FaissFlat",
)
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(client, *[, embedding, ...])
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_images(uris[, metadatas, ids, ...])
Run more images through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, batch_size])
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.
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])
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.
count(collection_name)
decode_image(base64_image)
delete([ids, collection_name, constraints])
Delete by ID.
encode_image(image_path)
from_documents(documents[, embedding, ids, ...])
Create a VDMS vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create a VDMS vectorstore from a raw documents.
get(collection_name[, constraints, limit, ...])
Gets the collection.
get_descriptor_response(command_str, setname)
get_k_candidates(setname, fetch_k[, ...])
max_marginal_relevance_search(query[, k, ...])
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.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance.
query_collection_embeddings([...])
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, fetch_k, filter])
Run similarity search with VDMS.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
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, ...])
Run similarity search with VDMS with distance.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to embedding vector and similarity score.
update_document(collection_name, ...)
Update a document in the collection.
update_documents(collection_name, ids, documents)
Update a document in the collection.
__init__(client: vdms.vdms, *, embedding: Optional[Embeddings] = None, collection_name: str = 'langchain', distance_strategy: DISTANCE_METRICS = 'L2', engine: ENGINES = 'FaissFlat', relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶
Parameters
client (vdms.vdms) – 
embedding (Optional[Embeddings]) – 
collection_name (str) – 
distance_strategy (DISTANCE_METRICS) – 
engine (ENGINES) – 
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) – 
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
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_images(uris: List[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, add_path: Optional[bool] = True, **kwargs: Any) → List[str][source]¶
Run more images through the embeddings and add to the vectorstore.
Images are added as embeddings (AddDescriptor) instead of separate
entity (AddImage) within VDMS to leverage similarity search capability
Parameters
uris (List[str]) – List of paths to the images to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
batch_size (int) – Number of concurrent requests to send to the server.
add_path (Optional[bool]) – Bool to add image path as metadata
kwargs (Any) – 
Returns
List of ids from adding images into the vectorstore.
Return type
List[str]
List of ids from adding images into the vectorstore.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – List of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional list of unique IDs.
batch_size (int) – Number of concurrent requests to send to the server.
kwargs (Any) – 
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.
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, **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
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
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]
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
# 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}
)
# 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
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]
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]]
count(collection_name: str) → int[source]¶
Parameters
collection_name (str) – 
Return type
int
decode_image(base64_image: str) → bytes[source]¶
Parameters
base64_image (str) – 
Return type
bytes
Parameters
base64_image (str) – 
Return type
bytes
delete(ids: Optional[List[str]] = None, collection_name: Optional[str] = None, constraints: Optional[Dict] = None, **kwargs: Any) → bool[source]¶
Delete by ID. These are the IDs in the vectorstore.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
collection_name (Optional[str]) – 
constraints (Optional[Dict]) – 
kwargs (Any) – 
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
encode_image(image_path: str) → str[source]¶
Parameters
image_path (str) – 
Return type
str
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, batch_size: int = 32, collection_name: str = 'langchain', **kwargs: Any) → VDMS[source]¶
Create a VDMS vectorstore from a list of documents.
Parameters
collection_name (str) – Name of the collection to create.
documents (List[Document]) – List of documents to add to vectorstore.
embedding (Embeddings) – Embedding function. Defaults to None.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
batch_size (int) – Number of concurrent requests to send to the server.
kwargs (Any) – 
Returns
VDMS vectorstore.
Return type
VDMS
Returns
VDMS vectorstore.
Return type
VDMS
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, collection_name: str = 'langchain', **kwargs: Any) → VDMS[source]¶
Create a VDMS vectorstore from a raw documents.
Parameters
texts (List[str]) – List of texts to add to the collection.
embedding (Embeddings) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
batch_size (int) – Number of concurrent requests to send to the server.
collection_name (str) – Name of the collection to create.
kwargs (Any) – 
Returns
VDMS vectorstore.
Return type
VDMS
get(collection_name: str, constraints: Optional[Dict] = None, limit: Optional[int] = None, include: List[str] = ['metadata']) → Tuple[Any, Any][source]¶
Gets the collection.
Get embeddings and their associated data from the data store.
If no constraints provided returns all embeddings up to limit.
Parameters
constraints (Optional[Dict]) – A dict used to filter results by.
E.g. {“color” : [“==”, “red”], “price”: [“>”, 4.00]}. Optional.
limit (Optional[int]) – The number of documents to return. Optional.
include (List[str]) – A list of what to include in the results.
Can contain “embeddings”, “metadatas”, “documents”.
Ids are always included.
Ids are always included.
Defaults to [“metadatas”, “documents”]. Optional.
collection_name (str) – 
Return type
Tuple[Any, Any]
get_descriptor_response(command_str: str, setname: str, k_neighbors: int = 3, fetch_k: int = 15, constraints: Optional[dict] = None, results: Optional[Dict[str, Any]] = None, query_embedding: Optional[List[float]] = None, normalize_distance: bool = False) → Tuple[List[Dict[str, Any]], List][source]¶
Parameters
command_str (str) – 
setname (str) – 
k_neighbors (int) – 
fetch_k (int) – 
constraints (Optional[dict]) – 
results (Optional[Dict[str, Any]]) – 
query_embedding (Optional[List[float]]) – 
normalize_distance (bool) – 
Return type
Tuple[List[Dict[str, Any]], List]
get_k_candidates(setname: str, fetch_k: Optional[int], results: Optional[Dict[str, Any]] = None, all_blobs: Optional[List] = None, normalize: Optional[bool] = False) → Tuple[List[Dict[str, Any]], List, float][source]¶
Parameters
setname (str) – 
fetch_k (Optional[int]) – 
results (Optional[Dict[str, Any]]) – 
all_blobs (Optional[List]) – 
normalize (Optional[bool]) – 
Return type
Tuple[List[Dict[str, Any]], List, float]
max_marginal_relevance_search(query: str, k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding 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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_with_score(query: str, k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Tuple[Document, float]]
max_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, lambda_mult: float = 0.5, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
Parameters
embedding (List[float]) – Embedding 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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Tuple[Document, float]]
query_collection_embeddings(query_embeddings: Optional[List[List[float]]] = None, collection_name: Optional[str] = None, n_results: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, Any]] = None, results: Optional[Dict[str, Any]] = None, normalize_distance: bool = False, **kwargs: Any) → List[Tuple[Dict[str, Any], List]][source]¶
Parameters
query_embeddings (Optional[List[List[float]]]) – 
collection_name (Optional[str]) – 
n_results (int) – 
fetch_k (int) – 
filter (Optional[Dict[str, Any]]) – 
results (Optional[Dict[str, Any]]) – 
normalize_distance (bool) – 
kwargs (Any) – 
Return type
List[Tuple[Dict[str, Any], List]]
search(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”,
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]
similarity_search(query: str, k: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with VDMS.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 3.
fetch_k (int) – Number of candidates to fetch for knn (>= k).
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of documents most similar to the query text.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 3, fetch_k: int = 15, filter: Optional[Dict[str, List]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
:param embedding: Embedding to look up documents similar to.
:type embedding: List[float]
:param k: Number of Documents to return. Defaults to 3.
:type k: int
:param fetch_k: Number of candidates to fetch for knn (>= k).
:type fetch_k: int
:param filter: Filter by metadata. Defaults to None.
:type filter: Optional[Dict[str, str]]
Returns
List of Documents most similar to the query vector.
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) –