Tony20100/langchain_full huggingface dataset:
Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
max_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]
max_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.
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.
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]
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”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Run similarity search with AtlasDB
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
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 = 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]
similarity_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].
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]]
similarity_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]]
Examples using AtlasDB¶
Atlas
langchain_community.vectorstores.sklearn.BsonSerializer¶
class langchain_community.vectorstores.sklearn.BsonSerializer(persist_path: str)[source]¶
Serialize data in Binary JSON using the bson python package.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)
Saves the data to the persist_path
Parameters
persist_path (str) – 
__init__(persist_path: str) → None[source]¶
Parameters
persist_path (str) – 
Return type
None
classmethod extension() → str[source]¶
The file extension suggested by this serializer (without dot).
Return type
str
load() → Any[source]¶
Loads the data from the persist_path
Return type
Any
save(data: Any) → None[source]¶
Saves the data to the persist_path
Parameters
data (Any) – 
Return type
None
langchain_community.vectorstores.jaguar.Jaguar¶
class langchain_community.vectorstores.jaguar.Jaguar(pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, url: str, embedding: Embeddings)[source]¶
Jaguar API vector store.
See http://www.jaguardb.com
See http://github.com/fserv/jaguar-sdk
Example
from langchain_community.vectorstores.jaguar import Jaguar
vectorstore = Jaguar(
    pod = 'vdb',
    store = 'mystore',
    vector_index = 'v',
    vector_type = 'cosine_fraction_float',
    vector_dimension = 1536,
    url='http://192.168.8.88:8080/fwww/',
    embedding=openai_model
)
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(pod, store, vector_index, ...)
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_texts(texts[, metadatas])
add_texts(texts[, metadatas])
Add  texts through the embeddings and add to the vectorstore. :param texts: list of text strings to add to the jaguar vector store. :param metadatas: Optional list of metadatas associated with the texts. [{"m1": "v11", "m2": "v12", "m3": "v13", "filecol": "path_file1.jpg" },                    {"m1": "v21", "m2": "v22", "m3": "v23", "filecol": "path_file2.jpg" },                    {"m1": "v31", "m2": "v32", "m3": "v33", "filecol": "path_file3.jpg" },                    {"m1": "v41", "m2": "v42", "m3": "v43", "filecol": "path_file4.jpg" }] :param kwargs: vector_index=name_of_vector_index                file_column=name_of_file_column.
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])
Return docs most similar to query.
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.
clear()
Delete all records in jaguardb Args: No args Returns: None
count()
Count records of a store in jaguardb Args: no args Returns: (int) number of records in pod store
create(metadata_str, text_size)
create the vector store on the backend database :param metadata_str: columns and their types :type metadata_str: str
delete(zids, **kwargs)
Delete records in jaguardb by a list of zero-ids :param pod: name of a Pod :type pod: str :param ids: a list of zid as string :type ids: List[str]
drop()
Drop or remove a store in jaguardb Args: no args Returns: None
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding, url, pod, ...)
Return VectorStore initialized from texts and embeddings.
is_anomalous(query, **kwargs)
Detect if given text is anomalous from the dataset :param query: Text to detect if it is anomaly
login([jaguar_api_key])
login([jaguar_api_key])
login to jaguardb server with a jaguar_api_key or let self._jag find a key :param pod: name of a Pod :type pod: str :param store: name of a vector store :type store: str :param optional jaguar_api_key: API key of user to jaguardb server :type optional jaguar_api_key: str
logout()
Logout to cleanup resources Args: no args Returns: None
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.
prt(msg)
run(query[, withFile])
Run any query statement in jaguardb :param query: query statement to jaguardb :type query: str
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, where, metadatas])
Return Jaguar documents most similar to query, along with scores. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 5. :param where: the where clause in select similarity. For example a               where can be "rating > 3.0 and (state = 'NV' or state = 'CA')".
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, ...])
similarity_search_with_score(query[, k, ...])
Return Jaguar documents most similar to query, along with scores. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 3. :param lambda_val: lexical match parameter for hybrid search. :param where: the where clause in select similarity. For example a               where can be "rating > 3.0 and (state = 'NV' or state = 'CA')" :param args: extra options passed to select similarity :param kwargs: vector_index=vcol, vector_type=cosine_fraction_float.
Parameters
pod (str) – 
store (str) – 
vector_index (str) – 
vector_type (str) – 
vector_dimension (int) – 
url (str) – 
embedding (Embeddings) – 
__init__(pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, url: str, embedding: Embeddings)[source]¶
Parameters
pod (str) – 
store (str) – 
vector_index (str) – 
vector_type (str) – 
vector_dimension (int) – 
url (str) – 
embedding (Embeddings) – 
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
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_texts(texts: List[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Add  texts through the embeddings and add to the vectorstore.
:param texts: list of text strings to add to the jaguar vector store.
:param metadatas: Optional list of metadatas associated with the texts.
[{“m1”: “v11”, “m2”: “v12”, “m3”: “v13”, “filecol”: “path_file1.jpg” },{“m1”: “v21”, “m2”: “v22”, “m3”: “v23”, “filecol”: “path_file2.jpg” },
{“m1”: “v31”, “m2”: “v32”, “m3”: “v33”, “filecol”: “path_file3.jpg” },
{“m1”: “v41”, “m2”: “v42”, “m3”: “v43”, “filecol”: “path_file4.jpg” }]
Parameters
Parameters
kwargs (Any) – vector_index=name_of_vector_index
file_column=name_of_file_column
texts (List[str]) – 
metadatas (Optional[List[dict]]) – 
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
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]]
clear() → None[source]¶
Delete all records in jaguardb
Args: No args
Returns: None
Return type
None
count() → int[source]¶
Count records of a store in jaguardb
Args: no args
Returns: (int) number of records in pod store
Return type
int
create(metadata_str: str, text_size: int) → None[source]¶
create the vector store on the backend database
:param metadata_str: columns and their types
:type metadata_str: str
Returns
True if successful; False if not successful
Parameters
metadata_str (str) – 
text_size (int) –
Parameters
metadata_str (str) – 
text_size (int) – 
Return type
None
delete(zids: List[str], **kwargs: Any) → None[source]¶
Delete records in jaguardb by a list of zero-ids
:param pod: name of a Pod
:type pod: str
:param ids: a list of zid as string
:type ids: List[str]
Returns
Do not return anything
Parameters
zids (List[str]) – 
kwargs (Any) – 
Return type
None
drop() → None[source]¶
Drop or remove a store in jaguardb
Args: no args
Returns: None
Return type
None
classmethod from_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
classmethod from_texts(texts: List[str], embedding: Embeddings, url: str, pod: str, store: str, vector_index: str, vector_type: str, vector_dimension: int, metadatas: Optional[List[dict]] = None, jaguar_api_key: Optional[str] = '', **kwargs: Any) → Jaguar[source]¶
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.
url (str) – 
pod (str) – 
store (str) – 
vector_index (str) –
store (str) – 
vector_index (str) – 
vector_type (str) – 
vector_dimension (int) – 
jaguar_api_key (Optional[str]) – 
kwargs (Any) – 
Return type
Jaguar
is_anomalous(query: str, **kwargs: Any) → bool[source]¶
Detect if given text is anomalous from the dataset
:param query: Text to detect if it is anomaly
Returns
True or False
Parameters
query (str) – 
kwargs (Any) – 
Return type
bool
login(jaguar_api_key: Optional[str] = '') → bool[source]¶
login to jaguardb server with a jaguar_api_key or let self._jag find a key
:param pod: name of a Pod
:type pod: str
:param store: name of a vector store
:type store: str
:param optional jaguar_api_key: API key of user to jaguardb server
:type optional jaguar_api_key: str
Returns
True if successful; False if not successful
Parameters
jaguar_api_key (Optional[str]) – 
Return type
bool
logout() → None[source]¶
Logout to cleanup resources
Args: no args
Returns: None
Return type
None
max_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.
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]
max_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.
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]
prt(msg: str) → None[source]¶
Parameters
msg (str) – 
Return type
None
run(query: str, withFile: bool = False) → dict[source]¶
Run any query statement in jaguardb
:param query: query statement to jaguardb
:type query: str
Returns
None for invalid token, or
json result string
Parameters
:type query: str
Returns
None for invalid token, or
json result string
Parameters
query (str) – 
withFile (bool) – 
Return type
dict
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”,
“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, where: Optional[str] = None, metadatas: Optional[List[str]] = None, **kwargs: Any) → List[Document][source]¶
Return Jaguar documents most similar to query, along with scores.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 5.
:param where: the where clause in select similarity. For example a
where can be “rating > 3.0 and (state = ‘NV’ or state = ‘CA’)”
Returns
List of Documents most similar to the query
Parameters
query (str) – 
k (int) – 
where (Optional[str]) – 
metadatas (Optional[List[str]]) – 
kwargs (Any) – 
Return type
List[Document]
similarity_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.
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]
similarity_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]]
similarity_search_with_score(query: str, k: int = 3, fetch_k: int = -1, where: Optional[str] = None, args: Optional[str] = None, metadatas: Optional[List[str]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return Jaguar documents most similar to query, along with scores.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 3.
:param lambda_val: lexical match parameter for hybrid search.
:param where: the where clause in select similarity. For example a
where can be “rating > 3.0 and (state = ‘NV’ or state = ‘CA’)”
Parameters
args (Optional[str]) – extra options passed to select similarity
kwargs (Any) – vector_index=vcol, vector_type=cosine_fraction_float
kwargs (Any) – vector_index=vcol, vector_type=cosine_fraction_float
query (str) – 
k (int) – 
fetch_k (int) – 
where (Optional[str]) – 
metadatas (Optional[List[str]]) – 
Returns
List of Documents most similar to the query and score for each.
List of Tuples of (doc, similarity_score):
[ (doc, score), (doc, score), …]
Return type
List[Tuple[Document, float]]
Examples using Jaguar¶
Jaguar
Jaguar Vector Database
JaguarDB Vector Database
langchain_community.vectorstores.pathway.PathwayVectorClient¶
class langchain_community.vectorstores.pathway.PathwayVectorClient(host: Optional[str] = None, port: Optional[int] = None, url: Optional[str] = None)[source]¶
VectorStore connecting to Pathway Vector Store.
A client you can use to query Pathway Vector Store.
Please provide aither the url, or host and port.
Parameters
host (-) – host on which Pathway Vector Store listens
port (-) – port on which Pathway Vector Store listens
url (-) – url at which Pathway Vector Store listens
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([host, port, url])
A client you can use to query Pathway Vector 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_texts(texts[, metadatas])
Pathway is not suitable for this method.
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.