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81b746c66a31-90 | },
"table": {
"title": "Table",
"default": "langchain",
"env_names": "{'myscale_table'}",
"type": "string"
},
"metric": {
"title": "Metric",
"default": "cosine",
"env_names": "{'myscale_metric'}",
"type": "string"
}
},
"additionalProperties": false
}
Config
env_file: str = .env
env_file_encoding: str = utf-8
env_prefix: str = myscale_
Fields
column_map (Dict[str, str])
database (str)
host (str)
index_param (Optional[Dict[str, str]])
index_type (str)
metric (str)
password (Optional[str])
port (int)
table (str)
username (Optional[str])
attribute column_map: Dict[str, str] = {'id': 'id', 'metadata': 'metadata', 'text': 'text', 'vector': 'vector'}ο
attribute database: str = 'default'ο
attribute host: str = 'localhost'ο
attribute index_param: Optional[Dict[str, str]] = Noneο
attribute index_type: str = 'IVFFLAT'ο
attribute metric: str = 'cosine'ο
attribute password: Optional[str] = Noneο
attribute port: int = 8443ο
attribute table: str = 'langchain'ο
attribute username: Optional[str] = Noneο
class langchain.vectorstores.Pinecone(index, embedding_function, text_key, namespace=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Pinecone vector database. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-91 | Bases: langchain.vectorstores.base.VectorStore
Wrapper around Pinecone vector database.
To use, you should have the pinecone-client python package installed.
Example
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone
# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(api_key="***", environment="...")
index = pinecone.Index("langchain-demo")
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone(index, embeddings.embed_query, "text")
Parameters
index (Any) β
embedding_function (Callable) β
text_key (str) β
namespace (Optional[str]) β
add_texts(texts, metadatas=None, ids=None, namespace=None, batch_size=32, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) β Optional list of ids to associate with the texts.
namespace (Optional[str]) β Optional pinecone namespace to add the texts to.
batch_size (int) β
kwargs (Any) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search_with_score(query, k=4, filter=None, namespace=None)[source]ο
Return pinecone documents most similar to query, along with scores.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-92 | k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[dict]) β Dictionary of argument(s) to filter on metadata
namespace (Optional[str]) β Namespace to search in. Default will search in ββ namespace.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search(query, k=4, filter=None, namespace=None, **kwargs)[source]ο
Return pinecone documents most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[dict]) β Dictionary of argument(s) to filter on metadata
namespace (Optional[str]) β Namespace to search in. Default will search in ββ namespace.
kwargs (Any) β
Returns
List of Documents most similar to the query and score for each
Return type
List[langchain.schema.Document]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, filter=None, namespace=None, **kwargs)[source]ο
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]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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]) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-93 | Defaults to 0.5.
filter (Optional[dict]) β
namespace (Optional[str]) β
kwargs (Any) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, filter=None, namespace=None, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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]) β
namespace (Optional[str]) β
kwargs (Any) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, batch_size=32, text_key='text', index_name=None, namespace=None, **kwargs)[source]ο
Construct Pinecone wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Adds the documents to a provided Pinecone index
This is intended to be a quick way to get started.
Example
from langchain import Pinecone
from langchain.embeddings import OpenAIEmbeddings
import pinecone
# The environment should be the one specified next to the API key
# in your Pinecone console | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-94 | # in your Pinecone console
pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
ids (Optional[List[str]]) β
batch_size (int) β
text_key (str) β
index_name (Optional[str]) β
namespace (Optional[str]) β
kwargs (Any) β
Return type
langchain.vectorstores.pinecone.Pinecone
classmethod from_existing_index(index_name, embedding, text_key='text', namespace=None)[source]ο
Load pinecone vectorstore from index name.
Parameters
index_name (str) β
embedding (langchain.embeddings.base.Embeddings) β
text_key (str) β
namespace (Optional[str]) β
Return type
langchain.vectorstores.pinecone.Pinecone
delete(ids, namespace=None)[source]ο
Delete by vector IDs.
:param ids: List of ids to delete.
Parameters
ids (List[str]) β
namespace (Optional[str]) β
Return type
None
class langchain.vectorstores.Qdrant(client, collection_name, embeddings=None, content_payload_key='page_content', metadata_payload_key='metadata', embedding_function=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Qdrant vector database.
To use you should have the qdrant-client package installed.
Example
from qdrant_client import QdrantClient
from langchain import Qdrant
client = QdrantClient()
collection_name = "MyCollection" | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-95 | client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
Parameters
client (Any) β
collection_name (str) β
embeddings (Optional[Embeddings]) β
content_payload_key (str) β
metadata_payload_key (str) β
embedding_function (Optional[Callable]) β
CONTENT_KEY = 'page_content'ο
METADATA_KEY = 'metadata'ο
add_texts(texts, metadatas=None, ids=None, batch_size=64, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
ids (Optional[Sequence[str]]) β Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size (int) β How many vectors upload per-request.
Default: 64
kwargs (Any) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search(query, k=4, filter=None, search_params=None, offset=0, score_threshold=None, consistency=None, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) β Filter by metadata. Defaults to None.
search_params (Optional[common_types.SearchParams]) β Additional search params
offset (int) β Offset of the first result to return.
May be used to paginate results. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-96 | May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) β Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) β Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
βmajorityβ - query all replicas, but return values present in themajority of replicas
βquorumβ - query the majority of replicas, return values present inall of them
βallβ - query all replicas, and return values present in all replicas
kwargs (Any) β
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_with_score(query, k=4, filter=None, search_params=None, offset=0, score_threshold=None, consistency=None, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) β Filter by metadata. Defaults to None.
search_params (Optional[common_types.SearchParams]) β Additional search params
offset (int) β Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) β Define a minimal score threshold for the result.
If defined, less similar results will not be returned. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-97 | If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) β Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
βmajorityβ - query all replicas, but return values present in themajority of replicas
βquorumβ - query the majority of replicas, return values present inall of them
βallβ - query all replicas, and return values present in all replicas
kwargs (Any) β
Returns
List of documents most similar to the query text and cosine
distance in float for each.
Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
similarity_search_by_vector(embedding, k=4, filter=None, search_params=None, offset=0, score_threshold=None, consistency=None, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding vector to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) β Filter by metadata. Defaults to None.
search_params (Optional[common_types.SearchParams]) β Additional search params
offset (int) β Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) β Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-98 | Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) β Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
βmajorityβ - query all replicas, but return values present in themajority of replicas
βquorumβ - query the majority of replicas, return values present inall of them
βallβ - query all replicas, and return values present in all replicas
kwargs (Any) β
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_with_score_by_vector(embedding, k=4, filter=None, search_params=None, offset=0, score_threshold=None, consistency=None, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding vector to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) β Filter by metadata. Defaults to None.
search_params (Optional[common_types.SearchParams]) β Additional search params
offset (int) β Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold (Optional[float]) β Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-99 | E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) β Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
βmajorityβ - query all replicas, but return values present in themajority of replicas
βquorumβ - query the majority of replicas, return values present inall of them
βallβ - query all replicas, and return values present in all replicas
kwargs (Any) β
Returns
List of documents most similar to the query text and cosine
distance in float for each.
Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-100 | Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, location=None, url=None, port=6333, grpc_port=6334, prefer_grpc=False, https=None, api_key=None, prefix=None, timeout=None, host=None, path=None, collection_name=None, distance_func='Cosine', content_payload_key='page_content', metadata_payload_key='metadata', batch_size=64, shard_number=None, replication_factor=None, write_consistency_factor=None, on_disk_payload=None, hnsw_config=None, optimizers_config=None, wal_config=None, quantization_config=None, init_from=None, **kwargs)[source]ο
Construct Qdrant wrapper from a list of texts.
Parameters
texts (List[str]) β A list of texts to be indexed in Qdrant.
embedding (Embeddings) β A subclass of Embeddings, responsible for text vectorization.
metadatas (Optional[List[dict]]) β An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids (Optional[Sequence[str]]) β Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location (Optional[str]) β If :memory: - use in-memory Qdrant instance.
If str - use it as a url parameter.
If None - fallback to relying on host and port parameters.
url (Optional[str]) β either host or str of βOptional[scheme], host, Optional[port],
Optional[prefix]β. Default: None
port (Optional[int]) β Port of the REST API interface. Default: 6333
grpc_port (int) β Port of the gRPC interface. Default: 6334
prefer_grpc (bool) β If true - use gPRC interface whenever possible in custom methods.
Default: False | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-101 | Default: False
https (Optional[bool]) β If true - use HTTPS(SSL) protocol. Default: None
api_key (Optional[str]) β API key for authentication in Qdrant Cloud. Default: None
prefix (Optional[str]) β If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout (Optional[float]) β Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host (Optional[str]) β Host name of Qdrant service. If url and host are None, set to
βlocalhostβ. Default: None
path (Optional[str]) β Path in which the vectors will be stored while using local mode.
Default: None
collection_name (Optional[str]) β Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func (str) β Distance function. One of: βCosineβ / βEuclidβ / βDotβ.
Default: βCosineβ
content_payload_key (str) β A payload key used to store the content of the document.
Default: βpage_contentβ
metadata_payload_key (str) β A payload key used to store the metadata of the document.
Default: βmetadataβ
batch_size (int) β How many vectors upload per-request.
Default: 64
shard_number (Optional[int]) β Number of shards in collection. Default is 1, minimum is 1.
replication_factor (Optional[int]) β Replication factor for collection. Default is 1, minimum is 1.
Defines how many copies of each shard will be created.
Have effect only in distributed mode. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-102 | Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor (Optional[int]) β Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any performance impact.
Have effect only in distributed mode.
on_disk_payload (Optional[bool]) β If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config (Optional[common_types.HnswConfigDiff]) β Params for HNSW index
optimizers_config (Optional[common_types.OptimizersConfigDiff]) β Params for optimizer
wal_config (Optional[common_types.WalConfigDiff]) β Params for Write-Ahead-Log
quantization_config (Optional[common_types.QuantizationConfig]) β Params for quantization, if None - quantization will be disabled
init_from (Optional[common_types.InitFrom]) β Use data stored in another collection to initialize this collection
**kwargs β Additional arguments passed directly into REST client initialization
kwargs (Any) β
Return type
Qdrant
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-103 | This is intended to be a quick way to get started.
Example
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
class langchain.vectorstores.Redis(redis_url, index_name, embedding_function, content_key='content', metadata_key='metadata', vector_key='content_vector', relevance_score_fn=<function _default_relevance_score>, **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Redis vector database.
To use, you should have the redis python package installed.
Example
from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Redis(
redis_url="redis://username:password@localhost:6379"
index_name="my-index",
embedding_function=embeddings.embed_query,
)
Parameters
redis_url (str) β
index_name (str) β
embedding_function (Callable) β
content_key (str) β
metadata_key (str) β
vector_key (str) β
relevance_score_fn (Optional[Callable[[float], float]]) β
kwargs (Any) β
add_texts(texts, metadatas=None, embeddings=None, batch_size=1000, **kwargs)[source]ο
Add more texts to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional) β Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional) β Optional pre-generated
embeddings. Defaults to None. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-104 | embeddings. Defaults to None.
keys (List[str]) or ids (List[str]) β Identifiers of entries.
Defaults to None.
batch_size (int, optional) β Batch size to use for writes. Defaults to 1000.
kwargs (Any) β
Returns
List of ids added to the vectorstore
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]ο
Returns the most similar indexed documents to the query text.
Parameters
query (str) β The query text for which to find similar documents.
k (int) β The number of documents to return. Default is 4.
kwargs (Any) β
Returns
A list of documents that are most similar to the query text.
Return type
List[Document]
similarity_search_limit_score(query, k=4, score_threshold=0.2, **kwargs)[source]ο
Returns the most similar indexed documents to the query text within the
score_threshold range.
Parameters
query (str) β The query text for which to find similar documents.
k (int) β The number of documents to return. Default is 4.
score_threshold (float) β The minimum matching score required for a document
0.2. (to be considered a match. Defaults to) β
similarity (Because the similarity calculation algorithm is based on cosine) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
:param :
:param the smaller the angle:
:param the higher the similarity.:
Returns
A list of documents that are most similar to the query text,
including the match score for each document.
Return type
List[Document]
Parameters
query (str) β
k (int) β
score_threshold (float) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-105 | k (int) β
score_threshold (float) β
kwargs (Any) β
Note
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
similarity_search_with_score(query, k=4)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
classmethod from_texts_return_keys(texts, embedding, metadatas=None, index_name=None, content_key='content', metadata_key='metadata', vector_key='content_vector', distance_metric='COSINE', **kwargs)[source]ο
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in Redis.
Adds the documents to the newly created Redis index.
Returns the keys of the newly created documents.
This is intended to be a quick way to get started.
.. rubric:: Example
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
index_name (Optional[str]) β
content_key (str) β
metadata_key (str) β
vector_key (str) β
distance_metric (Literal['COSINE', 'IP', 'L2']) β
kwargs (Any) β
Return type
Tuple[langchain.vectorstores.redis.Redis, List[str]] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-106 | Return type
Tuple[langchain.vectorstores.redis.Redis, List[str]]
classmethod from_texts(texts, embedding, metadatas=None, index_name=None, content_key='content', metadata_key='metadata', vector_key='content_vector', **kwargs)[source]ο
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in Redis.
Adds the documents to the newly created Redis index.
This is intended to be a quick way to get started.
.. rubric:: Example
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
index_name (Optional[str]) β
content_key (str) β
metadata_key (str) β
vector_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.redis.Redis
static delete(ids, **kwargs)[source]ο
Delete a Redis entry.
Parameters
ids (List[str]) β List of ids (keys) to delete.
kwargs (Any) β
Returns
Whether or not the deletions were successful.
Return type
bool
static drop_index(index_name, delete_documents, **kwargs)[source]ο
Drop a Redis search index.
Parameters
index_name (str) β Name of the index to drop.
delete_documents (bool) β Whether to drop the associated documents.
kwargs (Any) β
Returns
Whether or not the drop was successful.
Return type
bool
classmethod from_existing_index(embedding, index_name, content_key='content', metadata_key='metadata', vector_key='content_vector', **kwargs)[source]ο
Connect to an existing Redis index.
Parameters | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-107 | Connect to an existing Redis index.
Parameters
embedding (langchain.embeddings.base.Embeddings) β
index_name (str) β
content_key (str) β
metadata_key (str) β
vector_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.redis.Redis
as_retriever(**kwargs)[source]ο
Parameters
kwargs (Any) β
Return type
langchain.vectorstores.redis.RedisVectorStoreRetriever
class langchain.vectorstores.Rockset(client, embeddings, collection_name, text_key, embedding_key)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper arpund Rockset vector database.
To use, you should have the rockset python package installed. Note that to use
this, the collection being used must already exist in your Rockset instance.
You must also ensure you use a Rockset ingest transformation to apply
VECTOR_ENFORCE on the column being used to store embedding_key in the
collection.
See: https://rockset.com/blog/introducing-vector-search-on-rockset/ for more details
Everything below assumes commons Rockset workspace.
TODO: Add support for workspace args.
Example
from langchain.vectorstores import Rockset
from langchain.embeddings.openai import OpenAIEmbeddings
import rockset
# Make sure you use the right host (region) for your Rockset instance
# and APIKEY has both read-write access to your collection.
rs = rockset.RocksetClient(host=rockset.Regions.use1a1, api_key="***")
collection_name = "langchain_demo"
embeddings = OpenAIEmbeddings()
vectorstore = Rockset(rs, collection_name, embeddings,
"description", "description_embedding")
Parameters
client (Any) β
embeddings (Embeddings) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-108 | Parameters
client (Any) β
embeddings (Embeddings) β
collection_name (str) β
text_key (str) β
embedding_key (str) β
add_texts(texts, metadatas=None, ids=None, batch_size=32, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids to associate with the texts.
batch_size: Send documents in batches to rockset.
Returns
List of ids from adding the texts into the vectorstore.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
ids (Optional[List[str]]) β
batch_size (int) β
kwargs (Any) β
Return type
List[str]
classmethod from_texts(texts, embedding, metadatas=None, client=None, collection_name='', text_key='', embedding_key='', ids=None, batch_size=32, **kwargs)[source]ο
Create Rockset wrapper with existing texts.
This is intended as a quicker way to get started.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
client (Any) β
collection_name (str) β
text_key (str) β
embedding_key (str) β
ids (Optional[List[str]]) β
batch_size (int) β
kwargs (Any) β
Return type
langchain.vectorstores.rocksetdb.Rockset | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-109 | Return type
langchain.vectorstores.rocksetdb.Rockset
class DistanceFunction(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]ο
Bases: enum.Enum
COSINE_SIM = 'COSINE_SIM'ο
EUCLIDEAN_DIST = 'EUCLIDEAN_DIST'ο
DOT_PRODUCT = 'DOT_PRODUCT'ο
order_by()[source]ο
Return type
str
similarity_search_with_relevance_scores(query, k=4, distance_func=DistanceFunction.COSINE_SIM, where_str=None, **kwargs)[source]ο
Perform a similarity search with Rockset
Parameters
query (str) β Text to look up documents similar to.
distance_func (DistanceFunction) β how to compute distance between two
vectors in Rockset.
k (int, optional) β Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) β Metadata filters supplied as a
SQL where condition string. Defaults to None.
eg. βprice<=70.0 AND brand=βNintendoββ
NOTE β Please do not let end-user to fill this and always be aware
of SQL injection.
kwargs (Any) β
Returns
List of documents with their relevance score
Return type
List[Tuple[Document, float]]
similarity_search(query, k=4, distance_func=DistanceFunction.COSINE_SIM, where_str=None, **kwargs)[source]ο
Same as similarity_search_with_relevance_scores but
doesnβt return the scores.
Parameters
query (str) β
k (int) β
distance_func (DistanceFunction) β
where_str (Optional[str]) β
kwargs (Any) β
Return type
List[Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-110 | kwargs (Any) β
Return type
List[Document]
similarity_search_by_vector(embedding, k=4, distance_func=DistanceFunction.COSINE_SIM, where_str=None, **kwargs)[source]ο
Accepts a query_embedding (vector), and returns documents with
similar embeddings.
Parameters
embedding (List[float]) β
k (int) β
distance_func (DistanceFunction) β
where_str (Optional[str]) β
kwargs (Any) β
Return type
List[Document]
similarity_search_by_vector_with_relevance_scores(embedding, k=4, distance_func=DistanceFunction.COSINE_SIM, where_str=None, **kwargs)[source]ο
Accepts a query_embedding (vector), and returns documents with
similar embeddings along with their relevance scores.
Parameters
embedding (List[float]) β
k (int) β
distance_func (DistanceFunction) β
where_str (Optional[str]) β
kwargs (Any) β
Return type
List[Tuple[Document, float]]
delete_texts(ids)[source]ο
Delete a list of docs from the Rockset collection
Parameters
ids (List[str]) β
Return type
None
class langchain.vectorstores.SKLearnVectorStore(embedding, *, persist_path=None, serializer='json', metric='cosine', **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
A simple in-memory vector store based on the scikit-learn library
NearestNeighbors implementation.
Parameters
embedding (langchain.embeddings.base.Embeddings) β
persist_path (Optional[str]) β
serializer (Literal['json', 'bson', 'parquet']) β
metric (str) β
kwargs (Any) β
Return type
None
persist()[source]ο
Return type | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-111 | Return type
None
persist()[source]ο
Return type
None
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β vectorstore specific parameters
ids (Optional[List[str]]) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search_with_score(query, *, k=4, **kwargs)[source]ο
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param embedding: Embedding to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param lambda_mult: 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. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-112 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
Parameters
embedding (List[float]) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param lambda_mult: 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.
Returns
List of Documents selected by maximal marginal relevance.
Parameters
query (str) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, persist_path=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
ids (Optional[List[str]]) β
persist_path (Optional[str]) β
kwargs (Any) β
Return type | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-113 | persist_path (Optional[str]) β
kwargs (Any) β
Return type
langchain.vectorstores.sklearn.SKLearnVectorStore
class langchain.vectorstores.StarRocks(embedding, config=None, **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around StarRocks vector database
You need a pymysql python package, and a valid account
to connect to StarRocks.
Right now StarRocks has only implemented cosine_similarity function to
compute distance between two vectors. And there is no vector inside right now,
so we have to iterate all vectors and compute spatial distance.
For more information, please visit[StarRocks official site](https://www.starrocks.io/)
[StarRocks github](https://github.com/StarRocks/starrocks)
Parameters
embedding (Embeddings) β
config (Optional[StarRocksSettings]) β
kwargs (Any) β
Return type
None
escape_str(value)[source]ο
Parameters
value (str) β
Return type
str
add_texts(texts, metadatas=None, batch_size=32, ids=None, **kwargs)[source]ο
Insert more texts through the embeddings and add to the VectorStore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the VectorStore.
ids (Optional[Iterable[str]]) β Optional list of ids to associate with the texts.
batch_size (int) β Batch size of insertion
metadata β Optional column data to be inserted
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Returns
List of ids from adding the texts into the VectorStore.
Return type
List[str] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-114 | List of ids from adding the texts into the VectorStore.
Return type
List[str]
classmethod from_texts(texts, embedding, metadatas=None, config=None, text_ids=None, batch_size=32, **kwargs)[source]ο
Create StarRocks wrapper with existing texts
Parameters
embedding_function (Embeddings) β Function to extract text embedding
texts (Iterable[str]) β List or tuple of strings to be added
config (StarRocksSettings, Optional) β StarRocks configuration
text_ids (Optional[Iterable], optional) β IDs for the texts.
Defaults to None.
batch_size (int, optional) β Batchsize when transmitting data to StarRocks.
Defaults to 32.
metadata (List[dict], optional) β metadata to texts. Defaults to None.
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[Dict[Any, Any]]]) β
kwargs (Any) β
Returns
StarRocks Index
Return type
langchain.vectorstores.starrocks.StarRocks
similarity_search(query, k=4, where_str=None, **kwargs)[source]ο
Perform a similarity search with StarRocks
Parameters
query (str) β query string
k (int, optional) β Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) β where condition string.
Defaults to None.
NOTE β Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
kwargs (Any) β
Returns
List of Documents
Return type
List[Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-115 | Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding, k=4, where_str=None, **kwargs)[source]ο
Perform a similarity search with StarRocks by vectors
Parameters
query (str) β query string
k (int, optional) β Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) β where condition string.
Defaults to None.
NOTE β Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
embedding (List[float]) β
kwargs (Any) β
Returns
List of (Document, similarity)
Return type
List[Document]
similarity_search_with_relevance_scores(query, k=4, where_str=None, **kwargs)[source]ο
Perform a similarity search with StarRocks
Parameters
query (str) β query string
k (int, optional) β Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) β where condition string.
Defaults to None.
NOTE β Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
kwargs (Any) β
Returns
List of documents
Return type
List[Document]
drop()[source]ο
Helper function: Drop data
Return type
None
property metadata_column: strο
class langchain.vectorstores.SupabaseVectorStore(client, embedding, table_name, query_name=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-116 | Bases: langchain.vectorstores.base.VectorStore
VectorStore for a Supabase postgres database. Assumes you have the pgvector
extension installed and a match_documents (or similar) function. For more details:
https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/supabase
You can implement your own match_documents function in order to limit the search
space to a subset of documents based on your own authorization or business logic.
Note that the Supabase Python client does not yet support async operations.
If youβd like to use max_marginal_relevance_search, please review the instructions
below on modifying the match_documents function to return matched embeddings.
Parameters
client (supabase.client.Client) β
embedding (Embeddings) β
table_name (str) β
query_name (Union[str, None]) β
Return type
None
table_name: strο
query_name: strο
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict[Any, Any]]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β vectorstore specific parameters
ids (Optional[List[str]]) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
classmethod from_texts(texts, embedding, metadatas=None, client=None, table_name='documents', query_name='match_documents', ids=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (Embeddings) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-117 | Parameters
texts (List[str]) β
embedding (Embeddings) β
metadatas (Optional[List[dict]]) β
client (Optional[supabase.client.Client]) β
table_name (Optional[str]) β
query_name (Union[str, None]) β
ids (Optional[List[str]]) β
kwargs (Any) β
Return type
SupabaseVectorStore
add_vectors(vectors, documents, ids)[source]ο
Parameters
vectors (List[List[float]]) β
documents (List[langchain.schema.Document]) β
ids (List[str]) β
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
similarity_search_by_vector(embedding, k=4, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
kwargs (Any) β
Returns
List of Documents most similar to the query vector.
Return type
List[langchain.schema.Document]
similarity_search_with_relevance_scores(query, k=4, **kwargs)[source]ο
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) β input text
k (int) β Number of Documents to return. Defaults to 4.
**kwargs β kwargs to be passed to similarity search. Should include: | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-118 | **kwargs β 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
kwargs (Any) β
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_by_vector_with_relevance_scores(query, k)[source]ο
Parameters
query (List[float]) β
k (int) β
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search_by_vector_returning_embeddings(query, k)[source]ο
Parameters
query (List[float]) β
k (int) β
Return type
List[Tuple[langchain.schema.Document, float, numpy.ndarray[numpy.float32, Any]]]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
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]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-119 | Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
max_marginal_relevance_search requires that query_name returns matched
embeddings alongside the match documents. The following function
demonstrates how to do this:
```sql
CREATE FUNCTION match_documents_embeddings(query_embedding vector(1536),
match_count int)
RETURNS TABLE(id bigint,
content text,
metadata jsonb,
embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGINRETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROMdocstore
ORDER BYdocstore.embedding <=> query_embedding
LIMIT match_count;
END;
$$;
```
delete(ids)[source]ο
Delete by vector IDs.
Parameters
ids (List[str]) β List of ids to delete.
Return type
None | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-120 | Parameters
ids (List[str]) β List of ids to delete.
Return type
None
class langchain.vectorstores.Tair(embedding_function, url, index_name, content_key='content', metadata_key='metadata', search_params=None, **kwargs)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Tair Vector store.
Parameters
embedding_function (Embeddings) β
url (str) β
index_name (str) β
content_key (str) β
metadata_key (str) β
search_params (Optional[dict]) β
kwargs (Any) β
create_index_if_not_exist(dim, distance_type, index_type, data_type, **kwargs)[source]ο
Parameters
dim (int) β
distance_type (str) β
index_type (str) β
data_type (str) β
kwargs (Any) β
Return type
bool
add_texts(texts, metadatas=None, **kwargs)[source]ο
Add texts data to an existing index.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]ο
Returns the most similar indexed documents to the query text.
Parameters
query (str) β The query text for which to find similar documents.
k (int) β The number of documents to return. Default is 4.
kwargs (Any) β
Returns
A list of documents that are most similar to the query text.
Return type
List[Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-121 | Return type
List[Document]
classmethod from_texts(texts, embedding, metadatas=None, index_name='langchain', content_key='content', metadata_key='metadata', **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
index_name (str) β
content_key (str) β
metadata_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.tair.Tair
classmethod from_documents(documents, embedding, metadatas=None, index_name='langchain', content_key='content', metadata_key='metadata', **kwargs)[source]ο
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[langchain.schema.Document]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
index_name (str) β
content_key (str) β
metadata_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.tair.Tair
static drop_index(index_name='langchain', **kwargs)[source]ο
Drop an existing index.
Parameters
index_name (str) β Name of the index to drop.
kwargs (Any) β
Returns
True if the index is dropped successfully.
Return type
bool
classmethod from_existing_index(embedding, index_name='langchain', content_key='content', metadata_key='metadata', **kwargs)[source]ο
Connect to an existing Tair index.
Parameters
embedding (langchain.embeddings.base.Embeddings) β
index_name (str) β
content_key (str) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-122 | index_name (str) β
content_key (str) β
metadata_key (str) β
kwargs (Any) β
Return type
langchain.vectorstores.tair.Tair
class langchain.vectorstores.Tigris(client, embeddings, index_name)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Parameters
client (TigrisClient) β
embeddings (Embeddings) β
index_name (str) β
property search_index: TigrisVectorStoreο
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) β Optional list of ids for documents.
Ids will be autogenerated if not provided.
kwargs (Any) β vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search(query, k=4, filter=None, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
filter (Optional[TigrisFilter]) β
kwargs (Any) β
Return type
List[Document]
similarity_search_with_score(query, k=4, filter=None)[source]ο
Run similarity search with Chroma with distance.
Parameters
query (str) β Query text to search for.
k (int) β Number of results to return. Defaults to 4.
filter (Optional[TigrisFilter]) β Filter by metadata. Defaults to None.
Returns | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-123 | filter (Optional[TigrisFilter]) β Filter by metadata. Defaults to None.
Returns
List of documents most similar to the querytext with distance in float.
Return type
List[Tuple[Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, ids=None, client=None, index_name=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (Embeddings) β
metadatas (Optional[List[dict]]) β
ids (Optional[List[str]]) β
client (Optional[TigrisClient]) β
index_name (Optional[str]) β
kwargs (Any) β
Return type
Tigris
class langchain.vectorstores.Typesense(typesense_client, embedding, *, typesense_collection_name=None, text_key='text')[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Typesense vector search.
To use, you should have the typesense python package installed.
Example
from langchain.embedding.openai import OpenAIEmbeddings
from langchain.vectorstores import Typesense
import typesense
node = {
"host": "localhost", # For Typesense Cloud use xxx.a1.typesense.net
"port": "8108", # For Typesense Cloud use 443
"protocol": "http" # For Typesense Cloud use https
}
typesense_client = typesense.Client(
{
"nodes": [node],
"api_key": "<API_KEY>",
"connection_timeout_seconds": 2
}
)
typesense_collection_name = "langchain-memory"
embedding = OpenAIEmbeddings()
vectorstore = Typesense(
typesense_client=typesense_client,
embedding=embedding, | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-124 | typesense_client=typesense_client,
embedding=embedding,
typesense_collection_name=typesense_collection_name,
text_key="text",
)
Parameters
typesense_client (Client) β
embedding (Embeddings) β
typesense_collection_name (Optional[str]) β
text_key (str) β
add_texts(texts, metadatas=None, ids=None, **kwargs)[source]ο
Run more texts through the embedding and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) β Optional list of ids to associate with the texts.
kwargs (Any) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search_with_score(query, k=10, filter='')[source]ο
Return typesense documents most similar to query, along with scores.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter (Optional[str]) β typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search(query, k=10, filter='', **kwargs)[source]ο
Return typesense documents most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-125 | Minimum 10 results would be returned.
filter (Optional[str]) β typesense filter_by expression to filter documents on
kwargs (Any) β
Returns
List of Documents most similar to the query and score for each
Return type
List[langchain.schema.Document]
classmethod from_client_params(embedding, *, host='localhost', port='8108', protocol='http', typesense_api_key=None, connection_timeout_seconds=2, **kwargs)[source]ο
Initialize Typesense directly from client parameters.
Example
from langchain.embedding.openai import OpenAIEmbeddings
from langchain.vectorstores import Typesense
# Pass in typesense_api_key as kwarg or set env var "TYPESENSE_API_KEY".
vectorstore = Typesense(
OpenAIEmbeddings(),
host="localhost",
port="8108",
protocol="http",
typesense_collection_name="langchain-memory",
)
Parameters
embedding (langchain.embeddings.base.Embeddings) β
host (str) β
port (Union[str, int]) β
protocol (str) β
typesense_api_key (Optional[str]) β
connection_timeout_seconds (int) β
kwargs (Any) β
Return type
langchain.vectorstores.typesense.Typesense
classmethod from_texts(texts, embedding, metadatas=None, ids=None, typesense_client=None, typesense_client_params=None, typesense_collection_name=None, text_key='text', **kwargs)[source]ο
Construct Typesense wrapper from raw text.
Parameters
texts (List[str]) β
embedding (Embeddings) β
metadatas (Optional[List[dict]]) β
ids (Optional[List[str]]) β
typesense_client (Optional[Client]) β
typesense_client_params (Optional[dict]) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-126 | typesense_client_params (Optional[dict]) β
typesense_collection_name (Optional[str]) β
text_key (str) β
kwargs (Any) β
Return type
Typesense
class langchain.vectorstores.Vectara(vectara_customer_id=None, vectara_corpus_id=None, vectara_api_key=None)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Implementation of Vector Store using Vectara (https://vectara.com).
.. rubric:: Example
from langchain.vectorstores import Vectara
vectorstore = Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key
)
Parameters
vectara_customer_id (Optional[str]) β
vectara_corpus_id (Optional[str]) β
vectara_api_key (Optional[str]) β
add_texts(texts, metadatas=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
similarity_search_with_score(query, k=5, lambda_val=0.025, filter=None, n_sentence_context=0, **kwargs)[source]ο
Return Vectara documents most similar to query, along with scores.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 5.
lambda_val (float) β lexical match parameter for hybrid search. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-127 | lambda_val (float) β lexical match parameter for hybrid search.
filter (Optional[str]) β Dictionary of argument(s) to filter on metadata. For example a
filter can be βdoc.rating > 3.0 and part.lang = βdeuββ} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
n_sentence_context (int) β number of sentences before/after the matching segment
to add
kwargs (Any) β
Returns
List of Documents most similar to the query and score for each.
Return type
List[Tuple[langchain.schema.Document, float]]
similarity_search(query, k=5, lambda_val=0.025, filter=None, n_sentence_context=0, **kwargs)[source]ο
Return Vectara documents most similar to query, along with scores.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 5.
filter (Optional[str]) β Dictionary of argument(s) to filter on metadata. For example a
filter can be βdoc.rating > 3.0 and part.lang = βdeuββ} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview for more
details.
n_sentence_context (int) β number of sentences before/after the matching segment
to add
lambda_val (float) β
kwargs (Any) β
Returns
List of Documents most similar to the query
Return type
List[langchain.schema.Document]
classmethod from_texts(texts, embedding=None, metadatas=None, **kwargs)[source]ο
Construct Vectara wrapper from raw documents.
This is intended to be a quick way to get started.
.. rubric:: Example
from langchain import Vectara | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-128 | .. rubric:: Example
from langchain import Vectara
vectara = Vectara.from_texts(
texts,
vectara_customer_id=customer_id,
vectara_corpus_id=corpus_id,
vectara_api_key=api_key,
)
Parameters
texts (List[str]) β
embedding (Optional[langchain.embeddings.base.Embeddings]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
langchain.vectorstores.vectara.Vectara
as_retriever(**kwargs)[source]ο
Parameters
kwargs (Any) β
Return type
langchain.vectorstores.vectara.VectaraRetriever
class langchain.vectorstores.VectorStore[source]ο
Bases: abc.ABC
Interface for vector stores.
abstract add_texts(texts, metadatas=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) β Optional list of metadatas associated with the texts.
kwargs (Any) β vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
delete(ids)[source]ο
Delete by vector ID.
Parameters
ids (List[str]) β List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async aadd_texts(texts, metadatas=None, **kwargs)[source]ο
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-129 | texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
List[str]
add_documents(documents, **kwargs)[source]ο
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) β Documents to add to the vectorstore.
documents (List[langchain.schema.Document]) β
kwargs (Any) β
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_documents(documents, **kwargs)[source]ο
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) β Documents to add to the vectorstore.
documents (List[langchain.schema.Document]) β
kwargs (Any) β
Returns
List of IDs of the added texts.
Return type
List[str]
search(query, search_type, **kwargs)[source]ο
Return docs most similar to query using specified search type.
Parameters
query (str) β
search_type (str) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
async asearch(query, search_type, **kwargs)[source]ο
Return docs most similar to query using specified search type.
Parameters
query (str) β
search_type (str) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
abstract similarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-130 | kwargs (Any) β
Return type
List[langchain.schema.Document]
similarity_search_with_relevance_scores(query, k=4, **kwargs)[source]ο
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) β input text
k (int) β Number of Documents to return. Defaults to 4.
**kwargs β 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
kwargs (Any) β
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[langchain.schema.Document, float]]
async asimilarity_search_with_relevance_scores(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[Tuple[langchain.schema.Document, float]]
async asimilarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
similarity_search_by_vector(embedding, k=4, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
kwargs (Any) β
Returns
List of Documents most similar to the query vector.
Return type
List[langchain.schema.Document] | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-131 | Return type
List[langchain.schema.Document]
async asimilarity_search_by_vector(embedding, k=4, **kwargs)[source]ο
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
async amax_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Parameters
query (str) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-132 | 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]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
async amax_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance.
Parameters
embedding (List[float]) β
k (int) β
fetch_k (int) β
lambda_mult (float) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
classmethod from_documents(documents, embedding, **kwargs)[source]ο
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[langchain.schema.Document]) β
embedding (langchain.embeddings.base.Embeddings) β
kwargs (Any) β
Return type
langchain.vectorstores.base.VST
async classmethod afrom_documents(documents, embedding, **kwargs)[source]ο
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[langchain.schema.Document]) β
embedding (langchain.embeddings.base.Embeddings) β
kwargs (Any) β
Return type | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-133 | kwargs (Any) β
Return type
langchain.vectorstores.base.VST
abstract classmethod from_texts(texts, embedding, metadatas=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
langchain.vectorstores.base.VST
async classmethod afrom_texts(texts, embedding, metadatas=None, **kwargs)[source]ο
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
langchain.vectorstores.base.VST
as_retriever(**kwargs)[source]ο
Parameters
kwargs (Any) β
Return type
langchain.vectorstores.base.VectorStoreRetriever
class langchain.vectorstores.Weaviate(client, index_name, text_key, embedding=None, attributes=None, relevance_score_fn=<function _default_score_normalizer>, by_text=True)[source]ο
Bases: langchain.vectorstores.base.VectorStore
Wrapper around Weaviate vector database.
To use, you should have the weaviate-client python package installed.
Example
import weaviate
from langchain.vectorstores import Weaviate
client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
weaviate = Weaviate(client, index_name, text_key)
Parameters
client (Any) β
index_name (str) β
text_key (str) β
embedding (Optional[Embeddings]) β
attributes (Optional[List[str]]) β | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-134 | embedding (Optional[Embeddings]) β
attributes (Optional[List[str]]) β
relevance_score_fn (Optional[Callable[[float], float]]) β
by_text (bool) β
add_texts(texts, metadatas=None, **kwargs)[source]ο
Upload texts with metadata (properties) to Weaviate.
Parameters
texts (Iterable[str]) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
List[str]
similarity_search(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
kwargs (Any) β
Returns
List of Documents most similar to the query.
Return type
List[langchain.schema.Document]
similarity_search_by_text(query, k=4, **kwargs)[source]ο
Return docs most similar to query.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
kwargs (Any) β
Returns
List of Documents most similar to the query.
Return type
List[langchain.schema.Document]
similarity_search_by_vector(embedding, k=4, **kwargs)[source]ο
Look up similar documents by embedding vector in Weaviate.
Parameters
embedding (List[float]) β
k (int) β
kwargs (Any) β
Return type
List[langchain.schema.Document]
max_marginal_relevance_search(query, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
Return docs selected using the maximal marginal relevance. | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-135 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) β Text to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
max_marginal_relevance_search_by_vector(embedding, k=4, fetch_k=20, lambda_mult=0.5, **kwargs)[source]ο
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]) β Embedding to look up documents similar to.
k (int) β Number of Documents to return. Defaults to 4.
fetch_k (int) β Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) β 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) β
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[langchain.schema.Document]
similarity_search_with_score(query, k=4, **kwargs)[source]ο
Return list of documents most similar to the query
text and cosine distance in float for each.
Lower score represents more similarity.
Parameters | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
81b746c66a31-136 | text and cosine distance in float for each.
Lower score represents more similarity.
Parameters
query (str) β
k (int) β
kwargs (Any) β
Return type
List[Tuple[langchain.schema.Document, float]]
classmethod from_texts(texts, embedding, metadatas=None, **kwargs)[source]ο
Construct Weaviate wrapper from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new index for the embeddings in the Weaviate instance.
Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Example
from langchain.vectorstores.weaviate import Weaviate
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
weaviate = Weaviate.from_texts(
texts,
embeddings,
weaviate_url="http://localhost:8080"
)
Parameters
texts (List[str]) β
embedding (langchain.embeddings.base.Embeddings) β
metadatas (Optional[List[dict]]) β
kwargs (Any) β
Return type
langchain.vectorstores.weaviate.Weaviate
delete(ids)[source]ο
Delete by vector IDs.
Parameters
ids (List[str]) β List of ids to delete.
Return type
None | https://api.python.langchain.com/en/latest/modules/vectorstores.html |
39d4921beaba-0 | Agent Toolkitsο
Agent toolkits. | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-1 | langchain.agents.agent_toolkits.create_json_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to interact with JSON.\nYour goal is to return a final answer by interacting with the JSON.\nYou have access to the following tools which help you learn more about the JSON you are interacting with.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nDo not make up any information that is not contained in the JSON.\nYour input to the tools should be in the form of `data["key"][0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. \nYou should only use keys that you know for a fact exist. You must validate that a key exists by seeing it previously when calling `json_spec_list_keys`. \nIf you have not seen a key in one of those responses, you cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlways begin your interaction with the `json_spec_list_keys` tool with input "data" to see what keys exist in the JSON.\n\nNote that sometimes the value at a given path is large. In this case, you will get an error "Value is a large dictionary, should explore its keys directly".\nIn this case, you should ALWAYS follow up by using the `json_spec_list_keys` tool to see what keys exist at that path.\nDo not simply refer the user to the JSON or a section of the JSON, as this is not a valid answer. Keep digging until you find the | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-2 | a section of the JSON, as this is not a valid answer. Keep digging until you find the answer and explicitly return it.\n', suffix='Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{agent_scratchpad}', format_instructions='Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables=None, verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-3 | Construct a json agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.json.toolkit.JsonToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str) β
input_variables (Optional[List[str]]) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-4 | langchain.agents.agent_toolkits.create_sql_agent(llm, toolkit, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager=None, prefix='You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix=None, format_instructions='Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables=None, top_k=10, max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-5 | max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-6 | Construct a sql agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit) β
agent_type (langchain.agents.agent_types.AgentType) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (Optional[str]) β
format_instructions (str) β
input_variables (Optional[List[str]]) β
top_k (int) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-7 | langchain.agents.agent_toolkits.create_openapi_agent(llm, toolkit, callback_manager=None, prefix="You are an agent designed to answer questions by making web requests to an API given the openapi spec.\n\nIf the question does not seem related to the API, return I don't know. Do not make up an answer.\nOnly use information provided by the tools to construct your response.\n\nFirst, find the base URL needed to make the request.\n\nSecond, find the relevant paths needed to answer the question. Take note that, sometimes, you might need to make more than one request to more than one path to answer the question.\n\nThird, find the required parameters needed to make the request. For GET requests, these are usually URL parameters and for POST requests, these are request body parameters.\n\nFourth, make the requests needed to answer the question. Ensure that you are sending the correct parameters to the request by checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using a path that actually exists in the spec.\n", suffix='Begin!\n\nQuestion: {input}\nThought: I should explore the spec to find the base url for the API.\n{agent_scratchpad}', format_instructions='Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-8 | Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables=None, max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, return_intermediate_steps=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-9 | Construct a json agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str) β
input_variables (Optional[List[str]]) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
verbose (bool) β
return_intermediate_steps (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-10 | langchain.agents.agent_toolkits.create_pbi_agent(llm, toolkit, powerbi=None, callback_manager=None, prefix='You are an agent designed to help users interact with a PowerBI Dataset.\n\nAgent has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.\n\nGiven an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in a easily readable format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix='Begin!\n\nQuestion: {input}\nThought: I can first ask which tables I have, then how each table is defined and then ask the query tool the question I need, and finally create a nice sentence that answers the question.\n{agent_scratchpad}', format_instructions='Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', examples=None, | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-11 | know the final answer\nFinal Answer: the final answer to the original input question', examples=None, input_variables=None, top_k=10, verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-12 | Construct a pbi agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit]) β
powerbi (Optional[langchain.utilities.powerbi.PowerBIDataset]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str) β
examples (Optional[str]) β
input_variables (Optional[List[str]]) β
top_k (int) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-13 | Return type
langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_pbi_chat_agent(llm, toolkit, powerbi=None, callback_manager=None, output_parser=None, prefix='Assistant is a large language model built to help users interact with a PowerBI Dataset.\n\nAssistant has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.\n\nGiven an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in a easily readable format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix="TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}\n", examples=None, input_variables=None, memory=None, top_k=10, verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο
Construct a pbi agent from an Chat LLM and tools. | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-14 | Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
Parameters
llm (langchain.chat_models.base.BaseChatModel) β
toolkit (Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit]) β
powerbi (Optional[langchain.utilities.powerbi.PowerBIDataset]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
output_parser (Optional[langchain.agents.agent.AgentOutputParser]) β
prefix (str) β
suffix (str) β
examples (Optional[str]) β
input_variables (Optional[List[str]]) β
memory (Optional[langchain.memory.chat_memory.BaseChatMemory]) β
top_k (int) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_python_agent(llm, tool, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager=None, verbose=False, prefix='You are an agent designed to write and execute python code to answer questions.\nYou have access to a python REPL, which you can use to execute python code.\nIf you get an error, debug your code and try again.\nOnly use the output of your code to answer the question. \nYou might know the answer without running any code, but you should still run the code to get the answer.\nIf it does not seem like you can write code to answer the question, just return "I don\'t know" as the answer.\n', agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-15 | Construct a python agent from an LLM and tool.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
tool (langchain.tools.python.tool.PythonREPLTool) β
agent_type (langchain.agents.agent_types.AgentType) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
verbose (bool) β
prefix (str) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_vectorstore_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.\nIf the question does not seem relevant to any of the tools provided, just return "I don\'t know" as the answer.\n', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο
Construct a vectorstore agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
class langchain.agents.agent_toolkits.JsonToolkit(*, spec)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-16 | class langchain.agents.agent_toolkits.JsonToolkit(*, spec)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with a JSON spec.
Parameters
spec (langchain.tools.json.tool.JsonSpec) β
Return type
None
attribute spec: langchain.tools.json.tool.JsonSpec [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.SQLDatabaseToolkit(*, db, llm)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with SQL databases.
Parameters
db (langchain.sql_database.SQLDatabase) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute db: langchain.sql_database.SQLDatabase [Required]ο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
property dialect: strο
Return string representation of dialect to use.
class langchain.agents.agent_toolkits.SparkSQLToolkit(*, db, llm)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with Spark SQL.
Parameters
db (langchain.utilities.spark_sql.SparkSQL) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute db: langchain.utilities.spark_sql.SparkSQL [Required]ο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-17 | get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.NLAToolkit(*, nla_tools)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Natural Language API Toolkit Definition.
Parameters
nla_tools (Sequence[langchain.agents.agent_toolkits.nla.tool.NLATool]) β
Return type
None
attribute nla_tools: Sequence[langchain.agents.agent_toolkits.nla.tool.NLATool] [Required]ο
List of API Endpoint Tools.
classmethod from_llm_and_ai_plugin(llm, ai_plugin, requests=None, verbose=False, **kwargs)[source]ο
Instantiate the toolkit from an OpenAPI Spec URL
Parameters
llm (langchain.base_language.BaseLanguageModel) β
ai_plugin (langchain.tools.plugin.AIPlugin) β
requests (Optional[langchain.requests.Requests]) β
verbose (bool) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit
classmethod from_llm_and_ai_plugin_url(llm, ai_plugin_url, requests=None, verbose=False, **kwargs)[source]ο
Instantiate the toolkit from an OpenAPI Spec URL
Parameters
llm (langchain.base_language.BaseLanguageModel) β
ai_plugin_url (str) β
requests (Optional[langchain.requests.Requests]) β
verbose (bool) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit
classmethod from_llm_and_spec(llm, spec, requests=None, verbose=False, **kwargs)[source]ο
Instantiate the toolkit by creating tools for each operation.
Parameters | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-18 | Instantiate the toolkit by creating tools for each operation.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
spec (langchain.utilities.openapi.OpenAPISpec) β
requests (Optional[langchain.requests.Requests]) β
verbose (bool) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit
classmethod from_llm_and_url(llm, open_api_url, requests=None, verbose=False, **kwargs)[source]ο
Instantiate the toolkit from an OpenAPI Spec URL
Parameters
llm (langchain.base_language.BaseLanguageModel) β
open_api_url (str) β
requests (Optional[langchain.requests.Requests]) β
verbose (bool) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit
get_tools()[source]ο
Get the tools for all the API operations.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.PowerBIToolkit(*, powerbi, llm, examples=None, max_iterations=5, callback_manager=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with PowerBI dataset.
Parameters
powerbi (langchain.utilities.powerbi.PowerBIDataset) β
llm (langchain.base_language.BaseLanguageModel) β
examples (Optional[str]) β
max_iterations (int) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
Return type
None
attribute callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = Noneο
attribute examples: Optional[str] = Noneο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-19 | attribute examples: Optional[str] = Noneο
attribute llm: langchain.base_language.BaseLanguageModel [Required]ο
attribute max_iterations: int = 5ο
attribute powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.OpenAPIToolkit(*, json_agent, requests_wrapper)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with a OpenAPI api.
Parameters
json_agent (langchain.agents.agent.AgentExecutor) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
Return type
None
attribute json_agent: langchain.agents.agent.AgentExecutor [Required]ο
attribute requests_wrapper: langchain.requests.TextRequestsWrapper [Required]ο
classmethod from_llm(llm, json_spec, requests_wrapper, **kwargs)[source]ο
Create json agent from llm, then initialize.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
json_spec (langchain.tools.json.tool.JsonSpec) β
requests_wrapper (langchain.requests.TextRequestsWrapper) β
kwargs (Any) β
Return type
langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.VectorStoreToolkit(*, vectorstore_info, llm=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with a vector store.
Parameters | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-20 | Toolkit for interacting with a vector store.
Parameters
vectorstore_info (langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute llm: langchain.base_language.BaseLanguageModel [Optional]ο
attribute vectorstore_info: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
langchain.agents.agent_toolkits.create_vectorstore_router_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to answer questions.\nYou have access to tools for interacting with different sources, and the inputs to the tools are questions.\nYour main task is to decide which of the tools is relevant for answering question at hand.\nFor complex questions, you can break the question down into sub questions and use tools to answers the sub questions.\n', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο
Construct a vectorstore router agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
class langchain.agents.agent_toolkits.VectorStoreInfo(*, vectorstore, name, description)[source]ο
Bases: pydantic.main.BaseModel
Information about a vectorstore.
Parameters | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-21 | Bases: pydantic.main.BaseModel
Information about a vectorstore.
Parameters
vectorstore (langchain.vectorstores.base.VectorStore) β
name (str) β
description (str) β
Return type
None
attribute description: str [Required]ο
attribute name: str [Required]ο
attribute vectorstore: langchain.vectorstores.base.VectorStore [Required]ο
class langchain.agents.agent_toolkits.VectorStoreRouterToolkit(*, vectorstores, llm=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for routing between vector stores.
Parameters
vectorstores (List[langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo]) β
llm (langchain.base_language.BaseLanguageModel) β
Return type
None
attribute llm: langchain.base_language.BaseLanguageModel [Optional]ο
attribute vectorstores: List[langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo] [Required]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
langchain.agents.agent_toolkits.create_pandas_dataframe_agent(llm, df, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager=None, prefix=None, suffix=None, input_variables=None, verbose=False, return_intermediate_steps=False, max_iterations=15, max_execution_time=None, early_stopping_method='force', agent_executor_kwargs=None, include_df_in_prompt=True, **kwargs)[source]ο
Construct a pandas agent from an LLM and dataframe.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
df (Any) β
agent_type (langchain.agents.agent_types.AgentType) β | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-22 | agent_type (langchain.agents.agent_types.AgentType) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (Optional[str]) β
suffix (Optional[str]) β
input_variables (Optional[List[str]]) β
verbose (bool) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
include_df_in_prompt (Optional[bool]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_spark_dataframe_agent(llm, df, callback_manager=None, prefix='\nYou are working with a spark dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:', suffix='\nThis is the result of `print(df.first())`:\n{df}\n\nBegin!\nQuestion: {input}\n{agent_scratchpad}', input_variables=None, verbose=False, return_intermediate_steps=False, max_iterations=15, max_execution_time=None, early_stopping_method='force', agent_executor_kwargs=None, **kwargs)[source]ο
Construct a spark agent from an LLM and dataframe.
Parameters
llm (langchain.llms.base.BaseLLM) β
df (Any) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
input_variables (Optional[List[str]]) β
verbose (bool) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-23 | max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-24 | langchain.agents.agent_toolkits.create_spark_sql_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to interact with Spark SQL.\nGiven an input question, create a syntactically correct Spark SQL query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix='Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions='Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables=None, top_k=10, | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-25 | Answer: the final answer to the original input question', input_variables=None, top_k=10, max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-26 | Construct a sql agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str) β
input_variables (Optional[List[str]]) β
top_k (int) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
langchain.agents.agent_toolkits.create_csv_agent(llm, path, pandas_kwargs=None, **kwargs)[source]ο
Create csv agent by loading to a dataframe and using pandas agent.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
path (Union[str, List[str]]) β
pandas_kwargs (Optional[dict]) β
kwargs (Any) β
Return type
langchain.agents.agent.AgentExecutor
class langchain.agents.agent_toolkits.ZapierToolkit(*, tools=[])[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Zapier Toolkit.
Parameters
tools (List[langchain.tools.base.BaseTool]) β
Return type
None
attribute tools: List[langchain.tools.base.BaseTool] = []ο
async classmethod async_from_zapier_nla_wrapper(zapier_nla_wrapper)[source]ο
Create a toolkit from a ZapierNLAWrapper.
Parameters | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-27 | Create a toolkit from a ZapierNLAWrapper.
Parameters
zapier_nla_wrapper (langchain.utilities.zapier.ZapierNLAWrapper) β
Return type
langchain.agents.agent_toolkits.zapier.toolkit.ZapierToolkit
classmethod from_zapier_nla_wrapper(zapier_nla_wrapper)[source]ο
Create a toolkit from a ZapierNLAWrapper.
Parameters
zapier_nla_wrapper (langchain.utilities.zapier.ZapierNLAWrapper) β
Return type
langchain.agents.agent_toolkits.zapier.toolkit.ZapierToolkit
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.GmailToolkit(*, api_resource=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with Gmail.
Parameters
api_resource (Resource) β
Return type
None
attribute api_resource: Resource [Optional]ο
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.JiraToolkit(*, tools=[])[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Jira Toolkit.
Parameters
tools (List[langchain.tools.base.BaseTool]) β
Return type
None
attribute tools: List[langchain.tools.base.BaseTool] = []ο
classmethod from_jira_api_wrapper(jira_api_wrapper)[source]ο
Parameters
jira_api_wrapper (langchain.utilities.jira.JiraAPIWrapper) β
Return type
langchain.agents.agent_toolkits.jira.toolkit.JiraToolkit
get_tools()[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-28 | get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.FileManagementToolkit(*, root_dir=None, selected_tools=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for interacting with a Local Files.
Parameters
root_dir (Optional[str]) β
selected_tools (Optional[List[str]]) β
Return type
None
attribute root_dir: Optional[str] = Noneο
If specified, all file operations are made relative to root_dir.
attribute selected_tools: Optional[List[str]] = Noneο
If provided, only provide the selected tools. Defaults to all.
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.PlayWrightBrowserToolkit(*, sync_browser=None, async_browser=None)[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for web browser tools.
Parameters
sync_browser (Optional['SyncBrowser']) β
async_browser (Optional['AsyncBrowser']) β
Return type
None
attribute async_browser: Optional['AsyncBrowser'] = Noneο
attribute sync_browser: Optional['SyncBrowser'] = Noneο
classmethod from_browser(sync_browser=None, async_browser=None)[source]ο
Instantiate the toolkit.
Parameters
sync_browser (Optional[SyncBrowser]) β
async_browser (Optional[AsyncBrowser]) β
Return type
PlayWrightBrowserToolkit
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool]
class langchain.agents.agent_toolkits.AzureCognitiveServicesToolkit[source]ο | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
39d4921beaba-29 | class langchain.agents.agent_toolkits.AzureCognitiveServicesToolkit[source]ο
Bases: langchain.agents.agent_toolkits.base.BaseToolkit
Toolkit for Azure Cognitive Services.
Return type
None
get_tools()[source]ο
Get the tools in the toolkit.
Return type
List[langchain.tools.base.BaseTool] | https://api.python.langchain.com/en/latest/modules/agent_toolkits.html |
57cc15045056-0 | Output Parsersο
class langchain.output_parsers.BooleanOutputParser(*, true_val='YES', false_val='NO')[source]ο
Bases: langchain.schema.BaseOutputParser[bool]
Parameters
true_val (str) β
false_val (str) β
Return type
None
attribute false_val: str = 'NO'ο
attribute true_val: str = 'YES'ο
parse(text)[source]ο
Parse the output of an LLM call to a boolean.
Parameters
text (str) β output of language model
Returns
boolean
Return type
bool
class langchain.output_parsers.CombiningOutputParser(*, parsers)[source]ο
Bases: langchain.schema.BaseOutputParser
Class to combine multiple output parsers into one.
Parameters
parsers (List[langchain.schema.BaseOutputParser]) β
Return type
None
attribute parsers: List[langchain.schema.BaseOutputParser] [Required]ο
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(text)[source]ο
Parse the output of an LLM call.
Parameters
text (str) β
Return type
Dict[str, Any]
class langchain.output_parsers.CommaSeparatedListOutputParser[source]ο
Bases: langchain.output_parsers.list.ListOutputParser
Parse out comma separated lists.
Return type
None
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(text)[source]ο
Parse the output of an LLM call.
Parameters
text (str) β
Return type
List[str] | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-1 | Parameters
text (str) β
Return type
List[str]
class langchain.output_parsers.DatetimeOutputParser(*, format='%Y-%m-%dT%H:%M:%S.%fZ')[source]ο
Bases: langchain.schema.BaseOutputParser[datetime.datetime]
Parameters
format (str) β
Return type
None
attribute format: str = '%Y-%m-%dT%H:%M:%S.%fZ'ο
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(response)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text β output of language model
response (str) β
Returns
structured output
Return type
datetime.datetime
class langchain.output_parsers.EnumOutputParser(*, enum)[source]ο
Bases: langchain.schema.BaseOutputParser
Parameters
enum (Type[enum.Enum]) β
Return type
None
attribute enum: Type[enum.Enum] [Required]ο
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(response)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text β output of language model
response (str) β
Returns
structured output
Return type
Any
class langchain.output_parsers.GuardrailsOutputParser(*, guard=None, api=None, args=None, kwargs=None)[source]ο
Bases: langchain.schema.BaseOutputParser
Parameters
guard (Any) β | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-2 | Bases: langchain.schema.BaseOutputParser
Parameters
guard (Any) β
api (Optional[Callable]) β
args (Any) β
kwargs (Any) β
Return type
None
attribute api: Optional[Callable] = Noneο
attribute args: Any = Noneο
attribute guard: Any = Noneο
attribute kwargs: Any = Noneο
classmethod from_pydantic(output_class, num_reasks=1, api=None, *args, **kwargs)[source]ο
Parameters
output_class (Any) β
num_reasks (int) β
api (Optional[Callable]) β
args (Any) β
kwargs (Any) β
Return type
langchain.output_parsers.rail_parser.GuardrailsOutputParser
classmethod from_rail(rail_file, num_reasks=1, api=None, *args, **kwargs)[source]ο
Parameters
rail_file (str) β
num_reasks (int) β
api (Optional[Callable]) β
args (Any) β
kwargs (Any) β
Return type
langchain.output_parsers.rail_parser.GuardrailsOutputParser
classmethod from_rail_string(rail_str, num_reasks=1, api=None, *args, **kwargs)[source]ο
Parameters
rail_str (str) β
num_reasks (int) β
api (Optional[Callable]) β
args (Any) β
kwargs (Any) β
Return type
langchain.output_parsers.rail_parser.GuardrailsOutputParser
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(text)[source]ο
Parse the output of an LLM call. | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-3 | str
parse(text)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text (str) β output of language model
Returns
structured output
Return type
Dict
class langchain.output_parsers.ListOutputParser[source]ο
Bases: langchain.schema.BaseOutputParser
Class to parse the output of an LLM call to a list.
Return type
None
abstract parse(text)[source]ο
Parse the output of an LLM call.
Parameters
text (str) β
Return type
List[str]
class langchain.output_parsers.OutputFixingParser(*, parser, retry_chain)[source]ο
Bases: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T]
Wraps a parser and tries to fix parsing errors.
Parameters
parser (langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T]) β
retry_chain (langchain.chains.llm.LLMChain) β
Return type
None
attribute parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T] [Required]ο
attribute retry_chain: langchain.chains.llm.LLMChain [Required]ο | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-4 | attribute retry_chain: langchain.chains.llm.LLMChain [Required]ο
classmethod from_llm(llm, parser, prompt=PromptTemplate(input_variables=['completion', 'error', 'instructions'], output_parser=None, partial_variables={}, template='Instructions:\n--------------\n{instructions}\n--------------\nCompletion:\n--------------\n{completion}\n--------------\n\nAbove, the Completion did not satisfy the constraints given in the Instructions.\nError:\n--------------\n{error}\n--------------\n\nPlease try again. Please only respond with an answer that satisfies the constraints laid out in the Instructions:', template_format='f-string', validate_template=True))[source]ο
Parameters
llm (langchain.base_language.BaseLanguageModel) β
parser (langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T]) β
prompt (langchain.prompts.base.BasePromptTemplate) β
Return type
langchain.output_parsers.fix.OutputFixingParser[langchain.output_parsers.fix.T]
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(completion)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text β output of language model
completion (str) β
Returns
structured output
Return type
langchain.output_parsers.fix.T
class langchain.output_parsers.PydanticOutputParser(*, pydantic_object)[source]ο
Bases: langchain.schema.BaseOutputParser[langchain.output_parsers.pydantic.T]
Parameters
pydantic_object (Type[langchain.output_parsers.pydantic.T]) β
Return type
None | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-5 | Return type
None
attribute pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]ο
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(text)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text (str) β output of language model
Returns
structured output
Return type
langchain.output_parsers.pydantic.T
class langchain.output_parsers.RegexDictParser(*, regex_pattern="{}:\\s?([^.'\\n']*)\\.?", output_key_to_format, no_update_value=None)[source]ο
Bases: langchain.schema.BaseOutputParser
Class to parse the output into a dictionary.
Parameters
regex_pattern (str) β
output_key_to_format (Dict[str, str]) β
no_update_value (Optional[str]) β
Return type
None
attribute no_update_value: Optional[str] = Noneο
attribute output_key_to_format: Dict[str, str] [Required]ο
attribute regex_pattern: str = "{}:\\s?([^.'\\n']*)\\.?"ο
parse(text)[source]ο
Parse the output of an LLM call.
Parameters
text (str) β
Return type
Dict[str, str]
class langchain.output_parsers.RegexParser(*, regex, output_keys, default_output_key=None)[source]ο
Bases: langchain.schema.BaseOutputParser
Class to parse the output into a dictionary.
Parameters
regex (str) β
output_keys (List[str]) β
default_output_key (Optional[str]) β
Return type
None | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-6 | default_output_key (Optional[str]) β
Return type
None
attribute default_output_key: Optional[str] = Noneο
attribute output_keys: List[str] [Required]ο
attribute regex: str [Required]ο
parse(text)[source]ο
Parse the output of an LLM call.
Parameters
text (str) β
Return type
Dict[str, str]
class langchain.output_parsers.ResponseSchema(*, name, description, type='string')[source]ο
Bases: pydantic.main.BaseModel
Parameters
name (str) β
description (str) β
type (str) β
Return type
None
attribute description: str [Required]ο
attribute name: str [Required]ο
attribute type: str = 'string'ο
class langchain.output_parsers.RetryOutputParser(*, parser, retry_chain)[source]ο
Bases: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
Parameters
parser (langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T]) β
retry_chain (langchain.chains.llm.LLMChain) β
Return type
None
attribute parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]ο
attribute retry_chain: langchain.chains.llm.LLMChain [Required]ο | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-7 | attribute retry_chain: langchain.chains.llm.LLMChain [Required]ο
classmethod from_llm(llm, parser, prompt=PromptTemplate(input_variables=['completion', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nPlease try again:', template_format='f-string', validate_template=True))[source]ο
Parameters
llm (langchain.base_language.BaseLanguageModel) β
parser (langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T]) β
prompt (langchain.prompts.base.BasePromptTemplate) β
Return type
langchain.output_parsers.retry.RetryOutputParser[langchain.output_parsers.retry.T]
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(completion)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text β output of language model
completion (str) β
Returns
structured output
Return type
langchain.output_parsers.retry.T
parse_with_prompt(completion, prompt_value)[source]ο
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion (str) β output of language model
prompt β prompt value
prompt_value (langchain.schema.PromptValue) β
Returns
structured output
Return type
langchain.output_parsers.retry.T | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-8 | Returns
structured output
Return type
langchain.output_parsers.retry.T
class langchain.output_parsers.RetryWithErrorOutputParser(*, parser, retry_chain)[source]ο
Bases: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T]
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another language model and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
Parameters
parser (langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T]) β
retry_chain (langchain.chains.llm.LLMChain) β
Return type
None
attribute parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T] [Required]ο
attribute retry_chain: langchain.chains.llm.LLMChain [Required]ο
classmethod from_llm(llm, parser, prompt=PromptTemplate(input_variables=['completion', 'error', 'prompt'], output_parser=None, partial_variables={}, template='Prompt:\n{prompt}\nCompletion:\n{completion}\n\nAbove, the Completion did not satisfy the constraints given in the Prompt.\nDetails: {error}\nPlease try again:', template_format='f-string', validate_template=True))[source]ο
Parameters
llm (langchain.base_language.BaseLanguageModel) β
parser (langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T]) β
prompt (langchain.prompts.base.BasePromptTemplate) β
Return type
langchain.output_parsers.retry.RetryWithErrorOutputParser[langchain.output_parsers.retry.T] | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-9 | get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(completion)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text β output of language model
completion (str) β
Returns
structured output
Return type
langchain.output_parsers.retry.T
parse_with_prompt(completion, prompt_value)[source]ο
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion (str) β output of language model
prompt β prompt value
prompt_value (langchain.schema.PromptValue) β
Returns
structured output
Return type
langchain.output_parsers.retry.T
class langchain.output_parsers.StructuredOutputParser(*, response_schemas)[source]ο
Bases: langchain.schema.BaseOutputParser
Parameters
response_schemas (List[langchain.output_parsers.structured.ResponseSchema]) β
Return type
None
attribute response_schemas: List[langchain.output_parsers.structured.ResponseSchema] [Required]ο
classmethod from_response_schemas(response_schemas)[source]ο
Parameters
response_schemas (List[langchain.output_parsers.structured.ResponseSchema]) β
Return type
langchain.output_parsers.structured.StructuredOutputParser
get_format_instructions()[source]ο
Instructions on how the LLM output should be formatted.
Return type
str
parse(text)[source]ο
Parse the output of an LLM call. | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
57cc15045056-10 | str
parse(text)[source]ο
Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Parameters
text (str) β output of language model
Returns
structured output
Return type
Any | https://api.python.langchain.com/en/latest/modules/output_parsers.html |
46d47edcfcf7-0 | Embeddingsο
Wrappers around embedding modules.
class langchain.embeddings.OpenAIEmbeddings(*, client=None, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version=None, openai_api_base=None, openai_api_type=None, openai_proxy=None, embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special={}, disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None)[source]ο
Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around OpenAI embedding models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to βazureβ and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080" | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-1 | from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
Parameters
client (Any) β
model (str) β
deployment (str) β
openai_api_version (Optional[str]) β
openai_api_base (Optional[str]) β
openai_api_type (Optional[str]) β
openai_proxy (Optional[str]) β
embedding_ctx_length (int) β
openai_api_key (Optional[str]) β
openai_organization (Optional[str]) β
allowed_special (Union[Literal['all'], typing.Set[str]]) β
disallowed_special (Union[Literal['all'], typing.Set[str], typing.Sequence[str]]) β
chunk_size (int) β
max_retries (int) β
request_timeout (Optional[Union[float, Tuple[float, float]]]) β
headers (Any) β
tiktoken_model_name (Optional[str]) β
Return type
None
attribute chunk_size: int = 1000ο
Maximum number of texts to embed in each batch
attribute max_retries: int = 6ο
Maximum number of retries to make when generating.
attribute request_timeout: Optional[Union[float, Tuple[float, float]]] = Noneο
Timeout in seconds for the OpenAPI request.
attribute tiktoken_model_name: Optional[str] = Noneο
The model name to pass to tiktoken when using this class. | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-2 | The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
async aembed_documents(texts, chunk_size=0)[source]ο
Call out to OpenAIβs embedding endpoint async for embedding search docs.
Parameters
texts (List[str]) β The list of texts to embed.
chunk_size (Optional[int]) β The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
async aembed_query(text)[source]ο
Call out to OpenAIβs embedding endpoint async for embedding query text.
Parameters
text (str) β The text to embed.
Returns
Embedding for the text.
Return type
List[float]
embed_documents(texts, chunk_size=0)[source]ο
Call out to OpenAIβs embedding endpoint for embedding search docs.
Parameters
texts (List[str]) β The list of texts to embed.
chunk_size (Optional[int]) β The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text)[source]ο | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-3 | Return type
List[List[float]]
embed_query(text)[source]ο
Call out to OpenAIβs embedding endpoint for embedding query text.
Parameters
text (str) β The text to embed.
Returns
Embedding for the text.
Return type
List[float]
class langchain.embeddings.HuggingFaceEmbeddings(*, client=None, model_name='sentence-transformers/all-mpnet-base-v2', cache_folder=None, model_kwargs=None, encode_kwargs=None)[source]ο
Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
Example
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
Parameters
client (Any) β
model_name (str) β
cache_folder (Optional[str]) β
model_kwargs (Dict[str, Any]) β
encode_kwargs (Dict[str, Any]) β
Return type
None
attribute cache_folder: Optional[str] = Noneο
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.
attribute encode_kwargs: Dict[str, Any] [Optional]ο
Key word arguments to pass when calling the encode method of the model.
attribute model_kwargs: Dict[str, Any] [Optional]ο
Key word arguments to pass to the model.
attribute model_name: str = 'sentence-transformers/all-mpnet-base-v2'ο
Model name to use. | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-4 | Model name to use.
embed_documents(texts)[source]ο
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts (List[str]) β The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text)[source]ο
Compute query embeddings using a HuggingFace transformer model.
Parameters
text (str) β The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
class langchain.embeddings.CohereEmbeddings(*, client=None, model='embed-english-v2.0', truncate=None, cohere_api_key=None)[source]ο
Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around Cohere embedding models.
To use, you should have the cohere python package installed, and the
environment variable COHERE_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import CohereEmbeddings
cohere = CohereEmbeddings(
model="embed-english-light-v2.0", cohere_api_key="my-api-key"
)
Parameters
client (Any) β
model (str) β
truncate (Optional[str]) β
cohere_api_key (Optional[str]) β
Return type
None
attribute model: str = 'embed-english-v2.0'ο
Model name to use.
attribute truncate: Optional[str] = Noneο
Truncate embeddings that are too long from start or end (βNONEβ|βSTARTβ|βENDβ)
embed_documents(texts)[source]ο
Call out to Cohereβs embedding endpoint.
Parameters
texts (List[str]) β The list of texts to embed.
Returns | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-5 | Parameters
texts (List[str]) β The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text)[source]ο
Call out to Cohereβs embedding endpoint.
Parameters
text (str) β The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
class langchain.embeddings.ElasticsearchEmbeddings(client, model_id, *, input_field='text_field')[source]ο
Bases: langchain.embeddings.base.Embeddings
Wrapper around Elasticsearch embedding models.
This class provides an interface to generate embeddings using a model deployed
in an Elasticsearch cluster. It requires an Elasticsearch connection object
and the model_id of the model deployed in the cluster.
In Elasticsearch you need to have an embedding model loaded and deployed.
- https://www.elastic.co/guide/en/elasticsearch/reference/current/infer-trained-model.html
- https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html
Parameters
client (MlClient) β
model_id (str) β
input_field (str) β
classmethod from_credentials(model_id, *, es_cloud_id=None, es_user=None, es_password=None, input_field='text_field')[source]ο
Instantiate embeddings from Elasticsearch credentials.
Parameters
model_id (str) β The model_id of the model deployed in the Elasticsearch
cluster.
input_field (str) β The name of the key for the input text field in the
document. Defaults to βtext_fieldβ.
es_cloud_id (Optional[str]) β (str, optional): The Elasticsearch cloud ID to connect to.
es_user (Optional[str]) β (str, optional): Elasticsearch username.
es_password (Optional[str]) β (str, optional): Elasticsearch password.
Return type | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-6 | es_password (Optional[str]) β (str, optional): Elasticsearch password.
Return type
langchain.embeddings.elasticsearch.ElasticsearchEmbeddings
Example
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Credentials can be passed in two ways. Either set the env vars
# ES_CLOUD_ID, ES_USER, ES_PASSWORD and they will be automatically
# pulled in, or pass them in directly as kwargs.
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
input_field=input_field,
# es_cloud_id="foo",
# es_user="bar",
# es_password="baz",
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
classmethod from_es_connection(model_id, es_connection, input_field='text_field')[source]ο
Instantiate embeddings from an existing Elasticsearch connection.
This method provides a way to create an instance of the ElasticsearchEmbeddings
class using an existing Elasticsearch connection. The connection object is used
to create an MlClient, which is then used to initialize the
ElasticsearchEmbeddings instance.
Args:
model_id (str): The model_id of the model deployed in the Elasticsearch cluster.
es_connection (elasticsearch.Elasticsearch): An existing Elasticsearch
connection object. input_field (str, optional): The name of the key for the
input text field in the document. Defaults to βtext_fieldβ.
Returns:
ElasticsearchEmbeddings: An instance of the ElasticsearchEmbeddings class.
Example
from elasticsearch import Elasticsearch | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-7 | Example
from elasticsearch import Elasticsearch
from langchain.embeddings import ElasticsearchEmbeddings
# Define the model ID and input field name (if different from default)
model_id = "your_model_id"
# Optional, only if different from 'text_field'
input_field = "your_input_field"
# Create Elasticsearch connection
es_connection = Elasticsearch(
hosts=["localhost:9200"], http_auth=("user", "password")
)
# Instantiate ElasticsearchEmbeddings using the existing connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
model_id,
es_connection,
input_field=input_field,
)
documents = [
"This is an example document.",
"Another example document to generate embeddings for.",
]
embeddings_generator.embed_documents(documents)
Parameters
model_id (str) β
es_connection (Elasticsearch) β
input_field (str) β
Return type
ElasticsearchEmbeddings
embed_documents(texts)[source]ο
Generate embeddings for a list of documents.
Parameters
texts (List[str]) β A list of document text strings to generate embeddings
for.
Returns
A list of embeddings, one for each document in the inputlist.
Return type
List[List[float]]
embed_query(text)[source]ο
Generate an embedding for a single query text.
Parameters
text (str) β The query text to generate an embedding for.
Returns
The embedding for the input query text.
Return type
List[float]
class langchain.embeddings.LlamaCppEmbeddings(*, client=None, model_path, n_ctx=512, n_parts=- 1, seed=- 1, f16_kv=False, logits_all=False, vocab_only=False, use_mlock=False, n_threads=None, n_batch=8, n_gpu_layers=None)[source]ο | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-8 | Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around llama.cpp embedding models.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
Parameters
client (Any) β
model_path (str) β
n_ctx (int) β
n_parts (int) β
seed (int) β
f16_kv (bool) β
logits_all (bool) β
vocab_only (bool) β
use_mlock (bool) β
n_threads (Optional[int]) β
n_batch (Optional[int]) β
n_gpu_layers (Optional[int]) β
Return type
None
attribute f16_kv: bool = Falseο
Use half-precision for key/value cache.
attribute logits_all: bool = Falseο
Return logits for all tokens, not just the last token.
attribute n_batch: Optional[int] = 8ο
Number of tokens to process in parallel.
Should be a number between 1 and n_ctx.
attribute n_ctx: int = 512ο
Token context window.
attribute n_gpu_layers: Optional[int] = Noneο
Number of layers to be loaded into gpu memory. Default None.
attribute n_parts: int = -1ο
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
attribute n_threads: Optional[int] = Noneο
Number of threads to use. If None, the number | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-9 | Number of threads to use. If None, the number
of threads is automatically determined.
attribute seed: int = -1ο
Seed. If -1, a random seed is used.
attribute use_mlock: bool = Falseο
Force system to keep model in RAM.
attribute vocab_only: bool = Falseο
Only load the vocabulary, no weights.
embed_documents(texts)[source]ο
Embed a list of documents using the Llama model.
Parameters
texts (List[str]) β The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text)[source]ο
Embed a query using the Llama model.
Parameters
text (str) β The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
class langchain.embeddings.HuggingFaceHubEmbeddings(*, client=None, repo_id='sentence-transformers/all-mpnet-base-v2', task='feature-extraction', model_kwargs=None, huggingfacehub_api_token=None)[source]ο
Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.embeddings import HuggingFaceHubEmbeddings
repo_id = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
repo_id=repo_id,
task="feature-extraction",
huggingfacehub_api_token="my-api-key",
)
Parameters
client (Any) β | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-10 | )
Parameters
client (Any) β
repo_id (str) β
task (Optional[str]) β
model_kwargs (Optional[dict]) β
huggingfacehub_api_token (Optional[str]) β
Return type
None
attribute model_kwargs: Optional[dict] = Noneο
Key word arguments to pass to the model.
attribute repo_id: str = 'sentence-transformers/all-mpnet-base-v2'ο
Model name to use.
attribute task: Optional[str] = 'feature-extraction'ο
Task to call the model with.
embed_documents(texts)[source]ο
Call out to HuggingFaceHubβs embedding endpoint for embedding search docs.
Parameters
texts (List[str]) β The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text)[source]ο
Call out to HuggingFaceHubβs embedding endpoint for embedding query text.
Parameters
text (str) β The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
class langchain.embeddings.ModelScopeEmbeddings(*, embed=None, model_id='damo/nlp_corom_sentence-embedding_english-base')[source]ο
Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around modelscope_hub embedding models.
To use, you should have the modelscope python package installed.
Example
from langchain.embeddings import ModelScopeEmbeddings
model_id = "damo/nlp_corom_sentence-embedding_english-base"
embed = ModelScopeEmbeddings(model_id=model_id)
Parameters
embed (Any) β
model_id (str) β
Return type
None | https://api.python.langchain.com/en/latest/modules/embeddings.html |
46d47edcfcf7-11 | embed (Any) β
model_id (str) β
Return type
None
attribute model_id: str = 'damo/nlp_corom_sentence-embedding_english-base'ο
Model name to use.
embed_documents(texts)[source]ο
Compute doc embeddings using a modelscope embedding model.
Parameters
texts (List[str]) β The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]]
embed_query(text)[source]ο
Compute query embeddings using a modelscope embedding model.
Parameters
text (str) β The text to embed.
Returns
Embeddings for the text.
Return type
List[float]
class langchain.embeddings.TensorflowHubEmbeddings(*, embed=None, model_url='https://tfhub.dev/google/universal-sentence-encoder-multilingual/3')[source]ο
Bases: pydantic.main.BaseModel, langchain.embeddings.base.Embeddings
Wrapper around tensorflow_hub embedding models.
To use, you should have the tensorflow_text python package installed.
Example
from langchain.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
Parameters
embed (Any) β
model_url (str) β
Return type
None
attribute model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'ο
Model name to use.
embed_documents(texts)[source]ο
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts (List[str]) β The list of texts to embed.
Returns
List of embeddings, one for each text.
Return type
List[List[float]] | https://api.python.langchain.com/en/latest/modules/embeddings.html |