Tony20100/langchain_full huggingface dataset:
utilities.anthropic.get_token_ids_anthropic(text)
Get the token ids for a string of text.
utilities.cassandra.aexecute_cql(session, ...)
utilities.cassandra.wrapped_response_future(...)
Wrap a Cassandra response future in an asyncio future.
utilities.clickup.extract_dict_elements_from_component_fields(...)
Extract elements from a dictionary.
utilities.clickup.fetch_data(url, access_token)
Fetch data from a URL.
utilities.clickup.fetch_first_id(data, key)
Fetch the first id from a dictionary.
utilities.clickup.fetch_folder_id(space_id, ...)
Fetch the folder id.
utilities.clickup.fetch_list_id(space_id, ...)
Fetch the list id.
utilities.clickup.fetch_space_id(team_id, ...)
Fetch the space id.
utilities.clickup.fetch_team_id(access_token)
Fetch the team id.
utilities.clickup.load_query(query[, ...])
Parse a JSON string and return the parsed object.
utilities.clickup.parse_dict_through_component(...)
Parse a dictionary by creating a component and then turning it back into a dictionary.
utilities.opaqueprompts.desanitize(...)
Restore the original sensitive data from the sanitized text.
utilities.opaqueprompts.sanitize(input)
Sanitize input string or dict of strings by replacing sensitive data with placeholders.
utilities.passio_nutrition_ai.is_http_retryable(rsp)
Check if a HTTP response is retryable.
utilities.pebblo.get_full_path(path)
Return an absolute local path for a local file/directory, for a network related path, return as is.
utilities.pebblo.get_ip()
Fetch local runtime ip address.
utilities.pebblo.get_loader_full_path(loader)
Return an absolute source path of source of loader based on the keys present in Document.
utilities.pebblo.get_loader_type(loader)
utilities.pebblo.get_loader_type(loader)
Return loader type among, file, dir or in-memory.
utilities.pebblo.get_runtime()
Fetch the current Framework and Runtime details.
utilities.powerbi.fix_table_name(table)
Add single quotes around table names that contain spaces.
utilities.powerbi.json_to_md(json_contents)
Convert a JSON object to a markdown table.
utilities.redis.check_redis_module_exist(...)
Check if the correct Redis modules are installed.
utilities.redis.get_client(redis_url, **kwargs)
Get a redis client from the connection url given.
utilities.sql_database.truncate_word(...[, ...])
Truncate a string to a certain number of words, based on the max string length.
utilities.vertexai.create_retry_decorator(llm, *)
Create a retry decorator for Vertex / Palm LLMs.
utilities.vertexai.get_client_info([module])
Return a custom user agent header.
utilities.vertexai.init_vertexai([project, ...])
Init Vertex AI.
utilities.vertexai.load_image_from_gcs(path)
Load an image from Google Cloud Storage.
utilities.vertexai.raise_vertex_import_error([...])
Raise ImportError related to Vertex SDK being not available.
langchain_community.utils¶
Utility functions for LangChain.
Classes¶
utils.ernie_functions.FunctionDescription
Representation of a callable function to the Ernie API.
utils.ernie_functions.ToolDescription
Representation of a callable function to the Ernie API.
Functions¶
utils.ernie_functions.convert_pydantic_to_ernie_function(...)
Convert a Pydantic model to a function description for the Ernie API.
utils.ernie_functions.convert_pydantic_to_ernie_tool(...)
Convert a Pydantic model to a function description for the Ernie API.
utils.google.get_client_info([module])
Return a custom user agent header.
utils.google.get_client_info([module])
Return a custom user agent header.
utils.math.cosine_similarity(X, Y)
Row-wise cosine similarity between two equal-width matrices.
utils.math.cosine_similarity_top_k(X, Y[, ...])
Row-wise cosine similarity with optional top-k and score threshold filtering.
utils.openai.is_openai_v1()
Return whether OpenAI API is v1 or more.
utils.user_agent.get_user_agent()
Get user agent from environment variable.
langchain_community.vectorstores¶
Vector store stores embedded data and performs vector search.
One of the most common ways to store and search over unstructured data is to
embed it and store the resulting embedding vectors, and then query the store
and retrieve the data that are ‘most similar’ to the embedded query.
Class hierarchy:
VectorStore --> <name>  # Examples: Annoy, FAISS, Milvus
BaseRetriever --> VectorStoreRetriever --> <name>Retriever  # Example: VespaRetriever
Main helpers:
Embeddings, Document
Classes¶
vectorstores.aerospike.Aerospike(client, ...)
Aerospike vector store.
vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(...)
Alibaba Cloud OpenSearch vector store.
vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings(...)
Alibaba Cloud Opensearch` client configuration.
vectorstores.analyticdb.AnalyticDB(...[, ...])
AnalyticDB (distributed PostgreSQL) vector store.
vectorstores.annoy.Annoy(embedding_function, ...)
Annoy vector store.
vectorstores.apache_doris.ApacheDoris(...[, ...])
Apache Doris vector store.
vectorstores.apache_doris.ApacheDorisSettings
Apache Doris client configuration.
vectorstores.apache_doris.ApacheDorisSettings
Apache Doris client configuration.
vectorstores.astradb.AstraDB(*, embedding, ...)
[Deprecated]
vectorstores.atlas.AtlasDB(name[, ...])
Atlas vector store.
vectorstores.awadb.AwaDB([table_name, ...])
AwaDB vector store.
vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch(...)
Azure Cosmos DB for MongoDB vCore vector store.
vectorstores.azure_cosmos_db.CosmosDBSimilarityType(value)
Cosmos DB Similarity Type as enumerator.
vectorstores.azure_cosmos_db.CosmosDBVectorSearchType(value)
Cosmos DB Vector Search Type as enumerator.
vectorstores.azuresearch.AzureSearch(...[, ...])
Azure Cognitive Search vector store.
vectorstores.azuresearch.AzureSearchVectorStoreRetriever
Retriever that uses Azure Cognitive Search.
vectorstores.bagel.Bagel([cluster_name, ...])
Bagel.net Inference platform.
vectorstores.baiducloud_vector_search.BESVectorStore(...)
Baidu Elasticsearch vector store.
vectorstores.baiduvectordb.BaiduVectorDB(...)
Baidu VectorDB as a vector store.
vectorstores.baiduvectordb.ConnectionParams(...)
Baidu VectorDB Connection params.
vectorstores.baiduvectordb.TableParams(dimension)
Baidu VectorDB table params.
vectorstores.bigquery_vector_search.BigQueryVectorSearch(...)
[Deprecated] Google Cloud BigQuery vector store.
vectorstores.cassandra.Cassandra(embedding)
Apache Cassandra(R) for vector-store workloads.
vectorstores.chroma.Chroma([...])
ChromaDB vector store.
vectorstores.clarifai.Clarifai([user_id, ...])
Clarifai AI vector store.
vectorstores.clickhouse.Clickhouse(embedding)
Clarifai AI vector store.
vectorstores.clickhouse.Clickhouse(embedding)
ClickHouse VectorSearch vector store.
vectorstores.clickhouse.ClickhouseSettings
ClickHouse client configuration.
vectorstores.couchbase.CouchbaseVectorStore(...)
[Deprecated] Couchbase Vector Store vector store.
vectorstores.dashvector.DashVector(...)
DashVector vector store.
vectorstores.databricks_vector_search.DatabricksVectorSearch(...)
Databricks Vector Search vector store.
vectorstores.deeplake.DeepLake([...])
Activeloop Deep Lake vector store.
vectorstores.dingo.Dingo(embedding, text_key, *)
Dingo vector store.
vectorstores.docarray.base.DocArrayIndex(...)
Base class for DocArray based vector stores.
vectorstores.docarray.hnsw.DocArrayHnswSearch(...)
HnswLib storage using DocArray package.
vectorstores.docarray.in_memory.DocArrayInMemorySearch(...)
In-memory DocArray storage for exact search.
vectorstores.documentdb.DocumentDBSimilarityType(value)
DocumentDB Similarity Type as enumerator.
vectorstores.documentdb.DocumentDBVectorSearch(...)
Amazon DocumentDB (with MongoDB compatibility) vector store.
vectorstores.duckdb.DuckDB(*[, connection, ...])
DuckDB vector store.
vectorstores.ecloud_vector_search.EcloudESVectorStore(...)
ecloud Elasticsearch vector store.
vectorstores.elastic_vector_search.ElasticKnnSearch(...)
[Deprecated] [DEPRECATED] Elasticsearch with k-nearest neighbor search (k-NN) vector store.
vectorstores.elastic_vector_search.ElasticVectorSearch(...)
[Deprecated] ElasticVectorSearch uses the brute force method of searching on vectors.
vectorstores.elasticsearch.ApproxRetrievalStrategy([...])
[Deprecated] Approximate retrieval strategy using the HNSW algorithm.
[Deprecated] Approximate retrieval strategy using the HNSW algorithm.
vectorstores.elasticsearch.BaseRetrievalStrategy()
Base class for Elasticsearch retrieval strategies.
vectorstores.elasticsearch.ElasticsearchStore(...)
[Deprecated] Elasticsearch vector store.
vectorstores.elasticsearch.ExactRetrievalStrategy(...)
[Deprecated] Exact retrieval strategy using the script_score query.
vectorstores.elasticsearch.SparseRetrievalStrategy([...])
[Deprecated] Sparse retrieval strategy using the text_expansion processor.
vectorstores.epsilla.Epsilla(client, embeddings)
Wrapper around Epsilla vector database.
vectorstores.faiss.FAISS(embedding_function, ...)
Meta Faiss vector store.
vectorstores.hanavector.HanaDB(connection, ...)
SAP HANA Cloud Vector Engine
vectorstores.hippo.Hippo(embedding_function)
Hippo vector store.
vectorstores.hologres.Hologres(...[, ndims, ...])
Hologres API vector store.
vectorstores.infinispanvs.Infinispan(**kwargs)
Helper class for Infinispan REST interface.
vectorstores.infinispanvs.InfinispanVS([...])
Infinispan VectorStore interface.
vectorstores.inmemory.InMemoryVectorStore(...)
In-memory implementation of VectorStore using a dictionary.
vectorstores.jaguar.Jaguar(pod, store, ...)
Jaguar API vector store.
vectorstores.kdbai.KDBAI(table, embedding[, ...])
KDB.AI vector store.
vectorstores.kinetica.Dimension(value)
Some default dimensions for known embeddings.
vectorstores.kinetica.DistanceStrategy(value)
Enumerator of the Distance strategies.
vectorstores.kinetica.Kinetica(config, ...)
Kinetica vector store.
vectorstores.kinetica.KineticaSettings
Kinetica client configuration.
vectorstores.kinetica.KineticaSettings
Kinetica client configuration.
vectorstores.lancedb.LanceDB([connection, ...])
LanceDB vector store.
vectorstores.lantern.BaseEmbeddingStore()
Base class for the Lantern embedding store.
vectorstores.lantern.DistanceStrategy(value)
Enumerator of the Distance strategies.
vectorstores.lantern.Lantern(...[, ...])
Postgres with the lantern extension as a vector store.
vectorstores.lantern.QueryResult()
Result from a query.
vectorstores.llm_rails.LLMRails([...])
Implementation of Vector Store using LLMRails.
vectorstores.llm_rails.LLMRailsRetriever
Retriever for LLMRails.
vectorstores.manticore_search.ManticoreSearch(...)
ManticoreSearch Engine vector store.
vectorstores.manticore_search.ManticoreSearchSettings
Create a new model by parsing and validating input data from keyword arguments.
vectorstores.marqo.Marqo(client, index_name)
Marqo vector store.
vectorstores.matching_engine.MatchingEngine(...)
[Deprecated] Google Vertex AI Vector Search (previously Matching Engine) vector store.
vectorstores.meilisearch.Meilisearch(embedding)
Meilisearch vector store.
vectorstores.milvus.Milvus(embedding_function)
[Deprecated] Milvus vector store.
vectorstores.momento_vector_index.MomentoVectorIndex(...)
Momento Vector Index (MVI) vector store.
vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(...)
[Deprecated] MongoDB Atlas Vector Search vector store.
vectorstores.myscale.MyScale(embedding[, config])
MyScale vector store.
vectorstores.myscale.MyScaleSettings
MyScale client configuration.
vectorstores.myscale.MyScaleWithoutJSON(...)
MyScale client configuration.
vectorstores.myscale.MyScaleWithoutJSON(...)
MyScale vector store without metadata column
vectorstores.neo4j_vector.IndexType(value)
Enumerator of the index types.
vectorstores.neo4j_vector.Neo4jVector(...[, ...])
Neo4j vector index.
vectorstores.neo4j_vector.SearchType(value)
Enumerator of the Distance strategies.
vectorstores.nucliadb.NucliaDB(...[, ...])
NucliaDB vector store.
vectorstores.opensearch_vector_search.OpenSearchVectorSearch(...)
Amazon OpenSearch Vector Engine vector store.
vectorstores.oraclevs.OracleVS(client, ...)
OracleVS vector store.
vectorstores.pathway.PathwayVectorClient([...])
VectorStore connecting to Pathway Vector Store.
vectorstores.pgembedding.BaseModel(**kwargs)
Base model for all SQL stores.
vectorstores.pgembedding.CollectionStore(...)
Collection store.
vectorstores.pgembedding.EmbeddingStore(**kwargs)
Embedding store.
vectorstores.pgembedding.PGEmbedding(...[, ...])
Postgres with the pg_embedding extension as a vector store.
vectorstores.pgembedding.QueryResult()
Result from a query.
vectorstores.pgvecto_rs.PGVecto_rs(...[, ...])
VectorStore backed by pgvecto_rs.
vectorstores.pgvector.BaseModel(**kwargs)
Base model for the SQL stores.
vectorstores.pgvector.DistanceStrategy(value)
Enumerator of the Distance strategies.
vectorstores.pgvector.PGVector(...[, ...])
[Deprecated] Postgres/PGVector vector store.
vectorstores.pinecone.Pinecone(index, ...[, ...])
[Deprecated] Pinecone vector store.
vectorstores.qdrant.Qdrant(client, ...[, ...])
[Deprecated] Qdrant vector store.
[Deprecated] Qdrant vector store.
vectorstores.qdrant.QdrantException
Qdrant related exceptions.
vectorstores.redis.base.Redis(redis_url, ...)
Redis vector database.
vectorstores.redis.base.RedisVectorStoreRetriever
Retriever for Redis VectorStore.
vectorstores.redis.filters.RedisFilter()
Collection of RedisFilterFields.
vectorstores.redis.filters.RedisFilterExpression([...])
Logical expression of RedisFilterFields.
vectorstores.redis.filters.RedisFilterField(field)
Base class for RedisFilterFields.
vectorstores.redis.filters.RedisFilterOperator(value)
RedisFilterOperator enumerator is used to create RedisFilterExpressions.
vectorstores.redis.filters.RedisNum(field)
RedisFilterField representing a numeric field in a Redis index.
vectorstores.redis.filters.RedisTag(field)
RedisFilterField representing a tag in a Redis index.
vectorstores.redis.filters.RedisText(field)
RedisFilterField representing a text field in a Redis index.
vectorstores.redis.schema.FlatVectorField
Schema for flat vector fields in Redis.
vectorstores.redis.schema.HNSWVectorField
Schema for HNSW vector fields in Redis.
vectorstores.redis.schema.NumericFieldSchema
Schema for numeric fields in Redis.
vectorstores.redis.schema.RedisDistanceMetric(value)
Distance metrics for Redis vector fields.
vectorstores.redis.schema.RedisField
Base class for Redis fields.
vectorstores.redis.schema.RedisModel
Schema for Redis index.
vectorstores.redis.schema.RedisVectorField
Base class for Redis vector fields.
vectorstores.redis.schema.TagFieldSchema
Schema for tag fields in Redis.
vectorstores.redis.schema.TextFieldSchema
Schema for text fields in Redis.
vectorstores.relyt.Relyt(connection_string, ...)
Relyt (distributed PostgreSQL) vector store.
vectorstores.rocksetdb.Rockset(client, ...)
Rockset vector store.
vectorstores.rocksetdb.Rockset(client, ...)
Rockset vector store.
vectorstores.scann.ScaNN(embedding, index, ...)
ScaNN vector store.
vectorstores.semadb.SemaDB(collection_name, ...)
SemaDB vector store.
vectorstores.singlestoredb.SingleStoreDB(...)
SingleStore DB vector store.
vectorstores.sklearn.BaseSerializer(persist_path)
Base class for serializing data.
vectorstores.sklearn.BsonSerializer(persist_path)
Serialize data in Binary JSON using the bson python package.
vectorstores.sklearn.JsonSerializer(persist_path)
Serialize data in JSON using the json package from python standard library.
vectorstores.sklearn.ParquetSerializer(...)
Serialize data in Apache Parquet format using the pyarrow package.
vectorstores.sklearn.SKLearnVectorStore(...)
Simple in-memory vector store based on the scikit-learn library NearestNeighbors.
vectorstores.sklearn.SKLearnVectorStoreException
Exception raised by SKLearnVectorStore.
vectorstores.sqlitevss.SQLiteVSS(table, ...)
SQLite with VSS extension as a vector database.
vectorstores.starrocks.StarRocks(embedding)
StarRocks vector store.
vectorstores.starrocks.StarRocksSettings
StarRocks client configuration.
vectorstores.supabase.SupabaseVectorStore(...)
Supabase Postgres vector store.
vectorstores.surrealdb.SurrealDBStore(...)
SurrealDB as Vector Store.
vectorstores.tair.Tair(embedding_function, ...)
Tair vector store.
vectorstores.tencentvectordb.ConnectionParams(...)
Tencent vector DB Connection params.
vectorstores.tencentvectordb.IndexParams(...)
Tencent vector DB Index params.
vectorstores.tencentvectordb.MetaField
MetaData Field for Tencent vector DB.
vectorstores.tencentvectordb.MetaField
MetaData Field for Tencent vector DB.
vectorstores.tencentvectordb.TencentVectorDB(...)
Tencent VectorDB as a vector store.
vectorstores.thirdai_neuraldb.NeuralDBClientVectorStore(db)
Vectorstore that uses ThirdAI's NeuralDB Enterprise Python Client for NeuralDBs.
vectorstores.thirdai_neuraldb.NeuralDBVectorStore(db)
Vectorstore that uses ThirdAI's NeuralDB.
vectorstores.tidb_vector.TiDBVectorStore(...)
TiDB Vector Store.
vectorstores.tigris.Tigris(client, ...)
Tigris vector store.
vectorstores.tiledb.TileDB(embedding, ...[, ...])
TileDB vector store.
vectorstores.timescalevector.TimescaleVector(...)
Timescale Postgres vector store
vectorstores.typesense.Typesense(...[, ...])
Typesense vector store.
vectorstores.upstash.UpstashVectorStore([...])
Upstash Vector vector store
vectorstores.usearch.USearch(embedding, ...)
USearch vector store.
vectorstores.utils.DistanceStrategy(value)
Enumerator of the Distance strategies for calculating distances between vectors.
vectorstores.vald.Vald(embedding[, host, ...])
Vald vector database.
vectorstores.vdms.VDMS(client, *[, ...])
Intel Lab's VDMS for vector-store workloads.
vectorstores.vearch.Vearch(embedding_function)
Initialize vearch vector store flag 1 for cluster,0 for standalone
vectorstores.vectara.MMRConfig([is_enabled, ...])
Configuration for Maximal Marginal Relevance (MMR) search.
vectorstores.vectara.RerankConfig([...])
Configuration for Reranker.
vectorstores.vectara.SummaryConfig([...])
Configuration for summary generation.
vectorstores.vectara.SummaryConfig([...])
Configuration for summary generation.
vectorstores.vectara.Vectara([...])
Vectara API vector store.
vectorstores.vectara.VectaraQueryConfig([k, ...])
Configuration for Vectara query.
vectorstores.vectara.VectaraRAG(vectara, config)
vectorstores.vectara.VectaraRetriever
Vectara Retriever class.
vectorstores.vespa.VespaStore(app[, ...])
Vespa vector store.
vectorstores.vikingdb.VikingDB(...[, ...])
vikingdb as a vector store
vectorstores.vikingdb.VikingDBConfig([host, ...])
vikingdb connection config
vectorstores.vlite.VLite(embedding_function)
VLite is a simple and fast vector database for semantic search.
vectorstores.weaviate.Weaviate(client, ...)
Weaviate vector store.
vectorstores.xata.XataVectorStore(api_key, ...)
Xata vector store.
vectorstores.yellowbrick.Yellowbrick(...[, ...])
Yellowbrick as a vector database.
vectorstores.zep.CollectionConfig(name, ...)
Configuration for a Zep Collection.
vectorstores.zep.ZepVectorStore(...[, ...])
Zep vector store.
vectorstores.zep_cloud.ZepCloudVectorStore(...)
Zep vector store.
vectorstores.zilliz.Zilliz(embedding_function)
Zilliz vector store.
Functions¶
vectorstores.alibabacloud_opensearch.create_metadata(fields)
Create metadata from fields.
vectorstores.annoy.dependable_annoy_import()
Import annoy if available, otherwise raise error.
vectorstores.clickhouse.has_mul_sub_str(s, *args)
Check if a string contains multiple substrings.
Check if a string contains multiple substrings.
vectorstores.faiss.dependable_faiss_import([...])
Import faiss if available, otherwise raise error.
vectorstores.lancedb.import_lancedb()
Import lancedb package.
vectorstores.lantern.get_embedding_store(...)
Get the embedding store class.
vectorstores.myscale.has_mul_sub_str(s, *args)
Check if a string contains multiple substrings.
vectorstores.neo4j_vector.check_if_not_null(...)
Check if the values are not None or empty string
vectorstores.neo4j_vector.collect_params(...)
Transform the input data into the desired format.
vectorstores.neo4j_vector.combine_queries(...)
Combine multiple queries with an operator.
vectorstores.neo4j_vector.construct_metadata_filter(filter)
Construct a metadata filter.
vectorstores.neo4j_vector.dict_to_yaml_str(...)
Convert a dictionary to a YAML-like string without using external libraries.
vectorstores.neo4j_vector.remove_lucene_chars(text)
Remove Lucene special characters
vectorstores.neo4j_vector.sort_by_index_name(...)
Sort first element to match the index_name if exists
vectorstores.oraclevs.create_index(client, ...)
vectorstores.oraclevs.drop_index_if_exists(...)
vectorstores.oraclevs.drop_table_purge(...)
vectorstores.qdrant.sync_call_fallback(method)
Decorator to call the synchronous method of the class if the async method is not implemented.
vectorstores.redis.base.check_index_exists(...)
Check if Redis index exists.
vectorstores.redis.filters.check_operator_misuse(func)
Decorator to check for misuse of equality operators.
vectorstores.redis.schema.read_schema(...)
Read in the index schema from a dict or yaml file.
vectorstores.scann.dependable_scann_import()
Import scann if available, otherwise raise error.
vectorstores.scann.normalize(x)
Import scann if available, otherwise raise error.
vectorstores.scann.normalize(x)
Normalize vectors to unit length.
vectorstores.starrocks.debug_output(s)
Print a debug message if DEBUG is True.
vectorstores.starrocks.get_named_result(...)
Get a named result from a query.
vectorstores.starrocks.has_mul_sub_str(s, *args)
Check if a string has multiple substrings.
vectorstores.tencentvectordb.translate_filter(...)
Translate LangChain filter to Tencent VectorDB filter.
vectorstores.tiledb.dependable_tiledb_import()
Import tiledb-vector-search if available, otherwise raise error.
vectorstores.tiledb.get_documents_array_uri(uri)
Get the URI of the documents array.
vectorstores.tiledb.get_documents_array_uri_from_group(group)
Get the URI of the documents array from group.
vectorstores.tiledb.get_vector_index_uri(uri)
Get the URI of the vector index.
vectorstores.tiledb.get_vector_index_uri_from_group(group)
Get the URI of the vector index.
vectorstores.usearch.dependable_usearch_import()
Import usearch if available, otherwise raise error.
vectorstores.utils.filter_complex_metadata(...)
Filter out metadata types that are not supported for a vector store.
vectorstores.utils.maximal_marginal_relevance(...)
Calculate maximal marginal relevance.
vectorstores.vdms.VDMS_Client([host, port])
VDMS client for the VDMS server.
vectorstores.vdms.embedding2bytes(embedding)
Convert embedding to bytes.
langchain_robocorp 0.0.9.post1¶
langchain_robocorp.toolkits¶
Robocorp Action Server toolkit.
Classes¶
toolkits.ActionServerRequestTool
Requests POST tool with LLM-instructed extraction of truncated responses.
toolkits.ActionServerToolkit
Toolkit exposing Robocorp Action Server provided actions as individual tools.
toolkits.RunDetailsCallbackHandler(run_details)
Callback handler to add run details to the run.
toolkits.ToolArgs
Tool arguments.
toolkits.ToolInputSchema
Tool input schema.
langchain_milvus 0.1.1¶
langchain_milvus.retrievers¶
Classes¶
retrievers.milvus_hybrid_search.MilvusCollectionHybridSearchRetriever
This is a hybrid search retriever that uses Milvus Collection to retrieve documents based on multiple fields.
retrievers.zilliz_cloud_pipeline_retriever.ZillizCloudPipelineRetriever
Zilliz Cloud Pipeline retriever
langchain_milvus.utils¶
Classes¶
utils.sparse.BM25SparseEmbedding(corpus[, ...])
This is a class that inherits BaseSparseEmbedding and implements a sparse vector embedding model based on BM25.
utils.sparse.BaseSparseEmbedding()
Interface for Sparse embedding models.
langchain_milvus.vectorstores¶
Classes¶
vectorstores.milvus.Milvus(embedding_function)
Milvus vector store.
vectorstores.zilliz.Zilliz(embedding_function)
Zilliz vector store.
Functions¶
vectorstores.milvus.cosine_similarity(X, Y)
Row-wise cosine similarity between two equal-width matrices.
vectorstores.milvus.maximal_marginal_relevance(...)
Calculate maximal marginal relevance.