langchain-aws: 0.2.35

agents

Classes

agents.base.BedrockAgentsRunnable	Invoke a Bedrock Agent
agents.base.BedrockInlineAgentsRunnable	Invoke Bedrock Inline Agent as a Runnable.
agents.types.BedrockAgentAction	AgentAction with session id information.
agents.types.BedrockAgentFinish	AgentFinish with session id information.
agents.types.GuardrailConfiguration
agents.types.InlineAgentConfiguration	Configurations for an Inline Agent.
agents.types.KnowledgebaseConfiguration

Functions

agents.utils.get_boto_session([...])	Construct the boto3 session
agents.utils.parse_agent_response(response)	Parses the raw response from Bedrock Agent

chains

Functions

chains.graph_qa.neptune_cypher.create_neptune_opencypher_qa_chain(...)	Chain for question-answering against a Neptune graph by generating openCypher statements.
chains.graph_qa.neptune_cypher.extract_cypher(text)	Extract Cypher code from text using Regex.
chains.graph_qa.neptune_cypher.get_prompt(llm)	Selects the final prompt
chains.graph_qa.neptune_cypher.trim_query(query)	Trim the query to only include Cypher keywords.
chains.graph_qa.neptune_cypher.use_simple_prompt(llm)	Decides whether to use the simple prompt
chains.graph_qa.neptune_sparql.create_neptune_sparql_qa_chain(...)	Chain for question-answering against a Neptune graph by generating SPARQL statements.
chains.graph_qa.neptune_sparql.extract_sparql(query)	Extract SPARQL code from a text.
chains.graph_qa.neptune_sparql.get_prompt(...)	Selects the final prompt.

chat_models

Classes

chat_models.bedrock.ChatBedrock	A chat model that uses the Bedrock API.
chat_models.bedrock.ChatPromptAdapter()	Adapter class to prepare the inputs from Langchain to prompt format that Chat model expects.
chat_models.bedrock_converse.ChatBedrockConverse	Bedrock chat model integration built on the Bedrock converse API.
chat_models.sagemaker_endpoint.ChatLineIterator(stream)	A helper class for parsing the byte stream input.
chat_models.sagemaker_endpoint.ChatModelContentHandler()	Content handler for ChatSagemakerEndpoint class.
chat_models.sagemaker_endpoint.ChatSagemakerEndpoint	A chat model that uses a HuggingFace TGI compatible SageMaker Endpoint.

Functions

chat_models.bedrock.convert_messages_to_prompt_anthropic(...)	Format a list of messages into a full prompt for the Anthropic model
chat_models.bedrock.convert_messages_to_prompt_deepseek(...)	Convert a list of messages to a prompt for DeepSeek-R1.
chat_models.bedrock.convert_messages_to_prompt_llama(...)	Convert a list of messages to a prompt for llama.
chat_models.bedrock.convert_messages_to_prompt_llama3(...)	Convert a list of messages to a prompt for Llama 3.
chat_models.bedrock.convert_messages_to_prompt_llama4(...)	Convert a list of messages to a prompt for Llama 4.
chat_models.bedrock.convert_messages_to_prompt_mistral(...)	Convert a list of messages to a prompt for mistral.
chat_models.bedrock.convert_messages_to_prompt_openai(...)	Convert a list of messages to a Harmony format prompt for OpenAI Responses API.
chat_models.bedrock.convert_messages_to_prompt_writer(...)	Convert a list of messages to a prompt for Writer.

document_compressors

Classes

document_compressors.rerank.BedrockRerank

Document compressor that uses AWS Bedrock Rerank API.

embeddings

Classes

embeddings.bedrock.BedrockEmbeddings

Bedrock embedding models.

function_calling

Classes

function_calling.AnthropicTool
function_calling.FunctionDescription	Representation of a callable function to send to an LLM.
function_calling.ToolDescription	Representation of a callable function to the OpenAI API.
function_calling.ToolsOutputParser

Functions

function_calling.convert_to_anthropic_tool(tool)
function_calling.get_system_message(tools)

graphs

Classes

graphs.neptune_graph.BaseNeptuneGraph()
graphs.neptune_graph.NeptuneAnalyticsGraph(...)	Neptune Analytics wrapper for graph operations.
graphs.neptune_graph.NeptuneGraph(host[, ...])	Neptune wrapper for graph operations.
graphs.neptune_graph.NeptuneQueryException(...)	Exception for the Neptune queries.
graphs.neptune_rdf_graph.NeptuneRdfGraph(host)	Neptune wrapper for RDF graph operations.

llms

Classes

llms.bedrock.AnthropicTool
llms.bedrock.BedrockBase	Base class for Bedrock models.
llms.bedrock.BedrockLLM	Bedrock models.
llms.bedrock.LLMInputOutputAdapter()	Adapter class to prepare the inputs from Langchain to a format that LLM model expects.
llms.sagemaker_endpoint.LLMContentHandler()	Content handler for LLM class.
llms.sagemaker_endpoint.LineIterator(stream)	A helper class for parsing the byte stream input.
llms.sagemaker_endpoint.SagemakerEndpoint	Sagemaker Inference Endpoint models.

Functions

llms.bedrock.extract_tool_calls(content)
llms.sagemaker_endpoint.enforce_stop_tokens(...)	Cut off the text as soon as any stop words occur.

retrievers

Classes

retrievers.bedrock.AmazonKnowledgeBasesRetriever	Amazon Bedrock Knowledge Bases retrieval.
retrievers.bedrock.RetrievalConfig	Configuration for retrieval.
retrievers.bedrock.SearchFilter	Filter configuration for retrieval.
retrievers.bedrock.VectorSearchConfig	Configuration for vector search.
retrievers.kendra.AdditionalResultAttribute	Additional result attribute.
retrievers.kendra.AdditionalResultAttributeValue	Value of an additional result attribute.
retrievers.kendra.AmazonKendraRetriever	Amazon Kendra Index retriever.
retrievers.kendra.DocumentAttribute	Document attribute.
retrievers.kendra.DocumentAttributeValue	Value of a document attribute.
retrievers.kendra.Highlight	Information that highlights the keywords in the excerpt.
retrievers.kendra.QueryResult	Amazon Kendra Query API search result.
retrievers.kendra.QueryResultItem	Query API result item.
retrievers.kendra.ResultItem	Base class of a result item.
retrievers.kendra.RetrieveResult	Amazon Kendra Retrieve API search result.
retrievers.kendra.RetrieveResultItem	Retrieve API result item.
retrievers.kendra.TextWithHighLights	Text with highlights.
retrievers.s3_vectors.AmazonS3VectorsRetriever	AmazonS3VectorsRetriever is a retriever for Amazon S3 Vectors.

Functions

retrievers.kendra.clean_excerpt(excerpt)	Clean an excerpt from Kendra.
retrievers.kendra.combined_text(item)	Combine a ResultItem title and excerpt into a single string.

runnables

Classes

runnables.q_business.AmazonQ([region_name, ...])

utilities

Classes

utilities.redis.TokenEscaper([escape_chars_re])	Escape punctuation within an input string.
utilities.utils.DistanceStrategy(value)	Enumerator of the Distance strategies for calculating distances between vectors.

Functions

utilities.math.cosine_similarity(X, Y)	Row-wise cosine similarity between two equal-width matrices.
utilities.math.cosine_similarity_top_k(X, Y)	Row-wise cosine similarity with optional top-k and score threshold filtering.
utilities.redis.get_client(redis_url, **kwargs)	Get a redis client from the connection url given.
utilities.utils.filter_complex_metadata(...)	Filter out metadata types that are not supported for a vector store.
utilities.utils.maximal_marginal_relevance(...)	Calculate maximal marginal relevance.

utils

Classes

utils.ContentHandlerBase()

A handler class to transform input from LLM and BaseChatModel to a

Functions

utils.anthropic_tokens_supported()	Check if all requirements for Anthropic count_tokens() are met.
utils.count_tokens_api_supported_for_model(model)
utils.create_aws_client(service_name[, ...])	Helper function to validate AWS credentials and create an AWS client.
utils.enforce_stop_tokens(text, stop)	Cut off the text as soon as any stop words occur.
utils.get_num_tokens_anthropic(text)	Get the number of tokens in a string of text.
utils.get_token_ids_anthropic(text)	Get the token ids for a string of text.
utils.thinking_in_params(params)	Check if the thinking parameter is enabled in the request.
utils.trim_message_whitespace(messages)	Trim trailing whitespace from final AIMessage content.

vectorstores

Classes

vectorstores.inmemorydb.base.InMemoryVectorStore(...)	InMemoryVectorStore vector database.
vectorstores.inmemorydb.base.InMemoryVectorStoreRetriever	Retriever for InMemoryVectorStore.
vectorstores.inmemorydb.cache.InMemorySemanticCache(...)	Cache that uses MemoryDB as a vector-store backend.
vectorstores.inmemorydb.filters.InMemoryDBFilter()	Collection of InMemoryDBFilterFields.
vectorstores.inmemorydb.filters.InMemoryDBFilterExpression([...])	Logical expression of InMemoryDBFilterFields.
vectorstores.inmemorydb.filters.InMemoryDBFilterField(field)	Base class for InMemoryDBFilterFields.
vectorstores.inmemorydb.filters.InMemoryDBFilterOperator(value)	InMemoryDBFilterOperator enumerator is used to create InMemoryDBFilterExpressions
vectorstores.inmemorydb.filters.InMemoryDBNum(field)	InMemoryDBFilterField representing a numeric field in a InMemoryDB index.
vectorstores.inmemorydb.filters.InMemoryDBTag(field)	InMemoryDBFilterField representing a tag in a InMemoryDB index.
vectorstores.inmemorydb.filters.InMemoryDBText(field)	InMemoryDBFilterField representing a text field in a InMemoryDB index.
vectorstores.inmemorydb.schema.FlatVectorField	Schema for flat vector fields in Redis.
vectorstores.inmemorydb.schema.HNSWVectorField	Schema for HNSW vector fields in Redis.
vectorstores.inmemorydb.schema.InMemoryDBDistanceMetric(value)	Distance metrics for Redis vector fields.
vectorstores.inmemorydb.schema.InMemoryDBField	Base class for Redis fields.
vectorstores.inmemorydb.schema.InMemoryDBModel	Schema for MemoryDB index.
vectorstores.inmemorydb.schema.InMemoryDBVectorField	Base class for Redis vector fields.
vectorstores.inmemorydb.schema.NumericFieldSchema	Schema for numeric fields in Redis.
vectorstores.inmemorydb.schema.TagFieldSchema	Schema for tag fields in Redis.
vectorstores.inmemorydb.schema.TextFieldSchema	Schema for text fields in Redis.
vectorstores.s3_vectors.base.AmazonS3Vectors(*, ...)	S3Vectors is Amazon S3 Vectors database.

Functions

vectorstores.inmemorydb.base.check_index_exists(...)	Check if MemoryDB index exists.
vectorstores.inmemorydb.filters.check_operator_misuse(func)	Decorator to check for misuse of equality operators.
vectorstores.inmemorydb.schema.read_schema(...)	Read in the index schema from a dict or yaml file.