langchain library

Classes

Agent: An agent is a component that takes an input and a set of tools and decides what to do to get to the desired output.
AgentAction: Agent's action to take.
AgentExecutor: A chain responsible for executing the actions of an agent using tools. It receives user input and passes it to the agent, which then decides which tool/s to use and what action/s to take.
AgentFinish: Agent's return value.
AgentPlanInput: Input to the agent's plan method.
AgentStep: A action taken by the agent along with the observation of the action.
AIChatMessage: Type of message that is spoken by the AI.
AIChatMessagePromptTemplate: A chat message prompt template for a AIChatMessage (ChatMessageType.ai).
AIChatMessageToolCall: A tool that the model wants to call.
BaseActionAgent: Base class for action agents.
BaseAgentAction: Base class for agent actions.
BaseChain<MemoryType extends BaseMemory>: Base class for creating structured sequences of calls to components.
BaseChatMemory: Base interface for chat memory.
BaseChatMessageHistory: Base interface for chat message history.
BaseChatModel<Options extends ChatModelOptions>: Chat models base class. It should take in chat messages and return a chat message.
BaseChatPromptTemplate: Base class for chat prompt templates.
BaseCombineDocumentsChain: Base interface for chains combining documents, such as StuffDocumentsChain.
BaseDocumentLoader: @{template base_document_loader} Interface for loading documents. @{endtemplate}
BaseDocumentTransformer: Base interface for transforming documents.
BaseLangChain<RunInput extends Object, CallOptions extends RunnableOptions, RunOutput extends Object>: Base class for LangChain components (language models, chains, tools, etc.).
BaseLangChainOptions: Base options class for LangChain components.
BaseLanguageModel<Input extends Object, Options extends LanguageModelOptions, Output extends LanguageModelResult<Object>>: Base class for all language models.
BaseLLM<Options extends LLMOptions>: Large Language Models base class.
BaseMemory: Base interface for memory in chains.
BaseMultiActionAgent: Base class for multi action agents.
BaseOutputParser<ParserInput extends Object?, CallOptions extends OutputParserOptions, ParserOutput extends Object?>: Class to parse the output of a Runnable invocation.
BasePromptSelector: Base class for a prompt template.
BasePromptTemplate: Base class for a prompt template.
BaseSingleActionAgent: Base class for single action agents.
BaseStore<K, V>: Abstract interface for a key-value store.
CacheBackedEmbeddings: Wrapper around an embedder that caches embeddings in a key-value store to avoid recomputing embeddings for the same text.
ChainOptions: Options to pass to the chain.
CharacterTextSplitter: Implementation of TextSplitter that looks at characters.
ChatExample: An example of a conversation between the end-user and the model.
ChatMessage: A message that is part of a chat conversation.
ChatMessageContent: The content of a message.
ChatMessageContentImage: The content of a message that is an image.
ChatMessageContentMultiModal: {@template chat_message_content_multi_modal} The content of a message that is multi-modal. {@endtemplate
ChatMessageContentText: The content of a message that is text.
ChatMessageHistory: A simple in-memory implementation of a chat message history. Stores messages in an in memory list.
ChatMessagePromptTemplate: Base class for all message templates in a ChatPromptTemplate.
ChatModelOptions: Generation options to pass into the Chat Model.
ChatPromptTemplate: A prompt template for chat models.
ChatPromptValue: Prompt value whose value is a list of messages.
ChatResult: Result returned by the Chat Model.
ChatToolChoice: Controls how the model responds to tool calls.
ChatToolChoiceAuto: The model can pick between responding to the end-user or calling a tool.
ChatToolChoiceForced: The model is forced to to call the specified tool.
ChatToolChoiceNone: The model does not call a tool, and responds to the end-user.
ChatToolChoiceRequired: The model must call at least one tool, but doesn’t force a particular tool.
CodeTextSplitter: CodeTextSplitter allows you to split source code. It supports multiple languages (see CodeLanguage enum). It tries to split along class definitions, function definitions, and control flow statements.
ConditionalPromptSelector: Prompt collection that goes through conditionals to select the appropriate prompt template.
ConversationBufferMemory: Buffer for storing a conversation in-memory and then retrieving the messages at a later time.
ConversationBufferWindowMemory: ConversationBufferWindowMemory is a type of memory that stores a conversation in chatHistory and then retrieves the last k interactions with the model (i.e. the last k input messages and the last k output messages).
ConversationChain: Chain that carries on a conversation, loading context from memory and calling an LLM with it.
ConversationSummaryMemory<LLMType extends BaseLanguageModel<Object, LanguageModelOptions, LanguageModelResult<Object>>>: Memory that summarizes a conversation over time. This is useful for longer conversations where keeping the full message history would take up too many tokens.
ConversationTokenBufferMemory<LLMType extends BaseLanguageModel<Object, LanguageModelOptions, LanguageModelResult<Object>>>: Rolling buffer for storing a conversation and then retrieving the messages at a later time.
CustomChatMessage: Chat message with custom role.
CustomChatMessagePromptTemplate: A template for a CustomChatMessage (ChatMessageType.custom).
Document: Interface for interacting with a document.
Embeddings: Interface for embedding models.
EmbeddingsByteStoreEncoder: Encoder that transforms values to and from bytes.
EncoderBackedStore<K, V, EK, EV>: Wraps a store with key and value encoders/decoders.
ExceptionTool: A tool used when the agent throws an OutputParserException.
FakeChatModel: Fake Chat Model for testing. You can pass in a list of responses to return in order when called.
FakeChatModelOptions: Fake Echo Chat Model Options for testing.
FakeEchoChatModel: Fake Chat Model for testing. It just returns the content of the last message of the prompt or streams it char by char.
FakeEchoChatModelOptions: Fake Echo Chat Model Options for testing.
FakeEchoLLM: Fake LLM for testing. It just returns the prompt or streams it char by char.
FakeEmbeddings: Fake embeddings model for testing.
FakeHandlerLLM: Fake LLM for testing. It returns the string returned by the handler function.
FakeLLM: Fake LLM for testing. You can pass in a list of responses to return in order when called.
FakeLLMOptions: Fake LLM options for testing.
FakeRetriever: A retriever that returns a fixed list of documents. This class is meant for testing purposes only.
FakeTool: Fake tool for testing. It just returns the input string as is.
HumanChatMessage: Type of message that is spoken by the human.
HumanChatMessagePromptTemplate: A chat message prompt template for a HumanChatMessage (ChatMessageType.human).
InMemoryStore<K, V>: In-memory implementation of the BaseStore using a dictionary.
JsonOutputParser<ParserInput extends Object?>: Output parser that returns the output of the previous Runnable as a JSON Map.
LanguageModelOptions: Options to pass into the language model.
LanguageModelResult<O extends Object>: Result returned by the model.
LanguageModelUsage: Usage stats for the generation.
LLMChain<LLMType extends BaseLanguageModel<Object, LanguageModelOptions, LanguageModelResult<Object>>, LLMOptions extends LanguageModelOptions, MemoryType extends BaseMemory>: NOTE: Chains are the legacy way of using LangChain and will eventually be removed. Use LangChain Expression Language (LCEL) instead, a more flexible and powerful way to combine different components.
LLMOptions: Options to pass into the LLM.
LLMResult: Result returned by the LLM.
LocalFileStore: A simple file system implementation of a BaseStore.
MapReduceDocumentsChain: Chain that combines documents by mapping them individually and then reducing the results.
MemoryVector: Represents an entry of MemoryVectorStore.
MemoryVectorStore: Vector store that stores vectors in memory.
MessagePlaceholder: Prompt template that assumes the variable is a ChatMessage (ChatMessageType.messagePlaceholder).
MessagesPlaceholder: Prompt template that assumes the variable is a list of ChatMessage (ChatMessageType.messagesPlaceholder).
OutputParserOptions: Options to pass to an output parser.
ParsedToolCall: A parsed tool call from the model.
PipelinePromptTemplate: A prompt template for composing multiple prompts together.
PromptCondition: Condition for a prompt.
PromptTemplate: A prompt template for a language model.
PromptValue: Represents the input to an LLM or Chat model.
RecursiveCharacterTextSplitter: Implementation of splitting text that looks at characters. Recursively tries to split by different characters to find one that works.
ReduceDocumentsChain: Chain that combines documents by recursively reducing them if needed.
RetrievalQAChain: Chain for question-answering against the documents retrieved by the retriever.
Retriever<Options extends RetrieverOptions>: Base Index class. All indexes should extend this class.
RetrieverOptions: Base class for Retriever options.
RetryOptions: Options to pass into retryClient to control the retry behavior.
Runnable<RunInput extends Object?, CallOptions extends RunnableOptions, RunOutput extends Object?>: A Runnable is a generic unit of work that can be invoked, batched, streamed, and/or transformed. It is the basic building block of the LangChain Expression Language (LCEL).
RunnableAgent: An agent implemented from a Runnable.
RunnableBinding<RunInput extends Object?, CallOptions extends RunnableOptions, RunOutput extends Object?>: A RunnableBinding allows you to run a Runnable object with CallOptions.
RunnableFunction<RunInput extends Object, RunOutput extends Object>: A RunnableFunction allows you to run a Dart function as part of a chain.
RunnableMap<RunInput extends Object>: A RunnableMap allows you to run multiple Runnable objects in parallel on the same input returning a map of the results.
RunnableMapInput<RunInput extends Object, RunOutput extends Object>: A RunnableMapInput allows you to map the input to a different value.
RunnableMapInputStream<RunInput extends Object, RunOutput extends Object>: A RunnableMapInputStream allows you to map the input stream to a different stream of values.
RunnableOptions: Options to pass into a runnable.
RunnablePassthrough<RunInput extends Object>: A RunnablePassthrough takes the input it receives and passes it through as output.
RunnableRetry<RunInput extends Object?, RunOutput extends Object?>: A Runnable that automatically retries the operation if it fails.
RunnableRouter<RunInput extends Object, RunOutput extends Object>: A RunnableRouter takes the input it receives and routes it to the runnable returned by the router function.
RunnableSequence<RunInput extends Object?, RunOutput extends Object?>: A RunnableSequence allows you to run multiple Runnable objects sequentially, passing the output of the previous Runnable to the next one.
RunnableWithFallback<RunInput extends Object?, RunOutput extends Object?>: A Runnable that can fallback to other Runnables if it fails.
SequentialChain: Chain that combines multiple chains where the output of the one chain is the input of the next chain.
SimpleChatModel<Options extends ChatModelOptions>: SimpleChatModel provides a simplified interface for working with chat models, rather than expecting the user to implement the full SimpleChatModel.invoke method.
SimpleLLM<Options extends LLMOptions>: SimpleLLM provides a simplified interface for working with LLMs. Rather than expecting the user to implement the full SimpleLLM.invoke method, the user only needs to implement SimpleLLM.callInternal.
SimpleMemory: Simple memory for storing context or other bits of information that shouldn't ever change between prompts.
SimpleSequentialChain: SimpleSequentialChain is a simpler form of SequentialChain, where each step has a singular input/output, and the output of one step is the input to the next.
StoreEncoder<K, V, EK, EV>: Encoder/decoder for keys and values.
StringMessagePromptTemplate: Base class for all string message templates in a ChatPromptTemplate.
StringOutputParser<ParserInput extends Object?>: Output parser that returns the output of the previous Runnable as a String.
StringPromptValue: Prompt value whose value is a string.
StringTool<Options extends ToolOptions>: Base class for tools that accept a single string input and returns a string output.
StuffDocumentsChain: Chain that combines documents by stuffing into context.
StuffDocumentsQAChain: Stuffs documents with a question answering prompt.
SummarizeChain: Chain for summarizing documents.
SystemChatMessage: Type of message that is a system message.
SystemChatMessagePromptTemplate: A chat message prompt template for a SystemChatMessagePromptTemplate (ChatMessageType.human).
TextSplitter: Interface for splitting text into chunks.
Tool<Input extends Object, Options extends ToolOptions, Output extends Object>: A LangChain tool.
ToolChatMessage: Type of message that is the response of calling a tool.
ToolOptions: Generation options to pass into the Tool.
ToolsAgent: An agent powered by the tool calling API.
ToolsAgentOutputParser: Parser for ToolsAgent.
ToolsOutputParser: A parser that returns the list of tool calls returned by the model.
ToolSpec: The specification of a LangChain tool without the actual implementation.
VectorStore: Interface for vector stores.
VectorStoreMemory: Memory backed by a vector store.
VectorStoreMMRSearch: Maximal Marginal Relevance (MMR) search .
VectorStoreRetriever<V extends VectorStore>: A retriever that uses a vector store to retrieve documents.
VectorStoreRetrieverOptions: Options for VectorStoreRetriever.
VectorStoreSearchType: Vector store search type.
VectorStoreSimilaritySearch: Similarity search. Eg. using Cosine similarity.

Enums

AgentEarlyStoppingMethod: Method to use for early stopping if the agent never returns AgentFinish.
ChatMessageContentImageDetail: Specifies the detail level of the image.
ChatMessageRole: Role of a chat message
ChatMessageType: The type of a chat message prompt template.
CodeLanguage: Supported programming languages for CodeTextSplitter.
FinishReason: The reason the model stopped generating tokens.

Extensions

ChatMessagesX on List<ChatMessage>: Extensions on List<ChatMessage>.
RunnableX on Runnable<RunInput, CallOptions, RunOutput>: Extension methods for Runnables.

Functions

calculateSimilarity(List<double> embedding, List<List<double>> embeddings, {double similarityFunction(List<double> a, List<double> b) = cosineSimilarity}) → List<double>: Calculates the similarity between an embedding and a list of embeddings.
cosineSimilarity(List<double> a, List<double> b) → double: Measures the cosine of the angle between two vectors in a vector space. It ranges from -1 to 1, where 1 represents identical vectors, 0 represents orthogonal vectors, and -1 represents vectors that are diametrically opposed.
getIndexesMostSimilarEmbeddings(List<double> embedding, List<List<double>> embeddings, {double similarityFunction(List<double> a, List<double> b) = cosineSimilarity}) → List<int>: Returns a sorted list of indexes of embeddings that are most similar to the provided embedding (in descending order, most similar first).
getPromptInputKey(MemoryInputValues inputValues, Set<String> memoryKeys) → String: This function is used by memory classes to select the input value to use for the memory.

Typedefs

ChainValues = Map<String, dynamic>: Values to be used in the chain.
InputValues = Map<String, dynamic>: Input values used to format a prompt. The keys are the names of the variables in the prompt template and the values the actual values to use.
MemoryInputValues = Map<String, dynamic>: Input values to load from memory.
MemoryOutputValues = Map<String, dynamic>: Output values to save to memory.
MemoryVariables = Map<String, dynamic>: Key-value pairs loaded from memory.
PartialValues = Map<String, dynamic>: Partial input values used to format a prompt. The keys are the names of the variables in the prompt template and the values the actual values to use.

Exceptions / Errors

LangChainException: A base class for all exceptions thrown by LangChain.
OutputParserException: Exception that output parsers should raise to signify a parsing error.
PromptException: Exception thrown when some error occurs while working with a prompt.
TemplateValidationException: Exception thrown when a template validation fails.
ToolException: An exception that a tool throws when execution error occurs.

langchain library

Classes

Enums

Extensions

Functions

Typedefs

Exceptions / Errors

langchain package

langchain library