langchain_community 0.0.3¶
langchain_community.adapters¶
Classes¶
adapters.openai.Chat()
adapters.openai.ChatCompletion()
Chat completion.
adapters.openai.ChatCompletionChunk
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.ChatCompletions
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.Choice
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.ChoiceChunk
Create a new model by parsing and validating input data from keyword arguments.
adapters.openai.Completions()
Completion.
adapters.openai.IndexableBaseModel
Allows a BaseModel to return its fields by string variable indexing
Functions¶
adapters.openai.aenumerate(iterable[, start])
Async version of enumerate function.
adapters.openai.convert_dict_to_message(_dict)
Convert a dictionary to a LangChain message.
adapters.openai.convert_message_to_dict(message)
Convert a LangChain message to a dictionary.
adapters.openai.convert_messages_for_finetuning(...)
Convert messages to a list of lists of dictionaries for fine-tuning.
adapters.openai.convert_openai_messages(messages)
Convert dictionaries representing OpenAI messages to LangChain format.
langchain_community.agent_toolkits¶
Agent toolkits contain integrations with various resources and services.
LangChain has a large ecosystem of integrations with various external resources
like local and remote file systems, APIs and databases.
These integrations allow developers to create versatile applications that combine the
power of LLMs with the ability to access, interact with and manipulate external
resources.
When developing an application, developers should inspect the capabilities and
permissions of the tools that underlie the given agent toolkit, and determine
whether permissions of the given toolkit are appropriate for the application.