base

Create a message with a link to the LangChain troubleshooting guide.

Return a dict representation of an object.

Ensure that a config is a dict with all keys present.

Create a Pydantic model with the given field definitions.

Format a document into a string based on a prompt template.

First, this pulls information from the document from two sources:

1. page_content: This takes the information from the document.page_content and assigns it to a variable named page_content.
2. metadata: This takes information from document.metadata and assigns it to variables of the same name.

Those variables are then passed into the prompt to produce a formatted string.

Async format a document into a string based on a prompt template.

First, this pulls information from the document from two sources:

1. page_content: This takes the information from the document.page_content and assigns it to a variable named page_content.
2. metadata: This takes information from document.metadata and assigns it to variables of the same name.

Those variables are then passed into the prompt to produce a formatted string.

Error codes.

Base class to parse the output of an LLM call.

Output parsers help structure language model responses.

Chat prompt value which explicitly lists out the message types it accepts.

For use in external schemas.

Base abstract class for inputs to any language model.

PromptValues can be converted to both LLM (pure text-generation) inputs and chat model inputs.

String prompt value.

Configuration for a Runnable.

# Parent sets tags
chain.invoke(input, config={"tags": ["parent"]})
# Child automatically inherits and can add:
# ensure_config({"tags": ["child"]}) -> {"tags": ["parent", "child"]}

Runnable that can be serialized to JSON.

Class for storing a piece of text and associated metadata.

Base class for all prompt templates, returning a prompt.