A class for conducting conversations between a human and an AI. It extends the LLMChain class.
const model = new ChatOpenAI({});const chain = new ConversationChain({ llm: model });// Sending a greeting to the conversation chainconst res1 = await chain.call({ input: "Hi! I'm Jim." });console.log({ res1 });// Following up with a question in the conversationconst res2 = await chain.call({ input: "What's my name?" });console.log({ res2 }); Copy
const model = new ChatOpenAI({});const chain = new ConversationChain({ llm: model });// Sending a greeting to the conversation chainconst res1 = await chain.call({ input: "Hi! I'm Jim." });console.log({ res1 });// Following up with a question in the conversationconst res2 = await chain.call({ input: "What's my name?" });console.log({ res2 });
LLM Wrapper to use
Key to use for output, defaults to text
text
Prompt object to use
Optional
Kwargs to pass to LLM
OutputParser to use
Use .batch() instead. Will be removed in 0.2.0.
Call the chain on all inputs in the list
Run the core logic of this chain and add to output if desired.
Wraps _call and handles memory.
Invoke the chain with the provided input and returns the output.
Input values for the chain run.
Promise that resolves with the output of the chain run.
Format prompt with values and pass to LLM
keys to pass to prompt template
CallbackManager to use
Completion from LLM.
llm.predict({ adjective: "funny" }) Copy
llm.predict({ adjective: "funny" })
Use .invoke() instead. Will be removed in 0.2.0.
Static
Load a chain from a json-like object describing it.
A class for conducting conversations between a human and an AI. It extends the LLMChain class.
Example