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Merge pull request #158 from FlowiseAI/feature/ConversationalQAChain-Prompt 

Feature/Add prompt for conversationQA chain
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Henry Heng 2023-05-23 15:50:30 +01:00 committed by GitHub
parent 8d45412c38 ab812af587
commit db717ef410
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1 changed files with 27 additions and 1 deletions
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28
packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts
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@ -4,6 +4,20 @@ import { getBaseClasses } from '../../../src/utils'
import { ConversationalRetrievalQAChain } from 'langchain/chains'
import { BaseRetriever } from 'langchain/schema'
const default_qa_template = `Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:`
const qa_template = `Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
Helpful Answer:`
class ConversationalRetrievalQAChain_Chains implements INode {
label: string
name: string
@ -32,6 +46,16 @@ class ConversationalRetrievalQAChain_Chains implements INode {
label: 'Vector Store Retriever',
name: 'vectorStoreRetriever',
type: 'BaseRetriever'
},
{
label: 'System Message',
name: 'systemMessagePrompt',
type: 'string',
rows: 4,
additionalParams: true,
optional: true,
placeholder:
'I want you to act as a document that I am having a conversation with. Your name is "AI Assistant". You will provide me with answers from the given info. If the answer is not included, say exactly "Hmm, I am not sure." and stop after that. Refuse to answer any question not about the info. Never break character.'
}
]
}
@ -39,9 +63,11 @@ class ConversationalRetrievalQAChain_Chains implements INode {
async init(nodeData: INodeData): Promise<any> {
const model = nodeData.inputs?.model as BaseLanguageModel
const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
const systemMessagePrompt = nodeData.inputs?.systemMessagePrompt as string
const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, {
verbose: process.env.DEBUG === 'true' ? true : false
verbose: process.env.DEBUG === 'true' ? true : false,
qaTemplate: systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template
})
return chain
}