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- add refine chain type custom system message

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- allow all memory type to conver_re_qa_chain
- fix startsWith undefined where override config file is not passed
- ability to generate new session id when sharing chatbot
This commit is contained in:
Henry
2023-07-24 19:11:39 +01:00
parent e237a5f18c
commit bcf3946efb
10 changed files with 373 additions and 240 deletions
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packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts
+45 -13
View File
@@ -1,9 +1,9 @@
import { BaseLanguageModel } from 'langchain/base_language'
import { ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
import { getBaseClasses } from '../../../src/utils'
import { ConversationalRetrievalQAChain } from 'langchain/chains'
import { ConversationalRetrievalQAChain, QAChainParams } from 'langchain/chains'
import { AIMessage, BaseRetriever, HumanMessage } from 'langchain/schema'
import { BaseChatMemory, BufferMemory, ChatMessageHistory } from 'langchain/memory'
import { BaseChatMemory, BufferMemory, ChatMessageHistory, BufferMemoryInput } from 'langchain/memory'
import { PromptTemplate } from 'langchain/prompts'
import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
import {
@@ -11,7 +11,9 @@ import {
default_qa_template,
qa_template,
map_reduce_template,
CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT
CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT,
refine_question_template,
refine_template
} from './prompts'
class ConversationalRetrievalQAChain_Chains implements INode {
@@ -46,9 +48,9 @@ class ConversationalRetrievalQAChain_Chains implements INode {
{
label: 'Memory',
name: 'memory',
type: 'DynamoDBChatMemory | RedisBackedChatMemory | ZepMemory',
type: 'BaseMemory',
optional: true,
description: 'If no memory connected, default BufferMemory will be used'
description: 'If left empty, a default BufferMemory will be used'
},
{
label: 'Return Source Documents',
@@ -115,28 +117,43 @@ class ConversationalRetrievalQAChain_Chains implements INode {
combinePrompt: PromptTemplate.fromTemplate(
systemMessagePrompt ? `${systemMessagePrompt}\n${map_reduce_template}` : default_map_reduce_template
)
}
} as QAChainParams
} else if (chainOption === 'refine') {
// TODO: Add custom system message
const qprompt = new PromptTemplate({
inputVariables: ['context', 'question'],
template: refine_question_template(systemMessagePrompt)
})
const rprompt = new PromptTemplate({
inputVariables: ['context', 'question', 'existing_answer'],
template: refine_template
})
obj.qaChainOptions = {
type: 'refine',
questionPrompt: qprompt,
refinePrompt: rprompt
} as QAChainParams
} else {
obj.qaChainOptions = {
type: 'stuff',
prompt: PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template)
}
} as QAChainParams
}
if (memory) {
memory.inputKey = 'question'
memory.outputKey = 'text'
memory.memoryKey = 'chat_history'
if (chainOption === 'refine') memory.outputKey = 'output_text'
else memory.outputKey = 'text'
obj.memory = memory
} else {
obj.memory = new BufferMemory({
const fields: BufferMemoryInput = {
memoryKey: 'chat_history',
inputKey: 'question',
outputKey: 'text',
returnMessages: true
})
}
if (chainOption === 'refine') fields.outputKey = 'output_text'
else fields.outputKey = 'text'
obj.memory = new BufferMemory(fields)
}
const chain = ConversationalRetrievalQAChain.fromLLM(model, vectorStoreRetriever, obj)
@@ -147,6 +164,7 @@ class ConversationalRetrievalQAChain_Chains implements INode {
const chain = nodeData.instance as ConversationalRetrievalQAChain
const returnSourceDocuments = nodeData.inputs?.returnSourceDocuments as boolean
const memory = nodeData.inputs?.memory
const chainOption = nodeData.inputs?.chainOption as string
let model = nodeData.inputs?.model
@@ -176,8 +194,22 @@ class ConversationalRetrievalQAChain_Chains implements INode {
const loggerHandler = new ConsoleCallbackHandler(options.logger)
if (options.socketIO && options.socketIOClientId) {
const handler = new CustomChainHandler(options.socketIO, options.socketIOClientId, undefined, returnSourceDocuments)
const handler = new CustomChainHandler(
options.socketIO,
options.socketIOClientId,
chainOption === 'refine' ? 4 : undefined,
returnSourceDocuments
)
const res = await chain.call(obj, [loggerHandler, handler])
if (chainOption === 'refine') {
if (res.output_text && res.sourceDocuments) {
return {
text: res.output_text,
sourceDocuments: res.sourceDocuments
}
}
return res?.output_text
}
if (res.text && res.sourceDocuments) return res
return res?.text
} else {
packages/components/nodes/chains/ConversationalRetrievalQAChain/prompts.ts
+30 -1
View File
@@ -27,7 +27,36 @@ export const map_reduce_template = `Given the following extracted parts of a lon
Question: {question}
Helpful Answer:`
export const CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. include it in the standalone question.
export const refine_question_template = (sysPrompt?: string) => {
let returnPrompt = ''
if (sysPrompt)
returnPrompt = `Context information is below.
---------------------
{context}
---------------------
Given the context information and not prior knowledge, ${sysPrompt}
Answer the question: {question}.
Answer:`
if (!sysPrompt)
returnPrompt = `Context information is below.
---------------------
{context}
---------------------
Given the context information and not prior knowledge, answer the question: {question}.
Answer:`
return returnPrompt
}
export const refine_template = `The original question is as follows: {question}
We have provided an existing answer: {existing_answer}
We have the opportunity to refine the existing answer (only if needed) with some more context below.
------------
{context}
------------
Given the new context, refine the original answer to better answer the question.
If you can't find answer from the context, return the original answer.`
export const CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, answer in the same language as the follow up question. include it in the standalone question.
Chat History:
{chat_history}