farcasclaudiu/Flowise
1
0
Fork 0
mirror of https://github.com/farcasclaudiu/Flowise.git synced 2026-06-28 13:00:56 +03:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into feature/Credential

Browse Source 
# Conflicts:
#	README.md
#	docker/.env.example
#	packages/components/nodes/documentloaders/Notion/NotionDB.ts
#	packages/components/nodes/memory/DynamoDb/DynamoDb.ts
#	packages/components/nodes/memory/MotorheadMemory/MotorheadMemory.ts
#	packages/components/nodes/memory/ZepMemory/ZepMemory.ts
#	packages/components/package.json
#	packages/components/src/utils.ts
#	packages/server/.env.example
#	packages/server/README.md
#	packages/server/marketplaces/chatflows/Conversational Retrieval QA Chain.json
#	packages/server/src/ChildProcess.ts
#	packages/server/src/DataSource.ts
#	packages/server/src/commands/start.ts
#	packages/server/src/index.ts
#	packages/server/src/utils/index.ts
#	packages/server/src/utils/logger.ts
This commit is contained in:
Henry
2023-07-27 11:26:34 +01:00
parent 0665dcef55 e2d3f27842
commit 61dabbb7da
107 changed files with 4347 additions and 826 deletions
Download Patch File Download Diff File Expand all files Collapse all files
packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts
+66 -36
View File
@@ -1,32 +1,21 @@
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 { AIMessage, BaseRetriever, HumanMessage } from 'langchain/schema'
import { BaseChatMemory, BufferMemory, ChatMessageHistory } from 'langchain/memory'
import { ConversationalRetrievalQAChain, QAChainParams } from 'langchain/chains'
import { AIMessage, HumanMessage } from 'langchain/schema'
import { BaseRetriever } from 'langchain/schema/retriever'
import { BaseChatMemory, BufferMemory, ChatMessageHistory, BufferMemoryInput } from 'langchain/memory'
import { PromptTemplate } from 'langchain/prompts'
import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
const default_qa_template = `Use the following pieces of context to answer the question at the end, in its original language. If you don't know the answer, just say that you don't know in its original language, 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, in its original language.
{context}
Question: {question}
Helpful Answer:`
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.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:`
import {
default_map_reduce_template,
default_qa_template,
qa_template,
map_reduce_template,
CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT,
refine_question_template,
refine_template
} from './prompts'
class ConversationalRetrievalQAChain_Chains implements INode {
label: string
@@ -60,9 +49,9 @@ 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',
@@ -118,28 +107,54 @@ class ConversationalRetrievalQAChain_Chains implements INode {
const obj: any = {
verbose: process.env.DEBUG === 'true' ? true : false,
qaChainOptions: {
type: 'stuff',
prompt: PromptTemplate.fromTemplate(systemMessagePrompt ? `${systemMessagePrompt}\n${qa_template}` : default_qa_template)
},
questionGeneratorChainOptions: {
template: CUSTOM_QUESTION_GENERATOR_CHAIN_PROMPT
}
}
if (returnSourceDocuments) obj.returnSourceDocuments = returnSourceDocuments
if (chainOption) obj.qaChainOptions = { ...obj.qaChainOptions, type: chainOption }
if (chainOption === 'map_reduce') {
obj.qaChainOptions = {
type: 'map_reduce',
combinePrompt: PromptTemplate.fromTemplate(
systemMessagePrompt ? `${systemMessagePrompt}\n${map_reduce_template}` : default_map_reduce_template
)
} as QAChainParams
} else if (chainOption === 'refine') {
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)
@@ -150,6 +165,7 @@ 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
@@ -179,8 +195,22 @@ 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
+64
View File
@@ -0,0 +1,64 @@
export 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:`
export const qa_template = `Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
Helpful Answer:`
export const default_map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer.
If you don't know the answer, just say that you don't know. Don't try to make up an answer.
{summaries}
Question: {question}
Helpful Answer:`
export const map_reduce_template = `Given the following extracted parts of a long document and a question, create a final answer.
{summaries}
Question: {question}
Helpful Answer:`
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}
Follow Up Input: {question}
Standalone question:`