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Merge branch 'main' into FEATURE/Vision

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# Conflicts:
#	packages/server/src/index.ts
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Henry
2024-01-15 19:20:04 +00:00
parent 7f15494d60 24d227598b
commit f57daea946
51 changed files with 2231 additions and 1486 deletions
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packages/server/marketplaces/chatflows/API Agent.json
+1 -1
View File
@@ -936,7 +936,7 @@
"id": "conversationalAgent_0-input-tools-Tool"
},
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "conversationalAgent_0-input-model-BaseChatModel"
packages/server/marketplaces/chatflows/Chat with a Podcast.json
+22 -34
View File
@@ -13,7 +13,7 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
@@ -28,47 +28,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -89,9 +78,8 @@
"model": "{{chatOpenAI_0.data.instance}}",
"vectorStoreRetriever": "{{memoryVectorStore_0.data.instance}}",
"memory": "",
"returnSourceDocuments": "",
"systemMessagePrompt": "",
"chainOption": ""
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
@@ -625,9 +613,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/Claude LLM.json
+1 -1
View File
@@ -90,7 +90,7 @@
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "conversationChain_0-input-model-BaseChatModel"
packages/server/marketplaces/chatflows/Conversational Agent.json
+1 -1
View File
@@ -354,7 +354,7 @@
"id": "conversationalAgent_0-input-tools-Tool"
},
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "conversationalAgent_0-input-model-BaseChatModel"
packages/server/marketplaces/chatflows/Conversational Retrieval QA Chain.json
+25 -37
View File
@@ -249,10 +249,10 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -264,47 +264,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -325,16 +314,15 @@
"model": "{{chatOpenAI_0.data.instance}}",
"vectorStoreRetriever": "{{pinecone_0.data.instance}}",
"memory": "",
"returnSourceDocuments": "",
"systemMessagePrompt": "",
"chainOption": ""
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain"
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
@@ -704,9 +692,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/Flowise Docs QnA.json
+25 -36
View File
@@ -156,9 +156,9 @@
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"name": "conversationalRetrievalQAChain",
"version": 1,
"version": 2,
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -170,47 +170,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -232,15 +221,15 @@
"vectorStoreRetriever": "{{memoryVectorStore_0.data.instance}}",
"memory": "",
"returnSourceDocuments": true,
"systemMessagePrompt": "",
"chainOption": ""
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain"
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
@@ -668,9 +657,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/Local QnA.json
+26 -35
View File
@@ -83,10 +83,10 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -98,47 +98,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -158,14 +147,16 @@
"inputs": {
"model": "{{chatOllama_0.data.instance}}",
"vectorStoreRetriever": "{{faiss_0.data.instance}}",
"memory": ""
"memory": "",
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
@@ -649,9 +640,9 @@
"source": "chatOllama_0",
"sourceHandle": "chatOllama_0-output-chatOllama-ChatOllama|SimpleChatModel|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOllama_0-chatOllama_0-output-chatOllama-ChatOllama|SimpleChatModel|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOllama_0-chatOllama_0-output-chatOllama-ChatOllama|SimpleChatModel|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/Long Term Memory.json
+27 -36
View File
@@ -13,10 +13,10 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -28,47 +28,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -89,14 +78,16 @@
"model": "{{chatOpenAI_0.data.instance}}",
"vectorStoreRetriever": "{{qdrant_0.data.instance}}",
"memory": "{{ZepMemory_0.data.instance}}",
"returnSourceDocuments": true
"returnSourceDocuments": true,
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
@@ -232,7 +223,7 @@
"label": "Session Id",
"name": "sessionId",
"type": "string",
"description": "if empty, chatId will be used automatically",
"description": "If not specified, a random id will be used. Learn <a target=\"_blank\" href=\"https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat\">more</a>",
"default": "",
"additionalParams": true,
"optional": true,
@@ -709,9 +700,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/Metadata Filter.json
+26 -35
View File
@@ -249,10 +249,10 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -264,47 +264,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -323,14 +312,16 @@
],
"inputs": {
"model": "{{chatOpenAI_0.data.instance}}",
"vectorStoreRetriever": "{{pinecone_0.data.instance}}"
"vectorStoreRetriever": "{{pinecone_0.data.instance}}",
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
@@ -763,9 +754,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/Multiple VectorDB.json
+1 -1
View File
@@ -1567,7 +1567,7 @@
"id": "conversationalAgent_0-input-tools-Tool"
},
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "conversationalAgent_0-input-model-BaseChatModel"
packages/server/marketplaces/chatflows/Simple Conversation Chain.json
+1 -1
View File
@@ -262,7 +262,7 @@
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "conversationChain_0-input-model-BaseChatModel"
packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json
+48 -36
View File
@@ -190,7 +190,7 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
@@ -205,47 +205,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -267,8 +256,8 @@
"vectorStoreRetriever": "{{vectara_0.data.instance}}",
"memory": "",
"returnSourceDocuments": true,
"systemMessagePrompt": "",
"chainOption": ""
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
@@ -361,12 +350,33 @@
{
"label": "Top K",
"name": "topK",
"description": "Number of top results to fetch. Defaults to 4",
"placeholder": "4",
"description": "Number of top results to fetch. Defaults to 5",
"placeholder": "5",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "vectara_0-input-topK-number"
},
{
"label": "MMR K",
"name": "mmrK",
"description": "The number of results to rerank if MMR is enabled.",
"placeholder": "50",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "vectara_0-input-mmrK-number"
},
{
"label": "MMR Diversity Bias",
"name": "mmrDiversityBias",
"step": 0.1,
"description": "Diversity Bias parameter for MMR, if enabled. 0.0 means no diversiry bias, 1.0 means maximum diversity bias. Defaults to 0.0 (MMR disabled).",
"placeholder": "0.0",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "vectara_0-input-mmrDiversityBias-number"
}
],
"inputAnchors": [
@@ -385,7 +395,9 @@
"sentencesBefore": "",
"sentencesAfter": "",
"lambda": "",
"topK": ""
"topK": "",
"mmrK": "",
"mmrDiversityBias": ""
},
"outputAnchors": [
{
@@ -427,9 +439,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/marketplaces/chatflows/WebBrowser.json
+1 -1
View File
@@ -578,7 +578,7 @@
"id": "conversationalAgent_0-input-tools-Tool"
},
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseChatModel",
"id": "conversationalAgent_0-input-model-BaseChatModel"
packages/server/marketplaces/chatflows/WebPage QnA.json
+26 -37
View File
@@ -162,10 +162,10 @@
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"version": 1,
"version": 2,
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain"],
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
@@ -177,47 +177,36 @@
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"label": "Rephrase Prompt",
"name": "rephrasePrompt",
"type": "string",
"description": "Using previous chat history, rephrase question into a standalone question",
"warning": "Prompt must include input variables: {chat_history} and {question}",
"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.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"label": "Response Prompt",
"name": "responsePrompt",
"type": "string",
"description": "Taking the rephrased question, search for answer from the provided context",
"warning": "Prompt must include input variable: {context}",
"rows": 4,
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
}
],
"inputAnchors": [
{
"label": "Language Model",
"label": "Chat Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
"type": "BaseChatModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
},
{
"label": "Vector Store Retriever",
@@ -239,15 +228,15 @@
"vectorStoreRetriever": "{{pinecone_0.data.instance}}",
"memory": "{{RedisBackedChatMemory_0.data.instance}}",
"returnSourceDocuments": true,
"systemMessagePrompt": "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 context. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Do not make up any information that is not in the context. Refuse to answer any question not about the info. Never break character.",
"chainOption": ""
"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain",
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain"
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
}
],
"outputs": {},
@@ -589,7 +578,7 @@
"label": "Session Id",
"name": "sessionId",
"type": "string",
"description": "If not specified, the first CHAT_MESSAGE_ID will be used as sessionId",
"description": "If not specified, a random id will be used. Learn <a target=\"_blank\" href=\"https://docs.flowiseai.com/memory/long-term-memory#ui-and-embedded-chat\">more</a>",
"default": "",
"additionalParams": true,
"optional": true,
@@ -772,9 +761,9 @@
"source": "chatOpenAI_0",
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
"target": "conversationalRetrievalQAChain_0",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"type": "buttonedge",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseLanguageModel",
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
"data": {
"label": ""
}
packages/server/src/index.ts
+47 -82
View File
@@ -20,7 +20,6 @@ import {
ICredentialReturnResponse,
chatType,
IChatMessage,
IReactFlowEdge,
IDepthQueue,
INodeDirectedGraph
} from './Interface'
@@ -39,14 +38,14 @@ import {
databaseEntities,
transformToCredentialEntity,
decryptCredentialData,
clearAllSessionMemory,
replaceInputsWithConfig,
getEncryptionKey,
checkMemorySessionId,
clearSessionMemoryFromViewMessageDialog,
getMemorySessionId,
getUserHome,
replaceChatHistory,
getAllConnectedNodes
getSessionChatHistory,
getAllConnectedNodes,
clearSessionMemory,
findMemoryNode
} from './utils'
import { cloneDeep, omit, uniqWith, isEqual } from 'lodash'
import { getDataSource } from './DataSource'
@@ -362,7 +361,8 @@ export class App {
const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({
id: req.params.id
})
if (chatflow && chatflow.chatbotConfig) {
if (!chatflow) return res.status(404).send(`Chatflow ${req.params.id} not found`)
if (chatflow.chatbotConfig) {
try {
const parsedConfig = JSON.parse(chatflow.chatbotConfig)
return res.json(parsedConfig)
@@ -370,7 +370,7 @@ export class App {
return res.status(500).send(`Error parsing Chatbot Config for Chatflow ${req.params.id}`)
}
}
return res.status(404).send(`Chatbot Config for Chatflow ${req.params.id} not found`)
return res.status(200).send('OK')
})
// Save chatflow
@@ -533,7 +533,7 @@ export class App {
res.status(404).send(`Chatflow ${chatflowid} not found`)
return
}
const chatId = (req.query?.chatId as string) ?? (await getChatId(chatflowid))
const chatId = req.query?.chatId as string
const memoryType = req.query?.memoryType as string | undefined
const sessionId = req.query?.sessionId as string | undefined
const chatType = req.query?.chatType as string | undefined
@@ -543,20 +543,22 @@ export class App {
const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
const nodes = parsedFlowData.nodes
if (isClearFromViewMessageDialog) {
await clearSessionMemoryFromViewMessageDialog(
try {
await clearSessionMemory(
nodes,
this.nodesPool.componentNodes,
chatId,
this.AppDataSource,
sessionId,
memoryType
memoryType,
isClearFromViewMessageDialog
)
} else {
await clearAllSessionMemory(nodes, this.nodesPool.componentNodes, chatId, this.AppDataSource, sessionId)
} catch (e) {
return res.status(500).send('Error clearing chat messages')
}
const deleteOptions: FindOptionsWhere<ChatMessage> = { chatflowid, chatId }
const deleteOptions: FindOptionsWhere<ChatMessage> = { chatflowid }
if (chatId) deleteOptions.chatId = chatId
if (memoryType) deleteOptions.memoryType = memoryType
if (sessionId) deleteOptions.sessionId = sessionId
if (chatType) deleteOptions.chatType = chatType
@@ -644,7 +646,7 @@ export class App {
return res.json(result)
})
// Delete all chatmessages from chatflowid
// Delete all credentials from chatflowid
this.app.delete('/api/v1/credentials/:id', async (req: Request, res: Response) => {
const results = await this.AppDataSource.getRepository(Credential).delete({ id: req.params.id })
return res.json(results)
@@ -1454,26 +1456,6 @@ export class App {
return await this.AppDataSource.getRepository(ChatMessage).save(chatmessage)
}
/**
* Method that find memory label that is connected within chatflow
* In a chatflow, there should only be 1 memory node
* @param {IReactFlowNode[]} nodes
* @param {IReactFlowEdge[]} edges
* @returns {string | undefined}
*/
findMemoryLabel(nodes: IReactFlowNode[], edges: IReactFlowEdge[]): IReactFlowNode | undefined {
const memoryNodes = nodes.filter((node) => node.data.category === 'Memory')
const memoryNodeIds = memoryNodes.map((mem) => mem.data.id)
for (const edge of edges) {
if (memoryNodeIds.includes(edge.source)) {
const memoryNode = nodes.find((node) => node.data.id === edge.source)
return memoryNode
}
}
return undefined
}
async upsertVector(req: Request, res: Response, isInternal: boolean = false) {
try {
const chatflowid = req.params.id
@@ -1663,7 +1645,6 @@ export class App {
* - Still in sync (i.e the flow has not been modified since)
* - Existing overrideConfig and new overrideConfig are the same
* - Flow doesn't start with/contain nodes that depend on incomingInput.question
* - Its not an Upsert request
* TODO: convert overrideConfig to hash when we no longer store base64 string but filepath
***/
const isFlowReusable = () => {
@@ -1719,22 +1700,28 @@ export class App {
isStreamValid = isFlowValidForStream(nodes, endingNodeData)
}
let chatHistory: IMessage[] | string = incomingInput.history
let chatHistory: IMessage[] = incomingInput.history ?? []
// When {{chat_history}} is used in Prompt Template, fetch the chat conversations from memory
// When {{chat_history}} is used in Prompt Template, fetch the chat conversations from memory node
for (const endingNode of endingNodes) {
const endingNodeData = endingNode.data
if (!endingNodeData.inputs?.memory) continue
if (
endingNodeData.inputs?.memory &&
!incomingInput.history &&
(incomingInput.chatId || incomingInput.overrideConfig?.sessionId)
) {
const memoryNodeId = endingNodeData.inputs?.memory.split('.')[0].replace('{{', '')
const memoryNode = nodes.find((node) => node.data.id === memoryNodeId)
if (memoryNode) {
chatHistory = await replaceChatHistory(memoryNode, incomingInput, this.AppDataSource, databaseEntities, logger)
}
const memoryNodeId = endingNodeData.inputs?.memory.split('.')[0].replace('{{', '')
const memoryNode = nodes.find((node) => node.data.id === memoryNodeId)
if (!memoryNode) continue
if (!chatHistory.length && (incomingInput.chatId || incomingInput.overrideConfig?.sessionId)) {
chatHistory = await getSessionChatHistory(
memoryNode,
this.nodesPool.componentNodes,
incomingInput,
this.AppDataSource,
databaseEntities,
logger
)
}
}
@@ -1793,16 +1780,11 @@ export class App {
logger.debug(`[server]: Running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
let sessionId = undefined
if (nodeToExecuteData.instance) sessionId = checkMemorySessionId(nodeToExecuteData.instance, chatId)
const memoryNode = this.findMemoryLabel(nodes, edges)
const memoryNode = findMemoryNode(nodes, edges)
const memoryType = memoryNode?.data.label
let chatHistory: IMessage[] | string = incomingInput.history
if (memoryNode && !incomingInput.history && (incomingInput.chatId || incomingInput.overrideConfig?.sessionId)) {
chatHistory = await replaceChatHistory(memoryNode, incomingInput, this.AppDataSource, databaseEntities, logger)
}
let sessionId = undefined
if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
const nodeInstanceFilePath = this.nodesPool.componentNodes[nodeToExecuteData.name].filePath as string
const nodeModule = await import(nodeInstanceFilePath)
@@ -1810,26 +1792,26 @@ export class App {
let result = isStreamValid
? await nodeInstance.run(nodeToExecuteData, incomingInput.question, {
uploads: incomingInput.uploads,
chatId,
chatflowid,
chatHistory,
socketIO,
socketIOClientId: incomingInput.socketIOClientId,
chatHistory: incomingInput.history,
logger,
appDataSource: this.AppDataSource,
databaseEntities,
analytic: chatflow.analytic,
chatId
uploads: incomingInput.uploads,
socketIO,
socketIOClientId: incomingInput.socketIOClientId
})
: await nodeInstance.run(nodeToExecuteData, incomingInput.question, {
chatId,
chatflowid,
uploads: incomingInput.uploads,
chatHistory,
chatHistory: incomingInput.history,
logger,
appDataSource: this.AppDataSource,
databaseEntities,
analytic: chatflow.analytic,
chatId
uploads: incomingInput.uploads
})
result = typeof result === 'string' ? { text: result } : result
@@ -1893,23 +1875,6 @@ export class App {
}
}
/**
* Get first chat message id
* @param {string} chatflowid
* @returns {string}
*/
export async function getChatId(chatflowid: string): Promise<string> {
// first chatmessage id as the unique chat id
const firstChatMessage = await getDataSource()
.getRepository(ChatMessage)
.createQueryBuilder('cm')
.select('cm.id')
.where('chatflowid = :chatflowid', { chatflowid })
.orderBy('cm.createdDate', 'ASC')
.getOne()
return firstChatMessage ? firstChatMessage.id : ''
}
let serverApp: App | undefined
export async function getAllChatFlow(): Promise<IChatFlow[]> {
packages/server/src/utils/index.ts
+102 -73
View File
@@ -26,7 +26,8 @@ import {
getEncryptionKeyPath,
ICommonObject,
IDatabaseEntity,
IMessage
IMessage,
FlowiseMemory
} from 'flowise-components'
import { randomBytes } from 'crypto'
import { AES, enc } from 'crypto-js'
@@ -270,7 +271,7 @@ export const buildLangchain = async (
depthQueue: IDepthQueue,
componentNodes: IComponentNodes,
question: string,
chatHistory: IMessage[] | string,
chatHistory: IMessage[],
chatId: string,
chatflowid: string,
appDataSource: DataSource,
@@ -317,9 +318,10 @@ export const buildLangchain = async (
await newNodeInstance.vectorStoreMethods!['upsert']!.call(newNodeInstance, reactFlowNodeData, {
chatId,
chatflowid,
chatHistory,
logger,
appDataSource,
databaseEntities,
logger,
cachePool,
dynamicVariables
})
@@ -330,9 +332,10 @@ export const buildLangchain = async (
let outputResult = await newNodeInstance.init(reactFlowNodeData, question, {
chatId,
chatflowid,
chatHistory,
logger,
appDataSource,
databaseEntities,
logger,
cachePool,
dynamicVariables
})
@@ -424,66 +427,52 @@ export const buildLangchain = async (
}
/**
* Clear all session memories on the canvas
* @param {IReactFlowNode[]} reactFlowNodes
* @param {IComponentNodes} componentNodes
* @param {string} chatId
* @param {DataSource} appDataSource
* @param {string} sessionId
*/
export const clearAllSessionMemory = async (
reactFlowNodes: IReactFlowNode[],
componentNodes: IComponentNodes,
chatId: string,
appDataSource: DataSource,
sessionId?: string
) => {
for (const node of reactFlowNodes) {
if (node.data.category !== 'Memory' && node.data.type !== 'OpenAIAssistant') continue
const nodeInstanceFilePath = componentNodes[node.data.name].filePath as string
const nodeModule = await import(nodeInstanceFilePath)
const newNodeInstance = new nodeModule.nodeClass()
if (sessionId && node.data.inputs) {
node.data.inputs.sessionId = sessionId
}
if (newNodeInstance.memoryMethods && newNodeInstance.memoryMethods.clearSessionMemory) {
await newNodeInstance.memoryMethods.clearSessionMemory(node.data, { chatId, appDataSource, databaseEntities, logger })
}
}
}
/**
* Clear specific session memory from View Message Dialog UI
* Clear session memories
* @param {IReactFlowNode[]} reactFlowNodes
* @param {IComponentNodes} componentNodes
* @param {string} chatId
* @param {DataSource} appDataSource
* @param {string} sessionId
* @param {string} memoryType
* @param {string} isClearFromViewMessageDialog
*/
export const clearSessionMemoryFromViewMessageDialog = async (
export const clearSessionMemory = async (
reactFlowNodes: IReactFlowNode[],
componentNodes: IComponentNodes,
chatId: string,
appDataSource: DataSource,
sessionId?: string,
memoryType?: string
memoryType?: string,
isClearFromViewMessageDialog?: string
) => {
if (!sessionId) return
for (const node of reactFlowNodes) {
if (node.data.category !== 'Memory' && node.data.type !== 'OpenAIAssistant') continue
if (memoryType && node.data.label !== memoryType) continue
// Only clear specific session memory from View Message Dialog UI
if (isClearFromViewMessageDialog && memoryType && node.data.label !== memoryType) continue
const nodeInstanceFilePath = componentNodes[node.data.name].filePath as string
const nodeModule = await import(nodeInstanceFilePath)
const newNodeInstance = new nodeModule.nodeClass()
const options: ICommonObject = { chatId, appDataSource, databaseEntities, logger }
if (sessionId && node.data.inputs) node.data.inputs.sessionId = sessionId
if (newNodeInstance.memoryMethods && newNodeInstance.memoryMethods.clearSessionMemory) {
await newNodeInstance.memoryMethods.clearSessionMemory(node.data, { chatId, appDataSource, databaseEntities, logger })
return
// SessionId always take priority first because it is the sessionId used for 3rd party memory node
if (sessionId && node.data.inputs) {
if (node.data.type === 'OpenAIAssistant') {
await newNodeInstance.clearChatMessages(node.data, options, { type: 'threadId', id: sessionId })
} else {
node.data.inputs.sessionId = sessionId
const initializedInstance: FlowiseMemory = await newNodeInstance.init(node.data, '', options)
await initializedInstance.clearChatMessages(sessionId)
}
} else if (chatId && node.data.inputs) {
if (node.data.type === 'OpenAIAssistant') {
await newNodeInstance.clearChatMessages(node.data, options, { type: 'chatId', id: chatId })
} else {
node.data.inputs.sessionId = chatId
const initializedInstance: FlowiseMemory = await newNodeInstance.init(node.data, '', options)
await initializedInstance.clearChatMessages(chatId)
}
}
}
}
@@ -500,7 +489,7 @@ export const getVariableValue = (
paramValue: string,
reactFlowNodes: IReactFlowNode[],
question: string,
chatHistory: IMessage[] | string,
chatHistory: IMessage[],
isAcceptVariable = false
) => {
let returnVal = paramValue
@@ -533,10 +522,7 @@ export const getVariableValue = (
}
if (isAcceptVariable && variableFullPath === CHAT_HISTORY_VAR_PREFIX) {
variableDict[`{{${variableFullPath}}}`] = handleEscapeCharacters(
typeof chatHistory === 'string' ? chatHistory : convertChatHistoryToText(chatHistory),
false
)
variableDict[`{{${variableFullPath}}}`] = handleEscapeCharacters(convertChatHistoryToText(chatHistory), false)
}
// Split by first occurrence of '.' to get just nodeId
@@ -583,7 +569,7 @@ export const resolveVariables = (
reactFlowNodeData: INodeData,
reactFlowNodes: IReactFlowNode[],
question: string,
chatHistory: IMessage[] | string
chatHistory: IMessage[]
): INodeData => {
let flowNodeData = cloneDeep(reactFlowNodeData)
const types = 'inputs'
@@ -970,21 +956,43 @@ export const redactCredentialWithPasswordType = (
}
/**
* Replace sessionId with new chatId
* Ex: after clear chat history, use the new chatId as sessionId
* Get sessionId
* Hierarchy of sessionId (top down)
* API/Embed:
* (1) Provided in API body - incomingInput.overrideConfig: { sessionId: 'abc' }
* (2) Provided in API body - incomingInput.chatId
*
* API/Embed + UI:
* (3) Hard-coded sessionId in UI
* (4) Not specified on UI nor API, default to chatId
* @param {any} instance
* @param {IncomingInput} incomingInput
* @param {string} chatId
*/
export const checkMemorySessionId = (instance: any, chatId: string): string | undefined => {
if (instance.memory && instance.memory.isSessionIdUsingChatMessageId && chatId) {
instance.memory.sessionId = chatId
instance.memory.chatHistory.sessionId = chatId
export const getMemorySessionId = (
memoryNode: IReactFlowNode,
incomingInput: IncomingInput,
chatId: string,
isInternal: boolean
): string | undefined => {
if (!isInternal) {
// Provided in API body - incomingInput.overrideConfig: { sessionId: 'abc' }
if (incomingInput.overrideConfig?.sessionId) {
return incomingInput.overrideConfig?.sessionId
}
// Provided in API body - incomingInput.chatId
if (incomingInput.chatId) {
return incomingInput.chatId
}
}
if (instance.memory && instance.memory.sessionId) return instance.memory.sessionId
else if (instance.memory && instance.memory.chatHistory && instance.memory.chatHistory.sessionId)
return instance.memory.chatHistory.sessionId
return undefined
// Hard-coded sessionId in UI
if (memoryNode.data.inputs?.sessionId) {
return memoryNode.data.inputs.sessionId
}
// Default chatId
return chatId
}
/**
@@ -996,31 +1004,52 @@ export const checkMemorySessionId = (instance: any, chatId: string): string | un
* @param {any} logger
* @returns {string}
*/
export const replaceChatHistory = async (
export const getSessionChatHistory = async (
memoryNode: IReactFlowNode,
componentNodes: IComponentNodes,
incomingInput: IncomingInput,
appDataSource: DataSource,
databaseEntities: IDatabaseEntity,
logger: any
): Promise<string> => {
const nodeInstanceFilePath = memoryNode.data.filePath as string
): Promise<IMessage[]> => {
const nodeInstanceFilePath = componentNodes[memoryNode.data.name].filePath as string
const nodeModule = await import(nodeInstanceFilePath)
const newNodeInstance = new nodeModule.nodeClass()
// Replace memory's sessionId/chatId
if (incomingInput.overrideConfig?.sessionId && memoryNode.data.inputs) {
memoryNode.data.inputs.sessionId = incomingInput.overrideConfig.sessionId
} else if (incomingInput.chatId && memoryNode.data.inputs) {
memoryNode.data.inputs.sessionId = incomingInput.chatId
}
if (newNodeInstance.memoryMethods && newNodeInstance.memoryMethods.getChatMessages) {
return await newNodeInstance.memoryMethods.getChatMessages(memoryNode.data, {
chatId: incomingInput.chatId,
appDataSource,
databaseEntities,
logger
})
}
const initializedInstance: FlowiseMemory = await newNodeInstance.init(memoryNode.data, '', {
appDataSource,
databaseEntities,
logger
})
return ''
return (await initializedInstance.getChatMessages()) as IMessage[]
}
/**
* Method that find memory that is connected within chatflow
* In a chatflow, there should only be 1 memory node
* @param {IReactFlowNode[]} nodes
* @param {IReactFlowEdge[]} edges
* @returns {string | undefined}
*/
export const findMemoryNode = (nodes: IReactFlowNode[], edges: IReactFlowEdge[]): IReactFlowNode | undefined => {
const memoryNodes = nodes.filter((node) => node.data.category === 'Memory')
const memoryNodeIds = memoryNodes.map((mem) => mem.data.id)
for (const edge of edges) {
if (memoryNodeIds.includes(edge.source)) {
const memoryNode = nodes.find((node) => node.data.id === edge.source)
return memoryNode
}
}
return undefined
}
/**