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Whisper_Speech_to_Text.json
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Whisper_Speech_to_Text.json
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{
"id": "08ca082c-3bd7-4db0-aa63-6f79a22f575d",
"data": {
"nodes": [
{
"width": 384,
"height": 501,
"id": "RecursiveCharacterTextSplitter-vfMTH",
"type": "genericNode",
"position": {
"x": 715.1591932077537,
"y": 0.764544979573671
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"data": {
"type": "RecursiveCharacterTextSplitter",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"show": false,
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"value": "from typing import Optional\nfrom genflow import CustomComponent\nfrom langchain.schema import Document\nfrom genflow.utils.util import build_loader_repr_from_documents\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.aiplanet.com/components/text-splitters#recursivecharactertextsplitter\"\n \n def build_config(self):\n return {\n \"documents\": {\n \"display_name\": \"Documents\",\n \"info\": \"The documents to split.\",\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n documents: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Document]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n from langchain.text_splitter import RecursiveCharacterTextSplitter\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [x.encode().decode(\"unicode-escape\") for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n\n docs = splitter.split_documents(documents)\n self.repr_value = build_loader_repr_from_documents(docs)\n return docs\n",
"password": false,
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"info": "The amount of overlap between chunks.",
"type": "int",
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"required": false,
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"show": true,
"multiline": false,
"value": 1000,
"password": false,
"name": "chunk_size",
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"advanced": false,
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"info": "The maximum length of each chunk.",
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"placeholder": "",
"show": true,
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"name": "separators",
"display_name": "Separators",
"advanced": false,
"dynamic": false,
"info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
"type": "str",
"list": true
}
},
"description": "Split text into chunks of a specified length.",
"base_classes": [
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"display_name": "Recursive Character Text Splitter",
"custom_fields": {
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"chunk_size": null,
"documents": null,
"separators": null
},
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"documentation": "https://docs.aiplanet.com/components/text-splitters#recursivecharactertextsplitter",
"beta": true,
"error": null
},
"id": "RecursiveCharacterTextSplitter-vfMTH"
},
"positionAbsolute": {
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{
"width": 384,
"height": 735,
"id": "AzureChatOpenAI-W4KxI",
"type": "genericNode",
"position": {
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"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
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"value": "from typing import Optional\nfrom genflow.interface.custom import CustomComponent\nfrom langchain.llms.base import BaseLLM\nfrom langchain.chat_models import AzureChatOpenAI\n\n\nclass AzureChatOpenAILLM(CustomComponent):\n display_name: str = \"AzureChatOpenAI\"\n description: str = \"Azure Chat Open AI Chat&Completion large language models.\"\n documentation: str = \"https://docs.aiplanet.com/components/large-language-models#azurechatopenai\"\n \n AZURE_OPENAI_MODELS = [\n \"gpt-4\",\n \"gpt-4-32k\",\n \"gpt-4-vision\",\n ]\n\n def build_config(self):\n return {\n \"model\": {\n \"display_name\": \"Model Name\",\n \"value\": \"gpt-4\",\n \"options\": self.AZURE_OPENAI_MODELS,\n \"required\": True,\n },\n \"api_key\": {\n \"display_name\": \"AzureChatOpenAI API Key\",\n \"required\": True,\n \"password\": True,\n },\n \"api_base\": {\n \"display_name\": \"AzureChatOpenAI API Base\",\n \"required\": True,\n },\n \"api_type\": {\"display_name\": \"AzureChatOpenAI API Type\", \"required\": True},\n \"azure_deployment\": {\n \"display_name\": \"Deployment Name\",\n \"required\": True,\n },\n \"api_version\": {\n \"display_name\": \"API Version\",\n \"value\": \"2023-07-01-preview\",\n \"required\": True,\n \"advanced\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"value\": 0.5,\n \"field_type\": \"float\",\n \"required\": False,\n },\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"value\": 512,\n \"required\": False,\n \"field_type\": \"int\",\n \"advanced\": True,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n model: str,\n api_base: str,\n api_type: str,\n api_key: str,\n azure_deployment: str,\n api_version: str = \"2023-05-15\",\n temperature: Optional[float] = 0.7,\n max_tokens: Optional[int] = 512,\n ) -> BaseLLM:\n try:\n output = AzureChatOpenAI(\n model_name=model,\n openai_api_base=api_base,\n openai_api_type=api_type,\n openai_api_key=api_key,\n openai_api_version=api_version,\n deployment_name=azure_deployment,\n temperature=temperature,\n max_tokens=max_tokens,\n )\n except Exception as e:\n raise ValueError(\"Could not connect to Azure ChatOpenAI model.\") from e\n return output\n",
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},
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"password": false,
"name": "api_base",
"display_name": "AzureChatOpenAI API Base",
"advanced": false,
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"info": "",
"type": "str",
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},
"api_key": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": true,
"name": "api_key",
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"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false,
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},
"api_type": {
"required": true,
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"password": false,
"name": "api_type",
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"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false,
"value": ""
},
"api_version": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "2023-07-01-preview",
"password": false,
"name": "api_version",
"display_name": "API Version",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"azure_deployment": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "azure_deployment",
"display_name": "Deployment Name",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false,
"value": ""
},
"max_tokens": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": 512,
"password": false,
"name": "max_tokens",
"display_name": "Max Tokens",
"advanced": true,
"dynamic": false,
"info": "",
"type": "int",
"list": false
},
"model": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "gpt-4-32k",
"password": false,
"options": [
"gpt-4",
"gpt-4-32k",
"gpt-4-vision"
],
"name": "model",
"display_name": "Model Name",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": true
},
"temperature": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "0.1",
"password": false,
"name": "temperature",
"display_name": "Temperature",
"advanced": false,
"dynamic": false,
"info": "",
"type": "float",
"list": false
}
},
"description": "Azure Chat Open AI Chat&Completion large language models.",
"base_classes": [
"BaseLanguageModel",
"BaseLLM"
],
"display_name": "AzureChatOpenAI",
"custom_fields": {
"api_base": null,
"api_key": null,
"api_type": null,
"api_version": null,
"azure_deployment": null,
"max_tokens": null,
"model": null,
"temperature": null
},
"output_types": [
"AzureChatOpenAI"
],
"documentation": "https://docs.aiplanet.com/components/large-language-models#azurechatopenai",
"beta": true,
"error": null
},
"id": "AzureChatOpenAI-W4KxI"
},
"selected": false,
"positionAbsolute": {
"x": 1608.665462080407,
"y": -681.0252314723016
},
"dragging": false
},
{
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"height": 291,
"id": "Chroma-MSsd9",
"type": "genericNode",
"position": {
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"y": 827.7544464822664
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"template": {
"code": {
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"show": false,
"multiline": true,
"value": "from typing import Optional, Union\nfrom genflow import CustomComponent\n\nfrom langchain.vectorstores.chroma import Chroma\nfrom langchain.schema import Document\nfrom langchain.vectorstores.base import VectorStore\nfrom langchain.schema import BaseRetriever\nfrom langchain.embeddings.base import Embeddings\nimport chromadb # type: ignore\n\n\nclass ChromaComponent(CustomComponent):\n \"\"\"\n A custom component for implementing a Vector Store using Chroma.\n \"\"\"\n\n display_name: str = \"Chroma\"\n description: str = \"Implementation of Vector Store using Chroma\"\n documentation = \"https://docs.aiplanet.com/components/vector-store#chroma\"\n beta: bool = True\n\n def build_config(self):\n \"\"\"\n Builds the configuration for the component.\n\n Returns:\n - dict: A dictionary containing the configuration options for the component.\n \"\"\"\n return {\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"value\": \"genflow\",\n \"required\": False,\n \"advanced\": True,\n },\n \"persist\": {\n \"display_name\": \"Persist\",\n \"value\": True,\n \"required\": False,\n \"advanced\": True,\n },\n \"persist_directory\": {\n \"display_name\": \"Persist Directory\",\n \"value\": \"/mnt/models/chroma\",\n \"required\": False,\n \"advanced\": True,\n },\n \"code\": {\"show\": False, \"display_name\": \"Code\"},\n \"documents\": {\"display_name\": \"Documents\", \"is_list\": True},\n \"embedding\": {\"display_name\": \"Embedding\"},\n \"chroma_server_cors_allow_origins\": {\n \"display_name\": \"Server CORS Allow Origins\",\n \"advanced\": True,\n },\n \"chroma_server_host\": {\"display_name\": \"Server Host\", \"advanced\": True},\n \"chroma_server_port\": {\"display_name\": \"Server Port\", \"advanced\": True},\n \"chroma_server_grpc_port\": {\n \"display_name\": \"Server gRPC Port\",\n \"advanced\": True,\n },\n \"chroma_server_ssl_enabled\": {\n \"display_name\": \"Server SSL Enabled\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n chroma_server_ssl_enabled: bool,\n collection_name: Optional[str] = \"genflow\",\n persist: Optional[bool] = True,\n persist_directory: Optional[str] = \"/mnt/models/chroma\",\n embedding: Optional[Embeddings] = None,\n documents: Optional[Document] = None,\n chroma_server_cors_allow_origins: Optional[str] = None,\n chroma_server_host: Optional[str] = None,\n chroma_server_port: Optional[int] = None,\n chroma_server_grpc_port: Optional[int] = None,\n ) -> Union[VectorStore, BaseRetriever]:\n \"\"\"\n Builds the Vector Store or BaseRetriever object.\n\n Args:\n - collection_name (str): The name of the collection.\n - persist_directory (Optional[str]): The directory to persist the Vector Store to.\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\n - persist (bool): Whether to persist the Vector Store or not.\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\n - documents (Optional[Document]): The documents to use for the Vector Store.\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\n - chroma_server_host (Optional[str]): The host for the Chroma server.\n - chroma_server_port (Optional[int]): The port for the Chroma server.\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\n\n Returns:\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\n \"\"\"\n\n # Chroma settings\n chroma_settings = None\n\n if chroma_server_host is not None:\n chroma_settings = chromadb.config.Settings(\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins\n or None,\n chroma_server_host=chroma_server_host,\n chroma_server_port=chroma_server_port or None,\n chroma_server_grpc_port=chroma_server_grpc_port or None,\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\n )\n\n # If documents, then we need to create a Chroma instance using .from_documents\n if documents is not None and embedding is not None:\n return Chroma.from_documents(\n documents=documents, # type: ignore\n persist_directory=persist_directory if persist else None,\n collection_name=collection_name,\n embedding=embedding,\n client_settings=chroma_settings,\n )\n\n if embedding is not None:\n return Chroma(\n persist_directory=persist_directory,\n client_settings=chroma_settings,\n embedding_function=embedding,\n collection_name=collection_name,\n )\n\n return Chroma(\n persist_directory=persist_directory, client_settings=chroma_settings\n )\n",
"password": false,
"name": "code",
"advanced": false,
"type": "code",
"list": false
},
"_type": "CustomComponent",
"chroma_server_cors_allow_origins": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "chroma_server_cors_allow_origins",
"display_name": "Server CORS Allow Origins",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"chroma_server_grpc_port": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "chroma_server_grpc_port",
"display_name": "Server gRPC Port",
"advanced": true,
"dynamic": false,
"info": "",
"type": "int",
"list": false
},
"chroma_server_host": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "chroma_server_host",
"display_name": "Server Host",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"chroma_server_port": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "chroma_server_port",
"display_name": "Server Port",
"advanced": true,
"dynamic": false,
"info": "",
"type": "int",
"list": false
},
"chroma_server_ssl_enabled": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": false,
"password": false,
"name": "chroma_server_ssl_enabled",
"display_name": "Server SSL Enabled",
"advanced": true,
"dynamic": false,
"info": "",
"type": "bool",
"list": false
},
"collection_name": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "genflow",
"password": false,
"name": "collection_name",
"display_name": "Collection Name",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"documents": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "documents",
"display_name": "Documents",
"advanced": false,
"dynamic": false,
"info": "",
"type": "Document",
"list": true
},
"embedding": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "embedding",
"display_name": "Embedding",
"advanced": false,
"dynamic": false,
"info": "",
"type": "Embeddings",
"list": false
},
"persist": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": true,
"password": false,
"name": "persist",
"display_name": "Persist",
"advanced": true,
"dynamic": false,
"info": "",
"type": "bool",
"list": false
},
"persist_directory": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "/mnt/models/chroma",
"password": false,
"name": "persist_directory",
"display_name": "Persist Directory",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
}
},
"description": "Implementation of Vector Store using Chroma",
"base_classes": [
"VectorStore",
"BaseRetriever"
],
"display_name": "Chroma",
"custom_fields": {
"chroma_server_cors_allow_origins": null,
"chroma_server_grpc_port": null,
"chroma_server_host": null,
"chroma_server_port": null,
"chroma_server_ssl_enabled": null,
"collection_name": null,
"documents": null,
"embedding": null,
"persist": null,
"persist_directory": null
},
"output_types": [
"Chroma"
],
"documentation": "https://docs.aiplanet.com/components/vector-store#chroma",
"beta": true,
"error": null
},
"id": "Chroma-MSsd9"
},
"selected": false,
"positionAbsolute": {
"x": 1430.3803245394756,
"y": 827.7544464822664
},
"dragging": false
},
{
"width": 384,
"height": 387,
"id": "HuggingFaceEmbeddingInferenceAPI-USZgM",
"type": "genericNode",
"position": {
"x": 701.3217227112808,
"y": -598.0857498885518
},
"data": {
"type": "HuggingFaceEmbeddingInferenceAPI",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"show": false,
"multiline": true,
"value": "from genflow import CustomComponent\nfrom langchain.embeddings.base import Embeddings\nfrom langchain.embeddings import HuggingFaceInferenceAPIEmbeddings\n\n\nclass HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):\n display_name: str = \"HuggingFaceInferenceAPI Embeddings\"\n description: str = \"\"\"Access HuggingFaceEmbedding model via inference api,download models locally.\"\"\"\n documentation: str = \"https://docs.aiplanet.com/components/embeddings#huggingface-inference-api-embeddings\"\n beta = False\n\n def build_config(self):\n return {\n \"inference_api_key\": {\n \"display_name\": \"Inference API Key\",\n \"is_list\": False,\n \"required\": True,\n \"value\": \"\",\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"is_list\": False,\n \"required\": True,\n \"value\": \"\",\n },\n \"code\": {\"show\": False},\n }\n\n def build(self, inference_api_key: str, model_name: str) -> Embeddings:\n return HuggingFaceInferenceAPIEmbeddings(\n api_key=inference_api_key, model_name=model_name\n )\n",
"password": false,
"name": "code",
"advanced": false,
"type": "code",
"list": false
},
"_type": "CustomComponent",
"inference_api_key": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "",
"password": false,
"name": "inference_api_key",
"display_name": "Inference API Key",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"model_name": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "BAAI/bge-large-en-v1.5",
"password": false,
"name": "model_name",
"display_name": "Model Name",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false
}
},
"description": "Access HuggingFaceEmbedding model via inference api,download models locally.",
"base_classes": [
"Embeddings"
],
"display_name": "HuggingFaceInferenceAPI Embeddings",
"custom_fields": {
"inference_api_key": null,
"model_name": null
},
"output_types": [
"HuggingFaceEmbeddingInferenceAPI"
],
"documentation": "https://docs.aiplanet.com/components/embeddings#huggingface-inference-api-embeddings",
"beta": false,
"error": null
},
"id": "HuggingFaceEmbeddingInferenceAPI-USZgM"
},
"selected": false,
"positionAbsolute": {
"x": 701.3217227112808,
"y": -598.0857498885518
},
"dragging": false
},
{
"width": 384,
"height": 469,
"id": "CustomComponent-N37VV",
"type": "genericNode",
"position": {
"x": -26.27771861343183,
"y": -70.17469282079313
},
"data": {
"type": "CustomComponent",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"show": true,
"multiline": true,
"value": "from genflow import CustomComponent\nfrom typing import Optional, List, Dict, Union\nfrom openai import OpenAI\nfrom langchain.schema import Document\nfrom langchain.docstore.document import Document\n\nclass Component(CustomComponent):\n display_name: str = \"Whisper Transcriber\"\n description: str = \"Converts audio to text using OpenAI's Whisper.\"\n\n def build_config(self):\n return {\"audio\":\n {\"field_type\":\"file\",\n \"suffixes\":[\".mp3\", \".mp4\"],\"input_types\":[\"Input\"]},\n \"OpenAIKey\":\n {\"field_type\":\"str\",\"password\":True,\"input_types\":[\"Input\"]}\n }\n\n def build(self, audio:str, OpenAIKey:str) -> List[Document]:\n client = OpenAI(api_key=OpenAIKey)\n \n audio_file= open(audio, \"rb\")\n transcript = client.audio.transcriptions.create(\n model=\"whisper-1\", \n file=audio_file,\n response_format=\"text\"\n )\n \n self.status = transcript\n doc = Document(page_content=transcript, metadata={\"source\": \"audio\"})\n return doc",
"password": false,
"name": "code",
"advanced": false,
"type": "code",
"list": false
},
"_type": "CustomComponent",
"OpenAIKey": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": true,
"name": "OpenAIKey",
"display_name": "OpenAIKey",
"advanced": false,
"input_types": [
"Input"
],
"dynamic": false,
"info": "",
"type": "str",
"list": false,
"value": ""
},
"audio": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"suffixes": [
".mp3",
".mp4"
],
"password": false,
"name": "audio",
"display_name": "audio",
"advanced": false,
"input_types": [
"Input"
],
"dynamic": false,
"info": "",
"type": "file",
"list": false,
"file_path": "/mnt/models/files/837a52da-ca1d-471e-aa8a-385b804ed470/2749b44e3cfd3c70eea9097cf2e97d91d07a6ce6b44fca9e71bdd1c3317106ba.mp3",
"value": ""
}
},
"description": "Converts audio to text using OpenAI's Whisper.",
"base_classes": [
"Document"
],
"display_name": "Whisper Transcriber",
"custom_fields": {
"OpenAIKey": null,
"audio": null
},
"output_types": [
"Document"
],
"documentation": "",
"beta": true,
"error": null
},
"id": "CustomComponent-N37VV"
},
"selected": false,
"positionAbsolute": {
"x": -26.27771861343183,
"y": -70.17469282079313
},
"dragging": false
},
{
"width": 384,
"height": 457,
"id": "Input-V4esj",
"type": "genericNode",
"position": {
"x": -702.320697451038,
"y": 14.33095181856649
},
"data": {
"type": "Input",
"node": {
"template": {
"input_value": {
"required": false,
"placeholder": "",
"show": true,
"multiline": true,
"value": "",
"password": false,
"name": "input_value",
"display_name": "Input Value",
"advanced": false,
"dynamic": false,
"info": "",
"type": "file",
"list": false,
"file_path": null
},
"input_key": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "File ",
"password": false,
"name": "input_key",
"display_name": "Input Key",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"input_type": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "File",
"password": false,
"options": [
"File",
"Url",
"Text"
],
"name": "input_type",
"display_name": "Input Type",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"_type": "Input"
},
"description": "Input is used to specify the type of input.",
"base_classes": [
"Input"
],
"display_name": "Input",
"custom_fields": {},
"output_types": [
"Input"
],
"documentation": "https://docs.aiplanet.com/components/inputs",
"beta": false,
"error": null
},
"id": "Input-V4esj"
},
"selected": false,
"positionAbsolute": {
"x": -702.320697451038,
"y": 14.33095181856649
},
"dragging": false
},
{
"width": 384,
"height": 529,
"id": "ConversationBufferMemory-Bbv66",
"type": "genericNode",
"position": {
"x": 1801.2704708238798,
"y": 230.17579978325892
},
"data": {
"type": "ConversationBufferMemory",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"show": false,
"multiline": true,
"value": "from typing import Optional, Union\nfrom langchain.memory.chat_memory import BaseMemory, BaseChatMemory\nfrom langchain.memory.buffer import ConversationBufferMemory\nfrom langchain.memory.chat_message_histories.postgres import PostgresChatMessageHistory\n\nfrom genflow import CustomComponent\n\n\nclass ConversationBufferMemoryComponent(CustomComponent):\n display_name: str = \"ConversationBufferMemory\"\n description: str = \"Buffer for storing conversation memory.\"\n documentation: str = (\n \"https://docs.aiplanet.com/components/memories#conversationbuffermemory\"\n )\n beta = False\n\n def build_config(self):\n return {\n \"input_key\": {\n \"display_name\": \"Input Key\",\n \"required\": False,\n \"value\": \"\",\n \"info\": \"The variable to be used as Chat Input when more than one variable is available.\",\n },\n \"memory_key\": {\n \"display_name\": \"Memory Key\",\n \"required\": False,\n \"value\": \"history\",\n },\n \"output_key\": {\n \"display_name\": \"Output Key\",\n \"required\": False,\n \"value\": \"\",\n \"info\": \"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\n },\n \"return_messages\": {\n \"display_name\": \"Return Messages\",\n \"field_type\": \"bool\",\n \"required\": False,\n \"value\": True,\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"required\": False,\n \"advanced\": True,\n \"value\": \"genflow_memory_db\",\n },\n \"connection_string\": {\n \"display_name\": \"Connection String\",\n \"required\": False,\n \"advanced\": True,\n \"value\": \"\",\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n input_key: Optional[str] = \"\",\n memory_key: Optional[str] = \"history\",\n output_key: Optional[str] = \"\",\n return_messages: Optional[bool] = True,\n session_id: Optional[str] = \"genflow_memory_db\",\n connection_string: Optional[str] = \"\",\n ) -> Union[BaseMemory, BaseChatMemory]:\n chat_memory = PostgresChatMessageHistory(\n session_id=session_id, connection_string=connection_string\n )\n\n keys = {}\n\n if input_key and input_key != \"\":\n keys[\"input_key\"] = input_key\n\n if output_key and output_key != \"\":\n keys[\"output_key\"] = output_key\n\n return ConversationBufferMemory(\n chat_memory=chat_memory,\n memory_key=memory_key,\n return_messages=return_messages,\n **keys\n )\n",
"password": false,
"name": "code",
"advanced": false,
"type": "code",
"list": false
},
"_type": "CustomComponent",
"connection_string": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "",
"password": false,
"name": "connection_string",
"display_name": "Connection String",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"input_key": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "question",
"password": false,
"name": "input_key",
"display_name": "Input Key",
"advanced": false,
"dynamic": false,
"info": "The variable to be used as Chat Input when more than one variable is available.",
"type": "str",
"list": false
},
"memory_key": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "chat_history",
"password": false,
"name": "memory_key",
"display_name": "Memory Key",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": false
},
"output_key": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "answer",
"password": false,
"name": "output_key",
"display_name": "Output Key",
"advanced": false,
"dynamic": false,
"info": "The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)",
"type": "str",
"list": false
},
"return_messages": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": true,
"password": false,
"name": "return_messages",
"display_name": "Return Messages",
"advanced": false,
"dynamic": false,
"info": "",
"type": "bool",
"list": false
},
"session_id": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "genflow_memory_db",
"password": false,
"name": "session_id",
"display_name": "Session ID",
"advanced": true,
"dynamic": false,
"info": "",
"type": "str",
"list": false
}
},
"description": "Buffer for storing conversation memory.",
"base_classes": [
"BaseMemory",
"BaseChatMemory",
"BaseMemory"
],
"display_name": "ConversationBufferMemory",
"custom_fields": {
"connection_string": null,
"input_key": null,
"memory_key": null,
"output_key": null,
"return_messages": null,
"session_id": null
},
"output_types": [
"ConversationBufferMemory"
],
"documentation": "https://docs.aiplanet.com/components/memories#conversationbuffermemory",
"beta": false,
"error": null
},
"id": "ConversationBufferMemory-Bbv66"
},
"selected": false,
"positionAbsolute": {
"x": 1801.2704708238798,
"y": 230.17579978325892
},
"dragging": false
},
{
"width": 384,
"height": 497,
"id": "ConversationalRetrievalChain-x01sl",
"type": "genericNode",
"position": {
"x": 2531.265923045576,
"y": 243.75711052226725
},
"data": {
"type": "ConversationalRetrievalChain",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"show": false,
"multiline": true,
"value": "from typing import Optional, Union, Callable\nfrom genflow import CustomComponent\nfrom genflow.field_typing import (\n BasePromptTemplate,\n BaseLanguageModel,\n BaseMemory,\n Chain,\n BaseRetriever,\n)\nfrom langchain.chains import ConversationalRetrievalChain\nfrom langchain.prompts import PromptTemplate\n\n\nclass ConversationalRetrievalChainComponent(CustomComponent):\n \"\"\"\n A custom component for implementing a RetrievalQA using Prompt.\n \"\"\"\n\n display_name: str = \"ConversationalRetrievalChain\"\n description: str = \"Implementation of ConversationalRetrievalChain: allows Memory, Custom Prompt, Retriever\"\n documentation: str = \"https://docs.aiplanet.com/components/chains#conversationalretrievalchain\"\n beta: bool = True\n \n CHAIN_TYPE_OPTIONS = [\n 'stuff','map_reduce','refine','map_rerank'\n ]\n\n def build_config(self):\n \"\"\"\n Builds the configuration for the component.\n\n Returns:\n - dict: A dictionary containing the configuration options for the component.\n \"\"\"\n return {\n \"chain_type\":{\"display\":\"chain_type\",\"value\":\"stuff\",\"options\":self.CHAIN_TYPE_OPTIONS,\"required\":True},\n \"llm\":{\"display_name\":\"LLM\",\"required\":True},\n \"prompt\":{\"display_name\":\"Prompt\"},\n \"memory\":{\"display_name\":\"Memory\"},\n \"retriever\":{\"display_name\":\"Retriever\",\"required\":True},\n \"code\": {\"show\": False}, \n }\n\n def build(\n self,\n llm:BaseLanguageModel,\n retriever: BaseRetriever,\n prompt: Optional[BasePromptTemplate] = None,\n chain_type:str = \"stuff\",\n memory: BaseMemory = None,\n ) -> Chain:\n \"\"\"\n Builds the RetrievalQA with prompt\n\n Args:\n - llm: Large Language Models\n - chain_type: used to load a specific type of chain for question-answering\n - chain_type_kwargs: chain keywords argument to pass prompt\n - retriever: vector store to retrieve k relevant context information\n - memory: memory to provide chat_history for object\n\n Returns:\n - Chain: The ConversationalRetrievalChain.from_llm\n \"\"\"\n\n DEFAULT_TEMPLATE = \"\"\"\n Answer the question based on the chat history(delimited by <hs></hs>) and context(delimited by <ctx> </ctx>) below.\n -----------\n <ctx>\n {context}\n </ctx>\n -----------\n <hs>\n {chat_history}\n </hs>\n -----------\n Question: {question}\n Answer:\n \"\"\"\n\n DEFAULT_PROMPT = PromptTemplate(\n input_variables=[\"context\", \"question\", \"chat_history\"],\n template=DEFAULT_TEMPLATE\n )\n \n if prompt==None:\n prompt = DEFAULT_PROMPT\n\n return ConversationalRetrievalChain.from_llm(\n llm=llm,\n retriever=retriever.as_retriever(), chain_type=chain_type,return_source_documents=True,\n verbose = False,\n combine_docs_chain_kwargs={'prompt': prompt},\n memory = memory,\n )\n",
"password": false,
"name": "code",
"advanced": false,
"type": "code",
"list": false
},
"_type": "CustomComponent",
"chain_type": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"value": "stuff",
"password": false,
"options": [
"stuff",
"map_reduce",
"refine",
"map_rerank"
],
"name": "chain_type",
"display_name": "chain_type",
"advanced": false,
"dynamic": false,
"info": "",
"type": "str",
"list": true
},
"llm": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "llm",
"display_name": "LLM",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseLanguageModel",
"list": false
},
"memory": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "memory",
"display_name": "Memory",
"advanced": false,
"dynamic": false,
"info": "",
"type": "BaseMemory",
"list": false
},
"prompt": {
"required": false,