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OCR_Scanned_Chat.json
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{
"id": "7dd3ecf4-8d28-4787-a2d9-4072eb650705",
"data": {
"nodes": [
{
"width": 384,
"height": 501,
"id": "RecursiveCharacterTextSplitter-Zk55A",
"type": "genericNode",
"position": {
"x": 177.74123774779434,
"y": 898.1899836356408
},
"data": {
"type": "RecursiveCharacterTextSplitter",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"show": false,
"multiline": true,
"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.genflow.org/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,
"name": "code",
"advanced": false,
"type": "code",
"list": false
},
"_type": "CustomComponent",
"chunk_overlap": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "150",
"password": false,
"name": "chunk_overlap",
"display_name": "Chunk Overlap",
"advanced": false,
"dynamic": false,
"info": "The amount of overlap between chunks.",
"type": "int",
"list": false
},
"chunk_size": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"value": "500",
"password": false,
"name": "chunk_size",
"display_name": "Chunk Size",
"advanced": false,
"dynamic": false,
"info": "The maximum length of each chunk.",
"type": "int",
"list": false
},
"documents": {
"required": true,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"name": "documents",
"display_name": "Documents",
"advanced": false,
"dynamic": false,
"info": "The documents to split.",
"type": "Document",
"list": true
},
"separators": {
"required": false,
"placeholder": "",
"show": true,
"multiline": false,
"password": false,
"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,
"value": [
""
]
}
},
"description": "Split text into chunks of a specified length.",
"base_classes": [
"Document"
],
"display_name": "Recursive Character Text Splitter",
"custom_fields": {
"chunk_overlap": null,
"chunk_size": null,
"documents": null,
"separators": null
},
"output_types": [
"RecursiveCharacterTextSplitter"
],
"documentation": "https://docs.genflow.org/components/text-splitters#recursivecharactertextsplitter",
"beta": true,
"error": null
},
"id": "RecursiveCharacterTextSplitter-Zk55A"
},
"selected": false,
"positionAbsolute": {
"x": 177.74123774779434,
"y": 898.1899836356408
},
"dragging": false
},
{
"width": 384,
"height": 427,
"id": "HuggingFaceEmbeddingInferenceAPI-LRzFh",
"type": "genericNode",
"position": {
"x": 144.9517824752378,
"y": 274.25584108567
},
"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, \nwhich does not require to install sentence_transformers \nand download models locally.\"\"\"\n documentation: str = (\n \"https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub\"\n )\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-small-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, \nwhich does not require to install sentence_transformers \nand download models locally.",
"base_classes": [
"Embeddings"
],
"display_name": "HuggingFaceInferenceAPI Embeddings",
"custom_fields": {
"inference_api_key": null,
"model_name": null
},
"output_types": [
"HuggingFaceEmbeddingInferenceAPI"
],
"documentation": "https://python.langchain.com/docs/integrations/text_embedding/huggingfacehub",
"beta": false,
"error": null
},
"id": "HuggingFaceEmbeddingInferenceAPI-LRzFh"
},
"selected": false,
"positionAbsolute": {
"x": 144.9517824752378,
"y": 274.25584108567
},
"dragging": false
},
{
"width": 384,
"height": 291,
"id": "Chroma-RhZmo",
"type": "genericNode",
"position": {
"x": 1064.272935322748,
"y": 1287.5918335728254
},
"data": {
"type": "Chroma",
"node": {
"template": {
"code": {
"dynamic": true,
"required": true,
"placeholder": "",
"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": {
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