From 0a80e9b25ab24ef2292c39aaf5d77583fbbac34d Mon Sep 17 00:00:00 2001 From: Vedant Raikar <108983543+vedantRaikar@users.noreply.github.com> Date: Fri, 19 Apr 2024 18:55:07 +0530 Subject: [PATCH] Add files via upload --- vedantraikar.json | 1 + 1 file changed, 1 insertion(+) create mode 100644 vedantraikar.json diff --git a/vedantraikar.json b/vedantraikar.json new file mode 100644 index 0000000..26ec5db --- /dev/null +++ b/vedantraikar.json @@ -0,0 +1 @@ +{"id":"e74c2369-c537-490a-8e4f-66ea4dfc2877","data":{"nodes":[{"width":384,"height":366,"id":"PyPDFLoader-SLNUq","type":"genericNode","position":{"x":10,"y":792.1165582886154},"data":{"type":"PyPDFLoader","node":{"template":{"file_path":{"required":true,"placeholder":"","show":true,"multiline":false,"value":"dbn.pdf","suffixes":[".pdf"],"password":false,"name":"file_path","advanced":false,"dynamic":false,"info":"","type":"file","list":false,"fileTypes":["pdf"],"file_path":"/mnt/models/files/e74c2369-c537-490a-8e4f-66ea4dfc2877/57b5472b830cd739e4f2faf38c56bbb74d35f6a9fd8f971a483d3ffb72b2de59.pdf"},"metadata":{"required":false,"placeholder":"","show":true,"multiline":false,"value":[{"":""}],"password":false,"name":"metadata","display_name":"Metadata","advanced":false,"dynamic":false,"info":"","type":"dict","list":false},"_type":"PyPDFLoader"},"description":"Load PDF using pypdf into list of 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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":"20","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":"200","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 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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://python.langchain.com/docs/integrations/vectorstores/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\": {\"display_name\": \"Collection Name\", \"value\": \"genflow\"},\n \"persist\": {\"display_name\": \"Persist\"},\n \"persist_directory\": {\"display_name\": \"Persist Directory\"},\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 collection_name: str,\n persist: bool,\n chroma_server_ssl_enabled: bool,\n persist_directory: Optional[str] = None,\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 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)\n","password":false,"name":"code","advanced":false,"type":"code","list":false},"_type":"CustomComponent","inference_api_key":{"required":true,"placeholder":"","show":true,"multiline":false,"value":"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","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":"sentence-transformers/all-MiniLM-l6-v2","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 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