diff --git a/SonaliAssessmentOnGEnAi b/SonaliAssessmentOnGEnAi
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@@ -0,0 +1 @@
+{"id":"f0bb53cc-61cd-4131-a26e-3b08034d3136","data":{"nodes":[{"width":384,"height":375,"id":"PyPDFLoader-MwTV7","type":"genericNode","position":{"x":-375.83627167979563,"y":19.088913274188343},"data":{"type":"PyPDFLoader","node":{"template":{"file_path":{"required":true,"placeholder":"","show":true,"multiline":false,"value":"sonali_de.pdf","suffixes":[".pdf"],"password":false,"name":"file_path","advanced":false,"input_types":["Input"],"dynamic":false,"info":"","type":"file","list":false,"fileTypes":["pdf"],"file_path":"/mnt/models/files/f0bb53cc-61cd-4131-a26e-3b08034d3136/d5ac6b17764daaddbe3c4ecc152b2a08535b81cd0b3ad819c2f17ad053dd04f6.pdf"},"file_size":{"required":true,"placeholder":"","show":true,"multiline":false,"value":20,"password":false,"name":"file_size","display_name":"Fize 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documents.","base_classes":["Document"],"display_name":"PyPDFLoader","custom_fields":{},"output_types":["Document"],"documentation":"https://docs.aiplanet.com/components/document-loaders#pypdfloader","beta":false,"error":null},"id":"PyPDFLoader-MwTV7"},"selected":false,"dragging":false,"positionAbsolute":{"x":-375.83627167979563,"y":19.088913274188343}},{"width":384,"height":501,"id":"RecursiveCharacterTextSplitter-D49Hf","type":"genericNode","position":{"x":227.1640873840388,"y":43.76066667842758},"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.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,"name":"code","advanced":false,"type":"code","list":false},"_type":"CustomComponent","chunk_overlap":{"required":false,"placeholder":"","show":true,"multiline":false,"value":200,"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":1000,"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}},"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.aiplanet.com/components/text-splitters#recursivecharactertextsplitter","beta":true,"error":null},"id":"RecursiveCharacterTextSplitter-D49Hf"},"selected":false,"positionAbsolute":{"x":227.1640873840388,"y":43.76066667842758},"dragging":false},{"width":384,"height":387,"id":"HuggingFaceEmbeddingInferenceAPI-tmFx1","type":"genericNode","position":{"x":-17.747629736926,"y":643.0889912624864},"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":"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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":"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-tmFx1"},"selected":false,"positionAbsolute":{"x":-17.747629736926,"y":643.0889912624864},"dragging":false},{"width":384,"height":291,"id":"Chroma-foGoJ","type":"genericNode","position":{"x":764.999426977248,"y":497.0035943024951},"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 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