prerna.reactor.utils.GetEngineUsageReactor Maven / Gradle / Ivy
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package prerna.reactor.utils;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import prerna.auth.utils.SecurityEngineUtils;
import prerna.engine.api.IEngine;
import prerna.reactor.AbstractReactor;
import prerna.sablecc2.om.PixelDataType;
import prerna.sablecc2.om.ReactorKeysEnum;
import prerna.sablecc2.om.nounmeta.NounMetadata;
public class GetEngineUsageReactor extends AbstractReactor {
private static final String TYPE = "type";
private static final String LABEL = "label";
private static final String CODE = "code";
private static final String PYTHON = "python";
private static final String JAVA = "java";
private static final String PIXEL = "pixel";
public GetEngineUsageReactor() {
this.keysToGet = new String[]{ReactorKeysEnum.ENGINE.getKey()};
this.keyRequired = new int[] {1};
}
@Override
public NounMetadata execute() {
// get the selectors
this.organizeKeys();
String engineId = this.keyValue.get(this.keysToGet[0]);
Object[] typeAndSubtype = SecurityEngineUtils.getEngineTypeAndSubtype(engineId);
IEngine.CATALOG_TYPE engineType = (IEngine.CATALOG_TYPE) typeAndSubtype[0];
List> output;
switch(engineType) {
case DATABASE:
output = getDatabaseUsage(engineId);
break;
case STORAGE:
output = getStorageUsage(engineId);
break;
case MODEL:
output = getModelUsage(engineId);
break;
case VECTOR:
output = getVectorUsage(engineId);
break;
case FUNCTION:
output = getFunctionUsage(engineId);
break;
default:
output = getPendingUsage();
break;
}
return new NounMetadata(output, PixelDataType.VECTOR);
}
private List> getModelUsage(String engineId) {
List> usage = new ArrayList<>();
{
Map usageMap = fillMap(
PIXEL,
"How to use in Javascript",
"Generation\r\n" +
"```\r\n"+
"LLM(engine = \""+engineId+"\", command = \"Sample Question \", paramValues=[{'max_completion_tokens':2000,'temperature':0.3}]);\r\n" +
"```\r\n\r\n"+
"Geneartion with ChatML\r\n" +
"```\r\n"+
"LLM(engine = \""+engineId+"\", command = \"ignore \", paramValues=[\r\n"+
" {\"full_prompt\":[\r\n"+
" {\"role\":\"system\", \"content\": \"You are a helpful assistant.\"},\r\n"+
" {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"},\r\n"+
" {\"role\": \"assistant\", \"content\": \"The Los Angeles Dodgers won the World Series in 2020.\"},\r\n"+
" {\"role\": \"user\", \"content\": \"Where was it played?\"}\r\n"+
" ],\r\n"+
" 'max_completion_tokens':2000,\r\n"+
" 'temperature':0.3\r\n"+
" }]);\r\n"+
"```\r\n\r\n"+
"Embeddings\r\n" +
"```\r\n"+
"Embeddings(engine = \""+engineId+"\", values = [\"Sample String 1\", \"Sample String 2\"], paramValues=[{}]);\r\n" +
"```\r\n\r\n" +
"Additional parameters found at: [OpenAI Parameter Spec](https://platform.openai.com/docs/api-reference/chat/create)\r\n\r\n"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
PYTHON,
"How to use in Python",
"```python\r\n"+
"from gaas_gpt_model import ModelEngine\r\n" +
"model = ModelEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"\n# Generation\r\n" +
"question = 'Sample Question'\r\n" +
"output = model.ask(question = question, param_dict={'max_completion_tokens':2000,'temperature':0.3})\r\n" +
"\r\n# Geneartion with ChatML\r\n" +
"model.ask(question='ignore', param_dict=\r\n"+
" {\"full_prompt\":[\r\n"+
" {\"role\":\"system\", \"content\": \"You are a helpful assistant.\"},\r\n"+
" {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"},\r\n"+
" {\"role\": \"assistant\", \"content\": \"The Los Angeles Dodgers won the World Series in 2020.\"},\r\n"+
" {\"role\": \"user\", \"content\": \"Where was it played?\"}\r\n"+
" ],\r\n"+
" 'max_completion_tokens':2000,\r\n"+
" 'temperature':0.3\r\n"+
" });\r\n"+
"\n# Embeddings\r\n" +
"text_arr = ['Sample String 1', 'Sample String 2']\r\n" +
"model.embeddings(strings_to_embed = text_arr)\r\n"+
"```\r\n\r\n" +
"Additional chat parameters found at: [OpenAI Parameter Spec](https://platform.openai.com/docs/api-reference/chat/create)\r\n\r\n"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
"LANGCHAIN",
"How to use with Langchain API",
"```python\r\n"+
"from gaas_gpt_model import ModelEngine\r\n" +
"model = ModelEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"\n# Generation\r\n" +
"langhchain_llm = model.to_langchain_chat_model()\r\n" +
"question = 'Sample Question'\r\n" +
"output = langhchain_llm.invoke(input = question)\r\n" +
"\n# Embeddings\r\n" +
"langhchain_llm = model.to_langchain_embedder()\r\n" +
"text_arr = ['Sample String 1', 'Sample String 2']\r\n" +
"langhchain_llm.embed_query(text = text_arr[0])\r\n"+
"langhchain_llm.embed_documents(texts = text_arr)\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
"OPENAI",
"How to use externally with OpenAI API",
"```python\r\n"
+"# import the ai platform package - requires user access/secret, service account, or bearer_token"
+ "\r\n"
+ "import ai_server\r\n"
+ "server_connection=ai_server.ServerClient(\r\n"
+ " base=\"\", # example: https://{domain}/{direcotry/path segment}/Monolith/api\r\n"
+ " access_key=\"\", # example: 'd0033d40-ea83-4083-96ce-17a01451f831'\r\n"
+ " secret_key=\"\" # example: 'c2b3fae8-20d1-458c-8565-30ae935c4dfb'\r\n"
+ ")"
+ "\r\n"
+ "\r\n"
+ "# import the openai package and httpx\r\n"
+ "from openai import OpenAI\r\n"
+ "import httpx as httpx\r\n"
+ "http_client = httpx.Client()\r\n"
+ "http_client.cookies=server_connection.cookies\r\n"
+ "\r\n"
+ "# setup openai to point to this running instance\r\n"
+ "client = OpenAI(\r\n"
+ " api_key=\"EMPTY\",\r\n"
+ " base_url=server_connection.get_openai_endpoint(),\r\n"
+ " default_headers=server_connection.get_auth_headers(),\r\n"
+ " http_client=http_client\r\n"
+ ")"
+ "\r\n"
+ "\r\n"
+ "# chat completitions using openai\r\n"
+ "response = client.chat.completions.create(\r\n"
+ " model=\""+engineId+"\",\r\n"
+ " messages=[\r\n"
+ " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\r\n"
+ " {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"},\r\n"
+ " {\"role\": \"assistant\", \"content\": \"The Los Angeles Dodgers won the World Series in 2020.\"},\r\n"
+ " {\"role\": \"user\", \"content\": \"Where was it played?\"}\r\n"
+ " ],\r\n"
+ " extra_body={\"insight_id\":server_connection.cur_insight}\r\n"
+ ")"
+ "\r\n"
+ "\r\n"
+ "# completitions using openai - note this is marked deprecated by openai\r\n"
+ "response = client.completions.create(\r\n"
+ " model=\""+engineId+"\",\r\n"
+ " prompt=\"Write a tagline for an ice cream shop.\",\r\n"
+ " extra_body={\"insight_id\":server_connection.cur_insight}\r\n"
+ ")"
+ "\r\n"
+ "\r\n"
+ "# embeddings using openai\r\n"
+ "embeddings = client.embeddings.create(\r\n"
+ " model=\""+engineId+"\",\r\n"
+ " input=[\"Your text string goes here\"],\r\n"
+ " extra_body={\"insight_id\":server_connection.cur_insight}\r\n"
+ ")\r\n"
+ "```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
JAVA,
"How to use in Java",
"```java\r\n" +
"import prerna.util.Utility;\r\n" +
"import prerna.engine.api.IModelEngine;\r\n" +
"IModelEngine modelEngine = Utility.getModel(\""+engineId+"\");\r\n" +
"```"
);
usage.add(usageMap);
}
return usage;
}
private List> getStorageUsage(String engineId) {
List> usage = new ArrayList<>();
{
Map usageMap = fillMap(
PIXEL,
"How to use in Javascript",
"```\r\n"
+ "Storage(storage = \""+engineId+"\") | ListStoragePath(storagePath='/your/storage/path');\r\n"
+ "Storage(storage = \""+engineId+"\") | ListStoragePathDetails(storagePath='/your/storage/path');\r\n"
+ "Storage(storage = \""+engineId+"\") | PullFromStorage(storagePath='/your/storage/path', filePath='/your/local/path');\r\n"
+ "Storage(storage = \""+engineId+"\") | PushToStorage(storagePath='/your/storage/path', filePath='/your/local/path', metadata=[{'metaKey':'metaValue'}]);\r\n"
+ "Storage(storage = \""+engineId+"\") | SyncStorageToLocal(storagePath='/your/storage/path', filePath='/your/local/path');\r\n"
+ "Storage(storage = \""+engineId+"\") | SyncLocalToStorage(storagePath='/your/storage/path', filePath='/your/local/path', metadata=[{'metaKey':'metaValue'}]);\r\n"
+ "Storage(storage = \""+engineId+"\") | DeleteFromStorage(storagePath='/your/storage/path', leaveFolderStructure=false);\r\n"
+ "```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
PYTHON,
"How to use in Python",
"```python\r\n"+
"from gaas_gpt_storage import StorageEngine\r\n" +
"storageEngine = StorageEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"storageEngine.list(storagePath = '/your/path/')\r\n" +
"storageEngine.listDetails(storagePath = '/your/path/')\r\n" +
"storageEngine.syncLocalToStorage(localPath= 'your/local/path', storagePath = 'your/storage/path', metadata={'metaKey':'metaValue'})\r\n" +
"storageEngine.syncStorageToLocal(localPath= 'your/local/path', storagePath = 'your/storage/path')\r\n" +
"storageEngine.copyToLocal(localPath= 'your/local/file/path', storagePath = 'your/storage/file/path')\r\n" +
"storageEngine.copyToStorage(localPath= 'your/local/file/path', storagePath = 'your/storage/file/path', metadata={'metaKey':'metaValue'})\r\n" +
"storageEngine.deleteFromStorage(storagePath = 'your/storage/file/path', leaveFolderStructure=False)\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
"LANGCHAIN",
"How to use with Langchain API",
"```python\r\n"+
"from gaas_gpt_storage import StorageEngine\r\n" +
"storage = StorageEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"langhchain_storage = storage.to_langchain_storage()\r\n" +
"langhchain_storage.list(storagePath = '/your/path/')\r\n" +
"langhchain_storage.listDetails(storagePath = '/your/path/')\r\n" +
"langhchain_storage.syncLocalToStorage(localPath= 'your/local/path', storagePath = 'your/storage/path')\r\n" +
"langhchain_storage.syncStorageToLocal(localPath= 'your/local/path', storagePath = 'your/storage/path')\r\n" +
"langhchain_storage.copyToLocal(localPath= 'your/local/file/path', storagePath = 'your/storage/file/path')\r\n" +
"langhchain_storage.copyToStorage(localPath= 'your/local/file/path', storagePath = 'your/storage/file/path')\r\n" +
"langhchain_storage.deleteFromStorage(storagePath = 'your/storage/file/path')\r\n"+
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
JAVA,
"How to use in Java",
"```java\r\n" +
"import prerna.util.Utility;\r\n" +
"import prerna.engine.api.IStorageEngine;\r\n" +
"IStorageEngine storage = Utility.getStorage(\""+engineId+"\");" +
"```"
);
usage.add(usageMap);
}
return usage;
}
private List> getDatabaseUsage(String engineId) {
List> usage = new ArrayList<>();
{
Map usageMap = fillMap(
PIXEL,
"How to use in Javascript",
"```\r\n"+
"Database(database = \""+engineId+"\")|Query(\" your select query \")|Collect(500);\r\n" +
"Database(database = \""+engineId+"\")|Query(\" your insert/update/delete query \")|ExecQuery();\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
PYTHON,
"How to use in Python",
"```python\r\n"+
"from gaas_gpt_database import DatabaseEngine\r\n" +
"databaseEngine = DatabaseEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"databaseEngine.execQuery(query = 'SELECT * FROM table_name')\r\n" +
"databaseEngine.insertData(query = 'INSERT INTO table_name (column1, column2, column3, ...) VALUES (value1, value2, value3, ...)')\r\n" +
"databaseEngine.updateData(query = 'UPDATE table_name set column1=value1 WHERE condition')\r\n" +
"databaseEngine.removeData(query = 'DELETE FROM table_name WHERE condition')\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
"LANGCHAIN",
"How to use with Langchain API",
"```python\r\n"+
"from gaas_gpt_database import DatabaseEngine\r\n" +
"database = DatabaseEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"langhchain_db = database.to_langchain_database()\r\n" +
"langhchain_db.executeQuery(query = 'SELECT * FROM table_name')\r\n"+
"langhchain_db.insertQuery(query = 'INSERT INTO table_name (column1, column2, column3, ...) VALUES (value1, value2, value3, ...)')\r\n"+
"langhchain_db.updateQuery(query = 'UPDATE table_name set column1=value1 WHERE condition')\r\n"+
"langhchain_db.removeQuery(query = 'DELETE FROM table_name WHERE condition')\r\n"+
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
JAVA,
"How to use in Java",
"```\r\n"+
"import prerna.util.Utility;\r\n" +
"import prerna.engine.api.IDatabaseEngine;\r\n" +
"IDatabaseEngine database = Utility.getDatabase(\""+engineId+"\");\r\n" +
"```"
);
usage.add(usageMap);
}
return usage;
}
private List> getVectorUsage(String engineId) {
List> usage = new ArrayList<>();
{
Map usageMap = fillMap(
PIXEL,
"How to use in Javascript",
"#### List all the documents the vector database currently comprises of ##\r\n" +
"```\r\n" +
"ListDocumentsInVectorDatabase (engine = \""+engineId+"\");\r\n" +
"```\r\n" +
"\r\n#### Add document(s) that have been uploaded to the insight ##\r\n" +
"```\r\n" +
"CreateEmbeddingsFromDocuments (engine = \""+engineId+"\", filePaths = [\"fileName1.pdf\", \"fileName2.pdf\", ..., \"fileNameX.pdf\"]);\r\n" +
"```\r\n" +
"#### Add the VectorCSVFile Formatted CSVs that have been uploaded to the insight ##\r\n" +
"```\r\n" +
"CreateEmbeddingsFromVectorCSVFile (engine = \""+engineId+"\", filePaths = [\"fileName1.csv\", \"fileName2.csv\", ..., \"fileNameX.csv\"]);\r\n" +
"```\r\n" +
"\r\n#### Perform a nearest neighbor search on the embedded documents ##\r\n" +
"```\r\n" +
"##filters of the form Filter(Source == [\"your document name 1\", \"your document name 2\"])##\r\n" +
"##metaFilters of the form Filter( MetadataKey == \"Metadata Value\" )##\r\n" +
"VectorDatabaseQuery (engine = \""+engineId+"\", command = \"Sample Search Statement\", limit = 5, filters=[], metaFilters=[]);\r\n" +
"```\r\n" +
"\r\n#### Remove document(s) from the vector database ##\r\n" +
"```\r\n" +
"RemoveDocumentFromVectorDatabase (engine = \""+engineId+"\", filePaths = [\"fileName1.pdf\", \"fileName2.pdf\", ..., \"fileNameX.pdf\"]);\r\n" +
"```\r\n"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
PYTHON,
"How to use in Python",
"```python\r\n"+
"# import vector engine class and initialize\r\n" +
"from gaas_gpt_vector import VectorEngine\r\n" +
"vectorEngine = VectorEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"\n# List all the documents the vector database currently comprises of\r\n" +
"vectorEngine.listDocuments()\r\n" +
"\n# Add document(s) that have been uploaded to the insight\r\n" +
"vectorEngine.addDocument(file_paths = ['fileName1.pdf', 'fileName2.pdf', ..., 'fileNameX.pdf'])\r\n" +
"\n# Add the VectorCSVFile Formatted CSVs that have been uploaded to the insight\r\n" +
"vectorEngine.addVectorCSVFile(file_paths = ['fileName1.csv', 'fileName2.csv', ..., 'fileNameX.csv'])\r\n" +
"\n# Perform a nearest neighbor search on the embedded documents\r\n" +
"# filters is Optional[Dict] | Optional[str]\r\n" +
"# \tstr of the form 'Filter(Source == [\"your document name 1\", \"your document name 2\"])'\r\n" +
"# \tdict of the form {\"Source\": [\"constitution.pdf\", \"scientific_journal.pdf\"] and comparator is assumed to be '=' for all values\r\n" +
"# metafilters is Optional[Dict] | Optional[str].\r\n" +
"# \tstr of the form 'Filter( MetadataKey == \"Metadata Value\" )'\r\n" +
"# \tdict of the form {\"age\": [5,6,7]} and comparator is assumed to be '=' for all values\r\n" +
"vectorEngine.nearestNeighbor(search_statement = 'Sample Search Statement', limit = 5, param_dict={}, filters='', metafilters='')\r\n" +
"\n# Remove document(s) from the vector database\r\n" +
"vectorEngine.removeDocument(file_names = ['fileName1.pdf', 'fileName2.pdf', ..., 'fileNameX.pdf'])\r\n"+
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
"LANGCHAIN",
"How to use with Langchain API",
"```python\r\n"+
"from gaas_gpt_vector import VectorEngine\r\n" +
"vector = VectorEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"langhchain_vector = vector.to_langchain_vector_store()\r\n" +
"langhchain_vector.listDocs()\r\n" +
"langhchain_vector.addDocs(file_paths = ['file1.pdf','file2.pdf',...])\r\n" +
"langhchain_vector.removeDocs(file_names = ['file1.pdf','file2.pdf',...])\r\n" +
"langhchain_vector.similaritySearch(query = 'Sample Search Statement', k=5)\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
JAVA,
"How to use in Java",
"```java\r\n" +
"// imports\r\n" +
"import prerna.util.Utility;\r\n" +
"import prerna.engine.api.IVectorDatabaseEngine;\r\n\n" +
"// get the vector engine\r\n" +
"IVectorDatabaseEngine vectorEngine = Utility.getVectorDatabase(\""+engineId+"\");\r\n" +
"\n// List all the documents the vector database currently comprises of\r\n" +
"vectorEngine.listDocuments(Map parameters)\r\n" +
"\n// Add document(s) that have been uploaded to the insight\r\n" +
"vectorEngine.addDocument(List filePaths, Map parameters);\r\n" +
"\n// Add the VectorCSVFile Formatted CSVs that have been uploaded to the insight\r\n" +
"vectorEngine.addEmbeddings(List filePaths, Insight insight, Map parameters);\r\n" +
"\n// Perform a nearest neighbor search on the embedded documents\r\n" +
"vectorEngine.nearestNeighbor(String searchStatement, Number limit, Map parameters);\r\n" +
"\n// Remove document(s) from the vector database\r\n" +
"vectorEngine.removeDocument(List fileNames, Map parameters);\r\n" +
"```"
);
usage.add(usageMap);
}
return usage;
}
private List> getFunctionUsage(String engineId) {
List> usage = new ArrayList<>();
{
Map usageMap = fillMap(
PIXEL,
"How to use in Javascript",
"```\r\n"+
"ExecuteFunctionEngine(engine = \""+engineId+"\", map=[{'param1':'value1', ... , 'paramN':'valueN'}] );\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
PYTHON,
"How to use in Python",
"```python\r\n" +
"from gaas_gpt_function import FunctionEngine \r\n" +
"function = FunctionEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
"output = function.execute({'param1':'value1', ... , 'paramN':'valueN'})\r\n" +
"```"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
JAVA,
"How to use in Java",
"```java\r\n" +
"import prerna.util.Utility;\r\n" +
"import prerna.engine.api.IFunctionEngine;\r\n" +
"IFunctionEngine function = Utility.getFunction(\""+engineId+"\");\r\n" +
"```"
);
usage.add(usageMap);
}
return usage;
}
private List> getPendingUsage() {
List> usage = new ArrayList<>();
{
Map usageMap = fillMap(
PIXEL,
"How to use in Javascript",
"Documentation pending"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
PYTHON,
"How to use in Python",
"Documentation pending"
);
usage.add(usageMap);
}
{
Map usageMap = fillMap(
JAVA,
"How to use in Java",
"Documentation pending"
);
usage.add(usageMap);
}
return usage;
}
/**
*
* @param type
* @param label
* @param code
* @return
*/
private Map fillMap(String type, String label, String code) {
Map usageMap = new HashMap<>();
usageMap.put(TYPE, type);
usageMap.put(LABEL, label);
usageMap.put(CODE, code);
return usageMap;
}
/*
* Legacy structure
*/
// @Override
// public NounMetadata execute() {
// // get the selectors
// this.organizeKeys();
// String engineId = this.keyValue.get(this.keysToGet[0]);
// Object[] typeAndSubtype = SecurityEngineUtils.getEngineTypeAndSubtype(engineId);
// IEngine.CATALOG_TYPE engineType = (IEngine.CATALOG_TYPE) typeAndSubtype[0];
// Map outputMap;
// switch(engineType) {
// case DATABASE:
// outputMap = getDatabaseUsage(engineId);
// break;
// case STORAGE:
// outputMap = getStorageUsage(engineId);
// break;
// case MODEL:
// outputMap = getModelUsage(engineId);
// break;
// case VECTOR:
// outputMap = getVectorUsage(engineId);
// break;
// case FUNCTION:
// outputMap = getFunctionUsage(engineId);
// break;
// default:
// outputMap = new HashMap<>();
// outputMap.put(PYTHON, "Documentation pending");
// outputMap.put(JAVA, "Documentation pending");
// outputMap.put(PIXEL, "Documentation pending");
// break;
// }
// return new NounMetadata(outputMap, PixelDataType.MAP);
// }
//
// private Map getModelUsage(String engineId) {
// Map usageMap = new HashMap<>();
// usageMap.put(PYTHON,"from gaas_gpt_model import ModelEngine\r\n" +
// "question = 'Sample Question'\r\n" +
// "model = ModelEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
// "output = model.ask(question = question)");
// usageMap.put(JAVA,"import prerna.util.Utility;\r\n" +
// "import prerna.engine.api.IModelEngine;\r\n" +
// "IModelEngine eng = Utility.getModel(\""+engineId+"\");");
// usageMap.put(PIXEL,"LLM(engine = \""+engineId+"\", command = \"Sample Question \", paramValues = [ {} ] );");
//
// return usageMap;
// }
//
// private Map getStorageUsage(String engineId) {
// Map usageMap = new HashMap<>();
// usageMap.put(PYTHON,"from gaas_gpt_storage import StorageEngine\r\n" +
// "storageEngine = StorageEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
// "storageEngine.list(path = '/your/path/')\r\n" +
// "storageEngine.listDetails(path = '/your/path/')\r\n" +
// "storageEngine.syncLocalToStorage(localPath= 'your/local/path', storagePath = 'your/storage/path')\r\n" +
// "storageEngine.syncStorageToLocal(localPath= 'your/local/path', storagePath = 'your/storage/path')\r\n" +
// "storageEngine.copyToLocal(localFolderPath= 'your/local/file/path', storageFilePath = 'your/storage/file/path')\r\n" +
// "storageEngine.deleteFromStorage(storagePath = 'your/storage/file/path')");
// usageMap.put(JAVA,"import prerna.util.Utility;\r\n" +
// "import prerna.engine.api.IStorageEngine;\r\n" +
// "IStorageEngine storage = Utility.getStorage(\""+engineId+"\");");
// usageMap.put(PIXEL,"Storage(storage = \""+engineId+"\")");
// return usageMap;
// }
//
// private Map getDatabaseUsage(String engineId) {
// Map usageMap = new HashMap<>();
// usageMap.put(PYTHON,"from gaas_gpt_database import DatabaseEngine\r\n" +
// "databaseEngine = DatabaseEngine(engine_id = \""+engineId+"\", insight_id = '${i}')\r\n" +
// "databaseEngine.execQuery(query = 'SELECT * FROM table_name')\r\n" +
// "databaseEngine.insertData(query = 'INSERT INTO table_name (column1, column2, column3, ...) VALUES (value1, value2, value3, ...)')\r\n" +
// "databaseEngine.updateData(query = 'UPDATE table_name set column1=value1 WHERE condition')\r\n" +
// "databaseEngine.removeData(query = 'DELETE FROM table_name WHERE condition')");
// usageMap.put(JAVA,"import prerna.util.Utility;\r\n" +
// "import prerna.engine.api.IDatabaseEngine;\r\n" +
// "IDatabaseEngine database = Utility.getDatabase(\""+engineId+"\");");
// usageMap.put(PIXEL,"Database(database = \""+engineId+"\")");
// return usageMap;
// }
//
// private Map getVectorUsage(String engineId) {
// Map usageMap = new HashMap<>();
// usageMap.put(PYTHON,"# import vector engine class and initialize\r\nfrom gaas_gpt_vector import VectorEngine\r\n" +
// "vectorEngine = VectorEngine(engine_id = \""+engineId+"\", insight_id = '${i}', insight_folder = '${if}')\r\n" +
// "\n# Add document(s) that have been uploaded to the insight\r\n" +
// "vectorEngine.addDocument(file_paths = ['fileName1.pdf', 'fileName2.pdf', ..., 'fileNameX.pdf'])\r\n" +
// "\n# Perform a nearest neighbor search on the embedded documents\r\n" +
// "vectorEngine.nearestNeighbor(search_statement = 'Sample Search Statement', limit = 5)\r\n" +
// "\n# List all the documents the vector database currently comprises of\r\n" +
// "vectorEngine.listDocuments()\r\n" +
// "\n# Remove document(s) from the vector database\r\n" +
// "vectorEngine.removeDocument(file_names = ['fileName1.pdf', 'fileName2.pdf', ..., 'fileNameX.pdf'])");
// usageMap.put(JAVA,"// imports\r\nimport prerna.util.Utility;\r\n" +
// "import prerna.engine.api.IVectorDatabaseEngine;\r\n\n" +
// "// get the vector engine\r\nIVectorDatabaseEngine vectorEngine = Utility.getVectorDatabase(\""+engineId+"\");\r\n" +
// "\n// Add document(s) that have been uploaded to the insight\r\n" +
// "vectorEngine.addDocument(List filePaths, Map parameters);\r\n" +
// "\n// Perform a nearest neighbor search on the embedded documents\r\n" +
// "vectorEngine.nearestNeighbor(String searchStatement, Number limit, Map parameters);\r\n" +
// "\n// List all the documents the vector database currently comprises of\r\n" +
// "vectorEngine.listDocuments(Map parameters)\r\n" +
// "\n// Remove document(s) from the vector database\r\n" +
// "vectorEngine.removeDocument(List fileNames, Map parameters);"
// );
// usageMap.put(PIXEL,"## Add document(s) that have been uploaded to the insight ##\r\n" +
// "CreateEmbeddingsFromDocuments (engine = \""+engineId+"\", filePaths = [\"fileName1.pdf\", \"fileName2.pdf\", ..., \"fileNameX.pdf\"]);\r\n" +
// "\n## Perform a nearest neighbor search on the embedded documents ##\r\n" +
// "VectorDatabaseQuery (engine = \""+engineId+"\", command = \"Sample Search Statement\", limit = 5);\r\n" +
// "\n## List all the documents the vector database currently comprises of ##\r\n" +
// "ListDocumentsInVectorDatabase (engine = \""+engineId+"\");\r\n" +
// "\n## Remove document(s) from the vector database ##\r\n" +
// "RemoveDocumentFromVectorDatabase (engine = \""+engineId+"\", filePaths = [\"fileName1.pdf\", \"fileName2.pdf\", ..., \"fileNameX.pdf\"]);"
// );
// return usageMap;
// }
//
// private Map getFunctionUsage(String engineId) {
// Map usageMap = new HashMap<>();
// usageMap.put(PYTHON,"from gaas_gpt_function import FunctionEngine \r\n" +
// "function = FunctionEngine(engine_id = \"f3a4c8b2-7f3e-4d04-8c1f-2b0e3dabf5e9\", insight_id = '${i}')\r\n" +
// "output = function.execute({'param1':'value1', ... , 'paramN':'valueN'})");
// usageMap.put(JAVA,"import prerna.util.Utility;\r\n" +
// "import prerna.engine.api.IFunctionEngine;\r\n" +
// "IFunctionEngine function = Utility.getFunction(\""+engineId+"\");");
// usageMap.put(PIXEL,"ExecuteFunctionEngine(engine = \""+engineId+"\", map=[{'param1':'value1', ... , 'paramN':'valueN'}] )");
// return usageMap;
// }
}
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