I am new to Outlook VBA and have struggled for a day now to write a macro that creates rules from a CSV file in a shared mailbox with the trigger for Body or Subject in column 1 and the path to the folder to move the message to in column 2. I have the following code that runs OK but does not add any rules. Could someone please identify my errors?
PowerShell I cannot use due to organization's network restrictions.
Sub ImportRulesFromCSVTest()
Dim sharedMailboxName As String
sharedMailboxName = "email address removed for privacy reasons"
Dim olApp As Outlook.Application
Set olApp = Outlook.Application
Dim olNamespace As Outlook.NameSpace
Set olNamespace = olApp.GetNamespace("MAPI")
Dim olRecipient As Outlook.Recipient
Set olRecipient = olNamespace.CreateRecipient(sharedMailboxName)
olRecipient.Resolve
Dim olStore As Outlook.Store
Set olStore = olNamespace.GetSharedDefaultFolder(olRecipient, olFolderInbox).Store
Dim olRules As Outlook.Rules
Set olRules = olStore.GetRules
Dim csvFilePath As String
csvFilePath = "C:\Users\xyz\Test4.csv"
Open csvFilePath For Input As #1
Do While Not EOF(1)
Line Input #1, csvLine
Dim csvValues() As String
csvValues = Split(csvLine, ",")
If UBound(csvValues) = 2 Then
trigger = csvValues(0)
folderPath = csvValues(1)
'Create rule
Dim olRule As Outlook.Rule
Set olRule = olRules.Create(trigger, olRuleClientOnly)
'Set trigger condition
Dim olRuleConditions As Outlook.RuleConditions
Set olRuleConditions = olRule.Conditions
Dim olSubjectCondition As Outlook.TextRuleCondition
Set olSubjectCondition = olRuleConditions.BodyOrSubject
olSubjectCondition.Text = Array(trigger)
'Set exception exclude messages To (i.e. restrict rule to Cc and Bcc)
Dim oToCondition As Outlook.ToOrFromRuleCondition
Set oToCondition = oRule.olConditionNotTo
With oToCondition
.Enabled = True
.Recipients.Add ("email address removed for privacy reasons")
.Recipients.ResolveAll
End With
'Set move action
Dim olRuleActions As Outlook.RuleActions
Set olRuleActions = olRule.Actions
Dim olFolder As Outlook.Folder
Set olFolder = olStore.GetRootFolder.Folders(folderPath)
Dim olMoveOrCopyRuleAction As Outlook.MoveOrCopyRuleAction
Set olMoveOrCopyRuleAction = olRuleActions.MoveToFolder
With olMoveOrCopyRuleAction
.Enabled = True
.Folder = olFolder
End With
olRules.Save
End If
Loop
Close #1
MsgBox "Rules imported", vbInformation
End Sub
As far as I know, unfortunately, there is no way to get the database name from both (log analytics table and DMVs) The query will return only the result from the whole SQL serverless pool.
Thanks for the article, I found the article very helpful and well-explained.
Mabye you can help getting more information.
I've been attempting to get the serverless database name to a particular query is executed, by trying to combine logs and Dynamic Management Views (DMVs) within Synapse SQL Serverless. Regrettably, my efforts didn't get the desired results.
Would you happen to have any insights or suggestions on how to extract this information from Synapse SQL Serverless logs and other monitor data?
Thank you in advance for your assistance.
Regards,
Pedro Matos.
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Let's learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! In this post, we’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.
","body":"
Hi!
\n
\n
In this blog post we will learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! We’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.
\n
What is RAG?
\n
Before we dive into the demo, let’s quickly recap what RAG is. RAG is a hybrid approach that enhances the capabilities of a language model by incorporating external knowledge. In example: using a RAG approach we can retrieve relevant documents from a knowledge base and use them to generate more informed and accurate responses. This is particularly useful in scenarios where a LLM needs up-to-date information or specific domain knowledge that isn't contained within its initial training data.
\n
The Components
\n
To build our RAG setup, we’ll be using the following components:
\n
\n\n
\n
Phi-3: Our local LLM, which is a powerful tool for generating human-like text. Check out the Phi-3 Cookbook for more details.
\n
\n
\n
Smart Components Local Embeddings: This package will help us create embeddings, which are numerical representations of text that capture its semantic meaning. You can learn more about it in the Smart Components Local Embeddings documentation.
\n
\n
\n
Semantic Kernel: This acts as the main orchestrator, integrating Phi-3 and Smart Components to create a seamless RAG pipeline. Visit the Semantic Kernel GitHub page for more information.
\n
\n\n
Demo Scenario
\n
\n
The demo scenario below is designed to answer a specific question, “What is Bruno’s favourite super hero?“, using two different approaches.
\n
\n
\n
Ask the question directly to the Phi-3 model. The model will answer declining a response, Phi-3 does not talk about Bruno.
\n
Ask the question to the Phi-3 model, and add a semantic memory object with fan facts loaded. Now the response will be based on the semantic memory content.
\n
\n
This is the app running:
\n
\n
\n
Code Sample
\n
Let’s jump to the code. The code below is a C# console application that demonstrates the use of a local model hosted in Ollama and semantic memory for search.
\n
Here’s a step-by-step breakdown of the program:
\n
\n\n
The program starts by defining the question and announcing the two approaches it will use to answer it. The first approach is to ask the question directly to the Phi-3 model, and the second approach is to add facts to a semantic memory and ask the question again.
\n
The program creates a chat completion service using the Kernel.CreateBuilder() method. It adds Chat Completion using a local model, and local text embedding generation to the builder, then builds the kernel.
\n
The program then asks the question directly to the Phi-3 model and prints the response.
\n
The program gets the embeddings generator service and creates a new semantic text memory with a volatile memory store and the embedding generator.
\n
The program adds facts to the memory collection. These facts are about Bruno and Gisela’s favourite super heroes and the last super hero movies they watched.
\n
The program creates a new text memory plugin with the semantic text memory and imports the plugin into the kernel.
\n
The program sets up the prompt execution settings and the kernel arguments, which include the question and the memory collection.
\n
Finally, the program asks the question again, this time using the semantic memory, and prints the response.
\n\n
The program uses several external libraries, including:
\n
\n
Microsoft.Extensions.Configuration and Microsoft.Extensions.DependencyInjection for dependency injection and configuration.
\n
Microsoft.KernelMemory, Microsoft.SemanticKernel, Microsoft.SemanticKernel.ChatCompletion, Microsoft.SemanticKernel.Connectors.OpenAI, Microsoft.SemanticKernel.Embeddings, Microsoft.SemanticKernel.Memory, and Microsoft.SemanticKernel.Plugins.Memory for the semantic kernel and memory functionalities.
\n
\n
This program is a great example of how AI can be used to answer questions using both direct model querying and semantic memory.
\n
\n
// Copyright (c) 2024\n// Author : Bruno Capuano\n// Change Log :\n// - Sample console application to use a local model hosted in ollama and semantic memory for search\n//\n// The MIT License (MIT)\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#pragma warning disable SKEXP0001\n#pragma warning disable SKEXP0003\n#pragma warning disable SKEXP0010\n#pragma warning disable SKEXP0011\n#pragma warning disable SKEXP0050\n#pragma warning disable SKEXP0052\n\nusing Microsoft.Extensions.Configuration;\nusing Microsoft.Extensions.DependencyInjection;\nusing Microsoft.KernelMemory;\nusing Microsoft.SemanticKernel;\nusing Microsoft.SemanticKernel.ChatCompletion;\nusing Microsoft.SemanticKernel.Connectors.OpenAI;\nusing Microsoft.SemanticKernel.Embeddings;\nusing Microsoft.SemanticKernel.Memory;\nusing Microsoft.SemanticKernel.Plugins.Memory;\n\nvar question = \"What is Bruno's favourite super hero?\";\nConsole.WriteLine($\"This program will answer the following question: {question}\");\nConsole.WriteLine(\"1st approach will be to ask the question directly to the Phi-3 model.\");\nConsole.WriteLine(\"2nd approach will be to add facts to a semantic memory and ask the question again\");\nConsole.WriteLine(\"\");\n\n// Create a chat completion service\nvar builder = Kernel.CreateBuilder();\nbuilder.AddOpenAIChatCompletion(\n modelId: \"phi3\",\n endpoint: new Uri(\"http://localhost:11434\"),\n apiKey: \"apikey\");\nbuilder.AddLocalTextEmbeddingGeneration();\nKernel kernel = builder.Build();\n\nConsole.WriteLine($\"Phi-3 response (no memory).\");\nvar response = kernel.InvokePromptStreamingAsync(question);\nawait foreach (var result in response)\n{\n Console.Write(result);\n}\n\n// separator\nConsole.WriteLine(\"\");\nConsole.WriteLine(\"==============\");\nConsole.WriteLine(\"\");\n\n// get the embeddings generator service\nvar embeddingGenerator = kernel.Services.GetRequiredService<ITextEmbeddingGenerationService>();\nvar memory = new SemanticTextMemory(new VolatileMemoryStore(), embeddingGenerator);\n\n// add facts to the collection\nconst string MemoryCollectionName = \"fanFacts\";\n\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info1\", text: \"Gisela's favourite super hero is Batman\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info2\", text: \"The last super hero movie watched by Gisela was Guardians of the Galaxy Vol 3\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info3\", text: \"Bruno's favourite super hero is Invincible\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info4\", text: \"The last super hero movie watched by Bruno was Aquaman II\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info5\", text: \"Bruno don't like the super hero movie: Eternals\");\n\nTextMemoryPlugin memoryPlugin = new(memory);\n\n// Import the text memory plugin into the Kernel.\nkernel.ImportPluginFromObject(memoryPlugin);\n\nOpenAIPromptExecutionSettings settings = new()\n{\n ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions,\n};\n\n\nvar prompt = @\"\n Question: {{$input}}\n Answer the question using the memory content: {{Recall}}\";\n\nvar arguments = new KernelArguments(settings)\n{\n { \"input\", question },\n { \"collection\", MemoryCollectionName }\n};\n\nConsole.WriteLine($\"Phi-3 response (using semantic memory).\");\n\nresponse = kernel.InvokePromptStreamingAsync(prompt, arguments);\nawait foreach (var result in response)\n{\n Console.Write(result);\n}\n\nConsole.WriteLine($\"\");
\n
\n
The full source code is available here: Program.cs.
\n
Test This Scenario for Free Using CodeSpaces in the Phi-3 Cookbook
\n
To help you get started with Phi-3 and experience its capabilities firsthand, we are thrilled to introduce the support of Codespaces in the Phi-3 Cookbook.
\n
\n
The C# Ollama Labs are designed to test Phi-3 with C# samples directly in GitHub Codespaces as an easy way for anyone to try out Phi-3 with C# entirely in the browser.
Phi-3, local embeddings and Semantic Kernel are a great combination to support RAG scenarios in local mode.
\n
And using Semantic Kernel is easy to later switch to Azure OpenAI Services to scale at Enterprise level!
\n
\n
Happy Coding!
\n
Bruno Capuano
\n
","body@stringLength":"12097","rawBody":"
Hi!
\n
\n
In this blog post we will learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! We’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.
\n
What is RAG?
\n
Before we dive into the demo, let’s quickly recap what RAG is. RAG is a hybrid approach that enhances the capabilities of a language model by incorporating external knowledge. In example: using a RAG approach we can retrieve relevant documents from a knowledge base and use them to generate more informed and accurate responses. This is particularly useful in scenarios where a LLM needs up-to-date information or specific domain knowledge that isn't contained within its initial training data.
\n
The Components
\n
To build our RAG setup, we’ll be using the following components:
\n
\n\n
\n
Phi-3: Our local LLM, which is a powerful tool for generating human-like text. Check out the Phi-3 Cookbook for more details.
\n
\n
\n
Smart Components Local Embeddings: This package will help us create embeddings, which are numerical representations of text that capture its semantic meaning. You can learn more about it in the Smart Components Local Embeddings documentation.
\n
\n
\n
Semantic Kernel: This acts as the main orchestrator, integrating Phi-3 and Smart Components to create a seamless RAG pipeline. Visit the Semantic Kernel GitHub page for more information.
\n
\n\n
Demo Scenario
\n
\n
The demo scenario below is designed to answer a specific question, “What is Bruno’s favourite super hero?“, using two different approaches.
\n
\n
\n
Ask the question directly to the Phi-3 model. The model will answer declining a response, Phi-3 does not talk about Bruno.
\n
Ask the question to the Phi-3 model, and add a semantic memory object with fan facts loaded. Now the response will be based on the semantic memory content.
\n
\n
This is the app running:
\n
\n
\n
Code Sample
\n
Let’s jump to the code. The code below is a C# console application that demonstrates the use of a local model hosted in Ollama and semantic memory for search.
\n
Here’s a step-by-step breakdown of the program:
\n
\n\n
The program starts by defining the question and announcing the two approaches it will use to answer it. The first approach is to ask the question directly to the Phi-3 model, and the second approach is to add facts to a semantic memory and ask the question again.
\n
The program creates a chat completion service using the Kernel.CreateBuilder() method. It adds Chat Completion using a local model, and local text embedding generation to the builder, then builds the kernel.
\n
The program then asks the question directly to the Phi-3 model and prints the response.
\n
The program gets the embeddings generator service and creates a new semantic text memory with a volatile memory store and the embedding generator.
\n
The program adds facts to the memory collection. These facts are about Bruno and Gisela’s favourite super heroes and the last super hero movies they watched.
\n
The program creates a new text memory plugin with the semantic text memory and imports the plugin into the kernel.
\n
The program sets up the prompt execution settings and the kernel arguments, which include the question and the memory collection.
\n
Finally, the program asks the question again, this time using the semantic memory, and prints the response.
\n\n
The program uses several external libraries, including:
\n
\n
Microsoft.Extensions.Configuration and Microsoft.Extensions.DependencyInjection for dependency injection and configuration.
\n
Microsoft.KernelMemory, Microsoft.SemanticKernel, Microsoft.SemanticKernel.ChatCompletion, Microsoft.SemanticKernel.Connectors.OpenAI, Microsoft.SemanticKernel.Embeddings, Microsoft.SemanticKernel.Memory, and Microsoft.SemanticKernel.Plugins.Memory for the semantic kernel and memory functionalities.
\n
\n
This program is a great example of how AI can be used to answer questions using both direct model querying and semantic memory.
\n
\n// Copyright (c) 2024\n// Author : Bruno Capuano\n// Change Log :\n// - Sample console application to use a local model hosted in ollama and semantic memory for search\n//\n// The MIT License (MIT)\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#pragma warning disable SKEXP0001\n#pragma warning disable SKEXP0003\n#pragma warning disable SKEXP0010\n#pragma warning disable SKEXP0011\n#pragma warning disable SKEXP0050\n#pragma warning disable SKEXP0052\n\nusing Microsoft.Extensions.Configuration;\nusing Microsoft.Extensions.DependencyInjection;\nusing Microsoft.KernelMemory;\nusing Microsoft.SemanticKernel;\nusing Microsoft.SemanticKernel.ChatCompletion;\nusing Microsoft.SemanticKernel.Connectors.OpenAI;\nusing Microsoft.SemanticKernel.Embeddings;\nusing Microsoft.SemanticKernel.Memory;\nusing Microsoft.SemanticKernel.Plugins.Memory;\n\nvar question = \"What is Bruno's favourite super hero?\";\nConsole.WriteLine($\"This program will answer the following question: {question}\");\nConsole.WriteLine(\"1st approach will be to ask the question directly to the Phi-3 model.\");\nConsole.WriteLine(\"2nd approach will be to add facts to a semantic memory and ask the question again\");\nConsole.WriteLine(\"\");\n\n// Create a chat completion service\nvar builder = Kernel.CreateBuilder();\nbuilder.AddOpenAIChatCompletion(\n modelId: \"phi3\",\n endpoint: new Uri(\"http://localhost:11434\"),\n apiKey: \"apikey\");\nbuilder.AddLocalTextEmbeddingGeneration();\nKernel kernel = builder.Build();\n\nConsole.WriteLine($\"Phi-3 response (no memory).\");\nvar response = kernel.InvokePromptStreamingAsync(question);\nawait foreach (var result in response)\n{\n Console.Write(result);\n}\n\n// separator\nConsole.WriteLine(\"\");\nConsole.WriteLine(\"==============\");\nConsole.WriteLine(\"\");\n\n// get the embeddings generator service\nvar embeddingGenerator = kernel.Services.GetRequiredService<ITextEmbeddingGenerationService>();\nvar memory = new SemanticTextMemory(new VolatileMemoryStore(), embeddingGenerator);\n\n// add facts to the collection\nconst string MemoryCollectionName = \"fanFacts\";\n\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info1\", text: \"Gisela's favourite super hero is Batman\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info2\", text: \"The last super hero movie watched by Gisela was Guardians of the Galaxy Vol 3\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info3\", text: \"Bruno's favourite super hero is Invincible\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info4\", text: \"The last super hero movie watched by Bruno was Aquaman II\");\nawait memory.SaveInformationAsync(MemoryCollectionName, id: \"info5\", text: \"Bruno don't like the super hero movie: Eternals\");\n\nTextMemoryPlugin memoryPlugin = new(memory);\n\n// Import the text memory plugin into the Kernel.\nkernel.ImportPluginFromObject(memoryPlugin);\n\nOpenAIPromptExecutionSettings settings = new()\n{\n ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions,\n};\n\n\nvar prompt = @\"\n Question: {{$input}}\n Answer the question using the memory content: {{Recall}}\";\n\nvar arguments = new KernelArguments(settings)\n{\n { \"input\", question },\n { \"collection\", MemoryCollectionName }\n};\n\nConsole.WriteLine($\"Phi-3 response (using semantic memory).\");\n\nresponse = kernel.InvokePromptStreamingAsync(prompt, arguments);\nawait foreach (var result in response)\n{\n Console.Write(result);\n}\n\nConsole.WriteLine($\"\");\n
\n
The full source code is available here: Program.cs.
\n
Test This Scenario for Free Using CodeSpaces in the Phi-3 Cookbook
\n
To help you get started with Phi-3 and experience its capabilities firsthand, we are thrilled to introduce the support of Codespaces in the Phi-3 Cookbook.
\n
\n
The C# Ollama Labs are designed to test Phi-3 with C# samples directly in GitHub Codespaces as an easy way for anyone to try out Phi-3 with C# entirely in the browser.
Let's learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! In this post, we’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.
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Building Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide","moderationData":{"__ref":"ModerationData:moderation_data:4250464"},"body":"
Hi Bruno, I had a chance to watch your demo in the .NET focus on AI, and really enjoyed it. When trying this out for myself I eventually landed on this page and want to give a shout out to all the other people who helped here by posting their workarounds to some of the problems I ran into.
I have a question regarding the local Phi3 model:
I was under the impression that AutoInvokeKernelFunctions along with the Phi-3 model would be capable of planning and executing Kernel Functions without the need for me to be explicit (telling it what function to use by {{functionname}} as well as building the parameters for the plugin).
If we do indeed have to be explicit than how can we implement this so we can truly respond to the user in a dynamic way from various data sources? I've tried giving it options to call such as \"call the appropriate function from {{FunctionA}} or {{FunctionB}}\" but this didn't work because some of the kernel functions expected parameters.
Is that something that will be there in the future?
Thanks!!
James
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At the moment I applied a nice workaround found on the issue above - thanks to https://github.com/davidpuplava You can find a working implementation with latest NuGet packages at https://github.com/joslat/LocalRag (of course giving proper credit to the original code sources)
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It is some issue on dotnet-smartcomponents semantic kernel nuget package (or something changed on the semantic kernel and it is not invoking it properly/they are not aligned) - I played with changing the versions but did not get it to work...
elbruno, It would help me greatly if you could tell me which versions you are using that works 😄
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Really cool, still got a weird issue when executing the
Method not found: 'System.ValueTuple`3<System.ReadOnlyMemory`1<Int64>,System.ReadOnlyMemory`1<Int64>,System.ReadOnlyMemory`1<Int64>> FastBertTokenizer.BertTokenizer.Encode(System.String, Int32, System.Nullable`1<Int32>)'.
This is not from your code but from the embedding generator added by
builder.AddLocalTextEmbeddingGeneration();
Which comes from \"SmartComponents.LocalEmbeddings.SemanticKernel\" Version=\"0.1.0-preview10148\"
elbruno which version did you use in your sample? at thee moment used a previous version and just now updated to latest so wondering what might be the issue. Also tried with my laptop and the result is the same.
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Semantic Kernel version 1.18.0-rc puts Tool information even if the configuration says not to (OpenAIPromptExecutionSettings settings = new() { ToolCallBehavior = null };).
private ToolCallingConfig GetToolCallingConfiguration(Kernel? kernel, OpenAIPromptExecutionSettings executionSettings, int requestIndex)\n {\n if (executionSettings.ToolCallBehavior is null)\n {\n return new ToolCallingConfig(Tools: [s_nonInvocableFunctionTool], Choice: ChatToolChoice.None, AutoInvoke: false);\n }\n....\n}
With that new ToolCallingConfig, NonInvocableFunctionTool it will create a object inside json file, so when the request is called upon ollama phi3 it will always try to parse and execute the tool property. Phi3 does not support tools so it fails.
Use Microsoft.SemanticKernel version 1.17.1 instead.
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This started occurring for me when i updated Semantic kernel to the pre release versions. As a test, i ran phi3 in LM studio and ran a local server that way. Doing this, both embedded and non embedded scenarios work. (So this is a workaround for now)
This suggests that the issue is with the Ollama rest endpoint changing how it handles the requests formatted by semantic kernel
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{\n \"messages\": [\n {\n \"content\": \"\\r\\n Question: What is Bruno's favourite super hero?\\r\\n Answer the question using the memory content: Bruno's favourite super hero is Invincible\",\n \"role\": \"user\"\n }\n ],\n \"temperature\": 1,\n \"top_p\": 1,\n \"n\": 1,\n \"presence_penalty\": 0,\n \"frequency_penalty\": 0,\n \"model\": \"phi3\",\n \"tools\": [\n {\n \"function\": {\n \"name\": \"TextMemoryPlugin-Retrieve\",\n \"description\": \"Key-based lookup for a specific memory\",\n \"parameters\": {\n \"type\": \"object\",\n \"required\": [\n \"key\"\n ],\n \"properties\": {\n \"key\": {\n \"type\": \"string\",\n \"description\": \"The key associated with the memory to retrieve\"\n },\n \"collection\": {\n \"type\": \"string\",\n \"description\": \"Memories collection associated with the memory to retrieve (default value: generic)\"\n }\n }\n }\n },\n \"type\": \"function\"\n },\n {\n \"function\": {\n \"name\": \"TextMemoryPlugin-Recall\",\n \"description\": \"Semantic search and return up to N memories related to the input text\",\n \"parameters\": {\n \"type\": \"object\",\n \"required\": [\n \"input\"\n ],\n \"properties\": {\n \"input\": {\n \"type\": \"string\",\n \"description\": \"The input text to find related memories for\"\n },\n \"collection\": {\n \"type\": \"string\",\n \"description\": \"Memories collection to search (default value: generic)\"\n },\n \"relevance\": {\n \"type\": [\n \"number\",\n \"null\"\n ],\n \"description\": \"The relevance score, from 0.0 to 1.0, where 1.0 means perfect match (default value: 0)\"\n },\n \"limit\": {\n \"type\": [\n \"integer\",\n \"null\"\n ],\n \"description\": \"The maximum number of relevant memories to recall (default value: 1)\"\n }\n }\n }\n },\n \"type\": \"function\"\n },\n {\n \"function\": {\n \"name\": \"TextMemoryPlugin-Save\",\n \"description\": \"Save information to semantic memory\",\n \"parameters\": {\n \"type\": \"object\",\n \"required\": [\n \"input\",\n \"key\"\n ],\n \"properties\": {\n \"input\": {\n \"type\": \"string\",\n \"description\": \"The information to save\"\n },\n \"key\": {\n \"type\": \"string\",\n \"description\": \"The key associated with the information to save\"\n },\n \"collection\": {\n \"type\": \"string\",\n \"description\": \"Memories collection associated with the information to save (default value: generic)\"\n }\n }\n }\n },\n \"type\": \"function\"\n },\n {\n \"function\": {\n \"name\": \"TextMemoryPlugin-Remove\",\n \"description\": \"Remove specific memory\",\n \"parameters\": {\n \"type\": \"object\",\n \"required\": [\n \"key\"\n ],\n \"properties\": {\n \"key\": {\n \"type\": \"string\",\n \"description\": \"The key associated with the information to save\"\n },\n \"collection\": {\n \"type\": \"string\",\n \"description\": \"Memories collection associated with the information to save (default value: generic)\"\n }\n }\n }\n },\n \"type\": \"function\"\n }\n ],\n \"tool_choice\": \"auto\"\n}
This body was extracted from HttpRequest.Content object.
Results in:
{\n \"error\": \"json: cannot unmarshal array into Go struct field .tools.function.parameters.properties.type of type string\"\n}
Is it some configuration that we must make inside Ollama ? Or It is the format of data rendered by Import Plugin?
If I remove the very strange array types like this one: \"type\" : [ \"number\", \"null\" ] to just \"number\", the error does not happens but another error raises:
{\n \"error\": \"phi3 does not support tools\"\n}
Maybe something is missing inside current libraries that are generating invalid types as array types or ollama does not support these array types anymore. And are there some directions about installing tools for phi3 inside ollama?
Best regards,
Antonio Rabelo
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I just realized of that error while running on the Codespace. I'll try to fix it. I'm not really sure what change affected the whole demo.
\n
Best,
\n
Bruno
\n
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Hi all. Thanks for all the great work. I have been trying to run this scenario but get when trying to iterate through the response
Microsoft.SemanticKernel.HttpOperationException: 'json: cannot unmarshal array into Go struct field .tools.function.parameters.properties.type of type string Status: 400 (Bad Request)
I am running the code as is from the labs samples. Any ideas? Have there been breaking changes in the kernel libraries?
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