Add Agent state persistance

Author: ristoikonen

Agent_Llama.csproj

@@ -8,6 +8,7 @@ le</ImplicitUsings>
     <Nullable>enable</Nullable>
   </PropertyGroup>
   <ItemGroup>
+    <PackageReference Include="MemoryPack" Version="1.21.4" />
     <PackageReference Include="Microsoft.Agents.AI" Version="1.0.0" />
     <PackageReference Include="Microsoft.Agents.AI.A2A" Version="1.3.0-preview.260423.1" />
     <PackageReference Include="Microsoft.Extensions.AI" Version="10.4.1" />

DotNetAI.cs

@@ -99,9 +99,6 @@ public static async Task<string> GetBitcoinPrice(
     }
 
 
-
-
-
     [Description("Get the weather for a given location.")]
     public static string GetWeather([Description("The location to get the weather for.")] string location)
         => $"The weather in {location} is ...";

PDF_AI_Summariser.cs

@@ -1,14 +1,22 @@
-using Microsoft.SemanticKernel;
+using MemoryPack;
+using Microsoft.Extensions.AI;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Connectors.Ollama;
 using Microsoft.SemanticKernel.Embeddings;
 using OllamaSharp;
 using OllamaSharp.Models;
+using OpenAI.Chat;
+using PdfReader;
 using System;
 using System.Collections.Generic;
 using System.IO;
+using System.Net.Http;
 using System.Threading.Tasks;
+using UglyToad.PdfPig.Graphics;
 
 #pragma warning disable CA1861 // Avoid constant arrays as arguments
 #pragma warning disable SKEXP0070 // AddOllamaTextGeneration
+#pragma warning disable SKEXP0010
 
 namespace Agent_Llama;
 
@@ -49,7 +57,7 @@ public async Task SummarizeFileUsingPdfContentPlugin(string PDF_filename = @"C:\
             var builder = Kernel.CreateBuilder()
                 .AddOllamaTextGeneration(
                     endpoint: new Uri(ollamaEndpoint), // Use named argument for 'endpoint'
-                    modelId: ollamaModel              // Use named argument for 'modelId'
+                    modelId: ollamaModel              // Use named argument for 'modelId
                 );
 
             // Build the kernel instance
@@ -62,6 +70,8 @@ public async Task SummarizeFileUsingPdfContentPlugin(string PDF_filename = @"C:\
             var pdfContentPlugin = kernel.CreatePluginFromObject(new PdfContentPlugin());
             Console.WriteLine("PdfContentPlugin loaded successfully.");
 
+
+
             // --- Define the path to the text file ---
             //string filePath = Path.GetFullPath(sampleFileName);
             //Console.WriteLine($"Attempting to summarize file: {filePath}");
@@ -91,6 +101,8 @@ public async Task SummarizeFileUsingPdfContentPlugin(string PDF_filename = @"C:\
             var gen = ollamaClient.AsTextEmbeddingGenerationService();
             var embeds  = await gen.GenerateEmbeddingsAsync(Input);
 
+            System.Console.WriteLine($"Generated {embeds.Count} embeddings from Ollama for PDF: {PDF_filename_local}"   );
+            
             // Create the embedding service
             //var embeddingService = OllamaApiClient.AsEmbeddingGenerationService(ollamaClient, "nomic-embed-text");
 
@@ -136,6 +148,122 @@ public async Task SummarizeFileUsingPdfContentPlugin(string PDF_filename = @"C:\
         //Console.Read();
     }
 
+
+    public async Task SummarizeFileWithPdfContentPlugin(string PDF_filename = @"C:\Users\risto\source\repos\PDF_Llama\PDFs\VN.pdf")
+    {
+        // --- Configuration ---
+        const string PDF_filename_local = @"VN.pdf";
+
+        var httpClient = new HttpClient()
+        {
+            BaseAddress = ModelEndpoint,
+            Timeout = TimeSpan.FromMinutes(20)
+        };
+
+        try
+        {
+            var builder = Kernel.CreateBuilder()
+                .AddOllamaTextGeneration(
+                    endpoint: ModelEndpoint,
+                    modelId: ModelName      
+                );
+
+            var kernel = builder.Build();
+
+            Console.WriteLine($"Kernel initialized with Ollama model: {ModelName} at {ModelEndpoint}");
+
+
+            // --- Import your custom plugin ---
+            // The KernelPluginFactory.CreateFromType<T>() method is used to discover  kernel functions defined within the FileContentPlugin class.
+            //var pdfContentPlugin = kernel.CreatePluginFromObject(new PdfContentPlugin());
+            //Console.WriteLine("PdfContentPlugin loaded successfully.");
+
+
+            builder.Services.AddOllamaTextGeneration(
+                ModelName,
+                ModelEndpoint
+            );
+
+            var Input = new List<string> { "your text to embed" };
+
+            Reader reader = new Reader();
+            var pdftxtlist = reader.ReadPdfToList(PDF_filename);
+
+
+            // Assuming you have an IHttpClientFactory and a properly configured OllamaApiClient
+            //var ollamaClient = new OllamaApiClient(ModelEndpoint, ModelName);
+
+            var ollamaClient = new OllamaApiClient(httpClient)
+            {
+                SelectedModel = ModelName
+            };
+
+            //var gen = ollamaClient.AsTextEmbeddingGenerationService();
+            var embeds = await ollamaClient.AsTextEmbeddingGenerationService().GenerateEmbeddingsAsync(pdftxtlist);
+
+            // Serialize
+            byte[] bytearr = MemoryPackSerializer.Serialize(embeds);
+            File.WriteAllBytes(@"C:\tmp\embedding.bin", bytearr);
+
+            // Deserialize
+            ReadOnlyMemory<float> embeds_from_file = MemoryPackSerializer.Deserialize<ReadOnlyMemory<float>>(bytearr);
+            
+
+            System.Console.WriteLine($"Generated {embeds.Count}, seralised {embeds_from_file.Length} embeddings from Ollama for PDF: {PDF_filename_local}");
+
+
+
+            //var prompt = $"Summarize the following text in one sentence: {documentText}";
+            //var response = await chatClient.GetResponseAsync(prompt);
+
+            //Console.WriteLine($"\nSummary:\n{response.Message.Text}");
+
+            // Create the embedding service
+            //var embeddingService = OllamaApiClient.AsEmbeddingGenerationService(ollamaClient, "nomic-embed-text");
+
+            // Generate embeddings for a text
+            //var text = "This is a sample sentence.";
+            //var embedding = await embeddingService.GenerateEmbeddingAsync(text);
+
+
+            var embeddingRequest = new EmbedRequest
+            {
+                Input = Input,
+            };
+
+            var embeddingGenerator = new OllamaApiClient(ModelEndpoint, ModelName)
+                .EmbedAsync(embeddingRequest);
+
+            //OllamaApiClientExtensions
+            //    .AddOllamaTextEmbeddingGeneration(
+            //        builder.Services,
+
+            //        modelId: ollamaModel,
+            //        endpoint: new Uri(ollamaEndpoint)
+            //    );
+
+            //var embeddingGenerator = new OllamaEmbeddingGenerator(
+            //        modelId: ollamaModel,
+            //        endpoint: new Uri(ollamaEndpoint)
+            //    );
+
+            //var embeddingGenerator = new AddOllamaTextEmbeddingGeneration(ollamaEndpoint, ollamaModel)
+            //    .GetEmbeddingClient("your chosen model")
+            //    .AsIEmbeddingGenerator();
+
+        }
+        catch (Exception ex)
+        {
+            Console.WriteLine($"An error occurred: {ex.Message}");
+            Console.WriteLine("Please ensure Ollama is running and the specified model is downloaded.");
+            Console.WriteLine($"Check your Ollama endpoint: {ModelEndpoint.AbsoluteUri} and model: {ModelName}");
+        }
+
+        Console.WriteLine("Press any key to exit.");
+    }
+
+
+
     /// <summary>
     /// Creates a sample text file with some dummy content for testing.
     /// </summary>

PdfContentPlugin.cs

@@ -42,9 +42,13 @@ public async Task<string> SummarizeFile(
             Reader reader = new Reader(PDFPath);
             pdfpath = PDFPath + pdfFileName;
             string filePath = Path.GetFullPath(pdfpath);
-            var pdftxt = reader.ReadPdf(filePath); // Ensure the file can be read before proceeding.
-                                                 //json_chunks = reader.ReadPdfBlocks(pdfpath);
-                                                 // Create a prompt for the AI model.Instruct the model to summarize the provided text.
+            var pdftxt = reader.ReadPdf(filePath); 
+
+
+            // Ensure the file can be read before proceeding.
+            //json_chunks = reader.ReadPdfBlocks(pdfpath);
+            // Create a prompt for the AI model.Instruct the model to summarize the provided text.
+            
             prompt = @$"Summarize the following text concisely and accurately.
             If the text is too short or doesn't contain meaningful information, state that.
 

PdfReader.cs

@@ -33,6 +33,35 @@ public Reader(string directoryPath)
         }
     }
 
+    // get text and blocks
+    public string ReadPdf(string filePath)
+    {
+        StringBuilder text = new StringBuilder();
+        using (var document = UglyToad.PdfPig.PdfDocument.Open(filePath))
+        {
+            foreach (var page in document.GetPages())
+            {
+                var pageText = ContentOrderTextExtractor.GetText(page);
+                text.AppendLine(pageText);
+            }
+        }
+        return text.ToString();
+    }
+
+    public List<string> ReadPdfToList(string filePath)
+    {
+        List<string> pages = new List<string>();
+        using (var document = UglyToad.PdfPig.PdfDocument.Open(filePath))
+        {
+            foreach (var page in document.GetPages())
+            {
+                var pageText = ContentOrderTextExtractor.GetText(page);
+                pages.Add(pageText);
+            }
+        }
+        return pages;
+    }
+
     public List<string> Read_PDF_Blocks_AsJSON(string filePath)
     {
         List<string> jsonList = new List<string>();
@@ -97,20 +126,6 @@ public List<string> ReadDirectorysPdfsAsJSON()
         }
         return pdf_json_list;
     }
-    // get text and blocks
-    public string ReadPdf(string filePath)
-    {
-        StringBuilder text = new StringBuilder();
-        using (var document = UglyToad.PdfPig.PdfDocument.Open(filePath))
-        {
-            foreach (var page in document.GetPages())
-            {
-                var pageText = ContentOrderTextExtractor.GetText(page);
-                text.AppendLine(pageText);
-            }
-        }
-        return text.ToString();
-    }
 
     // C:\Users\risto\Downloads\Vacancy Notice EEA-AD-2024-17.pdf
     // VACANCY NOTICE EEA-AD-2024-17.pdf

Program.cs

@@ -68,7 +68,6 @@ static async Task Main(string[] args)
         //var config = configurationManager.GetRequiredSection("appSettings");
 
         var configvalue1 = configurationManager.Sources; // ("ModelEndpoint"); //.AppSettings["countoffiles"];
-        // write title
 
         SpectreConsoleOutput.DisplayTitleH3($"Use Semantic Kernel Plugin; PDF Summariser -  Get response from Ollama IChatClient");
 
@@ -83,25 +82,30 @@ static async Task Main(string[] args)
         AgentStructuredOutput agent_struct =  new(starts.ModelEndpoint, starts.ModelName);
 
         NestEd nested = new(starts.ModelEndpoint, starts.ModelName);
+        Embed embed = new(starts.ModelEndpoint, starts.ModelName);
 
         ////var cardResolver = new A2ACardResolver(starts.ModelEndpoint);
         //var agentCard = await cardResolver.GetAgentCardAsync();
 
         // present
         switch (scenario)
         {
-            // NestedDictionary
+            case "Embed":
+                await embed.CreateAgent("", "");
+                break;
+
             case "Nested":
                 await nested.CreateAgent("","");
                 break;
+
             case "AtoA":
                 await a2a.CreateAgent("What is the second largest city in France?",
                     "What is the third largest city in italy?");
                 break;
 
             case "PDF AI Summariser":
                 PDF_AI_Summariser pdf_AI_Summariser = new(starts.ModelEndpoint, starts.ModelName);
-                await pdf_AI_Summariser.SummarizeFileUsingPdfContentPlugin();
+                await pdf_AI_Summariser.SummarizeFileWithPdfContentPlugin();
                 break;
 
             case "Long Agent Task":

README.md

@@ -1,8 +1,8 @@
 # Agent_Llama
-##### Based on PDF_Llama solution, uses Microsoft Agent Framework.
 
 This repository demonstrates using Ollama (local LLM) with Microsoft Agent Framework 
 to **summarize PDF documents** and generate embeddings from documents for future use. 
+It is Based on PDF_Llama. Uses Microsoft Agent Framework.
 
 ## Usage scenarios
 
@@ -20,7 +20,16 @@ this helpful piggy gives us the ability to
 AI analyse documents images, links and metadata.
 
 ### Working on atm...
-- Use gemma3:4b model to add image analysis
+- Using gemma3:4b model to add image analysis
+- Serach for 'gemma3'
+
+### Choosing an embedding model
+ - Model accuracy and dimensionality.
+ - What is the size of the vectors generated by the model, 
+ - Is it configurable, as this will affect your vector storage cost.
+ - What type of elements does the generated vectors contain, e.g. float32, float16 - cost again.
+ - How fast does it generate vectors?
+
 
 ### Future enhancements