Merge pull request #82 from hchen2020/master

Enable knowledge in chatbot.

src/Infrastructure/BotSharp.Abstraction/Agents/Models/Agent.cs

@@ -18,6 +18,11 @@ public class Agent
     /// </summary>
     public string Samples { get; set; }
 
+    /// <summary>
+    /// Domain knowledges
+    /// </summary>
+    public string Knowledges { get; set;}
+
     /// <summary>
     /// Owner user id
     /// </summary>

src/Infrastructure/BotSharp.Abstraction/Conversations/Settings/ConversationSetting.cs

@@ -3,4 +3,5 @@ namespace BotSharp.Abstraction.Conversations.Settings;
 public class ConversationSetting
 {
     public string ChatCompletion { get; set; }
+    public bool EnableKnowledgeBase { get; set; }
 }

src/Infrastructure/BotSharp.Abstraction/Knowledges/IKnowledgeService.cs

@@ -5,5 +5,6 @@ namespace BotSharp.Abstraction.Knowledges;
 public interface IKnowledgeService
 {
     Task Feed(KnowledgeFeedModel knowledge);
+    Task<string> GetKnowledges(KnowledgeRetrievalModel retrievalModel);
     Task<string> GetAnswer(KnowledgeRetrievalModel retrievalModel);
 }

src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/ChunkOption.cs

@@ -3,12 +3,14 @@ namespace BotSharp.Abstraction.Knowledges.Models;
 public class ChunkOption
 {
     /// <summary>
-    /// Chunk size
+    /// Max chunk character size
     /// </summary>
     public int Size { get; set; }
 
     /// <summary>
-    /// Overlap length in between two chunks
+    /// Overlap word count in between two chunks
     /// </summary>
     public int Conjunction { get; set; }
+
+    public bool SplitByWord { get; set; }
 }

src/Infrastructure/BotSharp.Abstraction/VectorStorage/IVectorDb.cs

@@ -5,5 +5,5 @@ public interface IVectorDb
     Task<List<string>> GetCollections();
     Task CreateCollection(string collectionName, int dim);
     Task Upsert(string collectionName, int id, float[] vector, string text);
-    Task<List<string>> Search(string collectionName, float[] vector, int limit = 10);
+    Task<List<string>> Search(string collectionName, float[] vector, int limit = 5);
 }

src/Infrastructure/BotSharp.Core/Agents/Services/AgentService.GetAgents.cs

@@ -24,8 +24,17 @@ public partial class AgentService
         var profile = query.FirstOrDefault();
         var dir = GetAgentDataDir(id);
 
-        profile.Instruction = File.ReadAllText(Path.Combine(dir, "instruction.txt"));
-        profile.Samples = File.ReadAllText(Path.Combine(dir, "samples.txt"));
+        var instructionFile = Path.Combine(dir, "instruction.txt");
+        if (File.Exists(instructionFile))
+        {
+            profile.Instruction = File.ReadAllText(instructionFile);
+        }
+
+        var samplesFile = Path.Combine(dir, "samples.txt");
+        if (File.Exists(samplesFile))
+        {
+            profile.Samples = File.ReadAllText(Path.Combine(dir, "samples.txt"));
+        }
 
         return profile;
     }

src/Infrastructure/BotSharp.Core/Agents/Services/AgentService.UpdateAgent.cs

@@ -1,4 +1,5 @@
 using BotSharp.Abstraction.Agents.Models;
+using System.IO;
 
 namespace BotSharp.Core.Agents.Services;
 
@@ -16,5 +17,13 @@ public partial class AgentService
             record.Description = agent.Description;
             record.UpdatedDateTime = DateTime.UtcNow;
         });
+
+        // Save instruction to file
+        var dir = GetAgentDataDir(agent.Id);
+        var instructionFile = Path.Combine(dir, "instruction.txt");
+        File.WriteAllText(instructionFile, agent.Instruction);
+
+        var samplesFile = Path.Combine(dir, "samples.txt");
+        File.WriteAllText(samplesFile, agent.Samples);
     }
 }

src/Infrastructure/BotSharp.Core/Conversations/Services/ConversationService.cs

@@ -1,5 +1,6 @@
 using BotSharp.Abstraction.Conversations.Models;
 using BotSharp.Abstraction.Conversations.Settings;
+using BotSharp.Abstraction.Knowledges.Models;
 using BotSharp.Abstraction.MLTasks;
 
 namespace BotSharp.Core.Conversations.Services;
@@ -76,8 +77,20 @@ public class ConversationService : IConversationService
     public async Task<string> SendMessage(string agentId, string conversationId, List<RoleDialogModel> wholeDialogs)
     {
         var agent = await _services.GetRequiredService<IAgentService>().GetAgent(agentId);
-        var chat = GetChatCompletion();
-        var response = await chat.GetChatCompletionsAsync(agent, wholeDialogs);
+
+        // Get relevant domain knowledge
+        if (_settings.EnableKnowledgeBase)
+        {
+            var knowledge = _services.GetRequiredService<IKnowledgeService>();
+            agent.Knowledges = await knowledge.GetKnowledges(new KnowledgeRetrievalModel
+            {
+                AgentId = agentId,
+                Question = string.Join("\n", wholeDialogs.Select(x => x.Text))
+            });
+        }
+
+        var chatCompletion = GetChatCompletion();
+        var response = await chatCompletion.GetChatCompletionsAsync(agent, wholeDialogs);
         
         return response;
     }

src/Infrastructure/BotSharp.Core/Plugins/Knowledges/Services/KnowledgeService.cs

@@ -25,7 +25,8 @@ public class KnowledgeService : IKnowledgeService
         var lines = _textChopper.Chop(knowledge.Content, new ChunkOption
         {
             Size = 256,
-            Conjunction = 32
+            Conjunction = 5,
+            SplitByWord = true,
         });
 
         var db = GetVectorDb();
@@ -40,23 +41,24 @@ public class KnowledgeService : IKnowledgeService
         }
     }
 
-    public async Task<string> GetAnswer(KnowledgeRetrievalModel retrievalModel)
+    public async Task<string> GetKnowledges(KnowledgeRetrievalModel retrievalModel)
     {
         var textEmbedding = GetTextEmbedding();
         var vector = textEmbedding.GetVector(retrievalModel.Question);
 
         // Vector search
-        var result = await GetVectorDb().Search(retrievalModel.AgentId, vector);
+        var result = await GetVectorDb().Search(retrievalModel.AgentId, vector, limit: 10);
 
         // Restore 
-        var prompt = "";
-        foreach (var knowledge in result)
-        {
-            prompt += knowledge + "\n";
-        }
+        return "### Helpful domain knowledges:\r\n" + string.Join("\n", result.Select((x, i) => $"{i + 1}: {x}"));
+    }
 
-        prompt += "\r\n###\r\n";
-        prompt += "Answer the user's question based on the content provided above, and your reply should be as concise and organized as possible.\r\n";
+    public async Task<string> GetAnswer(KnowledgeRetrievalModel retrievalModel)
+    {
+        // Restore 
+        var prompt = await GetKnowledges(retrievalModel);
+
+        prompt += "\r\n### Answer user's question by utilizing the helpful domain knowledges above.\r\n";
         prompt += $"\r\nQuestion: {retrievalModel.Question}\r\nAnswer: ";
 
         var completion = await GetTextCompletion().GetCompletion(prompt);

src/Infrastructure/BotSharp.Core/Plugins/Knowledges/Services/TextChopperService.cs

@@ -1,10 +1,41 @@
 using BotSharp.Abstraction.Knowledges.Models;
+using System.Text.RegularExpressions;
 
 namespace BotSharp.Core.Plugins.Knowledges.Services;
 
 public class TextChopperService : ITextChopper
 {
     public List<string> Chop(string content, ChunkOption option)
+    {
+        content = Regex.Replace(content, @"\.{2,}", " ");
+        content = Regex.Replace(content, @"_{2,}", " ");
+        return option.SplitByWord ? ChopByWord(content, option) : ChopByChar(content, option);
+    }
+
+    private List<string> ChopByWord(string content, ChunkOption option)
+    {
+        var chunks = new List<string>();
+
+        var words = content.Split(' ')
+            .Where(x => !string.IsNullOrEmpty(x))
+            .ToList();
+
+        var chunk = "";
+        for (int i = 0; i < words.Count; i++)
+        {
+            chunk += words[i] + " ";
+            if (chunk.Length > option.Size)
+            {
+                chunks.Add(chunk.Trim());
+                chunk = "";
+                i -= option.Conjunction;
+            }
+        }
+
+        return chunks;
+    }
+
+    private List<string> ChopByChar(string content, ChunkOption option)
     {
         var chunks = new List<string>();
         var currentPos = 0;

src/Infrastructure/BotSharp.Core/Plugins/LLamaSharp/LlamaSharpSettings.cs

@@ -3,8 +3,6 @@ namespace BotSharp.Core.Plugins.LLamaSharp;
 public class LlamaSharpSettings
 {
     public string ModelPath { get; set; } = string.Empty;
-    public string InstructionFile { get; set; } = string.Empty;
-    public string ChatSampleFile { get; set; } = string.Empty;
     public int MaxContextLength { get; set; } = 512;
     public float RepeatPenalty { get; set; } = 1.0f;
     public bool VerbosePrompt { get; set; }

src/Infrastructure/BotSharp.Core/Plugins/MemVecDb/MemVectorDatabase.cs

@@ -1,5 +1,8 @@
 using BotSharp.Abstraction.VectorStorage;
+using System.Collections;
 using System.IO;
+using System.Numerics;
+using Tensorflow;
 using Tensorflow.NumPy;
 
 namespace BotSharp.Core.Plugins.MemVecDb;
@@ -20,13 +23,10 @@ public class MemVectorDatabase : IVectorDb
         return Task.FromResult(_collections.Select(x => x.Key).ToList());
     }
 
-    public Task<List<string>> Search(string collectionName, float[] vector, int limit = 10)
+    public Task<List<string>> Search(string collectionName, float[] vector, int limit = 5)
     {
-        var similarities = new float[_vectors[collectionName].Count];
-        for (int i = 0; i < _vectors[collectionName].Count; i++)
-        {
-            similarities[i] = CalCosineSimilarity(vector, _vectors[collectionName][i].Vector);
-        }
+        var similarities = CalCosineSimilarity(vector, _vectors[collectionName]);
+        // var similarities2 = CalEuclideanDistance(vector, _vectors[collectionName]);
 
         var texts = np.argsort(similarities).ToArray<int>()
             .Reverse()
@@ -49,8 +49,30 @@ public class MemVectorDatabase : IVectorDb
         return Task.CompletedTask;
     }
 
-    private float CalCosineSimilarity(float[] a, float[] b)
+    private float[] CalEuclideanDistance(float[] vec, List<VecRecord> records)
     {
-        return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b));
+        var a = np.zeros((records.Count, vec.Length), np.float32);
+        var b = np.zeros((records.Count, vec.Length), np.float32);
+        for (var i = 0; i < records.Count; i++)
+        {
+            a[i] = vec;
+            b[i] = records[i].Vector;
+        }
+
+        var c = np.sqrt(np.sum(np.square(a - b), axis: 1));
+        // var c = -np.prod(np.linalg.norm(a, axis: 1) * np.linalg.norm(b, axis: 1), axis: 1);
+        return c.ToArray<float>();
+    }
+
+    private float[] CalCosineSimilarity(float[] vec, List<VecRecord> records)
+    {
+        var similarities = new float[records.Count];
+        for (int i = 0; i < records.Count; i++)
+        {
+            var a = vec;
+            var b = records[i].Vector;
+            similarities[i] = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b));
+        }
+        return similarities;
     }
 }

src/Plugins/BotSharp.Plugin.AzureOpenAI/Providers/ChatCompletionProvider.cs

@@ -93,14 +93,18 @@ public class ChatCompletionProvider : IChatCompletion
 
     private ChatCompletionsOptions PrepareOptions(Agent agent, List<RoleDialogModel> conversations)
     {
-        var chatCompletionsOptions = new ChatCompletionsOptions()
-        {
-            Messages =
-            {
-                new ChatMessage(ChatRole.System, agent.Instruction)
-            }
-        };
+        var chatCompletionsOptions = new ChatCompletionsOptions();
 
+        if (!string.IsNullOrEmpty(agent.Instruction))
+        {
+            chatCompletionsOptions.Messages.Add(new ChatMessage(ChatRole.System, agent.Instruction));
+        }
+
+        if (!string.IsNullOrEmpty(agent.Knowledges))
+        {
+            chatCompletionsOptions.Messages.Add(new ChatMessage(ChatRole.System, agent.Knowledges));
+        }
+        
         foreach (var message in GetChatSamples(agent.Samples))
         {
             chatCompletionsOptions.Messages.Add(new ChatMessage(message.Role, message.Text));

src/Plugins/BotSharp.Plugin.AzureOpenAI/Settings/AzureOpenAiSettings.cs

@@ -6,6 +6,4 @@ public class AzureOpenAiSettings
     public string Endpoint { get; set; } = string.Empty;
     public DeploymentModelSetting DeploymentModel { get; set; } 
         = new DeploymentModelSetting();
-    public string InstructionFile { get; set; } = string.Empty;
-    public string ChatSampleFile { get; set; } = string.Empty;
 }

src/Plugins/BotSharp.Plugin.Qdrant/QdrantDb.cs

@@ -62,19 +62,19 @@ public class QdrantDb : IVectorDb
     public async Task Upsert(string collectionName, int id, float[] vector, string text)
     {
         // Insert vectors
-        await _client.Upsert(collectionName, points: new List<PointStruct>
+        /*await _client.Upsert(collectionName, points: new List<PointStruct>
         {
             new PointStruct(id: id, vector: vector)
-        });
+        });*/
 
         // Store chunks in local file system
         var agentService = _services.GetRequiredService<IAgentService>();
         var agentDataDir = agentService.GetAgentDataDir(collectionName);
         var knowledgePath = Path.Combine(agentDataDir, "knowledge.txt");
-        File.WriteAllLines(knowledgePath, new string[] { text });
+        File.AppendAllLines(knowledgePath, new[] { text });
     }
 
-    public async Task<List<string>> Search(string collectionName, float[] vector, int limit = 10)
+    public async Task<List<string>> Search(string collectionName, float[] vector, int limit = 5)
     {
         var result = await _client.Search(collectionName, vector, limit);
 

src/WebStarter/appsettings.json

@@ -21,8 +21,6 @@
   "LlamaSharp": {
     "Interactive": true,
     "ModelPath": "C:\\Users\\haipi\\Downloads\\wizard-vicuna-13B.ggmlv3.q8_0.bin",
-    "InstructionFile": "Prompts\\chat-with-bob.txt",
-    "ChatSampleFile": "Prompts\\chat-samples.txt",
     "MaxContextLength": 1024,
     "NumberOfGpuLayer": 10
   },
@@ -30,8 +28,6 @@
   "AzureOpenAi": {
     "ApiKey": "",
     "Endpoint": "",
-    "InstructionFile": "Prompts\\chat-with-bob.txt",
-    "ChatSampleFile": "Prompts\\chat-samples.txt",
     "DeploymentModel": {
       "ChatCompletionModel": "",
       "TextCompletionModel": ""