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refine file settings and knowledge service

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This commit is contained in:
Jicheng Lu 2024-08-09 16:03:29 -05:00
parent 608dc1b4cf
commit 280b1bcd2a
43 changed files with 495 additions and 470 deletions
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2
src/Infrastructure/BotSharp.Abstraction/Files/Converters/IPdf2ImageConverter.cs
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@ -2,6 +2,8 @@ namespace BotSharp.Abstraction.Files.Converters;
public interface IPdf2ImageConverter
{
public string Name { get; }
/// <summary>
/// Convert pdf pages to images, and return a list of image file paths
/// </summary>

10
src/Infrastructure/BotSharp.Abstraction/Files/FileCoreSettings.cs Normal file
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@ -0,0 +1,10 @@
using BotSharp.Abstraction.Repositories.Enums;
namespace BotSharp.Abstraction.Files;
public class FileCoreSettings
{
public string Storage { get; set; } = FileStorageEnum.LocalFileStorage;
public string Pdf2TextConverter { get; set; }
public string Pdf2ImageConverter { get; set; }
}

8
src/Infrastructure/BotSharp.Abstraction/Files/FileStorageSettings.cs
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@ -1,8 +0,0 @@
using BotSharp.Abstraction.Repositories.Enums;
namespace BotSharp.Abstraction.Files;
public class FileStorageSettings
{
public string Default { get; set; } = FileStorageEnum.LocalFileStorage;
}

2
src/Infrastructure/BotSharp.Abstraction/Knowledges/IKnowledgeHook.cs
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@ -1,5 +1,3 @@
using BotSharp.Abstraction.Knowledges.Models;
namespace BotSharp.Abstraction.Knowledges;
public interface IKnowledgeHook

13
src/Infrastructure/BotSharp.Abstraction/Knowledges/IKnowledgeService.cs
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@ -4,17 +4,8 @@ namespace BotSharp.Abstraction.Knowledges;
public interface IKnowledgeService
{
Task<List<KnowledgeChunk>> CollectChunkedKnowledge();
Task EmbedKnowledge(List<KnowledgeChunk> chunks);
Task Feed(KnowledgeFeedModel knowledge);
Task EmbedKnowledge(KnowledgeCreationModel knowledge);
Task<string> GetKnowledges(KnowledgeRetrievalModel retrievalModel);
Task<List<RetrievedResult>> GetAnswer(KnowledgeRetrievalModel retrievalModel);
#region List
Task<IEnumerable<KnowledgeRetrievalResult>> SearchKnowledge(KnowledgeRetrievalModel model);
Task FeedKnowledge(KnowledgeCreationModel model);
Task<StringIdPagedItems<KnowledgeCollectionData>> GetKnowledgeCollectionData(string collectionName, KnowledgeFilter filter);
Task<IEnumerable<KnowledgeCollectionData>> GetSimilarKnowledgeData(string collectionName, KnowledgeFilter filter);
Task<bool> DeleteKnowledgeCollectionData(string collectionName, string id);
#endregion
}

6
src/Infrastructure/BotSharp.Abstraction/Knowledges/IPdf2TextConverter.cs
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@ -1,12 +1,8 @@
using System;
using System.Collections.Generic;
using System.Text;
using Microsoft.AspNetCore.Http;
namespace BotSharp.Abstraction.Knowledges
{
public interface IPdf2TextConverter
{
public string Name { get; }
Task<string> ConvertPdfToText(string filePath, int? startPageNum, int? endPageNum);
}
}

7
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/KnowledgeCollectionInfo.cs
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@ -1,7 +0,0 @@
namespace BotSharp.Abstraction.Knowledges.Models;
public class KnowledgeCollectionInfo
{
public ulong DataCount { get; set; }
public ulong VectorCount { get; set; }
}

3
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/KnowledgeCreationModel.cs
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@ -1,6 +1,9 @@
using BotSharp.Abstraction.Knowledges.Enums;
namespace BotSharp.Abstraction.Knowledges.Models;
public class KnowledgeCreationModel
{
public string Collection { get; set; } = KnowledgeCollectionName.BotSharp;
public string Content { get; set; } = string.Empty;
}

7
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/KnowledgeFeedModel.cs
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@ -1,7 +0,0 @@
namespace BotSharp.Abstraction.Knowledges.Models;
public class KnowledgeFeedModel
{
public string AgentId { get; set; } = string.Empty;
public string Content { get; set; } = string.Empty;
}

10
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/KnowledgeRetrievalModel.cs
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@ -1,7 +1,13 @@
using BotSharp.Abstraction.Knowledges.Enums;
namespace BotSharp.Abstraction.Knowledges.Models;
public class KnowledgeRetrievalModel
{
public string AgentId { get; set; } = string.Empty;
public string Question { get; set; } = string.Empty;
public string Collection { get; set; } = KnowledgeCollectionName.BotSharp;
public string Text { get; set; } = string.Empty;
public IEnumerable<string>? Fields { get; set; } = new List<string> { KnowledgePayloadName.Text, KnowledgePayloadName.Answer };
public int? Limit { get; set; } = 5;
public float? Confidence { get; set; } = 0.5f;
public bool WithVector { get; set; }
}

9
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/KnowledgeRetrievalResult.cs
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@ -1,9 +0,0 @@
namespace BotSharp.Abstraction.Knowledges.Models;
public class KnowledgeRetrievalResult
{
public string Id { get; set; }
public string Text { get; set; }
public float Score { get; set; }
public float[]? Vector { get; set; }
}

12
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/KnowledgeSearchResult.cs Normal file
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@ -0,0 +1,12 @@
namespace BotSharp.Abstraction.Knowledges.Models;
public class KnowledgeSearchResult
{
public IDictionary<string, string> Data { get; set; } = new Dictionary<string, string>();
public double Score { get; set; }
public float[]? Vector { get; set; }
}
public class KnowledgeRetrievalResult : KnowledgeSearchResult
{
}

12
src/Infrastructure/BotSharp.Abstraction/Knowledges/Models/RetrievedResult.cs
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@ -1,12 +0,0 @@
namespace BotSharp.Abstraction.Knowledges.Models;
public class RetrievedResult
{
public int Paragraph { get; set; }
[JsonPropertyName("cite_source")]
public string CiteSource { get; set; } = "related text";
[JsonPropertyName("reasoning")]
public string Reasoning { get; set; } = "";
}

9
src/Infrastructure/BotSharp.Abstraction/Knowledges/Settings/KnowledgeBaseSettings.cs
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@ -3,7 +3,12 @@ namespace BotSharp.Abstraction.Knowledges.Settings;
public class KnowledgeBaseSettings
{
public string VectorDb { get; set; }
public string TextEmbedding { get; set; }
public string TextCompletion { get; set; }
public KnowledgeModelSetting TextEmbedding { get; set; }
public string Pdf2TextConverter { get; set; }
}
public class KnowledgeModelSetting
{
public string Provider { get; set; }
public string Model { get; set; }
}

3
src/Infrastructure/BotSharp.Abstraction/Using.cs
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@ -17,4 +17,5 @@ global using BotSharp.Abstraction.Templating;
global using BotSharp.Abstraction.Translation.Attributes;
global using BotSharp.Abstraction.Messaging.Enums;
global using BotSharp.Abstraction.Files.Models;
global using BotSharp.Abstraction.Files.Enums;
global using BotSharp.Abstraction.Files.Enums;
global using BotSharp.Abstraction.Knowledges.Models;

6
src/Infrastructure/BotSharp.Abstraction/VectorStorage/IVectorDb.cs
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@ -1,13 +1,13 @@
using BotSharp.Abstraction.Knowledges.Models;
namespace BotSharp.Abstraction.VectorStorage;
public interface IVectorDb
{
string Name { get; }
Task<IEnumerable<string>> GetCollections();
Task<StringIdPagedItems<KnowledgeCollectionData>> GetCollectionData(string collectionName, KnowledgeFilter filter);
Task CreateCollection(string collectionName, int dim);
Task<bool> Upsert(string collectionName, string id, float[] vector, string text, Dictionary<string, string>? payload = null);
Task<IEnumerable<string>> Search(string collectionName, float[] vector, string returnFieldName, int limit = 5, float confidence = 0.5f);
Task<IEnumerable<KnowledgeSearchResult>> Search(string collectionName, float[] vector, IEnumerable<string> fields, int limit = 5, float confidence = 0.5f, bool withVector = false);
Task<bool> DeleteCollectionData(string collectionName, string id);
}

12
src/Infrastructure/BotSharp.Core/Files/FilePlugin.cs → src/Infrastructure/BotSharp.Core/Files/FileCorePlugin.cs
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@ -4,22 +4,22 @@ using Microsoft.Extensions.Configuration;
namespace BotSharp.Core.Files;
public class FilePlugin : IBotSharpPlugin
public class FileCorePlugin : IBotSharpPlugin
{
public string Id => "6a8473c0-04eb-4346-be32-24755ce5973d";
public string Name => "File";
public string Name => "File Core";
public string Description => "Provides file storage and analysis.";
public void RegisterDI(IServiceCollection services, IConfiguration config)
{
var myFileStorageSettings = new FileStorageSettings();
config.Bind("FileStorage", myFileStorageSettings);
services.AddSingleton(myFileStorageSettings);
var fileCoreSettings = new FileCoreSettings();
config.Bind("FileCore", fileCoreSettings);
services.AddSingleton(fileCoreSettings);
if (myFileStorageSettings.Default == FileStorageEnum.LocalFileStorage)
if (fileCoreSettings.Storage == FileStorageEnum.LocalFileStorage)
{
services.AddScoped<IFileStorageService, LocalFileStorageService>();
}

3
src/Infrastructure/BotSharp.Core/Files/Services/Instruct/FileInstructService.Pdf.cs
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@ -97,7 +97,8 @@ public partial class FileInstructService
private async Task<IEnumerable<string>> ConvertPdfToImages(IEnumerable<string> files)
{
var images = new List<string>();
var converter = _services.GetServices<IPdf2ImageConverter>().FirstOrDefault();
var settings = _services.GetRequiredService<FileCoreSettings>();
var converter = _services.GetServices<IPdf2ImageConverter>().FirstOrDefault(x => x.Name == settings.Pdf2ImageConverter);
if (converter == null || files.IsNullOrEmpty())
{
return images;

5
src/Infrastructure/BotSharp.Core/Files/Services/Storage/LocalFileStorageService.Conversation.cs
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@ -275,8 +275,9 @@ public partial class LocalFileStorageService
private IPdf2ImageConverter? GetPdf2ImageConverter()
{
var converters = _services.GetServices<IPdf2ImageConverter>();
return converters.FirstOrDefault();
var settings = _services.GetRequiredService<FileCoreSettings>();
var converter = _services.GetServices<IPdf2ImageConverter>().FirstOrDefault(x => x.Name == settings.Pdf2ImageConverter);
return converter;
}
private async Task<IEnumerable<MessageFileModel>> GetScreenshots(string file, string parentDir, string messageId, string source)

121
src/Infrastructure/BotSharp.OpenAPI/Controllers/KnowledgeBaseController.cs
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@ -1,3 +1,4 @@
using BotSharp.Abstraction.Knowledges.Enums;
using BotSharp.Abstraction.Knowledges.Models;
using BotSharp.Abstraction.Knowledges.Settings;
using BotSharp.OpenAPI.ViewModels.Knowledges;
@ -17,86 +18,28 @@ public class KnowledgeBaseController : ControllerBase
_services = services;
}
[HttpGet("/knowledge/{agentId}")]
public async Task<List<RetrievedResult>> RetrieveKnowledge([FromRoute] string agentId, [FromQuery(Name = "q")] string question)
[HttpPost("/knowledge/search")]
public async Task<IEnumerable<KnowledgeRetrivalViewModel>> SearchKnowledge([FromBody] SearchKnowledgeModel model)
{
return await _knowledgeService.GetAnswer(new KnowledgeRetrievalModel
var searchModel = new KnowledgeRetrievalModel
{
AgentId = agentId,
Question = question
});
Collection = model.Collection,
Text = model.Text,
Fields = model.Fields,
Limit = model.Limit ?? 5,
Confidence = model.Confidence ?? 0.5f,
WithVector = model.WithVector
};
var results = await _knowledgeService.SearchKnowledge(searchModel);
return results.Select(x => KnowledgeRetrivalViewModel.From(x)).ToList();
}
[HttpPost("/knowledge-base/upload")]
public async Task<IActionResult> UploadKnowledge(IFormFile file, [FromQuery] int? startPageNum, [FromQuery] int? endPageNum)
{
var setttings = _services.GetRequiredService<KnowledgeBaseSettings>();
var textConverter = _services.GetServices<IPdf2TextConverter>()
.First(x => x.GetType().FullName.EndsWith(setttings.Pdf2TextConverter));
var filePath = Path.GetTempFileName();
using (var stream = System.IO.File.Create(filePath))
{
await file.CopyToAsync(stream);
}
var content = await textConverter.ConvertPdfToText(filePath, startPageNum, endPageNum);
// Process uploaded files
// Don't rely on or trust the FileName property without validation.
// Add FeedWithMetaData
await _knowledgeService.EmbedKnowledge(new KnowledgeCreationModel
{
Content = content
});
return Ok(new { count = 1, file.Length });
}
[HttpPost("/knowledge/{agentId}")]
public async Task<IActionResult> FeedKnowledge([FromRoute] string agentId, List<IFormFile> files, [FromQuery] int? startPageNum, [FromQuery] int? endPageNum, [FromQuery] bool? paddleModel)
{
var setttings = _services.GetRequiredService<KnowledgeBaseSettings>();
var textConverter = _services.GetServices<IPdf2TextConverter>().First(x => x.GetType().FullName.EndsWith(setttings.Pdf2TextConverter));
long size = files.Sum(f => f.Length);
foreach (var formFile in files)
{
var filePath = Path.GetTempFileName();
using (var stream = new FileStream(filePath, FileMode.Create, FileAccess.Write, FileShare.None))
{
await formFile.CopyToAsync(stream);
await stream.FlushAsync(); // Ensure all data is written to the file
}
var content = await textConverter.ConvertPdfToText(filePath, startPageNum, endPageNum);
// Process uploaded files
// Don't rely on or trust the FileName property without validation.
// Add FeedWithMetaData
await _knowledgeService.Feed(new KnowledgeFeedModel
{
AgentId = agentId,
Content = content
});
// Delete the temp file after processing to clean up
System.IO.File.Delete(filePath);
}
return Ok(new { count = files.Count, size });
}
[HttpPost("/knowledge/{collection}/data")]
public async Task<StringIdPagedItems<KnowledgeCollectionDataViewModel>> GetKnowledgeCollectionData([FromRoute] string collection, [FromBody] KnowledgeFilter filter)
{
var data = await _knowledgeService.GetKnowledgeCollectionData(collection, filter);
var items = data.Items?.Select(x => KnowledgeCollectionDataViewModel.ToViewModel(x))?
var items = data.Items?.Select(x => KnowledgeCollectionDataViewModel.From(x))?
.ToList() ?? new List<KnowledgeCollectionDataViewModel>();
return new StringIdPagedItems<KnowledgeCollectionDataViewModel>
@ -107,19 +50,33 @@ public class KnowledgeBaseController : ControllerBase
};
}
[HttpPost("/knowledge/{collection}/similar")]
public async Task<IEnumerable<KnowledgeCollectionDataViewModel>> GetSimilarKnowledgeData([FromRoute] string collection, [FromBody] KnowledgeFilter filter)
{
var data = await _knowledgeService.GetKnowledgeCollectionData(collection, filter);
var items = data.Items?.Select(x => KnowledgeCollectionDataViewModel.ToViewModel(x))?
.ToList() ?? new List<KnowledgeCollectionDataViewModel>();
return
}
[HttpDelete("/knowledge/{collection}/data/{id}")]
public async Task<bool> DeleteKnowledgeCollectionData([FromRoute] string collection, [FromRoute] string id)
{
return await _knowledgeService.DeleteKnowledgeCollectionData(collection, id);
}
[HttpPost("/knowledge/upload")]
public async Task<IActionResult> UploadKnowledge(IFormFile file, [FromQuery] string? collection, [FromQuery] int? startPageNum, [FromQuery] int? endPageNum)
{
var setttings = _services.GetRequiredService<KnowledgeBaseSettings>();
var textConverter = _services.GetServices<IPdf2TextConverter>().FirstOrDefault(x => x.Name == setttings.Pdf2TextConverter);
var filePath = Path.GetTempFileName();
using (var stream = new FileStream(filePath, FileMode.Create, FileAccess.Write, FileShare.None))
{
await file.CopyToAsync(stream);
await stream.FlushAsync();
}
var content = await textConverter.ConvertPdfToText(filePath, startPageNum, endPageNum);
await _knowledgeService.FeedKnowledge(new KnowledgeCreationModel
{
Collection = collection ?? KnowledgeCollectionName.BotSharp,
Content = content
});
System.IO.File.Delete(filePath);
return Ok(new { count = 1, file.Length });
}
}

2
src/Infrastructure/BotSharp.OpenAPI/ViewModels/Knowledges/KnowledgeCollectionDataViewModel.cs
View file

@ -20,7 +20,7 @@ public class KnowledgeCollectionDataViewModel
[JsonIgnore(Condition = JsonIgnoreCondition.WhenWritingNull)]
public float[]? Vector { get; set; }
public static KnowledgeCollectionDataViewModel ToViewModel(KnowledgeCollectionData data)
public static KnowledgeCollectionDataViewModel From(KnowledgeCollectionData data)
{
return new KnowledgeCollectionDataViewModel
{

27
src/Infrastructure/BotSharp.OpenAPI/ViewModels/Knowledges/KnowledgeRetrivalViewModel.cs Normal file
View file

@ -0,0 +1,27 @@
using BotSharp.Abstraction.Knowledges.Models;
using System.Text.Json.Serialization;
namespace BotSharp.OpenAPI.ViewModels.Knowledges;
public class KnowledgeRetrivalViewModel
{
[JsonPropertyName("data")]
public IDictionary<string, string> Data { get; set; }
[JsonPropertyName("score")]
public double Score { get; set; }
[JsonPropertyName("vector")]
[JsonIgnore(Condition = JsonIgnoreCondition.WhenWritingNull)]
public float[]? Vector { get; set; }
public static KnowledgeRetrivalViewModel From(KnowledgeRetrievalResult model)
{
return new KnowledgeRetrivalViewModel
{
Data = model.Data,
Score = model.Score,
Vector = model.Vector
};
}
}

25
src/Infrastructure/BotSharp.OpenAPI/ViewModels/Knowledges/SearchKnowledgeModel.cs Normal file
View file

@ -0,0 +1,25 @@
using BotSharp.Abstraction.Knowledges.Enums;
using System.Text.Json.Serialization;
namespace BotSharp.OpenAPI.ViewModels.Knowledges;
public class SearchKnowledgeModel
{
[JsonPropertyName("collection")]
public string Collection { get; set; } = KnowledgeCollectionName.BotSharp;
[JsonPropertyName("text")]
public string Text { get; set; } = string.Empty;
[JsonPropertyName("fields")]
public IEnumerable<string>? Fields { get; set; }
[JsonPropertyName("limit")]
public int? Limit { get; set; } = 5;
[JsonPropertyName("confidence")]
public float? Confidence { get; set; } = 0.5f;
[JsonPropertyName("with_vector")]
public bool WithVector { get; set; }
}

8
src/Plugins/BotSharp.Plugin.KnowledgeBase/Functions/KnowledgeRetrievalFn.cs
View file

@ -17,14 +17,14 @@ public class KnowledgeRetrievalFn : IFunctionCallback
{
var args = JsonSerializer.Deserialize<ExtractedKnowledge>(message.FunctionArgs ?? "{}");
var embedding = _services.GetServices<ITextEmbedding>()
.FirstOrDefault(x => x.GetType().FullName.EndsWith(_settings.TextEmbedding));
var embedding = _services.GetServices<ITextEmbedding>().FirstOrDefault(x => x.Provider == _settings.TextEmbedding.Provider);
embedding.SetModelName(_settings.TextEmbedding.Model);
var vector = await embedding.GetVectorAsync(args.Question);
var vectorDb = _services.GetRequiredService<IVectorDb>();
var knowledges = await vectorDb.Search(KnowledgeCollectionName.BotSharp, vector, KnowledgePayloadName.Answer);
var knowledges = await vectorDb.Search(KnowledgeCollectionName.BotSharp, vector, new List<string> { KnowledgePayloadName.Answer });
if (knowledges.Count > 0)
if (!knowledges.IsNullOrEmpty())
{
message.Content = string.Join("\r\n\r\n=====\r\n", knowledges);
}

9
src/Plugins/BotSharp.Plugin.KnowledgeBase/Functions/MemorizeKnowledgeFn.cs
View file

@ -1,5 +1,3 @@
using BotSharp.Core.Infrastructures;
namespace BotSharp.Plugin.KnowledgeBase.Functions;
public class MemorizeKnowledgeFn : IFunctionCallback
@ -19,8 +17,8 @@ public class MemorizeKnowledgeFn : IFunctionCallback
{
var args = JsonSerializer.Deserialize<ExtractedKnowledge>(message.FunctionArgs ?? "{}");
var embedding = _services.GetServices<ITextEmbedding>()
.First(x => x.GetType().FullName.EndsWith(_settings.TextEmbedding));
var embedding = _services.GetServices<ITextEmbedding>().FirstOrDefault(x => x.Provider == _settings.TextEmbedding.Provider);
embedding.SetModelName(_settings.TextEmbedding.Model);
var vector = await embedding.GetVectorsAsync(new List<string>
{
@ -28,10 +26,9 @@ public class MemorizeKnowledgeFn : IFunctionCallback
});
var vectorDb = _services.GetRequiredService<IVectorDb>();
await vectorDb.CreateCollection(KnowledgeCollectionName.BotSharp, vector[0].Length);
var id = Utilities.HashTextMd5(args.Question);
var id = Guid.NewGuid().ToString();
var result = await vectorDb.Upsert(KnowledgeCollectionName.BotSharp, id, vector[0],
args.Question,
new Dictionary<string, string>

2
src/Plugins/BotSharp.Plugin.KnowledgeBase/MemVecDb/MemVecDbPlugin.cs
View file

@ -9,6 +9,6 @@ public class MemVecDbPlugin : IBotSharpPlugin
public string Description => "Store text embedding, search similar text from memory.";
public void RegisterDI(IServiceCollection services, IConfiguration config)
{
services.AddSingleton<IVectorDb, MemVectorDatabase>();
services.AddSingleton<IVectorDb, MemoryVectorDb>();
}
}

139
src/Plugins/BotSharp.Plugin.KnowledgeBase/MemVecDb/MemVectorDatabase.cs
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@ -1,139 +0,0 @@
using Tensorflow.NumPy;
using static Tensorflow.Binding;
namespace BotSharp.Plugin.KnowledgeBase.MemVecDb;
public class MemVectorDatabase : IVectorDb
{
private readonly Dictionary<string, int> _collections = new Dictionary<string, int>();
private readonly Dictionary<string, List<VecRecord>> _vectors = new Dictionary<string, List<VecRecord>>();
public async Task CreateCollection(string collectionName, int dim)
{
_collections[collectionName] = dim;
_vectors[collectionName] = new List<VecRecord>();
}
public async Task<IEnumerable<string>> GetCollections()
{
return _collections.Select(x => x.Key).ToList();
}
public Task<StringIdPagedItems<KnowledgeCollectionData>> GetCollectionData(string collectionName, KnowledgeFilter filter)
{
throw new NotImplementedException();
}
public async Task<IEnumerable<string>> Search(string collectionName, float[] vector, string returnFieldName, int limit = 5, float confidence = 0.5f)
{
if (!_vectors.ContainsKey(collectionName))
{
return new List<string>();
}
var similarities = CalCosineSimilarity(vector, _vectors[collectionName]);
// var similarities2 = CalEuclideanDistance(vector, _vectors[collectionName]);
var texts = np.argsort(similarities).ToArray<int>()
.Reverse()
.Take(limit)
.Select(i => _vectors[collectionName][i].Text)
.ToList();
return texts;
}
public async Task<bool> Upsert(string collectionName, string id, float[] vector, string text, Dictionary<string, string>? payload = null)
{
_vectors[collectionName].Add(new VecRecord
{
Id = id,
Vector = vector,
Text = text
});
return true;
}
public Task<bool> DeleteCollectionData(string collectionName, string id)
{
throw new NotImplementedException();
}
#region Private methods
private float[] CalEuclideanDistance(float[] vec, List<VecRecord> records)
{
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 NDArray CalCosineSimilarity(float[] vec, List<VecRecord> records)
{
var recordsArray = np.zeros((records.Count, records[0].Vector.Length), dtype: np.float32);
for (int i = 0; i < records.Count; i++)
{
recordsArray[i] = records[i].Vector;
}
var vecArray = np.expand_dims(np.array(vec, dtype: np.float32), axis: 0); // [1. 300]
(var normVecArray, var _) = SafeNormalize(vecArray);
(var normRecordsArray, var _) = SafeNormalize(recordsArray);
var simiMatix = tf.matmul(tf.cast(normVecArray, tf.float32), tf.transpose(tf.cast(normRecordsArray, tf.float32))).numpy(); // [1, num_records]
simiMatix = np.squeeze(simiMatix, axis: 0);
return simiMatix;
}
public (int, float)[] CalCosineSimilarityTopK(float[] vec, List<VecRecord> records, int topK = 10, float filterProb = 0.75f)
{
var simiMatix = CalCosineSimilarity(vec, records);
topK = Math.Min(topK, records.Count);
var topIndex = np.argsort(simiMatix)["::-1"][$":{topK}"];
var resIndex = new List<(int, float)>();
for (int i = 0; i < topK; i++)
{
var index = topIndex[i];
var value = simiMatix[index];
if (value > filterProb)
{
resIndex.Add((topIndex[i], value));
}
}
return resIndex.ToArray();
}
private (NDArray, NDArray) SafeNormalize(NDArray x, double eps = 2.223E-15)
{
var squaredX = np.sum(np.multiply(x, x), axis: 1);
var normX = np.sqrt(squaredX);
var epsTensor = tf.cast(tf.convert_to_tensor(eps), dtype: tf.float32);
var normXTensor = tf.cast(normX, tf.float32);
var contantMask = (normXTensor < epsTensor);
var divideTensor = tf.ones_like(normXTensor, dtype: tf.float32);
normX = tf.where(contantMask, divideTensor, normXTensor).numpy();
normX = np.expand_dims(normX, axis: 1);
return (x / normX, normX);
}
#endregion
}

71
src/Plugins/BotSharp.Plugin.KnowledgeBase/MemVecDb/MemoryVectorDb.cs Normal file
View file

@ -0,0 +1,71 @@
using BotSharp.Plugin.KnowledgeBase.Utilities;
using Tensorflow.NumPy;
namespace BotSharp.Plugin.KnowledgeBase.MemVecDb;
public class MemoryVectorDb : IVectorDb
{
private readonly Dictionary<string, int> _collections = new Dictionary<string, int>();
private readonly Dictionary<string, List<VecRecord>> _vectors = new Dictionary<string, List<VecRecord>>();
public string Name => "MemoryVector";
public async Task CreateCollection(string collectionName, int dim)
{
_collections[collectionName] = dim;
_vectors[collectionName] = new List<VecRecord>();
}
public async Task<IEnumerable<string>> GetCollections()
{
return _collections.Select(x => x.Key).ToList();
}
public Task<StringIdPagedItems<KnowledgeCollectionData>> GetCollectionData(string collectionName, KnowledgeFilter filter)
{
throw new NotImplementedException();
}
public async Task<IEnumerable<KnowledgeSearchResult>> Search(string collectionName, float[] vector,
IEnumerable<string> fields, int limit = 5, float confidence = 0.5f, bool withVector = false)
{
if (!_vectors.ContainsKey(collectionName))
{
return new List<KnowledgeSearchResult>();
}
var similarities = VectorUtility.CalCosineSimilarity(vector, _vectors[collectionName]);
// var similarities = VectorUtility.CalEuclideanDistance(vector, _vectors[collectionName]);
var results = np.argsort(similarities).ToArray<int>()
.Reverse()
.Take(limit)
.Select(i => new KnowledgeSearchResult
{
Data = new Dictionary<string, string> { { "text", _vectors[collectionName][i].Text } },
Score = similarities[i],
Vector = withVector ? _vectors[collectionName][i].Vector : null,
})
.ToList();
return await Task.FromResult(results);
}
public async Task<bool> Upsert(string collectionName, string id, float[] vector, string text, Dictionary<string, string>? payload = null)
{
_vectors[collectionName].Add(new VecRecord
{
Id = id,
Vector = vector,
Text = text
});
return true;
}
public Task<bool> DeleteCollectionData(string collectionName, string id)
{
throw new NotImplementedException();
}
}

28
src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/KnowledgeService.Create.cs Normal file
View file

@ -0,0 +1,28 @@
namespace BotSharp.Plugin.KnowledgeBase.Services;
public partial class KnowledgeService
{
public async Task FeedKnowledge(KnowledgeCreationModel knowledge)
{
var index = 0;
var lines = _textChopper.Chop(knowledge.Content, new ChunkOption
{
Size = 1024,
Conjunction = 32,
SplitByWord = true,
});
var db = GetVectorDb();
var textEmbedding = GetTextEmbedding();
await db.CreateCollection(knowledge.Collection, textEmbedding.Dimension);
foreach (var line in lines)
{
var vec = await textEmbedding.GetVectorAsync(line);
var id = Guid.NewGuid().ToString();
await db.Upsert(knowledge.Collection, id, vec, line);
index++;
Console.WriteLine($"Saved vector {index}/{lines.Count}: {line}\n");
}
}
}

14
src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/KnowledgeService.List.cs → src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/KnowledgeService.Delete.cs
View file

@ -2,20 +2,6 @@ namespace BotSharp.Plugin.KnowledgeBase.Services;
public partial class KnowledgeService
{
public async Task<StringIdPagedItems<KnowledgeCollectionData>> GetKnowledgeCollectionData(string collectionName, KnowledgeFilter filter)
{
try
{
var db = GetVectorDb();
return await db.GetCollectionData(collectionName, filter);
}
catch (Exception ex)
{
_logger.LogWarning($"Error when getting knowledge collection data ({collectionName}). {ex.Message}\r\n{ex.InnerException}");
return new StringIdPagedItems<KnowledgeCollectionData>();
}
}
public async Task<bool> DeleteKnowledgeCollectionData(string collectionName, string id)
{
try

38
src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/KnowledgeService.Get.cs Normal file
View file

@ -0,0 +1,38 @@
namespace BotSharp.Plugin.KnowledgeBase.Services;
public partial class KnowledgeService
{
public async Task<StringIdPagedItems<KnowledgeCollectionData>> GetKnowledgeCollectionData(string collectionName, KnowledgeFilter filter)
{
try
{
var db = GetVectorDb();
return await db.GetCollectionData(collectionName, filter);
}
catch (Exception ex)
{
_logger.LogWarning($"Error when getting knowledge collection data ({collectionName}). {ex.Message}\r\n{ex.InnerException}");
return new StringIdPagedItems<KnowledgeCollectionData>();
}
}
public async Task<IEnumerable<KnowledgeRetrievalResult>> SearchKnowledge(KnowledgeRetrievalModel model)
{
var textEmbedding = GetTextEmbedding();
var vector = await textEmbedding.GetVectorAsync(model.Text);
// Vector search
var db = GetVectorDb();
var collection = !string.IsNullOrWhiteSpace(model.Collection) ? model.Collection : KnowledgeCollectionName.BotSharp;
var fields = !model.Fields.IsNullOrEmpty() ? model.Fields : new List<string> { KnowledgePayloadName.Text, KnowledgePayloadName.Answer };
var found = await db.Search(collection, vector, fields, limit: model.Limit ?? 5, confidence: model.Confidence ?? 0.5f, withVector: model.WithVector);
var results = found.Select(x => new KnowledgeRetrievalResult
{
Data = x.Data,
Score = x.Score,
Vector = x.Vector
}).ToList();
return results;
}
}

104
src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/KnowledgeService.cs
View file

@ -7,7 +7,8 @@ public partial class KnowledgeService : IKnowledgeService
private readonly ITextChopper _textChopper;
private readonly ILogger<KnowledgeService> _logger;
public KnowledgeService(IServiceProvider services,
public KnowledgeService(
IServiceProvider services,
KnowledgeBaseSettings settings,
ITextChopper textChopper,
ILogger<KnowledgeService> logger)
@ -18,104 +19,19 @@ public partial class KnowledgeService : IKnowledgeService
_logger = logger;
}
public async Task EmbedKnowledge(KnowledgeCreationModel knowledge)
private IVectorDb GetVectorDb()
{
var idStart = 0;
var lines = _textChopper.Chop(knowledge.Content, new ChunkOption
{
Size = 1024,
Conjunction = 32,
SplitByWord = true,
});
var db = GetVectorDb();
var textEmbedding = GetTextEmbedding();
await db.CreateCollection(KnowledgeCollectionName.BotSharp, textEmbedding.Dimension);
foreach (var line in lines)
{
var vec = await textEmbedding.GetVectorAsync(line);
await db.Upsert(KnowledgeCollectionName.BotSharp, idStart.ToString(), vec, line);
idStart++;
Console.WriteLine($"Saved vector {idStart}/{lines.Count}: {line}\n");
}
}
public async Task Feed(KnowledgeFeedModel knowledge)
{
var idStart = 0;
var lines = _textChopper.Chop(knowledge.Content, new ChunkOption
{
Size = 1024,
Conjunction = 32,
SplitByWord = true,
});
var db = GetVectorDb();
var textEmbedding = GetTextEmbedding();
await db.CreateCollection(knowledge.AgentId, textEmbedding.Dimension);
foreach (var line in lines)
{
var vec = await textEmbedding.GetVectorAsync(line);
await db.Upsert(knowledge.AgentId, idStart.ToString(), vec, line);
idStart++;
Console.WriteLine($"Saved vector {idStart}/{lines.Count}: {line}\n");
}
}
public async Task<string> GetKnowledges(KnowledgeRetrievalModel retrievalModel)
{
var textEmbedding = GetTextEmbedding();
var vector = await textEmbedding.GetVectorAsync(retrievalModel.Question);
// Vector search
var db = GetVectorDb();
var result = await db.Search(KnowledgeCollectionName.BotSharp, vector, KnowledgePayloadName.Answer, limit: 10);
// Restore
return string.Join("\n\n", result.Select((x, i) => $"### Paragraph {i + 1} ###\n{x.Trim()}"));
}
public async Task<List<RetrievedResult>> GetAnswer(KnowledgeRetrievalModel retrievalModel)
{
// Restore
var prompt = await GetKnowledges(retrievalModel);
var sb = new StringBuilder(prompt);
sb.AppendLine();
sb.AppendLine();
sb.AppendLine("------");
sb.AppendLine("Answer question based on the given information above. Keep your answers concise. Please response with paragraph number, cite sources and reasoning in JSON format, if multiple paragraphs are found, put them in a JSON array. make sure the paragraph number is real. If you don't know the answer just output empty.");
sb.AppendLine("[" + JsonSerializer.Serialize(new RetrievedResult()) + "]");
sb.AppendLine("------");
sb.AppendLine($"QUESTION: \"{retrievalModel.Question}\"");
sb.AppendLine("Which paragraphs are relevant in order to answer the above question?");
sb.AppendLine("ANSWER: ");
prompt = sb.ToString().Trim();
var completion = await GetTextCompletion().GetCompletion(prompt, Guid.Empty.ToString(), Guid.Empty.ToString());
return JsonSerializer.Deserialize<List<RetrievedResult>>(completion);
}
public IVectorDb GetVectorDb()
{
var db = _services.GetServices<IVectorDb>()
.FirstOrDefault(x => x.GetType().FullName.EndsWith(_settings.VectorDb));
var db = _services.GetServices<IVectorDb>().FirstOrDefault(x => x.Name == _settings.VectorDb);
return db;
}
public ITextEmbedding GetTextEmbedding()
private ITextEmbedding GetTextEmbedding()
{
var embedding = _services.GetServices<ITextEmbedding>()
.FirstOrDefault(x => x.GetType().FullName.EndsWith(_settings.TextEmbedding));
var embedding = _services.GetServices<ITextEmbedding>().FirstOrDefault(x => x.Provider == _settings.TextEmbedding.Provider);
if (embedding != null)
{
embedding.SetModelName(_settings.TextEmbedding.Model);
}
return embedding;
}
public ITextCompletion GetTextCompletion()
{
var textCompletion = _services.GetServices<ITextCompletion>()
.FirstOrDefault(x => x.GetType().FullName.EndsWith(_settings.TextCompletion));
return textCompletion;
}
}

14
src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/KnowledgeService.i.cs
View file

@ -1,14 +0,0 @@
namespace BotSharp.Plugin.KnowledgeBase.Services;
public partial class KnowledgeService
{
public async Task<List<KnowledgeChunk>> CollectChunkedKnowledge()
{
throw new NotImplementedException();
}
public async Task EmbedKnowledge(List<KnowledgeChunk> chunks)
{
throw new NotImplementedException();
}
}

2
src/Plugins/BotSharp.Plugin.KnowledgeBase/Services/PigPdf2TextConverter.cs
View file

@ -5,6 +5,8 @@ namespace BotSharp.Plugin.KnowledgeBase.Services;
public class PigPdf2TextConverter : IPdf2TextConverter
{
public string Name => "Pig";
public Task<string> ConvertPdfToText(string filePath, int? startPageNum, int? endPageNum)
{
// since PdfDocument.Open is not async, we dont need to make this method async

83
src/Plugins/BotSharp.Plugin.KnowledgeBase/Utilities/VectorUtility.cs Normal file
View file

@ -0,0 +1,83 @@
using BotSharp.Plugin.KnowledgeBase.MemVecDb;
using Tensorflow.NumPy;
using static Tensorflow.Binding;
namespace BotSharp.Plugin.KnowledgeBase.Utilities;
public static class VectorUtility
{
public static float[] CalEuclideanDistance(float[] vec, List<VecRecord> records)
{
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>();
}
public static NDArray CalCosineSimilarity(float[] vec, List<VecRecord> records)
{
var recordsArray = np.zeros((records.Count, records[0].Vector.Length), dtype: np.float32);
for (int i = 0; i < records.Count; i++)
{
recordsArray[i] = records[i].Vector;
}
var vecArray = np.expand_dims(np.array(vec, dtype: np.float32), axis: 0); // [1. 300]
(var normVecArray, var _) = SafeNormalize(vecArray);
(var normRecordsArray, var _) = SafeNormalize(recordsArray);
var simiMatix = tf.matmul(tf.cast(normVecArray, tf.float32), tf.transpose(tf.cast(normRecordsArray, tf.float32))).numpy(); // [1, num_records]
simiMatix = np.squeeze(simiMatix, axis: 0);
return simiMatix;
}
public static (int, float)[] CalCosineSimilarityTopK(float[] vec, List<VecRecord> records, int topK = 10, float filterProb = 0.75f)
{
var simiMatix = CalCosineSimilarity(vec, records);
topK = Math.Min(topK, records.Count);
var topIndex = np.argsort(simiMatix)["::-1"][$":{topK}"];
var resIndex = new List<(int, float)>();
for (int i = 0; i < topK; i++)
{
var index = topIndex[i];
var value = simiMatix[index];
if (value > filterProb)
{
resIndex.Add((topIndex[i], value));
}
}
return resIndex.ToArray();
}
private static (NDArray, NDArray) SafeNormalize(NDArray x, double eps = 2.223E-15)
{
var squaredX = np.sum(np.multiply(x, x), axis: 1);
var normX = np.sqrt(squaredX);
var epsTensor = tf.cast(tf.convert_to_tensor(eps), dtype: tf.float32);
var normXTensor = tf.cast(normX, tf.float32);
var contantMask = (normXTensor < epsTensor);
var divideTensor = tf.ones_like(normXTensor, dtype: tf.float32);
normX = tf.where(contantMask, divideTensor, normXTensor).numpy();
normX = np.expand_dims(normX, axis: 1);
return (x / normX, normX);
}
}

5
src/Plugins/BotSharp.Plugin.MetaAI/Providers/FaissDb.cs
View file

@ -9,6 +9,8 @@ namespace BotSharp.Plugin.MetaAI.Providers;
public class FaissDb : IVectorDb
{
public string Name => "Faiss";
public Task CreateCollection(string collectionName, int dim)
{
throw new NotImplementedException();
@ -24,7 +26,8 @@ public class FaissDb : IVectorDb
throw new NotImplementedException();
}
public Task<IEnumerable<string>> Search(string collectionName, float[] vector, string returnFieldName, int limit = 10, float confidence = 0.5f)
public Task<IEnumerable<KnowledgeSearchResult>> Search(string collectionName, float[] vector,
IEnumerable<string> fields, int limit = 10, float confidence = 0.5f, bool withVector = false)
{
throw new NotImplementedException();
}

5
src/Plugins/BotSharp.Plugin.PaddleSharp/Providers/Pdf2TextConverter.cs
View file

@ -19,15 +19,18 @@ using BotSharp.Plugin.PaddleSharp.Settings;
namespace BotSharp.Plugin.PaddleSharp.Providers;
public class Pdf2TextConverter : IPdf2TextConverter
{
{
private Dictionary<int, string> _mappings = new Dictionary<int, string>();
private FullOcrModel _model;
private PaddleSharpSettings _paddleSharpSettings;
public Pdf2TextConverter(PaddleSharpSettings paddleSharpSettings)
{
_paddleSharpSettings = paddleSharpSettings;
}
public string Name => "Paddle";
public async Task<string> ConvertPdfToText(string filePath, int? startPageNum, int? endPageNum)
{
await ConvertPdfToLocalImagesAsync(filePath, startPageNum, endPageNum);

62
src/Plugins/BotSharp.Plugin.Qdrant/QdrantDb.cs
View file

@ -10,14 +10,16 @@ public class QdrantDb : IVectorDb
private readonly QdrantSetting _setting;
private readonly IServiceProvider _services;
public QdrantDb(QdrantSetting setting,
public QdrantDb(
QdrantSetting setting,
IServiceProvider services)
{
_setting = setting;
_services = services;
}
public string Name => "Qdrant";
private QdrantClient GetClient()
{
if (_client == null)
@ -42,9 +44,8 @@ public class QdrantDb : IVectorDb
public async Task<StringIdPagedItems<KnowledgeCollectionData>> GetCollectionData(string collectionName, KnowledgeFilter filter)
{
var client = GetClient();
var exists = await client.CollectionExistsAsync(collectionName);
if (!exists)
var exist = await DoesCollectionExist(client, collectionName);
if (!exist)
{
return new StringIdPagedItems<KnowledgeCollectionData>();
}
@ -71,11 +72,12 @@ public class QdrantDb : IVectorDb
public async Task CreateCollection(string collectionName, int dim)
{
var collections = await GetCollections();
if (!collections.Contains(collectionName))
var client = GetClient();
var exist = await DoesCollectionExist(client, collectionName);
if (!exist)
{
// Create a new collection
await GetClient().CreateCollectionAsync(collectionName, new VectorParams()
await client.CreateCollectionAsync(collectionName, new VectorParams()
{
Size = (ulong)dim,
Distance = Distance.Cosine
@ -83,7 +85,7 @@ public class QdrantDb : IVectorDb
}
// Get collection info
var collectionInfo = await _client.GetCollectionInfoAsync(collectionName);
var collectionInfo = await client.GetCollectionInfoAsync(collectionName);
if (collectionInfo == null)
{
throw new Exception($"Create {collectionName} failed.");
@ -115,7 +117,6 @@ public class QdrantDb : IVectorDb
}
var client = GetClient();
var result = await client.UpsertAsync(collectionName, points: new List<PointStruct>
{
point
@ -124,19 +125,54 @@ public class QdrantDb : IVectorDb
return result.Status == UpdateStatus.Completed;
}
public async Task<IEnumerable<string>> Search(string collectionName, float[] vector, string returnFieldName, int limit = 5, float confidence = 0.5f)
public async Task<IEnumerable<KnowledgeSearchResult>> Search(string collectionName, float[] vector,
IEnumerable<string> fields, int limit = 5, float confidence = 0.5f, bool withVector = false)
{
var client = GetClient();
var points = await client.SearchAsync(collectionName, vector, limit: (ulong)limit, scoreThreshold: confidence);
return points.Select(x => x.Payload[returnFieldName].StringValue).ToList();
var results = new List<KnowledgeSearchResult>();
foreach (var point in points)
{
var data = new Dictionary<string, string>();
foreach (var field in fields)
{
if (point.Payload.ContainsKey(field))
{
data[field] = point.Payload[field].StringValue;
}
else
{
data[field] = "";
}
}
results.Add(new KnowledgeSearchResult
{
Data = data,
Score = point.Score,
Vector = withVector ? point.Vectors?.Vector?.Data?.ToArray() : null
});
}
return results;
}
public async Task<bool> DeleteCollectionData(string collectionName, string id)
{
if (!Guid.TryParse(id, out var guid))
{
return false;
}
var client = GetClient();
var guid = Guid.Parse(id);
var result = await client.DeleteAsync(collectionName, guid);
return result.Status == UpdateStatus.Completed;
}
private async Task<bool> DoesCollectionExist(QdrantClient client, string collectionName)
{
return await client.CollectionExistsAsync(collectionName);
}
}

8
src/Plugins/BotSharp.Plugin.RoutingSpeeder/Providers/IntentClassifier.cs
View file

@ -57,8 +57,8 @@ public class IntentClassifier
return;
}
var vector = _services.GetServices<ITextEmbedding>()
.FirstOrDefault(x => x.GetType().FullName.EndsWith(_knowledgeBaseSettings.TextEmbedding));
var vector = _services.GetServices<ITextEmbedding>().FirstOrDefault(x => x.Provider == _knowledgeBaseSettings.TextEmbedding.Provider);
vector.SetModelName(_knowledgeBaseSettings.TextEmbedding.Model);
var layers = new List<ILayer>
{
@ -136,8 +136,8 @@ public class IntentClassifier
public NDArray GetTextEmbedding(string text)
{
var knowledgeSettings = _services.GetRequiredService<KnowledgeBaseSettings>();
var embedding = _services.GetServices<ITextEmbedding>()
.FirstOrDefault(x => x.GetType().FullName.EndsWith(knowledgeSettings.TextEmbedding));
var embedding = _services.GetServices<ITextEmbedding>() .FirstOrDefault(x => x.Provider == knowledgeSettings.TextEmbedding.Provider);
embedding.SetModelName(knowledgeSettings.TextEmbedding.Model);
var x = np.zeros((1, embedding.Dimension), dtype: np.float32);
x[0] = embedding.GetVectorAsync(text).GetAwaiter().GetResult();

30
src/Plugins/BotSharp.Plugin.SemanticKernel/SemanticKernelMemoryStoreProvider.cs
View file

@ -20,6 +20,10 @@ namespace BotSharp.Plugin.SemanticKernel
{
this._memoryStore = memoryStore;
}
public string Name => "SemanticKernel";
public async Task CreateCollection(string collectionName, int dim)
{
await _memoryStore.CreateCollectionAsync(collectionName);
@ -40,18 +44,23 @@ namespace BotSharp.Plugin.SemanticKernel
return result;
}
public async Task<IEnumerable<string>> Search(string collectionName, float[] vector, string returnFieldName, int limit = 5, float confidence = 0.5f)
public async Task<IEnumerable<KnowledgeSearchResult>> Search(string collectionName, float[] vector,
IEnumerable<string> fields, int limit = 5, float confidence = 0.5f, bool withVector = false)
{
var results = _memoryStore.GetNearestMatchesAsync(collectionName, vector, limit);
var resultTexts = new List<string>();
await foreach (var (record, _) in results)
var resultTexts = new List<KnowledgeSearchResult>();
await foreach (var (record, score) in results)
{
resultTexts.Add(record.Metadata.Text);
resultTexts.Add(new KnowledgeSearchResult
{
Data = new Dictionary<string, string> { { "text", record.Metadata.Text } },
Score = score,
Vector = withVector ? record.Embedding.ToArray() : null
});
}
return resultTexts;
}
public async Task<bool> Upsert(string collectionName, string id, float[] vector, string text, Dictionary<string, string>? payload)
@ -62,9 +71,16 @@ namespace BotSharp.Plugin.SemanticKernel
return true;
}
public Task<bool> DeleteCollectionData(string collectionName, string id)
public async Task<bool> DeleteCollectionData(string collectionName, string id)
{
throw new NotImplementedException();
var exist = await _memoryStore.DoesCollectionExistAsync(collectionName);
if (exist)
{
await _memoryStore.RemoveAsync(collectionName, id);
return true;
}
return false;
}
}
}

6
src/Plugins/BotSharp.Plugin.TencentCos/Services/TencentCosService.Conversation.cs
View file

@ -1,3 +1,4 @@
using BotSharp.Abstraction.Files;
using BotSharp.Abstraction.Files.Converters;
using BotSharp.Abstraction.Files.Enums;
using BotSharp.Abstraction.Files.Utilities;
@ -252,8 +253,9 @@ public partial class TencentCosService
private IPdf2ImageConverter? GetPdf2ImageConverter()
{
var converters = _services.GetServices<IPdf2ImageConverter>();
return converters.FirstOrDefault();
var settings = _services.GetRequiredService<FileCoreSettings>();
var converter = _services.GetServices<IPdf2ImageConverter>().FirstOrDefault(x => x.Name == settings.Pdf2ImageConverter);
return converter;
}
private string BuilFileUrl(string file)

6
src/Plugins/BotSharp.Plugin.TencentCos/TencentCosPlugin.cs
View file

@ -18,10 +18,10 @@ public class TencentCosPlugin : IBotSharpPlugin
public void RegisterDI(IServiceCollection services, IConfiguration config)
{
var myFileStorageSettings = new FileStorageSettings();
config.Bind("FileStorage", myFileStorageSettings);
var fileCoreSettings = new FileCoreSettings();
config.Bind("FileCore", fileCoreSettings);
if (myFileStorageSettings.Default == FileStorageEnum.TencentCosStorage)
if (fileCoreSettings.Storage == FileStorageEnum.TencentCosStorage)
{
services.AddScoped(provider =>
{

17
src/WebStarter/appsettings.json
View file

@ -230,9 +230,13 @@
"FileRepository": "data",
"Assemblies": [ "BotSharp.Core" ]
},
"FileStorage": {
"Default": "LocalFileStorage"
"FileCore": {
"Storage": "LocalFileStorage",
"Pdf2TextConverter": "",
"Pdf2ImageConverter": ""
},
"TencentCos": {
"AppId": "",
"SecretId": "",
@ -254,10 +258,11 @@
},
"KnowledgeBase": {
"VectorDb": "MemVectorDatabase",
"TextEmbedding": "fastTextEmbeddingProvider",
"TextCompletion": "AzureOpenAI.Providers.TextCompletionProvider",
"Pdf2TextConverter": "PigPdf2TextConverter"
"VectorDb": "Qdrant",
"TextEmbedding": {
"Provider": "openai",
"Model": "text-embedding-3-small"
}
},
"SparkDesk": {