Support HuggingFace Inference API.

Author: Oceania2018

Directory.Build.props

@@ -2,7 +2,7 @@
  <PropertyGroup>
    <LangVersion>10.0</LangVersion>
    <OutputPath>..\..\..\packages</OutputPath>
-   <PackageVersion>0.12.1</PackageVersion>
+   <PackageVersion>0.12.3</PackageVersion>
    <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
  </PropertyGroup>
 </Project>

docs/architecture/assets/overview.drawio

@@ -1,11 +1,11 @@
-<mxfile host="Electron" modified="2023-09-11T03:31:43.410Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/21.6.8 Chrome/114.0.5735.289 Electron/25.5.0 Safari/537.36" etag="lRrajK2n_0sEKLZhDFCg" version="21.6.8" type="device">
+<mxfile host="Electron" modified="2023-09-17T13:34:27.318Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/21.6.8 Chrome/114.0.5735.289 Electron/25.5.0 Safari/537.36" etag="rOwRDk0nfgbjTZKasu5c" version="21.6.8" type="device">
   <diagram id="6jfvZ688tm8pOZHgqSFM" name="Page-1">
-    <mxGraphModel dx="1418" dy="820" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
+    <mxGraphModel dx="1418" dy="820" grid="0" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
       <root>
         <mxCell id="0" />
         <mxCell id="1" parent="0" />
-        <mxCell id="kNc4F-3AqUrd7lAFkTx2-3" value="BotSharp&lt;br&gt;&lt;span style=&quot;font-weight: normal;&quot;&gt;Agent&lt;br&gt;Conversation&lt;br&gt;Routing&lt;/span&gt;" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffcc99;strokeColor=#36393d;fontStyle=1;sketch=1;curveFitting=1;jiggle=2;" parent="1" vertex="1">
-          <mxGeometry x="345.71" y="340" width="214.29" height="60" as="geometry" />
+        <mxCell id="kNc4F-3AqUrd7lAFkTx2-3" value="BotSharp&lt;br&gt;&lt;span style=&quot;font-weight: normal;&quot;&gt;Agent&lt;br&gt;Conversation&lt;br&gt;Routing&lt;br&gt;Reasoning&lt;br&gt;&lt;/span&gt;" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#ffcc99;strokeColor=#36393d;fontStyle=1;sketch=1;curveFitting=1;jiggle=2;" parent="1" vertex="1">
+          <mxGeometry x="345.71" y="330.5" width="214.29" height="79" as="geometry" />
         </mxCell>
         <mxCell id="9DShFkwYbzccSmdIPQ0x-7" value="&lt;b&gt;Phone&amp;nbsp;Voice&lt;/b&gt;" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#f9f7ed;strokeColor=#36393d;sketch=1;curveFitting=1;jiggle=2;" parent="1" vertex="1">
           <mxGeometry x="240" y="240" width="90" height="40" as="geometry" />

docs/conf.py

@@ -64,9 +64,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '0.9'
+version = '0.12'
 # The full version, including alpha/beta/rc tags.
-release = '0.9.0'
+release = '0.12.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -170,7 +170,7 @@
 #  dir menu entry, description, category)
 texinfo_documents = [
     (master_doc, 'BotSharp', 'BotSharp Documentation',
-     author, 'BotSharp', 'The LLM powered Chatbot framework.',
+     author, 'BotSharp', 'The LLM application framework.',
      'Miscellaneous'),
 ]
 

docs/index.rst

@@ -3,23 +3,23 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-The Open Source AI Bot Platform Builder
+The Open Source LLM Application Framework
 ======================================================
 
 .. image:: https://img.shields.io/discord/1106946823282761851?label=Discord
     :target: `discord`_
 
-**Build the AI chatbot platform from scratch with .NET**
+**Build the AI bot from scratch with .NET**
 
-> The LLM powered Conversational Service framework
+> The LLM powered Conversational Service building blocks and best practice 
 
 *"Conversation as a platform (CaaP) is the future, so it's perfect that we're already offering the whole toolkits to .NET developers using BotSharp the Bot Platform Builder to build a CaaP. It opens up as much learning power as possible for your robots and precisely control every step of the AI processing pipeline."*
 
-**BotSharp** is an open source bot framework for AI Bot platform builders. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. 
+**BotSharp** is an open source AI framework for your enterprise-grade LLM applications. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in business oriented systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. 
 
 It's witten in C# running on .NET which is a full cross-platform framework. C# is an enterprise-grade programming language which is widely used to code business logic in information management related system. More friendly to corporate developers. BotSharp adopts machine learning algrithm in C/C++ interfaces directly which skips the python interfaces. That will facilitate the feature of the typed language C#, and be easier when refactoring code in system scope. 
 
-BotSharp is strictly in accordance with the components principle and decouples every part that is needed in the platform builder. So you can choose different UI/UX, or pick up a different NLP Tagger, or select a more advanced algorithm to do NER tasks. They are all modularized based on unified interfaces.
+BotSharp is strictly in accordance with the components principle and decouples every part that is needed in the platform builder. So you can choose different UI/UX, or pick up a different Vector Storage, or select a more advanced algorithm to do NLU tasks. They are all modularized based on unified interfaces.
 
 .. image:: static/logos/BotSharp.png
    :height: 64px

docs/quick-start/assets/overview.png

@@ -1,10 +1,10 @@
 # Overview
 
-BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out of the box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier.
+BotSharp is an open source application framework to speed up integrate LLMs into your current business system. This project involves natural language understanding and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out of the box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier.
 
 BotSharp is an high compatible and high scalable platform builder. It is in accordance with components princple strictly, decouples every part that needed in the platform builder. So you can choose different UI/UX, or pick up a different NLP Tagger, or select a more advanced algrithm to do NER task. They are all modulized based on unfied interfaces. 
 
-![ ](../static/screenshots/BotSharp_arch.png  "BotSharp Architecture Chart")
+![ ](./assets/overview.png  "BotSharp Architecture Chart")
 From the chart ahead we can see that based on botsharp you can launch your own chatbot platform with 3 components:
 
 - Storage module: Botsharp supports memory and redis DB 2 methods.
@@ -21,7 +21,7 @@ Even with this simple question, you can see conversational experience are hard t
 
 Your code would have to handle all these different types of requests ro carry out the same logic: looking up some forecast information for a feature. For this reason, a traditional computer interface would tend to force users to input a well-known, standard request at the detriment of the user experience, because it's just easier.
 
-However, BotSharp lets you easily achieve a conversational user experience by handling the natural language understanding (NLU) for you.When you use BotSharp, you can create agents that can understand the meaning of natural language and the nuances and trainslate that to structured meaning your software can understand.
+However, BotSharp lets you easily achieve a conversational user experience by handling the natural language understanding (NLU) for you. When you use BotSharp, you can create agents that can understand the meaning of natural language and the nuances and trainslate that to structured meaning your software can understand.
 
 Features
 -------------
@@ -30,6 +30,6 @@ Features
 * Integrate with multiple LLMs like ChatGPT and LLaMA.
 * Using plug-in design, it is easy to expand functions. 
 * Working with multiple Vector Stores for senmatic search.
-* Supporting different UI providers like [Chatbot UI](https://github.com/mckaywrigley/chatbot-ui) and [HuggingChat UI](https://github.com/huggingface/chat-ui).
+* Supporting different UI providers like [Chatbot UI](https://github.com/SciSharp/chatbot-ui) and [HuggingChat UI](https://github.com/huggingface/chat-ui).
 * Integrated with popular social platforms like Facebook Messenger, Slack and Telegram.
 * Providing REST APIs to work with your own UI.

docs/quick-start/overview.md

@@ -8,7 +8,7 @@ public interface IConversationStateService
     ConversationState Load(string conversationId);
     string GetState(string name, string defaultValue = "");
     ConversationState GetStates();
-    IConversationStateService SetState(string name, string value);
+    IConversationStateService SetState<T>(string name, T value);
     void CleanState();
     void Save();
 }

src/Infrastructure/BotSharp.Abstraction/Conversations/IConversationStateService.cs

@@ -8,11 +8,17 @@ public class IncomingMessageModel
 
     public virtual string Channel { get; set; } = string.Empty;
 
+    /// <summary>
+    /// Completion Provider
+    /// </summary>
+    [JsonPropertyName("provider")]
+    public virtual string? Provider { get; set; } = null;
+
     /// <summary>
     /// Model name
     /// </summary>
     [JsonPropertyName("model")]
-    public virtual string? ModelName { get; set; } = null;
+    public virtual string? Model { get; set; } = null;
 
     /// <summary>
     /// The sampling temperature to use that controls the apparent creativity of generated completions.

src/Infrastructure/BotSharp.Abstraction/Conversations/Models/IncomingMessageModel.cs

@@ -2,7 +2,7 @@ namespace BotSharp.Abstraction.MLTasks;
 
 public interface IChatCompletion
 {
-    string ModelName { get; }
+    string Provider { get; }
     Task<bool> GetChatCompletionsAsync(Agent agent, 
         List<RoleDialogModel> conversations, 
         Func<RoleDialogModel, Task> onMessageReceived,

src/Infrastructure/BotSharp.Abstraction/MLTasks/IChatCompletion.cs

@@ -1,4 +1,3 @@
-using System.Linq;
 using System.Text.RegularExpressions;
 
 namespace BotSharp.Abstraction.Utilities;