原始語料：ChatGLM是一個開源的清華技術(shù)成果轉(zhuǎn)化的公司智譜AI研發(fā)的支持中英雙語的對話機(jī)器人它支持中英雙語問答的對話語言模型，基于 General Language Model (GLM) 架構(gòu)，具有 62 億參數(shù)。結(jié)合模型量化技術(shù)，用戶可以在消費級的顯卡上進(jìn)行本地部署（INT4 量化級別下最低只需 6GB 顯存）。ChatGLM-6B 使用了和 ChatGLM 相同的技術(shù)，針對中文問答和對話進(jìn)行了優(yōu)化。經(jīng)過約 1T 標(biāo)識符的中英雙語訓(xùn)練，輔以監(jiān)督微調(diào)、反饋自助、人類反饋強(qiáng)化學(xué)習(xí)等技術(shù)的加持，62 億參數(shù)的 ChatGLM-6B 已經(jīng)能生成相當(dāng)符合人類偏好的回答。

var embeddings = await new OpenAiOptions() { ApiKey = key }.Embeddings.CreateEmbedding(new EmbeddingCreateRequest()
            {
                InputAsList = inputs.ToList(),
                Model = OpenAI.GPT3.ObjectModels.Models.TextEmbeddingAdaV2
            });
return embeddings.Data.Select(x => x.Embedding).ToList();

[-0.0020597207,-0.012355088,0.0037828966,-0.032127112,-0.04815184,0.016633095,-0.01277577,........]

    public class ChatGlmVector
    {
        public ChatGlmVector()
        {
            Id = Id ?? Guid.NewGuid().ToString();
        }
        [Keyword]
        public string Id { get; set; }

        [Text]
        public string Text { get; set; }

        [DenseVector(Dimensions = 1536)]
        public IList<double> Vector { get; set; }
    }

if (!elasticClient.Indices.Exists(IndexName).Exists)
  elasticClient.Indices.Create(IndexName, c => c.Map<ChatGlmVector>(m => m.AutoMap()));
await elasticClient.IndexAsync(item, idx => idx.Index(IndexName));

POST /my_index/_search
{
  "size": 3,    // 返回前3個最相似的文檔
  "query": {
    "function_score": {
      "query": {
        "match_all": {}
      },
      "functions": [
        {
          "script_score": {
            "script": {
              "source": "def cosineSim = cosineSimilarity(params.queryVector, 'vector'); if (cosineSim > 0.8) return cosineSim; else return 0;",
              "params": {
                "queryVector": [1.0, 2.0, 3.0]   // 要查詢的向量
              }
            }
          }
        }
      ],
      "boost_mode": "replace"
    }
  }
}

var scriptParams = new Dictionary<string, object>
{
    {"queryVector", new double[]{1.0, 2.0, 3.0}}
};

var script = new InlineScript("def cosineSim = cosineSimilarity(params.queryVector, 'vector'); if (cosineSim > 0.8) return cosineSim; else return 0;")
{
    Params = scriptParams
};

var searchResponse = client.Search<object>(s => s
    .Size(3)
    .Query(q => q
        .FunctionScore(fs => fs
            .Query(qq => qq
                .MatchAll()
            )
            .Functions(fu => fu
                .ScriptScore(ss => ss
                    .Script(sc => script)
                )
            )
            .BoostMode(FunctionBoostMode.Replace)
        )
    )
);

            return (await GetOpenAIService().ChatCompletion.CreateCompletion(new ChatCompletionCreateRequest()
            {
                Messages=new List<ChatMessage>() {
                ChatMessage.FromUser("你是一個智能助手，你需要根據(jù)下面的事實依據(jù)回答問題。如果用戶輸入不在事實依據(jù)范圍內(nèi)，請說\"抱歉,這個問題我不知道。\""),
ChatMessage.FromUser($"事實依據(jù)：{這里需要從ES查詢出相似度最高的文本作為LLM的長期記憶}"),
ChatMessage.FromUser($"用戶輸入：{這里是用戶的原始問題}")
                },
                Model = OpenAI.GPT3.ObjectModels.Models.ChatGpt3_5Turbo
            })).Choices.FirstOrDefault().Message;