new(basic.gblib): New batch features.

packages/gpt.gblib/services/ChatServices.ts

@@ -69,7 +69,7 @@ import {
   SQL_SQLITE_PROMPT,
   SQL_MSSQL_PROMPT,
   SQL_MYSQL_PROMPT
-   } from "langchain/chains/sql_db";
+} from 'langchain/chains/sql_db';
 
 export interface CustomOutputParserFields {}
 export type ExpectedOutput = any;
@@ -538,9 +538,9 @@ export class ChatServices {
        * Create the final prompt template which is tasked with getting the natural
        * language response to the SQL query.
        */
-      const finalResponsePrompt = SQL_SQLITE_PROMPT;
+      const finalResponsePrompt =
         PromptTemplate.fromTemplate(`Based on the table schema below, question, SQL query, and SQL response, write a natural language response:
-Optimize answers for KPI people.
+          Optimize answers for KPI people. ${systemPrompt}
               ------------
               SCHEMA: {schema}
               ------------
@@ -563,14 +563,15 @@ Optimize answers for KPI people.
       const finalChain = RunnableSequence.from([
         {
           input: input => input.question,
-          query: sqlQueryChain,
+          query: sqlQueryChain
         },
         {
           schema: async () => db.getTableInfo(),
           input: input => input.question,
           query: input => input.query,
           response: input => db.run(input.query),
-          top_k: ()=>10, table_info: ()=>'any'
+          top_k: () => 10,
+          table_info: () => 'any'
         },
         {
           result: finalResponsePrompt.pipe(model).pipe(new StringOutputParser()),
@@ -582,7 +583,6 @@ Optimize answers for KPI people.
       result = await finalChain.invoke({
         question: question
       });
-
     } else if (LLMMode === 'nochain') {
       result = await (tools.length > 0 ? modelWithTools : model).invoke(`
       ${systemPrompt}