Delete pipelines.py

pipelines.py

@@ -1,91 +0,0 @@
-from haystack import Pipeline
-from haystack.components.builders import PromptBuilder
-from haystack.components.generators.openai import OpenAIGenerator
-from haystack.components.routers import ConditionalRouter
-
-from functions import SQLiteQuery
-
-from typing import List
-import sqlite3
-
-import os
-from getpass import getpass
-from dotenv import load_dotenv
-
-load_dotenv()
-
-if "OPENAI_API_KEY" not in os.environ:
-    os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:") 
-
-from haystack.components.builders import PromptBuilder
-from haystack.components.generators import OpenAIGenerator
-
-llm = OpenAIGenerator(model="gpt-4o")
-sql_query = SQLiteQuery('data_source.db')
-
-connection = sqlite3.connect('data_source.db')
-cur=connection.execute('select * from data_source')
-columns = [i[0] for i in cur.description]
-cur.close()
-
-#Rag Pipeline
-prompt = PromptBuilder(template="""Please generate an SQL query. The query should answer the following Question: {{question}};
-            If the question cannot be answered given the provided table and columns, return 'no_answer'
-            The query is to be answered for the table is called 'data_source' with the following
-            Columns: {{columns}};
-            Answer:""")
-
-routes = [
-     {
-        "condition": "{{'no_answer' not in replies[0]}}",
-        "output": "{{replies}}",
-        "output_name": "sql",
-        "output_type": List[str],
-    },
-    {
-        "condition": "{{'no_answer' in replies[0]}}",
-        "output": "{{question}}",
-        "output_name": "go_to_fallback",
-        "output_type": str,
-    },
-]
-
-router = ConditionalRouter(routes)
-
-fallback_prompt = PromptBuilder(template="""User entered a query that cannot be answered with the given table.
-                                            The query was: {{question}} and the table had columns: {{columns}}.
-                                            Let the user know why the question cannot be answered""")
-fallback_llm = OpenAIGenerator(model="gpt-4")
-
-conditional_sql_pipeline = Pipeline()
-conditional_sql_pipeline.add_component("prompt", prompt)
-conditional_sql_pipeline.add_component("llm", llm)
-conditional_sql_pipeline.add_component("router", router)
-conditional_sql_pipeline.add_component("fallback_prompt", fallback_prompt)
-conditional_sql_pipeline.add_component("fallback_llm", fallback_llm)
-conditional_sql_pipeline.add_component("sql_querier", sql_query)
-
-conditional_sql_pipeline.connect("prompt", "llm")
-conditional_sql_pipeline.connect("llm.replies", "router.replies")
-conditional_sql_pipeline.connect("router.sql", "sql_querier.queries")
-conditional_sql_pipeline.connect("router.go_to_fallback", "fallback_prompt.question")
-conditional_sql_pipeline.connect("fallback_prompt", "fallback_llm")
-
-def rag_pipeline_func(queries: str, columns: str):
-   print("RAG PIPELINE FUNCTION")
-   result = conditional_sql_pipeline.run({"prompt": {"question": queries,
-                                                  "columns": columns},
-                                       "router": {"question": queries},
-                                       "fallback_prompt": {"columns": columns}})
-   
-   if 'sql_querier' in result:
-      reply = result['sql_querier']['results'][0]
-   elif 'fallback_llm' in result:
-      reply = result['fallback_llm']['replies'][0]
-   else:
-      reply = result["llm"]["replies"][0]
-
-   print("reply content") 
-   print(reply.content)  
-
-   return {"reply": reply.content}