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from haystack import Pipeline
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from haystack.components.builders import PromptBuilder
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from haystack.components.generators.openai import OpenAIGenerator
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from haystack.components.routers import ConditionalRouter
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from functions import SQLiteQuery
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from typing import List
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import sqlite3
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import os
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from getpass import getpass
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from dotenv import load_dotenv
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load_dotenv()
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if "OPENAI_API_KEY" not in os.environ:
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os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")
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'''
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prompt = PromptBuilder(template="""Please generate an SQL query. The query should answer the following Question: {{question}};
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The query is to be answered for the table is called 'data_source' with the following
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Columns: {{columns}};
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Answer:""")
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sql_query = SQLQuery('data_source.db')
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llm = OpenAIGenerator(model="gpt-4")
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sql_pipeline = Pipeline()
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sql_pipeline.add_component("prompt", prompt)
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sql_pipeline.add_component("llm", llm)
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sql_pipeline.add_component("sql_querier", sql_query)
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sql_pipeline.connect("prompt", "llm")
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sql_pipeline.connect("llm.replies", "sql_querier.queries")
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# If you want to draw the pipeline, uncomment below 👇
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sql_pipeline.show()
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print("PIPELINE RUNNING")
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result = sql_pipeline.run({"prompt": {"question": "On which days of the week are average sales highest?",
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"columns": columns}})
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print(result["sql_querier"]["results"][0])
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'''
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from haystack.components.builders import PromptBuilder
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from haystack.components.generators import OpenAIGenerator
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llm = OpenAIGenerator(model="gpt-4o")
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sql_query = SQLiteQuery('data_source.db')
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connection = sqlite3.connect('data_source.db')
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cur=connection.execute('select * from data_source')
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columns = [i[0] for i in cur.description]
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print("COLUMNS 2")
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print(columns)
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cur.close()
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prompt = PromptBuilder(template="""Please generate an SQL query. The query should answer the following Question: {{question}};
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If the question cannot be answered given the provided table and columns, return 'no_answer'
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The query is to be answered for the table is called 'data_source' with the following
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Columns: {{columns}};
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Answer:""")
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routes = [
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{
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"condition": "{{'no_answer' not in replies[0]}}",
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"output": "{{replies}}",
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"output_name": "sql",
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"output_type": List[str],
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},
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{
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"condition": "{{'no_answer' in replies[0]}}",
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"output": "{{question}}",
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"output_name": "go_to_fallback",
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"output_type": str,
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},
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]
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router = ConditionalRouter(routes)
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fallback_prompt = PromptBuilder(template="""User entered a query that cannot be answered with the given table.
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The query was: {{question}} and the table had columns: {{columns}}.
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Let the user know why the question cannot be answered""")
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fallback_llm = OpenAIGenerator(model="gpt-4")
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conditional_sql_pipeline = Pipeline()
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conditional_sql_pipeline.add_component("prompt", prompt)
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conditional_sql_pipeline.add_component("llm", llm)
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conditional_sql_pipeline.add_component("router", router)
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conditional_sql_pipeline.add_component("fallback_prompt", fallback_prompt)
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conditional_sql_pipeline.add_component("fallback_llm", fallback_llm)
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conditional_sql_pipeline.add_component("sql_querier", sql_query)
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conditional_sql_pipeline.connect("prompt", "llm")
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conditional_sql_pipeline.connect("llm.replies", "router.replies")
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conditional_sql_pipeline.connect("router.sql", "sql_querier.queries")
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conditional_sql_pipeline.connect("router.go_to_fallback", "fallback_prompt.question")
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conditional_sql_pipeline.connect("fallback_prompt", "fallback_llm")
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question = "When is my birthday?"
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result = conditional_sql_pipeline.run({"prompt": {"question": question,
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"columns": columns},
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"router": {"question": question},
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"fallback_prompt": {"columns": columns}})
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def rag_pipeline_func(question: str, columns: str):
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result = conditional_sql_pipeline.run({"prompt": {"question": question,
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"columns": columns},
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"router": {"question": question},
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"fallback_prompt": {"columns": columns}})
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if 'sql_querier' in result:
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reply = result['sql_querier']['results'][0]
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elif 'fallback_llm' in result:
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reply = result['fallback_llm']['replies'][0]
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else:
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reply = result["llm"]["replies"][0]
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print("reply content")
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print(reply.content)
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return {"reply": reply.content} |
