Update be.py

be.py

@@ -1,115 +1,115 @@
-from example import example
-from datetime import datetime
-import pandas as pd 
-# agent will directly create query and run the query in DB
-from langchain.agents import create_sql_agent 
-# Simple chain to create the SQL statements, it doesn't execute the query
-from langchain.chains import create_sql_query_chain 
-# to execute the query
-from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool 
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from langchain.sql_database import SQLDatabase
-from langchain.agents import AgentExecutor
-from langchain.agents.agent_types import AgentType
-from langchain_experimental.sql import SQLDatabaseChain
-from langchain_community.vectorstores import Chroma
-from langchain.prompts import SemanticSimilarityExampleSelector
-# Prompt input for MYSQL
-from langchain.chains.sql_database.prompt import PROMPT_SUFFIX, _mysql_prompt
-# Create the prompt template for creating the prompt for mysqlprompt
-from langchain.prompts.prompt import PromptTemplate
-from langchain.prompts import FewShotPromptTemplate
-# to create the tools to be used by agent
-from langchain.agents import Tool
-
-# create the agent prompts
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-# Huggingface embeddings using Langchain
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.prompts import HumanMessagePromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-# Load Env parameters
-from dotenv import load_dotenv
-from langchain_openai import ChatOpenAI
-from sqlalchemy import create_engine, text, URL
-
-
-def config():
-    load_dotenv()  # load env parameters
-    llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")   # create LLM
-    #llm = OpenAI(temperature=0.5)   # create LLM
-    return llm
-
-# Setting up URL parameter to connect to MySQL Database
-def get_db_chain(question):
-    db_user="root"
-    db_password="root"
-    db_host="localhost"
-    db_name="retail"
-    
-    # create LLM
-    llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
-    # Initialize SQL DB using Langchain
-    db = SQLDatabase.from_uri(f"mysql://{db_user}:{db_password}@{db_host}/{db_name}")
-    toolkit = SQLDatabaseToolkit(db=db, llm=llm)
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-    # create the list with only values and ready to be vectorized 
-    to_vectorize = [" ".join(example.values()) for example in example] # use join to convert Dict to list
-    # Setup the Chroma database and vectorize
-    vectorstore = Chroma.from_texts(to_vectorize, embedding=embeddings, metadatas=example)
-    # Based on the user question, convert them to vector and take the similar looking vectors from Chroma DB
-    example_selector = SemanticSimilarityExampleSelector(
-                        vectorstore = vectorstore,
-                        k=2)
-    example_prompt = PromptTemplate(
-                        input_variables=["Question", "SQLQuery", "SQLResult", "Answer",],
-                        template="\nQuestion: {Question}\nSQLQuery: {SQLQuery}\nSQLResult: {SQLResult}\n?Answer: {Answer}",)
-    few_shot_prompt = FewShotPromptTemplate( 
-                        example_selector=example_selector,                # Hey LLM, if you dont know refer the examples giving in vector DB
-                        example_prompt=example_prompt,                    # This is the Prompt template we have created        
-                        prefix=_mysql_prompt,                             # This is prefix of the prompt
-                        suffix=PROMPT_SUFFIX,                             # This is suffix of the prompt
-                        input_variables=["input", "table_info", "top_k"], # variables used in forming the prompt to LLM
-                        )
-    chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, prompt=few_shot_prompt)
-    response = chain.invoke(question)
-    return response
-    # Call the LLM with the question and the fewshotprompt
-    # write_query = create_sql_query_chain(llm=llm,db=db, prompt=few_shot_prompt)
-    #print(write_query)
-    # Execute the Query using QuerySQLDataBaseTool 
-    #execute_query = QuerySQLDataBaseTool(db=db)
-    # Chain to combine write SQL and Execute SQL 
-    #chain = write_query | execute_query | llm
-    #response = chain.invoke("Question")
-def get_store_address(store):
-    url_object = URL.create(
-    "mysql",
-    username="root",
-    password="root",  # plain (unescaped) text
-    host="localhost",
-    database="retail",
-    )
-    engine = create_engine(url_object)
-    #connect to engine
-    connection = engine.connect()
-    sql_query = "SELECT STORE_NUMBER, STORE_ADDRESS FROM STORES WHERE STORE_NUMBER = " + store
-    df = pd.read_sql(sql_query, con=engine)
-    response = df.to_string()
-    return response
-def outreach_sms_message(outreach_input):
-        # create LLM
-    llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo", verbose=True)
-    prompt = ChatPromptTemplate.from_template("You are a expert in writing a text message for appointment setup with less than 35 words." 
-                                              "With {outreach_input}, generate a text message for appointment to be sent to customer")
-    output_parser = StrOutputParser()
-    chain = prompt | llm | output_parser
-    response = chain.invoke({"outreach_input": outreach_input})
-    return response
-
-#if __name__ == "__main__":
-#    chain = get_db_chain()
-#    print(chain.run("List of all sales transactions for Trevor Nelson in June 2020"))
-# Setting up URL parameter to connect to MySQL Database
+from example import example
+from datetime import datetime
+import pandas as pd 
+# agent will directly create query and run the query in DB
+from langchain.agents import create_sql_agent 
+# Simple chain to create the SQL statements, it doesn't execute the query
+from langchain.chains import create_sql_query_chain 
+# to execute the query
+from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool 
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+from langchain.sql_database import SQLDatabase
+from langchain.agents import AgentExecutor
+from langchain.agents.agent_types import AgentType
+from langchain_experimental.sql import SQLDatabaseChain
+from langchain_community.vectorstores import Chroma
+from langchain.prompts import SemanticSimilarityExampleSelector
+# Prompt input for MYSQL
+from langchain.chains.sql_database.prompt import PROMPT_SUFFIX, _mysql_prompt
+# Create the prompt template for creating the prompt for mysqlprompt
+from langchain.prompts.prompt import PromptTemplate
+from langchain.prompts import FewShotPromptTemplate
+# to create the tools to be used by agent
+from langchain.agents import Tool
+
+# create the agent prompts
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+# Huggingface embeddings using Langchain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.prompts import HumanMessagePromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+# Load Env parameters
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+from sqlalchemy import create_engine, text, URL
+
+
+def config():
+    load_dotenv()  # load env parameters
+    llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")   # create LLM
+    #llm = OpenAI(temperature=0.5)   # create LLM
+    return llm
+
+# Setting up URL parameter to connect to MySQL Database
+def get_db_chain(question):
+    db_user="admin"
+    db_password="Epperson"
+    db_host="retail.cd6uaise2moh.us-west-2.rds.amazonaws.com"
+    db_name="retail"
+    
+    # create LLM
+    llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
+    # Initialize SQL DB using Langchain
+    db = SQLDatabase.from_uri(f"mysql://{db_user}:{db_password}@{db_host}/{db_name}")
+    toolkit = SQLDatabaseToolkit(db=db, llm=llm)
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+    # create the list with only values and ready to be vectorized 
+    to_vectorize = [" ".join(example.values()) for example in example] # use join to convert Dict to list
+    # Setup the Chroma database and vectorize
+    vectorstore = Chroma.from_texts(to_vectorize, embedding=embeddings, metadatas=example)
+    # Based on the user question, convert them to vector and take the similar looking vectors from Chroma DB
+    example_selector = SemanticSimilarityExampleSelector(
+                        vectorstore = vectorstore,
+                        k=2)
+    example_prompt = PromptTemplate(
+                        input_variables=["Question", "SQLQuery", "SQLResult", "Answer",],
+                        template="\nQuestion: {Question}\nSQLQuery: {SQLQuery}\nSQLResult: {SQLResult}\n?Answer: {Answer}",)
+    few_shot_prompt = FewShotPromptTemplate( 
+                        example_selector=example_selector,                # Hey LLM, if you dont know refer the examples giving in vector DB
+                        example_prompt=example_prompt,                    # This is the Prompt template we have created        
+                        prefix=_mysql_prompt,                             # This is prefix of the prompt
+                        suffix=PROMPT_SUFFIX,                             # This is suffix of the prompt
+                        input_variables=["input", "table_info", "top_k"], # variables used in forming the prompt to LLM
+                        )
+    chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, prompt=few_shot_prompt)
+    response = chain.invoke(question)
+    return response
+    # Call the LLM with the question and the fewshotprompt
+    # write_query = create_sql_query_chain(llm=llm,db=db, prompt=few_shot_prompt)
+    #print(write_query)
+    # Execute the Query using QuerySQLDataBaseTool 
+    #execute_query = QuerySQLDataBaseTool(db=db)
+    # Chain to combine write SQL and Execute SQL 
+    #chain = write_query | execute_query | llm
+    #response = chain.invoke("Question")
+def get_store_address(store):
+    url_object = URL.create(
+    "mysql",
+    username="root",
+    password="root",  # plain (unescaped) text
+    host="localhost",
+    database="retail",
+    )
+    engine = create_engine(url_object)
+    #connect to engine
+    connection = engine.connect()
+    sql_query = "SELECT STORE_NUMBER, STORE_ADDRESS FROM STORES WHERE STORE_NUMBER = " + store
+    df = pd.read_sql(sql_query, con=engine)
+    response = df.to_string()
+    return response
+def outreach_sms_message(outreach_input):
+        # create LLM
+    llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo", verbose=True)
+    prompt = ChatPromptTemplate.from_template("You are a expert in writing a text message for appointment setup with less than 35 words." 
+                                              "With {outreach_input}, generate a text message for appointment to be sent to customer")
+    output_parser = StrOutputParser()
+    chain = prompt | llm | output_parser
+    response = chain.invoke({"outreach_input": outreach_input})
+    return response
+
+#if __name__ == "__main__":
+#    chain = get_db_chain()
+#    print(chain.run("List of all sales transactions for Trevor Nelson in June 2020"))
+# Setting up URL parameter to connect to MySQL Database