--- a
+++ b/rag (1).py
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+# -*- coding: utf-8 -*-
+"""rag.ipynb
+
+
+"""### RAG on using this dataset -> # GPT 3.5"""
+
+!pip install openai
+
+#!pip install --upgrade pip
+
+!pip install transformers torch
+
+!pip install llama-index llama-index-experimental
+
+import logging
+import sys
+from IPython.display import Markdown, display
+
+import pandas as pd
+from llama_index.experimental.query_engine import PandasQueryEngine
+
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index.llms.openai import OpenAI
+
+llm = OpenAI(model="gpt-3.5-turbo", api_key="sk-proj-fPxf5ypW6BhdQP5XMgetT3BlbkFJI5hOUj2DsSwlUI5ZwN5b")
+
+import openai
+openai.api_key = 'sk-proj-fPxf5ypW6BhdQP5XMgetT3BlbkFJI5hOUj2DsSwlUI5ZwN5b'
+
+query_engine = PandasQueryEngine(df=haa_trainChronologies,llm=llm, verbose=True)
+
+
+
+file_formats = {
+    "csv": pd.read_csv,
+    "xls": pd.read_excel,
+    "xlsx": pd.read_excel,
+    "xlsm": pd.read_excel,
+    "xlsb": pd.read_excel,
+}
+
+def load_data(uploaded_file):
+    try:
+        ext = os.path.splitext(uploaded_file.name)[1][1:].lower()
+    except:
+        ext = uploaded_file.split(".")[-1]
+    if ext in file_formats:
+        return file_formats[ext](uploaded_file)
+    else:
+        st.error(f"Unsupported file format: {ext}")
+        return None
+
+# Read the Pandas DataFrame
+
+df = haa_develChronologies
+
+!pip install langchain
+
+!pip install langchain_openai
+
+from langchain_openai.chat_models import ChatOpenAI
+
+# Set your API key and model choice
+OPENAI_API_KEY = "sk-proj-0oig5Ll0qDjkUPCGlzq8T3BlbkFJHuYBzj0UJkkUy2Wgfh3O"
+MODEL_NAME = "gpt-3.5-turbo"  # Specify the model you want to use
+
+# Initialize the ChatOpenAI model with a specific temperature
+model = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model=MODEL_NAME, temperature=0.5)
+
+# Example usage
+response = model.invoke("What MLB team won the World Series during the COVID-19 pandemic?")
+print(response)
+
+!pip show langchain  # Check the installed version
+!pip install --upgrade langchain  # Update langchain
+
+!pip install langchain_experimental
+
+
+
+#from langchain.agents.agent_types import AgentType
+
+df= haa_develChronologies
+from langchain.agents.agent_types import AgentType  # Use the experimental version of AgentType
+from langchain_experimental.agents import create_pandas_dataframe_agent  # Assuming you also need this experimental feature
+
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chat_models import ChatOpenAI
+
+llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613", openai_api_key="sk-proj-0oig5Ll0qDjkUPCGlzq8T3BlbkFJHuYBzj0UJkkUy2Wgfh3O", streaming=True)
+pandas_df_agent = create_pandas_dataframe_agent(llm,df,verbose=True,agent_type=AgentType.OPENAI_FUNCTIONS,handle_parsing_errors=True,)
+
+response = pandas_df_agent.run("provide me time stemp for observations C0392747")
+
+
+
+dataFrameResponse = pandas_df_agent.run("provide me dataframe as string")
+
+type(dataFrameResponse)
+
+!pip install langchain_core
+
+!pip install ChatOpenAI
+
+from langchain_core.output_parsers import StrOutputParser
+
+parser = StrOutputParser()
+model = llm
+chain = model | parser
+
+try:
+    chain.invoke({
+        "context":dataFrameResponse,
+        "question":"which observations are more prone have Hospital acquired pressure injury? "
+    })
+except Exception as e:
+    print(e)
+
+#String Dataframe respone to Text Splitter by langchain
+
+# Note : Chunk size dependent on task you want to achieve with it
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+df_responseSplitter = RecursiveCharacterTextSplitter(chunk_size=50, chunk_overlap=15)
+df_responseSplitter.split_documents(dataFrameResponse)[:3]
+
+
+
+# openai embedding :
+
+from langchain_openai.embeddings import OpenAIEmbeddings
+
+ai_embeddings = OpenAIEmbeddings()
+ai_embed_query = embeddings.embed_query("provide me time stemp for observations C0392747")
+
+print(f"length: {len(ai_embed_query)}")
+
+
+
+medical_sentence1 = embeddings.embed_query("The timestamps for observations containing C0392747 are as follows - 2104-08-05 - 2104-08-07 - 2104-08-08")
+medical_sentence2 = embeddings.embed_query("Subject id : 75")
+
+# now let's examine performance of this medical query
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+def calculate_cosine_similarity(embedded_query, embedded_sentence):
+
+    similarities = [cosine_similarity([embedded_query], [sentence])[0][0] for sentence in embedded_sentences]
+    return similarities
+
+
+
+similarities_query1 = calculate_cosine_similarity(embedded_medical_query1, medical_sentence1)
+similarities_query2 = calculate_cosine_similarity(embedded_medical_query2, medical_sentence2)
+
+
+print('Similarities for Query 1:', similarities_query1)
+print('Similarities for Query 2:', similarities_query2)
+
+# now adding vector store
+
+# Concatenate the columns with a separator for clarity
+haa_develAdmittimes['combined'] = haa_develAdmittimes['hadm_id'].astype(str) + " at " + haa_develAdmittimes['admittime'].astype(str)
+
+# Now extract the combined text data
+text_data = haa_develAdmittimes['combined'].tolist()
+
+# here we can change multiple vector store as per application
+
+from langchain_community.vectorstores import DocArrayInMemorySearch
+
+# Create vector store from the combined text data
+admittime_vectorstore = DocArrayInMemorySearch.from_texts(text_data, embedding=embeddings)
+
+admittime_vectorstore.similarity_search_with_score(query="give me hadm_id of paintents accociated with 3rd january", k=2)
+
+#  retriever
+
+med_retriever = admittime_vectorstore.as_retriever()
+
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+
+setup = RunnableParallel(context=med_retriever, question=RunnablePassthrough())
+setup.invoke("give me hadm_id of paintents accociated with 3rd january?")
+
+chain = setup | prompt | model | parser
+chain.invoke("give me hadm_id of paintents accociated with 2nd january")
+