updated app

app.py

@@ -1,41 +1,130 @@
-import streamlit as st
-import re
 
+from flask import Flask
+from flask import request, jsonify
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain.retrievers.document_compressors import DocumentCompressorPipeline
+from langchain_community.document_transformers import EmbeddingsRedundantFilter
+from langchain.retrievers.document_compressors import EmbeddingsFilter
+#from langchain_text_splitters import CharacterTextSplitter
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain_groq import ChatGroq
+#from langchain.document_loaders import HuggingFaceDatasetLoader
+# from langchain_community.document_loaders import UnstructuredExcelLoader
+# from langchain.document_loaders import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+# from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+# from transformers import AutoTokenizer, pipeline
+# from langchain import HuggingFacePipeline
+import re
+import os
+import streamlit as st
 import requests
 
-# url = "http://127.0.0.1:4200/query_data"
-
-# payload = {}
-# files=[
-
-# ]
-# headers = {}
-
-# response = requests.request("POST", url, headers=headers, data=payload, files=files)
-
-# print(response.text)
+def start():
+    # Define the path to the pre-trained model you want to use
+    modelPath = "sentence-transformers/all-MiniLM-l6-v2"
+
+    # Create a dictionary with model configuration options, specifying to use the CPU for computations
+    model_kwargs = {'device': 'cpu'}
+
+    # Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to False
+    encode_kwargs = {'normalize_embeddings': False}
+
+    # Initialize an instance of HuggingFaceEmbeddings with the specified parameters
+    embeddings = HuggingFaceEmbeddings(
+        model_name=modelPath,  # Provide the pre-trained model's path
+        model_kwargs=model_kwargs,  # Pass the model configuration options
+        encode_kwargs=encode_kwargs  # Pass the encoding options
+    )
+
+
+    # Initialize the HuggingFaceEmbeddings
+    model_path = "sentence-transformers/all-MiniLM-l6-v2"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': False}
+    embeddings = HuggingFaceEmbeddings(
+        model_name=model_path,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+
+    # Load the FAISS index
+    db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+
+    retriever = db.as_retriever(search_kwargs={"k": 2})
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
+    relevant_filter = EmbeddingsFilter(embeddings=embeddings)
+    pipeline_compressor = DocumentCompressorPipeline(transformers=[text_splitter, redundant_filter, relevant_filter])
+    compression_retriever = ContextualCompressionRetriever(base_compressor=pipeline_compressor, base_retriever=retriever)
+
+    chat = ChatGroq(temperature=0, groq_api_key="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",
+                    model_name="llama3-70b-8192")
+
+    rag_template_str = ("""
+    Answer the following query based on the context given.
+    Stylization:
+    1)Do not include or reference quoted content verbatim in the answer. Don't say "According to context provided"
+    2)Include the source URLs 
+    3)Include the Category it belongs to
+    Formatting:
+    1)Use bullet points
+    Restriction:
+    1)Only use context to answer the question 
+    2)If you don't know the answer,reply with "No answer found, you can contact us on https://www.i2econsulting.com/contact-us/" 
+    context: {context}
+    query:{query}
+    """)
+
+    rag_prompt = ChatPromptTemplate.from_template(rag_template_str)
+    rag_chain = rag_prompt | chat | StrOutputParser()
+
+    llm = ChatGroq(groq_api_key="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",
+                model_name="mixtral-8x7b-32768")
+
+    prompt = ChatPromptTemplate.from_template(
+        """
+        Answer the questions based on the provided context only.
+        Please provide the most accurate response based on the question
+        <context>
+        {context}
+        <context>
+        Questions:{input}
+
+        """
+    )
+    rag_prompt = ChatPromptTemplate.from_template(rag_template_str)
+    rag_chain = rag_prompt | chat | StrOutputParser()
 
 st.title("i2e Enterprise Chatbot")
 
 prompt = st.text_input("Ask Question")
 
-
+def api_py_function(query):
+    context = compression_retriever.get_relevant_documents(query)
+    #print(context)
+    l = []
+    for documents in context[:5]:
+        if documents.state['query_similarity_score'] > 0.1:
+            content = documents.page_content + str(documents.metadata)
+            l.append(content)
+    final_context = ''.join(l)
+    if l != []:
+        response = rag_chain.invoke({"query": query, "context": final_context})
+    else:
+        response = "No answer found, Please rephrase your question or you can contact us on https://www.i2econsulting.com/contact-us/"
+    return response
 
 if prompt:
     print("processing request")
-
-    url = "http://127.0.0.1:4200/query_data"
-
-    payload = {'prompt': prompt}
-    files = [
-
-    ]
-    headers = {}
-
-    response = requests.request("POST", url, headers=headers, data=payload, files=files)
-    full_response = response.text.replace(u"\u2000", "")
-    full_response=response.text.replace("\\n\\n","  \\n")
-    full_response = full_response.replace("\\n", "  \\n")
-
-    print(full_response)
-    st.write(full_response)
+    full_response=api_py_function(prompt)
+    # full_response = response.text.replace(u"\u2000", "")
+    # full_response=response.text.replace("\\n\\n","  \\n")
+    # full_response = full_response.replace("\\n", "  \\n")
+
+    st.write(full_response)
+if __name__ == "__main__":
+    start()