upload

.env

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+OPENAI_API_KEY="sk-proj-Z0wca03PTJSq5bwRQlfAT3BlbkFJnqzpRC38JXdo1Fc0NyF0"
+LANGCHAI_API_KEY="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"
+LANGCHAIN_PROJECT="Tutorial_3"
+GROQ_API_KEY="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"

app.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+import time
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+## Load Groq API Key
+groq_api_key = os.environ['GROQ_API_KEY']
+
+if "vector" not in st.session_state:
+    st.session_state.embeddings=OllamaEmbeddings()
+    st.session_state.loader=WebBaseLoader("https://docs.smith.langchain.com/")
+    st.session_state.docs=st.session_state.loader.load()
+
+    st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:50])
+    st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+
+
+st.title("Chatgroq Demo")
+llm=ChatGroq(groq_api_key=groq_api_key,
+             model="gemma-7b-it")
+
+prompt = ChatPromptTemplate.from_template(
+    """
+Answer the question based on the provided context only.
+Please provide the most accurate response based on the question.
+<context>
+{context}
+<context>
+Question: {input}
+    """
+)
+
+
+document_chain = create_stuff_documents_chain(llm, prompt)
+retriver = st.session_state.vectors.as_retriever()
+retriver_chain = create_retrieval_chain(retriver, document_chain)
+
+prompt=st.text_input("Input your prompt here")
+
+if prompt:
+    start=time.process_time()
+    response = retriver_chain.invoke({"input": prompt})
+    print("Response time :",time.process_time() - start)
+    st.write(response['answer'])
+
+    # With a Streamlit expander
+    with st.expander("Document Similarity Search"):
+        # Find the relevant chunks
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("------------------------------------")

llama3.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_openai import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+## load the GroqAPI Key
+os.environ['OPENAI_API_KEY']=os.getenv("OPENAI_API_KEY")
+groq_api_key = os.getenv('GROQ_API_KEY')
+
+st.title("ChatBot Demo for Error Codes")
+
+llm=ChatGroq(groq_api_key=groq_api_key,
+             model="Llama3-8b-8192")
+
+
+prompt = ChatPromptTemplate.from_template(
+    """
+Answer the question based on the provided context only.
+Please provide the most accurate response based on the question.
+<context>
+{context}
+<context>
+Question: {input}
+    """
+)
+
+
+def vector_embedding():
+
+    if "vectors" not in st.session_state:
+
+        st.session_state.embeddings = OpenAIEmbeddings()
+        st.session_state.loader = PyPDFDirectoryLoader("./data")
+        st.session_state.docs = st.session_state.loader.load()
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings )
+
+
+
+
+
+prompt1=st.text_input("Enter your question from Documents")
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("VectorStore DB is ready")
+
+import time
+
+
+
+
+if prompt1:
+    start = time.process_time()
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = st.session_state.vectors.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+    response = retrieval_chain.invoke({'input': prompt1})
+    print("Response time : ", time.process_time() - start)
+    st.write(response['answer'])
+
+    # With a Streamlit expander
+    with st.expander("Document Similarity Search"):
+        # Find the relevant chunks
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("------------------------------------")
+
+

requirements.txt

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+langchain_openai 
+langchain_core
+python-dotenv
+streamlit
+langchain_community
+langserve
+fastapi
+uvicorn
+langchain-openai
+sse_starlette
+bs4
+pypdf
+chromadb
+ollama
+faiss-cpu
+arxiv
+wikipedia
+beautifulsoup4
+langchainhub
+groq
+langchain-groq