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.env

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+OPENAI_API_KEY="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"
+LANGCHAIN_TRACING_V2=true
+LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
+LANGCHAIN_API_KEY="ls__481915cb2eaa4a53876c4bcf592457b0"
+LANGCHAIN_PROJECT="Beitrag POC"
+ACCESS_TOKEN_SECRET="hpr;F3H678%H"

app.py

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+import os
+import tempfile
+import gradio as gr
+import torch
+import logging
+
+from operator import itemgetter
+from langchain_openai import ChatOpenAI
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.vectorstores.chroma import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema import AIMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from langchain.globals import set_debug
+from dotenv import load_dotenv
+
+# configure logging
+logging.basicConfig(level=logging.INFO)
+
+set_debug(True)
+load_dotenv()
+
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+persist_dir = "./chroma_db"
+device='cuda:0'
+model_name="all-mpnet-base-v2"
+model_kwargs = {'device': device if torch.cuda.is_available() else 'cpu'}
+logging.info(f"Using device {model_kwargs['device']}")
+# Create embeddings and store in vectordb
+embeddings = HuggingFaceEmbeddings(model_name=model_name, show_progress=True, model_kwargs=model_kwargs)
+
+def configure_retriever(local_files, chunk_size=12500, chunk_overlap=2500):
+    logging.info("Configuring retriever")
+    
+    if not os.path.exists(persist_dir):
+        logging.info(f"Persist directory {persist_dir} does not exist. Creating it.")
+        # Read documents
+        docs = []
+        temp_dir = tempfile.TemporaryDirectory()
+        for filename in local_files:
+            logging.info(f"Reading file {filename}")
+            # Read the file once
+            if not os.path.exists(os.path.join("docs", filename)):
+                file_content = open(os.path.join(".", filename), "rb").read()
+            else:
+                file_content = open(os.path.join("docs", filename), "rb").read()
+            temp_filepath = os.path.join(temp_dir.name, filename)
+            with open(temp_filepath, "wb") as f:
+                f.write(file_content)
+            loader = PyPDFLoader(temp_filepath)
+            docs.extend(loader.load())
+        
+        # Split documents
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+        splits = text_splitter.split_documents(docs)
+
+        vectordb = Chroma.from_documents(splits, embeddings, persist_directory=persist_dir)
+
+        # Define retriever
+        retriever = vectordb.as_retriever(
+                search_type="similarity_score_threshold",
+                search_kwargs={'score_threshold': 0.8}
+            )
+
+        return retriever
+    else:
+        logging.info(f"Persist directory {persist_dir} exists. Loading from it.")
+        vectordb = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
+
+        # Define retriever
+        retriever = vectordb.as_retriever(
+                search_type="similarity_score_threshold",
+                search_kwargs={'score_threshold': 0.8}
+            )
+
+        return retriever
+
+directory = "docs" if os.path.exists("docs") else "."
+local_files = [f for f in os.listdir(directory) if f.endswith(".pdf")]
+
+# Setup LLM
+llm = ChatOpenAI(
+    model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, temperature=0, streaming=True
+)
+
+retriever = configure_retriever(local_files)
+
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+
+Answer in German language.
+"""
+
+prompt = ChatPromptTemplate.from_template(template)
+
+chain = (
+    {
+        "context": itemgetter("question") | retriever,
+        "question": itemgetter("question"),
+    }
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+def predict(message, history):
+    message = f"Translate the following text to German: {message}"
+    history_langchain_format = []
+    for human, ai in history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
+    history_langchain_format.append(HumanMessage(content=message))
+    gpt_response = llm(history_langchain_format)
+    return chain.invoke({"question": gpt_response.content})
+
+demo = gr.ChatInterface(
+    predict,
+    chatbot=gr.Chatbot(height=500, show_share_button=True),
+    textbox=gr.Textbox(placeholder="stell mir Fragen", container=False, scale=7),
+    title="Beitrag Service",
+    description="Ich bin Ihr hilfreicher KI-Assistent",
+    theme="soft",
+    examples=["Hello"],
+    cache_examples=True,
+    retry_btn="Wiederholen",
+    undo_btn="Vorheriges löschen",
+    clear_btn="Löschen").launch(show_api= False)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+openai==1.12.0
+langchain==0.1.10
+langchain-openai==0.0.8
+pypdf==4.0.1
+python-dotenv==1.0.1
+chromadb==0.4.22