app.py

app.py

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+#================imports==============
+
+import uuid
+import os
+from typing import Dict, List, Any
+from dotenv import load_dotenv
+
+
+from langchain_core.globals import set_llm_cache
+from langchain_core.caches import InMemoryCache
+
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Weaviate
+from langchain_community.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+
+from langchain_classic.chains.combine_documents import create_stuff_documents_chain
+from langchain_classic.chains import create_retrieval_chain
+
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+
+#================== CONFIG==================
+
+load_dotenv()
+
+set_llm_cache(InMemoryCache())
+
+api_key=os.environ["GROQ_API_KEY"]
+print("api chargée:" if api_key else "y'a probleme!!")
+
+#========== charger et decouper documents=================
+
+urls=[
+    "https://fr.wikipedia.org/wiki/Intelligence_artificielle",
+    "https://fr.wikipedia.org/wiki/Apprentissage_automatique"
+]
+
+loader = WebBaseLoader(urls)
+docs =loader.load()
+
+splitter = RecursiveCharacterTextSplitter(chunk_size= 1000, chunk_overlap=200)
+chunks= splitter.split_documents(docs)
+
+#============embeding et indexation vers faiss_db================
+
+embeddings= HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+faiss_db=FAISS.from_documents(
+    documents=chunks,
+    embedding=embeddings
+    )
+
+retriever=faiss_db.as_retriever(search_type="similarity", search_kwargs={"k":3})
+
+#=============== LLM et Prompt=================
+
+llm = ChatGroq(
+    model="llama-3.3-70b-versatile",
+    temperature=0.0,
+    max_tokens=1200
+    )
+
+prompt=ChatPromptTemplate.from_template(
+  """
+  Tu es un assistant expert en IA, reponds clairement aux questions.Si tu ne connais pas n'invente pas.
+  Garde le ton amical.
+
+  Historique :
+  {{chat_history}}
+
+  Contexte:
+  {context}
+
+  Question:{input}
+
+  """
+  )
+
+#============= CHAINE DE RECUPERATION=======
+
+stuff_chain= create_stuff_documents_chain(llm, prompt)
+rag_chain=create_retrieval_chain(retriever, stuff_chain)
+
+import gradio as gr
+
+store = {}
+
+def get_session_history(session_id):
+    if session_id not in store:
+        store[session_id] = ChatMessageHistory()
+    return store[session_id]
+
+# ====== CHAIN AVEC MÉMOIRE ======
+convers_chain = RunnableWithMessageHistory(
+    rag_chain,
+    get_session_history,
+    input_messages_key="input",
+    history_messages_key="chat_history"
+)
+
+# ====== FONCTION CHAT ======
+def chat_fn(message, session_id):
+
+    result = convers_chain.invoke(
+        {"input": message},
+        config={"configurable": {"session_id": session_id}}
+    )
+
+#IMPORTANT : clé correcte = "answer"
+    return result.get("answer", str(result))
+
+# ====== GRADIO ======
+with gr.Blocks(theme= gr.themes.Soft(
+    primary_hue="yellow",
+    secondary_hue="gray",
+    neutral_hue="blue")) as demo:
+
+    gr.Markdown("🙋SUPER CHATTER👌")
+
+    session_id = gr.State(str(uuid.uuid4()))
+
+    chatbot = gr.Chatbot(height=800)  # version simple (tuples)
+    msg = gr.Textbox( label="❓",placeholder="Posez une question")
+
+    def respond(message, chat_history, session_id):
+
+        answer = chat_fn(message, session_id)
+
+        chat_history.append((message, answer))
+
+        return "", chat_history, session_id
+
+    msg.submit(
+        respond,
+        inputs=[msg, chatbot, session_id],
+        outputs=[msg, chatbot, session_id]
+    )
+
+# ===================LANCEMENT ===============
+
+demo.launch(share=True, debug=True)