Adds base agent and base infra

agent/conversational_rag.py

@@ -0,0 +1,74 @@
+import boto3
+from langchain_core.prompts import ChatPromptTemplate,MessagesPlaceholder
+from langchain_core.messages import HumanMessage,AIMessage
+from langchain.chains import create_history_aware_retriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+import langchain.chains
+from langchain.chains import create_retrieval_chain
+from langchain_aws import ChatBedrock
+from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever
+from langchain.chains import ConversationalRetrievalChain
+from dotenv import load_dotenv
+load_dotenv()
+import os
+llm = ChatBedrock(
+    model_id="arn:aws:bedrock:us-east-1:654654422992:application-inference-profile/d9blf0g3fzqz",
+    region_name="us-east-1",
+    aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
+    aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
+    aws_session_token=os.environ["AWS_SESSION_TOKEN"],
+    model_kwargs={"temperature": 0.2, 'max_tokens': 1000,},
+    provider='anthropic'
+)
+# Cria o prompt de busca
+prompt_search_query = ChatPromptTemplate.from_messages([
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("user", "{input}"),
+    ("assistant", "-"),
+    ("user", "Given the above conversation, generate a search query to look up to get information relevant to the conversation")
+])
+
+# Cria o prompt de resposta
+prompt_get_answer = ChatPromptTemplate.from_messages([
+    ("system", "Answer the user's questions based on the below context:\n\n{context}"),
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("user", "{input}")
+])
+
+# Conecta ao Qdrant
+retriever = AmazonKnowledgeBasesRetriever(
+    knowledge_base_id="RBD9TI5HYU",
+    region_name="us-east-1",
+    retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
+    
+)
+# Cria o retriever com histórico
+retriever_chain = create_history_aware_retriever(llm, retriever, prompt_search_query)
+
+# Cria o documento chain
+document_chain = create_stuff_documents_chain(llm, prompt_get_answer)
+
+# Cria o retrieval chain
+retrieval_chain= create_retrieval_chain(
+    retriever_chain,document_chain
+)
+
+def chat_with_bot(user_input, chat_history):
+    """
+    Função para interagir com o chatbot.
+
+    Args:
+        user_input (str): A entrada do usuário.
+        chat_history (list): O histórico da conversa, incluindo mensagens do usuário e do assistente.
+
+    Returns:
+        str: A resposta do chatbot.
+    """
+    # Chama o chain de recuperação com o histórico e a entrada do usuário
+    response = retrieval_chain.invoke({
+        "chat_history": chat_history,
+        "input": user_input,
+    })
+    # Retorna a resposta do assistente
+    return response['answer']
+print(chat_with_bot("Quanto é o auxilio?",chat_history=[{"role":"user","content":"Hello"}]))