feat: add agents/app

agents/app/rag/embeddings.py

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+from dotenv import load_dotenv
+from langchain_openai import OpenAIEmbeddings
+
+
+def load_embeddins():
+    load_dotenv()
+    # model = "text-embedding-ada-002"
+    model = "text-embedding-3-small"
+
+    return OpenAIEmbeddings(model=model)

agents/app/rag/llm.py

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+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+
+
+def load_llm_openai():
+    load_dotenv()
+    # model = "gpt-3.5-turbo-0125"
+    # model = "gpt-4o"
+    model = "gpt-4o-mini"
+
+    llm = ChatOpenAI(
+        model=model,
+        temperature=0.1,
+        max_tokens=2000,
+    )
+
+    return llm

agents/app/rag/rag_chain.py

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+from langchain.chains import create_history_aware_retriever
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+
+
+def create_rag_chain(llm, retriever):
+    contextualize_q_system_prompt = """
+    Given a chat history and the latest user question \
+    which might reference context in the chat history,
+    formulate a standalone question \
+    which can be understood without the chat history.
+    Do NOT answer the question, \
+    just reformulate it if needed and otherwise return it as is.
+    """
+    contextualize_q_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", contextualize_q_system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human", "{input}"),
+        ]
+    )
+    history_aware_retriever = create_history_aware_retriever(
+        llm, retriever, contextualize_q_prompt
+    )
+
+    # ___________________Chain con el chat history_______________________-
+    qa_system_prompt = """
+    You are an assistant for question-answering tasks.  \
+    Use the following pieces of retrieved context to answer the question. \
+    If you don't know the answer, just say that you don't know. \
+    The length of the answer should be sufficient to address
+    what is being asked, \
+    meaning don't limit yourself in length.\
+    {context}"""
+    qa_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", qa_system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human", "{input}"),
+        ]
+    )
+    question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+
+    return create_retrieval_chain(
+        history_aware_retriever, question_answer_chain)

agents/app/rag/retriever.py

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+from langchain_chroma import Chroma
+
+
+def create_retriever(embeddings, persist_directory: str):
+    # Cargamos la vectorstore
+    # vectordb = Chroma.from_documents(
+    #     persist_directory=st.session_state.persist_directory,
+    # Este es el directorio del la vs del docuemnto del usuario
+    # que se encuentra cargado en la session_state.
+    #     embedding_function=embeddings,
+    # )
+    vectordb = Chroma(
+        persist_directory=persist_directory,
+        embedding_function=embeddings,
+    )
+
+    # Creamos el retriver para que retorne los fragmentos mas relevantes.
+    return vectordb.as_retriever(search_kwargs={"k": 6})

agents/app/rag/split_docs.py

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+from langchain_community.document_loaders.pdf import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+
+
+# def load_split_docs(file_name: str) -> list:
+#     file_path: str = os.path.join("documents", "pdfs", file_name)
+#     loader = PyPDFLoader(file_path)
+#     docs: list = loader.load()
+#     chunk_size: int = 2000
+#     chunk_overlap: int = 300
+#
+#     splitter = RecursiveCharacterTextSplitter(
+#         chunk_size=chunk_size, chunk_overlap=chunk_overlap
+#     )
+#     docs_split: list = splitter.split_documents(docs)
+#
+#     return docs_split
+
+
+def load_split_docs(file_name: str) -> list:
+    # Obtener el directorio base del proyecto
+    base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+    # Construir la ruta absoluta al PDF
+    file_path = os.path.join(base_dir, "documents", "pdfs", file_name)
+
+    # Verificar si el archivo existe
+    if not os.path.exists(file_path):
+        print(f"Archivo no encontrado en: {file_path}")
+        raise FileNotFoundError(f"No se encontró el archivo en: {file_path}")
+
+    loader = PyPDFLoader(file_path)
+    docs: list = loader.load()
+
+    chunk_size: int = 2000
+    chunk_overlap: int = 300
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap
+    )
+    docs_split: list = splitter.split_documents(docs)
+    return docs_split

agents/app/rag/vectorstore.py

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+from langchain_chroma import Chroma
+import os
+
+#
+# def create_vectorstore(docs_split: list, embeddings, file_name: str):
+#     db_name: str = file_name.replace(".pdf", "").replace(" ", "_").lower()
+#     persist_directory: str = f"embeddings/{db_name}"
+#
+#     # Crear el directorio si no existe
+#     os.makedirs(persist_directory, exist_ok=True)
+#
+#     # Siempre crear/actualizar el vectorstore
+#     vectordb = Chroma.from_documents(
+#         persist_directory=persist_directory,
+#         documents=docs_split,
+#         embedding=embeddings,
+#     )
+#
+#     return vectordb
+
+
+def create_vectorstore(docs_split: list, embeddings, file_name: str):
+    # Obtener el directorio base del proyecto
+    base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+    # Crear el nombre de la base de datos
+    db_name: str = file_name.replace(".pdf", "").replace(" ", "_").lower()
+
+    # Construir la ruta absoluta para los embeddings
+    persist_directory: str = os.path.join(base_dir, "embeddings", db_name)
+
+    # Crear el directorio si no existe
+    os.makedirs(persist_directory, exist_ok=True)
+
+    # Debug log
+    print(f"Creando vectorstore en: {persist_directory}")
+
+    try:
+        # Crear/actualizar el vectorstore
+        vectordb = Chroma.from_documents(
+            persist_directory=persist_directory,
+            documents=docs_split,
+            embedding=embeddings,
+        )
+        return vectordb
+    except Exception as e:
+        print(f"Error al crear vectorstore: {e}")
+        raise