Function init_qa

streamlit_app.py:137–158

Initialise the Q/A engine with the selected LLM and embedding models. Args: model_name: Key from ``API_MODELS`` selecting the LLM. embeddings_name: Key from ``API_EMBEDDINGS`` selecting the embedding model. Returns: DocumentQAEngine: Ready-to-use engine

(model_name, embeddings_name)

# @st.cache_resource
def init_qa(model_name, embeddings_name):
    """Initialise the Q/A engine with the selected LLM and embedding models.

    Args:
        model_name: Key from ``API_MODELS`` selecting the LLM.
        embeddings_name: Key from ``API_EMBEDDINGS`` selecting the
            embedding model.

    Returns:
        DocumentQAEngine: Ready-to-use engine instance.
    """
    st.session_state["memory"] = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    chat = ChatOpenAI(model=model_name, temperature=0.0, base_url=API_MODELS[model_name], api_key=os.environ.get("API_KEY"))

    embeddings = ModalEmbeddings(
        url=API_EMBEDDINGS[embeddings_name], model_name=embeddings_name, api_key=os.environ.get("EMBEDS_API_KEY")
    )

    storage = DataStorage(embeddings)
    return DocumentQAEngine(
        chat, storage, grobid_url=os.environ["GROBID_URL"], memory=st.session_state["memory"], ping_grobid_server=False
    )


@st.cache_resource