create files

utils/prebuilt_chain.py

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+from langchain.chains import create_history_aware_retriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+
+def history_aware_retriever(llm, retriever):
+    """
+    Create a chain that takes conversation history and returns documents.
+    If there is no chat_history, then the input is just passed directly to the retriever.
+    If there is chat_history, then the prompt and LLM will be used to generate a search query.
+    That search query is then passed to the retriever.
+
+    Args:
+    llm: The language model.
+    retriever: The retriever to use for finding relevant documents.
+    """
+    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
+    )
+
+    return history_aware_retriever
+
+def documents_retriever(llm):
+    """
+    Create a chain for passing a list of Documents to a model.
+
+    Args:
+    llm: The language model.
+    """
+    system_prompt = (
+    "You are an helpfull assistant. "
+    "Use the following pieces of retrieved context to answer the question. "
+    "If you don't know the answer or the context is not retrieved, SAY THAT YOU DON'T KNOW!!. "
+    "Always response in Bahasa Indonesia or Indonesian Language. "
+    "Context: {context}"
+    )
+
+    qa_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            MessagesPlaceholder("chat_history"),
+            ("human", "{input}"),
+        ]
+    )
+    question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+
+    return question_answer_chain