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Membuat Kueri dan Menghasilkan Jawaban

Kembangkan logika untuk memproses kueri pengguna, mengambil konteks yang relevan, dan menyusun jawaban menggunakan LLM.

Membuat Kueri dan Menghasilkan Jawaban adalah pelajaran LangChain / RAG / Vector DBs gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar LangChain / RAG / Vector DBs, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus LangChain / RAG / Vector DBs mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

Querying RAG: The Answer Flow

After integrating RAG components, the next step is to use them to answer user questions. This lesson covers the full process from a user's query to a generated answer.

We'll focus on the 'query-time' logic: how your system takes a question, finds relevant context, and synthesizes a coherent response using an LLM.

Understanding the User Query

A RAG system starts with a user's question, just like a search engine. This raw input is the trigger for the entire process.

  • It defines what information needs to be retrieved.
  • It guides the LLM on what kind of answer to generate.

No special formatting is typically needed at this initial stage; it's just plain text.

Setting Up Your Retriever

To find relevant documents, you need a retriever. This component knows how to query your vector store. You typically obtain it from your VectorStore instance.

The as_retriever() method creates this component, and you can configure parameters like k (number of top documents to fetch).

from langchain_community.vectorstores import InMemoryVectorStore
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from typing import List

# A simple mock for embeddings
class MockEmbeddings(Embeddings):
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return [[i * 0.1] * 10 for i in range(len(texts))]
    def embed_query(self, text: str) -> List[float]:
        return [0.5] * 10

def main():
    # Create a dummy vector store with some content
    embeddings = MockEmbeddings()
    docs = [
        Document(page_content="The capital of France is Paris.", metadata={"source": "wiki"}),
        Document(page_content="Eiffel Tower is in Paris, France.", metadata={"source": "travel"})
    ]
    vectorstore = InMemoryVectorStore.from_documents(docs, embeddings)

    # Create a retriever from the vector store
    retriever = vectorstore.as_retriever(search_kwargs={"k": 1})
    print("Retriever created successfully!")

if __name__ == "__main__":
    main()

Fetching Contextual Documents

Once you have a retriever, you can invoke it with the user's query. It will perform a similarity search in your vector store and return the most relevant Document objects.

These documents form the context that will be passed to the LLM.

from langchain_community.vectorstores import InMemoryVectorStore
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from typing import List

class MockEmbeddings(Embeddings):
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return [[i * 0.1] * 10 for i in range(len(texts))]
    def embed_query(self, text: str) -> List[float]:
        return [0.5] * 10

def main():
    embeddings = MockEmbeddings()
    docs = [
        Document(page_content="The capital of France is Paris.", metadata={"source": "wiki"}),
        Document(page_content="Eiffel Tower is in Paris, France.", metadata={"source": "travel"}),
        Document(page_content="London is the capital of the UK.", metadata={"source": "wiki"})
    ]
    vectorstore = InMemoryVectorStore.from_documents(docs, embeddings)
    retriever = vectorstore.as_retriever(search_kwargs={"k": 2})

    user_query = "What is the capital of France?"
    retrieved_docs = retriever.invoke(user_query)

    print(f"Retrieved {len(retrieved_docs)} documents:")
    for doc in retrieved_docs:
        print(f"- {doc.page_content[:50]}...")

if __name__ == "__main__":
    main()

Preparing Context for the LLM

LLMs usually prefer a single string of text as context. The retrieved Document objects need to be combined into a coherent format.

A common approach is to concatenate their page_content fields, perhaps with separators, and include source metadata if desired.

  • Ensures all context fits within the LLM's token window.
  • Presents a clean input for the LLM to reason over.

Crafting the RAG Prompt

The prompt template is crucial. It instructs the LLM on how to use the provided context to answer the user's question. It typically includes placeholders for both the context and the question.

A well-designed prompt guides the LLM to be factual and avoid hallucination.

from langchain_core.prompts import ChatPromptTemplate

def main():
    # Define a RAG-specific prompt template
    rag_prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an AI assistant for Q&A. Use the context to answer. If you don't know, say that you don't know."),
        ("human", "Context: {context}\nQuestion: {question}")
    ])

    print("RAG Prompt Template created!")
    # Example of how it formats:
    # print(rag_prompt.format(context="some info", question="a query"))

if __name__ == "__main__":
    main()

Connecting the Generation Engine

The final step in generating an answer is to pass the prepared context and the user's question to a Large Language Model. LangChain allows you to easily plug in various LLM providers.

For this example, we'll use a mock LLM to demonstrate the integration without needing an API key.

from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage, AIMessage
from typing import List, Any

# A simple mock LLM
class MockChatLLM(BaseChatModel):
    def invoke(self, input: Any, config: Any = None) -> BaseMessage:
        # Simulate LLM response based on input
        if "Paris" in str(input):
            return AIMessage(content="Paris is the capital of France.")
        elif "London" in str(input):
            return AIMessage(content="London is the capital of the UK.")
        else:
            return AIMessage(content="I don't have enough info to answer.")

    async def ainvoke(self, input: Any, config: Any = None) -> BaseMessage:
        return self.invoke(input, config) # Simple async pass-through

    @property
    def _llm_type(self) -> str:
        return "mock-chat-llm"

def main():
    llm = MockChatLLM()
    print("Mock LLM initialized!")

    # Example invocation (not part of the RAG chain yet)
    response = llm.invoke("Tell me about Paris.")
    print(f"LLM Response: {response.content}")

if __name__ == "__main__":
    main()

Assembling the End-to-End RAG Chain

Now, we combine the retriever, prompt template, and LLM using LangChain Expression Language (LCEL) to create a powerful, flexible RAG chain. This chain handles the entire flow.

We'll use RunnablePassthrough to manage inputs and StrOutputParser to extract the final text answer.

from langchain_core.runnables import RunnablePassthrough, RunnableLambda
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.documents import Document
from langchain_community.vectorstores import InMemoryVectorStore
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage, AIMessage
from typing import List, Any

# Mock Embeddings
class MockEmbeddings(Embeddings):
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return [[i * 0.1] * 10 for i in range(len(texts))]
    def embed_query(self, text: str) -> List[float]:
        return [0.5] * 10

# Mock LLM
class MockChatLLM(BaseChatModel):
    def invoke(self, input: Any, config: Any = None) -> BaseMessage:
        input_str = str(input)
        if "Paris" in input_str and "capital of France" in input_str:
            return AIMessage(content="Based on context, Paris is the capital of France.")
        elif "Eiffel Tower" in input_str and "Paris" in input_str:
            return AIMessage(content="The Eiffel Tower is in Paris, France.")
        else:
            return AIMessage(content="I don't have enough info in the context.")
    async def ainvoke(self, input: Any, config: Any = None) -> BaseMessage:
        return self.invoke(input, config)
    @property
    def _llm_type(self) -> str:
        return "mock-chat-llm"

def main():
    # 1. Setup Retriever
    embeddings = MockEmbeddings()
    docs = [
        Document(page_content="The capital of France is Paris.", metadata={"source": "wiki"}),
        Document(page_content="Eiffel Tower is in Paris, France.", metadata={"source": "travel"})
    ]
    vectorstore = InMemoryVectorStore.from_documents(docs, embeddings)
    retriever = vectorstore.as_retriever(search_kwargs={"k": 1})

    # 2. Setup Prompt
    rag_prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an AI assistant. Use the following context to answer: {context}. If you don't know, say 'I don't know.'"),
        ("human", "Question: {question}")
    ])

    # 3. Setup LLM
    llm = MockChatLLM()

    # 4. Define how to format retrieved documents
    def format_docs(docs: List[Document]) -> str:
        return "\n\n".join(doc.page_content for doc in docs)

    # 5. Build the RAG chain
    rag_chain = (
        {"context": retriever | RunnableLambda(format_docs),
         "question": RunnablePassthrough()}
        | rag_prompt
        | llm
        | StrOutputParser()
    )

    print("RAG chain assembled!")

if __name__ == "__main__":
    main()

Querying Your RAG Application

With the RAG chain fully constructed, you can now invoke it with a user's question. The chain will internally handle retrieval, context formatting, prompting, and LLM generation, returning a direct answer.

This is the final step in getting a response from your RAG system.

from langchain_core.runnables import RunnablePassthrough, RunnableLambda
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.documents import Document
from langchain_community.vectorstores import InMemoryVectorStore
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage, AIMessage
from typing import List, Any

# Mock Embeddings
class MockEmbeddings(Embeddings):
    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        return [[i * 0.1] * 10 for i in range(len(texts))]
    def embed_query(self, text: str) -> List[float]:
        return [0.5] * 10

# Mock LLM
class MockChatLLM(BaseChatModel):
    def invoke(self, input: Any, config: Any = None) -> BaseMessage:
        input_str = str(input)
        if "Paris" in input_str and "capital of France" in input_str:
            return AIMessage(content="Based on context, Paris is the capital of France.")
        elif "Eiffel Tower" in input_str and "Paris" in input_str:
            return AIMessage(content="The Eiffel Tower is in Paris, France.")
        else:
            return AIMessage(content="I don't have enough info in the context.")
    async def ainvoke(self, input: Any, config: Any = None) -> BaseMessage:
        return self.invoke(input, config)
    @property
    def _llm_type(self) -> str:
        return "mock-chat-llm"

def main():
    # Setup Retriever
    embeddings = MockEmbeddings()
    docs = [
        Document(page_content="The capital of France is Paris.", metadata={"source": "wiki"}),
        Document(page_content="Eiffel Tower is in Paris, France.", metadata={"source": "travel"})
    ]
    vectorstore = InMemoryVectorStore.from_documents(docs, embeddings)
    retriever = vectorstore.as_retriever(search_kwargs={"k": 1})

    # Setup Prompt
    rag_prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an AI assistant. Use the following context to answer: {context}. If you don't know, say 'I don't know.'"),
        ("human", "Question: {question}")
    ])

    # Setup LLM
    llm = MockChatLLM()

    def format_docs(docs: List[Document]) -> str:
        return "\n\n".join(doc.page_content for doc in docs)

    # Build the RAG chain
    rag_chain = (
        {"context": retriever | RunnableLambda(format_docs),
         "question": RunnablePassthrough()}
        | rag_prompt
        | llm
        | StrOutputParser()
    )

    # Invoke the RAG chain with a query
    query = "Where is the Eiffel Tower?"
    result = rag_chain.invoke(query)
    print(f"Query: {query}")
    print(f"Answer: {result}")

    query_no_context = "What is the capital of Japan?"
    result_no_context = rag_chain.invoke(query_no_context)
    print(f"\nQuery: {query_no_context}")
    print(f"Answer: {result_no_context}")

if __name__ == "__main__":
    main()

Test Your RAG Flow Knowledge

Consider a LangChain RAG application designed to answer questions from a knowledge base.

Recap: Querying RAG

In this lesson, you learned how to bring all the RAG components together to process user queries and generate answers:

  • We transformed a user's question into a query for the retriever.
  • We instantiated a retriever from a vector store to fetch relevant documents.
  • We crafted a prompt template to guide the LLM.
  • We integrated an LLM to synthesize the final answer.
  • Finally, we assembled and invoked an end-to-end RAG chain using LangChain Expression Language (LCEL).

You can now build a functional RAG system that delivers grounded, factual answers!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Membuat Kueri dan Menghasilkan Jawaban” gratis?

Ya — teks lengkap “Membuat Kueri dan Menghasilkan Jawaban” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus LangChain / RAG / Vector DBs, upgrade ke CoddyKit PRO. Kursus LangChain / RAG / Vector DBs mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Membuat Kueri dan Menghasilkan Jawaban”?

Kembangkan logika untuk memproses kueri pengguna, mengambil konteks yang relevan, dan menyusun jawaban menggunakan LLM. Kamu berlatih LangChain / RAG / Vector DBs dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai LangChain / RAG / Vector DBs?

Tidak diperlukan pengalaman sebelumnya. LangChain / RAG / Vector DBs di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Membuat Kueri dan Menghasilkan Jawaban” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran LangChain / RAG / Vector DBs ini?

Ya. Setiap pelajaran LangChain / RAG / Vector DBs menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Mengintegrasikan Semua Komponen RAG
  2. Membuat Kueri dan Menghasilkan Jawaban
  3. Mengevaluasi Performa Sistem RAG
  4. Membangun Set Pengujian Emas untuk RAG
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