RAG Agentic

Construye sistemas RAG (Generación Aumentada por Recuperación) Agentic con LangGraph, nodos de recuperación, integración con almacén de documentos, planificación de consultas y recuperación en múltiples pasos.

El Agentic RAG combina la generación aumentada por recuperación con la toma de decisiones agéntica. En lugar de un único pipeline de recuperar y luego generar, el agente decide cuándo y qué recuperar, planifica consultas y refina los resultados de forma iterativa.

Del RAG Simple al RAG Agentic

RAG Simple (Recuperar → Generar)

Query → Embed → Retrieve → LLM → Answer

RAG Agentic

Query → Analyze → Plan queries → Retrieve → Evaluate → Need more? → Yes → Refine query → Retrieve
                                                                         ↓ No
                                                                    Generate answer

El agente tiene herramientas de recuperación y decide cómo usarlas.

Componentes Principales

python

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.tools import tool
from langchain_core.documents import Document
from langgraph.graph import StateGraph, START, END, add_messages
from typing_extensions import TypedDict, Annotated
from typing import List, Any
from langgraph.prebuilt import ToolExecutor

# Vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(
    collection_name="documents",
    embedding_function=embeddings,
    persist_directory="./chroma_db"
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

Definiendo Herramientas de Recuperación

python

@tool
def search_documents(query: str) -> str:
    """Search the knowledge base for relevant documents. Use for factual questions."""
    docs = retriever.invoke(query)
    return "\n\n".join(f"Source: {d.metadata.get('source', 'unknown')}\n{d.page_content}"
                       for d in docs)

@tool
def search_by_metadata(filters: str) -> str:
    """Search documents by metadata. Provide as JSON: {\"source\": \"report_2024.pdf\"}"""
    import json
    filters_dict = json.loads(filters)
    docs = vectorstore.similarity_search("", filter=filters_dict, k=5)
    return "\n\n".join(d.page_content for d in docs)

@tool
def web_search(query: str) -> str:
    """Search the web for current information not in the knowledge base."""
    return f"Web results for: {query}"

ℹ️Nota

Las herramientas de recuperación distintas atienden a necesidades distintas: search_documents para búsqueda semántica, search_by_metadata para acceso filtrado y web_search como alternativa para información actual.

Agente con Herramientas de Recuperación

python

llm = ChatOpenAI(model="gpt-4o")
tools = [search_documents, search_by_metadata, web_search]
llm_with_tools = llm.bind_tools(tools)
tool_executor = ToolExecutor(tools)

class RAGState(TypedDict):
    messages: Annotated[List[Any], add_messages]
    context: List[str]
    query_plan: List[str]

def agent_node(state: RAGState) -> dict:
    """Decides whether to retrieve or answer."""
    system = """You are an AI research assistant with access to a knowledge base.
    - If you need information, use search_documents or web_search
    - If the user asks for specific sources, use search_by_metadata
    - Once you have sufficient information, answer the question
    - Cite your sources"""
    messages = [SystemMessage(system)] + state["messages"]
    response = llm_with_tools.invoke(messages)
    return {"messages": [response]}

def tool_node(state: RAGState) -> dict:
    """Execute tool calls from the agent."""
    last = state["messages"][-1]
    if not last.tool_calls:
        return {}

    new_messages = []
    for tc in last.tool_calls:
        result = tool_executor.invoke(tc)
        new_messages.append(ToolMessage(content=str(result), tool_call_id=tc["id"]))
    return {"messages": new_messages}

def router(state: RAGState) -> str:
    last = state["messages"][-1]
    if hasattr(last, "tool_calls") and last.tool_calls:
        return "tool"
    return "end"

builder = StateGraph(RAGState)
builder.add_node("agent", agent_node)
builder.add_node("tool", tool_node)
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", router, {"tool": "tool", "end": END})
builder.add_edge("tool", "agent")  # Loop back

app = builder.compile()

Planificación de Consultas

En lugar de una sola recuperación, planifica varias subconsultas:

python

def plan_queries(state: RAGState) -> dict:
    prompt = ChatPromptTemplate.from_messages([
        ("system", "Break down the user's question into 3-5 specific sub-queries "
                   "that together cover all aspects. Return as a numbered list."),
        ("human", "{question}")
    ])
    chain = prompt | llm | StrOutputParser()
    plan = chain.invoke({"question": state["messages"][-1].content})
    queries = [q.strip() for q in plan.split("\n") if q.strip() and q[0].isdigit()]
    return {"query_plan": queries}

✅Éxito

La planificación de consultas garantiza una cobertura integral al dividir una pregunta amplia en subconsultas enfocadas, cada una orientada a un aspecto distinto del tema.

Recuperación de Múltiples Consultas

python

@tool
def multi_query_search(main_question: str) -> str:
    """Break down a question and search multiple aspects."""
    # Generate sub-queries
    prompt = f"Break this into 3 sub-queries: {main_question}"
    response = llm.invoke(prompt)
    queries = [q.strip() for q in response.content.split("\n")
               if q.strip() and not q.strip().startswith(("-", "1", "2", "3", "4", "5"))]

    # Search each
    all_results = []
    for q in queries[:5]:  # Limit to 5 queries
        docs = retriever.invoke(q)
        for d in docs:
            all_results.append(d)

    # Deduplicate and format
    seen = set()
    unique = []
    for d in all_results:
        if d.page_content[:100] not in seen:
            seen.add(d.page_content[:100])
            unique.append(d)

    return "\n\n---\n\n".join(
        f"[Relevance: {d.metadata.get('score', 'N/A')}]\n{d.page_content}"
        for d in unique[:10]
    )

Puntuación de Relevancia

Evalúa si los documentos recuperados son realmente relevantes:

python

def score_relevance(query: str, doc: str) -> float:
    prompt = f"""On a scale of 0.0 to 1.0, how relevant is this document to the query?
Query: {query}
Document: {doc[:500]}

Relevance score (respond with only the number):"""
    response = llm.invoke(prompt)
    try:
        return float(response.content.strip())
    except ValueError:
        return 0.0

def filter_relevant_docs(state: RAGState) -> dict:
    query = state["messages"][-1].content if state["messages"] else ""
    relevant = []
    for doc in state.get("context", []):
        score = score_relevance(query, doc)
        if score > 0.5:
            relevant.append(doc)
    return {"context": relevant}

⚠️Advertencia

La puntuación de relevancia añade una llamada a LLM por documento. Para conjuntos grandes de resultados, considera la similitud de embeddings como un sustituto más barato.

Sistema RAG Agentic Completo

python

from langgraph.graph import StateGraph, START, END, add_messages
from langgraph.checkpoint.memory import MemorySaver
from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, ToolMessage
from langchain_core.tools import tool
from langgraph.prebuilt import ToolExecutor
from typing_extensions import TypedDict, Annotated
from typing import List, Any

llm = ChatOpenAI(model="gpt-4o")
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

@tool
def retrieve(query: str) -> str:
    """Search the knowledge base."""
    docs = retriever.invoke(query)
    return "\n".join(d.page_content[:500] for d in docs)

tools = [retrieve]
llm_with_tools = llm.bind_tools(tools)
tool_executor = ToolExecutor(tools)

class AgentState(TypedDict):
    messages: Annotated[List[Any], add_messages]

def agent(state: AgentState) -> dict:
    messages = [SystemMessage("Answer using the knowledge base. "
               "Use the retrieve tool if you need information.")] + state["messages"]
    response = llm_with_tools.invoke(messages)
    return {"messages": [response]}

def tools_node(state: AgentState) -> dict:
    last = state["messages"][-1]
    if not last.tool_calls:
        return {}
    return {"messages": [ToolMessage(
        content=str(tool_executor.invoke(tc)),
        tool_call_id=tc["id"]
    ) for tc in last.tool_calls]}

def router(state: AgentState) -> str:
    last = state["messages"][-1]
    if hasattr(last, "tool_calls") and last.tool_calls:
        return "tools"
    return "end"

builder = StateGraph(AgentState)
builder.add_node("agent", agent)
builder.add_node("tools", tools_node)
builder.add_edge(START, "agent")
builder.add_conditional_edges("agent", router, {"tools": "tools", "end": END})
builder.add_edge("tools", "agent")
app = builder.compile(checkpointer=MemorySaver())