LangGraph

LangGraph is a library for building stateful, multi-actor applications, ideal for creating agentic workflows. It provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents.

You can define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.

LangGraph works seamlessly with all the components of LangChain. This means we can utilize Qdrant’s Langchain integration to create retrieval nodes in LangGraph, available in both Python and Javascript!

Usage

Install the required dependencies

$ pip install langgraph langchain_community langchain_qdrant fastembed

$ npm install @langchain/langgraph langchain @langchain/qdrant @langchain/openai

Create a retriever tool to add to the LangGraph workflow.


from langchain.tools.retriever import create_retriever_tool
from langchain_community.embeddings import FastEmbedEmbeddings

from langchain_qdrant import FastEmbedSparse, QdrantVectorStore, RetrievalMode

# We'll set up Qdrant to retrieve documents using Hybrid search.
# Learn more at https://qdrant.tech/articles/hybrid-search/
retriever = QdrantVectorStore.from_texts(
    url="http://localhost:6333/",
    collection_name="langgraph-collection",
    embedding=FastEmbedEmbeddings(model_name="BAAI/bge-small-en-v1.5"),
    sparse_embedding=FastEmbedSparse(model_name="Qdrant/bm25"),
    retrieval_mode=RetrievalMode.HYBRID,
    texts=["<SOME_KNOWLEDGE_TEXT>", "<SOME_OTHER_TEXT>", ...]
).as_retriever()

retriever_tool = create_retriever_tool(
    retriever,
    "retrieve_my_texts",
    "Retrieve texts stored in the Qdrant collection",
)

import { QdrantVectorStore } from "@langchain/qdrant";
import { OpenAIEmbeddings } from "@langchain/openai";
import { createRetrieverTool } from "langchain/tools/retriever";

const vectorStore = await QdrantVectorStore.fromTexts(
    ["<SOME_KNOWLEDGE_TEXT>", "<SOME_OTHER_TEXT>"],
    [{ id: 2 }, { id: 1 }],
    new OpenAIEmbeddings(),
    {
        url: "http://localhost:6333/",
        collectionName: "goldel_escher_bach",
    }
);

const retriever = vectorStore.asRetriever();

const tool = createRetrieverTool(
  retriever,
  {
    name: "retrieve_my_texts",
    description:
      "Retrieve texts stored in the Qdrant collection",
  },
);

Add the retriever tool as a node in LangGraph

from langgraph.graph import StateGraph
from langgraph.prebuilt import ToolNode

workflow = StateGraph()

# Define other the nodes which we'll cycle between.
workflow.add_node("retrieve_qdrant", ToolNode([retriever_tool]))

graph = workflow.compile()

import { StateGraph } from "@langchain/langgraph";
import { ToolNode } from "@langchain/langgraph/prebuilt";

// Define the graph
const workflow = new StateGraph(SomeGraphState)
  // Define the nodes which we'll cycle between.
  .addNode("retrieve", new ToolNode([tool]));

const graph = workflow.compile();

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LangGraph

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