app.py

from fastapi import FastAPI
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams

from langchain_qdrant import FastEmbedSparse, QdrantVectorStore, RetrievalMode
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings

from qdrant_client import QdrantClient, models
from qdrant_client.http.models import Distance, SparseVectorParams, VectorParams

from uuid import uuid4

from langchain_core.documents import Document

from typing import Union, List, Dict, Any
from pydantic import BaseModel, Field

class Data(BaseModel):
    items: Union[Dict[str, Any], List[Dict[str, Any]]] = Field(..., description="Either a dictionary or a list of dictionaries.")

# document_1 = Document(
#     page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
#     metadata={"source": "tweet"},
# )

# document_2 = Document(
#     page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees Fahrenheit.",
#     metadata={"source": "news"},
# )

# document_3 = Document(
#     page_content="Building an exciting new project with LangChain - come check it out!",
#     metadata={"source": "tweet"},
# )

# document_4 = Document(
#     page_content="Robbers broke into the city bank and stole $1 million in cash.",
#     metadata={"source": "news"},
# )

# document_5 = Document(
#     page_content="Wow! That was an amazing movie. I can't wait to see it again.",
#     metadata={"source": "tweet"},
# )

# document_6 = Document(
#     page_content="Is the new iPhone worth the price? Read this review to find out.",
#     metadata={"source": "website"},
# )

# document_7 = Document(
#     page_content="The top 10 soccer players in the world right now.",
#     metadata={"source": "website"},
# )

# document_8 = Document(
#     page_content="LangGraph is the best framework for building stateful, agentic applications!",
#     metadata={"source": "tweet"},
# )

# document_9 = Document(
#     page_content="The stock market is down 500 points today due to fears of a recession.",
#     metadata={"source": "news"},
# )

# document_10 = Document(
#     page_content="I have a bad feeling I am going to get deleted :(",
#     metadata={"source": "tweet"},
# )

# documents = [
#     document_1,
#     document_2,
#     document_3,
#     document_4,
#     document_5,
#     document_6,
#     document_7,
#     document_8,
#     document_9,
#     document_10,
# ]
# uuids = [str(uuid4()) for _ in range(len(documents))]

# docs = documents


# from uuid import uuid4
# from langchain.schema import Document

document_1 = Document(
    page_content="Aduan: Saya tidak bisa login ke sistem e-learning.\nJawaban: Kami menemukan bahwa akun Anda terkunci setelah tiga kali gagal login. Kami telah membuka kunci akun dan menyarankan Anda untuk melakukan reset password menggunakan fitur 'Lupa Kata Sandi'.",
    metadata={"source": "Aduan"},
)

document_2 = Document(
    page_content="Request: Mohon bantuannya untuk mendapatkan akses ke folder tim di server.\nJawaban: Kami telah menambahkan akun Anda ke grup pengguna 'Tim IT' di Active Directory. Akses ke folder sekarang dapat dilakukan setelah Anda login ulang.",
    metadata={"source": "Request"},
)

document_3 = Document(
    page_content="Incident: Laporan printer di lantai 3 tidak bisa mencetak.\nJawaban: Kami melakukan restart pada spooler service di perangkat printer dan membersihkan antrian cetak yang bermasalah. Printer sudah kembali normal.",
    metadata={"source": "Incident"},
)

document_4 = Document(
    page_content="Aduan: Email saya sering masuk ke folder spam penerima.\nJawaban: Kami periksa konfigurasi SPF, DKIM, dan DMARC pada domain Anda. Ternyata ada konfigurasi SPF yang tidak lengkap. Kami telah memperbaikinya dan hasil pengujian sudah menunjukkan pengiriman email berjalan normal.",
    metadata={"source": "Aduan"},
)

document_5 = Document(
    page_content="Request: Saya membutuhkan instalasi software AutoCAD untuk proyek desain.\nJawaban: Kami telah mengunduh versi terbaru dari situs resmi AutoDesk dan melakukan instalasi di laptop Anda. Lisensi telah diaktivasi menggunakan akun universitas.",
    metadata={"source": "Request"},
)

document_6 = Document(
    page_content="Incident: Sistem ERP tidak bisa mengakses modul keuangan sejak pagi.\nJawaban: Kami temukan bahwa service database MySQL berhenti secara tiba-tiba. Service telah kami nyalakan kembali dan modul keuangan kini dapat diakses kembali.",
    metadata={"source": "Incident"},
)

document_7 = Document(
    page_content="Aduan: Aplikasi mobile sering crash saat dibuka.\nJawaban: Kami analisis log error dan menemukan bug pada fitur notifikasi. Kami telah melakukan patch pada versi 1.2.3 dan memperbarui aplikasi Anda melalui MDM.",
    metadata={"source": "Aduan"},
)

document_8 = Document(
    page_content="Request: Mohon dibuatkan email dinas baru untuk staf baru di departemen HR.\nJawaban: Email telah dibuat dengan format [email protected] dan password default. Informasi login telah kami kirimkan melalui email pribadi yang terdaftar.",
    metadata={"source": "Request"},
)

document_9 = Document(
    page_content="Incident: Koneksi internet putus-putus di gedung B.\nJawaban: Kami lakukan pengecekan router dan mengganti kabel jaringan yang rusak di lantai 2. Koneksi kini stabil dan normal.",
    metadata={"source": "Incident"},
)

document_10 = Document(
    page_content="Aduan: Layar laptop saya berkedip-kedip.\nJawaban: Masalah disebabkan oleh driver grafis yang tidak kompatibel. Kami telah menginstal versi driver yang sesuai dengan perangkat Anda dan masalah layar sudah tidak muncul lagi.",
    metadata={"source": "Aduan"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]

uuids = [str(uuid4()) for _ in range(len(documents))]
docs = documents


# sparse_embeddings = FastEmbedSparse(model_name="Qdrant/bm25")
sparse_embeddings = FastEmbedEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")

client = QdrantClient(path="tmp/langchain_qdrant")

# Create a collection with sparse vectors
client.create_collection(
    collection_name="my_documents",
    # vectors_config={"dense": VectorParams(size=3072, distance=Distance.COSINE)},
    # sparse_vectors_config={
    #     "sparse": SparseVectorParams(index=models.SparseIndexParams(on_disk=False))
    # },
)

qdrant = QdrantVectorStore(
    client=client,
    collection_name="my_documents",
    sparse_embedding=sparse_embeddings,
    # retrieval_mode=RetrievalMode.SPARSE,
    # sparse_vector_name="sparse",
    
)

qdrant.add_documents(documents=documents, ids=uuids)

app = FastAPI()

@app.get("/get_data")
def get_data(query: str):
    # query = "How much money did the robbers steal?"
    found_docs = [x.model_dump() for x in qdrant.similarity_search(query)]

    for doc in found_docs:
        doc.pop("id", None)
        # key = 
        for k in list(doc["metadata"].keys()):
            if k[0] == "_":
                doc["metadata"].pop(k)

    return {
        "data": found_docs
    }


@app.post("/add_data")
def add_data(data: Data):
    global qdrant
    if isinstance(data.items, dict):
        qdrant.add_documents(documents=[Document(**data.items)])
    else:
        qdrant.add_documents(documents=[Document(**x.items) for x in data])

    return {"message":"Create data successfully!", "status_code":201}



@app.get("/")
def greet_json():
    return {"Hello": "World!"}