Update app.py

app.py

@@ -1,7 +1,122 @@
 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 qdrant_client import QdrantClient, models
+from qdrant_client.http.models import Distance, SparseVectorParams, VectorParams
+
+from uuid import uuid4
+
+from langchain_core.documents import Document
+
+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
+
+sparse_embeddings = FastEmbedSparse(model_name="Qdrant/bm25")
+
+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 qdrant.similarity_search(query)]
+    found_docs.pop("id", None)
+    for k,v in found_docs["metadata"].keys():
+        if k[0] == "_":
+            found_docs["metadata"].pop(k)
+
+    return {
+        "data": found_docs
+    }
+
+
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}