Delete app1.py

app1.py

@@ -1,71 +0,0 @@
-import asyncio
-from llama_index.core import Document
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.core.ingestion import IngestionPipeline
-from llama_index.core import SimpleDirectoryReader
-
-reader = SimpleDirectoryReader(input_dir=r"C:\Users\so7\AppData\Local\Programs\Python\Python313\RAG")
-documents = reader.load_data()
-
-# create the pipeline with transformations
-pipeline = IngestionPipeline(
-    transformations=[
-        SentenceSplitter(chunk_overlap=0),
-        HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
-    ]
-)
-
-# Define an async function to handle the pipeline
-async def main():
-    # Create the pipeline with transformations
-    pipeline = IngestionPipeline(
-        transformations=[
-            SentenceSplitter(chunk_overlap=0),
-            HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
-        ]
-    )
-    # Use await inside the async function
-    nodes =
- await pipeline.arun(documents=[Document.example()])
-    # Optional: Do something with the nodes (e.g., print them)
-    print(nodes)
-
-# Run the async function using asyncio
-if __name__ == "__main__":
-    asyncio.run(main())
-
-import chromadb
-from llama_index.vector_stores.chroma import ChromaVectorStore
-from llama_index.core.ingestion import IngestionPipeline
-from llama_index.core.node_parser import SentenceSplitter
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-
-db = chromadb.PersistentClient(path="./pl_db")
-chroma_collection = db.get_or_create_collection("ppgpl")
-vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
-
-pipeline = IngestionPipeline(
-    transformations=[
-        SentenceSplitter(chunk_size=25, chunk_overlap=0),
-        HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5"),
-    ],
-    vector_store=vector_store,
-)
-
-
-from llama_index.core import VectorStoreIndex
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-
-embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
-index = VectorStoreIndex.from_vector_store(vector_store, embed_model=embed_model)
-
-from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
-
-llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
-query_engine = index.as_query_engine(
-    llm=llm,
-    response_mode="tree_summarize",
-)
-query_engine.query("Солнце на третей ступени")
-# The meaning of life is 42