Create app1.py

app1.py

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+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