app1.py

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