DB-GPT/examples/rag/embedding_rag_example.py
Aries-ckt 58d08780d6
feat(ChatKnowledge): ChatKnowledge Support Keyword Retrieve (#1624)
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>
2024-06-13 13:49:17 +08:00

57 lines
1.7 KiB
Python

import asyncio
import os
from dbgpt.configs.model_config import MODEL_PATH, PILOT_PATH, ROOT_PATH
from dbgpt.rag import ChunkParameters
from dbgpt.rag.assembler import EmbeddingAssembler
from dbgpt.rag.embedding import DefaultEmbeddingFactory
from dbgpt.rag.knowledge import KnowledgeFactory
from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
"""Embedding rag example.
pre-requirements:
set your embedding model path in your example code.
```
embedding_model_path = "{your_embedding_model_path}"
```
Examples:
..code-block:: shell
python examples/rag/embedding_rag_example.py
"""
def _create_vector_connector():
"""Create vector connector."""
config = ChromaVectorConfig(
persist_path=PILOT_PATH,
name="embedding_rag_test",
embedding_fn=DefaultEmbeddingFactory(
default_model_name=os.path.join(MODEL_PATH, "text2vec-large-chinese"),
).create(),
)
return ChromaStore(config)
async def main():
file_path = os.path.join(ROOT_PATH, "docs/docs/awel/awel.md")
knowledge = KnowledgeFactory.from_file_path(file_path)
vector_store = _create_vector_connector()
chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE")
# get embedding assembler
assembler = EmbeddingAssembler.load_from_knowledge(
knowledge=knowledge,
chunk_parameters=chunk_parameters,
index_store=vector_store,
)
assembler.persist()
# get embeddings retriever
retriever = assembler.as_retriever(3)
chunks = await retriever.aretrieve_with_scores("what is awel talk about", 0.3)
print(f"embedding rag example results:{chunks}")
if __name__ == "__main__":
asyncio.run(main())