mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-10-02 15:48:21 +00:00
feat(ChatKnowledge): ChatKnowledge Support Keyword Retrieve (#1624)
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>
This commit is contained in:
@@ -31,8 +31,7 @@ from dbgpt.rag import ChunkParameters
|
||||
from dbgpt.rag.assembler import EmbeddingAssembler
|
||||
from dbgpt.rag.embedding import OpenAPIEmbeddings
|
||||
from dbgpt.rag.knowledge import KnowledgeFactory
|
||||
from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig
|
||||
from dbgpt.storage.vector_store.connector import VectorStoreConnector
|
||||
from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig
|
||||
|
||||
|
||||
def _create_embeddings(
|
||||
@@ -54,33 +53,32 @@ def _create_embeddings(
|
||||
|
||||
def _create_vector_connector():
|
||||
"""Create vector connector."""
|
||||
|
||||
return VectorStoreConnector.from_default(
|
||||
"Chroma",
|
||||
vector_store_config=ChromaVectorConfig(
|
||||
name="example_embedding_api_vector_store_name",
|
||||
persist_path=os.path.join(PILOT_PATH, "data"),
|
||||
),
|
||||
config = ChromaVectorConfig(
|
||||
persist_path=PILOT_PATH,
|
||||
name="embedding_api_rag_test",
|
||||
embedding_fn=_create_embeddings(),
|
||||
)
|
||||
|
||||
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_connector = _create_vector_connector()
|
||||
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,
|
||||
vector_store_connector=vector_connector,
|
||||
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}")
|
||||
vector_store.delete_vector_name("embedding_api_rag_test")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
Reference in New Issue
Block a user