mirror of
https://github.com/langgenius/dify.git
synced 2024-11-16 11:42:29 +08:00
cf93d8d6e2
Co-authored-by: jyong <718720800@qq.com> Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
from flask import current_app
|
|
from langchain.embeddings import OpenAIEmbeddings
|
|
from langchain.tools import BaseTool
|
|
|
|
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
|
|
from core.embedding.cached_embedding import CacheEmbedding
|
|
from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
|
|
from core.index.vector_index.vector_index import VectorIndex
|
|
from core.llm.llm_builder import LLMBuilder
|
|
from models.dataset import Dataset, DocumentSegment
|
|
|
|
|
|
class DatasetTool(BaseTool):
|
|
"""Tool for querying a Dataset."""
|
|
|
|
dataset: Dataset
|
|
k: int = 2
|
|
|
|
def _run(self, tool_input: str) -> str:
|
|
if self.dataset.indexing_technique == "economy":
|
|
# use keyword table query
|
|
kw_table_index = KeywordTableIndex(
|
|
dataset=self.dataset,
|
|
config=KeywordTableConfig(
|
|
max_keywords_per_chunk=5
|
|
)
|
|
)
|
|
|
|
documents = kw_table_index.search(tool_input, search_kwargs={'k': self.k})
|
|
return str("\n".join([document.page_content for document in documents]))
|
|
else:
|
|
model_credentials = LLMBuilder.get_model_credentials(
|
|
tenant_id=self.dataset.tenant_id,
|
|
model_provider=LLMBuilder.get_default_provider(self.dataset.tenant_id, 'text-embedding-ada-002'),
|
|
model_name='text-embedding-ada-002'
|
|
)
|
|
|
|
embeddings = CacheEmbedding(OpenAIEmbeddings(
|
|
**model_credentials
|
|
))
|
|
|
|
vector_index = VectorIndex(
|
|
dataset=self.dataset,
|
|
config=current_app.config,
|
|
embeddings=embeddings
|
|
)
|
|
|
|
documents = vector_index.search(
|
|
tool_input,
|
|
search_type='similarity',
|
|
search_kwargs={
|
|
'k': self.k
|
|
}
|
|
)
|
|
|
|
hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
|
|
hit_callback.on_tool_end(documents)
|
|
document_context_list = []
|
|
index_node_ids = [document.metadata['doc_id'] for document in documents]
|
|
segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
|
|
DocumentSegment.status == 'completed',
|
|
DocumentSegment.enabled == True,
|
|
DocumentSegment.index_node_id.in_(index_node_ids)
|
|
).all()
|
|
|
|
if segments:
|
|
for segment in segments:
|
|
if segment.answer:
|
|
document_context_list.append(segment.answer)
|
|
else:
|
|
document_context_list.append(segment.content)
|
|
|
|
return str("\n".join(document_context_list))
|
|
|
|
async def _arun(self, tool_input: str) -> str:
|
|
model_credentials = LLMBuilder.get_model_credentials(
|
|
tenant_id=self.dataset.tenant_id,
|
|
model_provider=LLMBuilder.get_default_provider(self.dataset.tenant_id, 'text-embedding-ada-002'),
|
|
model_name='text-embedding-ada-002'
|
|
)
|
|
|
|
embeddings = CacheEmbedding(OpenAIEmbeddings(
|
|
**model_credentials
|
|
))
|
|
|
|
vector_index = VectorIndex(
|
|
dataset=self.dataset,
|
|
config=current_app.config,
|
|
embeddings=embeddings
|
|
)
|
|
|
|
documents = await vector_index.asearch(
|
|
tool_input,
|
|
search_type='similarity',
|
|
search_kwargs={
|
|
'k': 10
|
|
}
|
|
)
|
|
|
|
hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
|
|
hit_callback.on_tool_end(documents)
|
|
return str("\n".join([document.page_content for document in documents]))
|