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fix: Weaviate document format.

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1.similar search: docs format
2.conf SUMMARY_CONFIG
This commit is contained in:
aries-ckt 2023-06-19 16:44:18 +08:00
parent b95084b89f
commit 05a74d89cd
3 changed files with 19 additions and 6 deletions
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2
pilot/configs/config.py
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@ -161,7 +161,7 @@ class Config(metaclass=Singleton):
self.KNOWLEDGE_CHUNK_SIZE = int(os.getenv("KNOWLEDGE_CHUNK_SIZE", 100)) self.KNOWLEDGE_CHUNK_SIZE = int(os.getenv("KNOWLEDGE_CHUNK_SIZE", 100))
self.KNOWLEDGE_SEARCH_TOP_SIZE = int(os.getenv("KNOWLEDGE_SEARCH_TOP_SIZE", 5)) self.KNOWLEDGE_SEARCH_TOP_SIZE = int(os.getenv("KNOWLEDGE_SEARCH_TOP_SIZE", 5))
### SUMMARY_CONFIG Configuration ### SUMMARY_CONFIG Configuration
self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "VECTOR") self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "FAST")
def set_debug_mode(self, value: bool) -> None: def set_debug_mode(self, value: bool) -> None:
"""Set the debug mode value""" """Set the debug mode value"""

3
pilot/scene/chat_knowledge/custom/chat.py
View File

@ -53,7 +53,8 @@ class ChatNewKnowledge(BaseChat):
docs = self.knowledge_embedding_client.similar_search( docs = self.knowledge_embedding_client.similar_search(
self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
) )
context = [d["page_content"] for d in docs] context = [d.page_content for d in docs]
self.metadata = [d.metadata for d in docs]
context = context[:2000] context = context[:2000]
input_values = {"context": context, "question": self.current_user_input} input_values = {"context": context, "question": self.current_user_input}
return input_values return input_values

20
pilot/vector_store/weaviate_store.py
View File

@ -1,7 +1,9 @@
import os import os
import json import json
import weaviate import weaviate
from langchain.schema import Document
from langchain.vectorstores import Weaviate from langchain.vectorstores import Weaviate
from weaviate.exceptions import WeaviateBaseError
from pilot.configs.config import Config from pilot.configs.config import Config
from pilot.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH from pilot.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH
@ -48,7 +50,13 @@ class WeaviateStore(VectorStoreBase):
.with_limit(topk) .with_limit(topk)
.do() .do()
) )
docs = response['data']['Get'][list(response['data']['Get'].keys())[0]] res = response['data']['Get'][list(response['data']['Get'].keys())[0]]
docs = []
for r in res:
docs.append(Document(
page_content=r['page_content'],
metadata={"metadata": r['metadata']},
))
return docs return docs
def vector_name_exists(self) -> bool: def vector_name_exists(self) -> bool:
@ -56,9 +64,13 @@ class WeaviateStore(VectorStoreBase):
Returns: Returns:
bool: True if the vector name exists, False otherwise. bool: True if the vector name exists, False otherwise.
""" """
if self.vector_store_client.schema.get(self.vector_name): try:
return True if self.vector_store_client.schema.get(self.vector_name):
return False return True
return False
except WeaviateBaseError as e:
logger.error("vector_name_exists error", e.message)
return False
def _default_schema(self) -> None: def _default_schema(self) -> None:
""" """