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fix:url embedding

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This commit is contained in:
aries-ckt
2023-06-01 22:07:33 +08:00
parent 8dd25815e1
commit 1d432e4d29
3 changed files with 18 additions and 8 deletions
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6
pilot/out_parser/base.py
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@@ -53,9 +53,11 @@ class BaseOutputParser(ABC):
"""
if data["error_code"] == 0:
if "vicuna" in CFG.LLM_MODEL:
output = data["text"][skip_echo_len + 11:].strip()
# output = data["text"][skip_echo_len + 11:].strip()
output = data["text"][skip_echo_len:].strip()
elif "guanaco" in CFG.LLM_MODEL:
output = data["text"][skip_echo_len + 14:].replace("<s>", "").strip()
# output = data["text"][skip_echo_len + 14:].replace("<s>", "").strip()
output = data["text"][skip_echo_len:].replace("<s>", "").strip()
else:
output = data["text"].strip()

2
pilot/scene/chat_knowledge/url/prompt.py
View File

@@ -11,7 +11,7 @@ from pilot.scene.chat_normal.out_parser import NormalChatOutputParser
CFG = Config()
PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge.
PROMPT_SCENE_DEFINE = """A chat between a curious human and an artificial intelligence assistant, who very familiar with database related knowledge.
The assistant gives helpful, detailed, professional and polite answers to the user's questions. """

18
pilot/source_embedding/url_embedding.py
View File

@@ -5,9 +5,12 @@ from langchain.document_loaders import WebBaseLoader
from langchain.schema import Document
from langchain.text_splitter import CharacterTextSplitter
from pilot.configs.config import Config
from pilot.configs.model_config import KNOWLEDGE_CHUNK_SPLIT_SIZE
from pilot.source_embedding import SourceEmbedding, register
from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
CFG = Config()
class URLEmbedding(SourceEmbedding):
"""url embedding for read url document."""
@@ -22,10 +25,15 @@ class URLEmbedding(SourceEmbedding):
def read(self):
"""Load from url path."""
loader = WebBaseLoader(web_path=self.file_path)
text_splitor = CharacterTextSplitter(
chunk_size=100, chunk_overlap=20, length_function=len
)
return loader.load_and_split(text_splitor)
if CFG.LANGUAGE == "en":
text_splitter = CharacterTextSplitter(
chunk_size=KNOWLEDGE_CHUNK_SPLIT_SIZE, chunk_overlap=20, length_function=len
)
else:
text_splitter = CHNDocumentSplitter(
pdf=True, sentence_size=1000
)
return loader.load_and_split(text_splitter)
@register
def data_process(self, documents: List[Document]):