@ -0,0 +1,2 @@
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||||
API_KEY=
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||||
FILE_PATH=
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@ -0,0 +1,8 @@
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||||
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||||
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||||
</Languages>
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
<project version="4">
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||||
<component name="Black">
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||||
<option name="sdkName" value="LLM" />
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||||
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||||
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||||
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||||
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||||
<?xml version="1.0" encoding="UTF-8"?>
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||||
<project version="4">
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||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/.idea/LLM.iml" filepath="$PROJECT_DIR$/.idea/LLM.iml" />
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||||
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||||
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||||
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||||
<change beforePath="$PROJECT_DIR$/.config/config.properties" beforeDir="false" afterPath="$PROJECT_DIR$/.config/config.properties" afterDir="false" />
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||||
<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
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||||
<change beforePath="$PROJECT_DIR$/requirements.txt" beforeDir="false" afterPath="$PROJECT_DIR$/requirements.txt" afterDir="false" />
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||||
<change beforePath="$PROJECT_DIR$/src/serve/HistoryRAG.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/serve/HistoryRAG.py" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/src/serve/RAG.py" beforeDir="false" afterPath="$PROJECT_DIR$/src/serve/RAG.py" afterDir="false" />
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||||
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||||
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||||
<option name="HIGHLIGHT_CONFLICTS" value="true" />
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<option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
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||||
<option name="LAST_RESOLUTION" value="IGNORE" />
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||||
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||||
<component name="FileTemplateManagerImpl">
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||||
<option name="RECENT_TEMPLATES">
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<list>
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||||
<option value="Python Script" />
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||||
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||||
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||||
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||||
<component name="Git.Settings">
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||||
<option name="RECENT_BRANCH_BY_REPOSITORY">
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<map>
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||||
<entry key="$PROJECT_DIR$" value="detached" />
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||||
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||||
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||||
<option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
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||||
<option name="RESET_MODE" value="HARD" />
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||||
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||||
<component name="GitHubPullRequestSearchHistory">{
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||||
"lastFilter": {
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||||
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||||
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||||
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||||
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||||
<option name="accountId" value="c877182e-149b-4367-a384-4c094fd3c76e" />
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||||
<option name="url" value="https://github.com/YDzzz/LLM.git" />
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<ConfirmationsSetting value="2" id="Add" />
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<component name="ProjectViewState">
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||||
<option name="hideEmptyMiddlePackages" value="true" />
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||||
<option name="showLibraryContents" value="true" />
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||||
</component>
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||||
<component name="PropertiesComponent"><![CDATA[{
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||||
"keyToString": {
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||||
"Python.HistoryRAG.executor": "Run",
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||||
"Python.RAG.executor": "Run",
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||||
"Python.embedding.executor": "Run",
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||||
"RunOnceActivity.OpenProjectViewOnStart": "true",
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||||
"RunOnceActivity.ShowReadmeOnStart": "true",
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||||
"com.google.cloudcode.ide_session_index": "20240528_0001",
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||||
"git-widget-placeholder": "032994f8",
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||||
"last_opened_file_path": "G:/code/py/LLM",
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||||
"node.js.detected.package.eslint": "true",
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||||
"node.js.detected.package.tslint": "true",
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||||
"node.js.selected.package.eslint": "(autodetect)",
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||||
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<task active="true" id="Default" summary="Default task">
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<created>1715274683455</created>
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||||
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<option name="project" value="LOCAL" />
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||||
<updated>1715966892912</updated>
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||||
</task>
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||||
<task id="LOCAL-00006" summary="complete RAG, History RAG">
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||||
<option name="closed" value="true" />
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||||
<created>1716789017012</created>
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||||
<option name="number" value="00006" />
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||||
<option name="presentableId" value="LOCAL-00006" />
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<option name="project" value="LOCAL" />
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||||
<updated>1716789017012</updated>
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||||
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||||
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<servers />
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||||
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||||
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||||
<option name="TAB_STATES">
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<entry key="MAIN">
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||||
<value>
|
||||
<State>
|
||||
<option name="FILTERS">
|
||||
<map>
|
||||
<entry key="branch">
|
||||
<value>
|
||||
<list>
|
||||
<option value="detached" />
|
||||
</list>
|
||||
</value>
|
||||
</entry>
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||||
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|
||||
</option>
|
||||
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|
||||
<component name="VcsManagerConfiguration">
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||||
<MESSAGE value="init" />
|
||||
<MESSAGE value="second commit" />
|
||||
<MESSAGE value="complete RAG, History RAG" />
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||||
<option name="LAST_COMMIT_MESSAGE" value="complete RAG, History RAG" />
|
||||
</component>
|
||||
<component name="com.intellij.coverage.CoverageDataManagerImpl">
|
||||
<SUITE FILE_PATH="coverage/LLM$RAG.coverage" NAME="RAG Coverage Results" MODIFIED="1716897848448" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/src/serve" />
|
||||
<SUITE FILE_PATH="coverage/LLM$embedding.coverage" NAME="embedding Coverage Results" MODIFIED="1715952325378" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/src/serve" />
|
||||
<SUITE FILE_PATH="coverage/LLM$HistoryRAG.coverage" NAME="HistoryRAG Coverage Results" MODIFIED="1716901838902" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/src/serve" />
|
||||
</component>
|
||||
</project>
|
||||
@ -1,9 +0,0 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) <year> <copyright holders>
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||
@ -0,0 +1,7 @@
|
||||
langchain>=0.1.20
|
||||
pypdf>=4.2.0
|
||||
zhipuai~=2.0.1.20240423.1
|
||||
langchainhub~=0.1.15
|
||||
httpx-sse~=0.4.0
|
||||
chardet~=5.2.0
|
||||
gradio
|
||||
@ -0,0 +1,47 @@
|
||||
import os
|
||||
from abc import ABC
|
||||
from typing import List
|
||||
|
||||
from zhipuai import ZhipuAI
|
||||
|
||||
from src.init.property import Property
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
os.environ["ZHIPUAI_API_KEY"] = Property.get_property("API_KEY")
|
||||
client = ZhipuAI()
|
||||
|
||||
|
||||
def _text_qualify(embedding_text):
|
||||
"""
|
||||
using ZhipuAI Embedding API to get embedding 1024 dimension support
|
||||
:param embedding_text:
|
||||
:return:
|
||||
"""
|
||||
if type(embedding_text) == str:
|
||||
e_t = embedding_text
|
||||
else:
|
||||
e_t = embedding_text.page_content
|
||||
# print usage of token number:
|
||||
# response.usage.total_tokens
|
||||
# embedding support:
|
||||
# response.data[0].embedding
|
||||
response = client.embeddings.create(
|
||||
model="embedding-2",
|
||||
input=e_t,
|
||||
)
|
||||
return response.data[0].embedding
|
||||
|
||||
|
||||
class ZhuPuAiEmbedding(Embeddings, ABC):
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
embeddings = []
|
||||
for i in texts:
|
||||
embeddings.append(_text_qualify(i))
|
||||
return embeddings
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
return _text_qualify(text)
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
@ -0,0 +1,26 @@
|
||||
from langchain_community.document_loaders import PyPDFLoader
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||||
|
||||
from src.base.ZhuPuAiEmbeddings import ZhuPuAiEmbedding
|
||||
|
||||
|
||||
class SentenceEmbedding:
|
||||
def __init__(self, file_path: str):
|
||||
self.file_path = file_path
|
||||
docs = PyPDFLoader(file_path).load()
|
||||
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200, add_start_index=True)
|
||||
sentences = text_splitter.split_documents(docs)
|
||||
self.vectorstore = Chroma.from_documents(sentences, ZhuPuAiEmbedding())
|
||||
|
||||
def get_vectorstore(self):
|
||||
return self.vectorstore
|
||||
|
||||
def search(self, query: str) -> str:
|
||||
docs = self.vectorstore.similarity_search(query)
|
||||
return docs[0].page_content
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
a = SentenceEmbedding("C:\\Users\\16922\\Desktop\\文档1.pdf")
|
||||
print(a.search("We will dedicate a segment of our project to discussing these ethical issues"))
|
||||
@ -0,0 +1,31 @@
|
||||
import os
|
||||
|
||||
|
||||
class Property(object):
|
||||
__props = {}
|
||||
|
||||
filepath = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, ".config\\config.properties")
|
||||
with open(filepath, "r") as f:
|
||||
for line in f:
|
||||
l = line.strip()
|
||||
if l and not l.startswith('#'):
|
||||
key_map = l.split('=')
|
||||
key_name = key_map[0].strip()
|
||||
key_value = '='.join(key_map[1:]).strip().strip()
|
||||
__props[key_name] = key_value
|
||||
|
||||
@staticmethod
|
||||
def get_property(property_name):
|
||||
try:
|
||||
return Property.__props[property_name]
|
||||
except KeyError:
|
||||
print("there is no that property name")
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
print(Property.get_property("API_KEY"))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -0,0 +1,84 @@
|
||||
import os
|
||||
|
||||
from langchain_core.callbacks import StreamingStdOutCallbackHandler, CallbackManager
|
||||
from langchain_community.chat_models.zhipuai import ChatZhipuAI
|
||||
from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain import hub
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
|
||||
from src.init.property import Property
|
||||
from src.base.embedding import SentenceEmbedding
|
||||
|
||||
# 初始化prompt工程
|
||||
_prompt = {
|
||||
'prompt_1': ChatPromptTemplate.from_template(
|
||||
"""你是问答任务的助手。使用以下检索到的上下文来回答问题。如果你不知道答案，就说你不知道。最多使用三个句子并保持答案简洁。
|
||||
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
|
||||
Helpful Answer:"""
|
||||
),
|
||||
'prompt_2': hub.pull("rlm/rag-prompt")
|
||||
}
|
||||
|
||||
|
||||
def format_docs(docs):
|
||||
return "\n\n".join(doc.page_content for doc in docs)
|
||||
|
||||
|
||||
class RAG:
|
||||
# 初始化ZHIPU API KEY
|
||||
os.environ["ZHIPUAI_API_KEY"] = Property.get_property("API_KEY")
|
||||
|
||||
# 初始化模型
|
||||
_llm = ChatZhipuAI(
|
||||
temperature=0.95,
|
||||
model="glm-4"
|
||||
)
|
||||
|
||||
__streaming_chat = ChatZhipuAI(
|
||||
model="glm-4",
|
||||
temperature=0.5,
|
||||
streaming=True,
|
||||
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
|
||||
)
|
||||
|
||||
def __init__(self, file_path: str):
|
||||
try:
|
||||
global _prompt
|
||||
self._example_prompt = _prompt["prompt_1"]
|
||||
except NameError:
|
||||
pass
|
||||
self.__file_path = file_path
|
||||
self.__sentenceEmbedding = SentenceEmbedding(file_path)
|
||||
self._retriever = self.__sentenceEmbedding.get_vectorstore().as_retriever(search_type="similarity",
|
||||
search_kwargs={"k": 5})
|
||||
|
||||
try:
|
||||
self.__rag_chain = (
|
||||
{"context": self._retriever | format_docs, "question": RunnablePassthrough()}
|
||||
| self._example_prompt
|
||||
| RAG._llm
|
||||
| StrOutputParser()
|
||||
)
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
def get_chat(self, message: str):
|
||||
for chunk in self.__rag_chain.stream(message):
|
||||
print(chunk, end="", flush=True)
|
||||
|
||||
def select_prompt(self, prompt_index: int = 1):
|
||||
global _prompt
|
||||
prompt_name = "prompt_" + str(prompt_index)
|
||||
self._example_prompt = _prompt[prompt_name]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
r = RAG("C:\\Users\\16922\\Desktop\\文档1.pdf")
|
||||
r.select_prompt(2)
|
||||
r.get_chat("what can Multimodal Agent AI systems do?")
|
||||
|
||||
Loading…
Reference in new issue