网站的服务器在哪里,sem推广软件选哪家,石家庄新钥匙建站,电子商务做网站设计在我之前的文章 “Elasticsearch#xff1a;与多个 PDF 聊天 | LangChain Python 应用教程#xff08;免费 LLMs 和嵌入#xff09;” 里#xff0c;我详述如何使用 Streamlit#xff0c;Langchain, Elasticsearch 及 OpenAI 来针对 PDF 进行聊天。在今天的文章中#xf…
在我之前的文章 “Elasticsearch与多个 PDF 聊天 | LangChain Python 应用教程免费 LLMs 和嵌入” 里我详述如何使用 StreamlitLangchain, Elasticsearch 及 OpenAI 来针对 PDF 进行聊天。在今天的文章中我将使用 Chainlit 来展示如使用 Langchain 及 Elasticsearch 针对 PDF 文件进行查询。
为方便大家学习我的代码在地址 GitHub - liu-xiao-guo/langchain-openai-chainlit: Chat with your documents (pdf, csv, text) using Openai model, LangChain and Chainlit 进行下载。 安装
安装 Elasticsearch 及 Kibana
如果你还没有安装好自己的 Elasticsearch 及 Kibana那么请参考一下的文章来进行安装 如何在 LinuxMacOS 及 Windows 上进行安装 Elasticsearch Kibana如何在 LinuxMacOS 及 Windows 上安装 Elastic 栈中的 Kibana
在安装的时候请选择 Elastic Stack 8.x 进行安装。在安装的时候我们可以看到如下的安装信息 拷贝 Elasticsearch 证书
我们把 Elasticsearch 的证书拷贝到当前的目录下
$ pwd
/Users/liuxg/python/elser
$ cp ~/elastic/elasticsearch-8.12.0/config/certs/http_ca.crt .
$ ls http_ca.crt
http_ca.crt 安装 Python 依赖包
我们在当前的目录下打入如下的命令
python3 -m venv .venv
source .venv/bin/activate
然后我们再打入如下的命令
$ pwd
/Users/liuxg/python/langchain-openai-chainlit
$ source .venv/bin/activate
(.venv) $ pip3 install -r requirements.txt 运行应用
有关 Chainlit 的更多知识请参考 Overview - Chainlit。这里就不再赘述。有关 pdf_qa.py 的代码如下
pdf_qa.py
# Import necessary modules and define env variables# from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import ChatOpenAI
from langchain.prompts.chat import (ChatPromptTemplate,SystemMessagePromptTemplate,HumanMessagePromptTemplate,
)
import os
import io
import chainlit as cl
import PyPDF2
from io import BytesIOfrom pprint import pprint
import inspect
# from langchain.vectorstores import ElasticsearchStore
from langchain_community.vectorstores import ElasticsearchStore
from elasticsearch import Elasticsearchfrom dotenv import load_dotenv# Load environment variables from .env file
load_dotenv()OPENAI_API_KEY os.getenv(OPENAI_API_KEY)
ES_USER os.getenv(ES_USER)
ES_PASSWORD os.getenv(ES_PASSWORD)
elastic_index_namepdf_docs# text_splitter and system templatetext_splitter RecursiveCharacterTextSplitter(chunk_size1000, chunk_overlap100)system_template Use the following pieces of context to answer the users question.
If you dont know the answer, just say that you dont know, dont try to make up an answer.
ALWAYS return a SOURCES part in your answer.
The SOURCES part should be a reference to the source of the document from which you got your answer.Example of your response should be:
The answer is foo
SOURCES: xyz
Begin!
----------------
{summaries}messages [SystemMessagePromptTemplate.from_template(system_template),HumanMessagePromptTemplate.from_template({question}),
]
prompt ChatPromptTemplate.from_messages(messages)
chain_type_kwargs {prompt: prompt}cl.on_chat_start
async def on_chat_start():# Sending an image with the local file pathelements [cl.Image(nameimage1, displayinline, path./robot.jpeg)]await cl.Message(contentHello there, Welcome to AskAnyQuery related to Data!, elementselements).send()files None# Wait for the user to upload a PDF filewhile files is None:files await cl.AskFileMessage(contentPlease upload a PDF file to begin!,accept[application/pdf],max_size_mb20,timeout180,).send()file files[0]# print(type: , type(file))# print(file: , file)# pprint(vars(file))# print(file.content)msg cl.Message(contentfProcessing {file.name}...)await msg.send()# Read the PDF file# pdf_stream BytesIO(file.content)with open(file.path, rb) as f:pdf_content f.read()pdf_stream BytesIO(pdf_content)pdf PyPDF2.PdfReader(pdf_stream)pdf_text for page in pdf.pages:pdf_text page.extract_text()# Split the text into chunkstexts text_splitter.split_text(pdf_text)# Create metadata for each chunkmetadatas [{source: f{i}-pl} for i in range(len(texts))]# Create a Chroma vector storeembeddings OpenAIEmbeddings()url fhttps://{ES_USER}:{ES_PASSWORD}localhost:9200connection Elasticsearch(hosts[url], ca_certs ./http_ca.crt, verify_certs True)docsearch Noneif not connection.indices.exists(indexelastic_index_name):print(The index does not exist, going to generate embeddings) docsearch await cl.make_async(ElasticsearchStore.from_texts)( texts,embedding embeddings, es_url url, es_connection connection,index_name elastic_index_name, es_user ES_USER,es_password ES_PASSWORD,metadatasmetadatas)else: print(The index already existed)docsearch ElasticsearchStore(es_connectionconnection,embeddingembeddings,es_url url, index_name elastic_index_name, es_user ES_USER,es_password ES_PASSWORD )# Create a chain that uses the Chroma vector storechain RetrievalQAWithSourcesChain.from_chain_type(ChatOpenAI(temperature0),chain_typestuff,retrieverdocsearch.as_retriever(search_kwargs{k: 4}),)# Save the metadata and texts in the user sessioncl.user_session.set(metadatas, metadatas)cl.user_session.set(texts, texts)# Let the user know that the system is readymsg.content fProcessing {file.name} done. You can now ask questions!await msg.update()cl.user_session.set(chain, chain)cl.on_message
async def main(message:str):chain cl.user_session.get(chain) # type: RetrievalQAWithSourcesChainprint(chain type: , type(chain))cb cl.AsyncLangchainCallbackHandler(stream_final_answerTrue, answer_prefix_tokens[FINAL, ANSWER])cb.answer_reached Trueprint(message: , message)pprint(vars(message))print(message.content)res await chain.acall(message.content, callbacks[cb])answer res[answer]sources res[sources].strip()source_elements []# Get the metadata and texts from the user sessionmetadatas cl.user_session.get(metadatas)all_sources [m[source] for m in metadatas]texts cl.user_session.get(texts)print(texts: , texts)if sources:found_sources []# Add the sources to the messagefor source in sources.split(,):source_name source.strip().replace(., )# Get the index of the sourcetry:index all_sources.index(source_name)except ValueError:continuetext texts[index]found_sources.append(source_name)# Create the text element referenced in the messagesource_elements.append(cl.Text(contenttext, namesource_name))if found_sources:answer f\nSources: {, .join(found_sources)}else:answer \nNo sources foundif cb.has_streamed_final_answer:cb.final_stream.elements source_elementsawait cb.final_stream.update()else:await cl.Message(contentanswer, elementssource_elements).send()
我们可以使用如下的命令来运行
export ES_USERelastic
export ES_PASSWORDxnLj56lTrH98Lf_6n76y
export OPENAI_API_KEYYourOpenAiKeychainlit run pdf_qa.py -w
(.venv) $ chainlit run pdf_qa.py -w
2024-02-14 10:58:30 - Loaded .env file
2024-02-14 10:58:33 - Your app is available at http://localhost:8000
2024-02-14 10:58:34 - Translation file for en not found. Using default translation en-US.
2024-02-14 10:58:35 - 2 changes detected
我们先选择项目自带的 pdf 文件
Is sample PDF download critical to an organization? Does comprehensive PDF testing have various advantages?
