| |
| """ |
| Created on Mon Dec 30 22:20:13 2024 |
| |
| @author: BM109X32G-10GPU-02 |
| """ |
| from langchain.chains import LLMChain, SimpleSequentialChain, RetrievalQA, ConversationalRetrievalChain |
|
|
| from langchain import PromptTemplate |
| |
| from langchain.tools import BaseTool |
| import os |
| from langchain_core.messages import HumanMessage, SystemMessage |
| from langchain.base_language import BaseLanguageModel |
| from langchain.text_splitter import CharacterTextSplitter |
| |
| |
| from langchain_community.document_loaders import PyPDFLoader |
| from langchain_community.vectorstores import FAISS |
| from langchain_openai import ChatOpenAI |
| from langchain_openai import OpenAIEmbeddings |
|
|
| template = """ |
| |
| You are an expert chemist and your task is to respond to the question or |
| solve the problem to the best of your ability. You need to answer in as much detail as possible. |
| You can only respond with a single "Final Answer" format. |
| Use the following pieces of context to answer the question at the end. |
| If you don't know the answer, just say that you don't know, don't try to make up an answer. |
| <context> |
| {context} |
| </context> |
| |
| Question: {question} |
| Answer: |
| |
| """ |
| |
| class pdfreader(BaseTool): |
| name: str = "pdfreader" |
| description: str = ( |
|
|
| "Used to read papers, summarize papers, Q&A based on papers, literature or publication" |
| "Input query , return the response" |
| ) |
|
|
| llm: BaseLanguageModel = None |
| path : str = None |
| return_direct: bool = True |
| def __init__(self, path: str = None): |
| super().__init__( ) |
| self.llm = ChatOpenAI(model="gpt-4o-2024-11-20",api_key=os.getenv("OPENAI_API_KEY"), |
| base_url=os.getenv("OPENAI_API_BASE")) |
| self.path = path |
| |
|
|
| def _run(self, query ) -> str: |
| |
| loader = PyPDFLoader(self.path) |
| documents = loader.load() |
| |
| text_splitter = CharacterTextSplitter(chunk_size=6000, chunk_overlap=1000) |
| docs = text_splitter.split_documents(documents) |
| embeddings = OpenAIEmbeddings(api_key=os.getenv("OPENAI_API_KEY"), |
| base_url=os.getenv("OPENAI_API_BASE")) |
|
|
| |
| vectorstore = FAISS.from_documents(docs, embeddings) |
| prompt = PromptTemplate(template=template, input_variables=[ "question"]) |
| qa_chain = RetrievalQA.from_chain_type( |
| llm= self.llm, |
| chain_type="stuff", |
| retriever=vectorstore.as_retriever(search_kwargs={"k": 2}), |
| return_source_documents=True, |
| chain_type_kwargs={"prompt": prompt}, |
| ) |
| |
| result = qa_chain.invoke(query) |
| return result['result'] |
| |
| |
| async def _arun(self, query) -> str: |
| """Use the tool asynchronously.""" |
| raise NotImplementedError("this tool does not support async") |
| |
| |
