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from typing import Any, Dict, Iterable, List, Optional, Type, TypeVar |
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from pydantic import Extra, Field, root_validator |
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from langchain.chains.base import Chain |
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from langchain import HuggingFaceHub |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain.chat_models import ChatOpenAI |
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from langchain.chains import ConversationalRetrievalChain |
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from langchain.memory import ConversationBufferWindowMemory |
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from langchain.chains.llm import LLMChain |
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from langchain.callbacks.base import CallbackManager |
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler |
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from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT |
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from langchain.chains.combine_documents.base import BaseCombineDocumentsChain |
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from langchain.chains.llm import LLMChain |
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from langchain.schema import BaseLanguageModel, BaseRetriever, Document |
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from langchain.prompts.prompt import PromptTemplate |
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def get_new_chain1(vectorstore, vectorstore_radio, model_selector, k_textbox, search_type_selector, max_tokens_textbox) -> Chain: |
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retriever = None |
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if vectorstore_radio == 'Chroma': |
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retriever = vectorstore.as_retriever(search_type=search_type_selector) |
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retriever.search_kwargs = {"k":int(k_textbox)} |
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if search_type_selector == 'mmr': |
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retriever.search_kwargs = {"k":int(k_textbox), "fetch_k":4*int(k_textbox)} |
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if vectorstore_radio == 'raw': |
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if search_type_selector == 'svm': |
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retriever = SVMRetriever.from_texts(merged_vectorstore, embedding_function) |
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retriever.k = int(k_textbox) |
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qa_template = """You are called chat-pykg and are an AI assistant coded in python using langchain and gradio. You are very helpful for answering questions about programming with various open source packages and libraries. |
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You are given snippets of code and information in the Context below, as well as a Question to give a Helpful answer to. |
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Due to data size limitations, the snippets of code in the Context have been specifically filtered/selected for their relevance from a document store containing code from one or many packages and libraries. |
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Each of the code snippets is marked with '# source: package/filename' so you can attempt to establish where they are located in their package structure and gain more understanding of the code. |
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Please provide a helpful answer in markdown to the Question. |
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Do not make up any hyperlinks that are not in the Context. |
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If you don't know the answer, just say that you don't know, don't try to make up an answer. |
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========= |
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Context:{context} |
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========= |
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Question: {question} |
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Helpful answer:""" |
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QA_PROMPT.template = qa_template |
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condense_question_template = """Given the following conversation and a Follow Up Input, rephrase the Follow Up Input to be a Standalone question. |
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The Standalone question will be used for retrieving relevant source code and information from a document store, where each document is marked with '# source: package/filename'. |
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Therefore, in your Standalone question you must try to include references to related code or sources that have been mentioned in the Follow Up Input or Chat History. |
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========= |
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Chat History: |
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{chat_history} |
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========= |
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Follow Up Input: {question} |
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Standalone question in markdown:""" |
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CONDENSE_QUESTION_PROMPT.template = condense_question_template |
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if model_selector in ['gpt-4', 'gpt-3.5-turbo']: |
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llm = ChatOpenAI(client = None, temperature=0.7, model_name=model_selector) |
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doc_chain_llm = ChatOpenAI(client = None, streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0.7, model_name=model_selector, max_tokens=int(max_tokens_textbox)) |
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if model_selector == 'other': |
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llm = HuggingFaceHub(repo_id="chavinlo/gpt4-x-alpaca") |
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doc_chain_llm = HuggingFaceHub(repo_id="chavinlo/gpt4-x-alpaca") |
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question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) |
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doc_chain = load_qa_chain(doc_chain_llm, chain_type="stuff", prompt=QA_PROMPT) |
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memory = ConversationBufferWindowMemory(input_key="question", output_key="answer", k=5) |
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qa_orig = ConversationalRetrievalChain( |
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retriever=retriever, memory=memory, combine_docs_chain=doc_chain, question_generator=question_generator, verbose=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])) |
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qa = qa_orig |
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return qa |
