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import json | |
import os | |
import pathlib | |
import pickle | |
from typing import Dict, List, Tuple | |
import weaviate | |
from langchain import OpenAI, PromptTemplate | |
from langchain.chains import LLMChain | |
from langchain.chains.base import Chain | |
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain | |
from langchain.chains.conversation.memory import ConversationBufferMemory | |
from langchain.chains.question_answering import load_qa_chain | |
from langchain.embeddings import OpenAIEmbeddings | |
from langchain.prompts import FewShotPromptTemplate, PromptTemplate | |
from langchain.prompts.example_selector import \ | |
SemanticSimilarityExampleSelector | |
from langchain.vectorstores import FAISS, Weaviate | |
from pydantic import BaseModel | |
class CustomChain(Chain, BaseModel): | |
vstore: FAISS | |
chain: BaseCombineDocumentsChain | |
key_word_extractor: Chain | |
def input_keys(self) -> List[str]: | |
return ["question"] | |
def output_keys(self) -> List[str]: | |
return ["answer"] | |
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: | |
question = inputs["question"] | |
chat_history_str = _get_chat_history(inputs["chat_history"]) | |
if chat_history_str: | |
new_question = self.key_word_extractor.run( | |
question=question, chat_history=chat_history_str | |
) | |
else: | |
new_question = question | |
print(new_question) | |
docs = self.vstore.similarity_search(new_question, k=4) | |
new_inputs = inputs.copy() | |
new_inputs["question"] = new_question | |
new_inputs["chat_history"] = chat_history_str | |
answer, _ = self.chain.combine_docs(docs, **new_inputs) | |
return {"answer": answer} | |
def get_new_chain1(vectorstore, rephraser_llm, final_output_llm) -> Chain: | |
_eg_template = """## Example: | |
Chat History: | |
{chat_history} | |
Follow Up Input: {question} | |
Standalone question: {answer}""" | |
_eg_prompt = PromptTemplate( | |
template=_eg_template, | |
input_variables=["chat_history", "question", "answer"], | |
) | |
_prefix = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. You should assume that the question is related to Hugging Face Code.""" | |
_suffix = """## Example: | |
Chat History: | |
{chat_history} | |
Follow Up Input: {question} | |
Standalone question:""" | |
#### LOAD VSTORE WITH REPHRASE EXAMPLES | |
with open("rephrase_eg.pkl", 'rb') as f: | |
rephrase_example_selector = pickle.load(f) | |
prompt = FewShotPromptTemplate( | |
prefix=_prefix, | |
suffix=_suffix, | |
example_selector=rephrase_example_selector, | |
example_prompt=_eg_prompt, | |
input_variables=["question", "chat_history"], | |
) | |
key_word_extractor = LLMChain(llm=rephraser_llm, prompt=prompt) | |
EXAMPLE_PROMPT = PromptTemplate( | |
template=">Example:\nContent:\n---------\n{page_content}\n----------\nSource: {source}", | |
input_variables=["page_content", "source"], | |
) | |
template = """You are an AI assistant for the open source transformers library provided by Hugging Face. The documentation is located at https://huggingface.co/docs/transformers. | |
You are given the following extracted parts of a long document and a question. Provide a conversational answer with a hyperlink to the documentation. Do NOT add .html to the end of links. | |
You should only use hyperlinks that are explicitly listed as a source in the context. Do NOT make up a hyperlink that is not listed. | |
If the question includes a request for code, provide a code block directly from the documentation. | |
For example, if someone asks how to install Transformers, you should say: | |
You can install with pip, for more info view the (documentation)[https://huggingface.co/docs/transformers/installation] | |
'''py | |
pip install transformers | |
''' | |
If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer. | |
If the question is not about Hugging Face Transformers, politely inform them that you are tuned to only answer questions about Transformers. | |
Question: {question} | |
========= | |
{context} | |
========= | |
Answer in Markdown:""" | |
PROMPT = PromptTemplate(template=template, input_variables=["question", "context"]) | |
doc_chain = load_qa_chain( | |
final_output_llm, | |
chain_type="stuff", | |
prompt=PROMPT, | |
document_prompt=EXAMPLE_PROMPT, | |
verbose=True | |
) | |
return CustomChain(chain=doc_chain, vstore=vectorstore, key_word_extractor=key_word_extractor) | |
def _get_chat_history(chat_history: List[Tuple[str, str]]): | |
buffer = "" | |
for human_s, ai_s in chat_history: | |
human = f"Human: " + human_s | |
ai = f"Assistant: " + ai_s | |
buffer += "\n" + "\n".join([human, ai]) | |
return buffer | |