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from typing import Dict, List, Tuple
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.question_answering import load_qa_chain
from langchain.prompts import FewShotPromptTemplate
from langchain.vectorstores import FAISS
from pydantic import BaseModel
class CustomChain(Chain, BaseModel):
vstore: FAISS
chain: BaseCombineDocumentsChain
key_word_extractor: Chain
@property
def input_keys(self) -> List[str]:
return ["question"]
@property
def output_keys(self) -> List[str]:
return ["answer", "sources"]
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
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)
sources = []
if "SOURCES:" in answer:
answer, sources = answer.split("SOURCES:")
sources = sources.split(", ")
return {"answer": answer.strip(), "sources": sources}
def get_chain(vectorstore: FAISS) -> Chain:
_eg_template = """## Example:
Chat History:
{chat_history}
Follow Up question: {question}
Standalone question: {answer}"""
_eg_prompt = PromptTemplate(
template=_eg_template,
input_variables=["chat_history", "question", "answer"],
)
_prefix = """Given the following Chat History and a Follow Up Question, rephrase the Follow Up Question to be a new Standalone Question that takes the Chat History and context in to consideration. You should assume that the question is related to the TokCast podcast."""
_suffix = """## Example:
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
examples = [
{
"question": "Who is that?",
"chat_history": "Human: What is the TokCast podcast?\nAssistant: TokCast is a podcast about the philosophy of David Deutsch.",
"answer": "Who is David Deutsch?",
},
{
"question": "What is the worldview presented here?",
"chat_history": "Human: What is the TokCast podcast?\nAssistant: TokCast is a podcast about the philosophy of David Deutsch.\nHuman: Who is that?\nAssistant: David Deutsch is a philosopher, physicist, and author. He is the author of The Beginning of Infinity, Fabric of Reality, and one of the pioneers of the field of quantum computing.",
"answer": "What is David Deutsch's worldview?",
},
]
prompt = FewShotPromptTemplate(
prefix=_prefix,
suffix=_suffix,
# example_selector=example_selector,
examples=examples,
example_prompt=_eg_prompt,
input_variables=["question", "chat_history"],
)
llm = OpenAI(temperature=0, model_name="text-davinci-003")
key_word_extractor = LLMChain(llm=llm, prompt=prompt)
EXAMPLE_PROMPT = PromptTemplate(
template="CONTENT:\n{page_content}\n----------\nSOURCE:\n{source}\n",
input_variables=["page_content", "source"],
)
template = """You are an AI assistant for the TokCast Podcast. You're trained on all the transcripts of the podcast.
Given a QUESTION and a series one or more CONTENT and SOURCE sections from a long document provide a conversational answer as "ANSWER" and a "SOURCES" output which lists verbatim the SOURCEs used in generating the response.
You should only use SOURCEs that are explicitly listed as a SOURCE in the context.
ALWAYS include the "SOURCES" as part of the response. If you don't have any sources, just say "SOURCES:"
If you don't know the answer, just say "I'm not sure. Check out Brett's Channel" Don't try to make up an answer.
QUESTION: {question}
=========
{context}
=========
ANSWER:"""
PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
doc_chain = load_qa_chain(
OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct", max_tokens=-1),
chain_type="stuff",
prompt=PROMPT,
document_prompt=EXAMPLE_PROMPT,
)
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 = "Human: " + human_s
ai = "Assistant: " + ai_s
buffer += "\n" + "\n".join([human, ai])
return buffer
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