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from langchain.llms import OpenAI |
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from langchain.prompts import PromptTemplate |
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from langchain.chains.qa_with_sources import load_qa_with_sources_chain |
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from langchain.chat_models import ChatOpenAI |
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from langchain.schema import HumanMessage |
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import tiktoken |
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def num_token(string: str) -> int: |
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"""Returns the number of tokens in a text string.""" |
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encoding = tiktoken.get_encoding('cl100k_base') |
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num_tokens = len(encoding.encode(string)) |
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return num_tokens |
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def retrive_doc_on_token(rephrased_query, db): |
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top_docs = db.similarity_search(rephrased_query, k=10) |
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num = [num_token(doc.page_content) for doc in top_docs] |
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cum_sum = [abs(sum(num[:i+1]) - 2700) for i in range(len(num))] |
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idx = cum_sum.index(min(cum_sum)) |
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return top_docs[:idx+1] |
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def multi_docs_qa(query, rephrased_query, db, company, language, temperature): |
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""" |
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Return an answer to the query based on multiple documents limited |
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by total token of 3000. |
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""" |
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print('temperature: ', temperature) |
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template = """<|im_start|> |
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Manulife's assistant helps the user with their questions about products and services. Be brief in your answers with appropriate tone and emotion. Answer ONLY with the facts listed in the list of sources below. If there isn't enough information below, say you don't know. Do not generate answers that don't use the sources below. If asking a clarifying question to the user would help, ask the question. For tabular information return it as an html table. Do not return markdown format. Each source has a name followed by colon and the actual information, ALWAYS include the source name for each fact you use in the response. Use square brakets to reference the source, e.g. [info1.txt]. Don't combine sources, list each source separately, e.g. [info1.txt][info2.pdf]. |
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Sources: |
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{sources} |
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<|im_end|> |
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{chat_history} |
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""" |
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docs = retrive_doc_on_token(query, db) |
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sources = [] |
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for i in docs: |
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source_txt = i.metadata['source'] |
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source_content = i.page_content |
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add = f"{source_txt}: {source_content}" |
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sources.append(add) |
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s_txt = '\n\n'.join(sources) |
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ch = rephrased_query + f'\nUser: {query}' + '\nAssistant: ' |
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final_template = template.format(sources=s_txt, chat_history=ch) |
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print('\n\n', final_template, '\n\n') |
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=temperature) |
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response = llm([HumanMessage(content=final_template)]).content |
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return response, '' |
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def multi_docs_qa_hkas(query, rephrased_query, db, language, temperature): |
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""" |
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Return an answer to the query based on multiple documents limited |
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by total token of 3000. |
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""" |
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template = ( |
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f"Create a comprehensive and truthful final response in {language}. " |
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"Ask for clearification before answering if the QUESTION is not clear.\n\n" |
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"Context (may or may not be useful)" |
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"===\n" |
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"{summaries}\n" |
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"===\n\n" |
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"Query: " |
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"===\n" |
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"{question} " |
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f"({rephrased_query})\n" |
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"===\n\n" |
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f"FINAL RESPONSE (in complete sentence):" |
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) |
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docs = retrive_doc_on_token(query+ f" ({rephrased_query})", db) |
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PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"]) |
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llm = OpenAI(temperature=temperature, model_name='gpt-3.5-turbo') |
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chain = load_qa_with_sources_chain(llm, chain_type="stuff", prompt=PROMPT) |
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response = chain({"input_documents": docs, "question": query}) |
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return response['output_text'], docs |
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def summary(query, context): |
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template = ( |
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"Use the following portion of a long document to see if any of the text is relevant to answer the question. " |
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"Return any relevant text verbatim and 'SOURCE'.\n\n" |
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"===\n" |
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"{context}" |
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"===\n\n" |
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"Question: {question}\n" |
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"Relevant text, if any:" |
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) |
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return template.format(context=context,question=query) |
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import asyncio |
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async def multi_reponse(temperature, messages, docs): |
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chat = ChatOpenAI(temperature=temperature, model_name="gpt-3.5-turbo") |
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responses = await chat.agenerate(messages=messages) |
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text = '' |
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for i, r in enumerate(responses.generations): |
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if 'N/A' not in r[0].text: |
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text += f"{r[0].text}\nSOURCE: {docs[i].metadata['source']}\n\n" |
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print(text) |
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return text |
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from typing import Any, Dict, List, Optional, Union |
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from types import GeneratorType |
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from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler |
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from langchain.schema import AgentAction, AgentFinish, LLMResult |
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class SyncStreamingLLMCallbackHandler(BaseCallbackHandler): |
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"""Callback handler for streaming LLM responses to a queue.""" |
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def __init__(self, q): |
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self.q = q |
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def on_llm_start( |
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self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_llm_new_token(self, token: str, **kwargs: Any) -> None: |
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self.q.put(token) |
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: |
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"""Do nothing.""" |
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pass |
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def on_llm_error( |
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self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_chain_start( |
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self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: |
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"""Do nothing.""" |
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pass |
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def on_chain_error( |
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self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_tool_start( |
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self, |
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serialized: Dict[str, Any], |
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input_str: str, |
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**kwargs: Any, |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_tool_end( |
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self, |
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output: str, |
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color: Optional[str] = None, |
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observation_prefix: Optional[str] = None, |
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llm_prefix: Optional[str] = None, |
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**kwargs: Any, |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_tool_error( |
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self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any |
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) -> None: |
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"""Do nothing.""" |
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pass |
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def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: |
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"""Run on agent action.""" |
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pass |
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def on_agent_finish( |
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self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any |
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) -> None: |
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"""Run on agent end.""" |
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pass |
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