Spaces:
Running
Running
File size: 14,427 Bytes
ba9f995 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 |
"""Conversational QA Chain"""
from __future__ import annotations
import inspect
import logging
from typing import Any, Dict, List, Optional
from pydantic import Field
from langchain.schema import BasePromptTemplate, BaseRetriever, Document
from langchain.schema.language_model import BaseLanguageModel
from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.base import (
BaseConversationalRetrievalChain,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from toolkit.utils import (
Config,
_get_chat_history,
_get_standalone_questions_list,
)
from toolkit.retrivers import MyRetriever
from toolkit.prompts import PromptTemplates
configs = Config("configparser.ini")
logger = logging.getLogger(__name__)
prompt_templates = PromptTemplates()
class ConvoRetrievalChain(BaseConversationalRetrievalChain):
"""Chain for having a conversation based on retrieved documents.
This chain takes in chat history (a list of messages) and new questions,
and then returns an answer to that question.
The algorithm for this chain consists of three parts:
1. Use the chat history and the new question to create a "standalone question".
This is done so that this question can be passed into the retrieval step to fetch
relevant documents. If only the new question was passed in, then relevant context
may be lacking. If the whole conversation was passed into retrieval, there may
be unnecessary information there that would distract from retrieval.
2. This new question is passed to the retriever and relevant documents are
returned.
3. The retrieved documents are passed to an LLM along with either the new question
(default behavior) or the original question and chat history to generate a final
response.
Example:
.. code-block:: python
from langchain.chains import (
StuffDocumentsChain, LLMChain, ConversationalRetrievalChain
)
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
combine_docs_chain = StuffDocumentsChain(...)
vectorstore = ...
retriever = vectorstore.as_retriever()
# This controls how the standalone question is generated.
# Should take `chat_history` and `question` as input variables.
template = (
"Combine the chat history and follow up question into "
"a standalone question. Chat History: {chat_history}"
"Follow up question: {question}"
)
prompt = PromptTemplate.from_template(template)
llm = OpenAI()
question_generator_chain = LLMChain(llm=llm, prompt=prompt)
chain = ConversationalRetrievalChain(
combine_docs_chain=combine_docs_chain,
retriever=retriever,
question_generator=question_generator_chain,
)
"""
retriever: MyRetriever = Field(exclude=True)
"""Retriever to use to fetch documents."""
file_names: List = Field(exclude=True)
"""file_names (List): List of file names used for retrieval."""
def _get_docs(
self,
question: str,
inputs: Dict[str, Any],
num_query: int,
*,
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> List[Document]:
"""Get docs."""
try:
docs = self.retriever.get_relevant_documents(
question, num_query=num_query, run_manager=run_manager
)
return docs
except (IOError, FileNotFoundError) as error:
logger.error("An error occurred in _get_docs: %s", error)
return []
def _retrieve(
self,
question_list: List[str],
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> List[str]:
num_query = len(question_list)
accepts_run_manager = (
"run_manager" in inspect.signature(self._get_docs).parameters
)
total_results = {}
for question in question_list:
docs_dict = (
self._get_docs(
question, inputs, num_query=num_query, run_manager=run_manager
)
if accepts_run_manager
else self._get_docs(question, inputs, num_query=num_query)
)
for file_name, docs in docs_dict.items():
if file_name not in total_results:
total_results[file_name] = docs
else:
total_results[file_name].extend(docs)
logger.info(
"-----step_done--------------------------------------------------",
)
snippets = ""
redundancy = set()
for file_name, docs in total_results.items():
sorted_docs = sorted(docs, key=lambda x: x.metadata["medium_chunk_idx"])
temp = "\n".join(
doc.page_content
for doc in sorted_docs
if doc.metadata["page_content_md5"] not in redundancy
)
redundancy.update(doc.metadata["page_content_md5"] for doc in sorted_docs)
snippets += f"\nContext about {file_name}:\n{{{temp}}}\n"
return snippets, docs_dict
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(inputs["chat_history"])
callbacks = _run_manager.get_child()
new_questions = self.question_generator.run(
question=question,
chat_history=chat_history_str,
database=self.file_names,
callbacks=callbacks,
)
logger.info("new_questions: %s", new_questions)
new_question_list = _get_standalone_questions_list(new_questions, question)[:3]
# print("new_question_list:", new_question_list)
logger.info("user_input: %s", question)
logger.info("new_question_list: %s", new_question_list)
snippets, source_docs = self._retrieve(
new_question_list, inputs, run_manager=_run_manager
)
docs = [
Document(
page_content=snippets,
metadata={},
)
]
new_inputs = inputs.copy()
new_inputs["chat_history"] = chat_history_str
answer = self.combine_docs_chain.run(
input_documents=docs,
database=self.file_names,
callbacks=_run_manager.get_child(),
**new_inputs,
)
output: Dict[str, Any] = {self.output_key: answer}
if self.return_source_documents:
output["source_documents"] = source_docs
if self.return_generated_question:
output["generated_question"] = new_questions
logger.info("*****response*****: %s", output["answer"])
logger.info(
"=====epoch_done============================================================",
)
return output
async def _aget_docs(
self,
question: str,
inputs: Dict[str, Any],
num_query: int,
*,
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> List[Document]:
"""Get docs."""
try:
docs = await self.retriever.aget_relevant_documents(
question, num_query=num_query, run_manager=run_manager
)
return docs
except (IOError, FileNotFoundError) as error:
logger.error("An error occurred in _get_docs: %s", error)
return []
async def _aretrieve(
self,
question_list: List[str],
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
num_query = len(question_list)
accepts_run_manager = (
"run_manager" in inspect.signature(self._get_docs).parameters
)
total_results = {}
for question in question_list:
docs_dict = (
await self._aget_docs(
question, inputs, num_query=num_query, run_manager=run_manager
)
if accepts_run_manager
else await self._aget_docs(question, inputs, num_query=num_query)
)
for file_name, docs in docs_dict.items():
if file_name not in total_results:
total_results[file_name] = docs
else:
total_results[file_name].extend(docs)
logger.info(
"-----step_done--------------------------------------------------",
)
snippets = ""
redundancy = set()
for file_name, docs in total_results.items():
sorted_docs = sorted(docs, key=lambda x: x.metadata["medium_chunk_idx"])
temp = "\n".join(
doc.page_content
for doc in sorted_docs
if doc.metadata["page_content_md5"] not in redundancy
)
redundancy.update(doc.metadata["page_content_md5"] for doc in sorted_docs)
snippets += f"\nContext about {file_name}:\n{{{temp}}}\n"
return snippets, docs_dict
async def _acall(
self,
inputs: Dict[str, Any],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
question = inputs["question"]
get_chat_history = self.get_chat_history or _get_chat_history
chat_history_str = get_chat_history(inputs["chat_history"])
callbacks = _run_manager.get_child()
new_questions = await self.question_generator.arun(
question=question,
chat_history=chat_history_str,
database=self.file_names,
callbacks=callbacks,
)
new_question_list = _get_standalone_questions_list(new_questions, question)[:3]
logger.info("new_questions: %s", new_questions)
logger.info("new_question_list: %s", new_question_list)
snippets, source_docs = await self._aretrieve(
new_question_list, inputs, run_manager=_run_manager
)
docs = [
Document(
page_content=snippets,
metadata={},
)
]
new_inputs = inputs.copy()
new_inputs["chat_history"] = chat_history_str
answer = await self.combine_docs_chain.arun(
input_documents=docs,
database=self.file_names,
callbacks=_run_manager.get_child(),
**new_inputs,
)
output: Dict[str, Any] = {self.output_key: answer}
if self.return_source_documents:
output["source_documents"] = source_docs
if self.return_generated_question:
output["generated_question"] = new_questions
logger.info("*****response*****: %s", output["answer"])
logger.info(
"=====epoch_done============================================================",
)
return output
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
retriever: BaseRetriever,
condense_question_prompt: BasePromptTemplate = prompt_templates.get_refine_qa_template(
configs.model_name
),
chain_type: str = "stuff", # only support stuff chain now
verbose: bool = False,
condense_question_llm: Optional[BaseLanguageModel] = None,
combine_docs_chain_kwargs: Optional[Dict] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> BaseConversationalRetrievalChain:
"""Convenience method to load chain from LLM and retriever.
This provides some logic to create the `question_generator` chain
as well as the combine_docs_chain.
Args:
llm: The default language model to use at every part of this chain
(eg in both the question generation and the answering)
retriever: The retriever to use to fetch relevant documents from.
condense_question_prompt: The prompt to use to condense the chat history
and new question into standalone question(s).
chain_type: The chain type to use to create the combine_docs_chain, will
be sent to `load_qa_chain`.
verbose: Verbosity flag for logging to stdout.
condense_question_llm: The language model to use for condensing the chat
history and new question into standalone question(s). If none is
provided, will default to `llm`.
combine_docs_chain_kwargs: Parameters to pass as kwargs to `load_qa_chain`
when constructing the combine_docs_chain.
callbacks: Callbacks to pass to all subchains.
**kwargs: Additional parameters to pass when initializing
ConversationalRetrievalChain
"""
combine_docs_chain_kwargs = combine_docs_chain_kwargs or {
"prompt": prompt_templates.get_retrieval_qa_template_selector(
configs.model_name
).get_prompt(llm)
}
doc_chain = load_qa_chain(
llm,
chain_type=chain_type,
verbose=verbose,
callbacks=callbacks,
**combine_docs_chain_kwargs,
)
_llm = condense_question_llm or llm
condense_question_chain = LLMChain(
llm=_llm,
prompt=condense_question_prompt,
verbose=verbose,
callbacks=callbacks,
)
return cls(
retriever=retriever,
combine_docs_chain=doc_chain,
question_generator=condense_question_chain,
callbacks=callbacks,
**kwargs,
)
|