import ast import glob import inspect import os import pathlib import pickle import shutil import subprocess import tempfile import time import traceback import types import uuid import zipfile from collections import defaultdict from datetime import datetime from functools import reduce from operator import concat import filelock from joblib import delayed from langchain.callbacks import streaming_stdout from langchain.embeddings import HuggingFaceInstructEmbeddings from langchain.schema import LLMResult from tqdm import tqdm from enums import DocumentSubset, no_lora_str, model_token_mapping, source_prefix, source_postfix, non_query_commands, \ LangChainAction, LangChainMode, DocumentChoice from evaluate_params import gen_hyper from gen import get_model, SEED from prompter import non_hf_types, PromptType, Prompter from utils import wrapped_partial, EThread, import_matplotlib, sanitize_filename, makedirs, get_url, flatten_list, \ get_device, ProgressParallel, remove, hash_file, clear_torch_cache, NullContext, get_hf_server, FakeTokenizer, \ have_libreoffice, have_arxiv, have_playwright, have_selenium, have_tesseract, have_pymupdf, set_openai from utils_langchain import StreamingGradioCallbackHandler import_matplotlib() import numpy as np import pandas as pd import requests from langchain.chains.qa_with_sources import load_qa_with_sources_chain # , GCSDirectoryLoader, GCSFileLoader # , OutlookMessageLoader # GPL3 # ImageCaptionLoader, # use our own wrapper # ReadTheDocsLoader, # no special file, some path, so have to give as special option from langchain.document_loaders import PyPDFLoader, TextLoader, CSVLoader, PythonLoader, TomlLoader, \ UnstructuredURLLoader, UnstructuredHTMLLoader, UnstructuredWordDocumentLoader, UnstructuredMarkdownLoader, \ EverNoteLoader, UnstructuredEmailLoader, UnstructuredODTLoader, UnstructuredPowerPointLoader, \ UnstructuredEPubLoader, UnstructuredImageLoader, UnstructuredRTFLoader, ArxivLoader, UnstructuredPDFLoader, \ UnstructuredExcelLoader from langchain.text_splitter import RecursiveCharacterTextSplitter, Language from langchain.chains.question_answering import load_qa_chain from langchain.docstore.document import Document from langchain import PromptTemplate, HuggingFaceTextGenInference from langchain.vectorstores import Chroma def get_db(sources, use_openai_embedding=False, db_type='faiss', persist_directory="db_dir", load_db_if_exists=True, langchain_mode='notset', collection_name=None, hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2"): if not sources: return None # get embedding model embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model) assert collection_name is not None or langchain_mode != 'notset' if collection_name is None: collection_name = langchain_mode.replace(' ', '_') # Create vector database if db_type == 'faiss': from langchain.vectorstores import FAISS db = FAISS.from_documents(sources, embedding) elif db_type == 'weaviate': import weaviate from weaviate.embedded import EmbeddedOptions from langchain.vectorstores import Weaviate if os.getenv('WEAVIATE_URL', None): client = _create_local_weaviate_client() else: client = weaviate.Client( embedded_options=EmbeddedOptions() ) index_name = collection_name.capitalize() db = Weaviate.from_documents(documents=sources, embedding=embedding, client=client, by_text=False, index_name=index_name) elif db_type == 'chroma': assert persist_directory is not None os.makedirs(persist_directory, exist_ok=True) # see if already actually have persistent db, and deal with possible changes in embedding db = get_existing_db(None, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode, hf_embedding_model, verbose=False) if db is None: from chromadb.config import Settings client_settings = Settings(anonymized_telemetry=False, chroma_db_impl="duckdb+parquet", persist_directory=persist_directory) db = Chroma.from_documents(documents=sources, embedding=embedding, persist_directory=persist_directory, collection_name=collection_name, client_settings=client_settings) db.persist() clear_embedding(db) save_embed(db, use_openai_embedding, hf_embedding_model) else: # then just add db, num_new_sources, new_sources_metadata = add_to_db(db, sources, db_type=db_type, use_openai_embedding=use_openai_embedding, hf_embedding_model=hf_embedding_model) else: raise RuntimeError("No such db_type=%s" % db_type) return db def _get_unique_sources_in_weaviate(db): batch_size = 100 id_source_list = [] result = db._client.data_object.get(class_name=db._index_name, limit=batch_size) while result['objects']: id_source_list += [(obj['id'], obj['properties']['source']) for obj in result['objects']] last_id = id_source_list[-1][0] result = db._client.data_object.get(class_name=db._index_name, limit=batch_size, after=last_id) unique_sources = {source for _, source in id_source_list} return unique_sources def add_to_db(db, sources, db_type='faiss', avoid_dup_by_file=False, avoid_dup_by_content=True, use_openai_embedding=False, hf_embedding_model=None): assert hf_embedding_model is not None num_new_sources = len(sources) if not sources: return db, num_new_sources, [] if db_type == 'faiss': db.add_documents(sources) elif db_type == 'weaviate': # FIXME: only control by file name, not hash yet if avoid_dup_by_file or avoid_dup_by_content: unique_sources = _get_unique_sources_in_weaviate(db) sources = [x for x in sources if x.metadata['source'] not in unique_sources] num_new_sources = len(sources) if num_new_sources == 0: return db, num_new_sources, [] db.add_documents(documents=sources) elif db_type == 'chroma': collection = get_documents(db) # files we already have: metadata_files = set([x['source'] for x in collection['metadatas']]) if avoid_dup_by_file: # Too weak in case file changed content, assume parent shouldn't pass true for this for now raise RuntimeError("Not desired code path") sources = [x for x in sources if x.metadata['source'] not in metadata_files] if avoid_dup_by_content: # look at hash, instead of page_content # migration: If no hash previously, avoid updating, # since don't know if need to update and may be expensive to redo all unhashed files metadata_hash_ids = set( [x['hashid'] for x in collection['metadatas'] if 'hashid' in x and x['hashid'] not in ["None", None]]) # avoid sources with same hash sources = [x for x in sources if x.metadata.get('hashid') not in metadata_hash_ids] num_nohash = len([x for x in sources if not x.metadata.get('hashid')]) print("Found %s new sources (%d have no hash in original source," " so have to reprocess for migration to sources with hash)" % (len(sources), num_nohash), flush=True) # get new file names that match existing file names. delete existing files we are overridding dup_metadata_files = set([x.metadata['source'] for x in sources if x.metadata['source'] in metadata_files]) print("Removing %s duplicate files from db because ingesting those as new documents" % len( dup_metadata_files), flush=True) client_collection = db._client.get_collection(name=db._collection.name, embedding_function=db._collection._embedding_function) for dup_file in dup_metadata_files: dup_file_meta = dict(source=dup_file) try: client_collection.delete(where=dup_file_meta) except KeyError: pass num_new_sources = len(sources) if num_new_sources == 0: return db, num_new_sources, [] db.add_documents(documents=sources) db.persist() clear_embedding(db) save_embed(db, use_openai_embedding, hf_embedding_model) else: raise RuntimeError("No such db_type=%s" % db_type) new_sources_metadata = [x.metadata for x in sources] return db, num_new_sources, new_sources_metadata def create_or_update_db(db_type, persist_directory, collection_name, sources, use_openai_embedding, add_if_exists, verbose, hf_embedding_model): if db_type == 'weaviate': import weaviate from weaviate.embedded import EmbeddedOptions if os.getenv('WEAVIATE_URL', None): client = _create_local_weaviate_client() else: client = weaviate.Client( embedded_options=EmbeddedOptions() ) index_name = collection_name.replace(' ', '_').capitalize() if client.schema.exists(index_name) and not add_if_exists: client.schema.delete_class(index_name) if verbose: print("Removing %s" % index_name, flush=True) elif db_type == 'chroma': if not os.path.isdir(persist_directory) or not add_if_exists: if os.path.isdir(persist_directory): if verbose: print("Removing %s" % persist_directory, flush=True) remove(persist_directory) if verbose: print("Generating db", flush=True) if not add_if_exists: if verbose: print("Generating db", flush=True) else: if verbose: print("Loading and updating db", flush=True) db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type, persist_directory=persist_directory, langchain_mode=collection_name, hf_embedding_model=hf_embedding_model) return db def get_embedding(use_openai_embedding, hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2"): # Get embedding model if use_openai_embedding: assert os.getenv("OPENAI_API_KEY") is not None, "Set ENV OPENAI_API_KEY" from langchain.embeddings import OpenAIEmbeddings embedding = OpenAIEmbeddings(disallowed_special=()) else: # to ensure can fork without deadlock from langchain.embeddings import HuggingFaceEmbeddings device, torch_dtype, context_class = get_device_dtype() model_kwargs = dict(device=device) if 'instructor' in hf_embedding_model: encode_kwargs = {'normalize_embeddings': True} embedding = HuggingFaceInstructEmbeddings(model_name=hf_embedding_model, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs) else: embedding = HuggingFaceEmbeddings(model_name=hf_embedding_model, model_kwargs=model_kwargs) return embedding def get_answer_from_sources(chain, sources, question): return chain( { "input_documents": sources, "question": question, }, return_only_outputs=True, )["output_text"] """Wrapper around Huggingface text generation inference API.""" from functools import partial from typing import Any, Dict, List, Optional, Set from pydantic import Extra, Field, root_validator from langchain.callbacks.manager import CallbackManagerForLLMRun, Callbacks from langchain.llms.base import LLM class GradioInference(LLM): """ Gradio generation inference API. """ inference_server_url: str = "" temperature: float = 0.8 top_p: Optional[float] = 0.95 top_k: Optional[int] = None num_beams: Optional[int] = 1 max_new_tokens: int = 512 min_new_tokens: int = 1 early_stopping: bool = False max_time: int = 180 repetition_penalty: Optional[float] = None num_return_sequences: Optional[int] = 1 do_sample: bool = False chat_client: bool = False return_full_text: bool = True stream: bool = False sanitize_bot_response: bool = False prompter: Any = None context: Any = '' iinput: Any = '' client: Any = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that python package exists in environment.""" try: if values['client'] is None: import gradio_client values["client"] = gradio_client.Client( values["inference_server_url"] ) except ImportError: raise ImportError( "Could not import gradio_client python package. " "Please install it with `pip install gradio_client`." ) return values @property def _llm_type(self) -> str: """Return type of llm.""" return "gradio_inference" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: # NOTE: prompt here has no prompt_type (e.g. human: bot:) prompt injection, # so server should get prompt_type or '', not plain # This is good, so gradio server can also handle stopping.py conditions # this is different than TGI server that uses prompter to inject prompt_type prompting stream_output = self.stream gr_client = self.client client_langchain_mode = 'Disabled' client_add_chat_history_to_context = True client_langchain_action = LangChainAction.QUERY.value client_langchain_agents = [] top_k_docs = 1 chunk = True chunk_size = 512 client_kwargs = dict(instruction=prompt if self.chat_client else '', # only for chat=True iinput=self.iinput if self.chat_client else '', # only for chat=True context=self.context, # streaming output is supported, loops over and outputs each generation in streaming mode # but leave stream_output=False for simple input/output mode stream_output=stream_output, prompt_type=self.prompter.prompt_type, prompt_dict='', temperature=self.temperature, top_p=self.top_p, top_k=self.top_k, num_beams=self.num_beams, max_new_tokens=self.max_new_tokens, min_new_tokens=self.min_new_tokens, early_stopping=self.early_stopping, max_time=self.max_time, repetition_penalty=self.repetition_penalty, num_return_sequences=self.num_return_sequences, do_sample=self.do_sample, chat=self.chat_client, instruction_nochat=prompt if not self.chat_client else '', iinput_nochat=self.iinput if not self.chat_client else '', langchain_mode=client_langchain_mode, add_chat_history_to_context=client_add_chat_history_to_context, langchain_action=client_langchain_action, langchain_agents=client_langchain_agents, top_k_docs=top_k_docs, chunk=chunk, chunk_size=chunk_size, document_subset=DocumentSubset.Relevant.name, document_choice=[DocumentChoice.ALL.value], ) api_name = '/submit_nochat_api' # NOTE: like submit_nochat but stable API for string dict passing if not stream_output: res = gr_client.predict(str(dict(client_kwargs)), api_name=api_name) res_dict = ast.literal_eval(res) text = res_dict['response'] return self.prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=self.sanitize_bot_response) else: text_callback = None if run_manager: text_callback = partial( run_manager.on_llm_new_token, verbose=self.verbose ) job = gr_client.submit(str(dict(client_kwargs)), api_name=api_name) text0 = '' while not job.done(): outputs_list = job.communicator.job.outputs if outputs_list: res = job.communicator.job.outputs[-1] res_dict = ast.literal_eval(res) text = res_dict['response'] text = self.prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=self.sanitize_bot_response) # FIXME: derive chunk from full for now text_chunk = text[len(text0):] # save old text0 = text if text_callback: text_callback(text_chunk) time.sleep(0.01) # ensure get last output to avoid race res_all = job.outputs() if len(res_all) > 0: res = res_all[-1] res_dict = ast.literal_eval(res) text = res_dict['response'] # FIXME: derive chunk from full for now else: # go with old if failure text = text0 text_chunk = text[len(text0):] if text_callback: text_callback(text_chunk) return self.prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=self.sanitize_bot_response) class H2OHuggingFaceTextGenInference(HuggingFaceTextGenInference): max_new_tokens: int = 512 do_sample: bool = False top_k: Optional[int] = None top_p: Optional[float] = 0.95 typical_p: Optional[float] = 0.95 temperature: float = 0.8 repetition_penalty: Optional[float] = None return_full_text: bool = False stop_sequences: List[str] = Field(default_factory=list) seed: Optional[int] = None inference_server_url: str = "" timeout: int = 300 headers: dict = None stream: bool = False sanitize_bot_response: bool = False prompter: Any = None context: Any = '' iinput: Any = '' tokenizer: Any = None client: Any = None @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that python package exists in environment.""" try: if values['client'] is None: import text_generation values["client"] = text_generation.Client( values["inference_server_url"], timeout=values["timeout"], headers=values["headers"], ) except ImportError: raise ImportError( "Could not import text_generation python package. " "Please install it with `pip install text_generation`." ) return values def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: if stop is None: stop = self.stop_sequences else: stop += self.stop_sequences # HF inference server needs control over input tokens assert self.tokenizer is not None from h2oai_pipeline import H2OTextGenerationPipeline prompt, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer) # NOTE: TGI server does not add prompting, so must do here data_point = dict(context=self.context, instruction=prompt, input=self.iinput) prompt = self.prompter.generate_prompt(data_point) gen_server_kwargs = dict(do_sample=self.do_sample, stop_sequences=stop, max_new_tokens=self.max_new_tokens, top_k=self.top_k, top_p=self.top_p, typical_p=self.typical_p, temperature=self.temperature, repetition_penalty=self.repetition_penalty, return_full_text=self.return_full_text, seed=self.seed, ) gen_server_kwargs.update(kwargs) # lower bound because client is re-used if multi-threading self.client.timeout = max(300, self.timeout) if not self.stream: res = self.client.generate( prompt, **gen_server_kwargs, ) if self.return_full_text: gen_text = res.generated_text[len(prompt):] else: gen_text = res.generated_text # remove stop sequences from the end of the generated text for stop_seq in stop: if stop_seq in gen_text: gen_text = gen_text[:gen_text.index(stop_seq)] text = prompt + gen_text text = self.prompter.get_response(text, prompt=prompt, sanitize_bot_response=self.sanitize_bot_response) else: text_callback = None if run_manager: text_callback = partial( run_manager.on_llm_new_token, verbose=self.verbose ) # parent handler of streamer expects to see prompt first else output="" and lose if prompt=None in prompter if text_callback: text_callback(prompt) text = "" # Note: Streaming ignores return_full_text=True for response in self.client.generate_stream(prompt, **gen_server_kwargs): text_chunk = response.token.text text += text_chunk text = self.prompter.get_response(prompt + text, prompt=prompt, sanitize_bot_response=self.sanitize_bot_response) # stream part is_stop = False for stop_seq in stop: if stop_seq in response.token.text: is_stop = True break if is_stop: break if not response.token.special: if text_callback: text_callback(response.token.text) return text from langchain.chat_models import ChatOpenAI from langchain.llms import OpenAI from langchain.llms.openai import _streaming_response_template, completion_with_retry, _update_response, \ update_token_usage class H2OOpenAI(OpenAI): """ New class to handle vLLM's use of OpenAI, no vllm_chat supported, so only need here Handles prompting that OpenAI doesn't need, stopping as well """ stop_sequences: Any = None sanitize_bot_response: bool = False prompter: Any = None context: Any = '' iinput: Any = '' tokenizer: Any = None @classmethod def all_required_field_names(cls) -> Set: all_required_field_names = super(OpenAI, cls).all_required_field_names() all_required_field_names.update( {'top_p', 'frequency_penalty', 'presence_penalty', 'stop_sequences', 'sanitize_bot_response', 'prompter', 'tokenizer'}) return all_required_field_names def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: stop = self.stop_sequences if not stop else self.stop_sequences + stop # HF inference server needs control over input tokens assert self.tokenizer is not None from h2oai_pipeline import H2OTextGenerationPipeline for prompti, prompt in enumerate(prompts): prompt, num_prompt_tokens = H2OTextGenerationPipeline.limit_prompt(prompt, self.tokenizer) # NOTE: OpenAI/vLLM server does not add prompting, so must do here data_point = dict(context=self.context, instruction=prompt, input=self.iinput) prompt = self.prompter.generate_prompt(data_point) prompts[prompti] = prompt params = self._invocation_params params = {**params, **kwargs} sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} # Get the token usage from the response. # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} text = '' for _prompts in sub_prompts: if self.streaming: text_with_prompt = "" prompt = _prompts[0] if len(_prompts) > 1: raise ValueError("Cannot stream results with multiple prompts.") params["stream"] = True response = _streaming_response_template() first = True for stream_resp in completion_with_retry( self, prompt=_prompts, **params ): if first: stream_resp["choices"][0]["text"] = prompt + stream_resp["choices"][0]["text"] first = False text_chunk = stream_resp["choices"][0]["text"] text_with_prompt += text_chunk text = self.prompter.get_response(text_with_prompt, prompt=prompt, sanitize_bot_response=self.sanitize_bot_response) if run_manager: run_manager.on_llm_new_token( text_chunk, verbose=self.verbose, logprobs=stream_resp["choices"][0]["logprobs"], ) _update_response(response, stream_resp) choices.extend(response["choices"]) else: response = completion_with_retry(self, prompt=_prompts, **params) choices.extend(response["choices"]) if not self.streaming: # Can't update token usage if streaming update_token_usage(_keys, response, token_usage) choices[0]['text'] = text return self.create_llm_result(choices, prompts, token_usage) class H2OChatOpenAI(ChatOpenAI): @classmethod def all_required_field_names(cls) -> Set: all_required_field_names = super(ChatOpenAI, cls).all_required_field_names() all_required_field_names.update({'top_p', 'frequency_penalty', 'presence_penalty'}) return all_required_field_names def get_llm(use_openai_model=False, model_name=None, model=None, tokenizer=None, inference_server=None, stream_output=False, do_sample=False, temperature=0.1, top_k=40, top_p=0.7, num_beams=1, max_new_tokens=256, min_new_tokens=1, early_stopping=False, max_time=180, repetition_penalty=1.0, num_return_sequences=1, prompt_type=None, prompt_dict=None, prompter=None, context=None, iinput=None, sanitize_bot_response=False, verbose=False, ): if inference_server is None: inference_server = '' if use_openai_model or inference_server.startswith('openai') or inference_server.startswith('vllm'): if use_openai_model and model_name is None: model_name = "gpt-3.5-turbo" # FIXME: Will later import be ignored? I think so, so should be fine openai, inf_type = set_openai(inference_server) kwargs_extra = {} if inference_server == 'openai_chat' or inf_type == 'vllm_chat': cls = H2OChatOpenAI # FIXME: Support context, iinput else: cls = H2OOpenAI if inf_type == 'vllm': terminate_response = prompter.terminate_response or [] stop_sequences = list(set(terminate_response + [prompter.PreResponse])) stop_sequences = [x for x in stop_sequences if x] kwargs_extra = dict(stop_sequences=stop_sequences, sanitize_bot_response=sanitize_bot_response, prompter=prompter, context=context, iinput=iinput, tokenizer=tokenizer, client=None) callbacks = [StreamingGradioCallbackHandler()] llm = cls(model_name=model_name, temperature=temperature if do_sample else 0, # FIXME: Need to count tokens and reduce max_new_tokens to fit like in generate.py max_tokens=max_new_tokens, top_p=top_p if do_sample else 1, frequency_penalty=0, presence_penalty=1.07 - repetition_penalty + 0.6, # so good default callbacks=callbacks if stream_output else None, openai_api_key=openai.api_key, openai_api_base=openai.api_base, logit_bias=None if inf_type == 'vllm' else {}, max_retries=2, streaming=stream_output, **kwargs_extra ) streamer = callbacks[0] if stream_output else None if inference_server in ['openai', 'openai_chat']: prompt_type = inference_server else: # vllm goes here prompt_type = prompt_type or 'plain' elif inference_server: assert inference_server.startswith( 'http'), "Malformed inference_server=%s. Did you add http:// in front?" % inference_server from gradio_utils.grclient import GradioClient from text_generation import Client as HFClient if isinstance(model, GradioClient): gr_client = model hf_client = None else: gr_client = None hf_client = model assert isinstance(hf_client, HFClient) inference_server, headers = get_hf_server(inference_server) # quick sanity check to avoid long timeouts, just see if can reach server requests.get(inference_server, timeout=int(os.getenv('REQUEST_TIMEOUT_FAST', '10'))) callbacks = [StreamingGradioCallbackHandler()] assert prompter is not None terminate_response = prompter.terminate_response or [] stop_sequences = list(set(terminate_response + [prompter.PreResponse])) stop_sequences = [x for x in stop_sequences if x] if gr_client: chat_client = False llm = GradioInference( inference_server_url=inference_server, return_full_text=True, temperature=temperature, top_p=top_p, top_k=top_k, num_beams=num_beams, max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens, early_stopping=early_stopping, max_time=max_time, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, do_sample=do_sample, chat_client=chat_client, callbacks=callbacks if stream_output else None, stream=stream_output, prompter=prompter, context=context, iinput=iinput, client=gr_client, sanitize_bot_response=sanitize_bot_response, ) elif hf_client: llm = H2OHuggingFaceTextGenInference( inference_server_url=inference_server, do_sample=do_sample, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty, return_full_text=True, seed=SEED, stop_sequences=stop_sequences, temperature=temperature, top_k=top_k, top_p=top_p, # typical_p=top_p, callbacks=callbacks if stream_output else None, stream=stream_output, prompter=prompter, context=context, iinput=iinput, tokenizer=tokenizer, client=hf_client, timeout=max_time, sanitize_bot_response=sanitize_bot_response, ) else: raise RuntimeError("No defined client") streamer = callbacks[0] if stream_output else None elif model_name in non_hf_types: if model_name == 'llama': callbacks = [StreamingGradioCallbackHandler()] streamer = callbacks[0] if stream_output else None else: # stream_output = False # doesn't stream properly as generator, but at least callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()] streamer = None if prompter: prompt_type = prompter.prompt_type else: prompter = Prompter(prompt_type, prompt_dict, debug=False, chat=False, stream_output=stream_output) pass # assume inputted prompt_type is correct from gpt4all_llm import get_llm_gpt4all llm = get_llm_gpt4all(model_name, model=model, max_new_tokens=max_new_tokens, temperature=temperature, repetition_penalty=repetition_penalty, top_k=top_k, top_p=top_p, callbacks=callbacks, verbose=verbose, streaming=stream_output, prompter=prompter, context=context, iinput=iinput, ) else: if model is None: # only used if didn't pass model in assert tokenizer is None prompt_type = 'human_bot' if model_name is None: model_name = 'h2oai/h2ogpt-oasst1-512-12b' # model_name = 'h2oai/h2ogpt-oig-oasst1-512-6_9b' # model_name = 'h2oai/h2ogpt-oasst1-512-20b' inference_server = '' model, tokenizer, device = get_model(load_8bit=True, base_model=model_name, inference_server=inference_server, gpu_id=0) max_max_tokens = tokenizer.model_max_length gen_kwargs = dict(do_sample=do_sample, temperature=temperature, top_k=top_k, top_p=top_p, num_beams=num_beams, max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens, early_stopping=early_stopping, max_time=max_time, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, return_full_text=True, handle_long_generation=None) assert len(set(gen_hyper).difference(gen_kwargs.keys())) == 0 if stream_output: skip_prompt = False from gen import H2OTextIteratorStreamer decoder_kwargs = {} streamer = H2OTextIteratorStreamer(tokenizer, skip_prompt=skip_prompt, block=False, **decoder_kwargs) gen_kwargs.update(dict(streamer=streamer)) else: streamer = None from h2oai_pipeline import H2OTextGenerationPipeline pipe = H2OTextGenerationPipeline(model=model, use_prompter=True, prompter=prompter, context=context, iinpout=iinput, prompt_type=prompt_type, prompt_dict=prompt_dict, sanitize_bot_response=sanitize_bot_response, chat=False, stream_output=stream_output, tokenizer=tokenizer, # leave some room for 1 paragraph, even if min_new_tokens=0 max_input_tokens=max_max_tokens - max(min_new_tokens, 256), **gen_kwargs) # pipe.task = "text-generation" # below makes it listen only to our prompt removal, # not built in prompt removal that is less general and not specific for our model pipe.task = "text2text-generation" from langchain.llms import HuggingFacePipeline llm = HuggingFacePipeline(pipeline=pipe) return llm, model_name, streamer, prompt_type def get_device_dtype(): # torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently import torch n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0 device = 'cpu' if n_gpus == 0 else 'cuda' # from utils import NullContext # context_class = NullContext if n_gpus > 1 or n_gpus == 0 else context_class context_class = torch.device torch_dtype = torch.float16 if device == 'cuda' else torch.float32 return device, torch_dtype, context_class def get_wiki_data(title, first_paragraph_only, text_limit=None, take_head=True): """ Get wikipedia data from online :param title: :param first_paragraph_only: :param text_limit: :param take_head: :return: """ filename = 'wiki_%s_%s_%s_%s.data' % (first_paragraph_only, title, text_limit, take_head) url = f"https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&explaintext=1&titles={title}" if first_paragraph_only: url += "&exintro=1" import json if not os.path.isfile(filename): data = requests.get(url).json() json.dump(data, open(filename, 'wt')) else: data = json.load(open(filename, "rt")) page_content = list(data["query"]["pages"].values())[0]["extract"] if take_head is not None and text_limit is not None: page_content = page_content[:text_limit] if take_head else page_content[-text_limit:] title_url = str(title).replace(' ', '_') return Document( page_content=page_content, metadata={"source": f"https://en.wikipedia.org/wiki/{title_url}"}, ) def get_wiki_sources(first_para=True, text_limit=None): """ Get specific named sources from wikipedia :param first_para: :param text_limit: :return: """ default_wiki_sources = ['Unix', 'Microsoft_Windows', 'Linux'] wiki_sources = list(os.getenv('WIKI_SOURCES', default_wiki_sources)) return [get_wiki_data(x, first_para, text_limit=text_limit) for x in wiki_sources] def get_github_docs(repo_owner, repo_name): """ Access github from specific repo :param repo_owner: :param repo_name: :return: """ with tempfile.TemporaryDirectory() as d: subprocess.check_call( f"git clone --depth 1 https://github.com/{repo_owner}/{repo_name}.git .", cwd=d, shell=True, ) git_sha = ( subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d) .decode("utf-8") .strip() ) repo_path = pathlib.Path(d) markdown_files = list(repo_path.glob("*/*.md")) + list( repo_path.glob("*/*.mdx") ) for markdown_file in markdown_files: with open(markdown_file, "r") as f: relative_path = markdown_file.relative_to(repo_path) github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}" yield Document(page_content=f.read(), metadata={"source": github_url}) def get_dai_pickle(dest="."): from huggingface_hub import hf_hub_download # True for case when locally already logged in with correct token, so don't have to set key token = os.getenv('HUGGINGFACE_API_TOKEN', True) path_to_zip_file = hf_hub_download('h2oai/dai_docs', 'dai_docs.pickle', token=token, repo_type='dataset') shutil.copy(path_to_zip_file, dest) def get_dai_docs(from_hf=False, get_pickle=True): """ Consume DAI documentation, or consume from public pickle :param from_hf: get DAI docs from HF, then generate pickle for later use by LangChain :param get_pickle: Avoid raw DAI docs, just get pickle directly from HF :return: """ import pickle if get_pickle: get_dai_pickle() dai_store = 'dai_docs.pickle' dst = "working_dir_docs" if not os.path.isfile(dai_store): from create_data import setup_dai_docs dst = setup_dai_docs(dst=dst, from_hf=from_hf) import glob files = list(glob.glob(os.path.join(dst, '*rst'), recursive=True)) basedir = os.path.abspath(os.getcwd()) from create_data import rst_to_outputs new_outputs = rst_to_outputs(files) os.chdir(basedir) pickle.dump(new_outputs, open(dai_store, 'wb')) else: new_outputs = pickle.load(open(dai_store, 'rb')) sources = [] for line, file in new_outputs: # gradio requires any linked file to be with app.py sym_src = os.path.abspath(os.path.join(dst, file)) sym_dst = os.path.abspath(os.path.join(os.getcwd(), file)) if os.path.lexists(sym_dst): os.remove(sym_dst) os.symlink(sym_src, sym_dst) itm = Document(page_content=line, metadata={"source": file}) # NOTE: yield has issues when going into db, loses metadata # yield itm sources.append(itm) return sources image_types = ["png", "jpg", "jpeg"] non_image_types = ["pdf", "txt", "csv", "toml", "py", "rst", "rtf", "md", "html", "mhtml", "enex", "eml", "epub", "odt", "pptx", "ppt", "zip", "urls", ] # "msg", GPL3 if have_libreoffice or True: # or True so it tries to load, e.g. on MAC/Windows, even if don't have libreoffice since works without that non_image_types.extend(["docx", "doc", "xls", "xlsx"]) file_types = non_image_types + image_types def add_meta(docs1, file): file_extension = pathlib.Path(file).suffix hashid = hash_file(file) doc_hash = str(uuid.uuid4())[:10] if not isinstance(docs1, (list, tuple, types.GeneratorType)): docs1 = [docs1] [x.metadata.update(dict(input_type=file_extension, date=str(datetime.now()), hashid=hashid, doc_hash=doc_hash)) for x in docs1] def file_to_doc(file, base_path=None, verbose=False, fail_any_exception=False, chunk=True, chunk_size=512, n_jobs=-1, is_url=False, is_txt=False, enable_captions=True, captions_model=None, enable_ocr=False, enable_pdf_ocr='auto', caption_loader=None, headsize=50): if file is None: if fail_any_exception: raise RuntimeError("Unexpected None file") else: return [] doc1 = [] # in case no support, or disabled support if base_path is None and not is_txt and not is_url: # then assume want to persist but don't care which path used # can't be in base_path dir_name = os.path.dirname(file) base_name = os.path.basename(file) # if from gradio, will have its own temp uuid too, but that's ok base_name = sanitize_filename(base_name) + "_" + str(uuid.uuid4())[:10] base_path = os.path.join(dir_name, base_name) if is_url: file = file.strip() # in case accidental spaces in front or at end if file.lower().startswith('arxiv:'): query = file.lower().split('arxiv:') if len(query) == 2 and have_arxiv: query = query[1] docs1 = ArxivLoader(query=query, load_max_docs=20, load_all_available_meta=True).load() # ensure string, sometimes None [[x.metadata.update({k: str(v)}) for k, v in x.metadata.items()] for x in docs1] query_url = f"https://arxiv.org/abs/{query}" [x.metadata.update( dict(source=x.metadata.get('entry_id', query_url), query=query_url, input_type='arxiv', head=x.metadata.get('Title', ''), date=str(datetime.now))) for x in docs1] else: docs1 = [] else: if not (file.startswith("http://") or file.startswith("file://") or file.startswith("https://")): file = 'http://' + file docs1 = UnstructuredURLLoader(urls=[file]).load() if len(docs1) == 0 and have_playwright: # then something went wrong, try another loader: from langchain.document_loaders import PlaywrightURLLoader docs1 = PlaywrightURLLoader(urls=[file]).load() if len(docs1) == 0 and have_selenium: # then something went wrong, try another loader: # but requires Chrome binary, else get: selenium.common.exceptions.WebDriverException: Message: unknown error: cannot find Chrome binary from langchain.document_loaders import SeleniumURLLoader from selenium.common.exceptions import WebDriverException try: docs1 = SeleniumURLLoader(urls=[file]).load() except WebDriverException as e: print("No web driver: %s" % str(e), flush=True) [x.metadata.update(dict(input_type='url', date=str(datetime.now))) for x in docs1] docs1 = clean_doc(docs1) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif is_txt: base_path = "user_paste" source_file = os.path.join(base_path, "_%s" % str(uuid.uuid4())[:10]) makedirs(os.path.dirname(source_file), exist_ok=True) with open(source_file, "wt") as f: f.write(file) metadata = dict(source=source_file, date=str(datetime.now()), input_type='pasted txt') doc1 = Document(page_content=file, metadata=metadata) doc1 = clean_doc(doc1) elif file.lower().endswith('.html') or file.lower().endswith('.mhtml'): docs1 = UnstructuredHTMLLoader(file_path=file).load() add_meta(docs1, file) docs1 = clean_doc(docs1) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size, language=Language.HTML) elif (file.lower().endswith('.docx') or file.lower().endswith('.doc')) and (have_libreoffice or True): docs1 = UnstructuredWordDocumentLoader(file_path=file).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif (file.lower().endswith('.xlsx') or file.lower().endswith('.xls')) and (have_libreoffice or True): docs1 = UnstructuredExcelLoader(file_path=file).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.odt'): docs1 = UnstructuredODTLoader(file_path=file).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('pptx') or file.lower().endswith('ppt'): docs1 = UnstructuredPowerPointLoader(file_path=file).load() add_meta(docs1, file) docs1 = clean_doc(docs1) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.txt'): # use UnstructuredFileLoader ? docs1 = TextLoader(file, encoding="utf8", autodetect_encoding=True).load() # makes just one, but big one doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) doc1 = clean_doc(doc1) add_meta(doc1, file) elif file.lower().endswith('.rtf'): docs1 = UnstructuredRTFLoader(file).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.md'): docs1 = UnstructuredMarkdownLoader(file).load() add_meta(docs1, file) docs1 = clean_doc(docs1) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size, language=Language.MARKDOWN) elif file.lower().endswith('.enex'): docs1 = EverNoteLoader(file).load() add_meta(doc1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.epub'): docs1 = UnstructuredEPubLoader(file).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.jpeg') or file.lower().endswith('.jpg') or file.lower().endswith('.png'): docs1 = [] if have_tesseract and enable_ocr: # OCR, somewhat works, but not great docs1.extend(UnstructuredImageLoader(file).load()) add_meta(docs1, file) if enable_captions: # BLIP if caption_loader is not None and not isinstance(caption_loader, (str, bool)): # assumes didn't fork into this process with joblib, else can deadlock caption_loader.set_image_paths([file]) docs1c = caption_loader.load() add_meta(docs1c, file) [x.metadata.update(dict(head=x.page_content[:headsize].strip())) for x in docs1c] docs1.extend(docs1c) else: from image_captions import H2OImageCaptionLoader caption_loader = H2OImageCaptionLoader(caption_gpu=caption_loader == 'gpu', blip_model=captions_model, blip_processor=captions_model) caption_loader.set_image_paths([file]) docs1c = caption_loader.load() add_meta(docs1c, file) [x.metadata.update(dict(head=x.page_content[:headsize].strip())) for x in docs1c] docs1.extend(docs1c) for doci in docs1: doci.metadata['source'] = doci.metadata['image_path'] doci.metadata['hash'] = hash_file(doci.metadata['source']) if docs1: doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.msg'): raise RuntimeError("Not supported, GPL3 license") # docs1 = OutlookMessageLoader(file).load() # docs1[0].metadata['source'] = file elif file.lower().endswith('.eml'): try: docs1 = UnstructuredEmailLoader(file).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) except ValueError as e: if 'text/html content not found in email' in str(e): # e.g. plain/text dict key exists, but not # doc1 = TextLoader(file, encoding="utf8").load() docs1 = UnstructuredEmailLoader(file, content_source="text/plain").load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) else: raise # elif file.lower().endswith('.gcsdir'): # doc1 = GCSDirectoryLoader(project_name, bucket, prefix).load() # elif file.lower().endswith('.gcsfile'): # doc1 = GCSFileLoader(project_name, bucket, blob).load() elif file.lower().endswith('.rst'): with open(file, "r") as f: doc1 = Document(page_content=f.read(), metadata={"source": file}) add_meta(doc1, file) doc1 = chunk_sources(doc1, chunk=chunk, chunk_size=chunk_size, language=Language.RST) elif file.lower().endswith('.pdf'): env_gpt4all_file = ".env_gpt4all" from dotenv import dotenv_values env_kwargs = dotenv_values(env_gpt4all_file) pdf_class_name = env_kwargs.get('PDF_CLASS_NAME', 'PyMuPDFParser') doc1 = [] handled = False if have_pymupdf and pdf_class_name == 'PyMuPDFParser': # GPL, only use if installed from langchain.document_loaders import PyMuPDFLoader # load() still chunks by pages, but every page has title at start to help doc1 = PyMuPDFLoader(file).load() # remove empty documents handled |= len(doc1) > 0 doc1 = [x for x in doc1 if x.page_content] doc1 = clean_doc(doc1) if len(doc1) == 0: doc1 = UnstructuredPDFLoader(file).load() handled |= len(doc1) > 0 # remove empty documents doc1 = [x for x in doc1 if x.page_content] # seems to not need cleaning in most cases if len(doc1) == 0: # open-source fallback # load() still chunks by pages, but every page has title at start to help doc1 = PyPDFLoader(file).load() handled |= len(doc1) > 0 # remove empty documents doc1 = [x for x in doc1 if x.page_content] doc1 = clean_doc(doc1) if have_pymupdf and len(doc1) == 0: # GPL, only use if installed from langchain.document_loaders import PyMuPDFLoader # load() still chunks by pages, but every page has title at start to help doc1 = PyMuPDFLoader(file).load() handled |= len(doc1) > 0 # remove empty documents doc1 = [x for x in doc1 if x.page_content] doc1 = clean_doc(doc1) if len(doc1) == 0 and enable_pdf_ocr == 'auto' or enable_pdf_ocr == 'on': # try OCR in end since slowest, but works on pure image pages well doc1 = UnstructuredPDFLoader(file, strategy='ocr_only').load() handled |= len(doc1) > 0 # remove empty documents doc1 = [x for x in doc1 if x.page_content] # seems to not need cleaning in most cases # Some PDFs return nothing or junk from PDFMinerLoader if len(doc1) == 0: # if literally nothing, show failed to parse so user knows, since unlikely nothing in PDF at all. if handled: raise ValueError("%s had no valid text, but meta data was parsed" % file) else: raise ValueError("%s had no valid text and no meta data was parsed" % file) doc1 = chunk_sources(doc1, chunk=chunk, chunk_size=chunk_size) add_meta(doc1, file) elif file.lower().endswith('.csv'): doc1 = CSVLoader(file).load() add_meta(doc1, file) elif file.lower().endswith('.py'): doc1 = PythonLoader(file).load() add_meta(doc1, file) doc1 = chunk_sources(doc1, chunk=chunk, chunk_size=chunk_size, language=Language.PYTHON) elif file.lower().endswith('.toml'): doc1 = TomlLoader(file).load() add_meta(doc1, file) elif file.lower().endswith('.urls'): with open(file, "r") as f: docs1 = UnstructuredURLLoader(urls=f.readlines()).load() add_meta(docs1, file) doc1 = chunk_sources(docs1, chunk=chunk, chunk_size=chunk_size) elif file.lower().endswith('.zip'): with zipfile.ZipFile(file, 'r') as zip_ref: # don't put into temporary path, since want to keep references to docs inside zip # so just extract in path where zip_ref.extractall(base_path) # recurse doc1 = path_to_docs(base_path, verbose=verbose, fail_any_exception=fail_any_exception, n_jobs=n_jobs) else: raise RuntimeError("No file handler for %s" % os.path.basename(file)) # allow doc1 to be list or not. If not list, did not chunk yet, so chunk now # if list of length one, don't trust and chunk it if not isinstance(doc1, list): if chunk: docs = chunk_sources([doc1], chunk=chunk, chunk_size=chunk_size) else: docs = [doc1] elif isinstance(doc1, list) and len(doc1) == 1: if chunk: docs = chunk_sources(doc1, chunk=chunk, chunk_size=chunk_size) else: docs = doc1 else: docs = doc1 assert isinstance(docs, list) return docs def path_to_doc1(file, verbose=False, fail_any_exception=False, return_file=True, chunk=True, chunk_size=512, n_jobs=-1, is_url=False, is_txt=False, enable_captions=True, captions_model=None, enable_ocr=False, enable_pdf_ocr='auto', caption_loader=None): if verbose: if is_url: print("Ingesting URL: %s" % file, flush=True) elif is_txt: print("Ingesting Text: %s" % file, flush=True) else: print("Ingesting file: %s" % file, flush=True) res = None try: # don't pass base_path=path, would infinitely recurse res = file_to_doc(file, base_path=None, verbose=verbose, fail_any_exception=fail_any_exception, chunk=chunk, chunk_size=chunk_size, n_jobs=n_jobs, is_url=is_url, is_txt=is_txt, enable_captions=enable_captions, captions_model=captions_model, enable_ocr=enable_ocr, enable_pdf_ocr=enable_pdf_ocr, caption_loader=caption_loader) except BaseException as e: print("Failed to ingest %s due to %s" % (file, traceback.format_exc())) if fail_any_exception: raise else: exception_doc = Document( page_content='', metadata={"source": file, "exception": '%s Exception: %s' % (file, str(e)), "traceback": traceback.format_exc()}) res = [exception_doc] if return_file: base_tmp = "temp_path_to_doc1" if not os.path.isdir(base_tmp): os.makedirs(base_tmp, exist_ok=True) filename = os.path.join(base_tmp, str(uuid.uuid4()) + ".tmp.pickle") with open(filename, 'wb') as f: pickle.dump(res, f) return filename return res def path_to_docs(path_or_paths, verbose=False, fail_any_exception=False, n_jobs=-1, chunk=True, chunk_size=512, url=None, text=None, enable_captions=True, captions_model=None, caption_loader=None, enable_ocr=False, enable_pdf_ocr='auto', existing_files=[], existing_hash_ids={}, ): # path_or_paths could be str, list, tuple, generator globs_image_types = [] globs_non_image_types = [] if not path_or_paths and not url and not text: return [] elif url: globs_non_image_types = url if isinstance(url, (list, tuple, types.GeneratorType)) else [url] elif text: globs_non_image_types = text if isinstance(text, (list, tuple, types.GeneratorType)) else [text] elif isinstance(path_or_paths, str) and os.path.isdir(path_or_paths): # single path, only consume allowed files path = path_or_paths # Below globs should match patterns in file_to_doc() [globs_image_types.extend(glob.glob(os.path.join(path, "./**/*.%s" % ftype), recursive=True)) for ftype in image_types] [globs_non_image_types.extend(glob.glob(os.path.join(path, "./**/*.%s" % ftype), recursive=True)) for ftype in non_image_types] else: if isinstance(path_or_paths, str): if os.path.isfile(path_or_paths) or os.path.isdir(path_or_paths): path_or_paths = [path_or_paths] else: # path was deleted etc. return [] # list/tuple of files (consume what can, and exception those that selected but cannot consume so user knows) assert isinstance(path_or_paths, (list, tuple, types.GeneratorType)), \ "Wrong type for path_or_paths: %s %s" % (path_or_paths, type(path_or_paths)) # reform out of allowed types globs_image_types.extend(flatten_list([[x for x in path_or_paths if x.endswith(y)] for y in image_types])) # could do below: # globs_non_image_types = flatten_list([[x for x in path_or_paths if x.endswith(y)] for y in non_image_types]) # But instead, allow fail so can collect unsupported too set_globs_image_types = set(globs_image_types) globs_non_image_types.extend([x for x in path_or_paths if x not in set_globs_image_types]) # filter out any files to skip (e.g. if already processed them) # this is easy, but too aggressive in case a file changed, so parent probably passed existing_files=[] assert not existing_files, "DEV: assume not using this approach" if existing_files: set_skip_files = set(existing_files) globs_image_types = [x for x in globs_image_types if x not in set_skip_files] globs_non_image_types = [x for x in globs_non_image_types if x not in set_skip_files] if existing_hash_ids: # assume consistent with add_meta() use of hash_file(file) # also assume consistent with get_existing_hash_ids for dict creation # assume hashable values existing_hash_ids_set = set(existing_hash_ids.items()) hash_ids_all_image = set({x: hash_file(x) for x in globs_image_types}.items()) hash_ids_all_non_image = set({x: hash_file(x) for x in globs_non_image_types}.items()) # don't use symmetric diff. If file is gone, ignore and don't remove or something # just consider existing files (key) having new hash or not (value) new_files_image = set(dict(hash_ids_all_image - existing_hash_ids_set).keys()) new_files_non_image = set(dict(hash_ids_all_non_image - existing_hash_ids_set).keys()) globs_image_types = [x for x in globs_image_types if x in new_files_image] globs_non_image_types = [x for x in globs_non_image_types if x in new_files_non_image] # could use generator, but messes up metadata handling in recursive case if caption_loader and not isinstance(caption_loader, (bool, str)) and \ caption_loader.device != 'cpu' or \ get_device() == 'cuda': # to avoid deadlocks, presume was preloaded and so can't fork due to cuda context n_jobs_image = 1 else: n_jobs_image = n_jobs return_file = True # local choice is_url = url is not None is_txt = text is not None kwargs = dict(verbose=verbose, fail_any_exception=fail_any_exception, return_file=return_file, chunk=chunk, chunk_size=chunk_size, n_jobs=n_jobs, is_url=is_url, is_txt=is_txt, enable_captions=enable_captions, captions_model=captions_model, caption_loader=caption_loader, enable_ocr=enable_ocr, enable_pdf_ocr=enable_pdf_ocr, ) if n_jobs != 1 and len(globs_non_image_types) > 1: # avoid nesting, e.g. upload 1 zip and then inside many files # harder to handle if upload many zips with many files, inner parallel one will be disabled by joblib documents = ProgressParallel(n_jobs=n_jobs, verbose=10 if verbose else 0, backend='multiprocessing')( delayed(path_to_doc1)(file, **kwargs) for file in globs_non_image_types ) else: documents = [path_to_doc1(file, **kwargs) for file in tqdm(globs_non_image_types)] # do images separately since can't fork after cuda in parent, so can't be parallel if n_jobs_image != 1 and len(globs_image_types) > 1: # avoid nesting, e.g. upload 1 zip and then inside many files # harder to handle if upload many zips with many files, inner parallel one will be disabled by joblib image_documents = ProgressParallel(n_jobs=n_jobs, verbose=10 if verbose else 0, backend='multiprocessing')( delayed(path_to_doc1)(file, **kwargs) for file in globs_image_types ) else: image_documents = [path_to_doc1(file, **kwargs) for file in tqdm(globs_image_types)] # add image docs in documents += image_documents if return_file: # then documents really are files files = documents.copy() documents = [] for fil in files: with open(fil, 'rb') as f: documents.extend(pickle.load(f)) # remove temp pickle remove(fil) else: documents = reduce(concat, documents) return documents def prep_langchain(persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode, langchain_mode_paths, hf_embedding_model, n_jobs=-1, kwargs_make_db={}): """ do prep first time, involving downloads # FIXME: Add github caching then add here :return: """ assert langchain_mode not in ['MyData'], "Should not prep scratch data" db_dir_exists = os.path.isdir(persist_directory) user_path = langchain_mode_paths.get(langchain_mode) if db_dir_exists and user_path is None: print("Prep: persist_directory=%s exists, using" % persist_directory, flush=True) db = get_existing_db(None, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode, hf_embedding_model) else: if db_dir_exists and user_path is not None: print("Prep: persist_directory=%s exists, user_path=%s passed, adding any changed or new documents" % ( persist_directory, user_path), flush=True) elif not db_dir_exists: print("Prep: persist_directory=%s does not exist, regenerating" % persist_directory, flush=True) db = None if langchain_mode in ['All', 'DriverlessAI docs']: # FIXME: Could also just use dai_docs.pickle directly and upload that get_dai_docs(from_hf=True) if langchain_mode in ['All', 'wiki']: get_wiki_sources(first_para=kwargs_make_db['first_para'], text_limit=kwargs_make_db['text_limit']) langchain_kwargs = kwargs_make_db.copy() langchain_kwargs.update(locals()) db, num_new_sources, new_sources_metadata = make_db(**langchain_kwargs) return db import posthog posthog.disabled = True class FakeConsumer(object): def __init__(self, *args, **kwargs): pass def run(self): pass def pause(self): pass def upload(self): pass def next(self): pass def request(self, batch): pass posthog.Consumer = FakeConsumer def check_update_chroma_embedding(db, use_openai_embedding, hf_embedding_model, langchain_mode): changed_db = False if load_embed(db) != (use_openai_embedding, hf_embedding_model): print("Detected new embedding, updating db: %s" % langchain_mode, flush=True) # handle embedding changes db_get = get_documents(db) sources = [Document(page_content=result[0], metadata=result[1] or {}) for result in zip(db_get['documents'], db_get['metadatas'])] # delete index, has to be redone persist_directory = db._persist_directory shutil.move(persist_directory, persist_directory + "_" + str(uuid.uuid4()) + ".bak") db_type = 'chroma' load_db_if_exists = False db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type, persist_directory=persist_directory, load_db_if_exists=load_db_if_exists, langchain_mode=langchain_mode, collection_name=None, hf_embedding_model=hf_embedding_model) if False: # below doesn't work if db already in memory, so have to switch to new db as above # upsert does new embedding, but if index already in memory, complains about size mismatch etc. client_collection = db._client.get_collection(name=db._collection.name, embedding_function=db._collection._embedding_function) client_collection.upsert(ids=db_get['ids'], metadatas=db_get['metadatas'], documents=db_get['documents']) changed_db = True print("Done updating db for new embedding: %s" % langchain_mode, flush=True) return db, changed_db def get_existing_db(db, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode, hf_embedding_model, verbose=False, check_embedding=True): if load_db_if_exists and db_type == 'chroma' and os.path.isdir(persist_directory) and os.path.isdir( os.path.join(persist_directory, 'index')): if db is None: if verbose: print("DO Loading db: %s" % langchain_mode, flush=True) embedding = get_embedding(use_openai_embedding, hf_embedding_model=hf_embedding_model) from chromadb.config import Settings client_settings = Settings(anonymized_telemetry=False, chroma_db_impl="duckdb+parquet", persist_directory=persist_directory) db = Chroma(persist_directory=persist_directory, embedding_function=embedding, collection_name=langchain_mode.replace(' ', '_'), client_settings=client_settings) if verbose: print("DONE Loading db: %s" % langchain_mode, flush=True) else: if verbose: print("USING already-loaded db: %s" % langchain_mode, flush=True) if check_embedding: db_trial, changed_db = check_update_chroma_embedding(db, use_openai_embedding, hf_embedding_model, langchain_mode) if changed_db: db = db_trial # only call persist if really changed db, else takes too long for large db if db is not None: db.persist() clear_embedding(db) save_embed(db, use_openai_embedding, hf_embedding_model) return db return None def clear_embedding(db): if db is None: return # don't keep on GPU, wastes memory, push back onto CPU and only put back on GPU once again embed db._embedding_function.client.cpu() clear_torch_cache() def make_db(**langchain_kwargs): func_names = list(inspect.signature(_make_db).parameters) missing_kwargs = [x for x in func_names if x not in langchain_kwargs] defaults_db = {k: v.default for k, v in dict(inspect.signature(run_qa_db).parameters).items()} for k in missing_kwargs: if k in defaults_db: langchain_kwargs[k] = defaults_db[k] # final check for missing missing_kwargs = [x for x in func_names if x not in langchain_kwargs] assert not missing_kwargs, "Missing kwargs for make_db: %s" % missing_kwargs # only keep actual used langchain_kwargs = {k: v for k, v in langchain_kwargs.items() if k in func_names} return _make_db(**langchain_kwargs) def save_embed(db, use_openai_embedding, hf_embedding_model): if db is not None: embed_info_file = os.path.join(db._persist_directory, 'embed_info') with open(embed_info_file, 'wb') as f: pickle.dump((use_openai_embedding, hf_embedding_model), f) return use_openai_embedding, hf_embedding_model def load_embed(db): embed_info_file = os.path.join(db._persist_directory, 'embed_info') if os.path.isfile(embed_info_file): with open(embed_info_file, 'rb') as f: use_openai_embedding, hf_embedding_model = pickle.load(f) else: # migration, assume defaults use_openai_embedding, hf_embedding_model = False, "sentence-transformers/all-MiniLM-L6-v2" return use_openai_embedding, hf_embedding_model def get_persist_directory(langchain_mode): return 'db_dir_%s' % langchain_mode # single place, no special names for each case def _make_db(use_openai_embedding=False, hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2", first_para=False, text_limit=None, chunk=True, chunk_size=512, langchain_mode=None, langchain_mode_paths=None, db_type='faiss', load_db_if_exists=True, db=None, n_jobs=-1, verbose=False): persist_directory = get_persist_directory(langchain_mode) user_path = langchain_mode_paths.get(langchain_mode) # see if can get persistent chroma db db_trial = get_existing_db(db, persist_directory, load_db_if_exists, db_type, use_openai_embedding, langchain_mode, hf_embedding_model, verbose=verbose) if db_trial is not None: db = db_trial sources = [] if not db: if langchain_mode in ['wiki_full']: from read_wiki_full import get_all_documents small_test = None print("Generating new wiki", flush=True) sources1 = get_all_documents(small_test=small_test, n_jobs=os.cpu_count() // 2) print("Got new wiki", flush=True) if chunk: sources1 = chunk_sources(sources1, chunk=chunk, chunk_size=chunk_size) print("Chunked new wiki", flush=True) sources.extend(sources1) elif langchain_mode in ['wiki']: sources1 = get_wiki_sources(first_para=first_para, text_limit=text_limit) if chunk: sources1 = chunk_sources(sources1, chunk=chunk, chunk_size=chunk_size) sources.extend(sources1) elif langchain_mode in ['github h2oGPT']: # sources = get_github_docs("dagster-io", "dagster") sources1 = get_github_docs("h2oai", "h2ogpt") # FIXME: always chunk for now sources1 = chunk_sources(sources1, chunk=chunk, chunk_size=chunk_size) sources.extend(sources1) elif langchain_mode in ['DriverlessAI docs']: sources1 = get_dai_docs(from_hf=True) if chunk and False: # FIXME: DAI docs are already chunked well, should only chunk more if over limit sources1 = chunk_sources(sources1, chunk=chunk, chunk_size=chunk_size) sources.extend(sources1) if user_path: # UserData or custom, which has to be from user's disk if db is not None: # NOTE: Ignore file names for now, only go by hash ids # existing_files = get_existing_files(db) existing_files = [] existing_hash_ids = get_existing_hash_ids(db) else: # pretend no existing files so won't filter existing_files = [] existing_hash_ids = [] # chunk internally for speed over multiple docs # FIXME: If first had old Hash=None and switch embeddings, # then re-embed, and then hit here and reload so have hash, and then re-embed. sources1 = path_to_docs(user_path, n_jobs=n_jobs, chunk=chunk, chunk_size=chunk_size, existing_files=existing_files, existing_hash_ids=existing_hash_ids) new_metadata_sources = set([x.metadata['source'] for x in sources1]) if new_metadata_sources: print("Loaded %s new files as sources to add to %s" % (len(new_metadata_sources), langchain_mode), flush=True) if verbose: print("Files added: %s" % '\n'.join(new_metadata_sources), flush=True) sources.extend(sources1) print("Loaded %s sources for potentially adding to %s" % (len(sources), langchain_mode), flush=True) # see if got sources if not sources: if verbose: if db is not None: print("langchain_mode %s has no new sources, nothing to add to db" % langchain_mode, flush=True) else: print("langchain_mode %s has no sources, not making new db" % langchain_mode, flush=True) return db, 0, [] if verbose: if db is not None: print("Generating db", flush=True) else: print("Adding to db", flush=True) if not db: if sources: db = get_db(sources, use_openai_embedding=use_openai_embedding, db_type=db_type, persist_directory=persist_directory, langchain_mode=langchain_mode, hf_embedding_model=hf_embedding_model) if verbose: print("Generated db", flush=True) else: print("Did not generate db since no sources", flush=True) new_sources_metadata = [x.metadata for x in sources] elif user_path is not None: print("Existing db, potentially adding %s sources from user_path=%s" % (len(sources), user_path), flush=True) db, num_new_sources, new_sources_metadata = add_to_db(db, sources, db_type=db_type, use_openai_embedding=use_openai_embedding, hf_embedding_model=hf_embedding_model) print("Existing db, added %s new sources from user_path=%s" % (num_new_sources, user_path), flush=True) else: new_sources_metadata = [x.metadata for x in sources] return db, len(new_sources_metadata), new_sources_metadata def get_metadatas(db): from langchain.vectorstores import FAISS if isinstance(db, FAISS): metadatas = [v.metadata for k, v in db.docstore._dict.items()] elif isinstance(db, Chroma): metadatas = get_documents(db)['metadatas'] else: # FIXME: Hack due to https://github.com/weaviate/weaviate/issues/1947 # seems no way to get all metadata, so need to avoid this approach for weaviate metadatas = [x.metadata for x in db.similarity_search("", k=10000)] return metadatas def get_documents(db): if hasattr(db, '_persist_directory'): name_path = os.path.basename(db._persist_directory) base_path = 'locks' makedirs(base_path) with filelock.FileLock(os.path.join(base_path, "getdb_%s.lock" % name_path)): # get segfaults and other errors when multiple threads access this return _get_documents(db) else: return _get_documents(db) def _get_documents(db): from langchain.vectorstores import FAISS if isinstance(db, FAISS): documents = [v for k, v in db.docstore._dict.items()] elif isinstance(db, Chroma): documents = db.get() else: # FIXME: Hack due to https://github.com/weaviate/weaviate/issues/1947 # seems no way to get all metadata, so need to avoid this approach for weaviate documents = [x for x in db.similarity_search("", k=10000)] return documents def get_docs_and_meta(db, top_k_docs, filter_kwargs={}): if hasattr(db, '_persist_directory'): name_path = os.path.basename(db._persist_directory) base_path = 'locks' makedirs(base_path) with filelock.FileLock(os.path.join(base_path, "getdb_%s.lock" % name_path)): return _get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs) else: return _get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs) def _get_docs_and_meta(db, top_k_docs, filter_kwargs={}): from langchain.vectorstores import FAISS if isinstance(db, Chroma): db_get = db._collection.get(where=filter_kwargs.get('filter')) db_metadatas = db_get['metadatas'] db_documents = db_get['documents'] elif isinstance(db, FAISS): import itertools db_metadatas = get_metadatas(db) # FIXME: FAISS has no filter # slice dict first db_documents = list(dict(itertools.islice(db.docstore._dict.items(), top_k_docs)).values()) else: db_metadatas = get_metadatas(db) db_documents = get_documents(db) return db_documents, db_metadatas def get_existing_files(db): metadatas = get_metadatas(db) metadata_sources = set([x['source'] for x in metadatas]) return metadata_sources def get_existing_hash_ids(db): metadatas = get_metadatas(db) # assume consistency, that any prior hashed source was single hashed file at the time among all source chunks metadata_hash_ids = {x['source']: x.get('hashid') for x in metadatas} return metadata_hash_ids def run_qa_db(**kwargs): func_names = list(inspect.signature(_run_qa_db).parameters) # hard-coded defaults kwargs['answer_with_sources'] = True kwargs['show_rank'] = False missing_kwargs = [x for x in func_names if x not in kwargs] assert not missing_kwargs, "Missing kwargs for run_qa_db: %s" % missing_kwargs # only keep actual used kwargs = {k: v for k, v in kwargs.items() if k in func_names} try: return _run_qa_db(**kwargs) finally: clear_torch_cache() def _run_qa_db(query=None, iinput=None, context=None, use_openai_model=False, use_openai_embedding=False, first_para=False, text_limit=None, top_k_docs=4, chunk=True, chunk_size=512, langchain_mode_paths={}, detect_user_path_changes_every_query=False, db_type='faiss', model_name=None, model=None, tokenizer=None, inference_server=None, hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2", stream_output=False, prompter=None, prompt_type=None, prompt_dict=None, answer_with_sources=True, cut_distance=1.64, add_chat_history_to_context=True, sanitize_bot_response=False, show_rank=False, use_llm_if_no_docs=False, load_db_if_exists=False, db=None, do_sample=False, temperature=0.1, top_k=40, top_p=0.7, num_beams=1, max_new_tokens=256, min_new_tokens=1, early_stopping=False, max_time=180, repetition_penalty=1.0, num_return_sequences=1, langchain_mode=None, langchain_action=None, langchain_agents=None, document_subset=DocumentSubset.Relevant.name, document_choice=[DocumentChoice.ALL.value], n_jobs=-1, verbose=False, cli=False, reverse_docs=True, lora_weights='', auto_reduce_chunks=True, max_chunks=100, ): """ :param query: :param use_openai_model: :param use_openai_embedding: :param first_para: :param text_limit: :param top_k_docs: :param chunk: :param chunk_size: :param langchain_mode_paths: dict of langchain_mode -> user path to glob recursively from :param db_type: 'faiss' for in-memory db or 'chroma' or 'weaviate' for persistent db :param model_name: model name, used to switch behaviors :param model: pre-initialized model, else will make new one :param tokenizer: pre-initialized tokenizer, else will make new one. Required not None if model is not None :param answer_with_sources :return: """ assert langchain_mode_paths is not None if model is not None: assert model_name is not None # require so can make decisions assert query is not None assert prompter is not None or prompt_type is not None or model is None # if model is None, then will generate if prompter is not None: prompt_type = prompter.prompt_type prompt_dict = prompter.prompt_dict if model is not None: assert prompt_type is not None if prompt_type == PromptType.custom.name: assert prompt_dict is not None # should at least be {} or '' else: prompt_dict = '' assert len(set(gen_hyper).difference(inspect.signature(get_llm).parameters)) == 0 # pass in context to LLM directly, since already has prompt_type structure # can't pass through langchain in get_chain() to LLM: https://github.com/hwchase17/langchain/issues/6638 llm, model_name, streamer, prompt_type_out = get_llm(use_openai_model=use_openai_model, model_name=model_name, model=model, tokenizer=tokenizer, inference_server=inference_server, stream_output=stream_output, do_sample=do_sample, temperature=temperature, top_k=top_k, top_p=top_p, num_beams=num_beams, max_new_tokens=max_new_tokens, min_new_tokens=min_new_tokens, early_stopping=early_stopping, max_time=max_time, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, prompt_type=prompt_type, prompt_dict=prompt_dict, prompter=prompter, context=context if add_chat_history_to_context else '', iinput=iinput if add_chat_history_to_context else '', sanitize_bot_response=sanitize_bot_response, verbose=verbose, ) use_docs_planned = False scores = [] chain = None if isinstance(document_choice, str): # support string as well document_choice = [document_choice] func_names = list(inspect.signature(get_chain).parameters) sim_kwargs = {k: v for k, v in locals().items() if k in func_names} missing_kwargs = [x for x in func_names if x not in sim_kwargs] assert not missing_kwargs, "Missing: %s" % missing_kwargs docs, chain, scores, use_docs_planned, have_any_docs = get_chain(**sim_kwargs) if document_subset in non_query_commands: formatted_doc_chunks = '\n\n'.join([get_url(x) + '\n\n' + x.page_content for x in docs]) if not formatted_doc_chunks and not use_llm_if_no_docs: yield "No sources", '' return # if no souces, outside gpt_langchain, LLM will be used with '' input yield formatted_doc_chunks, '' return if not use_llm_if_no_docs: if not docs and langchain_action in [LangChainAction.SUMMARIZE_MAP.value, LangChainAction.SUMMARIZE_ALL.value, LangChainAction.SUMMARIZE_REFINE.value]: ret = 'No relevant documents to summarize.' if have_any_docs else 'No documents to summarize.' extra = '' yield ret, extra return if not docs and langchain_mode not in [LangChainMode.DISABLED.value, LangChainMode.LLM.value]: ret = 'No relevant documents to query.' if have_any_docs else 'No documents to query.' extra = '' yield ret, extra return if chain is None and model_name not in non_hf_types: # here if no docs at all and not HF type # can only return if HF type return # context stuff similar to used in evaluate() import torch device, torch_dtype, context_class = get_device_dtype() with torch.no_grad(): have_lora_weights = lora_weights not in [no_lora_str, '', None] context_class_cast = NullContext if device == 'cpu' or have_lora_weights else torch.autocast with context_class_cast(device): if stream_output and streamer: answer = None import queue bucket = queue.Queue() thread = EThread(target=chain, streamer=streamer, bucket=bucket) thread.start() outputs = "" prompt = None # FIXME try: for new_text in streamer: # print("new_text: %s" % new_text, flush=True) if bucket.qsize() > 0 or thread.exc: thread.join() outputs += new_text if prompter: # and False: # FIXME: pipeline can already use prompter output1 = prompter.get_response(outputs, prompt=prompt, sanitize_bot_response=sanitize_bot_response) yield output1, '' else: yield outputs, '' except BaseException: # if any exception, raise that exception if was from thread, first if thread.exc: raise thread.exc raise finally: # in case no exception and didn't join with thread yet, then join if not thread.exc: answer = thread.join() # in case raise StopIteration or broke queue loop in streamer, but still have exception if thread.exc: raise thread.exc # FIXME: answer is not string outputs from streamer. How to get actual final output? # answer = outputs else: answer = chain() if not use_docs_planned: ret = answer['output_text'] extra = '' yield ret, extra elif answer is not None: ret, extra = get_sources_answer(query, answer, scores, show_rank, answer_with_sources, verbose=verbose) yield ret, extra return def get_chain(query=None, iinput=None, context=None, # FIXME: https://github.com/hwchase17/langchain/issues/6638 use_openai_model=False, use_openai_embedding=False, first_para=False, text_limit=None, top_k_docs=4, chunk=True, chunk_size=512, langchain_mode_paths=None, detect_user_path_changes_every_query=False, db_type='faiss', model_name=None, inference_server='', hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2", prompt_type=None, prompt_dict=None, cut_distance=1.1, add_chat_history_to_context=True, # FIXME: https://github.com/hwchase17/langchain/issues/6638 load_db_if_exists=False, db=None, langchain_mode=None, langchain_action=None, langchain_agents=None, document_subset=DocumentSubset.Relevant.name, document_choice=[DocumentChoice.ALL.value], n_jobs=-1, # beyond run_db_query: llm=None, tokenizer=None, verbose=False, reverse_docs=True, # local auto_reduce_chunks=True, max_chunks=100, ): assert langchain_agents is not None # should be at least [] # determine whether use of context out of docs is planned if not use_openai_model and prompt_type not in ['plain'] or model_name in non_hf_types: if langchain_mode in ['Disabled', 'LLM']: use_docs_planned = False else: use_docs_planned = True else: use_docs_planned = True # https://github.com/hwchase17/langchain/issues/1946 # FIXME: Seems to way to get size of chroma db to limit top_k_docs to avoid # Chroma collection MyData contains fewer than 4 elements. # type logger error if top_k_docs == -1: k_db = 1000 if db_type == 'chroma' else 100 else: # top_k_docs=100 works ok too k_db = 1000 if db_type == 'chroma' else top_k_docs # FIXME: For All just go over all dbs instead of a separate db for All if not detect_user_path_changes_every_query and db is not None: # avoid looking at user_path during similarity search db handling, # if already have db and not updating from user_path every query # but if db is None, no db yet loaded (e.g. from prep), so allow user_path to be whatever it was if langchain_mode_paths is None: langchain_mode_paths = {} langchain_mode_paths = langchain_mode_paths.copy() langchain_mode_paths[langchain_mode] = None db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=use_openai_embedding, hf_embedding_model=hf_embedding_model, first_para=first_para, text_limit=text_limit, chunk=chunk, chunk_size=chunk_size, langchain_mode=langchain_mode, langchain_mode_paths=langchain_mode_paths, db_type=db_type, load_db_if_exists=load_db_if_exists, db=db, n_jobs=n_jobs, verbose=verbose) have_any_docs = db is not None if langchain_action == LangChainAction.QUERY.value: if iinput: query = "%s\n%s" % (query, iinput) if 'falcon' in model_name: extra = "According to only the information in the document sources provided within the context above, " prefix = "Pay attention and remember information below, which will help to answer the question or imperative after the context ends." elif inference_server in ['openai', 'openai_chat']: extra = "According to (primarily) the information in the document sources provided within context above, " prefix = "Pay attention and remember information below, which will help to answer the question or imperative after the context ends. If the answer cannot be primarily obtained from information within the context, then respond that the answer does not appear in the context of the documents." else: extra = "" prefix = "" if langchain_mode in ['Disabled', 'LLM'] or not use_docs_planned: template_if_no_docs = template = """%s{context}{question}""" % prefix else: template = """%s \"\"\" {context} \"\"\" %s{question}""" % (prefix, extra) template_if_no_docs = """%s{context}%s{question}""" % (prefix, extra) elif langchain_action in [LangChainAction.SUMMARIZE_ALL.value, LangChainAction.SUMMARIZE_MAP.value]: none = ['', '\n', None] if query in none and iinput in none: prompt_summary = "Using only the text above, write a condensed and concise summary:\n" elif query not in none: prompt_summary = "Focusing on %s, write a condensed and concise Summary:\n" % query elif iinput not in None: prompt_summary = iinput else: prompt_summary = "Focusing on %s, %s:\n" % (query, iinput) # don't auto reduce auto_reduce_chunks = False if langchain_action == LangChainAction.SUMMARIZE_MAP.value: fstring = '{text}' else: fstring = '{input_documents}' template = """In order to write a concise single-paragraph or bulleted list summary, pay attention to the following text: \"\"\" %s \"\"\"\n%s""" % (fstring, prompt_summary) template_if_no_docs = "Exactly only say: There are no documents to summarize." elif langchain_action in [LangChainAction.SUMMARIZE_REFINE]: template = '' # unused template_if_no_docs = '' # unused else: raise RuntimeError("No such langchain_action=%s" % langchain_action) if not use_openai_model and prompt_type not in ['plain'] or model_name in non_hf_types: use_template = True else: use_template = False if db and use_docs_planned: base_path = 'locks' makedirs(base_path) if hasattr(db, '_persist_directory'): name_path = "sim_%s.lock" % os.path.basename(db._persist_directory) else: name_path = "sim.lock" lock_file = os.path.join(base_path, name_path) if not isinstance(db, Chroma): # only chroma supports filtering filter_kwargs = {} else: assert document_choice is not None, "Document choice was None" if len(document_choice) >= 1 and document_choice[0] == DocumentChoice.ALL.value: filter_kwargs = {} elif len(document_choice) >= 2: if document_choice[0] == DocumentChoice.ALL.value: # remove 'All' document_choice = document_choice[1:] or_filter = [{"source": {"$eq": x}} for x in document_choice] filter_kwargs = dict(filter={"$or": or_filter}) elif len(document_choice) == 1: # degenerate UX bug in chroma one_filter = [{"source": {"$eq": x}} for x in document_choice][0] filter_kwargs = dict(filter=one_filter) else: # shouldn't reach filter_kwargs = {} if langchain_mode in [LangChainMode.LLM.value]: docs = [] scores = [] elif document_subset == DocumentSubset.TopKSources.name or query in [None, '', '\n']: db_documents, db_metadatas = get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs) # similar to langchain's chroma's _results_to_docs_and_scores docs_with_score = [(Document(page_content=result[0], metadata=result[1] or {}), 0) for result in zip(db_documents, db_metadatas)] # order documents doc_hashes = [x.get('doc_hash', 'None') for x in db_metadatas] doc_chunk_ids = [x.get('chunk_id', 0) for x in db_metadatas] docs_with_score = [x for _, _, x in sorted(zip(doc_hashes, doc_chunk_ids, docs_with_score), key=lambda x: (x[0], x[1])) ] docs_with_score = docs_with_score[:top_k_docs] docs = [x[0] for x in docs_with_score] scores = [x[1] for x in docs_with_score] have_any_docs |= len(docs) > 0 else: # FIXME: if langchain_action == LangChainAction.SUMMARIZE_MAP.value # if map_reduce, then no need to auto reduce chunks if top_k_docs == -1 or auto_reduce_chunks: # docs_with_score = db.similarity_search_with_score(query, k=k_db, **filter_kwargs)[:top_k_docs] top_k_docs_tokenize = 100 with filelock.FileLock(lock_file): docs_with_score = db.similarity_search_with_score(query, k=k_db, **filter_kwargs)[ :top_k_docs_tokenize] if hasattr(llm, 'pipeline') and hasattr(llm.pipeline, 'tokenizer'): # more accurate tokens = [len(llm.pipeline.tokenizer(x[0].page_content)['input_ids']) for x in docs_with_score] template_tokens = len(llm.pipeline.tokenizer(template)['input_ids']) elif inference_server in ['openai', 'openai_chat'] or use_openai_model or db_type in ['faiss', 'weaviate']: # use ticktoken for faiss since embedding called differently tokens = [llm.get_num_tokens(x[0].page_content) for x in docs_with_score] template_tokens = llm.get_num_tokens(template) elif isinstance(tokenizer, FakeTokenizer): tokens = [tokenizer.num_tokens_from_string(x[0].page_content) for x in docs_with_score] template_tokens = tokenizer.num_tokens_from_string(template) else: # in case model is not our pipeline with HF tokenizer tokens = [db._embedding_function.client.tokenize([x[0].page_content])['input_ids'].shape[1] for x in docs_with_score] template_tokens = db._embedding_function.client.tokenize([template])['input_ids'].shape[1] tokens_cumsum = np.cumsum(tokens) if hasattr(llm, 'pipeline') and hasattr(llm.pipeline, 'max_input_tokens'): max_input_tokens = llm.pipeline.max_input_tokens elif inference_server in ['openai']: max_tokens = llm.modelname_to_contextsize(model_name) # leave some room for 1 paragraph, even if min_new_tokens=0 max_input_tokens = max_tokens - 256 elif inference_server in ['openai_chat']: max_tokens = model_token_mapping[model_name] # leave some room for 1 paragraph, even if min_new_tokens=0 max_input_tokens = max_tokens - 256 elif isinstance(tokenizer, FakeTokenizer): max_input_tokens = tokenizer.model_max_length - 256 else: # leave some room for 1 paragraph, even if min_new_tokens=0 max_input_tokens = 2048 - 256 max_input_tokens -= template_tokens # FIXME: Doesn't account for query, == context, or new lines between contexts where_res = np.where(tokens_cumsum < max_input_tokens)[0] if where_res.shape[0] == 0: # then no chunk can fit, still do first one top_k_docs_trial = 1 else: top_k_docs_trial = 1 + where_res[-1] if 0 < top_k_docs_trial < max_chunks: # avoid craziness if top_k_docs == -1: top_k_docs = top_k_docs_trial else: top_k_docs = min(top_k_docs, top_k_docs_trial) if top_k_docs == -1: # if here, means 0 and just do best with 1 doc print("Unexpected large chunks and can't add to context, will add 1 anyways", flush=True) top_k_docs = 1 docs_with_score = docs_with_score[:top_k_docs] else: with filelock.FileLock(lock_file): docs_with_score = db.similarity_search_with_score(query, k=k_db, **filter_kwargs)[:top_k_docs] # put most relevant chunks closest to question, # esp. if truncation occurs will be "oldest" or "farthest from response" text that is truncated # BUT: for small models, e.g. 6_9 pythia, if sees some stuff related to h2oGPT first, it can connect that and not listen to rest if reverse_docs: docs_with_score.reverse() # cut off so no high distance docs/sources considered have_any_docs |= len(docs_with_score) > 0 # before cut docs = [x[0] for x in docs_with_score if x[1] < cut_distance] scores = [x[1] for x in docs_with_score if x[1] < cut_distance] if len(scores) > 0 and verbose: print("Distance: min: %s max: %s mean: %s median: %s" % (scores[0], scores[-1], np.mean(scores), np.median(scores)), flush=True) else: docs = [] scores = [] if not docs and use_docs_planned and model_name not in non_hf_types: # if HF type and have no docs, can bail out return docs, None, [], False, have_any_docs if document_subset in non_query_commands: # no LLM use return docs, None, [], False, have_any_docs common_words_file = "data/NGSL_1.2_stats.csv.zip" if os.path.isfile(common_words_file) and langchain_mode == LangChainAction.QUERY.value: df = pd.read_csv("data/NGSL_1.2_stats.csv.zip") import string reduced_query = query.translate(str.maketrans(string.punctuation, ' ' * len(string.punctuation))).strip() reduced_query_words = reduced_query.split(' ') set_common = set(df['Lemma'].values.tolist()) num_common = len([x.lower() in set_common for x in reduced_query_words]) frac_common = num_common / len(reduced_query) if reduced_query else 0 # FIXME: report to user bad query that uses too many common words if verbose: print("frac_common: %s" % frac_common, flush=True) if len(docs) == 0: # avoid context == in prompt then use_docs_planned = False template = template_if_no_docs if langchain_action == LangChainAction.QUERY.value: if use_template: # instruct-like, rather than few-shot prompt_type='plain' as default # but then sources confuse the model with how inserted among rest of text, so avoid prompt = PromptTemplate( # input_variables=["summaries", "question"], input_variables=["context", "question"], template=template, ) chain = load_qa_chain(llm, prompt=prompt) else: # only if use_openai_model = True, unused normally except in testing chain = load_qa_with_sources_chain(llm) if not use_docs_planned: chain_kwargs = dict(input_documents=[], question=query) else: chain_kwargs = dict(input_documents=docs, question=query) target = wrapped_partial(chain, chain_kwargs) elif langchain_action in [LangChainAction.SUMMARIZE_MAP.value, LangChainAction.SUMMARIZE_REFINE, LangChainAction.SUMMARIZE_ALL.value]: from langchain.chains.summarize import load_summarize_chain if langchain_action == LangChainAction.SUMMARIZE_MAP.value: prompt = PromptTemplate(input_variables=["text"], template=template) chain = load_summarize_chain(llm, chain_type="map_reduce", map_prompt=prompt, combine_prompt=prompt, return_intermediate_steps=True) target = wrapped_partial(chain, {"input_documents": docs}) # , return_only_outputs=True) elif langchain_action == LangChainAction.SUMMARIZE_ALL.value: assert use_template prompt = PromptTemplate(input_variables=["text"], template=template) chain = load_summarize_chain(llm, chain_type="stuff", prompt=prompt, return_intermediate_steps=True) target = wrapped_partial(chain) elif langchain_action == LangChainAction.SUMMARIZE_REFINE.value: chain = load_summarize_chain(llm, chain_type="refine", return_intermediate_steps=True) target = wrapped_partial(chain) else: raise RuntimeError("No such langchain_action=%s" % langchain_action) else: raise RuntimeError("No such langchain_action=%s" % langchain_action) return docs, target, scores, use_docs_planned, have_any_docs def get_sources_answer(query, answer, scores, show_rank, answer_with_sources, verbose=False): if verbose: print("query: %s" % query, flush=True) print("answer: %s" % answer['output_text'], flush=True) if len(answer['input_documents']) == 0: extra = '' ret = answer['output_text'] + extra return ret, extra # link answer_sources = [(max(0.0, 1.5 - score) / 1.5, get_url(doc)) for score, doc in zip(scores, answer['input_documents'])] answer_sources_dict = defaultdict(list) [answer_sources_dict[url].append(score) for score, url in answer_sources] answers_dict = {} for url, scores_url in answer_sources_dict.items(): answers_dict[url] = np.max(scores_url) answer_sources = [(score, url) for url, score in answers_dict.items()] answer_sources.sort(key=lambda x: x[0], reverse=True) if show_rank: # answer_sources = ['%d | %s' % (1 + rank, url) for rank, (score, url) in enumerate(answer_sources)] # sorted_sources_urls = "Sources [Rank | Link]:
" + "
".join(answer_sources) answer_sources = ['%s' % url for rank, (score, url) in enumerate(answer_sources)] sorted_sources_urls = "Ranked Sources:
" + "
".join(answer_sources) else: answer_sources = ['
  • %.2g | %s
  • ' % (score, url) for score, url in answer_sources] sorted_sources_urls = f"{source_prefix}

    {source_postfix}" if not answer['output_text'].endswith('\n'): answer['output_text'] += '\n' if answer_with_sources: extra = '\n' + sorted_sources_urls else: extra = '' ret = answer['output_text'] + extra return ret, extra def clean_doc(docs1): if not isinstance(docs1, (list, tuple, types.GeneratorType)): docs1 = [docs1] for doci, doc in enumerate(docs1): docs1[doci].page_content = '\n'.join([x.strip() for x in doc.page_content.split("\n") if x.strip()]) return docs1 def chunk_sources(sources, chunk=True, chunk_size=512, language=None): if not chunk: [x.metadata.update(dict(chunk_id=chunk_id)) for chunk_id, x in enumerate(sources)] return sources if not isinstance(sources, (list, tuple, types.GeneratorType)) and not callable(sources): # if just one document sources = [sources] if language and False: # Bug in langchain, keep separator=True not working # https://github.com/hwchase17/langchain/issues/2836 # so avoid this for now keep_separator = True separators = RecursiveCharacterTextSplitter.get_separators_for_language(language) else: separators = ["\n\n", "\n", " ", ""] keep_separator = False splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0, keep_separator=keep_separator, separators=separators) source_chunks = splitter.split_documents(sources) # currently in order, but when pull from db won't be, so mark order and document by hash [x.metadata.update(dict(chunk_id=chunk_id)) for chunk_id, x in enumerate(source_chunks)] return source_chunks def get_db_from_hf(dest=".", db_dir='db_dir_DriverlessAI_docs.zip'): from huggingface_hub import hf_hub_download # True for case when locally already logged in with correct token, so don't have to set key token = os.getenv('HUGGINGFACE_API_TOKEN', True) path_to_zip_file = hf_hub_download('h2oai/db_dirs', db_dir, token=token, repo_type='dataset') import zipfile with zipfile.ZipFile(path_to_zip_file, 'r') as zip_ref: persist_directory = os.path.dirname(zip_ref.namelist()[0]) remove(persist_directory) zip_ref.extractall(dest) return path_to_zip_file # Note dir has space in some cases, while zip does not some_db_zips = [['db_dir_DriverlessAI_docs.zip', 'db_dir_DriverlessAI docs', 'CC-BY-NC license'], ['db_dir_UserData.zip', 'db_dir_UserData', 'CC-BY license for ArXiv'], ['db_dir_github_h2oGPT.zip', 'db_dir_github h2oGPT', 'ApacheV2 license'], ['db_dir_wiki.zip', 'db_dir_wiki', 'CC-BY-SA Wikipedia license'], # ['db_dir_wiki_full.zip', 'db_dir_wiki_full.zip', '23GB, 05/04/2023 CC-BY-SA Wiki license'], ] all_db_zips = some_db_zips + \ [['db_dir_wiki_full.zip', 'db_dir_wiki_full.zip', '23GB, 05/04/2023 CC-BY-SA Wiki license'], ] def get_some_dbs_from_hf(dest='.', db_zips=None): if db_zips is None: db_zips = some_db_zips for db_dir, dir_expected, license1 in db_zips: path_to_zip_file = get_db_from_hf(dest=dest, db_dir=db_dir) assert os.path.isfile(path_to_zip_file), "Missing zip in %s" % path_to_zip_file if dir_expected: assert os.path.isdir(os.path.join(dest, dir_expected)), "Missing path for %s" % dir_expected assert os.path.isdir(os.path.join(dest, dir_expected, 'index')), "Missing index in %s" % dir_expected def _create_local_weaviate_client(): WEAVIATE_URL = os.getenv('WEAVIATE_URL', "http://localhost:8080") WEAVIATE_USERNAME = os.getenv('WEAVIATE_USERNAME') WEAVIATE_PASSWORD = os.getenv('WEAVIATE_PASSWORD') WEAVIATE_SCOPE = os.getenv('WEAVIATE_SCOPE', "offline_access") resource_owner_config = None try: import weaviate if WEAVIATE_USERNAME is not None and WEAVIATE_PASSWORD is not None: resource_owner_config = weaviate.AuthClientPassword( username=WEAVIATE_USERNAME, password=WEAVIATE_PASSWORD, scope=WEAVIATE_SCOPE ) client = weaviate.Client(WEAVIATE_URL, auth_client_secret=resource_owner_config) return client except Exception as e: print(f"Failed to create Weaviate client: {e}") return None if __name__ == '__main__': pass