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+accelerate = "^0.21.0" [build-system] diff --git a/repository_data.json b/repository_data.json index 3a095ff..a09ddae 100644 --- a/repository_data.json +++ b/repository_data.json @@ -121,14 +121,6 @@ } } }, - "search": { - "word_score": { - "local": { - "__is_async": true, - "__is_base": true, - "model_class": "Model", - "request_class": "ModelRequest" - } } } } diff --git a/src/dsp/README.md b/src/dsp/README.md new file mode 100644 index 0000000..ac99b13 --- /dev/null +++ b/src/dsp/README.md @@ -0,0 +1,3 @@ +# DSP (Demonstrate, Search, Predict) + +[DSP](https://github.com/stanfordnlp/dspy) to further augment RAG. This module is to specifically make DSP work for long passage answers required for questions rather than short factoid answers. \ No newline at end of file diff --git a/src/dsp/__init__.py b/src/dsp/__init__.py new file mode 100644 index 0000000..49202e0 --- /dev/null +++ b/src/dsp/__init__.py @@ -0,0 +1 @@ +from .local import * \ No newline at end of file diff --git a/src/dsp/local/Dockerfile b/src/dsp/local/Dockerfile new file mode 100644 index 0000000..3cea204 --- /dev/null +++ b/src/dsp/local/Dockerfile @@ -0,0 +1,15 @@ +# Use an official Python runtime as a parent image +FROM python:3.9-slim + +WORKDIR /app + + +#install requirements +COPY requirements.txt requirements.txt +RUN pip3 install -r requirements.txt + +# Copy the rest of the application code to the working directory +COPY . /app/ +EXPOSE 8000 +# Set the entrypoint for the container +CMD ["hypercorn", "--bind", "0.0.0.0:8000", "api:app"] \ No newline at end of file diff --git a/src/dsp/local/README.md b/src/dsp/local/README.md new file mode 100644 index 0000000..f7c1a85 --- /dev/null +++ b/src/dsp/local/README.md @@ -0,0 +1,12 @@ +# DSP + +## Test Deployment + +- Git clone the repo and cd to the project location. +- cd to `local`, i.e., `cd ./src/dsp/local`. +- Use openai api key. +- Start your docker engine and `docker build -t dsp .`. +- Do `docker run -p 8000:8000 dsp`. +- `curl -X POST -H "Content-Type: application/json" -d '{"text": TEXT, "train": TRAIN, "server": SERVER, "model": MODEL}' http://0.0.0.0:8000`. + +`TEXT` is the question. `TRAIN` is the labeled samples required in list format. Ex: `[("Question1", ["Answer1"]), ("Question2", ["Answer2"])]`. `SERVER` is the retrieval model server's api endpoint. Make sure to implement the server so the endpoints work as required for [this](https://github.com/stanfordnlp/dspy/blob/main/dsp/modules/colbertv2.py). `MODEL` is the hugging face model that you may want to use instead of gpt-3+, it is optional. Leave it blank if you want to use gpt-3+. diff --git a/src/dsp/local/__init__.py b/src/dsp/local/__init__.py new file mode 100644 index 0000000..3940fff --- /dev/null +++ b/src/dsp/local/__init__.py @@ -0,0 +1,2 @@ +from .request import ModelRequest +from .model import Model diff --git a/src/dsp/local/api.py b/src/dsp/local/api.py new file mode 100644 index 0000000..74c6979 --- /dev/null +++ b/src/dsp/local/api.py @@ -0,0 +1,24 @@ +from model import Model +from request import ModelRequest +from quart import Quart, request +import aiohttp + +app = Quart(__name__) + +model = None + +@app.before_serving +async def startup(): + app.client = aiohttp.ClientSession() + global model + model = Model(app) + +@app.route('/', methods=['POST']) +async def embed(): + global model + data = await request.get_json() + req = ModelRequest(**data) + return model.inference(req) + +if __name__ == "__main__": + app.run() \ No newline at end of file diff --git a/src/dsp/local/model.py b/src/dsp/local/model.py new file mode 100644 index 0000000..b6762f2 --- /dev/null +++ b/src/dsp/local/model.py @@ -0,0 +1,18 @@ +from request import ModelRequest +import dsp +from utils import DSP + + +class Model(): + def __new__(cls, context): + cls.context = context + if not hasattr(cls, 'instance'): + cls.dsp = DSP() + cls.instance = super(Model, cls).__new__(cls) + return cls.instance + + + def inference(self, request: ModelRequest): + train = [dsp.Example(question=question, answer=answer) for question, answer in request.train] + answer, history = self.dsp(request.text, train, request.server, request.hf_model) + return {"text": answer, "history": history} diff --git a/src/dsp/local/request.py b/src/dsp/local/request.py new file mode 100644 index 0000000..9840c93 --- /dev/null +++ b/src/dsp/local/request.py @@ -0,0 +1,13 @@ +import json + + +class ModelRequest(): + def __init__(self, text, train, server: str, model: str=None): + self.text = text + self.train = train + self.server = server + self.hf_model = model + + def to_json(self): + return json.dumps(self, default=lambda o: o.__dict__, + sort_keys=True, indent=4) \ No newline at end of file diff --git a/src/dsp/local/requirements.txt b/src/dsp/local/requirements.txt new file mode 100644 index 0000000..2113f4b --- /dev/null +++ b/src/dsp/local/requirements.txt @@ -0,0 +1,4 @@ +dsp-ml +accelerate +torch +tiktoken \ No newline at end of file diff --git a/src/dsp/local/utils.py b/src/dsp/local/utils.py new file mode 100644 index 0000000..a6c9386 --- /dev/null +++ b/src/dsp/local/utils.py @@ -0,0 +1,191 @@ +import os +import dsp +import tiktoken +from dsp.utils import deduplicate + + +openai_key = os.getenv("OPENAI_API_KEY") + + +class DSP(): + def __init__(self): + self.lm = dsp.GPT3(model='gpt-3.5-turbo-16k', api_key=openai_key, model_type='chat') + self.sbert_reranker = dsp.SentenceTransformersCrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2") + + self.encoding = tiktoken.get_encoding("cl100k_base") + + self.qa_template_with_CoT = None + self.rewrite_template = None + self.hop_template = None + self.train = None + self.__generate_templates() + + def __call__(self, server: str, hf_model: str, question: str, train) -> str: + self.rm = dsp.ColBERTv2(url=server) + try: + self.lm = dsp.HFModel(hf_model) + except: + self.lm = dsp.GPT3(model=hf_model, api_key=openai_key, model_type='chat') + dsp.settings.configure(lm=self.lm, rm=self.rm, reranker=self.sbert_reranker) + self.train = train + return self.multihop_QA_v2(question), self.lm.inspect_history(n=3) + + def __generate_templates(self): + Question = dsp.Type(prefix="Question:", desc="${the question to be answered}") + Answer = dsp.Type(prefix="Answer:", desc="${a crisp and concise answer without redundant information}", format=dsp.format_answers) + + qa_template = dsp.Template(instructions="Answer questions with concise answers without redundant information.", question=Question(), answer=Answer()) + + Rationale = dsp.Type( + prefix="Rationale: Let's think step by step.", + desc="${a step-by-step deduction that identifies the correct response, which will be provided below}" + ) + + self.qa_template_with_CoT = dsp.Template( + instructions=qa_template.instructions, + context=Context(), question=Question(), rationale=Rationale(), answer=Answer() + ) + + Context = dsp.Type( + prefix="Context:\n", + desc="${sources that may contain relevant content}", + format=dsp.passages2text + ) + + SearchRationale = dsp.Type( + prefix="Rationale: Let's think step by step. To answer this question, we first need to find out", + desc="${the missing information}" + ) + + SearchQuery = dsp.Type( + prefix="Search Query:", + desc="${a simple question for seeking the missing information}" + ) + + self.rewrite_template = dsp.Template( + instructions="Write a search query that will help answer a complex question.", + question=Question(), rationale=SearchRationale(), query=SearchQuery() + ) + + CondenseRationale = dsp.Type( + prefix="Rationale: Let's think step by step. Based on the context, we have learned the following.", + desc="${information from the context that provides useful clues}" + ) + + self.hop_template = dsp.Template( + instructions=self.rewrite_template.instructions, + context=Context(), question=Question(), rationale=CondenseRationale(), query=SearchQuery() + ) + + True_Answer = dsp.Type(prefix="The correct answer: ", desc="${The true answer}") + Predicted_Answer = dsp.Type(prefix="Another answer: ", desc="${The answer to compare to}") + Resp = dsp.Type(prefix="Response: ", desc="${True (or) False}") + + self.answer_match_template = dsp.Template( + instructions="Return True if the essence of both answers are same else return False. Respond with only 'True' and 'False'.", + true_answer=True_Answer(), predicted_answer=Predicted_Answer(), response=Resp() + ) + + def __answer_match(self, true_ans, pred_ans): + match_example = dsp.Example(true_answer=true_ans, predicted_answer=pred_ans, demos=[]) + _, completions = dsp.generate(self.answer_match_template)(match_example, stage='answer_match') + return completions[0].response + + def __count_tokens(self, text): + num_tokens = len(self.encoding.encode(text)) + return num_tokens + + @dsp.transformation + def QA_predict(self, example: dsp.Example, sc=True, return_store=False): + temp = str(example) + if sc: + example, completions = dsp.generate(self.qa_template_with_CoT, n=20, temperature=0.7)(example, stage='qa') + completions = dsp.majority(completions) + else: + example, completions = dsp.generate(self.qa_template_with_CoT)(example, stage='qa') + + if return_store: + len_tokens = self.__count_tokens(temp) + store = { + "question": example.question, + "context": example.context, + "rationale": completions[0].rationale, + "len_tokens": len_tokens + } + return example.copy(answer=completions.answer), store + return example.copy(answer=completions.answer) + + @dsp.transformation + def multihop_search_v1(self, example: dsp.Example, max_hops=2, k=2) -> dsp.Example: + example.context = [] + + for hop in range(max_hops): + # Generate a query based + template = self.rewrite_template if hop == 0 else self.hop_template + example, completions = dsp.generate(template)(example, stage=f'h{hop}') + + # Retrieve k results based on the query generated + passages = dsp.retrieve(completions.query, k=k) + + # Update the context by concatenating old and new passages + example.context = deduplicate(example.context + passages) + + return example + + @dsp.transformation + def multihop_attempt(self, d: dsp.Example) -> dsp.Example: + # Prepare unaugmented demonstrations for the example. + x = dsp.Example(question=d.question, demos=dsp.all_but(self.train, d)) + + # Search. + # Annotate demonstrations for multihop_search_v2 with the simpler multihop_search_v1 pipeline. + x = self.multihop_search_v1(x) + + # Predict. And skip examples where predict fails. + x = self.QA_predict(x, sc=False) + if not self.__answer_match(x.answer, d.answer) == "True": return None + + return d.copy(**x) + + @dsp.transformation + def multihop_demonstrate(self, x: dsp.Example) -> dsp.Example: + demos = dsp.sample(self.train, k=7) + x.demos = dsp.annotate(self.multihop_attempt)(demos, k=3, return_all=True) + return x + + @dsp.transformation + def multihop_search_v2(self, example: dsp.Example, max_hops=2, k=5) -> dsp.Example: + example.context = [] + store = [] + + for hop in range(max_hops): + # Generate queries + template = self.rewrite_template if hop == 0 else self.hop_template + + len_tokens = self.__count_tokens(str(example)) + example, completions = dsp.generate(template, n=10, temperature=0.7)(example, stage=f'h{hop}') + + # Collect the queries and search with result fusion + queries = [c.query for c in completions] + [example.question] + example.context = dsp.retrieveEnsemble(queries, k=k) + + # Arrange the passages for the next hop + if hop > 0: + example.context = [completions[0].rationale] + example.context + + store.append({ + "question": queries, + "rationale": completions[0].rationale, + "context": example.context, + "len_tokens": len_tokens + }) + + return example, store + + def multihop_QA_v2(self, question: str) -> str: + x = dsp.Example(question=question) + x = self.multihop_demonstrate(x) + x, stores = self.multihop_search_v2(x) + x, store = self.QA_predict(x, return_store=True) + stores.append(store) + return x.answer, stores \ No newline at end of file