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get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import dspy from dspy.evaluate import Evaluate from dspy.datasets.hotpotqa import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch, BootstrapFinetune ports = [7140, 7141, 7142, 7143, 7144, 7145] llamaChat = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=ports, max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llamaChat) dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=1000, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] testset = [x.with_inputs('question') for x in dataset.test] len(trainset), len(devset), len(testset) trainset[0] from dsp.utils.utils import deduplicate class BasicMH(dspy.Module): def __init__(self, passages_per_hop=3): super().__init__() self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_query = [dspy.ChainOfThought("context, question -> search_query") for _ in range(2)] self.generate_answer = dspy.ChainOfThought("context, question -> answer") def forward(self, question): context = [] for hop in range(2): search_query = self.generate_query[hop](context=context, question=question).search_query passages = self.retrieve(search_query).passages context = deduplicate(context + passages) return self.generate_answer(context=context, question=question).copy(context=context) RECOMPILE_INTO_LLAMA_FROM_SCRATCH = False NUM_THREADS = 24 metric_EM = dspy.evaluate.answer_exact_match if RECOMPILE_INTO_LLAMA_FROM_SCRATCH: tp = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_threads=NUM_THREADS) basicmh_bs = tp.compile(BasicMH(), trainset=trainset[:50], valset=trainset[50:200]) ensemble = [prog for *_, prog in basicmh_bs.candidate_programs[:4]] for idx, prog in enumerate(ensemble): pass if not RECOMPILE_INTO_LLAMA_FROM_SCRATCH: ensemble = [] for idx in range(4): prog = BasicMH() prog.load(f'multihop_llama213b_{idx}.json') ensemble.append(prog) llama_program = ensemble[0] evaluate_hotpot = Evaluate(devset=devset[:1000], metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=0) evaluate_hotpot(llama_program) llama_program(question="How many storeys are in the castle that David Gregory inherited?") llamaChat.inspect_history(n=3) unlabeled_train = HotPotQA(train_seed=1, train_size=3000, eval_seed=2023, dev_size=0, test_size=0).train unlabeled_train = [
dspy.Example(question=x.question)
dspy.Example
get_ipython().system('pip install clarifai') get_ipython().system('pip install dspy-ai') import dspy from dspy.retrieve.clarifai_rm import ClarifaiRM MODEL_URL = "https://clarifai.com/meta/Llama-2/models/llama2-70b-chat" PAT = "CLARIFAI_PAT" USER_ID = "YOUR_ID" APP_ID = "YOUR_APP" from langchain.text_splitter import CharacterTextSplitter from langchain.document_loaders import TextLoader from langchain.vectorstores import Clarifai as clarifaivectorstore loader = TextLoader("YOUR_TEXT_FILE") #replace with your file path documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200) docs = text_splitter.split_documents(documents) clarifai_vector_db = clarifaivectorstore.from_documents( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=PAT ) llm=dspy.Clarifai(model=MODEL_URL, api_key=PAT, n=2, inference_params={"max_tokens":100,'temperature':0.6}) retriever_model=ClarifaiRM(clarifai_user_id=USER_ID, clarfiai_app_id=APP_ID, clarifai_pat=PAT, k=2) dspy.settings.configure(lm=llm, rm=retriever_model) sentence = "disney again ransacks its archives for a quick-buck sequel ." # example from the SST-2 dataset. classify = dspy.Predict('sentence -> sentiment') print(classify(sentence=sentence).sentiment) retrieve = dspy.Retrieve() topK_passages = retrieve("can I test my vehicle engine in pit?").passages print(topK_passages) class GenerateAnswer(dspy.Signature): """Think and Answer questions based on the context provided.""" context = dspy.InputField(desc="may contain relevant facts about user query") question = dspy.InputField(desc="User query") answer = dspy.OutputField(desc="Answer in one or two lines") class RAG(dspy.Module): def __init__(self): super().__init__() self.retrieve = dspy.Retrieve() self.generate_answer = dspy.ChainOfThought(GenerateAnswer) def forward(self, question): context = self.retrieve(question).passages prediction = self.generate_answer(context=context, question=question) return dspy.Prediction(context=context, answer=prediction.answer) my_question = "can I test my vehicle engine in pit before inspection?" Rag_obj= RAG() predict_response_llama70b=Rag_obj(my_question) print(f"Question: {my_question}") print(f"Predicted Answer: {predict_response_llama70b.answer}") print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_llama70b.context]}") mistral_lm = dspy.Clarifai(model="https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct", api_key=PAT, n=2, inference_params={'temperature':0.6}) dspy.settings.configure(lm=mistral_lm, rm=retriever_model) my_question = "can I test my vehicle engine in pit before inspection?" Rag_obj= RAG() predict_response_mistral=Rag_obj(my_question) print(f"Question: {my_question}") print(f"Predicted Answer: {predict_response_mistral.answer}") print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_mistral.context]}") gemini_lm = dspy.Clarifai(model="https://clarifai.com/gcp/generate/models/gemini-pro", api_key=PAT, n=2)
dspy.settings.configure(lm=gemini_lm, rm=retriever_model)
dspy.settings.configure
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import dspy get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy') import dspy from dspy.evaluate import Evaluate from dspy.datasets.hotpotqa import HotPotQA from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llama) train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'), ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'), ('In what year was the star of To Hell and Back born?', '1925'), ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'), ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'), ('Which author is English: John Braine or Studs Terkel?', 'John Braine'), ('Who produced the album that included a re-recording of "Lithium"?', 'Butch Vig')] train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train] dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'), ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'), ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'), ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'), ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'), ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'), ('Kyle Moran was born in the town on what river?', 'Castletown River'), ("The actress who played the niece in the Priest film was born in what city, country?", 'Surrey, England'), ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'), ('What year was the father of the Princes in the Tower born?', '1442'), ('What river is near the Crichton Collegiate Church?', 'the River Tyne'), ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'), ('André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')] dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev] predict = dspy.Predict('question -> answer') predict(question="What is the capital of Germany?") class CoT(dspy.Module): # let's define a new module def __init__(self): super().__init__() self.generate_answer = dspy.ChainOfThought('question -> answer') def forward(self, question): return self.generate_answer(question=question) # here we use the module metric_EM = dspy.evaluate.answer_exact_match teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2) cot_compiled = teleprompter.compile(CoT(), trainset=train) cot_compiled("What is the capital of Germany?") llama.inspect_history(n=1) NUM_THREADS = 32 evaluate_hotpot =
Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15)
dspy.evaluate.Evaluate
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)
dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)
dspy.settings.configure
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]] score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0 return score / 5.0 metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) example = devset[10] tweet = tweeter(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) class TweeterWithAssertions(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet dspy.Suggest(has_no_hashtags(generated_tweet), f"Please revise the tweet to remove hashtag phrases following it.", target_module=GenerateTweet) dspy.Suggest(is_within_length_limit(generated_tweet, 280), f"Please ensure the tweet is within {280} characters.", target_module=GenerateTweet) dspy.Suggest(has_correct_answer(generated_tweet, answer), "The tweet does not include the correct answer to the question. Please revise accordingly.", target_module=GenerateTweet) engaging_question = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging_assessment =
dspy.Predict(AssessTweet)
dspy.Predict
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful =
dspy.Predict(AssessTweet)
dspy.Predict
get_ipython().system('pip install clarifai') get_ipython().system('pip install dspy-ai') import dspy from dspy.retrieve.clarifai_rm import ClarifaiRM MODEL_URL = "https://clarifai.com/meta/Llama-2/models/llama2-70b-chat" PAT = "CLARIFAI_PAT" USER_ID = "YOUR_ID" APP_ID = "YOUR_APP" from langchain.text_splitter import CharacterTextSplitter from langchain.document_loaders import TextLoader from langchain.vectorstores import Clarifai as clarifaivectorstore loader = TextLoader("YOUR_TEXT_FILE") #replace with your file path documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200) docs = text_splitter.split_documents(documents) clarifai_vector_db = clarifaivectorstore.from_documents( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=PAT ) llm=dspy.Clarifai(model=MODEL_URL, api_key=PAT, n=2, inference_params={"max_tokens":100,'temperature':0.6}) retriever_model=ClarifaiRM(clarifai_user_id=USER_ID, clarfiai_app_id=APP_ID, clarifai_pat=PAT, k=2) dspy.settings.configure(lm=llm, rm=retriever_model) sentence = "disney again ransacks its archives for a quick-buck sequel ." # example from the SST-2 dataset. classify = dspy.Predict('sentence -> sentiment') print(classify(sentence=sentence).sentiment) retrieve = dspy.Retrieve() topK_passages = retrieve("can I test my vehicle engine in pit?").passages print(topK_passages) class GenerateAnswer(dspy.Signature): """Think and Answer questions based on the context provided.""" context = dspy.InputField(desc="may contain relevant facts about user query") question =
dspy.InputField(desc="User query")
dspy.InputField
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo =
dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)
dspy.OpenAI
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import pkg_resources try: # When on Colab, let's install pyserini, Pytorch, and Faiss import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') get_ipython().run_line_magic('cd', '$repo_path') get_ipython().system('pip install -e .') if not "pyserini" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install pyserini') if not "torch" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install torch') if not "faiss-cpu" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install faiss-cpu') except: repo_path = '.' if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') if repo_path not in sys.path: sys.path.append(repo_path) import dspy pys_ret_prebuilt = dspy.Pyserini(index='beir-v1.0.0-nfcorpus.contriever-msmarco', query_encoder='facebook/contriever-msmarco', id_field='_id', text_fields=['title', 'text']) dspy.settings.configure(rm=pys_ret_prebuilt) example_question = "How Curry Can Kill Cancer Cells" retrieve = dspy.Retrieve(k=3) topK_passages = retrieve(example_question).passages print(f"Top {retrieve.k} passages for question: {example_question} \n", '-' * 30, '\n') for idx, passage in enumerate(topK_passages): print(f'{idx+1}]', passage, '\n') get_ipython().system('wget https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip -P collections') get_ipython().system('unzip collections/nfcorpus.zip -d collections') get_ipython().system('python -m pyserini.encode input --corpus collections/nfcorpus/corpus.jsonl --fields title text output --embeddings indexes/faiss.nfcorpus.contriever-msmarco --to-faiss encoder --encoder facebook/contriever-msmarco --device cuda:0 --pooling mean --fields title text') from datasets import load_dataset dataset = load_dataset(path='json', data_files='collections/nfcorpus/corpus.jsonl', split='train') pys_ret_local =
dspy.Pyserini(index='indexes/faiss.nfcorpus.contriever-msmarco', query_encoder='facebook/contriever-msmarco', dataset=dataset, id_field='_id', text_fields=['title', 'text'])
dspy.Pyserini
import dspy from dspy.evaluate import Evaluate from dspy.datasets.gsm8k import GSM8K, gsm8k_metric from dspy.teleprompt import BootstrapFewShotWithRandomSearch gms8k = GSM8K() turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250) trainset, devset = gms8k.train, gms8k.dev dspy.settings.configure(lm=turbo) NUM_THREADS = 4 evaluate =
Evaluate(devset=devset[:], metric=gsm8k_metric, num_threads=NUM_THREADS, display_progress=True, display_table=0)
dspy.evaluate.Evaluate
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]] score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0 return score / 5.0 metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) example = devset[10] tweet = tweeter(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) class TweeterWithAssertions(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet dspy.Suggest(has_no_hashtags(generated_tweet), f"Please revise the tweet to remove hashtag phrases following it.", target_module=GenerateTweet) dspy.Suggest(is_within_length_limit(generated_tweet, 280), f"Please ensure the tweet is within {280} characters.", target_module=GenerateTweet) dspy.Suggest(has_correct_answer(generated_tweet, answer), "The tweet does not include the correct answer to the question. Please revise accordingly.", target_module=GenerateTweet) engaging_question = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging_assessment = dspy.Predict(AssessTweet)(context=context, assessed_text=generated_tweet, assessment_question=engaging_question) dspy.Suggest(is_assessment_yes(engaging_assessment.assessment_answer), "The text is not engaging enough. Please revise to make it more captivating.", target_module=GenerateTweet) faithful_question = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful_assessment =
dspy.Predict(AssessTweet)
dspy.Predict
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset =
HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0)
dspy.datasets.HotPotQA
import dspy from dspy.evaluate.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShotWithRandomSearch colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.configure(rm=colbertv2)
dspy.configure
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]] score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0 return score / 5.0 metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) example = devset[10] tweet = tweeter(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate =
Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5)
dspy.evaluate.evaluate.Evaluate
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import pkg_resources try: # When on Colab, let's install pyserini, Pytorch, and Faiss import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') get_ipython().run_line_magic('cd', '$repo_path') get_ipython().system('pip install -e .') if not "pyserini" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install pyserini') if not "torch" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install torch') if not "faiss-cpu" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install faiss-cpu') except: repo_path = '.' if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') if repo_path not in sys.path: sys.path.append(repo_path) import dspy pys_ret_prebuilt =
dspy.Pyserini(index='beir-v1.0.0-nfcorpus.contriever-msmarco', query_encoder='facebook/contriever-msmarco', id_field='_id', text_fields=['title', 'text'])
dspy.Pyserini
import dspy from dspy.evaluate.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShotWithRandomSearch colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.configure(rm=colbertv2) from langchain_openai import OpenAI from langchain.globals import set_llm_cache from langchain.cache import SQLiteCache set_llm_cache(SQLiteCache(database_path="cache.db")) llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0) retrieve = lambda x: dspy.Retrieve(k=5)(x["question"]).passages from langchain_core.prompts import PromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough prompt = PromptTemplate.from_template("Given {context}, answer the question `{question}` as a tweet.") vanilla_chain = RunnablePassthrough.assign(context=retrieve) | prompt | llm | StrOutputParser() from dspy.predict.langchain import LangChainPredict, LangChainModule zeroshot_chain = RunnablePassthrough.assign(context=retrieve) | LangChainPredict(prompt, llm) | StrOutputParser() zeroshot_chain =
LangChainModule(zeroshot_chain)
dspy.predict.langchain.LangChainModule
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo =
dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)
dspy.OpenAI
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] def validate_query_distinction_local(previous_queries, query): """check if query is distinct from previous queries""" if previous_queries == []: return True if dspy.evaluate.answer_exact_match_str(query, previous_queries, frac=0.8): return False return True def validate_context_and_answer_and_hops(example, pred, trace=None): if not dspy.evaluate.answer_exact_match(example, pred): return False if not dspy.evaluate.answer_passage_match(example, pred): return False return True def gold_passages_retrieved(example, pred, trace=None): gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles'])) found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context])) return gold_titles.issubset(found_titles) class GenerateAnswer(dspy.Signature): """Answer questions with short factoid answers.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words") class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() def all_queries_distinct(prev_queries): query_distinct = True for i, query in enumerate(prev_queries): if validate_query_distinction_local(prev_queries[:i], query) == False: query_distinct = False break return query_distinct class SimplifiedBaleen(dspy.Module): def __init__(self, passages_per_hop=2, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) self.max_hops = max_hops self.passed_suggestions = 0 def forward(self, question): context = [] prev_queries = [question] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query prev_queries.append(query) passages = self.retrieve(query).passages context = deduplicate(context + passages) if all_queries_distinct(prev_queries): self.passed_suggestions += 1 pred = self.generate_answer(context=context, question=question) pred = dspy.Prediction(context=context, answer=pred.answer) return pred class SimplifiedBaleenAssertions(dspy.Module): def __init__(self, passages_per_hop=2, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) self.max_hops = max_hops self.passed_suggestions = 0 def forward(self, question): context = [] prev_queries = [question] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query dspy.Suggest( len(query) <= 100, "Query should be short and less than 100 characters", ) dspy.Suggest( validate_query_distinction_local(prev_queries, query), "Query should be distinct from: " + "; ".join(f"{i+1}) {q}" for i, q in enumerate(prev_queries)), ) prev_queries.append(query) passages = self.retrieve(query).passages context = deduplicate(context + passages) if all_queries_distinct(prev_queries): self.passed_suggestions += 1 pred = self.generate_answer(context=context, question=question) pred =
dspy.Prediction(context=context, answer=pred.answer)
dspy.Prediction
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_QuizGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_QuizGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_QuizGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_QuizGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts =
dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.ColBERTv2
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts =
dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.ColBERTv2
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') get_ipython().run_line_magic('pip', 'install datasets') import datasets ds = datasets.load_dataset("openai_humaneval") ds['test'][0] import dspy, dotenv, os dotenv.load_dotenv(os.path.expanduser("~/.env")) # load OpenAI API key from .env file lm = dspy.OpenAI(model="gpt-3.5-turbo", max_tokens=4000)
dspy.settings.configure(lm=lm)
dspy.settings.configure
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') import dspy turbo = dspy.OpenAI(model='gpt-3.5-turbo') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(lm=turbo, rm=colbertv2_wiki17_abstracts) from dspy.datasets import HotPotQA dataset = HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] len(trainset), len(devset) train_example = trainset[0] print(f"Question: {train_example.question}") print(f"Answer: {train_example.answer}") dev_example = devset[18] print(f"Question: {dev_example.question}") print(f"Answer: {dev_example.answer}") print(f"Relevant Wikipedia Titles: {dev_example.gold_titles}") print(f"For this dataset, training examples have input keys {train_example.inputs().keys()} and label keys {train_example.labels().keys()}") print(f"For this dataset, dev examples have input keys {dev_example.inputs().keys()} and label keys {dev_example.labels().keys()}") class BasicQA(dspy.Signature): """Answer questions with short factoid answers.""" question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words") generate_answer = dspy.Predict(BasicQA) pred = generate_answer(question=dev_example.question) print(f"Question: {dev_example.question}") print(f"Predicted Answer: {pred.answer}") turbo.inspect_history(n=1) generate_answer_with_chain_of_thought = dspy.ChainOfThought(BasicQA) pred = generate_answer_with_chain_of_thought(question=dev_example.question) print(f"Question: {dev_example.question}") print(f"Thought: {pred.rationale.split('.', 1)[1].strip()}") print(f"Predicted Answer: {pred.answer}") retrieve = dspy.Retrieve(k=3) topK_passages = retrieve(dev_example.question).passages print(f"Top {retrieve.k} passages for question: {dev_example.question} \n", '-' * 30, '\n') for idx, passage in enumerate(topK_passages): print(f'{idx+1}]', passage, '\n') retrieve("When was the first FIFA World Cup held?").passages[0] class GenerateAnswer(dspy.Signature): """Answer questions with short factoid answers.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words") class RAG(dspy.Module): def __init__(self, num_passages=3): super().__init__() self.retrieve = dspy.Retrieve(k=num_passages) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) def forward(self, question): context = self.retrieve(question).passages prediction = self.generate_answer(context=context, question=question) return dspy.Prediction(context=context, answer=prediction.answer) from dspy.teleprompt import BootstrapFewShot def validate_context_and_answer(example, pred, trace=None): answer_EM = dspy.evaluate.answer_exact_match(example, pred) answer_PM =
dspy.evaluate.answer_passage_match(example, pred)
dspy.evaluate.answer_passage_match
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_QuizGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_QuizGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_QuizGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_QuizGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo =
dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)
dspy.OpenAI
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] train_example = trainset[0] print(f"Question: {train_example.question}") print(f"Answer: {train_example.answer}") print(f"Relevant Wikipedia Titles: {train_example.gold_titles}") dev_example = devset[18] print(f"Question: {dev_example.question}") print(f"Answer: {dev_example.answer}") print(f"Relevant Wikipedia Titles: {dev_example.gold_titles}") from dsp.utils import deduplicate class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateCitedParagraph(dspy.Signature): """Generate a paragraph with citations.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() paragraph = dspy.OutputField(desc="includes citations") class LongFormQA(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph) self.max_hops = max_hops def forward(self, question): context = [] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages context = deduplicate(context + passages) pred = self.generate_cited_paragraph(context=context, question=question) pred = dspy.Prediction(context=context, paragraph=pred.paragraph) return pred class CheckCitationFaithfulness(dspy.Signature): """Verify that the text is based on the provided context.""" context = dspy.InputField(desc="may contain relevant facts") text = dspy.InputField(desc="between 1 to 2 sentences") faithfulness = dspy.OutputField(desc="boolean indicating if text is faithful to context") def citation_faithfulness(example, pred, trace): paragraph, context = pred.paragraph, pred.context citation_dict = extract_text_by_citation(paragraph) if not citation_dict: return False, None context_dict = {str(i): context[i].split(' | ')[1] for i in range(len(context))} faithfulness_results = [] unfaithful_citations = [] check_citation_faithfulness = dspy.ChainOfThought(CheckCitationFaithfulness) for citation_num, texts in citation_dict.items(): if citation_num not in context_dict: continue current_context = context_dict[citation_num] for text in texts: try: result = check_citation_faithfulness(context=current_context, text=text) is_faithful = result.faithfulness.lower() == 'true' faithfulness_results.append(is_faithful) if not is_faithful: unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'context': current_context}) except ValueError as e: faithfulness_results.append(False) unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'error': str(e)}) final_faithfulness = all(faithfulness_results) if not faithfulness_results: return False, None return final_faithfulness, unfaithful_citations def extract_cited_titles_from_paragraph(paragraph, context): cited_indices = [int(m.group(1)) for m in re.finditer(r'\[(\d+)\]\.', paragraph)] cited_indices = [index - 1 for index in cited_indices if index <= len(context)] cited_titles = [context[index].split(' | ')[0] for index in cited_indices] return cited_titles def calculate_recall(example, pred, trace=None): gold_titles = set(example['gold_titles']) found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context)) intersection = gold_titles.intersection(found_cited_titles) recall = len(intersection) / len(gold_titles) if gold_titles else 0 return recall def calculate_precision(example, pred, trace=None): gold_titles = set(example['gold_titles']) found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context)) intersection = gold_titles.intersection(found_cited_titles) precision = len(intersection) / len(found_cited_titles) if found_cited_titles else 0 return precision def answer_correctness(example, pred, trace=None): assert hasattr(example, 'answer'), "Example does not have 'answer'." normalized_context = normalize_text(pred.paragraph) if isinstance(example.answer, str): gold_answers = [example.answer] elif isinstance(example.answer, list): gold_answers = example.answer else: raise ValueError("'example.answer' is not string or list.") return 1 if any(normalize_text(answer) in normalized_context for answer in gold_answers) else 0 def evaluate(module): correctness_values = [] recall_values = [] precision_values = [] citation_faithfulness_values = [] for i in range(len(devset)): example = devset[i] try: pred = module(question=example.question) correctness_values.append(answer_correctness(example, pred)) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) citation_faithfulness_values.append(citation_faithfulness_score) recall = calculate_recall(example, pred) precision = calculate_precision(example, pred) recall_values.append(recall) precision_values.append(precision) except Exception as e: print(f"Failed generation with error: {e}") average_correctness = sum(correctness_values) / len(devset) if correctness_values else 0 average_recall = sum(recall_values) / len(devset) if recall_values else 0 average_precision = sum(precision_values) / len(devset) if precision_values else 0 average_citation_faithfulness = sum(citation_faithfulness_values) / len(devset) if citation_faithfulness_values else 0 print(f"Average Correctness: {average_correctness}") print(f"Average Recall: {average_recall}") print(f"Average Precision: {average_precision}") print(f"Average Citation Faithfulness: {average_citation_faithfulness}") longformqa = LongFormQA() evaluate(longformqa) question = devset[6].question pred = longformqa(question) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) print(f"Question: {question}") print(f"Predicted Paragraph: {pred.paragraph}") print(f"Citation Faithfulness: {citation_faithfulness_score}") class LongFormQAWithAssertions(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph) self.max_hops = max_hops def forward(self, question): context = [] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages context = deduplicate(context + passages) pred = self.generate_cited_paragraph(context=context, question=question) pred = dspy.Prediction(context=context, paragraph=pred.paragraph) dspy.Suggest(citations_check(pred.paragraph), f"Make sure every 1-2 sentences has citations. If any 1-2 sentences lack citations, add them in 'text... [x].' format.", target_module=GenerateCitedParagraph) _, unfaithful_outputs = citation_faithfulness(None, pred, None) if unfaithful_outputs: unfaithful_pairs = [(output['text'], output['context']) for output in unfaithful_outputs] for _, context in unfaithful_pairs: dspy.Suggest(len(unfaithful_pairs) == 0, f"Make sure your output is based on the following context: '{context}'.", target_module=GenerateCitedParagraph) else: return pred return pred longformqa_with_assertions = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler) evaluate(longformqa_with_assertions) question = devset[6].question pred = longformqa_with_assertions(question) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) print(f"Question: {question}") print(f"Predicted Paragraph: {pred.paragraph}") print(f"Citation Faithfulness: {citation_faithfulness_score}") longformqa = LongFormQA() teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6) cited_longformqa = teleprompter.compile(student = longformqa, teacher = longformqa, trainset=trainset, valset=devset[:100]) evaluate(cited_longformqa) longformqa = LongFormQA() teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6) cited_longformqa_teacher = teleprompter.compile(student=longformqa, teacher = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler), trainset=trainset, valset=devset[:100]) evaluate(cited_longformqa_teacher) longformqa = LongFormQA() teleprompter =
BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)
dspy.teleprompt.BootstrapFewShotWithRandomSearch
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts =
dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.ColBERTv2
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo =
dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500)
dspy.OpenAI
get_ipython().system('pip install clarifai') get_ipython().system('pip install dspy-ai') import dspy from dspy.retrieve.clarifai_rm import ClarifaiRM MODEL_URL = "https://clarifai.com/meta/Llama-2/models/llama2-70b-chat" PAT = "CLARIFAI_PAT" USER_ID = "YOUR_ID" APP_ID = "YOUR_APP" from langchain.text_splitter import CharacterTextSplitter from langchain.document_loaders import TextLoader from langchain.vectorstores import Clarifai as clarifaivectorstore loader = TextLoader("YOUR_TEXT_FILE") #replace with your file path documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200) docs = text_splitter.split_documents(documents) clarifai_vector_db = clarifaivectorstore.from_documents( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=PAT ) llm=dspy.Clarifai(model=MODEL_URL, api_key=PAT, n=2, inference_params={"max_tokens":100,'temperature':0.6}) retriever_model=ClarifaiRM(clarifai_user_id=USER_ID, clarfiai_app_id=APP_ID, clarifai_pat=PAT, k=2) dspy.settings.configure(lm=llm, rm=retriever_model) sentence = "disney again ransacks its archives for a quick-buck sequel ." # example from the SST-2 dataset. classify = dspy.Predict('sentence -> sentiment') print(classify(sentence=sentence).sentiment) retrieve = dspy.Retrieve() topK_passages = retrieve("can I test my vehicle engine in pit?").passages print(topK_passages) class GenerateAnswer(dspy.Signature): """Think and Answer questions based on the context provided.""" context = dspy.InputField(desc="may contain relevant facts about user query") question = dspy.InputField(desc="User query") answer = dspy.OutputField(desc="Answer in one or two lines") class RAG(dspy.Module): def __init__(self): super().__init__() self.retrieve = dspy.Retrieve() self.generate_answer = dspy.ChainOfThought(GenerateAnswer) def forward(self, question): context = self.retrieve(question).passages prediction = self.generate_answer(context=context, question=question) return dspy.Prediction(context=context, answer=prediction.answer) my_question = "can I test my vehicle engine in pit before inspection?" Rag_obj= RAG() predict_response_llama70b=Rag_obj(my_question) print(f"Question: {my_question}") print(f"Predicted Answer: {predict_response_llama70b.answer}") print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_llama70b.context]}") mistral_lm = dspy.Clarifai(model="https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct", api_key=PAT, n=2, inference_params={'temperature':0.6}) dspy.settings.configure(lm=mistral_lm, rm=retriever_model) my_question = "can I test my vehicle engine in pit before inspection?" Rag_obj= RAG() predict_response_mistral=Rag_obj(my_question) print(f"Question: {my_question}") print(f"Predicted Answer: {predict_response_mistral.answer}") print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in predict_response_mistral.context]}") gemini_lm =
dspy.Clarifai(model="https://clarifai.com/gcp/generate/models/gemini-pro", api_key=PAT, n=2)
dspy.Clarifai
import dspy from dspy.evaluate.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShotWithRandomSearch colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.configure(rm=colbertv2) from langchain_openai import OpenAI from langchain.globals import set_llm_cache from langchain.cache import SQLiteCache set_llm_cache(SQLiteCache(database_path="cache.db")) llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0) retrieve = lambda x: dspy.Retrieve(k=5)(x["question"]).passages from langchain_core.prompts import PromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough prompt = PromptTemplate.from_template("Given {context}, answer the question `{question}` as a tweet.") vanilla_chain = RunnablePassthrough.assign(context=retrieve) | prompt | llm | StrOutputParser() from dspy.predict.langchain import LangChainPredict, LangChainModule zeroshot_chain = RunnablePassthrough.assign(context=retrieve) | LangChainPredict(prompt, llm) | StrOutputParser() zeroshot_chain = LangChainModule(zeroshot_chain) # then wrap the chain in a DSPy module. question = "In what region was Eddy Mazzoleni born?" zeroshot_chain.invoke({"question": question}) from tweet_metric import metric, trainset, valset, devset len(trainset), len(valset), len(devset) evaluate = Evaluate(metric=metric, devset=devset, num_threads=8, display_progress=True, display_table=5) evaluate(zeroshot_chain) optimizer =
BootstrapFewShotWithRandomSearch(metric=metric, max_bootstrapped_demos=3, num_candidate_programs=3)
dspy.teleprompt.BootstrapFewShotWithRandomSearch
get_ipython().system('pip install clarifai') get_ipython().system('pip install dspy-ai') import dspy from dspy.retrieve.clarifai_rm import ClarifaiRM MODEL_URL = "https://clarifai.com/meta/Llama-2/models/llama2-70b-chat" PAT = "CLARIFAI_PAT" USER_ID = "YOUR_ID" APP_ID = "YOUR_APP" from langchain.text_splitter import CharacterTextSplitter from langchain.document_loaders import TextLoader from langchain.vectorstores import Clarifai as clarifaivectorstore loader = TextLoader("YOUR_TEXT_FILE") #replace with your file path documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1024, chunk_overlap=200) docs = text_splitter.split_documents(documents) clarifai_vector_db = clarifaivectorstore.from_documents( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=PAT ) llm=dspy.Clarifai(model=MODEL_URL, api_key=PAT, n=2, inference_params={"max_tokens":100,'temperature':0.6}) retriever_model=ClarifaiRM(clarifai_user_id=USER_ID, clarfiai_app_id=APP_ID, clarifai_pat=PAT, k=2) dspy.settings.configure(lm=llm, rm=retriever_model) sentence = "disney again ransacks its archives for a quick-buck sequel ." # example from the SST-2 dataset. classify = dspy.Predict('sentence -> sentiment') print(classify(sentence=sentence).sentiment) retrieve = dspy.Retrieve() topK_passages = retrieve("can I test my vehicle engine in pit?").passages print(topK_passages) class GenerateAnswer(dspy.Signature): """Think and Answer questions based on the context provided.""" context = dspy.InputField(desc="may contain relevant facts about user query") question = dspy.InputField(desc="User query") answer =
dspy.OutputField(desc="Answer in one or two lines")
dspy.OutputField
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] train_example = trainset[0] print(f"Question: {train_example.question}") print(f"Answer: {train_example.answer}") print(f"Relevant Wikipedia Titles: {train_example.gold_titles}") dev_example = devset[18] print(f"Question: {dev_example.question}") print(f"Answer: {dev_example.answer}") print(f"Relevant Wikipedia Titles: {dev_example.gold_titles}") from dsp.utils import deduplicate class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateCitedParagraph(dspy.Signature): """Generate a paragraph with citations.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() paragraph = dspy.OutputField(desc="includes citations") class LongFormQA(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph) self.max_hops = max_hops def forward(self, question): context = [] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages context = deduplicate(context + passages) pred = self.generate_cited_paragraph(context=context, question=question) pred = dspy.Prediction(context=context, paragraph=pred.paragraph) return pred class CheckCitationFaithfulness(dspy.Signature): """Verify that the text is based on the provided context.""" context = dspy.InputField(desc="may contain relevant facts") text = dspy.InputField(desc="between 1 to 2 sentences") faithfulness = dspy.OutputField(desc="boolean indicating if text is faithful to context") def citation_faithfulness(example, pred, trace): paragraph, context = pred.paragraph, pred.context citation_dict = extract_text_by_citation(paragraph) if not citation_dict: return False, None context_dict = {str(i): context[i].split(' | ')[1] for i in range(len(context))} faithfulness_results = [] unfaithful_citations = [] check_citation_faithfulness = dspy.ChainOfThought(CheckCitationFaithfulness) for citation_num, texts in citation_dict.items(): if citation_num not in context_dict: continue current_context = context_dict[citation_num] for text in texts: try: result = check_citation_faithfulness(context=current_context, text=text) is_faithful = result.faithfulness.lower() == 'true' faithfulness_results.append(is_faithful) if not is_faithful: unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'context': current_context}) except ValueError as e: faithfulness_results.append(False) unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'error': str(e)}) final_faithfulness = all(faithfulness_results) if not faithfulness_results: return False, None return final_faithfulness, unfaithful_citations def extract_cited_titles_from_paragraph(paragraph, context): cited_indices = [int(m.group(1)) for m in re.finditer(r'\[(\d+)\]\.', paragraph)] cited_indices = [index - 1 for index in cited_indices if index <= len(context)] cited_titles = [context[index].split(' | ')[0] for index in cited_indices] return cited_titles def calculate_recall(example, pred, trace=None): gold_titles = set(example['gold_titles']) found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context)) intersection = gold_titles.intersection(found_cited_titles) recall = len(intersection) / len(gold_titles) if gold_titles else 0 return recall def calculate_precision(example, pred, trace=None): gold_titles = set(example['gold_titles']) found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context)) intersection = gold_titles.intersection(found_cited_titles) precision = len(intersection) / len(found_cited_titles) if found_cited_titles else 0 return precision def answer_correctness(example, pred, trace=None): assert hasattr(example, 'answer'), "Example does not have 'answer'." normalized_context = normalize_text(pred.paragraph) if isinstance(example.answer, str): gold_answers = [example.answer] elif isinstance(example.answer, list): gold_answers = example.answer else: raise ValueError("'example.answer' is not string or list.") return 1 if any(normalize_text(answer) in normalized_context for answer in gold_answers) else 0 def evaluate(module): correctness_values = [] recall_values = [] precision_values = [] citation_faithfulness_values = [] for i in range(len(devset)): example = devset[i] try: pred = module(question=example.question) correctness_values.append(answer_correctness(example, pred)) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) citation_faithfulness_values.append(citation_faithfulness_score) recall = calculate_recall(example, pred) precision = calculate_precision(example, pred) recall_values.append(recall) precision_values.append(precision) except Exception as e: print(f"Failed generation with error: {e}") average_correctness = sum(correctness_values) / len(devset) if correctness_values else 0 average_recall = sum(recall_values) / len(devset) if recall_values else 0 average_precision = sum(precision_values) / len(devset) if precision_values else 0 average_citation_faithfulness = sum(citation_faithfulness_values) / len(devset) if citation_faithfulness_values else 0 print(f"Average Correctness: {average_correctness}") print(f"Average Recall: {average_recall}") print(f"Average Precision: {average_precision}") print(f"Average Citation Faithfulness: {average_citation_faithfulness}") longformqa = LongFormQA() evaluate(longformqa) question = devset[6].question pred = longformqa(question) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) print(f"Question: {question}") print(f"Predicted Paragraph: {pred.paragraph}") print(f"Citation Faithfulness: {citation_faithfulness_score}") class LongFormQAWithAssertions(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph) self.max_hops = max_hops def forward(self, question): context = [] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages context = deduplicate(context + passages) pred = self.generate_cited_paragraph(context=context, question=question) pred = dspy.Prediction(context=context, paragraph=pred.paragraph) dspy.Suggest(citations_check(pred.paragraph), f"Make sure every 1-2 sentences has citations. If any 1-2 sentences lack citations, add them in 'text... [x].' format.", target_module=GenerateCitedParagraph) _, unfaithful_outputs = citation_faithfulness(None, pred, None) if unfaithful_outputs: unfaithful_pairs = [(output['text'], output['context']) for output in unfaithful_outputs] for _, context in unfaithful_pairs: dspy.Suggest(len(unfaithful_pairs) == 0, f"Make sure your output is based on the following context: '{context}'.", target_module=GenerateCitedParagraph) else: return pred return pred longformqa_with_assertions = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler) evaluate(longformqa_with_assertions) question = devset[6].question pred = longformqa_with_assertions(question) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) print(f"Question: {question}") print(f"Predicted Paragraph: {pred.paragraph}") print(f"Citation Faithfulness: {citation_faithfulness_score}") longformqa = LongFormQA() teleprompter = BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6) cited_longformqa = teleprompter.compile(student = longformqa, teacher = longformqa, trainset=trainset, valset=devset[:100]) evaluate(cited_longformqa) longformqa = LongFormQA() teleprompter =
BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)
dspy.teleprompt.BootstrapFewShotWithRandomSearch
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] train_example = trainset[0] print(f"Question: {train_example.question}") print(f"Answer: {train_example.answer}") print(f"Relevant Wikipedia Titles: {train_example.gold_titles}") dev_example = devset[18] print(f"Question: {dev_example.question}") print(f"Answer: {dev_example.answer}") print(f"Relevant Wikipedia Titles: {dev_example.gold_titles}") from dsp.utils import deduplicate class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateCitedParagraph(dspy.Signature): """Generate a paragraph with citations.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() paragraph =
dspy.OutputField(desc="includes citations")
dspy.OutputField
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install -e $repo_path') get_ipython().system('pip install transformers') import dspy from dspy.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llama) train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'), ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'), ('In what year was the star of To Hell and Back born?', '1925'), ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'), ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'), ('Which author is English: John Braine or Studs Terkel?', 'John Braine'), ('Who produced the album that included a re-recording of "Lithium"?', 'Butch Vig')] train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train] dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'), ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'), ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'), ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'), ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'), ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'), ('Kyle Moran was born in the town on what river?', 'Castletown River'), ("The actress who played the niece in the Priest film was born in what city, country?", 'Surrey, England'), ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'), ('What year was the father of the Princes in the Tower born?', '1442'), ('What river is near the Crichton Collegiate Church?', 'the River Tyne'), ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'), ('André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')] dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev] predict = dspy.Predict('question -> answer') predict(question="What is the capital of Germany?") class CoT(dspy.Module): # let's define a new module def __init__(self): super().__init__() self.generate_answer = dspy.ChainOfThought('question -> answer') def forward(self, question): return self.generate_answer(question=question) # here we use the module metric_EM = dspy.evaluate.answer_exact_match teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2) cot_compiled = teleprompter.compile(CoT(), trainset=train) cot_compiled("What is the capital of Germany?") llama.inspect_history(n=1) NUM_THREADS = 32 evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15) evaluate_hotpot(cot_compiled) class RAG(dspy.Module): def __init__(self, num_passages=3): super().__init__() self.retrieve = dspy.Retrieve(k=num_passages) self.generate_query = dspy.ChainOfThought("question -> search_query") self.generate_answer = dspy.ChainOfThought("context, question -> answer") def forward(self, question): search_query = self.generate_query(question=question).search_query passages = self.retrieve(search_query).passages return self.generate_answer(context=passages, question=question) evaluate_hotpot(RAG(), display_table=0) teleprompter2 = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_candidate_programs=8, num_threads=NUM_THREADS) rag_compiled = teleprompter2.compile(RAG(), trainset=train, valset=dev) evaluate_hotpot(rag_compiled) rag_compiled("What year was the party of the winner of the 1971 San Francisco mayoral election founded?") llama.inspect_history(n=1) from dsp.utils.utils import deduplicate class MultiHop(dspy.Module): def __init__(self, num_passages=3): super().__init__() self.retrieve = dspy.Retrieve(k=num_passages) self.generate_query =
dspy.ChainOfThought("question -> search_query")
dspy.ChainOfThought
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] train_example = trainset[0] print(f"Question: {train_example.question}") print(f"Answer: {train_example.answer}") print(f"Relevant Wikipedia Titles: {train_example.gold_titles}") dev_example = devset[18] print(f"Question: {dev_example.question}") print(f"Answer: {dev_example.answer}") print(f"Relevant Wikipedia Titles: {dev_example.gold_titles}") from dsp.utils import deduplicate class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateCitedParagraph(dspy.Signature): """Generate a paragraph with citations.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() paragraph = dspy.OutputField(desc="includes citations") class LongFormQA(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve =
dspy.Retrieve(k=passages_per_hop)
dspy.Retrieve
import glob import os import pandas as pd import random import dspy from dspy.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShotWithRandomSearch os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join('.', 'cache') turbo = dspy.OpenAI(model='gpt-3.5-turbo-1106', max_tokens=250, model_type='chat') dspy.settings.configure(lm=turbo) gpt4T = dspy.OpenAI(model='gpt-4-1106-preview', max_tokens=350, model_type='chat') RUN_FROM_SCRATCH = False get_ipython().system('git clone https://github.com/selenashe/ScoNe.git') def load_scone(dirname): dfs = [] for filename in glob.glob(dirname + "/*.csv"): df = pd.read_csv(filename, index_col=0) df['category'] = os.path.basename(filename).replace(".csv", "") dfs.append(df) data_df = pd.concat(dfs) def as_example(row): suffix = '' if row['category'] == 'one_scoped' else '_edited' hkey = 'sentence2' + suffix question = row[hkey][0].lower() + row[hkey][1: ].strip(".") question = f"Can we logically conclude for sure that {question}?" label = "Yes" if row['gold_label' + suffix] == 'entailment' else "No" return dspy.Example({ "context": row['sentence1' + suffix], "question": question, "answer": label, "category": row['category'] }).with_inputs("context", "question") return list(data_df.apply(as_example, axis=1).values) all_train = load_scone("ScoNe/scone_nli/train") random.seed(1) random.shuffle(all_train) train, dev = all_train[: 200], all_train[200: 250] len(train), len(dev) random.seed(1) test = load_scone(dirname=f"ScoNe/scone_nli/test") test = [ex for ex in test if ex.category == "one_scoped"] pd.Series([ex.answer for ex in test]).value_counts() scone_accuracy = dspy.evaluate.metrics.answer_exact_match evaluator = Evaluate(devset=test, num_threads=1, display_progress=True, display_table=0) class ScoNeSignature(dspy.Signature): ("""You are given some context (a premise) and a question (a hypothesis). """ """You must indicate with Yes/No answer whether we can logically """ """conclude the hypothesis from the premise.""") context = dspy.InputField() question = dspy.InputField() answer =
dspy.OutputField(desc="Yes or No")
dspy.OutputField
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install -e $repo_path') get_ipython().system('pip install transformers') import dspy from dspy.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llama) train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'), ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'), ('In what year was the star of To Hell and Back born?', '1925'), ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'), ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'), ('Which author is English: John Braine or Studs Terkel?', 'John Braine'), ('Who produced the album that included a re-recording of "Lithium"?', 'Butch Vig')] train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train] dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'), ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'), ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'), ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'), ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'), ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'), ('Kyle Moran was born in the town on what river?', 'Castletown River'), ("The actress who played the niece in the Priest film was born in what city, country?", 'Surrey, England'), ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'), ('What year was the father of the Princes in the Tower born?', '1442'), ('What river is near the Crichton Collegiate Church?', 'the River Tyne'), ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'), ('André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')] dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev] predict = dspy.Predict('question -> answer') predict(question="What is the capital of Germany?") class CoT(dspy.Module): # let's define a new module def __init__(self): super().__init__() self.generate_answer = dspy.ChainOfThought('question -> answer') def forward(self, question): return self.generate_answer(question=question) # here we use the module metric_EM = dspy.evaluate.answer_exact_match teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2) cot_compiled = teleprompter.compile(CoT(), trainset=train) cot_compiled("What is the capital of Germany?") llama.inspect_history(n=1) NUM_THREADS = 32 evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15) evaluate_hotpot(cot_compiled) class RAG(dspy.Module): def __init__(self, num_passages=3): super().__init__() self.retrieve =
dspy.Retrieve(k=num_passages)
dspy.Retrieve
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] def validate_query_distinction_local(previous_queries, query): """check if query is distinct from previous queries""" if previous_queries == []: return True if dspy.evaluate.answer_exact_match_str(query, previous_queries, frac=0.8): return False return True def validate_context_and_answer_and_hops(example, pred, trace=None): if not
dspy.evaluate.answer_exact_match(example, pred)
dspy.evaluate.answer_exact_match
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]] score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0 return score / 5.0 metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) example = devset[10] tweet = tweeter(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) class TweeterWithAssertions(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet dspy.Suggest(has_no_hashtags(generated_tweet), f"Please revise the tweet to remove hashtag phrases following it.", target_module=GenerateTweet) dspy.Suggest(is_within_length_limit(generated_tweet, 280), f"Please ensure the tweet is within {280} characters.", target_module=GenerateTweet) dspy.Suggest(has_correct_answer(generated_tweet, answer), "The tweet does not include the correct answer to the question. Please revise accordingly.", target_module=GenerateTweet) engaging_question = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging_assessment = dspy.Predict(AssessTweet)(context=context, assessed_text=generated_tweet, assessment_question=engaging_question) dspy.Suggest(is_assessment_yes(engaging_assessment.assessment_answer), "The text is not engaging enough. Please revise to make it more captivating.", target_module=GenerateTweet) faithful_question = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful_assessment = dspy.Predict(AssessTweet)(context='N/A', assessed_text=generated_tweet, assessment_question=faithful_question) dspy.Suggest(is_assessment_yes(faithful_assessment.assessment_answer), "The text contains unfaithful elements or significant facts not in the context. Please revise for accuracy.", target_module=GenerateTweet) return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter_with_assertions = assert_transform_module(TweeterWithAssertions().map_named_predictors(Retry), backtrack_handler) metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter_with_assertions) example = devset[10] tweet = tweeter_with_assertions(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter_with_assertions) teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6) compiled_tweeter = teleprompter.compile(student = tweeter, teacher = tweeter, trainset=trainset, valset=devset[:100]) for metric in metrics: evaluate =
Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)
dspy.evaluate.evaluate.Evaluate
import dspy from dspy.evaluate.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShotWithRandomSearch colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.configure(rm=colbertv2) from langchain_openai import OpenAI from langchain.globals import set_llm_cache from langchain.cache import SQLiteCache set_llm_cache(SQLiteCache(database_path="cache.db")) llm = OpenAI(model_name="gpt-3.5-turbo-instruct", temperature=0) retrieve = lambda x: dspy.Retrieve(k=5)(x["question"]).passages from langchain_core.prompts import PromptTemplate from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnablePassthrough prompt = PromptTemplate.from_template("Given {context}, answer the question `{question}` as a tweet.") vanilla_chain = RunnablePassthrough.assign(context=retrieve) | prompt | llm | StrOutputParser() from dspy.predict.langchain import LangChainPredict, LangChainModule zeroshot_chain = RunnablePassthrough.assign(context=retrieve) |
LangChainPredict(prompt, llm)
dspy.predict.langchain.LangChainPredict
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import dspy get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy') import dspy from dspy.evaluate import Evaluate from dspy.datasets.hotpotqa import HotPotQA from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama =
dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150)
dspy.HFClientTGI
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] def validate_query_distinction_local(previous_queries, query): """check if query is distinct from previous queries""" if previous_queries == []: return True if dspy.evaluate.answer_exact_match_str(query, previous_queries, frac=0.8): return False return True def validate_context_and_answer_and_hops(example, pred, trace=None): if not dspy.evaluate.answer_exact_match(example, pred): return False if not dspy.evaluate.answer_passage_match(example, pred): return False return True def gold_passages_retrieved(example, pred, trace=None): gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles'])) found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context])) return gold_titles.issubset(found_titles) class GenerateAnswer(dspy.Signature): """Answer questions with short factoid answers.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words") class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() def all_queries_distinct(prev_queries): query_distinct = True for i, query in enumerate(prev_queries): if validate_query_distinction_local(prev_queries[:i], query) == False: query_distinct = False break return query_distinct class SimplifiedBaleen(dspy.Module): def __init__(self, passages_per_hop=2, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) self.max_hops = max_hops self.passed_suggestions = 0 def forward(self, question): context = [] prev_queries = [question] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query prev_queries.append(query) passages = self.retrieve(query).passages context = deduplicate(context + passages) if all_queries_distinct(prev_queries): self.passed_suggestions += 1 pred = self.generate_answer(context=context, question=question) pred = dspy.Prediction(context=context, answer=pred.answer) return pred class SimplifiedBaleenAssertions(dspy.Module): def __init__(self, passages_per_hop=2, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_answer = dspy.ChainOfThought(GenerateAnswer) self.max_hops = max_hops self.passed_suggestions = 0 def forward(self, question): context = [] prev_queries = [question] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query dspy.Suggest( len(query) <= 100, "Query should be short and less than 100 characters", ) dspy.Suggest( validate_query_distinction_local(prev_queries, query), "Query should be distinct from: " + "; ".join(f"{i+1}) {q}" for i, q in enumerate(prev_queries)), ) prev_queries.append(query) passages = self.retrieve(query).passages context = deduplicate(context + passages) if all_queries_distinct(prev_queries): self.passed_suggestions += 1 pred = self.generate_answer(context=context, question=question) pred = dspy.Prediction(context=context, answer=pred.answer) return pred evaluate_on_hotpotqa =
Evaluate(devset=devset, num_threads=10, display_progress=True, display_table=False)
dspy.evaluate.evaluate.Evaluate
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import dspy from dspy.evaluate import Evaluate from dspy.datasets.hotpotqa import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch, BootstrapFinetune ports = [7140, 7141, 7142, 7143, 7144, 7145] llamaChat = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=ports, max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llamaChat) dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=1000, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] testset = [x.with_inputs('question') for x in dataset.test] len(trainset), len(devset), len(testset) trainset[0] from dsp.utils.utils import deduplicate class BasicMH(dspy.Module): def __init__(self, passages_per_hop=3): super().__init__() self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_query = [dspy.ChainOfThought("context, question -> search_query") for _ in range(2)] self.generate_answer = dspy.ChainOfThought("context, question -> answer") def forward(self, question): context = [] for hop in range(2): search_query = self.generate_query[hop](context=context, question=question).search_query passages = self.retrieve(search_query).passages context = deduplicate(context + passages) return self.generate_answer(context=context, question=question).copy(context=context) RECOMPILE_INTO_LLAMA_FROM_SCRATCH = False NUM_THREADS = 24 metric_EM = dspy.evaluate.answer_exact_match if RECOMPILE_INTO_LLAMA_FROM_SCRATCH: tp = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_threads=NUM_THREADS) basicmh_bs = tp.compile(BasicMH(), trainset=trainset[:50], valset=trainset[50:200]) ensemble = [prog for *_, prog in basicmh_bs.candidate_programs[:4]] for idx, prog in enumerate(ensemble): pass if not RECOMPILE_INTO_LLAMA_FROM_SCRATCH: ensemble = [] for idx in range(4): prog = BasicMH() prog.load(f'multihop_llama213b_{idx}.json') ensemble.append(prog) llama_program = ensemble[0] evaluate_hotpot = Evaluate(devset=devset[:1000], metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=0) evaluate_hotpot(llama_program) llama_program(question="How many storeys are in the castle that David Gregory inherited?") llamaChat.inspect_history(n=3) unlabeled_train =
HotPotQA(train_seed=1, train_size=3000, eval_seed=2023, dev_size=0, test_size=0)
dspy.datasets.hotpotqa.HotPotQA
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]] score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0 return score / 5.0 metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) example = devset[10] tweet = tweeter(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) class TweeterWithAssertions(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet dspy.Suggest(has_no_hashtags(generated_tweet), f"Please revise the tweet to remove hashtag phrases following it.", target_module=GenerateTweet) dspy.Suggest(is_within_length_limit(generated_tweet, 280), f"Please ensure the tweet is within {280} characters.", target_module=GenerateTweet) dspy.Suggest(has_correct_answer(generated_tweet, answer), "The tweet does not include the correct answer to the question. Please revise accordingly.", target_module=GenerateTweet) engaging_question = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging_assessment = dspy.Predict(AssessTweet)(context=context, assessed_text=generated_tweet, assessment_question=engaging_question) dspy.Suggest(is_assessment_yes(engaging_assessment.assessment_answer), "The text is not engaging enough. Please revise to make it more captivating.", target_module=GenerateTweet) faithful_question = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful_assessment = dspy.Predict(AssessTweet)(context='N/A', assessed_text=generated_tweet, assessment_question=faithful_question) dspy.Suggest(is_assessment_yes(faithful_assessment.assessment_answer), "The text contains unfaithful elements or significant facts not in the context. Please revise for accuracy.", target_module=GenerateTweet) return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter_with_assertions = assert_transform_module(TweeterWithAssertions().map_named_predictors(Retry), backtrack_handler) metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter_with_assertions) example = devset[10] tweet = tweeter_with_assertions(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter_with_assertions) teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6) compiled_tweeter = teleprompter.compile(student = tweeter, teacher = tweeter, trainset=trainset, valset=devset[:100]) for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(compiled_tweeter) teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6) compiled_with_assertions_tweeter = teleprompter.compile(student=tweeter, teacher = tweeter_with_assertions, trainset=trainset, valset=devset[:100]) for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(compiled_with_assertions_tweeter) teleprompter = BootstrapFewShotWithRandomSearch(metric = overall_metric, max_bootstrapped_demos=2, num_candidate_programs=6, num_threads=1) compiled_tweeter_with_assertions = teleprompter.compile(student=tweeter_with_assertions, teacher = tweeter_with_assertions, trainset=trainset, valset=devset[:100]) for metric in metrics: evaluate =
Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5)
dspy.evaluate.evaluate.Evaluate
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset =
HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True)
dspy.datasets.HotPotQA
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import dspy get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys; sys.path.append('/future/u/okhattab/repos/public/stanfordnlp/dspy') import dspy from dspy.evaluate import Evaluate from dspy.datasets.hotpotqa import HotPotQA from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llama) train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'), ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'), ('In what year was the star of To Hell and Back born?', '1925'), ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'), ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'), ('Which author is English: John Braine or Studs Terkel?', 'John Braine'), ('Who produced the album that included a re-recording of "Lithium"?', 'Butch Vig')] train = [
dspy.Example(question=question, answer=answer)
dspy.Example
import dspy from dsp.utils import deduplicate from dspy.datasets import HotPotQA from dspy.predict.retry import Retry from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler import os import openai openai.api_key = os.getenv('OPENAI_API_KEY') colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] def validate_query_distinction_local(previous_queries, query): """check if query is distinct from previous queries""" if previous_queries == []: return True if dspy.evaluate.answer_exact_match_str(query, previous_queries, frac=0.8): return False return True def validate_context_and_answer_and_hops(example, pred, trace=None): if not dspy.evaluate.answer_exact_match(example, pred): return False if not dspy.evaluate.answer_passage_match(example, pred): return False return True def gold_passages_retrieved(example, pred, trace=None): gold_titles = set(map(dspy.evaluate.normalize_text, example['gold_titles'])) found_titles = set(map(dspy.evaluate.normalize_text, [c.split(' | ')[0] for c in pred.context])) return gold_titles.issubset(found_titles) class GenerateAnswer(dspy.Signature): """Answer questions with short factoid answers.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() answer = dspy.OutputField(desc="often between 1 and 5 words") class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query =
dspy.OutputField()
dspy.OutputField
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_LongFormQA_Cache') get_ipython().run_line_magic('cd', 'DSPy_LongFormQA_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_LongFormQA_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_LongFormQA_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import EM, normalize_text from dspy.primitives.assertions import assert_transform_module, backtrack_handler get_ipython().run_line_magic('cd', 'dspy/examples/longformqa') from utils import extract_text_by_citation, correct_citation_format, has_citations, citations_check colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] train_example = trainset[0] print(f"Question: {train_example.question}") print(f"Answer: {train_example.answer}") print(f"Relevant Wikipedia Titles: {train_example.gold_titles}") dev_example = devset[18] print(f"Question: {dev_example.question}") print(f"Answer: {dev_example.answer}") print(f"Relevant Wikipedia Titles: {dev_example.gold_titles}") from dsp.utils import deduplicate class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateCitedParagraph(dspy.Signature): """Generate a paragraph with citations.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() paragraph = dspy.OutputField(desc="includes citations") class LongFormQA(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph) self.max_hops = max_hops def forward(self, question): context = [] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages context = deduplicate(context + passages) pred = self.generate_cited_paragraph(context=context, question=question) pred = dspy.Prediction(context=context, paragraph=pred.paragraph) return pred class CheckCitationFaithfulness(dspy.Signature): """Verify that the text is based on the provided context.""" context = dspy.InputField(desc="may contain relevant facts") text = dspy.InputField(desc="between 1 to 2 sentences") faithfulness = dspy.OutputField(desc="boolean indicating if text is faithful to context") def citation_faithfulness(example, pred, trace): paragraph, context = pred.paragraph, pred.context citation_dict = extract_text_by_citation(paragraph) if not citation_dict: return False, None context_dict = {str(i): context[i].split(' | ')[1] for i in range(len(context))} faithfulness_results = [] unfaithful_citations = [] check_citation_faithfulness = dspy.ChainOfThought(CheckCitationFaithfulness) for citation_num, texts in citation_dict.items(): if citation_num not in context_dict: continue current_context = context_dict[citation_num] for text in texts: try: result = check_citation_faithfulness(context=current_context, text=text) is_faithful = result.faithfulness.lower() == 'true' faithfulness_results.append(is_faithful) if not is_faithful: unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'context': current_context}) except ValueError as e: faithfulness_results.append(False) unfaithful_citations.append({'paragraph': paragraph, 'text': text, 'error': str(e)}) final_faithfulness = all(faithfulness_results) if not faithfulness_results: return False, None return final_faithfulness, unfaithful_citations def extract_cited_titles_from_paragraph(paragraph, context): cited_indices = [int(m.group(1)) for m in re.finditer(r'\[(\d+)\]\.', paragraph)] cited_indices = [index - 1 for index in cited_indices if index <= len(context)] cited_titles = [context[index].split(' | ')[0] for index in cited_indices] return cited_titles def calculate_recall(example, pred, trace=None): gold_titles = set(example['gold_titles']) found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context)) intersection = gold_titles.intersection(found_cited_titles) recall = len(intersection) / len(gold_titles) if gold_titles else 0 return recall def calculate_precision(example, pred, trace=None): gold_titles = set(example['gold_titles']) found_cited_titles = set(extract_cited_titles_from_paragraph(pred.paragraph, pred.context)) intersection = gold_titles.intersection(found_cited_titles) precision = len(intersection) / len(found_cited_titles) if found_cited_titles else 0 return precision def answer_correctness(example, pred, trace=None): assert hasattr(example, 'answer'), "Example does not have 'answer'." normalized_context = normalize_text(pred.paragraph) if isinstance(example.answer, str): gold_answers = [example.answer] elif isinstance(example.answer, list): gold_answers = example.answer else: raise ValueError("'example.answer' is not string or list.") return 1 if any(normalize_text(answer) in normalized_context for answer in gold_answers) else 0 def evaluate(module): correctness_values = [] recall_values = [] precision_values = [] citation_faithfulness_values = [] for i in range(len(devset)): example = devset[i] try: pred = module(question=example.question) correctness_values.append(answer_correctness(example, pred)) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) citation_faithfulness_values.append(citation_faithfulness_score) recall = calculate_recall(example, pred) precision = calculate_precision(example, pred) recall_values.append(recall) precision_values.append(precision) except Exception as e: print(f"Failed generation with error: {e}") average_correctness = sum(correctness_values) / len(devset) if correctness_values else 0 average_recall = sum(recall_values) / len(devset) if recall_values else 0 average_precision = sum(precision_values) / len(devset) if precision_values else 0 average_citation_faithfulness = sum(citation_faithfulness_values) / len(devset) if citation_faithfulness_values else 0 print(f"Average Correctness: {average_correctness}") print(f"Average Recall: {average_recall}") print(f"Average Precision: {average_precision}") print(f"Average Citation Faithfulness: {average_citation_faithfulness}") longformqa = LongFormQA() evaluate(longformqa) question = devset[6].question pred = longformqa(question) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) print(f"Question: {question}") print(f"Predicted Paragraph: {pred.paragraph}") print(f"Citation Faithfulness: {citation_faithfulness_score}") class LongFormQAWithAssertions(dspy.Module): def __init__(self, passages_per_hop=3, max_hops=2): super().__init__() self.generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_cited_paragraph = dspy.ChainOfThought(GenerateCitedParagraph) self.max_hops = max_hops def forward(self, question): context = [] for hop in range(self.max_hops): query = self.generate_query[hop](context=context, question=question).query passages = self.retrieve(query).passages context = deduplicate(context + passages) pred = self.generate_cited_paragraph(context=context, question=question) pred = dspy.Prediction(context=context, paragraph=pred.paragraph) dspy.Suggest(citations_check(pred.paragraph), f"Make sure every 1-2 sentences has citations. If any 1-2 sentences lack citations, add them in 'text... [x].' format.", target_module=GenerateCitedParagraph) _, unfaithful_outputs = citation_faithfulness(None, pred, None) if unfaithful_outputs: unfaithful_pairs = [(output['text'], output['context']) for output in unfaithful_outputs] for _, context in unfaithful_pairs: dspy.Suggest(len(unfaithful_pairs) == 0, f"Make sure your output is based on the following context: '{context}'.", target_module=GenerateCitedParagraph) else: return pred return pred longformqa_with_assertions = assert_transform_module(LongFormQAWithAssertions().map_named_predictors(Retry), backtrack_handler) evaluate(longformqa_with_assertions) question = devset[6].question pred = longformqa_with_assertions(question) citation_faithfulness_score, _ = citation_faithfulness(None, pred, None) print(f"Question: {question}") print(f"Predicted Paragraph: {pred.paragraph}") print(f"Citation Faithfulness: {citation_faithfulness_score}") longformqa = LongFormQA() teleprompter =
BootstrapFewShotWithRandomSearch(metric = answer_correctness, max_bootstrapped_demos=2, num_candidate_programs=6)
dspy.teleprompt.BootstrapFewShotWithRandomSearch
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install -e $repo_path') get_ipython().system('pip install transformers') import dspy from dspy.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llama) train = [('Who was the director of the 2009 movie featuring Peter Outerbridge as William Easton?', 'Kevin Greutert'), ('The heir to the Du Pont family fortune sponsored what wrestling team?', 'Foxcatcher'), ('In what year was the star of To Hell and Back born?', '1925'), ('Which award did the first book of Gary Zukav receive?', 'U.S. National Book Award'), ('What documentary about the Gilgo Beach Killer debuted on A&E?', 'The Killing Season'), ('Which author is English: John Braine or Studs Terkel?', 'John Braine'), ('Who produced the album that included a re-recording of "Lithium"?', 'Butch Vig')] train = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in train] dev = [('Who has a broader scope of profession: E. L. Doctorow or Julia Peterkin?', 'E. L. Doctorow'), ('Right Back At It Again contains lyrics co-written by the singer born in what city?', 'Gainesville, Florida'), ('What year was the party of the winner of the 1971 San Francisco mayoral election founded?', '1828'), ('Anthony Dirrell is the brother of which super middleweight title holder?', 'Andre Dirrell'), ('The sports nutrition business established by Oliver Cookson is based in which county in the UK?', 'Cheshire'), ('Find the birth date of the actor who played roles in First Wives Club and Searching for the Elephant.', 'February 13, 1980'), ('Kyle Moran was born in the town on what river?', 'Castletown River'), ("The actress who played the niece in the Priest film was born in what city, country?", 'Surrey, England'), ('Name the movie in which the daughter of Noel Harrison plays Violet Trefusis.', 'Portrait of a Marriage'), ('What year was the father of the Princes in the Tower born?', '1442'), ('What river is near the Crichton Collegiate Church?', 'the River Tyne'), ('Who purchased the team Michael Schumacher raced for in the 1995 Monaco Grand Prix in 2000?', 'Renault'), ('André Zucca was a French photographer who worked with a German propaganda magazine published by what Nazi organization?', 'the Wehrmacht')] dev = [dspy.Example(question=question, answer=answer).with_inputs('question') for question, answer in dev] predict = dspy.Predict('question -> answer') predict(question="What is the capital of Germany?") class CoT(dspy.Module): # let's define a new module def __init__(self): super().__init__() self.generate_answer = dspy.ChainOfThought('question -> answer') def forward(self, question): return self.generate_answer(question=question) # here we use the module metric_EM = dspy.evaluate.answer_exact_match teleprompter = BootstrapFewShot(metric=metric_EM, max_bootstrapped_demos=2) cot_compiled = teleprompter.compile(CoT(), trainset=train) cot_compiled("What is the capital of Germany?") llama.inspect_history(n=1) NUM_THREADS = 32 evaluate_hotpot = Evaluate(devset=dev, metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=15) evaluate_hotpot(cot_compiled) class RAG(dspy.Module): def __init__(self, num_passages=3): super().__init__() self.retrieve = dspy.Retrieve(k=num_passages) self.generate_query = dspy.ChainOfThought("question -> search_query") self.generate_answer =
dspy.ChainOfThought("context, question -> answer")
dspy.ChainOfThought
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import dspy from dspy.evaluate import Evaluate from dspy.datasets.hotpotqa import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch, BootstrapFinetune ports = [7140, 7141, 7142, 7143, 7144, 7145] llamaChat = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=ports, max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2, lm=llamaChat) dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=1000, test_size=0) trainset = [x.with_inputs('question') for x in dataset.train] devset = [x.with_inputs('question') for x in dataset.dev] testset = [x.with_inputs('question') for x in dataset.test] len(trainset), len(devset), len(testset) trainset[0] from dsp.utils.utils import deduplicate class BasicMH(dspy.Module): def __init__(self, passages_per_hop=3): super().__init__() self.retrieve = dspy.Retrieve(k=passages_per_hop) self.generate_query = [dspy.ChainOfThought("context, question -> search_query") for _ in range(2)] self.generate_answer = dspy.ChainOfThought("context, question -> answer") def forward(self, question): context = [] for hop in range(2): search_query = self.generate_query[hop](context=context, question=question).search_query passages = self.retrieve(search_query).passages context = deduplicate(context + passages) return self.generate_answer(context=context, question=question).copy(context=context) RECOMPILE_INTO_LLAMA_FROM_SCRATCH = False NUM_THREADS = 24 metric_EM = dspy.evaluate.answer_exact_match if RECOMPILE_INTO_LLAMA_FROM_SCRATCH: tp = BootstrapFewShotWithRandomSearch(metric=metric_EM, max_bootstrapped_demos=2, num_threads=NUM_THREADS) basicmh_bs = tp.compile(BasicMH(), trainset=trainset[:50], valset=trainset[50:200]) ensemble = [prog for *_, prog in basicmh_bs.candidate_programs[:4]] for idx, prog in enumerate(ensemble): pass if not RECOMPILE_INTO_LLAMA_FROM_SCRATCH: ensemble = [] for idx in range(4): prog = BasicMH() prog.load(f'multihop_llama213b_{idx}.json') ensemble.append(prog) llama_program = ensemble[0] evaluate_hotpot = Evaluate(devset=devset[:1000], metric=metric_EM, num_threads=NUM_THREADS, display_progress=True, display_table=0) evaluate_hotpot(llama_program) llama_program(question="How many storeys are in the castle that David Gregory inherited?") llamaChat.inspect_history(n=3) unlabeled_train = HotPotQA(train_seed=1, train_size=3000, eval_seed=2023, dev_size=0, test_size=0).train unlabeled_train = [dspy.Example(question=x.question).with_inputs('question') for x in unlabeled_train] len(unlabeled_train) always_true = lambda g, p, trace=None: True for prog_ in ensemble: evaluate_hotpot(prog_, devset=unlabeled_train[:3000], metric=always_true) RECOMPILE_INTO_T5_FROM_SCRATCH = False if RECOMPILE_INTO_T5_FROM_SCRATCH: config = dict(target='t5-large', epochs=2, bf16=True, bsize=6, accumsteps=2, lr=5e-5) tp = BootstrapFinetune(metric=None) t5_program = tp.compile(BasicMH(), teacher=ensemble, trainset=unlabeled_train[:3000], **config) for p in t5_program.predictors(): p.activated = False if not RECOMPILE_INTO_T5_FROM_SCRATCH: t5_program = BasicMH() ckpt_path = "colbert-ir/dspy-Oct11-T5-Large-MH-3k-v1" LM =
dspy.HFModel(checkpoint=ckpt_path, model='t5-large')
dspy.HFModel
get_ipython().system('git clone https://huggingface.co/arnavs11/DSPy_TweetGen_Cache') get_ipython().run_line_magic('cd', 'DSPy_TweetGen_Cache/') get_ipython().system('git checkout master') get_ipython().run_line_magic('cd', '..') import os repo_clone_path = '/content/DSPy_TweetGen_Cache' if not os.access('/content', os.W_OK): repo_clone_path = os.path.join(os.getcwd(), 'DSPy_TweetGen_Cache') os.environ["DSP_NOTEBOOK_CACHEDIR"] = repo_clone_path get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os import regex as re import json try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install dspy-ai') get_ipython().system('pip install openai~=0.28.1') get_ipython().system('pip install -e $repo_path') import dspy from dspy.predict import Retry from dspy.datasets import HotPotQA from dspy.teleprompt import BootstrapFewShotWithRandomSearch from dsp.utils import deduplicate from dspy.evaluate.evaluate import Evaluate from dspy.primitives.assertions import assert_transform_module, backtrack_handler colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') dspy.settings.configure(rm=colbertv2_wiki17_abstracts) turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=500) dspy.settings.configure(lm=turbo, trace=[], temperature=0.7) dataset = HotPotQA(train_seed=1, train_size=300, eval_seed=2023, dev_size=300, test_size=0, keep_details=True) trainset = [x.with_inputs('question', 'answer') for x in dataset.train] devset = [x.with_inputs('question', 'answer') for x in dataset.dev] class GenerateSearchQuery(dspy.Signature): """Write a simple search query that will help answer a complex question.""" context = dspy.InputField(desc="may contain relevant facts") question = dspy.InputField() query = dspy.OutputField() class GenerateTweet(dspy.Signature): """Generate an engaging tweet that effectively answers a question staying faithful to the context, is less than 280 characters, and has no hashtags.""" question = dspy.InputField() context = dspy.InputField(desc="may contain relevant facts") tweet = dspy.OutputField() class Tweeter(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet return dspy.Prediction(generated_tweet=generated_tweet, context=context) tweeter = Tweeter() def has_no_hashtags(text): return len(re.findall(r"#\w+", text)) == 0 def is_within_length_limit(text, length_limit=280): return len(text) <= length_limit def is_assessment_yes(assessment_answer): """Check if the first word of the assessment answer is 'yes'.""" return assessment_answer.split()[0].lower() == 'yes' def has_correct_answer(text, answer): return answer in text class AssessTweet(dspy.Signature): """Assess the quality of a tweet along the specified dimension.""" context = dspy.InputField(desc='ignore if N/A') assessed_text = dspy.InputField() assessment_question = dspy.InputField() assessment_answer = dspy.OutputField(desc="Yes or No") def no_hashtags_metric(gold, pred, trace=None): tweet = pred.generated_tweet no_hashtags = has_no_hashtags(tweet) score = no_hashtags return score def is_correct_metric(gold, pred, trace=None): answer, tweet = gold.answer, pred.generated_tweet correct = has_correct_answer(tweet, answer) score = correct return score def within_length_metric(gold, pred, trace=None): tweet = pred.generated_tweet within_length_limit = is_within_length_limit(tweet, 280) score = within_length_limit return score def engaging_metric(gold, pred, trace=None): tweet = pred.generated_tweet engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging = engaging.assessment_answer.split()[0].lower() == 'yes' score = engaging return score def faithful_metric(gold, pred, trace=None): context, tweet = pred.context, pred.generated_tweet faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) faithful = faithful.assessment_answer.split()[0].lower() == 'yes' score = faithful return score def overall_metric(gold, pred, trace=None): answer, context, tweet = gold.answer, pred.context, pred.generated_tweet no_hashtags = has_no_hashtags(tweet) within_length_limit = is_within_length_limit(tweet, 280) correct = has_correct_answer(tweet, answer) engaging = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." faithful = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful = dspy.Predict(AssessTweet)(context=context, assessed_text=tweet, assessment_question=faithful) engaging = dspy.Predict(AssessTweet)(context='N/A', assessed_text=tweet, assessment_question=engaging) engaging, faithful = [m.assessment_answer.split()[0].lower() == 'yes' for m in [engaging, faithful]] score = (correct + engaging + faithful + no_hashtags + within_length_limit) if correct and within_length_limit else 0 return score / 5.0 metrics = [no_hashtags_metric, is_correct_metric, within_length_metric, engaging_metric, faithful_metric, overall_metric] for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset, num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) example = devset[10] tweet = tweeter(question=example.question, answer = example.answer) print(f'Generated Tweet: ', tweet.generated_tweet) tweet.context for metric in metrics: evaluate = Evaluate(metric=metric, devset=devset[10:11], num_threads=1, display_progress=True, display_table=5) evaluate(tweeter) class TweeterWithAssertions(dspy.Module): def __init__(self): super().__init__() self.generate_tweet = dspy.ChainOfThought(GenerateTweet) def forward(self, question, answer): context = [] max_hops=2 passages_per_hop=3 generate_query = [dspy.ChainOfThought(GenerateSearchQuery) for _ in range(max_hops)] retrieve = dspy.Retrieve(k=passages_per_hop) for hop in range(max_hops): query = generate_query[hop](context=context, question=question).query passages = retrieve(query).passages context = deduplicate(context + passages) generated_tweet = self.generate_tweet(question=question, context=context).tweet dspy.Suggest(has_no_hashtags(generated_tweet), f"Please revise the tweet to remove hashtag phrases following it.", target_module=GenerateTweet) dspy.Suggest(is_within_length_limit(generated_tweet, 280), f"Please ensure the tweet is within {280} characters.", target_module=GenerateTweet) dspy.Suggest(has_correct_answer(generated_tweet, answer), "The tweet does not include the correct answer to the question. Please revise accordingly.", target_module=GenerateTweet) engaging_question = "Does the assessed text make for a self-contained, engaging tweet? Say no if it is not engaging." engaging_assessment = dspy.Predict(AssessTweet)(context=context, assessed_text=generated_tweet, assessment_question=engaging_question) dspy.Suggest(is_assessment_yes(engaging_assessment.assessment_answer), "The text is not engaging enough. Please revise to make it more captivating.", target_module=GenerateTweet) faithful_question = "Is the assessed text grounded in the context? Say no if it includes significant facts not in the context." faithful_assessment = dspy.Predict(AssessTweet)(context='N/A', assessed_text=generated_tweet, assessment_question=faithful_question) dspy.Suggest(is_assessment_yes(faithful_assessment.assessment_answer), "The text contains unfaithful elements or significant facts not in the context. Please revise for accuracy.", target_module=GenerateTweet) return
dspy.Prediction(generated_tweet=generated_tweet, context=context)
dspy.Prediction
get_ipython().run_line_magic('load_ext', 'autoreload') get_ipython().run_line_magic('autoreload', '2') import sys import os try: # When on google Colab, let's clone the notebook so we download the cache. import google.colab repo_path = 'dspy' get_ipython().system('git -C $repo_path pull origin || git clone https://github.com/stanfordnlp/dspy $repo_path') except: repo_path = '.' if repo_path not in sys.path: sys.path.append(repo_path) os.environ["DSP_NOTEBOOK_CACHEDIR"] = os.path.join(repo_path, 'cache') import pkg_resources # Install the package if it's not installed if not "dspy-ai" in {pkg.key for pkg in pkg_resources.working_set}: get_ipython().system('pip install -U pip') get_ipython().system('pip install -e $repo_path') get_ipython().system('pip install transformers') import dspy from dspy.evaluate import Evaluate from dspy.teleprompt import BootstrapFewShot, BootstrapFewShotWithRandomSearch, BootstrapFinetune llama = dspy.HFClientTGI(model="meta-llama/Llama-2-13b-chat-hf", port=[7140, 7141, 7142, 7143], max_tokens=150) colbertv2 = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
dspy.settings.configure(rm=colbertv2, lm=llama)
dspy.settings.configure
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