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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) | dspy.settings.configure |
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) | dspy.Retrieve |
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 |
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.settings.configure(rm=pys_ret_local) | dspy.settings.configure |
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.settings.configure(rm=pys_ret_local)
dev_example = "How Curry Can Kill Cancer Cells"
retrieve = | dspy.Retrieve(k=3) | dspy.Retrieve |
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 |
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.ColBERTv2 |
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) | 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') | dspy.Predict |
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) | dspy.teleprompt.BootstrapFewShot |
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 |
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)
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) | 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 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') | dspy.ChainOfThought |
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)
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 |
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)
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") | dspy.ChainOfThought |
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)
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 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)
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) | dspy.Retrieve |
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)
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().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)
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")
self.generate_query_from_context = | dspy.ChainOfThought("context, question -> search_query") | dspy.ChainOfThought |
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)
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")
self.generate_query_from_context = dspy.ChainOfThought("context, 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 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 |
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) | dspy.Example |
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)
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")
self.generate_query_from_context = dspy.ChainOfThought("context, question -> search_query")
self.generate_answer = dspy.ChainOfThought("context, question -> answer")
def forward(self, question):
passages = []
search_query = self.generate_query(question=question).search_query
passages += self.retrieve(search_query).passages
search_query = self.generate_query_from_context(context=passages, question=question).search_query
passages += self.retrieve(search_query).passages
return self.generate_answer(context= | deduplicate(passages) | dsp.utils.utils.deduplicate |
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') | dspy.OpenAI |
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.ColBERTv2 |
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) | 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) | 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 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) | dspy.Predict |
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) | dspy.ChainOfThought |
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) | dspy.Retrieve |
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)
return answer_EM and answer_PM
teleprompter = | BootstrapFewShot(metric=validate_context_and_answer) | dspy.teleprompt.BootstrapFewShot |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = | Evaluate(devset=devset, 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 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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = dspy.ChainOfThought(GenerateAnswer)
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_answer(context=context, question=question)
return dspy.Prediction(context=context, answer=pred.answer)
my_question = "How many storeys are in the castle that David Gregory inherited?"
uncompiled_baleen = SimplifiedBaleen() # uncompiled (i.e., zero-shot) program
pred = uncompiled_baleen(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=3)
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
hops = [example.question] + [outputs.query for *_, outputs in trace if 'query' in outputs]
if max([len(h) for h in hops]) > 100: return False
if any(dspy.evaluate.answer_exact_match_str(hops[idx], hops[:idx], frac=0.8) for idx in range(2, len(hops))): return False
return True
teleprompter = | BootstrapFewShot(metric=validate_context_and_answer_and_hops) | dspy.teleprompt.BootstrapFewShot |
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() | dspy.InputField |
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") | 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 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") | dspy.InputField |
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() | dspy.InputField |
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") | 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 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) | dspy.evaluate.answer_exact_match |
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().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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
class GenerateSearchQuery(dspy.Signature):
"""Write a simple search query that will help answer a complex question."""
context = | dspy.InputField(desc="may contain relevant facts") | dspy.InputField |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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() | dspy.InputField |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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().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) | dspy.Retrieve |
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) | dspy.ChainOfThought |
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) | 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 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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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 |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = | dspy.ChainOfThought(GenerateAnswer) | dspy.ChainOfThought |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = dspy.ChainOfThought(GenerateAnswer)
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_answer(context=context, question=question)
return | dspy.Prediction(context=context, answer=pred.answer) | 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 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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = dspy.ChainOfThought(GenerateAnswer)
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_answer(context=context, question=question)
return dspy.Prediction(context=context, answer=pred.answer)
my_question = "How many storeys are in the castle that David Gregory inherited?"
uncompiled_baleen = SimplifiedBaleen() # uncompiled (i.e., zero-shot) program
pred = uncompiled_baleen(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=3)
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().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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = dspy.ChainOfThought(GenerateAnswer)
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_answer(context=context, question=question)
return dspy.Prediction(context=context, answer=pred.answer)
my_question = "How many storeys are in the castle that David Gregory inherited?"
uncompiled_baleen = SimplifiedBaleen() # uncompiled (i.e., zero-shot) program
pred = uncompiled_baleen(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=3)
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) | dspy.evaluate.answer_passage_match |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(dspy.Module):
def __init__(self, passages_per_hop=3, max_hops=2):
super().__init__()
self.generate_query = [ | dspy.ChainOfThought(GenerateSearchQuery) | dspy.ChainOfThought |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = dspy.ChainOfThought(GenerateAnswer)
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) | dsp.utils.deduplicate |
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)
return answer_EM and answer_PM
teleprompter = BootstrapFewShot(metric=validate_context_and_answer)
compiled_rag = teleprompter.compile(RAG(), trainset=trainset)
my_question = "What castle did David Gregory inherit?"
pred = compiled_rag(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=1)
for name, parameter in compiled_rag.named_predictors():
print(name)
print(parameter.demos[0])
print()
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5)
metric = dspy.evaluate.answer_exact_match
evaluate_on_hotpotqa(compiled_rag, metric=metric)
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)
compiled_rag_retrieval_score = evaluate_on_hotpotqa(compiled_rag, metric=gold_passages_retrieved)
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()
from dsp.utils import deduplicate
class SimplifiedBaleen(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_answer = dspy.ChainOfThought(GenerateAnswer)
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_answer(context=context, question=question)
return dspy.Prediction(context=context, answer=pred.answer)
my_question = "How many storeys are in the castle that David Gregory inherited?"
uncompiled_baleen = SimplifiedBaleen() # uncompiled (i.e., zero-shot) program
pred = uncompiled_baleen(my_question)
print(f"Question: {my_question}")
print(f"Predicted Answer: {pred.answer}")
print(f"Retrieved Contexts (truncated): {[c[:200] + '...' for c in pred.context]}")
turbo.inspect_history(n=3)
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
hops = [example.question] + [outputs.query for *_, outputs in trace if 'query' in outputs]
if max([len(h) for h in hops]) > 100: return False
if any( | dspy.evaluate.answer_exact_match_str(hops[idx], hops[:idx], frac=0.8) | dspy.evaluate.answer_exact_match_str |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
| dspy.settings.configure(lm=lm) | dspy.settings.configure |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
dspy.settings.configure(lm=lm)
from dspy.datasets import HotPotQA
dataset = | HotPotQA(train_seed=1, train_size=20, eval_seed=2023, dev_size=50, test_size=0) | dspy.datasets.HotPotQA |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
dspy.settings.configure(lm=lm)
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]
train_example = trainset[0]
print(train_example)
print(f"Question: {train_example.question}")
print(f"Answer: {train_example.answer}")
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = dspy.InputField()
answer = dspy.OutputField(desc="often between 1 and 5 words")
class BasicQABot(dspy.Module):
def __init__(self):
super().__init__()
self.generate = dspy.Predict(BasicQA)
def forward(self,question):
prediction = self.generate(question = question)
return dspy.Prediction(answer = prediction.answer)
qa_bot = BasicQABot()
pred = qa_bot.forward("In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?")
pred.answer
from dspy.teleprompt import KNNFewShot
from dspy.predict.knn import KNN
knn_teleprompter = | KNNFewShot(KNN, 7, trainset) | dspy.teleprompt.KNNFewShot |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
dspy.settings.configure(lm=lm)
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]
train_example = trainset[0]
print(train_example)
print(f"Question: {train_example.question}")
print(f"Answer: {train_example.answer}")
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = dspy.InputField()
answer = dspy.OutputField(desc="often between 1 and 5 words")
class BasicQABot(dspy.Module):
def __init__(self):
super().__init__()
self.generate = dspy.Predict(BasicQA)
def forward(self,question):
prediction = self.generate(question = question)
return dspy.Prediction(answer = prediction.answer)
qa_bot = BasicQABot()
pred = qa_bot.forward("In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?")
pred.answer
from dspy.teleprompt import KNNFewShot
from dspy.predict.knn import KNN
knn_teleprompter = KNNFewShot(KNN, 7, trainset)
compiled_knn = knn_teleprompter.compile(BasicQABot(), trainset=trainset)
example = devset[0]
pred = compiled_knn(question = example.question)
print("Question: ", example.question)
print("Expected answer: ", example.answer)
print("Prediction: ", pred.answer)
lm.inspect_history(1)
from dspy.evaluate.evaluate import Evaluate
evaluate_on_hotpotqa = | Evaluate(devset=devset, num_threads=1, display_progress=True, display_table=5) | dspy.evaluate.evaluate.Evaluate |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
dspy.settings.configure(lm=lm)
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]
train_example = trainset[0]
print(train_example)
print(f"Question: {train_example.question}")
print(f"Answer: {train_example.answer}")
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = | dspy.InputField() | dspy.InputField |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
dspy.settings.configure(lm=lm)
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]
train_example = trainset[0]
print(train_example)
print(f"Question: {train_example.question}")
print(f"Answer: {train_example.answer}")
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = dspy.InputField()
answer = | dspy.OutputField(desc="often between 1 and 5 words") | dspy.OutputField |
import openai
import dspy
import json
with open("creds.json", "r") as creds:
api_key = json.loads(creds.read())["openai_key"]
lm = dspy.OpenAI(model='gpt-4', api_key=api_key, model_type='chat', max_tokens = 500)
dspy.settings.configure(lm=lm)
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]
train_example = trainset[0]
print(train_example)
print(f"Question: {train_example.question}")
print(f"Answer: {train_example.answer}")
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = dspy.InputField()
answer = dspy.OutputField(desc="often between 1 and 5 words")
class BasicQABot(dspy.Module):
def __init__(self):
super().__init__()
self.generate = | dspy.Predict(BasicQA) | dspy.Predict |