Merge branch 'development' into release

src/examples/dspy_example/math_problems_cot.py

@@ -0,0 +1,42 @@
+import dspy
+from dspy.datasets.gsm8k import GSM8K, gsm8k_metric
+from dspy.teleprompt import BootstrapFewShot
+
+# flake8: noqa
+from langtrace_python_sdk import langtrace, with_langtrace_root_span
+
+langtrace.init()
+
+turbo = dspy.OpenAI(model="gpt-3.5-turbo", max_tokens=250)
+dspy.settings.configure(lm=turbo)
+
+# Load math questions from the GSM8K dataset
+gsm8k = GSM8K()
+gsm8k_trainset, gsm8k_devset = gsm8k.train[:10], gsm8k.dev[:10]
+
+
+class CoT(dspy.Module):
+    def __init__(self):
+        super().__init__()
+        self.prog = dspy.ChainOfThought("question -> answer")
+
+    def forward(self, question):
+        return self.prog(question=question)
+
+
+@with_langtrace_root_span(name="math_problems_cot_example")
+def example():
+
+    # Set up the optimizer: we want to "bootstrap" (i.e., self-generate) 4-shot examples of our CoT program.
+    config = dict(max_bootstrapped_demos=4, max_labeled_demos=4)
+
+    # Optimize! Use the `gsm8k_metric` here. In general, the metric is going to tell the optimizer how well it's doing.
+    teleprompter = BootstrapFewShot(metric=gsm8k_metric, **config)
+    optimized_cot = teleprompter.compile(CoT(), trainset=gsm8k_trainset)
+
+    ans = optimized_cot(question="What is the sqrt of 345?")
+    print(ans)
+
+
+if __name__ == "__main__":
+    example()

src/examples/dspy_example/program_of_thought_basic.py

@@ -0,0 +1,35 @@
+import dspy
+
+# flake8: noqa
+from langtrace_python_sdk import langtrace, with_langtrace_root_span
+
+langtrace.init()
+
+turbo = dspy.OpenAI(model="gpt-3.5-turbo", max_tokens=250)
+dspy.settings.configure(lm=turbo)
+
+
+# Define a simple signature for basic question answering
+class BasicQA(dspy.Signature):
+    """Answer questions with short factoid answers."""
+
+    question = dspy.InputField()
+    answer = dspy.OutputField(desc="often between 1 and 5 words")
+
+
+@with_langtrace_root_span(name="pot_example")
+def example():
+
+    # Pass signature to ProgramOfThought Module
+    pot = dspy.ProgramOfThought(BasicQA)
+
+    # Call the ProgramOfThought module on a particular input
+    question = "Sarah has 5 apples. She buys 7 more apples from the store. How many apples does Sarah have now?"
+    result = pot(question=question)
+
+    print(f"Question: {question}")
+    print(f"Final Predicted Answer (after ProgramOfThought process): {result.answer}")
+
+
+if __name__ == "__main__":
+    example()

src/examples/dspy_example/quiz_gen.py

@@ -0,0 +1,113 @@
+import dspy
+import json
+from dspy.datasets import HotPotQA
+from dspy.teleprompt import BootstrapFewShot
+from dspy.evaluate.evaluate import Evaluate
+
+# flake8: noqa
+from langtrace_python_sdk import langtrace, with_langtrace_root_span
+
+langtrace.init()
+
+
+colbertv2_wiki17_abstracts = dspy.ColBERTv2(
+    url="http://20.102.90.50:2017/wiki17_abstracts"
+)
+dspy.settings.configure(rm=colbertv2_wiki17_abstracts)
+turbo = dspy.OpenAI(model="gpt-3.5-turbo-0613", max_tokens=500)
+dspy.settings.configure(lm=turbo, trace=[], temperature=0.7)
+
+dataset = HotPotQA(
+    train_seed=1,
+    train_size=300,
+    eval_seed=2023,
+    dev_size=300,
+    test_size=0,
+    keep_details=True,
+)
+trainset = [x.with_inputs("question", "answer") for x in dataset.train]
+devset = [x.with_inputs("question", "answer") for x in dataset.dev]
+
+
+class GenerateAnswerChoices(dspy.Signature):
+    """Generate answer choices in JSON format that include the correct answer and plausible distractors for the specified question."""
+
+    question = dspy.InputField()
+    correct_answer = dspy.InputField()
+    number_of_choices = dspy.InputField()
+    answer_choices = dspy.OutputField(desc="JSON key-value pairs")
+
+
+class QuizAnswerGenerator(dspy.Module):
+    def __init__(self):
+        super().__init__()
+        self.prog = dspy.ChainOfThought(GenerateAnswerChoices)
+
+    def forward(self, question, answer):
+        choices = self.prog(
+            question=question, correct_answer=answer, number_of_choices="4"
+        ).answer_choices
+        # dspy.Suggest(
+        #     format_checker(choices),
+        #     "The format of the answer choices should be in JSON format. Please revise accordingly.",
+        #     target_module=GenerateAnswerChoices,
+        # )
+        return dspy.Prediction(choices=choices)
+
+
+def format_checker(choice_string):
+    try:
+        choices = json.loads(choice_string)
+        if isinstance(choices, dict) and all(
+            isinstance(key, str) and isinstance(value, str)
+            for key, value in choices.items()
+        ):
+            return True
+    except json.JSONDecodeError:
+        return False
+
+    return False
+
+
+def format_valid_metric(gold, pred, trace=None):
+    generated_choices = pred.choices
+    format_valid = format_checker(generated_choices)
+    score = format_valid
+    return score
+
+
+@with_langtrace_root_span(name="quiz_generator_1")
+def quiz_generator_1():
+    quiz_generator = QuizAnswerGenerator()
+
+    example = devset[67]
+    print("Example Question: ", example.question)
+    print("Example Answer: ", example.answer)
+    # quiz_choices = quiz_generator(question=example.question, answer=example.answer)
+    # print("Generated Quiz Choices: ", quiz_choices.choices)
+
+    optimizer = BootstrapFewShot(
+        metric=format_valid_metric, max_bootstrapped_demos=4, max_labeled_demos=4
+    )
+    compiled_quiz_generator = optimizer.compile(
+        quiz_generator,
+        trainset=trainset,
+    )
+    quiz_choices = compiled_quiz_generator(
+        question=example.question, answer=example.answer
+    )
+    print("Generated Quiz Choices: ", quiz_choices.choices)
+
+    # Evaluate
+    evaluate = Evaluate(
+        metric=format_valid_metric,
+        devset=devset[67:70],
+        num_threads=1,
+        display_progress=True,
+        display_table=5,
+    )
+    evaluate(quiz_generator)
+
+
+if __name__ == "__main__":
+    quiz_generator_1()

src/examples/dspy_example/react.py

@@ -0,0 +1,39 @@
+import sys
+import os
+import dspy
+
+# Add the local src folder to the Python path
+sys.path.insert(0, os.path.abspath('/Users/karthikkalyanaraman/work/langtrace/langtrace-python-sdk/src'))
+
+# flake8: noqa
+from langtrace_python_sdk import langtrace, with_langtrace_root_span
+langtrace.init()
+
+turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=250)
+dspy.settings.configure(lm=turbo)
+
+colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')
+dspy.settings.configure(rm=colbertv2_wiki17_abstracts)
+retriever = dspy.Retrieve(k=3)
+
+# Define a simple signature for basic question answering
+class BasicQA(dspy.Signature):
+    """Answer questions with short factoid answers."""
+    question = dspy.InputField()
+    answer = dspy.OutputField(desc="often between 1 and 5 words")
+
+@with_langtrace_root_span(name="react_example")
+def example():
+
+    # Pass signature to ReAct module
+    react_module = dspy.ReAct(BasicQA)
+
+    # Call the ReAct module on a particular input
+    question = 'Aside from the Apple Remote, what other devices can control the program Apple Remote was originally designed to interact with?'
+    result = react_module(question=question)
+
+    print(f"Question: {question}")
+    print(f"Final Predicted Answer (after ReAct process): {result.answer}")
+
+if __name__ == '__main__':
+    example()

src/langtrace_python_sdk/constants/instrumentation/common.py

@@ -11,6 +11,7 @@
     "ANTHROPIC": "Anthropic",
     "AZURE": "Azure",
     "CHROMA": "Chroma",
+    "DSPY": "DSPy",
     "GROQ": "Groq",
     "LANGCHAIN": "Langchain",
     "LANGCHAIN_COMMUNITY": "Langchain Community",

src/langtrace_python_sdk/instrumentation/__init__.py

@@ -12,6 +12,7 @@
 from .qdrant import QdrantInstrumentation
 from .weaviate import WeaviateInstrumentation
 from .ollama import OllamaInstrumentor
+from .dspy import DspyInstrumentor
 
 __all__ = [
     "AnthropicInstrumentation",
@@ -28,4 +29,5 @@
     "QdrantInstrumentation",
     "WeaviateInstrumentation",
     "OllamaInstrumentor",
+    "DspyInstrumentor",
 ]

src/langtrace_python_sdk/instrumentation/dspy/__init__.py

@@ -0,0 +1,3 @@
+from .instrumentation import DspyInstrumentor
+
+__all__ = ["DspyInstrumentor"]

src/langtrace_python_sdk/instrumentation/dspy/instrumentation.py

@@ -0,0 +1,85 @@
+"""
+Copyright (c) 2024 Scale3 Labs
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
+from opentelemetry.trace import get_tracer
+from wrapt import wrap_function_wrapper as _W
+from typing import Collection
+from importlib_metadata import version as v
+from .patch import patch_bootstrapfewshot_optimizer, patch_signature, patch_evaluate
+
+
+class DspyInstrumentor(BaseInstrumentor):
+    """
+    The DspyInstrumentor class represents the DSPy instrumentation"""
+
+    def instrumentation_dependencies(self) -> Collection[str]:
+        return ["dspy >= 0.1.5"]
+
+    def _instrument(self, **kwargs):
+        tracer_provider = kwargs.get("tracer_provider")
+        tracer = get_tracer(__name__, "", tracer_provider)
+        version = v("dspy")
+        _W(
+            "dspy.teleprompt.bootstrap",
+            "BootstrapFewShot.compile",
+            patch_bootstrapfewshot_optimizer(
+                "BootstrapFewShot.compile", version, tracer
+            ),
+        )
+        _W(
+            "dspy.predict.predict",
+            "Predict.forward",
+            patch_signature("Predict.forward", version, tracer),
+        )
+        _W(
+            "dspy.predict.chain_of_thought",
+            "ChainOfThought.forward",
+            patch_signature("ChainOfThought.forward", version, tracer),
+        )
+        _W(
+            "dspy.predict.chain_of_thought_with_hint",
+            "ChainOfThoughtWithHint.forward",
+            patch_signature("ChainOfThoughtWithHint.forward", version, tracer),
+        )
+        _W(
+            "dspy.predict.react",
+            "ReAct.forward",
+            patch_signature("ReAct.forward", version, tracer),
+        )
+        _W(
+            "dspy.predict.program_of_thought",
+            "ProgramOfThought.forward",
+            patch_signature("ProgramOfThought.forward", version, tracer),
+        )
+        _W(
+            "dspy.predict.multi_chain_comparison",
+            "MultiChainComparison.forward",
+            patch_signature("MultiChainComparison.forward", version, tracer),
+        )
+        _W(
+            "dspy.predict.retry",
+            "Retry.forward",
+            patch_signature("Retry.forward", version, tracer),
+        )
+        _W(
+            "dspy.evaluate.evaluate",
+            "Evaluate.__call__",
+            patch_evaluate("Evaluate", version, tracer),
+        )
+
+    def _uninstrument(self, **kwargs):
+        pass