diff --git a/.gitignore b/.gitignore index a2aad6c35..45557810a 100644 --- a/.gitignore +++ b/.gitignore @@ -47,3 +47,5 @@ assertion.log *.log *.db /.devcontainer/.personalization.sh + +.mypy_cache diff --git a/README.md b/README.md index a15661a71..a69bdbf4f 100644 --- a/README.md +++ b/README.md @@ -105,6 +105,7 @@ The DSPy documentation is divided into **tutorials** (step-by-step illustration - Interviews: [Weaviate Podcast in-person](https://www.youtube.com/watch?v=CDung1LnLbY), and you can find 6-7 other remote podcasts on YouTube from a few different perspectives/audiences. - **Tracing in DSPy** with Arize Phoenix: [Tutorial for tracing your prompts and the steps of your DSPy programs](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/dspy_tracing_tutorial.ipynb) - [DSPy: Not Your Average Prompt Engineering](https://jina.ai/news/dspy-not-your-average-prompt-engineering), why it's crucial for future prompt engineering, and yet why it is challenging for prompt engineers to learn. +- **Tracing & Optimization Tracking in DSPy** with Parea AI: [Tutorial on tracing & evaluating a DSPy RAG program](https://docs.parea.ai/tutorials/dspy-rag-trace-evaluate/tutorial) ### B) Guides @@ -136,24 +137,28 @@ You can find other examples tweeted by [@lateinteraction](https://twitter.com/la **Some other examples (not exhaustive, feel free to add more via PR):** + +- [DSPy Optimizers Benchmark on a bunch of different tasks, by Michael Ryan](https://github.com/stanfordnlp/dspy/tree/main/testing/tasks) +- [Sophisticated Extreme Multi-Class Classification, IReRa, by Karel D’Oosterlinck](https://github.com/KarelDO/xmc.dspy) +- [Haize Lab's Red Teaming with DSPy](https://blog.haizelabs.com/posts/dspy/) and see [their DSPy code](https://github.com/haizelabs/dspy-redteam) - Applying DSPy Assertions - [Long-form Answer Generation with Citations, by Arnav Singhvi](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/longformqa/longformqa_assertions.ipynb) - [Generating Answer Choices for Quiz Questions, by Arnav Singhvi](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/quiz/quiz_assertions.ipynb) - [Generating Tweets for QA, by Arnav Singhvi](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/tweets/tweets_assertions.ipynb) - [Compiling LCEL runnables from LangChain in DSPy](https://github.com/stanfordnlp/dspy/blob/main/examples/tweets/compiling_langchain.ipynb) - [AI feedback, or writing LM-based metrics in DSPy](https://github.com/stanfordnlp/dspy/blob/main/examples/tweets/tweet_metric.py) -- [DSPy Optimizers Benchmark on a bunch of different tasks, by Michael Ryan](https://github.com/stanfordnlp/dspy/tree/main/testing/tasks) +- [DSPy Optimizers Benchmark on a bunch of different tasks, by Michael Ryan](https://github.com/stanfordnlp/dspy/tree/main/testing/README.md) - [Indian Languages NLI with gains due to compiling by Saiful Haq](https://github.com/saifulhaq95/DSPy-Indic/blob/main/indicxlni.ipynb) -- [Sophisticated Extreme Multi-Class Classification, IReRa, by Karel D’Oosterlinck](https://github.com/KarelDO/xmc.dspy) - [DSPy on BIG-Bench Hard Example, by Chris Levy](https://drchrislevy.github.io/posts/dspy/dspy.html) - [Using Ollama with DSPy for Mistral (quantized) by @jrknox1977](https://gist.github.com/jrknox1977/78c17e492b5a75ee5bbaf9673aee4641) -- [Using DSPy, "The Unreasonable Effectiveness of Eccentric Automatic Prompts" (paper) by VMware's Rick Battle & Teja Gollapudi, and interview at TheRegister](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) +- [Using DSPy, "The Unreasonable Effectiveness of Eccentric Automatic Prompts" (paper) by VMware's Rick Battle & Teja Gollapudi](https://arxiv.org/abs/2402.10949), and [interview at TheRegister](https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/) - [Optimizing Performance of Open Source LM for Text-to-SQL using DSPy and vLLM, by Juan Ovalle](https://github.com/jjovalle99/DSPy-Text2SQL) - Typed DSPy (contributed by [@normal-computing](https://github.com/normal-computing)) - [Using DSPy to train Gpt 3.5 on HumanEval by Thomas Ahle](https://github.com/stanfordnlp/dspy/blob/main/examples/functional/functional.ipynb) - [Building a chess playing agent using DSPy by Franck SN](https://medium.com/thoughts-on-machine-learning/building-a-chess-playing-agent-using-dspy-9b87c868f71e) -TODO: Add links to the state-of-the-art results on Theory of Mind (ToM) by Plastic Labs, the results by Haize Labs for Red Teaming with DSPy, and the DSPy pipeline from Replit. + +TODO: Add links to the state-of-the-art results by the University of Toronto on Clinical NLP, on Theory of Mind (ToM) by Plastic Labs, and the DSPy pipeline from Replit. There are also recent cool examples at [Weaviate's DSPy cookbook](https://github.com/weaviate/recipes/tree/main/integrations/dspy) by Connor Shorten. [See tutorial on YouTube](https://www.youtube.com/watch?v=CEuUG4Umfxs). diff --git a/docs/api/language_model_clients/AzureOpenAI.md b/docs/api/language_model_clients/AzureOpenAI.md index 1f173eb5e..80ac7962c 100644 --- a/docs/api/language_model_clients/AzureOpenAI.md +++ b/docs/api/language_model_clients/AzureOpenAI.md @@ -14,6 +14,8 @@ lm = dspy.AzureOpenAI(api_base='...', api_version='2023-12-01-preview', model='g The constructor initializes the base class `LM` and verifies the provided arguments like the `api_provider`, `api_key`, and `api_base` to set up OpenAI request retrieval through Azure. The `kwargs` attribute is initialized with default values for relevant text generation parameters needed for communicating with the GPT API, such as `temperature`, `max_tokens`, `top_p`, `frequency_penalty`, `presence_penalty`, and `n`. +Azure requires that the deployment id of the Azure deployment to be also provided using the argument `deployment_id`. + ```python class AzureOpenAI(LM): def __init__( @@ -53,4 +55,4 @@ After generation, the completions are post-processed based on the `model_type` p - `**kwargs`: Additional keyword arguments for completion request. **Returns:** -- `List[Dict[str, Any]]`: List of completion choices. \ No newline at end of file +- `List[Dict[str, Any]]`: List of completion choices. diff --git a/docs/api/language_model_clients/Watsonx.md b/docs/api/language_model_clients/Watsonx.md new file mode 100644 index 000000000..2afaabbd8 --- /dev/null +++ b/docs/api/language_model_clients/Watsonx.md @@ -0,0 +1,55 @@ +# Watsonx Usage Guide + +This guide provides instructions on how to use the `Watsonx` class to interact with IBM Watsonx.ai API for text and code generation. + +## Requirements + +- Python 3.10 or higher. +- The `ibm-watsonx-ai` package installed, which can be installed via pip. +- An IBM Cloud account and a Watsonx configured project. + +## Installation + +Ensure you have installed the `ibm-watsonx-ai` package along with other necessary dependencies: + +## Configuration + +Before using the `Watsonx` class, you need to set up access to IBM Cloud: + +1. Create an IBM Cloud account +2. Enable a Watsonx service from the catalog +3. Create a new project and associate a Watson Machine Learning service instance. +4. Create an IAM authentication credentials and save them in a JSON file. + +## Usage + +Here's an example of how to instantiate the `Watsonx` class and send a generation request: + +```python +import dspy + +''' Initialize the class with the model name and parameters for Watsonx.ai + You can choose between many different models: + * (Mistral) mistralai/mixtral-8x7b-instruct-v01 + * (Meta) meta-llama/llama-3-70b-instruct + * (IBM) ibm/granite-13b-instruct-v2 + * and many others. +''' +watsonx=dspy.Watsonx( + model='mistralai/mixtral-8x7b-instruct-v01', + credentials={ + "apikey": "your-api-key", + "url": "https://us-south.ml.cloud.ibm.com" + }, + project_id="your-watsonx-project-id", + max_new_tokens=500, + max_tokens=1000 + ) + +dspy.settings.configure(lm=watsonx) +``` + +## Customizing Requests + +You can customize requests by passing additional parameters such as `decoding_method`,`max_new_tokens`, `stop_sequences`, `repetition_penalty`, and others supported by the Watsonx.ai API. This allows you to control the behavior of the generation. +Refer to [`ibm-watsonx-ai library`](https://ibm.github.io/watsonx-ai-python-sdk/index.html) documentation. diff --git a/docs/api/modules/ChainOfThought.md b/docs/api/modules/ChainOfThought.md index ab9bd0e5f..9d7939283 100644 --- a/docs/api/modules/ChainOfThought.md +++ b/docs/api/modules/ChainOfThought.md @@ -13,23 +13,20 @@ class ChainOfThought(Predict): self.activated = activated - signature = self.signature - *keys, last_key = signature.kwargs.keys() - - DEFAULT_RATIONALE_TYPE = dsp.Type(prefix="Reasoning: Let's think step by step in order to", - desc="${produce the " + last_key + "}. We ...") - - rationale_type = rationale_type or DEFAULT_RATIONALE_TYPE - - extended_kwargs = {key: signature.kwargs[key] for key in keys} - extended_kwargs.update({'rationale': rationale_type, last_key: signature.kwargs[last_key]}) - - self.extended_signature = dsp.Template(signature.instructions, **extended_kwargs) + signature = ensure_signature(self.signature) + *_keys, last_key = signature.output_fields.keys() + + rationale_type = rationale_type or dspy.OutputField( + prefix="Reasoning: Let's think step by step in order to", + desc="${produce the " + last_key + "}. We ...", + ) + + self.extended_signature = signature.prepend("rationale", rationale_type, type_=str) ``` **Parameters:** - `signature` (_Any_): Signature of predictive model. -- `rationale_type` (_dsp.Type_, _optional_): Rationale type for reasoning steps. Defaults to `None`. +- `rationale_type` (_dspy.OutputField_, _optional_): Rationale type for reasoning steps. Defaults to `None`. - `activated` (_bool_, _optional_): Flag for activated chain of thought processing. Defaults to `True`. - `**config` (_dict_): Additional configuration parameters for model. @@ -64,3 +61,15 @@ pred = generate_answer(question=question) print(f"Question: {question}") print(f"Predicted Answer: {pred.answer}") ``` + +The following example shows how to specify your custom rationale. Here `answer` corresponds to the last key to produce, it may be different in your case. + +```python +#define a custom rationale +rationale_type = dspy.OutputField( + prefix="Reasoning: Let's think step by step in order to", + desc="${produce the answer}. We ...", + ) +#Pass signature to ChainOfThought module +generate_answer = dspy.ChainOfThought(BasicQA, rationale_type=rationale_type) +``` \ No newline at end of file diff --git a/docs/api/modules/ChainOfThoughtWithHint.md b/docs/api/modules/ChainOfThoughtWithHint.md index 1b4b964e7..26a41814d 100644 --- a/docs/api/modules/ChainOfThoughtWithHint.md +++ b/docs/api/modules/ChainOfThoughtWithHint.md @@ -8,32 +8,23 @@ The constructor initializes the `ChainOfThoughtWithHint` class and sets up its a class ChainOfThoughtWithHint(Predict): def __init__(self, signature, rationale_type=None, activated=True, **config): super().__init__(signature, **config) - self.activated = activated - signature = self.signature - *keys, last_key = signature.kwargs.keys() - - DEFAULT_HINT_TYPE = dsp.Type(prefix="Hint:", desc="${hint}") - - DEFAULT_RATIONALE_TYPE = dsp.Type(prefix="Reasoning: Let's think step by step in order to", - desc="${produce the " + last_key + "}. We ...") - rationale_type = rationale_type or DEFAULT_RATIONALE_TYPE - - extended_kwargs1 = {key: signature.kwargs[key] for key in keys} - extended_kwargs1.update({'rationale': rationale_type, last_key: signature.kwargs[last_key]}) + *keys, last_key = signature.fields.keys() + rationale_type = rationale_type or dspy.OutputField( + prefix="Reasoning: Let's think step by step in order to", + desc="${produce the " + last_key + "}. We ...", + ) + self.extended_signature1 = self.signature.insert(-2, "rationale", rationale_type, type_=str) - extended_kwargs2 = {key: signature.kwargs[key] for key in keys} - extended_kwargs2.update({'hint': DEFAULT_HINT_TYPE, 'rationale': rationale_type, last_key: signature.kwargs[last_key]}) - - self.extended_signature1 = dsp.Template(signature.instructions, **extended_kwargs1) - self.extended_signature2 = dsp.Template(signature.instructions, **extended_kwargs2) + DEFAULT_HINT_TYPE = dspy.OutputField() + self.extended_signature2 = self.extended_signature1.insert(-2, "hint", DEFAULT_HINT_TYPE, type_=str) ``` **Parameters:** - `signature` (_Any_): Signature of predictive model. -- `rationale_type` (_dsp.Type_, _optional_): Rationale type for reasoning steps. Defaults to `None`. +- `rationale_type` (_dspy.OutputField_, _optional_): Rationale type for reasoning steps. Defaults to `None`. - `activated` (_bool_, _optional_): Flag for activated chain of thought processing. Defaults to `True`. - `**config` (_dict_): Additional configuration parameters for model. diff --git a/docs/docs/building-blocks/1-language_models.md b/docs/docs/building-blocks/1-language_models.md index 13595ff08..ddf5f815e 100644 --- a/docs/docs/building-blocks/1-language_models.md +++ b/docs/docs/building-blocks/1-language_models.md @@ -10,7 +10,7 @@ Let's first make sure you can set up your language model. DSPy support clients f ## Setting up the LM client. -You can just call the constructor that connects to the LM. Then, use `dspy.configure` to declare this as the dexfault LM. +You can just call the constructor that connects to the LM. Then, use `dspy.configure` to declare this as the default LM. For example, to use OpenAI language models, you can do it as follows. diff --git a/docs/docs/building-blocks/3-modules.md b/docs/docs/building-blocks/3-modules.md index dad351596..c4563f004 100644 --- a/docs/docs/building-blocks/3-modules.md +++ b/docs/docs/building-blocks/3-modules.md @@ -6,7 +6,7 @@ sidebar_position: 3 A **DSPy module** is a building block for programs that use LMs. -- Each built-in module abstracts a **prompting technique** (like chain of thought or ReAct). Crucially, they are generalized to handle any [DSPy Signature]. +- Each built-in module abstracts a **prompting technique** (like chain of thought or ReAct). Crucially, they are generalized to handle any [DSPy Signature](https://dspy-docs.vercel.app/docs/building-blocks/signatures). - A DSPy module has **learnable parameters** (i.e., the little pieces comprising the prompt and the LM weights) and can be invoked (called) to process inputs and return outputs. @@ -17,7 +17,7 @@ A **DSPy module** is a building block for programs that use LMs. Let's start with the most fundamental module, `dspy.Predict`. Internally, all other DSPy modules are just built using `dspy.Predict`. -We'll assume you are already at least a little familiar with [DSPy signatures], which are declarative specs for defining the behavior of any module we use in DSPy. +We'll assume you are already at least a little familiar with [DSPy signatures](https://dspy-docs.vercel.app/docs/building-blocks/signatures), which are declarative specs for defining the behavior of any module we use in DSPy. To use a module, we first **declare** it by giving it a signature. Then we **call** the module with the input arguments, and extract the output fields! diff --git a/docs/docs/building-blocks/7-assertions.md b/docs/docs/building-blocks/7-assertions.md index 71ad16313..ae6a49859 100644 --- a/docs/docs/building-blocks/7-assertions.md +++ b/docs/docs/building-blocks/7-assertions.md @@ -30,7 +30,7 @@ Specifically, when a constraint is not met: - Past Output: your model's past output that did not pass the validation_fn - Instruction: your user-defined feedback message on what went wrong and what possibly to fix -If the error continues past the `max_backtracking_attempts`, then `dspy.Assert` will halt the pipeline execution, altering you with an `dspy.AssertionError`. This ensures your program doesn't continue executing with “bad” LM behavior and immediately highlights sample failure outputs for user assessment. +If the error continues past the `max_backtracking_attempts`, then `dspy.Assert` will halt the pipeline execution, alerting you with an `dspy.AssertionError`. This ensures your program doesn't continue executing with “bad” LM behavior and immediately highlights sample failure outputs for user assessment. - **dspy.Suggest vs. dspy.Assert**: `dspy.Suggest` on the other hand offers a softer approach. It maintains the same retry backtracking as `dspy.Assert` but instead serves as a gentle nudger. If the model outputs cannot pass the model constraints after the `max_backtracking_attempts`, `dspy.Suggest` will log the persistent failure and continue execution of the program on the rest of the data. This ensures the LM pipeline works in a "best-effort" manner without halting execution. diff --git a/docs/docs/building-blocks/8-typed_predictors.md b/docs/docs/building-blocks/8-typed_predictors.md index 9fe1785f8..7b0bd38fc 100644 --- a/docs/docs/building-blocks/8-typed_predictors.md +++ b/docs/docs/building-blocks/8-typed_predictors.md @@ -67,8 +67,8 @@ prediction = predictor(input=doc_query_pair) Let's see the output and its type. ```python -answer = prediction.answer -confidence_score = prediction.confidence +answer = prediction.output.answer +confidence_score = prediction.output.confidence print(f"Prediction: {prediction}\n\n") print(f"Answer: {answer}, Answer Type: {type(answer)}") diff --git a/docs/docs/cheatsheet.md b/docs/docs/cheatsheet.md index 995e92921..448dc722e 100644 --- a/docs/docs/cheatsheet.md +++ b/docs/docs/cheatsheet.md @@ -177,7 +177,7 @@ print(f"Question: {question}") print(f"Final Predicted Answer (after ReAct process): {result.answer}") ``` -### dspy.Retreive +### dspy.Retrieve ```python colbertv2_wiki17_abstracts = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts') @@ -233,7 +233,7 @@ class FactJudge(dspy.Signature): context = dspy.InputField(desc="Context for the prediciton") question = dspy.InputField(desc="Question to be answered") answer = dspy.InputField(desc="Answer for the question") - factually_correct = dspy.OutputField(desc="Is the answer factually correct based on the context?", prefix="Facual[Yes/No]:") + factually_correct = dspy.OutputField(desc="Is the answer factually correct based on the context?", prefix="Factual[Yes/No]:") judge = dspy.ChainOfThought(FactJudge) @@ -374,7 +374,7 @@ compiled_program_optimized_signature = copro_teleprompter.compile(your_dspy_prog ```python from dspy.teleprompt import MIPRO -teleprompter = MIPRO(prompt_model=model_to_generate_prompts, task_model=model_that_solves_task, metric=your_defined_metric, n=num_new_prompts_generated, init_temperature=prompt_generation_temperature) +teleprompter = MIPRO(prompt_model=model_to_generate_prompts, task_model=model_that_solves_task, metric=your_defined_metric, num_candidates=num_new_prompts_generated, init_temperature=prompt_generation_temperature) kwargs = dict(num_threads=NUM_THREADS, display_progress=True, display_table=0) diff --git a/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.mdx b/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.mdx index 7b8d9c4c7..927288135 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.mdx +++ b/docs/docs/deep-dive/retrieval_models_clients/custom-rm-client.mdx @@ -61,7 +61,7 @@ def __call__(self, query:str, k:int) -> List[str]: params = {"query": query, "k": k} response = requests.get(self.url, params=params) - response = response.json()["retreived_passages"] # List of top-k passages + response = response.json()["retrieved_passages"] # List of top-k passages return response ``` @@ -238,4 +238,4 @@ If an `rm` is not initialized in `dsp.settings`, this would raise an error. *** - \ No newline at end of file + diff --git a/docs/docs/faqs.md b/docs/docs/faqs.md index 183958826..eb34d39f0 100644 --- a/docs/docs/faqs.md +++ b/docs/docs/faqs.md @@ -32,6 +32,21 @@ You can specify multiple output fields. For the short-form signature, you can li You can specify the generation of long responses as a `dspy.OutputField`. To ensure comprehensive checks of the content within the long-form generations, you can indicate the inclusion of citations per referenced context. Such constraints such as response length or citation inclusion can be stated through Signature descriptions, or concretely enforced through DSPy Assertions. Check out the [LongFormQA notebook](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/examples/longformqa/longformqa_assertions.ipynb) to learn more about **Generating long-form length responses to answer questions**. +- **How can I ensure that DSPy doesn't strip new line characters from my inputs or outputs?** + +DSPy uses [Signatures](https://dspy-docs.vercel.app/docs/deep-dive/signature/understanding-signatures) to format prompts passed into LMs. In order to ensure that new line characters aren't stripped from longer inputs, you must specify `format=str` when creating a field. + +```python +class UnstrippedSignature(dspy.Signature): + """Enter some information for the model here.""" + + title = dspy.InputField() + object = dspy.InputField(format=str) + result = dspy.OutputField(format=str) +``` + +`object` can now be a multi-line string without issue. + - **How do I define my own metrics? Can metrics return a float?** You can define metrics as simply Python functions that process model generations and evaluate them based on user-defined requirements. Metrics can compare existent data (e.g. gold labels) to model predictions or they can be used to assess various components of an output using validation feedback from LMs (e.g. LLMs-as-Judges). Metrics can return `bool`, `int`, and `float` types scores. Check out the official [Metrics docs](https://dspy-docs.vercel.app/docs/building-blocks/metrics) to learn more about defining custom metrics and advanced evaluations using AI feedback and/or DSPy programs. diff --git a/docs/docs/quick-start/minimal-example.mdx b/docs/docs/quick-start/minimal-example.mdx index 66534adc8..06e442fa1 100644 --- a/docs/docs/quick-start/minimal-example.mdx +++ b/docs/docs/quick-start/minimal-example.mdx @@ -12,7 +12,7 @@ We make use of the [GSM8K dataset](https://huggingface.co/datasets/gsm8k) and th ## Setup -Before we delve into the example, let's ensure our environment is properly configured. We'll start by importing the necessary modules and configuring our language model: +Before we jump into the example, let's ensure our environment is properly configured. We'll start by importing the necessary modules and configuring our language model: ```python import dspy @@ -33,7 +33,7 @@ Let's take a look at what `gsm8k_trainset` and `gsm8k_devset` are: print(gsm8k_trainset) ``` -The `gsm8k_trainset` and `gsm8k_devset` datasets contain a list of Examples with each example having `question` and `answer` field. We'll use these datasets to train and evaluate our model. +The `gsm8k_trainset` and `gsm8k_devset` datasets contain a list of Examples with each example having `question` and `answer` field. ## Define the Module @@ -51,7 +51,7 @@ class CoT(dspy.Module): ## Compile and Evaluate the Model -With our simple program in place, let's move on to optimizing it using the [`BootstrapFewShotWithRandomSearch`](/api/optimizers/BootstrapFewShotWithRandomSearch) teleprompter: +With our simple program in place, let's move on to compiling it with the [`BootstrapFewShot`](/api/optimizers/BootstrapFewShot) teleprompter: ```python from dspy.teleprompt import BootstrapFewShot @@ -61,9 +61,11 @@ 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, valset=gsm8k_devset) +optimized_cot = teleprompter.compile(CoT(), trainset=gsm8k_trainset) ``` +Note that BootstrapFewShot is not an optimizing teleprompter, i.e. it simple creates and validates examples for steps of the pipeline (in this case, the chain-of-thought reasoning) but does not optimize the metric. Other teleprompters like `BootstrapFewShotWithRandomSearch` and `MIPRO` will apply direct optimization. + ## Evaluate Now that we have a compiled (optimized) DSPy program, let's move to evaluating its performance on the dev dataset. diff --git a/docs/docs/tutorials/other_tutorial.md b/docs/docs/tutorials/other_tutorial.md index d276a5db1..c37caed12 100644 --- a/docs/docs/tutorials/other_tutorial.md +++ b/docs/docs/tutorials/other_tutorial.md @@ -24,4 +24,5 @@ sidebar_position: 99999 - [DSPy webinar with MLOps Learners](https://www.youtube.com/watch?v=im7bCLW2aM4), a bit longer with Q&A. - Hands-on Overviews of DSPy by the community: [DSPy Explained! by Connor Shorten](https://www.youtube.com/watch?v=41EfOY0Ldkc), [DSPy explained by code_your_own_ai](https://www.youtube.com/watch?v=ycfnKPxBMck), [DSPy Crash Course by AI Bites](https://youtu.be/5-zgASQKkKQ?si=3gnmVouT5_rpk_nu) - Interviews: [Weaviate Podcast in-person](https://www.youtube.com/watch?v=CDung1LnLbY), and you can find 6-7 other remote podcasts on YouTube from a few different perspectives/audiences. -- **Tracing in DSPy** with Arize Phoenix: [Tutorial for tracing your prompts and the steps of your DSPy programs](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/dspy_tracing_tutorial.ipynb) \ No newline at end of file +- **Tracing in DSPy** with Arize Phoenix: [Tutorial for tracing your prompts and the steps of your DSPy programs](https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/tracing/dspy_tracing_tutorial.ipynb) +- **Tracing & Optimization Tracking in DSPy** with Parea AI: [Tutorial on tracing & evaluating a DSPy RAG program](https://docs.parea.ai/tutorials/dspy-rag-trace-evaluate/tutorial) \ No newline at end of file diff --git a/docs/docusaurus.config.ts b/docs/docusaurus.config.ts index b4fa8159b..ffbbf9f93 100644 --- a/docs/docusaurus.config.ts +++ b/docs/docusaurus.config.ts @@ -121,16 +121,8 @@ const config: Config = { title: 'Community', items: [ { - label: 'Omar Khattab', - href: 'https://twitter.com/lateinteraction', - }, - { - label: 'Herumb Shandilya', - href: 'https://twitter.com/krypticmouse', - }, - { - label: 'Arnav Singhvi', - href: 'https://twitter.com/arnav_thebigman', + label: 'See all 130+ contributors on GitHub', + href: 'https://github.com/stanfordnlp/dspy', }, ], }, diff --git a/dsp/modules/__init__.py b/dsp/modules/__init__.py index a57515e94..d480490a7 100644 --- a/dsp/modules/__init__.py +++ b/dsp/modules/__init__.py @@ -23,3 +23,5 @@ from .sbert import * from .sentence_vectorizer import * from .snowflake import * +from .watsonx import * + diff --git a/dsp/modules/anthropic.py b/dsp/modules/anthropic.py index 3d7d76c90..0c4cd2e08 100644 --- a/dsp/modules/anthropic.py +++ b/dsp/modules/anthropic.py @@ -8,6 +8,7 @@ try: import anthropic + anthropic_rate_limit = anthropic.RateLimitError except ImportError: anthropic_rate_limit = Exception @@ -15,6 +16,7 @@ logger = logging.getLogger(__name__) BASE_URL = "https://api.anthropic.com/v1/messages" + def backoff_hdlr(details): """Handler from https://pypi.org/project/backoff/.""" print( @@ -23,14 +25,17 @@ def backoff_hdlr(details): "{kwargs}".format(**details), ) + def giveup_hdlr(details): """Wrapper function that decides when to give up on retry.""" if "rate limits" in details.message: return False return True + class Claude(LM): """Wrapper around anthropic's API. Supports both the Anthropic and Azure APIs.""" + def __init__( self, model: str = "claude-3-opus-20240229", @@ -64,7 +69,7 @@ def log_usage(self, response): usage_data = response.usage if usage_data: total_tokens = usage_data.input_tokens + usage_data.output_tokens - logger.info(f'{total_tokens}') + logger.debug(f"Anthropic Total Token Response Usage: {total_tokens}") def basic_request(self, prompt: str, **kwargs): raw_kwargs = kwargs @@ -114,10 +119,8 @@ def __call__(self, prompt, only_completed=True, return_sorted=False, **kwargs): completions = [] for _ in range(n): response = self.request(prompt, **kwargs) - # TODO: Log llm usage instead of hardcoded openai usage - # if dsp.settings.log_openai_usage: - # self.log_usage(response) + self.log_usage(response) if only_completed and response.stop_reason == "max_tokens": continue completions = [c.text for c in response.content] - return completions \ No newline at end of file + return completions diff --git a/dsp/modules/aws_models.py b/dsp/modules/aws_models.py index 94b07cad4..4c8937404 100644 --- a/dsp/modules/aws_models.py +++ b/dsp/modules/aws_models.py @@ -49,6 +49,9 @@ def __init__( self._max_context_size: int = max_context_size self._max_new_tokens: int = max_new_tokens + # make it consistent with equivalent LM::max_token + self.kwargs["max_tokens"] = max_new_tokens + self.kwargs = { **self.kwargs, **kwargs, @@ -63,7 +66,7 @@ def _call_model(self, body: str) -> str | list[str]: """Call model, get generated input without the formatted prompt.""" def _estimate_tokens(self, text: str) -> int: - return len(text)/CHARS2TOKENS + return len(text) / CHARS2TOKENS def _extract_input_parameters( self, diff --git a/dsp/modules/azure_openai.py b/dsp/modules/azure_openai.py index 6288875de..c80f82088 100644 --- a/dsp/modules/azure_openai.py +++ b/dsp/modules/azure_openai.py @@ -6,7 +6,6 @@ import backoff import openai -import dsp from dsp.modules.cache_utils import CacheMemory, NotebookCacheMemory, cache_turn_on from dsp.modules.lm import LM @@ -57,11 +56,14 @@ def __init__( model: str = "gpt-3.5-turbo-instruct", api_key: Optional[str] = None, model_type: Literal["chat", "text"] = "chat", + system_prompt: Optional[str] = None, **kwargs, ): super().__init__(model) self.provider = "openai" + self.system_prompt = system_prompt + # Define Client if OPENAI_LEGACY: # Assert that all variables are available @@ -125,7 +127,7 @@ def log_usage(self, response): usage_data = response.get("usage") if usage_data: total_tokens = usage_data.get("total_tokens") - logging.info(f"{total_tokens}") + logging.debug(f"Azure OpenAI Total Token Usage: {total_tokens}") def basic_request(self, prompt: str, **kwargs): raw_kwargs = kwargs @@ -133,7 +135,11 @@ def basic_request(self, prompt: str, **kwargs): kwargs = {**self.kwargs, **kwargs} if self.model_type == "chat": # caching mechanism requires hashable kwargs - kwargs["messages"] = [{"role": "user", "content": prompt}] + messages = [{"role": "user", "content": prompt}] + if self.system_prompt: + messages.insert(0, {"role": "system", "content": self.system_prompt}) + + kwargs["messages"] = messages kwargs = {"stringify_request": json.dumps(kwargs)} response = chat_request(self.client, **kwargs) @@ -192,8 +198,7 @@ def __call__( response = self.request(prompt, **kwargs) - if dsp.settings.log_openai_usage: - self.log_usage(response) + self.log_usage(response) choices = response["choices"] @@ -223,17 +228,17 @@ def __call__( completions = [c for _, c in scored_completions] return completions - + def copy(self, **kwargs): """Returns a copy of the language model with the same parameters.""" kwargs = {**self.kwargs, **kwargs} model = kwargs.pop("model") return self.__class__( - model=model, - api_key=self.api_key, - api_version=self.api_version, - api_base=self.api_base, + model=model, + api_key=self.api_key, + api_version=self.api_version, + api_base=self.api_base, **kwargs, ) diff --git a/dsp/modules/cohere.py b/dsp/modules/cohere.py index b24390e55..1100206d4 100644 --- a/dsp/modules/cohere.py +++ b/dsp/modules/cohere.py @@ -10,6 +10,9 @@ except ImportError: cohere_api_error = Exception # print("Not loading Cohere because it is not installed.") +except AttributeError: + cohere_api_error = Exception + def backoff_hdlr(details): """Handler from https://pypi.org/project/backoff/""" @@ -84,13 +87,14 @@ def basic_request(self, prompt: str, **kwargs): kwargs.pop("n") response = self.co.chat(**kwargs) - - self.history.append({ - "prompt": prompt, - "response": response, - "kwargs": kwargs, - "raw_kwargs": raw_kwargs, - }) + self.history.append( + { + "prompt": prompt, + "response": response, + "kwargs": kwargs, + "raw_kwargs": raw_kwargs, + }, + ) return response diff --git a/dsp/modules/gpt3.py b/dsp/modules/gpt3.py index b2e7e8589..8bead7414 100644 --- a/dsp/modules/gpt3.py +++ b/dsp/modules/gpt3.py @@ -6,7 +6,6 @@ import backoff import openai -import dsp from dsp.modules.cache_utils import CacheMemory, NotebookCacheMemory, cache_turn_on from dsp.modules.lm import LM @@ -19,9 +18,7 @@ import openai.error from openai.openai_object import OpenAIObject - ERRORS = ( - openai.error.RateLimitError, - ) + ERRORS = (openai.error.RateLimitError,) except Exception: ERRORS = (openai.RateLimitError,) OpenAIObject = dict @@ -69,8 +66,7 @@ def __init__( default_model_type = ( "chat" - if ("gpt-3.5" in model or "turbo" in model or "gpt-4" in model) - and ("instruct" not in model) + if ("gpt-3.5" in model or "turbo" in model or "gpt-4" in model) and ("instruct" not in model) else "text" ) self.model_type = model_type if model_type else default_model_type @@ -105,7 +101,7 @@ def log_usage(self, response): usage_data = response.get("usage") if usage_data: total_tokens = usage_data.get("total_tokens") - logging.info(f"{total_tokens}") + logging.debug(f"OpenAI Response Token Usage: {total_tokens}") def basic_request(self, prompt: str, **kwargs): raw_kwargs = kwargs @@ -181,9 +177,7 @@ def __call__( response = self.request(prompt, **kwargs) - if dsp.settings.log_openai_usage: - self.log_usage(response) - + self.log_usage(response) choices = response["choices"] completed_choices = [c for c in choices if c["finish_reason"] != "length"] diff --git a/dsp/modules/groq_client.py b/dsp/modules/groq_client.py index 2632d13b9..6650897ae 100644 --- a/dsp/modules/groq_client.py +++ b/dsp/modules/groq_client.py @@ -12,7 +12,6 @@ groq_api_error = Exception -import dsp from dsp.modules.lm import LM @@ -65,10 +64,10 @@ def __init__( def log_usage(self, response): """Log the total tokens from the Groq API response.""" - usage_data = response.get("usage") + usage_data = response.usage # Directly accessing the 'usage' attribute if usage_data: - total_tokens = usage_data.get("total_tokens") - logging.info(f"{total_tokens}") + total_tokens = usage_data.total_tokens + logging.debug(f"Groq Total Tokens Response Usage: {total_tokens}") def basic_request(self, prompt: str, **kwargs): raw_kwargs = kwargs @@ -132,8 +131,7 @@ def __call__( assert return_sorted is False, "for now" response = self.request(prompt, **kwargs) - if dsp.settings.log_openai_usage: - self.log_usage(response) + self.log_usage(response) choices = response.choices diff --git a/dsp/modules/hf_client.py b/dsp/modules/hf_client.py index 421569bc0..810c9d3a0 100644 --- a/dsp/modules/hf_client.py +++ b/dsp/modules/hf_client.py @@ -130,13 +130,15 @@ def __init__(self, model, port, model_type: Literal['chat', 'text'] = 'text', ur else: raise ValueError(f"The url provided to `HFClientVLLM` is neither a string nor a list of strings. It is of type {type(url)}.") + self.urls_const = tuple(self.urls) + self.port = port self.model_type = model_type self.headers = {"Content-Type": "application/json"} self.kwargs |= kwargs # kwargs needs to have model, port and url for the lm.copy() to work properly self.kwargs.update({ 'port': port, - 'url': url, + 'url': self.urls_const, }) @@ -157,8 +159,10 @@ def _generate(self, prompt, **kwargs): "messages": messages, **kwargs, } - response = send_hfvllm_chat_request_v00( + response = send_hfvllm_request_v01_wrapped( f"{url}/v1/chat/completions", + url=self.urls_const, + port=self.port, json=payload, headers=self.headers, ) @@ -181,9 +185,11 @@ def _generate(self, prompt, **kwargs): "prompt": prompt, **kwargs, } - - response = send_hfvllm_request_v00( + + response = send_hfvllm_request_v01_wrapped( f"{url}/v1/completions", + url=self.urls_const, + port=self.port, json=payload, headers=self.headers, ) @@ -201,12 +207,19 @@ def _generate(self, prompt, **kwargs): print("Failed to parse JSON response:", response.text) raise Exception("Received invalid JSON response from server") +@CacheMemory.cache(ignore=['arg']) +def send_hfvllm_request_v01(arg, url, port, **kwargs): + return requests.post(arg, **kwargs) + +# @functools.lru_cache(maxsize=None if cache_turn_on else 0) +@NotebookCacheMemory.cache(ignore=['arg']) +def send_hfvllm_request_v01_wrapped(arg, url, port, **kwargs): + return send_hftgi_request_v01(arg, url, port, **kwargs) @CacheMemory.cache def send_hfvllm_request_v00(arg, **kwargs): return requests.post(arg, **kwargs) - @CacheMemory.cache def send_hfvllm_chat_request_v00(arg, **kwargs): return requests.post(arg, **kwargs) diff --git a/dsp/modules/lm.py b/dsp/modules/lm.py index cfe8b88d0..34f903c44 100644 --- a/dsp/modules/lm.py +++ b/dsp/modules/lm.py @@ -55,6 +55,8 @@ def inspect_history(self, n: int = 1, skip: int = 0): printed.append((prompt, x["response"].text)) elif provider == "mistral": printed.append((prompt, x['response'].choices)) + elif provider == "ibm": + printed.append((prompt, x)) else: printed.append((prompt, x["response"]["choices"])) @@ -76,19 +78,21 @@ def inspect_history(self, n: int = 1, skip: int = 0): text = choices elif provider == "openai" or provider == "ollama": text = ' ' + self._get_choice_text(choices[0]).strip() - elif provider == "clarifai" or provider == "claude" : - text=choices + elif provider == "clarifai" or provider == "claude": + text = choices elif provider == "groq": text = ' ' + choices elif provider == "google": text = choices[0].parts[0].text elif provider == "mistral": text = choices[0].message.content + elif provider == "ibm": + text = choices else: text = choices[0]["text"] printing_value += self.print_green(text, end="") - if len(choices) > 1: + if len(choices) > 1 and isinstance(choices, list): printing_value += self.print_red(f" \t (and {len(choices)-1} other completions)", end="") printing_value += "\n\n\n" diff --git a/dsp/modules/ollama.py b/dsp/modules/ollama.py index 27304d271..7781583be 100644 --- a/dsp/modules/ollama.py +++ b/dsp/modules/ollama.py @@ -180,3 +180,15 @@ def __call__( completions = [self._get_choice_text(c) for c in choices] return completions + + def copy(self, **kwargs): + """Returns a copy of the language model with the same parameters.""" + kwargs = {**self.kwargs, **kwargs} + + return self.__class__( + model=self.model_name, + model_type=self.model_type, + base_url=self.base_url, + timeout_s=self.timeout_s, + **kwargs, + ) diff --git a/dsp/modules/sentence_vectorizer.py b/dsp/modules/sentence_vectorizer.py index b878420f7..c5b70cccb 100644 --- a/dsp/modules/sentence_vectorizer.py +++ b/dsp/modules/sentence_vectorizer.py @@ -203,3 +203,48 @@ def __call__(self, inp_examples: List["Example"]) -> np.ndarray: embeddings = np.array(embeddings_list, dtype=np.float32) return embeddings + +class FastEmbedVectorizer(BaseSentenceVectorizer): + """Sentence vectorizer implementaion using FastEmbed - https://qdrant.github.io/fastembed.""" + + def __init__( + self, + model_name: str = "BAAI/bge-small-en-v1.5", + batch_size: int = 256, + cache_dir: Optional[str] = None, + threads: Optional[int] = None, + parallel: Optional[int] = None, + **kwargs, + ): + """Initialize fastembed.TextEmbedding. + + Args: + model_name (str): The name of the model to use. Defaults to `"BAAI/bge-small-en-v1.5"`. + batch_size (int): Batch size for encoding. Higher values will use more memory, but be faster.\ + Defaults to 256. + cache_dir (str, optional): The path to the model cache directory.\ + Can also be set using the `FASTEMBED_CACHE_PATH` env variable. + threads (int, optional): The number of threads single onnxruntime session can use. + parallel (int, optional): If `>1`, data-parallel encoding will be used, recommended for large datasets.\ + If `0`, use all available cores.\ + If `None`, don't use data-parallel processing, use default onnxruntime threading.\ + Defaults to None. + **kwargs: Additional options to pass to fastembed.TextEmbedding + Raises: + ValueError: If the model_name is not in the format / e.g. BAAI/bge-small-en-v1.5. + """ + try: + from fastembed import TextEmbedding + except ImportError as e: + raise ValueError( + "The 'fastembed' package is not installed. Please install it with `pip install fastembed`", + ) from e + self._batch_size = batch_size + self._parallel = parallel + self._model = TextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=threads, **kwargs) + + def __call__(self, inp_examples: List["Example"]) -> np.ndarray: + texts_to_vectorize = self._extract_text_from_examples(inp_examples) + embeddings = self._model.embed(texts_to_vectorize, batch_size=self._batch_size, parallel=self._parallel) + + return np.array([embedding.tolist() for embedding in embeddings], dtype=np.float32) \ No newline at end of file diff --git a/dsp/modules/watsonx.py b/dsp/modules/watsonx.py new file mode 100644 index 000000000..eb5746344 --- /dev/null +++ b/dsp/modules/watsonx.py @@ -0,0 +1,132 @@ +from typing import Any + +from dsp.modules.lm import LM + +ibm_watsonx_ai_api_error = False + +try: + import ibm_watsonx_ai # noqa: F401 + from ibm_watsonx_ai.foundation_models import Model # type: ignore + +except ImportError: + ibm_watsonx_ai_api_error = Exception + + +class Watsonx(LM): + """Wrapper around Watsonx AI's API. + + The constructor initializes the base class LM to support prompting requests to Watsonx models. + This requires the following parameters: + Args: + model (str): the type of model to use from IBM Watsonx AI. + credentials ([dict]): credentials to Watson Machine Learning instance. + project_id (str): ID of the Watson Studio project. + **kwargs: Additional arguments to pass to the API provider. This is initialized with default values for relevant + text generation parameters needed for communicating with Watsonx API, such as: + - decoding_method + - max_new_tokens + - min_new_tokens + - stop_sequences + - repetition_penalty + """ + + def __init__(self, model, credentials, project_id, **kwargs): + """Parameters + + model : str + Which pre-trained model from Watsonx.ai to use? + Choices are [ + `mistralai/mixtral-8x7b-instruct-v01`, + `ibm/granite-13b-instruct-v2`, + `meta-llama/llama-3-70b-instruct`] + credentials : [dict] + Credentials to Watson Machine Learning instance. + project_id : str + ID of the Watson Studio project. + **kwargs: dict + Additional arguments to pass to the API provider. + """ + self.model = model + self.credentials = credentials + self.project_id = project_id + self.provider = "ibm" + self.model_type = "instruct" + self.kwargs = { + "temperature": 0, + "decoding_method": "greedy", + "max_new_tokens": 150, + "min_new_tokens": 0, + "stop_sequences": [], + "repetition_penalty": 1, + "num_generations": 1, + **kwargs, + } + + self.client = Model( + model_id=self.model, + params=self.kwargs, + credentials=self.credentials, + project_id=self.project_id, + ) + + self.history: list[dict[str, Any]] = [] + + def basic_request(self, prompt: str, **kwargs) -> Any: + raw_kwargs = kwargs + kwargs = {**self.kwargs, **kwargs} + + response = self.client.generate(prompt, params={**kwargs}) + + history = { + "prompt": prompt, + "response": response, + "kwargs": kwargs, + "raw_kwargs": raw_kwargs, + } + self.history.append(history) + + return response + + def request(self, prompt: str, **kwargs) -> Any: + # Handles the specific prompting for each supported model and the retrieval of completions from IBM Watsonx AI + + if self.model == "mistralai/mixtral-8x7b-instruct-v01": + prompt = "[INST]" + prompt + "" + elif self.model == "meta-llama/llama-3-70b-instruct": + prompt = ( + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>" + + prompt + + "<|eot_id|><|start_header_id|>user<|end_header_id|>" + ) + + return self.basic_request(prompt, **kwargs) + + def __call__( + self, + prompt: str, + only_completed: bool = True, + return_sorted: bool = False, + **kwargs, + ) -> list[dict[str, Any]]: + """Retrieves completions from Watsonx. + + Args: + prompt (str): prompt to send to Watsonx + only_completed (bool, optional): return only completed responses and ignores completion due to length. + Defaults to True. + return_sorted (bool, optional): sort the completion choices using the returned probabilities. + Defaults to False. + **kwargs: Additional arguments to pass + + Returns: + list[dict[str, Any]]: list of completion choices + """ + if only_completed is False: + raise ValueError("only_completed is True for now") + + if return_sorted: + raise ValueError("return_sorted is False for now") + + response = self.request(prompt, **kwargs) + + return [result["generated_text"] for result in response["results"]] diff --git a/dsp/primitives/search.py b/dsp/primitives/search.py index 0202098f0..bfabed495 100644 --- a/dsp/primitives/search.py +++ b/dsp/primitives/search.py @@ -49,7 +49,7 @@ def retrieveEnsemble(queries: list[str], k: int, by_prob: bool = True,**kwargs) if not dsp.settings.rm: raise AssertionError("No RM is loaded.") if dsp.settings.reranker: - return retrieveRerankEnsemble(queries, k) + return retrieveRerankEnsemble(queries, k, **kwargs) queries = [q for q in queries if q] diff --git a/dsp/utils/settings.py b/dsp/utils/settings.py index 0d0846800..716375b70 100644 --- a/dsp/utils/settings.py +++ b/dsp/utils/settings.py @@ -20,9 +20,7 @@ def __new__(cls): cls._instance.main_tid = threading.get_ident() cls._instance.main_stack = [] cls._instance.stack_by_thread = {} - cls._instance.stack_by_thread[ - threading.get_ident() - ] = cls._instance.main_stack + cls._instance.stack_by_thread[threading.get_ident()] = cls._instance.main_stack # TODO: remove first-class support for re-ranker and potentially combine with RM to form a pipeline of sorts # eg: RetrieveThenRerankPipeline(RetrievalModel, Reranker) @@ -38,7 +36,6 @@ def __new__(cls): skip_logprobs=False, trace=[], release=0, - log_openai_usage=False, bypass_assert=False, bypass_suggest=False, assert_failures=0, diff --git a/dspy/__init__.py b/dspy/__init__.py index bf6b630f3..70bb35d26 100644 --- a/dspy/__init__.py +++ b/dspy/__init__.py @@ -5,11 +5,11 @@ from .primitives import * from .retrieve import * from .signatures import * -from .utils.logging import logger, set_log_level, set_log_output +from .utils.logging import logger, set_log_output + # Functional must be imported after primitives, predict and signatures from .functional import * # isort: skip -from .utils.logging import logger, set_log_output settings = dsp.settings @@ -27,6 +27,7 @@ GoogleVertexAI = dsp.GoogleVertexAI GROQ = dsp.GroqLM Snowflake = dsp.Snowflake +Claude = dsp.Claude HFClientTGI = dsp.HFClientTGI HFClientVLLM = HFClientVLLM @@ -43,5 +44,7 @@ AWSAnthropic = dsp.AWSAnthropic AWSMeta = dsp.AWSMeta +Watsonx = dsp.Watsonx + configure = settings.configure context = settings.context diff --git a/dspy/evaluate/evaluate.py b/dspy/evaluate/evaluate.py index 03c9623ed..93524c3c2 100644 --- a/dspy/evaluate/evaluate.py +++ b/dspy/evaluate/evaluate.py @@ -1,3 +1,5 @@ +import contextlib +import signal import sys import threading import types @@ -14,18 +16,14 @@ except ImportError: ipython_display = print - def HTML(x): + def HTML(x) -> str: # noqa: N802 return x from concurrent.futures import ThreadPoolExecutor, as_completed -from dsp.evaluation.utils import * - -""" -TODO: Counting failures and having a max_failure count. When that is exceeded (also just at the end), -we print the number of failures, the first N examples that failed, and the first N exceptions raised. -""" +# TODO: Counting failures and having a max_failure count. When that is exceeded (also just at the end), +# we print the number of failures, the first N examples that failed, and the first N exceptions raised. class Evaluate: @@ -49,11 +47,13 @@ def __init__( self.max_errors = max_errors self.error_count = 0 self.error_lock = threading.Lock() + self.cancel_jobs = threading.Event() self.return_outputs = return_outputs if "display" in _kwargs: dspy.logger.warning( - "DeprecationWarning: 'display' has been deprecated. To see all information for debugging, use 'dspy.set_log_level('debug')'. In the future this will raise an error.", + "DeprecationWarning: 'display' has been deprecated. To see all information for debugging," + " use 'dspy.set_log_level('debug')'. In the future this will raise an error.", ) def _execute_single_thread(self, wrapped_program, devset, display_progress): @@ -78,19 +78,52 @@ def _execute_multi_thread(self, wrapped_program, devset, num_threads, display_pr ncorrect = 0 ntotal = 0 reordered_devset = [] - - with ThreadPoolExecutor(max_workers=num_threads) as executor: - futures = {executor.submit(wrapped_program, idx, arg) for idx, arg in devset} + job_cancelled = "cancelled" + + # context manger to handle sigint + @contextlib.contextmanager + def interrupt_handler_manager(): + """Sets the cancel_jobs event when a SIGINT is received.""" + default_handler = signal.getsignal(signal.SIGINT) + + def interrupt_handler(sig, frame): + self.cancel_jobs.set() + dspy.logger.warning("Received SIGINT. Cancelling evaluation.") + default_handler(sig, frame) + + signal.signal(signal.SIGINT, interrupt_handler) + yield + # reset to the default handler + signal.signal(signal.SIGINT, default_handler) + + def cancellable_wrapped_program(idx, arg): + # If the cancel_jobs event is set, return the cancelled_job literal + if self.cancel_jobs.is_set(): + return None, None, job_cancelled, None + return wrapped_program(idx, arg) + + with ThreadPoolExecutor(max_workers=num_threads) as executor, interrupt_handler_manager(): + futures = {executor.submit(cancellable_wrapped_program, idx, arg) for idx, arg in devset} pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True, disable=not display_progress) for future in as_completed(futures): example_idx, example, prediction, score = future.result() + + # use the cancelled_job literal to check if the job was cancelled - use "is" not "==" + # in case the prediction is "cancelled" for some reason. + if prediction is job_cancelled: + continue + reordered_devset.append((example_idx, example, prediction, score)) ncorrect += score ntotal += 1 self._update_progress(pbar, ncorrect, ntotal) pbar.close() + if self.cancel_jobs.is_set(): + dspy.logger.warning("Evaluation was cancelled. The results may be incomplete.") + raise KeyboardInterrupt + return reordered_devset, ncorrect, ntotal def _update_progress(self, pbar, ncorrect, ntotal): @@ -152,6 +185,7 @@ def wrapped_program(example_idx, example): del thread_stacks[threading.get_ident()] devset = list(enumerate(devset)) + tqdm.tqdm._instances.clear() if num_threads == 1: reordered_devset, ncorrect, ntotal = self._execute_single_thread(wrapped_program, devset, display_progress) @@ -174,24 +208,21 @@ def wrapped_program(example_idx, example): merge_dicts(example, prediction) | {"correct": score} for _, example, prediction, score in predicted_devset ] - df = pd.DataFrame(data) + result_df = pd.DataFrame(data) - # Truncate every cell in the DataFrame - if hasattr(df, "map"): # DataFrame.applymap was renamed to DataFrame.map in Pandas 2.1.0 - df = df.map(truncate_cell) - else: - df = df.applymap(truncate_cell) + # Truncate every cell in the DataFrame (DataFrame.applymap was renamed to DataFrame.map in Pandas 2.1.0) + result_df = result_df.map(truncate_cell) if hasattr(result_df, "map") else result_df.applymap(truncate_cell) # Rename the 'correct' column to the name of the metric object metric_name = metric.__name__ if isinstance(metric, types.FunctionType) else metric.__class__.__name__ - df.rename(columns={"correct": metric_name}, inplace=True) + result_df = result_df.rename(columns={"correct": metric_name}) if display_table: if isinstance(display_table, int): - df_to_display = df.head(display_table).copy() - truncated_rows = len(df) - display_table + df_to_display = result_df.head(display_table).copy() + truncated_rows = len(result_df) - display_table else: - df_to_display = df.copy() + df_to_display = result_df.copy() truncated_rows = 0 styled_df = configure_dataframe_display(df_to_display, metric_name) @@ -214,15 +245,15 @@ def wrapped_program(example_idx, example): if return_all_scores and return_outputs: return round(100 * ncorrect / ntotal, 2), results, [score for *_, score in predicted_devset] - elif return_all_scores: + if return_all_scores: return round(100 * ncorrect / ntotal, 2), [score for *_, score in predicted_devset] - elif return_outputs: + if return_outputs: return round(100 * ncorrect / ntotal, 2), results return round(100 * ncorrect / ntotal, 2) -def merge_dicts(d1, d2): +def merge_dicts(d1, d2) -> dict: merged = {} for k, v in d1.items(): if k in d2: @@ -239,7 +270,7 @@ def merge_dicts(d1, d2): return merged -def truncate_cell(content): +def truncate_cell(content) -> str: """Truncate content of a cell to 25 words.""" words = str(content).split() if len(words) > 25: @@ -247,14 +278,13 @@ def truncate_cell(content): return content -def configure_dataframe_display(df, metric_name): +def configure_dataframe_display(df, metric_name) -> pd.DataFrame: """Set various pandas display options for DataFrame.""" pd.options.display.max_colwidth = None pd.set_option("display.max_colwidth", 20) # Adjust the number as needed pd.set_option("display.width", 400) # Adjust - # df[metric_name] = df[metric_name].apply(lambda x: f'✔️ [{x}]' if x is True else f'❌ [{x}]') - df.loc[:, metric_name] = df[metric_name].apply(lambda x: f"✔️ [{x}]" if x is True else f"{x}") + df[metric_name] = df[metric_name].apply(lambda x: f"✔️ [{x}]" if x else str(x)) # Return styled DataFrame return df.style.set_table_styles( @@ -273,4 +303,5 @@ def configure_dataframe_display(df, metric_name): # FIXME: TODO: The merge_dicts stuff above is way too quick and dirty. -# TODO: the display_table can't handle False but can handle 0! Not sure how it works with True exactly, probably fails too. +# TODO: the display_table can't handle False but can handle 0! +# Not sure how it works with True exactly, probably fails too. diff --git a/dspy/functional/functional.py b/dspy/functional/functional.py index 7624ac6f2..667c86d4b 100644 --- a/dspy/functional/functional.py +++ b/dspy/functional/functional.py @@ -22,7 +22,7 @@ def _predictor(func) -> dspy.Module: return _StripOutput(TypedPredictor(signature, **kwargs), output_key) # if we have only a single callable argument, the decorator was invoked with no key word arguments - # so we just return the wrapped function + # so we just return the wrapped function if len(args) == 1 and callable(args[0]) and len(kwargs) == 0: return _predictor(args[0]) return _predictor @@ -36,7 +36,7 @@ def _cot(func) -> dspy.Module: return _StripOutput(TypedChainOfThought(signature, **kwargs), output_key) # if we have only a single callable argument, the decorator was invoked with no key word arguments - # so we just return the wrapped function + # so we just return the wrapped function if len(args) == 1 and callable(args[0]) and len(kwargs) == 0: return _cot(args[0]) return _cot @@ -217,7 +217,7 @@ def parse(x): format=lambda x: x if isinstance(x, str) else str(x), parser=parse, ) - elif type_ in (str, int, float, bool): + elif type_ in (str, int, float): signature = signature.with_updated_fields( name, desc=field.json_schema_extra.get("desc", "") diff --git a/dspy/predict/react.py b/dspy/predict/react.py index a465ee710..e358abd46 100644 --- a/dspy/predict/react.py +++ b/dspy/predict/react.py @@ -8,6 +8,8 @@ # TODO: Simplify a lot. # TODO: Divide Action and Action Input like langchain does for ReAct. +# TODO: There's a lot of value in having a stopping condition in the LM calls at `\n\nObservation:` + class ReAct(Module): def __init__(self, signature, max_iters=5, num_results=3, tools=None): @@ -91,18 +93,15 @@ def act(self, output, hop): if action_name == "Finish": return action_val - try: - output[f"Observation_{hop+1}"] = self.tools[action_name](action_val).passages - except AttributeError: - # Handle the case where 'passages' attribute is missing - # TODO: This is a hacky way to handle this. Need to fix this. - output[f"Observation_{hop+1}"] = self.tools[action_name](action_val) + result = self.tools[action_name](action_val) + # Handle the case where 'passages' attribute is missing + output[f"Observation_{hop+1}"] = getattr(result, "passages", result) - except Exception as e: + except Exception: output[f"Observation_{hop+1}"] = ( "Failed to parse action. Bad formatting or incorrect action name." ) - raise e + # raise e def forward(self, **kwargs): args = {key: kwargs[key] for key in self.input_fields.keys() if key in kwargs} @@ -115,5 +114,7 @@ def forward(self, **kwargs): break args.update(output) + observations = [args[key] for key in args if key.startswith("Observation")] + # assumes only 1 output field for now - TODO: handling for multiple output fields - return dspy.Prediction(**{list(self.output_fields.keys())[0]: action_val or ""}) + return dspy.Prediction(observations=observations, **{list(self.output_fields.keys())[0]: action_val or ""}) diff --git a/dspy/primitives/module.py b/dspy/primitives/module.py index 33690c924..5cf0b2231 100644 --- a/dspy/primitives/module.py +++ b/dspy/primitives/module.py @@ -4,17 +4,48 @@ import ujson +# NOTE: Note: It's important (temporary decision) to maintain named_parameters that's different in behavior from +# named_sub_modules for the time being. class BaseModule: def __init__(self): pass def named_parameters(self): - """Unlike PyTorch, handles lists of parameters too.""" + """ + Unlike PyTorch, handles (non-recursive) lists of parameters too. + """ + + import dspy from dspy.predict.parameter import Parameter - # Remove the 'self.' prefix from the names - return [(name[5:], param) for name, param in self.named_sub_modules(Parameter)] + visited = set() + named_parameters = [] + + def add_parameter(param_name, param_value): + if isinstance(param_value, Parameter) and id(param_value) not in visited: + visited.add(id(param_value)) + named_parameters.append((param_name, param_value)) + + for name, value in self.__dict__.items(): + if isinstance(value, Parameter): + add_parameter(name, value) + + elif isinstance(value, dspy.Module): + # When a sub-module is pre-compiled, keep it frozen. + if not getattr(value, "_compiled", False): + for sub_name, param in value.named_parameters(): + add_parameter(f"{name}.{sub_name}", param) + + elif isinstance(value, (list, tuple)): + for idx, item in enumerate(value): + add_parameter(f"{name}[{idx}]", item) + + elif isinstance(value, dict): + for key, item in value.items(): + add_parameter(f"{name}['{key}']", item) + + return named_parameters def named_sub_modules(self, type_=None, skip_compiled=False) -> Generator[tuple[str, "BaseModule"], None, None]: """Find all sub-modules in the module, as well as their names. diff --git a/dspy/retrieve/chromadb_rm.py b/dspy/retrieve/chromadb_rm.py index 4dc93661d..aa8589d75 100644 --- a/dspy/retrieve/chromadb_rm.py +++ b/dspy/retrieve/chromadb_rm.py @@ -26,9 +26,6 @@ from chromadb.config import Settings from chromadb.utils import embedding_functions except ImportError: - chromadb = None - -if chromadb is None: raise ImportError( "The chromadb library is required to use ChromadbRM. Install it with `pip install dspy-ai[chromadb]`", ) @@ -46,6 +43,7 @@ class ChromadbRM(dspy.Retrieve): persist_directory (str): chromadb persist directory embedding_function (Optional[EmbeddingFunction[Embeddable]]): Optional function to use to embed documents. Defaults to DefaultEmbeddingFunction. k (int, optional): The number of top passages to retrieve. Defaults to 7. + client(Optional[chromadb.Client]): Optional chromadb client provided by user, default to None Returns: dspy.Prediction: An object containing the retrieved passages. @@ -54,12 +52,25 @@ class ChromadbRM(dspy.Retrieve): Below is a code snippet that shows how to use this as the default retriever: ```python llm = dspy.OpenAI(model="gpt-3.5-turbo") + # using default chromadb client retriever_model = ChromadbRM('collection_name', 'db_path') dspy.settings.configure(lm=llm, rm=retriever_model) # to test the retriever with "my query" retriever_model("my query") ``` + Use provided chromadb client + ```python + import chromadb + llm = dspy.OpenAI(model="gpt-3.5-turbo") + # say you have a chromadb running on a different port + client = chromadb.HttpClient(host='localhost', port=8889) + retriever_model = ChromadbRM('collection_name', 'db_path', client=client) + dspy.settings.configure(lm=llm, rm=retriever_model) + # to test the retriever with "my query" + retriever_model("my query") + ``` + Below is a code snippet that shows how to use this in the forward() function of a module ```python self.retrieve = ChromadbRM('collection_name', 'db_path', k=num_passages) @@ -73,9 +84,10 @@ def __init__( embedding_function: Optional[ EmbeddingFunction[Embeddable] ] = ef.DefaultEmbeddingFunction(), + client: Optional[chromadb.Client] = None, k: int = 7, ): - self._init_chromadb(collection_name, persist_directory) + self._init_chromadb(collection_name, persist_directory, client=client) self.ef = embedding_function super().__init__(k=k) @@ -84,22 +96,26 @@ def _init_chromadb( self, collection_name: str, persist_directory: str, + client: Optional[chromadb.Client] = None, ) -> chromadb.Collection: """Initialize chromadb and return the loaded index. Args: collection_name (str): chromadb collection name persist_directory (str): chromadb persist directory + client (chromadb.Client): chromadb client provided by user - - Returns: + Returns: collection per collection_name """ - self._chromadb_client = chromadb.Client( - Settings( - persist_directory=persist_directory, - is_persistent=True, - ), + if client: + self._chromadb_client = client + else: + self._chromadb_client = chromadb.Client( + Settings( + persist_directory=persist_directory, + is_persistent=True, + ), ) self._chromadb_collection = self._chromadb_client.get_or_create_collection( name=collection_name, diff --git a/dspy/retrieve/databricks_rm.py b/dspy/retrieve/databricks_rm.py index 62ab06bc6..d2ac1701c 100644 --- a/dspy/retrieve/databricks_rm.py +++ b/dspy/retrieve/databricks_rm.py @@ -87,7 +87,7 @@ def __init__(self, databricks_index_name = None, databricks_endpoint = None, dat self.docs_id_column_name = docs_id_column_name self.text_column_name = text_column_name - def forward(self, query: Union[str, List[float]], query_type: str = 'text') -> dspy.Prediction: + def forward(self, query: Union[str, List[float]], query_type: str = 'text', filters_json: str = None) -> dspy.Prediction: """Search with Databricks Vector Search Client for self.k top results for query Args: @@ -115,8 +115,9 @@ def forward(self, query: Union[str, List[float]], query_type: str = 'text') -> d payload["query_text"] = query else: raise ValueError("Invalid query type specified. Use 'vector' or 'text'.") - if self.filters_json: - payload["filters_json"] = self.filters_json + + payload["filters_json"] = filters_json if filters_json else self.filters_json + response = requests.post( f"{self.databricks_endpoint}/api/2.0/vector-search/indexes/{self.databricks_index_name}/query", json=payload, diff --git a/dspy/teleprompt/bootstrap.py b/dspy/teleprompt/bootstrap.py index 8bbecef8f..9030ee6a4 100644 --- a/dspy/teleprompt/bootstrap.py +++ b/dspy/teleprompt/bootstrap.py @@ -52,9 +52,8 @@ def __init__( self.error_count = 0 self.error_lock = threading.Lock() - def compile(self, student, *, teacher=None, trainset, valset=None): + def compile(self, student, *, teacher=None, trainset): self.trainset = trainset - self.valset = valset self._prepare_student_and_teacher(student, teacher) self._prepare_predictor_mappings() @@ -133,7 +132,7 @@ def _bootstrap(self, *, max_bootstraps=None): self.validation = [x for idx, x in enumerate(self.trainset) if idx not in bootstrapped] random.Random(0).shuffle(self.validation) - self.validation = self.valset or self.validation + self.validation = self.validation # NOTE: Can't yet use evaluate because we need to trace *per example* # evaluate = Evaluate(program=self.teacher, metric=self.metric, num_threads=12) diff --git a/dspy/utils/logging.py b/dspy/utils/logging.py index c990e7e2a..a2fa66680 100644 --- a/dspy/utils/logging.py +++ b/dspy/utils/logging.py @@ -42,7 +42,10 @@ def _configure_structlog(self): ) def set_log_output( - self, method: t.Optional[str] = None, file_name: t.Optional[str] = None, output_type: t.Optional[str] = None, + self, + method: t.Optional[str] = None, + file_name: t.Optional[str] = None, + output_type: t.Optional[str] = None, ): if method is not None and method not in ["console", "file"]: raise ValueError("method provided can only be 'console', 'file'") @@ -78,12 +81,19 @@ def set_log_output( level = os.environ.get("LOG_LEVEL", "info").upper() + # Set Defaults -logging.basicConfig( - format="%(message)s", - stream=sys.stdout, - level=level, -) +def show_logging(level: str = level) -> None: + logging.basicConfig( + format="%(message)s", + stream=sys.stdout, + level=level, + ) + + # 'backoff' is used by OpenAI, and defaults their log level to INFO. + # this can clobber up dspy relevant logging for users + # this silences there logs. + logging.getLogger("backoff").setLevel(logging.WARNING) settings = LogSettings(output_type="str", method="console", file_name=None) diff --git a/examples/agents/multi_agent.ipynb b/examples/agents/multi_agent.ipynb new file mode 100644 index 000000000..ec63a9c21 --- /dev/null +++ b/examples/agents/multi_agent.ipynb @@ -0,0 +1,893 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "### Multi-Agent DSPy Programs: Bootstrapping & Aggregating Multiple `ReAct` Agents\n", + "\n", + "This is a quick (somewhat advanced) example of DSPy. You're given a hard QA task and an agent architecture (`dspy.ReAct`), how do you get high scores without tinkering with prompts?\n", + "\n", + "There are many ways, but this notebook shows one complex strategy that DSPy makes near-trivial to achieve: we'll automatically bootstrap five different highly-effective prompts for ReAct, then optimize an aggregator that combines their powers.\n", + "\n", + "As is usually the case with DSPy, the code to do this is probably shorter than describing it in English, so let's jump right into that." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0) TLDR.\n", + "\n", + "We'll build a ReAct agent in DSPy that scores 30% accuracy on a retrieval-based question answering task.\n", + "\n", + "Then, we'll optimize it with `BootstrapFewShotWithRandomSearch` to get 46% accuracy.\n", + "\n", + "Then, we'll build a multi-agent aggregator over five different optimized versions of the agent.\n", + "\n", + "Our unoptimized aggregator will score 26%. It doesn't understand the task. Hence, we'll optimize the aggregator too.\n", + "\n", + "We'll end up with an optimized multi-agent system that scores a whopping 60% accuracy on the same task.\n", + "\n", + "The core portion of the code to do this can be fit into 10 lines of DSPy, but we'll sprinkle some short explanations below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Setting Up.\n", + "\n", + "We'll configure the language model (GPT-3.5) and the retrieval model (ColBERTv2 over Wikipedia)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets.hotpotqa import HotPotQA\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "\n", + "gpt3 = dspy.OpenAI('gpt-3.5-turbo-0125', max_tokens=1000)\n", + "colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.configure(lm=gpt3, rm=colbert)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Loading some data.\n", + "\n", + "We'll load 150 examples for training (`trainset`), 50 examples for validation & optimization (`valset`), and 300 examples for evaluation (`devset`)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=300, test_size=0)\n", + "trainset = [x.with_inputs('question') for x in dataset.train[0:150]]\n", + "valset = [x.with_inputs('question') for x in dataset.train[150:200]]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]\n", + "\n", + "# show an example datapoint; it's just a question-answer pair\n", + "trainset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) ReAct Agent.\n", + "\n", + "Our agent will just be a DSPy ReAct agent that takes a `question` and outputs the `answer` by using a ColBERTv2 retrieval tool." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate this **unoptimized** ReAct agent on the `devset`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 91 / 300 (30.3): 100%|██████████| 300/300 [00:01<00:00, 161.84it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...No, Cangzhou is in the Hebei province, while Qionghai is in the Hainan province of China.False
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}[[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18–20, 2017 to...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...Tweed RiverFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}[['Æthelweard (son of Alfred) | Æthelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.']]King Alfred the Great✔️ [True]
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Examples will be bootstrapped starting from the `trainset` and optimized over our tiny `valset`. We'll evaluate later on the `devset`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 14 / 50 (28.0): 100%|██████████| 50/50 [00:00<00:00, 151.32it/s]\n", + "Average Metric: 14 / 50 (28.0): 100%|██████████| 50/50 [00:00<00:00, 1191.35it/s]\n", + " 4%|▍ | 6/150 [00:00<00:00, 216.36it/s]\n", + "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 158.43it/s]\n", + " 3%|▎ | 4/150 [00:00<00:00, 258.73it/s]\n", + "Average Metric: 21 / 50 (42.0): 100%|██████████| 50/50 [00:00<00:00, 184.63it/s]\n", + " 3%|▎ | 4/150 [00:00<00:01, 125.61it/s]\n", + "Average Metric: 24 / 50 (48.0): 100%|██████████| 50/50 [00:00<00:00, 130.39it/s]\n", + " 1%|▏ | 2/150 [00:00<00:00, 213.13it/s]\n", + "Average Metric: 20 / 50 (40.0): 100%|██████████| 50/50 [00:00<00:00, 158.40it/s]\n", + " 3%|▎ | 4/150 [00:00<00:00, 387.38it/s]\n", + "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 168.50it/s]\n", + " 4%|▍ | 6/150 [00:00<00:00, 201.99it/s]\n", + "Average Metric: 12 / 50 (24.0): 100%|██████████| 50/50 [00:00<00:00, 152.09it/s]\n", + " 6%|▌ | 9/150 [00:00<00:00, 203.21it/s]\n", + "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 231.82it/s]\n", + " 8%|▊ | 12/150 [00:00<00:00, 275.48it/s]\n", + "Average Metric: 16 / 50 (32.0): 100%|██████████| 50/50 [00:00<00:00, 165.50it/s]\n", + " 9%|▊ | 13/150 [00:00<00:00, 238.67it/s]\n", + "Average Metric: 20 / 50 (40.0): 100%|██████████| 50/50 [00:00<00:00, 167.18it/s]\n", + " 1%| | 1/150 [00:00<00:00, 624.06it/s]\n", + "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 963.47it/s] \n", + " 3%|▎ | 5/150 [00:00<00:00, 335.88it/s]\n", + "Average Metric: 21 / 50 (42.0): 100%|██████████| 50/50 [00:00<00:00, 173.26it/s]\n", + " 1%| | 1/150 [00:00<00:00, 196.26it/s]\n", + "Average Metric: 25 / 50 (50.0): 100%|██████████| 50/50 [00:00<00:00, 156.35it/s]\n", + " 7%|▋ | 10/150 [00:00<00:00, 415.36it/s]\n", + "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 180.18it/s]\n", + " 11%|█▏ | 17/150 [00:00<00:00, 265.33it/s]\n", + "Average Metric: 23 / 50 (46.0): 100%|██████████| 50/50 [00:00<00:00, 94.46it/s] \n", + " 4%|▍ | 6/150 [00:00<00:00, 273.37it/s]\n", + "Average Metric: 19 / 50 (38.0): 100%|██████████| 50/50 [00:00<00:00, 193.94it/s]\n", + " 1%|▏ | 2/150 [00:00<00:00, 290.68it/s]\n", + "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 1055.39it/s]\n", + " 1%| | 1/150 [00:00<00:00, 268.11it/s]\n", + "Average Metric: 16 / 50 (32.0): 100%|██████████| 50/50 [00:00<00:00, 148.74it/s]\n", + " 3%|▎ | 4/150 [00:00<00:00, 204.06it/s]\n", + "Average Metric: 18 / 50 (36.0): 100%|██████████| 50/50 [00:00<00:00, 183.84it/s]\n", + " 9%|▉ | 14/150 [00:00<00:00, 343.80it/s]\n", + "Average Metric: 23 / 50 (46.0): 100%|██████████| 50/50 [00:00<00:00, 148.82it/s]\n", + " 1%|▏ | 2/150 [00:00<00:00, 590.25it/s]\n", + "Average Metric: 17 / 50 (34.0): 100%|██████████| 50/50 [00:00<00:00, 223.29it/s]\n", + " 1%| | 1/150 [00:00<00:00, 169.02it/s]\n", + "Average Metric: 15 / 50 (30.0): 100%|██████████| 50/50 [00:00<00:00, 128.94it/s]\n" + ] + } + ], + "source": [ + "config = dict(max_bootstrapped_demos=2, max_labeled_demos=0, num_candidate_programs=20, num_threads=8)\n", + "tp = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match, **config)\n", + "optimized_react = tp.compile(agent, trainset=trainset, valset=valset)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Average Metric: 138 / 300 (46.0): 100%|██████████| 300/300 [00:00<00:00, 512.74it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...no✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}[['2017–18 Pittsburgh Penguins season | The 2017–18 Pittsburgh Penguins season will be the 51st season for the National Hockey League ice hockey team that was...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Steve Yzerman | Stephen Gregory \"Steve\" Yzerman ( ; born May 9, 1965) is a Canadian retired professional ice hockey player and current general manager...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...Crichton Collegiate Church is located in Midlothian, Scotland, near the hamlet of Crichton, about 7.5 miles south of Edinburgh.False
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}[['Æthelweard (son of Alfred) | Æthelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.'], ['Æthelstan of Kent |...Alfred the GreatFalse
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 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}determine if both Cangzhou and Qionghai are in the Hebei province of China. We need to carefully analyze the information provided in the context to...No, only Cangzhou is in the Hebei province of China. Qionghai is located in Hainan province.False
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}produce the answer. We know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. Looking at the context provided, we...The 2017 NHL Expansion Draft conducted by the National Hockey League filled the roster of the Vegas Golden Knights, including selecting Marc-Andre Fleury for the...False
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}identify the retired Canadian professional ice hockey player and current general manager of the Tampa Bay Lightning of the National Hockey League (NHL) whose retirement...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}identify the river near the Crichton Collegiate Church. We know that the church is situated in Midlothian, Scotland, and the River Esk flows through Midlothian...The River EskFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}produce the answer. We know from the context that Ealhswith had a son named Æthelweard in the 10th century A.D. Now, looking at the information...King Alfred the Great✔️ [True]
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 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Cangzhou', 'Qionghai'}produce the answer. From the context, we know that Cangzhou is a prefecture-level city in eastern Hebei province, while Qionghai is one of the seven...no✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017 NHL Expansion Draft', '2017–18 Pittsburgh Penguins season'}produce the answer. From the context, we know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. The draft that...National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}produce the answer. We know from the context that Steve Yzerman is a Canadian retired professional ice hockey player and the current general manager of...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}produce the answer. We know that Crichton Collegiate Church is located in Midlothian, Scotland, near the hamlet of Crichton. Since it is close to Edinburgh,...River EskFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Ealhswith', 'Æthelweard (son of Alfred)'}produce the answer. From the context, we know that Ealhswith was the wife of King Alfred the Great. Therefore, in the 10th Century A.D., Ealhswith...King Alfred the Great✔️ [True]
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "60.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized_aggregator2 = optimized_aggregator.deepcopy()\n", + "optimized_aggregator2.temperature = 0.7\n", + "\n", + "evaluate(optimized_aggregator2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7) Conclusion.\n", + "\n", + "Normally, we like to release notebooks with pre-computed caches and to inspect the prompts with `gpt3.inspect_history` to explore the behavior of optimization. See the intro notebook (or any of the Colab notebooks on the README) for such annotated examples!\n", + "\n", + "To keep the current release super quick, Omar will extend this notebook into an annotated version if there's significant interest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8) Post-Conclusion Note.\n", + "\n", + "With a little bit of syntactic sugar, the main code in this notebook could be as short as 10 lines excluding whitespace:\n", + "\n", + "```python\n", + "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])\n", + "\n", + "optimizer = BootstrapFewShotWithRandomSearch(metric=dspy.evaluate.answer_exact_match)\n", + "optimized_react = optimizer.compile(agent, trainset=trainset, valset=valset)\n", + "\n", + "class Aggregator(dspy.Module):\n", + "\tdef __init__(self):\n", + "\t\tself.aggregate = dspy.ChainOfThought('context, question -> answer')\n", + "\n", + "\tdef forward(self, question):\n", + " preds = [agent(question=question) for agent in optimized_react.best_programs[:5]]\n", + "\t\treturn self.aggregate(context=deduplicate(flatten([p.observations for p in preds])), question=question)\n", + "\t\n", + "optimized_aggregator = optimizer.compile(aggregator, trainset=trainset, valset=valset)\n", + "\n", + "# Use it!\n", + "optimized_aggregator(question=\"How many storeys are in the castle that David Gregory inherited?\")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/agents/multi_agent_llama3.ipynb b/examples/agents/multi_agent_llama3.ipynb new file mode 100644 index 000000000..55f358458 --- /dev/null +++ b/examples/agents/multi_agent_llama3.ipynb @@ -0,0 +1,1026 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"DSPy7\n", + "\n", + "### Multi-Agent DSPy Programs: Bootstrapping & Aggregating Multiple `ReAct` Agents\n", + "\n", + "This is a quick (somewhat advanced) example of DSPy. You're given a hard QA task and an agent architecture (`dspy.ReAct`), how do you get high scores without tinkering with prompts?\n", + "\n", + "There are many ways, but this notebook shows one complex strategy that DSPy makes near-trivial to achieve: we'll automatically bootstrap five different highly-effective prompts for ReAct, then optimize an aggregator that combines their powers.\n", + "\n", + "As is usually the case with DSPy, the code to do this is probably shorter than describing it in English, so let's jump right into that." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 0) TLDR.\n", + "\n", + "This is a copy of the `multi_agent.ipynb` example in the same directory, but now with **Llama3-8b** instead of **GPT-3.5**.\n", + "\n", + "We'll build a ReAct agent in DSPy that scores 24% accuracy on a retrieval-based question answering task.\n", + "\n", + "Then, we'll optimize it with `BootstrapFewShotWithRandomSearch` to get 35% accuracy.\n", + "\n", + "Then, we'll build a multi-agent aggregator over five different optimized versions of the agent.\n", + "\n", + "Our unoptimized aggregator will score 21%. It doesn't understand the task. Hence, we'll optimize the aggregator too.\n", + "\n", + "We'll end up with an optimized multi-agent system that scores a whopping 59% accuracy on the same task.\n", + "\n", + "The core portion of the code to do this can be fit into 15 lines of DSPy, but we'll sprinkle some short explanations below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1) Setting Up.\n", + "\n", + "We'll configure the language model (GPT-3.5) and the retrieval model (ColBERTv2 over Wikipedia)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import dspy\n", + "from dspy.evaluate import Evaluate\n", + "from dspy.datasets.hotpotqa import HotPotQA\n", + "from dspy.teleprompt import BootstrapFewShotWithRandomSearch\n", + "\n", + "# Set up llama3 with a VLLM client, served on four GPUs. Please note that these URLs will not work for you; you'd need to refer to the documentation to set up your own VLLM/SGLANG server(s).\n", + "llama3 = dspy.HFClientVLLM(model=\"meta-llama/Meta-Llama-3-8B-Instruct\", port=None, url=[\"http://future-hgx-3:7411\", \"http://future-hgx-3:7412\", \"http://future-hgx-3:7413\", \"http://future-hgx-1:7414\"], max_tokens=500, stop=('\\n',))\n", + "colbert = dspy.ColBERTv2(url='http://20.102.90.50:2017/wiki17_abstracts')\n", + "dspy.configure(lm=llama3, rm=colbert)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2) Loading some data.\n", + "\n", + "We'll load 150 examples for training (`trainset`), 50 examples for validation & optimization (`valset`), and 300 examples for evaluation (`devset`)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Example({'question': 'At My Window was released by which American singer-songwriter?', 'answer': 'John Townes Van Zandt'}) (input_keys={'question'})" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset = HotPotQA(train_seed=1, train_size=200, eval_seed=2023, dev_size=300, test_size=0)\n", + "trainset = [x.with_inputs('question') for x in dataset.train[0:150]]\n", + "valset = [x.with_inputs('question') for x in dataset.train[150:200]]\n", + "devset = [x.with_inputs('question') for x in dataset.dev]\n", + "\n", + "# show an example datapoint; it's just a question-answer pair\n", + "trainset[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3) ReAct Agent.\n", + "\n", + "Our agent will just be a DSPy ReAct agent that takes a `question` and outputs the `answer` by using a ColBERTv2 retrieval tool." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "agent = dspy.ReAct(\"question -> answer\", tools=[dspy.Retrieve(k=1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate this **unoptimized** ReAct agent on the `devset`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/300 [00:00\n", + "#T_fc33c th {\n", + " text-align: left;\n", + "}\n", + "#T_fc33c td {\n", + " text-align: left;\n", + "}\n", + "#T_fc33c_row0_col0, #T_fc33c_row0_col1, #T_fc33c_row0_col2, #T_fc33c_row0_col3, #T_fc33c_row0_col4, #T_fc33c_row0_col5, #T_fc33c_row1_col0, #T_fc33c_row1_col1, #T_fc33c_row1_col2, #T_fc33c_row1_col3, #T_fc33c_row1_col4, #T_fc33c_row1_col5, #T_fc33c_row2_col0, #T_fc33c_row2_col1, #T_fc33c_row2_col2, #T_fc33c_row2_col3, #T_fc33c_row2_col4, #T_fc33c_row2_col5, #T_fc33c_row3_col0, #T_fc33c_row3_col1, #T_fc33c_row3_col2, #T_fc33c_row3_col3, #T_fc33c_row3_col4, #T_fc33c_row3_col5, #T_fc33c_row4_col0, #T_fc33c_row4_col1, #T_fc33c_row4_col2, #T_fc33c_row4_col3, #T_fc33c_row4_col4, #T_fc33c_row4_col5 {\n", + " text-align: left;\n", + " white-space: pre-wrap;\n", + " word-wrap: break-word;\n", + " max-width: 400px;\n", + "}\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Qionghai', 'Cangzhou'}[['Hebei | Hebei (; postal: Hopeh) is a province of China in the North China region. Its one-character abbreviation is \"冀 \" (Jì), named after...No, Qionghai is not in the Hebei province of ChinaFalse
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017–18 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}[['2017 NHL Entry Draft | The 2017 NHL Entry Draft was the 55th NHL Entry Draft. The draft was held from June 23–24, 2017, at...answer=\"Vegas Golden Knights\"False
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Jay Feaster | Jay Harry Feaster (born July 30, 1962 in Harrisburg, Pennsylvania) is a National Hockey League (NHL) executive currently serving as the Executive...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne✔️ [True]
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}[['Æthelweard (son of Alfred) | Æthelweard (d. 920 or 922) was the younger son of King Alfred the Great and Ealhswith.'], ['Ealhswith | Ealhswith or...King Alfred the Great✔️ [True]
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 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Qionghai', 'Cangzhou'}[]no✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017–18 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}[[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18–20, 2017 to...George McPheeFalse
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Gretzky (disambiguation) | Wayne Gretzky is a retired National Hockey League player.'], [\"2006–07 Detroit Red Wings season | The 2006–07 Detroit Red Wings season was...\"Steve Yzerman\"✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[['Dumfries | Dumfries ( ; possibly from Scottish Gaelic: \"Dùn Phris\" ) is a market town and former royal burgh within the Dumfries and Galloway...River NithFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}[['Ealhswith | Ealhswith or Ealswitha (died 5 December 902) was the wife of King Alfred the Great. Her father was a Mercian nobleman, Æthelred Mucel,...\"Edward the Elder\"False
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 questionexample_answergold_titlesobservationspred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Qionghai', 'Cangzhou'}[['Cangzhou | Cangzhou () is a prefecture-level city in eastern Hebei province, People\\'s Republic of China. At the 2010 census, Cangzhou\\'s built-up (\"or metro\") area...no✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017–18 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}[[\"2017 NHL Expansion Draft | The 2017 NHL Expansion Draft was an expansion draft conducted by the National Hockey League on June 18–20, 2017 to...George McPheeFalse
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}[['Tony Resch | Tony Resch is a retired lacrosse player, and current field and box lacrosse head coach. He is the former head coach of...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}[[\"Crichton Collegiate Church | Crichton Collegiate Church is situated about 0.6 mi south west of the hamlet of Crichton in Midlothian, Scotland. Crichton itself is...River Tyne✔️ [True]
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}[['Steve Ellsworth | Steven Clark Ellsworth (born July 30, 1960 in Chicago) is the son of Dick Ellsworth and is a former Major League Baseball...answerFalse
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 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Qionghai', 'Cangzhou'}determine whether both Cangzhou and Qionghai are in the Hebei province of China. We know that Cangzhou is a prefecture-level city in eastern Hebei province,...No, only Cangzhou is in Hebei province, while Qionghai is not.assistantFalse
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017–18 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}answer this question. We know that Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season. We also know that the Vegas...The National Hockey League (NHL).assistant`False
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}answer this question. We know that the Wings entered a new era following the retirement of a Canadian retired professional ice hockey player and current...Steve Yzerman.✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}find the answer. We know that the Crichton Collegiate Church is situated near the hamlet of Crichton in Midlothian, Scotland. We also know that the...The River Esk.False
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}find the answer. We know that Ealhswith was the wife of King Alfred the Great. We also know that Æthelweard was the younger son of...King Alfred the Great.✔️ [True]
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 questionexample_answergold_titlesrationalepred_answeranswer_exact_match
0Are both Cangzhou and Qionghai in the Hebei province of China?no{'Qionghai', 'Cangzhou'}find the answer. We know that Cangzhou is a prefecture-level city in eastern Hebei province, while Qionghai is one of the seven county-level cities of...No✔️ [True]
1Who conducts the draft in which Marc-Andre Fleury was drafted to the Vegas Golden Knights for the 2017-18 season?National Hockey League{'2017–18 Pittsburgh Penguins season', '2017 NHL Expansion Draft'}find the answer. We know that Marc-André Fleury was drafted to the Vegas Golden Knights in the 2017 NHL Expansion Draft, and we also know...The National Hockey League✔️ [True]
2The Wings entered a new era, following the retirement of which Canadian retired professional ice hockey player and current general manager of the Tampa Bay...Steve Yzerman{'2006–07 Detroit Red Wings season', 'Steve Yzerman'}find the answer. We know that the Wings entered a new era, following the retirement of Steve Yzerman, and we also know that Yzerman is...Steve Yzerman✔️ [True]
3What river is near the Crichton Collegiate Church?the River Tyne{'Crichton Collegiate Church', 'Crichton Castle'}find the answer. We know that the Crichton Collegiate Church is situated in Midlothian, Scotland, and we also know that the River Esk flows through...the River EskFalse
4In the 10th Century A.D. Ealhswith had a son called Æthelweard by which English king?King Alfred the Great{'Æthelweard (son of Alfred)', 'Ealhswith'}find the answer. We know that Ealhswith was the wife of King Alfred the Great, and we also know that Æthelweard was the younger son...King Alfred the Great✔️ [True]
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "58.67" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimized_aggregator2 = optimized_aggregator.deepcopy()\n", + "optimized_aggregator2.temperature = 0.7\n", + "\n", + "evaluate(optimized_aggregator2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7) Conclusion.\n", + "\n", + "Normally, we like to release notebooks with pre-computed caches and to inspect the prompts with `llama3.inspect_history` to explore the behavior of optimization. See the intro notebook (or any of the Colab notebooks on the README) for such annotated examples!\n", + "\n", + "To keep the current release super quick, Omar will extend this notebook into an annotated version if there's significant interest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/qa/hotpot/multihop_finetune.ipynb b/examples/qa/hotpot/multihop_finetune.ipynb index 3e4642158..ec67735e6 100644 --- a/examples/qa/hotpot/multihop_finetune.ipynb +++ b/examples/qa/hotpot/multihop_finetune.ipynb @@ -156,7 +156,7 @@ " ensemble = [prog for *_, prog in basicmh_bs.candidate_programs[:4]]\n", "\n", " for idx, prog in enumerate(ensemble):\n", - " # prog.save(f'multihop_llama213b_{idx}.json')\n", + " # prog.save(f'checkpoints/multihop_llama213b_{idx}.json')\n", " pass" ] }, @@ -171,7 +171,7 @@ "\n", " for idx in range(4):\n", " prog = BasicMH()\n", - " prog.load(f'multihop_llama213b_{idx}.json')\n", + " prog.load(f'checkpoints/multihop_llama213b_{idx}.json')\n", " ensemble.append(prog)" ] }, diff --git a/poetry.lock b/poetry.lock index efdd6580b..e88f6b6f1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "aiohttp" @@ -265,6 +265,23 @@ cffi = ">=1.0.1" dev = ["cogapp", "pre-commit", "pytest", "wheel"] tests = ["pytest"] +[[package]] +name = "asgiref" +version = "3.8.1" +description = "ASGI specs, helper code, and adapters" +optional = true +python-versions = ">=3.8" +files = [ + {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"}, + {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""} + +[package.extras] +tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"] + [[package]] name = "asttokens" version = "2.4.1" @@ -339,13 +356,12 @@ cryptography = "*" [[package]] name = "autodoc-pydantic" -version = "2.1.0" +version = "2.2.0" description = "Seamlessly integrate pydantic models in your Sphinx documentation." optional = true -python-versions = ">=3.8,<4.0.0" +python-versions = "<4.0.0,>=3.8.1" files = [ - {file = "autodoc_pydantic-2.1.0-py3-none-any.whl", hash = "sha256:9f1f82ee3667589dfa08b21697be8bbd80b15110e838cd765bb1bf3ce1b0ea8f"}, - {file = "autodoc_pydantic-2.1.0.tar.gz", hash = "sha256:3cf1b973e2f5ff0fbbe9b951c11827b5e32d3409e238f7f5782359426ab8d360"}, + {file = "autodoc_pydantic-2.2.0-py3-none-any.whl", hash = "sha256:8c6a36fbf6ed2700ea9c6d21ea76ad541b621fbdf16b5a80ee04673548af4d95"}, ] [package.dependencies] @@ -354,10 +370,12 @@ pydantic-settings = ">=2.0,<3.0.0" Sphinx = ">=4.0" [package.extras] -dev = ["coverage (>=7,<8)", "flake8 (>=3,<4)", "pytest (>=7,<8)", "sphinx-copybutton (>=0.4,<0.5)", "sphinx-rtd-theme (>=1.0,<2.0)", "sphinx-tabs (>=3,<4)", "sphinxcontrib-mermaid (>=0.7,<0.8)", "tox (>=3,<4)"] -docs = ["sphinx-copybutton (>=0.4,<0.5)", "sphinx-rtd-theme (>=1.0,<2.0)", "sphinx-tabs (>=3,<4)", "sphinxcontrib-mermaid (>=0.7,<0.8)"] -erdantic = ["erdantic (>=0.6,<0.7)"] -test = ["coverage 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-docs-online = ["mkdocs (>=1.5.2)", "mkdocs-git-revision-date-localized-plugin (>=1.2.0)", "mkdocs-material (>=9.2.6)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"] -hooks = ["pre-commit (>=3.3.3)"] -package = ["build (>=1.0.0)", "twine (>=4.0.2)"] -runner = ["tox (>=4.11.1)"] -sast = ["bandit[toml] (>=1.7.5)"] -testing = ["pytest (>=7.4.0)"] -tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"] -tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"] - [[package]] name = "verspec" version = "0.1.0" @@ -6091,13 +6535,13 @@ test = ["coverage", "flake8 (>=3.7)", "mypy", "pretend", "pytest"] [[package]] name = "virtualenv" -version = "20.25.3" +version = "20.26.1" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.25.3-py3-none-any.whl", hash = "sha256:8aac4332f2ea6ef519c648d0bc48a5b1d324994753519919bddbb1aff25a104e"}, - {file = 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{file = "weaviate_client-4.5.7.tar.gz", hash = "sha256:130f7371c4af31949e6f3537683813f0e5adb7ec94a5536e2278768c5fe057bf"}, ] [package.dependencies] @@ -6264,24 +6708,24 @@ authlib = ">=1.2.1,<2.0.0" grpcio = ">=1.57.0,<2.0.0" grpcio-health-checking = ">=1.57.0,<2.0.0" grpcio-tools = ">=1.57.0,<2.0.0" -httpx = "0.27.0" +httpx = ">=0.25.0,<=0.27.0" pydantic = ">=2.5.0,<3.0.0" requests = ">=2.30.0,<3.0.0" -validators = "0.22.0" +validators = "0.28.1" [[package]] name = "websocket-client" -version = "1.7.0" +version = "1.8.0" description = "WebSocket client for Python with low level API options" optional = true python-versions = ">=3.8" files = [ - {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"}, - {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"}, + {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"}, + {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"}, ] [package.extras] -docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"] +docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"] optional = ["python-socks", "wsaccel"] test = ["websockets"] @@ -6698,6 +7142,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p aws = ["boto3"] chromadb = ["chromadb"] docs = ["autodoc_pydantic", "docutils", "furo", "m2r2", "myst-nb", "myst-parser", "sphinx", "sphinx-autobuild", "sphinx-automodapi", "sphinx-reredirects", "sphinx_rtd_theme"] +fastembed = ["fastembed"] marqo = ["marqo"] milvus = ["pymilvus"] pinecone = ["pinecone-client"] @@ -6707,5 +7152,6 @@ weaviate = ["weaviate-client"] [metadata] lock-version = "2.0" -python-versions = ">=3.9,<3.12" -content-hash = "3988e0bedd832c87fda15828d8c6f08b2c3a9e75a9bca6d4201c5b8bdf5e3c9e" +python-versions = ">=3.9,<3.13" +content-hash = "dfb44251298e064041c90ee3a63a1cf5baaf2c6ce5c4bbaa4a036247c74852a8" + diff --git a/pyproject.toml b/pyproject.toml index ef683f595..0b988e7ef 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,12 +4,12 @@ build-backend = "setuptools.build_meta" [project] name = "dspy-ai" -version = "2.4.3" +version = "2.4.9" description = "DSPy" readme = "README.md" authors = [{ name = "Omar Khattab", email = "okhattab@stanford.edu" }] license = { text = "MIT License" } -requires-python = ">=3.9, <3.12" +requires-python = ">=3.9, <3.13" classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", @@ -32,17 +32,18 @@ dependencies = [ "regex", "ujson", "tqdm", - "datasets~=2.14.6,<3.0.0", + "datasets>=2.14.6,<3.0.0", "requests", "optuna", "pydantic~=2.0", "structlog", + "jinja2" ] [project.optional-dependencies] anthropic = ["anthropic~=0.18.0"] chromadb = ["chromadb~=0.4.14"] -qdrant = ["qdrant-client>=1.6.2", "fastembed>=0.1.0"] +qdrant = ["qdrant-client>=1.6.2", "fastembed>=0.2.0"] marqo = ["marqo"] pinecone = ["pinecone-client~=2.2.4"] weaviate = ["weaviate-client~=4.5.4"] @@ -62,6 +63,7 @@ docs = [ "sphinx-automodapi==0.16.0", ] dev = ["pytest>=6.2.5"] +fastembed = ["fastembed>=0.2.0"] [project.urls] homepage = "https://github.com/stanfordnlp/dspy" @@ -81,7 +83,7 @@ keywords = ["dspy", "ai", "language models", "llm", "openai"] [tool.poetry.dependencies] -python = ">=3.9,<3.12" +python = ">=3.9,<3.13" pydantic = "^2.0" backoff = "^2.2.1" joblib = "^1.3.2" @@ -95,7 +97,7 @@ requests = "^2.31.0" optuna = "^3.4.0" anthropic = { version = "^0.18.0", optional = true } chromadb = { version = "^0.4.14", optional = true } -fastembed = { version = "^0.1.0", optional = true } +fastembed = { version = ">=0.2.0", optional = true } marqo = { version = "*", optional = true } qdrant-client = { version = "^1.6.2", optional = true } pinecone-client = { version = "^2.2.4", optional = true } @@ -119,6 +121,8 @@ psycopg2 = { version = "^2.9.9", optional = true } pgvector = { version = "^0.2.5", optional = true } structlog = "^24.1.0" snowflake-snowpark-python = { version = "*",optional=true} +jinja2 = "^3.1.3" + [tool.poetry.group.dev.dependencies] @@ -152,6 +156,7 @@ docs = [ "sphinx-reredirects", "sphinx-automodapi", ] +fastembed = ["fastembed"] [tool.poetry.group.doc.dependencies] mkdocs = ">=1.5.3" diff --git a/setup.py b/setup.py index 36b3e6e41..7d1e97365 100644 --- a/setup.py +++ b/setup.py @@ -8,19 +8,20 @@ with open("requirements.txt", encoding="utf-8") as f: requirements = f.read().splitlines() -setup( - name="dspy-ai", - version="2.4.3", - description="DSPy", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/stanfordnlp/dsp", - author="Omar Khattab", - author_email="okhattab@stanford.edu", - license="MIT License", - packages=find_packages(include=["dsp.*", "dspy.*", "dsp", "dspy"]), - python_requires=">=3.9", - install_requires=requirements, +setup( + name="dspy-ai", + version="2.4.9", + description="DSPy", + long_description=long_description, + long_description_content_type='text/markdown', + url="https://github.com/stanfordnlp/dsp", + author="Omar Khattab", + author_email="okhattab@stanford.edu", + license="MIT License", + packages=find_packages(include=['dsp.*', 'dspy.*', 'dsp', 'dspy']), + python_requires='>=3.9', + install_requires=requirements, + extras_require={ "chromadb": ["chromadb~=0.4.14"], "qdrant": ["qdrant-client", "fastembed"], @@ -32,14 +33,15 @@ "milvus": ["pymilvus~=2.3.7"], "google-vertex-ai": ["google-cloud-aiplatform==1.43.0"], "snowflake": ["snowflake-snowpark-python"], - }, - classifiers=[ - "Development Status :: 3 - Alpha", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", - "Operating System :: POSIX :: Linux", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - ], -) + "fastembed": ["fastembed"], + }, + classifiers=[ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + ], +) diff --git a/skycamp2023.ipynb b/skycamp2023.ipynb index 5590509da..e1dc7e27f 100644 --- a/skycamp2023.ipynb +++ b/skycamp2023.ipynb @@ -46,8 +46,8 @@ "import pkg_resources # Install the package if it's not installed\n", "if not \"dspy-ai\" in {pkg.key for pkg in pkg_resources.working_set}:\n", " !pip install -U pip\n", - " # !pip install dspy-ai\n", - " !pip install -e $repo_path\n", + " !pip install dspy-ai==2.1\n", + " # !pip install -e $repo_path\n", "\n", "!pip install transformers" ] diff --git a/tests/evaluate/test_evaluate.py b/tests/evaluate/test_evaluate.py index 3af3ae9da..a0ba93331 100644 --- a/tests/evaluate/test_evaluate.py +++ b/tests/evaluate/test_evaluate.py @@ -1,15 +1,23 @@ -import dsp, dspy +import signal +import threading + +import pytest + +import dsp +import dspy from dspy.evaluate.evaluate import Evaluate from dspy.evaluate.metrics import answer_exact_match from dspy.predict import Predict from dspy.utils.dummies import DummyLM + def new_example(question, answer): """Helper function to create a new example.""" return dspy.Example( question=question, answer=answer, - ).with_inputs("question") + ).with_inputs("question") + def test_evaluate_initialization(): devset = [new_example("What is 1+1?", "2")] @@ -23,6 +31,7 @@ def test_evaluate_initialization(): assert ev.num_threads == len(devset) assert ev.display_progress == False + def test_evaluate_call(): dspy.settings.configure(lm=DummyLM({"What is 1+1?": "2", "What is 2+2?": "4"})) devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")] @@ -36,6 +45,60 @@ def test_evaluate_call(): score = ev(program) assert score == 100.0 + +def test_multithread_evaluate_call(): + dspy.settings.configure(lm=DummyLM({"What is 1+1?": "2", "What is 2+2?": "4"})) + devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")] + program = Predict("question -> answer") + assert program(question="What is 1+1?").answer == "2" + ev = Evaluate( + devset=devset, + metric=answer_exact_match, + display_progress=False, + num_threads=2, + ) + score = ev(program) + assert score == 100.0 + + +def test_multi_thread_evaluate_call_cancelled(monkeypatch): + # slow LM that sleeps for 1 second before returning the answer + class SlowLM(DummyLM): + def __call__(self, prompt, **kwargs): + import time + + time.sleep(1) + return super().__call__(prompt, **kwargs) + + dspy.settings.configure(lm=SlowLM({"What is 1+1?": "2", "What is 2+2?": "4"})) + + devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")] + program = Predict("question -> answer") + assert program(question="What is 1+1?").answer == "2" + + # spawn a thread that will sleep for .1 seconds then send a KeyboardInterrupt + def sleep_then_interrupt(): + import time + + time.sleep(0.1) + import os + + os.kill(os.getpid(), signal.SIGINT) + + input_thread = threading.Thread(target=sleep_then_interrupt) + input_thread.start() + + with pytest.raises(KeyboardInterrupt): + ev = Evaluate( + devset=devset, + metric=answer_exact_match, + display_progress=False, + num_threads=2, + ) + score = ev(program) + assert score == 100.0 + + def test_evaluate_call_bad(): dspy.settings.configure(lm=DummyLM({"What is 1+1?": "0", "What is 2+2?": "0"})) devset = [new_example("What is 1+1?", "2"), new_example("What is 2+2?", "4")] @@ -48,6 +111,7 @@ def test_evaluate_call_bad(): score = ev(program) assert score == 0.0 + def test_evaluate_display_table(): devset = [new_example("What is 1+1?", "2")] ev = Evaluate( @@ -56,4 +120,3 @@ def test_evaluate_display_table(): display_table=True, ) assert ev.display_table == True - diff --git a/tests/modules/vectorizer/test_fastembed.py b/tests/modules/vectorizer/test_fastembed.py new file mode 100644 index 000000000..d6064cc66 --- /dev/null +++ b/tests/modules/vectorizer/test_fastembed.py @@ -0,0 +1,43 @@ +from dsp.modules.sentence_vectorizer import FastEmbedVectorizer +import pytest + +from dspy.primitives.example import Example + +# Skip the test if the 'fastembed' package is not installed +pytest.importorskip("fastembed", reason="'fastembed' is not installed. Use `pip install fastembed` to install it.") + + +@pytest.mark.parametrize( + "n_dims,model_name", [(384, "BAAI/bge-small-en-v1.5"), (512, "jinaai/jina-embeddings-v2-small-en")] +) +def test_fastembed_with_examples(n_dims, model_name): + vectorizer = FastEmbedVectorizer(model_name) + + examples = [ + Example(query="What's the price today?", response="The price is $10.00").with_inputs("query", "response"), + Example(query="What's the weather today?", response="The weather is sunny").with_inputs("query", "response"), + Example(query="Who was leading the team?", response="It was Jim. Rather enthusiastic guy.").with_inputs( + "query", "response" + ), + ] + + embeddings = vectorizer(examples) + + assert embeddings.shape == (len(examples), n_dims) + + +@pytest.mark.parametrize( + "n_dims,model_name", [(384, "BAAI/bge-small-en-v1.5"), (512, "jinaai/jina-embeddings-v2-small-en")] +) +def test_fastembed_with_strings(n_dims, model_name): + vectorizer = FastEmbedVectorizer(model_name) + + inputs = [ + "Jonathan Kent is a fictional character appearing in American comic books published by DC Comics.", + "Clark Kent is a fictional character appearing in American comic books published by DC Comics.", + "Martha Kent is a fictional character appearing in American comic books published by DC Comics.", + ] + + embeddings = vectorizer(inputs) + + assert embeddings.shape == (len(inputs), n_dims) diff --git a/examples/qa/hotpot/multihop_llama213b_0.json b/tests/multihop_llama213b_0.json similarity index 100% rename from examples/qa/hotpot/multihop_llama213b_0.json rename to tests/multihop_llama213b_0.json diff --git a/examples/qa/hotpot/multihop_llama213b_1.json b/tests/multihop_llama213b_1.json similarity index 100% rename from examples/qa/hotpot/multihop_llama213b_1.json rename to tests/multihop_llama213b_1.json diff --git a/examples/qa/hotpot/multihop_llama213b_2.json b/tests/multihop_llama213b_2.json similarity index 100% rename from examples/qa/hotpot/multihop_llama213b_2.json rename to tests/multihop_llama213b_2.json diff --git a/examples/qa/hotpot/multihop_llama213b_3.json b/tests/multihop_llama213b_3.json similarity index 100% rename from examples/qa/hotpot/multihop_llama213b_3.json rename to tests/multihop_llama213b_3.json diff --git a/tests/predict/test_react.py b/tests/predict/test_react.py index f28e905e7..79f8e0bdf 100644 --- a/tests/predict/test_react.py +++ b/tests/predict/test_react.py @@ -1,3 +1,5 @@ +from dataclasses import dataclass + import dspy from dspy.utils.dummies import dummy_rm @@ -84,3 +86,71 @@ def test_example_search(): "Thought 2: More thoughts\n\n" "Action 2: Finish[blue]" ) + + +class DummyTool1: + name = "Tool1" + input_variable = "query" + desc = "" + num_calls = 0 + + def __call__(self, *args, **kwargs): + # test case with no passages attribute + assert args[0] == "foo" + self.num_calls += 1 + return "tool 1 output" + + +@dataclass +class DummyOutput: + passages: str + + +class DummyTool2: + name = "Tool2" + input_variable = "query" + desc = "" + num_calls = 0 + + def __call__(self, *args, **kwargs): + # test case with passages attribute + assert args[0] == "bar" + self.num_calls += 1 + return DummyOutput(passages="tool 2 output") + + +def test_custom_tools(): + lm = dspy.utils.DummyLM( + [ + "Initial thoughts", + "Tool1[foo]", + "More thoughts", + "Tool2[bar]", + "Even more thoughts", + "Finish[baz]", + ] + ) + dspy.settings.configure(lm=lm) + + tool1 = DummyTool1() + tool2 = DummyTool2() + program = dspy.ReAct("question -> answer", tools=[tool1, tool2]) + + question = "What is the color of the sky?" + result = program(question=question) + assert result.answer == "baz" + + # each tool should be called only once + assert tool1.num_calls == 1 + assert tool2.num_calls == 1 + assert lm.get_convo(-1).endswith( + "Question: What is the color of the sky?\n\n" + "Thought 1: Initial thoughts\n\n" + "Action 1: Tool1[foo]\n\n" + "Observation 1: tool 1 output\n\n" + "Thought 2: More thoughts\n\n" + "Action 2: Tool2[bar]\n\n" + "Observation 2: tool 2 output\n\n" + "Thought 3: Even more thoughts\n\n" + "Action 3: Finish[baz]" + ) diff --git a/tests/primitives/test_program.py b/tests/primitives/test_program.py index 3f8600515..219ad5259 100644 --- a/tests/primitives/test_program.py +++ b/tests/primitives/test_program.py @@ -126,9 +126,9 @@ def test_complex_module_traversal(): "self", "self.sub_module", "self.sub_module.nested_list[0]", - "self.sub_module.nested_list[1][key]", + "self.sub_module.nested_list[1][key]", # NOTE: named_sub_modules allows recursive structures "self.sub_module.nested_tuple[0]", - "self.sub_module.nested_tuple[1][0]", + "self.sub_module.nested_tuple[1][0]", # NEW: named_sub_modules allows recursive structures, but named_prameters does not # "self.sub_module.nested_tuple[1][1]", This should not be included, as it's the same module as the previous one } found_names = {name for name, _ in root.named_sub_modules()} diff --git a/tests/teleprompt/test_bootstrap.py b/tests/teleprompt/test_bootstrap.py index 4758a5aae..ba2acd556 100644 --- a/tests/teleprompt/test_bootstrap.py +++ b/tests/teleprompt/test_bootstrap.py @@ -54,7 +54,7 @@ def test_compile_with_predict_instances(): metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1 ) compiled_student = bootstrap.compile( - student, teacher=teacher, trainset=trainset, valset=valset + student, teacher=teacher, trainset=trainset ) assert compiled_student is not None, "Failed to compile student" @@ -74,7 +74,7 @@ def test_bootstrap_effectiveness(): metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1 ) compiled_student = bootstrap.compile( - student, teacher=teacher, trainset=trainset, valset=valset + student, teacher=teacher, trainset=trainset ) # Check that the compiled student has the correct demos @@ -149,7 +149,7 @@ def forward(self, **kwargs): ) with pytest.raises(RuntimeError, match="Simulated error"): - bootstrap.compile(student, teacher=teacher, trainset=trainset, valset=valset) + bootstrap.compile(student, teacher=teacher, trainset=trainset) def test_validation_set_usage(): @@ -171,7 +171,7 @@ def test_validation_set_usage(): metric=simple_metric, max_bootstrapped_demos=1, max_labeled_demos=1 ) compiled_student = bootstrap.compile( - student, teacher=teacher, trainset=trainset, valset=valset + student, teacher=teacher, trainset=trainset ) # Check that validation examples are part of student's demos after compilation