Update the Learn DSPy docs (#1790)

* Update the Learn DSPy docs * fixes
stanfordnlp · Nov 12, 2024 · 5ebf2f7 · 5ebf2f7
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diff --git a/docs/docs/building-blocks/1-language_models.md b/docs/docs/building-blocks/1-language_models.md
diff --git a/docs/docs/building-blocks/3-modules.md b/docs/docs/building-blocks/3-modules.md
diff --git a/docs/docs/building-blocks/6-optimizers.md b/docs/docs/building-blocks/6-optimizers.md
diff --git a/docs/docs/building-blocks/_category_.json b/docs/docs/building-blocks/_category_.json
diff --git a/docs/docs/deep-dive/assertions.md b/docs/docs/deep-dive/assertions.md
@@ -4,6 +4,9 @@ sidebar_position: 7
 
 # DSPy Assertions
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 Language models (LMs) have transformed how we interact with machine learning, offering vast capabilities in natural language understanding and generation. However, ensuring these models adhere to domain-specific constraints remains a challenge. Despite the growth of techniques like fine-tuning or “prompt engineering”, these approaches are extremely tedious and rely on heavy, manual hand-waving to guide the LMs in adhering to specific constraints. Even DSPy's modularity of programming prompting pipelines lacks mechanisms to effectively and automatically enforce these constraints. 
 
 To address this, we introduce DSPy Assertions, a feature within the DSPy framework designed to automate the enforcement of computational constraints on LMs. DSPy Assertions empower developers to guide LMs towards desired outcomes with minimal manual intervention, enhancing the reliability, predictability, and correctness of LM outputs.

diff --git a/docs/docs/deep-dive/data-handling/built-in-datasets.md b/docs/docs/deep-dive/data-handling/built-in-datasets.md
@@ -2,6 +2,7 @@
 sidebar_position: 2
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+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
 
 # Utilizing Built-in Datasets
 

diff --git a/docs/docs/deep-dive/data-handling/examples.md b/docs/docs/deep-dive/data-handling/examples.md
@@ -2,6 +2,7 @@
 sidebar_position: 1
 ---
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
 
 # Examples in DSPy
 

diff --git a/docs/docs/deep-dive/data-handling/loading-custom-data.md b/docs/docs/deep-dive/data-handling/loading-custom-data.md
@@ -2,6 +2,8 @@
 sidebar_position: 3
 ---
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 # Creating a Custom Dataset
 
 We've seen how to work with with `Example` objects and use the `HotPotQA` class to load the HuggingFace HotPotQA dataset as a list of `Example` objects. But in production, such structured datasets are rare. Instead, you'll find yourself working on a custom dataset and might question: how do I create my own dataset or what format should it be?

diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientTGI.md
@@ -2,6 +2,9 @@ import AuthorDetails from '@site/src/components/AuthorDetails';
 
 # HFClientTGI
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 ## Prerequisites
 
 Docker must be installed on your system. If you don't have Docker installed, you can get it from [here](https://docs.docker.com/get-docker/).

diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientVLLM.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/HFClientVLLM.md
@@ -1,5 +1,8 @@
 # HFClient vLLM
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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+
 ## Prerequisites
 
 Follow these steps to set up the vLLM Server:

diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/LlamaCpp.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/LlamaCpp.md
@@ -1,5 +1,8 @@
 # LlamaCpp
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 ## Prerequisites
 
 Install Llama Cpp Python by following the instructions provided in the [Llama Cpp Python repository](https://github.com/abetlen/llama-cpp-python).

diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/MLC.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/MLC.md
@@ -1,5 +1,8 @@
 # ChatModuleClient
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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+
 ## Prerequisites
 
 1. Install the required packages using the following commands:

diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/Ollama.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/Ollama.md
@@ -1,5 +1,7 @@
 # OllamaLocal
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 !!! note
     Adapted from documentation provided by https://github.com/insop
 

diff --git a/docs/docs/deep-dive/language_model_clients/lm_local_models/TensorRTLLM.md b/docs/docs/deep-dive/language_model_clients/lm_local_models/TensorRTLLM.md
@@ -1,5 +1,7 @@
 # TensorRTModel
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 TensorRT LLM by Nvidia happens to be one of the most optimized inference engines to run open-source Large Language Models locally or in production.
 
 ### Prerequisites

diff --git a/docs/docs/deep-dive/modules/chain-of-thought-with-hint.md b/docs/docs/deep-dive/modules/chain-of-thought-with-hint.md
@@ -4,6 +4,8 @@ sidebar_position: 2
 
 # ChainOfThoughtWithHint
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
+
 This class builds upon the `ChainOfThought` class by introducing an additional input field to provide hints for reasoning. The inclusion of a hint allows for a more directed problem-solving process, which can be especially useful in complex scenarios.
 
 

diff --git a/docs/docs/deep-dive/modules/chain-of-thought.md b/docs/docs/deep-dive/modules/chain-of-thought.md
@@ -1,5 +1,8 @@
 # dspy.ChainOfThought
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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+
 ### Constructor
 
 The constructor initializes the `ChainOfThought` class and sets up its attributes. It inherits from the `Predict` class and adds specific functionality for chain of thought processing. 

diff --git a/docs/docs/deep-dive/modules/guide.md b/docs/docs/deep-dive/modules/guide.md
@@ -1,5 +1,8 @@
 # DSPy Modules
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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+
 [<img align="center" src="https://colab.research.google.com/assets/colab-badge.svg" />](https://colab.research.google.com/github/stanfordnlp/dspy/blob/main/docs/guides/modules.ipynb)
 
 ## Quick Recap

diff --git a/docs/docs/deep-dive/modules/multi-chain-comparison.md b/docs/docs/deep-dive/modules/multi-chain-comparison.md
@@ -1,5 +1,8 @@
 # dspy.MultiChainComparison
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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+
 ## Constructor
 
 The constructor initializes the `MultiChainComparison` class and sets up its attributes. It inherits from the `Predict` class and adds specific functionality for multiple chain comparisons.

diff --git a/docs/docs/deep-dive/modules/predict.md b/docs/docs/deep-dive/modules/predict.md
@@ -1,5 +1,8 @@
 # dspy.Predict
 
+!!! warning "This page is outdated and may not be fully accurate in DSPy 2.5"
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 ## Constructor
 
 The constructor initializes the `Predict` class and sets up its attributes, taking in the `signature` and additional config options. If the `signature` is a string, it processes the input and output fields, generates instructions, and creates a template for the specified `signature` type.