Details Details All the documentation should b

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Sweep: Ensure `retrieve` in the `dspy/` folder has comprehensive documentation. about dspy HOT 1 OPEN

darinkishore commented on July 22, 2024 2

Sweep: Ensure `retrieve` in the `dspy/` folder has comprehensive documentation.

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sweep-ai commented on July 22, 2024

🚀 Here's the PR! #47

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Sandbox Execution ✓

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Sandbox logs for c7a522a

Checking docs/DOCS_README.md for syntax errors... ✅ docs/DOCS_README.md has no syntax errors! 1/1 ✓
Checking docs/DOCS_README.md for syntax errors...
✅ docs/DOCS_README.md has no syntax errors!

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Step 1: 🔎 Searching

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I think are relevant in decreasing order of relevance (click to expand). If some file is missing from here, you can mention the path in the ticket description.

dspy/dspy/retrieve/retrieve.py

Lines 7 to 41 in c7a522a

    
           class Retrieve(Parameter): 
        
               name = "Search" 
        
               input_variable = "query" 
        
               desc = "takes a search query and returns one or more potentially relevant passages from a corpus" 
        
               def __init__(self, k=3): 
        
                   self.stage = random.randbytes(8).hex() 
        
                   self.k = k 
        
               def reset(self): 
        
                   pass 
        
               def dump_state(self): 
        
                   state_keys = ["k"] 
        
                   return {k: getattr(self, k) for k in state_keys} 
        
               def load_state(self, state): 
        
                   for name, value in state.items(): 
        
                       setattr(self, name, value) 
        
               def __call__(self, *args, **kwargs): 
        
                   return self.forward(*args, **kwargs) 
        
               def forward(self, query_or_queries): 
        
                   queries = [query_or_queries] if isinstance(query_or_queries, str) else query_or_queries 
        
                   queries = [query.strip().split('\n')[0].strip() for query in queries] 
        
                   # print(queries) 
        
                   # TODO: Consider removing any quote-like markers that surround the query too. 
        
                   passages = dsp.retrieveEnsemble(queries, k=self.k) 
        
                   return Prediction(passages=passages)

dspy/README.md

Lines 15 to 25 in c7a522a

    
           **DSPy** is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). **DSPy** unifies techniques for **prompting** and **fine-tuning** LMs — and approaches for **reasoning**, **self-improvement**, and **augmentation with retrieval and tools**. All of these are expressed through modules that compose and learn. 
        
           To make this possible: 
        
           - **DSPy** provides **composable and declarative modules** for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted _string manipulation tricks_ into truly modular _generalized operations that learn to adapt to your task_. 
        
           - **DSPy** introduces an **automatic compiler that teaches LMs** how to conduct the declarative steps in your program. Specifically, the **DSPy compiler** will internally _trace_ your program and then **craft high-quality prompts for large LMs (or train automatic finetunes for small LMs)** to teach them the steps of your task. 
        
           The **DSPy compiler** _bootstraps_ prompts and finetunes from minimal data **without needing manual labels for the intermediate steps** in your program. Instead of brittle "prompt engineering" with hacky string manipulation, you can explore a systematic space of modular and trainable pieces.

Step 2: ⌨️ Coding

Create docs/retrieve.md ✓ 867f563 Edit

Create docs/retrieve.md with contents:
• Start by creating a new markdown file named `retrieve.md` in the `docs/` directory.
• Begin the documentation with a brief introduction to the `retrieve` module, explaining that it is part of the DSPy framework used for advanced tasks with language and retrieval models.
• Describe the functionality of the `retrieve` module, explaining that it takes a search query and returns one or more potentially relevant passages from a corpus. Reference the `retrieve.py` file in the `dspy/retrieve/` directory for this.
• Draw from the README.md file to identify the philosophical usage/purpose of `retrieve`. Explain how the `retrieve` module fits into the overall philosophy and purpose of the DSPy framework.
• Provide examples of how to use the `retrieve` module. Reference the jupyter notebooks for these examples. Ensure to provide a variety of examples to cover different use cases.
• Conclude the documentation with any additional information that users might find useful when using the `retrieve` module.

Running GitHub Actions for docs/retrieve.md ✓ Edit

Check docs/retrieve.md with contents:
Ran GitHub Actions for 867f5637636604102bebf8dd7b5f76ac87e87bfa:

Modify docs/DOCS_README.md ✓ 8485a44 Edit

Modify docs/DOCS_README.md with contents:
• If a section for `retrieve` already exists in the `DOCS_README.md` file, modify it to include a link to the newly created `retrieve.md` file.
• If no such section exists, add a new section for `retrieve` and include a link to the `retrieve.md` file.
• Ensure to provide a brief description of the `retrieve` module in this section, summarizing its purpose and functionality.
--- 
+++ 
@@ -43,7 +43,11 @@
 ##### Watch Docs
 
 We recommend using sphinx-autobuild during development, which provides a live-reloading
-server, that rebuilds the documentation and refreshes any open pages automatically when
+## Retrieve Module
+
+For comprehensive information about the `retrieve` module, check out the [Retrieve Module Documentation](retrieve.md).
+
+The `retrieve` module provides a way to retrieve relevant information from a dataset based on a query. It is designed to work seamlessly within the DSPy framework, aiding in the retrieval tasks that form the backbone of many language and data processing operations.server, that rebuilds the documentation and refreshes any open pages automatically when
 changes are saved. This enables a much shorter feedback loop which can help boost
 productivity when writing documentation.
 

Running GitHub Actions for docs/DOCS_README.md ✓ Edit

Check docs/DOCS_README.md with contents:
Ran GitHub Actions for 8485a4411bad33dde778c275f8e3f1f6cf972533:

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Sweep: Ensure `retrieve` in the `dspy/` folder has comprehensive documentation. about dspy HOT 1 OPEN

Comments (1)

🚀 Here's the PR! #47

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	class Retrieve(Parameter):
	name = "Search"
	input_variable = "query"
	desc = "takes a search query and returns one or more potentially relevant passages from a corpus"

	def __init__(self, k=3):
	self.stage = random.randbytes(8).hex()
	self.k = k

	def reset(self):
	pass

	def dump_state(self):
	state_keys = ["k"]
	return {k: getattr(self, k) for k in state_keys}

	def load_state(self, state):
	for name, value in state.items():
	setattr(self, name, value)

	def __call__(self, args, *kwargs):
	return self.forward(args, *kwargs)

	def forward(self, query_or_queries):
	queries = [query_or_queries] if isinstance(query_or_queries, str) else query_or_queries
	queries = [query.strip().split('\n')[0].strip() for query in queries]


	# print(queries)
	# TODO: Consider removing any quote-like markers that surround the query too.

	passages = dsp.retrieveEnsemble(queries, k=self.k)
	return Prediction(passages=passages)


	DSPy is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). DSPy unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools. All of these are expressed through modules that compose and learn.

	To make this possible:

	- DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted _string manipulation tricks_ into truly modular _generalized operations that learn to adapt to your task_.

	- DSPy introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally _trace_ your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.

	The DSPy compiler _bootstraps_ prompts and finetunes from minimal data without needing manual labels for the intermediate steps in your program. Instead of brittle "prompt engineering" with hacky string manipulation, you can explore a systematic space of modular and trainable pieces.