simplify prompt
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zachary62
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assets/prompt
268
assets/prompt
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# Example App for text summarization & QA using minillmflow
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from minillmflow import Node, BatchNode, Flow, BatchFlow, AsyncNode, AsyncFlow, BatchAsyncFlow
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import os
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from minillmflow import *
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import openai, os, yaml
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# 1) Implement a simple LLM helper (OpenAI in this example).
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# Minimal LLM wrapper
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def call_llm(prompt):
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# Users must set an OpenAI API key; can also load from env var, etc.
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openai.api_key = "YOUR_API_KEY_HERE"
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r = openai.ChatCompletion.create(
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model="gpt-4",
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@ -12,211 +10,113 @@ def call_llm(prompt):
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)
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return r.choices[0].message.content
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# 2) Create a shared store (dict) for Node/Flow data exchange.
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# This can be replaced with a DB or other storage.
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# Design the structure / schema based on the app requirements.
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shared = {"data": {}, "summary": {}}
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# 3) Create a Node that loads data from disk into shared['data'].
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# Load data into shared['data']
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class LoadData(Node):
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# For compute-intensive operations, do them in prep().
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def prep(self, shared):
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path = "../data/PaulGrahamEssaysLarge"
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for filename in os.listdir(path):
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with open(os.path.join(path, filename), 'r') as f:
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shared['data'][filename] = f.read()
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# If LLM was needed, we'd handle it in exec(). Not needed here.
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# (idempotent so it can be retried if needed)
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def exec(self,shared,prep_res): pass
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# post() can update shared again or decide the next node (by return the action).
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def post(self,shared,prep_res,exec_res): pass
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for fn in os.listdir(path):
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with open(os.path.join(path, fn), 'r') as f:
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shared['data'][fn] = f.read()
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def exec(self, res): pass
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def post(self, s, pr, er): pass
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load_data = LoadData()
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# Run the data-loading node once
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load_data.run(shared)
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LoadData().run(shared)
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# 4) Create a Node that summarizes a single file using the LLM.
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# Summarize one file
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class SummarizeFile(Node):
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def prep(self, shared):
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# Use self.params (which must remain immutable during prep/exec/post).
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# Typically, we only store identifying info in params (e.g., filename).
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content = shared['data'][self.params['filename']]
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return content
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def exec(self, shared, prep_res):
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content = prep_res
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prompt = f"{content} Respond a summary of above in 10 words"
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summary = call_llm(prompt)
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return summary
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def post(self, shared, prep_res, exec_res):
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shared["summary"][self.params['filename']] = exec_res
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def prep(self, s): return s['data'][self.params['filename']]
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def exec(self, content):
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return call_llm(f"{content} Summarize in 10 words.")
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def post(self, s, pr, sr): s["summary"][self.params['filename']] = sr
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summarize_file = SummarizeFile()
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# For testing, we set params directly on the node.
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# In real usage, you'd set them in a Flow or BatchFlow.
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summarize_file.set_params({"filename":"addiction.txt"})
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summarize_file.run(shared)
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node_summ = SummarizeFile()
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node_summ.set_params({"filename":"addiction.txt"})
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node_summ.run(shared)
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# 5) If data is large, we can apply a map-reduce pattern:
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# - MapSummaries(BatchNode) => chunk the file and summarize each chunk
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# - ReduceSummaries(Node) => combine those chunk-level summaries
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# Map-Reduce summarization
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class MapSummaries(BatchNode):
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def prep(self, shared):
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content = shared['data'][self.params['filename']]
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chunk_size = 10000
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chunks = [content[i:i+chunk_size] for i in range(0, len(content), chunk_size)]
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# Must return an iterable (list or generator) for a BatchNode.
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return chunks
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def exec(self, shared, prep_res):
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# Each iteration of prep_res corresponds to a single chunk.
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chunk = prep_res
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prompt = f"{chunk} Respond a summary of above in 10 words"
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summary = call_llm(prompt)
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return summary
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def post(self, shared, prep_res, exec_res):
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# exec_res is a list of exec() results (summaries for each chunk).
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combined_summary = [f"{i}. {summary}" for i, summary in enumerate(exec_res)]
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shared["summary"][self.params['filename']] = combined_summary
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def prep(self, s):
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text = s['data'][self.params['filename']]
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return [text[i:i+10000] for i in range(0, len(text), 10000)]
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def exec(self, chunk):
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return call_llm(f"{chunk} Summarize in 10 words.")
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def post(self, s, pr, er):
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s["summary"][self.params['filename']] = [f"{i}. {r}" for i,r in enumerate(er)]
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class ReduceSummaries(Node):
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def prep(self, shared):
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# Retrieve the list of chunk summaries from shared storage
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return shared["summary"][self.params['filename']]
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def exec(self, shared, prep_res):
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combined_summary = prep_res
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prompt = f"{combined_summary} Respond a summary of above in 10 words"
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summary = call_llm(prompt)
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return summary
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def post(self, shared, prep_res, exec_res):
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# Store the combined summary as the final summary for this file.
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shared["summary"][self.params['filename']] = exec_res
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map_summaries = MapSummaries()
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reduce_summaries = ReduceSummaries()
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# Link map_summaries to reduce_summaries with an action
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# By default, the action is "default" (when post returns None, it takes "default" action)
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# This is the same as map_summaries - "default" >> reduce_summaries
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map_summaries >> reduce_summaries
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def prep(self, s): return s["summary"][self.params['filename']]
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def exec(self, chunks):
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return call_llm(f"{chunks} Combine into 10 words summary.")
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def post(self, s, pr, sr): s["summary"][self.params['filename']] = sr
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# We don't directly call map_summaries.run(shared),
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# because that alone would process only the map step without reduce.
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map_summ = MapSummaries()
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reduce_summ = ReduceSummaries()
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map_summ >> reduce_summ
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# 6) Instead, create a Flow that starts from map_summaries (a Node)
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# and automatically includes reduce_summaries.
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# Note: A Flow can also start from any other Flow or BatchFlow.
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flow = Flow(start=map_summ)
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flow.set_params({"filename":"before.txt"})
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flow.run(shared)
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file_summary_flow = Flow(start=map_summaries)
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# When a flow params is set, it will recursively set its params to all nodes in the flow
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file_summary_flow.set_params({"filename":"before.txt"})
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file_summary_flow.run(shared)
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# 7) Summarize all files using a BatchFlow that reruns file_summary_flow for each file
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# Summarize all files
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class SummarizeAllFiles(BatchFlow):
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def prep(self, shared):
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# Return a list of parameters to apply in each flow iteration.
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# Each individual param will be merged with this node's own params
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# Allowing nesting of multi-level BatchFlow.
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# E.g., first level diretcory, second level file.
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return [{"filename":filename} for filename in shared['data']]
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def prep(self, s): return [{"filename":fn} for fn in s['data']]
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summarize_all_files = SummarizeAllFiles(start=file_summary_flow)
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summarize_all_files.run(shared)
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SummarizeAllFiles(start=flow).run(shared)
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# 8) QA Agent: Find the most relevant file based on summary with actions
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# if no question is asked:
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# (a) end: terminate the flow
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# if question is asked:
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# if relevant file is found:
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# (b) answer: move to answer node and read the whole file to answer the question
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# if no relevant file is found:
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# (c) retry: retry the process to find the relevant file
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# QA agent
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class FindRelevantFile(Node):
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def prep(self, shared):
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question = input("Enter a question: ")
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formatted_list = [f"- '{filename}': {shared['summary'][filename]}"
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for filename in shared['summary']]
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return question, formatted_list
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def exec(self, shared, prep_res):
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question, formatted_list = prep_res
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if not question:
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return {"think":"no question", "has_relevant":False}
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# Provide a structured YAML output that includes:
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# - The chain of thought
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# - Whether any relevant file was found
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# - The most relevant file if found
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prompt = f"""Question: {question}
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Find the most relevant file from:
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{formatted_list}
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If no relevant file, explain why
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Respond in yaml without additional information:
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think: the question has/has no relevant file ...
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has_relevant: true/false
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most_relevant: filename"""
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response = call_llm(prompt)
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import yaml
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result = yaml.safe_load(response)
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# Ensure required fields are present
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assert "think" in result
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assert "has_relevant" in result
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assert "most_relevant" in result if result["has_relevant"] else True
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return result
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# handle errors by returning a default response in case of exception after retries
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def exec_fallback(self,shared,prep_res,exc):
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# if not overridden, the default is to throw the exception
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return {"think":"error finding the file", "has_relevant":False}
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def post(self, shared, prep_res, exec_res):
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question, _ = prep_res
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# Decide what to do next based on the results
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if not question:
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print(f"No question asked")
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return "end"
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if exec_res["has_relevant"]:
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# Store the question and most relevant file in shared
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shared["question"] = question
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shared["relevant_file"] = exec_res['most_relevant']
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print(f"Relevant file found: {exec_res['most_relevant']}")
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def prep(self, s):
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q = input("Enter a question: ")
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summ = [f"- '{fn}': {s['summary'][fn]}" for fn in s['summary']]
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return q, summ
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def exec(self, p):
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q, summ = p
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if not q:
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return {"think":"no question","has_relevant":False}
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resp = call_llm(f"""
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Question: {q}
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Find the most relevant file from: {summ}
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If none, explain why
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Respond in YAML:
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think: ...
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has_relevant: ...
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most_relevant: ...
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""")
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r = yaml.safe_load(resp)
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return r
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def exec_fallback(self, p, exc): return {"think":"error","has_relevant":False}
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def post(self, s, pr, res):
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q, _ = pr
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if not q:
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print("No question asked"); return "end"
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if res["has_relevant"]:
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s["question"], s["relevant_file"] = q, res["most_relevant"]
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print("Relevant file:", res["most_relevant"])
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return "answer"
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else:
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print(f"No relevant file found: {exec_res['think']}")
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print("No relevant file:", res["think"])
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return "retry"
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class AnswerQuestion(Node):
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def prep(self, shared):
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question = shared['question']
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relevant_file = shared['relevant_file']
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# Read the whole file content
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file_content = shared['data'][relevant_file]
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return question, file_content
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def exec(self, shared, prep_res):
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question, file_content = prep_res
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prompt = f"""Question: {question}
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File: {file_content}
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Answer the question in 50 words"""
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response = call_llm(prompt)
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return response
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def post(self, shared, prep_res, exec_res):
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print(f"Answer: {exec_res}")
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def prep(self, s):
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return s['question'], s['data'][s['relevant_file']]
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def exec(self, p):
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q, txt = p
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return call_llm(f"Question: {q}\nText: {txt}\nAnswer in 50 words.")
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def post(self, s, pr, ex):
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print("Answer:", ex)
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class NoOp(Node):
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pass
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class NoOp(Node): pass
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# Configure the QA agent with appropriate transitions and retries
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find_relevant_file = FindRelevantFile(max_retries=3)
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answer_question = AnswerQuestion()
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no_op = NoOp()
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frf = FindRelevantFile(max_retries=3)
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aq = AnswerQuestion()
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noop = NoOp()
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# Connect the nodes based on the actions they return
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find_relevant_file - "answer" >> answer_question >> find_relevant_file
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find_relevant_file - "retry" >> find_relevant_file
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find_relevant_file - "end" >> no_op
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frf - "answer" >> aq >> frf
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frf - "retry" >> frf
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frf - "end" >> noop
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qa_agent = Flow(start=find_relevant_file)
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qa_agent.run(shared)
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# Above example demonstrates the use of minillmflow
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# Next, build another app based on the same principles
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# First, given the app's requirements, design the Node/Flow structure
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# Then, design the data structure within shared storage, and how it's updated
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# Finally, implement the Nodes and Flows to achieve the desired functionality
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qa = Flow(start=frf)
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qa.run(shared)
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@ -355,7 +355,7 @@
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" assert \"most_relevant\" in result if result[\"has_relevant\"] else True\n",
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" return result\n",
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" # handle errors by returning a default response in case of exception after retries\n",
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" def exec_fallback(self,shared,prep_res,exc):\n",
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" def exec_fallback(self,prep_res,exc):\n",
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" # if not overridden, the default is to throw the exception\n",
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" return {\"think\":\"error finding the file\", \"has_relevant\":False}\n",
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" def post(self, shared, prep_res, exec_res):\n",
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