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app.py

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+# Summarize a long document by chunking and summarizing parts.  Uses
+# aynchronous calls to the API.  Adapted from LangChain [Map-Reduce
+# summary](https://langchain.readthedocs.io/en/stable/_modules/langchain/chains/mapreduce.html).
+
+import trio
+
+from minichain import TemplatePrompt, show_log, start_chain
+
+# Prompt that asks LLM to produce a bash command.
+
+
+class SummaryPrompt(TemplatePrompt):
+    template_file = "summary.pmpt.tpl"
+
+
+def chunk(f, width=4000, overlap=800):
+    "Split a documents into 4800 character overlapping chunks"
+    text = open(f).read().replace("\n\n", "\n")
+    chunks = []
+    for i in range(4):
+        if i * width > len(text):
+            break
+        chunks.append({"text": text[i * width : (i + 1) * width + overlap]})
+    return chunks
+
+
+with start_chain("summary") as backend:
+    prompt = SummaryPrompt(backend.OpenAI())
+    list_prompt = prompt.map()
+
+    # Map - Summarize each chunk in parallel
+    out = trio.run(list_prompt.arun, chunk("../state_of_the_union.txt"))
+
+    # Reduce - Summarize the summarized chunks
+    print(prompt({"text": "\n".join(out)}))
+
+# + tags=["hide_inp"]
+SummaryPrompt().show(
+    {"text": "One way to fight is to drive down wages and make Americans poorer."},
+    "Make Americans poorer",
+)
+# -
+
+show_log("summary.log")

requirements.txt

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+gradio
+git+https://github.com/srush/minichain
+manifest-ml

summary.pmpt.tpl

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+Write a concise summary of the following:
+
+
+"{{text}}"
+
+
+CONCISE SUMMARY: