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app.py

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+# Questions answering with Hugging Face embeddings.  Adapted from the
+# [LlamaIndex
+# example](https://github.com/jerryjliu/gpt_index/blob/main/examples/gatsby/TestGatsby.ipynb).
+
+import datasets
+import numpy as np
+
+from minichain import EmbeddingPrompt, TemplatePrompt, show_log, start_chain
+
+# Load data with embeddings (computed beforehand)
+
+gatsby = datasets.load_from_disk("gatsby")
+gatsby.add_faiss_index("embeddings")
+
+# Fast KNN retieval prompt
+
+
+class KNNPrompt(EmbeddingPrompt):
+    def find(self, out, inp):
+        res = gatsby.get_nearest_examples("embeddings", np.array(out), 1)
+        return {"question": inp, "docs": res.examples["passages"]}
+
+
+# QA prompt to ask question with examples
+
+
+class QAPrompt(TemplatePrompt):
+    template_file = "gatsby.pmpt.tpl"
+
+
+with start_chain("gatsby") as backend:
+    question = "What did Gatsby do before he met Daisy?"
+    prompt = KNNPrompt(
+        backend.HuggingFaceEmbed("sentence-transformers/all-mpnet-base-v2")
+    ).chain(QAPrompt(backend.OpenAI()))
+    result = prompt(question)
+    print(result)
+
+# + tags=["hide_inp"]
+QAPrompt().show({"question": "Who was Gatsby?", "docs": ["doc1", "doc2", "doc3"]}, "")
+# -
+
+show_log("gatsby.log")

gatsby.pmpt.tpl

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+Context information is below. 
+---------------------
+
+{% for doc in docs %}
+* {{doc}}
+{% endfor %}
+
+---------------------
+
+Given the context information and not prior knowledge, answer the question: {{question}}

requirements.txt

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