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app.py

@@ -1,12 +1,19 @@
-# # QA
+# + tags=["hide_inp"]
+desc = """
+### Question Answering with Retrieval
+
+Chain that answers questions with embeedding based retrieval. [[Code](https://github.com/srush/MiniChain/blob/main/examples/qa.py)]
+
+(Adapted from [OpenAI Notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).)
+"""
+# -
 
-# Questions answering with embeddings.  Adapted from [OpenAI
-# Notebook](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).
+# $
 
 import datasets
 import numpy as np
-
-from minichain import EmbeddingPrompt, TemplatePrompt, show_log, start_chain
+from minichain import prompt, show, OpenAIEmbed, OpenAI
+from manifest import Manifest
 
 # We use Hugging Face Datasets as the database by assigning
 # a FAISS index.
@@ -17,30 +24,45 @@ olympics.add_faiss_index("embeddings")
 
 # Fast KNN retieval prompt
 
+@prompt(OpenAIEmbed())
+def get_neighbors(model, inp, k):
+    embedding = model(inp)
+    res = olympics.get_nearest_examples("embeddings", np.array(embedding), k)
+    return res.examples["content"]
 
-class KNNPrompt(EmbeddingPrompt):
-    def find(self, out, inp):
-        res = olympics.get_nearest_examples("embeddings", np.array(out), 3)
-        return {"question": inp, "docs": res.examples["content"]}
+@prompt(OpenAI(),
+        template_file="qa.pmpt.tpl")
+def get_result(model, query, neighbors):
+    return model(dict(question=query, docs=neighbors))
 
+def qa(query):
+    n = get_neighbors(query, 3)
+    return get_result(query, n)
 
-# QA prompt to ask question with examples
+# $
 
 
-class QAPrompt(TemplatePrompt):
-    template_file = "qa.pmpt.tpl"
+questions = ["Who won the 2020 Summer Olympics men's high jump?",
+             "Why was the 2020 Summer Olympics originally postponed?",
+             "In the 2020 Summer Olympics, how many gold medals did the country which won the most medals win?",
+             "What is the total number of medals won by France?",
+             "What is the tallest mountain in the world?"]
 
+gradio = show(qa,
+              examples=questions,
+              subprompts=[get_neighbors, get_result],
+              description=desc,
+              code=open("qa.py", "r").read().split("$")[1].strip().strip("#").strip(),
+              )
+if __name__ == "__main__":
+    gradio.launch()
 
-with start_chain("qa") as backend:
-    question = "Who won the 2020 Summer Olympics men's high jump?"
-    prompt = KNNPrompt(backend.OpenAIEmbed()).chain(QAPrompt(backend.OpenAI()))
-    result = prompt(question)
-    print(result)
 
-# + tags=["hide_inp"]
-QAPrompt().show(
-    {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
-)
-# -
 
-show_log("qa.log")
+# # + tags=["hide_inp"]
+# QAPrompt().show(
+#     {"question": "Who won the race?", "docs": ["doc1", "doc2", "doc3"]}, "Joe Bob"
+# )
+# # -
+
+# show_log("qa.log")

qa.pmpt.tpl

@@ -8,5 +8,5 @@ Context:
 
  Q: {{question}}
 
- A: 
+ A:
 

requirements.txt

@@ -1,3 +1,4 @@
-gradio
+gradio==3.21.0
 git+https://github.com/srush/minichain@gradio
 manifest-ml
+faiss-cpu