add app.py

.gitignore

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+__pycache__

app.py

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+import json
+import openai
+import tiktoken
+import numpy as np
+import pandas as pd
+import gradio as gr
+import pickle
+
+COMPLETIONS_MODEL = "text-davinci-003"
+EMBEDDING_MODEL = "text-embedding-ada-002"
+
+with open("document_embeddings_clean_code.pickle", "rb") as document_embeddings_clean_code:
+  document_embeddings = pickle.load(document_embeddings_clean_code)
+  
+df = pd.read_csv('./clean_code_processed.csv')
+df = df.set_index(["title", "section"])
+df = df[df.tokens > 40]
+df_for_embeddings = df
+
+
+def get_embedding(text, model=EMBEDDING_MODEL):
+    result = openai.Embedding.create(
+      model=model,
+      input=text
+    )
+    return result["data"][0]["embedding"]
+
+def vector_similarity(x, y):
+    """
+    Returns the similarity between two vectors.
+    
+    Because OpenAI Embeddings are normalized to length 1, the cosine similarity is the same as the dot product.
+    """
+    return np.dot(np.array(x), np.array(y))
+
+def order_document_sections_by_query_similarity(query, contexts):
+    """
+    Find the query embedding for the supplied query, and compare it against all of the pre-calculated document embeddings
+    to find the most relevant sections. 
+    
+    Return the list of document sections, sorted by relevance in descending order.
+    """
+    query_embedding = get_embedding(query)
+    
+    document_similarities = sorted([
+        (vector_similarity(query_embedding, doc_embedding), doc_index) for doc_index, doc_embedding in contexts.items()
+    ], reverse=True)
+    
+    return document_similarities
+
+def construct_prompt(question, context_embeddings, df):
+    """
+    Fetch relevant 
+    """
+    most_relevant_document_sections = order_document_sections_by_query_similarity(question, context_embeddings)
+    
+    chosen_sections = []
+    chosen_sections_len = 0
+    chosen_sections_indexes = []
+     
+    for _, section_index in most_relevant_document_sections:
+        # Add contexts until we run out of space.        
+        tokens = df._get_value(section_index, "tokens")
+        if type(tokens) != np.int64:
+            continue
+        
+        chosen_sections_len += df._get_value(section_index, "tokens") + separator_len
+        if chosen_sections_len > MAX_SECTION_LEN:
+            break
+            
+        chosen_sections.append(SEPARATOR + df._get_value(section_index, "content").replace("\n", " "))
+        chosen_sections_indexes.append(section_index)
+            
+    # Useful diagnostic information
+    print(f"Selected {len(chosen_sections)} document sections:")
+    print("\n".join(str(index) for index in chosen_sections_indexes))
+    
+    header = """Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."\n\nContext:\n"""
+    
+    return (header + "".join(chosen_sections) + "\n\n Q: " + question + "\n A:", chosen_sections_indexes)
+
+
+
+MAX_SECTION_LEN = 2000
+SEPARATOR = "\n* "
+ENCODING = "gpt2"  # encoding for text-davinci-003
+
+encoding = tiktoken.get_encoding(ENCODING)
+separator_len = len(encoding.encode(SEPARATOR))
+
+COMPLETIONS_API_PARAMS = {
+    # We use temperature of 0.0 because it gives the most predictable, factual answer.
+    "temperature": 0.0,
+    "max_tokens": 1500,
+    "model": COMPLETIONS_MODEL,
+}
+
+def answer_query_with_context(
+    query,
+    df,
+    document_embeddings
+):
+    prompt, chosen_sections_indexes = construct_prompt(
+        query,
+        document_embeddings,
+        df
+    )
+
+    for i in range(len(chosen_sections_indexes)):
+        chosen_sections_indexes[i] = chosen_sections_indexes[i][0]
+    
+    response = openai.Completion.create(
+                prompt=prompt,
+                **COMPLETIONS_API_PARAMS
+            )
+
+    return (response["choices"][0]["text"].strip(" \n"), chosen_sections_indexes)
+
+def handle(question):
+    answer, related_documents = answer_query_with_context(question, df_for_embeddings, document_embeddings)
+    return answer + "\n\nRelated chapters:\n" + "\n".join(related_documents)
+
+demo = gr.Interface(
+    fn=handle,
+    inputs="text",
+    outputs="text",
+    cache_examples=False,
+)
+
+demo.launch()   

document_embeddings_clean_code.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:600168ec8f03d7f4c56ab5493d7ed71cb283cababcc0e24121b85b9512a41914
+size 2216273

requirements.txt

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+openai
+tiktoken
+numpy
+pandas