Duplicate from NeoConsulting/Jarvis_QuAn_v01

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README.md

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+---
+title: Jarvis QuAn v01
+emoji: 🌖
+colorFrom: gray
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.19.1
+python_version: 3.11.2
+app_file: app.py
+pinned: false
+license: openrail
+duplicated_from: NeoConsulting/Jarvis_QuAn_v01
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import numpy as np
+import openai
+import pandas as pd
+import tiktoken
+import gradio as gr
+
+COMPLETIONS_MODEL = "text-davinci-003"
+EMBEDDING_MODEL = "text-embedding-ada-002"
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# 1) Preprocess the document library
+df = pd.read_csv("olympics_sections_text.csv")
+df = df.set_index(["title", "heading"])
+
+def get_embedding(text: str, model: str=EMBEDDING_MODEL) -> list[float]:
+    result = openai.Embedding.create(
+      model=model,
+      input=text
+    )
+    return result["data"][0]["embedding"]
+
+# uncomment the below line to caculate embeddings from scratch. ========
+
+#def compute_doc_embeddings(df: pd.DataFrame) -> dict[tuple[str, str], list[float]]:
+#    return {
+#        idx: get_embedding(r.content) for idx, r in df.iterrows()
+#    }
+
+#document_embeddings = compute_doc_embeddings(df)
+
+def load_embeddings(fname: str) -> dict[tuple[str, str], list[float]]:
+    """
+    Read the document embeddings and their keys from a CSV.
+    
+    fname is the path to a CSV with exactly these named columns: 
+        "title", "heading", "0", "1", ... up to the length of the embedding vectors.
+    """
+    
+    df = pd.read_csv(fname, header=0)
+    max_dim = max([int(c) for c in df.columns if c != "title" and c != "heading"])
+    return {
+           (r.title, r.heading): [r[str(i)] for i in range(max_dim + 1)] for _, r in df.iterrows()
+    }
+
+document_embeddings = load_embeddings("olympics_sections_document_embeddings.csv")
+
+# 2) Find the most similar document embeddings to the question embedding
+
+def vector_similarity(x: list[float], y: list[float]) -> float:
+    """
+    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: str, contexts: dict[(str, str), np.array]) -> list[(float, (str, str))]:
+    """
+    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
+
+# 3) Add the most relevant document sections to the query prompt
+
+MAX_SECTION_LEN = 500
+SEPARATOR = "\n* "
+ENCODING = "gpt2"  # encoding for text-davinci-003
+
+encoding = tiktoken.get_encoding(ENCODING)
+separator_len = len(encoding.encode(SEPARATOR))
+
+def construct_prompt(question: str, context_embeddings: dict, df: pd.DataFrame) -> str:
+    """
+    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.        
+        document_section = df.loc[section_index]
+        
+        chosen_sections_len += document_section.tokens + separator_len
+        if chosen_sections_len > MAX_SECTION_LEN:
+            break
+            
+        chosen_sections.append(SEPARATOR + document_section.content.replace("\n", " "))
+        chosen_sections_indexes.append(str(section_index))
+    
+    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:"
+
+prompt = construct_prompt(
+    "Who won the 2020 Summer Olympics men's high jump?",
+    document_embeddings,
+    df
+)
+
+# 4) Answer the user's question based on the context.
+
+COMPLETIONS_API_PARAMS = {
+    # We use temperature of 0.0 because it gives the most predictable, factual answer.
+    "temperature": 0.0,
+    "max_tokens": 300,
+    "model": COMPLETIONS_MODEL,
+}
+
+def answer_query_with_context(
+    query: str,
+    df: pd.DataFrame,
+    document_embeddings: dict[(str, str), np.array]
+) -> str:
+    prompt = construct_prompt(
+        query,
+        document_embeddings,
+        df
+    )
+
+    response = openai.Completion.create(
+                prompt=prompt,
+                **COMPLETIONS_API_PARAMS
+            )
+
+    return response["choices"][0]["text"].strip(" \n")
+
+def answer_question(query):
+    return answer_query_with_context(query, df, document_embeddings)
+
+iface = gr.Interface(fn=answer_question, inputs="text", outputs="text")
+iface.launch()

olympics_sections_document_embeddings.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdf6cfaa80e7db13902fbd8064942ceb7ad4861aa8bc0348d5edd89bf7f971f3
+size 118432915

requirements.txt

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+openai
+tiktoken