skills/retrieval_augmented_generation/evaluation/prompts.py

import json
import os
from typing import Callable, List, Dict, Any, Tuple, Set
from vectordb import VectorDB, SummaryIndexedVectorDB
from anthropic import Anthropic

client = Anthropic(api_key=os.environ.get('ANTHROPIC_API_KEY'))

# Initialize the VectorDB
db = VectorDB("anthropic_docs")
# Load the Anthropic documentation
with open('../data/anthropic_docs.json', 'r') as f:
    anthropic_docs = json.load(f)
db.load_data(anthropic_docs)

def _retrieve_base(query, db):
    results = db.search(query, k=3)
    context = ""
    for result in results:
        chunk = result['metadata']
        context += f"\n{chunk['text']}\n"
    return results, context

def answer_query_base(context):
    input_query = context['vars']['query']
    documents, document_context = _retrieve_base(input_query, db)
    prompt = f"""
    You have been tasked with helping us to answer the following query: 
    <query>
    {input_query}
    </query>
    You have access to the following documents which are meant to provide context as you answer the query:
    <documents>
    {document_context}
    </documents>
    Please remain faithful to the underlying context, and only deviate from it if you are 100% sure that you know the answer already. 
    Answer the question now, and avoid providing preamble such as 'Here is the answer', etc
    """

    return prompt

# Initialize the VectorDB
db_summary = SummaryIndexedVectorDB("anthropic_docs_summaries")
# Load the Anthropic documentation
with open("../data/anthropic_summary_indexed_docs.json", 'r') as f:
    anthropic_docs_summaries = json.load(f)
db_summary.load_data(anthropic_docs_summaries)

def retrieve_level_two(query):
    results = db_summary.search(query, k=3)
    context = ""
    for result in results:
        chunk = result['metadata']
        context += f"\n <document> \n {chunk['chunk_heading']}\n\nText\n {chunk['text']} \n\nSummary: \n {chunk['summary']} \n </document> \n" #show model all 3 items
    return results, context

def answer_query_level_two(context):
    input_query = context['vars']['query']
    documents, document_context = retrieve_level_two(input_query)
    prompt = f"""
    You have been tasked with helping us to answer the following query: 
    <query>
    {input_query}
    </query>
    You have access to the following documents which are meant to provide context as you answer the query:
    <documents>
    {document_context}
    </documents>
    Please remain faithful to the underlying context, and only deviate from it if you are 100% sure that you know the answer already. 
    Answer the question now, and avoid providing preamble such as 'Here is the answer', etc
    """

    return prompt

# Initialize the VectorDB
db_rerank = SummaryIndexedVectorDB("anthropic_docs_rerank")
# Load the Anthropic documentation
with open("../data/anthropic_summary_indexed_docs.json", 'r') as f:
    anthropic_docs_summaries = json.load(f)
db_rerank.load_data(anthropic_docs_summaries)

def _rerank_results(query: str, results: List[Dict], k: int = 5) -> List[Dict]:
    # Prepare the summaries with their indices
    summaries = []
    print(len(results))
    for i, result in enumerate(results):
        summary = "[{}] Document: {}".format(
            i,
            result['metadata']['chunk_heading'],
            result['metadata']['summary']
        )
        summary += " \n {}".format(result['metadata']['text'])
        summaries.append(summary)
    
    # Join summaries with newlines
    joined_summaries = "\n".join(summaries)
    
    prompt = f"""
    Query: {query}
    You are about to be given a group of documents, each preceded by its index number in square brackets. Your task is to select the only {k} most relevant documents from the list to help us answer the query.
    
    {joined_summaries}
    
    Output only the indices of {k} most relevant documents in order of relevance, separated by commas, enclosed in XML tags here:
    <relevant_indices>put the numbers of your indices here, seeparted by commas</relevant_indices>
    """
    try:
        response = client.messages.create(
            model="claude-3-haiku-20240307",
            max_tokens=50,
            messages=[{"role": "user", "content": prompt}, {"role": "assistant", "content": "<relevant_indices>"}],
            temperature=0,
            stop_sequences=["</relevant_indices>"]
        )
        
        # Extract the indices from the response
        response_text = response.content[0].text.strip()
        indices_str = response_text
        relevant_indices = []
        for idx in indices_str.split(','):
            try:
                relevant_indices.append(int(idx.strip()))
            except ValueError:
                continue  # Skip invalid indices
        print(indices_str)
        print(relevant_indices)
        # If we didn't get enough valid indices, fall back to the top k by original order
        if len(relevant_indices) == 0:
            relevant_indices = list(range(min(k, len(results))))
        
        # Ensure we don't have out-of-range indices
        relevant_indices = [idx for idx in relevant_indices if idx < len(results)]
        
        # Return the reranked results
        reranked_results = [results[idx] for idx in relevant_indices[:k]]
        # Assign descending relevance scores
        for i, result in enumerate(reranked_results):
            result['relevance_score'] = 100 - i  # Highest score is 100, decreasing by 1 for each rank
        
        return reranked_results
    
    except Exception as e:
        print(f"An error occurred during reranking: {str(e)}")
        # Fall back to returning the top k results without reranking
        return results[:k]

def _retrieve_advanced(query: str, k: int = 3, initial_k: int = 20) -> Tuple[List[Dict], str]:
    # Step 1: Get initial results
    initial_results = db_rerank.search(query, k=initial_k)

    # Step 2: Re-rank results
    reranked_results = _rerank_results(query, initial_results, k=k)
    
    # Step 3: Generate new context string from re-ranked results
    new_context = ""
    for result in reranked_results:
        chunk = result['metadata']
        new_context += f"\n <document> \n {chunk['chunk_heading']}\n\n{chunk['text']} \n </document> \n"
    
    return reranked_results, new_context

# The answer_query_advanced function remains unchanged
def answer_query_level_three(context):
    input_query = context['vars']['query']
    documents, document_context = _retrieve_advanced(input_query)
    prompt = f"""
    You have been tasked with helping us to answer the following query: 
    <query>
    {input_query}
    </query>
    You have access to the following documents which are meant to provide context as you answer the query:
    <documents>
    {document_context}
    </documents>
    Please remain faithful to the underlying context, and only deviate from it if you are 100% sure that you know the answer already. 
    Answer the question now, and avoid providing preamble such as 'Here is the answer', etc
    """
    return prompt