Unlocking the Power of LlamaIndex: Advanced Techniques in Python
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Chapter 1: Recap and Overview
In the previous installment of this LlamaIndex series, we delved into concepts such as Retriever Mode, Optimizer, and Response Synthesizer. If you haven't yet reviewed Part Three, make sure to check it out for a comprehensive understanding.
Chapter 2: Customizing Prompt Templates
Prompt Templates allow for the customization of the default query structure. The get_response_synthesizer function can be used to implement these templates, which include three main types:
- text_qa_template: A basic prompt that solicits an answer based on the provided context.
- refine_template: This template is designed to enhance (or refine) a previously given answer with new context.
- simple_template: A straightforward prompt that forwards the user's question to the LLM without modifications.
Here’s an example of how to configure these templates:
from llama_index.response_synthesizers import get_response_synthesizer
from llama_index.response_synthesizers import ResponseMode
from llama_index.prompts.prompts import Prompt
from llama_index.prompts.prompt_type import PromptType
from llama_index.prompts.default_prompts import DEFAULT_TEXT_QA_PROMPT_TMPL, DEFAULT_SIMPLE_INPUT_TMPL, DEFAULT_REFINE_PROMPT_TMPL
response_synthesizer = get_response_synthesizer(response_mode=ResponseMode.COMPACT,
text_qa_template=Prompt(DEFAULT_TEXT_QA_PROMPT_TMPL, prompt_type=PromptType.QUESTION_ANSWER),
refine_template=Prompt(DEFAULT_REFINE_PROMPT_TMPL, prompt_type=PromptType.REFINE),
simple_template=Prompt(DEFAULT_SIMPLE_INPUT_TMPL, prompt_type=PromptType.SIMPLE_INPUT)
)
The default mode, ResponseMode.COMPACT, first employs the text_qa_template for the initial node, followed by the refine_template using the previous result. The simple_template is included for adaptability but isn't utilized in this example.
Section 2.1: Understanding Templates
text_qa_template
The text_qa_template is designed to ask for an answer based on the given context:
DEFAULT_TEXT_QA_PROMPT_TMPL = (
"Context information is below.n"
"---------------------n"
"{context_str}n"
"---------------------n"
"Given the context information and not prior knowledge, "
"answer the question: {query_str}n"
)
refine_template
The refine_template is slightly more complex. It allows for the refinement of responses when new context is provided:
DEFAULT_REFINE_PROMPT_TMPL = (
"The original question is as follows: {query_str}n"
"We have provided an existing answer: {existing_answer}n"
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.n"
"------------n"
"{context_msg}n"
"------------n"
"Given the new context, refine the original answer to better "
"answer the question. "
"If the context isn't useful, return the original answer."
)
simple_template
The simple_template forwards the query as is, without any contextual input:
DEFAULT_SIMPLE_INPUT_TMPL = "{query_str}"
Chapter 3: Practical Application
To illustrate how these templates can be integrated into your application, here’s a sample implementation that focuses on the Query Engine:
from llama_index import StorageContext
from llama_index.storage.docstore import SimpleDocumentStore
from llama_index.storage.index_store import SimpleIndexStore
from llama_index.vector_stores import SimpleVectorStore
from llama_index import ServiceContext
from llama_index.node_parser import SimpleNodeParser
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index import LLMPredictor
from llama_index.indices.prompt_helper import PromptHelper
from llama_index.logger.base import LlamaLogger
from llama_index.callbacks.base import CallbackManager
from llama_index.indices.list.base import ListRetrieverMode
from llama_index.response_synthesizers import ResponseMode
from llama_index.response_synthesizers import get_response_synthesizer
# Setting up the Storage Context
storage_context = StorageContext.from_defaults(
docstore=SimpleDocumentStore(),
vector_store=SimpleVectorStore(),
index_store=SimpleIndexStore()
)
# Creating the Service Context
llm_predictor = LLMPredictor()
service_context = ServiceContext.from_defaults(
node_parser=SimpleNodeParser(),
embed_model=OpenAIEmbedding(),
llm_predictor=llm_predictor,
prompt_helper=PromptHelper.from_llm_metadata(llm_metadata=llm_predictor.metadata),
llama_logger=LlamaLogger(),
callback_manager=CallbackManager([])
)
# Indexing documents with context
list_index = ListIndex.from_documents(
documents,
storage_context=storage_context,
service_context=service_context
)
# Synthesizing responses
response_synthesizer = get_response_synthesizer(response_mode=ResponseMode.COMPACT,
text_qa_template=Prompt(DEFAULT_TEXT_QA_PROMPT_TMPL, prompt_type=PromptType.QUESTION_ANSWER),
refine_template=Prompt(DEFAULT_REFINE_PROMPT_TMPL, prompt_type=PromptType.REFINE),
simple_template=Prompt(DEFAULT_SIMPLE_INPUT_TMPL, prompt_type=PromptType.SIMPLE_INPUT)
)
# Query Engine setup
query_engine = list_index.as_query_engine(
retriever_mode=ListRetrieverMode.DEFAULT,
node_postprocessors=[],
response_synthesizer=response_synthesizer
)
response = query_engine.query("Please summarize this article in 300 characters")
for i in response.response.split("。"):
print(i + "。")
Conclusion: Embracing LlamaIndex
LlamaIndex represents a significant advancement in artificial intelligence development. By enabling the enhancement of private data for LLMs, it facilitates more tailored, precise, and detailed AI responses. Whether you are a business aiming to enhance customer service interfaces, a researcher seeking rapid access to specific data, or a developer eager to expand the capabilities of AI, LlamaIndex provides a promising avenue for innovation.
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