Overview of LLM Predictor

The LLM Predictor is an integral component of the language model designed to generate text responses (Completions). Below, you’ll find a class named LLMPredictor that showcases its functionality.

LlamaIndex's LLM class offers a cohesive interface for defining LLM modules, whether they originate from OpenAI, Hugging Face, or LangChain. Initially, it served as a wrapper for LangChain's LLMChain class but has evolved into a standalone module.

You can utilize a variety of modules, including but not limited to OpenAI, Anthropic, and Palm. Here are the attributes associated with LLMPredictor:

{'_llm': OpenAI(model='text-davinci-003', temperature=0.0, max_tokens=None, additional_kwargs={}, max_retries=10),

'callback_manager': }

The OpenAI class is primarily responsible for the core processing tasks.

Prompt Helper Functionality

The PromptHelper is designed to segment text while adhering to token count restrictions. Its functionality is similar to NodeParser, but it specifically aims to align with the token limits on the LLM's side. Below is a glimpse of the configured PromptHelper:

{'context_window': 4097,

'num_output': 256,

'chunk_overlap_ratio': 0.1,

'chunk_size_limit': None,

'_tokenizer': >,

'_separator': ' '}

Callback Manager Overview

Callbacks can be set at both the initiation and conclusion of various processes within LlamaIndex. When you assign a CallbackHandler to the CallbackManager, the on_event_start and on_event_end methods of each CallbackHandler will trigger.

These methods receive CBEventType and payload, which represent the processing times. The list of CBEventType includes events such as text chunking, node parsing, embedding, LLM calls, query execution, node retrieval, response synthesis, and summary processing.

The only predefined CallbackHandler is LlamaDebugHandler. You can view the attributes of CallbackManager below:

{'handlers': [],

'_trace_map': defaultdict(list, {...}),

'_trace_event_stack': ['root'],

'_trace_id_stack': []}

Understanding Llama Logger

LlamaLogger, although not extensively documented, seems to primarily log queries directed at the LLM. You can obtain logs post-query execution by enabling the Logger.

Copylist_index.service_context.llama_logger.get_logs()

Exploring QueryEngine

The QueryEngine class is constructed using the as_query_engine method from the index class. It allows for the adjustment of settings, excluding Storage Context and Service Context.

query_engine = list_index.as_query_engine()

Various types of Query Engines are instantiated by the Index, including List-index, Vector index, and Tree index. Here’s a look at the classes involved in the Query Engine:

query_engine

Despite the lack of detailed documentation, the following options are available with as_query_engine:

• Retriever Mode: Switch modes as indicated in the Index.
• Node Postprocessor: Implement post-processing following node extraction.
• Response Synthesizer: Handle the synthesis of responses from the LLM.

Response Synthesizer and Prompt Templates

The Response Synthesizer is responsible for merging responses from the LLM. Previously an internal class, it is now defined as an external API as of v0.7.9. The default settings are in use, but explicit creation is possible using a factory pattern:

from llama_index.response_synthesizers import get_response_synthesizer

from llama_index.response_synthesizers import ResponseMode

response_synthesizer = get_response_synthesizer(response_mode=ResponseMode.COMPACT)

The default ResponseMode.COMPACT utilizes the text_qa_template for the initial node, followed by the refine_template for subsequent responses.

In the next section, we will delve deeper into topics such as Retriever, Optimizer, and Prompt Templates.

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