can llamaindex work with multiple llms simultaneously

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LlamaIndex and Concurrent LLMs: A Deep Dive

LlamaIndex, a powerful data framework for LLM applications, is designed to connect custom data sources to large language models (LLMs). While its core functionality revolves around facilitating this data connectivity and orchestrating interactions with a single LLM at a time for specific tasks like querying or summarization, the underlying architecture doesn't fundamentally preclude the use of multiple LLMs simultaneously within a broader application context. However, the direct usage of LlamaIndex to explicitly orchestrate a workflow that leverages multiple LLMs in parallel or in tandem to achieve a single specific task seamlessly isn't a built-in feature, requiring careful planning and custom implementation to achieve. The ability to integrate and utilize multiple LLMs comes down to how efficiently you coordinate their roles and integrate their outputs within your overall LlamaIndex-powered application. This article will discuss the possibility of leveraging multiple LLMs simultaneously with LlamaIndex, elaborating on strategies, limitations, and providing practical examples of how this can be achieved. This means exploring how you can strategically employ external tools (like orchestration frameworks or custom loops) to manage the concurrent application of various LLMs, each contributing uniquely to the final output or decision-making process driven by the context provided by LlamaIndex.

Is Direct Concurrent LLM Orchestration Native to LlamaIndex?

Before diving into strategies, it's important to clearly state that LlamaIndex, as of current versions, doesn't offer a first-class, built-in mechanism for directly orchestrating multiple LLMs concurrently to solve a single query. The standard usage pattern involves selecting a specific LLM for the duration of a query pipeline. However, this limitation doesn't mean it is impossible to use different LLMs within a single application that uses LlamaIndex. Imagine a chatbot built with LlamaIndex to answer questions about a company's product catalog. The chatbot's main query engine might use GPT-4 for high-quality responses. Simultaneously, a separate module could be running, using a smaller, faster, and cheaper LLM like Google's Gemma or even a distilled version of Llama 3, to monitor user input for specific keywords triggering pre-defined actions, such as displaying a relevant promotion. These two LLMs work independently within the application, both leveraging different LlamaIndex functionalities, but are not directly communicating in a structured manner at an LLM level. What matters is the use of one LLM at a time, as the framework wasn't designed to use many in real time at once, in the way we might envision.

Strategies for Integrating Multiple LLMs with LlamaIndex

Despite the lack of native support, several strategies can be employed to integrate multiple LLMs into a LlamaIndex-driven application, allowing the application to benefit from the strengths of each model. These strategies generally involve using LlamaIndex to manage the data context and then leveraging external tools or writing custom logic to orchestrate the LLMs based on that context.

External Orchestration Tools and Frameworks

Tools like LangChain or even general-purpose workflow management systems (e.g., Airflow, Prefect), can act as orchestrators. LlamaIndex provides the data retrieval and indexing capabilities, while these orchestrators manage the flow and dependencies between different LLM calls. For example, you could use LlamaIndex to retrieve relevant documents given a user query. Then, LangChain could be used to implement a chain that first summarizes each document with one LLM and then uses another LLM to synthesize a final answer based on the summaries. This approach separates the data retrieval logic from the LLM orchestration logic, promoting modularity and maintainability. The LlamaIndex part of the application could then be updated or modified in case the data or user queries change, and the changes wouldn't affect the LangChain workflow. Keep in mind that using LangChain would add extra steps to implement the program.

Custom Logic and Workflow Control

For simpler use cases, custom logic can be implemented to control which LLM is used based on the query or the intermediate results. For example, one might use a smaller, faster LLM for initial filtering or classification of a query, and then use a more powerful (but slower) LLM for the actual answer generation if the query is deemed complex enough. If/Then conditions or rule-based systems are commonly used to implement this logic. This could be a very simple program that leverages LlamaIndex to preprocess the information, and then a python function (or a set of functions) that takes different actions. Such approach doesn't rely on external frameworks, but it can lead to more complex codes.

Parallel Processing with Asynchronous Calls

Python's asyncio library can be used if the application needs to make requests to multiple LLMs concurrently, as it enables parallel execution of tasks within the same application. For instance, the application migth need to compare the quality or consistency of a LlamaIndex query by sending the same query to multiple LLMs and comparing the respective responses in parallel. This can be implemented by using a "worker" based approach, where workers are responsible for a specific subtask of a bigger goal. For example, they might be in charge of summarazing documents, extracting key information from a document, or extracting a list of named entities from a text extracted from LlamaIndex.

Practical Examples of Multi-LLM Integration with LlamaIndex

Here are detailed examples of how one might implement such strategies:

Example 1: Query Classification and Routing

Imagine a LlamaIndex-powered support chatbot. You can achieve to classify incoming queries into categories like "billing," "technical support," or "feature requests." This classification can then be used to route the query to a specialized LLM trained on data relevant to that category.

1. Data Indexing (LlamaIndex): Load and index all support documents, including billing FAQs, technical manuals, and feature request documentation.
2. Query Classification (LLM 1): A small, fast LLM (e.g., a fine-tuned BERT model) is used to classify the user's query. This LLM can be directly integrated using LlamaIndex's LLM interface. You create an instance of the classification LLM and use it to predict the class.
3. Query Routing (Custom Logic): Based on the classification, route the query to a specific LlamaIndex query engine configured with the appropriate data and powered by specialized LLM. For example, all billing queries go to a query engine that is tuned with billing FAQs, and powered by a dedicated LLM instance. The same goes for technical support (it uses a separate query engine and LLM instance). You will also need to configure custom documents for each class. The documents will include data and information regarding each query category.

Example 2: Summarization and Synthesis

User uploads a large document. The goal is to provide a concise summary highlighting the key insights.

1. Data Indexing (LlamaIndex): Load and index the large document.
2. Document Summarization (LLM 1): Use a series of LlamaIndex calls in a loop. In each of these calls, the method retrieves each chunk of the document and sends it to LLM 1 (a fast summarization model) to generate a shorter summary of each chunk.
3. Summary Synthesis (LLM 2): Take the summaries of all the individual document chunks and use another LLM (LLM 2, a more powerful model) to synthesize a final overall summary.

Example 3: Parallel Comparison of LLM outputs

The goal is to use each LLM, compare them, and return the answers.

1. Data Indexing (LlamaIndex): Index the data using LlamaIndex's loader.
2. Asynchronous Queries: Use asyncio to send to different queries concurrently to these LLMs. Using asyncio.gather allows you to run multiple LLM calls concurrently.
3. Response Comparison: After all responses are received, implement logic to compare them, using metrics like similarity, relevance, or factual accuracy. The logic can be done using other LLMs or simple functions.
4. Final Answer Generation: Based on the comparison, either select the best answer from one of the LLMs of combine them to generate a new (final) answer with proper explanation.

Limitations and Challenges

While the aforementioned strategies demonstrate the feasibility of employing multiple LLMs in LlamaIndex-driven applications, it's essential to acknowledge the limitations and challenges associated with this approach.

Complexity and Overhead

Managing multiple LLMs significantly increases the complexity of the application. Each LLM requires its own configuration, API keys, and potentially different pricing models, requiring careful management to ensure efficient and cost-effective operation. The logic required to orchestrate these LLMs, handle errors, and manage concurrency can also be complex, potentially adding significant development overhead.

Latency

Orchestrating multiple LLMs can introduce additional latency into the application, especially if the LLMs are called sequentially. Each API call to an LLM takes time, and these delays can accumulate, resulting in slower response times for the end user. Therefore, it's important to carefully consider the trade-offs between accuracy, cost, and latency when designing a multi-LLM workflow. If some LLMs do not provide speed, consider creating an ensemble with faster LLMs to get an output.

Consistency and Reliability

Ensuring consistency and reliability across multiple LLMs can also be challenging. Different LLMs may have different biases and limitations, and their responses to the same query can vary significantly. This can make it difficult to provide a consistent and reliable experience for the end user. Moreover, LLM API availability and reliability can also vary, requiring the application to handle potential errors and failures gracefully.

Conclusion

While LlamaIndex doesn't offer native, built-in concurrency for multiple LLMs in a single query pipeline, it is, however, not that hard to create one. By leveraging external orchestration tools, implementing custom logic, and utilizing asynchronous processing, it's possible to create LlamaIndex-powered applications that effectively leverage the strengths of multiple LLMs. As the field of LLMs evolves, we can expect to see more tools and frameworks emerging to simplify the implementation and management of multi-LLM workflows, but for now, manual (but simple) techniques can be employed to create a multi-LLM system.

from Anakin Blog http://anakin.ai/blog/can-llamaindex-work-with-multiple-llms-simultaneously/
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