Optimizing Graph RAG with Local LLMs: Challenges and Insights

In this section, we will explore the use of local language models (LLMs) with the Graph RAG (Retrieval-Augmented Generation) framework. While using a local model can provide some benefits, there are also significant challenges to consider.

One of the main advantages of using a local LLM is the potential cost savings. Accessing a remote API like OpenAI can be expensive, especially for large-scale applications. By running a local model, you can avoid these API costs and potentially reduce the overall operational expenses of your Graph RAG system.

However, the choice of LLM is critical when it comes to Graph RAG. Unlike traditional retrieval-augmented systems, where the embedding model plays a more crucial role, the LLM in Graph RAG is responsible for extracting entities, recognizing relationships, and generating summaries. A smaller or less capable LLM, such as the Llama-38B model used in the example, may struggle to perform these tasks effectively, leading to suboptimal results.

The example demonstrates that the performance of the Llama-38B model is not as good as the GPT-4 model used in the previous video. The summary generated by Llama-38B fails to capture the main theme of the book as accurately as the GPT-4 output. This highlights the importance of using a larger and more capable LLM for Graph RAG applications.

To address this challenge, the video suggests exploring the use of larger models, such as the Llama-370B model from Gro. However, this comes with its own set of considerations, such as the need to manage rate limits and the potentially longer processing times required for indexing and querying.

In conclusion, while using a local LLM can provide cost savings, the choice of the LLM is critical for the success of a Graph RAG system. Carefully evaluating the capabilities of different LLMs and finding the right balance between performance and cost is essential for deploying an effective Graph RAG solution.

To set up the local LLM environment for the Graph Retrieval Augmented Generation (Graph RAG) system, follow these steps:

1. Download and Install AMA: First, you need to download and install the AMA (Anthropic Model API) on your local machine. This will allow you to use a local language model, such as the Llama 3 model, for your Graph RAG application.

2. Choose the LLM Model: Once you have AMA set up, you need to choose the language model you want to use. In this case, we'll be using the Llama 3 model, but it's recommended to use a larger model if your hardware can support it, as larger models tend to perform better with Graph RAG.

3. Configure the Graph RAG Settings: Next, you need to update the settings.yml file in your Graph RAG project. Set the llm section to use the AMA API, providing the API key (which is "AMA" in this case), the model name (Llama 3), and the base API URL (http://localhost:11434/v1).

4. Run the Local Indexing: To create the index for your documents, run the python dm_craft_rank.index command. This will process the input files in the specified folder and generate the necessary embeddings and index files.

5. Test the Local LLM with Graph RAG: Once the indexing is complete, you can test the local LLM by running the python dm_graph_rag.query command with the prompt "what is the main theme of the book". This will use the local Llama 3 model to generate a response based on the created graph.

Keep in mind that using a local LLM, such as Llama 3, may not provide the same level of performance as a larger, cloud-based model like GPT-4. The choice of LLM is critical for Graph RAG, as the model's ability to accurately extract entities and relationships from the text is crucial for building a high-quality knowledge graph. If possible, consider using a larger LLM, such as the Llama 370 billion model, which may provide better results, but be aware of the potential rate limits and longer processing times when using a remote API like Gro.

To use a local model with Graph RAG, you'll first need to download and set up the AMA (Anthropic) model on your local machine. In this case, we'll be using the Llama 3 model, but it's recommended to use a larger model if your hardware can support it.

The Llama 3 model follows the same API standard as OpenAI, making it easy to replace the OpenAI API server with the new AMA endpoint. The default base URL for the AMA API is http://localhost:11434/v1, and the API key is simply "AMA".

Next, you'll need to update the settings.yml file in your Graph RAG project to point to the new AMA API endpoint and model. Set the llm.api_key to "AMA", the llm.model to "Llama 3", and the llm.base_api to the local AMA API endpoint.

If you're using the Gro API to serve the model, you'll need to update the llm.api_key to the Gro API endpoint and the llm.model to the larger Llama 370 billion model. Additionally, you'll need to set the llm.requests_per_minute to a lower value (e.g., 30) to avoid timeouts.

The indexing process can take a significant amount of time, especially when using a larger model. On an M2 MacBook Pro with 96GB of RAM, the indexing process took around 27 minutes to complete 50-58% of the task.

Once the indexing is complete, you can run the Graph RAG query using the same prompt as in the previous video. The response from the Llama 3 model may not be as good as the response from the GPT-4 model, as the choice of the language model is more critical for Graph RAG compared to traditional QA systems.

The reason for this is that Graph RAG relies heavily on the language model's ability to accurately extract entities and relationships from the text, which is then used to build the knowledge graph. A smaller language model like Llama 3 may not be able to perform this task as effectively as a larger model like Llama 370 billion or GPT-4.

To improve the results, you may need to experiment with different prompts that are tailored to the specific language model you're using. Additionally, using a larger and more capable language model, such as the Llama 370 billion model, can significantly improve the quality of the Graph RAG output.

Using a local language model like Llama 3 for the Graph Rack system can be challenging compared to using a more powerful model like GPT-4. The key reasons are:

1. Entity and Relationship Extraction: The quality of the knowledge graph built by Graph Rack heavily depends on the LLM's ability to accurately extract entities and their relationships from the input text. Smaller models like Llama 3 may struggle with this task, leading to an inferior knowledge graph.

2. Summary Generation: Graph Rack relies on the LLM to generate summaries of the identified communities within the knowledge graph. A more capable LLM like GPT-4 is better suited for this task, producing more coherent and informative summaries.

3. Prompt Engineering: Different LLMs respond differently to the same prompt. Optimizing the prompts for a smaller model like Llama 3 requires more effort and experimentation compared to using GPT-4, which has shown better few-shot performance.

The results demonstrate that using a larger, more powerful LLM like the Llama 370B model from Cohere can provide significantly better performance for the Graph Rack system compared to the Llama 3 model. However, this comes at the cost of increased processing time and potential rate limiting issues when using the Cohere API.

Overall, the choice of LLM is a critical factor in the success of the Graph Rack approach, and users should carefully consider the trade-offs between model size, performance, and cost when selecting the appropriate LLM for their use case.

Prompt engineering is a critical aspect when working with large language models (LLMs) in the context of graph-based retrieval and augmented generation (graph-RAG) systems. The choice of LLM is more crucial in graph-RAG compared to traditional retrieval systems, as the LLM plays a pivotal role in accurately extracting entities, recognizing relationships, and generating coherent summaries.

When using a smaller LLM like Lama-38B, the model may struggle to accurately extract entities and relationships from the text, leading to the creation of an inferior knowledge graph. This, in turn, results in suboptimal summaries and responses. In contrast, larger LLMs like Lama-370B or GPT-4 have a greater capacity to understand the nuances of the text and generate more accurate and informative outputs.

However, simply using a larger LLM is not a silver bullet. Prompt engineering becomes crucial to ensure that the LLM is provided with the appropriate context and instructions to generate the desired responses. Prompts that work well for one LLM may not be as effective for another, as different models have unique strengths and weaknesses.

To unlock the full potential of graph-RAG systems, it is essential to carefully craft prompts that are tailored to the specific LLM being used. This may involve experimenting with different prompt formats, lengths, and styles to find the most effective approach for a given LLM and task. Additionally, monitoring the model's performance and iteratively refining the prompts can lead to significant improvements in the overall system's effectiveness.

By investing time and effort into prompt engineering, you can maximize the capabilities of your graph-RAG system and ensure that the LLM is able to leverage the knowledge graph to generate high-quality, contextually relevant responses.

In this section, we will explore alternative implementations of the Graph RAG framework and discuss potential future directions for this approach.

While the previous video demonstrated the use of a local Llama model with the Graph RAG system, the results highlighted the importance of selecting a high-quality language model for optimal performance. The choice of the language model is a critical factor in the Graph RAG approach, as it directly impacts the entity extraction and relationship identification, which are crucial for building the knowledge graph.

One potential future direction is to explore the use of larger and more powerful language models, such as the Llama 370 billion model, which demonstrated improved results compared to the smaller Llama 38 billion model. However, the use of these larger models may come with additional challenges, such as increased computational requirements and potential rate limiting issues when using external API services like Gro.

Another area of exploration could be the investigation of alternative embedding models to replace the OpenAI embedding model, which currently lacks a standardized API across different providers. Developing a more flexible and interoperable embedding model integration could enhance the overall flexibility and portability of the Graph RAG system.

Additionally, the importance of prompt engineering for different language models should not be overlooked. As mentioned in the video, the same prompt may not work equally well for different language models, and customizing the prompts to the specific strengths and capabilities of each model could lead to improved performance.

Furthermore, exploring alternative implementations of the Graph RAG framework, such as those developed by other researchers or organizations, could provide valuable insights and opportunities for further advancements. Comparing and contrasting different approaches may uncover new techniques or optimizations that can be incorporated into the Graph RAG system.

In summary, the future directions for exploring alternative Graph RAG implementations include:

1. Investigating the use of larger and more powerful language models, such as the Llama 370 billion model, while addressing the associated challenges.
2. Developing a more flexible and interoperable embedding model integration to replace the current OpenAI-based approach.
3. Emphasizing the importance of prompt engineering for different language models to optimize performance.
4. Exploring and comparing alternative implementations of the Graph RAG framework developed by other researchers or organizations.

By pursuing these avenues of exploration, the Graph RAG system can be further refined and enhanced, leading to improved performance and broader applicability in various domains.

The use of local models like Lama 3 with Graph Rag is not recommended, as the choice of the language model (LLM) plays a critical role in the performance of the Graph Rag system. Larger and more capable LLMs, such as the Lama 370 billion model, are necessary to accurately extract entities and relationships from the text, which is essential for building a high-quality knowledge graph.

While using a local model can reduce costs, the trade-off is a significant decrease in the quality of the results. The author's experiments have shown that the summaries generated by the Lama 3 model are not as good as those produced by the GPT-4 model used in the previous video.

To achieve better results with Graph Rag, it is important to use a larger and more capable LLM, even if it comes at a higher cost. Additionally, the prompts used to interact with the LLM should be carefully crafted and tailored to the specific model being used, as different LLMs may respond differently to the same prompt.

The author plans to continue exploring Graph Rag and other implementations of the framework in future videos, as they believe it is a promising approach that deserves further investigation and refinement.