What is LIMP re-ranking

 LIMP re-ranking refers to a Latent Interaction Model with Pooling (LIMP) technique used for re-ranking documents or search results. It is a method to improve the ranking of documents retrieved by an initial retrieval model by incorporating interactions between the query and the documents in a more nuanced way.

Here’s a breakdown of LIMP re-ranking:

Key Concepts of LIMP:

Latent Interaction Model:

LIMP focuses on latent interactions between the query and the document. Instead of only relying on pre-encoded representations of documents and queries (as in a traditional bi-encoder model), LIMP allows the model to capture more granular word-to-word interactions between them.

Pooling:

The pooling step in LIMP aggregates information from all latent interactions between the query and the document to compute a relevance score. This pooling mechanism can take multiple forms (e.g., max-pooling, average pooling), and it allows the model to focus on the most relevant parts of the document when determining relevance.

Re-ranking:

Re-ranking is the process of refining the order of documents after an initial retrieval phase. In the context of LIMP, once an initial set of documents is retrieved (usually using a simpler and more scalable retrieval model like a bi-encoder), LIMP is used to re-rank the documents by analyzing the deeper interactions between the query and each document. This step improves the relevance of the top-ranked documents presented to the user.

How LIMP Re-ranking Works:

Initial Retrieval:

The system first retrieves a set of documents using a traditional retrieval method, such as BM25, a bi-encoder, or another retrieval model. These documents may contain some relevant ones, but the ranking might not be optimal.

Interaction Modeling:

LIMP then applies latent interaction modeling, where the words or embeddings of the query and the document are compared directly at various levels (e.g., word-level interactions or higher-level embeddings).

Pooling Mechanism:

The latent interaction scores are aggregated using a pooling mechanism, which captures the most relevant interactions between the query and document content. Pooling could prioritize strong matches (max-pooling) or capture an average similarity across all terms (average-pooling), depending on the implementation.

Re-ranking:

The pooled interaction score is used to re-rank the set of retrieved documents. The new ranking reflects a more detailed and fine-grained relevance scoring compared to the initial retrieval method.

Benefits of LIMP Re-ranking:

Captures Deeper Query-Document Interactions: Unlike traditional models that may only consider holistic similarity (like cosine similarity between embeddings), LIMP focuses on word-to-word and phrase-level interactions, leading to better ranking precision.

Improved Precision: By refining the initial set of retrieved documents, LIMP can significantly improve the relevance of the top results, making it useful in applications where high accuracy is critical.

Flexible Pooling: The pooling mechanism allows the model to focus on the most important aspects of the query-document relationship, further enhancing the precision of re-ranking.

Comparison with Other Re-ranking Methods:

LIMP vs. Bi-Encoders: A bi-encoder retrieves documents by encoding both the query and document separately and comparing their embeddings. In contrast, LIMP performs more detailed latent interaction modeling, which enables it to capture more nuanced relationships and improve the ranking of the results.

LIMP vs. Cross-Encoders: While cross-encoders also encode the query and document jointly, LIMP explicitly models the interactions at a finer level and uses pooling to summarize them. It offers an intermediate approach between bi-encoders (efficiency) and cross-encoders (precision).

Use Case in RAG (Retrieval-Augmented Generation):

In RAG systems, LIMP can be used for re-ranking retrieved documents to improve the quality of the documents fed into the generator (LLM). After an initial retrieval (e.g., via a bi-encoder), LIMP can re-rank the documents by looking at the finer interactions between the query and each document, ensuring the most relevant documents are presented for further processing or generation.

Conclusion:

LIMP re-ranking is a powerful tool that combines the benefits of interaction modeling and pooling to improve the relevance of search results or retrieved documents. It is especially useful in scenarios where precision is key, and it fits well within larger RAG systems as a re-ranking mechanism after initial retrieval.

What is Cross Encoder and Bi Encoder in RAG

 In the context of Retrieval-Augmented Generation (RAG), cross encoders and bi-encoders are two different methods for encoding query-document pairs to evaluate relevance. They represent two different approaches for measuring similarity between a query and potential documents during retrieval.

1. Bi-Encoder:

What it is: In a bi-encoder architecture, both the query and the documents are encoded independently into vector representations, typically using the same model. These vectors are then compared (e.g., using cosine similarity) to determine relevance.

How it works: The bi-encoder first encodes the query and the document separately into their respective embeddings. The similarity between the query and the document is computed after both have been encoded, without direct interaction between the two during encoding.

Pros:

Efficient retrieval: Since both queries and documents are independently encoded, you can pre-compute and store document embeddings in a vector database, making it fast and scalable for large datasets.

Scalability: Works well for large-scale retrieval tasks where embeddings of many documents are compared to a query.

Cons:

Lower precision: Since the query and document are encoded separately, there is no interaction between them during encoding, which might result in lower relevance compared to a cross-encoder.

Loss of interaction: The model cannot leverage interactions between query and document words, which might miss nuanced relevance.

Use case: Ideal for tasks requiring fast retrieval over large document sets, where embeddings can be precomputed and stored for quick lookup.

2. Cross-Encoder:

What it is: In a cross-encoder architecture, the query and the document are encoded together in a single pass through the model. This allows for cross-attention between the query and document, making the similarity judgment more precise.

How it works: The query and document are concatenated and passed through a model (like a transformer). The model processes them jointly, allowing direct interaction between the two. The model then outputs a relevance score based on the joint encoding.

Pros:

Higher precision: Since the query and document are encoded together, the model can take into account word-level interactions between them, leading to more accurate relevance judgments.

Better understanding of context: By processing both query and document together, the cross-encoder can capture subtle relationships and semantic nuances between the two.

Cons:

Slow for large-scale retrieval: Since every query-document pair needs to be encoded together, it's computationally expensive for large-scale retrieval tasks.

No pre-computation: Unlike bi-encoders, you can't pre-compute the document embeddings, which limits scalability.

Use case: Best suited for re-ranking a small set of documents retrieved by a bi-encoder or other methods. It is typically used in a two-step process where a bi-encoder retrieves a broad set of documents, and a cross-encoder refines the ranking.

Comparison in RAG:

In RAG (Retrieval-Augmented Generation), typically, a bi-encoder is used to perform initial retrieval from a large corpus (due to its efficiency and scalability), followed by a cross-encoder to re-rank or refine the results for better accuracy, especially when precision is critical.

Bi-Encoder: Used for fast, scalable retrieval.

Cross-Encoder: Used for accurate re-ranking of a small subset of documents retrieved by the bi-encoder.

Example Workflow in RAG:

Bi-Encoder Retrieval: The system first uses a bi-encoder to retrieve a broad set of candidate documents that are relevant to the user's query by comparing the query's embedding with precomputed document embeddings.

Cross-Encoder Re-Ranking: Once a smaller subset of relevant documents is retrieved, the system can apply a cross-encoder to re-rank the documents by jointly encoding the query and each document and generating a more precise relevance score.

Both methods have their place in RAG-based systems: bi-encoders handle large-scale retrieval, while cross-encoders improve precision for re-ranking and final selection.

embed_query in langchain's OpenAIEmbeddings

In langchain, the embed_query method of the OpenAIEmbeddings class is used to generate an embedding vector for a query (text input). The idea behind embeddings is to convert text into numerical vectors, which represent semantic meanings and are used for similarity searches, such as when comparing queries with stored documents or other text.

How it works:

Query Embeddings: When you call embed_query, it sends the input query (a piece of text) to the OpenAI API, which then returns a vector representation of that text.

Usage: This embedding is typically used to match queries with stored document embeddings in a vector database to find the most relevant document or answer. It helps in similarity search tasks by comparing how "close" the query vector is to other document vectors.

Example:

from langchain.embeddings import OpenAIEmbeddings

# Initialize OpenAI Embeddings object

openai_embeddings = OpenAIEmbeddings()

# Get embedding for a query (a string of text)

query = "What is the version of this device?"

query_embedding = openai_embeddings.embed_query(query)

# Now, you can use this embedding for similarity searches, etc.

Main Purpose: embed_query is used when you want to search or match a user's query with similar documents stored in a vector database or embedding store.

No, not all embedding models in Langchain support the embed_query method directly. The availability of the embed_query method depends on the specific embedding model you are using. Here’s a breakdown of how this works:

1. Models that support embed_query:

OpenAIEmbeddings: OpenAI models, such as text-embedding-ada-002, natively support the embed_query method, which allows you to generate query embeddings for similarity search or document retrieval tasks.

Other Cloud/Managed API Models: Similar to OpenAI, some managed services like Cohere, Hugging Face embeddings, etc., also provide embed_query functionality depending on the model's API.

2. Models that may not support embed_query:

Self-Hosted Models: Some self-hosted or custom models (e.g., using locally trained models or models running on frameworks like transformers or Sentence Transformers) may not have the embed_query method, unless specifically implemented.

Custom Embedding Models: If you are using a custom embedding model or provider, you may need to implement the method yourself if it’s not already included.

3. General Implementation:

The embed_query method is generally a convenience function that converts a query into an embedding. For models that don't provide this directly, you may still be able to call a generic embedding method like embed_documents or embed_text and apply that to queries. It might just not be explicitly named embed_query.

Alternative Methods:

If embed_query isn’t supported, you can usually still use the model’s general embedding method for queries by treating queries like any other document or text.

Example:

query_embedding = model.embed_documents([query]) 

In summary, many embedding models do support embed_query, especially those from major providers like OpenAI, Cohere, etc., but custom, self-hosted, or specialized models may require you to handle the embedding process for queries manually. Always check the specific embedding model’s documentation in Langchain to confirm support.

What is difference between Multi Query retriever, TimeBasedVectoreStoreRetriever, and Self Query retrievers in Langchain

 In Langchain, different retrievers serve as mechanisms to extract relevant information from various data sources for LLM-based applications. Here’s a breakdown of the key retrievers you mentioned:

1. Multi Query Retriever:

The Multi Query Retriever allows an LLM to generate multiple variations of a query to improve retrieval results. This helps address scenarios where different wordings of the same query might lead to different but relevant results in a vector store or database.

Purpose: Enhance recall by increasing the chances of retrieving relevant information through multiple reformulated queries.

Process: The retriever generates alternative queries (e.g., rephrases the user's original query) and uses them to search the data store. The combined results from these queries are then ranked and returned.

Use Case: Useful when you want to cover diverse interpretations or wordings of the user's question for more comprehensive results.

Example: When a user asks, "What is the best way to secure a database?", the retriever might generate alternative queries like:

"How to improve database security?"

"Best practices for securing a database?"

"How to safeguard databases?"

This helps in retrieving different but complementary documents or information.

2. TimeBasedVectorStoreRetriever:

The TimeBasedVectorStoreRetriever is designed for retrieving information based on time relevance from a vector store. In addition to vector similarity search, it factors in the timestamp associated with documents, ensuring that results are time-ordered or time-filtered.

Purpose: To prioritize or filter documents based on their recency or relevance to a specific time range, in addition to vector similarity.

Process: This retriever can either rank results by their timestamp or restrict retrieval to a certain time window, depending on how it's set up.

Use Case: Ideal for applications dealing with time-sensitive information, like news archives, logs, or research articles.

Example: If the user asks, "What were the latest advancements in AI?", this retriever ensures that the most recent articles or documents are prioritized over older content.

3. Self Query Retriever:

The Self Query Retriever is an advanced retriever that uses an LLM to automatically generate structured queries (with filters) for more specific searches based on the user's query.

Purpose: Automatically apply metadata-based filters (e.g., date ranges, categories) to retrieve more targeted results.

Process: It involves the LLM analyzing the user's query to generate a structured query with filter conditions. These filters can be based on attributes like date, author, or document type, enhancing retrieval precision.

Use Case: Useful in situations where the data has rich metadata and users may have implicit requirements. For example, finding "recent research papers on deep learning by a specific author."

Example: If the user query is "Show me articles on machine learning from 2020," the retriever will automatically generate a query that filters for "machine learning" and restricts results to documents from 2020.

Key Differences:

Multi Query Retriever: Focuses on reformulating the query to improve recall, covering multiple possible variations.

TimeBasedVectorStoreRetriever: Prioritizes or filters results by time, useful for retrieving time-sensitive information.

Self Query Retriever: Automatically creates more precise queries with filtering based on metadata.

Each of these retrievers has its own specialized purpose, and the right one depends on the specific data retrieval needs of the application.

What is Berkley Function calling tool

Berkeley Function Calling Leaderboard (BFCL) is a great resource for comparing how different models perform on function calling tasks. It also provides an evaluation suite to compare your own fine-tuned model on various challenging tool calling tasks. In fact, the latest dataset, BFCL v3, was just released and now includes multi-step, multi-turn function calling, further raising the bar for tool based reasoning tasks.

Both types of reasoning are powerful independently, and when combined, they have the potential to create agents that can effectively breakdown complicated tasks and autonomously interact with their environment. For more examples of AI agent architectures for reasoning, planning, and tool calling check out my team’s survey paper on ArXiv.

refernces:

https://gorilla.cs.berkeley.edu/leaderboard.html

What is contextual Embedding in Anthropic RAG? - Anthropic

The recent paper Anthropic introduced the concept of “contextual embedding” that solves the problem of lacking context by adding relevant context to each chunk before embedding.

What you can do is to leverage LLM for Contextual Retrieval. You can develope a prompt for an LLM that instructs the model to generate concise, chunk-specific context based on the overall document in order to provide contextual information for each chunk.

Consider a query about a specific company’s quarterly revenue growth. A relevant chunk might be something like this: “The company’s revenue grew by 3% over the previous quarter” contain the growth percentage but lack crucial details like the company name or time period. This absence of context can hinder accurate retrieval.

By sending the overall document to an LLM for every chunk, we get a contextualized_chunk like this:

This ‘contextualized_chunk’ is then sent to an embedding model to create the embedding of the chunk.

Hybrid search approach

While this contextual embeddings has proven to improve upon traditional semantic search RAG, a hybrid approach incorporating BM25 can produce even better results.

The same chunk-specific context can also be used with BM25 search to further improve retrieval performance.

Okapi BM25

BM25 is an algorithm that addresses some drawbacks of TF-IDF which concern with term saturation and document length.

Term Saturation and diminishing return

If a document contains 100 occurrences of the term “computer,” is it really twice as relevant as a document that contains 50 occurrences? We want to control the contribution of TF when a term is likely to be saturated. BM25 solves this issue by introducing a parameter k1 that controls the shape of this saturation curve. Parameter k1 can be tuned in a way that if the TF increases, at some point, the BM25 score will be saturated , meaning that the increase in TF no longer contributes much to the score.

References:

https://levelup.gitconnected.com/the-best-rag-technique-yet-anthropics-contextual-retrieval-and-hybrid-search-62320d99004e

What's the differences between Chain of Thought (CoT), ReAct, Prompt Decomposition approaches

Chain-of-Thought (CoT), ReAct (Reasoning + Acting), and Prompt Decomposition are all advanced prompting techniques for improving the reasoning capabilities of large language models (LLMs). Each approach differs in how they manage complex tasks and guide the model’s reasoning. Here’s a breakdown of the differences:

1. Chain-of-Thought (CoT):

Purpose: CoT is designed to enhance the reasoning capabilities of an LLM by encouraging it to think step-by-step.

Approach: In CoT, the model is explicitly guided to break down its reasoning process for complex questions or tasks. Instead of jumping to an answer, the model generates intermediate steps or thoughts that lead to the final result.

How It Works: When given a question, the model first generates a "chain of thought" — a logical sequence of steps that helps it arrive at a conclusion.

Use Case: CoT is useful for multi-step problems, arithmetic reasoning, logical deduction, and scenarios where intermediate steps are important for accuracy.

Example: Prompt: "If a train travels 60 miles in 2 hours, how far will it travel in 5 hours at the same speed?" Model's response using CoT:

The train travels 60 miles in 2 hours.

So, its speed is 60 ÷ 2 = 30 miles per hour.

In 5 hours, it will travel 5 × 30 = 150 miles.

2. ReAct (Reasoning + Acting):

Purpose: ReAct combines reasoning (thought process) and actions (interactions with external tools or APIs) to solve tasks that require external input or real-time actions.

Approach: The model alternates between reasoning and acting steps. The reasoning process helps the model figure out what information or action is needed, and the acting step involves interacting with external systems (e.g., querying a database, using a calculator, calling an API). This combination leads to more effective decision-making in tasks that involve dynamic responses or external actions.

How It Works: ReAct prompts the model to first reason about the problem and then take action based on that reasoning. It can repeat this cycle multiple times if needed.

Use Case: ReAct is ideal for interactive tasks like searching a knowledge base, answering questions that involve retrieving data from external sources, or interacting with APIs.

Example: Prompt: "What is the current temperature in New York?" Model's response using ReAct:

First, I need to find the current temperature in New York (Reasoning).

Let me call a weather API to get the temperature (Acting).

The temperature in New York is 72°F (Result).

3. Prompt Decomposition:

Purpose: Prompt Decomposition breaks down a complex task or query into smaller, manageable subtasks or steps. Each subtask is handled separately, and the results are combined to address the original query.

Approach: Instead of giving a single, complex prompt, the task is divided into multiple sub-prompts, where each part of the task is processed independently. The results from these sub-prompts are then aggregated.

How It Works: The original query is split into smaller, more focused prompts, which may be handled by different agents or functions. This modular approach ensures that each part of the query is processed accurately, especially for multi-step or multi-domain tasks.

Use Case: Prompt Decomposition is used for complex tasks that involve multiple steps, specialized sub-tasks, or require integration from multiple sources. It is common in multi-agent systems and workflows that need to be handled in parts.

Example: Prompt: "How do I configure a router, ensure it meets security standards, and monitor network traffic?"

First sub-prompt: "What are the steps to configure a router?"

Second sub-prompt: "What are the security standards for routers?"

Third sub-prompt: "What are the best practices for monitoring network traffic?"

Key Differences:

Chain-of-Thought (CoT): Focuses on internal reasoning by prompting the model to think in logical steps without external action. It’s ideal for solving reasoning-based problems.

ReAct: Combines reasoning with external actions, where the model alternates between thought processes and interactions with tools or APIs.

Prompt Decomposition: Breaks down complex tasks into smaller, simpler components to handle them individually, often involving multiple steps or agents.

Summary:

CoT is mainly for reasoning step-by-step and is self-contained within the model’s thought process.

ReAct involves reasoning combined with taking external actions (e.g., tool usage or calling APIs).

Prompt Decomposition breaks a problem into multiple smaller tasks, which can be handled independently and in parallel by different agents or processes.

Each approach is useful depending on the complexity and type of task you are dealing with, whether it requires reasoning, external actions, or task breakdown.

references:

OpenAI