Using the Gemini embedding model to Develop a RAG System with observability via W&B Weave

Build a powerful RAG system with Gemini embeddings (gemini-embedding-001) and monitor queries, retrievals, and outputs using W&B Weave.
Dave Davies
Created on July 25|Last edited on July 25
Comment
The Gemini embedding model is one of the latest advancements in natural language processing, providing high-quality text embeddings that capture semantic meaning. It converts text into numerical vector representations (embeddings) that reflect the context and meaning of the input. These rich embeddings can significantly enhance applications like search and question-answering by finding related information based on meaning rather than keywords.
In Retrieval Augmented Generation (RAG) systems – which combine knowledge retrieval with generative AI – Gemini’s embeddings help retrieve the most relevant facts to ground the language model’s responses. This article will explore how to leverage the Gemini embedding model (known by its API ID gemini-embedding-001) in a RAG pipeline and how tools from Weights & Biases, such as W&B Weave, provide observability into the process.
We will start by understanding what the Gemini embedding model is and its key use cases in NLP. Then, we’ll walk through using the Gemini API to generate embeddings, including how to specify task types for optimal performance. Next, we’ll develop a simple RAG system that uses Gemini for embeddings, highlighting how those embeddings improve the system’s accuracy.
Throughout, we’ll integrate W&B Weave for monitoring and debugging – so you can see how queries, embeddings, and retrieved documents flow through the system in real time. By the end, you’ll have a clear roadmap for building your own “Gemini RAG” system with robust observability, using W&B to track performance and gain insights.
Understanding the Gemini Embedding Model
Key use cases in NLP tasksThe Gemini embedding model is designed to generate dense vector embeddings for text, enabling more intelligent NLP applications. These embeddings power tasks such as semantic search, text classification, and clustering, by representing text in a way that captures meaning and context. For example, in semantic search, a query and documents are embedded into vectors, and the closest vectors indicate the most relevant documents even if they don’t share literal keywords. In classification tasks, embeddings of sentences or documents can be fed into classifiers to categorize text by topic or sentiment. In clustering, similar documents yield nearby embeddings in vector space, allowing grouping of related content. By using Gemini’s embeddings, these tasks benefit from a deeper understanding of language, yielding more accurate and context-aware results than simple keyword matching. 
Another key use case is Retrieval Augmented Generation (RAG). Here, embeddings are the bridge between a knowledge base and a generative language model. Each document in a knowledge corpus is converted to a vector once using the Gemini model, and those vectors are stored in a vector index. At query time, the user’s question is also converted to an embedding, and the system finds which document vectors are closest (most semantically similar) to the query. The content from those top documents is then provided to an LLM (such as a Gemini LLM or another large language model) as additional context to generate a factually correct answer. This approach substantially improves the factual accuracy and relevance of AI-generated responses by grounding them in actual retrieved data. In summary, Gemini embeddings are versatile: from powering smarter search engines to enabling AI assistants to fetch knowledge on the fly, they enhance a wide range of NLP tasks with contextual intelligence.
Supported task types and their applicationsTo maximize performance across different applications, the Gemini embedding model supports specifying task types when generating embeddings. A task type tells the model what kind of semantic relationship or use-case to prioritize in the embedding space. By choosing the right task type, you optimize the embeddings for the intended relationships, which improves accuracy and efficiency for that task. For instance, if your goal is to measure how similar in meaning two pieces of text are, you would use the SEMANTIC_SIMILARITY task type. This optimizes the embeddings to place semantically alike texts closer together, which is ideal for applications like duplicate question detection or recommendation systems. On the other hand, a task type like CLASSIFICATION produces embeddings geared toward distinguishing categories or labels – useful for tasks such as sentiment analysis or spam filtering. 
The Gemini API supports several task types tuned for common scenarios. Semantic similarity embeddings make it easy to find texts with similar meaning (e.g., detecting if a user query is answered by an FAQ entry). Classification embeddings help in tagging or categorizing text by themes or sentiment. Clustering embeddings group similar texts together, useful for document organization or discovering topics in unlabeled data. Notably, there are also specialized retrieval task types: RETRIEVAL_DOCUMENT and RETRIEVAL_QUERY. These are used together to improve search – documents in your knowledge base would be embedded with RETRIEVAL_DOCUMENT, while incoming queries use RETRIEVAL_QUERY. This pairing ensures that the embeddings of queries and documents are created in a complementary way, improving the relevance of search results. There’s even a mode for code search: CODE_RETRIEVAL_QUERY can be used for embedding natural language descriptions of code, while code snippets themselves use the document embedding type, enabling better code snippet search. In summary, Gemini provides a range of embedding flavors that you can choose from based on your use case – specifying the task type guides the model to generate embeddings that are most effective for that particular application.
Generating Embeddings with the Gemini API
Process for generating embeddingsUsing the Gemini API to generate embeddings is straightforward and flexible. The core method provided is typically called embedContent, which accepts your input text (or a batch of texts) and returns the embedding vectors. Under the hood, this method is part of Google’s Generative AI SDK and is available in multiple programming languages, including Python, JavaScript, and Go, as well as via a REST HTTP endpoint. This means you can easily integrate Gemini embeddings into a variety of environments (backend services, web apps, etc.). For example, in Python you would initialize the Generative AI client with your API key and then call client.models.embed_content(...) with the model name and the text you want to embed. In JavaScript, a similar ai.models.embedContent({...}) function is available through Google’s @google/genai package. All of these interfaces require specifying the model ID for embeddings – currently this is "gemini-embedding-001", which is the latest version of the Gemini text embedding model.
When calling the API, you can embed either single strings or multiple pieces of text in one call. Passing a list of texts to embedContent will return a list of embeddings in the same order, which is convenient for processing batches of data. The resulting embedding is an array of floating-point numbers (vector) that encodes the semantic information of your input. The dimensionality of each Gemini embedding vector is fixed by the model – for gemini-embedding-001, each embedding is a 768-dimensional vector. This dimensionality is chosen by the model designers to provide a rich representation of meaning (768 is fairly large, allowing the encoding of nuanced semantic features). You don’t typically change the embedding size; instead, you select a different model if a smaller or larger embedding is needed. In practice, 768 dimensions is a good trade-off between expressiveness and manageability. After obtaining the embedding, it’s common to normalize the vector (Gemini’s embeddings can be normalized to unit length, which makes similarity comparisons easier, as indicated by the norm being ~1.0 after normalization in examples) – though depending on the use case, this might not be necessary. 
Once generated, embeddings should be stored for later use, especially for building RAG systems. Usually, you will pre-compute embeddings for your entire knowledge corpus and store them in a specialized vector database or index. These databases (such as Weaviate, Pinecone, or FAISS) are optimized to store high-dimensional vectors and perform similarity search efficiently. You can also store embeddings in a simple in-memory list or NumPy array for small-scale applications or testing. The key is that each embedding is stored alongside metadata (like an ID or the original text) so that when you later find the nearest vectors, you can retrieve the corresponding content. By controlling when and how you generate embeddings (for example, embedding all documents once upfront, then caching them), you also control the computational overhead of your system. In summary, the process involves using the embedContent API call with the desired model and content, handling the output vectors (which are 768-dimensional for Gemini) and saving them in a way that your RAG system can quickly access for similarity searches.
Specifying task types for optimized performanceWhen generating embeddings with the Gemini API, it’s important to specify a task type (as discussed earlier) to tailor the embeddings to your needs. The API allows you to pass a configuration or parameter indicating the task type for the embedding. In the Python SDK, for example, you can use types.EmbedContentConfig(task_type="SEMANTIC_SIMILARITY") and pass that config into the embed_content call. In JavaScript, a similar config: { taskType: "SEMANTIC_SIMILARITY" } can be provided. By doing so, you instruct the model to optimize the embedding generation for a specific purpose. This extra context helps the model emphasize the aspects of the text that matter for your task. 
For instance, if you are building a semantic search or duplicate-question detector, you would specify SEMANTIC_SIMILARITY. The embeddings returned will be optimized so that texts with similar meaning end up with vectors that are close together (high cosine similarity). If you are instead building a classifier (say to detect topics or sentiment), using CLASSIFICATION will yield embeddings that better separate texts by classes, which can improve downstream classifier accuracy. For clustering tasks (like grouping news articles by theme without predefined labels), CLUSTERING as the task type will aim to place related articles near each other in the vector space, making the clusters more meaningful. For RAG systems specifically, you should pay attention to the retrieval-oriented task types. Gemini offers RETRIEVAL_DOCUMENT and RETRIEVAL_QUERY to handle search from both sides. In practice, you would embed all your knowledge base documents with RETRIEVAL_DOCUMENT, and at query time embed the user’s query with RETRIEVAL_QUERY. This dual approach optimizes the vectors so that a query’s embedding will directly align with the document embeddings if the content is relevant. Using mismatched types (or not specifying any) might lead to suboptimal retrieval performance, so it’s a best practice to set these parameters. In summary, always choose the task type that matches your use case when calling the embed API – this simple step can maximize the accuracy of your embeddings for that scenario, giving your application a notable boost in performance.
Developing a RAG System with Gemini Embeddings
How embeddings improve RAG systemsRetrieval Augmented Generation (RAG) systems combine an information retrieval component with a generative AI model to produce answers that are both accurate and contextually relevant. Embeddings like those from Gemini play a vital role in these systems by bridging the gap between unstructured knowledge and the language model. Instead of relying solely on an LLM’s internal knowledge (which might be limited or become outdated), a RAG system uses embeddings to fetch relevant external information on the fly. The Gemini embedding model converts queries and documents into vectors that capture their meaning, which makes it possible to match a user’s question with supporting documents based on semantic similarity. This dramatically improves the factual accuracy of the generated responses. The language model is guided by real data retrieved via the embeddings, so it’s less likely to hallucinate or give irrelevant answers. In essence, high-quality embeddings ensure that the RAG system finds the right pieces of text to feed into the LLM, resulting in answers that are better grounded in truth.
Embeddings also contribute to the coherence and contextual richness of RAG-based answers. By pulling in text passages that closely relate to the query, the system provides the generative model with ample context. This means the LLM can focus on composing a well-structured and clear answer using that context, rather than guessing or generalizing. The retrieved facts, figures, or definitions give specificity to the response. For example, if a user asks, “What are the health benefits of green tea?”, the embedding-powered retriever might fetch a scientific article or a Wikipedia paragraph on green tea’s health effects. The LLM then uses that content to produce a precise answer with references to antioxidants or metabolism – details it might not recall correctly on its own. The result is a response that is both informative and trustworthy. Without embeddings, an LLM might respond with generic information, but with a Gemini-augmented RAG pipeline, the response will likely include up-to-date and context-specific information. In summary, embeddings improve RAG systems by ensuring the right context is retrieved and provided to the model, leading to answers that are more accurate, relevant, and deeply informed by the actual data available.
Step-by-step tutorial using W&B WeaveNow, let’s put everything together and build a simple RAG system using Gemini embeddings, and see how to monitor it with W&B Weave. This tutorial will walk through the key steps: setting up the environment, generating and storing embeddings, performing retrieval, and integrating observability. We’ll use Python for the example code. By following these steps, you can adapt the approach to your own dataset and applicat
Step 1: Set up your environment and API accessFirst, install the required libraries and configure your API keys. You’ll need Google’s Generative AI SDK (for the Gemini API) and Weights & Biases. You can install them via pip if needed (pip install google-generativeai wandb). In your Python script or notebook, import the libraries and initialize W&B Weave for observability:
import os
import google.generativeai as genai
import weave
﻿
# Initialize W&B Weave for logging and observability
weave.init(project_name="gemini-rag-demo")
﻿
# Configure the Google Gemini API client with your API key
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
In the above snippet, we call weave.init() to set up a Weave run (make sure you’re logged into W&B or have set your W&B API key in the environment). We give the project a name (e.g., "gemini-rag-demo"), which will be used to organize runs in W&B. The genai.configure() line authenticates the Google Generative AI client with your Gemini API key – ensure you have this key from Google Cloud or the Gemini service and set it as an environment variable or directly in the code (for security, using an environment variable is recommended).
Step 2: Prepare your documents (knowledge base)For this tutorial, let’s assume we have a small set of documents. In a real scenario, these could be paragraphs from articles, FAQ entries, or any text data relevant to your domain. We’ll create a list of documents as example data:
# Example knowledge base documents
documents = [
    "Green tea contains antioxidants called catechins that may improve metabolism and overall health.",
    "Black tea and green tea both come from the Camellia sinensis plant, but are processed differently.",
    "Some studies suggest green tea consumption is linked to improved brain function and reduced risk of Alzheimer's."
]
Here we have three sample texts about tea and health. In practice, you might load documents from files or a database. Each item in the documents list is a string that we plan to embed and store. If your documents are large, you might consider splitting them into smaller chunks before embedding (since very long texts might be truncated or less efficient to embed in one go). For simplicity, our example texts are short.
Step 3: Generate embeddings for the documentsUsing the Gemini embedding model, we will convert each document into an embedding vector. We also specify an appropriate task type for our use case. Suppose we want to use these embeddings for semantic search (to find which document is most relevant to a query); we’ll use the RETRIEVAL_DOCUMENT task type for the document embeddings:
# Embed the documents using Gemini embedding model
doc_embeddings = []
for doc in documents:
    response = genai.embed_text(  # Using a hypothetical embed_text function for clarity
        model="gemini-embedding-001",
        content=doc,
        task_type="RETRIEVAL_DOCUMENT"  # optimize for document retrieval
    )
    embedding_vector = response["embedding"]  # Assuming the response is a dict with the embedding
    doc_embeddings.append(embedding_vector)
A few notes on the above code: We loop through each document and call a function to get its embedding. In the actual Google Generative AI Python SDK, the method might be genai.models.embed_content – the exact function name and return format can differ, but the concept remains: provide the model name, the content, and a config specifying task_type. We use "gemini-embedding-001" as the model, and we set task_type="RETRIEVAL_DOCUMENT". The response from the API will include the embedding values (often under a field like response.embeddings or similar). We extract the embedding vector (which is a list or array of 768 floats) and store it in doc_embeddings. After this loop, we have a list of embedding vectors, where doc_embeddings[i] corresponds to documents[i]. In a real application, you might embed documents in batches rather than one by one (the API supports passing a list of contents in a single call), but iterating is easier to illustrate here. Also, note that W&B Weave is automatically tracking these calls to genai.embed_text because we initialized Weave – every call to the Gemini API is being logged behind the scenes, which we’ll inspect later.
Step 4: Store embeddings in a vector indexNow that we have the embeddings, we need to store them in a way that allows similarity search. For a simple demonstration, we can just keep them in a Python list and perform a brute-force similarity check. However, in a production system, you would likely use a dedicated vector database or index (such as FAISS, Pinecone, or Weaviate) for faster lookups. Regardless of the storage, ensure you keep an association between each embedding and its source document (or an ID for the document). In our simple approach, the index of the embedding in the list will correspond to the index of the document in the original documents list, so we maintain that relationship implicitly. Our doc_embeddings list effectively is our vector index for now.
Step 5: Handle user queries with retrieval and generationWith the knowledge base ready, we can accept a user query, embed it, find the most similar document(s), and then feed those to an LLM to generate an answer. Let’s simulate a user query and go through the retrieval step:
import numpy as np
﻿
# Example user query
query = "What are the health benefits of green tea?"
﻿
# Embed the query using the Gemini model, optimized for queries
query_response = genai.embed_text(
    model="gemini-embedding-001",
    content=query,
    task_type="RETRIEVAL_QUERY"
)
query_embedding = query_response["embedding"]
﻿
# Compute cosine similarity between the query embedding and each document embedding
similarities = []
for emb in doc_embeddings:
    # Cosine similarity: dot product divided by product of norms
    sim = np.dot(query_embedding, emb) / (np.linalg.norm(query_embedding) * np.linalg.norm(emb))
    similarities.append(sim)
﻿
# Find the index of the most similar document
top_doc_index = int(np.argmax(similarities))
print("Most relevant document index:", top_doc_index)
print("Most relevant document:", documents[top_doc_index])
In this code, we take a sample question about green tea’s health benefits. We embed the query using the same Gemini model but specify task_type="RETRIEVAL_QUERY" this time, since this embedding will be used to match against document embeddings. The result query_embedding is the vector representation of the query. We then calculate the cosine similarity between this query vector and each of the document vectors we stored earlier. Cosine similarity is a common measure to compare embeddings – it ranges from -1 to 1, where 1 means the vectors are pointing in the same direction (very similar in meaning). We use NumPy to do the dot product and norm calculations. After computing a similarity score for each document, we pick the highest score to identify the most relevant document (np.argmax gives the index of the max value). We then print out which document was most similar. 
Running this retrieval step, we would expect the document about "antioxidants called catechins" to come out on top for this particular query, since it directly mentions health benefits of green tea. The print statements will confirm which document was selected. At this point, our RAG system has successfully found the relevant context in response to the user’s question.
Step 6: Generate an answer using the retrieved contextThe final step is to use a generative model to produce an answer, augmented by the content we just retrieved. In a fully implemented system, we would take the top document (or top few documents) and provide them to an LLM along with the user’s question (often by constructing a prompt that includes the documents as context or by using a system message if the LLM is chat-oriented). For example, if using the Gemini LLM or another model via an API, the prompt might say: "Use the following content to answer the question. Content: '<document text>'. Question: 'What are the health benefits of green tea?'. Answer:" and then we invoke the model to get a completion. For brevity, we won’t execute an actual LLM call here, but conceptually it would look like:
# Pseudo-code for generation (actual API call may differ):
retrieved_text = documents[top_doc_index]
prompt = f"Context: {retrieved_text}\nQuestion: {query}\nAnswer:"
generation = genai.generate_text(model="gemini-1.5-turbo", prompt=prompt)
print("AI Answer:", generation["content"])
In the above pseudo-code, genai.generate_text (or a similar method) would call an LLM (like a Gemini LLM named "gemini-1.5-turbo" in this hypothetical example) to get an answer. The retrieved document text is included as context. The result would be an answer that hopefully mentions the antioxidants and health benefits, grounded in the content we retrieved. This final step completes the RAG loop: we took a query, used embeddings to get relevant knowledge, and then generated an informed answer.
Step 7: Monitor and visualize with WeaveBecause we initialized W&B Weave at the start (weave.init(...)), all the steps above have been tracked. The calls to the embedding model (embed_text) and even our custom code execution can be logged as operations in W&B. Weave automatically captures the inputs and outputs of the Gemini API calls, so we have a trace of what embeddings were generated for which text. We printed some results to the console for illustration, but W&B has recorded these steps in the background as a sequence of events (a trace) within the run. 
After running the code, we can go to our W&B project (in the browser) and open the Weave dashboard for this run. There, we might see a list of operations that were captured. For instance, we’ll find entries corresponding to the embed_text calls for each document and the query, along with the model name gemini-embedding-001 and perhaps metadata like the timestamp and duration of each call. Weave may also capture the final generation call if we made one. Each operation can be inspected – you can click on an embedding operation to see the input (the text that was embedded) and the output (the embedding vector, or a reference to it). This is extremely useful for debugging: you can verify that the documents were embedded correctly (e.g. no empty outputs or errors) and that the query embedding was generated with the right task type.
Furthermore, you can add visualizations in the Weave interface to analyze performance. For example, you might log the similarity scores as a small chart to confirm that one document was clearly a better match than the others. We could also track the end-to-end latency of the RAG process for each query (embedding time + retrieval time + generation time) by logging timestamps or durations, and then use Weave to plot these metrics. W&B Weave allows creation of custom dashboards, so you could design a panel that lists queries and the documents returned, or a graph of similarity score distributions. If you have ground-truth answers for your queries, you could even use Weave to help evaluate the accuracy of the generated answers by comparing them (manually or with an eval harness). The key point is that with Weave’s observability, every step in the pipeline is recorded and available for analysis. This makes it much easier to iterate on your RAG system – for instance, if an irrelevant document was retrieved for a query, you would spot that in the Weave traces and could then fine-tune your approach (maybe by cleaning the documents or adjusting the embedding task type).
By following these steps, you have a basic RAG system powered by the Gemini embedding model, and you’ve instrumented it with W&B Weave for full transparency. In a real-world scenario, you would expand on this: use a larger dataset, incorporate a proper vector database for efficiency, handle multiple retrieved documents, and refine the prompt for generation. Throughout those improvements, W&B Weave would continue to be your ally in observing how the system behaves and ensuring everything works as expected.
Observability with W&B Weave
Benefits of using W&B WeaveBuilding a high-performing RAG system is not just about the model and retrieval algorithm – it’s also about being able to observe and debug the system in action. This is where W&B Weave adds tremendous value. Weave is an observability tool that allows you to track every step of your NLP pipeline, from inputs to outputs, along with any custom metrics or metadata. With Weave, you get end-to-end visibility into how your RAG system is performing. It automatically logs each interaction with the model (such as the API calls to Gemini for embeddings or generations) as well as any instrumented functions in your code. This means you have a detailed record of what your system did for each query – what it embedded, what it retrieved, what the LLM responded, etc. Such granular tracking is critical for diagnosing issues and optimizing performance.
One major benefit of Weave is the ability to visualize data and metrics in a flexible dashboard. Unlike traditional log files, Weave presents your data in an interactive interface. You can see tables of operations, filter or search through them (for example, find all queries where the similarity score was below a certain threshold), and create plots or charts. This helps in spotting patterns or anomalies. For instance, you might visualize the distribution of similarity scores for a batch of queries – if many are low, it might indicate your embeddings aren’t effective for those queries or that the knowledge base is missing information on certain topics. Weave also improves system transparency and reliability by exposing hidden failure modes. If the model ever returns an error or an unexpected result, it will be logged. Developers can then quickly pinpoint if the failure was in the embedding step, the retrieval, or the generation. Capturing this rich trace data means that issues like hallucinations (the LLM producing irrelevant or incorrect info), latency spikes, or mismatches between query and retrieved content can be detected and analyzed. In high-stakes applications (finance, healthcare, etc.), this level of observability isn’t just nice-to-have – it’s essential for trust and compliance. Teams can demonstrate that they are monitoring for bias, errors, or other problems, and they have the tools to trace and audit the system’s decisions. Overall, W&B Weave acts as a guardian for your RAG system, ensuring you can peek under the hood at any moment and understand exactly how your model arrived at a given answer.
Implementing observability in your RAG systemImplementing observability with W&B Weave in your own RAG system involves a few straightforward steps. As demonstrated in the tutorial, the first step is to initialize Weave in your code by calling weave.init(project_name="your-project"). This connects your script to Weave and ensures that all subsequent operations get logged to a W&B run. It’s important to do this early, before you start calling the model or doing critical operations, so nothing is missed. Next, leverage Weave’s ability to auto-capture data. If you’re using supported libraries (like the Google Generative AI SDK for Gemini), Weave will automatically intercept those function calls (such as generating or embedding content) and log their inputs/outputs. This means minimal effort on your part – simply use the models as you normally would, and Weave handles the logging behind the scenes.
For parts of your code that are unique or not using a supported SDK, you can use Weave’s custom logging. One approach is to annotate functions with the @weave.op decorator. For example, if you have a custom retrieval function or a post-processing step, you can define it as a Weave operation. This will cause Weave to capture the arguments and return values whenever that function runs, treating it as another step to visualize. You can also use wandb.log (from the core W&B library) to log scalar metrics or custom data at any point, which will appear in the Weave interface as well. For instance, you might log the number of documents retrieved, the similarity score of the top match, or the latency of the query processing. Logging such metrics allows you to easily plot them in Weave and see trends (e.g., which queries tend to have lower similarity scores or which ones take longer to answer).
After running your instrumented RAG system, you’ll head to the W&B app to view the results. In your project, each execution (or session) of your RAG pipeline will be recorded as a run. Opening a run in Weave lets you inspect all the captured operations in sequence. You might start by examining a specific query: see the embedding call for the query (was the task type correct and the output reasonable?), then the retrieval step (which documents were found and what were their similarity scores?), and then the generation step (did the model produce an answer using the provided context?). Weave’s interface often provides default panels to display this information, but you can also create custom dashboards. For example, you can create a table that lists each query alongside the document title that was retrieved and the answer given. This could help you qualitatively evaluate if the retrieved document was actually relevant and if the answer was correct. If you find an output that looks wrong, the dashboard lets you drill down: you could click on that particular run or operation and see all details (perhaps the query was phrased ambiguously, or maybe the embeddings pulled in a tangential document – you’ll have the evidence to diagnose it).
Tracking metrics is also crucial. Using Weave, you can track things like the average similarity of the top document for each query, the fraction of queries that get a “good” answer (if you have a way to label them), or system performance metrics like response time. Over time, these metrics can inform improvements. For instance, if you notice that some queries consistently have low similarity scores even for the top result, you might decide to expand your knowledge base or fine-tune the embedding model for those cases. Weave allows you to plot these metrics over multiple runs, so if you make changes to your system (say, switch to a different embedding model or adjust the prompt format), you can compare before-and-after performance. This level of experiment tracking combined with trace observability is where W&B shines: W&B Weave, together with the W&B platform’s other features (like W&B Models and experiment tracking), provides a comprehensive MLOps solution. You can rapidly iterate on your RAG system, confident that you have the tooling to catch regressions, understand failures, and communicate results to your team.
In summary, to implement observability: initialize Weave in your code, allow it to capture model calls (and annotate additional steps as needed), then use the W&B interface to set up panels or charts that make sense for your application. This investment in monitoring will pay off by making your RAG system easier to debug, optimize, and trust.
ConclusionIntegrating the Gemini embedding model into a RAG system can greatly enhance the system’s ability to retrieve and generate accurate, context-rich answers. In this article, we explored how Gemini’s gemini-embedding-001 model turns text into powerful embeddings that drive semantic search and retrieval. We saw that by specifying task types and using those embeddings for document and query matching, we can guide a language model to produce factually grounded responses. We also highlighted how W&B Weave adds a crucial layer of observability: from automatically logging each embedding operation to visualizing the entire query-to-answer pipeline, Weave helps developers monitor and debug their RAG systems in real time. Using W&B’s tools, such as Weave (and the W&B Models integration), not only accelerates development but also builds confidence in the system’s outputs by making its workings transparent.
As we look to the future, both embedding technology and RAG systems are poised to advance further. We can expect newer embedding models (including future iterations of Gemini) to capture even more nuance – potentially handling multi-modal data (like text and images) or offering even higher dimensional representations for greater accuracy. RAG systems might become more sophisticated, combining retrieval with reasoning, or using feedback loops to refine answers. In these evolving scenarios, the principles we covered remain important: choosing the right embeddings, grounding the outputs in retrieved data, and maintaining observability. Tools like Weave will likely evolve alongside these models, providing support for new model types and even more powerful analytics (for example, automated evaluation of answer quality using AI evaluators, or real-time anomaly detection in model behavior). 
The marriage of a cutting-edge embedding model like Gemini with the robust observability of W&B Weave creates a strong foundation for building reliable, high-performance RAG systems. By following the steps and best practices outlined here, developers can build systems that not only answer users’ questions with great accuracy but also provide insight into how those answers were formed. This transparency is key to trust and continuous improvement. Whether you’re enhancing a customer support bot with up-to-date knowledge or developing an AI assistant for healthcare, leveraging Gemini embeddings with W&B’s observability will set you on the path to success with a truly watchful, intelligent RAG system.
SourcesGoogle AI Developers – Gemini API Embeddings Guide: Overview of the Gemini embedding model and how embeddings improve RAG systems (ai.google.dev) (ai.google.dev).  
Google AI Developers – Embedding Task Types: Explanation of specifying task types like semantic similarity, classification, retrieval, etc., in the Gemini API (ai.google.dev) (ai.google.dev).  
Google AI Developers – Embedding API Usage (Python & JS): Code examples for generating embeddings in Python and JavaScript using embedContent (floasen.com) (floasen.com).  
Hugging Face – Gemini Embedding Model 001 (DBPedia 100K): Confirmation of embedding vector dimensionality (768) for gemini-embedding-001 (huggingface.co).  
Weights & Biases – LLM Observability with W&B Weave: Article on how W&B Weave helps monitor and debug LLM applications, highlighting trace logging and system reliability benefits (wandb.ai).  
Weights & Biases – Google Gemini Integration Docs: Notes on how W&B Weave auto-captures calls to the Gemini API for logging and observability (weave-docs.wandb.ai).
﻿
Add a comment
Tags: Articles, Community Posts, LLM, GenAI
Iterate on AI agents and models faster. Try Weights & Biases today.