Gemini Pro + LangChain - Chains, Mini RAG, PAL + Multimodal

Gemini Pro becomes a practical building block inside LangChain, enabling everything from simple prompt-to-response chains to mini RAG, PAL (program-aided language model) math, and multimodal image Q&A. The core takeaway is that the same LangChain workflow—define a Gemini-backed LLM, wire prompts and output parsing, then compose with retrievers or tool-like steps—scales across multiple task types without changing the overall architecture.

After setting up access through Google AI Studio (API key stored in Colab secrets), the walkthrough contrasts direct Gemini Pro API calls with the LangChain equivalent. In LangChain, the key move is swapping in the Gemini chat model wrapper (ChatGoogleGenerativeAI) and then invoking it with a prompt. Responses come back in structured markdown-like formatting and can stream in multiple chunks, but the developer-facing pattern stays straightforward: prompt in, model output out.

From there, a basic “joke chain” demonstrates LangChain’s composability. A chat prompt template (“tell me a joke about {topic}”) feeds into the Gemini Pro model with a temperature setting (0.7). An output parser converts the model’s response into a plain string, and the chain is assembled using LangChain Expression Language piping—prompt → model → parser. The example confirms the chain reliably produces jokes for different topics, even if the humor quality varies.

The next step is a mini RAG system built entirely in memory. Instead of persisting a vector store, the setup embeds a small set of “mini documents” using Google’s embeddings model (embedding-001). A retriever performs similarity search over these embedded snippets, returning the most relevant context for a query. A prompt instructs the model to answer only using the retrieved context, then the pipeline passes context and question through the prompt, into Gemini Pro, and finally through an output parser. The examples show retrieval working as intended: asking “What is Gemini pro?” surfaces the snippet mentioning Gemini Pro, and asking “Who made Gemini pro?” yields “Google DeepMind.” The prompt is also tweaked to force different output formats—full sentences, JSON dictionaries, or answers wrapped in custom formatting like triple backticks.

PAL chain performance is used to highlight Gemini Pro’s ability to follow instruction patterns that turn natural language into executable logic. Using a classic word problem (“The cafeteria had 23 apples…”) the PAL chain converts the question into a Python function representing the arithmetic, runs it, and returns the correct result (9). A second timing problem (“wake up at 7:00 AM…”) produces an answer expressed as 8.5, interpreted as 8:30—presented as a “better than many” outcome compared with models that might return nonsensical values or fail outright.

Finally, multimodal support is demonstrated by switching to Gemini Pro Vision and passing an image via URL. The example uses an Earth-from-space image and asks what’s in the image and who lives there; the model responds with visible details (clouds, land, water) and a population estimate. The transcript notes multiple ways to supply images—public URLs, local paths, Google Cloud Storage bucket URLs, or base64-encoded images—setting up the path toward multimodal RAG where images can be retrieved and queried.

Overall, the workflow shows how Gemini Pro + LangChain can be assembled into reusable chains that handle text generation, retrieval-augmented answering, tool-like computation via PAL, and image-based question answering—using consistent composition patterns across tasks.