feat: init gemini llm (#769)

.changeset/light-ears-begin.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+feat: add gemini llm and embedding

apps/docs/docs/modules/embeddings/available_embeddings/gemini.md

@@ -0,0 +1,33 @@
+# Gemini
+
+To use Gemini embeddings, you need to import `GeminiEmbedding` from `llamaindex`.
+
+```ts
+import { GeminiEmbedding, Settings } from "llamaindex";
+
+// Update Embed Model
+Settings.embedModel = new GeminiEmbedding();
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+Per default, `GeminiEmbedding` is using the `gemini-pro` model. You can change the model by passing the `model` parameter to the constructor.
+For example:
+
+```ts
+import { GEMINI_MODEL, GeminiEmbedding } from "llamaindex";
+
+Settings.embedModel = new GeminiEmbedding({
+  model: GEMINI_MODEL.GEMINI_PRO_LATEST,
+});
+```

apps/docs/docs/modules/llms/available_llms/gemini.md

@@ -0,0 +1,71 @@
+# Gemini
+
+## Usage
+
+```ts
+import { Gemini, Settings, GEMINI_MODEL } from "llamaindex";
+
+Settings.llm = new Gemini({
+  model: GEMINI_MODEL.GEMINI_PRO,
+});
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Gemini,
+  Document,
+  VectorStoreIndex,
+  Settings,
+  GEMINI_MODEL,
+} from "llamaindex";
+
+Settings.llm = new Gemini({
+  model: GEMINI_MODEL.GEMINI_PRO,
+});
+
+async function main() {
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document]);
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

examples/gemini.ts

@@ -0,0 +1,15 @@
+import { GEMINI_MODEL, GeminiEmbedding } from "llamaindex";
+
+async function main() {
+  if (!process.env.GOOGLE_API_KEY) {
+    throw new Error("Please set the GOOGLE_API_KEY environment variable.");
+  }
+  const embedModel = new GeminiEmbedding({
+    model: GEMINI_MODEL.GEMINI_PRO,
+  });
+  const texts = ["hello", "world"];
+  const embeddings = await embedModel.getTextEmbeddingsBatch(texts);
+  console.log(`\nWe have ${embeddings.length} embeddings`);
+}
+
+main().catch(console.error);

examples/gemini/chat.ts

@@ -0,0 +1,21 @@
+import { Gemini, GEMINI_MODEL } from "llamaindex";
+
+(async () => {
+  if (!process.env.GOOGLE_API_KEY) {
+    throw new Error("Please set the GOOGLE_API_KEY environment variable.");
+  }
+  const gemini = new Gemini({
+    model: GEMINI_MODEL.GEMINI_PRO,
+  });
+  const result = await gemini.chat({
+    messages: [
+      { content: "You want to talk in rhymes.", role: "system" },
+      {
+        content:
+          "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
+        role: "user",
+      },
+    ],
+  });
+  console.log(result);
+})();

packages/core/package.json

@@ -8,6 +8,7 @@
     "@anthropic-ai/sdk": "^0.20.6",
     "@aws-crypto/sha256-js": "^5.2.0",
     "@datastax/astra-db-ts": "^1.0.1",
+    "@google/generative-ai": "^0.8.0",
     "@grpc/grpc-js": "^1.10.6",
     "@llamaindex/cloud": "0.0.5",
     "@llamaindex/env": "workspace:*",

packages/core/src/embeddings/GeminiEmbedding.ts

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+import {
+  GEMINI_MODEL,
+  GeminiSessionStore,
+  type GeminiConfig,
+  type GeminiSession,
+} from "../llm/gemini.js";
+import { BaseEmbedding } from "./types.js";
+
+/**
+ * GeminiEmbedding is an alias for Gemini that implements the BaseEmbedding interface.
+ */
+export class GeminiEmbedding extends BaseEmbedding {
+  model: GEMINI_MODEL;
+  temperature: number;
+  topP: number;
+  maxTokens?: number;
+  session: GeminiSession;
+
+  constructor(init?: GeminiConfig) {
+    super();
+    this.model = init?.model ?? GEMINI_MODEL.GEMINI_PRO;
+    this.temperature = init?.temperature ?? 0.1;
+    this.topP = init?.topP ?? 1;
+    this.maxTokens = init?.maxTokens ?? undefined;
+    this.session = init?.session ?? GeminiSessionStore.get();
+  }
+
+  private async getEmbedding(prompt: string): Promise<number[]> {
+    const client = this.session.gemini.getGenerativeModel({
+      model: this.model,
+    });
+    const result = await client.embedContent(prompt);
+    return result.embedding.values;
+  }
+
+  getTextEmbedding(text: string): Promise<number[]> {
+    return this.getEmbedding(text);
+  }
+
+  getQueryEmbedding(query: string): Promise<number[]> {
+    return this.getTextEmbedding(query);
+  }
+}

packages/core/src/embeddings/index.ts

@@ -1,4 +1,5 @@
 export * from "./ClipEmbedding.js";
+export * from "./GeminiEmbedding.js";
 export * from "./HuggingFaceEmbedding.js";
 export * from "./JinaAIEmbedding.js";
 export * from "./MistralAIEmbedding.js";