mirror of
https://github.com/run-llama/LlamaIndexTS.git
synced 2026-07-05 12:05:56 -04:00
Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 0e61df60f7 |
@@ -1,5 +0,0 @@
|
||||
---
|
||||
"create-llama": patch
|
||||
---
|
||||
|
||||
Add LlamaParse option when selecting a pdf file or a folder
|
||||
@@ -1,5 +0,0 @@
|
||||
---
|
||||
"llamaindex": patch
|
||||
---
|
||||
|
||||
Add quantized parameter to HuggingFaceEmbedding
|
||||
@@ -44,7 +44,6 @@ test-results/
|
||||
playwright-report/
|
||||
blob-report/
|
||||
playwright/.cache/
|
||||
.tsbuildinfo
|
||||
|
||||
# intellij
|
||||
**/.idea
|
||||
|
||||
@@ -1,12 +1,5 @@
|
||||
# docs
|
||||
|
||||
## 0.0.4
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- Updated dependencies [5116ad8]
|
||||
- @llamaindex/env@0.0.5
|
||||
|
||||
## 0.0.3
|
||||
|
||||
### Patch Changes
|
||||
|
||||
@@ -23,15 +23,3 @@ const results = await queryEngine.query({
|
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query,
|
||||
});
|
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```
|
||||
|
||||
Per default, `HuggingFaceEmbedding` is using the `Xenova/all-MiniLM-L6-v2` model. You can change the model by passing the `modelType` parameter to the constructor.
|
||||
If you're not using a quantized model, set the `quantized` parameter to `false`.
|
||||
|
||||
For example, to use the not quantized `BAAI/bge-small-en-v1.5` model, you can use the following code:
|
||||
|
||||
```
|
||||
const embedModel = new HuggingFaceEmbedding({
|
||||
modelType: "BAAI/bge-small-en-v1.5",
|
||||
quantized: false,
|
||||
});
|
||||
```
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "docs",
|
||||
"version": "0.0.4",
|
||||
"version": "0.0.3",
|
||||
"private": true,
|
||||
"scripts": {
|
||||
"docusaurus": "docusaurus",
|
||||
|
||||
@@ -8,7 +8,7 @@ import {
|
||||
async function main() {
|
||||
// Load the documents
|
||||
const documents = await new SimpleDirectoryReader().loadData({
|
||||
directoryPath: "node_modules/llamaindex/examples",
|
||||
directoryPath: "node_modules/llamaindex/examples/",
|
||||
});
|
||||
|
||||
// Create a vector index from the documents
|
||||
@@ -32,31 +32,13 @@ async function main() {
|
||||
verbose: true,
|
||||
});
|
||||
|
||||
const task = agent.createTask("What was his salary?");
|
||||
// Chat with the agent
|
||||
const response = await agent.chat({
|
||||
message: "What was his salary?",
|
||||
});
|
||||
|
||||
let count = 0;
|
||||
|
||||
while (true) {
|
||||
const stepOutput = await agent.runStep(task.taskId);
|
||||
|
||||
console.log(`Runnning step ${count++}`);
|
||||
console.log(`======== OUTPUT ==========`);
|
||||
if (stepOutput.output.response) {
|
||||
console.log(stepOutput.output.response);
|
||||
} else {
|
||||
console.log(stepOutput.output.sources);
|
||||
}
|
||||
console.log(`==========================`);
|
||||
|
||||
if (stepOutput.isLast) {
|
||||
const finalResponse = await agent.finalizeResponse(
|
||||
task.taskId,
|
||||
stepOutput,
|
||||
);
|
||||
console.log({ finalResponse });
|
||||
break;
|
||||
}
|
||||
}
|
||||
// Print the response
|
||||
console.log(String(response));
|
||||
}
|
||||
|
||||
main().then(() => {
|
||||
@@ -8,7 +8,7 @@ import {
|
||||
async function main() {
|
||||
// Load the documents
|
||||
const documents = await new SimpleDirectoryReader().loadData({
|
||||
directoryPath: "node_modules/llamaindex/examples",
|
||||
directoryPath: "node_modules/llamaindex/examples/",
|
||||
});
|
||||
|
||||
// Create a vector index from the documents
|
||||
|
||||
@@ -1,95 +0,0 @@
|
||||
import { FunctionTool, OpenAIAgent } from "llamaindex";
|
||||
|
||||
// Define a function to sum two numbers
|
||||
function sumNumbers({ a, b }: { a: number; b: number }): number {
|
||||
return a + b;
|
||||
}
|
||||
|
||||
// Define a function to divide two numbers
|
||||
function divideNumbers({ a, b }: { a: number; b: number }): number {
|
||||
return a / b;
|
||||
}
|
||||
|
||||
// Define the parameters of the sum function as a JSON schema
|
||||
const sumJSON = {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: {
|
||||
type: "number",
|
||||
description: "The first number",
|
||||
},
|
||||
b: {
|
||||
type: "number",
|
||||
description: "The second number",
|
||||
},
|
||||
},
|
||||
required: ["a", "b"],
|
||||
};
|
||||
|
||||
const divideJSON = {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: {
|
||||
type: "number",
|
||||
description: "The dividend a to divide",
|
||||
},
|
||||
b: {
|
||||
type: "number",
|
||||
description: "The divisor b to divide by",
|
||||
},
|
||||
},
|
||||
required: ["a", "b"],
|
||||
};
|
||||
|
||||
async function main() {
|
||||
// Create a function tool from the sum function
|
||||
const functionTool = new FunctionTool(sumNumbers, {
|
||||
name: "sumNumbers",
|
||||
description: "Use this function to sum two numbers",
|
||||
parameters: sumJSON,
|
||||
});
|
||||
|
||||
// Create a function tool from the divide function
|
||||
const functionTool2 = new FunctionTool(divideNumbers, {
|
||||
name: "divideNumbers",
|
||||
description: "Use this function to divide two numbers",
|
||||
parameters: divideJSON,
|
||||
});
|
||||
|
||||
// Create an OpenAIAgent with the function tools
|
||||
const agent = new OpenAIAgent({
|
||||
tools: [functionTool, functionTool2],
|
||||
verbose: true,
|
||||
});
|
||||
|
||||
// Create a task to sum and divide numbers
|
||||
const task = agent.createTask("How much is 5 + 5? then divide by 2");
|
||||
|
||||
let count = 0;
|
||||
|
||||
while (true) {
|
||||
const stepOutput = await agent.runStep(task.taskId);
|
||||
|
||||
console.log(`Runnning step ${count++}`);
|
||||
console.log(`======== OUTPUT ==========`);
|
||||
if (stepOutput.output.response) {
|
||||
console.log(stepOutput.output.response);
|
||||
} else {
|
||||
console.log(stepOutput.output.sources);
|
||||
}
|
||||
console.log(`==========================`);
|
||||
|
||||
if (stepOutput.isLast) {
|
||||
const finalResponse = await agent.finalizeResponse(
|
||||
task.taskId,
|
||||
stepOutput,
|
||||
);
|
||||
console.log({ finalResponse });
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
main().then(() => {
|
||||
console.log("Done");
|
||||
});
|
||||
@@ -1,90 +0,0 @@
|
||||
import { FunctionTool, ReActAgent } from "llamaindex";
|
||||
|
||||
// Define a function to sum two numbers
|
||||
function sumNumbers({ a, b }: { a: number; b: number }): number {
|
||||
return a + b;
|
||||
}
|
||||
|
||||
// Define a function to divide two numbers
|
||||
function divideNumbers({ a, b }: { a: number; b: number }): number {
|
||||
return a / b;
|
||||
}
|
||||
|
||||
// Define the parameters of the sum function as a JSON schema
|
||||
const sumJSON = {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: {
|
||||
type: "number",
|
||||
description: "The first number",
|
||||
},
|
||||
b: {
|
||||
type: "number",
|
||||
description: "The second number",
|
||||
},
|
||||
},
|
||||
required: ["a", "b"],
|
||||
};
|
||||
|
||||
const divideJSON = {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: {
|
||||
type: "number",
|
||||
description: "The dividend",
|
||||
},
|
||||
b: {
|
||||
type: "number",
|
||||
description: "The divisor",
|
||||
},
|
||||
},
|
||||
required: ["a", "b"],
|
||||
};
|
||||
|
||||
async function main() {
|
||||
// Create a function tool from the sum function
|
||||
const functionTool = new FunctionTool(sumNumbers, {
|
||||
name: "sumNumbers",
|
||||
description: "Use this function to sum two numbers",
|
||||
parameters: sumJSON,
|
||||
});
|
||||
|
||||
// Create a function tool from the divide function
|
||||
const functionTool2 = new FunctionTool(divideNumbers, {
|
||||
name: "divideNumbers",
|
||||
description: "Use this function to divide two numbers",
|
||||
parameters: divideJSON,
|
||||
});
|
||||
|
||||
// Create an OpenAIAgent with the function tools
|
||||
const agent = new ReActAgent({
|
||||
tools: [functionTool, functionTool2],
|
||||
verbose: true,
|
||||
});
|
||||
|
||||
const task = agent.createTask("Divide 16 by 2 then add 20");
|
||||
|
||||
let count = 0;
|
||||
|
||||
while (true) {
|
||||
const stepOutput = await agent.runStep(task.taskId);
|
||||
|
||||
console.log(`Runnning step ${count++}`);
|
||||
console.log(`======== OUTPUT ==========`);
|
||||
console.log(stepOutput.output);
|
||||
console.log(`==========================`);
|
||||
|
||||
if (stepOutput.isLast) {
|
||||
const finalResponse = await agent.finalizeResponse(
|
||||
task.taskId,
|
||||
stepOutput,
|
||||
);
|
||||
console.log({ finalResponse });
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
main().then(() => {
|
||||
console.log("Done");
|
||||
});
|
||||
@@ -3,7 +3,6 @@ import { Anthropic } from "llamaindex";
|
||||
(async () => {
|
||||
const anthropic = new Anthropic({
|
||||
apiKey: process.env.ANTHROPIC_API_KEY,
|
||||
model: "claude-3-opus",
|
||||
});
|
||||
const result = await anthropic.chat({
|
||||
messages: [
|
||||
@@ -1,34 +0,0 @@
|
||||
import { Anthropic, SimpleChatEngine, SimpleChatHistory } from "llamaindex";
|
||||
import { stdin as input, stdout as output } from "node:process";
|
||||
import readline from "node:readline/promises";
|
||||
|
||||
(async () => {
|
||||
const llm = new Anthropic({
|
||||
apiKey: process.env.ANTHROPIC_API_KEY,
|
||||
model: "claude-3-opus",
|
||||
});
|
||||
// chatHistory will store all the messages in the conversation
|
||||
const chatHistory = new SimpleChatHistory({
|
||||
messages: [
|
||||
{
|
||||
content: "You want to talk in rhymes.",
|
||||
role: "system",
|
||||
},
|
||||
],
|
||||
});
|
||||
const chatEngine = new SimpleChatEngine({
|
||||
llm,
|
||||
chatHistory,
|
||||
});
|
||||
const rl = readline.createInterface({ input, output });
|
||||
|
||||
while (true) {
|
||||
const query = await rl.question("User: ");
|
||||
process.stdout.write("Assistant: ");
|
||||
const stream = await chatEngine.chat({ message: query, stream: true });
|
||||
for await (const chunk of stream) {
|
||||
process.stdout.write(chunk.response);
|
||||
}
|
||||
process.stdout.write("\n");
|
||||
}
|
||||
})();
|
||||
@@ -1,23 +0,0 @@
|
||||
import { Anthropic } from "llamaindex";
|
||||
|
||||
(async () => {
|
||||
const anthropic = new Anthropic({
|
||||
apiKey: process.env.ANTHROPIC_API_KEY,
|
||||
model: "claude-instant-1.2",
|
||||
});
|
||||
const stream = await anthropic.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",
|
||||
},
|
||||
],
|
||||
stream: true,
|
||||
});
|
||||
|
||||
for await (const chunk of stream) {
|
||||
process.stdout.write(chunk.delta);
|
||||
}
|
||||
})();
|
||||
+1
-7
@@ -1,4 +1,4 @@
|
||||
import { OpenAI, OpenAIEmbedding } from "llamaindex";
|
||||
import { OpenAI } from "llamaindex";
|
||||
|
||||
(async () => {
|
||||
const llm = new OpenAI({ model: "gpt-4-1106-preview", temperature: 0.1 });
|
||||
@@ -12,10 +12,4 @@ import { OpenAI, OpenAIEmbedding } from "llamaindex";
|
||||
messages: [{ content: "Tell me a joke.", role: "user" }],
|
||||
});
|
||||
console.log(response2.message.content);
|
||||
|
||||
// embeddings
|
||||
const embedModel = new OpenAIEmbedding();
|
||||
const texts = ["hello", "world"];
|
||||
const embeddings = await embedModel.getTextEmbeddingsBatch(texts);
|
||||
console.log(`\nWe have ${embeddings.length} embeddings`);
|
||||
})();
|
||||
|
||||
@@ -7,9 +7,8 @@ There are two scripts available here: load-docs.ts and query.ts
|
||||
You'll need a Pinecone account, project, and index. Pinecone does not allow automatic creation of indexes on the free plan,
|
||||
so this vector store does not check and create the index (unlike, e.g., the PGVectorStore)
|
||||
|
||||
Set the **PINECONE_API_KEY** and **PINECONE_ENVIRONMENT** environment variables to match your specific values.
|
||||
You will likely also need to set **PINECONE_INDEX_NAME**, unless your index is the default value "llama".
|
||||
By default, all operations take place inside the default namespace '', but you can set **PINECONE_NAMESPACE** to a different value if you need to.
|
||||
Set the **PINECONE_API_KEY** and **PINECONE_ENVIRONMENT** environment variables to match your specific values. You will likely also need to set **PINECONE_INDEX_NAME**, unless your
|
||||
index is the default value "llama".
|
||||
|
||||
You'll also need a value for OPENAI_API_KEY in your environment.
|
||||
|
||||
|
||||
@@ -1,3 +1 @@
|
||||
.turbo
|
||||
README.md
|
||||
LICENSE
|
||||
@@ -1,22 +1,5 @@
|
||||
# llamaindex
|
||||
|
||||
## 0.1.20
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- 64683a5: fix: prefix messages always true
|
||||
- 698cd9c: fix: step wise agent + examples
|
||||
- 7257751: fixed removeRefDocNode and persist store on delete
|
||||
- 5116ad8: fix: compatibility issue with Deno
|
||||
- Updated dependencies [5116ad8]
|
||||
- @llamaindex/env@0.0.5
|
||||
|
||||
## 0.1.19
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- 026d068: feat: enhance pinecone usage
|
||||
|
||||
## 0.1.18
|
||||
|
||||
### Patch Changes
|
||||
|
||||
@@ -1,8 +0,0 @@
|
||||
{
|
||||
"name": "@llamaindex/core",
|
||||
"version": "0.1.20",
|
||||
"exports": "./src/index.ts",
|
||||
"imports": {
|
||||
"@llamaindex/env": "jsr:@llamaindex/env@0.0.5"
|
||||
}
|
||||
}
|
||||
@@ -1,22 +1,22 @@
|
||||
{
|
||||
"name": "llamaindex",
|
||||
"version": "0.1.20",
|
||||
"version": "0.1.18",
|
||||
"license": "MIT",
|
||||
"type": "module",
|
||||
"dependencies": {
|
||||
"@anthropic-ai/sdk": "^0.15.0",
|
||||
"@anthropic-ai/sdk": "^0.13.0",
|
||||
"@aws-crypto/sha256-js": "^5.2.0",
|
||||
"@datastax/astra-db-ts": "^0.1.4",
|
||||
"@llamaindex/cloud": "0.0.4",
|
||||
"@types/lodash": "^4.14.202",
|
||||
"@types/node": "^18.19.14",
|
||||
"@types/papaparse": "^5.3.14",
|
||||
"@types/pg": "^8.11.0",
|
||||
"@llamaindex/cloud": "^0.0.1",
|
||||
"@llamaindex/env": "workspace:*",
|
||||
"@mistralai/mistralai": "^0.0.10",
|
||||
"@notionhq/client": "^2.2.14",
|
||||
"@pinecone-database/pinecone": "^2.0.1",
|
||||
"@qdrant/js-client-rest": "^1.7.0",
|
||||
"@types/lodash": "^4.14.202",
|
||||
"@types/node": "^18.19.14",
|
||||
"@types/papaparse": "^5.3.14",
|
||||
"@types/pg": "^8.11.0",
|
||||
"@xenova/transformers": "^2.15.0",
|
||||
"assemblyai": "^4.2.2",
|
||||
"chromadb": "~1.7.3",
|
||||
@@ -95,8 +95,7 @@
|
||||
"build:esm": "swc src -d dist --strip-leading-paths --config-file .swcrc",
|
||||
"build:cjs": "swc src -d dist/cjs --strip-leading-paths --config-file .cjs.swcrc",
|
||||
"build:type": "pnpm run -w type-check",
|
||||
"copy": "cp -r ../../README.md ../../LICENSE .",
|
||||
"postbuild": "pnpm run copy && node -e \"require('fs').writeFileSync('./dist/cjs/package.json', JSON.stringify({ type: 'commonjs' }))\"",
|
||||
"postbuild": "node -e \"require('fs').writeFileSync('./dist/cjs/package.json', JSON.stringify({ type: 'commonjs' }))\"",
|
||||
"circular-check": "madge -c ./src/index.ts",
|
||||
"dev": "concurrently \"pnpm run build:esm --watch\" \"pnpm run build:cjs --watch\" \"pnpm run build:type --watch\""
|
||||
}
|
||||
|
||||
@@ -37,6 +37,8 @@ export class OpenAIAgent extends AgentRunner {
|
||||
toolRetriever,
|
||||
systemPrompt,
|
||||
}: OpenAIAgentParams) {
|
||||
prefixMessages = prefixMessages || [];
|
||||
|
||||
llm = llm ?? new OpenAI({ model: "gpt-3.5-turbo-0613" });
|
||||
|
||||
if (systemPrompt) {
|
||||
|
||||
@@ -14,7 +14,7 @@ import { AgentState, BaseAgentRunner, TaskState } from "./types.js";
|
||||
|
||||
const validateStepFromArgs = (
|
||||
taskId: string,
|
||||
input?: string | null,
|
||||
input: string,
|
||||
step?: any,
|
||||
kwargs?: any,
|
||||
): TaskStep | undefined => {
|
||||
@@ -24,7 +24,6 @@ const validateStepFromArgs = (
|
||||
}
|
||||
return step;
|
||||
} else {
|
||||
if (!input) return;
|
||||
return new TaskStep(taskId, step, input, kwargs);
|
||||
}
|
||||
};
|
||||
@@ -195,7 +194,7 @@ export class AgentRunner extends BaseAgentRunner {
|
||||
*/
|
||||
async runStep(
|
||||
taskId: string,
|
||||
input?: string | null,
|
||||
input: string,
|
||||
step?: TaskStep,
|
||||
kwargs: any = {},
|
||||
): Promise<TaskStepOutput> {
|
||||
|
||||
@@ -161,13 +161,13 @@ export class TaskStep implements ITaskStep {
|
||||
* @param isLast: isLast
|
||||
*/
|
||||
export class TaskStepOutput {
|
||||
output: any;
|
||||
output: unknown;
|
||||
taskStep: TaskStep;
|
||||
nextSteps: TaskStep[];
|
||||
isLast: boolean;
|
||||
|
||||
constructor(
|
||||
output: any,
|
||||
output: unknown,
|
||||
taskStep: TaskStep,
|
||||
nextSteps: TaskStep[],
|
||||
isLast: boolean = false,
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
import type { PlatformApiClient } from "@llamaindex/cloud";
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import type { ClientParams } from "./types.js";
|
||||
import { DEFAULT_BASE_URL } from "./types.js";
|
||||
|
||||
@@ -8,8 +7,8 @@ export async function getClient({
|
||||
baseUrl,
|
||||
}: ClientParams = {}): Promise<PlatformApiClient> {
|
||||
// Get the environment variables or use defaults
|
||||
baseUrl = baseUrl ?? getEnv("LLAMA_CLOUD_BASE_URL") ?? DEFAULT_BASE_URL;
|
||||
apiKey = apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
|
||||
baseUrl = baseUrl ?? process.env.LLAMA_CLOUD_BASE_URL ?? DEFAULT_BASE_URL;
|
||||
apiKey = apiKey ?? process.env.LLAMA_CLOUD_API_KEY;
|
||||
|
||||
const { PlatformApiClient } = await import("@llamaindex/cloud");
|
||||
|
||||
|
||||
@@ -20,7 +20,6 @@ export enum HuggingFaceEmbeddingModelType {
|
||||
*/
|
||||
export class HuggingFaceEmbedding extends BaseEmbedding {
|
||||
modelType: string = HuggingFaceEmbeddingModelType.XENOVA_ALL_MINILM_L6_V2;
|
||||
quantized: boolean = true;
|
||||
|
||||
private extractor: any;
|
||||
|
||||
@@ -32,9 +31,7 @@ export class HuggingFaceEmbedding extends BaseEmbedding {
|
||||
async getExtractor() {
|
||||
if (!this.extractor) {
|
||||
const { pipeline } = await import("@xenova/transformers");
|
||||
this.extractor = await pipeline("feature-extraction", this.modelType, {
|
||||
quantized: this.quantized,
|
||||
});
|
||||
this.extractor = await pipeline("feature-extraction", this.modelType);
|
||||
}
|
||||
return this.extractor;
|
||||
}
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import { OpenAIEmbedding } from "./OpenAIEmbedding.js";
|
||||
|
||||
export class FireworksEmbedding extends OpenAIEmbedding {
|
||||
constructor(init?: Partial<OpenAIEmbedding>) {
|
||||
const {
|
||||
apiKey = getEnv("FIREWORKS_API_KEY"),
|
||||
apiKey = process.env.FIREWORKS_API_KEY,
|
||||
additionalSessionOptions = {},
|
||||
model = "nomic-ai/nomic-embed-text-v1.5",
|
||||
...rest
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import { OpenAIEmbedding } from "./OpenAIEmbedding.js";
|
||||
|
||||
export class TogetherEmbedding extends OpenAIEmbedding {
|
||||
constructor(init?: Partial<OpenAIEmbedding>) {
|
||||
const {
|
||||
apiKey = getEnv("TOGETHER_API_KEY"),
|
||||
apiKey = process.env.TOGETHER_API_KEY,
|
||||
additionalSessionOptions = {},
|
||||
model = "togethercomputer/m2-bert-80M-32k-retrieval",
|
||||
...rest
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import type OpenAILLM from "openai";
|
||||
import type { ClientOptions as OpenAIClientOptions } from "openai";
|
||||
import type {
|
||||
AnthropicStreamToken,
|
||||
CallbackManager,
|
||||
Event,
|
||||
EventType,
|
||||
@@ -12,7 +13,11 @@ import type { ChatCompletionMessageParam } from "openai/resources/index.js";
|
||||
import type { LLMOptions } from "portkey-ai";
|
||||
import { Tokenizers, globalsHelper } from "../GlobalsHelper.js";
|
||||
import type { AnthropicSession } from "./anthropic.js";
|
||||
import { getAnthropicSession } from "./anthropic.js";
|
||||
import {
|
||||
ANTHROPIC_AI_PROMPT,
|
||||
ANTHROPIC_HUMAN_PROMPT,
|
||||
getAnthropicSession,
|
||||
} from "./anthropic.js";
|
||||
import type { AzureOpenAIConfig } from "./azure.js";
|
||||
import {
|
||||
getAzureBaseUrl,
|
||||
@@ -255,7 +260,7 @@ export class OpenAI extends BaseLLM {
|
||||
stream: false,
|
||||
});
|
||||
|
||||
const content = response.choices[0].message?.content ?? null;
|
||||
const content = response.choices[0].message?.content ?? "";
|
||||
|
||||
const kwargsOutput: Record<string, any> = {};
|
||||
|
||||
@@ -608,30 +613,12 @@ If a question does not make any sense, or is not factually coherent, explain why
|
||||
}
|
||||
}
|
||||
|
||||
export const ALL_AVAILABLE_ANTHROPIC_LEGACY_MODELS = {
|
||||
"claude-2.1": {
|
||||
contextWindow: 200000,
|
||||
},
|
||||
"claude-instant-1.2": {
|
||||
contextWindow: 100000,
|
||||
},
|
||||
};
|
||||
|
||||
export const ALL_AVAILABLE_V3_MODELS = {
|
||||
"claude-3-opus": { contextWindow: 200000 },
|
||||
"claude-3-sonnet": { contextWindow: 200000 },
|
||||
};
|
||||
|
||||
export const ALL_AVAILABLE_ANTHROPIC_MODELS = {
|
||||
...ALL_AVAILABLE_ANTHROPIC_LEGACY_MODELS,
|
||||
...ALL_AVAILABLE_V3_MODELS,
|
||||
// both models have 100k context window, see https://docs.anthropic.com/claude/reference/selecting-a-model
|
||||
"claude-2": { contextWindow: 200000 },
|
||||
"claude-instant-1": { contextWindow: 100000 },
|
||||
};
|
||||
|
||||
const AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE: { [key: string]: string } = {
|
||||
"claude-3-opus": "claude-3-opus-20240229",
|
||||
"claude-3-sonnet": "claude-3-sonnet-20240229",
|
||||
} as { [key in keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS]: string };
|
||||
|
||||
/**
|
||||
* Anthropic LLM implementation
|
||||
*/
|
||||
@@ -653,7 +640,7 @@ export class Anthropic extends BaseLLM {
|
||||
|
||||
constructor(init?: Partial<Anthropic>) {
|
||||
super();
|
||||
this.model = init?.model ?? "claude-3-opus";
|
||||
this.model = init?.model ?? "claude-2";
|
||||
this.temperature = init?.temperature ?? 0.1;
|
||||
this.topP = init?.topP ?? 0.999; // Per Ben Mann
|
||||
this.maxTokens = init?.maxTokens ?? undefined;
|
||||
@@ -687,24 +674,21 @@ export class Anthropic extends BaseLLM {
|
||||
};
|
||||
}
|
||||
|
||||
getModelName = (model: string): string => {
|
||||
if (Object.keys(AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE).includes(model)) {
|
||||
return AVAILABLE_ANTHROPIC_MODELS_WITHOUT_DATE[model];
|
||||
}
|
||||
return model;
|
||||
};
|
||||
|
||||
formatMessages(messages: ChatMessage[]) {
|
||||
return messages.map((message) => {
|
||||
if (message.role !== "user" && message.role !== "assistant") {
|
||||
throw new Error("Unsupported Anthropic role");
|
||||
}
|
||||
|
||||
return {
|
||||
content: message.content,
|
||||
role: message.role,
|
||||
};
|
||||
});
|
||||
mapMessagesToPrompt(messages: ChatMessage[]) {
|
||||
return (
|
||||
messages.reduce((acc, message) => {
|
||||
return (
|
||||
acc +
|
||||
`${
|
||||
message.role === "system"
|
||||
? ""
|
||||
: message.role === "assistant"
|
||||
? ANTHROPIC_AI_PROMPT + " "
|
||||
: ANTHROPIC_HUMAN_PROMPT + " "
|
||||
}${message.content.trim()}`
|
||||
);
|
||||
}, "") + ANTHROPIC_AI_PROMPT
|
||||
);
|
||||
}
|
||||
|
||||
chat(
|
||||
@@ -714,67 +698,49 @@ export class Anthropic extends BaseLLM {
|
||||
async chat(
|
||||
params: LLMChatParamsNonStreaming | LLMChatParamsStreaming,
|
||||
): Promise<ChatResponse | AsyncIterable<ChatResponseChunk>> {
|
||||
let { messages } = params;
|
||||
|
||||
const { parentEvent, stream } = params;
|
||||
|
||||
let systemPrompt: string | null = null;
|
||||
|
||||
const systemMessages = messages.filter(
|
||||
(message) => message.role === "system",
|
||||
);
|
||||
|
||||
if (systemMessages.length > 0) {
|
||||
systemPrompt = systemMessages
|
||||
.map((message) => message.content)
|
||||
.join("\n");
|
||||
messages = messages.filter((message) => message.role !== "system");
|
||||
}
|
||||
|
||||
const { messages, parentEvent, stream } = params;
|
||||
//Streaming
|
||||
if (stream) {
|
||||
return this.streamChat(messages, parentEvent, systemPrompt);
|
||||
return this.streamChat(messages, parentEvent);
|
||||
}
|
||||
|
||||
//Non-streaming
|
||||
const response = await this.session.anthropic.messages.create({
|
||||
model: this.getModelName(this.model),
|
||||
messages: this.formatMessages(messages),
|
||||
max_tokens: this.maxTokens ?? 4096,
|
||||
const response = await this.session.anthropic.completions.create({
|
||||
model: this.model,
|
||||
prompt: this.mapMessagesToPrompt(messages),
|
||||
max_tokens_to_sample: this.maxTokens ?? 100000,
|
||||
temperature: this.temperature,
|
||||
top_p: this.topP,
|
||||
...(systemPrompt && { system: systemPrompt }),
|
||||
});
|
||||
|
||||
return {
|
||||
message: { content: response.content[0].text, role: "assistant" },
|
||||
message: { content: response.completion.trimStart(), role: "assistant" },
|
||||
//^ We're trimming the start because Anthropic often starts with a space in the response
|
||||
// That space will be re-added when we generate the next prompt.
|
||||
};
|
||||
}
|
||||
|
||||
protected async *streamChat(
|
||||
messages: ChatMessage[],
|
||||
parentEvent?: Event | undefined,
|
||||
systemPrompt?: string | null,
|
||||
): AsyncIterable<ChatResponseChunk> {
|
||||
const stream = await this.session.anthropic.messages.create({
|
||||
model: this.getModelName(this.model),
|
||||
messages: this.formatMessages(messages),
|
||||
max_tokens: this.maxTokens ?? 4096,
|
||||
temperature: this.temperature,
|
||||
top_p: this.topP,
|
||||
stream: true,
|
||||
...(systemPrompt && { system: systemPrompt }),
|
||||
});
|
||||
// AsyncIterable<AnthropicStreamToken>
|
||||
const stream: AsyncIterable<AnthropicStreamToken> =
|
||||
await this.session.anthropic.completions.create({
|
||||
model: this.model,
|
||||
prompt: this.mapMessagesToPrompt(messages),
|
||||
max_tokens_to_sample: this.maxTokens ?? 100000,
|
||||
temperature: this.temperature,
|
||||
top_p: this.topP,
|
||||
stream: true,
|
||||
});
|
||||
|
||||
let idx_counter: number = 0;
|
||||
for await (const part of stream) {
|
||||
const content =
|
||||
part.type === "content_block_delta" ? part.delta.text : null;
|
||||
|
||||
if (typeof content !== "string") continue;
|
||||
//TODO: LLM Stream Callback, pending re-work.
|
||||
|
||||
idx_counter++;
|
||||
yield { delta: content };
|
||||
yield { delta: part.completion };
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import type { ClientOptions } from "@anthropic-ai/sdk";
|
||||
import Anthropic, { AI_PROMPT, HUMAN_PROMPT } from "@anthropic-ai/sdk";
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import _ from "lodash";
|
||||
|
||||
export class AnthropicSession {
|
||||
@@ -8,7 +7,9 @@ export class AnthropicSession {
|
||||
|
||||
constructor(options: ClientOptions = {}) {
|
||||
if (!options.apiKey) {
|
||||
options.apiKey = getEnv("ANTHROPIC_API_KEY");
|
||||
if (typeof process !== undefined) {
|
||||
options.apiKey = process.env.ANTHROPIC_API_KEY;
|
||||
}
|
||||
}
|
||||
|
||||
if (!options.apiKey) {
|
||||
|
||||
@@ -1,5 +1,3 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
|
||||
export interface AzureOpenAIConfig {
|
||||
apiKey?: string;
|
||||
endpoint?: string;
|
||||
@@ -69,24 +67,24 @@ export function getAzureConfigFromEnv(
|
||||
return {
|
||||
apiKey:
|
||||
init?.apiKey ??
|
||||
getEnv("AZURE_OPENAI_KEY") ?? // From Azure docs
|
||||
getEnv("OPENAI_API_KEY") ?? // Python compatible
|
||||
getEnv("AZURE_OPENAI_API_KEY"), // LCJS compatible
|
||||
process.env.AZURE_OPENAI_KEY ?? // From Azure docs
|
||||
process.env.OPENAI_API_KEY ?? // Python compatible
|
||||
process.env.AZURE_OPENAI_API_KEY, // LCJS compatible
|
||||
endpoint:
|
||||
init?.endpoint ??
|
||||
getEnv("AZURE_OPENAI_ENDPOINT") ?? // From Azure docs
|
||||
getEnv("OPENAI_API_BASE") ?? // Python compatible
|
||||
getEnv("AZURE_OPENAI_API_INSTANCE_NAME"), // LCJS compatible
|
||||
process.env.AZURE_OPENAI_ENDPOINT ?? // From Azure docs
|
||||
process.env.OPENAI_API_BASE ?? // Python compatible
|
||||
process.env.AZURE_OPENAI_API_INSTANCE_NAME, // LCJS compatible
|
||||
apiVersion:
|
||||
init?.apiVersion ??
|
||||
getEnv("AZURE_OPENAI_API_VERSION") ?? // From Azure docs
|
||||
getEnv("OPENAI_API_VERSION") ?? // Python compatible
|
||||
getEnv("AZURE_OPENAI_API_VERSION") ?? // LCJS compatible
|
||||
process.env.AZURE_OPENAI_API_VERSION ?? // From Azure docs
|
||||
process.env.OPENAI_API_VERSION ?? // Python compatible
|
||||
process.env.AZURE_OPENAI_API_VERSION ?? // LCJS compatible
|
||||
DEFAULT_API_VERSION,
|
||||
deploymentName:
|
||||
init?.deploymentName ??
|
||||
getEnv("AZURE_OPENAI_DEPLOYMENT") ?? // From Azure docs
|
||||
getEnv("AZURE_OPENAI_API_DEPLOYMENT_NAME") ?? // LCJS compatible
|
||||
process.env.AZURE_OPENAI_DEPLOYMENT ?? // From Azure docs
|
||||
process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME ?? // LCJS compatible
|
||||
init?.model, // Fall back to model name, Python compatible
|
||||
};
|
||||
}
|
||||
@@ -115,8 +113,8 @@ export function getAzureModel(openAIModel: string) {
|
||||
|
||||
export function shouldUseAzure() {
|
||||
return (
|
||||
getEnv("AZURE_OPENAI_ENDPOINT") ||
|
||||
getEnv("AZURE_OPENAI_API_INSTANCE_NAME") ||
|
||||
getEnv("OPENAI_API_TYPE") === "azure"
|
||||
process.env.AZURE_OPENAI_ENDPOINT ||
|
||||
process.env.AZURE_OPENAI_API_INSTANCE_NAME ||
|
||||
process.env.OPENAI_API_TYPE === "azure"
|
||||
);
|
||||
}
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import { OpenAI } from "./LLM.js";
|
||||
|
||||
export class FireworksLLM extends OpenAI {
|
||||
constructor(init?: Partial<OpenAI>) {
|
||||
const {
|
||||
apiKey = getEnv("FIREWORKS_API_KEY"),
|
||||
apiKey = process.env.FIREWORKS_API_KEY,
|
||||
additionalSessionOptions = {},
|
||||
model = "accounts/fireworks/models/mixtral-8x7b-instruct",
|
||||
...rest
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import { OpenAI } from "./LLM.js";
|
||||
|
||||
export class Groq extends OpenAI {
|
||||
constructor(init?: Partial<OpenAI>) {
|
||||
const {
|
||||
apiKey = getEnv("GROQ_API_KEY"),
|
||||
apiKey = process.env.GROQ_API_KEY,
|
||||
additionalSessionOptions = {},
|
||||
model = "mixtral-8x7b-32768",
|
||||
...rest
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import type {
|
||||
CallbackManager,
|
||||
Event,
|
||||
@@ -28,7 +27,9 @@ export class MistralAISession {
|
||||
if (init?.apiKey) {
|
||||
this.apiKey = init?.apiKey;
|
||||
} else {
|
||||
this.apiKey = getEnv("MISTRAL_API_KEY");
|
||||
if (typeof process !== undefined) {
|
||||
this.apiKey = process.env.MISTRAL_API_KEY;
|
||||
}
|
||||
}
|
||||
if (!this.apiKey) {
|
||||
throw new Error("Set Mistral API key in MISTRAL_API_KEY env variable"); // Overriding MistralAI package's error message
|
||||
|
||||
@@ -37,18 +37,14 @@ export class Ollama extends BaseEmbedding implements LLM {
|
||||
additionalChatOptions?: Record<string, unknown>;
|
||||
callbackManager?: CallbackManager;
|
||||
|
||||
protected modelMetadata: Partial<LLMMetadata>;
|
||||
|
||||
constructor(
|
||||
init: Partial<Ollama> & {
|
||||
// model is required
|
||||
model: string;
|
||||
modelMetadata?: Partial<LLMMetadata>;
|
||||
},
|
||||
) {
|
||||
super();
|
||||
this.model = init.model;
|
||||
this.modelMetadata = init.modelMetadata ?? {};
|
||||
Object.assign(this, init);
|
||||
}
|
||||
|
||||
@@ -60,7 +56,6 @@ export class Ollama extends BaseEmbedding implements LLM {
|
||||
maxTokens: undefined,
|
||||
contextWindow: this.contextWindow,
|
||||
tokenizer: undefined,
|
||||
...this.modelMetadata,
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import _ from "lodash";
|
||||
import type { ClientOptions } from "openai";
|
||||
import OpenAI from "openai";
|
||||
@@ -14,7 +13,9 @@ export class OpenAISession {
|
||||
|
||||
constructor(options: ClientOptions & { azure?: boolean } = {}) {
|
||||
if (!options.apiKey) {
|
||||
options.apiKey = getEnv("OPENAI_API_KEY");
|
||||
if (typeof process !== undefined) {
|
||||
options.apiKey = process.env.OPENAI_API_KEY;
|
||||
}
|
||||
}
|
||||
|
||||
if (!options.apiKey) {
|
||||
|
||||
@@ -1,8 +1,17 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import _ from "lodash";
|
||||
import type { LLMOptions } from "portkey-ai";
|
||||
import { Portkey } from "portkey-ai";
|
||||
|
||||
export const readEnv = (
|
||||
env: string,
|
||||
default_val?: string,
|
||||
): string | undefined => {
|
||||
if (typeof process !== "undefined") {
|
||||
return process.env?.[env] ?? default_val;
|
||||
}
|
||||
return default_val;
|
||||
};
|
||||
|
||||
interface PortkeyOptions {
|
||||
apiKey?: string;
|
||||
baseURL?: string;
|
||||
@@ -15,11 +24,11 @@ export class PortkeySession {
|
||||
|
||||
constructor(options: PortkeyOptions = {}) {
|
||||
if (!options.apiKey) {
|
||||
options.apiKey = getEnv("PORTKEY_API_KEY");
|
||||
options.apiKey = readEnv("PORTKEY_API_KEY");
|
||||
}
|
||||
|
||||
if (!options.baseURL) {
|
||||
options.baseURL = getEnv("PORTKEY_BASE_URL") ?? "https://api.portkey.ai";
|
||||
options.baseURL = readEnv("PORTKEY_BASE_URL", "https://api.portkey.ai");
|
||||
}
|
||||
|
||||
this.portkey = new Portkey({});
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import Replicate from "replicate";
|
||||
|
||||
export class ReplicateSession {
|
||||
@@ -8,8 +7,8 @@ export class ReplicateSession {
|
||||
constructor(replicateKey: string | null = null) {
|
||||
if (replicateKey) {
|
||||
this.replicateKey = replicateKey;
|
||||
} else if (getEnv("REPLICATE_API_TOKEN")) {
|
||||
this.replicateKey = getEnv("REPLICATE_API_TOKEN") as string;
|
||||
} else if (process.env.REPLICATE_API_TOKEN) {
|
||||
this.replicateKey = process.env.REPLICATE_API_TOKEN;
|
||||
} else {
|
||||
throw new Error(
|
||||
"Set Replicate token in REPLICATE_API_TOKEN env variable",
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import { OpenAI } from "./LLM.js";
|
||||
|
||||
export class TogetherLLM extends OpenAI {
|
||||
constructor(init?: Partial<OpenAI>) {
|
||||
const {
|
||||
apiKey = getEnv("TOGETHER_API_KEY"),
|
||||
apiKey = process.env.TOGETHER_API_KEY,
|
||||
additionalSessionOptions = {},
|
||||
model = "togethercomputer/llama-2-7b-chat",
|
||||
...rest
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import type {
|
||||
BaseServiceParams,
|
||||
SubtitleFormat,
|
||||
@@ -29,7 +28,7 @@ abstract class AssemblyAIReader implements BaseReader {
|
||||
options = {};
|
||||
}
|
||||
if (!options.apiKey) {
|
||||
options.apiKey = getEnv("ASSEMBLYAI_API_KEY");
|
||||
options.apiKey = process.env.ASSEMBLYAI_API_KEY;
|
||||
}
|
||||
if (!options.apiKey) {
|
||||
throw new Error(
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import { defaultFS, getEnv, type GenericFileSystem } from "@llamaindex/env";
|
||||
import type { GenericFileSystem } from "@llamaindex/env";
|
||||
import { defaultFS } from "@llamaindex/env";
|
||||
import { Document } from "../Node.js";
|
||||
import type { FileReader } from "./type.js";
|
||||
|
||||
@@ -23,7 +24,7 @@ export class LlamaParseReader implements FileReader {
|
||||
|
||||
constructor(params: Partial<LlamaParseReader> = {}) {
|
||||
Object.assign(this, params);
|
||||
params.apiKey = params.apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
|
||||
params.apiKey = params.apiKey ?? process.env.LLAMA_CLOUD_API_KEY;
|
||||
if (!params.apiKey) {
|
||||
throw new Error(
|
||||
"API Key is required for LlamaParseReader. Please pass the apiKey parameter or set the LLAMA_CLOUD_API_KEY environment variable.",
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import _ from "lodash";
|
||||
import _, * as lodash from "lodash";
|
||||
import type { BaseNode } from "../../Node.js";
|
||||
import { ObjectType } from "../../Node.js";
|
||||
import { DEFAULT_NAMESPACE } from "../constants.js";
|
||||
@@ -123,10 +123,10 @@ export class KVDocumentStore extends BaseDocumentStore {
|
||||
|
||||
const refDocInfo = await this.kvstore.get(refDocId, this.refDocCollection);
|
||||
if (!_.isNil(refDocInfo)) {
|
||||
!_.pull(refDocInfo.nodeIds, docId);
|
||||
lodash.pull(refDocInfo.docIds, docId);
|
||||
|
||||
if (refDocInfo.nodeIds.length > 0) {
|
||||
this.kvstore.put(refDocId, refDocInfo, this.refDocCollection);
|
||||
if (refDocInfo.docIds.length > 0) {
|
||||
this.kvstore.put(refDocId, refDocInfo.toDict(), this.refDocCollection);
|
||||
}
|
||||
this.kvstore.delete(refDocId, this.metadataCollection);
|
||||
}
|
||||
|
||||
@@ -54,9 +54,6 @@ export class SimpleKVStore extends BaseKVStore {
|
||||
): Promise<boolean> {
|
||||
if (key in this.data[collection]) {
|
||||
delete this.data[collection][key];
|
||||
if (this.persistPath) {
|
||||
await this.persist(this.persistPath, this.fs);
|
||||
}
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import { AstraDB } from "@datastax/astra-db-ts";
|
||||
import type { Collection } from "@datastax/astra-db-ts/dist/collections";
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import type { BaseNode } from "../../Node.js";
|
||||
import { MetadataMode } from "../../Node.js";
|
||||
import type {
|
||||
@@ -35,8 +34,9 @@ export class AstraDBVectorStore implements VectorStore {
|
||||
if (init?.astraDBClient) {
|
||||
this.astraDBClient = init.astraDBClient;
|
||||
} else {
|
||||
const token = init?.params?.token ?? getEnv("ASTRA_DB_APPLICATION_TOKEN");
|
||||
const endpoint = init?.params?.endpoint ?? getEnv("ASTRA_DB_ENDPOINT");
|
||||
const token =
|
||||
init?.params?.token ?? process.env.ASTRA_DB_APPLICATION_TOKEN;
|
||||
const endpoint = init?.params?.endpoint ?? process.env.ASTRA_DB_ENDPOINT;
|
||||
|
||||
if (!token) {
|
||||
throw new Error(
|
||||
@@ -48,7 +48,7 @@ export class AstraDBVectorStore implements VectorStore {
|
||||
}
|
||||
const namespace =
|
||||
init?.params?.namespace ??
|
||||
getEnv("ASTRA_DB_NAMESPACE") ??
|
||||
process.env.ASTRA_DB_NAMESPACE ??
|
||||
"default_keyspace";
|
||||
this.astraDBClient = new AstraDB(token, endpoint, namespace);
|
||||
}
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
import { getEnv } from "@llamaindex/env";
|
||||
import type { BulkWriteOptions, Collection } from "mongodb";
|
||||
import { MongoClient } from "mongodb";
|
||||
import type { BaseNode } from "../../Node.js";
|
||||
@@ -45,7 +44,7 @@ export class MongoDBAtlasVectorSearch implements VectorStore {
|
||||
if (init.mongodbClient) {
|
||||
this.mongodbClient = init.mongodbClient;
|
||||
} else {
|
||||
const mongoUri = getEnv("MONGODB_URI");
|
||||
const mongoUri = process.env.MONGODB_URI;
|
||||
if (!mongoUri) {
|
||||
throw new Error(
|
||||
"Must specify MONGODB_URI via env variable if not directly passing in client.",
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import type pg from "pg";
|
||||
import pg from "pg";
|
||||
import pgvector from "pgvector/pg";
|
||||
|
||||
import type {
|
||||
VectorStore,
|
||||
@@ -82,18 +83,16 @@ export class PGVectorStore implements VectorStore {
|
||||
private async getDb(): Promise<pg.Client> {
|
||||
if (!this.db) {
|
||||
try {
|
||||
const { Client } = await import("pg");
|
||||
const { registerType } = await import("pgvector/pg");
|
||||
// Create DB connection
|
||||
// Read connection params from env - see comment block above
|
||||
const db = new Client({
|
||||
const db = new pg.Client({
|
||||
connectionString: this.connectionString,
|
||||
});
|
||||
await db.connect();
|
||||
|
||||
// Check vector extension
|
||||
db.query("CREATE EXTENSION IF NOT EXISTS vector");
|
||||
await registerType(db);
|
||||
await pgvector.registerType(db);
|
||||
|
||||
// Check schema, table(s), index(es)
|
||||
await this.checkSchema(db);
|
||||
|
||||
@@ -6,21 +6,19 @@ import type {
|
||||
VectorStoreQueryResult,
|
||||
} from "./types.js";
|
||||
|
||||
import { getEnv, type GenericFileSystem } from "@llamaindex/env";
|
||||
import type { GenericFileSystem } from "@llamaindex/env";
|
||||
import type {
|
||||
FetchResponse,
|
||||
Index,
|
||||
ScoredPineconeRecord,
|
||||
} from "@pinecone-database/pinecone";
|
||||
import { type Pinecone } from "@pinecone-database/pinecone";
|
||||
import { Pinecone } from "@pinecone-database/pinecone";
|
||||
import type { BaseNode, Metadata } from "../../Node.js";
|
||||
import { metadataDictToNode, nodeToMetadata } from "./utils.js";
|
||||
|
||||
type PineconeParams = {
|
||||
indexName?: string;
|
||||
chunkSize?: number;
|
||||
namespace?: string;
|
||||
textKey?: string;
|
||||
};
|
||||
|
||||
/**
|
||||
@@ -39,23 +37,18 @@ export class PineconeVectorStore implements VectorStore {
|
||||
*/
|
||||
db?: Pinecone;
|
||||
indexName: string;
|
||||
namespace: string;
|
||||
chunkSize: number;
|
||||
textKey: string;
|
||||
|
||||
constructor(params?: PineconeParams) {
|
||||
this.indexName =
|
||||
params?.indexName ?? getEnv("PINECONE_INDEX_NAME") ?? "llama";
|
||||
this.namespace = params?.namespace ?? getEnv("PINECONE_NAMESPACE") ?? "";
|
||||
params?.indexName ?? process.env.PINECONE_INDEX_NAME ?? "llama";
|
||||
this.chunkSize =
|
||||
params?.chunkSize ??
|
||||
Number.parseInt(getEnv("PINECONE_CHUNK_SIZE") ?? "100");
|
||||
this.textKey = params?.textKey ?? "text";
|
||||
Number.parseInt(process.env.PINECONE_CHUNK_SIZE ?? "100");
|
||||
}
|
||||
|
||||
private async getDb(): Promise<Pinecone> {
|
||||
if (!this.db) {
|
||||
const { Pinecone } = await import("@pinecone-database/pinecone");
|
||||
this.db = await new Pinecone();
|
||||
}
|
||||
|
||||
@@ -155,23 +148,24 @@ export class PineconeVectorStore implements VectorStore {
|
||||
};
|
||||
|
||||
const idx = await this.index();
|
||||
const results = await idx.namespace(this.namespace).query(options);
|
||||
const results = await idx.query(options);
|
||||
|
||||
const idList = results.matches.map((row) => row.id);
|
||||
const records: FetchResponse<any> = await idx
|
||||
.namespace(this.namespace)
|
||||
.fetch(idList);
|
||||
const records: FetchResponse<any> = await idx.fetch(idList);
|
||||
const rows = Object.values(records.records);
|
||||
|
||||
const nodes = rows.map((row) => {
|
||||
const node = metadataDictToNode(row.metadata, {
|
||||
const metadata = this.metaWithoutText(row.metadata);
|
||||
const text = this.textFromResultRow(row);
|
||||
const node = metadataDictToNode(metadata, {
|
||||
fallback: {
|
||||
id: row.id,
|
||||
text: this.textFromResultRow(row),
|
||||
metadata: this.metaWithoutText(row.metadata),
|
||||
text,
|
||||
metadata,
|
||||
embedding: row.values,
|
||||
},
|
||||
});
|
||||
node.setContent(text);
|
||||
return node;
|
||||
});
|
||||
|
||||
@@ -205,12 +199,12 @@ export class PineconeVectorStore implements VectorStore {
|
||||
}
|
||||
|
||||
textFromResultRow(row: ScoredPineconeRecord<Metadata>): string {
|
||||
return row.metadata?.[this.textKey] ?? "";
|
||||
return row.metadata?.text ?? "";
|
||||
}
|
||||
|
||||
metaWithoutText(meta: Metadata): any {
|
||||
return Object.keys(meta)
|
||||
.filter((key) => key != this.textKey)
|
||||
.filter((key) => key != "text")
|
||||
.reduce((acc: any, key: string) => {
|
||||
acc[key] = meta[key];
|
||||
return acc;
|
||||
|
||||
@@ -82,9 +82,6 @@ export class SimpleVectorStore implements VectorStore {
|
||||
delete this.data.embeddingDict[textId];
|
||||
delete this.data.textIdToRefDocId[textId];
|
||||
}
|
||||
if (this.persistPath) {
|
||||
await this.persist(this.persistPath, this.fs);
|
||||
}
|
||||
return Promise.resolve();
|
||||
}
|
||||
|
||||
|
||||
@@ -6,13 +6,5 @@
|
||||
"moduleResolution": "node16",
|
||||
"target": "ESNext"
|
||||
},
|
||||
"include": ["./**/*.ts"],
|
||||
"references": [
|
||||
{
|
||||
"path": "../../core/tsconfig.json"
|
||||
},
|
||||
{
|
||||
"path": "../../env/tsconfig.json"
|
||||
}
|
||||
]
|
||||
"include": ["./**/*.ts"]
|
||||
}
|
||||
|
||||
@@ -3,7 +3,6 @@
|
||||
"compilerOptions": {
|
||||
"rootDir": "./src",
|
||||
"outDir": "./dist/type",
|
||||
"tsBuildInfoFile": "./dist/.tsbuildinfo",
|
||||
"emitDeclarationOnly": true,
|
||||
"module": "node16",
|
||||
"moduleResolution": "node16",
|
||||
@@ -11,10 +10,5 @@
|
||||
"strict": true
|
||||
},
|
||||
"include": ["./src"],
|
||||
"exclude": ["node_modules"],
|
||||
"references": [
|
||||
{
|
||||
"path": "../env/tsconfig.json"
|
||||
}
|
||||
]
|
||||
"exclude": ["node_modules"]
|
||||
}
|
||||
|
||||
@@ -32,7 +32,6 @@ export async function createApp({
|
||||
eslint,
|
||||
frontend,
|
||||
openAiKey,
|
||||
llamaCloudKey,
|
||||
model,
|
||||
communityProjectPath,
|
||||
llamapack,
|
||||
@@ -78,7 +77,6 @@ export async function createApp({
|
||||
isOnline,
|
||||
eslint,
|
||||
openAiKey,
|
||||
llamaCloudKey,
|
||||
model,
|
||||
communityProjectPath,
|
||||
llamapack,
|
||||
|
||||
@@ -26,7 +26,6 @@ const createEnvLocalFile = async (
|
||||
root: string,
|
||||
opts?: {
|
||||
openAiKey?: string;
|
||||
llamaCloudKey?: string;
|
||||
vectorDb?: TemplateVectorDB;
|
||||
model?: string;
|
||||
framework?: TemplateFramework;
|
||||
@@ -47,10 +46,6 @@ const createEnvLocalFile = async (
|
||||
content += `OPENAI_API_KEY=${opts?.openAiKey}\n`;
|
||||
}
|
||||
|
||||
if (opts?.llamaCloudKey) {
|
||||
content += `LLAMA_CLOUD_API_KEY=${opts?.llamaCloudKey}\n`;
|
||||
}
|
||||
|
||||
switch (opts?.vectorDb) {
|
||||
case "mongo": {
|
||||
content += `# For generating a connection URI, see https://www.mongodb.com/docs/guides/atlas/connection-string\n`;
|
||||
@@ -210,7 +205,6 @@ export const installTemplate = async (
|
||||
// Copy the environment file to the target directory.
|
||||
await createEnvLocalFile(props.root, {
|
||||
openAiKey: props.openAiKey,
|
||||
llamaCloudKey: props.llamaCloudKey,
|
||||
vectorDb: props.vectorDb,
|
||||
model: props.model,
|
||||
framework: props.framework,
|
||||
|
||||
@@ -8,7 +8,6 @@ import { templatesDir } from "./dir";
|
||||
import { isPoetryAvailable, tryPoetryInstall } from "./poetry";
|
||||
import { Tool } from "./tools";
|
||||
import {
|
||||
FileSourceConfig,
|
||||
InstallTemplateArgs,
|
||||
TemplateDataSource,
|
||||
TemplateVectorDB,
|
||||
@@ -245,16 +244,13 @@ export const installPythonTemplate = async ({
|
||||
|
||||
const dataSourceType = dataSource?.type;
|
||||
if (dataSourceType !== undefined && dataSourceType !== "none") {
|
||||
let loaderFolder: string;
|
||||
if (dataSourceType === "file" || dataSourceType === "folder") {
|
||||
const dataSourceConfig = dataSource?.config as FileSourceConfig;
|
||||
loaderFolder = dataSourceConfig.useLlamaParse ? "llama_parse" : "file";
|
||||
} else {
|
||||
loaderFolder = dataSourceType;
|
||||
}
|
||||
const loaderPath =
|
||||
dataSourceType === "folder"
|
||||
? path.join(compPath, "loaders", "python", "file")
|
||||
: path.join(compPath, "loaders", "python", dataSourceType);
|
||||
await copy("**", enginePath, {
|
||||
parents: true,
|
||||
cwd: path.join(compPath, "loaders", "python", loaderFolder),
|
||||
cwd: loaderPath,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -15,7 +15,6 @@ export type TemplateDataSourceType = "none" | "file" | "folder" | "web";
|
||||
// Config for both file and folder
|
||||
export type FileSourceConfig = {
|
||||
path?: string;
|
||||
useLlamaParse?: boolean;
|
||||
};
|
||||
export type WebSourceConfig = {
|
||||
baseUrl?: string;
|
||||
@@ -36,7 +35,6 @@ export interface InstallTemplateArgs {
|
||||
eslint: boolean;
|
||||
customApiPath?: string;
|
||||
openAiKey?: string;
|
||||
llamaCloudKey?: string;
|
||||
forBackend?: string;
|
||||
model: string;
|
||||
communityProjectPath?: string;
|
||||
|
||||
@@ -154,18 +154,6 @@ const program = new Commander.Command(packageJson.name)
|
||||
`
|
||||
|
||||
Specify the tools you want to use by providing a comma-separated list. For example, 'wikipedia.WikipediaToolSpec,google.GoogleSearchToolSpec'. Use 'none' to not using any tools.
|
||||
`,
|
||||
)
|
||||
.option(
|
||||
"--llama-parse",
|
||||
`
|
||||
Enable LlamaParse.
|
||||
`,
|
||||
)
|
||||
.option(
|
||||
"--llama-cloud-key <key>",
|
||||
`
|
||||
Provide a LlamaCloud API key.
|
||||
`,
|
||||
)
|
||||
.allowUnknownOption()
|
||||
@@ -183,9 +171,6 @@ if (process.argv.includes("--tools")) {
|
||||
program.tools = getTools(program.tools.split(","));
|
||||
}
|
||||
}
|
||||
if (process.argv.includes("--no-llama-parse")) {
|
||||
program.llamaParse = false;
|
||||
}
|
||||
|
||||
const packageManager = !!program.useNpm
|
||||
? "npm"
|
||||
@@ -279,7 +264,6 @@ async function run(): Promise<void> {
|
||||
eslint: program.eslint,
|
||||
frontend: program.frontend,
|
||||
openAiKey: program.openAiKey,
|
||||
llamaCloudKey: program.llamaCloudKey,
|
||||
model: program.model,
|
||||
communityProjectPath: program.communityProjectPath,
|
||||
llamapack: program.llamapack,
|
||||
|
||||
@@ -5,11 +5,7 @@ import path from "path";
|
||||
import { blue, green, red } from "picocolors";
|
||||
import prompts from "prompts";
|
||||
import { InstallAppArgs } from "./create-app";
|
||||
import {
|
||||
FileSourceConfig,
|
||||
TemplateDataSourceType,
|
||||
TemplateFramework,
|
||||
} from "./helpers";
|
||||
import { TemplateDataSourceType, TemplateFramework } from "./helpers";
|
||||
import { COMMUNITY_OWNER, COMMUNITY_REPO } from "./helpers/constant";
|
||||
import { templatesDir } from "./helpers/dir";
|
||||
import { getAvailableLlamapackOptions } from "./helpers/llama-pack";
|
||||
@@ -19,7 +15,7 @@ import { supportedTools, toolsRequireConfig } from "./helpers/tools";
|
||||
export type QuestionArgs = Omit<
|
||||
InstallAppArgs,
|
||||
"appPath" | "packageManager"
|
||||
> & { files?: string; llamaParse?: boolean };
|
||||
> & { files?: string };
|
||||
const supportedContextFileTypes = [
|
||||
".pdf",
|
||||
".doc",
|
||||
@@ -67,7 +63,6 @@ const defaults: QuestionArgs = {
|
||||
eslint: true,
|
||||
frontend: false,
|
||||
openAiKey: "",
|
||||
llamaCloudKey: "",
|
||||
model: "gpt-3.5-turbo",
|
||||
communityProjectPath: "",
|
||||
llamapack: "",
|
||||
@@ -526,64 +521,6 @@ export const askQuestions = async (
|
||||
}
|
||||
}
|
||||
|
||||
if (
|
||||
program.dataSource?.type === "file" ||
|
||||
(program.dataSource?.type === "folder" && program.framework === "fastapi")
|
||||
) {
|
||||
if (ciInfo.isCI) {
|
||||
program.llamaCloudKey = getPrefOrDefault("llamaCloudKey");
|
||||
} else {
|
||||
const dataSourceConfig = program.dataSource.config as FileSourceConfig;
|
||||
dataSourceConfig.useLlamaParse = program.llamaParse;
|
||||
|
||||
// Is pdf file selected as data source or is it a folder data source
|
||||
const askingLlamaParse =
|
||||
dataSourceConfig.useLlamaParse === undefined &&
|
||||
(program.dataSource.type === "folder"
|
||||
? true
|
||||
: dataSourceConfig.path &&
|
||||
path.extname(dataSourceConfig.path) === ".pdf");
|
||||
|
||||
// Ask if user wants to use LlamaParse
|
||||
if (askingLlamaParse) {
|
||||
const { useLlamaParse } = await prompts(
|
||||
{
|
||||
type: "toggle",
|
||||
name: "useLlamaParse",
|
||||
message:
|
||||
"Would you like to use LlamaParse (improved parser for RAG - requires API key)?",
|
||||
initial: true,
|
||||
active: "yes",
|
||||
inactive: "no",
|
||||
},
|
||||
handlers,
|
||||
);
|
||||
dataSourceConfig.useLlamaParse = useLlamaParse;
|
||||
program.dataSource.config = dataSourceConfig;
|
||||
}
|
||||
|
||||
// Ask for LlamaCloud API key
|
||||
if (
|
||||
dataSourceConfig.useLlamaParse &&
|
||||
program.llamaCloudKey === undefined
|
||||
) {
|
||||
const { llamaCloudKey } = await prompts(
|
||||
{
|
||||
type: "text",
|
||||
name: "llamaCloudKey",
|
||||
message: "Please provide your LlamaIndex Cloud API key:",
|
||||
validate: (value) =>
|
||||
value
|
||||
? true
|
||||
: "LlamaIndex Cloud API key is required. You can get it from: https://cloud.llamaindex.ai/api-key",
|
||||
},
|
||||
handlers,
|
||||
);
|
||||
program.llamaCloudKey = llamaCloudKey;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (program.dataSource?.type === "web" && program.framework === "fastapi") {
|
||||
let { baseUrl } = await prompts(
|
||||
{
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from llama_index.core.readers import SimpleDirectoryReader
|
||||
|
||||
DATA_DIR = "data" # directory containing the documents
|
||||
DATA_DIR = "data" # directory to cache the generated index
|
||||
|
||||
|
||||
def get_documents():
|
||||
|
||||
@@ -1,14 +0,0 @@
|
||||
from llama_parse import LlamaParse
|
||||
from llama_index.core import SimpleDirectoryReader
|
||||
|
||||
DATA_DIR = "data" # directory containing the documents
|
||||
|
||||
|
||||
def get_documents():
|
||||
parser = LlamaParse(
|
||||
result_type="markdown",
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
reader = SimpleDirectoryReader(DATA_DIR, file_extractor={".pdf": parser})
|
||||
return reader.load_data()
|
||||
Vendored
-6
@@ -1,11 +1,5 @@
|
||||
# @llamaindex/env
|
||||
|
||||
## 0.0.5
|
||||
|
||||
### Patch Changes
|
||||
|
||||
- 5116ad8: fix: compatibility issue with Deno
|
||||
|
||||
## 0.0.4
|
||||
|
||||
### Patch Changes
|
||||
|
||||
Vendored
-8
@@ -1,8 +0,0 @@
|
||||
{
|
||||
"name": "@llamaindex/env",
|
||||
"version": "0.0.5",
|
||||
"exports": {
|
||||
".": "./src/index.ts",
|
||||
"./type": "./src/type.ts"
|
||||
}
|
||||
}
|
||||
Vendored
+1
-1
@@ -1,7 +1,7 @@
|
||||
{
|
||||
"name": "@llamaindex/env",
|
||||
"description": "environment wrapper",
|
||||
"version": "0.0.5",
|
||||
"version": "0.0.4",
|
||||
"type": "module",
|
||||
"types": "dist/type/index.d.ts",
|
||||
"main": "dist/cjs/index.js",
|
||||
|
||||
Vendored
-1
@@ -39,4 +39,3 @@ export function randomUUID(): string {
|
||||
return crypto.randomUUID();
|
||||
}
|
||||
export * from "./type.js";
|
||||
export { getEnv } from "./utils.js";
|
||||
|
||||
Vendored
+1
-2
@@ -34,6 +34,5 @@ export const defaultFS: CompleteFileSystem = {
|
||||
stat: fs.stat,
|
||||
};
|
||||
|
||||
export type * from "./type.js";
|
||||
export { getEnv } from "./utils.js";
|
||||
export * from "./type.js";
|
||||
export { EOL, ok, path, randomUUID };
|
||||
|
||||
Vendored
+2
-1
@@ -58,8 +58,9 @@ export class InMemoryFileSystem implements CompleteFileSystem {
|
||||
}
|
||||
}
|
||||
|
||||
async mkdir(path: string): Promise<undefined> {
|
||||
async mkdir(path: string) {
|
||||
this.files[path] = _.get(this.files, path, null);
|
||||
return undefined;
|
||||
}
|
||||
|
||||
async readdir(path: string): Promise<string[]> {
|
||||
|
||||
Vendored
-12
@@ -1,12 +0,0 @@
|
||||
export function getEnv(name: string): string | undefined {
|
||||
if (typeof process === "undefined" || typeof process.env === "undefined") {
|
||||
// @ts-expect-error
|
||||
if (typeof Deno === "undefined") {
|
||||
throw new Error("Current environment is not supported");
|
||||
} else {
|
||||
// @ts-expect-error
|
||||
return Deno.env.get(name);
|
||||
}
|
||||
}
|
||||
return process.env[name];
|
||||
}
|
||||
Vendored
-1
@@ -3,7 +3,6 @@
|
||||
"compilerOptions": {
|
||||
"rootDir": "./src",
|
||||
"outDir": "./dist/type",
|
||||
"tsBuildInfoFile": "./dist/.tsbuildinfo",
|
||||
"emitDeclarationOnly": true,
|
||||
"module": "node16",
|
||||
"moduleResolution": "node16",
|
||||
|
||||
@@ -36,7 +36,6 @@ module.exports = {
|
||||
"PINECONE_INDEX_NAME",
|
||||
"PINECONE_CHUNK_SIZE",
|
||||
"PINECONE_INDEX_NAME",
|
||||
"PINECONE_NAMESPACE",
|
||||
|
||||
"AZURE_OPENAI_API_KEY",
|
||||
"AZURE_OPENAI_API_INSTANCE_NAME",
|
||||
|
||||
Generated
+87
-200
@@ -53,7 +53,7 @@ importers:
|
||||
version: link:../../packages/env
|
||||
'@mdx-js/react':
|
||||
specifier: ^3.0.0
|
||||
version: 3.0.0(@types/react@18.2.61)(react@18.2.0)
|
||||
version: 3.0.0(@types/react@18.2.55)(react@18.2.0)
|
||||
clsx:
|
||||
specifier: ^2.1.0
|
||||
version: 2.1.0
|
||||
@@ -65,7 +65,7 @@ importers:
|
||||
version: 2.3.1(react@18.2.0)
|
||||
raw-loader:
|
||||
specifier: ^4.0.2
|
||||
version: 4.0.2(webpack@5.90.3)
|
||||
version: 4.0.2(webpack@5.90.1)
|
||||
react:
|
||||
specifier: ^18.2.0
|
||||
version: 18.2.0
|
||||
@@ -78,10 +78,10 @@ importers:
|
||||
version: 3.1.0(react-dom@18.2.0)(react@18.2.0)
|
||||
'@docusaurus/preset-classic':
|
||||
specifier: ^3.1.1
|
||||
version: 3.1.1(@algolia/client-search@4.22.1)(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3)
|
||||
version: 3.1.1(@algolia/client-search@4.22.1)(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3)
|
||||
'@docusaurus/theme-classic':
|
||||
specifier: ^3.1.1
|
||||
version: 3.1.1(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
version: 3.1.1(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/types':
|
||||
specifier: ^3.1.1
|
||||
version: 3.1.1(react-dom@18.2.0)(react@18.2.0)
|
||||
@@ -160,8 +160,8 @@ importers:
|
||||
packages/core:
|
||||
dependencies:
|
||||
'@anthropic-ai/sdk':
|
||||
specifier: ^0.15.0
|
||||
version: 0.15.0
|
||||
specifier: ^0.13.0
|
||||
version: 0.13.0
|
||||
'@aws-crypto/sha256-js':
|
||||
specifier: ^5.2.0
|
||||
version: 5.2.0
|
||||
@@ -169,8 +169,8 @@ importers:
|
||||
specifier: ^0.1.4
|
||||
version: 0.1.4
|
||||
'@llamaindex/cloud':
|
||||
specifier: 0.0.4
|
||||
version: 0.0.4
|
||||
specifier: ^0.0.1
|
||||
version: 0.0.1
|
||||
'@llamaindex/env':
|
||||
specifier: workspace:*
|
||||
version: link:../env
|
||||
@@ -406,16 +406,16 @@ importers:
|
||||
dependencies:
|
||||
eslint-config-next:
|
||||
specifier: ^13.5.6
|
||||
version: 13.5.6(eslint@8.57.0)(typescript@5.3.3)
|
||||
version: 13.5.6(eslint@8.56.0)(typescript@5.3.3)
|
||||
eslint-config-prettier:
|
||||
specifier: ^8.10.0
|
||||
version: 8.10.0(eslint@8.57.0)
|
||||
version: 8.10.0(eslint@8.56.0)
|
||||
eslint-config-turbo:
|
||||
specifier: ^1.11.3
|
||||
version: 1.11.3(eslint@8.57.0)
|
||||
version: 1.11.3(eslint@8.56.0)
|
||||
eslint-plugin-react:
|
||||
specifier: 7.28.0
|
||||
version: 7.28.0(eslint@8.57.0)
|
||||
version: 7.28.0(eslint@8.56.0)
|
||||
devDependencies:
|
||||
next:
|
||||
specifier: ^13.5.6
|
||||
@@ -574,8 +574,8 @@ packages:
|
||||
'@jridgewell/gen-mapping': 0.3.3
|
||||
'@jridgewell/trace-mapping': 0.3.22
|
||||
|
||||
/@anthropic-ai/sdk@0.15.0:
|
||||
resolution: {integrity: sha512-QMNEFcwGGB64oEIL+U9b+mxSbat5TCdNxvQVV0qCNGQvg/nlnbOmq2/x/0mKhuKD0n5bioL75oCkTbQaAgyYtw==}
|
||||
/@anthropic-ai/sdk@0.13.0:
|
||||
resolution: {integrity: sha512-wn315W4tCfCO+Z6FMa/67HgdgaWbjs4ie0Zbx5A6lq8RPEA3sEDknYzw0gCIrSlnSgRHxvUP/bT9KncFmOrmTg==}
|
||||
dependencies:
|
||||
'@types/node': 18.19.14
|
||||
'@types/node-fetch': 2.6.9
|
||||
@@ -2055,7 +2055,7 @@ packages:
|
||||
resolution: {integrity: sha512-SPiDHaWKQZpwR2siD0KQUwlStvIAnEyK6tAE2h2Wuoq8ue9skzhlyVQ1ddzOxX6khULnAALDiR/isSF3bnuciA==}
|
||||
dev: true
|
||||
|
||||
/@docsearch/react@3.5.2(@algolia/client-search@4.22.1)(@types/react@18.2.61)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0):
|
||||
/@docsearch/react@3.5.2(@algolia/client-search@4.22.1)(@types/react@18.2.55)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0):
|
||||
resolution: {integrity: sha512-9Ahcrs5z2jq/DcAvYtvlqEBHImbm4YJI8M9y0x6Tqg598P40HTEkX7hsMcIuThI+hTFxRGZ9hll0Wygm2yEjng==}
|
||||
peerDependencies:
|
||||
'@types/react': '>= 16.8.0 < 19.0.0'
|
||||
@@ -2075,7 +2075,7 @@ packages:
|
||||
'@algolia/autocomplete-core': 1.9.3(@algolia/client-search@4.22.1)(algoliasearch@4.22.1)(search-insights@2.13.0)
|
||||
'@algolia/autocomplete-preset-algolia': 1.9.3(@algolia/client-search@4.22.1)(algoliasearch@4.22.1)
|
||||
'@docsearch/css': 3.5.2
|
||||
'@types/react': 18.2.61
|
||||
'@types/react': 18.2.55
|
||||
algoliasearch: 4.22.1
|
||||
react: 18.2.0
|
||||
react-dom: 18.2.0(react@18.2.0)
|
||||
@@ -2581,7 +2581,7 @@ packages:
|
||||
- webpack-cli
|
||||
dev: true
|
||||
|
||||
/@docusaurus/preset-classic@3.1.1(@algolia/client-search@4.22.1)(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3):
|
||||
/@docusaurus/preset-classic@3.1.1(@algolia/client-search@4.22.1)(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3):
|
||||
resolution: {integrity: sha512-jG4ys/hWYf69iaN/xOmF+3kjs4Nnz1Ay3CjFLDtYa8KdxbmUhArA9HmP26ru5N0wbVWhY+6kmpYhTJpez5wTyg==}
|
||||
engines: {node: '>=18.0'}
|
||||
peerDependencies:
|
||||
@@ -2597,9 +2597,9 @@ packages:
|
||||
'@docusaurus/plugin-google-gtag': 3.1.1(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/plugin-google-tag-manager': 3.1.1(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/plugin-sitemap': 3.1.1(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/theme-classic': 3.1.1(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/theme-classic': 3.1.1(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/theme-common': 3.1.1(@docusaurus/types@3.1.1)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3)
|
||||
'@docusaurus/theme-search-algolia': 3.1.1(@algolia/client-search@4.22.1)(@docusaurus/types@3.1.1)(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3)
|
||||
'@docusaurus/theme-search-algolia': 3.1.1(@algolia/client-search@4.22.1)(@docusaurus/types@3.1.1)(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3)
|
||||
'@docusaurus/types': 3.1.1(react-dom@18.2.0)(react@18.2.0)
|
||||
react: 18.2.0
|
||||
react-dom: 18.2.0(react@18.2.0)
|
||||
@@ -2630,7 +2630,7 @@ packages:
|
||||
peerDependencies:
|
||||
react: '*'
|
||||
dependencies:
|
||||
'@types/react': 18.2.48
|
||||
'@types/react': 18.2.55
|
||||
prop-types: 15.8.1
|
||||
react: 18.2.0
|
||||
|
||||
@@ -2647,7 +2647,7 @@ packages:
|
||||
- supports-color
|
||||
dev: false
|
||||
|
||||
/@docusaurus/theme-classic@3.1.1(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3):
|
||||
/@docusaurus/theme-classic@3.1.1(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(typescript@5.3.3):
|
||||
resolution: {integrity: sha512-GiPE/jbWM8Qv1A14lk6s9fhc0LhPEQ00eIczRO4QL2nAQJZXkjPG6zaVx+1cZxPFWbAsqSjKe2lqkwF3fGkQ7Q==}
|
||||
engines: {node: '>=18.0'}
|
||||
peerDependencies:
|
||||
@@ -2666,7 +2666,7 @@ packages:
|
||||
'@docusaurus/utils': 3.1.1(@docusaurus/types@3.1.1)
|
||||
'@docusaurus/utils-common': 3.1.1(@docusaurus/types@3.1.1)
|
||||
'@docusaurus/utils-validation': 3.1.1(@docusaurus/types@3.1.1)
|
||||
'@mdx-js/react': 3.0.0(@types/react@18.2.61)(react@18.2.0)
|
||||
'@mdx-js/react': 3.0.0(@types/react@18.2.55)(react@18.2.0)
|
||||
clsx: 2.1.0
|
||||
copy-text-to-clipboard: 3.2.0
|
||||
infima: 0.2.0-alpha.43
|
||||
@@ -2745,14 +2745,14 @@ packages:
|
||||
- webpack-cli
|
||||
dev: true
|
||||
|
||||
/@docusaurus/theme-search-algolia@3.1.1(@algolia/client-search@4.22.1)(@docusaurus/types@3.1.1)(@types/react@18.2.61)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3):
|
||||
/@docusaurus/theme-search-algolia@3.1.1(@algolia/client-search@4.22.1)(@docusaurus/types@3.1.1)(@types/react@18.2.55)(eslint@8.56.0)(react-dom@18.2.0)(react@18.2.0)(search-insights@2.13.0)(typescript@5.3.3):
|
||||
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|
||||
events: 3.3.0
|
||||
@@ -15292,7 +15179,7 @@ packages:
|
||||
neo-async: 2.6.2
|
||||
schema-utils: 3.3.0
|
||||
tapable: 2.2.1
|
||||
terser-webpack-plugin: 5.3.10(webpack@5.90.3)
|
||||
terser-webpack-plugin: 5.3.10(webpack@5.90.1)
|
||||
watchpack: 2.4.0
|
||||
webpack-sources: 3.2.3
|
||||
transitivePeerDependencies:
|
||||
|
||||
+1
-7
@@ -11,13 +11,7 @@
|
||||
"outDir": "./lib",
|
||||
"tsBuildInfoFile": "./lib/.tsbuildinfo",
|
||||
"incremental": true,
|
||||
"composite": true,
|
||||
"paths": {
|
||||
"llamaindex": ["./packages/core/src/index.ts"],
|
||||
"llamaindex/*": ["./packages/core/src/*.ts"],
|
||||
"@llamaindex/env": ["./packages/env/src/index.ts"],
|
||||
"@llamaindex/env/*": ["./packages/env/src/*.ts"]
|
||||
}
|
||||
"composite": true
|
||||
},
|
||||
"files": [],
|
||||
"references": [
|
||||
|
||||
Reference in New Issue
Block a user