mirror of
https://github.com/langchain-ai/retrieval-agent-template-js.git
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Polish template (#3)
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
This commit is contained in:
Jacob Lee
committed by
William Fu-Hinthorn
William Fu-Hinthorn
parent
4451066f7d
commit
0b37680e4c
+5
-1
@@ -8,6 +8,10 @@ ANTHROPIC_API_KEY=....
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FIREWORKS_API_KEY=...
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OPENAI_API_KEY=...
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# Embeddings choice
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OPENAI_API_KEY=...
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COHERE_API_KEY=...
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# Retrieval provider
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## Elastic cloud:
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@@ -23,5 +27,5 @@ ELASTICSEARCH_PASSWORD=changeme
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PINECONE_API_KEY=...
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PINECONE_INDEX_NAME=...
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## Mongo Atlas
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## MongoDB Atlas
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MONGODB_URI=... # Full connection string
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@@ -0,0 +1,62 @@
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module.exports = {
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extends: [
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"eslint:recommended",
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"prettier",
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"plugin:@typescript-eslint/recommended",
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],
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parserOptions: {
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ecmaVersion: 12,
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parser: "@typescript-eslint/parser",
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project: "./tsconfig.json",
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sourceType: "module",
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},
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plugins: ["import", "@typescript-eslint", "no-instanceof"],
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ignorePatterns: [
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".eslintrc.cjs",
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"scripts",
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"node_modules",
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"dist",
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"dist-cjs",
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"*.js",
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"*.cjs",
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"*.d.ts",
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],
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rules: {
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"no-process-env": 0,
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"no-instanceof/no-instanceof": 2,
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"@typescript-eslint/explicit-module-boundary-types": 0,
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"@typescript-eslint/no-empty-function": 0,
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"@typescript-eslint/no-non-null-assertion": 0,
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"@typescript-eslint/no-shadow": 0,
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"@typescript-eslint/no-empty-interface": 0,
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"@typescript-eslint/no-use-before-define": ["error", "nofunc"],
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"@typescript-eslint/no-unused-vars": ["warn", { args: "none" }],
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"@typescript-eslint/no-floating-promises": "error",
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"@typescript-eslint/no-misused-promises": "error",
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camelcase: 0,
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"class-methods-use-this": 0,
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"import/extensions": [2, "ignorePackages"],
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"import/no-extraneous-dependencies": [
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"error",
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{ devDependencies: ["**/*.test.ts"] },
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],
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"import/no-unresolved": 0,
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"import/prefer-default-export": 0,
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"keyword-spacing": "error",
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"max-classes-per-file": 0,
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"max-len": 0,
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"no-await-in-loop": 0,
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"no-bitwise": 0,
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"no-console": 0,
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"no-restricted-syntax": 0,
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"no-shadow": 0,
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"no-continue": 0,
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"no-underscore-dangle": 0,
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"no-use-before-define": 0,
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"no-useless-constructor": 0,
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"no-return-await": 0,
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"consistent-return": 0,
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"no-else-return": 0,
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"new-cap": ["error", { properties: false, capIsNew: false }],
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},
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};
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@@ -32,7 +32,6 @@ Assuming you have already [installed LangGraph Studio](https://github.com/langch
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```bash
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cp .env.example .env
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```
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2. Select your retriever & index, and save the access instructions to your `.env` file.
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@@ -83,28 +82,6 @@ ELASTICSEARCH_USER=elastic
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ELASTICSEARCH_PASSWORD=changeme
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```
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### MongoDB Atlas
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MongoDB Atlas is a fully-managed cloud database that includes vector search capabilities for AI-powered applications.
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1. Create a free Atlas cluster:
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- Go to the [MongoDB Atlas website](https://www.mongodb.com/cloud/atlas/register) and sign up for a free account.
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- After logging in, create a free cluster by following the on-screen instructions.
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2. Set up your environment:
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- In the Atlas dashboard, click on "Connect" for your cluster.
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- Choose "Connect your application" and copy the provided connection string.
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- Create a `.env` file in your project root if you haven't already.
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- Add your MongoDB Atlas connection string to the `.env` file:
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```
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MONGODB_URI="mongodb+srv://username:password@your-cluster-url.mongodb.net/?retryWrites=true&w=majority&appName=your-cluster-name"
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```
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Replace `username`, `password`, `your-cluster-url`, and `your-cluster-name` with your actual credentials and cluster information.
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### Pinecone Serverless
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Pinecone is a managed, cloud-native vector database that provides long-term memory for high-performance AI applications.
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@@ -128,6 +105,49 @@ PINECONE_API_KEY=your-api-key
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PINECONE_INDEX_NAME=your-index-name
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```
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### MongoDB Atlas
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MongoDB Atlas is a fully-managed cloud database that includes vector search capabilities for AI-powered applications.
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1. Create a free Atlas cluster:
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- Go to the [MongoDB Atlas website](https://www.mongodb.com/cloud/atlas/register) and sign up for a free account.
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- After logging in, create a free cluster by following the on-screen instructions.
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2. Set up your environment:
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- In the Atlas dashboard, click on "Connect" for your cluster.
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- Choose "Connect your application" and copy the provided connection string.
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- Create a `.env` file in your project root if you haven't already.
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- Add your MongoDB Atlas connection string to the `.env` file:
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```
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MONGODB_URI="mongodb+srv://username:password@your-cluster-url.mongodb.net/?retryWrites=true&w=majority&appName=your-cluster-name"
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```
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Replace `username`, `password`, `your-cluster-url`, and `your-cluster-name` with your actual credentials and cluster information.
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3. Set up a vector search index. After configuring your cluster, you’ll need to create an index on the collection field you want to search over.
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Switch to the Atlas Search tab and click Create Search Index. From there, make sure you select Atlas Vector Search - JSON Editor, then select the appropriate database and collection and paste the following into the textbox:
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```json
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{
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"fields": [
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{
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"numDimensions": 1536,
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"path": "embedding",
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"similarity": "euclidean",
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"type": "vector"
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}
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]
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}
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```
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Note that the dimensions property should match the dimensionality of the embeddings you are using. For example, Cohere embeddings have 1024 dimensions, and by default OpenAI embeddings have 1536.
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[See this page](https://js.langchain.com/docs/integrations/vectorstores/mongodb_atlas/#setup) for more detailed information.
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</details>
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<details>
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@@ -338,7 +358,8 @@ Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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"PINECONE_INDEX_NAME"
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]
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}
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]
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],
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"type": "string"
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}
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}
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},
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@@ -430,7 +451,8 @@ Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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"PINECONE_INDEX_NAME"
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]
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}
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]
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],
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"type": "string"
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},
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"responseModel": {
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"type": "string",
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@@ -675,4 +697,3 @@ Configuration auto-generated by `langgraph template lock`. DO NOT EDIT MANUALLY.
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}
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}
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-->
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@@ -12,4 +12,7 @@ export default {
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],
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},
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extensionsToTreatAsEsm: [".ts"],
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setupFiles: ["dotenv/config"],
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passWithNoTests: true,
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testTimeout: 20_000,
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};
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+23
-9
@@ -1,17 +1,19 @@
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{
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"name": "retrieval-graph",
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"version": "0.0.1",
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"description": "A starter template for creating a LangGraph workflow.",
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"description": "A starter template containing an example retrieval agent.",
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"main": "src/retrieval_graph/graph.ts",
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"author": "Your Name",
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"license": "MIT",
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"private": true,
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"type": "module",
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"packageManager": "yarn@1.22.22",
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"scripts": {
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"build": "tsc",
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"clean": "rm -rf dist",
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"test": "node --experimental-vm-modules node_modules/jest/bin/jest.js --testPathPattern=\\.test\\.ts$ --testPathIgnorePatterns=\\.int\\.test\\.ts$",
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"test:int": "node --experimental-vm-modules node_modules/jest/bin/jest.js --testPathPattern=\\.int\\.test\\.ts$",
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"lint": "eslint src",
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"format": "prettier --write ."
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},
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"dependencies": {
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@@ -22,20 +24,32 @@
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"@langchain/core": "^0.3.2",
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"@langchain/langgraph": "^0.2.8",
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"@langchain/mongodb": "^0.1.0",
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"@langchain/openai": "^0.3.0",
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"@langchain/pinecone": "^0.1.0",
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"@pinecone-database/pinecone": "^3.0.3",
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"langchain": "^0.3.2",
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"ts-node": "^10.9.2"
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},
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"resolutions": {
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"@langchain/core": "^0.3.0"
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"mongodb": "^6.9.0",
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"ts-node": "^10.9.2",
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"uuid": "^10.0.0",
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"zod": "^3.23.8"
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},
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"devDependencies": {
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"@langchain/openai": "^0.3.0",
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"@eslint/eslintrc": "^3.1.0",
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"@eslint/js": "^9.9.1",
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"@jest/globals": "^29.7.0",
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"@tsconfig/recommended": "^1.0.7",
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"@types/jest": "^29.5.13",
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"@types/jest": "^29.5.0",
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"@typescript-eslint/eslint-plugin": "^5.59.8",
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"@typescript-eslint/parser": "^5.59.8",
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"dotenv": "^16.4.5",
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"eslint": "^8.41.0",
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"eslint-config-prettier": "^8.8.0",
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"eslint-plugin-import": "^2.27.5",
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"eslint-plugin-no-instanceof": "^1.0.1",
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"eslint-plugin-prettier": "^4.2.1",
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"jest": "^29.7.0",
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"prettier": "^3.3.3",
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"ts-jest": "^29.2.5",
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"typescript": "^5.6.2"
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"ts-jest": "^29.1.0",
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"typescript": "^5.3.3"
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}
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}
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@@ -1,7 +1,6 @@
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/**
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* Define the configurable parameters for the agent.
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*/
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import { RunnableConfig } from "@langchain/core/runnables";
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import {
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RESPONSE_SYSTEM_PROMPT_TEMPLATE,
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@@ -20,12 +19,12 @@ export const IndexConfigurationAnnotation = Annotation.Root({
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/**
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* Unique identifier for the user.
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*/
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userId: Annotation<string>(),
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userId: Annotation<string>,
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/**
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* Name of the embedding model to use. Must be a valid embedding model name.
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*/
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embeddingModel: Annotation<string>(),
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embeddingModel: Annotation<string>,
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/**
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* The vector store provider to use for retrieval.
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@@ -33,12 +32,13 @@ export const IndexConfigurationAnnotation = Annotation.Root({
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*/
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retrieverProvider: Annotation<
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"elastic" | "elastic-local" | "pinecone" | "mongodb"
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>(),
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>,
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/**
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* Additional keyword arguments to pass to the search function of the retriever.
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*/
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searchKwargs: Annotation<Record<string, any>>(),
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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searchKwargs: Annotation<Record<string, any>>,
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});
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/**
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@@ -57,7 +57,7 @@ export function ensureIndexConfiguration(
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userId: configurable.userId || "default", // Give a default user for shared docs
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embeddingModel:
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configurable.embeddingModel || "openai/text-embedding-3-small",
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retrieverProvider: configurable.retrieverProvider || "mongodb",
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retrieverProvider: configurable.retrieverProvider || "elastic",
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searchKwargs: configurable.searchKwargs || {},
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};
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}
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@@ -1,4 +1,3 @@
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import { ChatPromptTemplate } from "@langchain/core/prompts";
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import { RunnableConfig } from "@langchain/core/runnables";
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import { StateGraph } from "@langchain/langgraph";
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import {
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@@ -17,8 +16,8 @@ const SearchQuery = z.object({
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async function generateQuery(
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state: typeof StateAnnotation.State,
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config?: RunnableConfig
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): Promise<{ queries: string[] }> {
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config?: RunnableConfig,
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): Promise<typeof StateAnnotation.Update> {
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const messages = state.messages;
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if (messages.length === 1) {
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// It's the first user question. We will use the input directly to search.
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@@ -27,23 +26,19 @@ async function generateQuery(
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} else {
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const configuration = ensureConfiguration(config);
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// Feel free to customize the prompt, model, and other logic!
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const prompt = ChatPromptTemplate.fromMessages([
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["system", configuration.querySystemPromptTemplate],
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["placeholder", "{messages}"],
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]);
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const systemMessage = configuration.querySystemPromptTemplate
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.replace("{queries}", (state.queries || []).join("\n- "))
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.replace("{systemTime}", new Date().toISOString());
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const messageValue = [
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{ role: "system", content: systemMessage },
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...state.messages,
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];
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const model = (
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await loadChatModel(configuration.responseModel)
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).withStructuredOutput(SearchQuery);
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const messageValue = await prompt.invoke(
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{
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...state,
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queries: (state.queries || []).join("\n- "),
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systemTime: new Date().toISOString(),
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},
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config
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);
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const generated = await model.invoke(messageValue, config);
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const generated = await model.invoke(messageValue);
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return {
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queries: [generated.query],
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};
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@@ -52,54 +47,47 @@ async function generateQuery(
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async function retrieve(
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state: typeof StateAnnotation.State,
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config: RunnableConfig
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): Promise<{ retrievedDocs: any[] }> {
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config: RunnableConfig,
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): Promise<typeof StateAnnotation.Update> {
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const query = state.queries[state.queries.length - 1];
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const retriever = await makeRetriever(config);
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const firstResopnse = await retriever.vectorStore.similaritySearch(query);
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console.log("FIRSTRESP", firstResopnse);
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const response = await retriever.invoke(query);
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return { retrievedDocs: response };
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}
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async function respond(
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state: typeof StateAnnotation.State,
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config: RunnableConfig
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) {
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config: RunnableConfig,
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): Promise<typeof StateAnnotation.Update> {
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/**
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* Call the LLM powering our "agent".
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*/
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const configuration = ensureConfiguration(config);
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// Feel free to customize the prompt, model, and other logic!
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const prompt = ChatPromptTemplate.fromMessages([
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["system", configuration.responseSystemPromptTemplate],
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["placeholder", "{messages}"],
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]);
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const model = await loadChatModel(configuration.responseModel);
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const retrievedDocs = formatDocs(state.retrievedDocs);
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const messageValue = await prompt.invoke(
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{
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||||
...state,
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||||
retrievedDocs,
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||||
systemTime: new Date().toISOString(),
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||||
},
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||||
config
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||||
);
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||||
const response = await model.invoke(messageValue, config);
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||||
// Feel free to customize the prompt, model, and other logic!
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||||
const systemMessage = configuration.responseSystemPromptTemplate
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||||
.replace("{retrievedDocs}", retrievedDocs)
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||||
.replace("{systemTime}", new Date().toISOString());
|
||||
const messageValue = [
|
||||
{ role: "system", content: systemMessage },
|
||||
...state.messages,
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||||
];
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||||
const response = await model.invoke(messageValue);
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// We return a list, because this will get added to the existing list
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return { messages: [response] };
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}
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||||
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// Define a new graph (It's just a pipe)
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||||
// Lay out the nodes and edges to define a graph
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||||
const builder = new StateGraph(
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{
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||||
stateSchema: StateAnnotation,
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// Just the user
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// The only input field is the user
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input: InputStateAnnotation,
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}
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// ConfigurationAnnotation
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},
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ConfigurationAnnotation,
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)
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.addNode("generateQuery", generateQuery)
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.addNode("retrieve", retrieve)
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@@ -7,13 +7,15 @@ import { RunnableConfig } from "@langchain/core/runnables";
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import { StateGraph } from "@langchain/langgraph";
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import { IndexStateAnnotation } from "./state.js";
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// import { IndexConfigurationAnnotation } from "./configuration.js";
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import { makeRetriever } from "./retrieval.js";
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import { ensureIndexConfiguration } from "./configuration.js";
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import {
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||||
ensureIndexConfiguration,
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IndexConfigurationAnnotation,
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||||
} from "./configuration.js";
|
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function ensureDocsHaveUserId(
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docs: Document[],
|
||||
config: RunnableConfig
|
||||
config: RunnableConfig,
|
||||
): Document[] {
|
||||
const configuration = ensureIndexConfiguration(config);
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const userId = configuration.userId;
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||||
@@ -27,8 +29,8 @@ function ensureDocsHaveUserId(
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async function indexDocs(
|
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state: typeof IndexStateAnnotation.State,
|
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config?: RunnableConfig
|
||||
): Promise<{ docs: string }> {
|
||||
config?: RunnableConfig,
|
||||
): Promise<typeof IndexStateAnnotation.Update> {
|
||||
if (!config) {
|
||||
throw new Error("ConfigurationAnnotation required to run index_docs.");
|
||||
}
|
||||
@@ -36,17 +38,15 @@ async function indexDocs(
|
||||
const retriever = await makeRetriever(config);
|
||||
const stampedDocs = ensureDocsHaveUserId(docs, config);
|
||||
|
||||
const results = await retriever.addDocuments(stampedDocs);
|
||||
await retriever.addDocuments(stampedDocs);
|
||||
return { docs: "delete" };
|
||||
}
|
||||
|
||||
// Define a new graph
|
||||
|
||||
const builder = new StateGraph(
|
||||
{
|
||||
stateSchema: IndexStateAnnotation,
|
||||
}
|
||||
// IndexConfigurationAnnotation
|
||||
IndexStateAnnotation,
|
||||
IndexConfigurationAnnotation,
|
||||
)
|
||||
.addNode("indexDocs", indexDocs)
|
||||
.addEdge("__start__", "indexDocs");
|
||||
|
||||
@@ -2,13 +2,13 @@
|
||||
* Default prompts.
|
||||
*/
|
||||
|
||||
export const RESPONSE_SYSTEM_PROMPT_TEMPLATE: string = `You are a helpful AI assistant. Answer the user's questions based on the retrieved documents.
|
||||
export const RESPONSE_SYSTEM_PROMPT_TEMPLATE = `You are a helpful AI assistant. Answer the user's questions based on the retrieved documents.
|
||||
|
||||
{retrievedDocs}
|
||||
|
||||
System time: {systemTime}`;
|
||||
|
||||
export const QUERY_SYSTEM_PROMPT_TEMPLATE: string = `Generate search queries to retrieve documents that may help answer the user's question. Previously, you made the following queries:
|
||||
export const QUERY_SYSTEM_PROMPT_TEMPLATE = `Generate search queries to retrieve documents that may help answer the user's question. Previously, you made the following queries:
|
||||
|
||||
<previous_queries/>
|
||||
{queries}
|
||||
|
||||
@@ -13,7 +13,7 @@ import { OpenAIEmbeddings } from "@langchain/openai";
|
||||
|
||||
async function makeElasticRetriever(
|
||||
configuration: ReturnType<typeof ensureConfiguration>,
|
||||
embeddingModel: Embeddings
|
||||
embeddingModel: Embeddings,
|
||||
): Promise<VectorStoreRetriever> {
|
||||
const elasticUrl = process.env.ELASTICSEARCH_URL;
|
||||
if (!elasticUrl) {
|
||||
@@ -26,7 +26,7 @@ async function makeElasticRetriever(
|
||||
const password = process.env.ELASTICSEARCH_PASSWORD;
|
||||
if (!username || !password) {
|
||||
throw new Error(
|
||||
"ELASTICSEARCH_USER or ELASTICSEARCH_PASSWORD environment variable is not defined"
|
||||
"ELASTICSEARCH_USER or ELASTICSEARCH_PASSWORD environment variable is not defined",
|
||||
);
|
||||
}
|
||||
auth = { username, password };
|
||||
@@ -34,7 +34,7 @@ async function makeElasticRetriever(
|
||||
const apiKey = process.env.ELASTICSEARCH_API_KEY;
|
||||
if (!apiKey) {
|
||||
throw new Error(
|
||||
"ELASTICSEARCH_API_KEY environment variable is not defined"
|
||||
"ELASTICSEARCH_API_KEY environment variable is not defined",
|
||||
);
|
||||
}
|
||||
auth = { apiKey };
|
||||
@@ -49,19 +49,18 @@ async function makeElasticRetriever(
|
||||
client,
|
||||
indexName: "langchain_index",
|
||||
});
|
||||
|
||||
const searchKwargs = configuration.searchKwargs || {};
|
||||
searchKwargs.filter = searchKwargs.filter || [];
|
||||
searchKwargs.filter.push({
|
||||
term: { "metadata.user_id": configuration.userId },
|
||||
});
|
||||
const filter = {
|
||||
...searchKwargs,
|
||||
user_id: configuration.userId,
|
||||
};
|
||||
|
||||
return vectorStore.asRetriever({ searchKwargs });
|
||||
return vectorStore.asRetriever({ filter });
|
||||
}
|
||||
|
||||
async function makePineconeRetriever(
|
||||
configuration: ReturnType<typeof ensureConfiguration>,
|
||||
embeddingModel: Embeddings
|
||||
embeddingModel: Embeddings,
|
||||
): Promise<VectorStoreRetriever> {
|
||||
const indexName = process.env.PINECONE_INDEX_NAME;
|
||||
if (!indexName) {
|
||||
@@ -74,15 +73,17 @@ async function makePineconeRetriever(
|
||||
});
|
||||
|
||||
const searchKwargs = configuration.searchKwargs || {};
|
||||
searchKwargs.filter = searchKwargs.filter || {};
|
||||
searchKwargs.filter.user_id = configuration.userId;
|
||||
const filter = {
|
||||
...searchKwargs,
|
||||
user_id: configuration.userId,
|
||||
};
|
||||
|
||||
return vectorStore.asRetriever({ searchKwargs });
|
||||
return vectorStore.asRetriever({ filter });
|
||||
}
|
||||
|
||||
async function makeMongoDBRetriever(
|
||||
configuration: ReturnType<typeof ensureConfiguration>,
|
||||
embeddingModel: Embeddings
|
||||
embeddingModel: Embeddings,
|
||||
): Promise<VectorStoreRetriever> {
|
||||
if (!process.env.MONGODB_URI) {
|
||||
throw new Error("MONGODB_URI environment variable is not defined");
|
||||
@@ -105,7 +106,7 @@ async function makeMongoDBRetriever(
|
||||
return vectorStore.asRetriever({ filter: searchKwargs });
|
||||
}
|
||||
|
||||
function makeTextEncoder(modelName: string): Embeddings {
|
||||
function makeTextEmbeddings(modelName: string): Embeddings {
|
||||
/**
|
||||
* Connect to the configured text encoder.
|
||||
*/
|
||||
@@ -129,10 +130,10 @@ function makeTextEncoder(modelName: string): Embeddings {
|
||||
}
|
||||
|
||||
export async function makeRetriever(
|
||||
config: RunnableConfig
|
||||
config: RunnableConfig,
|
||||
): Promise<VectorStoreRetriever> {
|
||||
const configuration = ensureConfiguration(config);
|
||||
const embeddingModel = makeTextEncoder(configuration.embeddingModel);
|
||||
const embeddingModel = makeTextEmbeddings(configuration.embeddingModel);
|
||||
|
||||
const userId = configuration.userId;
|
||||
if (!userId) {
|
||||
@@ -149,7 +150,7 @@ export async function makeRetriever(
|
||||
return makeMongoDBRetriever(configuration, embeddingModel);
|
||||
default:
|
||||
throw new Error(
|
||||
`Unrecognized retrieverProvider in configuration: ${configuration.retrieverProvider}`
|
||||
`Unrecognized retrieverProvider in configuration: ${configuration.retrieverProvider}`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2,6 +2,7 @@ import { Document } from "@langchain/core/documents";
|
||||
import { BaseMessage } from "@langchain/core/messages";
|
||||
import { Annotation, MessagesAnnotation } from "@langchain/langgraph";
|
||||
import { v4 as uuidv4 } from "uuid";
|
||||
|
||||
/**
|
||||
* Reduces the document array based on the provided new documents or actions.
|
||||
*
|
||||
@@ -13,6 +14,7 @@ export function reduceDocs(
|
||||
existing?: Document[],
|
||||
newDocs?:
|
||||
| Document[]
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
| { [key: string]: any }[]
|
||||
| string[]
|
||||
| string
|
||||
@@ -66,6 +68,7 @@ export const IndexStateAnnotation = Annotation.Root({
|
||||
*/
|
||||
docs: Annotation<
|
||||
Document[],
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
Document[] | { [key: string]: any }[] | string[] | string | "delete"
|
||||
>({
|
||||
reducer: reduceDocs,
|
||||
|
||||
@@ -5,11 +5,12 @@ import { BaseChatModel } from "@langchain/core/language_models/chat_models";
|
||||
import { initChatModel } from "langchain/chat_models/universal";
|
||||
|
||||
export function getMessageText(msg: BaseMessage): string {
|
||||
/**Get the text content of a message. */
|
||||
/** Get the text content of a message. */
|
||||
const content = msg.content;
|
||||
if (typeof content === "string") {
|
||||
return content;
|
||||
} else {
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
const txts = (content as any[]).map((c) =>
|
||||
typeof c === "string" ? c : c.text || "",
|
||||
);
|
||||
@@ -35,6 +36,7 @@ export function formatDocs(docs?: Document[]): string {
|
||||
const formatted = docs.map(formatDoc).join("\n");
|
||||
return `<documents>\n${formatted}\n</documents>`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Load a chat model from a fully specified name.
|
||||
* @param fullySpecifiedName - String in the format 'provider/model' or 'provider/account/provider/model'.
|
||||
|
||||
Reference in New Issue
Block a user