update readme

README.md

@@ -1,40 +1,25 @@
 # 🦜️🔗 Chat LangChain
 
 This repo is an implementation of a locally hosted chatbot specifically focused on question answering over the [LangChain documentation](https://langchain.readthedocs.io/en/latest/).
-Built with [LangChain](https://github.com/hwchase17/langchain/), [FastAPI](https://fastapi.tiangolo.com/), and [Next.js](https://nextjs.org).
+Built with [LangChain](https://github.com/langchain-ai/langchainjs/), and [Next.js](https://nextjs.org).
 
-Deployed version: [chat.langchain.com](https://chat.langchain.com)
+Deployed version: [chatjs.langchain.com](https://chatjs.langchain.com)
 
-The app leverages LangChain's streaming support and async API to update the page in real time for multiple users.
+> Looking for the Python? Click [here](https://chat.langchain.com)
+
+The app leverages LangChain's streaming API to update the page in real time for multiple users.
 
 ## ✅ Running locally
-1. Install backend dependencies: `poetry install`.
-1. Make sure to enter your environment variables to configure the application:
-```
-export OPENAI_API_KEY=
-export WEAVIATE_URL=
-export WEAVIATE_API_KEY=
-export RECORD_MANAGER_DB_URL=
-
-# for tracing
-export LANGCHAIN_TRACING_V2=true
-export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
-export LANGCHAIN_API_KEY=
-export LANGCHAIN_PROJECT=
-```
-1. Run `python ingest.py` to ingest LangChain docs data into the Weaviate vectorstore (only needs to be done once).
-   1. You can use other [Document Loaders](https://langchain.readthedocs.io/en/latest/modules/document_loaders.html) to load your own data into the vectorstore.
-1. Start the Python backend with `poetry run make start`.
-1. Install frontend dependencies by running `cd chat-langchain`, then `yarn`.
-1. Run the frontend with `yarn dev` for frontend.
-1. Open [localhost:3000](http://localhost:3000) in your browser.
-
-## ☕ Running locally (JS backend)
-1. Follow the first three steps above to ingest LangChain docs data into the vectorstore.
-1. Install frontend dependencies by running `cd chat-langchain`, then `yarn`.
-1. Populate a `chat-langchain/.env.local` file with your own versions of keys from the `chat-langchain/.env.example` file, and set `NEXT_PUBLIC_API_BASE_URL` to `"http://localhost:3000/api"`.
-1. Run the app with `yarn dev`.
-1. Open [localhost:3000](http://localhost:3000) in your browser.
+1. Install dependencies via: `yarn.install`.
+2. Set the required environment variables listed inside [`backend/.env.example`](backend/.env.example) for the backend, and [`frontend/.env.example`](frontend/.env.example) for the frontend.
+
+### Ingest
+1. Build the backend via `yarn build --filter=backend` (from root).
+2. Run the ingestion script by navigating into `./backend` and running `yarn ingest`.
+
+### Frontend
+1. Navigate into `./frontend` and run `yarn dev` to start the frontend.
+2. Open [localhost:3000](http://localhost:3000) in your browser.
 
 ## 📚 Technical description
 
@@ -43,9 +28,9 @@ There are two components: ingestion and question-answering.
 Ingestion has the following steps:
 
 1. Pull html from documentation site as well as the Github Codebase
-2. Load html with LangChain's [RecursiveURLLoader](https://python.langchain.com/docs/integrations/document_loaders/recursive_url_loader) and [SitemapLoader](https://python.langchain.com/docs/integrations/document_loaders/sitemap)
-3. Split documents with LangChain's [RecursiveCharacterTextSplitter](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.RecursiveCharacterTextSplitter.html)
-4. Create a vectorstore of embeddings, using LangChain's [Weaviate vectorstore wrapper](https://python.langchain.com/docs/integrations/vectorstores/weaviate) (with OpenAI's embeddings).
+2. Load html with LangChain's [RecursiveUrlLoader](https://api.js.langchain.com/classes/langchain_document_loaders_web_recursive_url.RecursiveUrlLoader.html) and [SitemapLoader](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap)
+3. Split documents with LangChain's [RecursiveCharacterTextSplitter](https://js.langchain.com/docs/modules/data_connection/document_transformers/recursive_text_splitter)
+4. Create a vectorstore of embeddings, using LangChain's [Weaviate vectorstore wrapper](https://js.langchain.com/docs/integrations/vectorstores/weaviate) (with OpenAI's embeddings).
 
 Question-Answering has the following steps:
 
@@ -56,5 +41,4 @@ Question-Answering has the following steps:
 
 ## 🚀 Deployment
 
-Deploy the frontend Next.js app as a serverless Edge function on Vercel [by clicking here]().
-You'll need to populate the `NEXT_PUBLIC_API_BASE_URL` environment variable with the base URL you've deployed the backend under (no trailing slash!).
+Deploy the frontend Next.js app as a serverless Edge function on Vercel.

backend/.env.example

@@ -1,5 +1,13 @@
+# ------------------LangSmith tracing------------------
+LANGCHAIN_TRACING_V2=true
+LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
+LANGCHAIN_API_KEY=
+LANGCHAIN_PROJECT=
+# -----------------------------------------------------
+
 WEAVIATE_API_KEY=
-WEAVIATE_HOST=
+WEAVIATE_URL=
+WEAVIATE_INDEX_NAME=
 FORCE_UPDATE=true
 RECORD_MANAGER_DB_URL=
 OPENAI_API_KEY=

frontend/.env.example

@@ -1,12 +1,14 @@
-## For JS backend:
+# ------------------LangSmith tracing------------------
+LANGCHAIN_TRACING_V2=true
+LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
+LANGCHAIN_API_KEY=
+LANGCHAIN_PROJECT=
+# -----------------------------------------------------
 
-# LANGCHAIN_TRACING_V2=true
-# LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
-# LANGCHAIN_API_KEY="YOUR_LANGSMITH_KEY"
-# LANGCHAIN_PROJECT="YOUR_PROJECT_NAME"
+WEAVIATE_API_KEY=
+WEAVIATE_URL=
+WEAVIATE_INDEX_NAME=
 
-# NEXT_PUBLIC_API_BASE_URL="http://localhost:3000/api"
-# OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
-# WEAVIATE_HOST="YOUR_WEAVIATE_HOST"
-# WEAVIATE_API_KEY="YOUR_WEAVIATE_API_KEY"
-# WEAVIATE_INDEX_NAME="YOUR_WEAVIATE_INDEX_NAME"
+FIREWORKS_API_KEY=
+
+OPENAI_API_KEY=

frontend/app/utils/constants.tsx

@@ -1,3 +1,2 @@
-export const apiBaseUrl =
-  process.env.NEXT_PUBLIC_API_BASE_URL ??
-  `https://${process.env.VERCEL_URL}/api`;
+const apiBasePath = process.env.VERCEL_URL ? `https://${process.env.VERCEL_URL}` : `http://localhost:3000`
+export const apiBaseUrl = `${apiBasePath}/api`;