Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/cutwell/langchain-in-y-seconds
Learn the entire LangChain tech-stack fast 🏃♀️
https://github.com/cutwell/langchain-in-y-seconds
generative-ai langchain langserve langsmith llm tutorial
Last synced: about 1 month ago
JSON representation
Learn the entire LangChain tech-stack fast 🏃♀️
- Host: GitHub
- URL: https://github.com/cutwell/langchain-in-y-seconds
- Owner: Cutwell
- Created: 2023-11-14T22:39:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-15T20:02:44.000Z (about 1 year ago)
- Last Synced: 2024-10-15T19:28:52.664Z (3 months ago)
- Topics: generative-ai, langchain, langserve, langsmith, llm, tutorial
- Language: Python
- Homepage: https://youtu.be/q76MWApFIt4
- Size: 5.62 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 
Learn LangChain in Y seconds
Learn the entire LangChain tech-stack fast 🏃♀️
[](https://opensource.org/licenses/MIT)
## Prerequisites
1. Install Python (3.11.6 used in video).
2. Install Poetry.
3. Install DirEnv (or use another secret manager).
4. Get your [OpenAI API key](https://platform.openai.com/api-keys).
5. Sign up for LangSmith and get your LangSmith API key (https://smith.langchain.com/ and access API keys via the 🔑 icon on the bottom left).
## Tutorial walkthrough
0. If using a secret file (e.g.: `.envrc`), create a `.gitignore` file similar to the one in this repository, to prevent accidentally sharing your API keys.
1. Create a `.envrc` file and populate it with this template:
```bash
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
export LANGCHAIN_API_KEY=
export LANGCHAIN_PROJECT=langchain-quickstart # if not specified, defaults to "default"
export OPENAI_API_KEY=
```
2. Open a new terminal and run these commands:
```bash
poetry init -n --python=3.11.6 && poetry add langchain-cli
source ./.venv/bin/activate
poetry run langchain app new api --package pirate-speak
```
_Type 'Y' when prompted to install pirate-speak as a mirrored module_
3. Replace the `NotImplemented` route in `api/app/server.py` with a route for your component chain:
```python
from pirate_speak.chain import chain as pirate_speak_chain
add_routes(app, pirate_speak_chain, path="/pirate-speak")
```
4. Update the project `name` in `api/pyproject.toml`:
```toml
name = "api"
```
_This can be any name other than `__app_name__`_
5. Open a new terminal in `/api` and run these commands:
```bash
poetry install
source ./.venv/bin/activate
poetry run langchain serve
```
6. Visit http://127.0.0.1:8000/pirate-speak/playground/ in your web browser and play with your chain.
7. Open LangSmith (https://smith.langchain.com/) and visit the project page. View one of your traces (created when you tested the playground demo) and use it to create a dataset.
8. View the dataset and click `New Test Run` to get a code snippet:
```python
client = langsmith.Client()
chain_results = client.run_on_dataset(
dataset_name="quickstart-dataset", # this will change if you use a different dataset name.
llm_or_chain_factory=chain,
project_name="test-formal-project-4",
concurrency_level=5,
verbose=True,
)
```
9. Create a new PyTest file `test_chain.py` in `api/packages/pirate-speak/tests/`:
```python
from pirate_assistant.chain import chain
import langsmith
from datetime import datetime # import datetime module to get a timestamp
def test_chain():
client = langsmith.Client()
chain_results = client.run_on_dataset(
dataset_name="quickstart-dataset",
llm_or_chain_factory=chain,
project_name=f"quickstart-dataset-test-{int(datetime.now().strftime('%Y%m%d%H%M%S'))}", # use a timestamped unique project name each re-run
concurrency_level=5,
verbose=True,
)
```
_To enable PyTest re-runs, we want to use a timestamped unique project name to store each successive test result._
10. Open a terminal in `/api` and run these commands:
```bash
poetry add pytest --group=dev
poetry run python -m pytest -s .
```
11. View your dataset test runs, and add the trace to a new annotation queue.
12. View your annotation queue and explore the review interface.
🎉🎉 **Congratulations!** 🎉🎉
You've completed this tutorial and now have a complete LangChain project. If you need more help with navigating the LangSmith interface, go view the video version of this tutorial [on Youtube](https://youtu.be/q76MWApFIt4).
## License
MIT