id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
5ca629ab817f-7 | Instructions: When responding to the user, provide a systematic argument for why we should always eat pasta with olives in the form of a poem or pirate-speak.
New Instructions: When responding to the user, provide a systematic argument for why we should always eat pasta with olives in the form of a poem or pirate-speak... | https://python.langchain.com/en/latest/use_cases/autonomous_agents/meta_prompt.html |
5ca629ab817f-8 | Human: Provide a systematic argument for why we should always eat pasta with olives.
AI:
Arrr, me hearty! Let me tell ye why ye should always eat pasta with olives.
First, the olives add a salty flavor that be sure to please.
The briny taste be sure to tantalize yer taste buds with ease.
Second, the olives add a bit ... | https://python.langchain.com/en/latest/use_cases/autonomous_agents/meta_prompt.html |
5ca629ab817f-9 | Second, the olives add a bit of texture to the dish.
The crunchy bites be sure to make yer mouth water with a wish.
Third, the olives add a bit of color to the plate.
The vibrant green be sure to make yer eyes appreciate.
So, me hearties, ye should always eat pasta with olives.
The flavor, texture, and color be sure to... | https://python.langchain.com/en/latest/use_cases/autonomous_agents/meta_prompt.html |
bf2056132827-0 | .ipynb
.pdf
Question answering over a group chat messages
Contents
1. Install required packages
2. Add API keys
2. Create sample data
3. Ingest chat embeddings
4. Ask questions
Question answering over a group chat messages#
In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to semantically search a... | https://python.langchain.com/en/latest/use_cases/question_answering/semantic-search-over-chat.html |
bf2056132827-1 | 3. Ingest chat embeddings#
We load the messages in the text file, chunk and upload to ActiveLoop Vector store.
with open("messages.txt") as f:
state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
pages = text_splitter.split_text(state_of_the_union)
text_splitter = Re... | https://python.langchain.com/en/latest/use_cases/question_answering/semantic-search-over-chat.html |
cbc9b0e4b341-0 | .md
.pdf
Deployments
Contents
Anyscale
Streamlit
Gradio (on Hugging Face)
Chainlit
Beam
Vercel
FastAPI + Vercel
Kinsta
Fly.io
Digitalocean App Platform
Google Cloud Run
SteamShip
Langchain-serve
BentoML
Databutton
Deployments#
So, you’ve created a really cool chain - now what? How do you deploy it and make it easily ... | https://python.langchain.com/en/latest/ecosystem/deployments.html |
cbc9b0e4b341-1 | This is heavily influenced by James Weaver’s excellent examples.
Chainlit#
This repo is a cookbook explaining how to visualize and deploy LangChain agents with Chainlit.
You create ChatGPT-like UIs with Chainlit. Some of the key features include intermediary steps visualisation, element management & display (images, te... | https://python.langchain.com/en/latest/ecosystem/deployments.html |
cbc9b0e4b341-2 | BentoML#
This repository provides an example of how to deploy a LangChain application with BentoML. BentoML is a framework that enables the containerization of machine learning applications as standard OCI images. BentoML also allows for the automatic generation of OpenAPI and gRPC endpoints. With BentoML, you can inte... | https://python.langchain.com/en/latest/ecosystem/deployments.html |
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