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langchain/templates
lvliang-intel 0175906437
templates: add RAG template for Intel Xeon Scalable Processors (#18424)
**Description:**
This template utilizes Chroma and TGI (Text Generation Inference) to
execute RAG on the Intel Xeon Scalable Processors. It serves as a
demonstration for users, illustrating the deployment of the RAG service
on the Intel Xeon Scalable Processors and showcasing the resulting
performance enhancements.

**Issue:**
None

**Dependencies:**
The template contains the poetry project requirements to run this
template.
CPU TGI batching is WIP.

**Twitter handle:**
None

---------

Signed-off-by: lvliang-intel <liang1.lv@intel.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
6 months ago
..
anthropic-iterative-search
basic-critique-revise templates, cli: more security deps (#19006) 6 months ago
bedrock-jcvd
cassandra-entomology-rag
cassandra-synonym-caching
chain-of-note-wiki templates, cli: more security deps (#19006) 6 months ago
chat-bot-feedback templates, cli: more security deps (#19006) 6 months ago
cohere-librarian templates, cli: more security deps (#19006) 6 months ago
csv-agent
docs
elastic-query-generator
extraction-anthropic-functions
extraction-openai-functions
gemini-functions-agent
guardrails-output-parser templates, cli: more security deps (#19006) 6 months ago
hybrid-search-weaviate
hyde templates, cli: more security deps (#19006) 6 months ago
intel-rag-xeon templates: add RAG template for Intel Xeon Scalable Processors (#18424) 6 months ago
llama2-functions
mongo-parent-document-retrieval templates, cli: more security deps (#19006) 6 months ago
neo4j-advanced-rag
neo4j-cypher
neo4j-cypher-ft
neo4j-cypher-memory
neo4j-generation templates: Switch neo4j generation template to LLMGraphTransformer (#19024) 6 months ago
neo4j-parent
neo4j-semantic-layer
neo4j-semantic-ollama
neo4j-vector-memory
nvidia-rag-canonical
openai-functions-agent
openai-functions-agent-gmail
openai-functions-tool-retrieval-agent Update README.md (#19172) 6 months ago
pii-protected-chatbot templates, cli: more security deps (#19006) 6 months ago
pirate-speak templates, cli: more security deps (#19006) 6 months ago
pirate-speak-configurable templates, cli: more security deps (#19006) 6 months ago
plate-chain templates, cli: more security deps (#19006) 6 months ago
propositional-retrieval templates, cli: more security deps (#19006) 6 months ago
python-lint infra: Update package version to apply CVE-related patch (#19490) 6 months ago
rag-astradb
rag-aws-bedrock
rag-aws-kendra templates, cli: more security deps (#19006) 6 months ago
rag-chroma templates, cli: more security deps (#19006) 6 months ago
rag-chroma-multi-modal templates, cli: more security deps (#19006) 6 months ago
rag-chroma-multi-modal-multi-vector templates, cli: more security deps (#19006) 6 months ago
rag-chroma-private templates, cli: more security deps (#19006) 6 months ago
rag-codellama-fireworks templates, cli: more security deps (#19006) 6 months ago
rag-conversation
rag-conversation-zep templates, cli: more security deps (#19006) 6 months ago
rag-elasticsearch
rag-fusion
rag-gemini-multi-modal templates, cli: more security deps (#19006) 6 months ago
rag-google-cloud-sensitive-data-protection templates, cli: more security deps (#19006) 6 months ago
rag-google-cloud-vertexai-search templates, cli: more security deps (#19006) 6 months ago
rag-gpt-crawler templates, cli: more security deps (#19006) 6 months ago
rag-jaguardb templates: Added template for JaguarDB (#16757) 6 months ago
rag-lancedb templates: fix rag-lancedb dep (#19010) 6 months ago
rag-lantern templates: Add rag lantern template (#16523) 6 months ago
rag-matching-engine templates, cli: more security deps (#19006) 6 months ago
rag-momento-vector-index templates, cli: more security deps (#19006) 6 months ago
rag-mongo
rag-multi-index-fusion templates, cli: more security deps (#19006) 6 months ago
rag-multi-index-router templates, cli: more security deps (#19006) 6 months ago
rag-multi-modal-local templates, cli: more security deps (#19006) 6 months ago
rag-multi-modal-mv-local templates, cli: more security deps (#19006) 6 months ago
rag-ollama-multi-query templates, cli: more security deps (#19006) 6 months ago
rag-opensearch templates, cli: more security deps (#19006) 6 months ago
rag-pinecone
rag-pinecone-multi-query
rag-pinecone-rerank templates, cli: more security deps (#19006) 6 months ago
rag-redis templates, cli: more security deps (#19006) 6 months ago
rag-redis-multi-modal-multi-vector templates, cli: more security deps (#19006) 6 months ago
rag-self-query
rag-semi-structured templates, cli: more security deps (#19006) 6 months ago
rag-singlestoredb templates, cli: more security deps (#19006) 6 months ago
rag-supabase
rag-timescale-conversation
rag-timescale-hybrid-search-time templates, cli: more security deps (#19006) 6 months ago
rag-vectara
rag-vectara-multiquery
rag-weaviate
research-assistant templates, cli: more security deps (#19006) 6 months ago
retrieval-agent templates, cli: more security deps (#19006) 6 months ago
retrieval-agent-fireworks templates, cli: more security deps (#19006) 6 months ago
rewrite-retrieve-read
robocorp-action-server templates, cli: more security deps (#19006) 6 months ago
self-query-qdrant templates: update qdrant self query (#19218) 6 months ago
self-query-supabase
shopping-assistant
skeleton-of-thought templates, cli: more security deps (#19006) 6 months ago
solo-performance-prompting-agent templates, cli: more security deps (#19006) 6 months ago
sql-llama2
sql-llamacpp
sql-ollama
sql-pgvector templates, cli: more security deps (#19006) 6 months ago
sql-research-assistant templates, cli: more security deps (#19006) 6 months ago
stepback-qa-prompting
summarize-anthropic
vertexai-chuck-norris templates, cli: more security deps (#19006) 6 months ago
xml-agent
.gitignore
Makefile
README.md
poetry.lock
pyproject.toml

README.md

LangChain Templates

LangChain Templates are the easiest and fastest way to build a production-ready LLM application. These templates serve as a set of reference architectures for a wide variety of popular LLM use cases. They are all in a standard format which make it easy to deploy them with LangServe.

🚩 We will be releasing a hosted version of LangServe for one-click deployments of LangChain applications. Sign up here to get on the waitlist.

Quick Start

To use, first install the LangChain CLI.

pip install -U langchain-cli

Next, create a new LangChain project:

langchain app new my-app

This will create a new directory called my-app with two folders:

  • app: This is where LangServe code will live
  • packages: This is where your chains or agents will live

To pull in an existing template as a package, you first need to go into your new project:

cd my-app

And you can the add a template as a project. In this getting started guide, we will add a simple pirate-speak project. All this project does is convert user input into pirate speak.

langchain app add pirate-speak

This will pull in the specified template into packages/pirate-speak

You will then be prompted if you want to install it. This is the equivalent of running pip install -e packages/pirate-speak. You should generally accept this (or run that same command afterwards). We install it with -e so that if you modify the template at all (which you likely will) the changes are updated.

After that, it will ask you if you want to generate route code for this project. This is code you need to add to your app to start using this chain. If we accept, we will see the following code generated:

from pirate_speak.chain import chain as pirate_speak_chain

add_routes(app, pirate_speak_chain, path="/pirate-speak")

You can now edit the template you pulled down. You can change the code files in packages/pirate-speak to use a different model, different prompt, different logic. Note that the above code snippet always expects the final chain to be importable as from pirate_speak.chain import chain, so you should either keep the structure of the package similar enough to respect that or be prepared to update that code snippet.

Once you have done as much of that as you want, it is In order to have LangServe use this project, you then need to modify app/server.py. Specifically, you should add the above code snippet to app/server.py so that file looks like:

from fastapi import FastAPI
from langserve import add_routes
from pirate_speak.chain import chain as pirate_speak_chain

app = FastAPI()

add_routes(app, pirate_speak_chain, path="/pirate-speak")

(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

For this particular application, we will use OpenAI as the LLM, so we need to export our OpenAI API key:

export OPENAI_API_KEY=sk-...

You can then spin up production-ready endpoints, along with a playground, by running:

langchain serve

This now gives a fully deployed LangServe application. For example, you get a playground out-of-the-box at http://127.0.0.1:8000/pirate-speak/playground/:

Screenshot of the LangServe Playground interface with input and output fields demonstrating pirate speak conversion.

Access API documentation at http://127.0.0.1:8000/docs

Screenshot of the API documentation interface showing available endpoints for the pirate-speak application.

Use the LangServe python or js SDK to interact with the API as if it were a regular Runnable.

from langserve import RemoteRunnable

api = RemoteRunnable("http://127.0.0.1:8000/pirate-speak")
api.invoke({"text": "hi"})

That's it for the quick start! You have successfully downloaded your first template and deployed it with LangServe.

Additional Resources

Index of Templates

Explore the many templates available to use - from advanced RAG to agents.

Contributing

Want to contribute your own template? It's pretty easy! These instructions walk through how to do that.

Launching LangServe from a Package

You can also launch LangServe from a package directly (without having to create a new project). These instructions cover how to do that.