From 38ab523fc8d1cd22916e626230bfbb66a205fc9a Mon Sep 17 00:00:00 2001
From: Aaron <29749331+aarnphm@users.noreply.github.com>
Date: Thu, 22 Jun 2023 04:19:51 -0400
Subject: [PATCH] docs: update LangChain integration

Finally it is merged onto LangChain upstream

Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
---
 README.md | 48 ++++++++++++++++++++++++++++++++++++------------
 1 file changed, 36 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 6e8d6c28..8473775b 100644
--- a/README.md
+++ b/README.md
@@ -28,9 +28,9 @@ StarCoder and more.
 🔥 **Flexible APIs**: serve LLMs over RESTful API or gRPC with one command,
 query via WebUI, CLI, our Python/Javascript client, or any HTTP client.
 
-⛓️ **Freedom To Build**: First-class support for LangChain, BentoML and
-Hugging Face that allows you to easily create your own AI apps by composing LLMs
-with other models and services.
+⛓️ **Freedom To Build**: First-class support for LangChain, BentoML and Hugging
+Face that allows you to easily create your own AI apps by composing LLMs with
+other models and services.
 
 🎯 **Streamline Deployment**: Automatically generate your LLM server Docker
 Images or deploy as serverless endpoint via
@@ -376,26 +376,50 @@ client.ask_agent(
 )
 ```
 
-### LangChain (⏳Coming Soon!)
+### LangChain
 
-In future LangChain releases, you'll be able to effortlessly invoke OpenLLM
-models, like so:
+To quickly start a local LLM with `langchain`, simply do the following:
 
 ```python
 from langchain.llms import OpenLLM
-llm = OpenLLM.for_model(model_name='flan-t5')
-llm("What is the difference between a duck and a goose?")
+
+llm = OpenLLM(model_name="dolly-v2", model_id='databricks/dolly-v2-7b', device_map='auto')
+
+llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?")
 ```
 
-if you have an OpenLLM server deployed elsewhere, you can connect to it by
-specifying its URL:
+`langchain.llms.OpenLLM` has the capabilities to interact with remote OpenLLM
+Server. Given there is an OpenLLM server deployed elsewhere, you can connect to
+it by specifying its URL:
 
 ```python
 from langchain.llms import OpenLLM
-llm = OpenLLM.for_model(server_url='http://localhost:8000', server_type='http')
-llm("What is the difference between a duck and a goose?")
+
+llm = OpenLLM(server_url='http://44.23.123.1:3000', server_type='grpc')
+llm("What is the difference between a duck and a goose? And why there are so many Goose in Canada?")
 ```
 
+To integrate a LangChain agent with BentoML, you can do the following:
+
+```python
+llm = OpenLLM(
+    model_name='flan-t5',
+    model_id='google/flan-t5-large',
+    embedded=False,
+)
+tools = load_tools(["serpapi", "llm-math"], llm=llm)
+agent = initialize_agent(
+    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
+)
+svc = bentoml.Service("langchain-openllm", runners=[llm.runner])
+@svc.api(input=Text(), output=Text())
+def chat(input_text: str):
+    return agent.run(input_text)
+```
+
+> **Note** You can find out more examples under the
+> [examples](https://github.com/bentoml/OpenLLM/tree/main/examples) folder.
+
 ## 🚀 Deploying to Production
 
 To deploy your LLMs into production: