add agent examples (#33)

.github/workflows/langserve_ci.yml

@@ -11,6 +11,7 @@ on:
       - '.github/workflows/_test.yml'
       - '.github/workflows/langserve_ci.yml'
       - 'langserve/**'
+      - 'examples/**'
       - 'pyproject.toml'
       - 'Makefile'
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI

examples/agent/client.ipynb

@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Client\n",
+    "\n",
+    "Demo of a client interacting with a remote agent. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable(\"http://localhost:8000/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remote runnable has the same interface as local runnables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await remote_runnable.ainvoke({\"input\": \"hi!\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': 'Eugene thinks that cats like fish.'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "remote_runnable.invoke({\"input\": \"what does eugene think of cats?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

examples/agent/server.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+"""Example LangChain server exposes a conversational retrieval chain."""
+from fastapi import FastAPI
+from langchain.agents import AgentExecutor, tool
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.tools.render import format_tool_to_openai_function
+from langchain.vectorstores import FAISS
+from pydantic import BaseModel
+
+from langserve import add_routes
+
+vectorstore = FAISS.from_texts(
+    ["cats like fish", "dogs like sticks"], embedding=OpenAIEmbeddings()
+)
+retriever = vectorstore.as_retriever()
+
+
+@tool
+def get_eugene_thoughts(query: str) -> list:
+    """Returns Eugene's thoughts on a topic."""
+    return retriever.get_relevant_documents(query)
+
+
+tools = [get_eugene_thoughts]
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful assistant."),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+llm = ChatOpenAI()
+
+llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
+
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "agent_scratchpad": lambda x: format_to_openai_functions(
+            x["intermediate_steps"]
+        ),
+    }
+    | prompt
+    | llm_with_tools
+    | OpenAIFunctionsAgentOutputParser()
+)
+
+agent_executor = AgentExecutor(agent=agent, tools=tools)
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+
+
+# We need to add these input/output schemas because the current AgentExecutor
+# is lacking in schemas.
+class Input(BaseModel):
+    input: str
+
+
+class Output(BaseModel):
+    output: str
+
+
+# Adds routes to the app for using the chain under:
+# /invoke
+# /batch
+# /stream
+add_routes(app, agent_executor, input_type=Input, output_type=Output)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="localhost", port=8000)

examples/conversational_retrieval_chain/client.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Client\n",
     "\n",
-    "Demo of a client interacting with a remote retriever. "
+    "Demo of a client interacting with a remote conversational retrieval chain. "
    ]
   },
   {
@@ -70,7 +70,9 @@
     }
    ],
    "source": [
-    "await remote_runnable.ainvoke({\"question\": \"what do cats like?\", \"chat_history\": [(\"hi\", \"hi\")]})"
+    "await remote_runnable.ainvoke(\n",
+    "    {\"question\": \"what do cats like?\", \"chat_history\": [(\"hi\", \"hi\")]}\n",
+    ")"
    ]
   },
   {

examples/conversational_retrieval_chain/server.py

@@ -1,10 +1,10 @@
 #!/usr/bin/env python
 """Example LangChain server exposes a conversational retrieval chain."""
 from fastapi import FastAPI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
 from langchain.chains import ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
 
 from langserve import add_routes