Clean-up template READMEs (#12403)

Normalize, and update notebooks.

templates/extraction-openai-functions/README.md

@@ -10,32 +10,4 @@ By default, it will extract the title and author of papers.
 
 This template will use `OpenAI` by default. 
 
-Be sure that `OPENAI_API_KEY` is set in your environment.
-
-## Adding the template
-
-Install the langchain package
-```
-pip install -e packages/extraction_openai_functions
-```
-
-Edit app/server.py to add that package to the routes
-```
-from fastapi import FastAPI
-from langserve import add_routes 
-from extraction_openai_functions.chain import chain
-
-app = FastAPI()
-add_routes(app, chain)
-```
-
-Run the app
-```
-python app/server.py
-```
-
-You can use this template in the Playground:
-
-http://127.0.0.1:8000/extraction-openai-functions/playground/
-
-Also, see Jupyter notebook `openai_functions` for various other ways to connect to the template.
+Be sure that `OPENAI_API_KEY` is set in your environment.

templates/extraction-openai-functions/extraction_openai_functions.ipynb

@@ -29,22 +29,10 @@
    "source": [
     "## Run Template\n",
     "\n",
-    "\n",
-    "As shown in the README, add template and start server:\n",
+    "In `server.py`, set -\n",
     "```\n",
-    "langchain serve add openai-functions\n",
-    "langchain start\n",
-    "```\n",
-    "\n",
-    "We can now look at the endpoints:\n",
-    "\n",
-    "http://127.0.0.1:8000/docs#\n",
-    "\n",
-    "And specifically at our loaded template:\n",
-    "\n",
-    "http://127.0.0.1:8000/docs#/default/invoke_openai_functions_invoke_post\n",
-    " \n",
-    "We can also use remote runnable to call it."
+    "add_routes(app, chain_ext, path=\"/extraction_openai_functions\")\n",
+    "```"
    ]
   },
   {
@@ -55,7 +43,7 @@
    "outputs": [],
    "source": [
     "from langserve.client import RemoteRunnable\n",
-    "oai_function = RemoteRunnable('http://localhost:8000/openai-functions')"
+    "oai_function = RemoteRunnable('http://0.0.0.0:8001/extraction_openai_functions')"
    ]
   },
   {
@@ -63,32 +51,30 @@
    "id": "68046695",
    "metadata": {},
    "source": [
-    "The function call will perform tagging:\n",
-    "\n",
-    "* summarize\n",
-    "* provide keywords\n",
-    "* provide language"
+    "The function wille extract paper titles and authors from an input."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "6dace748",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'Overview', 'arguments': '{\\n  \"summary\": \"This article discusses the concept of building agents with LLM (large language model) as their core controller. It explores the potentiality of LLM as a general problem solver and describes the key components of an LLM-powered autonomous agent system, including planning, memory, and tool use. The article also presents case studies and challenges related to building LLM-powered agents.\",\\n  \"language\": \"English\",\\n  \"keywords\": \"LLM, autonomous agents, planning, memory, tool use, case studies, challenges\"\\n}'}})"
+       "[{'title': 'Chain of Thought', 'author': 'Wei et al. 2022'},\n",
+       " {'title': 'Tree of Thoughts', 'author': 'Yao et al. 2023'},\n",
+       " {'title': 'LLM+P', 'author': 'Liu et al. 2023'}]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "oai_function.invoke(text[0].page_content[0:1500])"
+    "oai_function.invoke({\"input\":text[0].page_content[0:4000]})"
    ]
   }
  ],