Tried out an app version; the chat playground has browser issues, and the default playground is not ideal. Langsmith seems best for inspecting post-facto.

app_notebook.ipynb

@@ -0,0 +1,69 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langserve import RemoteRunnable\n",
+    "\n",
+    "remote_runnable = RemoteRunnable('http://localhost:8000/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "HTTPStatusError",
+     "evalue": "Server error '500 Internal Server Error' for url 'http://localhost:8000/invoke'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500 for Internal Server Error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mHTTPStatusError\u001b[0m                           Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langserve\\client.py:157\u001b[0m, in \u001b[0;36m_raise_for_status\u001b[1;34m(response)\u001b[0m\n\u001b[0;32m    156\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 157\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    158\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m httpx\u001b[38;5;241m.\u001b[39mHTTPStatusError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\httpx\\_models.py:761\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    760\u001b[0m message \u001b[38;5;241m=\u001b[39m message\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m, error_type\u001b[38;5;241m=\u001b[39merror_type)\n\u001b[1;32m--> 761\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPStatusError(message, request\u001b[38;5;241m=\u001b[39mrequest, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
+      "\u001b[1;31mHTTPStatusError\u001b[0m: Server error '500 Internal Server Error' for url 'http://localhost:8000/invoke'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mHTTPStatusError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# remote_runnable.invoke(dict(input = 'Do you know the tragedy of Darth Plagueis the Wise?'))\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mremote_runnable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDo you know the tragedy of Darth Plagueis the Wise?\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langserve\\client.py:356\u001b[0m, in \u001b[0;36mRemoteRunnable.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m    354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs:\n\u001b[0;32m    355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs not implemented yet.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langchain_core\\runnables\\base.py:1625\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[1;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[0;32m   1621\u001b[0m     context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[0;32m   1622\u001b[0m     context\u001b[38;5;241m.\u001b[39mrun(var_child_runnable_config\u001b[38;5;241m.\u001b[39mset, child_config)\n\u001b[0;32m   1623\u001b[0m     output \u001b[38;5;241m=\u001b[39m cast(\n\u001b[0;32m   1624\u001b[0m         Output,\n\u001b[1;32m-> 1625\u001b[0m         \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1626\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1627\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1628\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   1629\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1630\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1631\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1632\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[0;32m   1633\u001b[0m     )\n\u001b[0;32m   1634\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m   1635\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langchain_core\\runnables\\config.py:347\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[1;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[0;32m    346\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[1;32m--> 347\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langserve\\client.py:343\u001b[0m, in \u001b[0;36mRemoteRunnable._invoke\u001b[1;34m(self, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[0;32m    334\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke the runnable with the given input and config.\"\"\"\u001b[39;00m\n\u001b[0;32m    335\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msync_client\u001b[38;5;241m.\u001b[39mpost(\n\u001b[0;32m    336\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/invoke\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    337\u001b[0m     json\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    341\u001b[0m     },\n\u001b[0;32m    342\u001b[0m )\n\u001b[1;32m--> 343\u001b[0m output, callback_events \u001b[38;5;241m=\u001b[39m \u001b[43m_decode_response\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    344\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_lc_serializer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mis_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[0;32m    345\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_use_server_callback_events \u001b[38;5;129;01mand\u001b[39;00m callback_events:\n\u001b[0;32m    348\u001b[0m     handle_callbacks(run_manager, callback_events)\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langserve\\client.py:230\u001b[0m, in \u001b[0;36m_decode_response\u001b[1;34m(serializer, response, is_batch)\u001b[0m\n\u001b[0;32m    223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_decode_response\u001b[39m(\n\u001b[0;32m    224\u001b[0m     serializer: Serializer,\n\u001b[0;32m    225\u001b[0m     response: httpx\u001b[38;5;241m.\u001b[39mResponse,\n\u001b[0;32m    226\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m    227\u001b[0m     is_batch: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    228\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[Any, Union[List[CallbackEventDict], List[List[CallbackEventDict]]]]:\n\u001b[0;32m    229\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Decode the response.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 230\u001b[0m     \u001b[43m_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    231\u001b[0m     obj \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mjson()\n\u001b[0;32m    232\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, \u001b[38;5;28mdict\u001b[39m):\n",
+      "File \u001b[1;32mc:\\Users\\Dr-Lo\\miniconda3\\envs\\ML11\\Lib\\site-packages\\langserve\\client.py:165\u001b[0m, in \u001b[0;36m_raise_for_status\u001b[1;34m(response)\u001b[0m\n\u001b[0;32m    162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mtext:\n\u001b[0;32m    163\u001b[0m     message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mtext\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 165\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m httpx\u001b[38;5;241m.\u001b[39mHTTPStatusError(\n\u001b[0;32m    166\u001b[0m     message\u001b[38;5;241m=\u001b[39mmessage,\n\u001b[0;32m    167\u001b[0m     request\u001b[38;5;241m=\u001b[39m_sanitize_request(e\u001b[38;5;241m.\u001b[39mrequest),\n\u001b[0;32m    168\u001b[0m     response\u001b[38;5;241m=\u001b[39me\u001b[38;5;241m.\u001b[39mresponse,\n\u001b[0;32m    169\u001b[0m )\n",
+      "\u001b[1;31mHTTPStatusError\u001b[0m: Server error '500 Internal Server Error' for url 'http://localhost:8000/invoke'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500 for Internal Server Error"
+     ]
+    }
+   ],
+   "source": [
+    "# remote_runnable.invoke(dict(input = 'Do you know the tragedy of Darth Plagueis the Wise?'))\n",
+    "remote_runnable.invoke('Do you know the tragedy of Darth Plagueis the Wise?')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ML11",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

serve.py

@@ -0,0 +1,205 @@
+from langchain_openai import ChatOpenAI#, OpenAIEmbeddings # No need to pay for using embeddings as well when have free alternatives
+
+# Data
+from langchain_community.document_loaders import DirectoryLoader, TextLoader, WebBaseLoader
+# from langchain_chroma import Chroma # The documentation uses this one, but it is extremely recent, and the same functionality is available in langchain_community and langchain (which imports community)
+from langchain_community.vectorstores import Chroma # This has documentation on-hover, while the indirect import through non-community does not
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings # The free alternative (also the default in docs, with model_name = 'all-MiniLM-L6-v2')
+from langchain.text_splitter import RecursiveCharacterTextSplitter#, TextSplitter # Recursive to better keep related bits contiguous (also recommended in docs: https://python.langchain.com/docs/modules/data_connection/document_transformers/)
+
+# Chains
+from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.output_parsers import StrOutputParser
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.tools.retriever import create_retriever_tool
+from langchain_core.runnables import RunnablePassthrough, RunnableParallel
+
+# Agents
+from langchain import hub
+from langchain.agents import create_tool_calling_agent, AgentExecutor
+
+# To manually create inputs to test pipelines
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.documents import Document
+
+# To serve the app
+from fastapi import FastAPI
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain_core.messages import BaseMessage
+from langserve import add_routes, CustomUserType
+
+import requests
+from bs4 import BeautifulSoup
+
+import os
+import shutil
+import re
+
+import dotenv
+dotenv.load_dotenv()
+
+
+## Vector stores
+
+script_db = Chroma(embedding_function = SentenceTransformerEmbeddings(model_name = 'all-MiniLM-L6-v2'), persist_directory = r'scripts\db')
+woo_db = Chroma(embedding_function = SentenceTransformerEmbeddings(model_name = 'all-MiniLM-L6-v2'), persist_directory = r'wookieepedia_db')
+
+
+
+## Wookieepedia functions
+
+def first_wookieepedia_result(query: str) -> str:
+    '''Get the url of the first result when searching Wookieepedia for a query
+    (best for simple names as queries, ideally generated by the llm for something like
+    "Produce a input consisting of the name of the most important element in the query so that its article can be looked up")
+    '''
+    search_results = requests.get(f'https://starwars.fandom.com/wiki/Special:Search?query={"+".join(query.split(" "))}')
+    soup = BeautifulSoup(search_results.content, 'html.parser')
+    first_res = soup.find('a', class_ = 'unified-search__result__link')
+    return first_res['href']
+
+
+def get_new_wookieepedia_chunks(query: str, previous_sources: set[str]) -> list[Document]:
+    '''Retrieve and return chunks of the content of the first result of query on Wookieepedia, then return the closest matches for.
+    '''
+    url = first_wookieepedia_result(query)
+
+    if url in previous_sources: return []
+    else:
+        doc = WebBaseLoader(url).load()[0] # Only one url passed in => only one Document out; no need to assert
+    
+        # There probably is a very long preamble before the real content, however, if more than one gap then ignore and proceed with full document
+        trimmed = parts[1] if len(parts := doc.page_content.split('\n\n\n\n\n\n\n\n\n\n\n\n\n\n \xa0 \xa0')) == 2 else doc.page_content
+        doc.page_content = re.sub(r'[\n\t]{2,}', '\n', trimmed) # And remove excessive spacing
+
+        return RecursiveCharacterTextSplitter(chunk_size = 800, chunk_overlap = 100).split_documents([doc])
+
+def get_wookieepedia_context(original_query: str, simple_query: str, wdb: Chroma) -> list[Document]:
+    try:
+        new_chunks = get_new_wookieepedia_chunks(simple_query, previous_sources = set(md.get('source') for md in wdb.get()['metadatas']))
+        if new_chunks: wdb.add_documents(new_chunks)
+    except: return []
+
+    return wdb.similarity_search(original_query, k = 10)
+
+
+
+# Chains
+
+llm = ChatOpenAI(model = 'gpt-3.5-turbo-0125', temperature = 0)
+
+
+document_prompt_system_text = '''
+You are very knowledgeable about Star Wars and your job is to answer questions about its plot, characters, etc.
+Use the context below to produce your answers with as much detail as possible.
+If you do not know an answer, say so; do not make up information not in the context.
+
+<context>
+{context}
+</context>
+'''
+document_prompt = ChatPromptTemplate.from_messages([
+    ('system', document_prompt_system_text),
+    MessagesPlaceholder(variable_name = 'chat_history', optional = True),
+    ('user', '{input}')
+])
+document_chain = create_stuff_documents_chain(llm, document_prompt)
+
+
+script_retriever_prompt = ChatPromptTemplate.from_messages([
+    MessagesPlaceholder(variable_name = 'chat_history'),
+    ('user', '{input}'),
+    ('user', '''Given the above conversation, generate a search query to look up relevant information in a database containing the full scripts from the Star Wars films (i.e. just dialogue and brief scene descriptions).
+     The query need not be a proper sentence, but a list of keywords likely to be in dialogue or scene descriptions''')
+])
+script_retriever_chain = create_history_aware_retriever(llm, script_db.as_retriever(), script_retriever_prompt) # Essentially just: prompt | llm | StrOutputParser() | retriever
+
+
+woo_retriever_prompt = ChatPromptTemplate.from_messages([
+    MessagesPlaceholder(variable_name = 'chat_history'),
+    ('user', '{input}'),
+    ('user', 'Given the above conversation, generate a search query to find a relevant page in the Star Wars fandom wiki; the query should be something simple, such as the name of a character, place, event, item, etc.')
+])
+woo_retriever_chain = create_history_aware_retriever(llm, woo_db.as_retriever(), woo_retriever_prompt) # Essentially just: prompt | llm | StrOutputParser() | retriever
+
+
+full_chain = create_retrieval_chain(script_retriever_chain, document_chain)
+
+
+
+## Agent version
+
+script_tool = create_retriever_tool(
+    script_db.as_retriever(search_kwargs = dict(k = 4)),
+    'search_film_scripts',
+    '''Search the Star Wars film scripts. This tool should be the first choice for Star Wars related questions.
+    Queries passed to this tool should be lists of keywords likely to be in dialogue or scene descriptions, and should not include film titles.'''
+)
+wookieepedia_tool = create_retriever_tool(
+    woo_db.as_retriever(search_kwargs = dict(k = 4)),
+    'search_wookieepedia',
+    'Search the Star Wars fandom wiki. This tool should be used for queries about details of a particular character, location, event, weapon, etc., and the query should be something simple, such as the name of a character, place, event, item, etc.',
+)
+tools = [script_tool, wookieepedia_tool]
+
+agent_system_text = '''
+You are a helpful agent who is very knowledgeable about Star Wars and your job is to answer questions about its plot, characters, etc.
+Use the context provided in the exchanges to come to produce your answers with as much detail as possible.
+If you do not know an answer, say so; do not make up information.
+'''
+agent_prompt = ChatPromptTemplate.from_messages([
+    ('system', agent_system_text),
+    MessagesPlaceholder('chat_history', optional = True), # Using this form since not clear how to have optional = True in the tuple form
+    ('human', '{input}'),
+    ('placeholder', '{agent_scratchpad}') # Required for chat history and the agent's intermediate processing values
+])
+agent = create_tool_calling_agent(llm, tools, agent_prompt)
+agent_executor = AgentExecutor(agent = agent, tools = tools, verbose = True)
+
+
+
+
+class StrInput(BaseModel):
+    input: str
+
+class Input(BaseModel):
+    input: str
+    chat_history: list[BaseMessage] = Field(
+        ...,
+        extra = {'widget': {'type': 'chat', 'input': 'location'}},
+    )
+
+class Output(BaseModel):
+    output: str
+
+
+
+
+## App definition
+
+app = FastAPI(
+  title = 'LangChain Server',
+  version = '1.0',
+  description = 'Simple version of a Star Wars expert',
+)
+
+
+
+## Adding chain route
+
+# add_routes(app, script_db.as_retriever())
+add_routes(app, full_chain.with_types(input_type = StrInput, output_type = Output), playground_type = 'default')
+
+# NOTE: The chat playground type has a web page issue (flashes and becomes white, hence non-interactable; this was supposedly solved in an issue late last year)
+
+# add_routes(app, agent_executor, playground_type = 'chat')
+# add_routes(app, agent_executor.with_types(input_type = StrInput, output_type = Output))
+
+
+if __name__ == '__main__':
+    import uvicorn
+
+    uvicorn.run(app, host = 'localhost', port = 8000)
+
+