assn13.ipynb

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     "text": [
      "content='According to the text, Jones, Aaronson, and Rutherford were guilty of the crimes they were charged with.'\n",
      "content='He wrote \"2+2=5\" in the dust on the table.'\n",
      "content='Julia is a character who was involved with the protagonist in the story.'\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.document_loaders import UnstructuredFileLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.embeddings import OpenAIEmbeddings, CacheBackedEmbeddings\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain.storage import LocalFileStore\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
    "\n",
    "\n",
    "llm = ChatOpenAI(\n",
    "    temperature=0.1,\n",
    ")\n",
    "\n",
    "memory = ConversationBufferMemory(return_messages=True)\n",
    "\n",
    "\n",
    "def load_memory(_):\n",
    "    return memory.load_memory_variables({})[\"history\"]\n",
    "\n",
    "cache_dir = LocalFileStore(\"./.cache/\")\n",
    "\n",
    "splitter = CharacterTextSplitter.from_tiktoken_encoder(\n",
    "    separator=\"\\n\",\n",
    "    chunk_size=600,\n",
    "    chunk_overlap=100,\n",
    ")\n",
    "loader = UnstructuredFileLoader(\"./files/document.txt\")\n",
    "\n",
    "docs = loader.load_and_split(text_splitter=splitter)\n",
    "\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "cached_embeddings = CacheBackedEmbeddings.from_bytes_store(embeddings, cache_dir)\n",
    "\n",
    "vectorstore = FAISS.from_documents(docs, cached_embeddings)\n",
    "\n",
    "retriever = vectorstore.as_retriever()\n",
    "\n",
    "prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\n",
    "            \"system\",\n",
    "            \"You are a helpful assistant. Answer questions using only the following context. If you don't know the answer just say you don't know, don't make it up:\\n\\n{context}\",\n",
    "        ),\n",
    "        MessagesPlaceholder(variable_name=\"history\"),\n",
    "        (\"human\", \"{question}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "chain = (\n",
    "    {\n",
    "        \"context\": retriever,\n",
    "        \"question\": RunnablePassthrough(),\n",
    "        \"history\": RunnableLambda(load_memory),\n",
    "    }\n",
    "    | prompt\n",
    "    | llm\n",
    ")\n",
    "questions = [\"Is Aaronson guilty?\",\n",
    "             \"What message did he write in the table?\",\n",
    "             \"Who is Julia?\",\n",
    "]\n",
    "for question in questions:\n",
    "    result = chain.invoke(question)\n",
    "    print(result)\n",
    "    memory.save_context({\"input\":question}, {\"output\":result.content})"
   ]
  }
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