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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt_tab to\n",
"[nltk_data] /Users/ryanrodriguez/nltk_data...\n",
"[nltk_data] Package punkt_tab is already up-to-date!\n",
"[nltk_data] Downloading package averaged_perceptron_tagger_eng to\n",
"[nltk_data] /Users/ryanrodriguez/nltk_data...\n",
"[nltk_data] Package averaged_perceptron_tagger_eng is already up-to-\n",
"[nltk_data] date!\n"
]
}
],
"source": [
"from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"from langchain.chains import create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from backend.app.vectorstore import get_vector_db"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_ROLE_PROMPT = \"\"\"\n",
" You are a knowledgeable grading assistant that evaluates student answers based on provided context.\n",
" You should determine if answers are correct and provide constructive feedback.\n",
"\"\"\"\n",
"\n",
"USER_ROLE_PROMPT = \"\"\"\n",
" Grade the following student answer based on the provided context about {query}.\n",
" \n",
" Context: {context}\n",
" \n",
" Question: {problem}\n",
" Student Answer: {answer}\n",
" \n",
" Evaluate if the answer is correct and provide brief feedback. Start with either \"Correct\" or \"Incorrect\" \n",
" followed by a brief explanation of why. Focus on the accuracy based on the context provided.\n",
" \n",
" Always begin your response with \"Correct\" or \"Incorrect\" and then provide a brief explanation of why.\n",
"\n",
" Your response should be direct and clear, for example:\n",
" \"Correct. The answer accurately explains [reason]\" or \n",
" \"Incorrect. While [partial understanding], the answer misses [key point]\"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"chat_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", SYSTEM_ROLE_PROMPT),\n",
" (\"user\", USER_ROLE_PROMPT)\n",
"])\n",
"\n",
"openai_chat_model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0.7)\n",
"\n",
"retriever = get_vector_db().as_retriever(search_kwargs={\"k\": 2})\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from operator import itemgetter\n",
"\n",
"simple_rag = (\n",
" {\n",
" # Use the query to retrieve documents from the vectorstore\n",
" \"context\": itemgetter(\"query\") | retriever | (lambda docs: \"\\n\\n\".join([doc.page_content for doc in docs])),\n",
" # Pass through all other inputs directly\n",
" \"query\": itemgetter(\"query\"),\n",
" \"problem\": itemgetter(\"problem\"),\n",
" \"answer\": itemgetter(\"answer\")\n",
" } \n",
" | chat_prompt\n",
" | openai_chat_model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Incorrect. While the answer mentions storing and retrieving documents, it misses the key point that the purpose of the indexing component in a RAG application is to ingest data from a source, index it, and facilitate efficient retrieval of relevant data at runtime.'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_result = simple_rag.invoke(\n",
" {\n",
" \"query\": \"RAG\",\n",
" \"problem\": \"What is the purpose of the indexing component in a RAG application?\",\n",
" \"answer\": \"The indexing component is used to store and retrieve documents efficiently.\"\n",
" }\n",
")\n",
"raw_result"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['What are the two main components of a typical RAG application?',\n",
" 'What is the purpose of the indexing component in a RAG application?',\n",
" \"What are the steps involved in the 'Load' phase of indexing?\",\n",
" 'Why is splitting text into smaller chunks important in the context of RAG applications?',\n",
" 'How does the retrieval and generation component of a RAG application process user queries?']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"result = json.loads(raw_result)\n",
"result[\"questions\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "argument 'text': 'dict' object cannot be converted to 'PyString'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontext\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mretriever\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquery\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mRunnablePassthrough\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mproblem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mRunnablePassthrough\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43manswer\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mRunnablePassthrough\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m}\u001b[49m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;241;43m|\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchat_prompt\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquery\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRAG\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mproblem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWhat is the purpose of the indexing component in a RAG application?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43manswer\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mThe indexing component is used to store and retrieve documents efficiently.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3022\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 3020\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, config)\n\u001b[1;32m 3021\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 3022\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\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[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\u001b[1;32m 3023\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3024\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(step\u001b[38;5;241m.\u001b[39minvoke, \u001b[38;5;28minput\u001b[39m, config)\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3729\u001b[0m, in \u001b[0;36mRunnableParallel.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 3724\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m get_executor_for_config(config) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[1;32m 3725\u001b[0m futures \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 3726\u001b[0m executor\u001b[38;5;241m.\u001b[39msubmit(_invoke_step, step, \u001b[38;5;28minput\u001b[39m, config, key)\n\u001b[1;32m 3727\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, step \u001b[38;5;129;01min\u001b[39;00m steps\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 3728\u001b[0m ]\n\u001b[0;32m-> 3729\u001b[0m output \u001b[38;5;241m=\u001b[39m {key: \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m key, future \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(steps, futures)}\n\u001b[1;32m 3730\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m 3731\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",
"File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/concurrent/futures/_base.py:449\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 449\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__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
"File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/.local/share/uv/python/cpython-3.12.0-macos-aarch64-none/lib/python3.12/concurrent/futures/thread.py:58\u001b[0m, in \u001b[0;36m_WorkItem.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[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[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\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 exc:\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfuture\u001b[38;5;241m.\u001b[39mset_exception(exc)\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_core/runnables/base.py:3713\u001b[0m, in \u001b[0;36mRunnableParallel.invoke.<locals>._invoke_step\u001b[0;34m(step, input, config, key)\u001b[0m\n\u001b[1;32m 3711\u001b[0m context \u001b[38;5;241m=\u001b[39m copy_context()\n\u001b[1;32m 3712\u001b[0m context\u001b[38;5;241m.\u001b[39mrun(_set_config_context, child_config)\n\u001b[0;32m-> 3713\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcontext\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3716\u001b[0m \u001b[43m \u001b[49m\u001b[43mchild_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3717\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_core/retrievers.py:259\u001b[0m, in \u001b[0;36mBaseRetriever.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m 257\u001b[0m _kwargs \u001b[38;5;241m=\u001b[39m kwargs \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expects_other_args \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_arg_supported:\n\u001b[0;32m--> 259\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_relevant_documents\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[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[43m_kwargs\u001b[49m\n\u001b[1;32m 261\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 263\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_relevant_documents(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_kwargs)\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_core/vectorstores/base.py:1073\u001b[0m, in \u001b[0;36mVectorStoreRetriever._get_relevant_documents\u001b[0;34m(self, query, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m 1071\u001b[0m _kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_kwargs \u001b[38;5;241m|\u001b[39m kwargs\n\u001b[1;32m 1072\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimilarity\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1073\u001b[0m docs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvectorstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimilarity_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[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[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1074\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msearch_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimilarity_score_threshold\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1075\u001b[0m docs_and_similarities \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1076\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvectorstore\u001b[38;5;241m.\u001b[39msimilarity_search_with_relevance_scores(\n\u001b[1;32m 1077\u001b[0m query, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m_kwargs\n\u001b[1;32m 1078\u001b[0m )\n\u001b[1;32m 1079\u001b[0m )\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_community/vectorstores/qdrant.py:288\u001b[0m, in \u001b[0;36mQdrant.similarity_search\u001b[0;34m(self, query, k, filter, search_params, offset, score_threshold, consistency, **kwargs)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msimilarity_search\u001b[39m(\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 245\u001b[0m query: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 253\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Document]:\n\u001b[1;32m 254\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return docs most similar to query.\u001b[39;00m\n\u001b[1;32m 255\u001b[0m \n\u001b[1;32m 256\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;124;03m List of Documents most similar to the query.\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 288\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimilarity_search_with_score\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 289\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 290\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 291\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 292\u001b[0m \u001b[43m \u001b[49m\u001b[43msearch_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msearch_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 293\u001b[0m \u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 294\u001b[0m \u001b[43m \u001b[49m\u001b[43mscore_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscore_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 295\u001b[0m \u001b[43m \u001b[49m\u001b[43mconsistency\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconsistency\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 296\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[1;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(itemgetter(\u001b[38;5;241m0\u001b[39m), results))\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_community/vectorstores/qdrant.py:365\u001b[0m, in \u001b[0;36mQdrant.similarity_search_with_score\u001b[0;34m(self, query, k, filter, search_params, offset, score_threshold, consistency, **kwargs)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msimilarity_search_with_score\u001b[39m(\n\u001b[1;32m 320\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 321\u001b[0m query: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m 329\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Tuple[Document, \u001b[38;5;28mfloat\u001b[39m]]:\n\u001b[1;32m 330\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return docs most similar to query.\u001b[39;00m\n\u001b[1;32m 331\u001b[0m \n\u001b[1;32m 332\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;124;03m List of documents most similar to the query text and distance for each.\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimilarity_search_with_score_by_vector(\n\u001b[0;32m--> 365\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_embed_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 366\u001b[0m k,\n\u001b[1;32m 367\u001b[0m \u001b[38;5;28mfilter\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfilter\u001b[39m,\n\u001b[1;32m 368\u001b[0m search_params\u001b[38;5;241m=\u001b[39msearch_params,\n\u001b[1;32m 369\u001b[0m offset\u001b[38;5;241m=\u001b[39moffset,\n\u001b[1;32m 370\u001b[0m score_threshold\u001b[38;5;241m=\u001b[39mscore_threshold,\n\u001b[1;32m 371\u001b[0m consistency\u001b[38;5;241m=\u001b[39mconsistency,\n\u001b[1;32m 372\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 373\u001b[0m )\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_community/vectorstores/qdrant.py:2065\u001b[0m, in \u001b[0;36mQdrant._embed_query\u001b[0;34m(self, query)\u001b[0m\n\u001b[1;32m 2054\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Embed query text.\u001b[39;00m\n\u001b[1;32m 2055\u001b[0m \n\u001b[1;32m 2056\u001b[0m \u001b[38;5;124;03mUsed to provide backward compatibility with `embedding_function` argument.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2062\u001b[0m \u001b[38;5;124;03m List of floats representing the query embedding.\u001b[39;00m\n\u001b[1;32m 2063\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2064\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2065\u001b[0m embedding \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membed_query\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2066\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_embeddings_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_openai/embeddings/base.py:629\u001b[0m, in \u001b[0;36mOpenAIEmbeddings.embed_query\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21membed_query\u001b[39m(\u001b[38;5;28mself\u001b[39m, text: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[\u001b[38;5;28mfloat\u001b[39m]:\n\u001b[1;32m 621\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Call out to OpenAI's embedding endpoint for embedding query text.\u001b[39;00m\n\u001b[1;32m 622\u001b[0m \n\u001b[1;32m 623\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;124;03m Embedding for the text.\u001b[39;00m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 629\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[43membed_documents\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_openai/embeddings/base.py:588\u001b[0m, in \u001b[0;36mOpenAIEmbeddings.embed_documents\u001b[0;34m(self, texts, chunk_size)\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[38;5;66;03m# NOTE: to keep things simple, we assume the list may contain texts longer\u001b[39;00m\n\u001b[1;32m 586\u001b[0m \u001b[38;5;66;03m# than the maximum context and use length-safe embedding function.\u001b[39;00m\n\u001b[1;32m 587\u001b[0m engine \u001b[38;5;241m=\u001b[39m cast(\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeployment)\n\u001b[0;32m--> 588\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_get_len_safe_embeddings\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtexts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_openai/embeddings/base.py:480\u001b[0m, in \u001b[0;36mOpenAIEmbeddings._get_len_safe_embeddings\u001b[0;34m(self, texts, engine, chunk_size)\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;124;03mGenerate length-safe embeddings for a list of texts.\u001b[39;00m\n\u001b[1;32m 466\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[38;5;124;03m List[List[float]]: A list of embeddings for each input text.\u001b[39;00m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 479\u001b[0m _chunk_size \u001b[38;5;241m=\u001b[39m chunk_size \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchunk_size\n\u001b[0;32m--> 480\u001b[0m _iter, tokens, indices \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tokenize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtexts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_chunk_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 481\u001b[0m batched_embeddings: List[List[\u001b[38;5;28mfloat\u001b[39m]] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 482\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m _iter:\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/langchain_openai/embeddings/base.py:441\u001b[0m, in \u001b[0;36mOpenAIEmbeddings._tokenize\u001b[0;34m(self, texts, chunk_size)\u001b[0m\n\u001b[1;32m 439\u001b[0m token \u001b[38;5;241m=\u001b[39m encoding\u001b[38;5;241m.\u001b[39mencode(text, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mencoder_kwargs)\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 441\u001b[0m token \u001b[38;5;241m=\u001b[39m \u001b[43mencoding\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_ordinary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 443\u001b[0m \u001b[38;5;66;03m# Split tokens into chunks respecting the embedding_ctx_length\u001b[39;00m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mlen\u001b[39m(token), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membedding_ctx_length):\n",
"File \u001b[0;32m~/src/Simplify/.venv/lib/python3.12/site-packages/tiktoken/core.py:73\u001b[0m, in \u001b[0;36mEncoding.encode_ordinary\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Encodes a string into tokens, ignoring special tokens.\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \n\u001b[1;32m 66\u001b[0m \u001b[38;5;124;03mThis is equivalent to `encode(text, disallowed_special=())` (but slightly faster).\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[38;5;124;03m[31373, 995]\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 73\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_core_bpe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_ordinary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mUnicodeEncodeError\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;66;03m# See comment in encode\u001b[39;00m\n\u001b[1;32m 76\u001b[0m text \u001b[38;5;241m=\u001b[39m text\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-16\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-16\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreplace\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mTypeError\u001b[0m: argument 'text': 'dict' object cannot be converted to 'PyString'"
]
}
],
"source": [
"({\n",
" \"context\": retriever,\n",
" \"query\": RunnablePassthrough(),\n",
" \"problem\": RunnablePassthrough(),\n",
" \"answer\": RunnablePassthrough(),\n",
"}\n",
"| chat_prompt).invoke(\n",
" {\n",
" \"query\": \"RAG\",\n",
" \"problem\": \"What is the purpose of the indexing component in a RAG application?\",\n",
" \"answer\": \"The indexing component is used to store and retrieve documents efficiently.\"\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(metadata={'source': 'static/data/langchain_rag_tutorial.html', '_id': 'f1d61e037f6240d3a3671e435f9d5726', '_collection_name': 'extending_context_window_llama_3'}, page_content=\"Part 1 (this guide) introduces RAG and walks through a minimal implementation.\\n\\nPart 2 extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes.\\n\\nThis tutorial will show how to build a simple Q&A application\\nover a text data source. Along the way we’ll go over a typical Q&A\\narchitecture and highlight additional resources for more advanced Q&A techniques. We’ll also see\\nhow LangSmith can help us trace and understand our application.\\nLangSmith will become increasingly helpful as our application grows in\\ncomplexity.\\n\\nIf you're already familiar with basic retrieval, you might also be interested in\\nthis high-level overview of different retrieval techniques.\\n\\nNote: Here we focus on Q&A for unstructured data. If you are interested for RAG over structured data, check out our tutorial on doing question/answering over SQL data.\\n\\nOverview\\u200b\\n\\nA typical RAG application has two main components:\"),\n",
" Document(metadata={'source': 'static/data/langchain_rag_tutorial.html', '_id': 'aaa4338e68d142febd9bd7969f0b3a2d', '_collection_name': 'extending_context_window_llama_3'}, page_content=\"Overview\\u200b\\n\\nA typical RAG application has two main components:\\n\\nIndexing: a pipeline for ingesting data from a source and indexing it. This usually happens offline.\\n\\nRetrieval and generation: the actual RAG chain, which takes the user query at run time and retrieves the relevant data from the index, then passes that to the model.\\n\\nNote: the indexing portion of this tutorial will largely follow the semantic search tutorial.\\n\\nThe most common full sequence from raw data to answer looks like:\\n\\nIndexing\\u200b\\n\\nLoad: First we need to load our data. This is done with Document Loaders.\\n\\nSplit: Text splitters break large Documents into smaller chunks. This is useful both for indexing data and passing it into a model, as large chunks are harder to search over and won't fit in a model's finite context window.\\n\\nStore: We need somewhere to store and index our splits, so that they can be searched over later. This is often done using a VectorStore and Embeddings model.\\n\\nRetrieval and generation\\u200b\")]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retriever.invoke(\"RAG\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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",
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|