From fcb108ced63a9e6906ac753d03ca12ebbdc3ba82 Mon Sep 17 00:00:00 2001 From: Taqi Jaffri Date: Thu, 28 Mar 2024 11:24:12 -0700 Subject: [PATCH] notebooks --- docugami_kg_rag/agent.py | 16 +- .../eval-sec-10-q.ipynb | 83 ++-- .../eval-csv.ipynb => notebooks/run-csv.ipynb | 46 ++- poetry.lock | 388 +++++++++++------- pyproject.toml | 5 +- 5 files changed, 344 insertions(+), 194 deletions(-) rename evals/sec-10-q.ipynb => notebooks/eval-sec-10-q.ipynb (81%) rename evals/eval-csv.ipynb => notebooks/run-csv.ipynb (64%) diff --git a/docugami_kg_rag/agent.py b/docugami_kg_rag/agent.py index 7f5942c..8579c80 100644 --- a/docugami_kg_rag/agent.py +++ b/docugami_kg_rag/agent.py @@ -107,13 +107,15 @@ class AgentInput(BaseModel): ) -def agent_output_to_string(streaming_state: AgentState) -> str: - if streaming_state: - react_output = streaming_state.get("generate_re_act") - if react_output: - cited_answer = react_output.get("cited_answer") - if cited_answer and cited_answer.is_final: - return cited_answer.answer +def agent_output_to_string(state: AgentState) -> str: + if state: + streaming_output = state.get("generate_re_act") + if streaming_output: + state = streaming_output + + cited_answer = state.get("cited_answer") + if cited_answer and cited_answer.is_final: + return cited_answer.answer return "" diff --git a/evals/sec-10-q.ipynb b/notebooks/eval-sec-10-q.ipynb similarity index 81% rename from evals/sec-10-q.ipynb rename to notebooks/eval-sec-10-q.ipynb index 212d28d..e075f85 100644 --- a/evals/sec-10-q.ipynb +++ b/notebooks/eval-sec-10-q.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ "\n", "DOCSET_NAME = \"SEC 10Q Filings\"\n", "EVAL_NAME = DOCSET_NAME + \" \" + datetime.now().strftime(\"%Y-%m-%d\")\n", - "FILES_DIR = Path(os.getcwd()) / \"temp/sec-10-q/docs\"\n", + "FILES_DIR = Path(os.getcwd()) / \"temp/sec-10-q/data/v1/docs\"\n", "FILE_NAMES = [\n", " \"2022 Q3 AAPL.pdf\",\n", " \"2022 Q3 AMZN.pdf\",\n", @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -154,9 +154,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'2022 Q3 AAPL.pdf': '/tmp/tmpwj7qvnpc',\n", + " '2022 Q3 AMZN.pdf': '/tmp/tmp_b1v53gw',\n", + " '2022 Q3 INTC.pdf': '/tmp/tmp7rbwg3oo',\n", + " '2022 Q3 MSFT.pdf': '/tmp/tmplb2qcrzz',\n", + " '2022 Q3 NVDA.pdf': '/tmp/tmpudk140xq',\n", + " '2023 Q1 AAPL.pdf': '/tmp/tmpy49blnkv',\n", + " '2023 Q1 AMZN.pdf': '/tmp/tmp9z7c6swf',\n", + " '2023 Q1 INTC.pdf': '/tmp/tmpfi4rli61',\n", + " '2023 Q1 MSFT.pdf': '/tmp/tmprbk948a0',\n", + " '2023 Q1 NVDA.pdf': '/tmp/tmp779afiom',\n", + " '2023 Q2 AAPL.pdf': '/tmp/tmpn22kjw46',\n", + " '2023 Q2 AMZN.pdf': '/tmp/tmp3fadq9kp',\n", + " '2023 Q2 INTC.pdf': '/tmp/tmpe0gim1ke',\n", + " '2023 Q2 MSFT.pdf': '/tmp/tmpb5mb3x0a',\n", + " '2023 Q2 NVDA.pdf': '/tmp/tmpgc20mcnv',\n", + " '2023 Q3 AAPL.pdf': '/tmp/tmprnrsxhhs',\n", + " '2023 Q3 AMZN.pdf': '/tmp/tmpuatxzleg',\n", + " '2023 Q3 INTC.pdf': '/tmp/tmpgnyhizig',\n", + " '2023 Q3 MSFT.pdf': '/tmp/tmp5dg44pcy',\n", + " '2023 Q3 NVDA.pdf': '/tmp/tmp9dogliao'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Wait for files to finish processing (OCR, and zero-shot creation of XML knowledge graph)\n", "\n", @@ -166,9 +196,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Torch not installed...\n", + "Loading default rankgpt3 model for language en\n", + "Loading RankGPTRanker model gpt-3.5-turbo\n", + "Indexing SEC 10Q Filings (ID: s85dxu9aie2h)\n" + ] + } + ], "source": [ "# Run indexing\n", "from docugami_kg_rag.indexing import index_docset\n", @@ -193,23 +234,15 @@ "metadata": {}, "outputs": [], "source": [ - "from docugami_kg_rag.agent import agent as docugami_agent, _get_tools, AgentInput\n", - "\n", - "def predict_docugami_agent(input: dict) -> dict:\n", - " question = input[\"question\"]\n", - " chain = AgentExecutor(\n", - " agent=docugami_agent,\n", - " tools=_get_tools(),\n", - " ).with_types(\n", - " input_type=AgentInput,\n", - " )\n", - " result = chain.invoke({\n", - " \"input\": question,\n", - " \"use_reports\": False,\n", - " \"chat_history\": [],\n", - " })\n", + "from docugami_kg_rag.agent import agent as docugami_agent\n", + "from langchain_core.messages import HumanMessage\n", "\n", - " return result[\"output\"]" + "def predict_docugami_agent(question: str) -> str:\n", + " return docugami_agent.invoke(\n", + " {\n", + " \"messages\": [HumanMessage(content=question)],\n", + " }\n", + " )" ] }, { @@ -219,7 +252,7 @@ "outputs": [], "source": [ "# Test the agent to make sure it is working\n", - "predict_docugami_agent({\"question\": \"How much did Microsoft spend for opex in the latest quarter?\"})" + "predict_docugami_agent(\"How much did Microsoft spend for opex in the latest quarter?\")" ] }, { diff --git a/evals/eval-csv.ipynb b/notebooks/run-csv.ipynb similarity index 64% rename from evals/eval-csv.ipynb rename to notebooks/run-csv.ipynb index 22547db..b095b97 100644 --- a/evals/eval-csv.ipynb +++ b/notebooks/run-csv.ipynb @@ -11,16 +11,28 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "!rm -rf temp\n", + "!git clone https://github.com/docugami/KG-RAG-datasets.git temp" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", "import os\n", "import pandas as pd\n", "from pathlib import Path\n", "\n", - "INPUT_CSV_PATH = Path(os.getcwd()) / \"temp/questions.csv\"\n", - "OUTPUT_CSV_PATH = INPUT_CSV_PATH.with_name(INPUT_CSV_PATH.stem + '_answers' + INPUT_CSV_PATH.suffix)" + "INPUT_CSV_PATH = Path(os.getcwd()) / \"temp/sec-10-q/data/raw_questions/questions_mini.csv\"\n", + "EVAL_NAME = INPUT_CSV_PATH.stem + \"_\" + datetime.now().strftime(\"%Y-%m-%d\")\n", + "OUTPUT_CSV_PATH = INPUT_CSV_PATH.with_name(EVAL_NAME + \"_answers\" + INPUT_CSV_PATH.suffix)" ] }, { @@ -32,52 +44,48 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "from docugami_langchain.agents import ReActAgent\n", "from docugami_kg_rag.agent import agent as docugami_agent\n", + "from langchain_core.messages import HumanMessage\n", + "\n", "\n", "def predict_docugami_agent(question: str) -> str:\n", - " result = docugami_agent.invoke(\n", + " return docugami_agent.invoke(\n", " {\n", - " \"question\": question,\n", - " \"chat_history\": [],\n", - " \"agent_outcome\": None,\n", - " \"intermediate_steps\": [],\n", + " \"messages\": [HumanMessage(content=question)],\n", " }\n", - " )\n", - "\n", - " return ReActAgent.to_human_readable(result)" + " )" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Output CSV created at: /root/Source/github/langchain-template-docugami-kg-rag/evals/temp/questions_answers.csv\n" + "Output CSV created at: /root/Source/github/langchain-template-docugami-kg-rag/notebooks/temp/sec-10-q/data/raw_questions/questions_mini_2024-03-28_answers.csv\n" ] } ], "source": [ - "os.environ['LANGCHAIN_PROJECT'] = 'adp_eval_3_11'\n", + "os.environ['LANGCHAIN_PROJECT'] = EVAL_NAME\n", "\n", "# Eval the CSV\n", "df = pd.read_csv(INPUT_CSV_PATH)\n", - "if \"question\" in df.columns:\n", + "if \"Question\" in df.columns:\n", " # Apply the predict function to each question and create a new column for the answers\n", - " df[\"answer\"] = df[\"question\"].apply(predict_docugami_agent)\n", + " df[\"Answer\"] = df[\"Question\"].apply(predict_docugami_agent)\n", " # Write the dataframe with questions and answers to the output CSV\n", " df.to_csv(OUTPUT_CSV_PATH, index=False)\n", " print(f\"Output CSV created at: {OUTPUT_CSV_PATH}\")\n", "else:\n", - " print(\"Error: The 'question' column does not exist in the input CSV.\")" + " print(\"Error: The 'Question' column does not exist in the input CSV.\")" ] } ], diff --git a/poetry.lock b/poetry.lock index 8df6690..0116df6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -110,6 +110,17 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + [[package]] name = "anyio" version = "4.3.0" @@ -307,26 +318,27 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] name = "build" -version = "1.1.1" +version = "1.2.1" description = "A simple, correct Python build frontend" optional = true -python-versions = ">= 3.7" +python-versions = ">=3.8" files = [ - 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