From fe87c43375ac380b94410c404d94c9c056146e2a Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Wed, 3 Apr 2024 15:10:25 -0700 Subject: [PATCH] Update notebook --- .../langgraph_code_assistant.ipynb | 520 +++++++++--------- 1 file changed, 255 insertions(+), 265 deletions(-) diff --git a/examples/code_assistant/langgraph_code_assistant.ipynb b/examples/code_assistant/langgraph_code_assistant.ipynb index e9e41fb22..6cf6e2501 100644 --- a/examples/code_assistant/langgraph_code_assistant.ipynb +++ b/examples/code_assistant/langgraph_code_assistant.ipynb @@ -2,8 +2,8 @@ "cells": [ { "attachments": { - "fb3f0be0-4884-4ad2-b9b3-cf92cfc51273.png": { - "image/png": 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" } }, "cell_type": "markdown", @@ -22,7 +22,7 @@ "2. We use a long context LLM to ingest it, and answer a question based upon it \n", "3. We perform two unit tests: Check imports and code execution\n", "\n", - "![Screenshot 2024-02-16 at 11.43.52 AM.png](attachment:fb3f0be0-4884-4ad2-b9b3-cf92cfc51273.png)" + "![Screenshot 2024-04-03 at 1.54.25 PM.png](attachment:a7336696-1044-4f8a-b4c6-79d3eb619cd7.png)" ] }, { @@ -40,9 +40,9 @@ "id": "38330223-d8c8-4156-82b6-93e63343bc01", "metadata": {}, "source": [ - "## Documentation\n", + "## Docs\n", "\n", - "Load [LangChain Expression Language](https://python.langchain.com/docs/expression_language/) (LCEL) docs." + "Load [LangChain Expression Language](https://python.langchain.com/docs/expression_language/) (LCEL) docs as an example." ] }, { @@ -62,28 +62,9 @@ ")\n", "docs = loader.load()\n", "\n", - "# LCEL w/ PydanticOutputParser (outside the primary LCEL docs)\n", - "url = \"https://python.langchain.com/docs/modules/model_io/output_parsers/quick_start\"\n", - "loader = RecursiveUrlLoader(\n", - " url=url, max_depth=1, extractor=lambda x: Soup(x, \"html.parser\").text\n", - ")\n", - "docs_pydantic = loader.load()\n", - "\n", - "# LCEL w/ Self Query (outside the primary LCEL docs)\n", - "url = \"https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/\"\n", - "loader = RecursiveUrlLoader(\n", - " url=url, max_depth=1, extractor=lambda x: Soup(x, \"html.parser\").text\n", - ")\n", - "docs_sq = loader.load()\n", - "\n", - "# Add\n", - "docs.extend([*docs_pydantic, *docs_sq])\n", - "\n", - "# Sort the list based on the URLs in 'metadata' -> 'source'\n", + "# Sort the list based on the URLs and get the text\n", "d_sorted = sorted(docs, key=lambda x: x.metadata[\"source\"])\n", "d_reversed = list(reversed(d_sorted))\n", - "\n", - "# Concatenate the 'page_content' of each sorted dictionary\n", "concatenated_content = \"\\n\\n\\n --- \\n\\n\\n\".join(\n", " [doc.page_content for doc in d_reversed]\n", ")" @@ -96,16 +77,14 @@ "source": [ "## LLMs\n", "\n", - "We de-couple code solution and code formatting so that any LLM can be used for code solution. \n", + "### Code solution\n", "\n", - "The structured output generation is handled in a seperate step.\n", - "\n", - "### Code solution" + "We de-couple code solution and code formatting so that any LLM can be used for code solution. " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "3ba3df70-f6b4-4ea5-a210-e10944960bc6", "metadata": {}, "outputs": [], @@ -130,8 +109,7 @@ "\n", "code_gen_chain = code_gen_prompt | code_gen_llm | StrOutputParser()\n", "question = \"How do I build a RAG chain in LCEL?\"\n", - "solution = code_gen_chain.invoke({\"context\" : concatenated_content,\n", - " \"messages\" : [(\"user\",question)], ..., ...})" + "solution = code_gen_chain.invoke({\"context\" : concatenated_content, \"messages\" : [(\"user\",question)]})" ] }, { @@ -139,7 +117,9 @@ "id": "eb01dcde-b446-4fae-90af-a303222f90e7", "metadata": {}, "source": [ - "### Formatted code" + "### Formatted code\n", + "\n", + "The structured output generation is handled in a seperate step." ] }, { @@ -174,6 +154,24 @@ "output = structured_code_formatter.invoke([(\"code\",solution)])" ] }, + { + "cell_type": "markdown", + "id": "00d6a0b4-9b7e-4067-8622-832201ad9b56", + "metadata": {}, + "source": [ + "## Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "01598aee-f272-4d72-aefc-2cb15dcbfdfc", + "metadata": {}, + "outputs": [], + "source": [ + "max_iterations = 3" + ] + }, { "cell_type": "markdown", "id": "131f2055-2f64-4d19-a3d1-2d3cb8b42894", @@ -186,32 +184,28 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "id": "c185f1a2-e943-4bed-b833-4243c9c64092", "metadata": {}, "outputs": [], "source": [ - "from typing import Dict, TypedDict\n", + "from typing import Dict, TypedDict, List\n", "\n", "class GraphState(TypedDict):\n", " \"\"\"\n", " Represents the state of our graph.\n", "\n", " Attributes:\n", - " question : Use question\n", - " generation : LLM generation\n", - " prefix : Parsed code prefix\n", - " imports : Parsed code imports\n", - " code : Parsed code block\n", - " error : Errors from unit tests\n", + " error : Binary flag for control flow to indicate whether test error was tripped\n", + " messages : With user question, error messages, reasoning\n", + " generation : Code solution\n", + " iterations : Number of tries \n", " \"\"\"\n", "\n", - " question : str\n", + " error : str\n", + " messages : List\n", " generation : str\n", - " prefix : str\n", - " imports: str\n", - " code : str\n", - " error : str" + " iterations : int" ] }, { @@ -221,44 +215,23 @@ "source": [ "## Graph \n", "\n", - "Our graph lays out the logical flow shown in the figure above.\n", - "\n", - "Error handling loop is a sub-graph: \n", - "\n", - "start\n", - "- errors \n", - "\n", - "* generate node\n", - "\n", - "* condititional edge: pass validation\n", - " - if yes: END\n", - " - if no: format error\n", - "\n", - "* format error" + "Our graph lays out the logical flow shown in the figure above." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "id": "b70e8301-63ae-4f7e-ad8f-c9a052fe3566", "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "unterminated string literal (detected at line 51) (1848823301.py, line 51)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[22], line 51\u001b[0;36m\u001b[0m\n\u001b[0;31m messages = state[\"messages\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unterminated string literal (detected at line 51)\n" - ] - } - ], + "outputs": [], "source": [ "from operator import itemgetter\n", - "\n", "from langchain.prompts import PromptTemplate\n", "from langchain_core.pydantic_v1 import BaseModel, Field\n", "from langchain_core.runnables import RunnablePassthrough\n", "\n", + "### Nodes\n", + "\n", "def generate(state: GraphState):\n", " \"\"\"\n", " Generate a code solution\n", @@ -267,68 +240,29 @@ " state (dict): The current graph state\n", "\n", " Returns:\n", - " state (dict): New key added to state, documents, that contains retrieved documents\n", + " state (dict): New key added to state, generation\n", " \"\"\"\n", "\n", + " print(\"---GENERATING CODE SOLUTION---\")\n", + " \n", " # State\n", - " question = state[\"question\"]\n", + " messages = state[\"messages\"]\n", " iterations = state[\"iterations\"]\n", + " \n", + " # Solution\n", + " solution = code_gen_chain.invoke({\"context\" : concatenated_content, \"messages\" : messages})\n", "\n", - " # No unit test errors \n", - " if \"error\" not in state:\n", - " print(\"---GENERATE SOLUTION---\")\n", - " solution = code_gen_chain.invoke({\"context\" : concatenated_content,\n", - " \"messages\" : [(\"user\",question)] })\n", - "\n", - " # We have a unit test error\n", - " elif \"error\" in state:\n", - " print(\"---RE-GENERATE SOLUTION w/ ERROR FEEDBACK---\")\n", - " \n", - " # Get error and the prior generation that produced it\n", - " error = state[\"error\"]\n", - " code_solution = state[\"generation\"]\n", - "\n", - " # New message\n", - " error_message = \"\"\" \\n --- --- --- \\n You previously tried to solve this problem. \\n Here is your solution: \n", - " \\n --- --- --- \\n {generation} \\n --- --- --- \\n Here is the resulting error from code \n", - " execution: \\n --- --- --- \\n {error} \\n --- --- --- \\n Please re-try to answer this. \n", - " Structure your answer with a description of the code solution. \\n Then list the imports. \n", - " And finally list the functioning code block. Structure your answer with a description of \n", - " the code solution. \\n Then list the imports. And finally list the functioning code block. \n", - " \\n Here is the user question: \\n --- --- --- \\n {question}\"\"\"\n", - "\n", - "\n", - " ### Here we need to get all prior messages ### \n", - " ### Do we need to have these in state? ###\n", - " ### More elegant way? ###\n", - "\n", - " \n", - " messages = state[\"messages\"]\n", - " solution = code_gen_chain.invoke({\"context\" : concatenated_content,\n", - " \"messages\" : [(\"user\",question)], ..., ...})\n", - " \n", - " messages = xxx\n", - " messages += [\n", - " (\n", - " \"user\",\n", - " error_message,\n", - " )\n", - " ]\n", - " solution = code_gen_chain.invoke({\"context\" : concatenated_content,\n", - " \"messages\" : messages] })\n", - " \n", - "\n", - " # Get structured output\n", + " # Structured output\n", " code_solution = structured_code_formatter.invoke([(\"code\",solution)])\n", + " \n", " # Increment\n", " iterations = iterations + 1\n", - " return {\n", - " \"keys\": {\"generation\": code_solution, \"question\": question, \"iterations\": iterations}\n", - " }\n", + " return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n", "\n", - "def check_code_imports(state: GraphState):\n", + "\n", + "def code_check(state: GraphState):\n", " \"\"\"\n", - " Check imports\n", + " Check code\n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -337,110 +271,74 @@ " state (dict): New key added to state, error\n", " \"\"\"\n", "\n", + " print(\"---CHECKING CODE---\")\n", + " \n", " ## State\n", - " print(\"---CHECKING CODE IMPORTS---\")\n", - " question = state[\"question\"]\n", + " messages = state[\"messages\"]\n", " code_solution = state[\"generation\"]\n", - " imports = code_solution[0].imports\n", - " iterations = state_dict[\"iterations\"]\n", + " iterations = state[\"iterations\"]\n", "\n", + " # Get solution components\n", + " prefix = code_solution.prefix\n", + " imports = code_solution.imports\n", + " code = code_solution.code\n", + "\n", + " # Check imports\n", " try:\n", " exec(imports)\n", " except Exception as e:\n", " print(\"---CODE IMPORT CHECK: FAILED---\")\n", - " error = f\"Execution error: {e}\"\n", - " if \"error\" in state_dict:\n", - " error_prev_runs = state[\"error\"]\n", - " error = error_prev_runs + \"\\n --- Most recent run error --- \\n\" + error\n", - " else:\n", - " print(\"---CODE IMPORT CHECK: SUCCESS---\")\n", - " error = \"None\"\n", + " error_message = [(\"user\", f\"Import Test Failure: You are required to fix the import error: {e}\")]\n", + " messages += error_message\n", + " return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations, \"error\": \"yes\"}\n", + " \n", + " # Check execution\n", + " try:\n", + " exec(imports + \"\\n\" + code)\n", + " except Exception as e:\n", + " print(\"---CODE BLOCK CHECK: FAILED---\")\n", + " error_message = [(\"user\", f\"Execution Test Failure: You are required to fix the code execution error: {e}\")]\n", + " return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations, \"error\": \"yes\"}\n", + " \n", + " # No errors\n", + " print(\"---NO CODE TEST FAILURES---\")\n", + " return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations, \"error\": \"no\"}\n", "\n", - " return {\n", - " \"keys\": {\n", - " \"generation\": code_solution,\n", - " \"question\": question,\n", - " \"error\": error,\n", - " \"iterations\": iterations,\n", - " }\n", - " }\n", "\n", - "def check_code_execution(state: GraphState):\n", + "def reflect(state: GraphState):\n", " \"\"\"\n", - " Check code block execution\n", + " Reflect on errors\n", "\n", " Args:\n", " state (dict): The current graph state\n", "\n", " Returns:\n", - " state (dict): New key added to state, error\n", + " state (dict): New key added to state, generation\n", " \"\"\"\n", "\n", - " ## State\n", - " print(\"---CHECKING CODE EXECUTION---\")\n", - " question = state[\"question\"]\n", - " code_solution = state[\"generation\"]\n", - " prefix = code_solution[0].prefix\n", - " imports = code_solution[0].imports\n", - " code = code_solution[0].code\n", - " code_block = imports + \"\\n\" + code\n", - " iterations = state_dict[\"iterations\"]\n", + " print(\"---GENERATING CODE SOLUTION---\")\n", + " \n", + " # State\n", + " messages = state[\"messages\"]\n", + " iterations = state[\"iterations\"]\n", "\n", - " try:\n", - " exec(code_block)\n", - " except Exception as e:\n", - " print(\"---CODE BLOCK CHECK: FAILED---\")\n", - " error = f\"Execution error: {e}\"\n", - " if \"error\" in state_dict:\n", - " error_prev_runs = state_dict[\"error\"]\n", - " error = error_prev_runs + \"\\n --- Most recent run error --- \\n\" + error\n", - " else:\n", - " print(\"---CODE BLOCK CHECK: SUCCESS---\")\n", - " error = \"None\"\n", + " # Prompt reflection\n", + " reflection_message = [(\"user\", \"\"\"You tried to solve this problem and failed a unit test. Reflect on this failure\n", + " given the provided documentation. Carefully write suggestions based on the \n", + " documentation to avoid making this mistake again.\"\"\")]\n", + " messages += reflection_message\n", + " \n", + " # Suggesting\n", + " reflections = code_gen_chain.invoke({\"context\" : concatenated_content, \"messages\" : messages})\n", + " messages += reflections\n", + " return {\"generation\": code_solution, \"messages\": messages, \"iterations\": iterations}\n", "\n", - " return {\n", - " \"keys\": {\n", - " \"generation\": code_solution,\n", - " \"question\": question,\n", - " \"error\": error,\n", - " \"prefix\": prefix,\n", - " \"imports\": imports,\n", - " \"iterations\": iterations,\n", - " \"code\": code,\n", - " }\n", - " }\n", "\n", "### Edges\n", "\n", - "def decide_to_check_code_exec(state: GraphState):\n", - " \"\"\"\n", - " Determines whether to test code execution, or re-try answer generation.\n", - "\n", - " Args:\n", - " state (dict): The current graph state\n", - "\n", - " Returns:\n", - " str: Next node to call\n", - " \"\"\"\n", - "\n", - " print(\"---DECIDE TO TEST CODE EXECUTION---\")\n", - " state_dict = state[\"keys\"]\n", - " error = state_dict[\"error\"]\n", - "\n", - " if error == \"None\":\n", - " # All documents have been filtered check_relevance\n", - " # We will re-generate a new query\n", - " print(\"---DECISION: TEST CODE EXECUTION---\")\n", - " return \"check_code_execution\"\n", - " else:\n", - " # We have relevant documents, so generate answer\n", - " print(\"---DECISION: RE-TRY SOLUTION---\")\n", - " return \"generate\"\n", - "\n", - "\n", "def decide_to_finish(state: GraphState):\n", " \"\"\"\n", - " Determines whether to finish (re-try code 3 times.\n", + " Determines whether to finish.\n", "\n", " Args:\n", " state (dict): The current graph state\n", @@ -448,26 +346,20 @@ " Returns:\n", " str: Next node to call\n", " \"\"\"\n", + " error = state[\"error\"]\n", + " iterations = state[\"iterations\"]\n", "\n", - " print(\"---DECIDE TO TEST CODE EXECUTION---\")\n", - " state_dict = state[\"keys\"]\n", - " error = state_dict[\"error\"]\n", - " iter = state_dict[\"iterations\"]\n", - "\n", - " if error == \"None\" or iter == 3:\n", - " # All documents have been filtered check_relevance\n", - " # We will re-generate a new query\n", - " print(\"---DECISION: TEST CODE EXECUTION---\")\n", + " if error == \"no\" or iterations == max_iterations:\n", + " print(\"---DECISION: FINISH---\")\n", " return \"end\"\n", " else:\n", - " # We have relevant documents, so generate answer\n", " print(\"---DECISION: RE-TRY SOLUTION---\")\n", - " return \"generate\"" + " return \"reflect\" # Or, directly back to generate" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "id": "f66b4e00-4731-42c8-bc38-72dd0ff7c92c", "metadata": {}, "outputs": [], @@ -478,61 +370,65 @@ "\n", "# Define the nodes\n", "workflow.add_node(\"generate\", generate) # generation solution\n", - "workflow.add_node(\"check_code_imports\", check_code_imports) # check imports\n", - "workflow.add_node(\"check_code_execution\", check_code_execution) # check execution\n", + "workflow.add_node(\"check_code\", code_check) # check code\n", + "workflow.add_node(\"reflect\", reflect) # reflect\n", "\n", "# Build graph\n", "workflow.set_entry_point(\"generate\")\n", - "workflow.add_edge(\"generate\", \"check_code_imports\")\n", + "workflow.add_edge(\"generate\", \"check_code\")\n", "workflow.add_conditional_edges(\n", - " \"check_code_imports\",\n", - " decide_to_check_code_exec,\n", - " {\n", - " \"check_code_execution\": \"check_code_execution\",\n", - " \"generate\": \"generate\",\n", - " },\n", - ")\n", - "workflow.add_conditional_edges(\n", - " \"check_code_execution\",\n", + " \"check_code\",\n", " decide_to_finish,\n", " {\n", " \"end\": END,\n", - " \"generate\": \"generate\",\n", + " \"reflect\": \"reflect\",\n", " },\n", ")\n", - "\n", - "# Compile\n", + "workflow.add_edge(\"reflect\", \"generate\")\n", "app = workflow.compile()" ] }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c4b1b35b-c8bd-4c68-a4b1-f09a7dfc8707", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---GENERATING CODE SOLUTION---\n", + "---CHECKING CODE---\n", + "---NO CODE TEST FAILURES---\n", + "---DECISION: FINISH---\n" + ] + }, + { + "data": { + "text/plain": [ + "{'error': 'no',\n", + " 'messages': [('user', 'How do I build a RAG chain in LCEL?')],\n", + " 'generation': code(prefix=\"Problem: Building a Retrieval-Augmented Generation (RAG) Chain using LangChain Expression Language (LCEL).\\n\\nApproach: To construct a RAG chain in LCEL, it's essential to integrate a retriever, a prompt template, a language model, and an output parser. The process involves creating a retriever to fetch relevant documents based on a query, defining a prompt template that incorporates the retrieved context and a question, instantiating a language model, and creating a chain by connecting the retriever, prompt, model, and output parser. Finally, the chain is invoked with a question to obtain the answer.\", imports='from langchain_community.vectorstores import FAISS\\nfrom langchain_core.output_parsers import StrOutputParser\\nfrom langchain_core.prompts import ChatPromptTemplate\\nfrom langchain_core.runnables import RunnablePassthrough\\nfrom langchain_openai import ChatOpenAI, OpenAIEmbeddings', code='# Load documents into a vector store\\nvectorstore = FAISS.from_texts(\\n [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\\n)\\nretriever = vectorstore.as_retriever()\\n\\n# Define prompt template\\ntemplate = \"\"\"Answer the question based only on the following context:\\n{context}\\nQuestion: {question}\"\"\"\\nprompt = ChatPromptTemplate.from_template(template)\\n\\n# Instantiate model and output parser\\nmodel = ChatOpenAI()\\noutput_parser = StrOutputParser()\\n\\n# Create RAG chain \\nchain = (\\n {\"context\": retriever, \"question\": RunnablePassthrough()}\\n | prompt \\n | model\\n | output_parser\\n)\\n\\n# Run the chain\\nchain.invoke(\"where did harrison work?\")'),\n", + " 'iterations': 1}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "question = \"How do I build a RAG chain in LCEL?\"\n", + "app.invoke({\"messages\":[(\"user\",question)],\"iterations\":0})" + ] + }, { "cell_type": "markdown", "id": "744f48a5-9ad3-4342-899f-7dd4266a9a15", "metadata": {}, "source": [ - "## Eval\n", - "\n", - "Compare LangGraph to base case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8fa6bcb-b245-4422-b79a-582cd8a7d7ea", - "metadata": {}, - "outputs": [], - "source": [ - "def predict_base_case(example: dict):\n", - " \"\"\" Context stuffing \"\"\"\n", - " solution = code_gen_chain.invoke({\"context\" : concatenated_content,\n", - " \"messages\" : [(\"user\",example[\"question\"])] })\n", - " output = structured_code_formatter.invoke([(\"code\",solution)])\n", - " return {\"imports\": output.imports, \"code\": output.code}\n", - "\n", - "def predict_langgraph(example: dict):\n", - " \"\"\" LangGraph \"\"\"\n", - " graph = app.invoke([(\"question\",example[\"question\"])])\n", - " return {\"imports\": graph[\"imports\"], \"code\": graph[\"code\"]}" + "## Eval" ] }, { @@ -540,7 +436,22 @@ "id": "89852874-b538-4c8d-a4c3-1d68302db492", "metadata": {}, "source": [ - "[Here](https://smith.langchain.com/public/326674a6-62bd-462d-88ae-eea49d503f9d/d) is a public dataset of LCEL questions. " + "[Here](https://smith.langchain.com/public/326674a6-62bd-462d-88ae-eea49d503f9d/d) is a public dataset of LCEL questions. \n", + "\n", + "I saved this as `test-LCEL-code-gen`.\n", + "\n", + "You can also find the csv [here](https://github.com/langchain-ai/lcel-teacher/blob/main/eval/eval.csv)." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "678e8954-56b5-4cc6-be26-f7f2a060b242", + "metadata": {}, + "outputs": [], + "source": [ + "import langsmith\n", + "client = langsmith.Client()" ] }, { @@ -550,14 +461,8 @@ "metadata": {}, "outputs": [], "source": [ - "import langsmith\n", - "\n", - "client = langsmith.Client()\n", - "\n", - "public_dataset = (\n", - " \"https://smith.langchain.com/public/326674a6-62bd-462d-88ae-eea49d503f9d/d\"\n", - ")\n", "# Clone the dataset to your tenant to use it\n", + "public_dataset = (\"https://smith.langchain.com/public/326674a6-62bd-462d-88ae-eea49d503f9d/d\")\n", "client.clone_public_dataset(public_dataset)" ] }, @@ -571,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "455a34ea-52cb-4ae5-9f4a-7e4a08cd0c09", "metadata": {}, "outputs": [], @@ -596,18 +501,103 @@ " return {\"key\": \"code_execution_check\" , \"score\": 0} " ] }, + { + "cell_type": "markdown", + "id": "c90bf261-0d94-4779-bbde-c76adeefe3d7", + "metadata": {}, + "source": [ + "Compare LangGraph to Context Stuffing." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "c8fa6bcb-b245-4422-b79a-582cd8a7d7ea", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_base_case(example: dict):\n", + " \"\"\" Context stuffing \"\"\"\n", + " solution = code_gen_chain.invoke({\"context\" : concatenated_content, \"messages\" : [(\"user\",example[\"question\"])] })\n", + " solution_structured = structured_code_formatter.invoke([(\"code\",solution)])\n", + " return {\"imports\": solution_structured.imports, \"code\": solution_structured.code}\n", + "\n", + "def predict_langgraph(example: dict):\n", + " \"\"\" LangGraph \"\"\"\n", + " graph = app.invoke({\"messages\":[(\"user\",example[\"question\"])],\"iterations\":0})\n", + " solution = graph[\"generation\"]\n", + " return {\"imports\": solution.imports, \"code\": solution.code}" + ] + }, { "cell_type": "code", "execution_count": null, "id": "2dacccf0-d73f-4017-aaf0-9806ffe5bd2c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/l9/bpjxdmfx7lvd1fbdjn38y5dh0000gn/T/ipykernel_43078/1906326331.py:10: UserWarning: Function evaluate is in beta.\n", + " experiment_results = evaluate(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "View the evaluation results for experiment: 'test-without-langgraph:1f3259c' at:\n", + "https://smith.langchain.com/o/1fa8b1f4-fcb9-4072-9aa9-983e35ad61b8/datasets/76bc1d66-8cb6-4f6d-b633-7cd077937e46/compare?selectedSessions=1c92918c-b111-412d-b845-a96f2ee37f7c\n", + "\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "981c40cd7ce34b97a06d8f4daa339710", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n", + "Error running target function: Error code: 429 - {'type': 'error', 'error': {'type': 'rate_limit_error', 'message': 'Number of concurrent connections has exceeded your rate limit. Please try again later or contact sales at https://www.anthropic.com/contact-sales to discuss your options for a rate limit increase.'}}\n" + ] + } + ], "source": [ "from langsmith.evaluation import evaluate\n", "\n", "# Evaluator\n", "code_evalulator = [check_import,check_execution]\n", - "dataset_name = \"lcel-teacher-eval\"\n", + "\n", + "# Dataset\n", + "dataset_name = \"test-LCEL-code-gen\"\n", "\n", "# Run base case\n", "experiment_results = evaluate(\n",