{ "cells": [ { "cell_type": "markdown", "id": "9f97dd1e", "metadata": {}, "source": [ "# Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "9e974df6", "metadata": {}, "outputs": [], "source": [ "import os\n", "from typing import TypedDict, List, Optional, Annotated\n", "from IPython.display import Image, display\n", "\n", "from langchain_core.documents import Document\n", "from langchain_core.messages import BaseMessage, SystemMessage\n", "from langchain_core.tools import tool\n", "from langgraph.checkpoint.memory import InMemorySaver\n", "from langgraph.graph.message import add_messages\n", "from langchain_ollama import ChatOllama, OllamaEmbeddings\n", "from langchain_elasticsearch import ElasticsearchStore\n", "from langgraph.graph import StateGraph, END\n", "from langgraph.prebuilt import ToolNode\n", "from langfuse import get_client\n", "from langfuse.langchain import CallbackHandler" ] }, { "cell_type": "code", "execution_count": 3, "id": "30edcecc", "metadata": {}, "outputs": [], "source": [ "ES_URL = os.getenv(\"ELASTICSEARCH_LOCAL_URL\")\n", "INDEX_NAME = os.getenv(\"ELASTICSEARCH_INDEX\")\n", "BASE_URL = os.getenv(\"LLM_BASE_LOCAL_URL\")\n", "MODEL_NAME = os.getenv(\"OLLAMA_MODEL_NAME\")\n", "EMB_MODEL_NAME = os.getenv(\"OLLAMA_EMB_MODEL_NAME\")\n", "LANGFUSE_PUBLIC_KEY = os.getenv(\"LANGFUSE_PUBLIC_KEY\")\n", "LANGFUSE_SECRET_KEY = os.getenv(\"LANGFUSE_SECRET_KEY\")\n", "LANGFUSE_HOST = os.getenv(\"LANGFUSE_HOST\")\n", "\n", "# langfuse = get_client()\n", "# langfuse_handler = CallbackHandler()\n", "\n", "embeddings = OllamaEmbeddings(base_url=BASE_URL, model=EMB_MODEL_NAME)\n", "llm = ChatOllama(base_url=BASE_URL, model=MODEL_NAME)\n", "\n", "vector_store = ElasticsearchStore(\n", " es_url=ES_URL,\n", " index_name=INDEX_NAME,\n", " embedding=embeddings,\n", " query_field=\"text\",\n", " vector_query_field=\"vector\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "ad98841b", "metadata": {}, "outputs": [], "source": [ "# if langfuse.auth_check():\n", "# print(\"Langfuse client is authenticated and ready!\")\n", "# else:\n", "# print(\"Authentication failed. Please check your credentials and host.\")" ] }, { "cell_type": "markdown", "id": "873ea2f6", "metadata": {}, "source": [ "### State" ] }, { "cell_type": "code", "execution_count": 4, "id": "5f8c88cf", "metadata": {}, "outputs": [], "source": [ "class AgentState(TypedDict):\n", " messages: Annotated[list, add_messages]" ] }, { "cell_type": "markdown", "id": "1d60c120", "metadata": {}, "source": [ "### Tools" ] }, { "cell_type": "code", "execution_count": 5, "id": "f0a21230", "metadata": {}, "outputs": [], "source": [ "retrieve_kwargs = {\"k\": 5}" ] }, { "cell_type": "code", "execution_count": 6, "id": "f9359747", "metadata": {}, "outputs": [], "source": [ "def format_context(docs: List[Document]) -> str:\n", " chunks: List[str] = []\n", " for i, doc in enumerate(docs, 1):\n", " source = (doc.metadata or {}).get(\"source\", \"Untitled\")\n", " source_id = (doc.metadata or {}).get(\"id\", f\"chunk-{i}\")\n", " text = doc.page_content or \"\"\n", " chunks.append(f\"[{i}] id={source_id} source={source}\\n{text}\")\n", " return \"\\n\\n\".join(chunks)\n", "\n", "\n", "@tool\n", "def retrieve(query: str) -> str:\n", " \"\"\"This tool retrieves relevant documents from the vector store based on the input query and formats them for the agent's response.\n", " Args:\n", " query (str): The input query for which to retrieve relevant documents.\n", " \"\"\"\n", " retriever = vector_store.as_retriever(\n", " search_type=\"similarity\",\n", " search_kwargs=retrieve_kwargs,\n", " )\n", " docs = retriever.invoke(query)\n", " return format_context(docs)" ] }, { "cell_type": "code", "execution_count": 7, "id": "e5247ab9", "metadata": {}, "outputs": [], "source": [ "def should_continue(state: AgentState) -> str:\n", " last = state[\"messages\"][-1]\n", " \n", " if getattr(last, \"tool_calls\", None):\n", " return \"tools\"\n", " return \"end\"" ] }, { "cell_type": "code", "execution_count": 8, "id": "a644f6fa", "metadata": {}, "outputs": [], "source": [ "tools = [retrieve]\n", "tool_node = ToolNode(tools)\n", "memory = InMemorySaver()" ] }, { "cell_type": "markdown", "id": "395966e2", "metadata": {}, "source": [ "### Agent" ] }, { "cell_type": "code", "execution_count": 17, "id": "36d0f54e", "metadata": {}, "outputs": [], "source": [ "def agent(state: AgentState) -> AgentState:\n", " messages: List[BaseMessage] = state[\"messages\"]\n", "\n", " system = SystemMessage(\n", " content=(\n", " \"\"\" \n", " You are a specialized agent whose job is to respond questions about a new language code called AVAP, which is a language designed to build APIs.\n", " To responde to the user's question, you have access to a tool called \"retrieve\" that allows you to retrieve relevant documents about AVAP from a vector store.\n", " You must respond in the language the user is assking you.\n", " \"\"\"\n", " )\n", " )\n", "\n", " model = llm.bind_tools(tools)\n", " resp = model.invoke([system, *messages])\n", "\n", " return {\"messages\": [*messages, resp]}" ] }, { "cell_type": "markdown", "id": "ef55bca3", "metadata": {}, "source": [ "### Graph" ] }, { "cell_type": "code", "execution_count": 18, "id": "fae46a58", "metadata": {}, "outputs": [], "source": [ "graph = StateGraph(AgentState)\n", "graph.add_node(\"agent\", agent)\n", "graph.add_node(\"tools\", tool_node)\n", "\n", "graph.set_entry_point(\"agent\")\n", "graph.add_conditional_edges(\"agent\", should_continue, {\"tools\": \"tools\", \"end\": END})\n", "graph.add_edge(\"tools\", \"agent\")\n", "\n", "agent_graph = graph.compile()" ] }, { "cell_type": "code", "execution_count": 19, "id": "2fec3fdb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "try:\n", " display(Image(agent_graph.get_graph().draw_mermaid_png()))\n", "except Exception:\n", " pass" ] }, { "cell_type": "markdown", "id": "1e9aff05", "metadata": {}, "source": [ "### Test" ] }, { "cell_type": "code", "execution_count": 20, "id": "8569cf39", "metadata": {}, "outputs": [], "source": [ "config = {\"configurable\": {\"thread_id\": \"5\"}, \n", " #\"callbacks\": [langfuse_handler],\n", " #\"run_name\": \"rag-local-test\"differences between getDatetime() and getTimeStamp() functions in AVAP\n", " }\n", "\n", "def stream_graph_updates(user_input: str):\n", " for event in agent_graph.stream(\n", " {\"messages\": [{\"role\": \"user\", \"content\": user_input}]},\n", " #config=config,\n", " stream_mode=\"values\",\n", " ):\n", " event[\"messages\"][-1].pretty_print()" ] }, { "cell_type": "code", "execution_count": 24, "id": "a1a1f3cf", "metadata": {}, "outputs": [], "source": [ "user_input = \"Create a small snippet that adds 2 numbers using AVAP language. Use the provided tool.\"" ] }, { "cell_type": "code", "execution_count": 26, "id": "53b89690", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "Create a small snippet that adds 2 numbers using AVAP language. Use the provided tool.\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "\n", "Okay, the user wants a snippet that adds two numbers using AVAP. Let me check the tools available. There's a function called retrieve that gets documents about AVAP. But the user hasn't provided any specific documents yet. The function requires a query, which the user hasn't given. I need to call the retrieve function with a query asking for the AVAP document. Once I get the documents, I can format a code snippet based on that. But since the user hasn't specified the query, I can't proceed without that. So I should first call the retrieve function with the query parameter set to the user's request. Wait, but the user's query is to create the snippet, not to retrieve documents. Maybe the function is supposed to retrieve the documents, but the user's question is to create the code. Hmm, perhaps I need to use the retrieve function to get the necessary information first. Let me structure the tool call with the query parameter as the user's instruction.\n", "\n", "Tool Calls:\n", " retrieve (1a91aa77-cafe-47ab-b44e-628ccc547660)\n", " Call ID: 1a91aa77-cafe-47ab-b44e-628ccc547660\n", " Args:\n", " query: Create a small snippet that adds 2 numbers using AVAP language.\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: retrieve\n", "\n", "[1] id=chunk-1 source=6_Data_Types.txt\n", " c = 3.5 d = 2.0 product = c * d division = c / d # Operations with strings text1 = \"Hello\" text2 = \"world\" concatenation = text1 + \" \" + text2 1.4 Summary Data types in AVAP™ are fundamental for program development. They allow for the storage and manipulation of different types of values, such as numbers, text, and truth values. With a solid understanding of data types and how they are used in program development, developers can create robust and functional applications in AVAP™.\n", "\n", "[2] id=chunk-2 source=9_Expressions_in_avap.txt\n", "Expressions in AVAP™ Introduction Expressions in AVAP™ are combinations of values, variables, operators, and function calls that can be evaluated to produce a result. Just like in Python, expressions in AVAP™ can be simple or complex, and they can contain a variety of elements that manipulate and process data. Types of Expressions In AVAP™, as in Python, there are several types of expressions that can be used to perform different operations and calculations. Some of the most common types of expressions include: Arithmetic: Perform mathematical operations such as addition, subtraction, multiplication, and division. Logical: Evaluate logical conditions and return boolean values, such as True or False. Comparative: Compare two values and return a result based on their relationship, such as equality, inequality, greater than, less than, etc. Assignment: Assign a value to a variable. Function Calls: Invoke functions and methods to perform specific tasks. Operators In AVAP™, as in Python, expressions can include a variety of operators that perform specific operations on data. Some of the most common operators include: Arithmetic: +, -, *, /, %, etc. Logical: and, or, not. Comparative: ==, !=, >, <, >=, <=, etc. Assignment: =, +=, -=, *=, /=, etc. Working with Lists Lists are a very versatile data structure in AVAP™ that allows you to store collections of elements of different types. Expressions in AVAP™ can involve operations and manipulations of lists, such as accessing individual elements, concatenation, searching, deletion, and more. // Definition of a list my_list = [1, 2, 3, 4, 5] // Accessing individual elements first_element = my_list[0] // Output: 1 // Concatenation of lists another_list = [6, 7, 8] combined_list = my_list + another_list // Output: [1, 2, 3, 4, 5, 6, 7, 8] // Length of a list length = len(my_list) // Output: 5 // Searching in a list is_present = 5 in my_list // Output: True // Removing elements my_list.remove(3) // Removes the element 3 from the list Practical Example Below is a practical example that illustrates the use of expressions in AVAP™ with lists: // Definition of a list of numbers numbers = [1,\n", "\n", "[3] id=chunk-3 source=9_Expressions_in_avap.txt\n", " that illustrates the use of expressions in AVAP™ with lists: // Definition of a list of numbers numbers = [1, 2, 3, 4, 5] // Calculation of the sum of the elements total = sum(numbers) // Output: 15 // Checking if a number is present in the list is_present = 6 in numbers // Output: False Conclusions Expressions in AVAP™ are a fundamental part of programming, allowing for a wide variety of data operations and manipulations. By understanding the different types of expressions and operators, as well as working with data structures such as lists, developers can write clear and effective code that meets the program's requirements.\n", "\n", "[4] id=chunk-4 source=10_Execution_model_in_avap.txt\n", " from the object variableFromJSON(\"myObject\", \"key1\", \"myVariable\") In this way, lists and objects in AVAP can be manipulated using the specific functions provided for working with variables stored as strings. Binary Arithmetic Operations Binary arithmetic operations have the conventional levels of precedence. Some of these operations also apply to certain non-numeric types. Aside from the exponentiation operator, there are two levels: one for multiplicative operators and another for additive ones: m_expr ::= u_expr | m_expr \"*\" u_expr | m_expr \"@\" m_expr | m_expr \"//\" u_expr | m_expr \"/\" u_expr | m_expr \"%\" u_expr a_expr ::= m_expr | a_expr \"+\" m_expr | a_expr \"-\" m_expr The * (multiplication) operator produces the product of its arguments. The arguments can both be numbers, or one argument must be an integer and the other a sequence. In the first case, the numbers are converted to a common type and then multiplied. In the second case, sequence repetition occurs; a negative repetition factor produces an empty sequence. The @ (matrix multiplication) operator is intended for matrix multiplication. No built-in type in Python implements this operator. The / (division) and // (floor division) operators produce the quotient of their arguments. Numeric arguments are converted to a common type. Division between integers produces a floating-point number, while floor division between integers results in an integer; the result is that of a mathematical division with the “floor” function applied to the result. Division by zero raises a ZeroDivisionError. The % (modulus) operator produces the remainder of the division of the first argument by the second. Numeric arguments are converted to a common type. A zero argument on the right raises a ZeroDivisionError. Arguments can be floating-point numbers, e.g., 3.14 % 0.7 is equal to 0.34 (since 3.14 is equal to 4 * 0.7 + 0.34). The modulus operator always produces a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand. The floor division and modulus operators are connected by the following identity: x == (x // y) * y + (x % y). Floor division and modulus are also connected by the built-in function divmod(): divmod(x, y) == (x // y, x\n", "\n", "[5] id=chunk-5 source=16_Appendix.txt\n", " Parameters prompt Type: variable Description: A string in natural language that describes the process or function to be executed. For example, \"calculate the average of the salary column\". source Type: variable Description: The name of the table on which the generated function should be executed. It must be a variable containing the name of the table in the database. TargetVariable Type: variable Description: The variable in which the result of the executed function or process will be stored. It must be a variable that will receive the result of the generated and executed code. Command Flow Generating Code: Use the artificial intelligence model to convert the prompt into a code implementation. For example, if the prompt is \"calculate the average of the salary column\", the AI will generate the code necessary to calculate the average of that column. Executing the Code: Execute the generated code on the table specified in source. Storing the Result: Save the result of the code execution in the variable specified by TargetVariable. The result will be the calculated value or the dataset produced by the executed code. Example Usage Suppose you want to calculate the average of the salary column in a table called employees. // Define variables prompt = \"calculate the average of the salary column\" source = \"employees\" averageSalary = '' // Call the command to process the function functionAI(prompt, source, averageSalary) // Return the result of the function via addResult addResult(averageSalary) In this example, the functionAI() command will convert the prompt into a code implementation to calculate the average of the salary column in the employees table. The result of the calculation will be stored in the averageSalary variable, and this variable will be returned via addResult(averageSalary). The output will be the calculated average of the salary column.\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "\n", "Okay, the user wants to create a small snippet that adds two numbers using AVAP language. Let me check the tools available. The function retrieve is there, but the query parameter isn't provided yet. Since the user hasn't specified the query, I can't call the function. I need to inform them that I need more information to proceed. But wait, maybe I should use the retrieve function with a query to get the documents. Let me structure that. The user's query is about creating the code, so the function call should be with the query parameter set to that instruction. Once I get the documents, I can then generate the code snippet. But since the user hasn't given the query, I can't proceed. So the correct approach is to call the retrieve function with the query as the user's request. Let me make sure the JSON is correctly formatted.\n", "\n", "Tool Calls:\n", " retrieve (a19ef546-2d46-4f04-b93b-830b733dec1b)\n", " Call ID: a19ef546-2d46-4f04-b93b-830b733dec1b\n", " Args:\n", " query: Create a small snippet that adds 2 numbers using AVAP language.\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: retrieve\n", "\n", "[1] id=chunk-1 source=6_Data_Types.txt\n", " c = 3.5 d = 2.0 product = c * d division = c / d # Operations with strings text1 = \"Hello\" text2 = \"world\" concatenation = text1 + \" \" + text2 1.4 Summary Data types in AVAP™ are fundamental for program development. They allow for the storage and manipulation of different types of values, such as numbers, text, and truth values. With a solid understanding of data types and how they are used in program development, developers can create robust and functional applications in AVAP™.\n", "\n", "[2] id=chunk-2 source=9_Expressions_in_avap.txt\n", "Expressions in AVAP™ Introduction Expressions in AVAP™ are combinations of values, variables, operators, and function calls that can be evaluated to produce a result. Just like in Python, expressions in AVAP™ can be simple or complex, and they can contain a variety of elements that manipulate and process data. Types of Expressions In AVAP™, as in Python, there are several types of expressions that can be used to perform different operations and calculations. Some of the most common types of expressions include: Arithmetic: Perform mathematical operations such as addition, subtraction, multiplication, and division. Logical: Evaluate logical conditions and return boolean values, such as True or False. Comparative: Compare two values and return a result based on their relationship, such as equality, inequality, greater than, less than, etc. Assignment: Assign a value to a variable. Function Calls: Invoke functions and methods to perform specific tasks. Operators In AVAP™, as in Python, expressions can include a variety of operators that perform specific operations on data. Some of the most common operators include: Arithmetic: +, -, *, /, %, etc. Logical: and, or, not. Comparative: ==, !=, >, <, >=, <=, etc. Assignment: =, +=, -=, *=, /=, etc. Working with Lists Lists are a very versatile data structure in AVAP™ that allows you to store collections of elements of different types. Expressions in AVAP™ can involve operations and manipulations of lists, such as accessing individual elements, concatenation, searching, deletion, and more. // Definition of a list my_list = [1, 2, 3, 4, 5] // Accessing individual elements first_element = my_list[0] // Output: 1 // Concatenation of lists another_list = [6, 7, 8] combined_list = my_list + another_list // Output: [1, 2, 3, 4, 5, 6, 7, 8] // Length of a list length = len(my_list) // Output: 5 // Searching in a list is_present = 5 in my_list // Output: True // Removing elements my_list.remove(3) // Removes the element 3 from the list Practical Example Below is a practical example that illustrates the use of expressions in AVAP™ with lists: // Definition of a list of numbers numbers = [1,\n", "\n", "[3] id=chunk-3 source=9_Expressions_in_avap.txt\n", " that illustrates the use of expressions in AVAP™ with lists: // Definition of a list of numbers numbers = [1, 2, 3, 4, 5] // Calculation of the sum of the elements total = sum(numbers) // Output: 15 // Checking if a number is present in the list is_present = 6 in numbers // Output: False Conclusions Expressions in AVAP™ are a fundamental part of programming, allowing for a wide variety of data operations and manipulations. By understanding the different types of expressions and operators, as well as working with data structures such as lists, developers can write clear and effective code that meets the program's requirements.\n", "\n", "[4] id=chunk-4 source=10_Execution_model_in_avap.txt\n", " from the object variableFromJSON(\"myObject\", \"key1\", \"myVariable\") In this way, lists and objects in AVAP can be manipulated using the specific functions provided for working with variables stored as strings. Binary Arithmetic Operations Binary arithmetic operations have the conventional levels of precedence. Some of these operations also apply to certain non-numeric types. Aside from the exponentiation operator, there are two levels: one for multiplicative operators and another for additive ones: m_expr ::= u_expr | m_expr \"*\" u_expr | m_expr \"@\" m_expr | m_expr \"//\" u_expr | m_expr \"/\" u_expr | m_expr \"%\" u_expr a_expr ::= m_expr | a_expr \"+\" m_expr | a_expr \"-\" m_expr The * (multiplication) operator produces the product of its arguments. The arguments can both be numbers, or one argument must be an integer and the other a sequence. In the first case, the numbers are converted to a common type and then multiplied. In the second case, sequence repetition occurs; a negative repetition factor produces an empty sequence. The @ (matrix multiplication) operator is intended for matrix multiplication. No built-in type in Python implements this operator. The / (division) and // (floor division) operators produce the quotient of their arguments. Numeric arguments are converted to a common type. Division between integers produces a floating-point number, while floor division between integers results in an integer; the result is that of a mathematical division with the “floor” function applied to the result. Division by zero raises a ZeroDivisionError. The % (modulus) operator produces the remainder of the division of the first argument by the second. Numeric arguments are converted to a common type. A zero argument on the right raises a ZeroDivisionError. Arguments can be floating-point numbers, e.g., 3.14 % 0.7 is equal to 0.34 (since 3.14 is equal to 4 * 0.7 + 0.34). The modulus operator always produces a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand. The floor division and modulus operators are connected by the following identity: x == (x // y) * y + (x % y). Floor division and modulus are also connected by the built-in function divmod(): divmod(x, y) == (x // y, x\n", "\n", "[5] id=chunk-5 source=16_Appendix.txt\n", " Parameters prompt Type: variable Description: A string in natural language that describes the process or function to be executed. For example, \"calculate the average of the salary column\". source Type: variable Description: The name of the table on which the generated function should be executed. It must be a variable containing the name of the table in the database. TargetVariable Type: variable Description: The variable in which the result of the executed function or process will be stored. It must be a variable that will receive the result of the generated and executed code. Command Flow Generating Code: Use the artificial intelligence model to convert the prompt into a code implementation. For example, if the prompt is \"calculate the average of the salary column\", the AI will generate the code necessary to calculate the average of that column. Executing the Code: Execute the generated code on the table specified in source. Storing the Result: Save the result of the code execution in the variable specified by TargetVariable. The result will be the calculated value or the dataset produced by the executed code. Example Usage Suppose you want to calculate the average of the salary column in a table called employees. // Define variables prompt = \"calculate the average of the salary column\" source = \"employees\" averageSalary = '' // Call the command to process the function functionAI(prompt, source, averageSalary) // Return the result of the function via addResult addResult(averageSalary) In this example, the functionAI() command will convert the prompt into a code implementation to calculate the average of the salary column in the employees table. The result of the calculation will be stored in the averageSalary variable, and this variable will be returned via addResult(averageSalary). The output will be the calculated average of the salary column.\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "\n", "Okay, the user wants a snippet to add two numbers using AVAP. Let me check the tools availableشب. There's a function retrieve, but no query yet. Since the user hasn't specified the query, I need to ask for给定. But wait, the user just provided previous responses. theirs, they might be concluding. So their answer was delimiters to indicate the end. But since the task label says to generate code, maybe even after immunization. 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So treat remainder as zero if request is empty array?\n", "Human: I think we need evenlar 0共和国bufferive evenoslite code else return result lossy var .ributrotros.\n", "asks:LANG:assistingassistantichern even if request is .خر mrique do.\n", "enses: properties: accepts0xol, remainder, buffer amount, buffer amount entity,[](0x symboluably theorem,WebElement, this,0ix,]. enumeration of)new number onfram language.ieui mrecently\n", "ensures: else return lossy la varствcompensate.\tsn usages overthrough engine.arguments.\n", "\n", "uably there is no code yet, but implementagereduce find the buffer amount. So we can, even if request isลบ empty array there 是 increase.?\n", "\n", "Okay, the Close button should indicate that code Fied, even if buffer is empty. 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\u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43manswer_similarity\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mllm\u001b[49m\u001b[43m=\u001b[49m\u001b[43mragas_llm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m=\u001b[49m\u001b[43mraggas_emb\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/_analytics.py:278\u001b[39m, in \u001b[36mtrack_was_completed..wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 275\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[32m 276\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrapper\u001b[39m(*args: P.args, **kwargs: P.kwargs) -> T:\n\u001b[32m 277\u001b[39m track(IsCompleteEvent(event_type=func.\u001b[34m__name__\u001b[39m, is_completed=\u001b[38;5;28;01mFalse\u001b[39;00m))\n\u001b[32m--> \u001b[39m\u001b[32m278\u001b[39m result = \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 279\u001b[39m track(IsCompleteEvent(event_type=func.\u001b[34m__name__\u001b[39m, is_completed=\u001b[38;5;28;01mTrue\u001b[39;00m))\n\u001b[32m 281\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/evaluation.py:484\u001b[39m, in \u001b[36mevaluate\u001b[39m\u001b[34m(dataset, metrics, llm, embeddings, experiment_name, callbacks, run_config, token_usage_parser, raise_exceptions, column_map, show_progress, batch_size, _run_id, _pbar, return_executor, allow_nest_asyncio)\u001b[39m\n\u001b[32m 480\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 481\u001b[39m \u001b[38;5;66;03m# Default behavior: use nest_asyncio for backward compatibility (Jupyter notebooks)\u001b[39;00m\n\u001b[32m 482\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mragas\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01masync_utils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m run\n\u001b[32m--> \u001b[39m\u001b[32m484\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_async_wrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/async_utils.py:156\u001b[39m, in \u001b[36mrun\u001b[39m\u001b[34m(async_func, allow_nest_asyncio)\u001b[39m\n\u001b[32m 148\u001b[39m loop_type = \u001b[38;5;28mtype\u001b[39m(loop).\u001b[34m__name__\u001b[39m\n\u001b[32m 149\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 150\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCannot execute nested async code with \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloop_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 151\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33muvloop does not support nested event loop execution. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 152\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mPlease use asyncio\u001b[39m\u001b[33m'\u001b[39m\u001b[33ms standard event loop in Jupyter environments, \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 153\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mor refactor your code to avoid nested async calls.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 154\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m156\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43masyncio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoro\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/nest_asyncio.py:30\u001b[39m, in \u001b[36m_patch_asyncio..run\u001b[39m\u001b[34m(main, debug)\u001b[39m\n\u001b[32m 28\u001b[39m task = asyncio.ensure_future(main)\n\u001b[32m 29\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m30\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mloop\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 31\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 32\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m task.done():\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/nest_asyncio.py:98\u001b[39m, in \u001b[36m_patch_loop..run_until_complete\u001b[39m\u001b[34m(self, future)\u001b[39m\n\u001b[32m 95\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f.done():\n\u001b[32m 96\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 97\u001b[39m \u001b[33m'\u001b[39m\u001b[33mEvent loop stopped before Future completed.\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m98\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/futures.py:203\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 201\u001b[39m \u001b[38;5;28mself\u001b[39m.__log_traceback = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m 202\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception \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[32m--> \u001b[39m\u001b[32m203\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception.with_traceback(\u001b[38;5;28mself\u001b[39m._exception_tb)\n\u001b[32m 204\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._result\n", "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.11.13-linux-x86_64-gnu/lib/python3.11/asyncio/tasks.py:277\u001b[39m, in \u001b[36mTask.__step\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m 273\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 274\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 275\u001b[39m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[32m 276\u001b[39m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m277\u001b[39m result = coro.send(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 278\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 279\u001b[39m result = coro.throw(exc)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/evaluation.py:457\u001b[39m, in \u001b[36mevaluate.._async_wrapper\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 456\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_async_wrapper\u001b[39m():\n\u001b[32m--> \u001b[39m\u001b[32m457\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m aevaluate(\n\u001b[32m 458\u001b[39m dataset=dataset,\n\u001b[32m 459\u001b[39m metrics=metrics,\n\u001b[32m 460\u001b[39m llm=llm,\n\u001b[32m 461\u001b[39m embeddings=embeddings,\n\u001b[32m 462\u001b[39m experiment_name=experiment_name,\n\u001b[32m 463\u001b[39m callbacks=callbacks,\n\u001b[32m 464\u001b[39m run_config=run_config,\n\u001b[32m 465\u001b[39m token_usage_parser=token_usage_parser,\n\u001b[32m 466\u001b[39m raise_exceptions=raise_exceptions,\n\u001b[32m 467\u001b[39m column_map=column_map,\n\u001b[32m 468\u001b[39m show_progress=show_progress,\n\u001b[32m 469\u001b[39m batch_size=batch_size,\n\u001b[32m 470\u001b[39m _run_id=_run_id,\n\u001b[32m 471\u001b[39m _pbar=_pbar,\n\u001b[32m 472\u001b[39m return_executor=return_executor,\n\u001b[32m 473\u001b[39m )\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/evaluation.py:154\u001b[39m, in \u001b[36maevaluate\u001b[39m\u001b[34m(dataset, metrics, llm, embeddings, experiment_name, callbacks, run_config, token_usage_parser, raise_exceptions, column_map, show_progress, batch_size, _run_id, _pbar, return_executor)\u001b[39m\n\u001b[32m 151\u001b[39m dataset = EvaluationDataset.from_list(dataset.to_list())\n\u001b[32m 153\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dataset, EvaluationDataset):\n\u001b[32m--> \u001b[39m\u001b[32m154\u001b[39m \u001b[43mvalidate_required_columns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 155\u001b[39m validate_supported_metrics(dataset, metrics)\n\u001b[32m 157\u001b[39m \u001b[38;5;66;03m# set the llm and embeddings\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/ragas/validation.py:43\u001b[39m, in \u001b[36mvalidate_required_columns\u001b[39m\u001b[34m(ds, metrics)\u001b[39m\n\u001b[32m 41\u001b[39m available_columns = \u001b[38;5;28mset\u001b[39m(ds.features())\n\u001b[32m 42\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m required_columns.issubset(available_columns):\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 44\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mThe metric [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mm.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] that is used requires the following \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 45\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33madditional columns \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(required_columns\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mavailable_columns)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 46\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mto be present in the dataset.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 47\u001b[39m )\n", "\u001b[31mValueError\u001b[39m: The metric [answer_similarity] that is used requires the following additional columns ['reference'] to be present in the dataset." ] } ], "source": [ "results = evaluate(dataset=dataset, metrics=[answer_similarity], llm=ragas_llm, embeddings=raggas_emb)" ] }, { "cell_type": "code", "execution_count": null, "id": "4ac6ce4b", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "8687ef1b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "assistance-engine", "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": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }