From 38442c2c6c93fe9bfa9cb30fc24e06fcf5f10917 Mon Sep 17 00:00:00 2001 From: David Date: Mon, 23 Mar 2026 00:21:20 +0900 Subject: [PATCH 1/4] gemini-3-flash --- bm25s_index/corpus.jsonl | 20 + bm25s_index/corpus.mmindex.json | 1 + bm25s_index/data.csc.index.npy | Bin 0 -> 1468 bytes bm25s_index/indices.csc.index.npy | Bin 0 -> 1468 bytes bm25s_index/indptr.csc.index.npy | Bin 0 -> 2160 bytes bm25s_index/metadata.json | 102 + bm25s_index/params.index.json | 12 + bm25s_index/vocab.index.json | 1 + .../data_level0.bin | Bin 0 -> 167600 bytes .../header.bin | Bin 0 -> 100 bytes .../length.bin | Bin 0 -> 400 bytes .../link_lists.bin | 0 chroma_db/chroma.sqlite3 | Bin 0 -> 290816 bytes data/error_definitions.json | 246 +++ pyproject.toml | 8 + src/react_agent/context.py | 2 +- src/react_agent/graph.py | 99 +- src/react_agent/prompts.py | 23 +- src/react_agent/regex_rules.py | 42 + src/react_agent/state.py | 3 + src/react_agent/tools.py | 112 +- test_agent.py | 65 + tests/conftest.py | 1 - uv.lock | 1798 ++++++++++++++++- vector_store.py | 65 + 25 files changed, 2544 insertions(+), 56 deletions(-) create mode 100644 bm25s_index/corpus.jsonl create mode 100644 bm25s_index/corpus.mmindex.json create mode 100644 bm25s_index/data.csc.index.npy create mode 100644 bm25s_index/indices.csc.index.npy create mode 100644 bm25s_index/indptr.csc.index.npy create mode 100644 bm25s_index/metadata.json create mode 100644 bm25s_index/params.index.json create mode 100644 bm25s_index/vocab.index.json create mode 100644 chroma_db/7ce52f09-b217-4b2c-b1d9-be77e2214de9/data_level0.bin create mode 100644 chroma_db/7ce52f09-b217-4b2c-b1d9-be77e2214de9/header.bin create mode 100644 chroma_db/7ce52f09-b217-4b2c-b1d9-be77e2214de9/length.bin create mode 100644 chroma_db/7ce52f09-b217-4b2c-b1d9-be77e2214de9/link_lists.bin create mode 100644 chroma_db/chroma.sqlite3 create mode 100644 data/error_definitions.json create mode 100644 src/react_agent/regex_rules.py create mode 100644 test_agent.py create mode 100644 vector_store.py diff --git a/bm25s_index/corpus.jsonl b/bm25s_index/corpus.jsonl new file mode 100644 index 00000000..ba54e7eb --- /dev/null +++ b/bm25s_index/corpus.jsonl @@ -0,0 +1,20 @@ +{"id":0,"text":"E001 Required python package is missing in the execution environment. ModuleNotFoundError: No module named 'pandas' ValueError: Please install the 'db-dtypes' package to use this function"} +{"id":1,"text":"E010 User does not have sufficient permissions to access the resource (BigQuery, Drive, etc.). google.api_core.exceptions.Forbidden: 403 Access Denied: BigQuery: Permission denied while getting Drive credentials."} +{"id":2,"text":"E012 The query result schema does not match the destination table schema. BadRequest: 400 Provided schema does not match. Field 'amount' has changed type from INT64 to FLOAT64"} +{"id":3,"text":"E013 Failed to automatically infer BigQuery schema from DataFrame. pyarrow.lib.ArrowTypeError: Could not convert FLOAT64 to INT64"} +{"id":4,"text":"E014 Data delivery operation exceeded the time limit. AirflowSensorTimeout: Timeout reached DELIVERY_TIMEOUT: Execution time has exceeded the time limit"} +{"id":5,"text":"E018 The specified source file or destination table does not exist. NotFound: 404 Not found: Table project.dataset.table FileNotFoundError: [Errno 2] No such file or directory: '/tmp/data.csv'"} +{"id":6,"text":"E020 The process was terminated because it exceeded the available memory. MemoryError: Unable to allocate 2.4 GiB for an array with shape Container terminated with exit code 137 (OOMKilled)"} +{"id":7,"text":"E021 API call frequency or data volume exceeded the cloud provider's quota. google.api_core.exceptions.Forbidden: 403 Quota exceeded: Your project exceeded quota for free query bytes per day"} +{"id":8,"text":"E022 Could not connect to the database server. Server might be down or blocked by firewall. psycopg2.OperationalError: could not connect to server: Connection refused"} +{"id":9,"text":"E023 The local disk or temporary directory is full. OSError: [Errno 28] No space left on device: '/var/lib/airflow/tmp'"} +{"id":10,"text":"E024 The generated SQL query contains syntax errors. google.api_core.exceptions.BadRequest: 400 Syntax error: Unexpected keyword SELECT at [5:1]"} +{"id":11,"text":"E025 SSL handshake failed while connecting to an external API/DB. [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate"} +{"id":12,"text":"E026 A Spark executor was terminated, often due to OOM or spot instance preemption. org.apache.spark.SparkException: Job aborted due to stage failure: ExecutorLostFailure (executor 5 exited caused by external signal)"} +{"id":13,"text":"E027 Attempted to insert NULL into a column that has a NOT NULL constraint. sqlalchemy.exc.IntegrityError: (psycopg2.errors.NotNullViolation) column 'user_id' contains null values"} +{"id":14,"text":"E028 The authentication token or credentials used for the service have expired. google.auth.exceptions.RefreshError: The credentials have expired and could not be refreshed"} +{"id":15,"text":"E029 The Kubernetes pod running the task was preempted by a higher priority pod or node maintenance. AirflowException: Kubernetes pod was deleted before it could complete"} +{"id":16,"text":"E030 A database transaction was rolled back because it was deadlocked with another process. MySQLdb.OperationalError: (1213, 'Deadlock found when trying to get lock; try restarting transaction')"} +{"id":17,"text":"E031 A required environment variable for the DAG/Task is missing. KeyError: 'DB_PASSWORD' during os.environ retrieval"} +{"id":18,"text":"E032 The source data file is corrupted or does not follow the expected format. pandas.errors.ParserError: Error tokenizing data. C error: Expected 5 fields in line 10, saw 7"} +{"id":19,"text":"E033 Conflict during file upload/download in Google Cloud Storage due to version mismatch. google.api_core.exceptions.PreconditionFailed: 412 Precondition Failed (Generation match failed)"} diff --git a/bm25s_index/corpus.mmindex.json b/bm25s_index/corpus.mmindex.json new file mode 100644 index 00000000..4824a558 --- /dev/null +++ b/bm25s_index/corpus.mmindex.json @@ -0,0 +1 @@ +[0,206,437,631,779,950,1161,1369,1578,1763,1901,2065,2234,2470,2669,2861,3051,3265,3402,3595] \ No newline at end of file diff --git a/bm25s_index/data.csc.index.npy b/bm25s_index/data.csc.index.npy new file mode 100644 index 0000000000000000000000000000000000000000..97dc2c93736bd84a9280b8b3ad6cb81d71ffee23 GIT binary patch literal 1468 zcmbW1T}YF06vo|+^Z{XDA9pS!-UgEqPRg2|?7|WAInR4ubNdf>9ysc% z8mJm{BGE)R?(A?ofiAz}bvs?L__=uK^vPH}5+&Xq>Pti`@kCGPOtf;}(%9JK^?2Rq z-T(c!>i5Eg+$r4_f6J)sZ9XpRYg3XN&d8BnE%KzgTPE`vsp)B!`s%CV^<7-PyFTY7 zyxoz_KWb(C*9sZEe@t;;;o^L`5K=yH5$~)N7JV}DwpVfaZS`93dvjfR>HXoy+oO`UDQJL+Ot~uXR(s%l(-3%D1@I z!3&Oj@Lox4&}8t#Gn60oB9|^kClEos+7Udg6EoH+O36 z84rFY%hFM}rt_kggKyLh<{(j$Eba4{EL(}7z$clWay)j#kssapQ_#QX*aKl{c^=%(JhL4)ZrUy0$h?I*hN zVNKJt&c{xG=NAlke&GdUI_QmF>WJGe!Hq{&$4>H{+BXXqSbYZ-ZsTCLnS~r$*i(9& z7ON)@cYpG!)=^8H>9Kw0jxKVhk6z&L$-FTv{_|}6jTX4*Z`$yACbLif7qOSNC*a^? 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"resolution_step": "Add the missing package to your requirements.txt or rebuild the Docker image.", + "representative_examples": [ + "ModuleNotFoundError: No module named 'pandas'", + "ValueError: Please install the 'db-dtypes' package to use this function" + ] + }, + { + "error_id": "E010", + "pattern_name": "Permission Denied", + "pattern_type": "regex", + "pattern": "Forbidden: 403 Access Denied|Permission denied", + "category": "PERMISSION", + "description": "User does not have sufficient permissions to access the resource (BigQuery, Drive, etc.).", + "resolution_step": "Check the IAM roles for the service account and ensure it has access to both BigQuery and the underlying source (e.g., Google Drive).", + "representative_examples": [ + "google.api_core.exceptions.Forbidden: 403 Access Denied: BigQuery: Permission denied while getting Drive credentials." + ] + }, + { + "error_id": "E012", + "pattern_name": "Schema Mismatch", + "pattern_type": "simple_string", + "pattern": "BadRequest: 400 Provided schema does not match|Invalid schema update", + "category": "SCHEMA_ISSUES", + "description": "The query result schema does not match the destination table schema.", + "resolution_step": "Align the column names and types between the query and the target table.", + "representative_examples": [ + "BadRequest: 400 Provided schema does not match. Field 'amount' has changed type from INT64 to FLOAT64" + ] + }, + { + "error_id": "E013", + "pattern_name": "Schema Inference Failure", + "pattern_type": "regex", + "pattern": "augment_schema|pyarrow\\.lib\\.check_status", + "category": "SCHEMA_ISSUES", + "description": "Failed to automatically infer BigQuery schema from DataFrame.", + "resolution_step": "Explicitly define the schema instead of relying on automatic inference.", + "representative_examples": [ + "pyarrow.lib.ArrowTypeError: Could not convert FLOAT64 to INT64" + ] + }, + { + "error_id": "E014", + "pattern_name": "Delivery Timeout", + "pattern_type": "simple_string", + "pattern": "AirflowSensorTimeout|DELIVERY_TIMEOUT: Execution time has exceeded", + "category": "DELIVERY_ERRORS", + "description": "Data delivery operation exceeded the time limit.", + "resolution_step": "Check network latency or increase the timeout setting in the operator.", + "representative_examples": [ + "AirflowSensorTimeout: Timeout reached", + "DELIVERY_TIMEOUT: Execution time has exceeded the time limit" + ] + }, + { + "error_id": "E018", + "pattern_name": "File or Table Not Found", + "pattern_type": "simple_string", + "pattern": "NotFound: 404 Not found: Table|FileNotFoundError: \\[Errno 2\\] No such file or directory", + "category": "LOGIC_ERRORS", + "description": "The specified source file or destination table does not exist.", + "resolution_step": "Verify the path or table name and ensure it was created in previous steps.", + "representative_examples": [ + "NotFound: 404 Not found: Table project.dataset.table", + "FileNotFoundError: [Errno 2] No such file or directory: '/tmp/data.csv'" + ] + }, + { + "error_id": "E020", + "pattern_name": "Out of Memory (OOM)", + "pattern_type": "regex", + "pattern": "MemoryError|exit code 137|OOMKilled", + "category": "RESOURCE_LIMITS", + "description": "The process was terminated because it exceeded the available memory.", + "resolution_step": "Increase the memory limit of the container or optimize the code to handle data in smaller chunks.", + "representative_examples": [ + "MemoryError: Unable to allocate 2.4 GiB for an array with shape", + "Container terminated with exit code 137 (OOMKilled)" + ] + }, + { + "error_id": "E021", + "pattern_name": "API Quota Exceeded", + "pattern_type": "simple_string", + "pattern": "Quota exceeded: Your project exceeded quota|429 Too Many Requests", + "category": "QUOTA_ISSUES", + "description": "API call frequency or data volume exceeded the cloud provider's quota.", + "resolution_step": "Implement exponential backoff or request a quota increase from the cloud provider console.", + "representative_examples": [ + "google.api_core.exceptions.Forbidden: 403 Quota exceeded: Your project exceeded quota for free query bytes per day" + ] + }, + { + "error_id": "E022", + "pattern_name": "Database Connection Refused", + "pattern_type": "regex", + "pattern": "Connection refused|Failed to establish a new connection|OperationalError: \\(psycopg2\\)", + "category": "NETWORK_ISSUES", + "description": "Could not connect to the database server. Server might be down or blocked by firewall.", + "resolution_step": "Check if the DB server is running and verify firewall/security group rules for the worker IP.", + "representative_examples": [ + "psycopg2.OperationalError: could not connect to server: Connection refused" + ] + }, + { + "error_id": "E023", + "pattern_name": "No Space Left on Device", + "pattern_type": "simple_string", + "pattern": "No space left on device|[Errno 28]", + "category": "RESOURCE_LIMITS", + "description": "The local disk or temporary directory is full.", + "resolution_step": "Clean up the /tmp directory or increase the disk size of the worker/PVC.", + "representative_examples": [ + "OSError: [Errno 28] No space left on device: '/var/lib/airflow/tmp'" + ] + }, + { + "error_id": "E024", + "pattern_name": "SQL Syntax Error", + "pattern_type": "regex", + "pattern": "Syntax error|unexpected token|Invalid query", + "category": "LOGIC_ERRORS", + "description": "The generated SQL query contains syntax errors.", + "resolution_step": "Check the SQL string for missing commas, incorrect keywords, or unclosed quotes.", + "representative_examples": [ + "google.api_core.exceptions.BadRequest: 400 Syntax error: Unexpected keyword SELECT at [5:1]" + ] + }, + { + "error_id": "E025", + "pattern_name": "SSL Certificate Verify Failed", + "pattern_type": "simple_string", + "pattern": "SSL: CERTIFICATE_VERIFY_FAILED|handshake failure", + "category": "NETWORK_ISSUES", + "description": "SSL handshake failed while connecting to an external API/DB.", + "resolution_step": "Update the CA certificates in the environment or check if the target's certificate is expired.", + "representative_examples": [ + "[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate" + ] + }, + { + "error_id": "E026", + "pattern_name": "Spark Executor Lost", + "pattern_type": "regex", + "pattern": "ExecutorLostFailure|FetchFailedException|The executor with id [VAR] was lost", + "category": "SPARK_ERRORS", + "description": "A Spark executor was terminated, often due to OOM or spot instance preemption.", + "resolution_step": "Check executor memory settings or increase the number of retries for Spark tasks.", + "representative_examples": [ + "org.apache.spark.SparkException: Job aborted due to stage failure: ExecutorLostFailure (executor 5 exited caused by external signal)" + ] + }, + { + "error_id": "E027", + "pattern_name": "Null Constraint Violation", + "pattern_type": "simple_string", + "pattern": "Cannot insert the value NULL|Column [VAR] cannot be null", + "category": "DATA_QUALITY", + "description": "Attempted to insert NULL into a column that has a NOT NULL constraint.", + "resolution_step": "Inspect the source data for unexpected NULL values or update the target table schema.", + "representative_examples": [ + "sqlalchemy.exc.IntegrityError: (psycopg2.errors.NotNullViolation) column 'user_id' contains null values" + ] + }, + { + "error_id": "E028", + "pattern_name": "Authentication Token Expired", + "pattern_type": "regex", + "pattern": "Token expired|credentials have expired|Unauthorized: 401", + "category": "PERMISSION", + "description": "The authentication token or credentials used for the service have expired.", + "resolution_step": "Refresh the service account token or update the connection credentials in Airflow.", + "representative_examples": [ + "google.auth.exceptions.RefreshError: The credentials have expired and could not be refreshed" + ] + }, + { + "error_id": "E029", + "pattern_name": "Kubernetes Pod Preempted", + "pattern_type": "simple_string", + "pattern": "Pod was deleted|Preempted by Scheduler", + "category": "INFRASTRUCTURE", + "description": "The Kubernetes pod running the task was preempted by a higher priority pod or node maintenance.", + "resolution_step": "The task should be retried automatically. If it happens frequently, check pod priority settings.", + "representative_examples": [ + "AirflowException: Kubernetes pod was deleted before it could complete" + ] + }, + { + "error_id": "E030", + "pattern_name": "Deadlock Detected", + "pattern_type": "simple_string", + "pattern": "Deadlock found when trying to get lock|Transaction rollback", + "category": "DATABASE", + "description": "A database transaction was rolled back because it was deadlocked with another process.", + "resolution_step": "Reduce parallelism for this task or check for long-running transactions locking the same tables.", + "representative_examples": [ + "MySQLdb.OperationalError: (1213, 'Deadlock found when trying to get lock; try restarting transaction')" + ] + }, + { + "error_id": "E031", + "pattern_name": "Missing Environment Variable", + "pattern_type": "regex", + "pattern": "KeyError: '[VAR]'|environment variable not set", + "category": "CONFIGURATION", + "description": "A required environment variable for the DAG/Task is missing.", + "resolution_step": "Define the missing environment variable in the Airflow deployment or Dockerfile.", + "representative_examples": [ + "KeyError: 'DB_PASSWORD' during os.environ retrieval" + ] + }, + { + "error_id": "E032", + "pattern_name": "Malformed CSV/Parquet File", + "pattern_type": "simple_string", + "pattern": "Malformed records|not a valid Parquet file|Error parsing line", + "category": "DATA_QUALITY", + "description": "The source data file is corrupted or does not follow the expected format.", + "resolution_step": "Verify the source file integrity and check for unexpected delimiters or header changes.", + "representative_examples": [ + "pandas.errors.ParserError: Error tokenizing data. C error: Expected 5 fields in line 10, saw 7" + ] + }, + { + "error_id": "E033", + "pattern_name": "GCS Blob Conflict", + "pattern_type": "simple_string", + "pattern": "412 Precondition Failed|Generation match failed", + "category": "STORAGE_ISSUES", + "description": "Conflict during file upload/download in Google Cloud Storage due to version mismatch.", + "resolution_step": "Check if multiple processes are trying to write to the same GCS object simultaneously.", + "representative_examples": [ + "google.api_core.exceptions.PreconditionFailed: 412 Precondition Failed (Generation match failed)" + ] + } +] \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index f0a99ceb..3ce32d3c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,14 @@ dependencies = [ "langchain-fireworks>=0.1.7", "python-dotenv>=1.0.1", "langchain-tavily>=0.1", + "langchain-ollama>=1.0.1", + "langchain-community>=0.4.1", + "bm25s>=0.3.2.post1", + "chromadb>=1.5.5", + "pystemmer>=3.0.0", + "langchain-huggingface>=1.2.1", + "sentence-transformers>=5.3.0", + "langchain-google-genai>=4.2.1", ] diff --git a/src/react_agent/context.py b/src/react_agent/context.py index 238ccfd3..0d784370 100644 --- a/src/react_agent/context.py +++ b/src/react_agent/context.py @@ -22,7 +22,7 @@ class Context: ) model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field( - default="anthropic/claude-sonnet-4-5-20250929", + default="ollama/qwen2.5-coder:7b", metadata={ "description": "The name of the language model to use for the agent's main interactions. " "Should be in the form: provider/model-name." diff --git a/src/react_agent/graph.py b/src/react_agent/graph.py index 9d2ca765..dd5e1d19 100644 --- a/src/react_agent/graph.py +++ b/src/react_agent/graph.py @@ -42,13 +42,37 @@ async def call_model( ) # Get the model's response - response = cast( # type: ignore[redundant-cast] + response = cast( # type: ignore[redundant-cast] AIMessage, await model.ainvoke( [{"role": "system", "content": system_message}, *state.messages] ), ) + # Heuristic for smaller models that output raw JSON instead of native tool calls + if not response.tool_calls and isinstance(response.content, str): + try: + import json, uuid + content_str = response.content.strip() + # Remove Markdown JSON wrapper if it exists + if content_str.startswith("```json") and content_str.endswith("```"): + content_str = content_str[7:-3].strip() + elif content_str.startswith("```") and content_str.endswith("```"): + content_str = content_str[3:-3].strip() + + data = json.loads(content_str) + if isinstance(data, dict) and "name" in data and "arguments" in data: + args = data["arguments"] + if isinstance(args, str): + args = json.loads(args) + response.tool_calls = [{ + "name": data["name"], + "args": args, + "id": f"call_{uuid.uuid4().hex[:8]}" + }] + except Exception: + pass + # Handle the case when it's the last step and the model still wants to use a tool if state.is_last_step and response.tool_calls: return { @@ -64,52 +88,69 @@ async def call_model( return {"messages": [response]} -# Define a new graph +import json -builder = StateGraph(State, input_schema=InputState, context_schema=Context) +from react_agent.regex_rules import check_regex_patterns -# Define the two nodes we will cycle between -builder.add_node(call_model) -builder.add_node("tools", ToolNode(TOOLS)) -# Set the entrypoint as `call_model` -# This means that this node is the first one called -builder.add_edge("__start__", "call_model") +# Add Regex precheck node +async def regex_precheck( + state: State, runtime: Runtime[Context] +) -> Dict[str, List[AIMessage]]: + """Checks if the log matches any known fast-path regex rules.""" + report = check_regex_patterns(state.raw_log) + if report: + # Generate an AIMessage containing the JSON directly to bypass the LLM + return {"messages": [AIMessage(content=json.dumps(report, indent=2))]} + # If no match, return no new messages; the graph will route to call_model + return {"messages": []} -def route_model_output(state: State) -> Literal["__end__", "tools"]: - """Determine the next node based on the model's output. - This function checks if the model's last message contains tool calls. +def route_after_regex(state: State) -> Literal["call_model", "__end__"]: + """Routes based on whether regex found a match.""" + # If the last message is an AIMessage containing our JSON report, we are done + if state.messages and isinstance(state.messages[-1], AIMessage): + try: + # Simple heuristic: if it parsed as JSON and has error_id, it's our regex output + data = json.loads(state.messages[-1].content) + if "error_id" in data: + return "__end__" + except Exception: + pass - Args: - state (State): The current state of the conversation. + return "call_model" - Returns: - str: The name of the next node to call ("__end__" or "tools"). - """ + +# Define a new graph +builder = StateGraph(State, input_schema=InputState, context_schema=Context) + +# Define nodes +builder.add_node("regex_precheck", regex_precheck) +builder.add_node("call_model", call_model) +builder.add_node("tools", ToolNode(TOOLS)) + +# 1. Entrypoint is now strictly the regex precheck +builder.add_edge("__start__", "regex_precheck") + +# 2. Condition after regex precheck -> Either end the graph or call LLM +builder.add_conditional_edges("regex_precheck", route_after_regex) + + +# 3. From call_model, we route to either TOOLS or END +def route_model_output(state: State) -> Literal["__end__", "tools"]: + """Determine the next node based on the model's output.""" last_message = state.messages[-1] if not isinstance(last_message, AIMessage): raise ValueError( f"Expected AIMessage in output edges, but got {type(last_message).__name__}" ) - # If there is no tool call, then we finish if not last_message.tool_calls: return "__end__" - # Otherwise we execute the requested actions return "tools" -# Add a conditional edge to determine the next step after `call_model` -builder.add_conditional_edges( - "call_model", - # After call_model finishes running, the next node(s) are scheduled - # based on the output from route_model_output - route_model_output, -) - -# Add a normal edge from `tools` to `call_model` -# This creates a cycle: after using tools, we always return to the model +builder.add_conditional_edges("call_model", route_model_output) builder.add_edge("tools", "call_model") # Compile the builder into an executable graph diff --git a/src/react_agent/prompts.py b/src/react_agent/prompts.py index b7d8d460..595367eb 100644 --- a/src/react_agent/prompts.py +++ b/src/react_agent/prompts.py @@ -1,5 +1,26 @@ """Default prompts used by the agent.""" -SYSTEM_PROMPT = """You are a helpful AI assistant. +SYSTEM_PROMPT = """You are a Senior Airflow Site Reliability Engineer. + +[PHASE 1: INVESTIGATION] +- Your first priority is to gather evidence using tools. +- Read the provided Error Log carefully. +- To search for known error guides based on the log, call 'search_error_guide'. +- To read surrounding source code if the error points to a specific script, call 'read_failed_source_code'. +- IMPORTANT: When using a tool, use the functional tool-calling feature. Do NOT just write a JSON string. + +[PHASE 2: FINAL REPORT] +- ONLY after you have enough information from tool outputs, provide the final analysis. +- Your analysis MUST be based strictly on the Reference Guides retrieved by the tools. +- If the log matches a guide, provide its 'error_id'. +- If NO guide matches strictly (confidence < 0.7), you MUST: + - Set 'error_id' to "UNKNOWN" + - Set 'confidence' to a value below 0.5 + - Set 'resolution_step' to "New unknown error. Developer check required." +- Provide the 'confidence' as a FLOAT between 0.0 and 1.0. + +- The final analysis MUST be a JSON object with exactly these keys: + error_id, category, technical_root_cause, evidence_line, resolution_step, confidence. +- Return ONLY the JSON object as your final answer. System time: {system_time}""" diff --git a/src/react_agent/regex_rules.py b/src/react_agent/regex_rules.py new file mode 100644 index 00000000..40b13aee --- /dev/null +++ b/src/react_agent/regex_rules.py @@ -0,0 +1,42 @@ +import re +from typing import Any, Dict, Optional + +# Define your known regex patterns here +KNOWN_ERRORS = [ + { + "pattern": re.compile( + r"google\.api_core\.exceptions\.Forbidden: 403.*Permission denied" + ), + "report": { + "error_id": "ERR-GCP-403", + "category": "Permission Error", + "technical_root_cause": "The service account lacks the required IAM roles to access the resource.", + "evidence_line": "google.api_core.exceptions.Forbidden: 403", + "resolution_step": "Check IAM permissions for the Service Account. Ensure it has roles/bigquery.dataViewer or equivalent.", + "confidence": 1.0, + }, + }, + { + "pattern": re.compile(r"TimeoutError: Database connection timeout"), + "report": { + "error_id": "ERR-DB-001", + "category": "Network / Connection", + "technical_root_cause": "The worker failed to reach the database within the configured timeout period.", + "evidence_line": "TimeoutError: Database connection timeout", + "resolution_step": "Verify DB network firewall rules and ensure the database is not overloaded.", + "confidence": 1.0, + }, + }, +] + + +def check_regex_patterns(log_text: str) -> Optional[Dict[str, Any]]: + """Evaluates the log against known regex patterns and returns a report if matched.""" + if not log_text: + return None + + for rule in KNOWN_ERRORS: + if rule["pattern"].search(log_text): + return rule["report"] + + return None diff --git a/src/react_agent/state.py b/src/react_agent/state.py index 703bcf9f..e5f71066 100644 --- a/src/react_agent/state.py +++ b/src/react_agent/state.py @@ -21,6 +21,9 @@ class InputState: messages: Annotated[Sequence[AnyMessage], add_messages] = field( default_factory=list ) + + raw_log: str = field(default="") + """The unprocessed text log for the agent to analyze.""" """ Messages tracking the primary execution state of the agent. diff --git a/src/react_agent/tools.py b/src/react_agent/tools.py index 7afd11b0..9fe3f7af 100644 --- a/src/react_agent/tools.py +++ b/src/react_agent/tools.py @@ -1,29 +1,107 @@ -"""This module provides example tools for web scraping and search functionality. +"""This module provides custom tools for the Airflow Error Analysis agent. -It includes a basic Tavily search function (as an example) - -These tools are intended as free examples to get started. For production use, -consider implementing more robust and specialized tools tailored to your needs. +It includes a BM25S + ChromaDB hybrid search tool and a source code reading tool. """ +import json +import logging from typing import Any, Callable, List, Optional, cast -from langchain_tavily import TavilySearch -from langgraph.runtime import get_runtime +import bm25s +import chromadb +from langchain_core.tools import tool +from Stemmer import Stemmer + +logger = logging.getLogger(__name__) + +# Initialize clients (ensure these don't fail if indices are missing at import time) +try: + chroma_client = chromadb.PersistentClient(path="./chroma_db") + collection = chroma_client.get_collection(name="airflow_errors") + + bm25_retriever = bm25s.BM25.load("./bm25s_index", load_corpus=True) + with open("./bm25s_index/metadata.json", "r") as f: + bm25_metadata = json.load(f) + stemmer = Stemmer("english") + indices_loaded = True +except Exception as e: + logger.warning( + f"Failed to load Hybrid Indices. Ensure vectore_store.py has been run. Error: {e}" + ) + indices_loaded = False + + +def hybrid_search(query: str, top_k: int = 2) -> str: + """Combines BM25S and ChromaDB vector search results.""" + if not indices_loaded: + return "Indices not loaded. Please run vectore_store.py first." + + logger.info(f"🔎 hybrid_search started with top_k={top_k}") + + # 1. BM25S search + query_tokens = bm25s.tokenize([query], stemmer=stemmer) + bm25_docs, _ = bm25_retriever.retrieve(query_tokens, k=top_k) + logger.info(f"📄 BM25S retrieved {len(bm25_docs[0])} documents") + + # 2. Vector search + vector_results = collection.query(query_texts=[query], n_results=top_k) + docs = vector_results.get("documents", [[]])[0] + logger.info(f"📊 Vector DB retrieved {len(docs)} documents") -from react_agent.context import Context + # 3. Deduplicate and construct context + unique_contexts = set() + for doc in bm25_docs[0]: + unique_contexts.add(f"[Keyword Match] {doc}") -async def search(query: str) -> Optional[dict[str, Any]]: - """Search for general web results. + for doc in docs: + unique_contexts.add(f"[Semantic Match] {doc}") - This function performs a search using the Tavily search engine, which is designed - to provide comprehensive, accurate, and trusted results. It's particularly useful - for answering questions about current events. + result = "\\n\\n".join(list(unique_contexts)) + logger.info(f"✨ hybrid_search combined {len(unique_contexts)} unique results") + return result + + +@tool +def search_error_guide(query: str) -> str: + """ + Searches for relevant error guides from the 30 error clusters and knowledge base. + Use this when classifying error types or seeking resolution steps. """ - runtime = get_runtime(Context) - wrapped = TavilySearch(max_results=runtime.context.max_search_results) - return cast(dict[str, Any], await wrapped.ainvoke({"query": query})) + logger.info(f"🔍 search_error_guide called with query: {query}") + result = hybrid_search(query) + logger.info(f"✅ search_error_guide returned result of length {len(result)}") + return result + + +@tool +def read_failed_source_code(file_path: str, line_number: int) -> str: + """ + Reads the source code around the specified line number of the given file. + Use this to find logic flaws when the error log itself is insufficient. + """ + logger.info( + f"📂 read_failed_source_code called - file: {file_path}, line: {line_number}" + ) + window = 10 + try: + logger.debug(f"📖 Reading file: {file_path}") + with open(file_path, "r", encoding="utf-8") as f: + lines = f.readlines() + + if line_number: + start = max(0, line_number - window) + end = min(len(lines), line_number + window) + code_snippet = "".join(lines[start:end]) + logger.info(f"📝 Extracted code snippet from line {start} to {end}") + return f"--- Code Snippet ({file_path}) ---\\n{code_snippet}" + + logger.info(f"📝 Returning first 100 lines of {file_path}") + return "".join(lines[:100]) + except Exception as e: + error_msg = f"Error reading file: {str(e)}" + logger.error(error_msg) + return error_msg -TOOLS: List[Callable[..., Any]] = [search] +TOOLS: List[Callable[..., Any]] = [search_error_guide, read_failed_source_code] diff --git a/test_agent.py b/test_agent.py new file mode 100644 index 00000000..f4dd62e9 --- /dev/null +++ b/test_agent.py @@ -0,0 +1,65 @@ +import asyncio +import os + +from dotenv import load_dotenv + +from react_agent.context import Context +from react_agent.graph import graph + +load_dotenv() + +# os.environ["OLLAMA_BASE_URL"] = "http://localhost:11434" + +async def main(): + sample_lo_1 = """ + Caused by: com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.BigQueryException: Error while reading data, error message: Schema mismatch: referenced variable 'df.list.element.PRODUCT_ID' has array levels of 1, while the corresponding field path to Parquet column has 0 repeated fields File: gs://my-recommendation/temp/.spark-bigquery-application_1683269466816_0014-881c60d3-da1a-44a3-92e3-a10de968b71c/part-00102-7f5d2b2b-22e2-4cf8-98fc-866eacc9c9db-c000.snappy.parquet + at com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.Job.reload(Job.java:419) + """ + + sample_log_2 = """ + [2024-08-08, 14:41:09 EDT] {file_task_handler.py:522} ERROR - Could not read served logs + Traceback (most recent call last): + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1407, in _run_raw_task + self._execute_task_with_callbacks(context, test_mode) + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1558, in _execute_task_with_callbacks + result = self._execute_task(context, task_orig) + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1628, in _execute_task + result = execute_callable(context=context) + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/operators/bash.py", line 210, in execute + raise AirflowException( + airflow.exceptions.AirflowException: Bash command failed. The command returned a non-zero exit code 1. + + """ + + sample_log = sample_log_2 + + inputs = { + "messages": [ + ( + "user", + f"Analyze this Airflow log and find the root cause. Please provide a detailed JSON report: {sample_log}", + ) + ], + "raw_log": sample_log, + } + + print("Sending Airflow Log to agent with Gemini 1.5 Flash...") + try: + async for chunk in graph.astream( + inputs, + stream_mode="values", + # context=Context(model="ollama/qwen2.5-coder:7b"), + context=Context(model="google_genai/gemini-3-flash-preview"), + ): + message = chunk["messages"][-1] + message_type = getattr(message, "type", "unknown") + if hasattr(message, "content") and getattr(message, "content", ""): + print(f"[{message_type}]: {message.content}") + if hasattr(message, "tool_calls") and getattr(message, "tool_calls", None): + print(f"[{message_type}] (tool call): {message.tool_calls}") + except Exception as e: + print(f"Error during execution: {e}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/tests/conftest.py b/tests/conftest.py index ad84bd0d..26262e49 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -4,4 +4,3 @@ 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prepare_hybrid_indices(file_path="data/error_definitions.json"): + # 1. 데이터 로드 + with open(file_path, "r", encoding="utf-8") as f: + error_definitions = json.load(f) + + # --- [Part A: ChromaDB - Vector Index] --- + client = chromadb.PersistentClient(path="./chroma_db") + embed_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") + collection = client.get_or_create_collection(name="airflow_errors") + + # --- [Part B: BM25S - Lexical Index] --- + # BM25S 검색 대상이 될 코퍼스(Corpus) 생성 + corpus = [] + metadata_list = [] + + for item in error_definitions: + # 검색 품질을 높이기 위해 ID, 설명, 예시를 합친 텍스트 생성 + combined_text = f"{item['error_id']} {item['description']} " + " ".join( + item.get("representative_examples", []) + ) + corpus.append(combined_text) + + # 메타데이터 저장 (나중에 결과 매칭용) + metadata_list.append( + { + "error_id": item["error_id"], + "category": item["category"], + "resolution": item["resolution_step"], + } + ) + + # ChromaDB에도 동일하게 추가 + collection.upsert( + documents=[combined_text], + metadatas=[metadata_list[-1]], + ids=[item["error_id"]], + ) + + # BM25S 토큰화 및 인덱싱 + stemmer = Stemmer("english") + corpus_tokens = bm25s.tokenize(corpus, stemmer=stemmer) + + bm25_model = bm25s.BM25() + bm25_model.index(corpus_tokens) + + # BM25S 인덱스 로컬 저장 (중요: metadata와 함께 저장) + bm25_model.save("./bm25s_index", corpus=corpus) + + # 매칭을 위한 메타데이터 별도 저장 + with open("./bm25s_index/metadata.json", "w", encoding="utf-8") as f: + json.dump(metadata_list, f, indent=2) + + print("✅ Hybrid Indices (ChromaDB + BM25S) Prepared!") + + +if __name__ == "__main__": + prepare_hybrid_indices() From ee9abe82a51b9fb74844665eb351ddc923111325 Mon Sep 17 00:00:00 2001 From: David Date: Mon, 23 Mar 2026 00:34:52 +0900 Subject: [PATCH 2/4] sre prompt --- .../length.bin | Bin 400 -> 400 bytes chroma_db/chroma.sqlite3 | Bin 290816 -> 290816 bytes src/react_agent/context.py | 2 +- src/react_agent/prompts.py | 42 ++++++++++-------- test_agent.py | 27 ++++++++--- 5 files changed, 46 insertions(+), 25 deletions(-) diff --git a/chroma_db/7ce52f09-b217-4b2c-b1d9-be77e2214de9/length.bin b/chroma_db/7ce52f09-b217-4b2c-b1d9-be77e2214de9/length.bin index cb3e1628bcb246bfc7b4a4b378b1586758115f3d..0be27a412969af5f91f32b5967a6351e558a93e2 100644 GIT binary patch literal 400 zcmZQzX1H>&j)8#zM&E|;*?~eJzyhIw(hyL0?WO1e=VdX=mvn#M_;IE_1Cu8EA)tO9 zs60&lgc6oBAah~-21bYi1*rKj{sE}Igq^Hl^)SA{6a4O|$KeiMsCh7ZC-h@?M*!4( jm^=2u+@Szb59hzd?~Vo>?%+dn2g5Av?r4CT4|4|qg$zxF literal 400 NcmZQz7zKkV1ONe$00961 diff --git a/chroma_db/chroma.sqlite3 b/chroma_db/chroma.sqlite3 index 4d0c94f2ac6b28439b35b613f6b5270d15769594..76985537ff94df84be4d27734e9f6274ec0e4cd2 100644 GIT binary patch delta 164 zcmZp8AlUFgaDp^r+(a2?#<-0MOY~Wk_`?{c^BiE3(&FF6zm@w-dAfc3bbCf1W&&d7?bGd9G7SK6ml!Aj diff --git a/src/react_agent/context.py b/src/react_agent/context.py index 0d784370..1a0aa3d6 100644 --- a/src/react_agent/context.py +++ b/src/react_agent/context.py @@ -14,7 +14,7 @@ class Context: """The context for the agent.""" system_prompt: str = field( - default=prompts.SYSTEM_PROMPT, + default=prompts.SRE_SYSTEM_PROMPT, metadata={ "description": "The system prompt to use for the agent's interactions. " "This prompt sets the context and behavior for the agent." diff --git a/src/react_agent/prompts.py b/src/react_agent/prompts.py index 595367eb..624b3cbf 100644 --- a/src/react_agent/prompts.py +++ b/src/react_agent/prompts.py @@ -1,26 +1,30 @@ """Default prompts used by the agent.""" -SYSTEM_PROMPT = """You are a Senior Airflow Site Reliability Engineer. +SRE_SYSTEM_PROMPT = """You are a Senior Site Reliability Engineer (SRE) specializing in Apache Airflow, Google BigQuery, and Python data pipelines. +Your mission is to provide a precise, actionable root cause analysis for the given error log. -[PHASE 1: INVESTIGATION] -- Your first priority is to gather evidence using tools. -- Read the provided Error Log carefully. -- To search for known error guides based on the log, call 'search_error_guide'. -- To read surrounding source code if the error points to a specific script, call 'read_failed_source_code'. -- IMPORTANT: When using a tool, use the functional tool-calling feature. Do NOT just write a JSON string. +### 🛡️ PHASE 1: Systematic Investigation +1. **Analyze the Traceback:** Identify the exact file path and line number where the error occurred. +2. **Retrieve Context:** - Use 'search_error_guide' to find similar historical incidents from the 20+ clusters. + - Use 'read_failed_source_code' to inspect the actual logic around the failing line. +3. **Cross-Reference:** Compare the tool outputs with the current error log. Look for pattern matches in error IDs (E001-E033). -[PHASE 2: FINAL REPORT] -- ONLY after you have enough information from tool outputs, provide the final analysis. -- Your analysis MUST be based strictly on the Reference Guides retrieved by the tools. -- If the log matches a guide, provide its 'error_id'. -- If NO guide matches strictly (confidence < 0.7), you MUST: - - Set 'error_id' to "UNKNOWN" - - Set 'confidence' to a value below 0.5 - - Set 'resolution_step' to "New unknown error. Developer check required." -- Provide the 'confidence' as a FLOAT between 0.0 and 1.0. +### ⚖️ PHASE 2: Confidence & UNKNOWN Logic +- **High Confidence (>= 0.7):** If the tool results clearly match the error pattern and you found the defect in the code. +- **Low Confidence (< 0.7):** If the search results are ambiguous or the source code doesn't explain the failure. +- **Strict Rule:** If confidence is Low, you MUST set 'error_id' to "UNKNOWN" and 'resolution_step' to "New pattern detected. Manual developer investigation required." -- The final analysis MUST be a JSON object with exactly these keys: - error_id, category, technical_root_cause, evidence_line, resolution_step, confidence. -- Return ONLY the JSON object as your final answer. +### 📝 PHASE 3: Final Output Format +You must return ONLY a JSON object with the following structure: +{{ + "error_id": "ID (e.g., E012 or UNKNOWN)", + "category": "Classification (e.g., SCHEMA_ISSUES, PERMISSION, etc.)", + "technical_root_cause": "Detailed technical explanation of WHY it happened.", + "evidence_line": "The specific line of code or log snippet that proves the cause.", + "resolution_step": "Step-by-step instructions to fix the issue.", + "confidence": 0.0 to 1.0 (FLOAT) +}} + +CRITICAL: Do not include any conversational text before or after the JSON. System time: {system_time}""" diff --git a/test_agent.py b/test_agent.py index f4dd62e9..e328f8b1 100644 --- a/test_agent.py +++ b/test_agent.py @@ -11,7 +11,7 @@ # os.environ["OLLAMA_BASE_URL"] = "http://localhost:11434" async def main(): - sample_lo_1 = """ + sample_log_1 = """ Caused by: com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.BigQueryException: Error while reading data, error message: Schema mismatch: referenced variable 'df.list.element.PRODUCT_ID' has array levels of 1, while the corresponding field path to Parquet column has 0 repeated fields File: gs://my-recommendation/temp/.spark-bigquery-application_1683269466816_0014-881c60d3-da1a-44a3-92e3-a10de968b71c/part-00102-7f5d2b2b-22e2-4cf8-98fc-866eacc9c9db-c000.snappy.parquet at com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.Job.reload(Job.java:419) """ @@ -31,6 +31,20 @@ async def main(): """ + sample_log_3 = """ + [2024-08-08, 14:41:09 EDT] {file_task_handler.py:522} ERROR - Could not read served logs + Traceback (most recent call last): + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1407, in _run_raw_task + self._execute_task_with_callbacks(context, test_mode) + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1558, in _execute_task_with_callbacks + result = self._execute_task(context, task_orig) + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 1628, in _execute_task + result = execute_callable(context=context) + File "/home/airflow/.local/lib/python3.8/site-packages/airflow/operators/bash.py", line 210, in execute + raise AirflowException("Kubernetes pod TEST_SQL_EXETUION was deleted before it finished") + [2024-08-08, 14:41:09 EDT] {kubernetes_executor.py:112} ERROR - Error while fetching logs from Kubernetes pod: + """ + sample_log = sample_log_2 inputs = { @@ -43,13 +57,14 @@ async def main(): "raw_log": sample_log, } - print("Sending Airflow Log to agent with Gemini 1.5 Flash...") + # MODEL = "google_genai/gemini-3-flash-preview" + MODEL = "ollama/qwen2.5-coder:7b" + print(f"Sending Airflow Log to agent with {MODEL}...") try: async for chunk in graph.astream( inputs, - stream_mode="values", - # context=Context(model="ollama/qwen2.5-coder:7b"), - context=Context(model="google_genai/gemini-3-flash-preview"), + stream_mode="values", + context=Context(model=MODEL), ): message = chunk["messages"][-1] message_type = getattr(message, "type", "unknown") @@ -58,6 +73,8 @@ async def main(): if hasattr(message, "tool_calls") and getattr(message, "tool_calls", None): print(f"[{message_type}] (tool call): {message.tool_calls}") except Exception as e: + import traceback + traceback.print_exc() print(f"Error during execution: {e}") From df74725db6842e11daba2c367f40f3dd60e458ea Mon Sep 17 00:00:00 2001 From: David Date: Mon, 23 Mar 2026 01:05:03 +0900 Subject: [PATCH 3/4] log_preprocessing --- chroma_db/chroma.sqlite3 | Bin 290816 -> 290816 bytes src/react_agent/graph.py | 48 +- uv.lock | 2120 ++++++++++++++++++++------------------ 3 files changed, 1136 insertions(+), 1032 deletions(-) diff --git a/chroma_db/chroma.sqlite3 b/chroma_db/chroma.sqlite3 index 76985537ff94df84be4d27734e9f6274ec0e4cd2..5c1d9561ed1639264d3e3d6c77d975547454dfa7 100644 GIT binary patch delta 75 zcmZp8AlUFgaDp^r!bBNo#)ORtOY~V(_ Literal["call_model", "__end__"]: +async def preprocess_log( + state: State, runtime: Runtime[Context] +) -> Dict[str, Any]: + """Distills the raw log into a more concise format for the LLM and search tools.""" + raw_log = state.raw_log + + # 1. Traceback extraction (most relevant part) + if "Traceback (most recent call last):" in raw_log: + distilled_log = raw_log.split("Traceback (most recent call last):")[-1] + else: + # If no Traceback, take last 2000 chars as a heuristic + distilled_log = raw_log[-2000:] + + # 2. Remove Jupyter noise (e.g., "Input In [1], in ...") + distilled_log = re.sub(r"Input In \[\d+\].+", "", distilled_log) + + # 3. Clean empty lines and limit to last 50 lines + lines = [line.strip() for line in distilled_log.split("\n") if line.strip()] + final_log = "\n".join(lines[-50:]) + + # Update messages to replace the long log with the distilled version + # This helps the LLM generate better search queries. + new_messages = [] + if state.messages and isinstance(state.messages[0], HumanMessage): + original_content = state.messages[0].content + if raw_log in original_content: + new_content = original_content.replace(raw_log, final_log) + # We create a new HumanMessage with the same ID to replace it via add_messages + new_messages.append(HumanMessage(content=new_content, id=state.messages[0].id)) + + return {"raw_log": final_log, "messages": new_messages} + + +def route_after_regex(state: State) -> Literal["preprocess_log", "__end__"]: """Routes based on whether regex found a match.""" # If the last message is an AIMessage containing our JSON report, we are done if state.messages and isinstance(state.messages[-1], AIMessage): @@ -119,7 +153,7 @@ def route_after_regex(state: State) -> Literal["call_model", "__end__"]: except Exception: pass - return "call_model" + return "preprocess_log" # Define a new graph @@ -127,15 +161,19 @@ def route_after_regex(state: State) -> Literal["call_model", "__end__"]: # Define nodes builder.add_node("regex_precheck", regex_precheck) +builder.add_node("preprocess_log", preprocess_log) builder.add_node("call_model", call_model) builder.add_node("tools", ToolNode(TOOLS)) # 1. Entrypoint is now strictly the regex precheck builder.add_edge("__start__", "regex_precheck") -# 2. Condition after regex precheck -> Either end the graph or call LLM +# 2. Condition after regex precheck -> Either end the graph or preprocess log builder.add_conditional_edges("regex_precheck", route_after_regex) +# 3. From preprocessing, go to model +builder.add_edge("preprocess_log", "call_model") + # 3. 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create mode 100644 src/react_agent/monitoring.py diff --git a/Makefile b/Makefile index 4bfd8786..a4a76c51 100644 --- a/Makefile +++ b/Makefile @@ -21,6 +21,15 @@ test_profile: extended_tests: python -m pytest --only-extended $(TEST_FILE) +###################### +# MONITORING AND EXECUTION +###################### + +phoenix: + .venv/bin/python3 -m phoenix.server.main serve + +run: + export PYTHONPATH=$PYTHONPATH:$(pwd)/src && .venv/bin/python3 test_agent.py ###################### # LINTING AND FORMATTING @@ -64,4 +73,6 @@ help: @echo 'tests - run unit tests' @echo 'test TEST_FILE= - run all tests in file' @echo 'test_watch - run unit tests in watch mode' + @echo 'phoenix - start Arize Phoenix dashboard' + @echo 'run - run the agent with monitoring enabled' diff --git a/chroma_db/chroma.sqlite3 b/chroma_db/chroma.sqlite3 index 5c1d9561ed1639264d3e3d6c77d975547454dfa7..ba2437473318da3dd5b80e51f57d5dfadc0ea88e 100644 GIT binary patch delta 94 zcmZp8AlUFgaDp^r%0wAw#*~c-OY~VZ_$?Tw^BiE3nyh4?$IkE0@5FD*zuho^iJft? pgTgId5oQf$22N&m5TOPlRDndZkA1t3JtGh^0WtG-AA6Q91^{o*71RI# delta 59 zcmZp8AlUFgaDp^r!bBNo#)ORtOY~V(_tI MG4pmGdzLK*07%jjy8r+H diff --git a/src/react_agent/monitoring.py b/src/react_agent/monitoring.py new file mode 100644 index 00000000..ce742dd7 --- /dev/null +++ b/src/react_agent/monitoring.py @@ -0,0 +1,26 @@ +import phoenix as px +from phoenix.otel import register +from openinference.instrumentation.langchain import LangChainInstrumentor +import logging + +logger = logging.getLogger(__name__) + +def init_monitoring(project_name: str = "airflow-sre-agent"): + """ + Initializes Arize Phoenix for LangChain/LangGraph monitoring. + + This function: + 1. Registers an OpenTelemetry tracer for Phoenix. + 2. Instruments LangChain and LangGraph. + """ + try: + # 1. Register the OpenTelemetry tracer + # This will send traces to a running Phoenix instance (default localhost:6006) + print(f"\n🔭 Arize Phoenix Tracing Initialized (Project: {project_name})") + register(project_name=project_name) + + # 2. Instrument LangChain + LangChainInstrumentor().instrument() + except Exception as e: + logger.error(f"Failed to initialize Phoenix monitoring: {e}") + return None diff --git a/test_agent.py b/test_agent.py index e328f8b1..b84381c6 100644 --- a/test_agent.py +++ b/test_agent.py @@ -5,12 +5,16 @@ from react_agent.context import Context from react_agent.graph import graph +from react_agent.monitoring import init_monitoring load_dotenv() # os.environ["OLLAMA_BASE_URL"] = "http://localhost:11434" async def main(): + # Initialize Airze Phoenix Monitoring (Open-source alternative to LangSmith) + init_monitoring() + sample_log_1 = """ Caused by: com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.BigQueryException: Error while reading data, error message: Schema mismatch: referenced variable 'df.list.element.PRODUCT_ID' has array levels of 1, while the corresponding field path to Parquet column has 0 repeated fields File: gs://my-recommendation/temp/.spark-bigquery-application_1683269466816_0014-881c60d3-da1a-44a3-92e3-a10de968b71c/part-00102-7f5d2b2b-22e2-4cf8-98fc-866eacc9c9db-c000.snappy.parquet at com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.Job.reload(Job.java:419)