instrumentation know how and span json - user guide update

Monocle_User_Guide.md

@@ -73,4 +73,74 @@ setup_monocle_telemetry(
                 wrapper=atask_wrapper)
         ])
 
-```
+```
+
+### Default configuration of instrumented methods in Monocle
+
+The following files comprise of default configuration of instrumented methods and span names corresponding to them, for each framework respectively. 
+
+[src/monocle_apptrace/langchain/__init__.py](src/monocle_apptrace/langchain/__init__.py),
+ [src/monocle_apptrace/llamaindex/__init__.py](src/monocle_apptrace/llamaindex/__init__.py),
+ [src/monocle_apptrace/haystack/__init__.py](src/monocle_apptrace/haystack/__init__.py)
+
+Following configuration instruments  ```invoke(..)``` of ```RunnableSequence```, aka chain or worflow in Langchain parlance, to emit the span.
+
+```
+    {
+        "package": "langchain.schema.runnable",
+        "object": "RunnableSequence",
+        "method": "invoke",
+        "span_name": "langchain.workflow",
+        "wrapper": task_wrapper
+    }
+```
+
+#### span json 
+
+Monocle generates spans which adhere to [Tracing API | OpenTelemetry](https://opentelemetry.io/docs/specs/otel/trace/api/#span) format. Please note that ```trace_id``` groups related spans and is auto generated with-in Monocle. 
+
+| Span JSON      | Description     |
+| ------------- | ------------- |
+| {||
+|  "```name```": "langchain.workflow",|span name and is configurable in [__init.py__](src/monocle_apptrace/langchain/__init__.py) or in ```setup_okahu_telemetry(...)```|
+|  "```context```": {|this gets autogenerated|
+|      "```trace_id```": "0xe5269f0e534efa098b240f974220d6b7",||
+|         "```span_id```": "0x30b13075eca52f44",||
+|         "```trace_state```": "[]"||
+|     },||
+|"```kind```": "SpanKind.INTERNAL",| An enum that describes what this span is about. Default value is SpanKind.INTERNAL, as current enums do not cover ML apps |
+|"```parent_id```": null,|If null, this is root span|
+|"```start_time```": "2024-07-16T17:05:15.544861Z",||
+|"```end_time```": "2024-07-16T17:05:43.502007Z",||
+|"```status```": {||
+|   "```status_code```": "UNSET"| This to be updated to set to OK or ERROR. Default is UNSET|
+|  },||
+|"```attributes```": {||
+|  "workflow_name": "ml_rag_app",|defines the name of the service being set in ```setup_okahu_telemetry(...)``` during initialization of instrumentation|
+|  "workflow_type": "workflow.langchain"|type of framework that generated this span|
+|  },||
+|"```events```": [|Captures the log records|
+|  {||
+|    "```name```": "input",|name of the event. If the span is about LLM, then this will be 'input'. For vector store retrieval, this would be 'context_input'|
+|    "```timestamp```": "2024-07-16T17:05:15.544874Z",||
+|    "```attributes```": {|captures the 'input' attributes. Based on the workflow of the ML framework being used, the attributes change|
+|       "question": "What is Task Decomposition?",|represents LLM query|
+|       "q_a_pairs": "..." |represents questions and answers for a few shot LLM prompting |
+|                }||
+|          },||
+|  {||
+|    "```name```": "output",|represents 'ouput' event of LLM|
+|   "```timestamp```": "2024-07-16T17:05:43.501996Z",||
+|  "```attributes```": {||
+|    "response": "Task Decomposition is ..."|response to LLM query. |
+|   }||
+|  }||
+|     ],||
+|  "```links```": [],|unpopulated and unused|
+|    "```resource```": {|represents the service name or server or machine or container which generated the span|
+|     "```attributes```": {||
+|      "service.name": "ml_rag_app"|only service.name is being populated and defaults to the value of 'workflow_name' |
+|     },||
+|  "```schema_url```": ""|unpopulated and unused|
+|       }||
+|} | |