mongodb kickstart + openai embedding enrichment

src/examples/mongo_vector_search_example/main.py

@@ -0,0 +1,61 @@
+from langtrace_python_sdk import langtrace, with_langtrace_root_span
+import pymongo
+import os
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv()
+langtrace.init(write_spans_to_console=False, batch=False)
+MODEL = "text-embedding-ada-002"
+openai_client = OpenAI()
+client = pymongo.MongoClient(os.environ["MONGO_URI"])
+
+
+# Define a function to generate embeddings
+def get_embedding(text):
+    """Generates vector embeddings for the given text."""
+    embedding = (
+        openai_client.embeddings.create(input=[text], model=MODEL).data[0].embedding
+    )
+    return embedding
+
+
+@with_langtrace_root_span("mongo-vector-search")
+def vector_query():
+    db = client["sample_mflix"]
+
+    embedded_movies_collection = db["embedded_movies"]
+    # define pipeline
+    pipeline = [
+        {
+            "$vectorSearch": {
+                "index": "vector_index",
+                "path": "plot_embedding",
+                "queryVector": get_embedding("time travel"),
+                "numCandidates": 150,
+                "limit": 10,
+            }
+        },
+        {
+            "$project": {
+                "_id": 0,
+                "plot": 1,
+                "title": 1,
+                "score": {"$meta": "vectorSearchScore"},
+            }
+        },
+    ]
+
+    result = embedded_movies_collection.aggregate(pipeline)
+    for doc in result:
+        # print(doc)
+        pass
+
+
+if __name__ == "__main__":
+    try:
+        vector_query()
+    except Exception as e:
+        print("error", e)
+    finally:
+        client.close()

src/langtrace_python_sdk/constants/instrumentation/common.py

@@ -34,6 +34,7 @@
     "EMBEDCHAIN": "Embedchain",
     "AUTOGEN": "Autogen",
     "XAI": "XAI",
+    "MONGODB": "MongoDB",
     "AWS_BEDROCK": "AWS Bedrock",
     "CEREBRAS": "Cerebras",
 }

src/langtrace_python_sdk/constants/instrumentation/pymongo.py

@@ -0,0 +1,8 @@
+APIS = {
+    "AGGREGATE": {
+        "MODULE": "pymongo.collection",
+        "METHOD": "Collection.aggregate",
+        "OPERATION": "aggregate",
+        "SPAN_NAME": "MongoDB Aggregate",
+    },
+}

src/langtrace_python_sdk/instrumentation/__init__.py

@@ -21,6 +21,7 @@
 from .aws_bedrock import AWSBedrockInstrumentation
 from .embedchain import EmbedchainInstrumentation
 from .litellm import LiteLLMInstrumentation
+from .pymongo import PyMongoInstrumentation
 from .cerebras import CerebrasInstrumentation
 
 __all__ = [
@@ -46,6 +47,7 @@
     "VertexAIInstrumentation",
     "GeminiInstrumentation",
     "MistralInstrumentation",
+    "PyMongoInstrumentation",
     "AWSBedrockInstrumentation",
     "CerebrasInstrumentation",
 ]

src/langtrace_python_sdk/instrumentation/openai/patch.py

@@ -27,6 +27,7 @@
     set_event_completion,
     StreamWrapper,
     set_span_attributes,
+    set_usage_attributes,
 )
 from langtrace_python_sdk.types import NOT_GIVEN
 
@@ -450,6 +451,14 @@ def traced_method(
             span_attributes[SpanAttributes.LLM_REQUEST_EMBEDDING_INPUTS] = json.dumps(
                 [kwargs.get("input", "")]
             )
+            span_attributes[SpanAttributes.LLM_PROMPTS] = json.dumps(
+                [
+                    {
+                        "role": "user",
+                        "content": kwargs.get("input"),
+                    }
+                ]
+            )
 
         attributes = LLMSpanAttributes(**filter_valid_attributes(span_attributes))
 
@@ -463,6 +472,11 @@ def traced_method(
             try:
                 # Attempt to call the original method
                 result = wrapped(*args, **kwargs)
+                usage = getattr(result, "usage", None)
+                if usage:
+                    set_usage_attributes(
+                        span, {"prompt_tokens": getattr(usage, "prompt_tokens", 0)}
+                    )
                 span.set_status(StatusCode.OK)
                 return result
             except Exception as err:

src/langtrace_python_sdk/instrumentation/pymongo/__init__.py

@@ -0,0 +1,5 @@
+from .instrumentation import PyMongoInstrumentation
+
+__all__ = [
+    "PyMongoInstrumentation",
+]

src/langtrace_python_sdk/instrumentation/pymongo/instrumentation.py

@@ -0,0 +1,47 @@
+"""
+Copyright (c) 2024 Scale3 Labs
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+from opentelemetry.instrumentation.instrumentor import BaseInstrumentor
+from opentelemetry.trace import get_tracer
+
+from typing import Collection
+from importlib_metadata import version as v
+from wrapt import wrap_function_wrapper as _W
+from .patch import generic_patch
+from langtrace_python_sdk.constants.instrumentation.pymongo import APIS
+
+
+class PyMongoInstrumentation(BaseInstrumentor):
+    """
+    The PyMongoInstrumentation class represents the PyMongo instrumentation
+    """
+
+    def instrumentation_dependencies(self) -> Collection[str]:
+        return ["pymongo >= 4.0.0"]
+
+    def _instrument(self, **kwargs):
+        tracer_provider = kwargs.get("tracer_provider")
+        tracer = get_tracer(__name__, "", tracer_provider)
+        version = v("pymongo")
+        for api in APIS.values():
+            _W(
+                module=api["MODULE"],
+                name=api["METHOD"],
+                wrapper=generic_patch(api["SPAN_NAME"], version, tracer),
+            )
+
+    def _uninstrument(self, **kwargs):
+        pass

src/langtrace_python_sdk/instrumentation/pymongo/patch.py

@@ -0,0 +1,66 @@
+from langtrace_python_sdk.utils.llm import (
+    get_langtrace_attributes,
+    get_span_name,
+    set_span_attributes,
+    set_span_attribute,
+)
+from langtrace_python_sdk.utils import deduce_args_and_kwargs, handle_span_error
+from opentelemetry.trace import SpanKind
+from langtrace_python_sdk.constants.instrumentation.common import SERVICE_PROVIDERS
+from langtrace.trace_attributes import DatabaseSpanAttributes
+
+import json
+
+
+def generic_patch(name, version, tracer):
+    def traced_method(wrapped, instance, args, kwargs):
+        database = instance.database.__dict__
+        span_attributes = {
+            **get_langtrace_attributes(
+                version=version,
+                service_provider=SERVICE_PROVIDERS["MONGODB"],
+                vendor_type="vectordb",
+            ),
+            "db.system": "mongodb",
+            "db.query": "aggregate",
+        }
+
+        attributes = DatabaseSpanAttributes(**span_attributes)
+
+        with tracer.start_as_current_span(
+            get_span_name(name), kind=SpanKind.CLIENT
+        ) as span:
+            if span.is_recording():
+                set_input_attributes(
+                    span, deduce_args_and_kwargs(wrapped, *args, **kwargs)
+                )
+                set_span_attributes(span, attributes)
+
+            try:
+                result = wrapped(*args, **kwargs)
+                print(result)
+                for doc in result:
+                    if span.is_recording():
+                        span.add_event(
+                            name="db.query.match",
+                            attributes={**doc},
+                        )
+                return result
+            except Exception as err:
+                handle_span_error(span, err)
+                raise
+
+    return traced_method
+
+
+def set_input_attributes(span, args):
+    pipeline = args.get("pipeline", None)
+    for stage in pipeline:
+        for k, v in stage.items():
+            if k == "$vectorSearch":
+                set_span_attribute(span, "db.index", v.get("index", None))
+                set_span_attribute(span, "db.path", v.get("path", None))
+                set_span_attribute(span, "db.top_k", v.get("numCandidates"))
+                set_span_attribute(span, "db.limit", v.get("limit"))
+            else:
+                set_span_attribute(span, k, json.dumps(v))

src/langtrace_python_sdk/langtrace.py

@@ -64,6 +64,7 @@
     AutogenInstrumentation,
     VertexAIInstrumentation,
     WeaviateInstrumentation,
+    PyMongoInstrumentation,
     CerebrasInstrumentation,
 )
 from opentelemetry.util.re import parse_env_headers
@@ -281,6 +282,7 @@ def init(
         "mistralai": MistralInstrumentation(),
         "boto3": AWSBedrockInstrumentation(),
         "autogen": AutogenInstrumentation(),
+        "pymongo": PyMongoInstrumentation(),
         "cerebras-cloud-sdk": CerebrasInstrumentation(),
     }
 

src/langtrace_python_sdk/version.py

@@ -1 +1 @@
-__version__ = "3.3.2"
+__version__ = "3.3.3"