Observability of Retrieval-Augmented Generation pipelines

Large Language Models (LLMs) are trained on vast volumes of data. However, they can present certain limitations.

Training data is static and has a cut-off date on the knowledge.
LLMs provide false information when they don't have the answer.

This use case illustrates a common approach to overcoming these limitations, which is to use a Retrieval-Augmented Generation (RAG) pipeline to provide the LLM with additional contextual information from an authoritative knowledge source, resulting in more accurate answers and more control over the generated output.

Dynatrace Observability Lab

Try it yourself

Explore our sample dashboards on the Dynatrace Playground or get hands-on and deploy our GenAI demo app using GitHub Codespace.

What will you learn

In this tutorial, we create a simple Python API that uses LangChain to implement a chatbot that suggests travel destinations for your next trip.

The chatbot is a RAG pipeline that uses Pinecone to retrieve relevant information for the user's desired destination.
We generate the answer using Ollama as the LLM.
We instrument the code using OpenTelemetry to gather deep insights into how our API is performing.

Architecture of the RAG pipeline

Before you begin

Prerequisites

Access to your Kubernetes environment
Pinecone (free) account

Steps

The general steps are as follows:

Create API keys to connect to Pinecone and Dynatrace.
Deploy our application on a Kubernetes cluster.
Visualize important application signals to observe costs and the quality of the responses.

See below for the details of each step.

Prepare API keys

Create and deploy RAG pipeline

Observe your RAG pipeline

Prepare API keys

In this step, we create create two API keys and store the keys as Kubernetes secrets. The API keys will be used to connect to Dynatrace and Pinecone.

Create Dynatrace token

To create a Dynatrace token

In Dynatrace, go to Access Tokens.
To find Access Tokens, press CTRL+K to search for and select Access Tokens.
In Access Tokens, select Generate new token.
Enter a Token name for your new token.
Give your new token the following permissions:
Search for and select all of the following scopes.
- Ingest metrics (metrics.ingest)
- Ingest logs (logs.ingest)
- Ingest events (events.ingest)
- Ingest OpenTelemetry traces (openTelemetryTrace.ingest)
- Read metrics (metrics.read)
- Write settings (settings.write)
Select Generate token.
Copy the generated token to the clipboard. Store the token in a password manager for future use.
You can only access your token once upon creation. You can't reveal it afterward.

Store API key as Kubernetes secret

Now that you have a token with the necessary permissions, you can use the following command to store the Dynatrace API key as a Kubernetes secret. Our Python application will use it to send observability data to your tenant.

kubectl create secret generic dynatrace --from-literal token=<your-api-key> -n travel-advisor

If error due to missing namespace

If the command returns an error because the namespace is missing, you can create it by running kubectl create namespace travel-advisor

Connect to Pinecone

To connect to Pinecone

Create a new index called travel-advisor with the dimensions of 3200 and a cosine metric.

The index will store our knowledge source, which the RAG pipeline will use to augment the LLM's output of the travel recommendation. The reasoning behind the selected dimensions is discussed later, in the deployment section.
After creating and running the index, we can create an API key to connect.

Follow the Pinecone documentation on authentication to get the API key to connect to your Pinecone index and store it as Kubernetes secrets with the following command:
```
kubectl create secret generic pinecone --from-literal api-key=<your-api-key> -n travel-advisor
```

Create and deploy RAG pipeline

For our demo application, we use Ollama to generate the answer.

We also use Ollama for embedding, which is the process of converting a text into vectors that capture the semantic meaning of the text. Embedding allows us to convert our knowledge source into mathematical objects stored inside our Pinecone index. We can do the same with the user input and look for similar vectors in the Pinecone index to provide additional information to the LLM to generate the end response.

Create a new Kubernetes namespace called ollama.

apiVersion: v1
kind: Namespace
metadata:
  name: ollama
  labels:
    name: ollama

Deploy the latest version of Ollama running on port 11434.

The Ollama container, by default, doesn't have any model. We need to tell the container which model to download explicitly and make it available through its APIs. To do this, we tell Kubernetes to run ollama run orca-mini:3b after the container is up and running, which tells Ollama to download the orca-mini:3b LLM.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ollama
  namespace: ollama
spec:
  selector:
    matchLabels:
      name: ollama
  template:
    metadata:
      labels:
        name: ollama
    spec:
      containers:
        - name: ollama
          image: ollama/ollama:latest
          ports:
            - name: http
              containerPort: 11434
              protocol: TCP
          lifecycle:
            postStart:
              exec:
                command: [ "/bin/sh", "-c", "ollama run orca-mini:3b" ]

Tell Kubernetes to expose these APIs to other containers via a service over http://ollama.ollama.

apiVersion: v1
kind: Service
metadata:
  name: ollama
  namespace: ollama
spec:
  type: ClusterIP
  selector:
    name: ollama
  ports:
    - port: 80
      name: http
      targetPort: 11434
      protocol: TCP

Now that Ollama is running, we can create our LangChain RAG pipeline with some Python code.

Create the object that will be used to contact Ollama to perform the embedding step. Ollama orca-mini:3b for an input returns vectors with a fixed size of 3200. This is why we configured the dimensions property of our Pinecone index to be 3200.
```
from langchain_community.embeddings import OllamaEmbeddings
embeddings = OllamaEmbeddings(model="orca-mini:3b", base_url="http://ollama.ollama")
```

Load our documents from our local file system.

In this example, we have several HTML pages of suggestions of what to visit in these cities.

# Retrieve the source data
docs_list = []
for item in os.listdir(path="destinations"):
    if item.endswith(".html"):
        item_docs_list = BSHTMLLoader(file_path=f"destinations/{item}").load()
        for item in item_docs_list:
            docs_list.append(item)

Split our documents into chunks.

Splitting the text into chunks is important because LLMs have a known limitation called a context window, which defines the boundaries within the model that can process and understand the text. Splitting documents into chunks allows the LLM model to proficiently understand its content and use it to generate the answer.

# Split Document into tokens
text_splitter = RecursiveCharacterTextSplitter()
documents = text_splitter.split_documents(docs_list)

logger.info("Loading documents from PineCone...")
vector = PineconeVectorStore.from_documents(
    documents,
    index_name="travel-advisor", # PineCone index to use
    embedding=embeddings # we're telling LangChain to use Ollama for the embedding step
)
retriever = vector.as_retriever()

Initialize the LLM model and wrap the user input into a prompt that locks down the answer expected from the model.

We use a template with two variables:

input: this is filled with the user input text
context: this is filled with relevant information retrieved from the Pinecone index.

llm = ChatOllama(model="orca-mini:3b", base_url="http://ollama.ollama")

prompt = ChatPromptTemplate.from_template("""
1. Use the following pieces of context to answer the question as travel advise at the end.
2. Keep the answer crisp and limited to 3,4 sentences.

Context: {context}

Question: {input}

Helpful Answer:""")

document_prompt = PromptTemplate(
    input_variables=["page_content", "source"],
    template="content:{page_content}\nsource:{source}",
)

Piece everything together and create our RAG pipeline.

The pipeline process performs the following steps:
1. Contact Ollama to create an embedding vector for the user input
2. Contact Pinecone to find relevant documents based on the embedding vector
3. Contact Ollama to generate a traveling advisor answer with a prompt that contains context relevant to the user input
```
document_chain = create_stuff_documents_chain(
    llm=llm,
    prompt=prompt,
    document_prompt=document_prompt,
)
chain = create_retrieval_chain(retriever, document_chain)
response = chain.invoke({"input": prompt})
```

from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_community.document_loaders import BSHTMLLoader

from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
from langchain_pinecone import PineconeVectorStore

import logging
import os
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
import uvicorn

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from traceloop.sdk import Traceloop
from traceloop.sdk.decorators import workflow

from telemetry.token_count import TokenUsageCallbackHandler
from telemetry.langchain import LangchainInstrumentor

# Read secrets from the mounted volume
def read_token():
    return read_secret('token')


def read_pinecone_key():
    return read_secret('api-key')


def read_secret(secret: str):
    try:
        with open(f"/etc/secrets/{secret}", "r") as f:
            return f.read().rstrip()
    except Exception as e:
        print("No token was provided")
        print(e)
        return ""

# Expose the PineCone key as env var for initialization by LangChain
os.environ['PINECONE_API_KEY'] = read_pinecone_key()

OTEL_ENDPOINT = os.environ.get("OTEL_ENDPOINT", "http://localhost:4317")
OLLAMA_ENDPOINT = os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434")

# GLOBALS
AI_MODEL = os.environ.get("AI_MODEL", "orca-mini:3b")
AI_SYSTEM = "ollama"
AI_EMBEDDING_MODEL = os.environ.get("AI_EMBEDDING_MODEL", "orca-mini:3b")
MAX_PROMPT_LENGTH = 50
retrieval_chain = None

# Initialise the logger
logging.basicConfig(level=logging.INFO, filename="run.log")
logger = logging.getLogger(__name__)

# ################
# # CONFIGURE OPENTELEMETRY

resource = Resource.create({
    "service.name": "travel-advisor",
    "service.version": "0.1.0"
})

TOKEN = read_token()
headers = {
    "Authorization": f"Api-Token {TOKEN}"
}

provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint=f"{OTEL_ENDPOINT}", headers=headers))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
otel_tracer = trace.get_tracer("travel-advisor")

Traceloop.init(app_name="travel-advisor", api_endpoint=OTEL_ENDPOINT, disable_batch=True, headers=headers)


def prep_system():

    # Create the embedding
    embeddings = OllamaEmbeddings(model=AI_EMBEDDING_MODEL, base_url=OLLAMA_ENDPOINT)

    # Retrieve the source data
    docs_list = []
    for item in os.listdir(path="destinations"):
        if item.endswith(".html"):
            item_docs_list = BSHTMLLoader(file_path=f"destinations/{item}").load()
            for item in item_docs_list:
                docs_list.append(item)

    # Split Document into tokens
    text_splitter = RecursiveCharacterTextSplitter()
    documents = text_splitter.split_documents(docs_list)

    logger.info("Loading documents from PineCone...")
    vector = PineconeVectorStore.from_documents(
        documents,
        index_name="travel-advisor",
        embedding=embeddings
    )
    retriever = vector.as_retriever()

    logger.info("Initialising Llama LLM...")
    llm = ChatOllama(model=AI_MODEL, base_url=OLLAMA_ENDPOINT)

    prompt = ChatPromptTemplate.from_template("""
    1. Use the following pieces of context to answer the question as travel advise at the end.
    2. Keep the answer crisp and limited to 3,4 sentences.

    Context: {context}

    Question: {input}

    Helpful Answer:""")

    document_prompt = PromptTemplate(
        input_variables=["page_content", "source"],
        template="content:{page_content}\nsource:{source}",
    )

    document_chain = create_stuff_documents_chain(
        llm=llm,
        prompt=prompt,
        document_prompt=document_prompt,
    )

    return create_retrieval_chain(retriever, document_chain)


############
# CONFIGURE ENDPOINTS

app = FastAPI()

####################################
@app.get("/api/v1/completion")
def submit_completion(prompt: str):
    with otel_tracer.start_as_current_span(name="/api/v1/completion") as span:
        return submit_completion(prompt, span)


@workflow(name="travelgenerator")
def submit_completion(prompt: str, span):
        if prompt:
            logger.info(f"Calling RAG to get the answer to the question: {prompt}...")
            response = retrieval_chain.invoke({"input": prompt}, config={
                "callbacks": [TokenUsageCallbackHandler()],
            })

            # Log information for DQL to grab
            logger.info(f"Response: {response}. Using RAG. model={AI_MODEL}. prompt={prompt}")
            return {"message": response['answer']}
        else:  # No, or invalid prompt given
            span.add_event(f"No prompt provided or prompt too long (over {MAX_PROMPT_LENGTH} chars)")
            return {"message": f"No prompt provided or prompt too long (over {MAX_PROMPT_LENGTH} chars)"}


####################################
@app.get("/api/v1/thumbsUp")
@otel_tracer.start_as_current_span("/api/v1/thumbsUp")
def thumbs_up(prompt: str):
    logger.info(f"Positive user feedback for search term: {prompt}")


@app.get("/api/v1/thumbsDown")
@otel_tracer.start_as_current_span("/api/v1/thumbsDown")
def thumbs_down(prompt: str):
    logger.info(f"Negative user feedback for search term: {prompt}")


if __name__ == "__main__":
    retrieval_chain = prep_system()

    # Mount static files at the root
    app.mount("/", StaticFiles(directory="./public", html=True), name="public")
    #app.mount("/destinations", StaticFiles(directory="destinations", html = True), name="destinations")

    # Run the app using uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8080)

---
apiVersion: v1
kind: Namespace
metadata:
  name: travel-advisor
  labels:
    name: travel-advisor
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: travel-advisor
  namespace: travel-advisor
spec:
  selector:
    matchLabels:
      name: travel-advisor
  template:
    metadata:
      labels:
        name: travel-advisor
    spec:
      containers:
        - name: travel-advisor
          image: travel-advisor:v0.1.3
          ports:
            - name: http
              containerPort: 8080
              protocol: TCP
          env:
            - name: OTEL_ENDPOINT
              value: "https://<YOUR_ENV>.live.dynatrace.com/api/v2/otlp"
            - name: OLLAMA_ENDPOINT
              value: "http://ollama.ollama"
            - name: TRACELOOP_TELEMETRY
              value: "false"
          imagePullPolicy: Always
          volumeMounts:
            - name: secrets
              readOnly: true
              mountPath: "/etc/secrets"
      volumes:
        - name: secrets
          projected:
            sources:
              - secret:
                  name: dynatrace
              - secret:
                  name: pinecone
---
apiVersion: v1
kind: Service
metadata:
  name: travel-advisor
  namespace: travel-advisor
spec:
  type: LoadBalancer
  selector:
    name: travel-advisor
  ports:
    - port: 80
      name: http
      targetPort: 8080
      protocol: TCP

Observe your RAG pipeline

We have presented a simple RAG pipeline for this example, but it already contains a lot of hidden communication with external services. Comprehensive observability is mandatory to control the performance, costs, and quality of the response provided by the LLM.

Luckily, we don't need to manually instrument our code base and collect essential signals. We can leverage OpenTelemetry to provide traces and metrics, particularly OpenLLMetry.

Add the following line to our code and we can harness the power of Dynatrace to monitor our AI workloads.

headers = { "Authorization": "Api-Token <YOUR_DT_API_TOKEN>" }
Traceloop.init(
    app_name="travel-advisor",
    api_endpoint="https://<YOUR_ENV>.live.dynatrace.com/api/v2/otlp",
    disable_batch=True,
    headers=headers
)

And that's it! Progressive delivery

We can now see traces that describe each step taken by the LangChain RAG pipeline and identify bottlenecks, improvements, or monitor if a service is not reachable anymore.

However, just a trace is not enough to assess the health status of our AI workloads. To this end, we can configure dashboards that display important metrics of our services. For example, we can monitor the amount of input/output tokens or the latency of our services, or configure SLOs for when token consumption reaches a threshold.

OpenTelemetry offers a GenAI Semantic Convention that can be used to write DQL queries to chart important signals of our AI workloads. The relevant attributes for this domain start with the gen_ai prefix.

For example, we can list the names of the models used.

fetch spans Preview

fetch spans is currently in Preview and only accessible to selected customers. If you would like to share feedback or ideas, please join our dedicated Community user group, or reach out to your Customer Success Manager.

For more information, see

Preview releases

fetch spans
| summarize models = collectDistinct(gen_ai.request.model)
| expand models
| sort models

fetch spans Preview

For more information, see

Preview releases

fetch spans
| filter gen_ai.response.model == "orca-mini:3b"
| makeTimeseries total = max(gen_ai.usage.output_tokens + gen_ai.usage.input_tokens), baseline = avg(gen_ai.usage.output_tokens + gen_ai.usage.input_tokens)
| fieldsAdd sli = (baseline[]/total[])*100
| fieldsRemove baseline, total

fetch spans Preview

For more information, see

Preview releases

fetch spans
| filter gen_ai.request.model == "orca-mini:3b" and llm.request.type != ""
| fieldsKeep duration, gen_ai.request.model, llm.request.type, end_time
| makeTimeseries avg(duration), time: end_time, by: {llm.request.type}
| append [
  fetch spans
  | filter gen_ai.request.model == "orca-mini:3b" and llm.request.type != ""
  | makeTimeseries requests=count()
]

fetch spans Preview

For more information, see

Preview releases

fetch spans
| filter gen_ai.request.model == "orca-mini:3b" and llm.request.type == "chat"
| fieldsAdd prompt = gen_ai.prompt.0.content
| fieldsAdd response = gen_ai.completion.0.content
| fields prompt, response

The following example dashboard visualizes important health metrics of the travel-advisor application.

Dashboard example: health metrics of the travel-advisor application

Observability of Retrieval-Augmented Generation pipelines

Try it yourself

What will you learn

Before you begin

Prerequisites

Steps

Prepare API keys

Create Dynatrace token

Store API key as Kubernetes secret

Connect to Pinecone

Create and deploy RAG pipeline

Observe your RAG pipeline

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