Update docs with usage

docs/quickstart.md

@@ -52,7 +52,7 @@ Components are stateful - they're classes instead of functions. They can be usef
 
 HealthChain comes with a few pre-built components, but you can also easily add your own. You can find more details on the [Components](./reference/pipeline/component.md) and [Models](./reference/pipeline/models/models.md) documentation pages.
 
-Add components to your pipeline with the `.add()` method.
+Add components to your pipeline with the `.add()` method and compile with `.build()`.
 
 ```python
 from healthchain.pipeline import Pipeline
@@ -80,10 +80,9 @@ For a full list of available prebuilt pipelines and details on how to configure
 from healthchain.pipeline import MedicalCodingPipeline
 
 pipeline = MedicalCodingPipeline.load("./path/to/model")
-pipe = pipeline.build()
 
 doc = Document("Patient diagnosed with myocardial infarction.")
-doc = pipe(doc)
+doc = pipeline(doc)
 ```
 
 ### Sandbox 🧪
@@ -107,8 +106,7 @@ from healthchain.pipeline import MedicalCodingPipeline
 class MyCoolSandbox(ClinicalDocumentation):
     def __init__(self) -> None:
         # Load your pipeline
-        pipeline = MedicalCodingPipeline.load("./path/to/model")
-        self.pipe = self.pipeline.build()
+        self.pipeline = MedicalCodingPipeline.load("./path/to/model")
 
     @hc.ehr(workflow="sign-note-inpatient")
     def load_data_in_client(self) -> CcdData:
@@ -139,13 +137,6 @@ healthchain run my_sandbox.py
 
 This will start a server by default at `http://127.0.0.1:8000`, and you can interact with the exposed endpoints at `/docs`. Data generated from your sandbox runs is saved at `./output/` by default.
 
-## Inspect generated data in Streamlit 🎈
-The streamlit dashboard is run separately and is currently purely for visualisation purposes.
-
-You need to install streamlit separately first:
-```bash
-pip install streamlit
-```
 Then run:
 
 ```bash
@@ -158,7 +149,7 @@ streamlit run app.py
 
 You can use the data generator to generate synthetic data for your sandbox runs.
 
-The `.generate()` is dependent on use case and workflow. For example, `CdsDataGenerator` will generate synthetic [FHIR](https://build.fhir.org/) data suitable for the workflow specified by the use case.
+The `.generate()` is dependent on use case and workflow. For example, `CdsDataGenerator` will generate synthetic [FHIR](https://hl7.org/fhir/) data suitable for the workflow specified by the use case.
 
 We're currently working on generating synthetic [CDA](https://www.hl7.org.uk/standards/hl7-standards/cda-clinical-document-architecture/) data. If you're interested in contributing, please [reach out](https://discord.gg/UQC6uAepUz)!
 

docs/reference/pipeline/pipeline.md

@@ -15,18 +15,15 @@ HealthChain comes with a set of prebuilt pipelines that are out-of-the-box imple
 | **QAPipeline** [TODO] | `Document` | N/A | A Question Answering pipeline suitable for conversational AI applications | Developing a chatbot to answer patient queries about their medical records |
 | **ClassificationPipeline** [TODO] | `Tabular` | `FhirConnector` | A pipeline for machine learning classification tasks | Predicting patient readmission risk based on historical health data |
 
-To use a pipeline, compile it by running `.build()` on it. This will return a compiled pipeline that you can run on your data.
-
 Pipeline inputs and outputs are defined by the container type.
 
 ```python
 from healthchain.pipeline import Pipeline
 from healthchain.io.containers import Document
 
 pipeline = MedicalCodingPipeline.load('/path/to/model')
-pipeline = pipeline.build()
 
-doc = Document(text="Patient is diagnosed with diabetes")
+doc = Document("Patient is diagnosed with diabetes")
 doc = pipeline(doc)
 ```
 
@@ -36,7 +33,7 @@ To customize a prebuilt pipeline, you can use the [pipeline management methods](
 
 If you need even more control and don't mind writing more code, you can subclass `BasePipeline` and implement your own pipeline logic.
 
-(BasePipeline API Reference)
+[(BasePipeline API Reference)](../../api/pipeline.md#healthchain.pipeline.basepipeline.BasePipeline)
 
 ## Freestyle 🕺
 
@@ -50,6 +47,16 @@ from healthchain.io.containers import Document
 
 pipeline = Pipeline[Document]()
 ```
+
+To use a built pipeline, compile it by running `.build()` on it. This will return a compiled pipeline that you can run on your data.
+
+```python
+pipe = pipeline.build()
+doc = pipe(Document("Patient is diagnosed with diabetes"))
+
+print(doc.entities)
+```
+
 There are three types of nodes you can add to your pipeline:
 
 - Inline Functions
@@ -100,7 +107,7 @@ stopwords = ["the", "a", "an", "in", "on", "at"]
 pipeline.add(RemoveStopwords(stopwords))
 ```
 
-(BaseComponent API Reference)
+[(BaseComponent API Reference)](../../api/component.md#healthchain.pipeline.components.basecomponent.BaseComponent)
 
 ## Pipeline Management 🔨
 

docs/reference/sandbox/service.md

@@ -22,8 +22,7 @@ Here are minimal examples for each use case:
     class MyCoolSandbox(ClinicalDocumentation):
         def __init__(self) -> None:
             # Load your pipeline
-            pipeline = MedicalCodingPipeline.load("./path/to/model")
-            self.pipe = pipeline.build()
+            self.pipeline = MedicalCodingPipeline.load("./path/to/model")
 
         @hc.ehr(workflow="sign-note-inpatient")
         def load_data_in_client(self) -> CcdData:
@@ -36,7 +35,7 @@ Here are minimal examples for each use case:
         @hc.api
         def my_service(self, ccd_data: CcdData) -> CcdData:
             # Run your pipeline
-            results = self.pipe(ccd_data)
+            results = self.pipeline(ccd_data)
             return results
     ```
 
@@ -52,8 +51,7 @@ Here are minimal examples for each use case:
     @hc.sandbox
     class MyCoolSandbox(ClinicalDecisionSupport):
         def __init__(self):
-            pipeline = Pipeline.load("./path/to/pipeline")
-            self.pipe = pipeline.build()
+            self.pipeline = Pipeline.load("./path/to/pipeline")
 
         @hc.ehr(workflow="patient-view")
         def load_data_in_client(self) -> CdsFhirData:
@@ -64,7 +62,7 @@ Here are minimal examples for each use case:
 
         @hc.api
         def my_service(self, request: CDSRequest) -> List[Card]:
-            result = self.pipe(str(request.prefetch))
+            result = self.pipeline(str(request.prefetch))
             return [
                 Card(
                     summary="Patient summary",

docs/reference/sandbox/use_cases/cds.md

@@ -9,7 +9,7 @@ CDS workflows are based on [CDS Hooks](https://cds-hooks.org/). CDS Hooks is an
 | Triggered at certain events during a clinician's workflow, e.g. when a patient record is opened. | EHR  | The context of the event and FHIR resources that are requested by your service. e.g. patient ID, `Encounter` and `Patient`.  | “Cards” displaying text, actionable suggestions, or links to launch a [SMART](https://smarthealthit.org/) app from within the workflow.      |
 
 
-CDS hooks communicate using [HL7 FHIR (Fast Healthcare Interoperability Resources)](https://hl7.org/fhir/R4/). FHIR data are represented internally as `CdsFhirData` in HealthChain, so a CDS client must return a `CdsFhirData` object.
+CDS hooks communicate using [HL7 FHIR (Fast Healthcare Interoperability Resources)](https://hl7.org/fhir/). FHIR data are represented internally as `CdsFhirData` in HealthChain, so a CDS client must return a `CdsFhirData` object.
 
 CDS service functions receive `CdsRequest` and return a list of `Card`. [Improved documentation coming soon]