Pipeline Routes

routers.pipeline_routes

`PipelineRequest`

class PipelineRequest(
  names: List[str]
  pipeline_options: PipelineOptions = Field(default_factory=PipelineOptions)
)

This class takes the format of a request to the API

Attributes

name: str The drug name sent to a pipeline

pipeline_options: Optional[PipelineOptions] Optionally, the default values can be overridden by instantiating a PipelineOptions object. If none is supplied, default arguments are used

`generate_events`

async def generate_events(request: PipelineRequest)

Generate LLM output and OMOP results for a list of informal names

Workflow

For each informal name:

The first event is to Query the OMOP database for a match
The second event is to fetches relevant concepts from the OMOP database
Finally, the function yields results as they become available, allowing for real-time streaming.

Conditions

If the OMOP database returns a match, the LLM is not queried

If the OMOP database does not return a match, the LLM is used to find the formal name and the OMOP database is queried for the LLM output.

Finally, the function yields the results for real-time streaming.

Parameters

request: PipelineRequest The request containing the list of informal names.

Yields

str JSON encoded strings of the event results. Two types are yielded:

“llm_output”: The result from the language model processing.
“omop_output”: The result from the OMOP database matching.

`run_pipeline`

@router.post("/")
async def run_pipeline(
  request: PipelineRequest
)

Call generate_events to run the pipeline

Parameters

request: PipelineRequest The request containing a list of informal names

Returns

EventSourceResponse The response containing the events

`run_db`

@router.post("/db")
async def run_db(
  request: PipelineRequest
)

Fetch OMOP concepts for a name

Default options can be overridden by the pipeline_options in the request

Parameters

request: PipelineRequest An API request containing a list of informal names and the options of a pipeline

Returns

dict Details of OMOP concept(s) fetched from a database query

`run_vector_search`

@router.post("/vector_search")
async def run_vector_search(
  request: PipelineRequest
)

Search a vector database for a name

Default options can be overridden by pipeline_options A warning: if you don’t have a vector database set up under the embeddings_path, this method will build one for you. This takes a while, an hour using 2.8 GHz intel I7, 16 Gb RAM.

Parameters

request: PipelineRequest An API request containing a list of informal names and the options of a pipeline

Returns

list Details of OMOP concept(s) fetched from a vector database query

`vector_llm_pipeline`

@router.post("/vector_llm")
async def vector_llm_pipeline(
  request: PipelineRequest
)

Run a RAG pipeline that first checks a vector database, then uses an LLM

This has a conditional router in it that checks whether there’s an exact match for the term. If there is an exact match, the vector search results are returned. If there is not, the vector search results are used for retrieval augmented generation

Parameters

request: PipelineRequest

Returns

list

Routers Text input