Release v0.9.0

bedhost/_version.py

@@ -1 +1 @@
-__version__ = "0.8.0"
+__version__ = "0.9.0"

bedhost/data_models.py

@@ -77,3 +77,7 @@ class BaseListResponse(BaseModel):
     limit: int
     offset: int
     results: list
+
+
+class CreateBEDsetRequest(BaseModel):
+    registry_path: str

bedhost/routers/bed_api.py

@@ -15,8 +15,8 @@
     BEDFileNotFoundError,
     TokenizeFileNotExistError,
 )
+from bbconf.models.bed_models import BedClassification  # BedPEPHub,
 from bbconf.models.bed_models import (
-    BedClassification,  # BedPEPHub,
     BedEmbeddingResult,
     BedFiles,
     BedListResult,
@@ -27,6 +27,8 @@
     BedStatsModel,
     TokenizedBedResponse,
     TokenizedPathResponse,
+    QdrantSearchResult,
+    RefGenValidReturnModel,
 )
 from fastapi import APIRouter, File, HTTPException, Query, UploadFile
 from fastapi.responses import PlainTextResponse
@@ -193,6 +195,27 @@ async def get_bed_pephub(
         )
 
 
+@router.get(
+    "/{bed_id}/neighbours",
+    summary="Get nearest neighbours for a single BED record",
+    response_model=BedListSearchResult,
+    response_model_by_alias=False,
+    description=f"Returns most similar BED files in the database. "
+    f"Example\n bed_id: {EXAMPLE_BED}",
+)
+async def get_bed_neighbours(
+    bed_id: str = BedDigest,
+    limit: int = 10,
+    offset: int = 0,
+):
+    try:
+        return bbagent.bed.get_neighbours(bed_id, limit=limit, offset=offset)
+    except BEDFileNotFoundError as _:
+        raise HTTPException(
+            status_code=404,
+        )
+
+
 @router.get(
     "/{bed_id}/embedding",
     summary="Get embeddings for a single BED record",
@@ -335,7 +358,52 @@ async def text_to_bed_search(query, limit: int = 10, offset: int = 0):
     Example: query="cancer"
     """
     _LOGGER.info(f"Searching for: {query}")
-    results = bbagent.bed.text_to_bed_search(query, limit=limit, offset=offset)
+
+    # results_sql = bbagent.bed.sql_search(
+    #     query, limit=round(limit / 2, 0), offset=round(offset / 2, 0)
+    # )
+    #
+    # if results_sql.count > results_sql.offset:
+    #     qdrant_offset = offset - results_sql.offset
+    # else:
+    #     qdrant_offset = offset - results_sql.count
+    #
+    # results_qdr = bbagent.bed.text_to_bed_search(
+    #     query, limit=limit, offset=qdrant_offset - 1 if qdrant_offset > 0 else 0
+    # )
+    #
+    # results = BedListSearchResult(
+    #     count=results_qdr.count,
+    #     limit=limit,
+    #     offset=offset,
+    #     results=(results_sql.results + results_qdr.results)[0:limit],
+    # )
+    spaceless_query = query.replace(" ", "")
+    if len(spaceless_query) == 32 and spaceless_query == query:
+        try:
+            similar_results = bbagent.bed.get_neighbours(
+                query, limit=limit, offset=offset
+            )
+
+            if similar_results.results and offset == 0:
+
+                result = QdrantSearchResult(
+                    id=query,
+                    payload={},
+                    score=1.0,
+                    metadata=bbagent.bed.get(query),
+                )
+
+                similar_results.results.insert(0, result)
+            return similar_results
+        except Exception as _:
+            pass
+
+    results = bbagent.bed.text_to_bed_search(
+        query,
+        limit=limit,
+        offset=offset,
+    )
 
     if results:
         return results
@@ -414,3 +482,24 @@ async def get_tokens(
             status_code=404,
             detail="Tokenized file not found",
         )
+
+
+@router.get(
+    "/{bed_id}/genome-stats",
+    summary="Get reference genome validation results",
+    response_model=RefGenValidReturnModel,
+)
+async def get_ref_gen_results(
+    bed_id: str,
+):
+    """
+    Return reference genome validation results for a bed file
+    Example: bed: 0dcdf8986a72a3d85805bbc9493a1302
+    """
+    try:
+        return bbagent.bed.get_reference_validation(bed_id)
+    except BEDFileNotFoundError as _:
+        raise HTTPException(
+            status_code=404,
+            detail=f"Bed file {bed_id} not found",
+        )

bedhost/routers/bedset_api.py

@@ -1,17 +1,19 @@
 import logging
 
-from bbconf.exceptions import BedSetNotFoundError
+from bbconf.exceptions import BedSetNotFoundError, BedSetTrackHubLimitError
 from bbconf.models.bedset_models import (
     BedSetBedFiles,
     BedSetListResult,
     BedSetMetadata,
     BedSetPlots,
     BedSetStats,
 )
+from pephubclient.helpers import is_registry_path, unwrap_registry_path
 from fastapi import APIRouter, HTTPException, Request, Response
 
 from ..const import EXAMPLE_BEDSET, PKG_NAME
 from ..main import bbagent
+from ..data_models import CreateBEDsetRequest
 from ..utils import zip_pep
 
 router = APIRouter(prefix="/v1/bedset", tags=["bedset"])
@@ -165,22 +167,78 @@ async def get_trackDb_file_bedset(bedset_id: str):
     """
     Generate trackDb file for the BED set track hub
     """
+    # Response should be this type:
+    # trackDb_txt = (
+    #     trackDb_txt + f"track\t {metadata.name}\n"
+    #     "type\t bigBed\n"
+    #     f"bigDataUrl\t {metadata.files.bigbed_file.access_methods[0].access_url.url} \n"
+    #     f"shortLabel\t {metadata.name}\n"
+    #     f"longLabel\t {metadata.description}\n"
+    #     "visibility\t full\n\n"
+    # )
+    try:
+        trackDb_txt = bbagent.bedset.get_track_hub_file(bedset_id)
+    except BedSetTrackHubLimitError as _:
+        raise HTTPException(
+            status_code=400,
+            detail="Track hub limit reached. Please try smaller BEDset.",
+        )
+
+    return Response(trackDb_txt, media_type="text/plain")
 
-    hit = bbagent.bedset.get_bedset_bedfiles(bedset_id)
 
-    trackDb_txt = ""
-    for bed in hit.results:
-        metadata = bbagent.bed.get(bed.id, full=True)
+@router.post(
+    "/create/",
+    description="Create a new bedset by providing registry path to the PEPhub project",
+)
+async def create_bedset(bedset: CreateBEDsetRequest):
+    """
+    Create a new bedset
+    """
+    # Validate the PEPhub project string
+    if not is_registry_path(bedset.registry_path):
+        raise HTTPException(status_code=406, detail="Invalid registry path")
+
+    project_reg_path = unwrap_registry_path(bedset.registry_path)
 
-        if metadata.files.bigbed_file:
+    if project_reg_path.namespace not in ["databio", "bedbase", "pepkit"]:
+        raise HTTPException(status_code=403, detail="User is not in admin list")
 
-            trackDb_txt = (
-                trackDb_txt + f"track\t {metadata.name}\n"
-                "type\t bigBed\n"
-                f"bigDataUrl\t {metadata.files.bigbed_file.access_methods[0].access_url.url} \n"
-                f"shortLabel\t {metadata.name}\n"
-                f"longLabel\t {metadata.description}\n"
-                "visibility\t full\n\n"
-            )
+    try:
+        project = bbagent.config.phc.load_project(bedset.registry_path)
+    except Exception as _:
+        raise HTTPException(
+            status_code=404, detail=f"Project: '{bedset.registry_path}' not found"
+        )
+
+    bedfiles_list = [
+        bedfile_id.get("record_identifier") or bedfile_id.sample_name
+        for bedfile_id in project.samples
+    ]
+
+    if bbagent.bedset.exists(identifier=project.name):
+        raise HTTPException(
+            status_code=409,
+            detail=f"BEDset with identifier {project.name} already exists",
+        )
 
-    return Response(trackDb_txt, media_type="text/plain")
+    try:
+        bbagent.bedset.create(
+            identifier=project.name,
+            name=project.name,
+            bedid_list=bedfiles_list,
+            statistics=True,
+            description=project.description,
+            annotation={
+                "source": project.config.get("source", ""),
+                "author": project.config.get("author", project_reg_path.namespace),
+            },
+            no_fail=False,
+            overwrite=False,
+        )
+    except Exception as err:
+        raise HTTPException(
+            status_code=400, detail=f"Unable to create bedset. Error: {err}"
+        )
+
+    return {"status": "success"}

interactive.py

@@ -0,0 +1,56 @@
+import bbconf
+
+bba = bbconf.BedBaseAgent("deployment/config/api-dev.bedbase.org.yaml")
+
+bba.config._b2bsi = bba.config._init_b2bsi_object()
+bba.config._r2v = bba.config._init_r2v_object()
+bba.config._bivec = bba.config._init_bivec_object()
+
+
+# Here's some code to test the BiVectorSearchInterface
+
+from geniml.search.interfaces import BiVectorSearchInterface
+from geniml.search.backends import BiVectorBackend
+
+from geniml.search.query2vec import Text2Vec
+
+search_backend = BiVectorBackend(
+    metadata_backend=self._qdrant_text_engine, bed_backend=self._qdrant_engine
+)
+
+t2v = Text2Vec("sentence-transformers/all-MiniLM-L6-v2", v2v=None)
+
+bvsi = BiVectorSearchInterface()
+
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+import logging
+from typing import Union
+
+import numpy as np
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+
+from geniml.text2bednn import Vec2VecFNN
+from geniml.search.query2vec.abstract import Query2Vec
+
+# culprit:
+te = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Testing the sentence transformers:
+
+
+from sentence_transformers import SentenceTransformer
+
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+embeddings = model.encode(sentences)
+print(embeddings)
+
+
+from fastembed import TextEmbedding
+
+model = TextEmbedding(
+    model_name="sentence-transformers/all-MiniLM-L6-v2", max_length=512
+)
+sentences = ["This is an example sentence", "Each sentence is converted"]
+embeddings = list(model.embed(sentences))

requirements/requirements-all.txt

@@ -1,5 +1,5 @@
 # bbconf @ git+https://github.com/databio/bbconf.git@dev#egg=bbconf
-bbconf>=0.9.0
+bbconf>=0.10.0
 fastapi>=0.103.0
 logmuse>=0.2.7
 markdown
@@ -9,4 +9,4 @@ uvicorn
 yacman>=0.9.2
 pephubclient>=0.4.1
 psycopg[binary,pool]
-python-multipart>=0.0.9
+python-multipart>=0.0.9