diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
index b5db6f8bc146195d50e99901b2b118faee6437ca..f42bf9c28937419b29ae4429151a1fe3120c0487 100644
--- a/semantic_router/encoders/bm25.py
+++ b/semantic_router/encoders/bm25.py
@@ -57,7 +57,6 @@ class BM25Encoder(TfidfEncoder):
self.model.fit(corpus=utterances)
def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
- print(f"JBTEMP: {docs}")
if self.model is None:
raise ValueError("Model or index mapping is not initialized.")
if len(docs) == 1:
diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py
index 9751ea097027004431edcd96c0bd3e18fb970804..0025a0bbc652b69bf2a13e3839703416569e6283 100644
--- a/semantic_router/index/base.py
+++ b/semantic_router/index/base.py
@@ -384,7 +384,6 @@ class BaseIndex(BaseModel):
"""Lock/unlock the index for a given scope (if applicable). If index
already locked/unlocked, raises ValueError.
"""
- logger.warning(f"JBTEMP alock method called with {value=} {wait=} {scope=}")
start_time = datetime.now()
while True:
if await self._ais_locked(scope=scope) != value:
diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py
index c01769929c6d6cecbbac6781c9c63dc78fe8fc45..5e71da2a76b57fd59236d35469dcb9bfad77341f 100644
--- a/semantic_router/index/pinecone.py
+++ b/semantic_router/index/pinecone.py
@@ -292,7 +292,6 @@ class PineconeIndex(BaseIndex):
:type batch: List[Dict]
"""
if self.index is not None:
- print(f"JBTEMP upserting batch: {batch} to '{self.namespace}'")
self.index.upsert(vectors=batch, namespace=self.namespace)
else:
raise ValueError("Index is None, could not upsert.")
@@ -309,10 +308,6 @@ class PineconeIndex(BaseIndex):
**kwargs,
):
"""Add vectors to Pinecone in batches."""
- print(f"{routes=}")
- print(f"{utterances=}")
- print(f"{function_schemas=}")
- print(f"{metadata_list=}")
if self.index is None:
self.dimensions = self.dimensions or len(embeddings[0])
self.index = self._init_index(force_create=True)
@@ -324,7 +319,6 @@ class PineconeIndex(BaseIndex):
metadata_list=metadata_list,
sparse_embeddings=sparse_embeddings,
)
- print(f"{vectors_to_upsert=}")
for i in range(0, len(vectors_to_upsert), batch_size):
batch = vectors_to_upsert[i : i + batch_size]
self._batch_upsert(batch)
@@ -583,11 +577,9 @@ class PineconeIndex(BaseIndex):
scope=scope,
)
config_id = f"{field}#{scope}"
- logger.warning(f"JBTEMP Pinecone config id: {config_id}")
config_record = await self._async_fetch_metadata(
vector_id=config_id, namespace="sr_config"
)
- logger.warning(f"JBTEMP Pinecone config record: {config_record}")
if config_record:
try:
return ConfigParameter(
@@ -637,7 +629,6 @@ class PineconeIndex(BaseIndex):
if self.dimensions is None:
raise ValueError("Must set PineconeIndex.dimensions before writing config.")
pinecone_config = config.to_pinecone(dimensions=self.dimensions)
- logger.warning(f"JBTEMP Pinecone config to upsert: {pinecone_config}")
await self._async_upsert(
vectors=[pinecone_config],
namespace="sr_config",
@@ -750,13 +741,11 @@ class PineconeIndex(BaseIndex):
"vectors": vectors,
"namespace": namespace,
}
- logger.warning(f"JBTEMP Pinecone upsert params: {params}")
async with self.async_client.post(
f"https://{self.host}/vectors/upsert",
json=params,
) as response:
res = await response.json(content_type=None)
- logger.warning(f"JBTEMP Pinecone upsert response: {res}")
return res
async def _async_create_index(
@@ -878,7 +867,6 @@ class PineconeIndex(BaseIndex):
params = {
"ids": [vector_id],
}
- logger.warning(f"JBTEMP Pinecone fetch params: {params}")
if namespace:
params["namespace"] = [namespace]
diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py
index fb14aa661bea0e651a210a844828fc511ffdcf1f..b5136f97750e55f25358707774cbd2abead5da30 100644
--- a/semantic_router/routers/base.py
+++ b/semantic_router/routers/base.py
@@ -391,7 +391,6 @@ class BaseRouter(BaseModel):
def _init_index_state(self):
"""Initializes an index (where required) and runs auto_sync if active."""
- print("JBTEMP _init_index_state")
# initialize index now, check if we need dimensions
if self.index.dimensions is None:
dims = len(self.encoder(["test"])[0])
@@ -684,7 +683,6 @@ class BaseRouter(BaseModel):
:param strategy: The sync strategy to execute.
:type strategy: Dict[str, Dict[str, List[Utterance]]]
"""
- print(f"strategy: {strategy}")
if strategy["remote"]["delete"]:
data_to_delete = {} # type: ignore
for utt_obj in strategy["remote"]["delete"]:
@@ -1233,7 +1231,6 @@ class BaseRouter(BaseModel):
self, query_results: List[Dict]
) -> Dict[str, List[float]]:
scores_by_class: Dict[str, List[float]] = {}
- logger.warning(f"JBTEMP: {query_results=}")
for result in query_results:
score = result["score"]
route = result["route"]
diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py
index ecbe45d4121c4f91253aa15194e9ab0873ba181a..9a0eaf54075b7742f28dd38da678134563b58138 100644
--- a/semantic_router/routers/hybrid.py
+++ b/semantic_router/routers/hybrid.py
@@ -91,7 +91,6 @@ class HybridRouter(BaseRouter):
if current_remote_hash.value == "":
# if remote hash is empty, the index is to be initialized
current_remote_hash = current_local_hash
- logger.warning(f"JBTEMP: {routes}")
if isinstance(routes, Route):
routes = [routes]
# create embeddings for all routes
@@ -242,8 +241,6 @@ class HybridRouter(BaseRouter):
route_filter=route_filter,
sparse_vector=sparse_vector,
)
- logger.warning(f"JBTEMP: {scores}")
- logger.warning(f"JBTEMP: {route_names}")
query_results = [
{"route": d, "score": s.item()} for d, s in zip(route_names, scores)
]
@@ -252,8 +249,6 @@ class HybridRouter(BaseRouter):
top_class, top_class_scores = self._semantic_classify(
query_results=query_results
)
- logger.warning(f"JBTEMP: {top_class}")
- logger.warning(f"JBTEMP: {top_class_scores}")
passed = self._pass_threshold(top_class_scores, self.score_threshold)
if passed:
return RouteChoice(name=top_class, similarity_score=max(top_class_scores))
@@ -312,8 +307,8 @@ class HybridRouter(BaseRouter):
Xq_s: List[SparseEmbedding] = []
for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
emb_d = np.array(self.encoder(X[i : i + batch_size]))
- # TODO JB: for some reason the sparse encoder is receiving a tuple like `("Hello",)`
- print(f"JBTEMP: {X[i : i + batch_size]}")
+ # TODO JB: for some reason the sparse encoder is receiving a tuple
+ # like `("Hello",)`
emb_s = self.sparse_encoder(X[i : i + batch_size])
Xq_d.extend(emb_d)
Xq_s.extend(emb_s)
diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py
index 204cb2a7a096a8b494c9cfefbbfd08ada6795663..feae4b5d103519bea2fe8687a8b6dd64e3e662f5 100644
--- a/tests/unit/test_sync.py
+++ b/tests/unit/test_sync.py
@@ -1133,14 +1133,12 @@ class TestAsyncSemanticRouter:
index = init_index(
index_cls, init_async_index=True, index_name=router_cls.__name__
)
- print(f"1. {index.namespace=}")
route_layer = router_cls(
encoder=openai_encoder,
routes=routes_2,
index=index,
auto_sync="local",
)
- print(f"2. {route_layer.index.namespace=}")
route_layer = router_cls(
encoder=openai_encoder,
routes=routes,
@@ -1153,14 +1151,12 @@ class TestAsyncSemanticRouter:
await route_layer.async_sync("local")
if index_cls is PineconeIndex:
await asyncio.sleep(PINECONE_SLEEP)
- print(f"3. {route_layer.index.namespace=}")
# Lock should be released, allowing another sync
await route_layer.async_sync("local") # Should not raise exception
if index_cls is PineconeIndex:
await asyncio.sleep(PINECONE_SLEEP)
assert await route_layer.async_is_synced()
- print(f"4. {route_layer.index.namespace=}")
# clear index if pinecone
if index_cls is PineconeIndex: