Added in registry and tests passed

llama_stack/providers/adapters/memory/pinecone/config.py

@@ -12,6 +12,6 @@ class PineconeRequestProviderData(BaseModel):
 
 
 class PineconeConfig(BaseModel):
-    dimensions: int
+    dimension: int = 384
-    cloud: str
+    cloud: str = "aws"
-    region: str
+    region: str = "us-east-1"

llama_stack/providers/adapters/memory/pinecone/pinecone.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import json
+import time
 
 from numpy.typing import NDArray
 from pinecone import ServerlessSpec
@@ -34,15 +34,20 @@ class PineconeIndex(EmbeddingIndex):
         for i, chunk in enumerate(chunks):
             data_objects.append(
                 {
-                    "id": f"vec{i+1}",
+                    "id": chunk.document_id,
                     "values": embeddings[i].tolist(),
-                    "metadata": {"chunk": chunk},
+                    "metadata": {
+                        "content": chunk.content,
+                        "token_count": chunk.token_count,
+                        "document_id": chunk.document_id,
+                    },
                 }
             )
 
         # Inserting chunks into a prespecified Weaviate collection
         index = self.client.Index(self.index_name)
         index.upsert(vectors=data_objects)
+        time.sleep(1)
 
     async def query(
         self, embedding: NDArray, k: int, score_threshold: float
@@ -50,16 +55,16 @@ class PineconeIndex(EmbeddingIndex):
         index = self.client.Index(self.index_name)
 
         results = index.query(
-            vector=embedding, top_k=k, include_values=True, include_metadata=True
+            vector=embedding, top_k=k, include_values=False, include_metadata=True
         )
 
         chunks = []
         scores = []
         for doc in results["matches"]:
-            chunk_json = doc["metadata"]["chunk"]
+            chunk_json = doc["metadata"]
+            print(f"chunk_json: {chunk_json}")
             try:
-                chunk_dict = json.loads(chunk_json)
+                chunk = Chunk(**chunk_json)
-                chunk = Chunk(**chunk_dict)
             except Exception:
                 import traceback
 
@@ -130,11 +135,11 @@ class PineconeMemoryAdapter(
         if not self.check_if_index_exists(client, memory_bank.identifier):
             client.create_index(
                 name=memory_bank.identifier,
-                dimension=self.config.dimensions if self.config.dimensions else 1024,
+                dimension=self.config.dimension,
                 metric="cosine",
                 spec=ServerlessSpec(
-                    cloud=self.config.cloud if self.config.cloud else "aws",
+                    cloud=self.config.cloud,
-                    region=self.config.region if self.config.region else "us-east-1",
+                    region=self.config.region,
                 ),
             )
 
@@ -146,7 +151,7 @@ class PineconeMemoryAdapter(
 
     async def list_memory_banks(self) -> List[MemoryBankDef]:
         # TODO: right now the Llama Stack is the source of truth for these banks. That is
-        # not ideal. It should be Weaviate which is the source of truth. Unfortunately,
+        # not ideal. It should be pinecone which is the source of truth. Unfortunately,
         # list() happens at Stack startup when the Pinecone client (credentials) is not
         # yet available. We need to figure out a way to make this work.
         return [i.bank for i in self.cache.values()]

llama_stack/providers/registry/memory.py

@@ -84,4 +84,14 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.adapters.memory.qdrant.QdrantConfig",
             ),
         ),
+        remote_provider_spec(
+            Api.memory,
+            AdapterSpec(
+                adapter_type="pinecone",
+                pip_packages=EMBEDDING_DEPS + ["pinecone"],
+                module="llama_stack.providers.adapters.memory.pinecone",
+                config_class="llama_stack.providers.adapters.memory.pinecone.PineconeConfig",
+                provider_data_validator="llama_stack.providers.adapters.memory.pinecone.PineconeRequestProviderData",
+            ),
+        ),
     ]

llama_stack/providers/tests/memory/provider_config_example.yaml

@@ -20,10 +20,12 @@ providers:
     config:
       host: localhost
       port: 6333
+  - provider_id: test-pinecone
+    provider_type: remote::pinecone
+    config: {}
 # if a provider needs private keys from the client, they use the
 # "get_request_provider_data" function (see distribution/request_headers.py)
 # this is a place to provide such data.
 provider_data:
-  "test-weaviate":
+  "test-pinecone":
-    weaviate_api_key: 0xdeadbeefputrealapikeyhere
+    pinecone_api_key:
-    weaviate_cluster_url: http://foobarbaz

llama_stack/providers/tests/memory/test_memory.py

@@ -69,7 +69,7 @@ def sample_documents():
 
 async def register_memory_bank(banks_impl: MemoryBanks):
     bank = VectorMemoryBankDef(
-        identifier="test_bank",
+        identifier="test-bank",
         embedding_model="all-MiniLM-L6-v2",
         chunk_size_in_tokens=512,
         overlap_size_in_tokens=64,
@@ -95,7 +95,7 @@ async def test_banks_register(memory_settings):
     # but so far we don't have an unregister API unfortunately, so be careful
     banks_impl = memory_settings["memory_banks_impl"]
     bank = VectorMemoryBankDef(
-        identifier="test_bank_no_provider",
+        identifier="test-bank-no-provider",
         embedding_model="all-MiniLM-L6-v2",
         chunk_size_in_tokens=512,
         overlap_size_in_tokens=64,
@@ -119,33 +119,33 @@ async def test_query_documents(memory_settings, sample_documents):
     banks_impl = memory_settings["memory_banks_impl"]
 
     with pytest.raises(ValueError):
-        await memory_impl.insert_documents("test_bank", sample_documents)
+        await memory_impl.insert_documents("test-bank", sample_documents)
 
     await register_memory_bank(banks_impl)
-    await memory_impl.insert_documents("test_bank", sample_documents)
+    await memory_impl.insert_documents("test-bank", sample_documents)
 
     query1 = "programming language"
-    response1 = await memory_impl.query_documents("test_bank", query1)
+    response1 = await memory_impl.query_documents("test-bank", query1)
     assert_valid_response(response1)
     assert any("Python" in chunk.content for chunk in response1.chunks)
 
     # Test case 3: Query with semantic similarity
     query3 = "AI and brain-inspired computing"
-    response3 = await memory_impl.query_documents("test_bank", query3)
+    response3 = await memory_impl.query_documents("test-bank", query3)
     assert_valid_response(response3)
     assert any("neural networks" in chunk.content.lower() for chunk in response3.chunks)
 
     # Test case 4: Query with limit on number of results
     query4 = "computer"
     params4 = {"max_chunks": 2}
-    response4 = await memory_impl.query_documents("test_bank", query4, params4)
+    response4 = await memory_impl.query_documents("test-bank", query4, params4)
     assert_valid_response(response4)
     assert len(response4.chunks) <= 2
 
     # Test case 5: Query with threshold on similarity score
     query5 = "quantum computing"  # Not directly related to any document
     params5 = {"score_threshold": 0.2}
-    response5 = await memory_impl.query_documents("test_bank", query5, params5)
+    response5 = await memory_impl.query_documents("test-bank", query5, params5)
     assert_valid_response(response5)
     print("The scores are:", response5.scores)
     assert all(score >= 0.2 for score in response5.scores)