API Updates (#73)

* API Keys passed from Client instead of distro configuration

* delete distribution registry

* Rename the "package" word away

* Introduce a "Router" layer for providers

Some providers need to be factorized and considered as thin routing
layers on top of other providers. Consider two examples:

- The inference API should be a routing layer over inference providers,
  routed using the "model" key
- The memory banks API is another instance where various memory bank
  types will be provided by independent providers (e.g., a vector store
  is served by Chroma while a keyvalue memory can be served by Redis or
  PGVector)

This commit introduces a generalized routing layer for this purpose.

* update `apis_to_serve`

* llama_toolchain -> llama_stack

* Codemod from llama_toolchain -> llama_stack

- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent

* Moved some stuff out of common/; re-generated OpenAPI spec

* llama-toolchain -> llama-stack (hyphens)

* add control plane API

* add redis adapter + sqlite provider

* move core -> distribution

* Some more toolchain -> stack changes

* small naming shenanigans

* Removing custom tool and agent utilities and moving them client side

* Move control plane to distribution server for now

* Remove control plane from API list

* no codeshield dependency randomly plzzzzz

* Add "fire" as a dependency

* add back event loggers

* stack configure fixes

* use brave instead of bing in the example client

* add init file so it gets packaged

* add init files so it gets packaged

* Update MANIFEST

* bug fix

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>

llama_stack/providers/impls/meta_reference/memory/faiss.py

@@ -0,0 +1,125 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import logging
+import uuid
+
+from typing import Any, Dict, List, Optional
+
+import faiss
+import numpy as np
+from numpy.typing import NDArray
+
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+
+from llama_stack.apis.memory import *  # noqa: F403
+from llama_stack.providers.utils.memory.vector_store import (
+    ALL_MINILM_L6_V2_DIMENSION,
+    BankWithIndex,
+    EmbeddingIndex,
+)
+from llama_stack.providers.utils.telemetry import tracing
+
+from .config import FaissImplConfig
+
+logger = logging.getLogger(__name__)
+
+
+class FaissIndex(EmbeddingIndex):
+    id_by_index: Dict[int, str]
+    chunk_by_index: Dict[int, str]
+
+    def __init__(self, dimension: int):
+        self.index = faiss.IndexFlatL2(dimension)
+        self.id_by_index = {}
+        self.chunk_by_index = {}
+
+    @tracing.span(name="add_chunks")
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        indexlen = len(self.id_by_index)
+        for i, chunk in enumerate(chunks):
+            self.chunk_by_index[indexlen + i] = chunk
+            logger.info(f"Adding chunk #{indexlen + i} tokens={chunk.token_count}")
+            self.id_by_index[indexlen + i] = chunk.document_id
+
+        self.index.add(np.array(embeddings).astype(np.float32))
+
+    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
+        distances, indices = self.index.search(
+            embedding.reshape(1, -1).astype(np.float32), k
+        )
+
+        chunks = []
+        scores = []
+        for d, i in zip(distances[0], indices[0]):
+            if i < 0:
+                continue
+            chunks.append(self.chunk_by_index[int(i)])
+            scores.append(1.0 / float(d))
+
+        return QueryDocumentsResponse(chunks=chunks, scores=scores)
+
+
+class FaissMemoryImpl(Memory):
+    def __init__(self, config: FaissImplConfig) -> None:
+        self.config = config
+        self.cache = {}
+
+    async def initialize(self) -> None: ...
+
+    async def shutdown(self) -> None: ...
+
+    async def create_memory_bank(
+        self,
+        name: str,
+        config: MemoryBankConfig,
+        url: Optional[URL] = None,
+    ) -> MemoryBank:
+        assert url is None, "URL is not supported for this implementation"
+        assert (
+            config.type == MemoryBankType.vector.value
+        ), f"Only vector banks are supported {config.type}"
+
+        bank_id = str(uuid.uuid4())
+        bank = MemoryBank(
+            bank_id=bank_id,
+            name=name,
+            config=config,
+            url=url,
+        )
+        index = BankWithIndex(bank=bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION))
+        self.cache[bank_id] = index
+        return bank
+
+    async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
+        index = self.cache.get(bank_id)
+        if index is None:
+            return None
+        return index.bank
+
+    async def insert_documents(
+        self,
+        bank_id: str,
+        documents: List[MemoryBankDocument],
+        ttl_seconds: Optional[int] = None,
+    ) -> None:
+        index = self.cache.get(bank_id)
+        if index is None:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        await index.insert_documents(documents)
+
+    async def query_documents(
+        self,
+        bank_id: str,
+        query: InterleavedTextMedia,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryDocumentsResponse:
+        index = self.cache.get(bank_id)
+        if index is None:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        return await index.query_documents(query, params)