llama-stack/llama_stack/apis/memory_banks/memory_banks.py
Dinesh Yeduguru fcd6449519
Telemetry API redesign (#525)
# What does this PR do?
Change the Telemetry API to be able to support different use cases like
returning traces for the UI and ability to export for Evals.
Other changes:
* Add a new trace_protocol decorator to decorate all our API methods so
that any call to them will automatically get traced across all impls.
* There is some issue with the decorator pattern of span creation when
using async generators, where there are multiple yields with in the same
context. I think its much more explicit by using the explicit context
manager pattern using with. I moved the span creations in agent instance
to be using with
* Inject session id at the turn level, which should quickly give us all
traces across turns for a given session

Addresses #509

## Test Plan
```
llama stack run /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml
PYTHONPATH=. python -m examples.agents.rag_with_memory_bank localhost 5000


 curl -X POST 'http://localhost:5000/alpha/telemetry/query-traces' \
-H 'Content-Type: application/json' \
-d '{
  "attribute_filters": [
    {
      "key": "session_id",
      "op": "eq",
      "value": "dd667b87-ca4b-4d30-9265-5a0de318fc65" }],
  "limit": 100,
  "offset": 0,
  "order_by": ["start_time"]
}' | jq .
[
  {
    "trace_id": "6902f54b83b4b48be18a6f422b13e16f",
    "root_span_id": "5f37b85543afc15a",
    "start_time": "2024-12-04T08:08:30.501587",
    "end_time": "2024-12-04T08:08:36.026463"
  },
  {
    "trace_id": "92227dac84c0615ed741be393813fb5f",
    "root_span_id": "af7c5bb46665c2c8",
    "start_time": "2024-12-04T08:08:36.031170",
    "end_time": "2024-12-04T08:08:41.693301"
  },
  {
    "trace_id": "7d578a6edac62f204ab479fba82f77b6",
    "root_span_id": "1d935e3362676896",
    "start_time": "2024-12-04T08:08:41.695204",
    "end_time": "2024-12-04T08:08:47.228016"
  },
  {
    "trace_id": "dbd767d76991bc816f9f078907dc9ff2",
    "root_span_id": "f5a7ee76683b9602",
    "start_time": "2024-12-04T08:08:47.234578",
    "end_time": "2024-12-04T08:08:53.189412"
  }
]


curl -X POST 'http://localhost:5000/alpha/telemetry/get-span-tree' \
-H 'Content-Type: application/json' \
-d '{ "span_id" : "6cceb4b48a156913", "max_depth": 2, "attributes_to_return": ["input"] }' | jq .
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   875  100   790  100    85  18462   1986 --:--:-- --:--:-- --:--:-- 20833
{
  "span_id": "6cceb4b48a156913",
  "trace_id": "dafa796f6aaf925f511c04cd7c67fdda",
  "parent_span_id": "892a66d726c7f990",
  "name": "retrieve_rag_context",
  "start_time": "2024-12-04T09:28:21.781995",
  "end_time": "2024-12-04T09:28:21.913352",
  "attributes": {
    "input": [
      "{\"role\":\"system\",\"content\":\"You are a helpful assistant\"}",
      "{\"role\":\"user\",\"content\":\"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.\",\"context\":null}"
    ]
  },
  "children": [
    {
      "span_id": "1a2df181854064a8",
      "trace_id": "dafa796f6aaf925f511c04cd7c67fdda",
      "parent_span_id": "6cceb4b48a156913",
      "name": "MemoryRouter.query_documents",
      "start_time": "2024-12-04T09:28:21.787620",
      "end_time": "2024-12-04T09:28:21.906512",
      "attributes": {
        "input": null
      },
      "children": [],
      "status": "ok"
    }
  ],
  "status": "ok"
}

```

<img width="1677" alt="Screenshot 2024-12-04 at 9 42 56 AM"
src="https://github.com/user-attachments/assets/4d3cea93-05ce-415a-93d9-4b1628631bf8">
2024-12-04 11:22:45 -08:00

151 lines
4 KiB
Python

# 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.
from enum import Enum
from typing import (
Annotated,
List,
Literal,
Optional,
Protocol,
runtime_checkable,
Union,
)
from llama_models.schema_utils import json_schema_type, webmethod
from pydantic import BaseModel, Field
from llama_stack.apis.resource import Resource, ResourceType
from llama_stack.distribution.tracing import trace_protocol
@json_schema_type
class MemoryBankType(Enum):
vector = "vector"
keyvalue = "keyvalue"
keyword = "keyword"
graph = "graph"
# define params for each type of memory bank, this leads to a tagged union
# accepted as input from the API or from the config.
@json_schema_type
class VectorMemoryBankParams(BaseModel):
memory_bank_type: Literal[MemoryBankType.vector.value] = MemoryBankType.vector.value
embedding_model: str
chunk_size_in_tokens: int
overlap_size_in_tokens: Optional[int] = None
@json_schema_type
class KeyValueMemoryBankParams(BaseModel):
memory_bank_type: Literal[MemoryBankType.keyvalue.value] = (
MemoryBankType.keyvalue.value
)
@json_schema_type
class KeywordMemoryBankParams(BaseModel):
memory_bank_type: Literal[MemoryBankType.keyword.value] = (
MemoryBankType.keyword.value
)
@json_schema_type
class GraphMemoryBankParams(BaseModel):
memory_bank_type: Literal[MemoryBankType.graph.value] = MemoryBankType.graph.value
BankParams = Annotated[
Union[
VectorMemoryBankParams,
KeyValueMemoryBankParams,
KeywordMemoryBankParams,
GraphMemoryBankParams,
],
Field(discriminator="memory_bank_type"),
]
# Some common functionality for memory banks.
class MemoryBankResourceMixin(Resource):
type: Literal[ResourceType.memory_bank.value] = ResourceType.memory_bank.value
@property
def memory_bank_id(self) -> str:
return self.identifier
@property
def provider_memory_bank_id(self) -> str:
return self.provider_resource_id
@json_schema_type
class VectorMemoryBank(MemoryBankResourceMixin):
memory_bank_type: Literal[MemoryBankType.vector.value] = MemoryBankType.vector.value
embedding_model: str
chunk_size_in_tokens: int
overlap_size_in_tokens: Optional[int] = None
@json_schema_type
class KeyValueMemoryBank(MemoryBankResourceMixin):
memory_bank_type: Literal[MemoryBankType.keyvalue.value] = (
MemoryBankType.keyvalue.value
)
# TODO: KeyValue and Keyword are so similar in name, oof. Get a better naming convention.
@json_schema_type
class KeywordMemoryBank(MemoryBankResourceMixin):
memory_bank_type: Literal[MemoryBankType.keyword.value] = (
MemoryBankType.keyword.value
)
@json_schema_type
class GraphMemoryBank(MemoryBankResourceMixin):
memory_bank_type: Literal[MemoryBankType.graph.value] = MemoryBankType.graph.value
MemoryBank = Annotated[
Union[
VectorMemoryBank,
KeyValueMemoryBank,
KeywordMemoryBank,
GraphMemoryBank,
],
Field(discriminator="memory_bank_type"),
]
class MemoryBankInput(BaseModel):
memory_bank_id: str
params: BankParams
provider_memory_bank_id: Optional[str] = None
@runtime_checkable
@trace_protocol
class MemoryBanks(Protocol):
@webmethod(route="/memory-banks/list", method="GET")
async def list_memory_banks(self) -> List[MemoryBank]: ...
@webmethod(route="/memory-banks/get", method="GET")
async def get_memory_bank(self, memory_bank_id: str) -> Optional[MemoryBank]: ...
@webmethod(route="/memory-banks/register", method="POST")
async def register_memory_bank(
self,
memory_bank_id: str,
params: BankParams,
provider_id: Optional[str] = None,
provider_memory_bank_id: Optional[str] = None,
) -> MemoryBank: ...
@webmethod(route="/memory-banks/unregister", method="POST")
async def unregister_memory_bank(self, memory_bank_id: str) -> None: ...