forked from phoenix-oss/llama-stack-mirror
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>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
f294eac5f5
commit
9487ad8294
213 changed files with 1725 additions and 1204 deletions
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@ -0,0 +1,375 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import re
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import tempfile
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from abc import abstractmethod
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from typing import List, Optional
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import requests
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from termcolor import cprint
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from .ipython_tool.code_execution import (
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CodeExecutionContext,
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CodeExecutionRequest,
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CodeExecutor,
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TOOLS_ATTACHMENT_KEY_REGEX,
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)
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.apis.agents import * # noqa: F403
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from .base import BaseTool
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def interpret_content_as_attachment(content: str) -> Optional[Attachment]:
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match = re.search(TOOLS_ATTACHMENT_KEY_REGEX, content)
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if match:
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snippet = match.group(1)
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data = json.loads(snippet)
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return Attachment(
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content=URL(uri="file://" + data["filepath"]), mime_type=data["mimetype"]
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)
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return None
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class SingleMessageBuiltinTool(BaseTool):
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async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
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assert len(messages) == 1, f"Expected single message, got {len(messages)}"
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message = messages[0]
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assert len(message.tool_calls) == 1, "Expected a single tool call"
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tool_call = messages[0].tool_calls[0]
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query = tool_call.arguments["query"]
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response: str = await self.run_impl(query)
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message = ToolResponseMessage(
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call_id=tool_call.call_id,
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tool_name=tool_call.tool_name,
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content=response,
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)
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return [message]
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@abstractmethod
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async def run_impl(self, query: str) -> str:
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raise NotImplementedError()
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class PhotogenTool(SingleMessageBuiltinTool):
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def __init__(self, dump_dir: str) -> None:
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self.dump_dir = dump_dir
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def get_name(self) -> str:
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return BuiltinTool.photogen.value
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async def run_impl(self, query: str) -> str:
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"""
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Implement this to give the model an ability to generate images.
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Return:
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info = {
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"filepath": str(image_filepath),
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"mimetype": "image/png",
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}
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"""
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raise NotImplementedError()
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class SearchTool(SingleMessageBuiltinTool):
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def __init__(self, engine: SearchEngineType, api_key: str, **kwargs) -> None:
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self.api_key = api_key
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if engine == SearchEngineType.bing:
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self.engine = BingSearch(api_key, **kwargs)
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elif engine == SearchEngineType.brave:
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self.engine = BraveSearch(api_key, **kwargs)
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else:
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raise ValueError(f"Unknown search engine: {engine}")
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def get_name(self) -> str:
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return BuiltinTool.brave_search.value
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async def run_impl(self, query: str) -> str:
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return await self.engine.search(query)
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class BingSearch:
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def __init__(self, api_key: str, top_k: int = 3, **kwargs) -> None:
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self.api_key = api_key
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self.top_k = top_k
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async def search(self, query: str) -> str:
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url = "https://api.bing.microsoft.com/v7.0/search"
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headers = {
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"Ocp-Apim-Subscription-Key": self.api_key,
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}
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params = {
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"count": self.top_k,
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"textDecorations": True,
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"textFormat": "HTML",
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"q": query,
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}
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response = requests.get(url=url, params=params, headers=headers)
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response.raise_for_status()
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clean = self._clean_response(response.json())
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return json.dumps(clean)
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def _clean_response(self, search_response):
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clean_response = []
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query = search_response["queryContext"]["originalQuery"]
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if "webPages" in search_response:
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pages = search_response["webPages"]["value"]
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for p in pages:
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selected_keys = {"name", "url", "snippet"}
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clean_response.append(
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{k: v for k, v in p.items() if k in selected_keys}
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)
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if "news" in search_response:
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clean_news = []
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news = search_response["news"]["value"]
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for n in news:
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selected_keys = {"name", "url", "description"}
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clean_news.append({k: v for k, v in n.items() if k in selected_keys})
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clean_response.append(clean_news)
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return {"query": query, "top_k": clean_response}
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class BraveSearch:
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def __init__(self, api_key: str) -> None:
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self.api_key = api_key
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async def search(self, query: str) -> str:
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url = "https://api.search.brave.com/res/v1/web/search"
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headers = {
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"X-Subscription-Token": self.api_key,
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"Accept-Encoding": "gzip",
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"Accept": "application/json",
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}
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payload = {"q": query}
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response = requests.get(url=url, params=payload, headers=headers)
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return json.dumps(self._clean_brave_response(response.json()))
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def _clean_brave_response(self, search_response, top_k=3):
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query = None
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clean_response = []
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if "query" in search_response:
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if "original" in search_response["query"]:
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query = search_response["query"]["original"]
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if "mixed" in search_response:
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mixed_results = search_response["mixed"]
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for m in mixed_results["main"][:top_k]:
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r_type = m["type"]
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results = search_response[r_type]["results"]
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if r_type == "web":
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# For web data - add a single output from the search
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idx = m["index"]
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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"date",
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"extra_snippets",
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]
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cleaned = {
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k: v for k, v in results[idx].items() if k in selected_keys
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}
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elif r_type == "faq":
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# For faw data - take a list of all the questions & answers
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selected_keys = ["type", "question", "answer", "title", "url"]
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cleaned = []
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for q in results:
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cleaned.append(
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{k: v for k, v in q.items() if k in selected_keys}
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)
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elif r_type == "infobox":
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idx = m["index"]
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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"long_desc",
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]
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cleaned = {
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k: v for k, v in results[idx].items() if k in selected_keys
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}
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elif r_type == "videos":
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selected_keys = [
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"type",
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"url",
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"title",
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"description",
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"date",
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]
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cleaned = []
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for q in results:
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cleaned.append(
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{k: v for k, v in q.items() if k in selected_keys}
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)
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elif r_type == "locations":
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# For faw data - take a list of all the questions & answers
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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"coordinates",
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"postal_address",
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"contact",
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"rating",
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"distance",
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"zoom_level",
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]
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cleaned = []
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for q in results:
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cleaned.append(
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{k: v for k, v in q.items() if k in selected_keys}
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)
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elif r_type == "news":
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# For faw data - take a list of all the questions & answers
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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]
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cleaned = []
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for q in results:
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cleaned.append(
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{k: v for k, v in q.items() if k in selected_keys}
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)
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else:
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cleaned = []
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clean_response.append(cleaned)
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return {"query": query, "top_k": clean_response}
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class WolframAlphaTool(SingleMessageBuiltinTool):
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def __init__(self, api_key: str) -> None:
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self.api_key = api_key
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self.url = "https://api.wolframalpha.com/v2/query"
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def get_name(self) -> str:
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return BuiltinTool.wolfram_alpha.value
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async def run_impl(self, query: str) -> str:
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params = {
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"input": query,
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"appid": self.api_key,
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"format": "plaintext",
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"output": "json",
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}
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response = requests.get(
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self.url,
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params=params,
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)
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return json.dumps(self._clean_wolfram_alpha_response(response.json()))
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def _clean_wolfram_alpha_response(self, wa_response):
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remove = {
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"queryresult": [
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"datatypes",
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"error",
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"timedout",
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"timedoutpods",
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"numpods",
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"timing",
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"parsetiming",
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"parsetimedout",
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"recalculate",
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"id",
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"host",
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"server",
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"related",
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"version",
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{
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"pods": [
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"scanner",
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"id",
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"error",
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"expressiontypes",
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"states",
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"infos",
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"position",
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"numsubpods",
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]
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},
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"assumptions",
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],
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}
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for main_key in remove:
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for key_to_remove in remove[main_key]:
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try:
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if key_to_remove == "assumptions":
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if "assumptions" in wa_response[main_key]:
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del wa_response[main_key][key_to_remove]
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if isinstance(key_to_remove, dict):
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for sub_key in key_to_remove:
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if sub_key == "pods":
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for i in range(len(wa_response[main_key][sub_key])):
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if (
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wa_response[main_key][sub_key][i]["title"]
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== "Result"
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):
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del wa_response[main_key][sub_key][i + 1 :]
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break
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sub_items = wa_response[main_key][sub_key]
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for i in range(len(sub_items)):
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for sub_key_to_remove in key_to_remove[sub_key]:
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if sub_key_to_remove in sub_items[i]:
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del sub_items[i][sub_key_to_remove]
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elif key_to_remove in wa_response[main_key]:
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del wa_response[main_key][key_to_remove]
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except KeyError:
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pass
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return wa_response
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class CodeInterpreterTool(BaseTool):
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def __init__(self) -> None:
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ctx = CodeExecutionContext(
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matplotlib_dump_dir=tempfile.mkdtemp(),
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)
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self.code_executor = CodeExecutor(ctx)
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def get_name(self) -> str:
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return BuiltinTool.code_interpreter.value
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async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
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message = messages[0]
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assert len(message.tool_calls) == 1, "Expected a single tool call"
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tool_call = messages[0].tool_calls[0]
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script = tool_call.arguments["code"]
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req = CodeExecutionRequest(scripts=[script])
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res = self.code_executor.execute(req)
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pieces = [res["process_status"]]
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for out_type in ["stdout", "stderr"]:
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res_out = res[out_type]
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if res_out != "":
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pieces.extend([f"[{out_type}]", res_out, f"[/{out_type}]"])
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if out_type == "stderr":
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cprint(f"ipython tool error: ↓\n{res_out}", color="red")
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message = ToolResponseMessage(
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call_id=tool_call.call_id,
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tool_name=tool_call.tool_name,
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content="\n".join(pieces),
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)
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return [message]
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