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* add tools to chat completion request * use templates for generating system prompts * Moved ToolPromptFormat and jinja templates to llama_models.llama3.api * <WIP> memory changes - inlined AgenticSystemInstanceConfig so API feels more ergonomic - renamed it to AgentConfig, AgentInstance -> Agent - added a MemoryConfig and `memory` parameter - added `attachments` to input and `output_attachments` to the response - some naming changes * InterleavedTextAttachment -> InterleavedTextMedia, introduce memory tool * flesh out memory banks API * agentic loop has a RAG implementation * faiss provider implementation * memory client works * re-work tool definitions, fix FastAPI issues, fix tool regressions * fix agentic_system utils * basic RAG seems to work * small bug fixes for inline attachments * Refactor custom tool execution utilities * Bug fix, show memory retrieval steps in EventLogger * No need for api_key for Remote providers * add special unicode character ↵ to showcase newlines in model prompt templates * remove api.endpoints imports * combine datatypes.py and endpoints.py into api.py * Attachment / add TTL api * split batch_inference from inference * minor import fixes * use a single impl for ChatFormat.decode_assistant_mesage * use interleaved_text_media_as_str() utilityt * Fix api.datatypes imports * Add blobfile for tiktoken * Add ToolPromptFormat to ChatFormat.encode_message so that tools are encoded properly * templates take optional --format={json,function_tag} * Rag Updates * Add `api build` subcommand -- WIP * fix * build + run image seems to work * <WIP> adapters * bunch more work to make adapters work * api build works for conda now * ollama remote adapter works * Several smaller fixes to make adapters work Also, reorganized the pattern of __init__ inside providers so configuration can stay lightweight * llama distribution -> llama stack + containers (WIP) * All the new CLI for api + stack work * Make Fireworks and Together into the Adapter format * Some quick fixes to the CLI behavior to make it consistent * Updated README phew * Update cli_reference.md * llama_toolchain/distribution -> llama_toolchain/core * Add termcolor * update paths * Add a log just for consistency * chmod +x scripts * Fix api dependencies not getting added to configuration * missing import lol * Delete utils.py; move to agentic system * Support downloading of URLs for attachments for code interpreter * Simplify and generalize `llama api build` yay * Update `llama stack configure` to be very simple also * Fix stack start * Allow building an "adhoc" distribution * Remote `llama api []` subcommands * Fixes to llama stack commands and update docs * Update documentation again and add error messages to llama stack start * llama stack start -> llama stack run * Change name of build for less confusion * Add pyopenapi fork to the repository, update RFC assets * Remove conflicting annotation * Added a "--raw" option for model template printing --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com> Co-authored-by: Dalton Flanagan <6599399+dltn@users.noreply.github.com>
87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
# 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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"""
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A custom Matplotlib backend that overrides the show method to return image bytes.
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"""
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import base64
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import io
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import json as _json
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import matplotlib
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from matplotlib.backend_bases import FigureManagerBase
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# Import necessary components from Matplotlib
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from matplotlib.backends.backend_agg import FigureCanvasAgg
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class CustomFigureCanvas(FigureCanvasAgg):
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def show(self):
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# Save the figure to a BytesIO object
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buf = io.BytesIO()
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self.print_png(buf)
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image_bytes = buf.getvalue()
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buf.close()
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return image_bytes
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class CustomFigureManager(FigureManagerBase):
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def __init__(self, canvas, num):
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super().__init__(canvas, num)
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# Mimic module initialization that integrates with the Matplotlib backend system
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def _create_figure_manager(num, *args, **kwargs):
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"""
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Create a custom figure manager instance.
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"""
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FigureClass = kwargs.pop("FigureClass", None) # noqa: N806
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if FigureClass is None:
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from matplotlib.figure import Figure
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FigureClass = Figure # noqa: N806
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fig = FigureClass(*args, **kwargs)
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canvas = CustomFigureCanvas(fig)
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manager = CustomFigureManager(canvas, num)
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return manager
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def show():
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"""
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Handle all figures and potentially return their images as bytes.
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This function iterates over all figures registered with the custom backend,
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renders them as images in bytes format, and could return a list of bytes objects,
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one for each figure, or handle them as needed.
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"""
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image_data = []
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for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers():
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# Get the figure from the manager
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fig = manager.canvas.figure
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buf = io.BytesIO() # Create a buffer for the figure
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fig.savefig(buf, format="png") # Save the figure to the buffer in PNG format
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buf.seek(0) # Go to the beginning of the buffer
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image_bytes = buf.getvalue() # Retrieve bytes value
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image_base64 = base64.b64encode(image_bytes).decode("utf-8")
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image_data.append({"image_base64": image_base64})
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buf.close()
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req_con, resp_con = _open_connections()
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_json_dump = _json.dumps(
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{
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"type": "matplotlib",
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"image_data": image_data,
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}
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)
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req_con.send_bytes(_json_dump.encode("utf-8"))
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resp = _json.loads(resp_con.recv_bytes().decode("utf-8"))
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print(resp)
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FigureCanvas = CustomFigureCanvas
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FigureManager = CustomFigureManager
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