mirror of
https://github.com/cpacker/MemGPT.git
synced 2025-06-03 04:30:22 +00:00
Merge branch 'main' into bump-7-17
This commit is contained in:
Caren Thomas
commit
472811a563
15
.github/ISSUE_TEMPLATE/bug_report.md
vendored
15
.github/ISSUE_TEMPLATE/bug_report.md
vendored
@ -11,20 +11,25 @@ assignees: ''
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**Please describe your setup**
|
||||
- [ ] How did you install letta?
|
||||
- `pip install letta`? `pip install letta-nightly`? `git clone`?
|
||||
- [ ] How are you running Letta?
|
||||
- Docker
|
||||
- pip (legacy)
|
||||
- From source
|
||||
- Desktop
|
||||
- [ ] Describe your setup
|
||||
- What's your OS (Windows/MacOS/Linux)?
|
||||
- How are you running `letta`? (`cmd.exe`/Powershell/Anaconda Shell/Terminal)
|
||||
- What is your `docker run ...` command (if applicable)
|
||||
|
||||
**Screenshots**
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
|
||||
**Additional context**
|
||||
Add any other context about the problem here.
|
||||
- What model you are using
|
||||
|
||||
**Agent File (optional)**
|
||||
Please attach your `.af` file, as this helps with reproducing issues.
|
||||
|
||||
**Letta Config**
|
||||
Please attach your `~/.letta/config` file or copy paste it below.
|
||||
|
||||
---
|
||||
|
||||
|
||||
19
.github/workflows/notify-letta-cloud.yml
vendored
19
.github/workflows/notify-letta-cloud.yml
vendored
@ -1,19 +0,0 @@
|
||||
name: Notify Letta Cloud
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
|
||||
jobs:
|
||||
notify:
|
||||
runs-on: ubuntu-latest
|
||||
if: ${{ !contains(github.event.head_commit.message, '[sync-skip]') }}
|
||||
steps:
|
||||
- name: Trigger repository_dispatch
|
||||
run: |
|
||||
curl -X POST \
|
||||
-H "Authorization: token ${{ secrets.SYNC_PAT }}" \
|
||||
-H "Accept: application/vnd.github.v3+json" \
|
||||
https://api.github.com/repos/letta-ai/letta-cloud/dispatches \
|
||||
-d '{"event_type":"oss-update"}'
|
||||
155
.github/workflows/send-message-integration-tests.yaml
vendored
Normal file
155
.github/workflows/send-message-integration-tests.yaml
vendored
Normal file
@ -0,0 +1,155 @@
|
||||
name: Send Message SDK Tests
|
||||
on:
|
||||
pull_request_target:
|
||||
# branches: [main] # TODO: uncomment before merge
|
||||
types: [labeled]
|
||||
paths:
|
||||
- 'letta/**'
|
||||
|
||||
jobs:
|
||||
send-messages:
|
||||
# Only run when the "safe to test" label is applied
|
||||
if: contains(github.event.pull_request.labels.*.name, 'safe to test')
|
||||
runs-on: ubuntu-latest
|
||||
timeout-minutes: 15
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
config_file:
|
||||
- "openai-gpt-4o-mini.json"
|
||||
- "azure-gpt-4o-mini.json"
|
||||
- "claude-3-5-sonnet.json"
|
||||
- "claude-3-7-sonnet.json"
|
||||
- "claude-3-7-sonnet-extended.json"
|
||||
- "gemini-pro.json"
|
||||
- "gemini-vertex.json"
|
||||
services:
|
||||
qdrant:
|
||||
image: qdrant/qdrant
|
||||
ports:
|
||||
- 6333:6333
|
||||
postgres:
|
||||
image: pgvector/pgvector:pg17
|
||||
ports:
|
||||
- 5432:5432
|
||||
env:
|
||||
POSTGRES_HOST_AUTH_METHOD: trust
|
||||
POSTGRES_DB: postgres
|
||||
POSTGRES_USER: postgres
|
||||
options: >-
|
||||
--health-cmd pg_isready
|
||||
--health-interval 10s
|
||||
--health-timeout 5s
|
||||
--health-retries 5
|
||||
|
||||
steps:
|
||||
# Ensure secrets don't leak
|
||||
- name: Configure git to hide secrets
|
||||
run: |
|
||||
git config --global core.logAllRefUpdates false
|
||||
git config --global log.hideCredentials true
|
||||
- name: Set up secret masking
|
||||
run: |
|
||||
# Automatically mask any environment variable ending with _KEY
|
||||
for var in $(env | grep '_KEY=' | cut -d= -f1); do
|
||||
value="${!var}"
|
||||
if [[ -n "$value" ]]; then
|
||||
# Mask the full value
|
||||
echo "::add-mask::$value"
|
||||
|
||||
# Also mask partial values (first and last several characters)
|
||||
# This helps when only parts of keys appear in logs
|
||||
if [[ ${#value} -gt 8 ]]; then
|
||||
echo "::add-mask::${value:0:8}"
|
||||
echo "::add-mask::${value:(-8)}"
|
||||
fi
|
||||
|
||||
# Also mask with common formatting changes
|
||||
# Some logs might add quotes or other characters
|
||||
echo "::add-mask::\"$value\""
|
||||
echo "::add-mask::$value\""
|
||||
echo "::add-mask::\"$value"
|
||||
|
||||
echo "Masked secret: $var (length: ${#value})"
|
||||
fi
|
||||
done
|
||||
|
||||
# Check out base repository code, not the PR's code (for security)
|
||||
- name: Checkout base repository
|
||||
uses: actions/checkout@v4 # No ref specified means it uses base branch
|
||||
|
||||
# Only extract relevant files from the PR (for security, specifically prevent modification of workflow files)
|
||||
- name: Extract PR schema files
|
||||
run: |
|
||||
# Fetch PR without checking it out
|
||||
git fetch origin pull/${{ github.event.pull_request.number }}/head:pr-${{ github.event.pull_request.number }}
|
||||
|
||||
# Extract ONLY the schema files
|
||||
git checkout pr-${{ github.event.pull_request.number }} -- letta/
|
||||
- name: Set up python 3.12
|
||||
id: setup-python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: 3.12
|
||||
- name: Load cached Poetry Binary
|
||||
id: cached-poetry-binary
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: ~/.local
|
||||
key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-1.8.3
|
||||
- name: Install Poetry
|
||||
uses: snok/install-poetry@v1
|
||||
with:
|
||||
version: 1.8.3
|
||||
virtualenvs-create: true
|
||||
virtualenvs-in-project: true
|
||||
- name: Load cached venv
|
||||
id: cached-poetry-dependencies
|
||||
uses: actions/cache@v4
|
||||
with:
|
||||
path: .venv
|
||||
key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}${{ inputs.install-args || '-E dev -E postgres -E external-tools -E tests -E cloud-tool-sandbox' }}
|
||||
# Restore cache with this prefix if not exact match with key
|
||||
# Note cache-hit returns false in this case, so the below step will run
|
||||
restore-keys: |
|
||||
venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-
|
||||
- name: Install dependencies
|
||||
if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
|
||||
shell: bash
|
||||
run: poetry install --no-interaction --no-root ${{ inputs.install-args || '-E dev -E postgres -E external-tools -E tests -E cloud-tool-sandbox -E google' }}
|
||||
- name: Install letta packages via Poetry
|
||||
run: |
|
||||
poetry run pip install --upgrade letta-client letta
|
||||
- name: Migrate database
|
||||
env:
|
||||
LETTA_PG_PORT: 5432
|
||||
LETTA_PG_USER: postgres
|
||||
LETTA_PG_PASSWORD: postgres
|
||||
LETTA_PG_DB: postgres
|
||||
LETTA_PG_HOST: localhost
|
||||
run: |
|
||||
psql -h localhost -U postgres -d postgres -c 'CREATE EXTENSION vector'
|
||||
poetry run alembic upgrade head
|
||||
- name: Run integration tests for ${{ matrix.config_file }}
|
||||
env:
|
||||
LLM_CONFIG_FILE: ${{ matrix.config_file }}
|
||||
LETTA_PG_PORT: 5432
|
||||
LETTA_PG_USER: postgres
|
||||
LETTA_PG_PASSWORD: postgres
|
||||
LETTA_PG_DB: postgres
|
||||
LETTA_PG_HOST: localhost
|
||||
LETTA_SERVER_PASS: test_server_token
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
|
||||
AZURE_BASE_URL: ${{ secrets.AZURE_BASE_URL }}
|
||||
GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
|
||||
COMPOSIO_API_KEY: ${{ secrets.COMPOSIO_API_KEY }}
|
||||
DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}
|
||||
GOOGLE_CLOUD_PROJECT: ${{ secrets.GOOGLE_CLOUD_PROJECT }}
|
||||
GOOGLE_CLOUD_LOCATION: ${{ secrets.GOOGLE_CLOUD_LOCATION }}
|
||||
run: |
|
||||
poetry run pytest \
|
||||
-s -vv \
|
||||
tests/integration_test_send_message.py \
|
||||
--maxfail=1 --durations=10
|
||||
@ -28,7 +28,7 @@ First, install Poetry using [the official instructions here](https://python-poet
|
||||
Once Poetry is installed, navigate to the letta directory and install the Letta project with Poetry:
|
||||
```shell
|
||||
cd letta
|
||||
poetry shell
|
||||
eval $(poetry env activate)
|
||||
poetry install --all-extras
|
||||
```
|
||||
#### Setup PostgreSQL environment (optional)
|
||||
|
||||
@ -66,7 +66,6 @@ ENV LETTA_ENVIRONMENT=${LETTA_ENVIRONMENT} \
|
||||
POSTGRES_DB=letta \
|
||||
COMPOSIO_DISABLE_VERSION_CHECK=true
|
||||
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# Copy virtual environment and app from builder
|
||||
|
||||
17
README.md
17
README.md
@ -8,26 +8,13 @@
|
||||
|
||||
<div align="center">
|
||||
<h1>Letta (previously MemGPT)</h1>
|
||||
|
||||
**☄️ New release: Letta Agent Development Environment (_read more [here](#-access-the-ade-agent-development-environment)_) ☄️**
|
||||
|
||||
<p align="center">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/letta-ai/letta/refs/heads/main/assets/example_ade_screenshot.png">
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/letta-ai/letta/refs/heads/main/assets/example_ade_screenshot_light.png">
|
||||
<img alt="Letta logo" src="https://raw.githubusercontent.com/letta-ai/letta/refs/heads/main/assets/example_ade_screenshot.png" width="800">
|
||||
</picture>
|
||||
</p>
|
||||
|
||||
---
|
||||
|
||||
<h3>
|
||||
|
||||
[Homepage](https://letta.com) // [Documentation](https://docs.letta.com) // [ADE](https://docs.letta.com/agent-development-environment) // [Letta Cloud](https://forms.letta.com/early-access)
|
||||
|
||||
</h3>
|
||||
|
||||
**👾 Letta** is an open source framework for building stateful LLM applications. You can use Letta to build **stateful agents** with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic.
|
||||
**👾 Letta** is an open source framework for building **stateful agents** with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic.
|
||||
|
||||
[](https://discord.gg/letta)
|
||||
[](https://twitter.com/Letta_AI)
|
||||
@ -157,7 +144,7 @@ No, the data in your Letta server database stays on your machine. The Letta ADE
|
||||
|
||||
> _"Do I have to use your ADE? Can I build my own?"_
|
||||
|
||||
The ADE is built on top of the (fully open source) Letta server and Letta Agents API. You can build your own application like the ADE on top of the REST API (view the documention [here](https://docs.letta.com/api-reference)).
|
||||
The ADE is built on top of the (fully open source) Letta server and Letta Agents API. You can build your own application like the ADE on top of the REST API (view the documentation [here](https://docs.letta.com/api-reference)).
|
||||
|
||||
> _"Can I interact with Letta agents via the CLI?"_
|
||||
|
||||
|
||||
@ -28,7 +28,6 @@ services:
|
||||
- "8083:8083"
|
||||
- "8283:8283"
|
||||
environment:
|
||||
- SERPAPI_API_KEY=${SERPAPI_API_KEY}
|
||||
- LETTA_PG_DB=${LETTA_PG_DB:-letta}
|
||||
- LETTA_PG_USER=${LETTA_PG_USER:-letta}
|
||||
- LETTA_PG_PASSWORD=${LETTA_PG_PASSWORD:-letta}
|
||||
|
||||
@ -8,6 +8,7 @@ If you're using Letta Cloud, replace 'baseURL' with 'token'
|
||||
See: https://docs.letta.com/api-reference/overview
|
||||
|
||||
Execute this script using `poetry run python3 example.py`
|
||||
This will install `letta_client` and other dependencies.
|
||||
"""
|
||||
client = Letta(
|
||||
base_url="http://localhost:8283",
|
||||
|
||||
@ -2,22 +2,33 @@ from pprint import pprint
|
||||
|
||||
from letta_client import Letta
|
||||
|
||||
# Connect to Letta server
|
||||
client = Letta(base_url="http://localhost:8283")
|
||||
|
||||
# Use the "everything" mcp server:
|
||||
# https://github.com/modelcontextprotocol/servers/tree/main/src/everything
|
||||
mcp_server_name = "everything"
|
||||
mcp_tool_name = "echo"
|
||||
|
||||
# List all McpTool belonging to the "everything" mcp server.
|
||||
mcp_tools = client.tools.list_mcp_tools_by_server(
|
||||
mcp_server_name=mcp_server_name,
|
||||
)
|
||||
|
||||
# We can see that "echo" is one of the tools, but it's not
|
||||
# a letta tool that can be added to a client (it has no tool id).
|
||||
for tool in mcp_tools:
|
||||
pprint(tool)
|
||||
|
||||
# Create a Tool (with a tool id) using the server and tool names.
|
||||
mcp_tool = client.tools.add_mcp_tool(
|
||||
mcp_server_name=mcp_server_name,
|
||||
mcp_tool_name=mcp_tool_name
|
||||
)
|
||||
|
||||
# Create an agent with the tool, using tool.id -- note that
|
||||
# this is the ONLY tool in the agent, you typically want to
|
||||
# also include the default tools.
|
||||
agent = client.agents.create(
|
||||
memory_blocks=[
|
||||
{
|
||||
@ -31,6 +42,7 @@ agent = client.agents.create(
|
||||
)
|
||||
print(f"Created agent id {agent.id}")
|
||||
|
||||
# Ask the agent to call the tool.
|
||||
response = client.agents.messages.create(
|
||||
agent_id=agent.id,
|
||||
messages=[
|
||||
|
||||
@ -253,15 +253,18 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": null,
|
||||
"id": "7808912f-831b-4cdc-8606-40052eb809b4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Optional, List\n",
|
||||
"from typing import Optional, List, TYPE_CHECKING\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"def task_queue_push(self: \"Agent\", task_description: str):\n",
|
||||
"if TYPE_CHECKING:\n",
|
||||
" from letta import AgentState\n",
|
||||
"\n",
|
||||
"def task_queue_push(agent_state: \"AgentState\", task_description: str):\n",
|
||||
" \"\"\"\n",
|
||||
" Push to a task queue stored in core memory. \n",
|
||||
"\n",
|
||||
@ -273,12 +276,12 @@
|
||||
" does not produce a response.\n",
|
||||
" \"\"\"\n",
|
||||
" import json\n",
|
||||
" tasks = json.loads(self.memory.get_block(\"tasks\").value)\n",
|
||||
" tasks = json.loads(agent_state.memory.get_block(\"tasks\").value)\n",
|
||||
" tasks.append(task_description)\n",
|
||||
" self.memory.update_block_value(\"tasks\", json.dumps(tasks))\n",
|
||||
" agent_state.memory.update_block_value(\"tasks\", json.dumps(tasks))\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"def task_queue_pop(self: \"Agent\"):\n",
|
||||
"def task_queue_pop(agent_state: \"AgentState\"):\n",
|
||||
" \"\"\"\n",
|
||||
" Get the next task from the task queue \n",
|
||||
"\n",
|
||||
@ -288,12 +291,12 @@
|
||||
" None (the task queue is empty)\n",
|
||||
" \"\"\"\n",
|
||||
" import json\n",
|
||||
" tasks = json.loads(self.memory.get_block(\"tasks\").value)\n",
|
||||
" tasks = json.loads(agent_state.memory.get_block(\"tasks\").value)\n",
|
||||
" if len(tasks) == 0: \n",
|
||||
" return None\n",
|
||||
" task = tasks[0]\n",
|
||||
" print(\"CURRENT TASKS: \", tasks)\n",
|
||||
" self.memory.update_block_value(\"tasks\", json.dumps(tasks[1:]))\n",
|
||||
" agent_state.memory.update_block_value(\"tasks\", json.dumps(tasks[1:]))\n",
|
||||
" return task\n",
|
||||
"\n",
|
||||
"push_task_tool = client.tools.upsert_from_function(func=task_queue_push)\n",
|
||||
@ -310,7 +313,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": null,
|
||||
"id": "135fcf3e-59c4-4da3-b86b-dbffb21aa343",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -336,10 +339,12 @@
|
||||
" ),\n",
|
||||
" CreateBlock(\n",
|
||||
" label=\"tasks\",\n",
|
||||
" value=\"\",\n",
|
||||
" value=\"[]\",\n",
|
||||
" ),\n",
|
||||
" ],\n",
|
||||
" tool_ids=[push_task_tool.id, pop_task_tool.id],\n",
|
||||
" model=\"letta/letta-free\",\n",
|
||||
" embedding=\"letta/letta-free\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
__version__ = "0.7.14"
|
||||
__version__ = "0.7.16"
|
||||
|
||||
# import clients
|
||||
from letta.client.client import LocalClient, RESTClient, create_client
|
||||
|
||||
# # imports for easier access
|
||||
# imports for easier access
|
||||
from letta.schemas.agent import AgentState
|
||||
from letta.schemas.block import Block
|
||||
from letta.schemas.embedding_config import EmbeddingConfig
|
||||
|
||||
@ -483,7 +483,7 @@ class Agent(BaseAgent):
|
||||
response_message.function_call if response_message.function_call is not None else response_message.tool_calls[0].function
|
||||
)
|
||||
function_name = function_call.name
|
||||
self.logger.info(f"Request to call function {function_name} with tool_call_id: {tool_call_id}")
|
||||
self.logger.debug(f"Request to call function {function_name} with tool_call_id: {tool_call_id}")
|
||||
|
||||
# Failure case 1: function name is wrong (not in agent_state.tools)
|
||||
target_letta_tool = None
|
||||
|
||||
@ -235,7 +235,9 @@ def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None
|
||||
|
||||
if endpoint_type == "openai":
|
||||
return OpenAIEmbeddings(
|
||||
api_key=model_settings.openai_api_key, model=config.embedding_model, base_url=model_settings.openai_api_base
|
||||
api_key=model_settings.openai_api_key,
|
||||
model=config.embedding_model,
|
||||
base_url=model_settings.openai_api_base,
|
||||
)
|
||||
|
||||
elif endpoint_type == "azure":
|
||||
|
||||
@ -34,6 +34,19 @@ def resolve_type(annotation: str):
|
||||
return BUILTIN_TYPES[annotation]
|
||||
|
||||
try:
|
||||
if annotation.startswith("list["):
|
||||
inner_type = annotation[len("list[") : -1]
|
||||
resolve_type(inner_type)
|
||||
return list
|
||||
elif annotation.startswith("dict["):
|
||||
inner_types = annotation[len("dict[") : -1]
|
||||
key_type, value_type = inner_types.split(",")
|
||||
return dict
|
||||
elif annotation.startswith("tuple["):
|
||||
inner_types = annotation[len("tuple[") : -1]
|
||||
[resolve_type(t.strip()) for t in inner_types.split(",")]
|
||||
return tuple
|
||||
|
||||
parsed = ast.literal_eval(annotation)
|
||||
if isinstance(parsed, type):
|
||||
return parsed
|
||||
|
||||
@ -46,7 +46,7 @@ def conversation_search(self: "Agent", query: str, page: Optional[int] = 0) -> O
|
||||
count = RETRIEVAL_QUERY_DEFAULT_PAGE_SIZE
|
||||
# TODO: add paging by page number. currently cursor only works with strings.
|
||||
# original: start=page * count
|
||||
messages = self.message_manager.list_user_messages_for_agent(
|
||||
messages = self.message_manager.list_messages_for_agent(
|
||||
agent_id=self.agent_state.id,
|
||||
actor=self.user,
|
||||
query_text=query,
|
||||
|
||||
@ -3,14 +3,19 @@ from typing import Any, Dict, List
|
||||
|
||||
from anthropic import AnthropicBedrock
|
||||
|
||||
from letta.log import get_logger
|
||||
from letta.settings import model_settings
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
||||
|
||||
def has_valid_aws_credentials() -> bool:
|
||||
"""
|
||||
Check if AWS credentials are properly configured.
|
||||
"""
|
||||
valid_aws_credentials = os.getenv("AWS_ACCESS_KEY") and os.getenv("AWS_SECRET_ACCESS_KEY") and os.getenv("AWS_REGION")
|
||||
valid_aws_credentials = (
|
||||
os.getenv("AWS_ACCESS_KEY") is not None and os.getenv("AWS_SECRET_ACCESS_KEY") is not None and os.getenv("AWS_REGION") is not None
|
||||
)
|
||||
return valid_aws_credentials
|
||||
|
||||
|
||||
@ -20,6 +25,7 @@ def get_bedrock_client():
|
||||
"""
|
||||
import boto3
|
||||
|
||||
logger.debug(f"Getting Bedrock client for {model_settings.aws_region}")
|
||||
sts_client = boto3.client(
|
||||
"sts",
|
||||
aws_access_key_id=model_settings.aws_access_key,
|
||||
@ -51,12 +57,13 @@ def bedrock_get_model_list(region_name: str) -> List[dict]:
|
||||
"""
|
||||
import boto3
|
||||
|
||||
logger.debug(f"Getting model list for {region_name}")
|
||||
try:
|
||||
bedrock = boto3.client("bedrock", region_name=region_name)
|
||||
response = bedrock.list_inference_profiles()
|
||||
return response["inferenceProfileSummaries"]
|
||||
except Exception as e:
|
||||
print(f"Error getting model list: {str(e)}")
|
||||
logger.exception(f"Error getting model list: {str(e)}", e)
|
||||
raise e
|
||||
|
||||
|
||||
@ -67,6 +74,7 @@ def bedrock_get_model_details(region_name: str, model_id: str) -> Dict[str, Any]
|
||||
import boto3
|
||||
from botocore.exceptions import ClientError
|
||||
|
||||
logger.debug(f"Getting model details for {model_id}")
|
||||
try:
|
||||
bedrock = boto3.client("bedrock", region_name=region_name)
|
||||
response = bedrock.get_foundation_model(modelIdentifier=model_id)
|
||||
|
||||
@ -55,7 +55,6 @@ def openai_check_valid_api_key(base_url: str, api_key: Union[str, None]) -> None
|
||||
else:
|
||||
raise ValueError("No API key provided")
|
||||
|
||||
|
||||
def openai_get_model_list(url: str, api_key: Optional[str] = None, fix_url: bool = False, extra_params: Optional[dict] = None) -> dict:
|
||||
"""https://platform.openai.com/docs/api-reference/models/list"""
|
||||
from letta.utils import printd
|
||||
|
||||
@ -75,7 +75,8 @@ class LLMConfig(BaseModel):
|
||||
description="The reasoning effort to use when generating text reasoning models",
|
||||
)
|
||||
max_reasoning_tokens: int = Field(
|
||||
0, description="Configurable thinking budget for extended thinking, only used if enable_reasoner is True. Minimum value is 1024."
|
||||
0,
|
||||
description="Configurable thinking budget for extended thinking. Used for enable_reasoner and also for Google Vertex models like Gemini 2.5 Flash. Minimum value is 1024 when used with enable_reasoner.",
|
||||
)
|
||||
|
||||
# FIXME hack to silence pydantic protected namespace warning
|
||||
|
||||
@ -30,9 +30,7 @@ logger = get_logger(__name__)
|
||||
responses={
|
||||
200: {
|
||||
"description": "Successful response",
|
||||
"content": {
|
||||
"text/event-stream": {"description": "Server-Sent Events stream"},
|
||||
},
|
||||
"content": {"text/event-stream": {}},
|
||||
}
|
||||
},
|
||||
)
|
||||
|
||||
@ -669,9 +669,7 @@ async def send_message(
|
||||
responses={
|
||||
200: {
|
||||
"description": "Successful response",
|
||||
"content": {
|
||||
"text/event-stream": {"description": "Server-Sent Events stream"},
|
||||
},
|
||||
"content": {"text/event-stream": {}},
|
||||
}
|
||||
},
|
||||
)
|
||||
@ -696,7 +694,7 @@ async def send_message_streaming(
|
||||
feature_enabled = settings.use_experimental or experimental_header.lower() == "true"
|
||||
model_compatible = agent.llm_config.model_endpoint_type in ["anthropic", "openai"]
|
||||
|
||||
if agent_eligible and feature_enabled and model_compatible:
|
||||
if agent_eligible and feature_enabled and model_compatible and request.stream_tokens:
|
||||
experimental_agent = LettaAgent(
|
||||
agent_id=agent_id,
|
||||
message_manager=server.message_manager,
|
||||
|
||||
@ -78,6 +78,17 @@ def list_sources(
|
||||
return server.list_all_sources(actor=actor)
|
||||
|
||||
|
||||
@router.get("/count", response_model=int, operation_id="count_sources")
|
||||
def count_sources(
|
||||
server: "SyncServer" = Depends(get_letta_server),
|
||||
actor_id: Optional[str] = Header(None, alias="user_id"), # Extract user_id from header, default to None if not present
|
||||
):
|
||||
"""
|
||||
Count all data sources created by a user.
|
||||
"""
|
||||
return server.source_manager.size(actor=server.user_manager.get_user_or_default(user_id=actor_id))
|
||||
|
||||
|
||||
@router.post("/", response_model=Source, operation_id="create_source")
|
||||
def create_source(
|
||||
source_create: SourceCreate,
|
||||
|
||||
@ -98,6 +98,21 @@ async def list_tools(
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.get("/count", response_model=int, operation_id="count_tools")
|
||||
def count_tools(
|
||||
server: SyncServer = Depends(get_letta_server),
|
||||
actor_id: Optional[str] = Header(None, alias="user_id"),
|
||||
):
|
||||
"""
|
||||
Get a count of all tools available to agents belonging to the org of the user
|
||||
"""
|
||||
try:
|
||||
return server.tool_manager.size(actor=server.user_manager.get_user_or_default(user_id=actor_id))
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@router.post("/", response_model=Tool, operation_id="create_tool")
|
||||
def create_tool(
|
||||
request: ToolCreate = Body(...),
|
||||
|
||||
@ -26,9 +26,7 @@ logger = get_logger(__name__)
|
||||
responses={
|
||||
200: {
|
||||
"description": "Successful response",
|
||||
"content": {
|
||||
"text/event-stream": {"description": "Server-Sent Events stream"},
|
||||
},
|
||||
"content": {"text/event-stream": {}},
|
||||
}
|
||||
},
|
||||
)
|
||||
|
||||
@ -1191,8 +1191,13 @@ class AgentManager:
|
||||
|
||||
@enforce_types
|
||||
async def get_in_context_messages_async(self, agent_id: str, actor: PydanticUser) -> List[PydanticMessage]:
|
||||
<<<<<<< HEAD
|
||||
agent = await self.get_agent_by_id_async(agent_id=agent_id, actor=actor)
|
||||
return await self.message_manager.get_messages_by_ids_async(message_ids=agent.message_ids, actor=actor)
|
||||
=======
|
||||
message_ids = self.get_agent_by_id(agent_id=agent_id, actor=actor).message_ids
|
||||
return await self.message_manager.get_messages_by_ids_async(message_ids=message_ids, actor=actor)
|
||||
>>>>>>> main
|
||||
|
||||
@enforce_types
|
||||
def get_system_message(self, agent_id: str, actor: PydanticUser) -> PydanticMessage:
|
||||
|
||||
@ -373,17 +373,24 @@ class MessageManager:
|
||||
if group_id:
|
||||
query = query.filter(MessageModel.group_id == group_id)
|
||||
|
||||
# If query_text is provided, filter messages using subquery + json_array_elements.
|
||||
# If query_text is provided, filter messages by matching any "text" type content block
|
||||
# whose text includes the query string (case-insensitive).
|
||||
if query_text:
|
||||
content_element = func.json_array_elements(MessageModel.content).alias("content_element")
|
||||
query = query.filter(
|
||||
exists(
|
||||
select(1)
|
||||
.select_from(content_element)
|
||||
.where(text("content_element->>'type' = 'text' AND content_element->>'text' ILIKE :query_text"))
|
||||
.params(query_text=f"%{query_text}%")
|
||||
dialect_name = session.bind.dialect.name
|
||||
|
||||
if dialect_name == "postgresql": # using subquery + json_array_elements.
|
||||
content_element = func.json_array_elements(MessageModel.content).alias("content_element")
|
||||
subquery_sql = text("content_element->>'type' = 'text' AND content_element->>'text' ILIKE :query_text")
|
||||
subquery = select(1).select_from(content_element).where(subquery_sql)
|
||||
|
||||
elif dialect_name == "sqlite": # using `json_each` and JSON path expressions
|
||||
json_item = func.json_each(MessageModel.content).alias("json_item")
|
||||
subquery_sql = text(
|
||||
"json_extract(value, '$.type') = 'text' AND lower(json_extract(value, '$.text')) LIKE lower(:query_text)"
|
||||
)
|
||||
)
|
||||
subquery = select(1).select_from(json_item).where(subquery_sql)
|
||||
|
||||
query = query.filter(exists(subquery.params(query_text=f"%{query_text}%")))
|
||||
|
||||
# If role(s) are provided, filter messages by those roles.
|
||||
if roles:
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "letta"
|
||||
version = "0.7.14"
|
||||
version = "0.7.16"
|
||||
packages = [
|
||||
{include = "letta"},
|
||||
]
|
||||
@ -106,6 +106,7 @@ google = ["google-genai"]
|
||||
desktop = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pyright", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust"]
|
||||
all = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "pyright", "pytest-order", "autoflake", "isort", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust"]
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
black = "^24.4.2"
|
||||
ipykernel = "^6.29.5"
|
||||
|
||||
32
scripts/docker-compose.yml
Normal file
32
scripts/docker-compose.yml
Normal file
@ -0,0 +1,32 @@
|
||||
version: '3.7'
|
||||
services:
|
||||
redis:
|
||||
image: redis:alpine
|
||||
container_name: redis
|
||||
healthcheck:
|
||||
test: ['CMD-SHELL', 'redis-cli ping | grep PONG']
|
||||
interval: 1s
|
||||
timeout: 3s
|
||||
retries: 5
|
||||
ports:
|
||||
- '6379:6379'
|
||||
volumes:
|
||||
- ./data/redis:/data
|
||||
command: redis-server --appendonly yes
|
||||
postgres:
|
||||
image: ankane/pgvector
|
||||
container_name: postgres
|
||||
healthcheck:
|
||||
test: ['CMD-SHELL', 'pg_isready -U postgres']
|
||||
interval: 1s
|
||||
timeout: 3s
|
||||
retries: 5
|
||||
ports:
|
||||
- '5432:5432'
|
||||
environment:
|
||||
POSTGRES_USER: postgres
|
||||
POSTGRES_PASSWORD: postgres
|
||||
POSTGRES_DB: letta
|
||||
volumes:
|
||||
- ./data/postgres:/var/lib/postgresql/data
|
||||
- ./scripts/postgres-db-init/init.sql:/docker-entrypoint-initdb.d/init.sql
|
||||
@ -155,6 +155,7 @@ async def test_sleeptime_group_chat(server, actor):
|
||||
|
||||
# 6. Verify run status after sleep
|
||||
time.sleep(2)
|
||||
|
||||
for run_id in run_ids:
|
||||
job = server.job_manager.get_job_by_id(job_id=run_id, actor=actor)
|
||||
assert job.status == JobStatus.running or job.status == JobStatus.completed
|
||||
|
||||
@ -564,7 +564,6 @@ def _modify(group_id, server, actor, max_val, min_val):
|
||||
actor=actor,
|
||||
)
|
||||
|
||||
|
||||
def test_valid_buffer_lengths_above_four(group_id, server, actor):
|
||||
# both > 4 and max > min
|
||||
updated = _modify(group_id, server, actor, max_val=10, min_val=5)
|
||||
|
||||
@ -127,13 +127,14 @@ def test_archival(agent_obj):
|
||||
pass
|
||||
|
||||
|
||||
def test_recall(client, agent_obj):
|
||||
def test_recall_self(client, agent_obj):
|
||||
# keyword
|
||||
keyword = "banana"
|
||||
keyword_backwards = "".join(reversed(keyword))
|
||||
|
||||
# Send messages to agent
|
||||
client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="hello")
|
||||
client.send_message(agent_id=agent_obj.agent_state.id, role="user", message=keyword)
|
||||
client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="what word is '{}' backwards?".format(keyword_backwards))
|
||||
client.send_message(agent_id=agent_obj.agent_state.id, role="user", message="tell me a fun fact")
|
||||
|
||||
# Conversation search
|
||||
|
||||
@ -1612,6 +1612,46 @@ def test_modify_letta_message(server: SyncServer, sarah_agent, default_user):
|
||||
# TODO: tool calls/responses
|
||||
|
||||
|
||||
def test_list_messages_with_query_text_filter(server: SyncServer, sarah_agent, default_user):
|
||||
"""
|
||||
Ensure that list_messages_for_agent correctly filters messages by query_text.
|
||||
"""
|
||||
test_contents = [
|
||||
"This is a message about unicorns and rainbows.",
|
||||
"Another message discussing dragons in the sky.",
|
||||
"Plain message with no magical beasts.",
|
||||
"Mentioning unicorns again for good measure.",
|
||||
"Something unrelated entirely.",
|
||||
]
|
||||
|
||||
created_messages = []
|
||||
for content in test_contents:
|
||||
message = PydanticMessage(
|
||||
agent_id=sarah_agent.id,
|
||||
role=MessageRole.user,
|
||||
content=[{"type": "text", "text": content}],
|
||||
)
|
||||
created = server.message_manager.create_message(pydantic_msg=message, actor=default_user)
|
||||
created_messages.append(created)
|
||||
|
||||
# Query messages that include "unicorns"
|
||||
unicorn_messages = server.message_manager.list_messages_for_agent(agent_id=sarah_agent.id, actor=default_user, query_text="unicorns")
|
||||
assert len(unicorn_messages) == 2
|
||||
for msg in unicorn_messages:
|
||||
assert any(chunk.type == "text" and "unicorns" in chunk.text.lower() for chunk in msg.content or [])
|
||||
|
||||
# Query messages that include "dragons"
|
||||
dragon_messages = server.message_manager.list_messages_for_agent(agent_id=sarah_agent.id, actor=default_user, query_text="dragons")
|
||||
assert len(dragon_messages) == 1
|
||||
assert any(chunk.type == "text" and "dragons" in chunk.text.lower() for chunk in dragon_messages[0].content or [])
|
||||
|
||||
# Query with a word that shouldn't match any message
|
||||
no_match_messages = server.message_manager.list_messages_for_agent(
|
||||
agent_id=sarah_agent.id, actor=default_user, query_text="nonexistentcreature"
|
||||
)
|
||||
assert len(no_match_messages) == 0
|
||||
|
||||
|
||||
# ======================================================================================================================
|
||||
# AgentManager Tests - Blocks Relationship
|
||||
# ======================================================================================================================
|
||||
|
||||
@ -115,7 +115,7 @@ def test_shared_blocks(client: LettaSDKClient):
|
||||
)
|
||||
assert (
|
||||
"charles" in client.agents.blocks.retrieve(agent_id=agent_state2.id, block_label="human").value.lower()
|
||||
), f"Shared block update failed {client.agents.blocks.retrieve(agent_id=agent_state2.id, block_label="human").value}"
|
||||
), f"Shared block update failed {client.agents.blocks.retrieve(agent_id=agent_state2.id, block_label='human').value}"
|
||||
|
||||
# cleanup
|
||||
client.agents.delete(agent_state1.id)
|
||||
|
||||
@ -8,10 +8,9 @@ def adjust_menu_prices(percentage: float) -> str:
|
||||
str: A formatted string summarizing the price adjustments.
|
||||
"""
|
||||
import cowsay
|
||||
from tqdm import tqdm
|
||||
|
||||
from core.menu import Menu, MenuItem # Import a class from the codebase
|
||||
from core.utils import format_currency # Use a utility function to test imports
|
||||
from tqdm import tqdm
|
||||
|
||||
if not isinstance(percentage, (int, float)):
|
||||
raise TypeError("percentage must be a number")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user