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feat: google vertex integration (#980)
This commit is contained in:
Sarah Wooders
GitHub
parent
db216b7e91
commit
8955dd939b
@ -188,6 +188,19 @@ class GoogleEmbeddings:
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return response_json["embedding"]["values"]
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class GoogleVertexEmbeddings:
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def __init__(self, model: str, project_id: str, region: str):
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from google import genai
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self.client = genai.Client(vertexai=True, project=project_id, location=region, http_options={"api_version": "v1"})
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self.model = model
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def get_text_embedding(self, text: str):
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response = self.client.generate_embeddings(content=text, model=self.model)
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return response.embeddings[0].embedding
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def query_embedding(embedding_model, query_text: str):
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"""Generate padded embedding for querying database"""
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query_vec = embedding_model.get_text_embedding(query_text)
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@ -267,5 +280,13 @@ def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None
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)
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return model
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elif endpoint_type == "google_vertex":
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model = GoogleVertexEmbeddings(
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model=config.embedding_model,
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api_key=model_settings.gemini_api_key,
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base_url=model_settings.gemini_base_url,
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)
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return model
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else:
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raise ValueError(f"Unknown endpoint type {endpoint_type}")
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330
letta/llm_api/google_vertex.py
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330
letta/llm_api/google_vertex.py
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@ -0,0 +1,330 @@
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import uuid
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from typing import List, Optional, Tuple
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import requests
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from letta.constants import NON_USER_MSG_PREFIX
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from letta.local_llm.json_parser import clean_json_string_extra_backslash
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from letta.local_llm.utils import count_tokens
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from letta.schemas.openai.chat_completion_request import Tool
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from letta.schemas.openai.chat_completion_response import ChatCompletionResponse, Choice, FunctionCall, Message, ToolCall, UsageStatistics
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from letta.utils import get_tool_call_id, get_utc_time, json_dumps
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def add_dummy_model_messages(messages: List[dict]) -> List[dict]:
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"""Google AI API requires all function call returns are immediately followed by a 'model' role message.
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In Letta, the 'model' will often call a function (e.g. send_message) that itself yields to the user,
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so there is no natural follow-up 'model' role message.
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To satisfy the Google AI API restrictions, we can add a dummy 'yield' message
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with role == 'model' that is placed in-betweeen and function output
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(role == 'tool') and user message (role == 'user').
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"""
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dummy_yield_message = {"role": "model", "parts": [{"text": f"{NON_USER_MSG_PREFIX}Function call returned, waiting for user response."}]}
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messages_with_padding = []
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for i, message in enumerate(messages):
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messages_with_padding.append(message)
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# Check if the current message role is 'tool' and the next message role is 'user'
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if message["role"] in ["tool", "function"] and (i + 1 < len(messages) and messages[i + 1]["role"] == "user"):
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messages_with_padding.append(dummy_yield_message)
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return messages_with_padding
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# TODO use pydantic model as input
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def to_google_ai(openai_message_dict: dict) -> dict:
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# TODO supports "parts" as part of multimodal support
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assert not isinstance(openai_message_dict["content"], list), "Multi-part content is message not yet supported"
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if openai_message_dict["role"] == "user":
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google_ai_message_dict = {
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"role": "user",
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"parts": [{"text": openai_message_dict["content"]}],
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}
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elif openai_message_dict["role"] == "assistant":
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google_ai_message_dict = {
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"role": "model", # NOTE: diff
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"parts": [{"text": openai_message_dict["content"]}],
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}
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elif openai_message_dict["role"] == "tool":
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google_ai_message_dict = {
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"role": "function", # NOTE: diff
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"parts": [{"text": openai_message_dict["content"]}],
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}
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else:
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raise ValueError(f"Unsupported conversion (OpenAI -> Google AI) from role {openai_message_dict['role']}")
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# TODO convert return type to pydantic
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def convert_tools_to_google_ai_format(tools: List[Tool], inner_thoughts_in_kwargs: Optional[bool] = True) -> List[dict]:
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"""
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OpenAI style:
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"tools": [{
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"type": "function",
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"function": {
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"name": "find_movies",
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"description": "find ....",
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"parameters": {
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"type": "object",
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"properties": {
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PARAM: {
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"type": PARAM_TYPE, # eg "string"
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"description": PARAM_DESCRIPTION,
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},
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...
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},
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"required": List[str],
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}
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}
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}
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]
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Google AI style:
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"tools": [{
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"functionDeclarations": [{
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"name": "find_movies",
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"description": "find movie titles currently playing in theaters based on any description, genre, title words, etc.",
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"parameters": {
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"type": "OBJECT",
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"properties": {
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"location": {
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"type": "STRING",
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"description": "The city and state, e.g. San Francisco, CA or a zip code e.g. 95616"
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},
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"description": {
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"type": "STRING",
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"description": "Any kind of description including category or genre, title words, attributes, etc."
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}
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},
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"required": ["description"]
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}
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}, {
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"name": "find_theaters",
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...
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"""
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function_list = [
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dict(
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name=t.function.name,
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description=t.function.description,
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parameters=t.function.parameters, # TODO need to unpack
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)
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for t in tools
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]
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# Correct casing + add inner thoughts if needed
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for func in function_list:
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func["parameters"]["type"] = "OBJECT"
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for param_name, param_fields in func["parameters"]["properties"].items():
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param_fields["type"] = param_fields["type"].upper()
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# Add inner thoughts
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if inner_thoughts_in_kwargs:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG, INNER_THOUGHTS_KWARG_DESCRIPTION
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func["parameters"]["properties"][INNER_THOUGHTS_KWARG] = {
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"type": "STRING",
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"description": INNER_THOUGHTS_KWARG_DESCRIPTION,
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}
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func["parameters"]["required"].append(INNER_THOUGHTS_KWARG)
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return [{"functionDeclarations": function_list}]
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def convert_google_ai_response_to_chatcompletion(
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response,
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model: str, # Required since not returned
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input_messages: Optional[List[dict]] = None, # Required if the API doesn't return UsageMetadata
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pull_inner_thoughts_from_args: Optional[bool] = True,
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) -> ChatCompletionResponse:
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"""Google AI API response format is not the same as ChatCompletion, requires unpacking
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Example:
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{
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"candidates": [
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{
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"content": {
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"parts": [
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{
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"text": " OK. Barbie is showing in two theaters in Mountain View, CA: AMC Mountain View 16 and Regal Edwards 14."
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}
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]
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}
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}
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],
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"usageMetadata": {
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"promptTokenCount": 9,
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"candidatesTokenCount": 27,
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"totalTokenCount": 36
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}
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}
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"""
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try:
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choices = []
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index = 0
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for candidate in response.candidates:
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content = candidate.content
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role = content.role
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assert role == "model", f"Unknown role in response: {role}"
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parts = content.parts
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# TODO support parts / multimodal
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# TODO support parallel tool calling natively
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# TODO Alternative here is to throw away everything else except for the first part
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for response_message in parts:
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# Convert the actual message style to OpenAI style
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if response_message.function_call:
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function_call = response_message.function_call
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function_name = function_call.name
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function_args = function_call.args
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assert isinstance(function_args, dict), function_args
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# NOTE: this also involves stripping the inner monologue out of the function
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if pull_inner_thoughts_from_args:
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from letta.local_llm.constants import INNER_THOUGHTS_KWARG
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assert INNER_THOUGHTS_KWARG in function_args, f"Couldn't find inner thoughts in function args:\n{function_call}"
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inner_thoughts = function_args.pop(INNER_THOUGHTS_KWARG)
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assert inner_thoughts is not None, f"Expected non-null inner thoughts function arg:\n{function_call}"
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else:
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inner_thoughts = None
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# Google AI API doesn't generate tool call IDs
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openai_response_message = Message(
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role="assistant", # NOTE: "model" -> "assistant"
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content=inner_thoughts,
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tool_calls=[
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ToolCall(
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id=get_tool_call_id(),
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type="function",
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function=FunctionCall(
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name=function_name,
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arguments=clean_json_string_extra_backslash(json_dumps(function_args)),
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),
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)
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],
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)
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else:
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# Inner thoughts are the content by default
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inner_thoughts = response_message.text
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# Google AI API doesn't generate tool call IDs
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openai_response_message = Message(
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role="assistant", # NOTE: "model" -> "assistant"
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content=inner_thoughts,
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)
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# Google AI API uses different finish reason strings than OpenAI
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# OpenAI: 'stop', 'length', 'function_call', 'content_filter', null
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# see: https://platform.openai.com/docs/guides/text-generation/chat-completions-api
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# Google AI API: FINISH_REASON_UNSPECIFIED, STOP, MAX_TOKENS, SAFETY, RECITATION, OTHER
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# see: https://ai.google.dev/api/python/google/ai/generativelanguage/Candidate/FinishReason
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finish_reason = candidate.finish_reason.value
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if finish_reason == "STOP":
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openai_finish_reason = (
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"function_call"
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if openai_response_message.tool_calls is not None and len(openai_response_message.tool_calls) > 0
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else "stop"
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)
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elif finish_reason == "MAX_TOKENS":
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openai_finish_reason = "length"
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elif finish_reason == "SAFETY":
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openai_finish_reason = "content_filter"
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elif finish_reason == "RECITATION":
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openai_finish_reason = "content_filter"
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else:
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raise ValueError(f"Unrecognized finish reason in Google AI response: {finish_reason}")
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choices.append(
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Choice(
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finish_reason=openai_finish_reason,
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index=index,
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message=openai_response_message,
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)
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)
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index += 1
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# if len(choices) > 1:
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# raise UserWarning(f"Unexpected number of candidates in response (expected 1, got {len(choices)})")
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# NOTE: some of the Google AI APIs show UsageMetadata in the response, but it seems to not exist?
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# "usageMetadata": {
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# "promptTokenCount": 9,
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# "candidatesTokenCount": 27,
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# "totalTokenCount": 36
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# }
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if response.usage_metadata:
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usage = UsageStatistics(
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prompt_tokens=response.usage_metadata.prompt_token_count,
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completion_tokens=response.usage_metadata.candidates_token_count,
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total_tokens=response.usage_metadata.total_token_count,
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)
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else:
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# Count it ourselves
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assert input_messages is not None, f"Didn't get UsageMetadata from the API response, so input_messages is required"
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prompt_tokens = count_tokens(json_dumps(input_messages)) # NOTE: this is a very rough approximation
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completion_tokens = count_tokens(json_dumps(openai_response_message.model_dump())) # NOTE: this is also approximate
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total_tokens = prompt_tokens + completion_tokens
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usage = UsageStatistics(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=total_tokens,
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)
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response_id = str(uuid.uuid4())
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return ChatCompletionResponse(
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id=response_id,
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choices=choices,
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model=model, # NOTE: Google API doesn't pass back model in the response
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created=get_utc_time(),
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usage=usage,
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)
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except KeyError as e:
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raise e
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# TODO convert 'data' type to pydantic
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def google_vertex_chat_completions_request(
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model: str,
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project_id: str,
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region: str,
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contents: List[dict],
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config: dict,
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add_postfunc_model_messages: bool = True,
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# NOTE: Google AI API doesn't support mixing parts 'text' and 'function',
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# so there's no clean way to put inner thoughts in the same message as a function call
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inner_thoughts_in_kwargs: bool = True,
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) -> ChatCompletionResponse:
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"""https://ai.google.dev/docs/function_calling
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From https://ai.google.dev/api/rest#service-endpoint:
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"A service endpoint is a base URL that specifies the network address of an API service.
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One service might have multiple service endpoints.
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This service has the following service endpoint and all URIs below are relative to this service endpoint:
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https://xxx.googleapis.com
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"""
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from google import genai
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client = genai.Client(vertexai=True, project=project_id, location=region, http_options={"api_version": "v1"})
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# add dummy model messages to the end of the input
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if add_postfunc_model_messages:
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contents = add_dummy_model_messages(contents)
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# make request to client
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response = client.models.generate_content(model=model, contents=contents, config=config)
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print(response)
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# convert back response
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try:
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return convert_google_ai_response_to_chatcompletion(
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response=response,
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model=model,
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input_messages=contents,
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pull_inner_thoughts_from_args=inner_thoughts_in_kwargs,
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)
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except Exception as conversion_error:
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print(f"Error during response conversion: {conversion_error}")
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raise conversion_error
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@ -252,6 +252,32 @@ def create(
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inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs,
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)
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elif llm_config.model_endpoint_type == "google_vertex":
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from letta.llm_api.google_vertex import google_vertex_chat_completions_request
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if stream:
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raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}")
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if not use_tool_naming:
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raise NotImplementedError("Only tool calling supported on Google Vertex AI API requests")
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if functions is not None:
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tools = [{"type": "function", "function": f} for f in functions]
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tools = [Tool(**t) for t in tools]
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tools = convert_tools_to_google_ai_format(tools, inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs)
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else:
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tools = None
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config = {"tools": tools, "temperature": llm_config.temperature, "max_output_tokens": llm_config.max_tokens}
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return google_vertex_chat_completions_request(
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model=llm_config.model,
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project_id=model_settings.google_cloud_project,
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region=model_settings.google_cloud_location,
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contents=[m.to_google_ai_dict() for m in messages],
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config=config,
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inner_thoughts_in_kwargs=llm_config.put_inner_thoughts_in_kwargs,
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)
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elif llm_config.model_endpoint_type == "anthropic":
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if not use_tool_naming:
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raise NotImplementedError("Only tool calling supported on Anthropic API requests")
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@ -26,6 +26,7 @@ class EmbeddingConfig(BaseModel):
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"bedrock",
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"cohere",
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"google_ai",
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"google_vertex",
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"azure",
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"groq",
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"ollama",
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@ -25,6 +25,7 @@ class LLMConfig(BaseModel):
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"anthropic",
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"cohere",
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"google_ai",
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"google_vertex",
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"azure",
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"groq",
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"ollama",
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@ -327,7 +327,7 @@ class LMStudioOpenAIProvider(OpenAIProvider):
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embedding_endpoint_type="openai",
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embedding_endpoint=self.base_url,
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embedding_dim=context_window_size,
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embedding_chunk_size=300,
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embedding_chunk_size=300, # NOTE: max is 2048
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handle=self.get_handle(model_name),
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),
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)
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@ -737,6 +737,45 @@ class GoogleAIProvider(Provider):
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return google_ai_get_model_context_window(self.base_url, self.api_key, model_name)
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class GoogleVertexProvider(Provider):
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name: str = "google_vertex"
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google_cloud_project: str = Field(..., description="GCP project ID for the Google Vertex API.")
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google_cloud_location: str = Field(..., description="GCP region for the Google Vertex API.")
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def list_llm_models(self) -> List[LLMConfig]:
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from letta.llm_api.google_constants import GOOGLE_MODEL_TO_CONTEXT_LENGTH
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configs = []
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for model, context_length in GOOGLE_MODEL_TO_CONTEXT_LENGTH.items():
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configs.append(
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LLMConfig(
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model=model,
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model_endpoint_type="google_vertex",
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model_endpoint=f"https://{self.google_cloud_location}-aiplatform.googleapis.com/v1/projects/{self.google_cloud_project}/locations/{self.google_cloud_location}",
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context_window=context_length,
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handle=self.get_handle(model),
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)
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)
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return configs
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def list_embedding_models(self) -> List[EmbeddingConfig]:
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from letta.llm_api.google_constants import GOOGLE_EMBEDING_MODEL_TO_DIM
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||||
configs = []
|
||||
for model, dim in GOOGLE_EMBEDING_MODEL_TO_DIM.items():
|
||||
configs.append(
|
||||
EmbeddingConfig(
|
||||
embedding_model=model,
|
||||
embedding_endpoint_type="google_vertex",
|
||||
embedding_endpoint=f"https://{self.google_cloud_location}-aiplatform.googleapis.com/v1/projects/{self.google_cloud_project}/locations/{self.google_cloud_location}",
|
||||
embedding_dim=dim,
|
||||
embedding_chunk_size=300, # NOTE: max is 2048
|
||||
handle=self.get_handle(model, is_embedding=True),
|
||||
)
|
||||
)
|
||||
return configs
|
||||
|
||||
|
||||
class AzureProvider(Provider):
|
||||
name: str = "azure"
|
||||
latest_api_version: str = "2024-09-01-preview" # https://learn.microsoft.com/en-us/azure/ai-services/openai/api-version-deprecation
|
||||
@ -792,8 +831,8 @@ class AzureProvider(Provider):
|
||||
embedding_endpoint=model_endpoint,
|
||||
embedding_dim=768,
|
||||
embedding_chunk_size=300, # NOTE: max is 2048
|
||||
handle=self.get_handle(model_name, is_embedding=True),
|
||||
)
|
||||
handle=self.get_handle(model_name),
|
||||
),
|
||||
)
|
||||
return configs
|
||||
|
||||
|
||||
@ -47,6 +47,7 @@ from letta.schemas.providers import (
|
||||
AnthropicProvider,
|
||||
AzureProvider,
|
||||
GoogleAIProvider,
|
||||
GoogleVertexProvider,
|
||||
GroqProvider,
|
||||
LettaProvider,
|
||||
LMStudioOpenAIProvider,
|
||||
@ -352,6 +353,13 @@ class SyncServer(Server):
|
||||
api_key=model_settings.gemini_api_key,
|
||||
)
|
||||
)
|
||||
if model_settings.google_cloud_location and model_settings.google_cloud_project:
|
||||
self._enabled_providers.append(
|
||||
GoogleVertexProvider(
|
||||
google_cloud_project=model_settings.google_cloud_project,
|
||||
google_cloud_location=model_settings.google_cloud_location,
|
||||
)
|
||||
)
|
||||
if model_settings.azure_api_key and model_settings.azure_base_url:
|
||||
assert model_settings.azure_api_version, "AZURE_API_VERSION is required"
|
||||
self._enabled_providers.append(
|
||||
|
||||
@ -86,6 +86,11 @@ class ModelSettings(BaseSettings):
|
||||
# google ai
|
||||
gemini_api_key: Optional[str] = None
|
||||
gemini_base_url: str = "https://generativelanguage.googleapis.com/"
|
||||
|
||||
# google vertex
|
||||
google_cloud_project: Optional[str] = None
|
||||
google_cloud_location: Optional[str] = None
|
||||
|
||||
# together
|
||||
together_api_key: Optional[str] = None
|
||||
|
||||
|
||||
297
poetry.lock
generated
297
poetry.lock
generated
@ -539,6 +539,17 @@ files = [
|
||||
{file = "Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cachetools"
|
||||
version = "5.5.1"
|
||||
description = "Extensible memoizing collections and decorators"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "cachetools-5.5.1-py3-none-any.whl", hash = "sha256:b76651fdc3b24ead3c648bbdeeb940c1b04d365b38b4af66788f9ec4a81d42bb"},
|
||||
{file = "cachetools-5.5.1.tar.gz", hash = "sha256:70f238fbba50383ef62e55c6aff6d9673175fe59f7c6782c7a0b9e38f4a9df95"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.1.31"
|
||||
@ -1606,6 +1617,47 @@ benchmarks = ["httplib2", "httpx", "requests", "urllib3"]
|
||||
dev = ["dpkt", "pytest", "requests"]
|
||||
examples = ["oauth2"]
|
||||
|
||||
[[package]]
|
||||
name = "google-auth"
|
||||
version = "2.38.0"
|
||||
description = "Google Authentication Library"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "google_auth-2.38.0-py2.py3-none-any.whl", hash = "sha256:e7dae6694313f434a2727bf2906f27ad259bae090d7aa896590d86feec3d9d4a"},
|
||||
{file = "google_auth-2.38.0.tar.gz", hash = "sha256:8285113607d3b80a3f1543b75962447ba8a09fe85783432a784fdeef6ac094c4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cachetools = ">=2.0.0,<6.0"
|
||||
pyasn1-modules = ">=0.2.1"
|
||||
rsa = ">=3.1.4,<5"
|
||||
|
||||
[package.extras]
|
||||
aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
|
||||
enterprise-cert = ["cryptography", "pyopenssl"]
|
||||
pyjwt = ["cryptography (>=38.0.3)", "pyjwt (>=2.0)"]
|
||||
pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
|
||||
reauth = ["pyu2f (>=0.1.5)"]
|
||||
requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
|
||||
|
||||
[[package]]
|
||||
name = "google-genai"
|
||||
version = "1.2.0"
|
||||
description = "GenAI Python SDK"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "google_genai-1.2.0-py3-none-any.whl", hash = "sha256:609d61bee73f1a6ae5b47e9c7dd4b469d50318f050c5ceacf835b0f80f79d2d9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-auth = ">=2.14.1,<3.0.0dev"
|
||||
pydantic = ">=2.0.0,<3.0.0dev"
|
||||
requests = ">=2.28.1,<3.0.0dev"
|
||||
typing-extensions = ">=4.11.0,<5.0.0dev"
|
||||
websockets = ">=13.0,<15.0dev"
|
||||
|
||||
[[package]]
|
||||
name = "greenlet"
|
||||
version = "3.1.1"
|
||||
@ -2481,13 +2533,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.34"
|
||||
version = "0.3.35"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_core-0.3.34-py3-none-any.whl", hash = "sha256:a057ebeddd2158d3be14bde341b25640ddf958b6989bd6e47160396f5a8202ae"},
|
||||
{file = "langchain_core-0.3.34.tar.gz", hash = "sha256:26504cf1e8e6c310adad907b890d4e3c147581cfa7434114f6dc1134fe4bc6d3"},
|
||||
{file = "langchain_core-0.3.35-py3-none-any.whl", hash = "sha256:81a4097226e180fa6c64e2d2ab38dcacbbc23b64fc109fb15622910fe8951670"},
|
||||
{file = "langchain_core-0.3.35.tar.gz", hash = "sha256:328688228ece259da734417d477994a69cf8202dea9ed4271f2d792e3575c6fc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -2576,13 +2628,13 @@ pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4,<14.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "letta-client"
|
||||
version = "0.1.27"
|
||||
version = "0.1.29"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "letta_client-0.1.27-py3-none-any.whl", hash = "sha256:dbd8ac70993f8b2776bf2203e46e6d637a216a7b85974217534b3cf29479fabf"},
|
||||
{file = "letta_client-0.1.27.tar.gz", hash = "sha256:191b29810c02e4b4818542affca41da4a2f2de97d05aac04fdab32b5b52a5da5"},
|
||||
{file = "letta_client-0.1.29-py3-none-any.whl", hash = "sha256:72b1b5d69c9277e19fdb0ddeeb572fd0b8d10bd644ec6fd7ddf32c3704052ec6"},
|
||||
{file = "letta_client-0.1.29.tar.gz", hash = "sha256:3fa4c9522be20457887142d24f579bc246244d713bd0d92c253785f007446f67"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -2610,13 +2662,13 @@ pydantic = ">=1.10"
|
||||
|
||||
[[package]]
|
||||
name = "llama-cloud-services"
|
||||
version = "0.6.0"
|
||||
version = "0.6.1"
|
||||
description = "Tailored SDK clients for LlamaCloud services."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "llama_cloud_services-0.6.0-py3-none-any.whl", hash = "sha256:b9647e236ba4e13d0b04bf336ed4023e422a2a48d363258924056ef9eb8f688d"},
|
||||
{file = "llama_cloud_services-0.6.0.tar.gz", hash = "sha256:9c1ed2849f8ba7374df16bdfda69bed145eb4a425b546048f5e751c48efe293a"},
|
||||
{file = "llama_cloud_services-0.6.1-py3-none-any.whl", hash = "sha256:0427c98284bbfedbdf1686d29729d04b13e13f72017e184057892c8583c2b195"},
|
||||
{file = "llama_cloud_services-0.6.1.tar.gz", hash = "sha256:92c7ee4fcc80adaa60f26c0da805182fa56d771fff11e9abb873f9ddb11b5e37"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -2628,19 +2680,19 @@ python-dotenv = ">=1.0.1,<2.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "llama-index"
|
||||
version = "0.12.16"
|
||||
version = "0.12.17"
|
||||
description = "Interface between LLMs and your data"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "llama_index-0.12.16-py3-none-any.whl", hash = "sha256:c94d0cf6735219d97d91e2eca5bcfac89ec1583990917f934b075d5a45686cf6"},
|
||||
{file = "llama_index-0.12.16.tar.gz", hash = "sha256:4fd5f5b94eb3f8dd470bb8cc0e1b985d931e8f31473266ef69855488fd8ae3f2"},
|
||||
{file = "llama_index-0.12.17-py3-none-any.whl", hash = "sha256:d8938e5e6e5ff78b6865f7890a01d1a40818a5df798555ee6eb7f2c5ab65aeb0"},
|
||||
{file = "llama_index-0.12.17.tar.gz", hash = "sha256:761a2dad3eb74bd5242ecf8fd28337c0c8745fc8d39d2f9f9b18bf733ad679f4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
llama-index-agent-openai = ">=0.4.0,<0.5.0"
|
||||
llama-index-cli = ">=0.4.0,<0.5.0"
|
||||
llama-index-core = ">=0.12.16,<0.13.0"
|
||||
llama-index-core = ">=0.12.17,<0.13.0"
|
||||
llama-index-embeddings-openai = ">=0.3.0,<0.4.0"
|
||||
llama-index-indices-managed-llama-cloud = ">=0.4.0"
|
||||
llama-index-llms-openai = ">=0.3.0,<0.4.0"
|
||||
@ -2750,17 +2802,17 @@ llama-index-core = ">=0.12.0,<0.13.0"
|
||||
|
||||
[[package]]
|
||||
name = "llama-index-llms-openai"
|
||||
version = "0.3.18"
|
||||
version = "0.3.19"
|
||||
description = "llama-index llms openai integration"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "llama_index_llms_openai-0.3.18-py3-none-any.whl", hash = "sha256:e2e78ab94fafda8ac99fbfea1b19c5ba4e49d292557d2bdd9c7cc4b445f8745f"},
|
||||
{file = "llama_index_llms_openai-0.3.18.tar.gz", hash = "sha256:81807ba318bac28aca67873228c55242c5fe55f8beba35d23828af6e03b1b234"},
|
||||
{file = "llama_index_llms_openai-0.3.19-py3-none-any.whl", hash = "sha256:ad3c4a8c86aef181eba6b34cfff995a7c288d6bd5b99207438e25c051d80532d"},
|
||||
{file = "llama_index_llms_openai-0.3.19.tar.gz", hash = "sha256:2e2dad70e7a9cb7a1519be1af4ba60c651a0039bc88888332a17922be00b0299"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
llama-index-core = ">=0.12.4,<0.13.0"
|
||||
llama-index-core = ">=0.12.17,<0.13.0"
|
||||
openai = ">=1.58.1,<2.0.0"
|
||||
|
||||
[[package]]
|
||||
@ -2848,17 +2900,17 @@ llama-parse = ">=0.5.0"
|
||||
|
||||
[[package]]
|
||||
name = "llama-parse"
|
||||
version = "0.6.0"
|
||||
version = "0.6.1"
|
||||
description = "Parse files into RAG-Optimized formats."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "llama_parse-0.6.0-py3-none-any.whl", hash = "sha256:786f3b5cb0afdb784cd3d4b8b03489573b56d7d574212cc88a2eda508965121d"},
|
||||
{file = "llama_parse-0.6.0.tar.gz", hash = "sha256:ac54ce4a43929b401a3ae4643e02ba4214e14814efb06062586263e13996ec54"},
|
||||
{file = "llama_parse-0.6.1-py3-none-any.whl", hash = "sha256:5f96c2951bc3ad514b67bb6886c99224f567d08290fc016e5c8de22c2df60e90"},
|
||||
{file = "llama_parse-0.6.1.tar.gz", hash = "sha256:bd848d3ab7460f70f9e9acaef057fb14ae45f976bdf91830db86a8c40883ef34"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
llama-cloud-services = "*"
|
||||
llama-cloud-services = ">=0.6.1"
|
||||
|
||||
[[package]]
|
||||
name = "locust"
|
||||
@ -3376,13 +3428,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.61.1"
|
||||
version = "1.62.0"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "openai-1.61.1-py3-none-any.whl", hash = "sha256:72b0826240ce26026ac2cd17951691f046e5be82ad122d20a8e1b30ca18bd11e"},
|
||||
{file = "openai-1.61.1.tar.gz", hash = "sha256:ce1851507218209961f89f3520e06726c0aa7d0512386f0f977e3ac3e4f2472e"},
|
||||
{file = "openai-1.62.0-py3-none-any.whl", hash = "sha256:dcb7f9fb4fbc3f27e3ffd2d7bf045be9211510d7fafefcef7ad2302cb27484e0"},
|
||||
{file = "openai-1.62.0.tar.gz", hash = "sha256:ef3f6864ae2f75fa6296bc9811acf684b95557fcb611fe95734215a8b9150b43"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -4192,6 +4244,31 @@ files = [
|
||||
[package.extras]
|
||||
test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
|
||||
|
||||
[[package]]
|
||||
name = "pyasn1"
|
||||
version = "0.6.1"
|
||||
description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
|
||||
{file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pyasn1-modules"
|
||||
version = "0.4.1"
|
||||
description = "A collection of ASN.1-based protocols modules"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
|
||||
{file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pyasn1 = ">=0.4.6,<0.7.0"
|
||||
|
||||
[[package]]
|
||||
name = "pycparser"
|
||||
version = "2.22"
|
||||
@ -4443,13 +4520,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pyright"
|
||||
version = "1.1.393"
|
||||
version = "1.1.394"
|
||||
description = "Command line wrapper for pyright"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pyright-1.1.393-py3-none-any.whl", hash = "sha256:8320629bb7a44ca90944ba599390162bf59307f3d9fb6e27da3b7011b8c17ae5"},
|
||||
{file = "pyright-1.1.393.tar.gz", hash = "sha256:aeeb7ff4e0364775ef416a80111613f91a05c8e01e58ecfefc370ca0db7aed9c"},
|
||||
{file = "pyright-1.1.394-py3-none-any.whl", hash = "sha256:5f74cce0a795a295fb768759bbeeec62561215dea657edcaab48a932b031ddbb"},
|
||||
{file = "pyright-1.1.394.tar.gz", hash = "sha256:56f2a3ab88c5214a451eb71d8f2792b7700434f841ea219119ade7f42ca93608"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -5191,6 +5268,20 @@ files = [
|
||||
{file = "rpds_py-0.22.3.tar.gz", hash = "sha256:e32fee8ab45d3c2db6da19a5323bc3362237c8b653c70194414b892fd06a080d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rsa"
|
||||
version = "4.9"
|
||||
description = "Pure-Python RSA implementation"
|
||||
optional = true
|
||||
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||||
[[package]]
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||||
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@ -5862,83 +5953,80 @@ test = ["websockets"]
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[[package]]
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{file = "websockets-14.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:3bdc8c692c866ce5fefcaf07d2b55c91d6922ac397e031ef9b774e5b9ea42166"},
|
||||
{file = "websockets-14.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c93215fac5dadc63e51bcc6dceca72e72267c11def401d6668622b47675b097f"},
|
||||
{file = "websockets-14.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1c9b6535c0e2cf8a6bf938064fb754aaceb1e6a4a51a80d884cd5db569886910"},
|
||||
{file = "websockets-14.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0a52a6d7cf6938e04e9dceb949d35fbdf58ac14deea26e685ab6368e73744e4c"},
|
||||
{file = "websockets-14.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9f05702e93203a6ff5226e21d9b40c037761b2cfb637187c9802c10f58e40473"},
|
||||
{file = "websockets-14.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:22441c81a6748a53bfcb98951d58d1af0661ab47a536af08920d129b4d1c3473"},
|
||||
{file = "websockets-14.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:efd9b868d78b194790e6236d9cbc46d68aba4b75b22497eb4ab64fa640c3af56"},
|
||||
{file = "websockets-14.2-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:1a5a20d5843886d34ff8c57424cc65a1deda4375729cbca4cb6b3353f3ce4142"},
|
||||
{file = "websockets-14.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:34277a29f5303d54ec6468fb525d99c99938607bc96b8d72d675dee2b9f5bf1d"},
|
||||
{file = "websockets-14.2-cp311-cp311-win32.whl", hash = "sha256:02687db35dbc7d25fd541a602b5f8e451a238ffa033030b172ff86a93cb5dc2a"},
|
||||
{file = "websockets-14.2-cp311-cp311-win_amd64.whl", hash = "sha256:862e9967b46c07d4dcd2532e9e8e3c2825e004ffbf91a5ef9dde519ee2effb0b"},
|
||||
{file = "websockets-14.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:1f20522e624d7ffbdbe259c6b6a65d73c895045f76a93719aa10cd93b3de100c"},
|
||||
{file = "websockets-14.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:647b573f7d3ada919fd60e64d533409a79dcf1ea21daeb4542d1d996519ca967"},
|
||||
{file = "websockets-14.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6af99a38e49f66be5a64b1e890208ad026cda49355661549c507152113049990"},
|
||||
{file = "websockets-14.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:091ab63dfc8cea748cc22c1db2814eadb77ccbf82829bac6b2fbe3401d548eda"},
|
||||
{file = "websockets-14.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b374e8953ad477d17e4851cdc66d83fdc2db88d9e73abf755c94510ebddceb95"},
|
||||
{file = "websockets-14.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a39d7eceeea35db85b85e1169011bb4321c32e673920ae9c1b6e0978590012a3"},
|
||||
{file = "websockets-14.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0a6f3efd47ffd0d12080594f434faf1cd2549b31e54870b8470b28cc1d3817d9"},
|
||||
{file = "websockets-14.2-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:065ce275e7c4ffb42cb738dd6b20726ac26ac9ad0a2a48e33ca632351a737267"},
|
||||
{file = "websockets-14.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e9d0e53530ba7b8b5e389c02282f9d2aa47581514bd6049d3a7cffe1385cf5fe"},
|
||||
{file = "websockets-14.2-cp312-cp312-win32.whl", hash = "sha256:20e6dd0984d7ca3037afcb4494e48c74ffb51e8013cac71cf607fffe11df7205"},
|
||||
{file = "websockets-14.2-cp312-cp312-win_amd64.whl", hash = "sha256:44bba1a956c2c9d268bdcdf234d5e5ff4c9b6dc3e300545cbe99af59dda9dcce"},
|
||||
{file = "websockets-14.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:6f1372e511c7409a542291bce92d6c83320e02c9cf392223272287ce55bc224e"},
|
||||
{file = "websockets-14.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4da98b72009836179bb596a92297b1a61bb5a830c0e483a7d0766d45070a08ad"},
|
||||
{file = "websockets-14.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f8a86a269759026d2bde227652b87be79f8a734e582debf64c9d302faa1e9f03"},
|
||||
{file = "websockets-14.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:86cf1aaeca909bf6815ea714d5c5736c8d6dd3a13770e885aafe062ecbd04f1f"},
|
||||
{file = "websockets-14.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9b0f6c3ba3b1240f602ebb3971d45b02cc12bd1845466dd783496b3b05783a5"},
|
||||
{file = "websockets-14.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:669c3e101c246aa85bc8534e495952e2ca208bd87994650b90a23d745902db9a"},
|
||||
{file = "websockets-14.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:eabdb28b972f3729348e632ab08f2a7b616c7e53d5414c12108c29972e655b20"},
|
||||
{file = "websockets-14.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:2066dc4cbcc19f32c12a5a0e8cc1b7ac734e5b64ac0a325ff8353451c4b15ef2"},
|
||||
{file = "websockets-14.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ab95d357cd471df61873dadf66dd05dd4709cae001dd6342edafc8dc6382f307"},
|
||||
{file = "websockets-14.2-cp313-cp313-win32.whl", hash = "sha256:a9e72fb63e5f3feacdcf5b4ff53199ec8c18d66e325c34ee4c551ca748623bbc"},
|
||||
{file = "websockets-14.2-cp313-cp313-win_amd64.whl", hash = "sha256:b439ea828c4ba99bb3176dc8d9b933392a2413c0f6b149fdcba48393f573377f"},
|
||||
{file = "websockets-14.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7cd5706caec1686c5d233bc76243ff64b1c0dc445339bd538f30547e787c11fe"},
|
||||
{file = "websockets-14.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ec607328ce95a2f12b595f7ae4c5d71bf502212bddcea528290b35c286932b12"},
|
||||
{file = "websockets-14.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:da85651270c6bfb630136423037dd4975199e5d4114cae6d3066641adcc9d1c7"},
|
||||
{file = "websockets-14.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3ecadc7ce90accf39903815697917643f5b7cfb73c96702318a096c00aa71f5"},
|
||||
{file = "websockets-14.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1979bee04af6a78608024bad6dfcc0cc930ce819f9e10342a29a05b5320355d0"},
|
||||
{file = "websockets-14.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2dddacad58e2614a24938a50b85969d56f88e620e3f897b7d80ac0d8a5800258"},
|
||||
{file = "websockets-14.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:89a71173caaf75fa71a09a5f614f450ba3ec84ad9fca47cb2422a860676716f0"},
|
||||
{file = "websockets-14.2-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:6af6a4b26eea4fc06c6818a6b962a952441e0e39548b44773502761ded8cc1d4"},
|
||||
{file = "websockets-14.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:80c8efa38957f20bba0117b48737993643204645e9ec45512579132508477cfc"},
|
||||
{file = "websockets-14.2-cp39-cp39-win32.whl", hash = "sha256:2e20c5f517e2163d76e2729104abc42639c41cf91f7b1839295be43302713661"},
|
||||
{file = "websockets-14.2-cp39-cp39-win_amd64.whl", hash = "sha256:b4c8cef610e8d7c70dea92e62b6814a8cd24fbd01d7103cc89308d2bfe1659ef"},
|
||||
{file = "websockets-14.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:d7d9cafbccba46e768be8a8ad4635fa3eae1ffac4c6e7cb4eb276ba41297ed29"},
|
||||
{file = "websockets-14.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:c76193c1c044bd1e9b3316dcc34b174bbf9664598791e6fb606d8d29000e070c"},
|
||||
{file = "websockets-14.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fd475a974d5352390baf865309fe37dec6831aafc3014ffac1eea99e84e83fc2"},
|
||||
{file = "websockets-14.2-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2c6c0097a41968b2e2b54ed3424739aab0b762ca92af2379f152c1aef0187e1c"},
|
||||
{file = "websockets-14.2-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d7ff794c8b36bc402f2e07c0b2ceb4a2424147ed4785ff03e2a7af03711d60a"},
|
||||
{file = "websockets-14.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:dec254fcabc7bd488dab64846f588fc5b6fe0d78f641180030f8ea27b76d72c3"},
|
||||
{file = "websockets-14.2-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:bbe03eb853e17fd5b15448328b4ec7fb2407d45fb0245036d06a3af251f8e48f"},
|
||||
{file = "websockets-14.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a3c4aa3428b904d5404a0ed85f3644d37e2cb25996b7f096d77caeb0e96a3b42"},
|
||||
{file = "websockets-14.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:577a4cebf1ceaf0b65ffc42c54856214165fb8ceeba3935852fc33f6b0c55e7f"},
|
||||
{file = "websockets-14.2-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ad1c1d02357b7665e700eca43a31d52814ad9ad9b89b58118bdabc365454b574"},
|
||||
{file = "websockets-14.2-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f390024a47d904613577df83ba700bd189eedc09c57af0a904e5c39624621270"},
|
||||
{file = "websockets-14.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3c1426c021c38cf92b453cdf371228d3430acd775edee6bac5a4d577efc72365"},
|
||||
{file = "websockets-14.2-py3-none-any.whl", hash = "sha256:7a6ceec4ea84469f15cf15807a747e9efe57e369c384fa86e022b3bea679b79b"},
|
||||
{file = "websockets-14.2.tar.gz", hash = "sha256:5059ed9c54945efb321f097084b4c7e52c246f2c869815876a69d1efc4ad6eb5"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -6492,17 +6580,18 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
|
||||
cffi = ["cffi (>=1.11)"]
|
||||
|
||||
[extras]
|
||||
all = ["autoflake", "black", "datasets", "docker", "fastapi", "isort", "langchain", "langchain-community", "locust", "pexpect", "pg8000", "pgvector", "pre-commit", "psycopg2", "psycopg2-binary", "pyright", "pytest-asyncio", "pytest-order", "uvicorn", "websockets", "wikipedia"]
|
||||
all = ["autoflake", "black", "datasets", "docker", "fastapi", "isort", "langchain", "langchain-community", "locust", "pexpect", "pg8000", "pgvector", "pre-commit", "psycopg2", "psycopg2-binary", "pyright", "pytest-asyncio", "pytest-order", "uvicorn", "wikipedia"]
|
||||
bedrock = []
|
||||
cloud-tool-sandbox = ["e2b-code-interpreter"]
|
||||
dev = ["autoflake", "black", "datasets", "isort", "locust", "pexpect", "pre-commit", "pyright", "pytest-asyncio", "pytest-order"]
|
||||
external-tools = ["docker", "langchain", "langchain-community", "wikipedia"]
|
||||
google = ["google-genai"]
|
||||
postgres = ["pg8000", "pgvector", "psycopg2", "psycopg2-binary"]
|
||||
qdrant = ["qdrant-client"]
|
||||
server = ["fastapi", "uvicorn", "websockets"]
|
||||
server = ["fastapi", "uvicorn"]
|
||||
tests = ["wikipedia"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "<3.14,>=3.10"
|
||||
content-hash = "1cb8ed2407a871e0753b46cd32454b0c78fb166fe60624b12265f800430e6d28"
|
||||
content-hash = "45a69f6422acba29dff7f93352c2b8a42d1ce6e1a5f1b0549bc86c12a2aee3b6"
|
||||
|
||||
@ -27,7 +27,6 @@ prettytable = "^3.9.0"
|
||||
pgvector = { version = "^0.2.3", optional = true }
|
||||
pre-commit = {version = "^3.5.0", optional = true }
|
||||
pg8000 = {version = "^1.30.3", optional = true}
|
||||
websockets = {version = "^12.0", optional = true}
|
||||
docstring-parser = ">=0.16,<0.17"
|
||||
httpx = "^0.28.0"
|
||||
numpy = "^1.26.2"
|
||||
@ -79,6 +78,7 @@ e2b-code-interpreter = {version = "^1.0.3", optional = true}
|
||||
anthropic = "^0.43.0"
|
||||
letta_client = "^0.1.23"
|
||||
openai = "^1.60.0"
|
||||
google-genai = {version = "^1.1.0", optional = true}
|
||||
faker = "^36.1.0"
|
||||
colorama = "^0.4.6"
|
||||
|
||||
@ -93,6 +93,7 @@ external-tools = ["docker", "langchain", "wikipedia", "langchain-community"]
|
||||
tests = ["wikipedia"]
|
||||
all = ["pgvector", "pg8000", "psycopg2-binary", "psycopg2", "pytest", "pytest-asyncio", "pexpect", "black", "pre-commit", "datasets", "pyright", "pytest-order", "autoflake", "isort", "websockets", "fastapi", "uvicorn", "docker", "langchain", "wikipedia", "langchain-community", "locust"]
|
||||
bedrock = ["boto3"]
|
||||
google = ["google-genai"]
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
black = "^24.4.2"
|
||||
|
||||
7
tests/configs/llm_model_configs/gemini-vertex.json
Normal file
7
tests/configs/llm_model_configs/gemini-vertex.json
Normal file
@ -0,0 +1,7 @@
|
||||
{
|
||||
"model": "gemini-2.0-pro-exp-02-05",
|
||||
"model_endpoint_type": "google_vertex",
|
||||
"model_endpoint": "https://us-central1-aiplatform.googleapis.com/v1/projects/memgpt-428419/locations/us-central1",
|
||||
"context_window": 2097152,
|
||||
"put_inner_thoughts_in_kwargs": true
|
||||
}
|
||||
@ -303,6 +303,18 @@ def test_gemini_pro_15_edit_core_memory():
|
||||
print(f"Got successful response from client: \n\n{response}")
|
||||
|
||||
|
||||
# ======================================================================================================================
|
||||
# GOOGLE VERTEX TESTS
|
||||
# ======================================================================================================================
|
||||
@pytest.mark.vertex_basic
|
||||
@retry_until_success(max_attempts=1, sleep_time_seconds=2)
|
||||
def test_vertex_gemini_pro_20_returns_valid_first_message():
|
||||
filename = os.path.join(llm_config_dir, "gemini-vertex.json")
|
||||
response = check_first_response_is_valid_for_llm_endpoint(filename)
|
||||
# Log out successful response
|
||||
print(f"Got successful response from client: \n\n{response}")
|
||||
|
||||
|
||||
# ======================================================================================================================
|
||||
# TOGETHER TESTS
|
||||
# ======================================================================================================================
|
||||
|
||||
@ -5,6 +5,7 @@ from letta.schemas.providers import (
|
||||
AnthropicProvider,
|
||||
AzureProvider,
|
||||
GoogleAIProvider,
|
||||
GoogleVertexProvider,
|
||||
GroqProvider,
|
||||
MistralProvider,
|
||||
OllamaProvider,
|
||||
@ -66,6 +67,16 @@ def test_googleai():
|
||||
provider.list_embedding_models()
|
||||
|
||||
|
||||
def test_google_vertex():
|
||||
provider = GoogleVertexProvider(google_cloud_project=os.getenv("GCP_PROJECT_ID"), google_cloud_location=os.getenv("GCP_REGION"))
|
||||
models = provider.list_llm_models()
|
||||
print(models)
|
||||
print([m.model for m in models])
|
||||
|
||||
embedding_models = provider.list_embedding_models()
|
||||
print([m.embedding_model for m in embedding_models])
|
||||
|
||||
|
||||
def test_mistral():
|
||||
provider = MistralProvider(api_key=os.getenv("MISTRAL_API_KEY"))
|
||||
models = provider.list_llm_models()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user