feat: add psagentai agent with deployment and configuration

Initial project setup including the main agent script, configuration files, profile JSON state, systemd service unit, startup script, deployment documentation, and Python dependencies. Includes .env, venv/, and __pycache__/ which should likely be excluded via .gitignore to avoid committing secrets and artifacts.
2026-10-25 17:05:13 -03:00
commit 6b3b6541a7
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+# File generated from our OpenAPI spec by Stainless.
+
+from __future__ import annotations
+
+import base64
+from typing import List, Union, Iterable, cast
+from typing_extensions import Literal
+
+import httpx
+
+from .. import _legacy_response
+from ..types import CreateEmbeddingResponse, embedding_create_params
+from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
+from .._utils import is_given, maybe_transform
+from .._compat import cached_property
+from .._extras import numpy as np, has_numpy
+from .._resource import SyncAPIResource, AsyncAPIResource
+from .._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
+from .._base_client import (
+    make_request_options,
+)
+
+__all__ = ["Embeddings", "AsyncEmbeddings"]
+
+
+class Embeddings(SyncAPIResource):
+    @cached_property
+    def with_raw_response(self) -> EmbeddingsWithRawResponse:
+        return EmbeddingsWithRawResponse(self)
+
+    @cached_property
+    def with_streaming_response(self) -> EmbeddingsWithStreamingResponse:
+        return EmbeddingsWithStreamingResponse(self)
+
+    def create(
+        self,
+        *,
+        input: Union[str, List[str], Iterable[int], Iterable[Iterable[int]]],
+        model: Union[str, Literal["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"]],
+        dimensions: int | NotGiven = NOT_GIVEN,
+        encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+        user: str | NotGiven = NOT_GIVEN,
+        # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+        # The extra values given here take precedence over values defined on the client or passed to this method.
+        extra_headers: Headers | None = None,
+        extra_query: Query | None = None,
+        extra_body: Body | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+    ) -> CreateEmbeddingResponse:
+        """
+        Creates an embedding vector representing the input text.
+
+        Args:
+          input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+              inputs in a single request, pass an array of strings or array of token arrays.
+              The input must not exceed the max input tokens for the model (8192 tokens for
+              `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
+              dimensions or less.
+              [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
+              for counting tokens.
+
+          model: ID of the model to use. You can use the
+              [List models](https://platform.openai.com/docs/api-reference/models/list) API to
+              see all of your available models, or see our
+              [Model overview](https://platform.openai.com/docs/models/overview) for
+              descriptions of them.
+
+          dimensions: The number of dimensions the resulting output embeddings should have. Only
+              supported in `text-embedding-3` and later models.
+
+          encoding_format: The format to return the embeddings in. Can be either `float` or
+              [`base64`](https://pypi.org/project/pybase64/).
+
+          user: A unique identifier representing your end-user, which can help OpenAI to monitor
+              and detect abuse.
+              [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
+
+          extra_headers: Send extra headers
+
+          extra_query: Add additional query parameters to the request
+
+          extra_body: Add additional JSON properties to the request
+
+          timeout: Override the client-level default timeout for this request, in seconds
+        """
+        params = {
+            "input": input,
+            "model": model,
+            "user": user,
+            "dimensions": dimensions,
+            "encoding_format": encoding_format,
+        }
+        if not is_given(encoding_format) and has_numpy():
+            params["encoding_format"] = "base64"
+
+        def parser(obj: CreateEmbeddingResponse) -> CreateEmbeddingResponse:
+            if is_given(encoding_format):
+                # don't modify the response object if a user explicitly asked for a format
+                return obj
+
+            for embedding in obj.data:
+                data = cast(object, embedding.embedding)
+                if not isinstance(data, str):
+                    # numpy is not installed / base64 optimisation isn't enabled for this model yet
+                    continue
+
+                embedding.embedding = np.frombuffer(  # type: ignore[no-untyped-call]
+                    base64.b64decode(data), dtype="float32"
+                ).tolist()
+
+            return obj
+
+        return self._post(
+            "/embeddings",
+            body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
+            options=make_request_options(
+                extra_headers=extra_headers,
+                extra_query=extra_query,
+                extra_body=extra_body,
+                timeout=timeout,
+                post_parser=parser,
+            ),
+            cast_to=CreateEmbeddingResponse,
+        )
+
+
+class AsyncEmbeddings(AsyncAPIResource):
+    @cached_property
+    def with_raw_response(self) -> AsyncEmbeddingsWithRawResponse:
+        return AsyncEmbeddingsWithRawResponse(self)
+
+    @cached_property
+    def with_streaming_response(self) -> AsyncEmbeddingsWithStreamingResponse:
+        return AsyncEmbeddingsWithStreamingResponse(self)
+
+    async def create(
+        self,
+        *,
+        input: Union[str, List[str], Iterable[int], Iterable[Iterable[int]]],
+        model: Union[str, Literal["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"]],
+        dimensions: int | NotGiven = NOT_GIVEN,
+        encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+        user: str | NotGiven = NOT_GIVEN,
+        # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
+        # The extra values given here take precedence over values defined on the client or passed to this method.
+        extra_headers: Headers | None = None,
+        extra_query: Query | None = None,
+        extra_body: Body | None = None,
+        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
+    ) -> CreateEmbeddingResponse:
+        """
+        Creates an embedding vector representing the input text.
+
+        Args:
+          input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+              inputs in a single request, pass an array of strings or array of token arrays.
+              The input must not exceed the max input tokens for the model (8192 tokens for
+              `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
+              dimensions or less.
+              [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
+              for counting tokens.
+
+          model: ID of the model to use. You can use the
+              [List models](https://platform.openai.com/docs/api-reference/models/list) API to
+              see all of your available models, or see our
+              [Model overview](https://platform.openai.com/docs/models/overview) for
+              descriptions of them.
+
+          dimensions: The number of dimensions the resulting output embeddings should have. Only
+              supported in `text-embedding-3` and later models.
+
+          encoding_format: The format to return the embeddings in. Can be either `float` or
+              [`base64`](https://pypi.org/project/pybase64/).
+
+          user: A unique identifier representing your end-user, which can help OpenAI to monitor
+              and detect abuse.
+              [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
+
+          extra_headers: Send extra headers
+
+          extra_query: Add additional query parameters to the request
+
+          extra_body: Add additional JSON properties to the request
+
+          timeout: Override the client-level default timeout for this request, in seconds
+        """
+        params = {
+            "input": input,
+            "model": model,
+            "user": user,
+            "dimensions": dimensions,
+            "encoding_format": encoding_format,
+        }
+        if not is_given(encoding_format) and has_numpy():
+            params["encoding_format"] = "base64"
+
+        def parser(obj: CreateEmbeddingResponse) -> CreateEmbeddingResponse:
+            if is_given(encoding_format):
+                # don't modify the response object if a user explicitly asked for a format
+                return obj
+
+            for embedding in obj.data:
+                data = cast(object, embedding.embedding)
+                if not isinstance(data, str):
+                    # numpy is not installed / base64 optimisation isn't enabled for this model yet
+                    continue
+
+                embedding.embedding = np.frombuffer(  # type: ignore[no-untyped-call]
+                    base64.b64decode(data), dtype="float32"
+                ).tolist()
+
+            return obj
+
+        return await self._post(
+            "/embeddings",
+            body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
+            options=make_request_options(
+                extra_headers=extra_headers,
+                extra_query=extra_query,
+                extra_body=extra_body,
+                timeout=timeout,
+                post_parser=parser,
+            ),
+            cast_to=CreateEmbeddingResponse,
+        )
+
+
+class EmbeddingsWithRawResponse:
+    def __init__(self, embeddings: Embeddings) -> None:
+        self._embeddings = embeddings
+
+        self.create = _legacy_response.to_raw_response_wrapper(
+            embeddings.create,
+        )
+
+
+class AsyncEmbeddingsWithRawResponse:
+    def __init__(self, embeddings: AsyncEmbeddings) -> None:
+        self._embeddings = embeddings
+
+        self.create = _legacy_response.async_to_raw_response_wrapper(
+            embeddings.create,
+        )
+
+
+class EmbeddingsWithStreamingResponse:
+    def __init__(self, embeddings: Embeddings) -> None:
+        self._embeddings = embeddings
+
+        self.create = to_streamed_response_wrapper(
+            embeddings.create,
+        )
+
+
+class AsyncEmbeddingsWithStreamingResponse:
+    def __init__(self, embeddings: AsyncEmbeddings) -> None:
+        self._embeddings = embeddings
+
+        self.create = async_to_streamed_response_wrapper(
+            embeddings.create,
+        )