Merge branch 'main' into dataflow_changes

.github/workflows/run-unittests-py39-py310.yml

@@ -74,16 +74,16 @@ jobs:
         name: "Test env setup"
         timeout-minutes: 30
 
-      - name: "Run hpo tests"
-        timeout-minutes: 10
-        shell: bash
-        if: ${{ matrix.name }} == "unitary"
-        run: |
-          set -x # print commands that are executed
+      # - name: "Run hpo tests"
+      #   timeout-minutes: 10
+      #   shell: bash
+      #   if: ${{ matrix.name }} == "unitary"
+      #   run: |
+      #     set -x # print commands that are executed
 
-          # Run hpo tests, which hangs if run together with all unitary tests
-          python -m pytest -v -p no:warnings -n auto --dist loadfile \
-          tests/unitary/with_extras/hpo
+      #     # Run hpo tests, which hangs if run together with all unitary tests
+      #     python -m pytest -v -p no:warnings -n auto --dist loadfile \
+      #     tests/unitary/with_extras/hpo
 
       - name: "Run unitary tests folder with maximum ADS dependencies"
         timeout-minutes: 60

ads/llm/guardrails/base.py

@@ -1,17 +1,16 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
-# Copyright (c) 2023 Oracle and/or its affiliates.
+# Copyright (c) 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 
 import datetime
 import functools
-import operator
 import importlib.util
+import operator
 import sys
+from typing import Any, List, Optional, Union
 
-from typing import Any, List, Dict, Tuple
 from langchain.schema.prompt import PromptValue
 from langchain.tools.base import BaseTool, ToolException
 from pydantic import BaseModel, model_validator
@@ -207,7 +206,9 @@ def _preprocess(self, input: Any) -> str:
             return input.to_string()
         return str(input)
 
-    def _to_args_and_kwargs(self, tool_input: Any) -> Tuple[Tuple, Dict]:
+    def _to_args_and_kwargs(
+        self, tool_input: Union[str, dict], tool_call_id: Optional[str]
+    ) -> tuple[tuple, dict]:
         if isinstance(tool_input, dict):
             return (), tool_input
         else:

ads/llm/langchain/plugins/chat_models/oci_data_science.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
-# Copyright (c) 2023 Oracle and/or its affiliates.
+# Copyright (c) 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 """Chat model for OCI data science model deployment endpoint."""
 
@@ -50,6 +49,7 @@
 )
 
 logger = logging.getLogger(__name__)
+DEFAULT_INFERENCE_ENDPOINT_CHAT = "/v1/chat/completions"
 
 
 def _is_pydantic_class(obj: Any) -> bool:
@@ -93,6 +93,8 @@ class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
     Key init args — client params:
         auth: dict
             ADS auth dictionary for OCI authentication.
+        default_headers: Optional[Dict]
+            The headers to be added to the Model Deployment request.
 
     Instantiate:
         .. code-block:: python
@@ -109,6 +111,10 @@ class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment):
                     "temperature": 0.2,
                     # other model parameters ...
                 },
+                default_headers={
+                    "route": "/v1/chat/completions",
+                    # other request headers ...
+                },
             )
 
     Invocation:
@@ -291,6 +297,25 @@ def _default_params(self) -> Dict[str, Any]:
             "stream": self.streaming,
         }
 
+    def _headers(
+        self, is_async: Optional[bool] = False, body: Optional[dict] = None
+    ) -> Dict:
+        """Construct and return the headers for a request.
+
+        Args:
+            is_async (bool, optional): Indicates if the request is asynchronous.
+                Defaults to `False`.
+            body (optional): The request body to be included in the headers if
+                the request is asynchronous.
+
+        Returns:
+            Dict: A dictionary containing the appropriate headers for the request.
+        """
+        return {
+            "route": DEFAULT_INFERENCE_ENDPOINT_CHAT,
+            **super()._headers(is_async=is_async, body=body),
+        }
+
     def _generate(
         self,
         messages: List[BaseMessage],
@@ -704,7 +729,7 @@ def _process_response(self, response_json: dict) -> ChatResult:
 
         for choice in choices:
             message = _convert_dict_to_message(choice["message"])
-            generation_info = dict(finish_reason=choice.get("finish_reason"))
+            generation_info = {"finish_reason": choice.get("finish_reason")}
             if "logprobs" in choice:
                 generation_info["logprobs"] = choice["logprobs"]
 
@@ -794,7 +819,7 @@ class ChatOCIModelDeploymentVLLM(ChatOCIModelDeployment):
     """Number of most likely tokens to consider at each step."""
 
     min_p: Optional[float] = 0.0
-    """Float that represents the minimum probability for a token to be considered. 
+    """Float that represents the minimum probability for a token to be considered.
     Must be in [0,1]. 0 to disable this."""
 
     repetition_penalty: Optional[float] = 1.0
@@ -818,7 +843,7 @@ class ChatOCIModelDeploymentVLLM(ChatOCIModelDeployment):
     the EOS token is generated."""
 
     min_tokens: Optional[int] = 0
-    """Minimum number of tokens to generate per output sequence before 
+    """Minimum number of tokens to generate per output sequence before
     EOS or stop_token_ids can be generated"""
 
     stop_token_ids: Optional[List[int]] = None
@@ -836,7 +861,7 @@ class ChatOCIModelDeploymentVLLM(ChatOCIModelDeployment):
     tool_choice: Optional[str] = None
     """Whether to use tool calling.
     Defaults to None, tool calling is disabled.
-    Tool calling requires model support and the vLLM to be configured 
+    Tool calling requires model support and the vLLM to be configured
     with `--tool-call-parser`.
     Set this to `auto` for the model to make tool calls automatically.
     Set this to `required` to force the model to always call one or more tools.
@@ -956,9 +981,9 @@ class ChatOCIModelDeploymentTGI(ChatOCIModelDeployment):
     """Total probability mass of tokens to consider at each step."""
 
     top_logprobs: Optional[int] = None
-    """An integer between 0 and 5 specifying the number of most 
-    likely tokens to return at each token position, each with an 
-    associated log probability. logprobs must be set to true if 
+    """An integer between 0 and 5 specifying the number of most
+    likely tokens to return at each token position, each with an
+    associated log probability. logprobs must be set to true if
     this parameter is used."""
 
     @property

ads/llm/langchain/plugins/llms/oci_data_science_model_deployment_endpoint.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
-# Copyright (c) 2023 Oracle and/or its affiliates.
+# Copyright (c) 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 
@@ -24,6 +23,7 @@
 
 import aiohttp
 import requests
+from langchain_community.utilities.requests import Requests
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -34,14 +34,13 @@
 from langchain_core.utils import get_from_dict_or_env
 from pydantic import Field, model_validator
 
-from langchain_community.utilities.requests import Requests
-
 logger = logging.getLogger(__name__)
 
 
 DEFAULT_TIME_OUT = 300
 DEFAULT_CONTENT_TYPE_JSON = "application/json"
 DEFAULT_MODEL_NAME = "odsc-llm"
+DEFAULT_INFERENCE_ENDPOINT = "/v1/completions"
 
 
 class TokenExpiredError(Exception):
@@ -86,6 +85,9 @@ class BaseOCIModelDeployment(Serializable):
     max_retries: int = 3
     """Maximum number of retries to make when generating."""
 
+    default_headers: Optional[Dict[str, Any]] = None
+    """The headers to be added to the Model Deployment request."""
+
     @model_validator(mode="before")
     @classmethod
     def validate_environment(cls, values: Dict) -> Dict:
@@ -101,7 +103,7 @@ def validate_environment(cls, values: Dict) -> Dict:
                 "Please install it with `pip install oracle_ads`."
             ) from ex
 
-        if not values.get("auth", None):
+        if not values.get("auth"):
             values["auth"] = ads.common.auth.default_signer()
 
         values["endpoint"] = get_from_dict_or_env(
@@ -125,12 +127,12 @@ def _headers(
         Returns:
             Dict: A dictionary containing the appropriate headers for the request.
         """
+        headers = self.default_headers or {}
         if is_async:
             signer = self.auth["signer"]
             _req = requests.Request("POST", self.endpoint, json=body)
             req = _req.prepare()
             req = signer(req)
-            headers = {}
             for key, value in req.headers.items():
                 headers[key] = value
 
@@ -140,7 +142,7 @@ def _headers(
                 )
             return headers
 
-        return (
+        headers.update(
             {
                 "Content-Type": DEFAULT_CONTENT_TYPE_JSON,
                 "enable-streaming": "true",
@@ -152,6 +154,8 @@ def _headers(
             }
         )
 
+        return headers
+
     def completion_with_retry(
         self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
     ) -> Any:
@@ -357,7 +361,7 @@ def _refresh_signer(self) -> bool:
             self.auth["signer"].refresh_security_token()
             return True
         return False
-    
+
     @classmethod
     def is_lc_serializable(cls) -> bool:
         """Return whether this model can be serialized by LangChain."""
@@ -388,6 +392,10 @@ class OCIModelDeploymentLLM(BaseLLM, BaseOCIModelDeployment):
                 model="odsc-llm",
                 streaming=True,
                 model_kwargs={"frequency_penalty": 1.0},
+                headers={
+                    "route": "/v1/completions",
+                    # other request headers ...
+                }
             )
             llm.invoke("tell me a joke.")
 
@@ -477,6 +485,25 @@ def _identifying_params(self) -> Dict[str, Any]:
             **self._default_params,
         }
 
+    def _headers(
+        self, is_async: Optional[bool] = False, body: Optional[dict] = None
+    ) -> Dict:
+        """Construct and return the headers for a request.
+
+        Args:
+            is_async (bool, optional): Indicates if the request is asynchronous.
+                Defaults to `False`.
+            body (optional): The request body to be included in the headers if
+                the request is asynchronous.
+
+        Returns:
+            Dict: A dictionary containing the appropriate headers for the request.
+        """
+        return {
+            "route": DEFAULT_INFERENCE_ENDPOINT,
+            **super()._headers(is_async=is_async, body=body),
+        }
+
     def _generate(
         self,
         prompts: List[str],
@@ -712,9 +739,9 @@ def _process_response(self, response_json: dict) -> List[Generation]:
     def _generate_info(self, choice: dict) -> Any:
         """Extracts generation info from the response."""
         gen_info = {}
-        finish_reason = choice.get("finish_reason", None)
-        logprobs = choice.get("logprobs", None)
-        index = choice.get("index", None)
+        finish_reason = choice.get("finish_reason")
+        logprobs = choice.get("logprobs")
+        index = choice.get("index")
         if finish_reason:
             gen_info.update({"finish_reason": finish_reason})
         if logprobs is not None:

tests/unitary/with_extras/langchain/chat_models/test_oci_data_science.py

@@ -26,6 +26,7 @@
 CONST_ENDPOINT = "https://oci.endpoint/ocid/predict"
 CONST_PROMPT = "This is a prompt."
 CONST_COMPLETION = "This is a completion."
+CONST_COMPLETION_ROUTE = "/v1/chat/completions"
 CONST_COMPLETION_RESPONSE = {
     "id": "chat-123456789",
     "object": "chat.completion",
@@ -123,6 +124,7 @@ def mocked_requests_post(url: str, **kwargs: Any) -> MockResponse:
 def test_invoke_vllm(*args: Any) -> None:
     """Tests invoking vLLM endpoint."""
     llm = ChatOCIModelDeploymentVLLM(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME)
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert isinstance(output, AIMessage)
     assert output.content == CONST_COMPLETION
@@ -135,6 +137,7 @@ def test_invoke_vllm(*args: Any) -> None:
 def test_invoke_tgi(*args: Any) -> None:
     """Tests invoking TGI endpoint using OpenAI Spec."""
     llm = ChatOCIModelDeploymentTGI(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME)
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert isinstance(output, AIMessage)
     assert output.content == CONST_COMPLETION
@@ -149,6 +152,7 @@ def test_stream_vllm(*args: Any) -> None:
     llm = ChatOCIModelDeploymentVLLM(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = None
     count = 0
     for chunk in llm.stream(CONST_PROMPT):
@@ -187,6 +191,7 @@ async def test_stream_async(*args: Any) -> None:
     llm = ChatOCIModelDeploymentVLLM(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     with mock.patch.object(
         llm,
         "_aiter_sse",

tests/unitary/with_extras/langchain/llms/test_oci_model_deployment_endpoint.py

@@ -24,6 +24,7 @@
 CONST_ENDPOINT = "https://oci.endpoint/ocid/predict"
 CONST_PROMPT = "This is a prompt."
 CONST_COMPLETION = "This is a completion."
+CONST_COMPLETION_ROUTE = "/v1/completions"
 CONST_COMPLETION_RESPONSE = {
     "choices": [
         {
@@ -116,6 +117,7 @@ async def mocked_async_streaming_response(
 def test_invoke_vllm(*args: Any) -> None:
     """Tests invoking vLLM endpoint."""
     llm = OCIModelDeploymentVLLM(endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME)
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert output == CONST_COMPLETION
 
@@ -128,6 +130,7 @@ def test_stream_tgi(*args: Any) -> None:
     llm = OCIModelDeploymentTGI(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = ""
     count = 0
     for chunk in llm.stream(CONST_PROMPT):
@@ -145,6 +148,7 @@ def test_generate_tgi(*args: Any) -> None:
     llm = OCIModelDeploymentTGI(
         endpoint=CONST_ENDPOINT, api="/generate", model=CONST_MODEL_NAME
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     output = llm.invoke(CONST_PROMPT)
     assert output == CONST_COMPLETION
 
@@ -163,6 +167,7 @@ async def test_stream_async(*args: Any) -> None:
     llm = OCIModelDeploymentTGI(
         endpoint=CONST_ENDPOINT, model=CONST_MODEL_NAME, streaming=True
     )
+    assert llm._headers().get("route") == CONST_COMPLETION_ROUTE
     with mock.patch.object(
         llm,
         "_aiter_sse",