huggingface: fix model param population (#24743)

- **Description:** Fix the validation error for `endpoint_url` for
HuggingFaceEndpoint. I have given a descriptive detail of the isse in
the issue that I have created.
- **Issue:** #24742

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py

@@ -1,5 +1,6 @@
 import json  # type: ignore[import-not-found]
 import logging
+import os
 from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
 
 from langchain_core.callbacks import (
@@ -66,9 +67,10 @@ class HuggingFaceEndpoint(LLM):
     """  # noqa: E501
 
     endpoint_url: Optional[str] = None
-    """Endpoint URL to use."""
+    """Endpoint URL to use. If repo_id is not specified then this needs to given or 
+    should be pass as env variable in `HF_INFERENCE_ENDPOINT`"""
     repo_id: Optional[str] = None
-    """Repo to use."""
+    """Repo to use. If endpoint_url is not specified then this needs to given"""
     huggingfacehub_api_token: Optional[str] = None
     max_new_tokens: int = 512
     """Maximum number of generated tokens"""
@@ -146,19 +148,38 @@ class HuggingFaceEndpoint(LLM):
 
         values["model_kwargs"] = extra
 
-        values["endpoint_url"] = get_from_dict_or_env(
-            values, "endpoint_url", "HF_INFERENCE_ENDPOINT", None
-        )
+        # to correctly create the InferenceClient and AsyncInferenceClient
+        # in validate_environment, we need to populate values["model"].
+        # from InferenceClient docstring:
+        # model (`str`, `optional`):
+        #     The model to run inference with. Can be a model id hosted on the Hugging
+        #       Face Hub, e.g. `bigcode/starcoder`
+        #     or a URL to a deployed Inference Endpoint. Defaults to None, in which
+        #       case a recommended model is
+        #     automatically selected for the task.
 
-        if values["endpoint_url"] is None and "repo_id" not in values:
+        # this string could be in 3 places of descending priority:
+        # 2. values["model"] or values["endpoint_url"] or values["repo_id"]
+        #       (equal priority - don't allow both set)
+        # 3. values["HF_INFERENCE_ENDPOINT"] (if none above set)
+
+        model = values.get("model")
+        endpoint_url = values.get("endpoint_url")
+        repo_id = values.get("repo_id")
+
+        if sum([bool(model), bool(endpoint_url), bool(repo_id)]) > 1:
             raise ValueError(
-                "Please specify an `endpoint_url` or `repo_id` for the model."
+                "Please specify either a `model` OR an `endpoint_url` OR a `repo_id`,"
+                "not more than one."
             )
-        if values["endpoint_url"] is not None and "repo_id" in values:
+        values["model"] = (
+            model or endpoint_url or repo_id or os.environ.get("HF_INFERENCE_ENDPOINT")
+        )
+        if not values["model"]:
             raise ValueError(
-                "Please specify either an `endpoint_url` OR a `repo_id`, not both."
+                "Please specify a `model` or an `endpoint_url` or a `repo_id` for the "
+                "model."
             )
-        values["model"] = values.get("endpoint_url") or values.get("repo_id")
         return values
 
     @root_validator(pre=False, skip_on_failure=True)