community: extend Predibase integration to support fine-tuned LLM adapters (#19979)

- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
  - Example: "community: add foobar LLM"


- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Langchain-Predibase integration was failing, because
it was not current with the Predibase SDK; in addition, Predibase
integration tests were instantiating the Langchain Community `Predibase`
class with one required argument (`model`) missing. This change updates
the Predibase SDK usage and fixes the integration tests.
    - **Twitter handle:** `@alexsherstinsky`


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.

---------

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

docs/docs/integrations/llms/predibase.ipynb

@@ -50,7 +50,24 @@
     "from langchain_community.llms import Predibase\n",
     "\n",
     "model = Predibase(\n",
-    "    model=\"vicuna-13b\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
+    "    model=\"mistral-7b\",\n",
+    "    predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import Predibase\n",
+    "\n",
+    "# With an adapter, fine-tuned on the specified model\n",
+    "model = Predibase(\n",
+    "    model=\"mistral-7b\",\n",
+    "    adapter_id=\"predibase/e2e_nlg\",\n",
+    "    predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
     ")"
    ]
   },
@@ -66,19 +83,43 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
    "source": [
     "## Chain Call Setup"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import Predibase\n",
+    "\n",
+    "model = Predibase(\n",
+    "    model=\"mistral-7b\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# With an adapter, fine-tuned on the specified model\n",
     "llm = Predibase(\n",
-    "    model=\"vicuna-13b\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
+    "    model=\"mistral-7b\",\n",
+    "    adapter_id=\"predibase/e2e_nlg\",\n",
+    "    predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
     ")"
    ]
   },
@@ -169,7 +210,11 @@
     "from langchain_community.llms import Predibase\n",
     "\n",
     "model = Predibase(\n",
-    "    model=\"my-finetuned-LLM\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
+    "    model=\"my-base-LLM\",\n",
+    "    adapter_id=\"my-finetuned-adapter-id\",\n",
+    "    predibase_api_key=os.environ.get(\n",
+    "        \"PREDIBASE_API_TOKEN\"\n",
+    "    ),  # Adapter argument is optional.\n",
     ")\n",
     "# replace my-finetuned-LLM with the name of your model in Predibase"
    ]

docs/docs/integrations/providers/predibase.md

@@ -17,7 +17,21 @@ os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
 
 from langchain_community.llms import Predibase
 
-model =  Predibase(model = 'vicuna-13b', predibase_api_key=os.environ.get('PREDIBASE_API_TOKEN'))
+model = Predibase(model="mistral-7b"", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))
+
+response = model("Can you recommend me a nice dry wine?")
+print(response)
+```
+
+Predibase also supports adapters that are fine-tuned on the base model given by the `model` argument:
+
+```python
+import os
+os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
+
+from langchain_community.llms import Predibase
+
+model =  Predibase(model="mistral-7b"", adapter_id="predibase/e2e_nlg", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))
 
 response = model("Can you recommend me a nice dry wine?")
 print(response)

libs/community/langchain_community/llms/predibase.py

@@ -10,10 +10,18 @@ class Predibase(LLM):
 
     To use, you should have the ``predibase`` python package installed,
     and have your Predibase API key.
+
+    The `model` parameter is the Predibase "serverless" base_model ID
+    (see https://docs.predibase.com/user-guide/inference/models for the catalog).
+
+    An optional `adapter_id` parameter is the HuggingFace ID of a fine-tuned LLM
+    adapter, whose base model is the `model` parameter; the fine-tuned adapter
+    must be compatible with its base model; otherwise, an error is raised.
     """
 
     model: str
     predibase_api_key: SecretStr
+    adapter_id: Optional[str] = None
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     default_options_for_generation: dict = Field(
         {
@@ -38,7 +46,10 @@ class Predibase(LLM):
             from predibase import PredibaseClient
             from predibase.pql import get_session
             from predibase.pql.api import Session
-            from predibase.resource.llm.interface import LLMDeployment
+            from predibase.resource.llm.interface import (
+                HuggingFaceLLM,
+                LLMDeployment,
+            )
             from predibase.resource.llm.response import GeneratedResponse
 
             session: Session = get_session(
@@ -55,15 +66,23 @@ class Predibase(LLM):
         except ValueError as e:
             raise ValueError("Your API key is not correct. Please try again") from e
         options: Dict[str, Union[str, float]] = (
-            kwargs or self.default_options_for_generation
+            self.model_kwargs or self.default_options_for_generation
         )
         base_llm_deployment: LLMDeployment = pc.LLM(
             uri=f"pb://deployments/{self.model}"
         )
-        result: GeneratedResponse = base_llm_deployment.generate(
-            prompt=prompt,
-            options=options,
-        )
+        result: GeneratedResponse
+        if self.adapter_id:
+            adapter_model: HuggingFaceLLM = pc.LLM(uri=f"hf://{self.adapter_id}")
+            result = base_llm_deployment.with_adapter(model=adapter_model).generate(
+                prompt=prompt,
+                options=options,
+            )
+        else:
+            result = base_llm_deployment.generate(
+                prompt=prompt,
+                options=options,
+            )
         return result.response
 
     @property

libs/community/tests/unit_tests/llms/test_predibase.py

@@ -17,3 +17,20 @@ def test_api_key_masked_when_passed_via_constructor(
     captured = capsys.readouterr()
 
     assert captured.out == "**********"
+
+
+def test_specifying_adapter_id_argument() -> None:
+    llm = Predibase(model="my_llm", predibase_api_key="secret-api-key")
+    assert not llm.adapter_id
+
+    llm = Predibase(
+        model="my_llm", predibase_api_key="secret-api-key", adapter_id="my-hf-adapter"
+    )
+    assert llm.adapter_id == "my-hf-adapter"
+
+    llm = Predibase(
+        model="my_llm",
+        adapter_id="my-other-hf-adapter",
+        predibase_api_key="secret-api-key",
+    )
+    assert llm.adapter_id == "my-other-hf-adapter"