feat: allow passing API_BASE as optional parameter for openai provider

examples/getting_started.py

@@ -1,34 +1,43 @@
+# Disable pylint errors for logging basicConfig
+# pylint: disable=no-logging-basicconfig
+import logging
+
+from typing import Optional
+
 from haystack.document_stores import InMemoryDocumentStore
 from haystack.utils import build_pipeline, add_example_data, print_answers
 
+logging.basicConfig(level=logging.DEBUG)
 
-def getting_started(provider, API_KEY):
+
+def getting_started(provider, API_KEY, API_BASE: Optional[str] = None):
     """
     This getting_started example shows you how to use LLMs with your data with a technique called Retrieval Augmented Generation - RAG.
 
     :param provider: We are model agnostic :) Here, you can choose from: "anthropic", "cohere", "huggingface", and "openai".
     :param API_KEY: The API key matching the provider.
+    :param API_BASE: The URL to use for a custom endpoint, e.g., if using LM Studio.  Only openai provider supported.  /v1 at the end is needed (e.g., http://localhost:1234/v1)
 
     """
-
     # We support many different databases. Here we load a simple and lightweight in-memory database.
     document_store = InMemoryDocumentStore(use_bm25=True)
 
     # Pipelines are the main abstraction in Haystack, they connect components like LLMs and databases.
-    pipeline = build_pipeline(provider, API_KEY, document_store)
+    pipeline = build_pipeline(provider, API_KEY, document_store, API_BASE)
 
     # Download and add Game of Thrones TXT articles to Haystack's database.
     # You can also provide a folder with your local documents.
     # You might need to install additional dependencies - look inside the function for more information.
     add_example_data(document_store, "data/GoT_getting_started")
 
     # Ask a question on the data you just added.
-    result = pipeline.run(query="Who is the father of Arya Stark?")
+    result = pipeline.run(query="Who is the father of Arya Stark?", debug=True)
 
     # For details such as which documents were used to generate the answer, look into the <result> object.
     print_answers(result, details="medium")
     return result
 
 
 if __name__ == "__main__":
+    # getting_started(provider="openai", API_KEY="NOT NEEDED", API_BASE="http://192.168.1.100:1234/v1")
     getting_started(provider="openai", API_KEY="ADD KEY HERE")

haystack/nodes/prompt/invocation_layer/open_ai.py

@@ -35,7 +35,7 @@ def __init__(
         api_key: str,
         model_name_or_path: str = "gpt-3.5-turbo-instruct",
         max_length: Optional[int] = 100,
-        api_base: str = "https://api.openai.com/v1",
+        api_base: Optional[str] = "https://api.openai.com/v1",
         openai_organization: Optional[str] = None,
         timeout: Optional[float] = None,
         **kwargs,
@@ -65,7 +65,7 @@ def __init__(
                 f"api_key {api_key} must be a valid OpenAI key. Visit https://openai.com/api/ to get one."
             )
         self.api_key = api_key
-        self.api_base = api_base
+        self.api_base = "https://api.openai.com/v1" if api_base is None else api_base
         self.openai_organization = openai_organization
         self.timeout = timeout
 

haystack/nodes/prompt/prompt_model.py

@@ -37,6 +37,7 @@ def __init__(
         model_name_or_path: str = "google/flan-t5-base",
         max_length: Optional[int] = 100,
         api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
         timeout: Optional[float] = None,
         use_auth_token: Optional[Union[str, bool]] = None,
         use_gpu: Optional[bool] = None,
@@ -65,6 +66,7 @@ def __init__(
         self.model_name_or_path = model_name_or_path
         self.max_length = max_length
         self.api_key = api_key
+        self.api_base = api_base
         self.timeout = timeout
         self.use_auth_token = use_auth_token
         self.use_gpu = use_gpu
@@ -78,6 +80,7 @@ def create_invocation_layer(
     ) -> PromptModelInvocationLayer:
         kwargs = {
             "api_key": self.api_key,
+            "api_base": self.api_base,
             "timeout": self.timeout,
             "use_auth_token": self.use_auth_token,
             "use_gpu": self.use_gpu,

haystack/nodes/prompt/prompt_node.py

@@ -57,6 +57,7 @@ def __init__(
         output_variable: Optional[str] = None,
         max_length: Optional[int] = 100,
         api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
         timeout: Optional[float] = None,
         use_auth_token: Optional[Union[str, bool]] = None,
         use_gpu: Optional[bool] = None,
@@ -114,6 +115,7 @@ def __init__(
                 model_name_or_path=model_name_or_path,
                 max_length=max_length,
                 api_key=api_key,
+                api_base=api_base,
                 timeout=timeout,
                 use_auth_token=use_auth_token,
                 use_gpu=use_gpu,

haystack/utils/getting_started.py

@@ -7,7 +7,7 @@
 logger = logging.getLogger(__name__)
 
 
-def build_pipeline(provider, API_KEY, document_store):
+def build_pipeline(provider, API_KEY, document_store, API_BASE):
     # Importing top-level causes a circular import
     from haystack.nodes import AnswerParser, PromptNode, PromptTemplate, BM25Retriever
     from haystack.pipelines import Pipeline
@@ -42,6 +42,7 @@ def build_pipeline(provider, API_KEY, document_store):
         prompt_node = PromptNode(
             model_name_or_path="gpt-3.5-turbo-0301",
             api_key=API_KEY,
+            api_base=API_BASE,
             default_prompt_template=question_answering_with_references,
         )
     else:

releasenotes/notes/override-api-base-67bc046a5cc5f46d.yaml

@@ -0,0 +1,8 @@
+---
+enhancements:
+  - |
+    API_BASE can now be passed as an optional parameter in the getting_started sample.  Only openai provider is supported in this set of changes.
+    PromptNode and PromptModel were enhanced to allow passing of this parameter.
+    This allows RAG against a local endpoint (e.g, http://localhost:1234/v1), so long as it is OpenAI compatible (such as LM Studio)
+
+    Logging in the getting started sample was made more verbose, to make it easier for people to see what was happening under the covers.