feat: Allow end users to pass model instances for llm and embedding model

examples/azure/smart_scraper_azure_openai.py

@@ -0,0 +1,63 @@
+""" 
+Basic example of scraping pipeline using SmartScraper using Azure OpenAI Key
+"""
+
+import os
+from dotenv import load_dotenv
+from langchain_openai import AzureChatOpenAI
+from langchain_openai import AzureOpenAIEmbeddings
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+
+## required environment variable in .env
+# AZURE_OPENAI_ENDPOINT
+# AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
+# MODEL_NAME
+# AZURE_OPENAI_API_KEY
+# OPENAI_API_TYPE
+# AZURE_OPENAI_API_VERSION
+# AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME
+load_dotenv()
+
+
+# ************************************************
+# Initialize the model instances
+# ************************************************
+
+llm_model_instance = AzureChatOpenAI(
+    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+    azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
+)
+
+embedder_model_instance = AzureOpenAIEmbeddings(
+    azure_deployment=os.environ["AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"],
+    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+)
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+graph_config = {
+    "llm": {"model_instance": llm_model_instance},
+    "embeddings": {"model_instance": embedder_model_instance}
+}
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="List me all the events, with the following fields: company_name, event_name, event_start_date, event_start_time, event_end_date, event_end_time, location, event_mode, event_category, third_party_redirect, no_of_days, 
+time_in_hours, hosted_or_attending, refreshments_type,  registration_available, registration_link",
+    # also accepts a string with the already downloaded HTML code
+    source="https://www.hmhco.com/event",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

scrapegraphai/graphs/abstract_graph.py

@@ -19,7 +19,7 @@ class AbstractGraph(ABC):
         self.prompt = prompt
         self.source = source
         self.config = config
-        self.llm_model = self._create_llm(config["llm"])
+        self.llm_model = self._create_llm(config["llm"], chat=True)
         self.embedder_model = self.llm_model if "embeddings" not in config else self._create_llm(
             config["embeddings"])
 
@@ -32,7 +32,16 @@ class AbstractGraph(ABC):
         self.final_state = None
         self.execution_info = None
 
-    def _create_llm(self, llm_config: dict):
+    def _set_model_token(self, llm):
+
+        if 'Azure' in str(type(llm)):
+            try:
+                self.model_token = models_tokens["azure"][llm.model_name]
+            except KeyError:
+                raise KeyError("Model not supported")
+
+
+    def _create_llm(self, llm_config: dict, chat=False) -> object:
         """
         Creates an instance of the language model (OpenAI or Gemini) based on configuration.
         """
@@ -42,6 +51,12 @@ class AbstractGraph(ABC):
         }
         llm_params = {**llm_defaults, **llm_config}
 
+        # If model instance is passed directly instead of the model details
+        if 'model_instance' in llm_params:
+            if chat:
+                self._set_model_token(llm_params['model_instance'])
+            return llm_params['model_instance']
+        
         # Instantiate the language model based on the model name
         if "gpt-" in llm_params["model"]:
             try:
@@ -129,3 +144,4 @@ class AbstractGraph(ABC):
         Abstract method to execute the graph and return the result.
         """
         pass
+

scrapegraphai/helpers/models_tokens.py

@@ -18,7 +18,9 @@ models_tokens = {
         "gpt-4-32k": 32768,
         "gpt-4-32k-0613": 32768,
     },
-
+    "azure": {
+        "gpt-3.5-turbo": 4096
+    },
     "gemini": {
         "gemini-pro": 128000,
     },
@@ -45,3 +47,4 @@ models_tokens = {
         "claude3": 200000
     }
 }
+

scrapegraphai/nodes/rag_node.py

@@ -92,6 +92,8 @@ class RAGNode(BaseNode):
         if isinstance(embedding_model, OpenAI):
             embeddings = OpenAIEmbeddings(
                 api_key=embedding_model.openai_api_key)
+        elif isinstance(embedding_model, AzureOpenAIEmbeddings):
+            embeddings = embedding_model
         elif isinstance(embedding_model, AzureOpenAI):
             embeddings = AzureOpenAIEmbeddings()
         elif isinstance(embedding_model, Ollama):
@@ -133,3 +135,4 @@ class RAGNode(BaseNode):
 
         state.update({self.output[0]: compressed_docs})
         return state
+