feat(burr-bridge): BurrBridge class to integrate inside BaseGraph

Author: PeriniM

.python-version

@@ -0,0 +1,109 @@
+"""
+Example of custom graph using existing nodes
+"""
+
+import os
+from dotenv import load_dotenv
+
+from langchain_openai import OpenAIEmbeddings
+from scrapegraphai.models import OpenAI
+from scrapegraphai.graphs import BaseGraph
+from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode
+load_dotenv()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+        "temperature": 0,
+        "streaming": False
+    },
+}
+
+# ************************************************
+# Define the graph nodes
+# ************************************************
+
+llm_model = OpenAI(graph_config["llm"])
+embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)
+
+# define the nodes for the graph
+
+fetch_node = FetchNode(
+    input="url | local_dir",
+    output=["doc", "link_urls", "img_urls"],
+    node_config={
+        "verbose": True,
+        "headless": True,
+    }
+)
+parse_node = ParseNode(
+    input="doc",
+    output=["parsed_doc"],
+    node_config={
+        "chunk_size": 4096,
+        "verbose": True,
+    }
+)
+rag_node = RAGNode(
+    input="user_prompt & (parsed_doc | doc)",
+    output=["relevant_chunks"],
+    node_config={
+        "llm_model": llm_model,
+        "embedder_model": embedder,
+        "verbose": True,
+    }
+)
+generate_answer_node = GenerateAnswerNode(
+    input="user_prompt & (relevant_chunks | parsed_doc | doc)",
+    output=["answer"],
+    node_config={
+        "llm_model": llm_model,
+        "verbose": True,
+    }
+)
+
+# ************************************************
+# Create the graph by defining the connections
+# ************************************************
+
+graph = BaseGraph(
+    nodes=[
+        fetch_node,
+        parse_node,
+        rag_node,
+        generate_answer_node,
+    ],
+    edges=[
+        (fetch_node, parse_node),
+        (parse_node, rag_node),
+        (rag_node, generate_answer_node)
+    ],
+    entry_point=fetch_node,
+    use_burr=True,
+    burr_config={
+        "app_instance_id": "custom_graph_openai",
+        "inputs": {
+            "llm_model": graph_config["llm"].get("model", "gpt-3.5-turbo"),
+        }
+    }
+)
+
+# ************************************************
+# Execute the graph
+# ************************************************
+
+result, execution_info = graph.execute({
+    "user_prompt": "Describe the content",
+    "url": "https://example.com/"
+})
+
+# get the answer from the result
+result = result.get("answer", "No answer found.")
+print(result)

examples/openai/burr_integration_openai.py

@@ -29,6 +29,7 @@ dependencies = [
     "playwright==1.43.0",
     "google==3.0.0",
     "yahoo-search-py==0.3",
+    "burr[start]"
 ]
 
 license = "MIT"