new langflow endpoint

pyproject.toml

@@ -5,7 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
-    "agentd>=0.2.0.post2",
+    "agentd>=0.2.0.post3",
     "aiofiles>=24.1.0",
     "docling>=2.41.0",
     "opensearch-py[async]>=3.0.0",

src/agent.py

@@ -1,4 +1,4 @@
-messages = [{"role": "system", "content": "You are a helpful assistant. use your tools to answer questions."}]
+messages = [{"role": "system", "content": "You are a helpful assistant. Always use the search_tools to answer questions."}]
 
 # Async version for web server
 async def async_chat(async_client, prompt: str, model: str = "gpt-4.1-mini", previous_response_id: str = None) -> str:

src/app.py

@@ -11,9 +11,6 @@ os.environ['USE_CPU_ONLY'] = 'true'
 import hashlib
 import tempfile
 import asyncio
-import time
-import json
-import httpx
 
 from starlette.applications import Starlette
 from starlette.requests     import Request
@@ -27,6 +24,10 @@ from docling.document_converter import DocumentConverter
 from agentd.patch import patch_openai_with_mcp
 from openai import AsyncOpenAI
 from agentd.tool_decorator import tool
+from dotenv import load_dotenv
+
+load_dotenv()
+load_dotenv("../")
 
 # Initialize Docling converter
 converter = DocumentConverter()  # basic converter; tweak via PipelineOptions if you need OCR, etc.
@@ -35,7 +36,12 @@ converter = DocumentConverter()  # basic converter; tweak via PipelineOptions if
 opensearch_host = os.getenv("OPENSEARCH_HOST", "localhost")
 opensearch_port = int(os.getenv("OPENSEARCH_PORT", "9200"))
 opensearch_username = os.getenv("OPENSEARCH_USERNAME", "admin")
-opensearch_password = os.getenv("OPENSEARCH_PASSWORD", "OSisgendb1!")
+opensearch_password = os.getenv("OPENSEARCH_PASSWORD")
+langflow_url = os.getenv("LANGFLOW_URL", "http://localhost:7860")
+flow_id = os.getenv("FLOW_ID")
+langflow_key = os.getenv("LANGFLOW_SECRET_KEY")
+
+
 
 es = AsyncOpenSearch(
     hosts=[{"host": opensearch_host, "port": opensearch_port}],
@@ -81,7 +87,11 @@ index_body = {
     }
 }
 
-async_client = patch_openai_with_mcp(AsyncOpenAI())  # Get the patched client back
+langflow_client = AsyncOpenAI(
+    base_url=f"{langflow_url}/api/v1",
+    api_key=langflow_key
+)
+patched_async_client = patch_openai_with_mcp(AsyncOpenAI())  # Get the patched client back
 
 async def wait_for_opensearch():
     """Wait for OpenSearch to be ready with retries"""
@@ -175,7 +185,7 @@ async def process_file_common(file_path: str, file_hash: str = None):
     slim_doc = extract_relevant(full_doc)
 
     texts = [c["text"] for c in slim_doc["chunks"]]
-    resp = await async_client.embeddings.create(model=EMBED_MODEL, input=texts)
+    resp = await patched_async_client.embeddings.create(model=EMBED_MODEL, input=texts)
     embeddings = [d.embedding for d in resp.data]
 
     # Index each chunk as a separate document
@@ -264,7 +274,7 @@ async def search_tool(query: str)-> dict[str, Any]:
                      - {"results": [chunks]} on success
     """
     # Embed the query
-    resp = await async_client.embeddings.create(model=EMBED_MODEL, input=[query])
+    resp = await patched_async_client.embeddings.create(model=EMBED_MODEL, input=[query])
     query_embedding = resp.data[0].embedding
     # Search using vector similarity on individual chunks
     search_body = {
@@ -299,14 +309,38 @@ async def chat_endpoint(request):
     if not prompt:
         return JSONResponse({"error": "Prompt is required"}, status_code=400)
 
-    response = await async_chat(async_client, prompt)
+    response = await async_chat(patched_async_client, prompt)
     return JSONResponse({"response": response})
 
+async def langflow_endpoint(request):
+    data = await request.json()
+    prompt = data.get("prompt", "")
+    
+    if not prompt:
+        return JSONResponse({"error": "Prompt is required"}, status_code=400)
+
+    if not langflow_url or not flow_id or not langflow_key:
+        return JSONResponse({"error": "LANGFLOW_URL, FLOW_ID, and LANGFLOW_KEY environment variables are required"}, status_code=500)
+
+    try:
+        response = await langflow_client.responses.create(
+            model=flow_id,
+            input=prompt
+        )
+        
+        response_text = response.output_text
+        
+        return JSONResponse({"response": response_text})
+        
+    except Exception as e:
+        return JSONResponse({"error": f"Langflow request failed: {str(e)}"}, status_code=500)
+
 app = Starlette(debug=True, routes=[
     Route("/upload",      upload,           methods=["POST"]),
     Route("/upload_path", upload_path,      methods=["POST"]),
     Route("/search",      search,           methods=["POST"]),
     Route("/chat",        chat_endpoint,    methods=["POST"]),
+    Route("/langflow",    langflow_endpoint, methods=["POST"]),
 ])
 
 if __name__ == "__main__":

uv.lock

@@ -9,7 +9,7 @@ resolution-markers = [
 
 [[package]]
 name = "agentd"
-version = "0.2.0.post2"
+version = "0.2.0.post3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "litellm" },
@@ -18,9 +18,9 @@ dependencies = [
     { name = "openai-agents" },
     { name = "pyyaml" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/91/fb/177ce5c7e8f8e8c4a4771b1da26e09e62780bf6f4042622654d05b101534/agentd-0.2.0.post2.tar.gz", hash = "sha256:b4cf8f5b727c1f0c0c9685762415e5affbc501758c0641eb9bd9c7d972c3ef30", size = 114513, upload-time = "2025-07-16T05:13:30.646Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6e/89/2bc397c80764d6acfeb6de7ac6d3ce8914f6f37f57307d1e2b6f7e8b0923/agentd-0.2.0.post3.tar.gz", hash = "sha256:765cb51798791eed32687b44305b20dd4130990471f0f1914afa2b292d09cb5e", size = 114530, upload-time = "2025-07-16T06:13:11.423Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e4/5d/232b01286225d5e6eff6e4e741411950d60002eeeceb7b2ab1e84d96cc66/agentd-0.2.0.post2-py3-none-any.whl", hash = "sha256:000a058758843739061c93503db5977c9734f2e690545dd21923bf9ae8a8a161", size = 13266, upload-time = "2025-07-16T05:13:29.389Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/f8/ced474722557f11e0a2f7e691371cf25c6a823507cc297971baa71bfbaac/agentd-0.2.0.post3-py3-none-any.whl", hash = "sha256:d05c6123a33d9f0b466fba4f7b378618c352ca367c65cd2c5a54f867af7b3cff", size = 13299, upload-time = "2025-07-16T06:13:10.034Z" },
 ]
 
 [[package]]
@@ -434,7 +434,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "agentd", specifier = ">=0.2.0.post2" },
+    { name = "agentd", specifier = ">=0.2.0.post3" },
     { name = "aiofiles", specifier = ">=24.1.0" },
     { name = "docling", specifier = ">=2.41.0" },
     { name = "opensearch-py", extras = ["async"], specifier = ">=3.0.0" },