Update entrypoint.py

entrypoint.py

@@ -1,47 +1,47 @@
 import os
-import langchain
-import langchain_community
-from langchain_community.agents import tool
-from langchain_community.tools import searxng, qdrant, llma_server
+from fastapi import FastAPI
+from langchain_community.utilities import SearxSearchWrapper
+from langchain_community.vectorstores import Qdrant
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import LLaMAServer
+from langchain.schema import Document
 
 # Environment variables
-SEARXNG_URL = os.environ['SEARXNG_URL']
-QDRANT_URL = os.environ['QDRANT_URL']
-LLAMA_SERVER_URL = os.environ['LLAMA_SERVER_URL']
+SEARXNG_URL = os.environ["SEARXNG_URL"]
+QDRANT_URL = os.environ["QDRANT_URL"]
+LLAMA_SERVER_URL = os.environ["LLAMA_SERVER_URL"]
 
-# Define the LangChain agent
-class SearchAgent(langchain.Agent):
-    def __init__(self):
-        super().__init__()
-        self.searxng_tool = searxng.SearXNGTool(SEARXNG_URL=SEARXNG_URL)
-        self.qdrant_tool = qdrant.QDrantTool(QDRANT_URL=QDRANT_URL)
-        self.llma_server_tool = llma_server.LLaMAServerTool(LLAMA_SERVER_URL=LLAMA_SERVER_URL)
-
-    async def search(self, query):
-        # Send query to SearXNG API
-        results = await self.searxng_tool.search(query)
-        # Embed results with QDrant
-        await self.qdrant_tool.embed(results)
-        return results
-
-    async def query(self, query):
-        # Use LLaMA-Server to process query
-        response = await self.llma_server_tool.query(query)
-        return response
-
-# Create the LangChain agent
-agent = SearchAgent()
-
-# Define the API endpoints
-from fastapi import FastAPI
+# Initialize FastAPI
 app = FastAPI()
 
-@app.post("/search")
-async def search(query: str):
-    results = await agent.search(query)
-    return results
+# Initialize components
+searx = SearxSearchWrapper(searx_host=SEARXNG_URL)
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+qdrant = Qdrant(
+    url=QDRANT_URL,
+    collection_name="search_vectors",
+    embeddings=embedding_model
+)
+llm = LLaMAServer(endpoint_url=LLAMA_SERVER_URL)
 
-@app.post("/query")
-async def query(query: str):
-    response = await agent.query(query)
-    return response
+@app.post("/process")
+async def process(query: str):
+    # Step 1: Search with SearXNG
+    raw_results = searx.run(query)
+
+    # Step 2: Convert to LangChain Documents
+    docs = [Document(page_content=result) for result in raw_results.split("\n") if result.strip()]
+
+    # Step 3: Embed and store in Qdrant
+    qdrant.add_documents(docs)
+
+    # Step 4: Query Qdrant for similar vectors
+    matches = qdrant.similarity_search(query, k=3)
+
+    # Step 5: Combine raw + vector results
+    combined = "\n".join([doc.page_content for doc in docs + matches])
+
+    # Step 6: Send to LLaMA-Server
+    response = llm.invoke(combined)
+
+    return {"response": response}