asdf

.gitignore

@@ -0,0 +1,3 @@
+.env
+__pycache__/
+*.pyc 

Dockerfile

@@ -7,6 +7,7 @@ ENV PATH="/home/user/.local/bin:$PATH"
 WORKDIR /app
 
 COPY --chown=user ./requirements.txt requirements.txt
+COPY --chown=user ./.env .env
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY --chown=user . /app

app.py

@@ -1,7 +1,46 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from langchain_groq import ChatGroq
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+import os
 
+# Load environment variables
+load_dotenv()
+
+# Initialize FastAPI app
 app = FastAPI()
 
+# Create a request model
+class SearchQuery(BaseModel):
+    query: str
+
+# Initialize LangChain with Groq
+llm = ChatGroq(
+    temperature=0.7,
+    model_name="mixtral-8x7b-32768",  # You can also use "llama2-70b-4096"
+)
+
+prompt_template = PromptTemplate(
+    input_variables=["query"],
+    template="Please provide a detailed response to the following query: {query}"
+)
+chain = LLMChain(llm=llm, prompt=prompt_template)
+
+@app.post("/search")
+async def process_search(search_query: SearchQuery):
+    try:
+        # Process the query using LangChain
+        response = chain.run(query=search_query.query)
+        
+        return {
+            "status": "success",
+            "response": response
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
 @app.get("/")
-def greet_json():
-    return {"Hello": "World!"} 
+async def root():
+    return {"message": "Search API is running"} 

requirements.txt

@@ -1,2 +1,6 @@
 fastapi
-uvicorn[standard] 
+uvicorn
+python-dotenv
+langchain
+openai
+pydantic 

vercel.json

@@ -0,0 +1 @@
+