First commit

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from huggingface_hub import InferenceClient
+
+from langchain_core.messages import HumanMessage, AIMessage
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, MessagesState, StateGraph
+
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# HuggingFace token
+HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN", os.getenv("HUGGINGFACE_TOKEN"))
+
+# Initialize the HuggingFace model
+model = InferenceClient(
+    model="Qwen/Qwen2.5-72B-Instruct",
+    api_key=HUGGINGFACE_TOKEN
+)
+
+# Define the function that calls the model
+def call_model(state: MessagesState):
+    """
+    Call the model with the given messages
+
+    Args:
+        state: MessagesState
+
+    Returns:
+        dict: A dictionary containing the generated text and the thread ID
+    """
+    # Convert LangChain messages to HuggingFace format
+    hf_messages = []
+    for msg in state["messages"]:
+        if isinstance(msg, HumanMessage):
+            hf_messages.append({"role": "user", "content": msg.content})
+        elif isinstance(msg, AIMessage):
+            hf_messages.append({"role": "assistant", "content": msg.content})
+
+    # Call the API
+    response = model.chat_completion(
+        messages=hf_messages,
+        temperature=0.5,
+        max_tokens=64,
+        top_p=0.7
+    )
+
+    # Convert the response to LangChain format
+    ai_message = AIMessage(content=response.choices[0].message.content)
+    return {"messages": state["messages"] + [ai_message]}
+
+# Define the graph
+workflow = StateGraph(state_schema=MessagesState)
+
+# Define the node in the graph
+workflow.add_edge(START, "model")
+workflow.add_node("model", call_model)
+
+# Add memory
+memory = MemorySaver()
+graph_app = workflow.compile(checkpointer=memory)
+
+# Define the data model for the request
+class QueryRequest(BaseModel):
+    query: str
+    thread_id: str = "default"
+
+# Create the FastAPI application
+app = FastAPI(title="LangChain FastAPI", description="API to generate text using LangChain and LangGraph")
+
+# Welcome endpoint
+@app.get("/")
+async def api_home():
+    """Welcome endpoint"""
+    return {"detail": "Welcome to FastAPI, Langchain, Docker tutorial"}
+
+# Generate endpoint
+@app.post("/generate")
+async def generate(request: QueryRequest):
+    """
+    Endpoint to generate text using the language model
+
+    Args:
+        request: QueryRequest
+        query: str
+        thread_id: str = "default"
+
+    Returns:
+        dict: A dictionary containing the generated text and the thread ID
+    """
+    try:
+        # Configure the thread ID
+        config = {"configurable": {"thread_id": request.thread_id}}
+
+        # Create the input message
+        input_messages = [HumanMessage(content=request.query)]
+
+        # Invoke the graph
+        output = graph_app.invoke({"messages": input_messages}, config)
+
+        # Get the model response
+        response = output["messages"][-1].content
+
+        return {
+            "generated_text": response,
+            "thread_id": request.thread_id
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error al generar texto: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)