Feature/improve speed

README.md

@@ -78,16 +78,12 @@
 - Easy to use
 - Easy to deploy
 
-## 💻 Screenshots
+## 💻 Demo
 
 
 <p align="center">
   <a href="https://prometh.ai">
-    <img src="assets/img_5.png" alt="Description1" width="15%" height="50%" />
-    <img src="assets/img_1.png" alt="Description2" width="15%" height="50%" />
-    <img src="assets/img_2.png" alt="Description3" width="15%" height="50%" />
-    <img src="assets/img_3.png" alt="Description4" width="15%" height="50%" />
-    <img src="assets/img_4.png" alt="Description4" width="15%" height="50%" />
+    <img  src="https://promethai-public-assets.s3.eu-west-1.amazonaws.com/product_demo-min.gif" alt="AI assistant" width="25%" height="50%"/>
   </a>
 </p>
 

examples/level_2/.env.template

@@ -5,4 +5,6 @@ OPENAI_TEMPERATURE=0.0
 REDIS_HOST=redis
 ZAPIER_NLA_API_KEY
 LOCAL_DEV = False
-REPLICATE_API_TOKEN
+REPLICATE_API_TOKEN
+WEAVIATE_URL
+WEAVIATE_API_KEY

examples/level_2/Readme.md

@@ -30,5 +30,15 @@ The fast API endpoint accepts prompts and PDF files and returns a JSON object wi
     -X POST                                                                                             
     -F "prompt=The quick brown fox"                                                                     
     -F "file=@/path/to/file.pdf"                                                                       
-    http://localhost:8000/generate/                                                                    
-```
+    http://localhost:8000/upload/                                                                    
+```
+
+{
+  "payload": {
+    "user_id": "681",
+    "session_id": "471",
+    "model_speed": "slow",
+    "prompt": "Temperature=Cold;Food Type=Ice Cream",
+    "pdf_url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
+  }
+}

examples/level_2/api.py

@@ -1,6 +1,6 @@
 from langchain.document_loaders import PyPDFLoader
 
-from level_2_pdf_vectorstore__dlt_contracts import ShortTermMemory
+from level_2_pdf_vectorstore__dlt_contracts import Memory
 from fastapi import FastAPI
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
@@ -15,8 +15,7 @@
 from fastapi import HTTPException
 from fastapi import FastAPI, UploadFile, File
 from typing import List
-from level_2_pdf_vectorstore__dlt_contracts import ShortTermMemory
-
+import requests
 # Set up logging
 logging.basicConfig(
     level=logging.INFO,  # Set the logging level (e.g., DEBUG, INFO, WARNING, ERROR, CRITICAL)
@@ -36,8 +35,6 @@
 from fastapi import Depends
 
 
-class Payload(BaseModel):
-    payload: str
 class ImageResponse(BaseModel):
     success: bool
     message: str
@@ -66,39 +63,98 @@ def health_check():
 
 #curl -X POST -H "Content-Type: application/json" -d '{"data": "YourPayload"}' -F "files=@/path/to/your/pdf/file.pdf" http://127.0.0.1:8000/upload/
 
-@app.post("/upload/")
+from fastapi import FastAPI, UploadFile, File
+import requests
+import os
+import json
+
+app = FastAPI()
+
+
+from io import BytesIO
+
+
+class Payload(BaseModel):
+    payload: Dict[str, Any]
+
+@app.post("/upload/", response_model=dict)
 async def upload_pdf_and_payload(
         payload: Payload,
-        files: List[UploadFile] = File(...),
+        # files: List[UploadFile] = File(...),
 ):
     try:
         # Process the payload
-        payload_data = payload.payload
+        decoded_payload = payload.payload
+    # except:
+    #     pass
+    #
+    # return JSONResponse(content={"response": decoded_payload}, status_code=200)
+
+        # Download the remote PDF if URL is provided
+        if 'pdf_url' in decoded_payload:
+            pdf_response = requests.get(decoded_payload['pdf_url'])
+            pdf_content = pdf_response.content
+
+            logging.info("Downloaded PDF from URL")
+
+            # Create an in-memory file-like object for the PDF content
+            pdf_stream = BytesIO(pdf_content)
+
+            contents = pdf_stream.read()
 
-        decoded_payload = json.loads(payload_data)
-        # Process each uploaded PDF file
-        results = []
-        for file in files:
-            contents = await file.read()
             tmp_location = os.path.join('/tmp', "tmp.pdf")
             with open(tmp_location, 'wb') as tmp_file:
                 tmp_file.write(contents)
+
+            logging.info("Wrote PDF from URL")
+
+            # Process the PDF using PyPDFLoader
             loader = PyPDFLoader(tmp_location)
             pages = loader.load_and_split()
+            logging.info(" PDF split into pages")
+            Memory_ = Memory(index_name="my-agent", user_id='555' )
+            await Memory_.async_init()
+            Memory_._run_buffer(user_input="I want to get a schema for my data")
 
-            stm = ShortTermMemory( user_id=decoded_payload['user_id'])
-            stm.episodic_buffer.main_buffer(prompt=decoded_payload['prompt'], pages=pages)
-            # Here you can perform your processing on the PDF contents
-            results.append({"filename": file.filename, "size": len(contents)})
-
-        return {"message": "Upload successful", "results": results}
 
-    except Exception as e:
-        return {"error": str(e)}
+            # Run the buffer
+            response = Memory_._run_buffer(user_input="I want to get a schema for my data")
+            return JSONResponse(content={"response": response}, status_code=200)
 
+            #to do: add the user id to the payload
+            #to do add the raw pdf to payload
+            # bb = await Memory_._run_buffer(user_input=decoded_payload['prompt'])
+            # print(bb)
 
 
+    except Exception as e:
 
+        return {"error": str(e)}
+            # Here you can perform your processing on the PDF contents
+            # results.append({"filename": file.filename, "size": len(contents)})
+
+            # Append the in-memory file to the files list
+            # files.append(UploadFile(pdf_stream, filename="downloaded.pdf"))
+    #
+    #     # Process each uploaded PDF file
+    #     results = []
+    #     for file in files:
+    #         contents = await file.read()
+    #         tmp_location = os.path.join('/tmp', "tmp.pdf")
+    #         with open(tmp_location, 'wb') as tmp_file:
+    #             tmp_file.write(contents)
+    #         loader = PyPDFLoader(tmp_location)
+    #         pages = loader.load_and_split()
+    #
+    #         stm = ShortTermMemory(user_id=decoded_payload['user_id'])
+    #         stm.episodic_buffer.main_buffer(prompt=decoded_payload['prompt'], pages=pages)
+    #         # Here you can perform your processing on the PDF contents
+    #         results.append({"filename": file.filename, "size": len(contents)})
+    #
+    #     return {"message": "Upload successful", "results": results}
+    #
+    # except Exception as e:
+    #     return {"error": str(e)}
 
 
 # @app.post("/clear-cache", response_model=dict)