Create app.py

app.py

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+import weaviate
+import langchain
+import gradio as gr
+from langchain.embeddings import CohereEmbeddings
+from langchain.document_loaders import UnstructuredFileLoader, PyPDFLoader
+from langchain.vectorstores import Qdrant
+import os
+import urllib.request
+import ssl
+import mimetypes
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+openai_api_key = os.getenv('OPENAI')
+cohere_api_key = os.getenv('COHERE')
+weaviate_api_key = os.getenv('WEAVIATE')
+weaviate_url = os.getenv('WEAVIATE_URL')
+
+# Weaviate connection
+auth_config = weaviate.auth.AuthApiKey(api_key=weaviate_api_key)
+client = weaviate.Client(url=weaviate_url, auth_client_secret=auth_config, additional_headers={"X-Cohere-Api-Key": cohere_api_key})
+vectorstore = Qdrant(client, index_name="Articles", text_key="text")
+vectorstore._query_attrs = ["text", "title", "url", "views", "lang", "_additional {distance}"]
+vectorstore.embedding = CohereEmbeddings(model="embed-multilingual-v2.0", cohere_api_key=cohere_api_key)
+
+def embed_pdf(file, collection_name):
+    # Save the uploaded file
+    filename = file.name
+    file_path = os.path.join('./', filename)
+    with open(file_path, 'wb') as f:
+        f.write(file.read())
+
+    # Checking filetype for document parsing
+    mime_type = mimetypes.guess_type(file_path)[0]
+        loader = UnstructuredFileLoader(file_path)
+        docs = loader.load()
+
+    # Generate embeddings
+    embeddings = CohereEmbeddings(model="embed-multilingual-v2.0", cohere_api_key=cohere_api_key)
+    
+    # Store documents in vectorstore (Qdrant)
+    for doc in docs:
+        embedding = embeddings.embed([doc['text']])
+        vectorstore_document = {
+            "text": doc['text'],
+            "embedding": embedding
+        }
+    collection_name = request.json.get("collection_name")
+    file_url = request.json.get("file_url")
+
+    # Download the file
+    folder_path = f'./'
+    os.makedirs(folder_path, exist_ok=True)
+    filename = file_url.split('/')[-1]
+    file_path = os.path.join(folder_path, filename)
+    
+    ssl._create_default_https_context = ssl._create_unverified_context
+    urllib.request.urlretrieve(file_url, file_path)
+
+    # Check filetype for document parsing
+    mime_type = mimetypes.guess_type(file_path)[0]
+        loader = UnstructuredFileLoader(file_path)
+        docs = loader.load()
+
+    # Generate embeddings
+    embeddings = CohereEmbeddings(model="embed-multilingual-v2.0", cohere_api_key=cohere_api_key)
+    
+    # Store documents in Weaviate
+    for doc in docs:
+        embedding = embeddings.embed([doc['text']])
+        weaviate_document = {
+            "text": doc['text'],
+            "embedding": embedding
+        }
+        client.data_object.create(data_object=weaviate_document, class_name=collection_name)
+
+    os.remove(file_path)
+    return {"message": f"Documents embedded in Weaviate collection '{collection_name}'"}
+
+def retrieve_info():
+    query = request.json.get("query")
+    llm = OpenAI(temperature=0, openai_api_key=openai_api_key)
+    qa = RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever())
+    result = qa({"query": query})
+    return {"results": result}
+
+# Gradio interface
+iface = gr.Interface(
+    fn=retrieve_info,
+    inputs=[
+        gr.inputs.Textbox(label="Query"),
+        gr.inputs.File(label="PDF File", type="file", optional=True)
+    ],
+    outputs="text",
+    allow_flagging="never"
+)
+
+# Embed PDF function
+iface.add_endpoint(
+    fn=embed_pdf,
+    inputs=[
+        gr.inputs.File(label="PDF File", type="file"),
+        gr.inputs.Textbox(label="Collection Name")
+    ],
+    outputs="text"
+)
+
+iface.launch()