Set a different embedding model

app.py

@@ -10,104 +10,146 @@ from llama_index.vector_stores.types import MetadataFilters, ExactMatchFilter
 
 inference_api_key = st.secrets["INFRERENCE_API_TOKEN"]
 
-embed_model_name = st.text_input(
-    'Embed Model name', "Gooly/gte-small-en-fine-tuned-e-commerce")
+# embed_model_name = st.text_input(
+#     'Embed Model name', "Gooly/gte-small-en-fine-tuned-e-commerce")
 
-llm_model_name = st.text_input(
-    'Embed Model name', "mistralai/Mistral-7B-Instruct-v0.2")
+# llm_model_name = st.text_input(
+#     'Embed Model name', "mistralai/Mistral-7B-Instruct-v0.2")
+
+embed_model_name = "jinaai/jina-embedding-s-en-v1"
+llm_model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+
+llm = HuggingFaceInferenceAPI(
+    model_name=llm_model_name, token=inference_api_key)
+
+embed_model = HuggingFaceInferenceAPIEmbedding(
+    model_name=embed_model_name,
+    token=inference_api_key,
+    model_kwargs={"device": ""},
+    encode_kwargs={"normalize_embeddings": True},
+)
+
+service_context = ServiceContext.from_defaults(
+    embed_model=embed_model, llm=llm)
 
 query = st.text_input(
-    'Query', "What is the price of the product?")
+    'Query', "What is the price of the product?"
+)
 
 html_file = st.file_uploader("Upload a html file", type=["html"])
 
-if st.button('Start Pipeline'):
-    if html_file is not None and embed_model_name is not None and llm_model_name is not None and query is not None:
-        st.write('Running Pipeline')
-        llm = HuggingFaceInferenceAPI(
-            model_name=llm_model_name, token=inference_api_key)
+if html_file is not None:
+    stringio = StringIO(html_file.getvalue().decode("utf-8"))
+    string_data = stringio.read()
+    with st.expander("Uploaded HTML"):
+        st.write(string_data)
+
+    document_id = str(uuid.uuid4())
+
+    document = Document(text=string_data)
+    document.metadata["id"] = document_id
+    documents = [document]
+
+    filters = MetadataFilters(
+        filters=[ExactMatchFilter(key="id", value=document_id)])
+
+    index = VectorStoreIndex.from_documents(
+        documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
+
+    query_engine = index.as_query_engine(
+        filters=filters, service_context=service_context)
+
+    response = query_engine.query(query)
+
+    st.write(response.response)
+
+# if st.button('Start Pipeline'):
+#     if html_file is not None and embed_model_name is not None and llm_model_name is not None and query is not None:
+#         st.write('Running Pipeline')
+#         llm = HuggingFaceInferenceAPI(
+#             model_name=llm_model_name, token=inference_api_key)
 
-        embed_model = HuggingFaceInferenceAPIEmbedding(
-            model_name=embed_model_name,
-            token=inference_api_key,
-            model_kwargs={"device": ""},
-            encode_kwargs={"normalize_embeddings": True},
-        )
+#         embed_model = HuggingFaceInferenceAPIEmbedding(
+#             model_name=embed_model_name,
+#             token=inference_api_key,
+#             model_kwargs={"device": ""},
+#             encode_kwargs={"normalize_embeddings": True},
+#         )
 
-        service_context = ServiceContext.from_defaults(
-            embed_model=embed_model, llm=llm)
+#         service_context = ServiceContext.from_defaults(
+#             embed_model=embed_model, llm=llm)
 
-        stringio = StringIO(html_file.getvalue().decode("utf-8"))
-        string_data = stringio.read()
-        with st.expander("Uploaded HTML"):
-            st.write(string_data)
+#         stringio = StringIO(html_file.getvalue().decode("utf-8"))
+#         string_data = stringio.read()
+#         with st.expander("Uploaded HTML"):
+#             st.write(string_data)
 
-        document_id = str(uuid.uuid4())
+#         document_id = str(uuid.uuid4())
 
-        document = Document(text=string_data)
-        document.metadata["id"] = document_id
-        documents = [document]
+#         document = Document(text=string_data)
+#         document.metadata["id"] = document_id
+#         documents = [document]
 
-        filters = MetadataFilters(
-            filters=[ExactMatchFilter(key="id", value=document_id)])
+#         filters = MetadataFilters(
+#             filters=[ExactMatchFilter(key="id", value=document_id)])
 
-        index = VectorStoreIndex.from_documents(
-            documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
+#         index = VectorStoreIndex.from_documents(
+#             documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
 
-        retriever = index.as_retriever()
+#         retriever = index.as_retriever()
 
-        ranked_nodes = retriever.retrieve(
-            query)
+#         ranked_nodes = retriever.retrieve(
+#             query)
 
-        with st.expander("Ranked Nodes"):
-            for node in ranked_nodes:
-                st.write(node.node.get_content(), "-> Score:", node.score)
+#         with st.expander("Ranked Nodes"):
+#             for node in ranked_nodes:
+#                 st.write(node.node.get_content(), "-> Score:", node.score)
 
-        query_engine = index.as_query_engine(
-            filters=filters, service_context=service_context)
+#         query_engine = index.as_query_engine(
+#             filters=filters, service_context=service_context)
 
-        response = query_engine.query(query)
+#         response = query_engine.query(query)
 
-        st.write(response.response)
+#         st.write(response.response)
 
-        st.write(response.source_nodes)
+#         st.write(response.source_nodes)
 
-    else:
-        st.error('Please fill in all the fields')
-else:
-    st.write('Press start to begin')
+#     else:
+#         st.error('Please fill in all the fields')
+# else:
+#     st.write('Press start to begin')
 
-# if html_file is not None:
-#     stringio = StringIO(html_file.getvalue().decode("utf-8"))
-#     string_data = stringio.read()
-#     with st.expander("Uploaded HTML"):
-#         st.write(string_data)
+# # if html_file is not None:
+# #     stringio = StringIO(html_file.getvalue().decode("utf-8"))
+# #     string_data = stringio.read()
+# #     with st.expander("Uploaded HTML"):
+# #         st.write(string_data)
 
-#     document_id = str(uuid.uuid4())
+# #     document_id = str(uuid.uuid4())
 
-#     document = Document(text=string_data)
-#     document.metadata["id"] = document_id
-#     documents = [document]
+# #     document = Document(text=string_data)
+# #     document.metadata["id"] = document_id
+# #     documents = [document]
 
-#     filters = MetadataFilters(
-#         filters=[ExactMatchFilter(key="id", value=document_id)])
+# #     filters = MetadataFilters(
+# #         filters=[ExactMatchFilter(key="id", value=document_id)])
 
-#     index = VectorStoreIndex.from_documents(
-#         documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
+# #     index = VectorStoreIndex.from_documents(
+# #         documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
 
-#     retriever = index.as_retriever()
+# #     retriever = index.as_retriever()
 
-#     ranked_nodes = retriever.retrieve(
-#         "Get me all the information about the product")
+# #     ranked_nodes = retriever.retrieve(
+# #         "Get me all the information about the product")
 
-#     with st.expander("Ranked Nodes"):
-#         for node in ranked_nodes:
-#             st.write(node.node.get_content(), "-> Score:", node.score)
+# #     with st.expander("Ranked Nodes"):
+# #         for node in ranked_nodes:
+# #             st.write(node.node.get_content(), "-> Score:", node.score)
 
-#     query_engine = index.as_query_engine(
-#         filters=filters, service_context=service_context)
+# #     query_engine = index.as_query_engine(
+# #         filters=filters, service_context=service_context)
 
-#     response = query_engine.query(
-#         "Get me all the information about the product")
+# #     response = query_engine.query(
+# #         "Get me all the information about the product")
 
-#     st.write(response)
+# #     st.write(response)