Propose fix some typos and add newline if needed (#147)

Author: jeis4wpi

examples/notebooks/showcases/live-data-jupyter.ipynb

@@ -459,7 +459,7 @@
       "source": [
         "## Jupyter Notebooks & Streaming Data in Production\n",
         "\n",
-        "Congratulations! You have succesfully built a live data streaming pipeline with useful data visualisations and real-time alerts, right from a Jupyter notebook \ud83d\ude04\n",
+        "Congratulations! You have successfully built a live data streaming pipeline with useful data visualisations and real-time alerts, right from a Jupyter notebook \ud83d\ude04\n",
         "\n",
         "This is just a taste of what is possible. If you're interested in diving deeper and building a production-grade data science pipeline all the way from data exploration to deployment, you may want to check out the full-length [From Jupyter to Deploy](/developers/user-guide/deployment/from-jupyter-to-deploy) tutorial.\n",
         "\n",
@@ -499,4 +499,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/showcases/live_vector_indexing_pipeline.ipynb

@@ -66,7 +66,7 @@
         "\n",
         "Pathway Vectorstore enables building a document index on top of your documents without the\n",
         "complexity of ETL pipelines, managing different containers for storing, embedding, and serving.\n",
-        "It allows for easy to manage, always up-to-date, LLM pipelines accesible using a RESTful API\n",
+        "It allows for easy to manage, always up-to-date, LLM pipelines accessible using a RESTful API\n",
         "and with integrations to popular LLM toolkits such as Langchain and LlamaIndex.\n",
         "\n",
         "\n",

examples/notebooks/showcases/mistral_adaptive_rag_question_answering.ipynb

@@ -405,7 +405,7 @@
         "lines_to_next_cell": 2
       },
       "source": [
-        "#### 4. Local LLM Deployement\n",
+        "#### 4. Local LLM Deployment\n",
         "Due to its size and performance we decided to run the `Mistral 7B` Local Language Model. We deploy it as a service running on GPU, using `Ollama`.\n",
         "\n",
         "In order to run local LLM, refer to these steps:\n",
@@ -626,4 +626,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/tutorials/alert-deduplication.ipynb

@@ -173,7 +173,7 @@
       "id": "7",
       "metadata": {},
       "source": [
-        "To track the maximum value, we could write `input.groupby().reduce(max=pw.reducers.max(input.value))`. Here we want to keep track also *when* this maximum occured, therefore we use the `argmax_rows` utility function."
+        "To track the maximum value, we could write `input.groupby().reduce(max=pw.reducers.max(input.value))`. Here we want to keep track also *when* this maximum occurred, therefore we use the `argmax_rows` utility function."
       ]
     },
     {
@@ -242,7 +242,7 @@
       "id": "14",
       "metadata": {},
       "source": [
-        "Now we can send the alerts to e.g. Slack. We can do it similarily as in the [realtime log monitoring tutorial](/developers/templates/etl/realtime-log-monitoring#scenario-2-sending-the-alert-to-slack) by using `pw.io.subscribe`.\n",
+        "Now we can send the alerts to e.g. Slack. We can do it similarly as in the [realtime log monitoring tutorial](/developers/templates/etl/realtime-log-monitoring#scenario-2-sending-the-alert-to-slack) by using `pw.io.subscribe`.\n",
         "\n",
         "Here, for testing purposes, instead of sending an alert, we will store the accepted maxima in the list."
       ]
@@ -279,7 +279,7 @@
       "id": "17",
       "metadata": {},
       "source": [
-        "Let's run the program. Since the stream we defined is bounded (and we set high `input_rate` in the `generate_custom_stream`), the call to `pw.run` will finish quickly. Hovever, in most usecases, you will be streaming data (e.g. from kafka) indefinitely."
+        "Let's run the program. Since the stream we defined is bounded (and we set high `input_rate` in the `generate_custom_stream`), the call to `pw.run` will finish quickly. However, in most usecases, you will be streaming data (e.g. from kafka) indefinitely."
       ]
     },
     {
@@ -386,4 +386,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/tutorials/declarative_vs_imperative.ipynb

@@ -81,7 +81,7 @@
         "```\n",
         "we would expect three \"finished\" chunks: `(0,1,2)`, `(3,4,5,6)`, `(7,8)` and one unfinished chunk `(9,...)`.\n",
         "\n",
-        "One way to do this would be imperative style: go through rows one-by-one in order storing current chunk in a state and emiting it whenever `flag` is equal to True, while clearing the state.\n",
+        "One way to do this would be imperative style: go through rows one-by-one in order storing current chunk in a state and emitting it whenever `flag` is equal to True, while clearing the state.\n",
         "Even though, its not recommended approach, let's see how to code it in Pathway."
       ]
     },
@@ -181,7 +181,7 @@
       "source": [
         "Instead of manually managing state and control flow, Pathway allows you to define such logic using declarative constructs like `sort`, `iterate`, `groupby`. The result is a clear and concise pipeline that emits chunks of event times splitting the flag, showcasing the power and readability of declarative data processing.\n",
         "\n",
-        "In the following, we tell Pathway to propagate the starting time of each chunk across the rows. This is done by declaring a simple local rule: take the starting time of a chunk from previous row or use current event time. This rule is then iterated until fixed-point, so that the information is spreaded until all rows know the starting time of their chunk.\n",
+        "In the following, we tell Pathway to propagate the starting time of each chunk across the rows. This is done by declaring a simple local rule: take the starting time of a chunk from previous row or use current event time. This rule is then iterated until fixed-point, so that the information is spread until all rows know the starting time of their chunk.\n",
         "\n",
         "Then we can just group rows by starting time of the chunk to get a table of chunks."
       ]
@@ -389,4 +389,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}

examples/notebooks/tutorials/rag-evaluations.ipynb

@@ -1613,7 +1613,7 @@
         "\n",
         "Always structure your responses in the following format:\n",
         "Relevant contexts: [Write the relevant parts of the context for given question]\n",
-        "Answer: [Detailed reponse to the user's question that is grounded by the facts you listed]\n",
+        "Answer: [Detailed response to the user's question that is grounded by the facts you listed]\n",
         "\n",
         "If you don't know the answer, just say that you don't know.\n",
         "\n",
@@ -1757,4 +1757,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}