Tabnine Chat Feedback

[amirbilu]

This is the place to leave feedback / discuss issues on Tabnine Chat for nvim. Note this feature is still in BETA - to join the BETA - send us your Tabnine Pro email to [email protected].

[shuxiao9058]

Recently I have port TabNine Chat to Emacs

https://github.com/shuxiao9058/tabnine

[amirbilu]

@shuxiao9058 this is awesome!!! Please leave us a message at [email protected] to get Tabnine Pro credits and Tabnine swag.

[shuxiao9058]

@shuxiao9058 this is awesome!!! Please leave us a message at [email protected] to get Tabnine Pro credits and Tabnine swag.

Thanks, @amirbilu email already send.

[chuckpr]

Been experimenting with Tabnine Chat in Neovim. Works well except that I get this message frequently:

Error executing vim.schedule lua callback: ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:60: Expected value but found unexpected end of string at character 8193
stack traceback:
        [C]: in function 'decode'
        ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:60: in function 'cb'
        vim/_editor.lua:325: in function <vim/_editor.lua:324>

[amirbilu]

Thanks! I'll add tomorrow some debug info so we can figure it out together.

On Wed, Jul 19, 2023, 23:06 Charles Pepe-Ranney @.***> wrote:

Been experimenting with Tabnine Chat in Neovim. Works well except that I get this message frequently:

Error executing vim.schedule lua callback: ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lu a:60: Expected value but found unexpected end of string at character 8193 stack traceback: [C]: in function 'decode' ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:60: in function 'cb' vim/_editor.lua:325: in function <vim/_editor.lua:324>

— Reply to this email directly, view it on GitHub https://github.com/codota/tabnine-nvim/issues/92#issuecomment-1642684039, or unsubscribe https://github.com/notifications/unsubscribe-auth/AB2TIGA7PH7JFKXLAR7SA5DXRA437ANCNFSM6AAAAAA2HBHUEI . You are receiving this because you were mentioned.Message ID: @.***>

[aarondill]

That's the JSON decode function, which means the chat binaries are outputting a JSON which is not syntactically correct (idk why or how though)

[amirbilu]

Yeah, for sure. It's not supposed to happen so I wonder what it outputs

On Wed, Jul 19, 2023, 23:09 Aaron Dill @.***> wrote:

That's the JSON decode function, which means the chat binaries are outputting a JSON which is not syntactically correct (idk why or how though)

— Reply to this email directly, view it on GitHub https://github.com/codota/tabnine-nvim/issues/92#issuecomment-1642687842, or unsubscribe https://github.com/notifications/unsubscribe-auth/AB2TIGCFAPMX2BYGZEUCSZLXRA5IXANCNFSM6AAAAAA2HBHUEI . You are receiving this because you were mentioned.Message ID: @.***>

[amirbilu]

Been experimenting with Tabnine Chat in Neovim. Works well except that I get this message frequently:

Error executing vim.schedule lua callback: ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:60: Expected value but found unexpected end of string at character 8193
stack traceback:
        [C]: in function 'decode'
        ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:60: in function 'cb'
        vim/_editor.lua:325: in function <vim/_editor.lua:324>

Can you please try this https://github.com/codota/tabnine-nvim/pull/94, you should get a nicer debug message. When you get it - attach it here. Appreciate it!

[chuckpr]

Ok, here are the messages I am seeing using Chat built from the debug-message branch:

[tabnine-nvim] Failed to decode chat message: {"id":"25","command":"update_chat_conversation","data":{"id":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","messages":[{"id":"c2bd9e03-2eb1-46e7-a591-1d334959296c","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"/explain-code","isBot":false,"timestamp":"1689879814043","intent":"explain-code","editorContext":{"fileCode":"import altair as alt\nimport pandas as pd\nfrom vega_datasets import data\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\nprint(melted_cars.head())import altair as alt\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\n","selectedCode":"","selectedCodeUsages":[]},"retrievalContext":{"documents":[]}},{"id":"24cc71cd-95db-4493-8e0c-42620fc5af85","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"The selected code is a Python script that imports the Altair and Pandas libraries, loads the cars dataset from the Vega dataset, creates a bar chart using the Altair library, and displays a jitter chart.\n\nThe script starts by importing the Altair and Pandas libraries:\n```python\nimport altair as alt\nimport pandas as pd\n```\n\nIt then loads the cars dataset from the Vega dataset and creates a bar chart using the Altair library:\n```python\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n```\n\nNext, the script loads the cars dataset again and uses Pandas to melt the data frame:\n```python\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n```\n\nThe script then creates a jitter chart for all the values in the melted_cars dataframe using Altair:\n```python\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n```\n\nFinally, the script displays the jitter chart:\n```python\n# Display the jitter chart\njitter_chart\n```","isBot":true,"timestamp":"1689879832134"},{"id":"c52306dc-6b04-44b7-ab57-f2201531f047","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"/explain-code","isBot":false,"timestamp":"1689879843080","intent":"explain-code","editorContext":{"fileCode":"import altair as alt\nimport pandas as pd\nfrom vega_datasets import data\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\nprint(melted_cars.head())import altair as alt\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.C
[tabnine-nvim] Failed to decode chat message: hart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\n","selectedCode":"# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n","selectedCodeUsages":[]},"retrievalContext":{"documents":[]}},{"id":"39850d60-2884-449b-88c9-32cedbb5eda1","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"The selected code is a Python script that uses Pandas to melt a data frame.\n\nThe script starts by loading the cars dataset from the Vega dataset and using Pandas to create an id_vars list and a value_vars list:\n```python\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n```\n\nThe id_vars list contains the columns \"Name\" and \"Miles_per_Gallon\", while the value_vars list contains the columns \"Horsepower\", \"Cylinders\", \"Displacement\", \"Weight_in_lbs\", \"Acceleration\", and \"Year\".\n\nThe script then uses Pandas to melt the data frame, which creates a new column for each value variable and combines the id_vars into a single \"variable\" column.","isBot":true,"timestamp":"1689879851807"}]}}

I see these mesagges after highlighting some code and using /explain-code in Chat.

[gunslingerfry]

The rust package has an implicit dependency on webkit2gtk-4.1 in case anybody is having difficulty compiling.

edit: even with the package I'm getting linker errors. My version of webkit2gtk-4.1 may be too new? I'm not familiar enough with rust to figure this out.

EndeavourOS (Arch) NVIM v0.9.1 libwebkit2gtk-4.1 version: 0.8.4 rust version: 1.71.0 (just updated from rustup)

[gunslingerfry]

Here is a gist with the error output so I don't spam this thread: https://gist.github.com/gunslingerfry/8a8bcd1adeba6c8aba017a9dce0714a3

[aarondill]

Ok, here are the messages I am seeing using Chat built from the debug-message branch:

[tabnine-nvim] Failed to decode chat message: {"id":"25","command":"update_chat_conversation","data":{"id":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","messages":[{"id":"c2bd9e03-2eb1-46e7-a591-1d334959296c","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"/explain-code","isBot":false,"timestamp":"1689879814043","intent":"explain-code","editorContext":{"fileCode":"import altair as alt\nimport pandas as pd\nfrom vega_datasets import data\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\nprint(melted_cars.head())import altair as alt\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\n","selectedCode":"","selectedCodeUsages":[]},"retrievalContext":{"documents":[]}},{"id":"24cc71cd-95db-4493-8e0c-42620fc5af85","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"The selected code is a Python script that imports the Altair and Pandas libraries, loads the cars dataset from the Vega dataset, creates a bar chart using the Altair library, and displays a jitter chart.\n\nThe script starts by importing the Altair and Pandas libraries:\n```python\nimport altair as alt\nimport pandas as pd\n```\n\nIt then loads the cars dataset from the Vega dataset and creates a bar chart using the Altair library:\n```python\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n```\n\nNext, the script loads the cars dataset again and uses Pandas to melt the data frame:\n```python\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n```\n\nThe script then creates a jitter chart for all the values in the melted_cars dataframe using Altair:\n```python\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n```\n\nFinally, the script displays the jitter chart:\n```python\n# Display the jitter chart\njitter_chart\n```","isBot":true,"timestamp":"1689879832134"},{"id":"c52306dc-6b04-44b7-ab57-f2201531f047","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"/explain-code","isBot":false,"timestamp":"1689879843080","intent":"explain-code","editorContext":{"fileCode":"import altair as alt\nimport pandas as pd\nfrom vega_datasets import data\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.Chart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\nprint(melted_cars.head())import altair as alt\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Create a bar chart using the Altair library\nalt.Chart(cars).mark_bar().encode(\n    x=\"Horsepower:Q\", y=\"Miles_per_Gallon:Q\", color=\"Origin:N\"\n)\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n\n# Create a jitter chart for all the values in the melted_cars dataframe\njitter_chart = (\n    alt.C

[tabnine-nvim] Failed to decode chat message: hart(melted_cars)\n    .mark_circle(size=60)\n    .encode(\n        x=\"value:Q\",\n        y=\"variable:N\",\n        color=\"variable:N\",\n        tooltip=[\"value:Q\", \"variable:N\"],\n    )\n    .transform_calculate(jitter=\"random() - 0.5\")\n    .transform_joinaggregate(mean=\"mean(value)\", count=\"count(value)\")\n    .transform_calculate(\n        x=\"if(datum.count > 1, (datum.mean - datum.stddev) + jitter, mean)\",\n        y=\"if(datum.count > 1, (datum.mean + datum.stddev) + jitter, mean)\",\n    )\n    .transform_calculate(r=\"min(width, height) / 2\")\n    .transform_scale(x=[-r, r], y=[-r, r])\n)\n\n# Display the jitter chart\njitter_chart\n# Print the first few rows of the melted data frame\n","selectedCode":"# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n","selectedCodeUsages":[]},"retrievalContext":{"documents":[]}},{"id":"39850d60-2884-449b-88c9-32cedbb5eda1","conversationId":"dc4e2d3c-041b-4fe4-9b80-35c7116e78e6","text":"The selected code is a Python script that uses Pandas to melt a data frame.\n\nThe script starts by loading the cars dataset from the Vega dataset and using Pandas to create an id_vars list and a value_vars list:\n```python\n# Load the data from the Vega dataset\ncars = data.cars()\n\n# Melt the data frame\nmelted_cars = pd.melt(\n    cars,\n    id_vars=[\"Name\", \"Miles_per_Gallon\"],\n    value_vars=[\n        \"Horsepower\",\n        \"Cylinders\",\n        \"Displacement\",\n        \"Weight_in_lbs\",\n        \"Acceleration\",\n        \"Year\",\n    ],\n)\n```\n\nThe id_vars list contains the columns \"Name\" and \"Miles_per_Gallon\", while the value_vars list contains the columns \"Horsepower\", \"Cylinders\", \"Displacement\", \"Weight_in_lbs\", \"Acceleration\", and \"Year\".\n\nThe script then uses Pandas to melt the data frame, which creates a new column for each value variable and combines the id_vars into a single \"variable\" column.","isBot":true,"timestamp":"1689879851807"}]}}

I see these mesagges after highlighting some code and using /explain-code in Chat.

It seems like something is going wrong, which is putting a newline into the json message.

Something in the returned file code

[aarondill]

@chuckpr If you don't mind, can you attempt to create a minimal file where you can reproduce this, and share it (perhaps in a gist)? I suspect that something in the handling of user code (perhaps a length issue?) is going wrong, so being able to reproduce this locally would be very helpful.

[chuckpr]

Sure, to reproduce the error, I ran /expain-code five times. On the fifth invocation, I started to see the error.

Gist: https://gist.github.com/chuckpr/8a67b3685b9631f4d633821143df3747

[amirbilu]

@aarondill did you manage to reproduce this? it seems to work fine for me

[aarondill]

@aarondill did you manage to reproduce this? it seems to work fine for me

I haven't had the time to try to. I won't be able to try for at least a few days.

[shuxiao9058]

Recently I have port TabNine Chat to Emacs

https://github.com/shuxiao9058/tabnine

TabNine for Emacs now is on Melpa

[gunslingerfry]

Yay! @aarondill got me sorted. I had out of sync packages. Silly me not trying a system upgrade.

[aarondill]

@aarondill did you manage to reproduce this? it seems to work fine for me

@amirbilu Having just compiled and tested this on my machine, I can't seem to reproduce the error.

@chuckpr Can you still reproduce this issue on your machine? If so, does rerunning dl_binaries.sh fix the issue? If it does not, can you provide detailed system information and reproduction steps using the templates below?

An example for system information:

> uname -a
results_here
> cat /etc/os-release || cat /usr/lib/os-release
results_here
> cd /path/to/tabnine-nvim
> ls -A ./binaries
results_here
> cat chat_state.json
results_here
> cat ./chat/target/.rustc_info.json
results_here

Reproduction steps (an example):

1. install tabnine-nvim using this file (FILENAME):

contents of file

2. open nvim test.py
3. Go to line #
4. Press V
5. Select lines # through #
6. Press : and type TabnineChatNew
7. Type /explain-code repeatedly (5 times?)
8. See Error executing vim.schedule lua callback... error in original nvim window.

[aarondill]

@amirbilu I think we need to put a list of dependencies in the readme for the chat plugin. We seem to be attracting people who are new to compiling their own software, and without a list of dependencies, we are getting issues like #96 and @gunslingerfry's issue above.

For now, it could just be the same list from chat.yaml and we can add to it as we encounter further dependencies, but something should be there.

[aemonge]

Some feedback from me, and from neovim.

• Would be super useful to have the ability to select text for context. I've noticed the chat uses my current buffer as context for my questions, and I rarely want it to reply with a full file suggestion; usually I'm in querying for a specific function.
• A vi-mode for the input would be really nice. I know you can have binding to input-rc for emacs, or vi. That would be good.
• CLI chat would be cool too. We often forget commands, or want to "unit" test bulk files.

Finally, this isn't a request but just awareness. I'm paying for chat-GPT4 mainly to develop, so if this chat is smarter or same-ish than GPT-4 I wouldn't mind migrating my payment from GPT4 to here :). Having the chat editor-integrated and narrow to development, it's what I'm looking for and craving.

Furthermore, please take this feedback as it is, positive feedback from a delighted customer. <3

[amirbilu]

Andres,

Thanks for the feedback! I'm passing it to the team

On Wed, Aug 2, 2023, 10:27 Andres Monge @.***> wrote:

Some feedback from me, and from neovim.

• Would be super useful to have the ability to select text for context. I've noticed the chat uses my current buffer as context for my questions, and I rarely want it to reply with a full file suggestion; usually I'm in querying for a specific function.
• A vi-mode for the input would be really nice. I know you can have binding to input-rc for emacs, or vi. That would be good.
• CLI chat would be cool too. We often forget commands, or want to "unit" test bulk files.

Finally, this isn't a request but just awareness. I'm paying for chat-GPT4 mainly to develop, so if this chat is smarter or same-ish than GPT-4 I wouldn't mind migrating my payment from GPT4 to here :). Having the chat editor-integrated and narrow to development, it's what I'm looking for and craving.

Furthermore, please take this feedback as it is, positive feedback from a delighted customer. <3

— Reply to this email directly, view it on GitHub https://github.com/codota/tabnine-nvim/issues/92#issuecomment-1661654113, or unsubscribe https://github.com/notifications/unsubscribe-auth/AB2TIGE5D3IHO25CKIAXDZDXTH6OHANCNFSM6AAAAAA2HBHUEI . You are receiving this because you were mentioned.Message ID: @.***>

[allan-simon]

Hello my neovim is running inside a dockerized environment (so without X) , so the same as it possible to get tabnine hub opening by doing port redirection, is there a way to open the chat from my host and point it to my neovim instance ?

[gsharma-jiggzy]

It would be nice to have vim native like chatgpt.nvim

https://github.com/jackMort/ChatGPT.nvim

[aemonge]

Or a simple terminal-based integration such as https://github.com/kardolus/chatgpt-cli , this could serve more user that only neovim ones. And us neovim can simply :terminal chatgtp-cli. Right @gsharma-jiggzy ?

[nfwyst]

chat is not easy to use, maybe the model should be upgraded....

for example, i have code like:

function Hello(x) {
  console.log("Hello" + " " + x);
}

Hello("marvin");

tabnine's answer is

this is a good start point...

[AlexanderShvaykin]

I have the error:

tabnine-nvim/lua/tabnine/chat/codelens.lua:89: attempt to index field 'range' (a nil value)

[AlexanderShvaykin]

After responding from the chat, I get an error message

Error executing vim.schedule lua callback: ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:82: Expected value but found unexpected end of string at character 8193
stack traceback:
        [C]: in function 'decode'
        ...ck/packer/start/tabnine-nvim/lua/tabnine/chat/binary.lua:82: in function ''
        vim/_editor.lua: in function <vim/_editor.lua:0>

[amirbilu]

Hi @AlexanderShvaykin does it happen constantly?

[MJAS1]

I am trying to get TabnineChat to work, but the command only opens a blank window with nothing in it. I have not sent an email to [email protected] to request chat to be enabled as I noticed that the instruction asking to do so was removed from the README. Was it removed on purpose?