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1 month ago Pull Request Test Coverage Report for Build 11429325160
Details
- 0 of 50 (0.0%) changed or added relevant lines in 2 files are covered.
- 48 unchanged lines in 26 files lost coverage.
- Overall coverage decreased (-0.2%) to 60.052%
| Changes Missing Coverage | Covered Lines | Changed/Added Lines | % | ||
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| src/main/scala/com/johnsnowlabs/nlp/pretrained/ResourceDownloader.scala | 0 | 2 | 0.0% | ||
| src/main/scala/com/johnsnowlabs/nlp/PromptAssembler.scala | 0 | 48 | 0.0% | ||
| <!-- | Total: | 0 | 50 | 0.0% | --> |
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| Change from base Build 11409007316: | -0.2% |
| Covered Lines: | 8985 |
| Relevant Lines: | 14962 |
Description
This PR adds the a new annotator: The PromptAssembler.
This annotator takes a LLM chat template, a sequence of messages and produces a prompt for LLMs.
It expects an array of two-tuples as the type of the input column (one array of tuples per row). The first element of the tuples should be the role and the second element is the text of the message. Possible roles are "system", "user" and "assistant".
At the moment, this annotator uses llama.cpp as a backend to parse and apply the templates. llama.cpp uses basic pattern matching to determine the type of the template, then applies a basic version of the template to the messages. This means that more advanced templates are not supported.
Scala Example
```scala // Batches (whole conversations) of arrays of messages val data: Seq[Seq[(String, String)]] = Seq( Seq( ("system", "You are a helpful assistant."), ("assistant", "Hello there, how can I help you?"), ("user", "I need help with organizing my room."))) val dataDF = data.toDF("messages") // llama3.1 val template = "{{- bos_token }} {%- if custom_tools is defined %} {%- set tools = custom_tools %} {%- " + "endif %} {%- if not tools_in_user_message is defined %} {%- set tools_in_user_message = true %} {%- " + "endif %} {%- if not date_string is defined %} {%- set date_string = \"26 Jul 2024\" %} {%- endif %} " + "{%- if not tools is defined %} {%- set tools = none %} {%- endif %} {#- This block extracts the " + "system message, so we can slot it into the right place. #} {%- if messages[0]['role'] == 'system' %}" + " {%- set system_message = messages[0]['content']|trim %} {%- set messages = messages[1:] %} {%- else" + " %} {%- set system_message = \"\" %} {%- endif %} {#- System message + builtin tools #} {{- " + "\"<|start_header_id|>system<|end_header_id|>\\n\\n\" }} {%- if builtin_tools is defined or tools is " + "not none %} {{- \"Environment: ipython\\n\" }} {%- endif %} {%- if builtin_tools is defined %} {{- " + "\"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}} " + "{%- endif %} {{- \"Cutting Knowledge Date: December 2023\\n\" }} {{- \"Today Date: \" + date_string " + "+ \"\\n\\n\" }} {%- if tools is not none and not tools_in_user_message %} {{- \"You have access to " + "the following functions. To call a function, please respond with JSON for a function call.\" }} {{- " + "'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its" + " value}.' }} {{- \"Do not use variables.\\n\\n\" }} {%- for t in tools %} {{- t | tojson(indent=4) " + "}} {{- \"\\n\\n\" }} {%- endfor %} {%- endif %} {{- system_message }} {{- \"<|eot_id|>\" }} {#- " + "Custom tools are passed in a user message with some extra guidance #} {%- if tools_in_user_message " + "and not tools is none %} {#- Extract the first user message so we can plug it in here #} {%- if " + "messages | length != 0 %} {%- set first_user_message = messages[0]['content']|trim %} {%- set " + "messages = messages[1:] %} {%- else %} {{- raise_exception(\"Cannot put tools in the first user " + "message when there's no first user message!\") }} {%- endif %} {{- " + "'<|start_header_id|>user<|end_header_id|>\\n\\n' -}} {{- \"Given the following functions, please " + "respond with a JSON for a function call \" }} {{- \"with its proper arguments that best answers the " + "given prompt.\\n\\n\" }} {{- 'Respond in the format {\"name\": function name, \"parameters\": " + "dictionary of argument name and its value}.' }} {{- \"Do not use variables.\\n\\n\" }} {%- for t in " + "tools %} {{- t | tojson(indent=4) }} {{- \"\\n\\n\" }} {%- endfor %} {{- first_user_message + " + "\"<|eot_id|>\"}} {%- endif %} {%- for message in messages %} {%- if not (message.role == 'ipython' " + "or message.role == 'tool' or 'tool_calls' in message) %} {{- '<|start_header_id|>' + message['role']" + " + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }} {%- elif 'tool_calls' in " + "message %} {%- if not message.tool_calls|length == 1 %} {{- raise_exception(\"This model only " + "supports single tool-calls at once!\") }} {%- endif %} {%- set tool_call = message.tool_calls[0]" + ".function %} {%- if builtin_tools is defined and tool_call.name in builtin_tools %} {{- " + "'<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}} {{- \"<|python_tag|>\" + tool_call.name + " + "\".call(\" }} {%- for arg_name, arg_val in tool_call.arguments | items %} {{- arg_name + '=\"' + " + "arg_val + '\"' }} {%- if not loop.last %} {{- \", \" }} {%- endif %} {%- endfor %} {{- \")\" }} {%- " + "else %} {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}} {{- '{\"name\": \"' + " + "tool_call.name + '\", ' }} {{- '\"parameters\": ' }} {{- tool_call.arguments | tojson }} {{- \"}\" " + "}} {%- endif %} {%- if builtin_tools is defined %} {#- This means we're in ipython mode #} {{- " + "\"<|eom_id|>\" }} {%- else %} {{- \"<|eot_id|>\" }} {%- endif %} {%- elif message.role == \"tool\" " + "or message.role == \"ipython\" %} {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }} {%- " + "if message.content is mapping or message.content is iterable %} {{- message.content | tojson }} {%- " + "else %} {{- message.content }} {%- endif %} {{- \"<|eot_id|>\" }} {%- endif %} {%- endfor %} {%- if " + "add_generation_prompt %} {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }} {%- endif %} " val promptAssembler = new PromptAssembler() .setInputCol("messages") .setOutputCol("prompt") .setChatTemplate(template) promptAssembler.transform(dataDF).select("prompt.result").show(truncate = false) +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |result | +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ |[<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHello there, how can I help you?<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nI need help with organizing my room.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n]| +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ```A python end to end example is also included in this PR, which showcases how to use it: https://github.com/JohnSnowLabs/spark-nlp/blob/6d238692e9b71a01b99ffa3f5f6cfb385678c8a8/examples/python/llama.cpp/PromptAssember_with_AutoGGUFModel.ipynb
Motivation and Context
Users might want to model more complex message interactions before feeding them to AutoGGUFModel or similar LLMs. This annotator enables this with a template.
How Has This Been Tested?
Old and new tests passing on scala and python side.
Types of changes
Checklist: