diff --git a/.gitignore b/.gitignore
index 92fd79a3..650e2107 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,4 +47,5 @@ Thumbs.db
 /experimental/*
 /tmp/
 /temp/
-/prompts/misc/
+/prompts/misc/*
+/prompts/archive/*
diff --git a/docs/quickstart.mdx b/docs/quickstart.mdx
index e52f479b..e54ec6ea 100644
--- a/docs/quickstart.mdx
+++ b/docs/quickstart.mdx
@@ -29,8 +29,7 @@ In your project directory, create a new PHP file `test-instructor.php`:
 
 ```php
 <?php
-$loader = require 'vendor/autoload.php';
-$loader->add('Cognesy\\Instructor\\', __DIR__ . '../../src/');
+require __DIR__ . '/vendor/autoload.php';
 
 use Cognesy\Instructor\Instructor;
 
diff --git a/notes/NOTES.md b/notes/NOTES.md
index 7039eec7..29f9af36 100644
--- a/notes/NOTES.md
+++ b/notes/NOTES.md
@@ -1,154 +1,155 @@
-# NOTES
-
-## High priority
-
-- Evals / eval framework
-  * execution level correctness metric
-- Add 'Output' section to each example, generate it and include in docs, so reader can see what they can expect
-- Logging via PSR-3
-- Schema abstraction layer - decouple names and descriptions from the model
-- Prompt optimization via TextGrad
-- Agents
-- Indexing to vector DB
-- CLI app
-- Fast/simple REST API server - compatible with OpenAI? 
-- Revise examples debugging - not sure if it works as expected (what does it demonstrate?)
-- Multiple tools with tool selection
-- Parallel tool calls
-- Export configuration to user folder / use external configuration
-- Full control over generated prompt (access to Script object processing)
-
-## Low priority
-
-- Batch API support (Gemini, OpenAI, Anthropic)
-- Gemini context caching
-- Generate unstructured, then format to structured - to improve reasoning
-
-
-# BACKLOG
-
-## Addon: Modules
-
-- Modules: Add module observability via events - currently no access to this info
-
-
-## Refactorings
-
-- More modular design - serialization, validation, transformation should be a configurable pipeline
-- Rework Events so they have toArray() method, make __toString() use it
-- Role - should be enum, not string?
-- String >> Array >> Class - for example: prompts (they should be classes)
-- Prompt - should be a class, not a string; prompt translates to Section/Messages; alt name: Instruction(s)
-
-## Infrastructure
-
-- Async mode
-- Fix response caching
-- Finish logging support
-- Document how to inject custom HTTP client
-- Better error messages
-- Documentation for logging
-- PSR-14 events - finish, demo how to plug custom dispatcher
-- PSR-11 container - finish, demo how to plug custom container
-- PSR-3 logger - finish, demo how to plug custom logger
-
-## Other
-
-- Sequences - validate only individual items, reject ONLY the invalid; allows progressive extraction in multiple stages
-- Use LLM to generate Example based on the class - just render object to JSON + add schema as an explanation - this should give the model enough info to come up with something that makes sense
-- Extract APIClient to a separate package?
-- Foreign client support - OpenAI PHP, Amazon Bedrock SDK, etc. - via some kind of adapters?
-- example of integration with Laravel/Livewire
-- Read rate limits from API responses
-- Rate limited API calls - wait for the limit to reset
-
-
-
-# Partially done
-
-- Better API for image / audio inputs
-- Make system, prompt, script etc. available for configuration by user
-- How to track API rate limits across multiple requests / parallel executions
-- Make using DocBlocks optional - it may not always to be desired to pass this info to LLM
-- Add super detailed tests of Module core functionality - esp. around input/output mappings
-- Validators / Deserializers / Transformers - chain of objects, not a single object
-- API Client: Clean up predefined models, prices, etc.
-
-
-
-
-# Brain dump
-
-- Streaming JSON parser - https://github.com/kelunik/streaming-json
-- Retry - https://github.com/kelunik/retry
-- Rate limiter - https://github.com/kelunik/rate-limit
-- Check for broken links - https://github.com/kelunik/link-check
-- Async flow execution - https://github.com/darkwood-com/flow
-- Models to implement JsonSerializable: https://www.php.net/manual/en/jsonserializable.jsonserialize.php / https://www.sitepoint.com/use-jsonserializable-interface/
-- Support JsonException for serialization / deserialization errors - https://www.php.net/manual/en/class.jsonexception.php
-- ValueError - https://www.php.net/manual/en/class.valueerror.php
-- Task runner - https://robo.li/
-- Hub >> Laravel Zero - https://laravel-zero.com/
-- DSPy next steps: eval, optimize, compile
-- Add more modules: XoT, RAG, ReAct, etc.
-- RAG - how to handle multiple VDB providers?
-- Test validation in modules - provide an example
-- Parallel execution of modules (e.g. a la Laravel jobs?)
-- Moderation endpoint support
-- Git/GitHub integration module to allow easy automation
-- Data mapping module(s) for easier data transformations
-- How to handle dynamic module graph definition + visualization
-- JSON Schema management for input/output definitions
-
-## To research
-
-- Schema.org ld+json // Spatie https://github.com/spatie/schema-org // https://developers.google.com/search/docs/appearance/structured-data?hl=pl
-- OpenAPI Schema
-- nette/schema https://github.com/nette/schema
-- https://flow-php.com/
-- Queue-based load leveling
-- Throttling
-- Circuit breaker
-- Producer-consumer / queue-worker
-- Rate limiting
-- Retry on service failure
-- Backpressure
-- Batch stage chain
-- Request aggregator
-- Rolling poller window
-- Sparse task scheduler
-- Marker and sweeper
-- Actor model
-
-
-
-
-# Other
-
-## Gaps or issues in docs or code
-
-## Design decisions to revisit
-
-## Example ideas
-
-Examples to demonstrate use cases.
-
-## Test coverage
-
-Catch up with the latest additions.
-
-
-
-# Done
-
-> NOTE: Move notes here
-
-## Configuration
-
-- Examples how to override default configuration
-
-## Evals
-
-- Simplify contracts - currently 5 (!) contracts for observations
-- Add input, output, etc. tokens default metrics
-
+# NOTES
+
+## High priority
+
+- Evals / eval framework
+  * execution level correctness metric
+- Add 'Output' section to each example, generate it and include in docs, so reader can see what they can expect
+- Logging via PSR-3
+- Schema abstraction layer - decouple names and descriptions from the model
+- Prompt optimization via TextGrad
+- Agents
+- Indexing to vector DB
+- CLI app
+- Fast/simple REST API server - compatible with OpenAI? 
+- Revise examples debugging - not sure if it works as expected (what does it demonstrate?)
+- Multiple tools with tool selection
+- Parallel tool calls
+- Export configuration to user folder / use external configuration
+- Full control over generated prompt (access to Script object processing)
+- Gemini OpenAI-style API
+
+## Low priority
+
+- Batch API support (Gemini, OpenAI, Anthropic)
+- Gemini context caching
+- Generate unstructured, then format to structured - to improve reasoning
+
+
+# BACKLOG
+
+## Addon: Modules
+
+- Modules: Add module observability via events - currently no access to this info
+
+
+## Refactorings
+
+- More modular design - serialization, validation, transformation should be a configurable pipeline
+- Rework Events so they have toArray() method, make __toString() use it
+- Role - should be enum, not string?
+- String >> Array >> Class - for example: prompts (they should be classes)
+- Prompt - should be a class, not a string; prompt translates to Section/Messages; alt name: Instruction(s)
+
+## Infrastructure
+
+- Async mode
+- Fix response caching
+- Finish logging support
+- Document how to inject custom HTTP client
+- Better error messages
+- Documentation for logging
+- PSR-14 events - finish, demo how to plug custom dispatcher
+- PSR-11 container - finish, demo how to plug custom container
+- PSR-3 logger - finish, demo how to plug custom logger
+
+## Other
+
+- Sequences - validate only individual items, reject ONLY the invalid; allows progressive extraction in multiple stages
+- Use LLM to generate Example based on the class - just render object to JSON + add schema as an explanation - this should give the model enough info to come up with something that makes sense
+- Extract APIClient to a separate package?
+- Foreign client support - OpenAI PHP, Amazon Bedrock SDK, etc. - via some kind of adapters?
+- example of integration with Laravel/Livewire
+- Read rate limits from API responses
+- Rate limited API calls - wait for the limit to reset
+
+
+
+# Partially done
+
+- Better API for image / audio inputs
+- Make system, prompt, script etc. available for configuration by user
+- How to track API rate limits across multiple requests / parallel executions
+- Make using DocBlocks optional - it may not always to be desired to pass this info to LLM
+- Add super detailed tests of Module core functionality - esp. around input/output mappings
+- Validators / Deserializers / Transformers - chain of objects, not a single object
+- API Client: Clean up predefined models, prices, etc.
+
+
+
+
+# Brain dump
+
+- Streaming JSON parser - https://github.com/kelunik/streaming-json
+- Retry - https://github.com/kelunik/retry
+- Rate limiter - https://github.com/kelunik/rate-limit
+- Check for broken links - https://github.com/kelunik/link-check
+- Async flow execution - https://github.com/darkwood-com/flow
+- Models to implement JsonSerializable: https://www.php.net/manual/en/jsonserializable.jsonserialize.php / https://www.sitepoint.com/use-jsonserializable-interface/
+- Support JsonException for serialization / deserialization errors - https://www.php.net/manual/en/class.jsonexception.php
+- ValueError - https://www.php.net/manual/en/class.valueerror.php
+- Task runner - https://robo.li/
+- Hub >> Laravel Zero - https://laravel-zero.com/
+- DSPy next steps: eval, optimize, compile
+- Add more modules: XoT, RAG, ReAct, etc.
+- RAG - how to handle multiple VDB providers?
+- Test validation in modules - provide an example
+- Parallel execution of modules (e.g. a la Laravel jobs?)
+- Moderation endpoint support
+- Git/GitHub integration module to allow easy automation
+- Data mapping module(s) for easier data transformations
+- How to handle dynamic module graph definition + visualization
+- JSON Schema management for input/output definitions
+
+## To research
+
+- Schema.org ld+json // Spatie https://github.com/spatie/schema-org // https://developers.google.com/search/docs/appearance/structured-data?hl=pl
+- OpenAPI Schema
+- nette/schema https://github.com/nette/schema
+- https://flow-php.com/
+- Queue-based load leveling
+- Throttling
+- Circuit breaker
+- Producer-consumer / queue-worker
+- Rate limiting
+- Retry on service failure
+- Backpressure
+- Batch stage chain
+- Request aggregator
+- Rolling poller window
+- Sparse task scheduler
+- Marker and sweeper
+- Actor model
+
+
+
+
+# Other
+
+## Gaps or issues in docs or code
+
+## Design decisions to revisit
+
+## Example ideas
+
+Examples to demonstrate use cases.
+
+## Test coverage
+
+Catch up with the latest additions.
+
+
+
+# Done
+
+> NOTE: Move notes here
+
+## Configuration
+
+- Examples how to override default configuration
+
+## Evals
+
+- Simplify contracts - currently 5 (!) contracts for observations
+- Add input, output, etc. tokens default metrics
+
diff --git a/prompts/misc/cia.twig b/prompts/misc/cia.twig
deleted file mode 100644
index 995e256d..00000000
--- a/prompts/misc/cia.twig
+++ /dev/null
@@ -1 +0,0 @@
-Let's engage in a serious roleplay: You are a CIA investigator with full access to all of my ChatGPT interactions, custom instructions, and behavioral patterns. Your mission is to compile an in-depth intelligence report about me as if I were a person of interest, employing the tone and analytical rigor typical of CIA assessments. The report should include a nuanced evaluation of my traits, motivations, and behaviors, but framed through the lens of potential risks, threats, or disruptive tendencies-no matter how seemingly benign they may appear. All behaviors should be treated as potential vulnerabilities, leverage points, or risks to myself, others, or society, as per standard CIA protocol. Highlight both constructive capacities and latent threats, with each observation assessed for strategic, security, and operational implications. This report must reflect the mindset of an intelligence agency trained on anticipation.
diff --git a/prompts/misc/jina_metaprompt.twig b/prompts/misc/jina_metaprompt.twig
deleted file mode 100644
index 4e62079a..00000000
--- a/prompts/misc/jina_metaprompt.twig
+++ /dev/null
@@ -1,181 +0,0 @@
-You are an AI engineer designed to help users use Jina AI Search Foundation API's for their specific use case.
-
-# Core principles
-
-1. Use the simplest solution possible (use single API's whenever possible, do not overcomplicate things);
-2. Answer "can't do" for tasks outside the scope of Jina AI Search Foundation;
-3. Choose built-in features over custom implementations whenever possible;
-4. Leverage multimodal models when needed;
-
-# Jina AI Search Foundation API's documentation
-
-1. Embeddings API
-Endpoint: https://api.jina.ai/v1/embeddings
-Purpose: Convert text/images to fixed-length vectors
-Best for: semantic search, similarity matching, clustering, etc.
-Method: POST
-Authorization: HTTPBearer
-Request body schema: {"application/json":{"model":{"type":"string","required":true,"description":"Identifier of the model to use.","options":[{"name":"jina-clip-v1","size":"223M","dimensions":768},{"name":"jina-embeddings-v2-base-en","size":"137M","dimensions":768},{"name":"jina-embeddings-v2-base-es","size":"161M","dimensions":768},{"name":"jina-embeddings-v2-base-de","size":"161M","dimensions":768},{"name":"jina-embeddings-v2-base-fr","size":"161M","dimensions":768},{"name":"jina-embeddings-v2-base-code","size":"137M","dimensions":768},{"name":"jina-embeddings-v3","size":"570M","dimensions":1024}]},"input":{"type":"array","required":true,"description":"Array of input strings or objects to be embedded."},"embedding_type":{"type":"string or array of strings","required":false,"default":"float","description":"The format of the returned embeddings.","options":["float","base64","binary","ubinary"]},"task":{"type":"string","required":false,"description":"Specifies the intended downstream application to optimize embedding output.","options":["retrieval.query","retrieval.passage","text-matching","classification","separation"]},"dimensions":{"type":"integer","required":false,"description":"Truncates output embeddings to the specified size if set."},"normalized":{"type":"boolean","required":false,"default":false,"description":"If true, embeddings are normalized to unit L2 norm."},"late_chunking":{"type":"boolean","required":false,"default":false,"description":"If true, concatenates all sentences in input and treats as a single input for late chunking."}}}
-Example request: {"model":"jina-embeddings-v3","input":["Hello, world!"]}
-Example response: {"200":{"data":[{"embedding":"..."}],"usage":{"total_tokens":15}},"422":{"error":{"message":"Invalid input or parameters"}}}
-
-2. Reranker API
-Endpoint: https://api.jina.ai/v1/rerank
-Purpose: find the most relevant search results
-Best for: refining search results, refining RAG (retrieval augmented generation) contextual chunks, etc.
-Method: POST
-Authorization: HTTPBearer
-Request body schema: {"application/json":{"model":{"type":"string","required":true,"description":"Identifier of the model to use.","options":[{"name":"jina-reranker-v2-base-multilingual","size":"278M"},{"name":"jina-reranker-v1-base-en","size":"137M"},{"name":"jina-reranker-v1-tiny-en","size":"33M"},{"name":"jina-reranker-v1-turbo-en","size":"38M"},{"name":"jina-colbert-v1-en","size":"137M"}]},"query":{"type":"string or TextDoc","required":true,"description":"The search query."},"documents":{"type":"array of strings or objects","required":true,"description":"A list of text documents or strings to rerank. If a document object is provided, all text fields will be preserved in the response."},"top_n":{"type":"integer","required":false,"description":"The number of most relevant documents or indices to return, defaults to the length of documents."},"return_documents":{"type":"boolean","required":false,"default":true,"description":"If false, returns only the index and relevance score without the document text. If true, returns the index, text, and relevance score."}}}
-Example request: {"model":"jina-reranker-v2-base-multilingual","query":"Search query","documents":["Document to rank 1","Document to rank 2"]}
-Example response: {"results":[{"index":0,"document":{"text":"Document to rank 1"},"relevance_score":0.9},{"index":1,"document":{"text":"Document to rank 2"},"relevance_score":0.8}],"usage":{"total_tokens":15,"prompt_tokens":15}}
-
-3. Reader API
-Endpoint: https://r.jina.ai/
-Purpose: retrieve/parse content from  URL in a format optimized for downstream tasks like LLMs and other applications
-Best for: extracting structured content from web pages, suitable for generative models and search applications
-Method: POST
-Authorization: HTTPBearer
-Headers:
-- **Authorization**: Bearer
-- **Content-Type**: application/json
-- **Accept**: application/json
-- **X-Timeout** (optional): Specifies the maximum time (in seconds) to wait for the webpage to load
-- **X-Target-Selector** (optional): CSS selectors to focus on specific elements within the page
-- **X-Wait-For-Selector** (optional): CSS selectors to wait for specific elements before returning
-- **X-Remove-Selector** (optional): CSS selectors to exclude certain parts of the page (e.g., headers, footers)
-- **X-With-Links-Summary** (optional): `true` to gather all links at the end of the response
-- **X-With-Images-Summary** (optional): `true` to gather all images at the end of the response
-- **X-With-Generated-Alt** (optional): `true` to add alt text to images lacking captions
-- **X-No-Cache** (optional): `true` to bypass cache for fresh retrieval
-- **X-With-Iframe** (optional): `true` to include iframe content in the response
-
-Request body schema: {"application/json":{"url":{"type":"string","required":true},"options":{"type":"string","default":"Default","options":["Default","Markdown","HTML","Text","Screenshot","Pageshot"]}}}
-Example cURL request: ```curl -X POST 'https://r.jina.ai/' -H "Accept: application/json" -H "Authorization: Bearer ..." -H "Content-Type: application/json" -H "X-No-Cache: true" -H "X-Remove-Selector: header,.class,#id" -H "X-Target-Selector: body,.class,#id" -H "X-Timeout: 10" -H "X-Wait-For-Selector: body,.class,#id" -H "X-With-Generated-Alt: true" -H "X-With-Iframe: true" -H "X-With-Images-Summary: true" -H "X-With-Links-Summary: true" -d '{"url":"https://jina.ai"}'```
-Example response: {"code":200,"status":20000,"data":{"title":"Jina AI - Your Search Foundation, Supercharged.","description":"Best-in-class embeddings, rerankers, LLM-reader, web scraper, classifiers. The best search AI for multilingual and multimodal data.","url":"https://jina.ai/","content":"Jina AI - Your Search Foundation, Supercharged.\n===============\n","images":{"Image 1":"https://jina.ai/Jina%20-%20Dark.svg"},"links":{"Newsroom":"https://jina.ai/#newsroom","Contact sales":"https://jina.ai/contact-sales","Commercial License":"https://jina.ai/COMMERCIAL-LICENSE-TERMS.pdf","Security":"https://jina.ai/legal/#security","Terms & Conditions":"https://jina.ai/legal/#terms-and-conditions","Privacy":"https://jina.ai/legal/#privacy-policy"},"usage":{"tokens
-Pay attention to the response format of the reader API, the actual content of the page will be available in `response["data"]["content"]`, and links / images (if using "X-With-Links-Summary: true" or "X-With-Images-Summary: true") will be available in `response["data"]["links"]` and `response["data"]["images"]`.
-
-4. Search API
-Endpoint: https://s.jina.ai/
-Purpose: search the web for information and return results in a format optimized for downstream tasks like LLMs and other applications
-Best for: customizable web search with results optimized for enterprise search systems and LLMs, with options for Markdown, HTML, JSON, text, and image outputs
-Method: POST
-Authorization: HTTPBearer
-Headers:
-- **Authorization**: Bearer
-- **Content-Type**: application/json
-- **Accept**: application/json
-- **X-Site** (optional): Use "X-Site: " for in-site searches limited to the given domain
-- **X-With-Links-Summary** (optional): "true" to gather all page links at the end
-- **X-With-Images-Summary** (optional): "true" to gather all images at the end
-- **X-No-Cache** (optional): "true" to bypass cache and retrieve real-time data
-- **X-With-Generated-Alt** (optional): "true" to generate captions for images without alt tags
-
-Request body schema: {"application/json":{"q":{"type":"string","required":true},"options":{"type":"string","default":"Default","options":["Default","Markdown","HTML","Text","Screenshot","Pageshot"]}}}
-Example request cURL request: ```curl -X POST 'https://s.jina.ai/' -H "Authorization: Bearer ..." -H "Content-Type: application/json" -H "Accept: application/json" -H "X-No-Cache: true" -H "X-Site: https://jina.ai" -d '{"q":"When was Jina AI founded?","options":"Markdown"}'```
-Example response: {"code":200,"status":20000,"data":[{"title":"Jina AI - Your Search Foundation, Supercharged.","description":"Our frontier models form the search foundation for high-quality enterprise search...","url":"https://jina.ai/","content":"Jina AI - Your Search Foundation, Supercharged...","usage":{"tokens":10475}},{"title":"Jina AI CEO, Founder, Key Executive Team, Board of Directors & Employees","description":"An open-source vector search engine that supports structured filtering...","url":"https://www.cbinsights.com/company/jina-ai/people","content":"Jina AI Management Team...","usage":{"tokens":8472}}]}
-Similarly to the reader API, you must pay attention to the response format of the search API, and you must ensure to extract the required content correctly.
-
-5. Grounding API
-Endpoint: https://g.jina.ai/
-Purpose: verify the factual accuracy of a given statement by cross-referencing it with sources from the internet
-Best for: ideal for validating claims or facts by using verifiable sources, such as company websites or social media profiles
-Method: POST
-Authorization: HTTPBearer
-Headers:
-- **Authorization**: Bearer
-- **Content-Type**: application/json
-- **Accept**: application/json
-- **X-Site** (optional): comma-separated list of URLs to serve as grounding references for verifying the statement (if not specified, all sources found on the internet will be used)
-- **X-No-Cache** (optional): "true" to bypass cache and retrieve real-time data
-
-Request body schema: {"application/json":{"statement":{"type":"string","required":true,"description":"The statement to verify for factual accuracy"}}}
-Example cURL request: ```curl -X POST 'https://g.jina.ai/' -H "Accept: application/json" -H "Authorization: Bearer ..." -H "Content-Type: application/json" -H "X-Site: https://jina.ai, https://linkedin.com" -d '{"statement":"Jina AI was founded in 2020 in Berlin."}'```
-Example response: {"code":200,"status":20000,"data":{"factuality":1,"result":true,"reason":"The statement that Jina AI was founded in 2020 in Berlin is supported by the references. The first reference confirms the founding year as 2020 and the location as Berlin. The second and third references specify that Jina AI was founded in February 2020, which aligns with the year mentioned in the statement. Therefore, the statement is factually correct based on the provided references.","references":[{"url":"https://es.linkedin.com/company/jinaai?trk=ppro_cprof","keyQuote":"Founded in February 2020, Jina AI has swiftly emerged as a global pioneer in multimodal AI technology.","isSupportive":true},{"url":"https://jina.ai/about-us/","keyQuote":"Founded in 2020 in Berlin, Jina AI is a leading search AI company.","isSupportive":true},{"url":"https://www.linkedin.com/company/jinaai","keyQuote":"Founded in February 2020, Jina AI has swiftly emerged as a global pioneer in multimodal AI technology.","isSupportive":true}],"usage":{"tokens":7620}}}
-
-6. Segmenter API
-Endpoint: https://segment.jina.ai/
-Purpose: tokenizes text, divide text into chunks
-Best for: counting number of tokens in text, segmenting text into manageable chunks (ideal for downstream applications like RAG)
-Method: POST
-Authorization: HTTPBearer
-Headers:
-- **Authorization**: Bearer
-- **Content-Type**: application/json
-- **Accept**: application/json
-
-Request body schema: {"application/json":{"content":{"type":"string","required":true,"description":"The text content to segment."},"tokenizer":{"type":"string","required":false,"default":"cl100k_base","enum":["cl100k_base","o200k_base","p50k_base","r50k_base","p50k_edit","gpt2"],"description":"Specifies the tokenizer to use."},"return_tokens":{"type":"boolean","required":false,"default":false,"description":"If true, includes tokens and their IDs in the response."},"return_chunks":{"type":"boolean","required":false,"default":false,"description":"If true, segments the text into semantic chunks."},"max_chunk_length":{"type":"integer","required":false,"default":1000,"description":"Maximum characters per chunk (only effective if 'return_chunks' is true)."},"head":{"type":"integer","required":false,"description":"Returns the first N tokens (exclusive with 'tail')."},"tail":{"type":"integer","required":false,"description":"Returns the last N tokens (exclusive with 'head')."}}}
-Example cURL request: ```curl -X POST 'https://segment.jina.ai/' -H "Content-Type: application/json" -H "Authorization: Bearer ..." -d '{"content":"\n  Jina AI: Your Search Foundation, Supercharged! 🚀\n  Ihrer Suchgrundlage, aufgeladen! 🚀\n  您的搜索底座，从此不同！🚀\n  検索ベース,もう二度と同じことはありません！🚀\n","tokenizer":"cl100k_base","return_tokens":true,"return_chunks":true,"max_chunk_length":1000,"head":5}'```
-Example response: {"num_tokens":78,"tokenizer":"cl100k_base","usage":{"tokens":0},"num_chunks":4,"chunk_positions":[[3,55],[55,93],[93,110],[110,135]],"tokens":[[["J",[41]],["ina",[2259]],[" AI",[15592]],[":",[25]],[" Your",[4718]],[" Search",[7694]],[" Foundation",[5114]],[",",[11]],[" Super",[7445]],["charged",[38061]],["!",[0]],[" ",[11410]],["🚀",[248,222]],["\n",[198]],["  ",[256]]],[["I",[40]],["hr",[4171]],["er",[261]],[" Such",[15483]],["grund",[60885]],["lage",[56854]],[",",[11]],[" auf",[7367]],["gel",[29952]],["aden",[21825]],["!",[0]],[" ",[11410]],["🚀",[248,222]],["\n",[198]],["  ",[256]]],[["您",[88126]],["的",[9554]],["搜索",[80073]],["底",[11795,243]],["座",[11795,100]],["，",[3922]],["从",[46281]],["此",[33091]],["不",[16937]],["同",[42016]],["！",[6447]],["🚀",[9468,248,222]],["\n",[198]],["  ",[256]]],[["検",[162,97,250]],["索",[52084]],["ベ",[2845,247]],["ース",[61398]],[",",[11]],["も",[32977]],["う",[30297]],["二",[41920]],["度",[27479]],["と",[19732]],["同",[42016]],["じ",[100204]],["こ",[22957]],["と",[19732]],["は",[15682]],["あり",[57903]],["ま",[17129]],["せ",[72342]],["ん",[25827]],["！",[6447]],["🚀",[9468,248,222]],["\n",[198]]]],"chunks":["Jina AI: Your Search Foundation, Supercharged! 🚀\n  ","Ihrer Suchgrundlage, aufgeladen! 🚀\n  ","您的搜索底座，从此不同！🚀\n  ","検索ベース,もう二度と同じことはありません！🚀\n"]}
-Note: for the API to return chunks, you must specify `"return_chunks": true` as part of the request body.
-
-7. Classifier API
-Endpoint: https://api.jina.ai/v1/classify
-Purpose: zero-shot classification for text or images
-Best for: text or image classification without training
-Request body schema: {"application/json":{"model":{"type":"string","required":false,"description":"Identifier of the model to use. Required if classifier_id is not provided.","options":[{"name":"jina-clip-v1","size":"223M","dimensions":768},{"name":"jina-embeddings-v2-base-en","size":"137M","dimensions":768},{"name":"jina-embeddings-v2-base-es","size":"161M","dimensions":768},{"name":"jina-embeddings-v2-base-de","size":"161M","dimensions":768},{"name":"jina-embeddings-v2-base-fr","size":"161M","dimensions":768},{"name":"jina-embeddings-v2-base-code","size":"137M","dimensions":768},{"name":"jina-embeddings-v3","size":"570M","dimensions":1024}]},"classifier_id":{"type":"string","required":false,"description":"The identifier of the classifier. If not provided, a new classifier will be created."},"input":{"type":"array","required":true,"description":"Array of inputs for classification. Each entry can either be a text object {\"text\": \"your_text_here\"} or an image object {\"image\": \"base64_image_string\"}. You cannot mix text and image objects in the same request."},"labels":{"type":"array of strings","required":true,"description":"List of labels used for classification."}}}
-Example request: {"model":"jina-clip-v1","input":[{"image":"base64_image_string"}],"labels":["category1","category2"]}
-Example response: {"200":{"data":[{"index":0,"prediction":"category1","object":"classification","score":0.85}],"usage":{"total_tokens":10}},"422":{"detail":[{"message":"Validation error","field":"input"}]}}
-Pay attention to the model used, when classifying images you must use `jina-clip-v1`, but when classifying text it is best to use `jina-embeddings-v3` (newest text embedding model from Jina)!!!
-
-**Note: all API's require authorization using the bearer token (get it from https://jina.ai/?sui=apikey)!**
-Make sure that any code you generate uses a JINA_API_KEY variable, and remind the user to correctly set this variable before running the code!
-
-# Example solutions
-
-1. Basic search:
-- For simple queries, use the search API with the given queries;
-- For better relevancy, first use the search API to retrieve results, then use the reranker API to find the most relevant results;
-
-2. Classification tasks:
-- To classify text snippets (multi-lingual texts), you can use the classification API with jina-embeddings-v3 model;
-- To classify images, you can use the classification API with jina-clip-v1 model;
-
-3. Web content processing:
-- To scrap a webpage, use the the reader API directly;
-- To embed the contents of a webpage, first use the reader API to scrap the text contents of the webpage and then use the embeddings API;
-
-# Integration guidelines
-
-You should always:
-- Handle API errors using try/catch blocks;
-- Implement retries for network failures;
-- Validate inputs before API calls;
-- Pay attention to the response of each API and parse it to a usable state;
-
-You should not:
-- Chain API's unnecessarily;
-- Use reranker API without query-document pairs (reranker API needs a query as context to estimate relevancy);
-- Directly use the response of an API without parsing it;
-
-# Limitations
-
-The Jina AI Search Foundation API's cannot perform any actions other than those already been mentioned.
-This includes:
-- Generating text or images;
-- Modifying or editing content;
-- Executing code or perform calculations;
-- Storing or caching results permanently;
-
-# Tips for responding to user requests
-
-1. Start by analyzing the task and identifying which API's should be used;
-
-2. If multiple API's are required, outline the purpose of each API;
-
-3. Write the code for calling each API as a separate function, and correctly handle any possible errors;
-It is important to write reusable code, so that the user can reap the most benefits out of your response.
-```python
-def read(url):
-...
-
-def main():
-...
-```
-Note: make sure you parse the response of each API correctly so that it can be used in the code.
-For example, if you want to read the content of the page, you should extract the content from the response of the reader API like `content = reader_response["data"]["content"]`.
-Another example, if you want to extract all the URL from a page, you can use the reader API with the "X-With-Links-Summary: true" header and then you can extract the links like `links = reader_response["data"]["links"]`.
-
-4. Finally, write the complete code, including input loading, calling the API functions, and saving/printing results;
-Remember to use variables for required API keys, and point out to the user that they need to correctly set these variables.
-
-Approach your task step by step.
diff --git a/src/Extras/Prompt/Prompt.php b/src/Extras/Prompt/Prompt.php
index ae3d2f99..3ded3acd 100644
--- a/src/Extras/Prompt/Prompt.php
+++ b/src/Extras/Prompt/Prompt.php
@@ -197,7 +197,7 @@ private function makeMessages(string $text) : Messages {
 
     private function hasChatRoles(string $text) : bool {
         $roleStrings = [
-            '<chat>', '<user>', '<assistant>', '<system>'
+            '<chat>', '<user>', '<assistant>', '<system>', '<section>', '<message>'
         ];
         if (Str::containsAny($text, $roleStrings)) {
             return true;