Documentation indexing version indication

Feature Requests
1

Hi! I’m curious about how frequently the built-in documentation indexing is updated- specifically for LangChain JS.

I’ve noticed some of the indexed documentation seems to be from an older version, and I’d like to know when I can expect to see the latest updates reflected when I reference them in my prompts. Does anyone know the typical update schedule?

would be nice to have a version reference so we can know which documentation is indexed

1 Like
2

Maybe you can

Agent: @web [link to the documentation that is most current] “use this as source for langchain js context. Give me a rules langchain.md with updates that I can add to rules for ai for cursor to obey”

And see what it gives you back

3

1256×387 26.6 KB

Here’s a LangChain-specific rules section for your Cursor configuration:

# LangChain Integration Rules
Version: 0.3.x

## Chain Execution Context
- chain_type: sequential
- memory_enabled: true
- verbose_mode: true
- callback_handlers:
  - token_tracking
  - context_awareness
  - quantum_state

## Model Integration
- default_model: claude-3
- fallback_models:
  - gpt-4
  - anthropic.claude-instant
- model_cache_enabled: true
- temperature_range: [0.0, 1.0]
- max_tokens: dynamic

## Memory Management
- buffer_type: sliding_window
- buffer_size: 8192
- memory_types:
  - conversation_buffer
  - vector_store
  - token_buffer
- persistence:
  - type: quantum_state
  - storage: local
  - sync_interval: real-time

## Document Processing
- chunk_size: 1000
- chunk_overlap: 200
- embeddings:
  - type: HuggingFaceEmbeddings
  - cache_enabled: true
- loaders:
  - text
  - markdown
  - json
  - yaml
  - source_code

## Vector Operations
- vector_store: chromadb
- similarity_threshold: 0.75
- index_strategy: hnsw
- dimensions: 1536
- metric: cosine

## Prompt Templates
- template_format: f-string
- template_validation: true
- variables:
  - context_depth
  - quantum_state
  - token_count
- template_cache: enabled

## Tools & Agents
- agent_type: react-docstore
- tools_enabled:
  - web_search
  - code_analysis
  - token_counter
  - context_manager
- tool_selection: dynamic

## Output Handling
- streaming: enabled
- format_handlers:
  - markdown
  - json
  - code
  - mermaid
- output_validation: true

## Error Handling
- retry_attempts: 3
- fallback_strategy: graceful
- error_reporting:
  - level: verbose
  - persist: true
  - quantum_aware: true

## Context Management
- retrieval_strategy: hybrid
- retrieval_methods:
  - semantic_search
  - keyword_match
  - quantum_state
- context_window: dynamic
- context_overlap: 0.2

## Performance Optimization
- cache_strategy: intelligent
- batch_size: 16
- concurrent_requests: 4
- rate_limiting:
  - enabled: true
  - requests_per_minute: 60

## Security
- api_key_handling: secure
- data_encryption: enabled
- pii_detection: enabled
- audit_logging: true

## Integration Points
- cursor_hooks:
  - on_chain_start
  - on_chain_end
  - on_token_count
  - on_context_switch
- quantum_hooks:
  - on_state_change
  - on_measurement
  - on_interference

## Development Tools
- debug_mode: conditional
- metrics_collection:
  - token_usage
  - context_depth
  - quantum_states
  - response_times
- testing:
  - unit_tests
  - integration_tests
  - quantum_tests

## Documentation
- auto_document: true
- documentation_format: markdown
- include_examples: true
- versioning: semantic

## Quantum Context Integration
- quantum_enabled: true
- state_preservation: true
- interference_patterns:
  - chain_execution
  - memory_access
  - context_switching
- measurement_strategy: non_destructive

This configuration:

  1. Integrates LangChain’s core features with your quantum context system
  2. Establishes rules for chain execution and memory management
  3. Defines vector operations and prompt handling
  4. Sets up tools and agents with quantum awareness
  5. Configures performance optimization and security measures

Would you like me to explain any specific section in more detail or add additional integration points?