16 commits

Author	SHA1	Message	Date
jinliyl	1687179f84	feat: add Auto Fin cookbook and managed outbound proxy support (#392) ... * feat: add ssh proxy * feat: add ssh proxy * feat: add ssh proxy * feat: add ssh proxy * feat: add prompt * feat: add agent wrapper * feat: add agent wrapper * feat: add agent wrapper * feat: add tushare skill * feat: add tushare skill * feat: add tushare skill * feat: add none stream * chore(deps): update dependency versions in pyproject.toml - Bump claude-agent-sdk from 0.2.123 to 0.2.126 - Upgrade pre-commit to version 4.6.1 or higher - Upgrade pytest to version 9.1.1 or higher * feat(agent_wrapper): add session compaction support and unify session commands - Introduce compact_session method to BaseAgentWrapper and implement it in AsAgentWrapper, CcAgentWrapper, and CodexAgentWrapper - Add session_command module with SessionCommandResult dataclass and handle_session_command function for /clear and /compact commands - Update __init__.py exports to include session_command handlers - Modify DingTalkWaitStep to handle session commands via handle_session_command function - Remove streaming mode from DingTalkWaitStep and simplify reply handling to final Markdown replies only - Add unit tests for session compaction methods and session command handling across wrappers and DingTalk integration - Clean up and remove obsolete streaming and card rendering code from DingTalk wait step - Adjust daily_cookbook.yaml to remove stream and card_update_interval config entries for DingTalk wait step * feat(auto_fin): add Auto Fin simulated portfolio cookbook workflow - Add comprehensive Auto Fin schema exports for multiple models and enums - Implement base class and helpers for Auto Fin analysis steps - Create file, state, and formatting utilities for Auto Fin with atomic file writes and locking - Define Auto Fin pipeline with four analysis agents: backtest, event, portfolio, and US correlation - Register Auto Fin package in cookbook workflows and schema initialization - Add detailed documentation in markdown describing the system design, workflow, and data contracts * feat(outbound_proxy): add application-scoped outbound HTTP proxy components - Introduce BaseOutboundProxy and OutboundProxyEndpoint as core contracts - Implement FixedHttpOutboundProxy for external HTTP proxy integration - Add SshHttpOutboundProxy providing SSH-backed local HTTP proxy tunnels - Register outbound proxy components in component registry and enumeration - Update components package to include outbound_proxy module - Add dependency on pproxy for SSH HTTP proxy bridging - Include comprehensive unit tests covering proxy lifecycle, validation, environment merging, error handling, readiness, and monitoring mechanisms * refactor(network): replace SSH proxy with explicit HTTP outbound proxy - Remove SSH proxy helper implementation and references in codebase - Add support for explicit HTTP proxy URL in arXiv and HuggingFace clients - Modify clients to use async context manager for consistent resource handling - Update daily paper steps to forward outbound proxy configuration explicitly - Change tests to cover new proxy usage model and remove SSH proxy mocks - Add outbound proxy component configuration in daily_cookbook.yaml - Ensure proxy URL usage disables environment trust in HTTP clients - Fix app context component enum access to be defensive against missing keys * feat(agent_wrapper): add managed proxy support for command environments - Introduce BaseOutboundProxy binding in BaseAgentWrapper for outbound proxy management - Add bash_environment and command_proxy_environment properties to apply proxy settings - Update WorkspaceBackend instantiation in AsAgentWrapper to use bash_environment - Inject managed proxy export commands into Claude Code Bash commands via hooks - Enhance CodexAgentWrapper to include managed proxy in shell environment policy - Modify daily_cookbook.yaml steps to specify outbound_proxy as default where needed - Add comprehensive unit tests verifying managed proxy injection and environment isolation - Ensure subprocess_environment remains unchanged while proxy is applied selectively to commands * refactor(memory): replace search job_tools with memory in daily cookbook config - Change workspace_dir default from .reme to reme_workspace - Replace search job_tools with memory across multiple components and jobs - Update descriptions to reflect long-term memory retrieval instead of search - Modify system prompts to instruct using memory for retrieving notes - Adjust unit tests to verify memory job_tools and job presence instead of search - Ensure consistency in configuration and tests for memory backend usage * refactor(config): rename memory to memory_search in daily cookbook config - Change all occurrences of "memory" to "memory_search" in job_tools and job definitions - Update related system prompts to reflect the new memory_search terminology - Modify unit tests to assert the presence of memory_search instead of memory - Ensure consistency across skills, job tools, and backend configurations in multiple components * feat(auto_fin): add deterministic quantitative research and ranking fusion - Introduce new schema models: EtfScore, RankingMetrics, ExtremeAnalysis, DimensionRanking, and FusionRanking to represent deterministic research outputs - Add ranking data to event, backtest, us_correlation, and portfolio analysis outputs - Implement ranking_section renderer to format Top20 scores and diagnostics in Markdown - Develop AutoFinQuantStep for deterministic ETF ranking using TuShare data, Polars, and a custom extremely randomized tree ensemble - Integrate quantitative rankings into backtest and portfolio analysis steps and reports - Extend auto_fin pipeline with new quant_enabled and quant_required config options - Enforce ranking constraints like unique codes, contiguous ranks, and normalized fusion weights - Update analysis YAMLs with rules limiting data freshness, universe, and ranking usage - Incorporate ranking outputs into all major markdown report bodies in Auto Fin pipeline - Add concurrency-limited asynchronous TuShare client to fetch required market data - Introduce cross-sectional rank correlation and NDCG metrics for ranking quality evaluation * feat(auto_fin): implement stage-wise notification and reporting for analysis pipeline - Refactor notification config in daily_cookbook.yaml to support dispatch steps - Update AutoFinNotificationStep to deduplicate notifications per run stage - Add _notify_stage method in pipeline to send notifications for each analysis stage - Implement persistence and notification for event, backtest, US correlation, and portfolio stages - Modify pipeline flow to persist reports and notify after each stage completion - Adjust metadata to track notifications and errors per stage - Update tests to verify stage-wise notification sending and deduplication - Remove older combined report persistence in favor of modular stage handling * feat(auto_fin): add outbound proxy support for Tushare API usage - Introduce BaseOutboundProxy reference in AutoFinPipelineStep and AutoFinQuantStep - Update TushareResearchClient and trade calendar fetch to accept and use proxy URL - Create _ProxiedTushareApi adapter to route Tushare requests via explicit HTTP proxy - Modify create_tushare_api utility to optionally return proxied API client - Add unit tests covering proxy forwarding and client behavior with managed proxies - Ensure proxy usage respects explicit proxy URL over environment fallback - Integrate outbound proxy into data fetching and quantitative research steps * feat(auto_fin): enforce checkpoint time validation and add state models - Introduce AnalysisState base class and specific states for event, backtest, and US correlation analyses - Replace analysis output types with corresponding state classes in run schemas - Add require_checkpoint_reached method to validate decision_at/data_cutoff against current time - Enforce checkpoint time checks before analysis steps in event, backtest, portfolio, and quant analyses - Refactor quant data loading to include adjustment factors and apply price adjustments without fallback - Update analysis YAML docs to require real-time checkpoint validation and forbid using future data - Improve portfolio run serialization by excluding redundant legacy fields and nested proposed actions - Add helper to extract readable sections from persisted checkpoint documents - Fix event analysis output validation to reject events and sources with future timestamps * feat(auto_fin): auto-select latest reached checkpoint if none specified - Extend checkpoint config to accept empty string for auto selection - Add static method to compute latest checkpoint reached by current time - Modify pipeline step to auto-select checkpoint based on trade calendar and time - Adjust force flag default depending on whether checkpoint is explicit or auto - Log details when checkpoint is auto-selected to improve observability - Add comprehensive tests for auto checkpoint selection logic and edge cases - Remove deprecated default and required constraints from force parameter in config * refactor(auto_fin): unify datetime comparison with compare_datetimes utility - Replace direct datetime comparisons with compare_datetimes function calls - Use cmp_to_key with compare_datetimes for sorting datetime tuples and lists - Update validation logic in backtest, event, analysis, and ledger modules for consistent datetime handling - Add unit tests to verify handling of naive and aware datetime comparisons in event and backtest validations - Ensure marked_at and interval_end timestamps are set and compared consistently using compare_datetimes - Improve correctness of ordering and conditional checks related to timestamps throughout auto_fin steps and ledger code * feat(auto_fin): add datetime comparison helper for mixed timezone data - Implement compare_datetimes function to handle naive and aware datetimes - Ensure naive datetime is interpreted in the known timezone of the counterpart - Facilitate comparisons between legacy and timezone-aware Auto Fin data - Add module docstring explaining purpose of the helpers * docs(auto_fin): enforce unique ETF representative per sub-theme in analysis rules - Update backtest.yaml to recommend or highlight only one ETF per sub-theme for ETF analyses - Modify event.yaml to map only one representative ETF per sub-theme, avoiding duplicate recommendations - Revise portfolio.yaml to restrict holdings/buys to a single ETF per sub-theme, preventing repeated buys of highly overlapping ETFs - Adjust us_correlation.yaml to retain only one representative A-share ETF per sub-theme for mapping or recommendation - Add test to verify presence of new sub-theme uniqueness guidance in step prompts * feat(auto_fin): separate draft model and include deterministic fusion ranking - Introduce _PortfolioProposalDraft pydantic model for agent-authored fields before ranking - Discard any "fusion_ranking" data from draft to prevent conflicts with canonical ranking - Modify AutoFinPortfolioStep to receive draft, enrich with fusion_ranking, and produce final output - Update tests to use _PortfolioProposalDraft and validate deterministic fusion ranking propagation - Add async test verifying fusion ranking is correctly set in portfolio output with no errors * refactor(auto_fin): rewrite and simplify Auto Fin schema and steps - Remove legacy Auto Fin analysis step modules and helpers - Replace complex ranking and portfolio models with simplified current-news models - Update schema to focus on news-case workflow with new domain models - Remove A-share decision checkpoints and backtest details from schema - Simplify recommendation and decision output structures - Clean up deprecated state and utility functions - Update Auto Fin steps initialization to new pipeline steps only - Improve uniqueness validation for themes and ETFs in research plan * feat(auto_fin): implement full local cache and analysis workflow for Auto Fin - Add AutoFinDataStep to prepare and cache daily TuShare data with lookback - Add AutoFinAnalysisStep to analyze cached data and generate Markdown report - Implement detailed time window, ETF filtering, and historical case validation - Introduce YAML prompts for planning and decision-making steps - Update .gitignore to include reme_workspace/ - Clean up config and import structure for auto_fin steps - Remove old pipeline.py and consolidate functionality into new modules - Use polars for efficient CSV reading and data processing - Ensure atomic writes and strict JSON serialization for cache files - Enforce rules on news timing, ETF universe, and historical case usage * fix(auto_fin): restrict news data source to '财联社' in analysis and cache - Update analysis templates to specify current news as from '财联社' only - Modify news fetching functions to filter by source '财联社' - Add validation method to check cached news source correctness - Update news caching logic to exclude non-'财联社' news - Enhance unit tests with multiple sources to ensure filtering works - Confirm news API calls include source filter parameter as '财联社' * refactor(auto_fin): convert I/O methods to asynchronous implementations - Change _news, _dataset, and _theme_data methods to async for improved concurrency - Move JSONL and CSV reading operations to asynchronous wrappers using asyncio.to_thread - Remove synchronous _read_jsonl and _read_csv functions, integrate them as static async class methods - Update cache validation methods to async, awaiting I/O operations accordingly - Adjust usage of dataset and news retrieval in analysis step to await asynchronous methods - Add async unit test to validate JSONL reading with unicode line separators - Preserve existing functionality while enabling non-blocking file and data access * fix(nx_file_graph): defer networkx import and improve dependency handling - Move networkx import inside NxFileGraph constructor for lazy loading - Raise ImportError with original exception context if networkx is missing - Remove module-level fallback assignment of nx to None - Expand test to block loading of multiple optional core dependencies eagerly - Change exception type in test from ModuleNotFoundError to AssertionError - Update test comments to reflect broader optional dependency checks * feat(embedding_store): add quota retry delay mechanism for embedding requests - Introduce quota_retry_delay parameter to configure wait time before retry on quota exhaustion - Implement detection of insufficient quota errors in LocalEmbeddingStore without external SDK - Add retry logic with custom delay when quota is insufficient during embedding requests - Update configuration to set max_retries and quota_retry_delay defaults for embedding store - Add unit tests covering quota exhaustion retry behavior with delay and opt-in control - Ensure existing retry behavior remains unchanged if quota_retry_delay is not set * feat(auto_fin): add detailed logging to analysis and data fetching steps - Add _preview static method for bounded diagnostic output in analysis.py - Log prompt start, completion, errors, and validation details in _reply method - Add info logs for major processing steps in execute method of analysis.py - Add debug and info logs for cache validation, data fetching, and pagination in data.py - Log conditions for skipping reports and cache plans in data.py execute method - Log download summaries and cache writes for news and ETF data - Improve error logging with exception details in cache validation functions - Ensure all logs include context such as record counts, paths, and parameters * refactor(auto_fin): overhaul Auto Fin workflow and schema contracts - Replace old Auto Fin schema models with comprehensive new data classes - Remove legacy Auto Fin analysis step in favor of modular agent-based steps - Introduce AutoFinAgentStep for validating structured agent replies - Simplify data cleaning and JSONL writing utilities for news cache - Remove synchronous and asynchronous dataset methods from analysis step - Redefine Auto Fin analysis configuration for 360-day news retention and multi-step pipeline - Remove embedded analysis prompt templates and replace with agent-driven logic - Update __init__.py exports to match new step implementations and remove deprecated classes - Improve error handling and validation in agent step reply processing - Clean up redundant imports and unused code in analysis and data preparation modules * feat(auto_fin): add detailed logging for analysis and data processing steps - Add timing logs to measure agent prompt processing duration in analysis.py - Log news cache hits and news write paths with record counts in data.py - Include detailed info logs for news download start and completion in data.py - Add start, progress, and completion logs with topic and event counts in history.py - Log start and completion of merge step including path and ETF count in merge.py - Add start and done logs with window and news counts in topic.py * feat(auto_fin): enhance schema and steps with detailed ETF and event modeling - Replace and add multiple AutoFin schema classes to support detailed ETF selection, historical research, market analysis, forecast models, and report output with validation - Implement Shanghai timezone normalization and strict validation in schema models - Remove deprecated AutoFin analysis agent step and consolidate reply handling in base step - Introduce AutoFinStep base class with shared helpers for prompt handling, data fetching, logging, and JSONL file operations - Add AutoFinDataStep to manage daily news data complete with schedule validation, caching, and source validation logic - Update cookbook configuration to customize auto_fin step parameters and simplify outbound proxy settings - Refactor imports and clean unused code for better maintainability * feat(auto_fin): introduce detailed historical event resolution and market similarity analysis - Add AutoFinHistoricalEventReference and AutoFinHistoricalSimilarity models for refined event referencing and similarity judgment - Implement validation to ensure non-empty critical fields and uniqueness of historical news IDs - Develop method to resolve Agent-selected historical event references from workspace files with strict path and existence checks - Enrich historical events with market entry and future returns data after resolution - Redesign market step to calculate similarity-weighted ETF forecasts based on matched historical event similarities - Enforce validation on matched historical events for uniqueness and proper weight summation - Simplify merge step output to final Markdown report without YAML frontmatter and redundant fields - Update user instructions for history search, market, and merge steps to reflect new data structures and responsibilities - Adjust test suite to cover new schema and step behavior changes, including enhanced validation and JSON output formats * feat(auto_fin): add new cron jobs and output analysis jsonl - Add new cron jobs auto_fin_1145_cron and auto_fin_1800_cron with auto_fin_steps - Change auto_fin_0930_cron schedule to run Monday to Sunday - Extend merge step to write analysis data to auto_fin_analysis.jsonl - Update unit tests to verify new cron jobs and their steps configuration * fix(auto_fin): improve atomic file write and refresh daily index - Change temporary file naming to include UUID for uniqueness and hidden prefix - Replace atomic write method from using Path.replace to os.replace with safe unlink - Add import and use os.replace for safer file replace operation - Refresh daily index after writing auto finance markdown and JSONL files - Import and call refresh_day_index in merge step to update file index asynchronously * docs(cookbook): add optional SSH proxy configuration in README files - Introduce optional SSH proxy setup in auto-fin and daily_paper cookbooks - Provide instructions to enable outbound proxy via `daily_cookbook.yaml` and environment variables - Add `REME_PROXY_IP` and `REME_PROXY_ACCOUNT` environment variables descriptions in multiple README files - Update English and Chinese README and README_ZH documents with proxy details - Maintain consistent formatting of environment variable tables across documents * fix(file_io): include schema_version in hidden metadata keys - Added "schema_version" to _INDEX_HIDDEN_METADATA_KEYS in _daily_index.py - Updated _render_notes_block to always include additional keys regardless of schema_version fix(deps): move pproxy dependency to later in pyproject.toml - Removed pproxy from early dependencies list - Added pproxy back near the end of dependency list for better ordering fix(outbound_proxy): require pproxy package for ssh_http proxy - Added importlib.util check for pproxy package presence - Raise RuntimeError if pproxy is not installed when using SSH HTTP outbound proxy - Improved error message suggests installing reme-ai with 'core' extra * docs(readme): update News section with new Cookbook workflows - Clarify introduction of optional Cookbooks with Daily Paper and Auto Fin workflows - Update English README to reflect both paper discovery and file-native ETF event research - Revise Chinese README to include financial news and historical market data research capability - Maintain announcement of paper acceptance at Findings of ACL 2026 * feat(auto_fin): add calculation results to final Markdown output - Implement _calculation_results to summarize forecast for each ETF analyzed - Include program-calculated results in the JSON input for the Markdown report - Update YAML template to incorporate calculation results and adjust recommendation rules - Refine recommendation logic to rely on event impact judgments combined with calculation outputs - Modify tests to verify presence of calculation results and updated report content and format * up prompt * fix(keyword_index): ignore non-indexable chunks during keyword sync - Add is_indexable method to base and BM25 keyword index classes to check text tokenizability - Update local file store to exclude non-indexable chunks from expected document IDs to prevent rebuild - Fix JSONL chunker to correctly handle Unicode line separator U+2028 inside JSON strings without splitting - Add test to ensure non-empty but non-indexable chunk does not trigger keyword index rebuild - Add test to verify U+2028 character does not cause incorrect JSONL record splitting	2026-07-25 18:09:39 +08:00
jinliyl	206a53e5ed	init: reme version 0.4.0 (#284)	2026-06-22 15:41:19 +08:00
Sen Huang	16d2d84431	feat(dream): replace digester with abstraction-layer dreamer pipeline (#264) ... * feat(dream): replace digester with abstraction-layer dreamer pipeline Reframe digest as the abstract memory layer (details stay in the daily/ resource material; digest holds principles, patterns, precedents reachable via derived_from provenance edges). Replaces the old digester with a 2-phase ReAct workflow + a daily-tick wrapper: - Phase 1 (Dreamer extract): clusters material into orthogonal memory sub-units; each sub-unit maps 1:1 to a digest node (no inner atom enumeration). Biases toward fewer / richer sub-units. - Phase 2 (Dreamer integrate per sub-unit): cross-bucket recall + exactly one write decision (CREATE / UPDATE / SKIP); UPDATE shapes surfaced explicitly (corroborate / refine / correct). - CronDreamer: scans <daily_dir>/<today>.md + <daily_dir>/<today>/** + <resource_dir>/<today>/** and runs dream_one per file. Write tools are proper subclasses of the canonical file_io WriteStep / EditStep with only path-shape + bucket + E-1 edge-conservation rules layered on top: - DigestWriteStep(WriteStep): path = <digest_dir>/<bucket>/<slug>.md, must-not-exist, schema mirrors `write` (path / name / description / content) so frontmatter lands automatically. - DigestEditStep(EditStep): body-only find-and-replace + must-exist + E-1 conservation preflight (refuses if any outbound wikilink would be dropped). Configuration: - Bucket vocabulary structured in code (tuple[{name, description}]); prompt renders the heuristic block at runtime via {buckets}. - digest_dir / daily_dir / resource_dir come from app config (not tool params); prompts use {digest_dir} placeholder. - BaseStep walks class MRO when loading prompts, so subclasses inherit parent yaml without duplication. Tooling: agentscope register_tool_function schemas now wrap in the proper {"type":"function","function":{...}} envelope. OpenAIAsLLM routes base_url through client_kwargs so non-default endpoints work. Smoke: tests4/smoke/{_dreamer_fixture.py,test_dreamer_inproc.py, test_dreamer_cli.sh} drive the end-to-end pipeline. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * refactor(dreamer): split long description string across multiple lines * refactor(dream): remove hardcoded DEFAULT_DIGEST_DIR and use app_config * docs(auto-cognition): add comprehensive design document for auto-cognition system * refactor(steps): remove deprecated digest edit/write steps * refactor(config): remove redundant LLM formatter backend configuration * refactor(dreamer): improve code formatting and line breaks * feat(auto-dream): implement three-bucket classification system for knowledge organization * feat: rename dream_today step to auto-dream and refactor extraction logic	2026-06-01 19:09:59 +08:00
Sen Huang	7d0bec60be	feat: rename working_dir to vault_dir and update documentation (#254) ... * feat: rename working_dir to vault_dir and update documentation - Rename working_dir to vault_dir across the application - Update documentation to reflect vault_dir instead of working_dir - Change FileFrontMatter title field to name field - Update .gitignore to include vault directory - Modify file path descriptions to reference vault instead of working_dir - Update related configuration and property names accordingly * refactor(steps): rename working_path to vault_path in CRUD operations - Rename parameter from `working_path` to `vault_path` in `resolve_path` function - Update all usages in append, edit, read, and write steps to use `self.vault_path` - Update documentation comments to reflect the new parameter name - Update docstring in read.py to mention `vault_dir` instead of `vault` test(chunked_file_parser): update frontmatter field from title to name - Change frontmatter field from `title` to `name` in test cases - Update comment in background steps test to reference `vault_path` instead of `working_path` * refactor(schema): remove unused ResourceEntry import * feat(file_graph): add link scope filtering to get_inlinks/get_outlinks * feat(file-store): add scope parameter to link methods	2026-05-22 18:10:29 +08:00
诸岳	0dff85b9b5	feat(store): seekdb file and vector stores via pyseekdb (embedded + remote) (#207) ... * feat(seekdb): add Seekdb file and vector stores with pyseekdb>=1.2.0 * refactor(seekdb): add pyseekdb_conn and remote-only host/port config * refactor(embedding): remove env fallbacks from BaseEmbeddingModel; pass credentials in tests * refactor(seekdb): drop tenant from client kwargs; default database test and empty password * fix(deps): gate pyseekdb to Python >=3.11 for CI 3.10 compatibility * fix(seekdb): satisfy pre-commit pylint and formatting for seekdb stores	2026-05-22 10:55:48 +08:00
jinliyl	d0c9d89092	feat(memory): add ContextChecker component for context size management (#144) ... * feat(memory): add ContextChecker component for context size management * refactor(memory): restructure file-based memory tools and update imports * docs(readme): update documentation with detailed architecture and components * docs(readme): update Chinese documentation with enhanced memory management diagrams * refactor(cookbook): move cookbook files to test directory and clean up docs * docs(readme): update link path for old version documentation * docs(readme): update documentation with improved architecture diagrams and component details * docs(readme): update documentation with improved clarity and structure * refactor(docs): update in-memory memory documentation * docs(readme): add experiment reproduction link to quickstart guide	2026-03-06 23:43:42 +08:00
jinli.yl	2715c6fc90	docs: update documentation and configuration	2026-03-05 10:17:28 +08:00
jinliyl	9e2e98ef40	Dev/readme (#137) ... * refactor(memory): rename copaw to reme and update module structure * chore(release): bump version to 0.3.0.6b1 * refactor(docs): update README and ReMeLight implementation * refactor(reme): rename ReMeCopaw to ReMeLight and remove CLI module * docs(readme): update Chinese documentation with enhanced structure and content * docs(readme): update Chinese documentation for context compression * docs(readme): update context compression section header * docs(readme): update documentation with installation and usage guide * docs(readme): update Chinese documentation table * chore(deps): update dependency extras configuration * chore(test): remove deprecated test files for message operations * docs(readme): update documentation with ReMeLight implementation changes	2026-03-04 18:43:21 +08:00
jinli.yl	a753a81852	feat(core): upgrade version to 0.3.0.0b1 and add metadata configuration	2026-02-15 18:29:46 +08:00
jinli.yl	3fb2f82019	fix(agent): update ReactSearchOp import path	2025-11-23 14:20:39 +08:00
jinli.yl	ac72e6e021	feat(vector_store): add QdrantVectorStore implementation with async support ... - Added QdrantVectorStore backend with native async operations - Implemented advanced filtering capabilities for metadata queries - Added support for Qdrant Cloud and local deployments- Updated vector store comparison table with Qdrant features - Enhanced documentation with Qdrant setup and usage examples - Fixed code block formatting in vector store API guide - Updated embedding model integration for Qdrant compatibility	2025-10-23 17:15:12 +08:00
jinliyl	560e746cea	reformat code, support flowllm 0.1.9 (#26) ... * reformat code, support flowllm 0.1.9 * Update README.md add FLOW_USE_FRAMEWORK=true * Update README_ZH.md add FLOW_USE_FRAMEWORK=true * Update index.md add FLOW_USE_FRAMEWORK=true * add env FLOW_APP_NAME=ReMe	2025-09-16 16:49:56 +08:00
jinli.yl	0c2fb9a522	refactor(reme_ai): formatting and minor code improvements ... - Removed unnecessary comments - Improved code formatting across multiple files - Updated import statements and removed unused imports - Refactored some function definitions for better readability	2025-08-31 15:58:59 +08:00
jinli.yl	4739d39f3f	feat(reme_ai): implement memory retrieval and merging functionality ... - Add BuildQueryOp to construct query for memory retrieval-Implement MergeMemoryOp to combine retrieved memories - Create RecallVectorStoreOp to fetch memories from vector store - Develop memory representation and conversion methods - Establish initial project structure and dependencies	2025-08-25 16:10:53 +08:00
jinli.yl	dec8b45186	add add tavily search ... mv to sub folder add memory scope add memoryscope2 move name back	2025-08-11 12:04:08 +08:00
jinli.yl	15dda77a96	update readme & roadmap	2025-07-24 15:01:18 +08:00