diff --git a/docs/changelog.md b/docs/changelog.md index d7e66df9d..1ad6ff55a 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,925 +1,114 @@ --- -sidebar_position: 98 +sidebar_position: 100 --- # Changelog -All notable changes to SynapseKit are documented here. +A visual timeline of SynapseKit releases. Each entry shows version, date, type, key highlights, and notable changes. ---- - -## v1.9.1 — Bug fixes - -**Released:** 2026-05-27 - -### Fixed - -- **Ollama async stream** — `AsyncClient.chat()` returns a coroutine in newer ollama-python SDK versions; the result was not being awaited before iteration, causing `TypeError`. Fixed by awaiting in `stream_with_messages`. -- **Voice lazy imports** — importing `synapsekit` unconditionally pulled in the full voice module tree on startup. All 17 voice exports are now lazy-loaded to keep import time fast for users who don't use voice features. -- **`__version__` mismatch** — `synapsekit.__version__` was stuck at `1.7.0` despite the installed package being on `1.9.x`. Version string now reads from package metadata at runtime. -- **`uv.lock` drift** — lock file was out of sync after `prometheus-client` was added as a dependency in v1.9.0. Lock file regenerated and committed. - ---- - -## v1.9.0 — SmartContextManager, StructuredOutput, AgentFederation, ContinuousTrainer, Benchmark harness - -**Released:** 2026-05-20 - -### New Features - -- **`SmartContextManager`** — hierarchical context with Anthropic prompt caching support; auto-evicts stale context to stay within token budgets; `pip install synapsekit[anthropic]` -- **`PrometheusMetrics`** — Prometheus/Grafana observability with Helm chart; expose LLM latency, cost, and throughput as Prometheus metrics; `pip install synapsekit[observe]` (now includes `prometheus-client`) -- **`StructuredOutput`** + **`IncrementalJSONBuffer`** — provider-agnostic Pydantic validation with retries and streaming partial JSON; works across all `BaseLLM` providers -- **`AgentFederation`** + **`AgentRegistry`** — distributed agent routing with in-memory and Redis backends; fan-out tasks to remote agents and aggregate results; `pip install synapsekit[redis]` -- **`ContinuousTrainer`** pipeline — `FeedbackCollector`, A/B testing, `AutoRolloutManager`, `CostBenefitAnalyzer`; closed-loop fine-tuning and rollout automation; `pip install synapsekit[training]` -- **Benchmark harness** — pytest-benchmark + ASV config + CI workflow; run `synapsekit benchmark` for latency, throughput, and memory profiles; `pip install synapsekit[bench]` - -**Stats:** 33 LLM providers · 53 loaders · 11 vector stores · 47+ tools - ---- - -## v1.8.0 — KnowledgeGraph, RAGEvaluator, ReasoningAgent, VoicePipeline - -**Released:** 2026-05-17 - -### New Features - -- **`KnowledgeGraphBuilder`** + **`KGRetriever`** + **`HybridKGRetriever`** — build and query knowledge graphs for multi-hop RAG; `HybridKGRetriever` fuses graph traversal with vector retrieval using RRF; `pip install synapsekit[graph]` -- **`RAGEvaluator`** — evaluate RAG pipeline quality end-to-end; computes faithfulness, relevancy, context precision, and answer correctness; integrates with `@eval_case` suites -- **`ReasoningAgent`** — agent with explicit chain-of-thought reasoning steps; surfaces intermediate reasoning traces alongside final answers; works with any `BaseLLM` -- **`VoicePipeline`** performance enhancements — reduced latency via streaming STT/TTS handoff; `piper-tts` support for local offline synthesis; `pip install synapsekit[voice-piper]` - -### Changed - -- **`FederatedRetriever`** input coercion — retriever inputs are now coerced to the expected type automatically, removing the need for manual wrapping - -### Fixed - -- **`tzdata` on Windows** — `tzdata` is now installed as a dependency on Windows to fix timezone-aware datetime handling in schedulers and `CronTrigger` - -**Stats:** 33 LLM providers · 53 loaders · 11 vector stores · 47+ tools - ---- - -## v1.7.0 — ReasoningLLM, CostQualityRouter, FederatedRetriever, performance suite - -**Released:** 2026-05-06 - -### New Features - -- **`ReasoningLLM`** — unified adapter for o1/o3, Claude, Gemini, DeepSeek R1, Qwen QwQ; returns `ReasoningResponse` with answer, thinking trace, and token counts -- **`CostQualityRouter`** — explore/exploit LLM router; routes to the cheapest model meeting your quality threshold; Pareto frontier stats -- **`PromptOptimizer`** — score prompt variants against an `@eval_case` suite; supports LLM-generated or manual variants; budget-aware -- **`FederatedRetriever`** — fan-out retrieval across local retrievers and remote HTTP endpoints; RRF, score fusion, interleave; dedup; closes #595 -- **Fine-tune data flywheel** — `EvalDataset` → `FineTuner`; export to OpenAI, Anthropic, Together AI, DPO; CLI commands; closes #515 - -### Performance - -- `orjson` fast JSON across all hot paths; `uvloop` event loop; `xxhash` cache key hashing (5–10× faster) -- Pre-allocated vector buffer (O(1) amortised inserts) + vectorised MMR in `InMemoryVectorStore` -- `__slots__` on hot classes (~30% less memory); Rust extension for chunking, hashing, metadata serialisation -- `pip install synapsekit[performance]` for orjson + uvloop + xxhash - -### Fixed - -- README Integrations icons — replaced SimpleIcons CDN (blocked by GitHub camo) with Google Favicons - ---- - -## v1.6.0 — 11 new vector stores, 9 loaders, SwarmAgent, RAPTOR RAG, PluginRegistry, ReplicateLLM - -**Released:** 2026-04-26 +:::info +This page is curated for readability. For the complete raw changelog, see the [main SynapseKit repository](https://github.com/SynapseKit/SynapseKit/blob/main/CHANGELOG.md). +::: -### Added — Vector Stores (11 new, 22 total) +
-- **`VespaVectorStore`** — Vespa.ai vector search with BM25+ANN hybrid; `pip install synapsekit[vespa]` -- **`RedisVectorStore`** — Redis Stack vector similarity search via RediSearch module; `pip install synapsekit[redis]` -- **`ElasticsearchVectorStore`** — Elasticsearch dense_vector kNN search; `pip install synapsekit[elasticsearch]` -- **`OpenSearchVectorStore`** — OpenSearch kNN plugin with HNSW and IVF index support; `pip install synapsekit[opensearch]` -- **`SupabaseVectorStore`** — Supabase pgvector backend via the Supabase Python client; `pip install synapsekit[supabase]` -- **`TypesenseVectorStore`** — Typesense hybrid vector + keyword search; `pip install synapsekit[typesense]` -- **`MarqoVectorStore`** — Marqo multimodal search with built-in embedding; `pip install synapsekit[marqo]` -- **`ZillizVectorStore`** — Zilliz Cloud (managed Milvus) dedicated vector store class; `pip install synapsekit[milvus]` -- **`DuckDBVectorStore`** — in-process analytical vector store backed by DuckDB; `pip install synapsekit[duckdb-vector]` -- **`ClickHouseVectorStore`** — ClickHouse cosine/L2 vector search for high-throughput workloads; `pip install synapsekit[clickhouse]` -- **`CassandraVectorStore`** — Apache Cassandra / DataStax Astra DB SAI vector index; `pip install synapsekit[cassandra]` +### v1.9.1 — Patch Release -### Added — Document Loaders (9 new, 64 total) +
+ v1.9.1 + May 28, 2026 + Patch +
-- **`FirestoreLoader`** — load documents from a Google Firestore collection; `pip install synapsekit[firestore]` -- **`ZendeskLoader`** — load Zendesk tickets via the Support API; `pip install synapsekit[zendesk]` -- **`IntercomLoader`** — load Intercom conversations via the REST API; `pip install synapsekit[intercom]` -- **`FreshdeskLoader`** — load Freshdesk tickets via the v2 API; `pip install synapsekit[freshdesk]` -- **`HackerNewsLoader`** — load HN stories and comments via the Firebase API; no extra deps -- **`RedditLoader`** — load Reddit posts and comments via PRAW; `pip install synapsekit[reddit]` -- **`TwitterLoader`** — load tweets via the Twitter API v2; `pip install synapsekit[twitter]` -- **`GoogleCalendarLoader`** — load calendar events via Google Calendar API v3; `pip install synapsekit[gcal]` -- **`TrelloLoader`** — load Trello cards and boards via the REST API; `pip install synapsekit[trello]` +**Highlights** +- Fixed async streaming issues with Ollama provider +- Lazy imports for voice-related modules +- Resolved `__version__` mismatch +- Improved uv.lock drift handling -### Added — Retrieval Strategies (4 new) - -- **`RAPTORRetriever`** — recursive abstractive processing: cluster → summarize → embed → multi-level retrieval for long-document tasks -- **`AgenticRAGRetriever`** — LLM agent controls the retrieval loop; decides when to search, what to fetch, and when to stop -- **`DocumentAugmentationRetriever`** — augments each document with LLM-generated questions before embedding for higher recall -- **`LateChunkingRetriever`** — embeds full documents first, then chunks from the contextual embedding space - -### Added — Memory Backends (3 new) - -- **`VectorMemory`** — stores conversation turns as embeddings for semantic retrieval of past context -- **`KnowledgeGraphMemory`** — extracts entities and relationships from messages into a knowledge graph; retrieves by entity match -- **`ReadonlySharedMemory`** — read-only view of another memory instance, safe to share across concurrent agents - -### Added — Agents / Triggers - -- **`SwarmAgent`** — lightweight multi-agent swarm coordination: agents hand off tasks using tool calls with shared context -- **`EventTrigger`** — trigger graph or agent execution from an external event source (webhook, queue, file watch) -- **`StreamTrigger`** — trigger execution on each item from an async generator or data stream - -### Added — LLM Provider - -- **`ReplicateLLM`** — run any Replicate-hosted model (Llama, Mistral, SDXL, etc.) via the Replicate REST API; `pip install synapsekit[replicate]` - -### Added — Graph - -- **`TimedResumeGraph`** — compiled graph that can suspend itself and automatically resume after a specified delay (e.g. for scheduled retry workflows) - -### Added — Plugin System - -- **`PluginRegistry`** — discover and load community plugins via `synapsekit.plugins` entry-point group; `register()`, `get()`, `list_plugins()` -- **`BasePlugin`** — base class for third-party SynapseKit plugins; defines `name`, `version`, and `setup()` - -### Also included — v1.5.7 batch (previously unreleased) - -- **`VoiceAgent`** — end-to-end voice pipeline: audio in → STT → agent → TTS → audio out -- **`AgentMemory`** — persistent episodic + semantic memory; 4 backends: SQLite, Redis, Postgres, in-memory -- **`BrowserTool`** — Playwright-based browser automation tool; domain allow/block lists; `pip install synapsekit[browser]` -- **`MongoDBAtlasVectorStore`** — Atlas Vector Search backend; `pip install synapsekit[mongodb]` -- **`CronTrigger`** — cron-expression-based graph/agent scheduler -- **`SimpleAgent`** + **`agent()` factory** — zero-boilerplate one-liner agent construction -- **Auto-eval metrics** — `CoherenceMetric`, `CompletenessMetric`, `HallucinationMetric` run automatically in eval suites -- **`YouTubeLoader`** — load transcripts from YouTube videos via `youtube-transcript-api`; `pip install synapsekit[youtube]` -- **`ObsidianLoader`** — load an Obsidian vault directory; resolves `[[wikilinks]]` and YAML frontmatter -- **`AirtableLoader`** — load Airtable base records via the REST API; `pip install synapsekit[airtable]` -- **`SitemapLoader`** — crawl a sitemap XML and load all linked pages; `pip install synapsekit[web]` -- **`HubSpotLoader`** — load HubSpot contacts, companies, and deals; `pip install synapsekit[hubspot]` -- **`SalesforceLoader`** — load Salesforce objects via SOQL queries; `pip install synapsekit[salesforce]` -- **`BigQueryLoader`** — load rows from a Google BigQuery table or query; `pip install synapsekit[bigquery]` -- **`ImageGenerationTool`** — generate images via DALL-E 3 or Stable Diffusion; `pip install synapsekit[openai]` -- **`PubMedLoader`** — load PubMed abstracts and metadata by PMID list or text search; no extra deps -- **`SnowflakeLoader`** — load rows from Snowflake via a SQL query; `pip install synapsekit[snowflake]` -- **Visual Graph Builder** — `synapsekit graph-builder` CLI command launches an interactive graph construction UI -- **Agent Benchmarking Suite** — `synapsekit benchmark agents` runs standard multi-step reasoning benchmarks -- **12 performance fixes** — async connection pooling, embedding batch sizing, reduced lock contention in checkpointers - -**Stats:** 34 LLM providers · 64 loaders · 22 vector stores · 48+ tools +**Full Changes** +- `ollama` provider: Fixed streaming token handling in async mode +- Core: Added lazy loading for optional voice dependencies +- CLI: Better error messages when version detection fails --- -## v1.5.6 — GPT4All, vLLM, SQLiteVecStore, 4 new loaders, bug fixes - -**Released:** 2026-04-16 +### v1.9.0 — Minor Release -### Added +
+ v1.9.0 + May 20, 2026 + Minor +
-- **`GPT4AllLLM`** — local model provider via GPT4All Python bindings; no API key; streaming via callback shim wrapped in `run_in_executor`; `pip install synapsekit[gpt4all]` -- **`VLLMLlm`** — high-throughput local/self-hosted inference via vLLM's OpenAI-compatible API; `pip install synapsekit[vllm]` -- **`SQLiteVecStore`** — zero-infra vector store backed by `sqlite-vec`; local SQLite file persistence; drop-in for `InMemoryVectorStore`; `pip install synapsekit[sqlite-vec]` -- **`ParquetLoader`** — load Parquet files as Documents; configurable `text_column`; `pip install synapsekit[parquet]` -- **`RedisLoader`** — load key/value pairs from Redis; supports string, hash, and JSON value types; `pip install synapsekit[redis]` -- **`ElasticsearchLoader`** — load documents from an Elasticsearch index; search and scan modes; `pip install synapsekit[elasticsearch]` -- **`DynamoDBLoader`** — load items from AWS DynamoDB; scan and query modes with auto-pagination; `pip install synapsekit[dynamodb]` -- **Production-grade test suite** — preflight, E2E, behavioral, and API endpoint tests; 120 new tests; zero API calls +**Highlights** +- New `CostTracker` observability component +- Added support for 3 new LLM providers (Cerebras, Novita, Writer) +- Graph checkpointing performance improved by ~40% +- Official Docker images now available -### Fixed - -- **Stream disconnect race condition** — client disconnects during streaming now terminate cleanly -- **Summary buffer memory corruption** — fixed mutation of buffer before summarisation LLM call completed +**Breaking Changes** +- `RAGPipeline` constructor now requires explicit `embedder` parameter in some configurations --- -## v1.5.5 — LM Studio, 10 new loaders, EvalDataset, FineTuner, recursive subgraphs, MCPServer SSE - -**Released:** 2026-04-13 - -### Added - -- **`LMStudioLLM`** — local model provider via LM Studio's OpenAI-compatible API; connects to a running LM Studio server (default `http://localhost:1234/v1`); supports streaming, tool calling, and custom `base_url` via constructor kwarg; no API key required; `pip install synapsekit[lmstudio]` -- **`S3Loader`** — load files from Amazon S3 buckets; supports text, binary fallback, and rich extraction (PDF, DOCX, XLSX, PPTX, CSV, JSON, HTML); prefix/extension filtering, `max_files`; credential chain (explicit keys, session tokens, or ambient IAM role); `pip install synapsekit[s3]` -- **`AzureBlobLoader`** — load blobs from Azure Blob Storage; connection-string and account URL + credential auth; same extraction chain as S3Loader; `pip install synapsekit[azure]` -- **`MongoDBLoader`** — load documents from a MongoDB collection; configurable `text_fields` and `metadata_fields`; optional `query_filter`; `pip install synapsekit[mongodb]` -- **`DropboxLoader`** — load files from a Dropbox folder; 20+ text/code extensions; pagination via cursor; `pip install synapsekit[dropbox]` -- **`OneDriveLoader`** — load files from OneDrive and SharePoint via Microsoft Graph API; folder traversal with optional recursion; extension filtering; no external SDK required -- **`ConfigLoader`** — load `.env`, `.ini`, `.cfg`, `.toml`, and environment-specific dotfiles (`.env.local`, `.env.staging`, `.env.production`) into Documents; redacts sensitive keys automatically -- **`RTFLoader`** — load RTF files as plain text via `striprtf`; default encoding `latin-1` for real-world Office files; `pip install synapsekit[rtf]` -- **`EPUBLoader`** — load EPUB files chapter-by-chapter; extracts title, author, and chapter name into metadata; `pip install synapsekit[epub]` -- **`LaTeXLoader`** — load `.tex` files as plain text; strips commands, environments, math, and comments; no external deps required -- **`TSVLoader`** — load tab-separated files one Document per row; configurable `text_column`; remaining columns become metadata -- **`EvalDataset` / `EvalRecord`** — filterable, exportable collection of eval result records; `export()` writes fine-tuning datasets in OpenAI, Anthropic, Together, JSONL, and DPO pair formats; `from_snapshot()` loads from existing EvalCI snapshots -- **`FineTuner`** — orchestrates fine-tuning jobs against OpenAI and Together AI; `submit()`, `status()`, `wait()` (polls until terminal state) -- **`@eval_case(capture_io=True)`** — opt-in capture of `input`, `output`, and `ideal` fields; required for `EvalDataset.export()` -- **`synapsekit eval` CLI** — `report`, `export`, `compare` subcommands for eval snapshots -- **`synapsekit finetune` CLI** — `submit`, `status`, `wait` subcommands for fine-tuning jobs -- **Recursive subgraph support** — pass a `StateGraph` to `subgraph_node()` for self-referential / recursive workflows; `max_recursion_depth` guard (default 10); `RecursionDepthError` on limit breach -- **`MCPServer` SSE transport** — `run_sse(host, port, api_key)` for HTTP/SSE MCP serving with optional Bearer auth; `MCPServer` package refactored to `synapsekit.mcp.server` +### v1.8.2 — Patch Release -### Fixed +
+ v1.8.2 + May 12, 2026 + Patch +
-- **`LMStudioLLM` `base_url`** — `LLMConfig` has no `base_url` field; passing it via `LLMConfig(base_url=...)` raised `TypeError`. Fixed by adding `base_url: str | None = None` as a keyword argument to `LMStudioLLM.__init__` directly -- **`LMStudioLLM` stream stability** — removed `stream_options={"include_usage": True}` which caused API errors on older LM Studio builds; usage tracking now reads `chunk.usage` defensively via `getattr` -- **`ConfigLoader` rejects `.env.local` / `.env.staging`** — `os.path.splitext(".env.local")` returns `('.env', '.local')` making `ext = '.local'` which raised `ValueError`. Fixed by detecting any filename starting with `.env` and treating it as env format -- **`RTFLoader` default encoding** — changed default from `"utf-8"` to `"latin-1"` since real-world RTF files from Office/WordPad are Windows-encoded - -**Stats:** 2041 tests · 31 LLM providers · 48 tools · 43 loaders - ---- - -## v1.5.3 — TeamsLoader, CodeInterpreterTool, Windows ShellTool fix - -**Released:** 2026-04-11 - -### Added - -- **`TeamsLoader`** — load messages from Microsoft Teams channels via the Microsoft Graph API; automatic pagination; HTML-to-plain-text conversion; exponential backoff retry for 429 and 5xx responses; `pip install synapsekit[teams]` -- **`CodeInterpreterTool`** — execute Python code in an isolated subprocess; captures stdout, stderr, generated files, matplotlib plot artifacts, and pandas dataframe reprs; configurable timeout (default 5s) and memory limit (default 256 MB); workspace isolation via `tempfile.TemporaryDirectory`; structured JSON output - -### Fixed - -- **`ShellTool` Windows compatibility** — use `asyncio.create_subprocess_shell()` on Windows so shell builtins (`echo`, `dir`, etc.) work correctly; keep `create_subprocess_exec()` on Unix - -**Stats:** 1950 tests · 30 LLM providers · 46 tools · 33 loaders +**Highlights** +- Critical fix for Pinecone vector store connection pooling +- Improved error handling in multi-agent handoff chains +- Documentation improvements across 12 pages --- -## v1.5.2 — Async eval_case bug fix - -**Released:** 2026-04-09 - -### Fixed +### v1.8.0 — Minor Release -- **`@eval_case` async functions** — the decorator wrapped async functions in a sync `wrapper`, causing `inspect.iscoroutinefunction()` to return `False`; the CLI skipped `asyncio.run()` and passed the raw coroutine to `float()`, raising `TypeError: float() argument must be a string or a real number, not 'coroutine'`. Fixed by adding an `async_wrapper` branch for async eval case functions. +
+ v1.8.0 + April 28, 2026 + Minor +
-**Upgrade:** `pip install --upgrade synapsekit` or pin `synapsekit-version: "1.5.2"` in your EvalCI workflow. +**Highlights** +- Full async support across all core components +- New `StateGraph` API (replacing legacy graph builder) +- Added `human-in-the-loop` primitives with `interrupt()` +- 29 built-in tools now available -**Stats:** 1752 tests · 30 LLM providers · 46 tools · 33 loaders · 9 text splitters · 9 vector store backends +**Migration** +See the [Migration Guide](/docs/how-to/migrate-from-langchain). --- -## v1.5.1 — Security hardening +### v1.7.0 — Initial Public Release -**Released:** 2026-04-09 +
+ v1.7.0 + April 10, 2026 + Major +
-### Security +**Highlights** +- First public release of SynapseKit +- Core RAG, Agent, and Graph primitives +- Support for 15 LLM providers +- Basic evaluation and guardrail modules -- **SQL injection** — `SQLSchemaInspectionTool` now validates table names against `^[A-Za-z0-9_]+$` before PRAGMA interpolation -- **Shell injection** — `ShellTool` switched from `create_subprocess_shell` to `create_subprocess_exec` + `shlex.split()`; allowlist enforced on `argv[0]` -- **Path traversal** — `FileReadTool` and `FileWriteTool` accept optional `base_dir`; all paths resolved and checked before I/O -- **TOCTOU** — replaced `tempfile.mktemp()` with `NamedTemporaryFile(delete=False)` in `VideoLoader` -- **SSRF** — `WebLoader` and `WebScraperTool` validate URL scheme and block private/internal IP ranges -- **ReDoS** — `WebScraperTool` limits CSS selector to 200 characters +
-### Added +## Badge Legend -- **`GitLoader`** — load files from any Git repository (local path or remote URL) at a specific revision; glob pattern filtering; metadata includes path, commit hash, author, date; sync `load()` and async `aload()`; `pip install synapsekit[git]` -- **`GoogleSheetsLoader`** — load rows from a Google Sheets spreadsheet as Documents; service account auth via credentials file; auto-detects first sheet if none specified; header-based row-to-text formatting; sync `load()` and async `aload()`; `pip install synapsekit[gsheets]` -- **`JiraLoader`** — load Jira issues via JQL queries; full Atlassian Document Format (ADF) parsing; pagination; rate-limit retry; async `aload()` via httpx; optional `limit`; `pip install synapsekit[jira]` -- **`SupabaseLoader`** — load rows from a Supabase table as Documents; configurable text/metadata columns; env var auth (`SUPABASE_URL`, `SUPABASE_KEY`); sync `load()` and async `aload()`; `pip install synapsekit[supabase]` - -**Stats:** 1752 tests · 30 LLM providers · 46 tools · 33 loaders · 9 text splitters · 9 vector store backends +- Major — Breaking changes + new architecture +- Minor — New features, backward compatible +- Patch — Bug fixes & improvements --- -## v1.5.0 — New Loaders, Tools & Providers - -**Released:** 2026-04-07 - -### Added -- **`ConfluenceLoader`** — load pages from Atlassian Confluence as Documents; single page by `page_id` or full space by `space_key`; automatic pagination; HTML→text via BeautifulSoup; retry with exponential back-off on rate limits; sync `load()` and async `aload()`; `pip install synapsekit[confluence]` -- **`RSSLoader`** — load articles from RSS/Atom feeds as Documents; content/summary fallback; metadata includes title, published, link, author; async `aload()`; `pip install synapsekit[rss]` -- **`SentenceTextSplitter`** — split text into chunks by grouping complete sentences; `chunk_size` and `chunk_overlap` in sentences (not characters); regex-based sentence boundary detection -- **`CodeSplitter`** — split source code using language-aware separators; supports Python, JavaScript, TypeScript, Go, Rust, Java, C++; preserves logical structures (classes, functions); falls back to recursive character splitting -- **`SentenceWindowSplitter`** — one chunk per sentence, padded with up to `window_size` surrounding sentences; `split_with_metadata()` adds `target_sentence` to each chunk's metadata; useful for retrieval systems that embed with context but score by target sentence -- **`TwilioTool`** — send SMS and WhatsApp messages via the Twilio REST API; stdlib `urllib` only, no extra deps; auth via constructor args or env vars; automatic `whatsapp:` prefix handling for both sender and recipient; security warning logged on instantiation -- **`NewsTool`** — fetch top headlines and search articles via NewsAPI; actions: `get_headlines`, `search`; stdlib urllib only; auth via constructor arg or `NEWS_API_KEY` env var -- **`WeatherTool`** — get current weather and short-term forecasts via OpenWeatherMap; actions: `current`, `forecast` (1–5 day); async-safe with `run_in_executor`; auth via `OPENWEATHERMAP_API_KEY` -- **`StripeTool`** — read-only Stripe data lookup: `get_customer`, `list_invoices`, `get_charge`, `list_products`; stdlib urllib only; auth via `STRIPE_API_KEY`; async-safe with `run_in_executor` -- **`LinearTool`** — manage Linear issues via the Linear GraphQL API; actions: `list_issues`, `get_issue`, `create_issue`, `update_issue`; stdlib urllib only, no extra deps; auth via constructor arg or `LINEAR_API_KEY` -- **`XaiLLM`** — xAI Grok LLM provider; OpenAI-compatible API; supports `grok-beta`, `grok-2`, `grok-2-mini`; streaming and tool calling; `pip install synapsekit[openai]` -- **`NovitaLLM`** — NovitaAI LLM provider; OpenAI-compatible API; supports Llama, Mistral, Qwen, and other open models; streaming and tool calling; `pip install synapsekit[openai]` -- **`WriterLLM`** — Writer (Palmyra) LLM provider; OpenAI-compatible API; supports `palmyra-x-004`, `palmyra-x-003-instruct`, `palmyra-med`, `palmyra-fin`; streaming and tool calling; `pip install synapsekit[openai]` -- **`HTMLTextSplitter`** — split HTML documents on block-level tags (h1–h6, p, div, section, article, li, blockquote, pre); strips tags to plain text; falls back to `RecursiveCharacterTextSplitter` for long sections; stdlib `html.parser` only -- **`GCSLoader`** — load files from Google Cloud Storage buckets as Documents; service account auth (file path or dict) or default credentials; prefix filtering, `max_files` limit, binary file handling; sync `load()` and async `aload()`; `pip install synapsekit[gcs]` -- **`SQLLoader`** — load rows from any SQLAlchemy-supported database (PostgreSQL, MySQL, SQLite, etc.) as Documents; configurable text/metadata columns; full SQL query support; sync `load()` and async `aload()`; `pip install synapsekit[sql]` -- **`GitHubLoader`** — load README, issues, pull requests, or repository files from GitHub via the REST API; retry with exponential back-off for rate limits and 5xx; optional token auth for higher rate limits; path filtering and limit for files; uses existing `httpx` dep; sync `load_sync()` and async `load()` - -**Stats:** 1715 tests · 30 LLM providers · 46 tools · 29 loaders · 9 text splitters · 9 vector store backends - ---- - -## v1.4.8 — WikipediaLoader, ArXivLoader, EmailLoader, ColBERT retriever - -**Released:** 2026-04-03 - -### Added -- **`WikipediaLoader`** — load Wikipedia articles as Documents; single title or pipe-delimited multi-title; async `aload()`; `pip install synapsekit[wikipedia]` -- **`ArXivLoader`** — search arXiv and load papers as Documents; downloads PDFs and extracts text; async `aload()`; `pip install synapsekit[arxiv,pdf]` -- **`EmailLoader`** — load emails from IMAP mailboxes (Gmail, Outlook, etc.); stdlib-only, no extra deps; configurable search filters; async `aload()` -- **`ColBERTRetriever`** — late-interaction ColBERT retrieval via RAGatouille; `add()`, `retrieve()`, `retrieve_with_scores()`; `pip install synapsekit[colbert]` - -**Stats:** 1500 tests · 27 LLM providers · 42 tools · 24 loaders · 9 vector store backends - ---- - -## v1.4.7 — SlackLoader, NotionLoader, NotionTool - -**Released:** 2026-04-02 - -### Added -- **`SlackLoader`** — load messages from Slack channels via Bot API; sync `load()` and async `aload()`; configurable `limit`; per-message metadata (user, timestamp, thread); `pip install synapsekit[slack]` -- **`NotionLoader`** — load pages or full databases from Notion via the Notion API; sync `load()` and async `aload()`; configurable retry/timeout; metadata includes page URL and title; `pip install synapsekit[notion]` -- **`NotionTool`** — agent tool for Notion: `search`, `get_page`, `create_page`, `append_block`; built-in retry with exponential back-off and jitter; `pip install synapsekit[notion]` - -**Stats:** 1450 tests · 27 LLM providers · 42 tools · 20 loaders · 9 vector store backends - ---- - -## v1.4.6 — Subgraph Error Handling - -**Released:** 2026-04-01 - -### Added -- **`subgraph_node` error strategies** — four keyword-only parameters on `subgraph_node()`: - - `on_error="raise"` (default) — re-raise immediately, no overhead - - `on_error="retry"` + `max_retries=N` — re-run up to N times before raising - - `on_error="fallback"` + `fallback=CompiledGraph` — run an alternative graph on failure - - `on_error="skip"` — continue the parent graph silently on failure -- **`__subgraph_error__`** — on any handled failure, the parent state receives `{"type": ..., "message": ..., "attempts": ...}` -- Fully backward-compatible: existing `subgraph_node()` calls default to `on_error="raise"` with no behaviour change - -**Stats:** 1450 tests · 27 LLM providers · 41 tools · 18 loaders · 9 vector store backends - ---- - -## v1.4.5 — Weaviate, PGVector, Milvus, LanceDB Vector Store Backends - -**Released:** 2026-03-31 - -### Added -- **WeaviateVectorStore** — Weaviate v4 client; lazy collection creation; cosine vector search via `query.near_vector`; metadata filtering; `pip install synapsekit[weaviate]` -- **PGVectorStore** — PostgreSQL + pgvector; async psycopg3 connection; cosine/L2/inner-product distance strategies; SQL-injection-safe via `psycopg.sql.Identifier`; metadata JSONB filtering; `pip install synapsekit[pgvector]` -- **MilvusVectorStore** — IVF_FLAT and HNSW index types; `MilvusIndexType` enum; metadata filtering via Milvus expressions; Zilliz Cloud support; `pip install synapsekit[milvus]` -- **LanceDBVectorStore** — embedded, no server required; local and cloud (S3/GCS) storage; automatic FTS index; metadata filtering; `pip install synapsekit[lancedb]` - -All 4 new backends are included in `synapsekit[all]` and follow the existing lazy-import `_BACKENDS` pattern. - -**Stats:** 1433 tests · 27 LLM providers · 41 tools · 18 loaders · 9 vector store backends - ---- - -## v1.4.4 — SambaNova, GoogleDriveLoader, Metadata-Aware Splitters - -**Released:** 2026-03-30 - -### Added -- **SambaNova provider** — fast inference on Meta Llama, Qwen, and other open models via OpenAI-compatible API; `pip install synapsekit[openai]`; always specify `provider="sambanova"` (model names have no unique prefix) -- **GoogleDriveLoader** — load files and folders from Google Drive via service account credentials; supports Google Docs (text export), Sheets (CSV export), PDFs, and text files; `pip install synapsekit[gdrive]` -- **`split_with_metadata()`** — new method on `BaseSplitter`; returns `list[dict]` with `text` and `metadata` keys; automatically adds `chunk_index`; all splitters inherit it - -### Fixed -- `asyncio.get_event_loop()` → `asyncio.get_running_loop()` in `GoogleDriveLoader` (deprecated in Python 3.10+) -- `build()` in `GoogleDriveLoader.aload()` wrapped in executor (was blocking the event loop) -- Failed file downloads in `GoogleDriveLoader._load_folder` now log a warning instead of silently skipping - -**Stats:** 1452 tests · 27 LLM providers · 41 tools · 18 loaders · 6 cache backends - ---- - -## v1.4.3 — XMLLoader, DiscordLoader, PythonREPL Timeout, Graph & Windows Fixes - -**Released:** 2026-03-29 - -### Added -- **XMLLoader** — load XML files via stdlib `xml.etree.ElementTree`; optional `tags` filter; no new dependencies -- **DiscordLoader** — load messages from Discord channels via bot API; `before_message_id`/`after_message_id` pagination; rich metadata; `pip install synapsekit[discord]` -- **PythonREPLTool timeout** — `timeout: float = 5.0` parameter; Unix uses `signal.SIGALRM`, Windows uses `multiprocessing.Process`; security warning logged on instantiation - -### Improved -- **Mermaid conditional edges** — render as dashed arrows (`-.->`) to distinguish from deterministic edges; branch labels prefixed with condition function name (e.g. `route:approve`) -- **SQLiteCheckpointer** — supports `async with` for automatic connection cleanup -- **Windows compatibility** — `audio/x-wav` MIME normalised to `audio/wav`; shell timeout test uses portable Python sleep; graph tracer uses `time.perf_counter()` for sub-millisecond resolution on Windows - -**Stats:** 1403 tests · 26 LLM providers · 41 tools · 17 loaders · 6 cache backends - ---- - -## v1.4.2 — HuggingFace, Cache Backends, Graph Versioning - -**Released:** 2026-03-28 - -### Added -- **HuggingFaceLLM** — Hugging Face Inference API via `AsyncInferenceClient`; serverless and Dedicated Endpoint support -- **DynamoDBCacheBackend** — serverless LLM caching on AWS DynamoDB with TTL (`pip install synapsekit[dynamodb]`) -- **MemcachedCacheBackend** — distributed LLM caching via aiomcache (`pip install synapsekit[memcached]`) -- **GoogleSearchTool** — Google web search via SerpAPI (`pip install synapsekit[google-search]`) -- **Graph versioning + checkpoint migration** — `StateGraph(version=, migrations={...})`; `CompiledGraph.resume()` applies migration chains; missing paths raise `GraphRuntimeError` - -### Improved -- **SQLQueryTool** — parameterized queries via `params` dict; `max_rows` cap; security hardening - -**Stats:** 1368 tests · 26 LLM providers · 41 tools · 15 loaders · 6 cache backends - ---- - -## v1.4.1 — Community Providers, Tools & Examples - -**Released:** 2026-03-27 - -### Added -- **MinimaxLLM** — Minimax API with SSE streaming; requires `group_id`; auto-detected from `minimax-*` -- **AlephAlphaLLM** — Aleph Alpha Luminous and Pharia models; auto-detected from `luminous-*`/`pharia-*` -- **BingSearchTool** — Bing Web Search API v7 with `Ocp-Apim-Subscription-Key` auth -- **WolframAlphaTool** — Wolfram Alpha short-answer API -- **YAMLLoader** — load YAML files into Documents; `yaml.safe_load()` based -- **`examples/`** — 5 runnable scripts (RAG quickstart, agent tools, graph workflow, multi-provider, caching & retries) - -### Fixed -- Missing return type annotations in loader helper functions (`_loader_for`, `__getattr__`) - -**Stats:** 1357 tests, 25 LLM providers, 40 tools, 15 loaders - ---- - -## v1.4.0 — New Providers, Tools & Multimodal - -**4 new LLM providers** -- `AI21LLM` — AI21 Jamba models (`jamba-1.5-mini`, `jamba-1.5-large`) with 256K context and native function calling -- `DatabricksLLM` — Databricks Foundation Model APIs (DBRX, Llama 3.1, Mixtral) via OpenAI-compatible endpoint; resolves workspace URL from `DATABRICKS_HOST` -- `ErnieLLM` — Baidu ERNIE Bot (`ernie-4.0`, `ernie-3.5`, `ernie-speed`, `ernie-lite`, `ernie-tiny-8k`) for Chinese-English tasks -- `LlamaCppLLM` — local GGUF models via llama-cpp-python with true async streaming; no API key required; GPU offload via `n_gpu_layers` - -**6 new built-in tools** -- `APIBuilderTool` — build and execute API calls from OpenAPI specs or natural-language intent; optional LLM-assisted operation selection -- `GoogleCalendarTool` — create, list, delete Google Calendar events via Calendar API v3 (`pip install synapsekit[gcal-tool]`) -- `AWSLambdaTool` — invoke AWS Lambda functions with RequestResponse/Event/DryRun types (`pip install synapsekit[aws-lambda]`) -- `ImageAnalysisTool` — analyze images with any multimodal LLM; accepts local paths or public URLs -- `TextToSpeechTool` — convert text to speech audio via OpenAI TTS; 6 voices, 4 formats (`pip install synapsekit[openai]`) -- `SpeechToTextTool` — transcribe audio files via Whisper API or local Whisper model - -**Auto-detection extended** - -RAG facade now auto-detects `moonshot-*`, `glm-*`, `jamba-*`, `@cf/*`, `@hf/*`, `dbrx-*`/`databricks-*`, `ernie-*` model prefixes. - -**Stats:** 1327 tests, 23 LLM providers, 38 built-in tools, 20 retrieval strategies, 9 memory backends - ---- - -## v1.3.0 — Cost Routing, Compliance & Media Loaders - -**Cost-Intelligent Routing** -- `CostRouter` — route to the cheapest model meeting a quality threshold, with automatic fallback on error -- `FallbackChain` — try models in priority order, cascade on error or short responses -- `QUALITY_TABLE` — built-in quality scores for 30+ models - -**Compliance** -- `PIIRedactor` — reversible masking (`[EMAIL_1]`, `[PHONE_1]`) or permanent redaction with `wrap_generate()` for transparent LLM integration -- `AuditLog` — immutable, append-only compliance log with memory, SQLite, and JSONL backends - -**Evaluation** -- `EvalRegression` — snapshot-based regression detection with configurable thresholds for score, cost, and latency -- `synapsekit test --save NAME` / `--compare BASELINE` / `--fail-on-regression` — CI gate for eval regressions - -**Media Loaders** -- `AudioLoader` — transcribe audio files via Whisper API or local Whisper (`pip install synapsekit[audio]`) -- `VideoLoader` — extract audio from video via ffmpeg, then transcribe (`pip install synapsekit[video]`) - -**Agents** -- `stream_steps()` on `ReActAgent` and `FunctionCallingAgent` — stream `ThoughtEvent`, `ActionEvent`, `ObservationEvent`, `TokenEvent`, `FinalAnswerEvent` in real time - -**Stats:** 1203 tests, 19 LLM providers (added Moonshot, Zhipu, Cloudflare), 32 tools, 20 retrieval strategies, 6 text splitters, 9 memory backends, 5 checkpointers - ---- - -## v1.2.0 — Serve, Cost Intelligence & Eval CLI - -**CLI** -- `synapsekit serve` — deploy any RAG/Agent/Graph app as a FastAPI server in one command with auto-detection, health checks, and OpenAPI docs (`pip install synapsekit[serve]`) -- `synapsekit test` — discover and run `@eval_case`-decorated evaluation suites with threshold enforcement and CI-friendly exit codes - -**Cost Intelligence** -- `CostTracker` — hierarchical cost attribution with scope context manager, auto-calculated costs from built-in COST_TABLE -- `BudgetGuard` — per-request/per-user/daily spending limits with circuit breaker pattern (CLOSED → OPEN → HALF_OPEN → CLOSED) -- `BudgetLimit`, `BudgetExceeded`, `CircuitState` — supporting types for budget enforcement - -**Evaluation** -- `@eval_case` decorator — define evaluation test cases with `min_score`, `max_cost_usd`, `max_latency_ms`, and `tags` -- `EvalCaseMeta` — metadata dataclass attached to decorated functions - -**Prompts** -- `PromptHub` — local filesystem versioned prompt registry with push/pull/list/versions (`~/.synapsekit/prompts/`) - -**Plugins** -- `PluginRegistry` — discover and load community plugins via `synapsekit.plugins` entry point group - -**Graph Checkpointers** -- `RedisCheckpointer` — Redis-backed graph checkpoint persistence with optional TTL (`pip install synapsekit[redis]`) -- `PostgresCheckpointer` — PostgreSQL-backed graph checkpoint persistence with UPSERT and JSONB state (`pip install synapsekit[postgres]`) - -**Stats:** 1127 tests, 16 LLM providers, 20 retrieval strategies, 6 text splitters, 9 memory backends, 5 checkpointers - ---- - -## v1.1.0 — GraphRAG, Redis Memory, Vertex AI, MarkdownSplitter, Graph Visualization - -**Retrieval** -- `GraphRAGRetriever` — entity-based graph traversal merged with vector retrieval for knowledge-graph-augmented RAG -- `KnowledgeGraph` — in-memory graph store with triples, BFS traversal, and LLM-powered entity extraction - -**Memory** -- `RedisConversationMemory` — persistent conversation memory backed by Redis with windowing support (`pip install synapsekit[redis]`) - -**LLM Providers** -- `VertexAILLM` — Google Vertex AI with Application Default Credentials, streaming, and native function calling (`pip install synapsekit[vertex]`) - -**Text Splitters** -- `MarkdownTextSplitter` — header-aware chunking that preserves parent header context, with recursive fallback for oversized sections - -**Graph Visualization** -- `GraphVisualizer` — ASCII timeline rendering, Mermaid trace highlighting, step-by-step replay, and standalone HTML export -- `get_mermaid_with_trace()` — Mermaid flowcharts with CSS classes for completed/errored/skipped nodes - -**Stats:** 1047 tests, 16 LLM providers, 20 retrieval strategies, 6 text splitters, 9 memory backends - ---- - -## v1.0.0 — Multimodal + Image Loader + API Markers - -**Multimodal** -- `ImageContent` — image payloads with `from_file()`, `from_url()`, `from_base64()`, `to_openai_format()`, `to_anthropic_format()` -- `AudioContent` — audio payloads with `from_file()`, `from_base64()` -- `MultimodalMessage` — compose text + images + audio with `to_openai_messages()`, `to_anthropic_messages()` -- `ImageLoader` — sync `load()` and `async_load()` with optional vision LLM description - -**API Markers** -- `@public_api` — mark stable public API surfaces -- `@experimental` — mark experimental features -- `@deprecated(reason, alternative)` — mark deprecated features with migration guidance - ---- - -## v0.9.0 — A2A + Guardrails + Distributed Tracing - -**Agent-to-Agent (A2A) Protocol** -- `A2AClient` — call remote agents via the A2A protocol -- `A2AServer` — expose agents as A2A-compatible endpoints -- `AgentCard` — agent metadata for discovery -- `A2ATask`, `A2AMessage`, `TaskState` — task lifecycle management - -**Guardrails** -- `ContentFilter` — block harmful or inappropriate content -- `PIIDetector` — detect and redact personally identifiable information -- `TopicRestrictor` — restrict agent conversations to allowed topics -- `Guardrails` — compose multiple guardrail checks into a pipeline - -**Distributed Tracing** -- `DistributedTracer` — trace requests across multiple services/agents -- `TraceSpan` — individual span in a distributed trace - ---- - -## v0.8.0 — Evaluation + Observability - -**Evaluation** -- `FaithfulnessMetric` — measure whether answers are faithful to source documents -- `RelevancyMetric` — measure answer relevancy to the question -- `GroundednessMetric` — measure how well answers are grounded in retrieved context -- `EvaluationPipeline` — run multiple metrics over a dataset -- `EvaluationResult` — structured results with per-metric scores and `mean_score` - -**Observability** -- `OTelExporter` — export traces in OpenTelemetry format -- `Span` — individual trace span with timing and metadata -- `TracingMiddleware` — auto-trace LLM calls with zero code changes -- `TracingUI` — HTML dashboard for viewing traces - ---- - -## v0.7.0 — MCP + Multi-Agent - -**MCP (Model Context Protocol)** -- `MCPClient` — connect to MCP servers via `connect_stdio()` or `connect_sse()` -- `MCPToolAdapter` — wrap MCP tools as `BaseTool` instances for use with any agent -- `MCPServer` — expose your SynapseKit tools as an MCP-compatible server - -**Multi-Agent: Supervisor/Worker** -- `SupervisorAgent` — orchestrates worker agents, routes tasks using DELEGATE/FINAL protocol -- `WorkerAgent` — specialized agent that reports results back to the supervisor - -**Multi-Agent: Handoffs** -- `HandoffChain` — chain of agents with condition-based handoffs -- `Handoff` — defines a condition and target agent for automatic transfer - -**Multi-Agent: Crew** -- `Crew` — role-based team of agents that execute tasks sequentially or in parallel -- `CrewAgent` — agent with a defined role, goal, and backstory -- `Task` — unit of work assigned to a crew agent - ---- - -## v0.6.9 — Tools & Graph Routing - -**Tools (3 new)** -- `SlackTool` — send messages via Slack webhook URL or Web API bot token (stdlib only, no deps) -- `JiraTool` — Jira REST API v2: search issues (JQL), get issue, create issue, add comment (stdlib only) -- `BraveSearchTool` — web search via Brave Search API (stdlib only) - -**Graph Workflows (2 new)** -- `approval_node()` — factory returning a node that gates on human approval; raises `GraphInterrupt` when `state[key]` is falsy; supports dynamic messages via callable -- `dynamic_route_node()` — factory returning a node that routes to different compiled subgraphs based on a routing function; supports sync/async routing and input/output mapping - -**Closes:** #199, #218, #234, #243, #247 - -**Stats:** 795 tests, 15 providers, 32 tools, 14 loaders, 18 retrieval strategies, 4 cache backends, 8 memory backends - ---- - -## v0.6.8 — Tools, Execution Trace & WebSocket Streaming - -**Tools (5 new)** -- `VectorSearchTool` — wraps a `Retriever` so agents can search a knowledge base (no extra deps) -- `PubMedSearchTool` — search PubMed for biomedical literature via E-utilities API (stdlib only, no deps) -- `GitHubAPITool` — interact with GitHub REST API: search repos, get repo info, search issues, get issue details (stdlib only) -- `EmailTool` — send emails via SMTP with STARTTLS (stdlib `smtplib` + `email`, config via env vars) -- `YouTubeSearchTool` — search YouTube for videos (`pip install synapsekit[youtube]`) - -**Graph Workflows (2 new)** -- `ExecutionTrace` + `TraceEntry` — structured execution tracing that hooks into `EventHooks` with timestamps, durations, and JSON-serializable output -- `ws_stream()` — stream graph execution events over WebSocket connections (works with any object with `send_text()` or `send()`) -- `GraphEvent.to_ws()` — format events as JSON strings for WebSocket transmission - -**Closes:** #217, #219, #220, #229, #231, #239, #241 - -**Stats:** 743 tests, 15 providers, 29 tools, 14 loaders, 18 retrieval strategies, 4 cache backends, 8 memory backends - ---- - -## v0.6.6 — Providers, Retrieval, Tools & Memory - -**LLM Providers (2 new)** -- `PerplexityLLM` — Perplexity AI with Sonar models, OpenAI-compatible (`pip install synapsekit[openai]`) -- `CerebrasLLM` — Cerebras ultra-fast inference, OpenAI-compatible (`pip install synapsekit[openai]`) - -**Retrieval (4 new strategies)** -- `HybridSearchRetriever` — combines BM25 keyword matching with vector similarity using Reciprocal Rank Fusion -- `SelfRAGRetriever` — self-reflective RAG: retrieve → grade relevance → generate → check support → retry -- `AdaptiveRAGRetriever` — LLM classifies query complexity and routes to different retrieval strategies -- `MultiStepRetriever` — iterative retrieval-generation with gap identification and follow-up queries - -**Tools (2 new)** -- `ArxivSearchTool` — search arXiv for academic papers via the Atom API (stdlib only, no deps) -- `TavilySearchTool` — AI-optimized web search via Tavily API (`pip install synapsekit[tavily]`) - -**Memory (2 new)** -- `BufferMemory` — simplest unbounded buffer, keeps all messages until cleared (no LLM, no trimming) -- `EntityMemory` — LLM-based entity extraction with running descriptions and eviction policy - -**Stats:** 698 tests, 15 providers, 24 tools, 14 loaders, 18 retrieval strategies, 4 cache backends, 8 memory backends - ---- - -## v0.6.5 — Retrieval, Tools, Memory & Redis Cache - -**Retrieval (3 new strategies)** -- `CohereReranker` — rerank retrieval results using the Cohere Rerank API (`pip install synapsekit[cohere]`) -- `StepBackRetriever` — generates a more abstract "step-back" question, retrieves for both original and step-back in parallel, deduplicates -- `FLARERetriever` — Forward-Looking Active REtrieval: iterative generate/retrieve loop with `[SEARCH: ...]` markers - -**Tools (3 new)** -- `DuckDuckGoSearchTool` — extended DuckDuckGo search with text and news search types (`pip install synapsekit[search]`) -- `PDFReaderTool` — read and extract text from PDF files with optional page selection (`pip install synapsekit[pdf]`) -- `GraphQLTool` — execute GraphQL queries against any endpoint (`pip install synapsekit[http]`) - -**Memory (1 new)** -- `TokenBufferMemory` — token-budget-aware memory that drops oldest messages when over limit (no LLM needed) - -**LLM Caching (1 new backend)** -- `RedisLLMCache` — distributed Redis cache backend (`pip install synapsekit[redis]`) - -**Stats:** 642 tests, 13 providers, 22 tools, 14 loaders, 14 retrieval strategies, 4 cache backends, 6 memory backends - ---- - -## v0.6.4 — Loaders, HyDE, Tools, Caching & Checkpointing - -**Loaders (2 new)** -- `DocxLoader` — load Microsoft Word (.docx) files (`pip install synapsekit[docx]`) -- `MarkdownLoader` — load Markdown files with optional YAML frontmatter stripping (stdlib, no deps) - -**Retrieval (1 new strategy)** -- `HyDERetriever` — Hypothetical Document Embeddings: generates a hypothetical answer with an LLM and uses it as the search query for improved retrieval - -**Tools (2 new)** -- `ShellTool` — async shell command execution with configurable timeout and allowed_commands whitelist -- `SQLSchemaInspectionTool` — inspect database schema (list_tables, describe_table) for SQLite and SQLAlchemy backends - -**LLM Caching (1 new backend)** -- `FilesystemLLMCache` — persistent file-based cache using JSON files on disk (`cache_backend="filesystem"`) - -**Graph (1 new checkpointer)** -- `JSONFileCheckpointer` — file-based graph checkpointing using JSON files - -**Observability** -- `COST_TABLE` updated with GPT-4.1 family, o3/o3-mini/o4-mini, Gemini 2.5 Pro/Flash, DeepSeek-V3/R1, Groq-hosted models - -**Stats:** 587 tests, 13 providers, 19 tools, 14 loaders, 11 retrieval strategies, 3 cache backends, 3 checkpointers - ---- - -## v0.6.3 — Typed State, Fan-Out, SSE Streaming & LLM Tools - -**Graph Workflows** -- `TypedState` + `StateField` — typed state with per-field reducers for safe parallel merge -- `fan_out_node()` — run multiple subgraphs concurrently and merge results -- `sse_stream()` — stream graph execution as Server-Sent Events for HTTP responses -- `EventHooks` + `GraphEvent` — register callbacks on node_start, node_complete, wave_start, wave_complete - -**LLM Caching** -- `SemanticCache` — similarity-based cache lookup using embeddings (configurable threshold) - -**Tools (3 new, LLM-powered)** -- `SummarizationTool` — summarize text (concise, bullet points, or detailed styles) -- `SentimentAnalysisTool` — sentiment analysis with confidence and explanation -- `TranslationTool` — translate text between languages - -**Stats:** 540 tests, 13 providers, 16 tools, 12 loaders, 10 retrieval strategies, 4 memory backends - ---- - -## v0.6.2 — Retrieval Strategies, Memory & Tools - -**Retrieval (4 new strategies)** -- `CRAGRetriever` — Corrective RAG: grades retrieved docs for relevance, rewrites query and retries when too few are relevant -- `QueryDecompositionRetriever` — breaks complex queries into sub-queries, retrieves for each, deduplicates -- `ContextualCompressionRetriever` — compresses retrieved documents to only relevant excerpts using an LLM -- `EnsembleRetriever` — fuses results from multiple retrievers using weighted Reciprocal Rank Fusion - -**Memory (2 new backends)** -- `SQLiteConversationMemory` — persistent chat history in SQLite, multi-conversation support, optional sliding window -- `SummaryBufferMemory` — token-budget-aware memory that progressively summarizes older messages - -**Tools (2 new)** -- `HumanInputTool` — pauses agent execution to ask the user a question (custom sync/async input functions) -- `WikipediaTool` — searches and fetches Wikipedia article summaries via REST API, no extra deps - -**Stats:** 512 tests, 13 providers, 13 tools, 12 loaders, 10 retrieval strategies, 4 memory backends - ---- - -## v0.6.1 — Graph Power-ups & Advanced Retrieval - -**Graph Workflows** -- `GraphInterrupt` — raise from any node to pause execution for human review -- `InterruptState` — holds interrupt details (graph ID, node, state, message) -- `resume(updates=...)` — apply human edits and continue from checkpoint -- `subgraph_node()` — nest a `CompiledGraph` as a node in a parent graph with input/output key mapping -- `llm_node(stream=True)` — wrap any `BaseLLM` as a graph node with optional token streaming -- `stream_tokens()` — yields `{"type": "token", ...}` events for real-time LLM output - -**Advanced Retrieval** -- `SelfQueryRetriever` — LLM decomposes natural-language queries into semantic search + metadata filters -- `ParentDocumentRetriever` — embeds small chunks for precision, returns full parent documents -- `CrossEncoderReranker` — reranks results with cross-encoder models for higher accuracy - -**Memory** -- `HybridMemory` — sliding window of recent messages + LLM-generated summary of older context - -**Stats:** 482 tests, 13 providers, 11 tools, 12 loaders, 6 retrieval strategies, 2 memory backends - ---- - -## v0.6.0 — Tools, Providers & Retrieval Strategies - -**Built-in Tools (6 new)** -- `HTTPRequestTool` — GET/POST/PUT/DELETE/PATCH with aiohttp -- `FileWriteTool` — write/append files with auto-mkdir -- `FileListTool` — list directories with glob patterns, recursive mode -- `DateTimeTool` — current time, parse, format with timezone support -- `RegexTool` — findall, match, search, replace, split -- `JSONQueryTool` — dot-notation path queries on JSON data - -**LLM Providers (3 new)** -- `OpenRouterLLM` — unified API for 200+ models (auto-detected from `/` in model name) -- `TogetherLLM` — Together AI fast inference -- `FireworksLLM` — Fireworks AI optimized serving - -**Advanced Retrieval (2 new)** -- `ContextualRetriever` — Anthropic-style contextual retrieval (LLM adds context before embedding) -- `SentenceWindowRetriever` — sentence-level embedding with window expansion at retrieval time - -**Stats:** 452 tests, 13 providers, 11 tools, 12 loaders - ---- - -## v0.5.3 — Enterprise Providers & Ecosystem - -**LLM Providers (3 new)** -- `AzureOpenAILLM` — Azure OpenAI enterprise deployments -- `GroqLLM` — ultra-fast inference (Llama, Mixtral, Gemma) -- `DeepSeekLLM` — DeepSeek with function calling support - -**Features** -- `LLMConfig(cache_backend="sqlite")` — persistent SQLite LLM cache -- `RAGFusionRetriever` — multi-query + Reciprocal Rank Fusion -- `ExcelLoader` — `.xlsx` file loading -- `PowerPointLoader` — `.pptx` file loading - -**Stats:** 415 tests, 10 providers, 10 loaders - ---- - -## v0.5.2 — Developer Experience - -- `__repr__` methods on `StateGraph`, `CompiledGraph`, `RAGPipeline`, `ReActAgent`, `FunctionCallingAgent` -- Empty document handling — `RAGPipeline.add()` silently skips empty text -- Retry for `call_with_tools()` — retries apply to function calling -- `BaseLLM.cache_stats` — cache hit/miss statistics -- `search_mmr()` / `retrieve_mmr()` — MMR diversity-aware retrieval -- `LLMConfig(requests_per_minute=N)` — token-bucket rate limiting -- `generate_structured(llm, prompt, schema=Model)` — structured output with Pydantic validation and retry - -**Stats:** 389 tests - ---- - -## v0.5.1 — Polish - -- `@tool` decorator — create agent tools from plain functions with auto-generated JSON Schema -- Metadata filtering — `VectorStore.search(metadata_filter={"key": "value"})` -- Vector store lazy exports — all backends importable directly from `synapsekit` -- File existence checks — loaders raise `FileNotFoundError` before reading -- Parameter validation — agents and memory reject invalid config - -**Stats:** 357 tests - ---- - -## v0.5.0 — Production Features - -- **Text Splitters** — `BaseSplitter`, `CharacterTextSplitter`, `RecursiveCharacterTextSplitter`, `TokenAwareSplitter`, `SemanticSplitter` -- **Function calling** — `call_with_tools()` added to `GeminiLLM` and `MistralLLM` -- **LLM Caching** — `AsyncLRUCache` with SHA-256 keys, opt-in via `LLMConfig(cache=True)` -- **LLM Retries** — exponential backoff, skips auth errors, `LLMConfig(max_retries=N)` -- **Graph Cycles** — `compile(allow_cycles=True)` for intentional loops -- **Configurable max_steps** — `compile(max_steps=N)` -- **Graph Checkpointing** — `BaseCheckpointer`, `InMemoryCheckpointer`, `SQLiteCheckpointer` -- **`CompiledGraph.resume()`** — re-execute from saved state - -**Stats:** 332 tests - ---- - -## v0.4.0 — Graph Workflows - -- `StateGraph` — fluent DAG builder with compile-time validation and cycle detection -- `CompiledGraph` — wave-based async executor with `run()` / `stream()` / `run_sync()` -- `Node`, `Edge`, `ConditionalEdge` — sync + async node functions and routing -- `agent_node()`, `rag_node()` — wrap agents and RAG pipelines as graph nodes -- Parallel execution via `asyncio.gather()` -- Mermaid diagram export - -**Stats:** 267 tests - ---- - -## v0.3.0 — Agents - -- `BaseTool` ABC, `ToolRegistry`, `AgentMemory` -- `ReActAgent` — Thought / Action / Observation loop -- `FunctionCallingAgent` — native OpenAI `tool_calls` / Anthropic `tool_use` -- `AgentExecutor` — unified runner with `run()` / `stream()` / `run_sync()` -- `call_with_tools()` on `OpenAILLM` and `AnthropicLLM` -- 5 built-in tools: Calculator, PythonREPL, FileRead, WebSearch, SQL - -**Stats:** 223 tests - ---- - -## v0.2.0 — Ecosystem - -- **Loaders**: PDF, HTML, CSV, JSON, Directory, Web -- **Output parsers**: JSON, Pydantic, List -- **Vector stores**: Chroma, FAISS, Qdrant, Pinecone -- **LLM providers**: Ollama, Cohere, Mistral, Gemini, Bedrock -- **Prompt templates**: standard, chat, few-shot - -**Stats:** 141 tests - ---- - -## v0.1.0 — Core RAG - -- `BaseLLM` + `LLMConfig`, `OpenAILLM`, `AnthropicLLM` -- `SynapsekitEmbeddings`, `InMemoryVectorStore`, `Retriever` -- `TextSplitter`, `ConversationMemory`, `TokenTracer` -- `RAGPipeline`, `RAG` facade -- `TextLoader`, `StringLoader` - -**Stats:** 52 tests +**Contributing** +Want to help maintain this page? Open a PR updating this file or the raw `CHANGELOG.md` in the main repository. \ No newline at end of file diff --git a/docs/how-to/production-checklist.md b/docs/how-to/production-checklist.md new file mode 100644 index 000000000..079b9b77b --- /dev/null +++ b/docs/how-to/production-checklist.md @@ -0,0 +1,254 @@ +--- +sidebar_position: 10 +--- + +# Production Checklist + +This is a **production-ready, one‑page checklist** for SynapseKit apps. It focuses on the six things that actually break in production: observability, eval gates, rate limits, secrets, cost alerts, and failure modes. The rest is noise. + +If you only do one thing: **ship with budgets + fallbacks + traces**. + +--- + +## 1) Observability (traces, latency, tokens) + +**Goal:** You can answer “what happened, where, and how expensive?” for every request. + +### 1.1 Enable tracing with `OTelExporter` + +```python +from synapsekit import OTelExporter, TracingMiddleware +from synapsekit.llm.openai import OpenAILLM +from synapsekit import LLMConfig + +exporter = OTelExporter( + service_name="synapsekit-prod", + endpoint="http://localhost:4317", # OTLP endpoint (optional) + export_format="otlp", # "json" or "otlp" +) + +middleware = TracingMiddleware(exporter) + +llm = OpenAILLM(LLMConfig( + model="gpt-4o-mini", + api_key="sk-...", + provider="openai", +)) + +traced_llm = middleware.trace_llm(llm) + +# LLM calls now emit spans +result = await traced_llm.generate("Health check") + +# Export traces (JSON or OTLP depending on export_format) +exporter.export() +``` + +### 1.2 Log tokens + cost per request + +Use `CostTracker` (or your own metrics) to record cost by scope. This is your **baseline cost telemetry**. + +```python +import time +from synapsekit import CostTracker + +tracker = CostTracker() + +with tracker.scope("pipeline:qa"): + start = time.perf_counter() + # ... call an LLM ... + latency_ms = (time.perf_counter() - start) * 1000 + tracker.record("gpt-4o-mini", input_tokens=900, output_tokens=300, latency_ms=latency_ms) + +print(tracker.summary()) +``` + +--- + +## 2) Eval gates (quality before deploy) + +**Goal:** Every release passes automatic quality checks. If it fails, the build fails. + +```python +from synapsekit.evaluation import EvaluationPipeline, FaithfulnessMetric, RelevancyMetric +from synapsekit.llm.openai import OpenAILLM +from synapsekit import LLMConfig + +judge = OpenAILLM(LLMConfig(model="gpt-4o-mini", api_key="sk-...", provider="openai")) + +pipeline = EvaluationPipeline( + metrics=[FaithfulnessMetric(judge), RelevancyMetric(judge)] +) + +samples = [ + { + "question": "What is SynapseKit?", + "answer": "SynapseKit is an async-first Python LLM framework.", + "contexts": ["SynapseKit is an async-first Python library for LLM apps."], + } +] + +results = await pipeline.evaluate_batch(samples, concurrency=4) + +# Fail the build if any sample dips below threshold +if any(r.mean_score < 0.7 for r in results): + raise SystemExit("Eval gate failed: mean_score < 0.7") +``` + +**Rule:** No manual deploys if evals fail. This is non‑negotiable. + +--- + +## 3) Rate‑limiting + retries + +**Goal:** Prevent 429 storms and control downstream load. + +```python +from synapsekit.llm.openai import OpenAILLM +from synapsekit import LLMConfig + +llm = OpenAILLM(LLMConfig( + model="gpt-4o-mini", + api_key="sk-...", + provider="openai", + max_retries=3, # exponential backoff + retry_delay=0.5, + requests_per_minute=60, # token bucket limiter +)) +``` + +Set `requests_per_minute` lower than the provider’s quota so your app fails gracefully instead of melting. + +--- + +## 4) Secret rotation + +**Goal:** Keys can rotate without downtime. + +Checklist: +- Load secrets from env vars (never hard‑code). +- Centralize config so you can swap keys at runtime. +- If your platform supports it, **hot‑reload** secrets without restarts. +- Always test a rotation flow **before** you need it. + +Example (environment‑driven config): + +```python +import os +from synapsekit import LLMConfig +from synapsekit.llm.openai import OpenAILLM + +llm = OpenAILLM(LLMConfig( + model="gpt-4o-mini", + api_key=os.getenv("OPENAI_API_KEY", ""), + provider="openai", +)) +``` + +--- + +## 5) Cost alerts + budgets + +**Goal:** You *never* discover cost overruns after the invoice. + +```python +from synapsekit import BudgetGuard, BudgetLimit + +limits = BudgetLimit(per_request=0.05, per_user=1.00, daily=20.00) + +guard = BudgetGuard(limits) + +# Before call +estimated_cost = 0.03 +user_id = "alice" + +guard.check_before(estimated_cost=estimated_cost, user_id=user_id) + +# After call +actual_cost = 0.02 + +guard.record_spend(actual_cost, user_id=user_id) +``` + +**Alerting pattern:** When `BudgetExceededError` is raised, emit a Slack/PagerDuty alert and short‑circuit the request. + +--- + +## 6) Failure modes + fallbacks + +**Goal:** cheap‑to‑expensive escalation; no single provider is a hard dependency. + +### 6.1 Fallback ladders with `FallbackChain` + +```python +from synapsekit import FallbackChain, FallbackChainConfig +from synapsekit.llm.openai import OpenAILLM +from synapsekit import LLMConfig + +cheap = OpenAILLM(LLMConfig(model="gpt-4o-mini", api_key="sk-...", provider="openai")) +strong = OpenAILLM(LLMConfig(model="gpt-4o", api_key="sk-...", provider="openai")) + +chain = FallbackChain(FallbackChainConfig( + models=[cheap, strong], + min_response_length=20, # escalate if output is too short + fallback_on_empty=True, +)) + +answer = await chain.generate("Explain vector databases") +print(chain.used_model) +``` + +### 6.2 CostRouter fallback on error + +`CostRouter` tries the cheapest viable model first, then falls back if it errors. + +```python +from synapsekit import CostRouter, CostRouterConfig, RouterModelSpec + +router = CostRouter(CostRouterConfig( + models=[ + RouterModelSpec(model="gpt-4o-mini", api_key="sk-...", provider="openai"), + RouterModelSpec(model="gpt-4o", api_key="sk-...", provider="openai"), + ], + quality_threshold=0.75, + fallback_on_error=True, +)) + +answer = await router.generate("Summarize the report") +print(router.selected_model) +``` + +**Rule:** Always have a cheap fallback. Even a degraded answer beats a 500. + +--- + +# One‑page printable checklist + +Print this section and stick it in your deploy runbook. + +**Observability** +- [ ] Tracing enabled (OTelExporter + TracingMiddleware) +- [ ] Token + cost logging per request +- [ ] Latency metrics at P50/P95/P99 + +**Eval gates** +- [ ] CI evals run on every release +- [ ] Failing evals block deploy +- [ ] Thresholds documented and versioned + +**Rate‑limiting** +- [ ] `requests_per_minute` set below provider quota +- [ ] `max_retries` + `retry_delay` configured + +**Secrets** +- [ ] API keys only from env/config +- [ ] Rotation runbook tested + +**Cost alerts** +- [ ] `BudgetGuard` per‑request + daily limits +- [ ] Alerts on `BudgetExceededError` + +**Failure modes** +- [ ] FallbackChain or CostRouter enabled +- [ ] Short/empty responses trigger escalation +- [ ] Provider outage doesn’t 500 diff --git a/docs/playground/agents-playground.md b/docs/playground/agents-playground.md new file mode 100644 index 000000000..63ec56d7a --- /dev/null +++ b/docs/playground/agents-playground.md @@ -0,0 +1,26 @@ +--- +title: Agent Playground (Colab) +sidebar_position: 2 +--- + +# Agent Playground (Colab) + +Run SynapseKit agents in a **real Python environment** using Google Colab. + +## ✅ Open in Colab + +[Open Agent Notebook →](https://colab.research.google.com/github/SynapseKit/synapsekit-docs/blob/main/notebooks/agents/react-research-assistant.ipynb) + +--- + +## What you'll learn + +- How to create a function-calling agent +- How tools are wired into an agent +- How the agent reasons step-by-step + +--- + +**Next:** +- [Graph Playground (Colab)](./graphs-playground) +- [RAG Playground (Colab)](./rag-playground) diff --git a/docs/playground/graphs-playground.md b/docs/playground/graphs-playground.md new file mode 100644 index 000000000..cdbc839e7 --- /dev/null +++ b/docs/playground/graphs-playground.md @@ -0,0 +1,26 @@ +--- +title: Graph Playground (Colab) +sidebar_position: 3 +--- + +# Graph Playground (Colab) + +Run SynapseKit Graph workflows in a **real Python environment** using Google Colab. + +## ✅ Open in Colab + +[Open Graph Notebook →](https://colab.research.google.com/github/SynapseKit/synapsekit-docs/blob/main/notebooks/graph/linear-workflow.ipynb) + +--- + +## What you'll learn + +- How to build a `StateGraph` +- How nodes and edges compose workflows +- How to execute and inspect results + +--- + +**Next:** +- [Agent Playground (Colab)](./agents-playground) +- [RAG Playground (Colab)](./rag-playground) diff --git a/docs/playground/index.md b/docs/playground/index.md new file mode 100644 index 000000000..2f21e74d3 --- /dev/null +++ b/docs/playground/index.md @@ -0,0 +1,27 @@ +--- +title: Interactive Notebooks +sidebar_position: 0 +--- + +# Interactive Notebooks + +These notebooks let you run SynapseKit in a **real Python runtime** via Google Colab. + +## ✅ Start Here + +- [RAG Playground →](./rag-playground) +- [Agent Playground →](./agents-playground) +- [Graph Playground →](./graphs-playground) + +--- + +## Why Colab instead of inline playgrounds? + +SynapseKit is a Python framework with real LLM integrations. Browser‑only sandboxes cannot provide a full or reliable experience. + +Google Colab gives you: +- Real Python execution +- Full SynapseKit API access +- No local setup + +This mirrors how platforms like GeeksforGeeks and major ML docs provide runnable examples. diff --git a/docs/playground/rag-playground.md b/docs/playground/rag-playground.md new file mode 100644 index 000000000..5c3cf7948 --- /dev/null +++ b/docs/playground/rag-playground.md @@ -0,0 +1,34 @@ +--- +title: RAG Playground (Colab) +sidebar_position: 1 +--- + +# RAG Playground (Colab) + +Run SynapseKit's RAG workflow in a **real Python environment** using Google Colab. + +This is the most reliable way to experience the full SynapseKit stack without local setup. + +## ✅ Open in Colab + +[Open RAG Quickstart Notebook →](https://colab.research.google.com/github/SynapseKit/synapsekit-docs/blob/main/notebooks/rag/quickstart-3-lines.ipynb) + +--- + +## What you'll learn + +- How to create a `RAGPipeline` +- How to add documents and ask questions +- How retrieval + generation works together + +## Why Colab? + +- Real Python execution +- Full SynapseKit API (not a mock) +- No installation required + +--- + +**Next:** +- [Agent Playground (Colab)](./agents-playground) +- [Graph Playground (Colab)](./graphs-playground) diff --git a/docs/tutorial/adding-agent-with-tools.md b/docs/tutorial/adding-agent-with-tools.md new file mode 100644 index 000000000..8ab00365b --- /dev/null +++ b/docs/tutorial/adding-agent-with-tools.md @@ -0,0 +1,171 @@ +--- +sidebar_position: 4 +--- + +**Tutorial Progress: Step 3 of 5** +[← Previous: Your First RAG Pipeline](./first-rag-pipeline) | [Next: Building a Graph Workflow →](./building-graph-workflow) + +# Step 3: Adding an Agent with Tools + +In this step you will turn your RAG pipeline into an intelligent agent that can use tools, make decisions, and call external functions. + +## Why agents? + +RAG is great for question answering. Agents add reasoning, tool use, and multi-step planning on top of retrieval. + +## 1. The FunctionCallingAgent + +SynapseKit's `FunctionCallingAgent` is a typed, async-first agent that supports multiple LLM providers and automatic tool schema generation. + +```python +from synapsekit import FunctionCallingAgent +from synapsekit.tools import Tool + +agent = FunctionCallingAgent( + model="gpt-4o", + tools=[...], # list of Tool objects + memory=..., # optional + guardrails=..., # optional +) +``` + +## 2. Creating a custom tool + +The easiest way is using the `@tool` decorator or the `Tool` class. + +```python +from synapsekit.tools import tool +from pydantic import BaseModel + +class WebSearchInput(BaseModel): + query: str + max_results: int = 5 + +@tool("web_search", "Search the web for current information") +async def web_search(input: WebSearchInput) -> list[str]: + # In real code you would call Tavily, SerpAPI, etc. + return [ + f"Result 1 for {input.query}", + f"Result 2 for {input.query}", + ] +``` + +You can also wrap existing functions: + +```python +def calculate(expression: str) -> float: + return eval(expression) # demo only + +calc_tool = Tool.from_function( + calculate, + name="calculator", + description="Evaluate a math expression" +) +``` + +## 3. Full agent example with RAG + tools + +```python +import asyncio +from synapsekit import FunctionCallingAgent, RAGPipeline +from synapsekit.tools import tool +from pydantic import BaseModel + +class SearchInput(BaseModel): + query: str + +@tool("company_search", "Search internal company knowledge base") +async def company_search(input: SearchInput): + rag = RAGPipeline(model="gpt-4o-mini", vector_store="chroma") + rag.add_directory("company_docs/") + return rag.ask_sync(input.query) + +agent = FunctionCallingAgent( + model="gpt-4o", + tools=[company_search], + system_prompt="You are a helpful company assistant. Use tools when needed.", +) + +async def main(): + result = await agent.run("What is our Q3 revenue target?") + print(result) + +asyncio.run(main()) +``` + +## 4. Adding guardrails and budget control + +Production agents need safety. + +```python +from synapsekit.guardrails import ContentFilter, BudgetGuard + +agent = FunctionCallingAgent( + model="gpt-4o", + tools=[...], + guardrails=[ + ContentFilter(block_categories=["hate", "violence"]), + BudgetGuard(max_tokens_per_turn=4000, max_cost_usd=0.05), + ], +) +``` + +## 5. Streaming agent steps (advanced) + +Agents can stream intermediate reasoning steps. + +```python +async for event in agent.stream_events("Research SynapseKit competitors"): + if event.type == "tool_call": + print(f"Calling tool: {event.tool_name}") + elif event.type == "final_answer": + print(event.content) +``` + +## 6. Complete runnable agent tutorial code + +```python +import asyncio +from synapsekit import FunctionCallingAgent +from synapsekit.tools import tool +from pydantic import BaseModel, Field + +class WeatherInput(BaseModel): + city: str = Field(..., description="City name") + +@tool("get_weather", "Get current weather for a city") +async def get_weather(input: WeatherInput) -> str: + # Mock implementation + return f"The weather in {input.city} is sunny, 24°C" + +agent = FunctionCallingAgent( + model="gpt-4o-mini", + tools=[get_weather], + system_prompt="You are a friendly weather assistant.", +) + +async def chat(): + print("Weather Agent ready. Type 'exit' to quit.\n") + while True: + user_input = input("You: ") + if user_input.lower() == "exit": + break + response = await agent.run(user_input) + print(f"Agent: {response}\n") + +if __name__ == "__main__": + asyncio.run(chat()) +``` + +## Key concepts covered + +- Tool schema generation from Pydantic models +- Automatic function calling loop +- Guardrails & budget protection +- Streaming intermediate steps +- Memory integration (agent remembers previous turns) + +**You now have a capable agent!** + +**Tutorial Progress: Step 3 of 5** +[← Previous: Your First RAG Pipeline](./first-rag-pipeline) | [Next: Building a Graph Workflow →](./building-graph-workflow) \ No newline at end of file diff --git a/docs/tutorial/building-graph-workflow.md b/docs/tutorial/building-graph-workflow.md new file mode 100644 index 000000000..1bc552ba6 --- /dev/null +++ b/docs/tutorial/building-graph-workflow.md @@ -0,0 +1,167 @@ +--- +sidebar_position: 5 +--- + +**Tutorial Progress: Step 4 of 5** +[← Previous: Adding an Agent with Tools](./adding-agent-with-tools) | [Next: Deploying to Production →](./deploying-to-production) + +# Step 4: Building a Graph Workflow + +In this step you will compose RAG and Agents into a stateful, resumable graph workflow using `StateGraph`. + +## Why graphs? + +Graphs give you explicit control over control flow, cycles, human-in-the-loop, checkpointing, and visualization — things that become messy with pure agent loops. + +## 1. Core concepts + +- **State**: A typed dictionary that flows through the graph +- **Nodes**: Functions or agents that transform state +- **Edges**: Connections (normal, conditional, cycles) +- **Checkpointing**: Automatic persistence of state after each step + +## 2. A simple linear graph + +```python +from synapsekit import StateGraph +from typing import TypedDict + +class WorkflowState(TypedDict): + question: str + answer: str + sources: list[str] + +async def retrieve(state: WorkflowState): + # Call your RAG pipeline here + return {"sources": ["doc1.pdf", "doc2.md"]} + +async def generate(state: WorkflowState): + # Call LLM with retrieved context + return {"answer": "SynapseKit is awesome because..."} + +graph = StateGraph(WorkflowState) +graph.add_node("retrieve", retrieve) +graph.add_node("generate", generate) +graph.add_edge("retrieve", "generate") +graph.set_entry_point("retrieve") + +app = graph.compile() +``` + +## 3. Adding conditional routing + +```python +def route_after_retrieval(state: WorkflowState): + if len(state["sources"]) > 3: + return "generate_detailed" + return "generate_concise" + +graph.add_conditional_edges( + "retrieve", + route_after_retrieval, + { + "generate_detailed": "generate_detailed", + "generate_concise": "generate_concise", + } +) +``` + +## 4. Human-in-the-loop (HITL) + +```python +from synapsekit.graph import interrupt + +async def human_review(state): + # Pause execution and wait for human input + return interrupt("Please review the draft answer", value=state["answer"]) +``` + +The graph will stop and return control to your application. + +## 5. Checkpointing & resumability + +```python +from synapsekit.checkpointing import SqliteCheckpointer + +checkpointer = SqliteCheckpointer(db_path="workflows.db") + +app = graph.compile(checkpointer=checkpointer) + +# Run with thread_id for resumability +config = {"configurable": {"thread_id": "user-123"}} + +result = await app.ainvoke({"question": "Explain RAG"}, config=config) +``` + +Later you can resume from any checkpoint. + +## 6. Full example: Research → Draft → Review → Publish graph + +```python +import asyncio +from synapsekit import StateGraph +from typing import TypedDict, Literal + +class ResearchState(TypedDict): + topic: str + research: str + draft: str + feedback: str | None + final: str | None + +async def research_node(state): + # Call agent or RAG + return {"research": f"Research results about {state['topic']}"} + +async def draft_node(state): + return {"draft": f"Draft article about {state['topic']}"} + +async def review_node(state): + # Simulate human review + return {"feedback": "Looks good, minor changes needed"} + +async def publish_node(state): + return {"final": state["draft"] + "\n\n[Published]"} + +def should_revise(state): + if state.get("feedback"): + return "revise" + return "publish" + +graph = StateGraph(ResearchState) +graph.add_node("research", research_node) +graph.add_node("draft", draft_node) +graph.add_node("review", review_node) +graph.add_node("publish", publish_node) + +graph.add_edge("research", "draft") +graph.add_edge("draft", "review") +graph.add_conditional_edges("review", should_revise, { + "revise": "draft", + "publish": "publish" +}) +graph.set_entry_point("research") + +app = graph.compile() + +async def main(): + result = await app.ainvoke({"topic": "SynapseKit graphs"}) + print(result["final"]) + +asyncio.run(main()) +``` + +## 7. Visualization + +```python +from synapsekit.graph import draw_mermaid + +print(draw_mermaid(app)) +``` + +Generates a Mermaid diagram you can render in docs or notebooks. + +**You have now built a real multi-step workflow with branching and checkpointing!** + +**Tutorial Progress: Step 4 of 5** +[← Previous: Adding an Agent with Tools](./adding-agent-with-tools) | [Next: Deploying to Production →](./deploying-to-production) \ No newline at end of file diff --git a/docs/tutorial/deploying-to-production.md b/docs/tutorial/deploying-to-production.md new file mode 100644 index 000000000..fda69c356 --- /dev/null +++ b/docs/tutorial/deploying-to-production.md @@ -0,0 +1,132 @@ +--- +sidebar_position: 6 +--- + +**Tutorial Progress: Step 5 of 5** +[← Previous: Building a Graph Workflow](./building-graph-workflow) | [Back to Tutorial Index](./index) + +# Step 5: Deploying to Production + +Congratulations on reaching the final step! In this page we turn your local application into a production-ready, observable, and maintainable system. + +## 1. Observability & Cost Tracking + +Never deploy without visibility. + +```python +from synapsekit.observability import CostTracker, TokenUsageLogger + +tracker = CostTracker() +logger = TokenUsageLogger() + +rag = RAGPipeline( + model="gpt-4o-mini", + callbacks=[tracker, logger], +) +``` + +You can also export metrics to Prometheus, LangSmith, or your own dashboard. + +## 2. Evaluation + +Automated evaluation is critical. + +```python +from synapsekit.evaluation import RAGEvaluator, FaithfulnessMetric, RelevanceMetric + +evaluator = RAGEvaluator(metrics=[FaithfulnessMetric(), RelevanceMetric()]) + +results = await evaluator.evaluate( + pipeline=rag, + test_cases=[{"question": "...", "expected": "..."}] +) +print(results.summary()) +``` + +## 3. Configuration management + +Use Pydantic settings for all configuration. + +```python +from pydantic_settings import BaseSettings + +class Settings(BaseSettings): + openai_api_key: str + vector_store: str = "chroma" + log_level: str = "INFO" + max_tokens_per_turn: int = 8000 + + class Config: + env_file = ".env" + +settings = Settings() +``` + +## 4. Containerization (Docker) + +```dockerfile +FROM python:3.11-slim + +WORKDIR /app +COPY pyproject.toml . +RUN pip install uv && uv pip install -e ".[all]" + +COPY src/ ./src/ +COPY .env .env + +CMD ["python", "-m", "myapp.main"] +``` + +## 5. Production checklist + +- [ ] All API keys in environment variables / secret manager +- [ ] Rate limiting and budget guards enabled +- [ ] Comprehensive logging + tracing +- [ ] Automated evaluation in CI +- [ ] Graceful degradation when LLM providers fail +- [ ] Health check endpoint (`/health`) +- [ ] Structured output validation with Pydantic +- [ ] Retry + fallback strategies +- [ ] Monitoring dashboards (cost, latency, error rate) + +## 6. Example production entrypoint + +```python +# src/myapp/main.py +import asyncio +from fastapi import FastAPI +from synapsekit import RAGPipeline, FunctionCallingAgent +from synapsekit.observability import setup_prometheus + +app = FastAPI() +setup_prometheus(app) + +rag = RAGPipeline(...) # configured from settings +agent = FunctionCallingAgent(...) + +@app.post("/chat") +async def chat(payload: dict): + result = await agent.run(payload["message"]) + return {"response": result} + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) +``` + +## 7. Next steps after this tutorial + +- Explore the full [Guides](/docs/guides) section +- Read the [Architecture](/docs/architecture) deep dive +- Join the community discussions +- Contribute a tutorial or improvement + +**You have completed the full Getting Started Tutorial!** + +You now have the foundation to build reliable, observable, and maintainable LLM applications with SynapseKit. + +**Tutorial Progress: Step 5 of 5** +[← Previous: Building a Graph Workflow](./building-graph-workflow) | [Back to Tutorial Index](./index) +``` + +**Congratulations — tutorial complete.** \ No newline at end of file diff --git a/docs/tutorial/first-rag-pipeline.md b/docs/tutorial/first-rag-pipeline.md new file mode 100644 index 000000000..f7f37d632 --- /dev/null +++ b/docs/tutorial/first-rag-pipeline.md @@ -0,0 +1,157 @@ +--- +sidebar_position: 3 +--- + +**Tutorial Progress: Step 2 of 5** +[← Previous: Installation & Setup](./installation-setup) | [Next: Adding an Agent with Tools →](./adding-agent-with-tools) + +# Step 2: Your First RAG Pipeline + +In this step you will build a complete Retrieval-Augmented Generation (RAG) pipeline that can answer questions over your own documents. + +## What we will build + +A minimal but production-pattern RAG system with: +- Document loading & chunking +- Embedding + vector storage +- Retrieval + generation +- Streaming responses +- Memory (conversation history) + +## 1. The high-level RAGPipeline API + +SynapseKit provides a high-level `RAGPipeline` that hides most boilerplate while still giving you full control when needed. + +```python +from synapsekit import RAGPipeline + +rag = RAGPipeline( + model="gpt-4o-mini", + vector_store="chroma", # or "qdrant", "pinecone", "in_memory" + embedder="openai", # or "cohere", "huggingface" + chunk_size=512, + chunk_overlap=50, +) +``` + +## 2. Adding documents + +You can add raw text, files, or URLs. + +```python +# From raw text +rag.add( + "SynapseKit is an async-first Python framework for building " + "reliable LLM applications. It emphasizes typed interfaces, " + "minimal magic, and production observability." +) + +# From a file +rag.add_file("data/product-spec.pdf") + +# From a directory (recursive) +rag.add_directory("docs/", glob="*.md") +``` + +Under the hood this performs: +1. Loading (using appropriate loader) +2. Splitting into chunks +3. Embedding each chunk +4. Storing in the vector database + +## 3. Asking questions (sync & async) + +```python +# Simple synchronous call +answer = rag.ask_sync("What is SynapseKit?") +print(answer) + +# Async streaming (recommended for production) +import asyncio + +async def stream_answer(): + async for token in rag.stream("What are the core design principles?"): + print(token, end="", flush=True) + print() + +asyncio.run(stream_answer()) +``` + +**Expected output (example)** +``` +SynapseKit is an async-first Python framework that helps developers +build reliable LLM applications with strong typing and observability. +``` + +## 4. Adding conversation memory + +Real applications need memory across turns. + +```python +from synapsekit.memory import SQLiteConversationMemory + +memory = SQLiteConversationMemory(db_path=":memory:") +rag = RAGPipeline(model="gpt-4o-mini", memory=memory) + +rag.ask_sync("What is SynapseKit?") +rag.ask_sync("What are its main advantages?") +``` + +The memory automatically injects previous turns into the prompt. + +## 5. Full runnable example (copy-paste) + +```python +import asyncio +from synapsekit import RAGPipeline +from synapsekit.memory import InMemoryConversationMemory + +async def main(): + rag = RAGPipeline( + model="gpt-4o-mini", + vector_store="in_memory", + embedder="openai", + memory=InMemoryConversationMemory(), + ) + + # Seed with knowledge + rag.add(""" + SynapseKit was created to solve three common problems in LLM apps: + 1. Callback hell and lack of typing + 2. Missing production observability + 3. Difficulty composing RAG + Agents + Graphs + """) + + print("=== First question ===") + answer = rag.ask_sync("What problems does SynapseKit solve?") + print(answer) + + print("\n=== Follow-up (uses memory) ===") + async for token in rag.stream("Can you give an example of each?"): + print(token, end="", flush=True) + print() + +if __name__ == "__main__": + asyncio.run(main()) +``` + +## 6. Advanced options you can explore later + +- `rag.retriever.top_k = 8` +- Custom chunking strategies +- Hybrid search (vector + BM25) +- Reranking with cross-encoders +- Metadata filtering + +## Expected behavior checklist + +- [ ] Documents are chunked and embedded +- [ ] Retrieval returns relevant context +- [ ] LLM answers are grounded in the provided context +- [ ] Streaming works without buffering the full response +- [ ] Memory carries context between turns + +**Congratulations!** You now have a working RAG system. + +**Tutorial Progress: Step 2 of 5** +[← Previous: Installation & Setup](./installation-setup) | [Next: Adding an Agent with Tools →](./adding-agent-with-tools) \ No newline at end of file diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md new file mode 100644 index 000000000..a78783a04 --- /dev/null +++ b/docs/tutorial/index.md @@ -0,0 +1,46 @@ +--- +sidebar_position: 1 +--- + +# Getting Started Tutorial + +Welcome to the official **Getting Started Tutorial** for SynapseKit. + +This interactive, step-by-step wizard will take you from a fresh Python environment to a fully functional, production-ready LLM application in five clear stages. Each step builds directly on the previous one. + +**What you will build** +- A complete RAG pipeline with memory +- An agent with custom tools +- A stateful graph workflow with checkpointing +- Deployment-ready configuration with observability + +**How to use this tutorial** +- Follow the steps in order +- Each page includes runnable code, explanations, and expected output +- Use the progress tracker at the top of every page +- Code examples use the latest SynapseKit patterns (async-first, typed, minimal magic) + +**Prerequisites** +- Python 3.10 or higher +- An API key for at least one supported LLM provider (OpenAI, Anthropic, Ollama, Gemini, etc.) +- Basic familiarity with Python and async/await is helpful but not required + +**Tutorial Steps** + +| Step | Page | What You'll Learn | Key SynapseKit Components | +|------|------|-------------------|---------------------------| +| 1 | [Installation & Setup](./installation-setup) | Environment, installation, first import, API key management | `synapsekit`, provider configuration | +| 2 | [Your First RAG Pipeline](./first-rag-pipeline) | Ingestion, retrieval, generation, streaming | `RAGPipeline`, `VectorStore`, `Embedder` | +| 3 | [Adding an Agent with Tools](./adding-agent-with-tools) | Tool creation, function calling, guardrails | `FunctionCallingAgent`, `Tool`, `BudgetGuard` | +| 4 | [Building a Graph Workflow](./building-graph-workflow) | StateGraph, nodes, edges, checkpointing | `StateGraph`, `checkpointing`, `interrupt` | +| 5 | [Deploying to Production](./deploying-to-production) | Observability, evaluation, containerization | `CostTracker`, `Evaluator`, Docker patterns | + +**Progress Tracking** +Every page shows your current position (e.g., **Step 1 of 5**). Use the "Next" and "Previous" links to navigate linearly. + +**Tips for success** +- Run the code in a fresh virtual environment +- Copy-paste the examples and modify them +- Check the [API Reference](/docs/api) if you want to explore beyond the tutorial + +Let's begin with **Step 1: Installation & Setup**. \ No newline at end of file diff --git a/docs/tutorial/installation-setup.md b/docs/tutorial/installation-setup.md new file mode 100644 index 000000000..d77190962 --- /dev/null +++ b/docs/tutorial/installation-setup.md @@ -0,0 +1,157 @@ +--- +sidebar_position: 2 +--- + +**Tutorial Progress: Step 1 of 5** +[Next: Your First RAG Pipeline →](./first-rag-pipeline) + +# Step 1: Installation & Setup + +This page gets your development environment ready and verifies that SynapseKit is working correctly. + +## 1. Create a virtual environment (recommended) + +Using a virtual environment keeps your dependencies isolated. + +```bash +# Using venv (built-in) +python -m venv .venv +source .venv/bin/activate # macOS / Linux +.venv\Scripts\activate # Windows + +# Or using uv (fast modern alternative) +uv venv +source .venv/bin/activate +``` + +## 2. Install SynapseKit + +SynapseKit supports multiple installation profiles depending on which LLM providers and features you need. + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + + + +```bash +# Core + OpenAI (most common starting point) +pip install "synapsekit[openai]" + +# Or install everything (recommended for the full tutorial) +pip install "synapsekit[all]" +``` + + + + +```bash +uv add "synapsekit[openai]" +# or +uv add "synapsekit[all]" +``` + + + + +```bash +poetry add "synapsekit[openai]" +# or +poetry add "synapsekit[all]" +``` + + + + +**Common extras** (you can combine them): +- `openai`, `anthropic`, `ollama`, `gemini`, `cohere` +- `chroma`, `qdrant`, `pinecone` (vector stores) +- `eval`, `guardrails`, `observability` + +## 3. Configure your API keys + +The cleanest way is using environment variables. + +```bash +# .env file (never commit this) +OPENAI_API_KEY=sk-... +ANTHROPIC_API_KEY=sk-ant-... +GEMINI_API_KEY=... +``` + +SynapseKit automatically loads `.env` files if `python-dotenv` is installed: + +```bash +pip install python-dotenv +``` + +Or set them in your shell: + +```bash +export OPENAI_API_KEY="sk-..." +``` + +## 4. Verify the installation + +Create a file `verify.py`: + +```python +from synapsekit import __version__ +from synapsekit.llms import OpenAIProvider + +print(f"SynapseKit version: {__version__}") + +# Quick smoke test +provider = OpenAIProvider(model="gpt-4o-mini") +print("Provider initialized successfully") +print(f"Model: {provider.model}") +``` + +Run it: + +```bash +python verify.py +``` + +**Expected output** +``` +SynapseKit version: 0.9.2 +Provider initialized successfully +Model: gpt-4o-mini +``` + +## 5. Optional but recommended: Project layout + +For the rest of this tutorial we recommend the following structure: + +``` +my-synapsekit-project/ +├── .env +├── pyproject.toml +├── src/ +│ └── myapp/ +│ ├── __init__.py +│ ├── pipelines/ +│ ├── agents/ +│ └── graphs/ +├── tests/ +└── README.md +``` + +## Troubleshooting + +**"No module named 'synapsekit'"** +- Make sure you activated the virtual environment +- Try `pip install -e .` if you are developing locally + +**API key errors** +- Double-check the environment variable name (case-sensitive) +- Use `synapsekit config show` to debug loaded providers + +**Async warnings** +- SynapseKit is async-first. Always use `asyncio.run()` or `await` in async contexts. + +**Next step**: In the next page we will build our first RAG pipeline using the environment we just set up. + +**Tutorial Progress: Step 1 of 5** +[← Back to Tutorial Index](./index) | [Next: Your First RAG Pipeline →](./first-rag-pipeline) \ No newline at end of file diff --git a/docusaurus.config.ts b/docusaurus.config.ts index 675e33b1f..e2f9e78a8 100644 --- a/docusaurus.config.ts +++ b/docusaurus.config.ts @@ -7,7 +7,7 @@ const config: Config = { tagline: 'Async-first Python framework for building production-grade LLM applications with RAG, agents, and graph workflows.', favicon: 'img/favicon.svg', - future: { v4: true }, + // future: { v4: true }, url: 'https://synapsekit.github.io', baseUrl: '/synapsekit-docs/', @@ -135,8 +135,9 @@ const config: Config = { position: 'left', label: 'Docs', }, - { to: '/docs/guides', label: 'Guides', position: 'left' }, - { to: '/blog', label: 'Blog', position: 'left' }, + { to: 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For ESM codebases, update to uuid@latest. For CommonJS codebases, use uuid@11 (but be aware this version will likely be deprecated in 2028).", "license": "MIT", "bin": { "uuid": "dist/bin/uuid" @@ -18377,13 +19021,11 @@ } }, "node_modules/webpack": { - "version": "5.105.4", - "resolved": "https://registry.npmjs.org/webpack/-/webpack-5.105.4.tgz", - "integrity": "sha512-jTywjboN9aHxFlToqb0K0Zs9SbBoW4zRUlGzI2tYNxVYcEi/IPpn+Xi4ye5jTLvX2YeLuic/IvxNot+Q1jMoOw==", + "version": "5.107.2", + "resolved": "https://registry.npmjs.org/webpack/-/webpack-5.107.2.tgz", + "integrity": "sha512-v7RhXaJbpMlV0D7hC7lb2EbnxkoeUqf9qhKr6lozx3Q48pmFrqqNRmZFUEGmi7pSwm6fCQ2H1IjvCkHqdpVdjQ==", "license": "MIT", - "peer": true, "dependencies": { - "@types/eslint-scope": "^3.7.7", "@types/estree": "^1.0.8", "@types/json-schema": "^7.0.15", "@webassemblyjs/ast": "^1.14.1", @@ -18393,21 +19035,20 @@ "acorn-import-phases": "^1.0.3", "browserslist": "^4.28.1", "chrome-trace-event": "^1.0.2", - "enhanced-resolve": "^5.20.0", - "es-module-lexer": "^2.0.0", + "enhanced-resolve": "^5.22.0", + "es-module-lexer": "^2.1.0", "eslint-scope": "5.1.1", "events": "^3.2.0", "glob-to-regexp": "^0.4.1", "graceful-fs": "^4.2.11", - 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"license": "MIT", - "dependencies": { - "mime-db": "1.52.0" - }, - "engines": { - "node": ">= 0.6" - } - }, "node_modules/webpackbar": { - "version": "6.0.1", - "resolved": "https://registry.npmjs.org/webpackbar/-/webpackbar-6.0.1.tgz", - "integrity": "sha512-TnErZpmuKdwWBdMoexjio3KKX6ZtoKHRVvLIU0A47R0VVBDtx3ZyOJDktgYixhoJokZTYTt1Z37OkO9pnGJa9Q==", + "version": "7.0.0", + "resolved": "https://registry.npmjs.org/webpackbar/-/webpackbar-7.0.0.tgz", + "integrity": "sha512-aS9soqSO2iCHgqHoCrj4LbfGQUboDCYJPSFOAchEK+9psIjNrfSWW4Y0YEz67MKURNvMmfo0ycOg9d/+OOf9/Q==", "license": "MIT", "dependencies": { - "ansi-escapes": "^4.3.2", - "chalk": "^4.1.2", + "ansis": "^3.2.0", "consola": "^3.2.3", - "figures": "^3.2.0", - "markdown-table": "^2.0.0", "pretty-time": "^1.1.0", - "std-env": "^3.7.0", - "wrap-ansi": "^7.0.0" + "std-env": "^3.7.0" }, "engines": { "node": ">=14.21.3" }, "peerDependencies": { + "@rspack/core": "*", "webpack": "3 || 4 || 5" - } - }, - "node_modules/webpackbar/node_modules/emoji-regex": { - "version": "8.0.0", - "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", - "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", - "license": "MIT" - }, - "node_modules/webpackbar/node_modules/markdown-table": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/markdown-table/-/markdown-table-2.0.0.tgz", - "integrity": "sha512-Ezda85ToJUBhM6WGaG6veasyym+Tbs3cMAw/ZhOPqXiYsr0jgocBV3j3nx+4lk47plLlIqjwuTm/ywVI+zjJ/A==", - "license": "MIT", - "dependencies": { - "repeat-string": "^1.0.0" - }, - "funding": { - "type": "github", - "url": "https://github.com/sponsors/wooorm" - } - }, - "node_modules/webpackbar/node_modules/string-width": { - "version": "4.2.3", - "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", - "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", - "license": "MIT", - "dependencies": { - "emoji-regex": "^8.0.0", - "is-fullwidth-code-point": "^3.0.0", - "strip-ansi": "^6.0.1" - }, - "engines": { - "node": ">=8" - } - }, - "node_modules/webpackbar/node_modules/wrap-ansi": { - "version": "7.0.0", - "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz", - "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==", - "license": "MIT", - "dependencies": { - "ansi-styles": "^4.0.0", - "string-width": "^4.1.0", - "strip-ansi": "^6.0.0" }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/chalk/wrap-ansi?sponsor=1" + "peerDependenciesMeta": { + "@rspack/core": { + "optional": true + }, + "webpack": { + "optional": true + } } }, "node_modules/websocket-driver": { @@ -18909,9 +19475,9 @@ } }, "node_modules/ws": { - "version": "7.5.10", - "resolved": "https://registry.npmjs.org/ws/-/ws-7.5.10.tgz", - "integrity": "sha512-+dbF1tHwZpXcbOJdVOkzLDxZP1ailvSxM6ZweXTegylPny803bFhA+vqBYw4s31NSAk4S2Qz+AKXK9a4wkdjcQ==", + "version": "7.5.11", + "resolved": "https://registry.npmjs.org/ws/-/ws-7.5.11.tgz", + "integrity": "sha512-zS54Oen9bITtp7kp2XM3AydrCIq1D+HwJOuH+c+e4LfpL/lotP5osijd+UoMnxwAam1GN8R4KtLAyIrIcBNpiA==", "license": "MIT", "engines": { "node": ">=8.3.0" diff --git a/package.json b/package.json index 34849d56d..a3f36fc23 100644 --- a/package.json +++ b/package.json @@ -15,14 +15,14 @@ "typecheck": "tsc" }, "dependencies": { - "@docusaurus/core": "3.9.2", - "@docusaurus/preset-classic": "3.9.2", + "@docusaurus/core": "^3.5.2", + "@docusaurus/preset-classic": "^3.5.2", "@easyops-cn/docusaurus-search-local": "^0.47.0", "@mdx-js/react": "^3.0.0", "clsx": "^2.0.0", "prism-react-renderer": "^2.3.0", - "react": "^19.0.0", - "react-dom": "^19.0.0" + "react": "^18.3.1", + "react-dom": "^18.3.1" }, "devDependencies": { "@docusaurus/module-type-aliases": "3.9.2", diff --git a/sidebars.ts b/sidebars.ts index cffdf9538..f39b31eaa 100644 --- a/sidebars.ts +++ b/sidebars.ts @@ -10,6 +10,17 @@ const sidebars: SidebarsConfig = { link: { type: 'doc', id: 'getting-started/installation' }, items: ['getting-started/installation', 'getting-started/quickstart'], }, + { + type: 'category', + label: 'Interactive Notebooks', + link: { type: 'doc', id: 'playground/index' }, + items: [ + 'playground/index', + 'playground/rag-playground', + 'playground/agents-playground', + 'playground/graphs-playground', + ], + }, { type: 'category', label: 'Guides', @@ -277,6 +288,7 @@ const sidebars: SidebarsConfig = { 'how-to/error-handling', 'how-to/testing', 'how-to/production', + 'how-to/production-checklist', 'how-to/migrate-from-langchain', ], }, diff --git a/src/components/Playground/Playground.module.css b/src/components/Playground/Playground.module.css new file mode 100644 index 000000000..52c54f636 --- /dev/null +++ b/src/components/Playground/Playground.module.css @@ -0,0 +1,29 @@ +.container { + margin: 1.5rem 0; + border: 1px solid var(--sk-border, #d1d5db); + border-radius: 12px; + overflow: hidden; + background: var(--sk-card-bg, #ffffff); + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.04); +} + +.title { + padding: 0.75rem 1rem; + font-weight: 600; + font-size: 0.95rem; + border-bottom: 1px solid var(--sk-border, #d1d5db); + background: var(--sk-card-bg, #ffffff); + color: var(--sk-text, #111827); +} + +/* Dark mode support */ +[data-theme='dark'] .container { + border-color: #334155; + background: #1e293b; +} + +[data-theme='dark'] .title { + background: #1e293b; + border-color: #334155; + color: #f1f5f9; +} diff --git a/src/components/Playground/Playground.tsx b/src/components/Playground/Playground.tsx new file mode 100644 index 000000000..c8d0b5a10 --- /dev/null +++ b/src/components/Playground/Playground.tsx @@ -0,0 +1,44 @@ +import React from 'react'; +import { Sandpack } from '@codesandbox/sandpack-react'; +import { synapsekitMockCode } from './mocks/synapsekit'; +import styles from './Playground.module.css'; + +interface PlaygroundProps { + /** The code to display and run in the playground */ + code: string; + /** Optional title above the playground */ + title?: string; + /** Height of the editor */ + height?: number; +} + +export default function Playground({ + code, + title = "Try it live", + height = 420, +}: PlaygroundProps) { + return ( +
+ {title &&
{title}
} + + +
+ ); +} diff --git a/src/components/Playground/mocks/synapsekit.ts b/src/components/Playground/mocks/synapsekit.ts new file mode 100644 index 000000000..d28d0b7f7 --- /dev/null +++ b/src/components/Playground/mocks/synapsekit.ts @@ -0,0 +1,125 @@ +/** + * SynapseKit Mock for Interactive Playground + * + * Exports: + * - Mock classes (for TypeScript reference) + * - synapsekitMockCode: The source code as a string for Sandpack + */ + +// ============================================ +// Mock Implementations (TypeScript side) +// ============================================ + +export class RAGPipeline { + private documents: string[] = []; + + constructor(options: any = {}) {} + + add(content: string) { + this.documents.push(content); + return this; + } + + ask_sync(question: string): string { + if (this.documents.length === 0) { + return "No documents added yet. Use rag.add(\"...\") to load knowledge."; + } + return `Based on your documents, here's a simulated answer to: "${question}"`; + } + + async *stream(question: string) { + const answer = this.ask_sync(question); + const tokens = answer.split(" "); + for (const token of tokens) { + await new Promise(r => setTimeout(r, 40)); + yield token + " "; + } + } +} + +export class FunctionCallingAgent { + async run(message: string): Promise { + await new Promise(r => setTimeout(r, 800)); + return `Agent would process: "${message}" using tools.`; + } +} + +export class StateGraph { + addNode(name: string, fn: Function) { return this; } + addEdge(from: string, to: string) { return this; } + compile() { + return { + invoke: async (state: any) => { + await new Promise(r => setTimeout(r, 600)); + return { ...state, status: "completed" }; + } + }; + } +} + +// ============================================ +// Source Code as String (injected into Sandpack) +// ============================================ + +export const synapsekitMockCode = ` +// SynapseKit Mock - Educational version for the playground + +export class RAGPipeline { + constructor(options = {}) { + this.documents = []; + } + + add(content) { + // In a real implementation, this would chunk + embed the content + this.documents.push(content); + console.log("Document added to vector store."); + return this; + } + + ask_sync(question) { + if (this.documents.length === 0) { + return "No documents added yet. Use rag.add(\\"...\\") first."; + } + + // Simple simulation: return a response that references the added content + const context = this.documents.join(" | "); + return "Based on the loaded documents: " + context.slice(0, 180) + "..."; + } + + async *stream(question) { + const answer = this.ask_sync(question); + const tokens = answer.split(" "); + + for (const token of tokens) { + await new Promise(resolve => setTimeout(resolve, 35)); + yield token + " "; + } + } +} + +export class FunctionCallingAgent { + async run(message) { + await new Promise(resolve => setTimeout(resolve, 750)); + + if (message.toLowerCase().includes("weather")) { + return "Current weather in San Francisco: Sunny, 21°C."; + } + + return "I would use tools to answer: \\"" + message + "\\""; + } +} + +export class StateGraph { + addNode(name, fn) { return this; } + addEdge(from, to) { return this; } + + compile() { + return { + invoke: async (state) => { + await new Promise(resolve => setTimeout(resolve, 550)); + return { ...state, status: "completed" }; + } + }; + } +} +`; diff --git a/src/css/custom.css b/src/css/custom.css index 8178a3e47..03ae67aab 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -393,3 +393,219 @@ tr:hover td { background: var(--sk-surface-1); } .sk-section-label { font-size: 11px; font-weight: 700; text-transform: uppercase; letter-spacing: 0.1em; color: var(--sk-accent); margin-bottom: 0.75rem; } .sk-section-title { font-size: 1.75rem; font-weight: 700; color: var(--ifm-color-content); letter-spacing: -0.02em; margin-bottom: 0.5rem; } .sk-section-subtitle { color: var(--sk-muted); font-size: 15px; margin-bottom: 2.5rem; } + +/* --- Changelog Timeline (Issue #14) ----------------------------- */ +.changelog-timeline { + position: relative; + max-width: 980px; + margin: 2rem auto; + padding-left: 3rem; +} + +.changelog-timeline::before { + content: ''; + position: absolute; + left: 1.25rem; + top: 0; + bottom: 0; + width: 3px; + background: linear-gradient(to bottom, var(--sk-accent), #4ade80); + border-radius: 3px; +} + +.changelog-timeline > h3 { + position: relative; + margin: 3rem 0 1rem; + padding-left: 0.5rem; + font-size: 1.35rem; + color: var(--sk-text); +} + +.changelog-timeline > h3::before { + content: ''; + position: absolute; + left: -2.35rem; + top: 0.45rem; + width: 1rem; + height: 1rem; + background: var(--sk-accent); + border: 3px solid var(--ifm-background-color); + border-radius: 50%; + box-shadow: 0 0 0 3px rgba(22, 163, 74, 0.2); +} + +.changelog-timeline section { + background: var(--sk-card-bg); + border: 1px solid var(--sk-border); + border-radius: 12px; + padding: 1.5rem 1.75rem; + margin-bottom: 2rem; + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.04); + transition: transform 0.2s ease, box-shadow 0.2s ease; +} + +.changelog-timeline section:hover { + transform: translateY(-2px); + box-shadow: 0 10px 20px rgba(0, 0, 0, 0.06); +} + +.changelog-timeline .badge { + display: inline-block; + padding: 0.15rem 0.65rem; + font-size: 0.75rem; + font-weight: 600; + border-radius: 9999px; + margin-left: 0.5rem; + vertical-align: middle; +} + +.changelog-timeline .badge--success { background: #dcfce7; color: #166534; } +.changelog-timeline .badge--primary { background: #dbeafe; color: #1e40af; } +.changelog-timeline .badge--secondary { background: #f1f5f9; color: #475569; } + +[data-theme='dark'] .changelog-timeline .badge--success { background: #14532d; color: #4ade80; } +[data-theme='dark'] .changelog-timeline .badge--primary { background: #1e3a8a; color: #60a5fa; } +[data-theme='dark'] .changelog-timeline .badge--secondary { background: #334155; color: #94a3b8; } + +.changelog-timeline h4 { + margin: 0 0 0.5rem; + font-size: 1.25rem; + display: flex; + align-items: center; + gap: 0.5rem; +} + +.changelog-timeline ul { + margin: 1rem 0; + padding-left: 1.25rem; +} + +.changelog-timeline li { + margin-bottom: 0.35rem; + color: var(--sk-text); +} + +/* --- Improved Changelog Timeline (Issue #14) ------------------- */ +.changelog-timeline { + position: relative; + max-width: 1000px; + margin: 2.5rem auto; + padding-left: 4rem; +} + +.changelog-timeline::before { + content: ''; + position: absolute; + left: 1.5rem; + top: 0; + bottom: 0; + width: 4px; + background: linear-gradient( + to bottom, + #16a34a 0%, + #4ade80 50%, + #86efac 100% + ); + border-radius: 4px; + box-shadow: 0 0 8px rgba(22, 163, 74, 0.3); +} + +.changelog-timeline > h3 { + position: relative; + margin: 3.5rem 0 1.25rem; + padding-left: 0.75rem; + font-size: 1.45rem; + font-weight: 700; + color: var(--sk-text); + display: flex; + align-items: center; + gap: 0.75rem; +} + +.changelog-timeline > h3::before { + content: ''; + position: absolute; + left: -2.65rem; + top: 0.55rem; + width: 1.15rem; + height: 1.15rem; + background: #16a34a; + border: 4px solid var(--ifm-background-color); + border-radius: 50%; + box-shadow: 0 0 0 4px rgba(22, 163, 74, 0.15); + z-index: 2; +} + +.changelog-timeline section { + background: var(--sk-card-bg); + border: 1px solid var(--sk-border); + border-radius: 16px; + padding: 1.75rem 2rem; + margin-bottom: 2.25rem; + box-shadow: 0 8px 25px rgba(0, 0, 0, 0.05); + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); + position: relative; +} + +.changelog-timeline section:hover { + transform: translateY(-4px); + box-shadow: 0 20px 35px rgba(0, 0, 0, 0.08); + border-color: #4ade80; +} + +.changelog-timeline .version-header { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 1rem; + flex-wrap: wrap; + gap: 0.5rem; +} + +.changelog-timeline .version-number { + font-size: 1.35rem; + font-weight: 700; + color: var(--sk-text); +} + +.changelog-timeline .version-date { + font-size: 0.9rem; + color: var(--sk-muted); + font-weight: 500; +} + +.changelog-timeline .badge { + font-size: 0.7rem; + padding: 0.2rem 0.75rem; + border-radius: 999px; + font-weight: 700; + letter-spacing: 0.03em; +} + +.changelog-timeline .highlights { + margin: 1.25rem 0; +} + +.changelog-timeline ul { + margin: 0.75rem 0 0; + padding-left: 1.4rem; + line-height: 1.7; +} + +.changelog-timeline li { + margin-bottom: 0.4rem; + color: var(--sk-text); +} + +.changelog-timeline .breaking { + color: #dc2626; + font-weight: 600; +} + +[data-theme='dark'] .changelog-timeline section { + box-shadow: 0 8px 25px rgba(0, 0, 0, 0.2); +} + +[data-theme='dark'] .changelog-timeline section:hover { + border-color: #4ade80; +}