diff --git a/.gitignore b/.gitignore index 71af5be..c0e12ad 100644 --- a/.gitignore +++ b/.gitignore @@ -27,3 +27,4 @@ htmlcov/ .idea/ .DS_Store +uv.lock diff --git a/CHANGELOG.md b/CHANGELOG.md index 0e3dbe8..386d2c7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,56 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.4.0] - 2026-04-28 + +This is a feature release covering Phase 2 of the modernization roadmap. Adds five new public surfaces (retriever, Task API, FindAll, Monitor, MCP toolkit) and removes the deprecation paths that 0.3.0 introduced. + +### Added + +- **`ParallelSearchRetriever`** — `BaseRetriever` returning `list[Document]` with rich `metadata` (`url`, `title`, `publish_date`, `search_id`, `excerpts`, `query`). Drops in to any RAG pipeline. Sync + async. +- **Task API** (`langchain_parallel.tasks`): + - `ParallelTaskRunTool` — agent-callable `BaseTool` that runs a single Parallel Task synchronously via `client.task_run.execute(...)`. Surfaces the structured output, `basis` citations, and run id. Default processor: **`lite-fast`**. Supports the full processor menu (`lite`, `base`, `core`, `core2x`, `pro`, `ultra`, `ultra2x/4x/8x`, plus matching `-fast` variants — 18 total). + - `ParallelDeepResearch` — `Runnable[str|dict, dict]` for the single-input deep-research pattern. Default processor: **`pro-fast`** (the `-fast` variant of "Exploratory web research", 2-5x faster than `pro` at similar accuracy). Pass `processor="ultra"` for the most thorough multi-source reports. + - `ParallelTaskGroup` — low-level batch primitive: creates a Task Group, fans out runs, collects results. New `task_spec` field forwards as `default_task_spec` to `add_runs`. Default processor: **`lite-fast`**. + - `ParallelEnrichment` — `Runnable[list, list]` for the structured-batch enrichment pattern (typed counterpart to `ParallelDeepResearch`). Wraps `ParallelTaskGroup` with a `default_task_spec` built from pydantic input/output schemas; coerces pydantic instances → dicts on input. Default processor: **`core-fast`**. + + All four Task surfaces default to a **`-fast` processor variant** (2-5x faster than the corresponding non-fast tier at similar accuracy per https://docs.parallel.ai/task-api/guides/choose-a-processor). Drop the `-fast` suffix when latency is less of a concern than maximum quality. + - `build_task_spec(input_schema=, output_schema=)` helper — accepts pydantic classes, raw JSON-schema dicts, or text descriptions and returns a TaskSpec dict. + - `parse_basis(result)` helper — pulls per-field citations, low-confidence field names, and the `interaction_id` out of any Task-surface result dict. Removes the ~30 lines of result-shape walking every confidence-aware consumer would otherwise rewrite. + - **BYOMCP support** — pass `mcp_servers=[McpServer(...)]` to `ParallelTaskRunTool` / `ParallelDeepResearch` to expose your own Streamable-HTTP MCP endpoints to the run. + - **`previous_interaction_id`** — multi-turn context chaining; both `ParallelTaskRunTool` and `ParallelDeepResearch` accept it and result dicts surface `interaction_id` at the top level for easy chaining. + - **Webhook signature verification** — `verify_webhook(payload, *, webhook_id, webhook_timestamp, webhook_signature, secret)` implementing the Standard Webhooks scheme (HMAC-SHA256, base64, `v1,` format with replay protection) per https://docs.parallel.ai/resources/webhook-setup. +- **`ParallelFindAllTool`** (`langchain_parallel.findall`) — entity discovery via `client.beta.findall.create` + `result`. Returns ranked candidates that satisfy a natural-language objective and a set of boolean match conditions. Generators: `preview`, `base`, `core`, `pro`. Sync + async. +- **`ParallelMonitor`** (`langchain_parallel.monitors`) — thin httpx wrapper around `/v1alpha/monitors`. Create / retrieve / update / delete monitors; list event groups; simulate events. The Parallel SDK (0.5.1) does not yet expose this surface, so this module talks to the API directly. The Monitor API is **alpha** and shapes may change without notice. + +### Removed + +- **`mode="one-shot"` / `"agentic"` / `"fast"`** — the legacy `mode` strings deprecated in 0.3.0 are removed. Use `mode="basic"` (formerly `one-shot`/`fast`) or `mode="advanced"` (formerly `agentic`). +- **Search `objective`-only call (no `search_queries`)** — the `/v1beta` fallback path deprecated in 0.3.0 is removed. `search_queries` is now a required field on `ParallelWebSearchInput`. Pass at least one keyword query alongside any `objective`. +- **`Extract.excerpts=False`** — the no-op DeprecationWarning path is removed. The field is now `Optional[ExcerptSettings]` (was `Union[bool, ExcerptSettings]`); pass `ExcerptSettings(max_chars_per_result=…)` to control per-result size, or omit the field for the API default. +- **`search_metadata["endpoint"]`** key — no longer emitted (the v1beta fallback that introduced it is gone). + +### Changed + +- Bumped `langchain-core` constraint to `^1.3.0` and locked at 1.3.2 (was 1.1.0 → 1.2.31 in this PR's earlier pass). +- Refreshed `poetry.lock` against the latest within current pin constraints (rolls in the changes from dependabot PRs #7-#11): `langsmith` 0.4.37 → 0.7.37, `pydantic` 2.12.3 → 2.13.3, `pydantic-core` 2.41.4 → 2.46.3, `requests` 2.32.5 → 2.33.1, `orjson` 3.11.3 → 3.11.8, `pygments` 2.19.2 → 2.20.0, `mypy` 1.18.2 → 1.20.2, plus transitives. + +### Tests + +- Override `test_serdes` from `langchain-tests` 1.1.6 to handle `langchain-core` 1.2+'s new `allowed_objects=` allowlist on `load()`. The standard test still uses the pre-1.2 `valid_namespaces=` API and rejects partner integrations; our override opts in via `allowed_objects=[ChatParallelWeb]` and `valid_namespaces=["langchain_parallel"]` to keep the full serialize → load → equality round-trip intact. Marked `@pytest.mark.xfail(strict=False)` per `langchain-tests` rules; reports as XPASS. Remove the override once `langchain-tests` is updated for the new allowlist API. + +### Migration from 0.3.x + +```python +# 0.3.x (DeprecationWarning, still worked) # 0.4.x +tool.invoke({"objective": "..."}) # tool.invoke({"search_queries": ["..."], "objective": "..."}) +tool.invoke({"mode": "one-shot", ...}) # tool.invoke({"mode": "basic", ...}) +tool.invoke({"mode": "agentic", ...}) # tool.invoke({"mode": "advanced", ...}) +extract_tool.invoke({..., "excerpts": False}) # extract_tool.invoke({...}) # excerpts always returned in v1 +``` + +The new surfaces are all additive — existing 0.3.x code that was warning-free continues to work without changes. + ## [0.3.0] - 2026-04-27 This release migrates Search and Extract to Parallel's v1 GA endpoints, surfaces citations + structured output on the chat model, and bumps the SDK to `0.5.1`. diff --git a/README.md b/README.md index 9d7f7bd..3eedc92 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,24 @@ # LangChain Parallel Web Integration -This package provides LangChain integrations for [Parallel](https://docs.parallel.ai/), enabling real-time web research and AI capabilities through an OpenAI-compatible interface. +This package provides LangChain integrations for [Parallel](https://docs.parallel.ai/), covering Parallel's full developer-facing API surface. ## Features -- **Chat Models**: `ChatParallel` (formerly `ChatParallelWeb`) — real-time web research chat completions, with citations and structured output on the research models. -- **Search Tool**: `ParallelSearchTool` (formerly `ParallelWebSearchTool`) — direct access to Parallel's GA `/v1/search` endpoint. -- **Extract Tool**: `ParallelExtractTool` — clean content extraction from web pages via `/v1/extract`. -- **Streaming Support**: Real-time response streaming on chat. -- **Async/Await**: Full asynchronous operation support. -- **LangChain Integration**: Pydantic input schemas, `bind`-able tools, `with_structured_output()`, `lc_serializable`. - -> Note: the older names (`ChatParallelWeb`, `ParallelWebSearchTool`) continue to work as aliases. +| Surface | Class | Backed by | +|---|---|---| +| Chat completions with citations + structured output | [`ChatParallel`](#chat-models) | `/chat/completions` (lite/base/core) | +| Web search → Documents (RAG) | [`ParallelSearchRetriever`](#retriever-rag) | `/v1/search` | +| Web search tool (agents) | [`ParallelSearchTool`](#search-api) | `/v1/search` | +| Web content extraction | [`ParallelExtractTool`](#extract-api) | `/v1/extract` | +| Single Task Run + citations | [`ParallelTaskRunTool`](#task-api) | `/v1/tasks/runs` | +| Deep-research Runnable | [`ParallelDeepResearch`](#task-api) | `/v1/tasks/runs` | +| Bulk task batching | [`ParallelTaskGroup`](#task-api) | `/v1beta/tasks/groups` | +| Structured-batch enrichment | [`ParallelEnrichment`](#task-api) | `/v1beta/tasks/groups` + TaskSpec | +| Entity discovery | [`ParallelFindAllTool`](#findall-api) | `/v1beta/findall` | +| Scheduled web monitors | [`ParallelMonitor`](#monitor-api-alpha) | `/v1alpha/monitors` | +| Webhook signature verification | [`verify_webhook()`](#webhook-signature-verification) | HMAC-SHA256 | + +> Old names (`ChatParallelWeb`, `ParallelWebSearchTool`) continue to work as aliases for `ChatParallel` and `ParallelSearchTool`. ## Installation @@ -404,6 +411,233 @@ async def extract_async(): result = asyncio.run(extract_async()) ``` +## Retriever (RAG) + +`ParallelSearchRetriever` is a `BaseRetriever` that returns Parallel Search results as `Document`s. Drops in to any LangChain RAG pipeline. + +```python +from langchain_parallel import ParallelSearchRetriever, SourcePolicy + +retriever = ParallelSearchRetriever( + max_results=5, + mode="advanced", + source_policy=SourcePolicy(include_domains=["nature.com", "arxiv.org"]), + objective="Focus on peer-reviewed material", # forwarded on every call +) + +docs = retriever.invoke("recent advances in protein folding") +for doc in docs: + print(doc.metadata["title"], "-", doc.metadata["url"]) + print(doc.page_content[:200]) +``` + +`Document.metadata` carries `url`, `title`, `publish_date`, `search_id`, the original `excerpts` list, and the `query` that produced the document. + +## Task API + +The Task API exposes Parallel's research processors (`lite`, `base`, `core`, `core2x`, `pro`, `ultra`, `ultra2x/4x/8x`, plus matching `-fast` variants) and the `basis` citation graph. Four surfaces: + +| | Single input | List of inputs | +|---|---|---| +| **Untyped** | `ParallelTaskRunTool` (`BaseTool` for agents, defaults to `lite-fast`) | `ParallelTaskGroup` (low-level batch primitive, defaults to `lite-fast`) | +| **Typed (TaskSpec)** | `ParallelDeepResearch` (`Runnable`, defaults to `pro-fast`) | `ParallelEnrichment` (`Runnable`, defaults to `core-fast`) | + +`ParallelTaskRunTool` is the only surface designed for an LLM to call mid-conversation. The other three are application-side: `TaskGroup` is the manual primitive, and `DeepResearch` / `Enrichment` are opinionated `Runnable`s for the two most common patterns. + +> All four default to a **`-fast`** processor variant. The `-fast` family is 2-5x faster than the corresponding non-fast tier at similar accuracy and is the right pick for agent-loop / interactive workflows. Strip the suffix (`processor="pro"`, `processor="ultra"`, etc.) when latency is less of a concern than maximum quality. + +### Single Task with citations + +```python +from langchain_parallel import ParallelTaskRunTool + +tool = ParallelTaskRunTool() # defaults to processor="lite-fast" +result = tool.invoke({"input": "Who founded SpaceX, in one sentence?"}) +print(result["output"]["content"]) +print(result["output"]["basis"]) # per-field citations + reasoning + confidence +print(result["run"]["run_id"]) +``` + +### Deep research (Runnable) + +```python +from langchain_parallel import ParallelDeepResearch + +# Defaults to processor="pro-fast" (the -fast variant of pro, +# Exploratory web research, 2-5x faster than "pro" at similar accuracy). +# For the most thorough report, pass processor="ultra" (5-25 min). +research = ParallelDeepResearch() +result = research.invoke("Latest developments in renewable energy storage") +print(result["output"]["content"]) +for fact in result["output"].get("basis", []): + print(fact["field"], "->", fact["citations"]) +``` + +### Structured-batch enrichment + +```python +from pydantic import BaseModel, Field +from langchain_parallel import ParallelEnrichment + +class CompanyInput(BaseModel): + company: str = Field(description="Company name to enrich") + +class CompanyOutput(BaseModel): + headquarters: str + founding_year: int + +enricher = ParallelEnrichment( + input_schema=CompanyInput, + output_schema=CompanyOutput, + # Defaults to processor="core-fast"; pass "core" or "pro" for higher + # accuracy when latency is less of a concern. +) + +results = enricher.invoke([ + CompanyInput(company="Anthropic"), + {"company": "OpenAI"}, +]) +for r in results: + print(r["output"]["content"]) +# {'headquarters': 'San Francisco, California, USA', 'founding_year': 2021} +# {'headquarters': 'San Francisco, USA', 'founding_year': 2015} +``` + +### Structured output (pydantic) + +```python +from pydantic import BaseModel, Field +from langchain_parallel import ParallelTaskRunTool + +class CompanyFacts(BaseModel): + name: str + founded: int = Field(description="Year the company was founded") + headquarters: str + +tool = ParallelTaskRunTool( + processor="base", + task_output_schema=CompanyFacts, +) +result = tool.invoke({"input": "Tell me about Anthropic"}) +print(result["parsed"]) # CompanyFacts instance, fields populated +``` + +### `parse_basis()` — citations + low-confidence fields, in one call + +Every Task-surface result carries a `basis` (per-field citations + reasoning + confidence). `parse_basis()` walks it for you and returns the three things consumers actually want: + +```python +from langchain_parallel import ParallelDeepResearch, parse_basis + +result = ParallelDeepResearch().invoke("Founder of SpaceX, in one sentence?") +parsed = parse_basis(result) +# parsed["citations_by_field"] -> {field_name: [citation, ...]} +# parsed["low_confidence_fields"] -> ["year", ...] # confidence == "low" +# parsed["interaction_id"] -> str | None # for multi-turn chaining +``` + +### Batch (Task Group) + +```python +from langchain_parallel import ParallelTaskGroup + +group = ParallelTaskGroup() # defaults to processor="lite-fast" +results = group.run([ + "Founder of Anthropic?", + "Founder of OpenAI?", + "Founder of Google DeepMind?", +]) +for r in results: + print(r["output"]) +``` + +### BYOMCP (bring-your-own MCP servers) + +```python +from langchain_parallel import McpServer, ParallelTaskRunTool + +tool = ParallelTaskRunTool( + processor="base", + mcp_servers=[ + McpServer( + name="my_internal_data", + url="https://mcp.example.com/internal", + headers={"Authorization": "Bearer ..."}, + ), + ], +) +``` + +## FindAll API + +Discover entities from the web that satisfy a natural-language objective plus boolean match conditions. + +```python +from langchain_parallel import ( + ParallelFindAllTool, + FindAllMatchCondition, +) + +tool = ParallelFindAllTool(generator="base") +result = tool.invoke({ + "objective": "AI agent startups founded after 2023", + "entity_type": "company", + "match_conditions": [ + FindAllMatchCondition( + name="founded_after_2023", + description="Was this company founded after January 1 2023?", + ), + FindAllMatchCondition( + name="builds_ai_agents", + description="Does this company build AI agents as a core product?", + ), + ], + "match_limit": 25, +}) +for candidate in result["candidates"]: + print(candidate["name"], "-", candidate["url"]) +``` + +Generators: `preview` (small free sample), `base`, `core`, `pro` (highest quality, longest-running). + +## Monitor API (alpha) + +Schedule recurring web queries that emit webhook events on change. The Monitor API is **alpha**; shapes may change without notice. The current SDK doesn't expose this surface, so `ParallelMonitor` talks to `/v1alpha/monitors` directly. + +```python +from langchain_parallel import ParallelMonitor, MonitorWebhook + +monitors = ParallelMonitor() + +m = monitors.create( + query="Track new SEC filings related to Anthropic", + frequency="1h", + webhook=MonitorWebhook( + url="https://example.com/parallel-webhook", + secret="...", # used to HMAC-sign payloads + ), +) + +events = monitors.list_events(m["monitor_id"]) +print(len(events["event_groups"])) +``` + +## Webhook signature verification + +Validates HMAC-SHA256 signatures on incoming Task Run / FindAll / Monitor webhooks. + +```python +from langchain_parallel import verify_webhook + +@app.post("/parallel-webhook") +async def webhook(request): + body = await request.body() + signature = request.headers["parallel-signature"] + if not verify_webhook(body, signature, secret="..."): + return Response(status_code=401) + # ... process the event +``` + ## Error Handling ```python diff --git a/docs/findall.ipynb b/docs/findall.ipynb new file mode 100644 index 0000000..51d5104 --- /dev/null +++ b/docs/findall.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3324dc43", + "metadata": {}, + "source": [ + "# ParallelFindAllTool\n", + "\n", + "`ParallelFindAllTool` exposes the Parallel [FindAll API](https://docs.parallel.ai/findall-api/overview) — entity discovery against a natural-language objective and a set of boolean match conditions. Returns ranked candidates that satisfy all conditions.\n", + "\n", + "Generators: `preview` (free, capped at 10 results), then `base` / `core` / `pro` for higher-quality runs." + ] + }, + { + "cell_type": "markdown", + "id": "825fc0b8", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5115df49", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --quiet -U langchain-parallel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f104220", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if not os.environ.get(\"PARALLEL_API_KEY\"):\n", + " os.environ[\"PARALLEL_API_KEY\"] = getpass.getpass(\"Parallel API key:\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c4dab0c", + "metadata": {}, + "source": [ + "## Quick discovery (preview generator)\n", + "\n", + "The `preview` generator is the fastest way to sanity-check your conditions. It's capped at 10 candidates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77d38cbc", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import (\n", + " FindAllMatchCondition,\n", + " ParallelFindAllTool,\n", + ")\n", + "\n", + "tool = ParallelFindAllTool()\n", + "\n", + "result = await tool.ainvoke({\n", + " \"objective\": \"Pure-play public LLM API providers\",\n", + " \"entity_type\": \"company\",\n", + " \"match_conditions\": [\n", + " FindAllMatchCondition(\n", + " name=\"public_us\",\n", + " description=\"Company is publicly traded on a US exchange\",\n", + " ).model_dump(),\n", + " FindAllMatchCondition(\n", + " name=\"llm_api_revenue\",\n", + " description=\"Primary revenue is selling LLM inference via API\",\n", + " ).model_dump(),\n", + " ],\n", + " \"generator\": \"preview\",\n", + " \"match_limit\": 5,\n", + "})\n", + "\n", + "for c in result.get(\"candidates\", []):\n", + " print(c.get(\"name\"), \"-\", c.get(\"url\"))\n" + ] + }, + { + "cell_type": "markdown", + "id": "3e362146", + "metadata": {}, + "source": [ + "## Higher-quality runs\n", + "\n", + "For non-preview runs, use `match_limit` 5..1000 with `generator=\"base\"` / `\"core\"` / `\"pro\"`. These take longer (minutes); the tool polls under the hood and returns when status is terminal (`completed` / `cancelled` / `failed`).\n", + "\n", + "```python\n", + "result = await tool.ainvoke({\n", + " \"objective\": \"Independent solar-installer companies based in the EU\",\n", + " \"match_conditions\": [\n", + " FindAllMatchCondition(description=\"Headquartered in an EU country\").model_dump(),\n", + " FindAllMatchCondition(description=\"Primarily installs residential solar PV\").model_dump(),\n", + " ],\n", + " \"generator\": \"core\",\n", + " \"match_limit\": 50,\n", + "})\n", + "```\n", + "\n", + "You can also exclude entries you've already seen via `FindAllExcludeEntry(url=...)`." + ] + }, + { + "cell_type": "markdown", + "id": "90d51bfc", + "metadata": {}, + "source": [ + "## Cancel a long-running search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee80475f", + "metadata": {}, + "outputs": [], + "source": [ + "# Available cancellation API (no live call here so the notebook stays fast):\n", + "print(\"public methods:\", [m for m in dir(tool) if not m.startswith(\"_\") and \"cancel\" in m])\n" + ] + }, + { + "cell_type": "markdown", + "id": "042893cf", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "- [FindAll API guides](https://docs.parallel.ai/findall-api/overview)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/monitor.ipynb b/docs/monitor.ipynb new file mode 100644 index 0000000..482d9c6 --- /dev/null +++ b/docs/monitor.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2516afb9", + "metadata": {}, + "source": [ + "# ParallelMonitor (alpha)\n", + "\n", + "`ParallelMonitor` is a thin wrapper around Parallel's [Monitor API](https://docs.parallel.ai/monitor-api/overview) — schedule a Task to run on a recurring cadence and get notified when relevant new content shows up.\n", + "\n", + "> **Alpha:** the Monitor API is still in `/v1alpha`. Shapes may change without notice. The Parallel SDK doesn't yet expose this surface, so this module talks to the API directly via httpx." + ] + }, + { + "cell_type": "markdown", + "id": "87ac7c2d", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "312fb278", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --quiet -U langchain-parallel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c13550dd", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if not os.environ.get(\"PARALLEL_API_KEY\"):\n", + " os.environ[\"PARALLEL_API_KEY\"] = getpass.getpass(\"Parallel API key:\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "6181b6b3", + "metadata": {}, + "source": [ + "## Create a monitor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac77d5ea", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import ParallelMonitor, MonitorWebhook\n", + "\n", + "monitor_client = ParallelMonitor()\n", + "\n", + "created = monitor_client.create(\n", + " query=\"Latest peer-reviewed papers on net-energy-gain fusion\",\n", + " frequency=\"1d\", # in 1h..30d\n", + " metadata={\"label\": \"smoke-test-fusion\"},\n", + " # Optional: a webhook URL to receive new events.\n", + " # webhook=MonitorWebhook(\n", + " # url=\"https://example.com/parallel/webhook\",\n", + " # event_types=[\"new_event_group\"],\n", + " # ),\n", + ")\n", + "monitor_id = created[\"monitor_id\"]\n", + "print(\"created\", monitor_id, \"-\", created[\"status\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "14adf0e6", + "metadata": {}, + "source": [ + "## Retrieve / update / list events" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a541883", + "metadata": {}, + "outputs": [], + "source": [ + "got = monitor_client.retrieve(monitor_id)\n", + "print(\"query:\", got[\"query\"][:80])\n" + ] + }, + { + "cell_type": "markdown", + "id": "4efcc17e", + "metadata": {}, + "source": [ + "List events the monitor has surfaced. Newly-created monitors typically have none yet, so we just verify the call shape:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc64bfa0", + "metadata": {}, + "outputs": [], + "source": [ + "events = monitor_client.list_events(monitor_id, lookback_period=\"7d\")\n", + "print(\"events:\", len(events.get(\"events\", [])))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e1a86a2", + "metadata": {}, + "source": [ + "## Delete the monitor\n", + "\n", + "Always delete a monitor when you're done so you stop receiving events. (Smoke-test cleanup happens here too.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa12dcc2", + "metadata": {}, + "outputs": [], + "source": [ + "deleted = monitor_client.delete(monitor_id)\n", + "print(\"deleted:\", deleted[\"status\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "91977e73", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "- [Monitor API guides](https://docs.parallel.ai/monitor-api/overview)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/retriever.ipynb b/docs/retriever.ipynb new file mode 100644 index 0000000..712469e --- /dev/null +++ b/docs/retriever.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "486cae3a", + "metadata": {}, + "source": [ + "# ParallelSearchRetriever\n", + "\n", + "`ParallelSearchRetriever` is a LangChain [`BaseRetriever`](https://python.langchain.com/docs/concepts/retrievers/) backed by Parallel's [Search API](https://docs.parallel.ai/search-api/overview). It returns `list[Document]` with rich `metadata` (`url`, `title`, `publish_date`, `search_id`, `excerpts`, `query`) and slots into any RAG pipeline." + ] + }, + { + "cell_type": "markdown", + "id": "de3463fc", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f4f6caa", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --quiet -U langchain-parallel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3d5f69c", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if not os.environ.get(\"PARALLEL_API_KEY\"):\n", + " os.environ[\"PARALLEL_API_KEY\"] = getpass.getpass(\"Parallel API key:\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7bc0630b", + "metadata": {}, + "source": [ + "## Basic retrieval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa104e64", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import ParallelSearchRetriever\n", + "\n", + "retriever = ParallelSearchRetriever(\n", + " max_results=3,\n", + " excerpts={\"max_chars_per_result\": 800},\n", + ")\n", + "\n", + "docs = retriever.invoke(\"breakthroughs in fusion energy 2025\")\n", + "for d in docs:\n", + " print(d.metadata.get(\"title\"), \"-\", d.metadata.get(\"url\"))\n", + " print(d.page_content[:200], \"...\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b8c9579e", + "metadata": {}, + "source": [ + "Each `Document` has its excerpts joined into `page_content` and exposes the source URL, title, and publish date in `metadata` for downstream filters / citations." + ] + }, + { + "cell_type": "markdown", + "id": "46b28c19", + "metadata": {}, + "source": [ + "## Configured search\n", + "\n", + "Pass an `objective` for a richer retrieval objective alongside the keyword `search_queries`. The retriever forwards source/fetch policies to the underlying Search API." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f14f40c", + "metadata": {}, + "outputs": [], + "source": [ + "configured = ParallelSearchRetriever(\n", + " max_results=5,\n", + " excerpts={\"max_chars_per_result\": 1500},\n", + " mode=\"basic\",\n", + " source_policy={\n", + " \"include_domains\": [\"nature.com\", \"science.org\", \"iter.org\"],\n", + " },\n", + ")\n", + "\n", + "docs = configured.invoke(\n", + " \"What's the latest peer-reviewed result on net-energy-gain fusion?\"\n", + ")\n", + "print(f\"got {len(docs)} docs\")\n", + "print(\"first url:\", docs[0].metadata.get(\"url\") if docs else \"n/a\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "29dce95e", + "metadata": {}, + "source": [ + "## Async usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db0236de", + "metadata": {}, + "outputs": [], + "source": [ + "async def fetch():\n", + " return await retriever.ainvoke(\"Latest GLP-1 trial results 2025\")\n", + "\n", + "docs = await fetch()\n", + "print(f\"async got {len(docs)} docs\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "875d3cf6", + "metadata": {}, + "source": [ + "## Drop into a RAG chain\n", + "\n", + "The retriever plugs into any LangChain chain. Here's the simplest possible \"retrieve → join → answer\" wiring (replace `llm` with your tool-calling chat model — `ChatAnthropic`, `ChatOpenAI`, etc.). For a complete agent walkthrough, see [`docs/demo_agent.ipynb`](./demo_agent.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc89786e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "\n", + "prompt = ChatPromptTemplate.from_messages([\n", + " (\"system\", \"Answer using only the context below. Cite URLs.\"),\n", + " (\"human\", \"Context:\\n{context}\\n\\nQuestion: {question}\"),\n", + "])\n", + "\n", + "def format_docs(docs):\n", + " return \"\\n\\n\".join(f\"[{d.metadata.get('url')}] {d.page_content[:500]}\" for d in docs)\n", + "\n", + "rag_chain = (\n", + " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + ")\n", + "\n", + "# Render the prompt for an example query (we don't bind an LLM here so the\n", + "# notebook stays self-contained for smoke tests).\n", + "filled = rag_chain.invoke(\"What was the most recent fusion energy breakthrough?\")\n", + "print(filled.to_string()[:600], \"...\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "78815399", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "- [Search API guides](https://docs.parallel.ai/search-api/overview)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/search_tool.ipynb b/docs/search_tool.ipynb index 617df05..f824cb4 100644 --- a/docs/search_tool.ipynb +++ b/docs/search_tool.ipynb @@ -141,8 +141,7 @@ " }\n", ")\n", "\n", - "print(f\"Found {len(result.get('results', []))} results\")\n", - "print(f\"endpoint: {result['search_metadata']['endpoint']}\")\n" + "print(f\"Found {len(result.get('results', []))} results\")\n" ] }, { @@ -166,8 +165,7 @@ " \"include_metadata\": True,\n", " }\n", ")\n", - "\n", - "print(f\"Found {len(result.get('results', []))} results / endpoint={result['search_metadata']['endpoint']}\")\n" + "\n" ] }, { @@ -275,6 +273,10 @@ "# Example of comprehensive parameter usage\n", "result = tool.invoke(\n", " {\n", + " \"search_queries\": [\n", + " \"renewable energy policy Europe\",\n", + " \"European Union energy directives\",\n", + " ],\n", " \"objective\": \"Find comprehensive information about renewable energy policies in European countries\",\n", " \"max_results\": 15,\n", " \"excerpts\": {\n", @@ -298,8 +300,7 @@ "print(f\"Found {len(result['results'])} results\")\n", "if \"search_metadata\" in result:\n", " metadata = result[\"search_metadata\"]\n", - " print(f\"Search took {metadata['search_duration_seconds']}s\")\n", - " print(f\"Source policy applied: {metadata.get('source_policy_applied', False)}\")" + " print(f\"Search took {metadata['search_duration_seconds']}s\")\n" ] }, { diff --git a/docs/task_api.ipynb b/docs/task_api.ipynb new file mode 100644 index 0000000..b14d2a6 --- /dev/null +++ b/docs/task_api.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e93d2846", + "metadata": {}, + "source": [ + "# Parallel Task API\n", + "\n", + "Parallel's [Task API](https://docs.parallel.ai/task-api/overview) runs research-grade tasks across a tiered processor menu (`lite` → `ultra`, plus matching `-fast` variants).\n", + "\n", + "`langchain-parallel` exposes the Task API as four LangChain surfaces:\n", + "\n", + "| Surface | Shape | Use it for |\n", + "|---|---|---|\n", + "| [`ParallelTaskRunTool`](https://python.langchain.com/api_reference/parallel_web/tasks/langchain_parallel.tasks.ParallelTaskRunTool.html) | `BaseTool` | Single Task, callable by an LLM mid-conversation |\n", + "| [`ParallelDeepResearch`](https://python.langchain.com/api_reference/parallel_web/tasks/langchain_parallel.tasks.ParallelDeepResearch.html) | `Runnable` | Single deep-research task with structured output |\n", + "| [`ParallelTaskGroup`](https://python.langchain.com/api_reference/parallel_web/tasks/langchain_parallel.tasks.ParallelTaskGroup.html) | Plain class | Low-level batch primitive |\n", + "| [`ParallelEnrichment`](https://python.langchain.com/api_reference/parallel_web/tasks/langchain_parallel.tasks.ParallelEnrichment.html) | `Runnable` | Typed batch enrichment with pydantic schemas |\n", + "\n", + "**All four default to a `-fast` processor variant** (`lite-fast` / `pro-fast` / `lite-fast` / `core-fast` respectively) — 2-5x faster than the corresponding non-fast tier at similar accuracy. Drop the `-fast` suffix when latency is less of a concern than maximum quality." + ] + }, + { + "cell_type": "markdown", + "id": "3a99305a", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe99c583", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --quiet -U langchain-parallel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "73fa807c", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if not os.environ.get(\"PARALLEL_API_KEY\"):\n", + " os.environ[\"PARALLEL_API_KEY\"] = getpass.getpass(\"Parallel API key:\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "af572226", + "metadata": {}, + "source": [ + "## `ParallelTaskRunTool` — single Task with citations\n", + "\n", + "`ParallelTaskRunTool` is an agent-callable `BaseTool`. It runs one Task synchronously and returns the structured `output`, per-field `basis` citations, and the `run_id`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30bd40ba", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import ParallelTaskRunTool\n", + "\n", + "tool = ParallelTaskRunTool() # default processor=\"lite-fast\"\n", + "result = tool.invoke({\"input\": \"Who founded SpaceX, in one sentence?\"})\n", + "print(result[\"output\"][\"content\"] if isinstance(result[\"output\"], dict) else result[\"output\"])\n", + "print(\"run_id:\", result[\"run\"][\"run_id\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "2f55b62b", + "metadata": {}, + "source": [ + "The `output` shape depends on whether you pass a `task_output_schema`. With no schema (above), `result[\"output\"]` is the raw text answer (or a dict). With a pydantic schema, you get a parsed instance and per-field citations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6d8a462", + "metadata": {}, + "outputs": [], + "source": [ + "from pydantic import BaseModel, Field\n", + "\n", + "class FounderFact(BaseModel):\n", + " founder: str = Field(description=\"Full name of the founder\")\n", + " year: int = Field(description=\"Year the company was founded\")\n", + "\n", + "structured = ParallelTaskRunTool(task_output_schema=FounderFact)\n", + "res = structured.invoke({\"input\": \"Who founded SpaceX and in what year?\"})\n", + "print(res[\"output\"])\n", + "# `res[\"basis\"]` carries per-field citations, reasoning, confidence\n", + "print(\"basis fields:\", [b.get(\"field\") for b in res.get(\"basis\", [])])\n" + ] + }, + { + "cell_type": "markdown", + "id": "1f100b24", + "metadata": {}, + "source": [ + "### `parse_basis()` — citations + low-confidence fields, in one call\n", + "\n", + "Every consumer that cares about confidence ends up writing the same ~30 lines to walk a result and pull out citations + low-confidence fields + the `interaction_id` for chaining. `parse_basis()` does that for you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef0eeeae", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import parse_basis\n", + "\n", + "parsed = parse_basis(res)\n", + "print(\"citations per field:\", {f: len(c) for f, c in parsed[\"citations_by_field\"].items()})\n", + "print(\"low-confidence fields:\", parsed[\"low_confidence_fields\"])\n", + "print(\"interaction_id:\", parsed[\"interaction_id\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd7d5a2d", + "metadata": {}, + "source": [ + "## `ParallelDeepResearch` — single deep-research task\n", + "\n", + "`ParallelDeepResearch` is a `Runnable`. It defaults to `pro-fast` (the `-fast` variant of \"Exploratory web research\"). For the most thorough reports, pass `processor=\"ultra\"`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d0df442", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import ParallelDeepResearch\n", + "\n", + "research = ParallelDeepResearch() # default processor=\"pro-fast\"\n", + "result = research.invoke(\"In one sentence, what is the capital of France?\")\n", + "print(result[\"output\"][\"content\"] if isinstance(result[\"output\"], dict) else result[\"output\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "21219c49", + "metadata": {}, + "source": [ + "For typed deep research, pass an `output_schema`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8b3e3d9", + "metadata": {}, + "outputs": [], + "source": [ + "class CityFact(BaseModel):\n", + " capital: str\n", + " population_millions: float\n", + "\n", + "typed = ParallelDeepResearch(output_schema=CityFact)\n", + "out = typed.invoke(\"Capital and population (in millions) of France?\")\n", + "print(out[\"output\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "8c2f43bb", + "metadata": {}, + "source": [ + "## `ParallelTaskGroup` — low-level batch primitive\n", + "\n", + "`ParallelTaskGroup` creates a Task Group, fans out runs, and collects results. Use it directly when you need fine-grained control over the batch envelope; otherwise prefer `ParallelEnrichment` (below) for typed bulk runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3002ae3d", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import ParallelTaskGroup\n", + "\n", + "group = ParallelTaskGroup() # default processor=\"lite-fast\"\n", + "results = group.run([\n", + " \"Founder of Anthropic? One sentence.\",\n", + " \"Founder of OpenAI? One sentence.\",\n", + "])\n", + "for r in results:\n", + " out = r[\"output\"]\n", + " print(out[\"content\"] if isinstance(out, dict) else out)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4a80e1aa", + "metadata": {}, + "source": [ + "## `ParallelEnrichment` — typed batch enrichment\n", + "\n", + "`ParallelEnrichment` wraps `ParallelTaskGroup` with a `default_task_spec` built from your input/output pydantic schemas. It coerces pydantic instances → dicts, fans out the batch, and returns results in input order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b675f645", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import ParallelEnrichment\n", + "\n", + "class CompanyInput(BaseModel):\n", + " company: str = Field(description=\"Company name\")\n", + "\n", + "class CompanyOutput(BaseModel):\n", + " headquarters: str\n", + " founding_year: int\n", + "\n", + "enricher = ParallelEnrichment(\n", + " input_schema=CompanyInput,\n", + " output_schema=CompanyOutput,\n", + " # default processor=\"core-fast\" — pass \"core\" or \"pro\" for higher accuracy\n", + ")\n", + "results = enricher.invoke([\n", + " CompanyInput(company=\"Anthropic\"),\n", + " {\"company\": \"OpenAI\"},\n", + "])\n", + "for r in results:\n", + " print(r[\"output\"][\"content\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4480dd6", + "metadata": {}, + "source": [ + "## `build_task_spec` helper\n", + "\n", + "`build_task_spec` accepts pydantic classes, raw JSON-schema dicts, or text descriptions and returns a TaskSpec dict ready for `client.task_run.create` or `add_runs(default_task_spec=...)`. Useful when you want full control of the run envelope." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "124afea8", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import build_task_spec\n", + "\n", + "spec = build_task_spec(input_schema=CompanyInput, output_schema=CompanyOutput)\n", + "print(list(spec.keys()))\n", + "print(spec[\"output_schema\"][\"type\"]) # \"json\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "433f9cb1", + "metadata": {}, + "source": [ + "## BYOMCP — bring your own MCP server\n", + "\n", + "Both `ParallelTaskRunTool` and `ParallelDeepResearch` accept `mcp_servers=[McpServer(...)]` to expose your own Streamable-HTTP MCP endpoints to the run. We construct the spec without calling here so the notebook smoke stays self-contained." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9af7cc00", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_parallel import McpServer\n", + "\n", + "mcp = McpServer(\n", + " name=\"my_internal_mcp\",\n", + " url=\"https://example.com/mcp\",\n", + " headers={\"Authorization\": \"Bearer ...\"},\n", + ")\n", + "print(mcp.model_dump())\n", + "# Pass it as: ParallelTaskRunTool(mcp_servers=[mcp]).invoke({\"input\": \"...\"})\n" + ] + }, + { + "cell_type": "markdown", + "id": "6fd85137", + "metadata": {}, + "source": [ + "## Webhook signature verification\n", + "\n", + "Long-running Tasks can deliver results via webhook. Verify the signature with `verify_webhook` (Standard Webhooks scheme: HMAC-SHA256 over `..`, base64-encoded, `v1,` format)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "375a672e", + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "import hashlib\n", + "import hmac\n", + "import time\n", + "\n", + "from langchain_parallel import verify_webhook\n", + "\n", + "# Simulate what the Parallel webhook delivery looks like.\n", + "secret = \"test_secret\"\n", + "body = b'{\"run_id\":\"trun_abc\"}'\n", + "webhook_id = \"msg_demo\"\n", + "ts = str(int(time.time()))\n", + "signed = f\"{webhook_id}.{ts}.{body.decode()}\"\n", + "sig = base64.b64encode(\n", + " hmac.new(secret.encode(), signed.encode(), hashlib.sha256).digest()\n", + ").decode()\n", + "\n", + "assert verify_webhook(\n", + " body,\n", + " webhook_id=webhook_id,\n", + " webhook_timestamp=ts,\n", + " webhook_signature=f\"v1,{sig}\",\n", + " secret=secret,\n", + ")\n", + "print(\"signature verified\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "bfed901b", + "metadata": {}, + "source": [ + "## API reference\n", + "\n", + "- [Task API guides](https://docs.parallel.ai/task-api/overview)\n", + "- [Choose a processor](https://docs.parallel.ai/task-api/guides/choose-a-processor)\n", + "- [Webhook setup](https://docs.parallel.ai/resources/webhook-setup)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/deep_research_example.py b/examples/deep_research_example.py new file mode 100644 index 0000000..5f92e61 --- /dev/null +++ b/examples/deep_research_example.py @@ -0,0 +1,40 @@ +"""Deep-research example using ParallelDeepResearch.""" + +from __future__ import annotations + +from pydantic import BaseModel + +from langchain_parallel import ParallelDeepResearch + +# Set your API key: export PARALLEL_API_KEY="your-api-key" + + +def basic_deep_research() -> None: + print("=== ParallelDeepResearch: untyped ===") + # Default processor is "pro-fast" — the -fast variant of pro, + # 2-5x faster than "pro" at similar accuracy. + research = ParallelDeepResearch() + result = research.invoke("In one sentence, what is the capital of France?") + output = result["output"] + print("output:", output["content"] if isinstance(output, dict) else output) + + +def typed_deep_research() -> None: + print("\n=== ParallelDeepResearch: typed output ===") + + class CityFact(BaseModel): + capital: str + population_millions: float + + research = ParallelDeepResearch(output_schema=CityFact) + result = research.invoke("Capital and population (in millions) of France?") + print("output:", result["output"]) + + +def main() -> None: + basic_deep_research() + typed_deep_research() + + +if __name__ == "__main__": + main() diff --git a/examples/enrichment_example.py b/examples/enrichment_example.py new file mode 100644 index 0000000..49d169c --- /dev/null +++ b/examples/enrichment_example.py @@ -0,0 +1,43 @@ +"""Structured-batch enrichment example using ParallelEnrichment.""" + +from __future__ import annotations + +from pydantic import BaseModel, Field + +from langchain_parallel import ParallelEnrichment + +# Set your API key: export PARALLEL_API_KEY="your-api-key" + + +class CompanyInput(BaseModel): + company: str = Field(description="Company name") + + +class CompanyOutput(BaseModel): + headquarters: str + founding_year: int + + +def enrich_company_facts() -> None: + print("=== ParallelEnrichment: typed batch ===") + # Default processor is "core-fast" — pass "core" or "pro" for higher accuracy. + enricher = ParallelEnrichment( + input_schema=CompanyInput, + output_schema=CompanyOutput, + ) + results = enricher.invoke( + [ + CompanyInput(company="Anthropic"), + {"company": "OpenAI"}, + ], + ) + for r in results: + print(r["output"]["content"]) + + +def main() -> None: + enrich_company_facts() + + +if __name__ == "__main__": + main() diff --git a/examples/extract_tool_example.py b/examples/extract_tool_example.py index f8c1df9..f35d846 100644 --- a/examples/extract_tool_example.py +++ b/examples/extract_tool_example.py @@ -69,7 +69,6 @@ def focused_extraction_examples() -> None: "search_objective": ( "What are the main applications and ethical concerns of AI?" ), - "excerpts": True, "full_content": False, } ) @@ -88,7 +87,6 @@ def focused_extraction_examples() -> None: "training algorithms", "applications", ], - "excerpts": True, } ) diff --git a/examples/findall_example.py b/examples/findall_example.py new file mode 100644 index 0000000..a81721f --- /dev/null +++ b/examples/findall_example.py @@ -0,0 +1,42 @@ +"""ParallelFindAllTool example: entity discovery with match conditions.""" + +from __future__ import annotations + +import asyncio + +from langchain_parallel import FindAllMatchCondition, ParallelFindAllTool + +# Set your API key: export PARALLEL_API_KEY="your-api-key" + + +async def preview_discovery() -> None: + print("=== ParallelFindAllTool: preview generator ===") + tool = ParallelFindAllTool() + result = await tool.ainvoke( + { + "objective": "Pure-play public LLM API providers", + "entity_type": "company", + "match_conditions": [ + FindAllMatchCondition( + name="public_us", + description="Company is publicly traded on a US exchange", + ).model_dump(), + FindAllMatchCondition( + name="llm_api_revenue", + description="Primary revenue is selling LLM inference via API", + ).model_dump(), + ], + "generator": "preview", + "match_limit": 5, + }, + ) + for c in result.get("candidates", []): + print("-", c.get("name"), "/", c.get("url")) + + +def main() -> None: + asyncio.run(preview_discovery()) + + +if __name__ == "__main__": + main() diff --git a/examples/monitor_example.py b/examples/monitor_example.py new file mode 100644 index 0000000..1bab89b --- /dev/null +++ b/examples/monitor_example.py @@ -0,0 +1,37 @@ +"""ParallelMonitor example: create / retrieve / delete a monitor (alpha).""" + +from __future__ import annotations + +from langchain_parallel import ParallelMonitor + +# Set your API key: export PARALLEL_API_KEY="your-api-key" + + +def monitor_crud_cycle() -> None: + print("=== ParallelMonitor: create / retrieve / delete ===") + client = ParallelMonitor() + + created = client.create( + query="Latest peer-reviewed papers on net-energy-gain fusion", + frequency="1d", + metadata={"label": "example-monitor-fusion"}, + ) + monitor_id = created["monitor_id"] + print("created:", monitor_id, "/ status:", created["status"]) + + got = client.retrieve(monitor_id) + print("retrieved query:", got["query"][:80]) + + events = client.list_events(monitor_id, lookback_period="7d") + print("events:", len(events.get("events", []))) + + deleted = client.delete(monitor_id) + print("deleted status:", deleted["status"]) + + +def main() -> None: + monitor_crud_cycle() + + +if __name__ == "__main__": + main() diff --git a/examples/retriever_example.py b/examples/retriever_example.py new file mode 100644 index 0000000..1956cf1 --- /dev/null +++ b/examples/retriever_example.py @@ -0,0 +1,55 @@ +"""ParallelSearchRetriever example: drop-in BaseRetriever for RAG.""" + +from __future__ import annotations + +import asyncio + +from langchain_parallel import ParallelSearchRetriever + +# Set your API key: export PARALLEL_API_KEY="your-api-key" + + +def basic_retrieval() -> None: + print("=== ParallelSearchRetriever: basic ===") + retriever = ParallelSearchRetriever( + max_results=3, + excerpts={"max_chars_per_result": 800}, + ) + docs = retriever.invoke("breakthroughs in fusion energy 2025") + for d in docs: + print("-", d.metadata.get("title"), "/", d.metadata.get("url")) + + +def configured_retrieval() -> None: + print("\n=== ParallelSearchRetriever: domain-filtered ===") + retriever = ParallelSearchRetriever( + max_results=5, + excerpts={"max_chars_per_result": 1500}, + mode="basic", + source_policy={ + "include_domains": ["nature.com", "science.org", "iter.org"], + }, + ) + docs = retriever.invoke( + "What's the latest peer-reviewed result on net-energy-gain fusion?", + ) + print(f"got {len(docs)} docs") + for d in docs[:3]: + print("-", d.metadata.get("url")) + + +async def async_retrieval() -> None: + print("\n=== ParallelSearchRetriever: async ===") + retriever = ParallelSearchRetriever(max_results=3) + docs = await retriever.ainvoke("Latest GLP-1 trial results 2025") + print(f"got {len(docs)} docs") + + +def main() -> None: + basic_retrieval() + configured_retrieval() + asyncio.run(async_retrieval()) + + +if __name__ == "__main__": + main() diff --git a/examples/search_example.py b/examples/search_example.py index c91c1f7..c776ecf 100644 --- a/examples/search_example.py +++ b/examples/search_example.py @@ -184,7 +184,6 @@ def display_metadata(result: dict[str, Any]) -> None: return metadata = result["search_metadata"] print("\n Search Metadata:") - print(f" Endpoint: {metadata.get('endpoint', 'N/A')}") print(f" Duration: {metadata.get('search_duration_seconds', 'N/A')}s") print(f" Results: {metadata.get('actual_results_returned', 'N/A')}") diff --git a/examples/task_run_example.py b/examples/task_run_example.py new file mode 100644 index 0000000..43422ed --- /dev/null +++ b/examples/task_run_example.py @@ -0,0 +1,41 @@ +"""Single-task example using ParallelTaskRunTool.""" + +from __future__ import annotations + +from pydantic import BaseModel, Field + +from langchain_parallel import ParallelTaskRunTool + +# Set your API key: export PARALLEL_API_KEY="your-api-key" + + +def untyped_run() -> None: + print("=== ParallelTaskRunTool: untyped run ===") + tool = ParallelTaskRunTool() # default processor="lite-fast" + result = tool.invoke({"input": "Who founded SpaceX, in one sentence?"}) + output = result["output"] + print("output:", output["content"] if isinstance(output, dict) else output) + print("run_id:", result["run"]["run_id"]) + + +def typed_run_with_citations() -> None: + print("\n=== ParallelTaskRunTool: typed run with citations ===") + + class FounderFact(BaseModel): + founder: str = Field(description="Full name of the founder") + year: int = Field(description="Year the company was founded") + + tool = ParallelTaskRunTool(task_output_schema=FounderFact) + result = tool.invoke({"input": "Who founded SpaceX and in what year?"}) + print("output:", result["output"]) + for fact in result.get("basis", []): + print(f"- {fact.get('field')}: confidence={fact.get('confidence')}") + + +def main() -> None: + untyped_run() + typed_run_with_citations() + + +if __name__ == "__main__": + main() diff --git a/examples/webhook_handler_example.py b/examples/webhook_handler_example.py new file mode 100644 index 0000000..c61636d --- /dev/null +++ b/examples/webhook_handler_example.py @@ -0,0 +1,93 @@ +"""verify_webhook example: validate Standard Webhooks signatures. + +This script demonstrates the verification flow without standing up a +server. For an end-to-end FastAPI handler, the snippet at the bottom +shows the wiring. +""" + +from __future__ import annotations + +import base64 +import hashlib +import hmac +import time + +from langchain_parallel import verify_webhook + +# Set your webhook secret: export PARALLEL_WEBHOOK_SECRET="..." + + +def sign(payload: bytes, webhook_id: str, ts: str, secret: str) -> str: + """Standard Webhooks: HMAC-SHA256 over `..`, base64-encoded.""" + signed = f"{webhook_id}.{ts}.{payload.decode()}" + digest = hmac.new(secret.encode(), signed.encode(), hashlib.sha256).digest() + return base64.b64encode(digest).decode() + + +def round_trip_verification() -> None: + print("=== verify_webhook: synthetic round-trip ===") + secret = "test_secret" # noqa: S105 - demo + body = b'{"run_id":"trun_abc","status":"completed"}' + webhook_id = "msg_demo_001" + ts = str(int(time.time())) + sig = sign(body, webhook_id, ts, secret) + + ok = verify_webhook( + body, + webhook_id=webhook_id, + webhook_timestamp=ts, + webhook_signature=f"v1,{sig}", + secret=secret, + ) + print("verified:", ok) + + +def replay_attack_rejected() -> None: + print("\n=== verify_webhook: rejects stale timestamp ===") + secret = "test_secret" # noqa: S105 - demo + body = b"{}" + old_ts = "1000000000" # year 2001 + sig = sign(body, "msg", old_ts, secret) + ok = verify_webhook( + body, + webhook_id="msg", + webhook_timestamp=old_ts, + webhook_signature=f"v1,{sig}", + secret=secret, + ) + print("verified:", ok, "(expected False)") + + +# --- FastAPI wiring (illustrative; uncomment + run with `uvicorn`) ----------- +# +# from fastapi import FastAPI, Header, HTTPException, Request +# +# app = FastAPI() +# +# @app.post("/parallel/webhook") +# async def webhook( +# request: Request, +# webhook_id: str = Header(..., alias="webhook-id"), +# webhook_timestamp: str = Header(..., alias="webhook-timestamp"), +# webhook_signature: str = Header(..., alias="webhook-signature"), +# ) -> dict: +# body = await request.body() +# if not verify_webhook( +# body, +# webhook_id=webhook_id, +# webhook_timestamp=webhook_timestamp, +# webhook_signature=webhook_signature, +# secret=os.environ["PARALLEL_WEBHOOK_SECRET"], +# ): +# raise HTTPException(status_code=401, detail="invalid signature") +# # ... process the event +# return {"ok": True} + + +def main() -> None: + round_trip_verification() + replay_attack_rejected() + + +if __name__ == "__main__": + main() diff --git a/langchain_parallel/__init__.py b/langchain_parallel/__init__.py index c3ade94..0c194d0 100644 --- a/langchain_parallel/__init__.py +++ b/langchain_parallel/__init__.py @@ -8,7 +8,24 @@ ) from langchain_parallel.chat_models import ChatParallel, ChatParallelWeb from langchain_parallel.extract_tool import ParallelExtractTool +from langchain_parallel.findall import ( + FindAllExcludeEntry, + FindAllMatchCondition, + ParallelFindAllTool, +) +from langchain_parallel.monitors import MonitorWebhook, ParallelMonitor +from langchain_parallel.retrievers import ParallelSearchRetriever from langchain_parallel.search_tool import ParallelSearchTool, ParallelWebSearchTool +from langchain_parallel.tasks import ( + McpServer, + ParallelDeepResearch, + ParallelEnrichment, + ParallelTaskGroup, + ParallelTaskRunTool, + build_task_spec, + parse_basis, + verify_webhook, +) try: __version__ = metadata.version(__package__ or __name__) @@ -22,10 +39,24 @@ "ChatParallelWeb", "ExcerptSettings", "FetchPolicy", + "FindAllExcludeEntry", + "FindAllMatchCondition", "FullContentSettings", + "McpServer", + "MonitorWebhook", + "ParallelDeepResearch", + "ParallelEnrichment", "ParallelExtractTool", + "ParallelFindAllTool", + "ParallelMonitor", + "ParallelSearchRetriever", "ParallelSearchTool", + "ParallelTaskGroup", + "ParallelTaskRunTool", "ParallelWebSearchTool", "SourcePolicy", "__version__", + "build_task_spec", + "parse_basis", + "verify_webhook", ] diff --git a/langchain_parallel/_types.py b/langchain_parallel/_types.py index 7bb2f18..c5c863a 100644 --- a/langchain_parallel/_types.py +++ b/langchain_parallel/_types.py @@ -2,9 +2,10 @@ from __future__ import annotations -from typing import Optional +import datetime as _dt +from typing import Optional, Union -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator, model_validator class ExcerptSettings(BaseModel): @@ -38,10 +39,11 @@ class FetchPolicy(BaseModel): max_age_seconds: Optional[int] = Field( default=None, + ge=600, description=( - "Maximum age of cached content in seconds to trigger a live fetch. " - "Minimum value 600 seconds (10 minutes). If not provided, dynamic " - "age policy will be used." + "If cached content is older than this, fetch fresh content from the " + "source. Minimum 600 seconds (10 minutes); if not provided, the API " + "uses a dynamic age policy." ), ) timeout_seconds: Optional[float] = Field( @@ -61,10 +63,15 @@ class FetchPolicy(BaseModel): class SourcePolicy(BaseModel): - """Domain allow/deny lists and freshness floor for web research.""" + """Domain allow/deny lists and freshness floor for web research. + + Apex-domain semantics: include `nature.com`, not `https://www.nature.com`. + Wildcards permitted (e.g. `.org`). + """ include_domains: Optional[list[str]] = Field( default=None, + max_length=200, description=( "If provided, only sources from these apex domains are returned. " "Combined include + exclude lists are capped at 200 domains." @@ -72,12 +79,37 @@ class SourcePolicy(BaseModel): ) exclude_domains: Optional[list[str]] = Field( default=None, + max_length=200, description="If provided, sources from these apex domains are excluded.", ) - after_date: Optional[str] = Field( + after_date: Optional[Union[_dt.date, str]] = Field( default=None, description=( "ISO date (YYYY-MM-DD). Only return sources published on or after " "this date." ), ) + + @field_validator("after_date", mode="before") + @classmethod + def _parse_after_date(cls, v: object) -> object: + if v is None or isinstance(v, _dt.date): + return v + if isinstance(v, str): + try: + return _dt.date.fromisoformat(v) + except ValueError as e: + msg = f"after_date must be ISO YYYY-MM-DD; got {v!r} ({e!s})." + raise ValueError(msg) from e + return v + + @model_validator(mode="after") + def _check_domain_total(self) -> SourcePolicy: + total = len(self.include_domains or []) + len(self.exclude_domains or []) + if total > 200: + msg = ( + f"Combined include_domains + exclude_domains has {total} entries; " + f"the API caps the total at 200." + ) + raise ValueError(msg) + return self diff --git a/langchain_parallel/extract_tool.py b/langchain_parallel/extract_tool.py index 6404d45..a407986 100644 --- a/langchain_parallel/extract_tool.py +++ b/langchain_parallel/extract_tool.py @@ -2,7 +2,6 @@ from __future__ import annotations -import warnings from typing import Any, Optional, Union from langchain_core.callbacks import ( @@ -37,33 +36,16 @@ def _coerce_full_content( def _coerce_excerpts( - excerpts: Union[bool, ExcerptSettings, dict[str, Any], None], + excerpts: Optional[ExcerptSettings], ) -> Optional[dict[str, Any]]: - """Resolve the legacy ``Union[bool, ExcerptSettings]`` excerpts arg. + """Convert an ExcerptSettings to a kwargs dict, or None. - In v1 GA, excerpts are always returned and the API has no flag to disable - them — only their per-result size is configurable. We accept the legacy - boolean for backward compatibility: - - - ``None`` / ``True``: no excerpt-size override (API uses its default). - - ``False``: warn (the API can no longer disable excerpts) and treat as - no override. - - ``ExcerptSettings`` / ``dict``: pass through to advanced_settings. + The GA Extract API always returns excerpts; this controls only per-result + size. ``None`` means no override (API default). """ - if excerpts is None or excerpts is True: - return None - if excerpts is False: - warnings.warn( - "excerpts=False is no longer supported — the GA Extract API " - "always returns excerpts. Use ExcerptSettings(max_chars_per_result=…) " - "to control per-result size.", - DeprecationWarning, - stacklevel=4, - ) + if excerpts is None: return None - if isinstance(excerpts, ExcerptSettings): - return excerpts.model_dump(exclude_none=True) - return {k: v for k, v in excerpts.items() if v is not None} + return excerpts.model_dump(exclude_none=True) def _build_advanced_settings( @@ -89,10 +71,13 @@ class ParallelExtractInput(BaseModel): """Input schema for Parallel Extract Tool.""" urls: list[str] = Field( + min_length=1, + max_length=20, description="List of URLs to extract content from. Up to 20 per request.", ) search_objective: Optional[str] = Field( default=None, + max_length=5000, description=( "Natural-language objective to focus extraction. Up to 5000 characters." ), @@ -101,13 +86,12 @@ class ParallelExtractInput(BaseModel): default=None, description="Keyword queries to focus extracted content.", ) - excerpts: Union[bool, ExcerptSettings] = Field( - default=True, + excerpts: Optional[ExcerptSettings] = Field( + default=None, description=( - "Include excerpts from each URL. In v1 GA, excerpts are always " - "returned; the boolean is kept for backward compatibility and " - "controls nothing on the wire. Pass an ExcerptSettings to control " - "per-result size (the API has no flag to disable excerpts in v1)." + "Per-result excerpt-size settings. The GA Extract API always " + "returns excerpts; pass `ExcerptSettings(max_chars_per_result=…)` " + "to control their size, or leave None for the API default." ), ) full_content: Union[bool, FullContentSettings] = Field( @@ -292,7 +276,7 @@ def _build_call_kwargs( urls: list[str], search_objective: Optional[str], search_queries: Optional[list[str]], - excerpts: Union[bool, ExcerptSettings, dict[str, Any], None], + excerpts: Optional[ExcerptSettings], full_content: Union[bool, FullContentSettings, dict[str, Any]], fetch_policy: Optional[FetchPolicy], max_chars_total: Optional[int], @@ -358,7 +342,7 @@ def _run( urls: list[str], search_objective: Optional[str] = None, search_queries: Optional[list[str]] = None, - excerpts: Union[bool, ExcerptSettings] = True, + excerpts: Optional[ExcerptSettings] = None, full_content: Union[bool, FullContentSettings] = False, max_chars_total: Optional[int] = None, fetch_policy: Optional[FetchPolicy] = None, @@ -396,8 +380,20 @@ def _run( msg = f"Error calling Parallel Extract API: {e!s}" raise ValueError(msg) from e - formatted = self._format_response(response_obj.model_dump()) + envelope = response_obj.model_dump() + formatted = self._format_response(envelope) if run_manager: + for warning in envelope.get("warnings") or []: + warning_msg = ( + warning.get("message") + if isinstance(warning, dict) + else str(warning) + ) + if warning_msg: + run_manager.on_text( + f"[warning] {warning_msg}\n", + color="yellow", + ) run_manager.on_text( self._completion_text(formatted, async_=False), color="green", @@ -409,7 +405,7 @@ async def _arun( urls: list[str], search_objective: Optional[str] = None, search_queries: Optional[list[str]] = None, - excerpts: Union[bool, ExcerptSettings] = True, + excerpts: Optional[ExcerptSettings] = None, full_content: Union[bool, FullContentSettings] = False, max_chars_total: Optional[int] = None, fetch_policy: Optional[FetchPolicy] = None, @@ -453,8 +449,20 @@ async def _arun( msg = f"Error calling Parallel Extract API: {e!s}" raise ValueError(msg) from e - formatted = self._format_response(response_obj.model_dump()) + envelope = response_obj.model_dump() + formatted = self._format_response(envelope) if run_manager: + for warning in envelope.get("warnings") or []: + warning_msg = ( + warning.get("message") + if isinstance(warning, dict) + else str(warning) + ) + if warning_msg: + await run_manager.on_text( + f"[warning] {warning_msg}\n", + color="yellow", + ) await run_manager.on_text( self._completion_text(formatted, async_=True), color="green", diff --git a/langchain_parallel/findall.py b/langchain_parallel/findall.py new file mode 100644 index 0000000..a459fd2 --- /dev/null +++ b/langchain_parallel/findall.py @@ -0,0 +1,404 @@ +"""LangChain integration for Parallel's FindAll API. + +FindAll discovers entities from the web that match a natural-language +objective plus a set of boolean match conditions. Returns a ranked list of +candidates with citations. +""" + +from __future__ import annotations + +import asyncio +import contextlib +import time +from typing import Any, Literal, Optional + +from langchain_core.callbacks import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain_core.tools import BaseTool +from parallel import AsyncParallel, Parallel +from pydantic import BaseModel, Field, SecretStr, model_validator + +from ._client import get_api_key, get_async_parallel_client, get_parallel_client + +# Preview generator caps match_limit at 10; we still allow up to 1000 in the +# input schema and validate against the chosen generator at call time. +_PREVIEW_MAX_LIMIT = 10 +_DEFAULT_POLL_TIMEOUT = 600.0 +_POLL_INITIAL = 2.0 +_POLL_MAX = 10.0 + +FindAllEventType = Literal[ + "findall.candidate.generated", + "findall.candidate.matched", + "findall.candidate.unmatched", + "findall.candidate.enriched", + "findall.run.completed", + "findall.run.cancelled", + "findall.run.failed", +] + + +class FindAllWebhook(BaseModel): + """Webhook config for a FindAll run.""" + + url: str = Field(description="HTTPS URL the run will POST events to.") + event_types: Optional[list[FindAllEventType]] = Field( + default=None, + description="Optional subset of event types to forward. Defaults to all.", + ) + + def to_sdk(self) -> dict[str, Any]: + out: dict[str, Any] = {"url": self.url} + if self.event_types is not None: + out["event_types"] = list(self.event_types) + return out + + +class FindAllMatchCondition(BaseModel): + """One boolean condition the API uses to filter candidates. + + The pair (name, description) names a True/False question the API + answers per candidate; only candidates with all conditions True are + returned. + """ + + name: str = Field(description="Short identifier (slug-like).") + description: str = Field( + description=( + "Question the API answers per candidate, e.g. " + "'Is this company headquartered in the US?'" + ), + ) + + def to_sdk(self) -> dict[str, Any]: + return {"name": self.name, "description": self.description} + + +class FindAllExcludeEntry(BaseModel): + """One entry in the FindAll exclude_list.""" + + name: str + url: str + + def to_sdk(self) -> dict[str, Any]: + return {"name": self.name, "url": self.url} + + +class ParallelFindAllInput(BaseModel): + """Input schema for :class:`ParallelFindAllTool`.""" + + objective: str = Field( + description=( + "Natural-language description of what to find. e.g. 'AI startups " + "founded in 2024 that have raised more than $10M'." + ), + ) + entity_type: str = Field( + description=( + "Short noun describing the entity class, e.g. 'company', " + "'researcher', 'product'." + ), + ) + match_conditions: list[FindAllMatchCondition] = Field( + min_length=1, + description="Boolean conditions every candidate must satisfy.", + ) + match_limit: int = Field( + ge=5, + le=1000, + description=( + "Maximum number of matching candidates to return. Must be in " + "[5, 1000]; the `preview` generator further caps this at 10." + ), + ) + exclude_list: Optional[list[FindAllExcludeEntry]] = Field( + default=None, + description=( + "Entities to skip (use to refresh runs without re-discovering). " + "URL matching is sensitive to canonicalization; use the same " + "URL form across runs." + ), + ) + webhook: Optional[FindAllWebhook] = Field( + default=None, + description="Optional webhook to receive run/candidate events.", + ) + metadata: Optional[dict[str, Any]] = Field(default=None) + timeout: Optional[float] = Field( + default=None, + description=( + "Polling timeout (seconds). FindAll runs are typically multi-minute." + ), + ) + + +class ParallelFindAllTool(BaseTool): + """Run a Parallel FindAll discovery and return the matched candidates. + + FindAll discovers entities from the web that satisfy a natural-language + objective plus a set of boolean match conditions. Useful for lead + generation, market mapping, and entity enumeration. + + Setup: + ```bash + export PARALLEL_API_KEY="your-api-key" + ``` + + Key init args: + generator: Literal["preview", "base", "core", "pro"] + Discovery generator. Higher tiers find more candidates but + cost more and take longer. ``"preview"`` is a small free sample. + + Invocation: + ```python + from langchain_parallel import ( + ParallelFindAllTool, + FindAllMatchCondition, + ) + + tool = ParallelFindAllTool(generator="base") + result = tool.invoke({ + "objective": "AI agent startups founded after 2023", + "entity_type": "company", + "match_conditions": [ + FindAllMatchCondition( + name="founded_after_2023", + description="Was this company founded after January 1 2023?", + ), + FindAllMatchCondition( + name="builds_ai_agents", + description="Does this company build AI agents as a core product?", + ), + ], + "match_limit": 25, + }) + for candidate in result["candidates"]: + print(candidate["name"], candidate["url"]) + ``` + + Returns: + Dict with ``candidates`` (list), ``run`` (status info), and + ``last_event_id``. Each candidate carries: + + - ``candidate_id``, ``name``, ``url``, ``description`` + - ``match_status``: ``"generated" | "matched" | "unmatched"`` + - ``output``: ``{: {"type": "match_condition" | "enrichment", + "value": , "is_matched": }}`` + - ``basis``: list of citations / reasoning per output field + + Note: the ``preview`` generator returns at most 10 candidates and + does not support enrichments, cancellation, or extension. + """ + + name: str = "parallel_findall" + description: str = ( + "Discover entities from the web that match a natural-language objective " + "plus a set of boolean match conditions. Useful for lead generation, " + "market mapping, and entity enumeration. Returns a ranked candidate list." + ) + args_schema: type[BaseModel] = ParallelFindAllInput + + api_key: Optional[SecretStr] = Field(default=None) + base_url: str = Field(default="https://api.parallel.ai") + + generator: Literal["preview", "base", "core", "pro"] = Field(default="base") + """Discovery generator tier.""" + + _client: Optional[Parallel] = None + _async_client: Optional[AsyncParallel] = None + + @model_validator(mode="after") + def _initialize_clients(self) -> ParallelFindAllTool: + api_key_str = get_api_key( + self.api_key.get_secret_value() if self.api_key else None, + ) + self._client = get_parallel_client(api_key_str, self.base_url) + self._async_client = get_async_parallel_client(api_key_str, self.base_url) + return self + + def _build_create_kwargs( + self, + *, + objective: str, + entity_type: str, + match_conditions: list[FindAllMatchCondition], + match_limit: int, + exclude_list: Optional[list[FindAllExcludeEntry]], + webhook: Optional[FindAllWebhook], + metadata: Optional[dict[str, Any]], + ) -> dict[str, Any]: + if self.generator == "preview" and match_limit > _PREVIEW_MAX_LIMIT: + msg = ( + f"The 'preview' generator caps match_limit at " + f"{_PREVIEW_MAX_LIMIT}; got {match_limit}. Use 'base', " + f"'core', or 'pro' for larger runs." + ) + raise ValueError(msg) + kwargs: dict[str, Any] = { + "objective": objective, + "entity_type": entity_type, + "match_conditions": [m.to_sdk() for m in match_conditions], + "match_limit": match_limit, + "generator": self.generator, + } + if exclude_list: + kwargs["exclude_list"] = [e.to_sdk() for e in exclude_list] + if webhook is not None: + kwargs["webhook"] = webhook.to_sdk() + if metadata is not None: + kwargs["metadata"] = metadata + return kwargs + + def _format_result(self, result: Any) -> dict[str, Any]: + return result.model_dump() if hasattr(result, "model_dump") else dict(result) + + _TERMINAL_STATUSES = frozenset({"completed", "cancelled", "failed"}) + + def _wait_for_completion(self, findall_id: str, timeout: float) -> str: + """Poll ``retrieve()`` until the run terminates (or we time out). + + Returns the terminal status string (``"completed"``, ``"cancelled"``, + or ``"failed"``). + + On timeout, attempts a best-effort ``cancel()`` and re-raises + ``TimeoutError``. + """ + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + deadline = time.monotonic() + timeout + wait = _POLL_INITIAL + while True: + info = self._client.beta.findall.retrieve(findall_id) + status = info.status.status + if status in self._TERMINAL_STATUSES: + return status + if time.monotonic() >= deadline: + # Best-effort cancel so we don't leak the run server-side. + with contextlib.suppress(Exception): + self._client.beta.findall.cancel(findall_id) + msg = ( + f"FindAll run {findall_id} did not complete within " + f"{timeout}s (last status: {status})." + ) + raise TimeoutError(msg) + time.sleep(wait) + wait = min(wait * 1.5, _POLL_MAX) + + async def _await_completion(self, findall_id: str, timeout: float) -> str: + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + deadline = time.monotonic() + timeout + wait = _POLL_INITIAL + while True: + info = await self._async_client.beta.findall.retrieve(findall_id) + status = info.status.status + if status in self._TERMINAL_STATUSES: + return status + if time.monotonic() >= deadline: + with contextlib.suppress(Exception): + await self._async_client.beta.findall.cancel(findall_id) + msg = ( + f"FindAll run {findall_id} did not complete within " + f"{timeout}s (last status: {status})." + ) + raise TimeoutError(msg) + await asyncio.sleep(wait) + wait = min(wait * 1.5, _POLL_MAX) + + def cancel(self, findall_id: str) -> dict[str, Any]: + """Cancel a running FindAll run.""" + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + result: Any = self._client.beta.findall.cancel(findall_id) + return result.model_dump() if hasattr(result, "model_dump") else dict(result) + + async def acancel(self, findall_id: str) -> dict[str, Any]: + """Async cancel a running FindAll run.""" + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + result: Any = await self._async_client.beta.findall.cancel(findall_id) + return result.model_dump() if hasattr(result, "model_dump") else dict(result) + + def _run( + self, + objective: str, + entity_type: str, + match_conditions: list[FindAllMatchCondition], + match_limit: int, + exclude_list: Optional[list[FindAllExcludeEntry]] = None, + webhook: Optional[FindAllWebhook] = None, + metadata: Optional[dict[str, Any]] = None, + timeout: Optional[float] = None, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> dict[str, Any]: + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + kwargs = self._build_create_kwargs( + objective=objective, + entity_type=entity_type, + match_conditions=match_conditions, + match_limit=match_limit, + exclude_list=exclude_list, + webhook=webhook, + metadata=metadata, + ) + poll_timeout = timeout if timeout is not None else _DEFAULT_POLL_TIMEOUT + try: + run = self._client.beta.findall.create(**kwargs) + status = self._wait_for_completion(run.findall_id, poll_timeout) + if status == "failed": + msg = f"FindAll run {run.findall_id} terminated with status=failed." + raise ValueError(msg) + result = self._client.beta.findall.result(run.findall_id) + except (TimeoutError, ValueError): + raise + except Exception as e: + msg = f"Error calling Parallel FindAll API: {e!s}" + raise ValueError(msg) from e + return self._format_result(result) + + async def _arun( + self, + objective: str, + entity_type: str, + match_conditions: list[FindAllMatchCondition], + match_limit: int, + exclude_list: Optional[list[FindAllExcludeEntry]] = None, + webhook: Optional[FindAllWebhook] = None, + metadata: Optional[dict[str, Any]] = None, + timeout: Optional[float] = None, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> dict[str, Any]: + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + kwargs = self._build_create_kwargs( + objective=objective, + entity_type=entity_type, + match_conditions=match_conditions, + match_limit=match_limit, + exclude_list=exclude_list, + webhook=webhook, + metadata=metadata, + ) + poll_timeout = timeout if timeout is not None else _DEFAULT_POLL_TIMEOUT + try: + run = await self._async_client.beta.findall.create(**kwargs) + status = await self._await_completion(run.findall_id, poll_timeout) + if status == "failed": + msg = f"FindAll run {run.findall_id} terminated with status=failed." + raise ValueError(msg) + result = await self._async_client.beta.findall.result(run.findall_id) + except (TimeoutError, ValueError): + raise + except Exception as e: + msg = f"Error calling Parallel FindAll API: {e!s}" + raise ValueError(msg) from e + return self._format_result(result) diff --git a/langchain_parallel/monitors.py b/langchain_parallel/monitors.py new file mode 100644 index 0000000..f99979a --- /dev/null +++ b/langchain_parallel/monitors.py @@ -0,0 +1,398 @@ +"""LangChain integration for Parallel's Monitor API (alpha). + +The Monitor API runs scheduled web queries and emits webhook events when +results change. As of `parallel-web` 0.5.1 the SDK does not expose this +surface, so this module talks to ``/v1alpha/monitors`` directly via httpx. + +The Monitor API is currently in **alpha**; endpoints and shapes may change +without notice. Track https://docs.parallel.ai/monitor-api/monitor-quickstart +for updates. +""" + +from __future__ import annotations + +import re +from typing import Any, Literal, Optional + +import httpx +from pydantic import BaseModel, Field, SecretStr, model_validator + +from ._client import get_api_key + +_MONITORS_PATH = "/v1alpha/monitors" + +MonitorEventType = Literal[ + "monitor.event.detected", + "monitor.execution.completed", + "monitor.execution.failed", +] + +# `` where unit ∈ {h, d, w} and the resulting duration is between 1h +# and 30d. Validated as a regex; numeric range checked separately. +_FREQUENCY_RE = re.compile(r"^\d+[hdw]$") + + +def _validate_frequency(value: str) -> str: + """Frequency is `` from 1h to 30d (units: h/d/w).""" + if not _FREQUENCY_RE.match(value): + msg = ( + f"Invalid frequency '{value}'. Expected '' where unit is " + f"h, d, or w (e.g. '1h', '6h', '3d', '1w', '2w')." + ) + raise ValueError(msg) + n, unit = int(value[:-1]), value[-1] + hours = {"h": n, "d": n * 24, "w": n * 24 * 7}[unit] + if not (1 <= hours <= 30 * 24): + msg = f"frequency '{value}' is outside the supported range (1h-30d)." + raise ValueError(msg) + return value + + +class MonitorWebhook(BaseModel): + """Webhook config for a Parallel monitor. + + Per the create-monitor API, the webhook object is ``{url, event_types}``. + The signing secret is configured at the org webhook-endpoint level (in + the Parallel dashboard), not per-monitor — see + :func:`langchain_parallel.tasks.verify_webhook` for verification. + """ + + url: str = Field(description="HTTPS URL the monitor will POST events to.") + event_types: Optional[list[MonitorEventType]] = Field( + default=None, + description=( + "Optional subset of event types to forward. Defaults to all " + "(detected / completed / failed)." + ), + ) + + def to_sdk(self) -> dict[str, Any]: + out: dict[str, Any] = {"url": self.url} + if self.event_types is not None: + out["event_types"] = list(self.event_types) + return out + + +class ParallelMonitor(BaseModel): + """Manage scheduled web monitors via the Parallel Monitor API (alpha). + + Each method is a thin wrapper around an HTTP call. Returned dicts are + the API response bodies as-is. + + Setup: + ```bash + export PARALLEL_API_KEY="your-api-key" + ``` + + Example: + ```python + from langchain_parallel import ParallelMonitor, MonitorWebhook + + m = ParallelMonitor() + + monitor = m.create( + query="Track new SEC filings related to Anthropic", + frequency="6h", + webhook=MonitorWebhook( + url="https://example.com/parallel-webhook", + event_types=["monitor.event.detected"], + ), + metadata={"team": "research"}, + ) + print(monitor["monitor_id"]) + + # `/events` returns a flat list flattened out of event groups. + events = m.list_events(monitor["monitor_id"], lookback_period="7d") + for ev in events.get("events", []): + print(ev["type"], ev.get("event_date")) + ``` + """ + + api_key: Optional[SecretStr] = Field(default=None) + base_url: str = Field(default="https://api.parallel.ai") + timeout: float = Field(default=60.0) + + _resolved_key: Optional[str] = None + + @model_validator(mode="after") + def _resolve_key(self) -> ParallelMonitor: + self._resolved_key = get_api_key( + self.api_key.get_secret_value() if self.api_key else None, + ) + return self + + def _headers(self) -> dict[str, str]: + if self._resolved_key is None: + msg = "API key not initialized." + raise RuntimeError(msg) + return { + "x-api-key": self._resolved_key, + "Content-Type": "application/json", + } + + @staticmethod + def _build_create_body( + *, + query: str, + frequency: str, + webhook: Optional[MonitorWebhook], + metadata: Optional[dict[str, Any]], + output_schema: Optional[dict[str, Any]], + source_policy: Optional[dict[str, Any]], + include_backfill: Optional[bool], + ) -> dict[str, Any]: + body: dict[str, Any] = { + "query": query, + "frequency": _validate_frequency(frequency), + } + if webhook is not None: + body["webhook"] = webhook.to_sdk() + if metadata is not None: + body["metadata"] = metadata + if output_schema is not None: + body["output_schema"] = output_schema + if source_policy is not None: + body["source_policy"] = source_policy + if include_backfill is not None: + body["include_backfill"] = include_backfill + return body + + def _request( + self, + method: str, + path: str, + *, + json: Optional[dict[str, Any]] = None, + ) -> Any: + url = f"{self.base_url.rstrip('/')}{path}" + try: + with httpx.Client(timeout=self.timeout) as client: + response = client.request( + method, + url, + headers=self._headers(), + json=json, + ) + response.raise_for_status() + return response.json() if response.content else {} + except httpx.HTTPStatusError as e: + msg = ( + f"Parallel Monitor API error: {e.response.status_code} " + f"{e.response.text[:300]}" + ) + raise ValueError(msg) from e + except httpx.HTTPError as e: + msg = f"Parallel Monitor API request failed: {e!s}" + raise ValueError(msg) from e + + async def _arequest( + self, + method: str, + path: str, + *, + json: Optional[dict[str, Any]] = None, + ) -> Any: + url = f"{self.base_url.rstrip('/')}{path}" + try: + async with httpx.AsyncClient(timeout=self.timeout) as client: + response = await client.request( + method, + url, + headers=self._headers(), + json=json, + ) + response.raise_for_status() + return response.json() if response.content else {} + except httpx.HTTPStatusError as e: + msg = ( + f"Parallel Monitor API error: {e.response.status_code} " + f"{e.response.text[:300]}" + ) + raise ValueError(msg) from e + except httpx.HTTPError as e: + msg = f"Parallel Monitor API request failed: {e!s}" + raise ValueError(msg) from e + + # ---- CRUD ---- + + def create( + self, + *, + query: str, + frequency: str, + webhook: Optional[MonitorWebhook] = None, + metadata: Optional[dict[str, Any]] = None, + output_schema: Optional[dict[str, Any]] = None, + source_policy: Optional[dict[str, Any]] = None, + include_backfill: Optional[bool] = None, + ) -> dict[str, Any]: + """Create a new web monitor. + + Args: + query: What to monitor for material changes. + frequency: ```` where unit ∈ {h, d, w}, between 1h and 30d + (e.g. ``"1h"``, ``"6h"``, ``"3d"``, ``"2w"``). + webhook: Optional :class:`MonitorWebhook` to receive events. + metadata: Free-form string-valued metadata persisted on the run. + output_schema: Optional JSON schema for structured monitor events + (see https://docs.parallel.ai/monitor-api/monitor-structured-outputs). + source_policy: Domain include/exclude lists and freshness floor. + include_backfill: If True, the first execution returns historical + events matching the query. + """ + return self._request( + "POST", + _MONITORS_PATH, + json=self._build_create_body( + query=query, + frequency=frequency, + webhook=webhook, + metadata=metadata, + output_schema=output_schema, + source_policy=source_policy, + include_backfill=include_backfill, + ), + ) + + def retrieve(self, monitor_id: str) -> dict[str, Any]: + """Retrieve a monitor's current configuration and status.""" + return self._request("GET", f"{_MONITORS_PATH}/{monitor_id}") + + def list( + self, + *, + limit: Optional[int] = None, + ) -> Any: + """List active monitors.""" + path = _MONITORS_PATH + if limit is not None: + path = f"{path}?limit={limit}" + return self._request("GET", path) + + def delete(self, monitor_id: str) -> dict[str, Any]: + """Delete a monitor.""" + return self._request("DELETE", f"{_MONITORS_PATH}/{monitor_id}") + + # ---- Events ---- + + def list_events( + self, + monitor_id: str, + *, + lookback_period: Optional[str] = None, + ) -> dict[str, Any]: + """List recent events for a monitor. + + The response is a flat ``{"events": [...]}`` list with entries of + type ``event``, ``completion``, or ``error``. Event groups are + flattened into individual events per the Parallel API contract. + + Args: + monitor_id: The monitor id. + lookback_period: How far back to fetch (e.g. ``"10d"``, ``"1w"``). + Defaults to the API default of ``10d``. + """ + path = f"{_MONITORS_PATH}/{monitor_id}/events" + if lookback_period is not None: + path = f"{path}?lookback_period={lookback_period}" + return self._request("GET", path) + + def get_event_group( + self, + monitor_id: str, + event_group_id: str, + ) -> dict[str, Any]: + """Retrieve a single event group.""" + return self._request( + "GET", + f"{_MONITORS_PATH}/{monitor_id}/event_groups/{event_group_id}", + ) + + def simulate_event( + self, + monitor_id: str, + *, + event_type: Optional[MonitorEventType] = None, + ) -> dict[str, Any]: + """Trigger a synthetic event to test webhook wiring. + + Args: + monitor_id: The monitor id. + event_type: Which event to simulate; defaults to + ``monitor.event.detected``. + """ + path = f"{_MONITORS_PATH}/{monitor_id}/simulate_event" + if event_type is not None: + path = f"{path}?event_type={event_type}" + return self._request("POST", path) + + # ---- Async variants ---- + + async def acreate( + self, + *, + query: str, + frequency: str, + webhook: Optional[MonitorWebhook] = None, + metadata: Optional[dict[str, Any]] = None, + output_schema: Optional[dict[str, Any]] = None, + source_policy: Optional[dict[str, Any]] = None, + include_backfill: Optional[bool] = None, + ) -> dict[str, Any]: + return await self._arequest( + "POST", + _MONITORS_PATH, + json=self._build_create_body( + query=query, + frequency=frequency, + webhook=webhook, + metadata=metadata, + output_schema=output_schema, + source_policy=source_policy, + include_backfill=include_backfill, + ), + ) + + async def aretrieve(self, monitor_id: str) -> dict[str, Any]: + return await self._arequest("GET", f"{_MONITORS_PATH}/{monitor_id}") + + async def alist(self, *, limit: Optional[int] = None) -> Any: + path = _MONITORS_PATH + if limit is not None: + path = f"{path}?limit={limit}" + return await self._arequest("GET", path) + + async def adelete(self, monitor_id: str) -> dict[str, Any]: + return await self._arequest("DELETE", f"{_MONITORS_PATH}/{monitor_id}") + + async def alist_events( + self, + monitor_id: str, + *, + lookback_period: Optional[str] = None, + ) -> dict[str, Any]: + path = f"{_MONITORS_PATH}/{monitor_id}/events" + if lookback_period is not None: + path = f"{path}?lookback_period={lookback_period}" + return await self._arequest("GET", path) + + async def aget_event_group( + self, + monitor_id: str, + event_group_id: str, + ) -> dict[str, Any]: + return await self._arequest( + "GET", + f"{_MONITORS_PATH}/{monitor_id}/event_groups/{event_group_id}", + ) + + async def asimulate_event( + self, + monitor_id: str, + *, + event_type: Optional[MonitorEventType] = None, + ) -> dict[str, Any]: + path = f"{_MONITORS_PATH}/{monitor_id}/simulate_event" + if event_type is not None: + path = f"{path}?event_type={event_type}" + return await self._arequest("POST", path) diff --git a/langchain_parallel/retrievers.py b/langchain_parallel/retrievers.py new file mode 100644 index 0000000..7833027 --- /dev/null +++ b/langchain_parallel/retrievers.py @@ -0,0 +1,195 @@ +"""LangChain retrievers backed by the Parallel Search API.""" + +from __future__ import annotations + +from typing import Any, Optional, Union + +from langchain_core.callbacks import ( + AsyncCallbackManagerForRetrieverRun, + CallbackManagerForRetrieverRun, +) +from langchain_core.documents import Document +from langchain_core.retrievers import BaseRetriever +from parallel import AsyncParallel, Parallel +from pydantic import Field, SecretStr, model_validator + +from ._client import get_api_key, get_async_parallel_client, get_parallel_client +from ._types import ExcerptSettings, FetchPolicy, SourcePolicy + + +def _join_excerpts(excerpts: Optional[list[str]]) -> str: + """Combine the API's excerpt list into a single ``Document.page_content``.""" + return "\n\n".join(excerpts) if excerpts else "" + + +def _result_to_document( + result: dict[str, Any], + *, + search_id: Optional[str], + query: str, +) -> Document: + """Convert a single Parallel Search result into a LangChain Document.""" + metadata: dict[str, Any] = {"query": query} + for key in ("url", "title", "publish_date"): + if (value := result.get(key)) is not None: + metadata[key] = value + if search_id is not None: + metadata["search_id"] = search_id + if (excerpts := result.get("excerpts")) is not None: + metadata["excerpts"] = excerpts + return Document( + page_content=_join_excerpts(result.get("excerpts")), + metadata=metadata, + ) + + +class ParallelSearchRetriever(BaseRetriever): + """LangChain retriever that returns Parallel Search results as Documents. + + Drops in to any RAG pipeline that expects a `BaseRetriever`. Each + `Document.page_content` is the joined excerpt list from one Parallel + result; `metadata` carries `url`, `title`, `publish_date`, `search_id`, + the original `excerpts` list, and the `query` that produced it. + + Setup: + Install `langchain-parallel` and set environment variable + `PARALLEL_API_KEY`. + + Key init args: + api_key: Optional[SecretStr] + Parallel API key. Reads `PARALLEL_API_KEY` if not provided. + base_url: str + Defaults to "https://api.parallel.ai". + max_results: int + Maximum results returned per query. 1-40 (default 5). + mode: Optional[Literal["basic", "advanced"]] + Search mode; defaults to the API default ("advanced"). + objective: Optional[str] + Objective forwarded to the Search API on every call. Useful + when the same retriever is used for a focused research task + and the LangChain query is just one of several keyword forms. + excerpts: Optional[ExcerptSettings] + Per-result excerpt-size cap. + max_chars_total: Optional[int] + Cap on total excerpt characters across all results. + source_policy: Optional[SourcePolicy | dict] + Domain include/exclude lists and freshness floor. + fetch_policy: Optional[FetchPolicy] + Cache vs live-fetch policy. + location: Optional[str] + ISO 3166-1 alpha-2 country code for geo-targeting. + client_model: Optional[str] + Identifier of the calling LLM (e.g. ``"claude-opus-4-7"``). + + Instantiation and use: + ```python + from langchain_parallel import ParallelSearchRetriever + + retriever = ParallelSearchRetriever(max_results=5, mode="advanced") + docs = retriever.invoke("What's new in renewable energy this month?") + for doc in docs: + print(doc.metadata["title"], doc.metadata["url"]) + ``` + """ + + api_key: Optional[SecretStr] = Field(default=None) + """Parallel API key. Reads PARALLEL_API_KEY if not provided.""" + + base_url: str = Field(default="https://api.parallel.ai") + + max_results: int = Field(default=5) + """Number of results per query (1-40).""" + + mode: Optional[str] = Field(default=None) + """``"basic"`` or ``"advanced"``.""" + + objective: Optional[str] = Field(default=None) + """Optional persistent objective forwarded on every call.""" + + excerpts: Optional[ExcerptSettings] = Field(default=None) + max_chars_total: Optional[int] = Field(default=None) + source_policy: Optional[Union[SourcePolicy, dict[str, Any]]] = Field(default=None) + fetch_policy: Optional[FetchPolicy] = Field(default=None) + location: Optional[str] = Field(default=None) + client_model: Optional[str] = Field(default=None) + + _client: Optional[Parallel] = None + _async_client: Optional[AsyncParallel] = None + + @model_validator(mode="after") + def _initialize_clients(self) -> ParallelSearchRetriever: + api_key_str = get_api_key( + self.api_key.get_secret_value() if self.api_key else None, + ) + self._client = get_parallel_client(api_key_str, self.base_url) + self._async_client = get_async_parallel_client(api_key_str, self.base_url) + return self + + def _build_kwargs(self, query: str) -> dict[str, Any]: + kwargs: dict[str, Any] = {"search_queries": [query]} + if self.objective is not None: + kwargs["objective"] = self.objective + if self.mode is not None: + kwargs["mode"] = self.mode + if self.max_chars_total is not None: + kwargs["max_chars_total"] = self.max_chars_total + if self.client_model is not None: + kwargs["client_model"] = self.client_model + + advanced: dict[str, Any] = {"max_results": self.max_results} + if self.excerpts is not None: + advanced["excerpt_settings"] = self.excerpts.model_dump(exclude_none=True) + if self.fetch_policy is not None: + advanced["fetch_policy"] = self.fetch_policy.model_dump(exclude_none=True) + if self.source_policy is not None: + sp = ( + self.source_policy.model_dump(exclude_none=True) + if isinstance(self.source_policy, SourcePolicy) + else {k: v for k, v in self.source_policy.items() if v is not None} + ) + if sp: + advanced["source_policy"] = sp + if self.location is not None: + advanced["location"] = self.location + kwargs["advanced_settings"] = advanced + return kwargs + + def _get_relevant_documents( + self, + query: str, + *, + run_manager: CallbackManagerForRetrieverRun, + ) -> list[Document]: + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + try: + response = self._client.search(**self._build_kwargs(query)) + except Exception as e: + msg = f"Error calling Parallel Search API: {e!s}" + raise ValueError(msg) from e + payload = response.model_dump() + return [ + _result_to_document(r, search_id=payload.get("search_id"), query=query) + for r in payload.get("results") or [] + ] + + async def _aget_relevant_documents( + self, + query: str, + *, + run_manager: AsyncCallbackManagerForRetrieverRun, + ) -> list[Document]: + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + try: + response = await self._async_client.search(**self._build_kwargs(query)) + except Exception as e: + msg = f"Error calling Parallel Search API: {e!s}" + raise ValueError(msg) from e + payload = response.model_dump() + return [ + _result_to_document(r, search_id=payload.get("search_id"), query=query) + for r in payload.get("results") or [] + ] diff --git a/langchain_parallel/search_tool.py b/langchain_parallel/search_tool.py index 85827d0..e7cb2d9 100644 --- a/langchain_parallel/search_tool.py +++ b/langchain_parallel/search_tool.py @@ -2,9 +2,8 @@ from __future__ import annotations -import warnings from datetime import datetime -from typing import Any, Optional, Union +from typing import Any, Literal, Optional, Union from langchain_core.callbacks import ( AsyncCallbackManagerForToolRun, @@ -12,34 +11,22 @@ ) from langchain_core.tools import BaseTool from parallel import AsyncParallel, Parallel -from pydantic import BaseModel, Field, SecretStr, model_validator +from pydantic import BaseModel, Field, SecretStr, field_validator, model_validator from ._client import get_api_key, get_async_parallel_client, get_parallel_client from ._types import ExcerptSettings, FetchPolicy, SourcePolicy -_LEGACY_MODE_MAP: dict[str, str] = { - "fast": "basic", - "one-shot": "basic", - "agentic": "advanced", -} +_VALID_MODES = {"basic", "advanced"} -def _normalize_mode(mode: Optional[str]) -> Optional[str]: - """Translate legacy beta mode strings to the GA `basic` / `advanced` set.""" - if mode is None or mode in {"basic", "advanced"}: +def _validate_mode(mode: Optional[str]) -> Optional[str]: + """Validate that ``mode`` is ``"basic"``, ``"advanced"``, or ``None``.""" + if mode is None or mode in _VALID_MODES: return mode - if mode in _LEGACY_MODE_MAP: - new_mode = _LEGACY_MODE_MAP[mode] - warnings.warn( - f"mode='{mode}' is a legacy beta value; mapping to '{new_mode}'. " - f"Pass mode='{new_mode}' directly to silence this warning.", - DeprecationWarning, - stacklevel=3, - ) - return new_mode msg = ( f"Invalid mode '{mode}'. Expected one of: 'basic', 'advanced'. " - f"(Legacy values 'fast', 'one-shot', 'agentic' are accepted with a warning.)" + f"(Legacy values 'fast', 'one-shot', 'agentic' were removed in 0.4.0; " + f"map 'fast'/'one-shot' to 'basic' and 'agentic' to 'advanced'.)" ) raise ValueError(msg) @@ -79,28 +66,46 @@ def _build_advanced_settings( return settings or None +_MAX_QUERY_LEN = 200 + + class ParallelWebSearchInput(BaseModel): """Input schema for ParallelWeb search tool.""" + @field_validator("search_queries", check_fields=False) + @classmethod + def _check_query_length(cls, qs: list[str]) -> list[str]: + too_long = [q for q in qs if len(q) > _MAX_QUERY_LEN] + if too_long: + msg = ( + f"search_queries must each be <= {_MAX_QUERY_LEN} characters; " + f"got {len(too_long)} too-long entries." + ) + raise ValueError(msg) + return qs + objective: Optional[str] = Field( default=None, + max_length=5000, description=( "Natural-language description of the research goal. Up to 5000 " - "characters. Include any source or freshness guidance. Recommended " - "alongside `search_queries` for best results." + "characters. Include any source or freshness guidance. Pair with " + "`search_queries` for best results." ), ) - search_queries: Optional[list[str]] = Field( - default=None, + search_queries: list[str] = Field( + min_length=1, + max_length=5, description=( - "1-5 keyword search queries (3-6 words each, up to 200 characters). " - "Required by Parallel's GA endpoint; if omitted, the call routes " - "to the deprecated /v1beta endpoint with a DeprecationWarning. " - "Pair with an optional `objective` for best results." + "Required. 1-5 keyword search queries (3-6 words each, up to " + "200 characters). Pair with an optional `objective` for best " + "results." ), ) max_results: int = Field( default=10, + ge=1, + le=40, description="Maximum number of search results to return (1 to 40).", ) excerpts: Optional[ExcerptSettings] = Field( @@ -117,13 +122,11 @@ class ParallelWebSearchInput(BaseModel): "Useful for capping context size when feeding results to an LLM." ), ) - mode: Optional[str] = Field( + mode: Optional[Literal["basic", "advanced"]] = Field( default=None, description=( "Search mode: 'basic' for low-latency searches, 'advanced' (default) " - "for higher quality with more retrieval and compression. Legacy " - "values 'fast', 'one-shot' (-> 'basic') and 'agentic' (-> 'advanced') " - "are accepted with a deprecation warning." + "for higher quality with more retrieval and compression." ), ) source_policy: Optional[Union[SourcePolicy, dict[str, Any]]] = Field( @@ -253,7 +256,6 @@ class ParallelWebSearchTool(BaseTool): "search_metadata": { # added by this tool when include_metadata=True "search_duration_seconds": 2.451, "search_timestamp": "2026-04-27T10:30:00", - "endpoint": "v1", # or "v1beta" if search_queries was omitted "actual_results_returned": 5, } } @@ -303,7 +305,6 @@ def _build_metadata( self, *, start_time: datetime, - endpoint: str, response: dict[str, Any], ) -> dict[str, Any]: """Build client-side timing/result metadata.""" @@ -314,7 +315,6 @@ def _build_metadata( 3, ), "search_timestamp": start_time.isoformat(), - "endpoint": endpoint, "actual_results_returned": len(response.get("results") or []), } @@ -322,7 +322,7 @@ def _build_call_kwargs( self, *, objective: Optional[str], - search_queries: Optional[list[str]], + search_queries: list[str], mode: Optional[str], max_chars_total: Optional[int], client_model: Optional[str], @@ -333,55 +333,16 @@ def _build_call_kwargs( max_results: int, location: Optional[str], timeout: Optional[int], - ) -> tuple[str, dict[str, Any]]: - """Resolve params + endpoint (`v1` GA vs `v1beta` legacy fallback). - - The v1beta path is deprecated and will be removed in 0.4.0; it exists - so 0.2.x callers passing only ``objective`` keep working through the - Parallel API beta sunset (~June 2026). - """ - if not objective and not search_queries: - msg = "Either 'objective' or 'search_queries' must be provided." - raise ValueError(msg) - - normalized_mode = _normalize_mode(mode) - + ) -> dict[str, Any]: + """Resolve params into the GA `client.search(...)` kwargs shape.""" if not search_queries: - warnings.warn( - "Calling Parallel Search without `search_queries` routes to " - "the deprecated /v1beta endpoint and will be removed in " - "langchain-parallel 0.4.0. Pass `search_queries=[...]` (1-5 " - "keyword strings, 3-6 words each) to use the GA /v1 endpoint. " - "See https://docs.parallel.ai/search/search-migration-guide.", - DeprecationWarning, - stacklevel=4, + msg = ( + "search_queries is required (1-5 keyword strings, 3-6 words " + "each). Pair with an optional `objective` for best results." ) - beta_kwargs: dict[str, Any] = { - "objective": objective, - "max_results": max_results, - } - if excerpts is not None: - beta_kwargs["excerpts"] = excerpts.model_dump(exclude_none=True) - if fetch_policy is not None: - beta_kwargs["fetch_policy"] = fetch_policy.model_dump(exclude_none=True) - sp = _coerce_source_policy(source_policy) - if sp: - beta_kwargs["source_policy"] = sp - if normalized_mode is not None: - # v1beta speaks the legacy mode dialect. - beta_kwargs["mode"] = ( - "agentic" if normalized_mode == "advanced" else "one-shot" - ) - if client_model is not None: - beta_kwargs["client_model"] = client_model - if session_id is not None: - beta_kwargs["session_id"] = session_id - if location is not None: - beta_kwargs["location"] = location - if timeout is not None: - beta_kwargs["timeout"] = timeout - return "v1beta", beta_kwargs + raise ValueError(msg) + normalized_mode = _validate_mode(mode) advanced_settings = _build_advanced_settings( excerpts=excerpts, fetch_policy=fetch_policy, @@ -404,14 +365,13 @@ def _build_call_kwargs( kwargs["advanced_settings"] = advanced_settings if timeout is not None: kwargs["timeout"] = timeout - return "v1", kwargs + return kwargs def _finalize_response( self, response_obj: Any, *, start_time: datetime, - endpoint: str, include_metadata: bool, ) -> dict[str, Any]: """Convert SDK response to dict and attach client-side metadata.""" @@ -419,7 +379,6 @@ def _finalize_response( if include_metadata: response["search_metadata"] = self._build_metadata( start_time=start_time, - endpoint=endpoint, response=response, ) return response @@ -443,8 +402,8 @@ def _completion_text(response: dict[str, Any], *, async_: bool) -> str: def _run( self, + search_queries: list[str], objective: Optional[str] = None, - search_queries: Optional[list[str]] = None, max_results: int = 10, excerpts: Optional[ExcerptSettings] = None, max_chars_total: Optional[int] = None, @@ -471,7 +430,7 @@ def _run( color="blue", ) - endpoint, kwargs = self._build_call_kwargs( + kwargs = self._build_call_kwargs( objective=objective, search_queries=search_queries, mode=mode, @@ -487,11 +446,7 @@ def _run( ) try: - response_obj: Any = ( - self._client.search(**kwargs) - if endpoint == "v1" - else self._client.beta.search(**kwargs) - ) + response_obj = self._client.search(**kwargs) except Exception as e: if run_manager: run_manager.on_text(f"Search failed: {e!s}\n", color="red") @@ -501,7 +456,6 @@ def _run( response = self._finalize_response( response_obj, start_time=start_time, - endpoint=endpoint, include_metadata=include_metadata, ) if run_manager: @@ -513,8 +467,8 @@ def _run( async def _arun( self, + search_queries: list[str], objective: Optional[str] = None, - search_queries: Optional[list[str]] = None, max_results: int = 10, excerpts: Optional[ExcerptSettings] = None, max_chars_total: Optional[int] = None, @@ -541,7 +495,7 @@ async def _arun( color="blue", ) - endpoint, kwargs = self._build_call_kwargs( + kwargs = self._build_call_kwargs( objective=objective, search_queries=search_queries, mode=mode, @@ -557,11 +511,7 @@ async def _arun( ) try: - response_obj = ( - await self._async_client.search(**kwargs) - if endpoint == "v1" - else await self._async_client.beta.search(**kwargs) - ) + response_obj = await self._async_client.search(**kwargs) except Exception as e: if run_manager: await run_manager.on_text( @@ -574,7 +524,6 @@ async def _arun( response = self._finalize_response( response_obj, start_time=start_time, - endpoint=endpoint, include_metadata=include_metadata, ) if run_manager: diff --git a/langchain_parallel/tasks.py b/langchain_parallel/tasks.py new file mode 100644 index 0000000..d70e757 --- /dev/null +++ b/langchain_parallel/tasks.py @@ -0,0 +1,970 @@ +"""LangChain integration for Parallel's Task API. + +Four primary surfaces: + +- :class:`ParallelTaskRunTool` — agent-callable tool that runs a single + Parallel Task synchronously (via ``client.task_run.execute``) and + returns the structured result with citations. +- :class:`ParallelDeepResearch` — high-level :class:`~langchain_core.runnables.Runnable` + wrapper for deep-research tasks. Defaults to the ``core`` processor and + always returns the full ``basis`` (citations + reasoning + confidence). +- :class:`ParallelTaskGroup` — batch executor backed by the Task Group + API. The general building block for fan-out/fan-in workloads. +- :class:`ParallelEnrichment` — the structured-batch counterpart to + :class:`ParallelDeepResearch`. A typed Runnable that enriches a list + of records against a pydantic input/output schema using the Task + Group API under the hood. + +Helpers: :func:`build_task_spec` constructs a Task Spec dict from +pydantic classes; :func:`verify_webhook` validates HMAC signatures on +incoming Parallel webhooks. +""" + +from __future__ import annotations + +import hashlib +import hmac +from typing import Any, Literal, Optional, Union + +from langchain_core.callbacks import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain_core.runnables import Runnable +from langchain_core.tools import BaseTool +from parallel import AsyncParallel, Parallel +from pydantic import BaseModel, Field, SecretStr, model_validator + +from ._client import get_api_key, get_async_parallel_client, get_parallel_client + +_TIER_PROCESSORS = ( + "lite", + "base", + "core", + "core2x", + "pro", + "ultra", + "ultra2x", + "ultra4x", + "ultra8x", +) +ALL_PROCESSORS = tuple( + name + suffix for name in _TIER_PROCESSORS for suffix in ("", "-fast") +) +ProcessorLiteral = Literal[ + "lite", + "base", + "core", + "core2x", + "pro", + "ultra", + "ultra2x", + "ultra4x", + "ultra8x", + "lite-fast", + "base-fast", + "core-fast", + "core2x-fast", + "pro-fast", + "ultra-fast", + "ultra2x-fast", + "ultra4x-fast", + "ultra8x-fast", +] + +_MCP_BETA_HEADER = "mcp-server-2025-07-17" + + +_SDK_SCHEMA_TYPES = {"json", "text", "auto"} + + +def _to_schema_param( + value: Union[type[BaseModel], dict[str, Any], str, None], +) -> Optional[dict[str, Any]]: + """Normalize a schema value into the SDK's ``{type, ...}`` envelope. + + Accepts: + + - a pydantic ``BaseModel`` subclass → wrapped as a ``json`` envelope + whose ``json_schema`` is the class's ``model_json_schema()`` + - a plain string → wrapped as ``{"type": "text", "description": value}`` + - a dict already in SDK envelope shape (``type`` ∈ ``{json, text, auto}``) + → returned as-is + - any other dict (raw JSON schema) → wrapped as a ``json`` envelope + - ``None`` → ``None`` + """ + if value is None: + return None + if isinstance(value, str): + return {"type": "text", "description": value} + if isinstance(value, type) and issubclass(value, BaseModel): + return {"type": "json", "json_schema": value.model_json_schema()} + if isinstance(value, dict): + # An "envelope" dict carries the SDK's discriminator type. + if value.get("type") in _SDK_SCHEMA_TYPES: + return dict(value) + # Otherwise treat the dict as a raw JSON schema and wrap it. + return {"type": "json", "json_schema": dict(value)} + msg = ( + f"Unsupported schema type {type(value).__name__}: pass a pydantic " + f"BaseModel subclass, a JSON-schema dict, a str, or a fully-formed " + f"`{{type, ...}}` dict." + ) + raise TypeError(msg) + + +def build_task_spec( + *, + output_schema: Union[type[BaseModel], dict[str, Any], str], + input_schema: Union[type[BaseModel], dict[str, Any], str, None] = None, +) -> dict[str, Any]: + """Build a ``TaskSpecParam`` dict from pydantic classes / schemas / text. + + Mirrors the helper used in https://docs.parallel.ai/task-api/examples/task-enrichment. + """ + spec: dict[str, Any] = {"output_schema": _to_schema_param(output_schema)} + if input_schema is not None: + spec["input_schema"] = _to_schema_param(input_schema) + return spec + + +class McpServer(BaseModel): + """One BYOMCP server description for a Task Run. + + Mirrors :class:`parallel.types.McpServerParam` so callers don't have to + import from the SDK directly. + """ + + name: str = Field(description="Unique name for the MCP server.") + url: str = Field(description="HTTPS URL of a Streamable HTTP MCP endpoint.") + headers: Optional[dict[str, str]] = Field( + default=None, + description="Headers (e.g. Authorization) sent on every MCP call.", + ) + allowed_tools: Optional[list[str]] = Field( + default=None, + description=( + "If set, restrict the run to tools whose names appear in this list." + ), + ) + + def to_sdk(self) -> dict[str, Any]: + """Render to the SDK kwargs shape.""" + out: dict[str, Any] = {"type": "url", "name": self.name, "url": self.url} + if self.headers is not None: + out["headers"] = self.headers + if self.allowed_tools is not None: + out["allowed_tools"] = list(self.allowed_tools) + return out + + +_DEFAULT_WEBHOOK_TOLERANCE_SECONDS = 5 * 60 + + +def verify_webhook( + payload: bytes, + *, + webhook_id: str, + webhook_timestamp: str, + webhook_signature: str, + secret: str, + tolerance_seconds: int = _DEFAULT_WEBHOOK_TOLERANCE_SECONDS, +) -> bool: + """Verify a Parallel webhook signature (Standard Webhooks scheme). + + Parallel signs webhook payloads using HMAC-SHA256 over + ``".."``, base64-encoded + with padding. The signature is delivered as the ``webhook-signature`` + header (possibly with multiple space-delimited ``v1,`` entries). + See https://docs.parallel.ai/resources/webhook-setup. + + The verification flow is: + + 1. Reject if the timestamp drifts more than ``tolerance_seconds`` from now + (replay protection). + 2. Compute the expected signature. + 3. Compare against every ``v1,`` entry in the header. + + Args: + payload: Raw request body bytes (do not decode-then-encode — must be + byte-identical to what Parallel signed). + webhook_id: Value of the ``webhook-id`` header. + webhook_timestamp: Value of the ``webhook-timestamp`` header (Unix + seconds as a string). + webhook_signature: Value of the ``webhook-signature`` header. + secret: Your webhook signing secret (from the Parallel dashboard). + tolerance_seconds: Reject signatures whose timestamp differs from + ``time.time()`` by more than this many seconds. Defaults to 5 + minutes (the Standard Webhooks recommendation). + + Returns: + True if the signature is valid and within tolerance; False otherwise. + """ + import base64 + import time + + try: + ts = int(webhook_timestamp) + except (TypeError, ValueError): + return False + if abs(time.time() - ts) > tolerance_seconds: + return False + + signed = f"{webhook_id}.{webhook_timestamp}.{payload.decode('utf-8')}" + digest = hmac.new( + secret.encode("utf-8"), + signed.encode("utf-8"), + hashlib.sha256, + ).digest() + expected = base64.b64encode(digest).decode("ascii") + + # The header may carry multiple signatures, space-delimited, each prefixed + # with a version (e.g. "v1, v1,"). Match if any entry matches. + for entry in webhook_signature.split(): + if "," not in entry: + continue + version, sig = entry.split(",", 1) + if version != "v1": + continue + if hmac.compare_digest(expected, sig): + return True + return False + + +def _extract_basis(result: dict[str, Any]) -> list[Any]: + """Pull the basis list out of a Task result. + + The basis can sit at the top level (`result["basis"]`) or nested under + `result["output"]["basis"]` depending on the surface and whether a + structured output schema was used. + """ + direct = result.get("basis") + if isinstance(direct, list): + return direct + output = result.get("output") + if isinstance(output, dict): + nested = output.get("basis") + if isinstance(nested, list): + return nested + return [] + + +def parse_basis(result: dict[str, Any]) -> dict[str, Any]: + """Extract citations, low-confidence fields, and `interaction_id`. + + Saves Task-API consumers the boilerplate of walking the result shape to + pull out per-field citations, find which fields the model wasn't sure + about, and grab the `interaction_id` for multi-turn chaining. + + Works on results from :class:`ParallelTaskRunTool`, + :class:`ParallelDeepResearch`, and individual entries from + :class:`ParallelTaskGroup` / :class:`ParallelEnrichment` batches. + + Args: + result: A Task result dict — the value returned by ``.invoke()`` / + ``.run()`` on a Task surface, or one entry from a batch surface. + + Returns: + Dict with three keys: + + - ``citations_by_field``: ``{field_name: [citation_dict, ...]}`` + keyed by the output-schema field. Empty when there's no basis. + - ``low_confidence_fields``: list of field names whose + ``confidence`` is ``"low"`` (case-insensitive). Empty when all + fields are medium/high or unset. + - ``interaction_id``: the run's `interaction_id` for chaining + (or ``None`` if the API didn't return one). + + Example: + ```python + from langchain_parallel import ParallelDeepResearch, parse_basis + + result = ParallelDeepResearch().invoke("Founder of SpaceX?") + parsed = parse_basis(result) + for field in parsed["low_confidence_fields"]: + print(f"low confidence: {field}") + for field, cites in parsed["citations_by_field"].items(): + print(f"{field}: {len(cites)} citation(s)") + ``` + """ + basis = _extract_basis(result) + + citations_by_field: dict[str, list[dict[str, Any]]] = {} + low_confidence: list[str] = [] + for entry in basis: + if not isinstance(entry, dict): + continue + field = entry.get("field") + if not isinstance(field, str) or not field: + continue + citations_by_field[field] = list(entry.get("citations") or []) + confidence = entry.get("confidence") + if isinstance(confidence, str) and confidence.lower() == "low": + low_confidence.append(field) + + interaction_id = result.get("interaction_id") + if not interaction_id: + run = result.get("run") + if isinstance(run, dict): + interaction_id = run.get("interaction_id") or None + + return { + "citations_by_field": citations_by_field, + "low_confidence_fields": low_confidence, + "interaction_id": interaction_id, + } + + +class _TaskClientMixin(BaseModel): + """Shared API-key + client plumbing for the Task surfaces.""" + + api_key: Optional[SecretStr] = Field(default=None) + base_url: str = Field(default="https://api.parallel.ai") + + _client: Optional[Parallel] = None + _async_client: Optional[AsyncParallel] = None + + @model_validator(mode="after") + def _initialize_clients(self) -> _TaskClientMixin: + api_key_str = get_api_key( + self.api_key.get_secret_value() if self.api_key else None, + ) + self._client = get_parallel_client(api_key_str, self.base_url) + self._async_client = get_async_parallel_client(api_key_str, self.base_url) + return self + + +class ParallelTaskRunInput(BaseModel): + """Input schema for :class:`ParallelTaskRunTool`.""" + + input: Union[str, dict[str, Any]] = Field( + description=( + "The task input. Either a freeform string (matches a default text " + "input schema) or a dict matching the tool's configured " + "`input_schema` if a TaskSpec was set." + ), + ) + metadata: Optional[dict[str, Union[str, float, bool]]] = Field( + default=None, + description="Free-form metadata persisted on the run.", + ) + previous_interaction_id: Optional[str] = Field( + default=None, + description=( + "Chain context from a prior run. Pass the `interaction_id` from " + "an earlier `ParallelTaskRunTool` / `ParallelDeepResearch` result " + "to continue a multi-turn research thread. See " + "https://docs.parallel.ai/task-api/guides/interactions." + ), + ) + + +class ParallelTaskRunTool(BaseTool): + """Run a single Parallel Task synchronously and return the structured result. + + This is the agent-friendly path: an LLM calls the tool with `input`, the + tool blocks until the Parallel Task Run completes, and returns a dict + containing the output, citations (`basis`), and run metadata. + + For long-running deep-research tasks, prefer :class:`ParallelDeepResearch`, + which is a :class:`~langchain_core.runnables.Runnable` and returns the same + result shape. + + Setup: + ```bash + export PARALLEL_API_KEY="your-api-key" + ``` + + Key init args: + processor: Literal[...] + Which Parallel processor to run. Defaults to ``"lite-fast"`` — + the ``-fast`` variants are 2-5x faster than their non-fast + counterparts at similar accuracy. Use ``"core"`` / ``"pro"`` + for deep research and ``"ultra"`` for the highest-quality + long-running tasks. Add ``-fast`` to any tier for + agent-loop-friendly latency. + output_schema: Optional[type[BaseModel] | dict | str] + If a pydantic class, the SDK parses the response into an instance + of the class. If a dict, it's used as the JSON schema. If a + string, it's used as the natural-language output description + (text output mode). + mcp_servers: Optional[list[McpServer]] + BYOMCP servers exposed to the run. + api_key: Optional[SecretStr] + + Invocation: + ```python + from langchain_parallel import ParallelTaskRunTool + + tool = ParallelTaskRunTool() # processor="lite-fast" + result = tool.invoke({"input": "Who founded SpaceX, in one sentence?"}) + # The structured output is at result["output"]["content"]; per-field + # citations are at result["output"]["basis"]; the run id is at + # result["run"]["run_id"]. + print(result["output"]["content"]) + print(result["output"]["basis"]) + ``` + """ + + name: str = "parallel_task_run" + description: str = ( + "Run a single Parallel Task synchronously. Inputs are either freeform " + "text or a dict matching the configured input_schema. Returns the " + "structured output, per-field citations (basis), and run id." + ) + args_schema: type[BaseModel] = ParallelTaskRunInput + + api_key: Optional[SecretStr] = Field(default=None) + base_url: str = Field(default="https://api.parallel.ai") + + processor: ProcessorLiteral = Field(default="lite-fast") + """Which processor to run. + + Defaults to ``"lite-fast"``. The ``-fast`` variants are 2-5x faster + than their non-fast counterparts at similar accuracy and are the + right pick for agent-loop / interactive workflows. Strip the suffix + (``"lite"``, ``"core"``, etc.) when latency is less of a concern + than maximum quality. + """ + + task_output_schema: Optional[Union[type[BaseModel], dict[str, Any], str]] = None + """Output spec: pydantic class, JSON-schema dict, or text description. + + (Named `task_output_schema` to avoid shadowing `BaseTool.output_schema`.) + """ + + task_spec: Optional[dict[str, Any]] = None + """Optional full TaskSpec dict ``{input_schema, output_schema}``. + + When set, takes precedence over ``task_output_schema`` and unlocks + structured ``input_schema`` validation. Required for the Task Group + API's structured-batch pattern. See + https://docs.parallel.ai/task-api/guides/specify-a-task. + """ + + mcp_servers: Optional[list[McpServer]] = None + """BYOMCP servers to make available to the run.""" + + _client: Optional[Parallel] = None + _async_client: Optional[AsyncParallel] = None + + @model_validator(mode="after") + def _initialize_clients(self) -> ParallelTaskRunTool: + api_key_str = get_api_key( + self.api_key.get_secret_value() if self.api_key else None, + ) + self._client = get_parallel_client(api_key_str, self.base_url) + self._async_client = get_async_parallel_client(api_key_str, self.base_url) + return self + + def _build_execute_kwargs( + self, + task_input: Union[str, dict[str, Any]], + metadata: Optional[dict[str, Union[str, float, bool]]], + timeout: Optional[float], + ) -> dict[str, Any]: + kwargs: dict[str, Any] = { + "input": task_input, + "processor": self.processor, + } + if self.task_output_schema is not None: + # The SDK accepts the pydantic class directly via the `output=` kwarg + # and parses for you; for dicts/strings it treats them as schemas. + kwargs["output"] = self.task_output_schema + if metadata is not None: + kwargs["metadata"] = metadata + if timeout is not None: + kwargs["timeout"] = timeout + return kwargs + + def _build_create_kwargs( + self, + task_input: Union[str, dict[str, Any]], + metadata: Optional[dict[str, Union[str, float, bool]]], + previous_interaction_id: Optional[str], + timeout: Optional[float], + ) -> dict[str, Any]: + """Build kwargs for ``client.beta.task_run.create`` (the create+poll path). + + Used when the user passes ``mcp_servers``, ``task_spec``, or any + other field that ``execute()`` doesn't accept. + """ + kwargs: dict[str, Any] = { + "input": task_input, + "processor": self.processor, + } + if self.task_spec is not None: + kwargs["task_spec"] = self.task_spec + if metadata is not None: + kwargs["metadata"] = metadata + if previous_interaction_id is not None: + kwargs["previous_interaction_id"] = previous_interaction_id + if self.mcp_servers: + kwargs["mcp_servers"] = [m.to_sdk() for m in self.mcp_servers] + kwargs["betas"] = [_MCP_BETA_HEADER] + if timeout is not None: + kwargs["timeout"] = timeout + return kwargs + + def _format_result(self, result: Any) -> dict[str, Any]: + """Convert the SDK's TaskRunResult / ParsedTaskRunResult to a dict. + + Surfaces the run id, output, basis (citations + reasoning + confidence), + any usage info the API returned, and the top-level ``interaction_id`` + for multi-turn context chaining. + """ + payload = result.model_dump() if hasattr(result, "model_dump") else dict(result) + # ParsedTaskRunResult exposes `.parsed` as the typed pydantic instance; + # if our caller asked for a pydantic schema, surface the parsed object. + if hasattr(result, "parsed") and result.parsed is not None: + parsed = result.parsed + payload["parsed"] = ( + parsed.model_dump() if hasattr(parsed, "model_dump") else parsed + ) + # Promote interaction_id from the nested `run` dict so callers can + # chain multi-turn research without diving into the structure. + run_block = payload.get("run") or {} + if isinstance(run_block, dict) and run_block.get("interaction_id"): + payload["interaction_id"] = run_block["interaction_id"] + return payload + + def _needs_create_path(self) -> bool: + """`execute()` doesn't accept mcp_servers or task_spec; route via create.""" + return bool(self.mcp_servers) or self.task_spec is not None + + def _run( + self, + input: Union[str, dict[str, Any]], # noqa: A002 -- SDK uses `input` too + metadata: Optional[dict[str, Union[str, float, bool]]] = None, + previous_interaction_id: Optional[str] = None, + *, + timeout: Optional[float] = None, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> dict[str, Any]: + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + if run_manager: + run_manager.on_text( + f"Starting Parallel task ({self.processor})...\n", + color="blue", + ) + + try: + if self._needs_create_path(): + run = self._client.beta.task_run.create( + **self._build_create_kwargs( + input, + metadata, + previous_interaction_id, + timeout, + ), + ) + result = self._client.task_run.result(run.run_id, timeout=timeout) + else: + kwargs = self._build_execute_kwargs(input, metadata, timeout) + if previous_interaction_id is not None: + # `execute()` doesn't take previous_interaction_id; route via + # create() so multi-turn chaining works on the simple path too. + create_kwargs = self._build_create_kwargs( + input, + metadata, + previous_interaction_id, + timeout, + ) + if self.task_output_schema is not None: + create_kwargs["task_spec"] = create_kwargs.get( + "task_spec", + ) or {"output_schema": self.task_output_schema} + run = self._client.beta.task_run.create(**create_kwargs) + result = self._client.task_run.result(run.run_id, timeout=timeout) + else: + result = self._client.task_run.execute(**kwargs) + except Exception as e: + msg = f"Error calling Parallel Task API: {e!s}" + raise ValueError(msg) from e + + if run_manager: + run_manager.on_text("Task completed\n", color="green") + return self._format_result(result) + + async def _arun( + self, + input: Union[str, dict[str, Any]], # noqa: A002 + metadata: Optional[dict[str, Union[str, float, bool]]] = None, + previous_interaction_id: Optional[str] = None, + *, + timeout: Optional[float] = None, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> dict[str, Any]: + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + if run_manager: + await run_manager.on_text( + f"Starting Parallel task ({self.processor})...\n", + color="blue", + ) + + try: + if self._needs_create_path() or previous_interaction_id is not None: + create_kwargs = self._build_create_kwargs( + input, + metadata, + previous_interaction_id, + timeout, + ) + if ( + not self._needs_create_path() + and self.task_output_schema is not None + ): + create_kwargs["task_spec"] = create_kwargs.get( + "task_spec", + ) or {"output_schema": self.task_output_schema} + run = await self._async_client.beta.task_run.create(**create_kwargs) + result = await self._async_client.task_run.result( + run.run_id, + timeout=timeout, + ) + else: + result = await self._async_client.task_run.execute( + **self._build_execute_kwargs(input, metadata, timeout), + ) + except Exception as e: + msg = f"Error calling Parallel Task API: {e!s}" + raise ValueError(msg) from e + + if run_manager: + await run_manager.on_text("Task completed\n", color="green") + return self._format_result(result) + + +class ParallelDeepResearch(Runnable[Union[str, dict[str, Any]], dict[str, Any]]): + """High-level Runnable for Parallel deep-research tasks. + + Defaults to the ``pro-fast`` processor -- the ``-fast`` variant of + ``pro`` ("Exploratory web research") at 2-5x the speed for similar + accuracy. Drop the ``-fast`` suffix (``processor="pro"``, 2-10 min) + or step up to ``processor="ultra"`` (5-25 min) for the most thorough + multi-source investigative reports. For shorter, enrichment-style + structured tasks, use :class:`ParallelEnrichment` or pass + ``processor="core-fast"`` here. + + Always returns the full ``basis`` (citations + reasoning + confidence) + on the result; lower friction than wiring up + :class:`ParallelTaskRunTool` manually when all you want is "do deep + research on this question." + + Example: + ```python + research = ParallelDeepResearch() # processor="pro-fast" + result = research.invoke("Latest developments in renewable energy") + print(result["output"]["content"]) + for fact in result["output"].get("basis", []): + print(fact["field"], "->", fact["citations"]) + + # For the most thorough report: + research = ParallelDeepResearch(processor="ultra") + ``` + """ + + def __init__( + self, + *, + processor: ProcessorLiteral = "pro-fast", + output_schema: Optional[Union[type[BaseModel], dict[str, Any], str]] = None, + api_key: Optional[Union[str, SecretStr]] = None, + base_url: str = "https://api.parallel.ai", + mcp_servers: Optional[list[McpServer]] = None, + ) -> None: + self.processor = processor + self._output_schema = output_schema + self.mcp_servers = mcp_servers + self._tool = ParallelTaskRunTool( + processor=processor, + task_output_schema=output_schema, + api_key=( + api_key + if isinstance(api_key, SecretStr) or api_key is None + else SecretStr(api_key) + ), + base_url=base_url, + mcp_servers=mcp_servers, + ) + + def invoke( # type: ignore[override] + self, + input: Union[str, dict[str, Any]], # noqa: A002 + config: Any = None, + *, + previous_interaction_id: Optional[str] = None, + **kwargs: Any, + ) -> dict[str, Any]: + tool_input: dict[str, Any] = {"input": input} + if previous_interaction_id is not None: + tool_input["previous_interaction_id"] = previous_interaction_id + return self._tool.invoke(tool_input, config=config, **kwargs) + + async def ainvoke( # type: ignore[override] + self, + input: Union[str, dict[str, Any]], # noqa: A002 + config: Any = None, + *, + previous_interaction_id: Optional[str] = None, + **kwargs: Any, + ) -> dict[str, Any]: + tool_input: dict[str, Any] = {"input": input} + if previous_interaction_id is not None: + tool_input["previous_interaction_id"] = previous_interaction_id + return await self._tool.ainvoke(tool_input, config=config, **kwargs) + + +class ParallelTaskGroup(_TaskClientMixin): + """Batch task runner backed by the Task Group API. + + Use when you have a list of inputs and want them all processed in + parallel. Returns a list of result dicts in the same order as the + input list. + + Example: + ```python + group = ParallelTaskGroup() # processor="lite-fast" + results = group.run( + inputs=[ + "Founder of Anthropic?", + "Founder of OpenAI?", + "Founder of Google DeepMind?", + ] + ) + for inp, out in zip(inputs, results): + print(inp, "->", out["output"]) + ``` + """ + + processor: ProcessorLiteral = Field(default="lite-fast") + """Default processor for runs added to this group. + + Defaults to ``"lite-fast"``. Strip the ``-fast`` suffix + (``"lite"``, ``"core"``, etc.) when latency is less of a concern + than maximum quality. + """ + + task_spec: Optional[dict[str, Any]] = Field(default=None) + """Optional ``default_task_spec`` applied to every run in the group. + + Use :func:`build_task_spec` to construct this from pydantic classes, + or pass a dict directly. When set, every input added to the group + must conform to ``task_spec["input_schema"]``. + """ + + metadata: Optional[dict[str, Union[str, float, bool]]] = Field(default=None) + + def _kick_off_runs( + self, + inputs: list[Union[str, dict[str, Any]]], + ) -> list[str]: + """Create a group, add runs, and return the list of run_ids.""" + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + group = self._client.beta.task_group.create(metadata=self.metadata) + run_inputs: list[Any] = [ + {"input": inp, "processor": self.processor} for inp in inputs + ] + add_kwargs: dict[str, Any] = { + "inputs": run_inputs, + "refresh_status": True, + } + if self.task_spec is not None: + add_kwargs["default_task_spec"] = self.task_spec + response = self._client.beta.task_group.add_runs( + group.task_group_id, + **add_kwargs, + ) + return list(response.run_ids or []) + + async def _akick_off_runs( + self, + inputs: list[Union[str, dict[str, Any]]], + ) -> list[str]: + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + group = await self._async_client.beta.task_group.create( + metadata=self.metadata, + ) + run_inputs: list[Any] = [ + {"input": inp, "processor": self.processor} for inp in inputs + ] + add_kwargs: dict[str, Any] = { + "inputs": run_inputs, + "refresh_status": True, + } + if self.task_spec is not None: + add_kwargs["default_task_spec"] = self.task_spec + response = await self._async_client.beta.task_group.add_runs( + group.task_group_id, + **add_kwargs, + ) + return list(response.run_ids or []) + + def run( + self, + inputs: list[Union[str, dict[str, Any]]], + *, + timeout: Optional[float] = None, + ) -> list[dict[str, Any]]: + """Submit a batch of inputs and block until all results are ready.""" + if self._client is None: + msg = "Parallel client not initialized." + raise RuntimeError(msg) + try: + run_ids = self._kick_off_runs(inputs) + results: list[dict[str, Any]] = [] + for run_id in run_ids: + result = self._client.task_run.result(run_id, timeout=timeout) + results.append( + result.model_dump() + if hasattr(result, "model_dump") + else dict(result), + ) + return results + except Exception as e: + msg = f"Error calling Parallel Task Group API: {e!s}" + raise ValueError(msg) from e + + async def arun( + self, + inputs: list[Union[str, dict[str, Any]]], + *, + timeout: Optional[float] = None, + ) -> list[dict[str, Any]]: + if self._async_client is None: + msg = "Async Parallel client not initialized." + raise RuntimeError(msg) + try: + run_ids = await self._akick_off_runs(inputs) + results: list[dict[str, Any]] = [] + for run_id in run_ids: + result = await self._async_client.task_run.result( + run_id, + timeout=timeout, + ) + results.append( + result.model_dump() + if hasattr(result, "model_dump") + else dict(result), + ) + return results + except Exception as e: + msg = f"Error calling Parallel Task Group API: {e!s}" + raise ValueError(msg) from e + + +EnrichmentInput = Union[str, dict[str, Any], BaseModel] + + +class ParallelEnrichment( + Runnable[list[EnrichmentInput], list[dict[str, Any]]], +): + """High-level Runnable for the structured-batch enrichment pattern. + + Wraps :class:`ParallelTaskGroup` with a ``default_task_spec`` so callers + can pass a list of records (pydantic instances or dicts) and get back a + list of enriched output dicts. Mirrors the canonical enrichment pattern + documented at + https://docs.parallel.ai/task-api/examples/task-enrichment. + + Defaults to the ``core`` processor (the docs' recommendation for + enrichment workflows). + + Example: + ```python + from pydantic import BaseModel, Field + from langchain_parallel import ParallelEnrichment + + class CompanyInput(BaseModel): + company: str = Field(description="Company name to enrich") + + class CompanyOutput(BaseModel): + headquarters: str + founding_year: int = Field(description="Year the company was founded") + + enricher = ParallelEnrichment( + input_schema=CompanyInput, + output_schema=CompanyOutput, + # Defaults to processor="core-fast"; pass "core" or "pro" for + # higher accuracy when latency is less of a concern. + ) + + results = enricher.invoke([ + {"company": "Anthropic"}, + {"company": "OpenAI"}, + {"company": "Google DeepMind"}, + ]) + + for inp, out in zip(["Anthropic", "OpenAI", "Google DeepMind"], results): + content = out["output"]["content"] + print(inp, "->", content) + ``` + """ + + def __init__( + self, + *, + output_schema: Union[type[BaseModel], dict[str, Any], str], + input_schema: Union[type[BaseModel], dict[str, Any], str, None] = None, + processor: ProcessorLiteral = "core-fast", + api_key: Optional[Union[str, SecretStr]] = None, + base_url: str = "https://api.parallel.ai", + metadata: Optional[dict[str, Union[str, float, bool]]] = None, + ) -> None: + # Note: avoid attribute names `input_schema` / `output_schema` because + # `Runnable` exposes them as read-only properties. + self.processor = processor + self._input_schema = input_schema + self._output_schema = output_schema + self._group = ParallelTaskGroup( + processor=processor, + api_key=( + api_key + if isinstance(api_key, SecretStr) or api_key is None + else SecretStr(api_key) + ), + base_url=base_url, + task_spec=build_task_spec( + output_schema=output_schema, + input_schema=input_schema, + ), + metadata=metadata, + ) + + @staticmethod + def _coerce_inputs( + inputs: list[EnrichmentInput], + ) -> list[Union[str, dict[str, Any]]]: + coerced: list[Union[str, dict[str, Any]]] = [] + for item in inputs: + if isinstance(item, BaseModel): + coerced.append(item.model_dump(exclude_none=True)) + else: + coerced.append(item) + return coerced + + def invoke( # type: ignore[override] + self, + input: list[EnrichmentInput], # noqa: A002 + config: Any = None, + *, + timeout: Optional[float] = None, + ) -> list[dict[str, Any]]: + return self._group.run(self._coerce_inputs(input), timeout=timeout) + + async def ainvoke( # type: ignore[override] + self, + input: list[EnrichmentInput], # noqa: A002 + config: Any = None, + *, + timeout: Optional[float] = None, + ) -> list[dict[str, Any]]: + return await self._group.arun(self._coerce_inputs(input), timeout=timeout) diff --git a/poetry.lock b/poetry.lock index 5020db4..87c69e2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -14,35 +14,46 @@ files = [ [[package]] name = "anyio" -version = "4.11.0" +version = "4.13.0" description = "High-level concurrency and networking framework on top of asyncio or Trio" optional = false -python-versions = ">=3.9" +python-versions = ">=3.10" groups = ["main", "test", "typing"] files = [ - 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"sha256:6cc4eefbb542a5d6ffd6d70ea9c502957c925e800f998c5630ecc809d6702bae"}, +] [[package]] name = "zstandard" @@ -2566,4 +2937,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt [metadata] lock-version = "2.1" python-versions = ">=3.10,<4.0" -content-hash = "1f2192a6249d32118c66b8ab9f78754e93a50cde422fef85e96d70e19cdc6053" +content-hash = "721b1960f7f298e83a02e5e28de732db214957280734a77713f905185694521d" diff --git a/pyproject.toml b/pyproject.toml index 5836e53..f293bc8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "langchain-parallel" -version = "0.3.0" +version = "0.4.0" description = "A LangChain integration for Parallel Web AI services, including Chat and Search." authors = ["Parallel Team "] readme = "README.md" @@ -42,7 +42,7 @@ disable_error_code = ["arg-type"] [tool.poetry.dependencies] python = ">=3.10,<4.0" -langchain-core = ">=1.1.0,<2.0.0" +langchain-core = "^1.3.0" openai = "^1.88.0" pydantic = "^2.11.7" httpx = ">=0.28.1,<1.0.0" @@ -157,6 +157,8 @@ optional = true optional = true [tool.poetry.group.dev.dependencies] +nbformat = "^5.10.4" +nbclient = "^0.10.4" [tool.poetry.group.test.dependencies] pytest = "^7.4.3" @@ -179,4 +181,4 @@ ruff = "<0.13,>=0.12.2" [tool.poetry.group.typing.dependencies] mypy = "^1.10" types-requests = "^2.31.0" -langchain-core = ">=1.1.0,<2.0.0" +langchain-core = "^1.3.0" diff --git a/scripts/run_notebooks.py b/scripts/run_notebooks.py index 1bb1a7f..86ce853 100644 --- a/scripts/run_notebooks.py +++ b/scripts/run_notebooks.py @@ -30,6 +30,10 @@ DOCS / "chat.ipynb", DOCS / "search_tool.ipynb", DOCS / "extract_tool.ipynb", + DOCS / "retriever.ipynb", + DOCS / "task_api.ipynb", + DOCS / "findall.ipynb", + DOCS / "monitor.ipynb", ] @@ -39,7 +43,7 @@ def _is_interactive_cell(source: str) -> bool: return stripped.startswith(("%pip", "!pip")) or "getpass.getpass" in source -def run_notebook(path: Path, *, timeout: int = 180) -> bool: +def run_notebook(path: Path, *, timeout: int = 600) -> bool: """Execute a notebook in-place and report whether it succeeded.""" nb = nbformat.read(path, as_version=4) @@ -75,8 +79,8 @@ def main() -> int: parser.add_argument( "--timeout", type=int, - default=180, - help="Per-cell timeout in seconds (default 180)", + default=600, + help="Per-cell timeout in seconds (default 600)", ) args = parser.parse_args() diff --git a/tests/unit_tests/__snapshots__/test_chat_models.ambr b/tests/unit_tests/__snapshots__/test_chat_models.ambr index 057e0bd..1833ead 100644 --- a/tests/unit_tests/__snapshots__/test_chat_models.ambr +++ b/tests/unit_tests/__snapshots__/test_chat_models.ambr @@ -18,6 +18,7 @@ 'max_retries': 2, 'model': 'speed', 'model_name': 'speed', + 'output_version': None, }), 'lc': 1, 'name': 'ChatParallelWeb', @@ -43,6 +44,7 @@ 'max_retries': 2, 'model': 'lite', 'model_name': 'lite', + 'output_version': None, }), 'lc': 1, 'name': 'ChatParallelWeb', diff --git a/tests/unit_tests/test_chat_models.py b/tests/unit_tests/test_chat_models.py index 34d08a1..dd28839 100644 --- a/tests/unit_tests/test_chat_models.py +++ b/tests/unit_tests/test_chat_models.py @@ -2,14 +2,19 @@ from __future__ import annotations +import os from types import SimpleNamespace +from unittest import mock from unittest.mock import Mock import pytest +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.load import dumpd, load from langchain_core.messages import AIMessage from langchain_core.runnables import RunnableSequence from langchain_tests.unit_tests import ChatModelUnitTests from pydantic import BaseModel, SecretStr +from syrupy.assertion import SnapshotAssertion from langchain_parallel.chat_models import ChatParallelWeb @@ -131,6 +136,45 @@ def supports_system_messages(self) -> bool: """ return True + @pytest.mark.xfail( + reason=( + "Standard test_serdes from langchain-tests 1.1.6 calls " + "`load(...)` without `allowed_objects=`, which langchain-core 1.2+ " + "rejects for partner integrations. This override opts our class " + "in via `allowed_objects=[ChatParallelWeb]` and is reported as " + "XPASS. Remove the override after langchain-tests updates for " + "the new allowlist API." + ), + strict=False, + ) + def test_serdes( # type: ignore[override] + self, + model: BaseChatModel, + snapshot: SnapshotAssertion, + ) -> None: + """Override of the standard `test_serdes` for langchain-core 1.2+.""" + if not self.chat_model_class.is_lc_serializable(): + pytest.skip("Model is not serializable.") + env_params, _model_params, _expected_attrs = self.init_from_env_params + with mock.patch.dict(os.environ, env_params): + ser = dumpd(model) + assert ser == snapshot(name="serialized") + namespaces = model.get_lc_namespace()[:1] + try: + # langchain-core 1.2+: explicit class allowlist for our + # partner integration. `valid_namespaces` is still + # required to permit the `langchain_parallel.*` import + # path (the default rejects anything outside `langchain`). + roundtripped = load( + ser, + allowed_objects=[self.chat_model_class], + valid_namespaces=namespaces, + ) + except TypeError: + # Older langchain-core didn't have `allowed_objects`. + roundtripped = load(ser, valid_namespaces=namespaces) + assert model.dict() == roundtripped.dict() + @property def init_from_env_params(self) -> tuple[dict, dict, dict]: """Parameters for testing initialization from environment variables.""" diff --git a/tests/unit_tests/test_extract_tool.py b/tests/unit_tests/test_extract_tool.py index 887b9f7..571305f 100644 --- a/tests/unit_tests/test_extract_tool.py +++ b/tests/unit_tests/test_extract_tool.py @@ -253,12 +253,12 @@ def test_full_content_false_omits_key( @patch("langchain_parallel.extract_tool.get_parallel_client") @patch("langchain_parallel.extract_tool.get_async_parallel_client") - def test_excerpts_bool_true_is_no_op( + def test_excerpts_default_is_no_op( self, mock_async_factory: Mock, mock_sync_factory: Mock, ) -> None: - """Backward compat: excerpts=True (the default) adds no excerpt_settings.""" + """Default `excerpts=None` adds no excerpt_settings.""" sync_client = Mock() sync_client.extract.return_value = _make_response( {"extract_id": "e", "results": [], "errors": []}, @@ -270,19 +270,18 @@ def test_excerpts_bool_true_is_no_op( "langchain_parallel.extract_tool.get_api_key", return_value="test-key" ): tool = ParallelExtractTool() - tool._run(urls=["https://example.com"], excerpts=True) + tool._run(urls=["https://example.com"]) advanced = sync_client.extract.call_args.kwargs.get("advanced_settings") - # No advanced settings at all when only the bool default is set. assert advanced is None @patch("langchain_parallel.extract_tool.get_parallel_client") @patch("langchain_parallel.extract_tool.get_async_parallel_client") - def test_excerpts_bool_false_warns( + def test_excerpts_bool_now_rejected( self, mock_async_factory: Mock, mock_sync_factory: Mock, ) -> None: - """excerpts=False emits a DeprecationWarning (v1 always returns excerpts).""" + """The legacy `excerpts: bool` form was removed in 0.4.0.""" sync_client = Mock() sync_client.extract.return_value = _make_response( {"extract_id": "e", "results": [], "errors": []}, @@ -294,8 +293,15 @@ def test_excerpts_bool_false_warns( "langchain_parallel.extract_tool.get_api_key", return_value="test-key" ): tool = ParallelExtractTool() - with pytest.warns(DeprecationWarning, match="always returns excerpts"): - tool._run(urls=["https://example.com"], excerpts=False) + # The boolean form was removed in 0.4.0; both True and False now + # fail pydantic validation on the typed `Optional[ExcerptSettings]` + # field. + from pydantic import ValidationError + + with pytest.raises(ValidationError): + tool.invoke({"urls": ["https://example.com"], "excerpts": False}) + with pytest.raises(ValidationError): + tool.invoke({"urls": ["https://example.com"], "excerpts": True}) @patch("langchain_parallel.extract_tool.get_parallel_client") @patch("langchain_parallel.extract_tool.get_async_parallel_client") diff --git a/tests/unit_tests/test_findall.py b/tests/unit_tests/test_findall.py new file mode 100644 index 0000000..8a7c288 --- /dev/null +++ b/tests/unit_tests/test_findall.py @@ -0,0 +1,98 @@ +"""Unit tests for ParallelFindAllTool.""" + +from __future__ import annotations + +from types import SimpleNamespace +from unittest.mock import Mock, patch + +import pytest + +from langchain_parallel import ( + FindAllExcludeEntry, + FindAllMatchCondition, + ParallelFindAllTool, +) + + +def _result(payload: dict) -> SimpleNamespace: + return SimpleNamespace(model_dump=lambda: dict(payload)) + + +@patch("langchain_parallel.findall.get_parallel_client") +@patch("langchain_parallel.findall.get_async_parallel_client") +def test_findall_tool_run( + mock_async: Mock, + mock_sync: Mock, +) -> None: + sync_client = Mock() + sync_client.beta.findall.create.return_value = SimpleNamespace(findall_id="fa-1") + # `retrieve()` is polled until the run is no longer active. + sync_client.beta.findall.retrieve.return_value = SimpleNamespace( + status=SimpleNamespace(status="completed"), + ) + sync_client.beta.findall.result.return_value = _result( + { + "findall_id": "fa-1", + "candidates": [ + {"name": "Acme AI", "url": "https://acme.example.com"}, + ], + "status": "completed", + }, + ) + mock_async.return_value = Mock() + mock_sync.return_value = sync_client + + with patch("langchain_parallel.findall.get_api_key", return_value="k"): + tool = ParallelFindAllTool(generator="base") + result = tool.invoke( + { + "objective": "AI startups", + "entity_type": "company", + "match_conditions": [ + FindAllMatchCondition(name="ai", description="Builds AI?"), + ], + "match_limit": 5, + "exclude_list": [ + FindAllExcludeEntry(name="OpenAI", url="https://openai.com"), + ], + }, + ) + + create_kwargs = sync_client.beta.findall.create.call_args.kwargs + assert create_kwargs["objective"] == "AI startups" + assert create_kwargs["entity_type"] == "company" + assert create_kwargs["match_conditions"] == [ + {"name": "ai", "description": "Builds AI?"}, + ] + assert create_kwargs["match_limit"] == 5 + assert create_kwargs["generator"] == "base" + assert create_kwargs["exclude_list"] == [ + {"name": "OpenAI", "url": "https://openai.com"}, + ] + assert result["candidates"][0]["name"] == "Acme AI" + + +@patch("langchain_parallel.findall.get_parallel_client") +@patch("langchain_parallel.findall.get_async_parallel_client") +def test_findall_wraps_errors( + mock_async: Mock, + mock_sync: Mock, +) -> None: + sync_client = Mock() + sync_client.beta.findall.create.side_effect = Exception("nope") + mock_async.return_value = Mock() + mock_sync.return_value = sync_client + + with patch("langchain_parallel.findall.get_api_key", return_value="k"): + tool = ParallelFindAllTool() + with pytest.raises(ValueError, match="Error calling Parallel FindAll API"): + tool.invoke( + { + "objective": "x", + "entity_type": "company", + "match_conditions": [ + FindAllMatchCondition(name="a", description="?"), + ], + "match_limit": 5, # ge=5 minimum + }, + ) diff --git a/tests/unit_tests/test_monitors.py b/tests/unit_tests/test_monitors.py new file mode 100644 index 0000000..7356280 --- /dev/null +++ b/tests/unit_tests/test_monitors.py @@ -0,0 +1,138 @@ +"""Unit tests for ParallelMonitor (httpx wrapper).""" + +from __future__ import annotations + +from unittest.mock import patch + +import httpx +import pytest + +from langchain_parallel import MonitorWebhook, ParallelMonitor + + +@patch("langchain_parallel.monitors.get_api_key", return_value="k") +def test_create_monitor(_mock_key: object) -> None: + """create() POSTs to /v1alpha/monitors with the right body and headers.""" + captured: dict[str, object] = {} + + monitor = ParallelMonitor() + with patch.object( + ParallelMonitor, + "_request", + lambda self, method, path, *, json=None: ( + captured.update( + method=method, + path=path, + body=json, + api_key=self._resolved_key, + ), + {"monitor_id": "mon-1", "status": "active"}, + )[1], + ): + result = monitor.create( + query="SEC filings", + frequency="6h", + webhook=MonitorWebhook( + url="https://x", + event_types=["monitor.event.detected"], + ), + metadata={"team": "research"}, + include_backfill=True, + ) + + assert captured["method"] == "POST" + assert captured["path"] == "/v1alpha/monitors" + assert captured["api_key"] == "k" + body = captured["body"] + assert isinstance(body, dict) + assert body["query"] == "SEC filings" + assert body["frequency"] == "6h" + assert body["webhook"] == { + "url": "https://x", + "event_types": ["monitor.event.detected"], + } + assert body["metadata"] == {"team": "research"} + assert body["include_backfill"] is True + assert result["monitor_id"] == "mon-1" + + +def test_invalid_frequency_raises() -> None: + """Frequency must be `` from 1h to 30d.""" + import pytest + + from langchain_parallel.monitors import _validate_frequency + + with pytest.raises(ValueError, match="Invalid frequency"): + _validate_frequency("15m") # sub-hour not supported + with pytest.raises(ValueError, match="outside the supported range"): + _validate_frequency("60d") # over 30d + # Valid values pass through. + assert _validate_frequency("1h") == "1h" + assert _validate_frequency("3d") == "3d" + assert _validate_frequency("2w") == "2w" + + +@patch("langchain_parallel.monitors.get_api_key", return_value="k") +def test_list_events_path_and_query(_mock_key: object) -> None: + """list_events hits /events with lookback_period (not /event_groups).""" + captured: dict[str, object] = {} + + monitor = ParallelMonitor() + with patch.object( + ParallelMonitor, + "_request", + lambda self, method, path, *, json=None: ( + captured.update(method=method, path=path), + {"events": []}, + )[1], + ): + monitor.list_events("mon-1", lookback_period="7d") + + assert captured["method"] == "GET" + assert captured["path"] == "/v1alpha/monitors/mon-1/events?lookback_period=7d" + + +@patch("langchain_parallel.monitors.get_api_key", return_value="k") +def test_simulate_event_with_type(_mock_key: object) -> None: + captured: dict[str, object] = {} + + monitor = ParallelMonitor() + with patch.object( + ParallelMonitor, + "_request", + lambda self, method, path, *, json=None: ( + captured.update(method=method, path=path), + {}, + )[1], + ): + monitor.simulate_event("mon-1", event_type="monitor.execution.completed") + + assert captured["path"] == ( + "/v1alpha/monitors/mon-1/simulate_event?event_type=monitor.execution.completed" + ) + + +@patch("langchain_parallel.monitors.get_api_key", return_value="k") +def test_http_error_wrapped(_mock_key: object) -> None: + """A 4xx from the API surfaces as ValueError with the response code.""" + monitor = ParallelMonitor() + + def fake_request(*_args: object, **_kwargs: object) -> dict[str, object]: + # Simulate the body of `_request`'s raise-for-status path. + request = httpx.Request("GET", "https://api.parallel.ai/v1alpha/monitors/x") + response = httpx.Response(404, json={"error": "not found"}, request=request) + try: + response.raise_for_status() + except httpx.HTTPStatusError as e: + msg = ( + f"Parallel Monitor API error: {e.response.status_code} " + f"{e.response.text[:300]}" + ) + raise ValueError(msg) from e + return {} + + with ( + patch.object(ParallelMonitor, "_request", fake_request), + pytest.raises(ValueError, match="Parallel Monitor API error: 404"), + ): + monitor.retrieve("missing") diff --git a/tests/unit_tests/test_retrievers.py b/tests/unit_tests/test_retrievers.py new file mode 100644 index 0000000..6d2d480 --- /dev/null +++ b/tests/unit_tests/test_retrievers.py @@ -0,0 +1,129 @@ +"""Unit tests for ParallelSearchRetriever.""" + +from __future__ import annotations + +from types import SimpleNamespace +from unittest.mock import AsyncMock, Mock, patch + +import pytest + +from langchain_parallel import ParallelSearchRetriever, SourcePolicy + + +def _resp(payload: dict) -> SimpleNamespace: + return SimpleNamespace(model_dump=lambda: dict(payload)) + + +@patch("langchain_parallel.retrievers.get_parallel_client") +@patch("langchain_parallel.retrievers.get_async_parallel_client") +def test_invoke_returns_documents( + mock_async_factory: Mock, + mock_sync_factory: Mock, +) -> None: + sync_client = Mock() + sync_client.search.return_value = _resp( + { + "search_id": "s-1", + "results": [ + { + "url": "https://example.com/a", + "title": "A", + "publish_date": "2025-01-01", + "excerpts": ["snippet 1", "snippet 2"], + }, + ], + }, + ) + mock_sync_factory.return_value = sync_client + mock_async_factory.return_value = Mock() + + with patch("langchain_parallel.retrievers.get_api_key", return_value="k"): + retriever = ParallelSearchRetriever( + max_results=5, + mode="advanced", + source_policy=SourcePolicy(include_domains=["example.com"]), + ) + docs = retriever.invoke("test query") + + assert len(docs) == 1 + doc = docs[0] + assert doc.page_content == "snippet 1\n\nsnippet 2" + assert doc.metadata["url"] == "https://example.com/a" + assert doc.metadata["title"] == "A" + assert doc.metadata["publish_date"] == "2025-01-01" + assert doc.metadata["search_id"] == "s-1" + assert doc.metadata["query"] == "test query" + assert doc.metadata["excerpts"] == ["snippet 1", "snippet 2"] + + # Verify advanced_settings envelope built correctly. + kwargs = sync_client.search.call_args.kwargs + assert kwargs["search_queries"] == ["test query"] + assert kwargs["mode"] == "advanced" + assert kwargs["advanced_settings"]["max_results"] == 5 + assert kwargs["advanced_settings"]["source_policy"] == { + "include_domains": ["example.com"], + } + + +@patch("langchain_parallel.retrievers.get_parallel_client") +@patch("langchain_parallel.retrievers.get_async_parallel_client") +@pytest.mark.asyncio +async def test_async_invoke( + mock_async_factory: Mock, + mock_sync_factory: Mock, +) -> None: + async_client = Mock() + async_client.search = AsyncMock( + return_value=_resp( + { + "search_id": "s-2", + "results": [ + {"url": "https://x.com", "title": "X", "excerpts": ["a"]}, + ], + } + ), + ) + mock_async_factory.return_value = async_client + mock_sync_factory.return_value = Mock() + + with patch("langchain_parallel.retrievers.get_api_key", return_value="k"): + retriever = ParallelSearchRetriever() + docs = await retriever.ainvoke("q") + assert len(docs) == 1 + assert docs[0].metadata["url"] == "https://x.com" + + +@patch("langchain_parallel.retrievers.get_parallel_client") +@patch("langchain_parallel.retrievers.get_async_parallel_client") +def test_persistent_objective_forwarded( + mock_async_factory: Mock, + mock_sync_factory: Mock, +) -> None: + sync_client = Mock() + sync_client.search.return_value = _resp({"search_id": "s", "results": []}) + mock_sync_factory.return_value = sync_client + mock_async_factory.return_value = Mock() + + with patch("langchain_parallel.retrievers.get_api_key", return_value="k"): + retriever = ParallelSearchRetriever(objective="academic AI research only") + retriever.invoke("transformers") + kwargs = sync_client.search.call_args.kwargs + assert kwargs["objective"] == "academic AI research only" + assert kwargs["search_queries"] == ["transformers"] + + +@patch("langchain_parallel.retrievers.get_parallel_client") +@patch("langchain_parallel.retrievers.get_async_parallel_client") +def test_api_error_wrapped( + mock_async_factory: Mock, + mock_sync_factory: Mock, +) -> None: + sync_client = Mock() + sync_client.search.side_effect = Exception("boom") + mock_sync_factory.return_value = sync_client + mock_async_factory.return_value = Mock() + + with patch("langchain_parallel.retrievers.get_api_key", return_value="k"): + retriever = ParallelSearchRetriever() + with pytest.raises(ValueError, match="Error calling Parallel Search API"): + retriever.invoke("q") diff --git a/tests/unit_tests/test_search_tool.py b/tests/unit_tests/test_search_tool.py index 86441be..dd44991 100644 --- a/tests/unit_tests/test_search_tool.py +++ b/tests/unit_tests/test_search_tool.py @@ -8,7 +8,7 @@ import pytest from langchain_parallel._types import ExcerptSettings, FetchPolicy, SourcePolicy -from langchain_parallel.search_tool import ParallelWebSearchTool, _normalize_mode +from langchain_parallel.search_tool import ParallelWebSearchTool, _validate_mode def _make_response(payload: dict) -> SimpleNamespace: @@ -69,64 +69,25 @@ def test_run_uses_v1_endpoint_when_search_queries_provided( assert kwargs["advanced_settings"] == {"max_results": 3} assert isinstance(result, dict) assert result["search_id"] == "search-1" - assert result["search_metadata"]["endpoint"] == "v1" - - @patch("langchain_parallel.search_tool.get_parallel_client") - @patch("langchain_parallel.search_tool.get_async_parallel_client") - def test_run_falls_back_to_beta_when_objective_only( - self, - mock_async_factory: Mock, - mock_sync_factory: Mock, - ) -> None: - """Objective-only routes to /v1beta with a DeprecationWarning. - - This is a deprecated path slated for removal in 0.4.0; it exists so - 0.2.x callers passing only ``objective`` keep working. - """ - sync_client = Mock() - sync_client.beta.search.return_value = _make_response( - {"search_id": "beta-1", "results": []}, - ) - mock_sync_factory.return_value = sync_client - mock_async_factory.return_value = Mock() + assert "search_duration_seconds" in result["search_metadata"] + def test_run_requires_search_queries(self) -> None: + """The v1beta-fallback path was removed in 0.4.0; missing queries raises now.""" with patch( "langchain_parallel.search_tool.get_api_key", return_value="test-key" ): tool = ParallelWebSearchTool() - with pytest.warns(DeprecationWarning, match="0.4.0"): - result = tool._run( - objective="What is AI?", - mode="advanced", - source_policy={"include_domains": ["wikipedia.org"]}, - ) - sync_client.beta.search.assert_called_once() - sync_client.search.assert_not_called() - beta_kwargs = sync_client.beta.search.call_args.kwargs - # advanced -> agentic on the legacy endpoint - assert beta_kwargs["mode"] == "agentic" - assert beta_kwargs["source_policy"] == { - "include_domains": ["wikipedia.org"], - } - assert result["search_metadata"]["endpoint"] == "v1beta" - - def test_run_raises_when_neither_objective_nor_queries(self) -> None: - """At least one of objective or search_queries must be provided.""" - with patch( - "langchain_parallel.search_tool.get_api_key", return_value="test-key" - ): - tool = ParallelWebSearchTool() - with pytest.raises(ValueError, match="objective.*search_queries.*provided"): - tool._run() + with pytest.raises(ValueError, match="search_queries is required"): + tool._run(search_queries=[], objective="What is AI?") @patch("langchain_parallel.search_tool.get_parallel_client") @patch("langchain_parallel.search_tool.get_async_parallel_client") - def test_run_translates_legacy_mode( + def test_run_rejects_legacy_mode( self, mock_async_factory: Mock, mock_sync_factory: Mock, ) -> None: - """Legacy mode strings are mapped with a DeprecationWarning.""" + """Legacy mode strings now raise (the deprecation shim was removed in 0.4.0).""" sync_client = Mock() sync_client.search.return_value = _make_response( {"search_id": "s", "results": []}, @@ -138,9 +99,8 @@ def test_run_translates_legacy_mode( "langchain_parallel.search_tool.get_api_key", return_value="test-key" ): tool = ParallelWebSearchTool() - with pytest.warns(DeprecationWarning, match="legacy beta value"): + with pytest.raises(ValueError, match="Invalid mode"): tool._run(search_queries=["q"], mode="agentic") - assert sync_client.search.call_args.kwargs["mode"] == "advanced" @patch("langchain_parallel.search_tool.get_parallel_client") @patch("langchain_parallel.search_tool.get_async_parallel_client") @@ -181,7 +141,7 @@ def test_advanced_settings_envelope_pydantic( }, "source_policy": { "include_domains": ["nature.com"], - "after_date": "2025-01-01", + "after_date": __import__("datetime").date(2025, 1, 1), }, "max_results": 15, "location": "us", @@ -331,20 +291,19 @@ def test_parallel_search_tool_is_alias_of_parallel_web_search_tool() -> None: assert ParallelSearchTool is ParallelWebSearchTool -class TestNormalizeMode: +class TestValidateMode: def test_passthrough(self) -> None: - assert _normalize_mode("basic") == "basic" - assert _normalize_mode("advanced") == "advanced" - assert _normalize_mode(None) is None - - def test_legacy(self) -> None: - with pytest.warns(DeprecationWarning): - assert _normalize_mode("one-shot") == "basic" - with pytest.warns(DeprecationWarning): - assert _normalize_mode("agentic") == "advanced" - with pytest.warns(DeprecationWarning): - assert _normalize_mode("fast") == "basic" + assert _validate_mode("basic") == "basic" + assert _validate_mode("advanced") == "advanced" + assert _validate_mode(None) is None + + def test_legacy_now_raises(self) -> None: + # The DeprecationWarning shim was removed in 0.4.0; legacy mode strings + # now raise instead of being mapped. + for legacy in ("one-shot", "agentic", "fast"): + with pytest.raises(ValueError, match="Invalid mode"): + _validate_mode(legacy) def test_invalid(self) -> None: with pytest.raises(ValueError, match="Invalid mode"): - _normalize_mode("nonsense") + _validate_mode("nonsense") diff --git a/tests/unit_tests/test_tasks.py b/tests/unit_tests/test_tasks.py new file mode 100644 index 0000000..964c00e --- /dev/null +++ b/tests/unit_tests/test_tasks.py @@ -0,0 +1,446 @@ +"""Unit tests for the Task API surfaces.""" + +from __future__ import annotations + +from types import SimpleNamespace +from unittest.mock import Mock, patch + +import pytest +from pydantic import BaseModel, Field + +from langchain_parallel import ( + McpServer, + ParallelDeepResearch, + ParallelEnrichment, + ParallelTaskGroup, + ParallelTaskRunTool, + build_task_spec, + parse_basis, + verify_webhook, +) + + +def _result(payload: dict) -> SimpleNamespace: + return SimpleNamespace(model_dump=lambda: dict(payload)) + + +# --- verify_webhook --- + + +def _sign(payload: bytes, webhook_id: str, ts: str, secret: str) -> str: + import base64 + import hashlib + import hmac + + signed = f"{webhook_id}.{ts}.{payload.decode()}" + digest = hmac.new(secret.encode(), signed.encode(), hashlib.sha256).digest() + return base64.b64encode(digest).decode() + + +def test_verify_webhook_success() -> None: + import time + + payload = b'{"run_id":"abc"}' + secret = "sek" # noqa: S105 - test fixture + wid = "msg_1" + ts = str(int(time.time())) + sig = _sign(payload, wid, ts, secret) + assert ( + verify_webhook( + payload, + webhook_id=wid, + webhook_timestamp=ts, + webhook_signature=f"v1,{sig}", + secret=secret, + ) + is True + ) + + +def test_verify_webhook_multiple_signatures() -> None: + """Header carrying multiple `v1,` entries: match if any matches.""" + import time + + payload = b'{"x":1}' + secret = "sek" # noqa: S105 + wid, ts = "msg", str(int(time.time())) + good = _sign(payload, wid, ts, secret) + header = f"v1,deadbeef v1,{good}" + assert ( + verify_webhook( + payload, + webhook_id=wid, + webhook_timestamp=ts, + webhook_signature=header, + secret=secret, + ) + is True + ) + + +def test_verify_webhook_replay_rejected() -> None: + """Timestamps outside tolerance are rejected.""" + payload = b"{}" + secret = "sek" # noqa: S105 + old_ts = "1000000000" # well in the past + sig = _sign(payload, "m", old_ts, secret) + assert ( + verify_webhook( + payload, + webhook_id="m", + webhook_timestamp=old_ts, + webhook_signature=f"v1,{sig}", + secret=secret, + ) + is False + ) + + +def test_verify_webhook_failure() -> None: + import time + + assert ( + verify_webhook( + b"x", + webhook_id="m", + webhook_timestamp=str(int(time.time())), + webhook_signature="v1,deadbeef", + secret="sek", # noqa: S106 + ) + is False + ) + + +# --- parse_basis --- + + +def test_parse_basis_top_level() -> None: + """Basis at the top level (TaskRunTool typed-output shape).""" + result = { + "run": {"run_id": "tr-1", "interaction_id": "iact_1"}, + "output": {"founder": "Elon Musk", "year": 2002}, + "basis": [ + { + "field": "founder", + "confidence": "high", + "citations": [{"url": "https://example.com/a"}], + }, + { + "field": "year", + "confidence": "low", + "citations": [{"url": "https://example.com/b"}], + }, + ], + } + parsed = parse_basis(result) + assert parsed["citations_by_field"]["founder"][0]["url"] == "https://example.com/a" + assert parsed["low_confidence_fields"] == ["year"] + assert parsed["interaction_id"] == "iact_1" + + +def test_parse_basis_nested_under_output() -> None: + """Basis nested under `output` (DeepResearch / structured-output shape).""" + result = { + "interaction_id": "iact_top", + "output": { + "content": {"capital": "Paris"}, + "basis": [ + {"field": "capital", "confidence": "MEDIUM", "citations": []}, + ], + }, + } + parsed = parse_basis(result) + assert parsed["citations_by_field"] == {"capital": []} + assert parsed["low_confidence_fields"] == [] + assert parsed["interaction_id"] == "iact_top" + + +def test_parse_basis_handles_missing_basis() -> None: + """No basis present → empty results, no errors.""" + parsed = parse_basis({"output": "free text", "run": {"run_id": "tr-x"}}) + assert parsed == { + "citations_by_field": {}, + "low_confidence_fields": [], + "interaction_id": None, + } + + +def test_parse_basis_skips_malformed_entries() -> None: + """Non-dict entries and entries without a `field` are ignored.""" + result = { + "basis": [ + "not a dict", + {"confidence": "low"}, # missing field + {"field": "", "confidence": "low"}, # blank field + {"field": "ok", "confidence": "low", "citations": [{"url": "u"}]}, + ], + } + parsed = parse_basis(result) + assert parsed["citations_by_field"] == {"ok": [{"url": "u"}]} + assert parsed["low_confidence_fields"] == ["ok"] + + +# --- Default processors --- + + +def test_default_processors_are_fast_variants() -> None: + """All four Task surfaces default to a `-fast` processor variant.""" + + class _Out(BaseModel): + x: int + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + assert ParallelTaskRunTool().processor == "lite-fast" + assert ParallelDeepResearch().processor == "pro-fast" + assert ParallelTaskGroup().processor == "lite-fast" + assert ParallelEnrichment(output_schema=_Out).processor == "core-fast" + + +# --- ParallelTaskRunTool --- + + +@patch("langchain_parallel.tasks.get_parallel_client") +@patch("langchain_parallel.tasks.get_async_parallel_client") +def test_task_run_tool_executes( + mock_async: Mock, + mock_sync: Mock, +) -> None: + sync_client = Mock() + sync_client.task_run.execute.return_value = _result( + { + "run": {"run_id": "tr-1", "status": "completed"}, + "output": {"text": "hello"}, + "basis": [{"field": "answer", "citations": []}], + }, + ) + mock_sync.return_value = sync_client + mock_async.return_value = Mock() + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + tool = ParallelTaskRunTool(processor="lite") + result = tool.invoke({"input": "Hi"}) + sync_client.task_run.execute.assert_called_once() + kwargs = sync_client.task_run.execute.call_args.kwargs + assert kwargs["input"] == "Hi" + assert kwargs["processor"] == "lite" + assert result["output"] == {"text": "hello"} + assert result["basis"][0]["field"] == "answer" + + +@patch("langchain_parallel.tasks.get_parallel_client") +@patch("langchain_parallel.tasks.get_async_parallel_client") +def test_task_run_tool_with_mcp_servers_uses_create_path( + mock_async: Mock, + mock_sync: Mock, +) -> None: + """BYOMCP forces the create+result path so mcp_servers can be passed.""" + sync_client = Mock() + sync_client.beta.task_run.create.return_value = SimpleNamespace(run_id="tr-2") + sync_client.task_run.result.return_value = _result( + {"run": {"run_id": "tr-2"}, "output": "ok"}, + ) + mock_sync.return_value = sync_client + mock_async.return_value = Mock() + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + tool = ParallelTaskRunTool( + processor="base", + mcp_servers=[ + McpServer( + name="my_mcp", + url="https://example.com/mcp", + headers={"Authorization": "Bearer abc"}, + ), + ], + ) + tool.invoke({"input": "Hi"}) + + # Should NOT have called task_run.execute (no mcp_servers there). + sync_client.task_run.execute.assert_not_called() + sync_client.beta.task_run.create.assert_called_once() + kwargs = sync_client.beta.task_run.create.call_args.kwargs + assert kwargs["mcp_servers"] == [ + { + "type": "url", + "name": "my_mcp", + "url": "https://example.com/mcp", + "headers": {"Authorization": "Bearer abc"}, + }, + ] + assert "mcp-server-2025-07-17" in kwargs["betas"] + + +@patch("langchain_parallel.tasks.get_parallel_client") +@patch("langchain_parallel.tasks.get_async_parallel_client") +def test_task_run_tool_wraps_errors( + mock_async: Mock, + mock_sync: Mock, +) -> None: + sync_client = Mock() + sync_client.task_run.execute.side_effect = Exception("API down") + mock_sync.return_value = sync_client + mock_async.return_value = Mock() + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + tool = ParallelTaskRunTool(processor="lite") + with pytest.raises(ValueError, match="Error calling Parallel Task API"): + tool.invoke({"input": "Hi"}) + + +# --- ParallelDeepResearch --- + + +@patch("langchain_parallel.tasks.get_parallel_client") +@patch("langchain_parallel.tasks.get_async_parallel_client") +def test_deep_research_runnable( + mock_async: Mock, + mock_sync: Mock, +) -> None: + sync_client = Mock() + sync_client.task_run.execute.return_value = _result({"output": "report"}) + mock_sync.return_value = sync_client + mock_async.return_value = Mock() + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + research = ParallelDeepResearch(processor="core") + result = research.invoke("research question") + kwargs = sync_client.task_run.execute.call_args.kwargs + assert kwargs["processor"] == "core" + assert kwargs["input"] == "research question" + assert result["output"] == "report" + + +# --- ParallelTaskGroup --- + + +@patch("langchain_parallel.tasks.get_parallel_client") +@patch("langchain_parallel.tasks.get_async_parallel_client") +def test_task_group_run( + mock_async: Mock, + mock_sync: Mock, +) -> None: + sync_client = Mock() + sync_client.beta.task_group.create.return_value = SimpleNamespace( + task_group_id="tg-1", + ) + sync_client.beta.task_group.add_runs.return_value = SimpleNamespace( + run_ids=["r1", "r2", "r3"], + ) + sync_client.task_run.result.side_effect = [ + _result({"output": f"out-{i}"}) for i in range(3) + ] + mock_sync.return_value = sync_client + mock_async.return_value = Mock() + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + group = ParallelTaskGroup(processor="lite") + results = group.run(["a", "b", "c"]) + assert [r["output"] for r in results] == ["out-0", "out-1", "out-2"] + # add_runs got 3 inputs each tagged with the configured processor. + add_kwargs = sync_client.beta.task_group.add_runs.call_args.kwargs + assert len(add_kwargs["inputs"]) == 3 + assert all(i["processor"] == "lite" for i in add_kwargs["inputs"]) + + +# --- build_task_spec --- + + +class _CompanyIn(BaseModel): + company: str = Field(description="Company name") + + +class _CompanyOut(BaseModel): + headquarters: str + founding_year: int + + +def test_build_task_spec_pydantic() -> None: + spec = build_task_spec(input_schema=_CompanyIn, output_schema=_CompanyOut) + assert spec["output_schema"]["type"] == "json" + assert "headquarters" in spec["output_schema"]["json_schema"]["properties"] + assert spec["input_schema"]["type"] == "json" + assert "company" in spec["input_schema"]["json_schema"]["properties"] + + +def test_build_task_spec_mixed_shapes() -> None: + """Strings become text schemas; raw dicts are wrapped as json schemas.""" + spec = build_task_spec( + input_schema="A natural-language description of the input", + output_schema={"type": "object", "properties": {"x": {"type": "integer"}}}, + ) + assert spec["input_schema"]["type"] == "text" + assert spec["input_schema"]["description"].startswith("A natural-language") + assert spec["output_schema"]["type"] == "json" + assert spec["output_schema"]["json_schema"]["properties"]["x"]["type"] == "integer" + + +# --- ParallelEnrichment --- + + +@patch("langchain_parallel.tasks.get_parallel_client") +@patch("langchain_parallel.tasks.get_async_parallel_client") +def test_enrichment_invoke( + mock_async: Mock, + mock_sync: Mock, +) -> None: + """Coerces pydantic inputs, passes default_task_spec to add_runs.""" + sync_client = Mock() + sync_client.beta.task_group.create.return_value = SimpleNamespace( + task_group_id="tg-en-1", + ) + sync_client.beta.task_group.add_runs.return_value = SimpleNamespace( + run_ids=["r1", "r2"], + ) + sync_client.task_run.result.side_effect = [ + _result( + { + "output": { + "content": { + "headquarters": "San Francisco, CA", + "founding_year": 2021, + }, + }, + }, + ), + _result( + { + "output": { + "content": { + "headquarters": "San Francisco, CA", + "founding_year": 2015, + }, + }, + }, + ), + ] + mock_sync.return_value = sync_client + mock_async.return_value = Mock() + + with patch("langchain_parallel.tasks.get_api_key", return_value="k"): + enricher = ParallelEnrichment( + input_schema=_CompanyIn, + output_schema=_CompanyOut, + processor="core", + ) + results = enricher.invoke( + [ + _CompanyIn(company="Anthropic"), + {"company": "OpenAI"}, + ], + ) + + # `default_task_spec` was passed to add_runs. + add_kwargs = sync_client.beta.task_group.add_runs.call_args.kwargs + assert "default_task_spec" in add_kwargs + spec = add_kwargs["default_task_spec"] + assert spec["input_schema"]["type"] == "json" + assert spec["output_schema"]["type"] == "json" + # Pydantic input got coerced to a dict. + inputs = add_kwargs["inputs"] + assert inputs[0]["input"] == {"company": "Anthropic"} + assert inputs[1]["input"] == {"company": "OpenAI"} + # Each run uses the configured processor. + assert all(i["processor"] == "core" for i in inputs) + # Results come back in the same order. + assert results[0]["output"]["content"]["founding_year"] == 2021 + assert results[1]["output"]["content"]["founding_year"] == 2015