feat: Add Anthropic Claude and LM Studio provider support (#36)
* feat: Add Anthropic Claude and LM Studio provider support - Add Anthropic as LLM provider with full async support - Add LM Studio provider for local model inference - Fix JSON response format compatibility for local models - Update .env.example with configuration examples - Update docstrings with all supported providers Tested with: - Claude Sonnet 4 (claude-sonnet-4-20250514) - Claude Haiku 4.5 (claude-haiku-4-5-20251001) - Qwen 30B via LM Studio * feat: Add dynamic timeout for local LLM providers Add configurable timeout support for LLM API calls: - Environment variable override via HINDSIGHT_API_LLM_TIMEOUT - Dynamic heuristic for lmstudio/ollama: 20 mins for large models (30b, 33b, 34b, 65b, 70b, 72b, 8x7b, 8x22b), 5 mins for others - Pass timeout to Anthropic, OpenAI, and local model clients 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: Address PR review feedback - Remove CLAUDE.md from .gitignore (should stay in repository) - Pass max_completion_tokens to _call_anthropic instead of hardcoding 4096 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: Remove deleted AI assistant files from .gitignore 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: Add CLAUDE.md for Claude Code integration Provides project context and development commands for AI-assisted coding. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: Include local dev files and sync changes - Add docker-compose.yml for local development - Add test_internal.py for local testing - Sync uv.lock and llm_wrapper.py changes 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: Address PR review feedback for LLM provider support - Move LLM config to config.py with HINDSIGHT_API_ prefix - Add HINDSIGHT_API_LLM_MAX_CONCURRENT (default: 32) - Add HINDSIGHT_API_LLM_TIMEOUT (default: 120s) - Remove fragile model-size timeout heuristic - Apply markdown JSON extraction to all providers, not just local - Fix Anthropic markdown extraction bug (missing split) - Change LLM request/response logs from info to debug level 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: Remove local dev docker-compose.yml 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: Add local dev docker-compose.yml 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: Update LM Studio port to 2222 in docker-compose 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: Remove obsolete version attribute from docker-compose 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: Remove test file and docker-compose per PR review - Remove test_internal.py (debug file) - Remove docker-compose.yml (to be moved to hindsight-cookbook repo) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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7 changed files with 3088 additions and 3174 deletions
12
.env.example
12
.env.example
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@ -2,11 +2,23 @@
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# Copy this file to .env and fill in your values
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# LLM Configuration (Required)
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# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio
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HINDSIGHT_API_LLM_PROVIDER=openai
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HINDSIGHT_API_LLM_API_KEY=your-api-key-here
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HINDSIGHT_API_LLM_MODEL=o3-mini
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HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
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# Example: Anthropic Claude configuration
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# HINDSIGHT_API_LLM_PROVIDER=anthropic
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# HINDSIGHT_API_LLM_API_KEY=your-anthropic-api-key
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# HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-20250514
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# Example: LM Studio local configuration (Qwen 2.5 32B recommended)
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# HINDSIGHT_API_LLM_PROVIDER=lmstudio
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# HINDSIGHT_API_LLM_API_KEY=lmstudio
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# HINDSIGHT_API_LLM_BASE_URL=http://localhost:1234/v1
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# HINDSIGHT_API_LLM_MODEL=qwen2.5-32b-instruct
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# API Configuration (Optional)
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HINDSIGHT_API_HOST=0.0.0.0
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HINDSIGHT_API_PORT=8888
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125
CLAUDE.md
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125
CLAUDE.md
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@ -0,0 +1,125 @@
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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Hindsight is an agent memory system that provides long-term memory for AI agents using biomimetic data structures. It stores memories as World facts, Experiences, Opinions, and Observations across memory banks.
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## Development Commands
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### API Server (Python/FastAPI)
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```bash
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# Start API server (loads .env automatically)
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./scripts/dev/start-api.sh
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# Run tests
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cd hindsight-api && uv run pytest tests/
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# Run specific test file
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cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v
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# Lint
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cd hindsight-api && uv run ruff check .
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```
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### Control Plane (Next.js)
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```bash
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./scripts/dev/start-control-plane.sh
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# Or manually:
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cd hindsight-control-plane && npm run dev
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```
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### Documentation Site (Docusaurus)
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```bash
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./scripts/dev/start-docs.sh
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```
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### Generating Clients/OpenAPI
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```bash
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# Regenerate OpenAPI spec after API changes
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./scripts/generate-openapi.sh
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# Regenerate all client SDKs (Python, TypeScript, Rust)
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./scripts/generate-clients.sh
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```
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### Benchmarks
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```bash
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./scripts/benchmarks/run-longmemeval.sh
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./scripts/benchmarks/run-locomo.sh
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./scripts/benchmarks/start-visualizer.sh # View results at localhost:8001
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```
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## Architecture
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### Monorepo Structure
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- **hindsight-api/**: Core FastAPI server with memory engine (Python, uv)
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- **hindsight/**: Embedded Python bundle (hindsight-all package)
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- **hindsight-control-plane/**: Admin UI (Next.js, npm)
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- **hindsight-cli/**: CLI tool (Rust, cargo)
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- **hindsight-clients/**: Generated SDK clients (Python, TypeScript, Rust)
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- **hindsight-docs/**: Docusaurus documentation site
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- **hindsight-integrations/**: Framework integrations (LiteLLM, OpenAI)
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- **hindsight-dev/**: Development tools and benchmarks
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### Core Engine (hindsight-api/hindsight_api/engine/)
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- `memory_engine.py`: Main orchestrator for retain/recall/reflect operations
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- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio
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- `embeddings.py`: Embedding generation (local or TEI)
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- `cross_encoder.py`: Reranking (local or TEI)
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- `entity_resolver.py`: Entity extraction and normalization
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- `query_analyzer.py`: Query intent analysis
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- `retain/`: Memory ingestion pipeline
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- `search/`: Multi-strategy retrieval (semantic, BM25, graph, temporal)
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### API Layer (hindsight-api/hindsight_api/api/)
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FastAPI routers for all endpoints. Main operations:
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- **Retain**: Store memories, extracts facts/entities/relationships
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- **Recall**: Retrieve memories via parallel search strategies + reranking
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- **Reflect**: Deep analysis forming new opinions/observations
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### Database
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PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api/hindsight_api/alembic/`. Migrations run automatically on API startup.
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Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
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## Key Conventions
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### Memory Banks
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- Each bank is isolated (no cross-bank data access)
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- Banks have dispositions (skepticism, literalism, empathy traits 1-5) affecting reflect
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- Banks can have background context
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### API Design
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- All endpoints operate on a single bank per request
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- Multi-bank queries are client responsibility
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- Disposition traits only affect reflect, not recall
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### Python Style
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- Python 3.11+, type hints required
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- Async throughout (asyncpg, async FastAPI)
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- Pydantic models for request/response
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- Ruff for linting (line-length 120)
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### TypeScript Style
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- Next.js App Router for control plane
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- Tailwind CSS with shadcn/ui components
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## Environment Setup
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```bash
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cp .env.example .env
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# Edit .env with LLM API key
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# Python deps
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uv sync --directory hindsight-api/
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# Node deps (workspace)
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npm install
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```
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Required env vars:
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- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, ollama, lmstudio
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- `HINDSIGHT_API_LLM_API_KEY`: Your API key
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- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., o3-mini, claude-sonnet-4-20250514)
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@ -16,6 +16,8 @@ ENV_LLM_PROVIDER = "HINDSIGHT_API_LLM_PROVIDER"
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ENV_LLM_API_KEY = "HINDSIGHT_API_LLM_API_KEY"
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ENV_LLM_MODEL = "HINDSIGHT_API_LLM_MODEL"
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ENV_LLM_BASE_URL = "HINDSIGHT_API_LLM_BASE_URL"
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ENV_LLM_MAX_CONCURRENT = "HINDSIGHT_API_LLM_MAX_CONCURRENT"
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ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
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ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
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ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
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@ -45,6 +47,8 @@ ENV_LAZY_RERANKER = "HINDSIGHT_API_LAZY_RERANKER"
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DEFAULT_DATABASE_URL = "pg0"
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DEFAULT_LLM_PROVIDER = "openai"
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DEFAULT_LLM_MODEL = "gpt-5-mini"
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DEFAULT_LLM_MAX_CONCURRENT = 32
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DEFAULT_LLM_TIMEOUT = 120.0 # seconds
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DEFAULT_EMBEDDINGS_PROVIDER = "local"
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DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
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@ -99,6 +103,8 @@ class HindsightConfig:
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llm_api_key: str | None
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llm_model: str
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llm_base_url: str | None
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llm_max_concurrent: int
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llm_timeout: float
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# Embeddings
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embeddings_provider: str
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@ -138,6 +144,8 @@ class HindsightConfig:
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llm_api_key=os.getenv(ENV_LLM_API_KEY),
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llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
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llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
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llm_max_concurrent=int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT))),
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llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
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# Embeddings
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embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
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embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
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@ -171,6 +179,8 @@ class HindsightConfig:
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return "https://api.groq.com/openai/v1"
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elif provider == "ollama":
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return "http://localhost:11434/v1"
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elif provider == "lmstudio":
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return "http://localhost:1234/v1"
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else:
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return ""
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@ -15,6 +15,13 @@ from google.genai import errors as genai_errors
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from google.genai import types as genai_types
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from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError
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from ..config import (
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DEFAULT_LLM_MAX_CONCURRENT,
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DEFAULT_LLM_TIMEOUT,
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ENV_LLM_MAX_CONCURRENT,
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ENV_LLM_TIMEOUT,
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)
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# Seed applied to every Groq request for deterministic behavior.
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DEFAULT_LLM_SEED = 4242
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@ -24,7 +31,9 @@ logger = logging.getLogger(__name__)
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logging.getLogger("httpx").setLevel(logging.WARNING)
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# Global semaphore to limit concurrent LLM requests across all instances
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_global_llm_semaphore = asyncio.Semaphore(32)
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# Set HINDSIGHT_API_LLM_MAX_CONCURRENT=1 for local LLMs (LM Studio, Ollama)
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_llm_max_concurrent = int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT)))
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_global_llm_semaphore = asyncio.Semaphore(_llm_max_concurrent)
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class OutputTooLongError(Exception):
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@ -58,7 +67,7 @@ class LLMProvider:
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Initialize LLM provider.
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Args:
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provider: Provider name ("openai", "groq", "ollama", "gemini").
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provider: Provider name ("openai", "groq", "ollama", "gemini", "anthropic", "lmstudio").
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api_key: API key.
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base_url: Base URL for the API.
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model: Model name.
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@ -71,7 +80,7 @@ class LLMProvider:
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self.reasoning_effort = reasoning_effort
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# Validate provider
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valid_providers = ["openai", "groq", "ollama", "gemini"]
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valid_providers = ["openai", "groq", "ollama", "gemini", "anthropic", "lmstudio"]
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if self.provider not in valid_providers:
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raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")
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@ -81,25 +90,52 @@ class LLMProvider:
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self.base_url = "https://api.groq.com/openai/v1"
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elif self.provider == "ollama":
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self.base_url = "http://localhost:11434/v1"
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elif self.provider == "lmstudio":
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self.base_url = "http://localhost:1234/v1"
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# Validate API key (not needed for ollama)
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if self.provider != "ollama" and not self.api_key:
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# Validate API key (not needed for ollama or lmstudio)
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if self.provider not in ("ollama", "lmstudio") and not self.api_key:
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raise ValueError(f"API key not found for {self.provider}")
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# Get timeout config (set HINDSIGHT_API_LLM_TIMEOUT for local LLMs that need longer timeouts)
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self.timeout = float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
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# Create client based on provider
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self._client = None
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self._gemini_client = None
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self._anthropic_client = None
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if self.provider == "gemini":
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self._gemini_client = genai.Client(api_key=self.api_key)
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self._client = None
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elif self.provider == "ollama":
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self._client = AsyncOpenAI(api_key="ollama", base_url=self.base_url, max_retries=0)
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self._gemini_client = None
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elif self.provider == "anthropic":
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from anthropic import AsyncAnthropic
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# Only pass base_url if it's set (Anthropic uses default URL otherwise)
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anthropic_kwargs = {"api_key": self.api_key}
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if self.base_url:
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anthropic_kwargs["base_url"] = self.base_url
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if self.timeout:
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anthropic_kwargs["timeout"] = self.timeout
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self._anthropic_client = AsyncAnthropic(**anthropic_kwargs)
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elif self.provider in ("ollama", "lmstudio"):
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# Use dummy key if not provided for local
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api_key = self.api_key or "local"
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client_kwargs = {
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"api_key": api_key,
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"base_url": self.base_url,
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"max_retries": 0
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}
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if self.timeout:
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client_kwargs["timeout"] = self.timeout
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self._client = AsyncOpenAI(**client_kwargs)
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else:
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# Only pass base_url if it's set (OpenAI uses default URL otherwise)
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client_kwargs = {"api_key": self.api_key, "max_retries": 0}
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if self.base_url:
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client_kwargs["base_url"] = self.base_url
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self._client = AsyncOpenAI(**client_kwargs) # type: ignore[invalid-argument-type] - dict kwargs
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self._gemini_client = None
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if self.timeout:
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client_kwargs["timeout"] = self.timeout
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self._client = AsyncOpenAI(**client_kwargs)
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async def verify_connection(self) -> None:
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"""
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@ -166,6 +202,12 @@ class LLMProvider:
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messages, response_format, max_retries, initial_backoff, max_backoff, skip_validation, start_time
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)
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# Handle Anthropic provider separately
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if self.provider == "anthropic":
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return await self._call_anthropic(
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messages, response_format, max_completion_tokens, max_retries, initial_backoff, max_backoff, skip_validation, start_time
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)
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# Handle Ollama with native API for structured output (better schema enforcement)
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if self.provider == "ollama" and response_format is not None:
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return await self._call_ollama_native(
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@ -238,35 +280,54 @@ class LLMProvider:
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schema_msg + "\n\n" + call_params["messages"][0]["content"]
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)
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call_params["response_format"] = {"type": "json_object"}
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# LM Studio and Ollama don't support json_object response format reliably
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# We rely on the schema in the system message instead
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if self.provider not in ("lmstudio", "ollama"):
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call_params["response_format"] = {"type": "json_object"}
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logger.debug(f"Sending request to {self.provider}/{self.model} (timeout={self.timeout})")
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response = await self._client.chat.completions.create(**call_params)
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logger.debug(f"Received response from {self.provider}/{self.model}")
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content = response.choices[0].message.content
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# Log raw LLM response for debugging JSON parse issues
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try:
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json_data = json.loads(content)
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except json.JSONDecodeError as json_err:
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# Truncate content for logging (first 500 and last 200 chars)
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content_preview = content[:500] if content else "<empty>"
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if content and len(content) > 700:
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content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
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logger.warning(
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f"JSON parse error from LLM response (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
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f" Model: {self.provider}/{self.model}\n"
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f" Content length: {len(content) if content else 0} chars\n"
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f" Content preview: {content_preview!r}\n"
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f" Finish reason: {response.choices[0].finish_reason if response.choices else 'unknown'}"
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)
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# Retry on JSON parse errors - LLM may return valid JSON on next attempt
|
||||
if attempt < max_retries:
|
||||
backoff = min(initial_backoff * (2**attempt), max_backoff)
|
||||
await asyncio.sleep(backoff)
|
||||
last_exception = json_err
|
||||
continue
|
||||
else:
|
||||
logger.error(f"JSON parse error after {max_retries + 1} attempts, giving up")
|
||||
raise
|
||||
# For local models, they may wrap JSON in markdown code blocks
|
||||
if self.provider in ("lmstudio", "ollama"):
|
||||
clean_content = content
|
||||
if "```json" in content:
|
||||
clean_content = content.split("```json")[1].split("```")[0].strip()
|
||||
elif "```" in content:
|
||||
clean_content = content.split("```")[1].split("```")[0].strip()
|
||||
try:
|
||||
json_data = json.loads(clean_content)
|
||||
except json.JSONDecodeError:
|
||||
# Fallback to parsing raw content
|
||||
json_data = json.loads(content)
|
||||
else:
|
||||
# Log raw LLM response for debugging JSON parse issues
|
||||
try:
|
||||
json_data = json.loads(content)
|
||||
except json.JSONDecodeError as json_err:
|
||||
# Truncate content for logging (first 500 and last 200 chars)
|
||||
content_preview = content[:500] if content else "<empty>"
|
||||
if content and len(content) > 700:
|
||||
content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
|
||||
logger.warning(
|
||||
f"JSON parse error from LLM response (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
|
||||
f" Model: {self.provider}/{self.model}\n"
|
||||
f" Content length: {len(content) if content else 0} chars\n"
|
||||
f" Content preview: {content_preview!r}\n"
|
||||
f" Finish reason: {response.choices[0].finish_reason if response.choices else 'unknown'}"
|
||||
)
|
||||
# Retry on JSON parse errors - LLM may return valid JSON on next attempt
|
||||
if attempt < max_retries:
|
||||
backoff = min(initial_backoff * (2**attempt), max_backoff)
|
||||
await asyncio.sleep(backoff)
|
||||
last_exception = json_err
|
||||
continue
|
||||
else:
|
||||
logger.error(f"JSON parse error after {max_retries + 1} attempts, giving up")
|
||||
raise
|
||||
|
||||
if skip_validation:
|
||||
result = json_data
|
||||
|
|
@ -339,6 +400,142 @@ class LLMProvider:
|
|||
raise last_exception
|
||||
raise RuntimeError("LLM call failed after all retries with no exception captured")
|
||||
|
||||
async def _call_anthropic(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
response_format: Any | None,
|
||||
max_completion_tokens: int | None,
|
||||
max_retries: int,
|
||||
initial_backoff: float,
|
||||
max_backoff: float,
|
||||
skip_validation: bool,
|
||||
start_time: float,
|
||||
) -> Any:
|
||||
"""Handle Anthropic-specific API calls."""
|
||||
from anthropic import APIConnectionError, APIStatusError, RateLimitError
|
||||
|
||||
# Convert OpenAI-style messages to Anthropic format
|
||||
system_prompt = None
|
||||
anthropic_messages = []
|
||||
|
||||
for msg in messages:
|
||||
role = msg.get("role", "user")
|
||||
content = msg.get("content", "")
|
||||
|
||||
if role == "system":
|
||||
if system_prompt:
|
||||
system_prompt += "\n\n" + content
|
||||
else:
|
||||
system_prompt = content
|
||||
else:
|
||||
anthropic_messages.append({"role": role, "content": content})
|
||||
|
||||
# Add JSON schema instruction if response_format is provided
|
||||
if response_format is not None and hasattr(response_format, "model_json_schema"):
|
||||
schema = response_format.model_json_schema()
|
||||
schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
|
||||
if system_prompt:
|
||||
system_prompt += schema_msg
|
||||
else:
|
||||
system_prompt = schema_msg
|
||||
|
||||
# Prepare parameters
|
||||
call_params = {
|
||||
"model": self.model,
|
||||
"messages": anthropic_messages,
|
||||
"max_tokens": max_completion_tokens if max_completion_tokens is not None else 4096,
|
||||
}
|
||||
|
||||
if system_prompt:
|
||||
call_params["system"] = system_prompt
|
||||
|
||||
last_exception = None
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
response = await self._anthropic_client.messages.create(**call_params)
|
||||
|
||||
# Anthropic response content is a list of blocks
|
||||
content = ""
|
||||
for block in response.content:
|
||||
if block.type == "text":
|
||||
content += block.text
|
||||
|
||||
if response_format is not None:
|
||||
# Models may wrap JSON in markdown code blocks
|
||||
clean_content = content
|
||||
if "```json" in content:
|
||||
clean_content = content.split("```json")[1].split("```")[0].strip()
|
||||
elif "```" in content:
|
||||
clean_content = content.split("```")[1].split("```")[0].strip()
|
||||
|
||||
try:
|
||||
json_data = json.loads(clean_content)
|
||||
except json.JSONDecodeError:
|
||||
# Fallback to parsing raw content if markdown stripping failed
|
||||
json_data = json.loads(content)
|
||||
|
||||
if skip_validation:
|
||||
result = json_data
|
||||
else:
|
||||
result = response_format.model_validate(json_data)
|
||||
else:
|
||||
result = content
|
||||
|
||||
# Log slow calls
|
||||
duration = time.time() - start_time
|
||||
if duration > 10.0:
|
||||
input_tokens = response.usage.input_tokens
|
||||
output_tokens = response.usage.output_tokens
|
||||
logger.info(
|
||||
f"slow llm call: model={self.provider}/{self.model}, "
|
||||
f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
|
||||
f"time={duration:.3f}s"
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
last_exception = e
|
||||
if attempt < max_retries:
|
||||
logger.warning("Anthropic returned invalid JSON, retrying...")
|
||||
backoff = min(initial_backoff * (2**attempt), max_backoff)
|
||||
await asyncio.sleep(backoff)
|
||||
continue
|
||||
else:
|
||||
logger.error(f"Anthropic returned invalid JSON after {max_retries + 1} attempts")
|
||||
raise
|
||||
|
||||
except (APIConnectionError, RateLimitError, APIStatusError) as e:
|
||||
# Fast fail on 401/403
|
||||
if isinstance(e, APIStatusError) and e.status_code in (401, 403):
|
||||
logger.error(f"Anthropic auth error (HTTP {e.status_code}), not retrying: {str(e)}")
|
||||
raise
|
||||
|
||||
last_exception = e
|
||||
if attempt < max_retries:
|
||||
# Check if it's a rate limit or server error
|
||||
should_retry = isinstance(e, (APIConnectionError, RateLimitError)) or (
|
||||
isinstance(e, APIStatusError) and e.status_code >= 500
|
||||
)
|
||||
|
||||
if should_retry:
|
||||
backoff = min(initial_backoff * (2**attempt), max_backoff)
|
||||
jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
|
||||
await asyncio.sleep(backoff + jitter)
|
||||
continue
|
||||
|
||||
logger.error(f"Anthropic API error after {max_retries + 1} attempts: {str(e)}")
|
||||
raise
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error during Anthropic call: {type(e).__name__}: {str(e)}")
|
||||
raise
|
||||
|
||||
if last_exception:
|
||||
raise last_exception
|
||||
raise RuntimeError("Anthropic call failed after all retries")
|
||||
|
||||
async def _call_ollama_native(
|
||||
self,
|
||||
messages: list[dict[str, str]],
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@ dependencies = [
|
|||
"opentelemetry-exporter-prometheus>=0.41b0",
|
||||
"dateparser>=1.2.2",
|
||||
"google-genai>=1.0.0",
|
||||
"anthropic>=0.40.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
|
|
|||
|
|
@ -70,7 +70,7 @@ class Server:
|
|||
|
||||
Args:
|
||||
db_url: Database URL. Use "pg0" for embedded PostgreSQL.
|
||||
llm_provider: LLM provider ("groq", "openai", "ollama")
|
||||
llm_provider: LLM provider ("groq", "openai", "ollama", "gemini", "anthropic", "lmstudio")
|
||||
llm_api_key: API key for the LLM provider
|
||||
llm_model: Model name to use
|
||||
llm_base_url: Optional custom base URL for LLM API
|
||||
|
|
@ -236,7 +236,7 @@ def start_server(
|
|||
|
||||
Args:
|
||||
db_url: Database URL. Use "pg0" for embedded PostgreSQL.
|
||||
llm_provider: LLM provider ("groq", "openai", "ollama")
|
||||
llm_provider: LLM provider ("groq", "openai", "ollama", "gemini", "anthropic", "lmstudio")
|
||||
llm_api_key: API key for the LLM provider
|
||||
llm_model: Model name to use
|
||||
llm_base_url: Optional custom base URL for LLM API
|
||||
|
|
|
|||
Loading…
Reference in a new issue