26c7412339
* feat: add litellm.compress() for BM25-based context compression
Adds a compress() utility that reduces context size for LLM calls using
BM25 relevance scoring (with optional semantic embeddings via
litellm.embedding()). Messages below a token threshold pass through
unchanged; messages above are scored, ranked, and the lowest-relevance
ones replaced with stubs. Originals are cached and a retrieval tool is
injected so the model can recover dropped content on demand.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(compress): truncate high-scoring messages instead of fully stubbing them
When a relevant message was too large to fit in the token budget it was
replaced with a stub, leaving the LLM with no real content to work with.
Now the highest-scoring overflow message is truncated (first 70% + last 30%
of words) to fill the remaining budget, so the LLM always receives actual
content rather than just a retrieval pointer.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(bm25): add prefix expansion so query terms match inflected doc tokens
"cook" now matches "cooking", "auth" matches "authentication", etc.
Without this, short query terms scored 0 against longer inflected forms
in documents, causing the wrong message to be kept.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add routing correctness test and eval harness for litellm.compress()
- test_simple_compression: parametrized test verifying BM25 routes the
right message based on query ("How to cook?" keeps cooking, "Fix auth"
keeps auth content)
- eval_compression.py: end-to-end eval harness comparing baseline vs
compressed model performance on HumanEval-style coding problems
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat(eval): add SWE-bench Lite compression eval harness
Uses princeton-nlp/SWE-bench_Lite_bm25_27K which bundles ~27k tokens of
BM25-retrieved repo context per problem — large enough to meaningfully
stress litellm.compress() without Docker or GitHub API calls.
Proxy eval metrics (no test runner needed):
- has_diff: model produced a valid unified diff
- file_overlap: fraction of gold-patch files in generated patch
- exact_file_match: generated patch touches exactly the right files
Run: python tests/eval_swe_bench.py --model gpt-4o --problems 10
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(eval): robust dataset loading + sys.path fix for worktree imports
- Add HuggingFace API fallback so the SWE-bench loader doesn't need
the `datasets` library (avoids pyarrow/numpy binary compat issues)
- Insert repo root into sys.path so compression module resolves
from worktrees
- Use direct import of litellm_compress to avoid __getattr__ issues
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* improve compression quality: line-based truncation, multi-message budget, 70% default target
- Switch truncate_message from word-based to line-based splitting to
preserve code structure (function boundaries, indentation)
- Allow multiple messages to be truncated instead of burning entire
budget on one overflow message
- Raise default compression target from 50% to 70% of trigger for
better quality/cost tradeoff
- Add --compression-target CLI arg to SWE-bench eval harness
- Move tests to canonical locations (tests/test_litellm/, scripts/)
- Add docs page and sidebar entries for compress()
Eval results (5 problems, Opus, trigger=10k):
Hunk overlap delta improved from -0.417 to -0.221
Content similarity now matches baseline (+0.006)
Cost savings: 72%
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* docs: add SWE-bench performance results to compress() docs
Include benchmark table from Opus eval (5 problems, trigger=10k)
showing 72% cost savings with file-level quality fully preserved.
Add metric explanations and eval runner examples.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(eval): use tolerance-based hunk overlap metric
The exact line-number matching was too brittle — LLM-generated patches
often target the right code region but with slightly offset line numbers.
Switch to hunk-level overlap with a 10-line tolerance window so nearby
edits count as matches. This better reflects actual patch quality.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add compression_interception callback for LiteLLM Proxy
Add a proxy callback that automatically compresses incoming /v1/messages
payloads above a configurable token threshold, runs the retrieval tool
loop server-side, and returns the final response. This brings compress()
support to proxy deployments (e.g. Claude Code via /v1/messages).
- New callback: litellm/integrations/compression_interception/
- Proxy config: compression_interception_params in litellm_settings
- Support for input_type param in compress() (openai vs anthropic)
- Docs: proxy setup instructions with YAML config example
- Tests: 139-line unit test suite for the interception handler
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Revert "feat: add compression_interception callback for LiteLLM Proxy"
This reverts commit 72bd5cb152ca1df07f14a14e14a2816e188874a8.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
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|---|---|---|
| .. | ||
| agent_tests | ||
| audio_tests | ||
| basic_proxy_startup_tests | ||
| batches_tests | ||
| benchmarks | ||
| code_coverage_tests | ||
| documentation_tests | ||
| enterprise | ||
| guardrails_tests | ||
| image_gen_tests | ||
| litellm | ||
| litellm_core_utils | ||
| litellm_utils_tests | ||
| litellm-proxy-extras | ||
| llm_responses_api_testing | ||
| llm_translation | ||
| load_tests | ||
| local_testing | ||
| logging_callback_tests | ||
| mcp_tests | ||
| multi_instance_e2e_tests | ||
| ocr_tests | ||
| old_proxy_tests/tests | ||
| openai_endpoints_tests | ||
| otel_tests | ||
| pass_through_tests | ||
| pass_through_unit_tests | ||
| proxy_admin_ui_tests | ||
| proxy_e2e_anthropic_messages_tests | ||
| proxy_security_tests | ||
| proxy_unit_tests | ||
| router_unit_tests | ||
| scim_tests | ||
| search_tests | ||
| spend_tracking_tests | ||
| store_model_in_db_tests | ||
| test_litellm | ||
| ui_e2e_tests | ||
| unified_google_tests | ||
| vector_store_tests | ||
| windows_tests | ||
| __init__.py | ||
| eval_swe_bench.py | ||
| gettysburg.wav | ||
| large_text.py | ||
| openai_batch_completions.jsonl | ||
| README.MD | ||
| test_budget_management.py | ||
| test_callbacks_on_proxy.py | ||
| test_config.py | ||
| test_debug_warning.py | ||
| test_default_encoding_non_root.py | ||
| test_end_users.py | ||
| test_entrypoint.py | ||
| test_fallbacks.py | ||
| test_gpt5_azure_temperature_support.py | ||
| test_health.py | ||
| test_keys.py | ||
| test_litellm_proxy_responses_config.py | ||
| test_logging.conf | ||
| test_models.py | ||
| test_new_vector_store_endpoints.py | ||
| test_openai_endpoints.py | ||
| test_organizations.py | ||
| test_otel_thread_leak.py | ||
| test_passthrough_endpoints.py | ||
| test_presidio_latency.py | ||
| test_proxy_server_non_root.py | ||
| test_ratelimit.py | ||
| test_resource_cleanup.py | ||
| test_service_logger_otel.py | ||
| test_spend_logs.py | ||
| test_team_logging.py | ||
| test_team_members.py | ||
| test_team.py | ||
| test_users.py | ||
In total litellm runs 1000+ tests
[02/20/2025] Update:
To make it easier to contribute and map what behavior is tested,
we've started mapping the litellm directory in tests/test_litellm
This folder can only run mock tests.