From 4a52a71956a8d46fcb7294ac71734504bb09bcc2 Mon Sep 17 00:00:00 2001
From: S. Solomon Darnell
Date: Fri, 28 Mar 2025 21:52:21 -0500
Subject: two version of R2R are here
---
R2R/.dockerignore | 20 +
R2R/.flake8 | 3 +
R2R/.gitattributes | 2 +
R2R/.github/.codecov.yml | 5 +
R2R/.github/ISSUE_TEMPLATE/bug_report.md | 38 +
R2R/.github/ISSUE_TEMPLATE/custom.md | 8 +
R2R/.github/ISSUE_TEMPLATE/feature_request.md | 20 +
R2R/.github/workflows/build-main.yml | 83 +
R2R/.github/workflows/build-release.yml | 43 +
R2R/.github/workflows/ci.yml | 85 +
R2R/.github/workflows/links.yml | 39 +
R2R/.github/workflows/publish-to-pypi.yml | 27 +
R2R/.gitignore | 20 +
R2R/.isort.cfg | 10 +
R2R/.pre-commit-config.yaml | 32 +
R2R/CODE_OF_CONDUCT.md | 42 +
R2R/CONTRIBUTING.md | 18 +
R2R/Dockerfile | 37 +
R2R/LICENSE.md | 21 +
R2R/MANIFEST.md | 3 +
R2R/README.md | 317 ++
R2R/SECURITY.md | 39 +
R2R/assets/quickstart.gif | Bin 0 -> 5034602 bytes
R2R/assets/r2r.png | Bin 0 -> 1248884 bytes
R2R/compose.neo4j.yaml | 42 +
R2R/compose.og.yaml | 129 +
R2R/compose.postgres.yaml | 62 +
R2R/compose.yaml | 129 +
R2R/config.json | 59 +
R2R/ingesting/README.md | 21 +
R2R/ingesting/aging_pt1.json | 1 +
R2R/ingesting/cmds.txt | 1 +
R2R/ingesting/ingest_my_data.py | 99 +
R2R/poetry.lock | 3942 ++++++++++++++++++++
R2R/pyproject.toml | 108 +
R2R/r2r/__init__.py | 110 +
R2R/r2r/base/__init__.py | 160 +
R2R/r2r/base/abstractions/__init__.py | 0
R2R/r2r/base/abstractions/base.py | 93 +
R2R/r2r/base/abstractions/document.py | 242 ++
R2R/r2r/base/abstractions/exception.py | 16 +
R2R/r2r/base/abstractions/llama_abstractions.py | 439 +++
R2R/r2r/base/abstractions/llm.py | 112 +
R2R/r2r/base/abstractions/prompt.py | 31 +
R2R/r2r/base/abstractions/search.py | 84 +
R2R/r2r/base/abstractions/vector.py | 66 +
R2R/r2r/base/logging/__init__.py | 0
R2R/r2r/base/logging/kv_logger.py | 547 +++
R2R/r2r/base/logging/log_processor.py | 196 +
R2R/r2r/base/logging/run_manager.py | 56 +
R2R/r2r/base/parsers/__init__.py | 5 +
R2R/r2r/base/parsers/base_parser.py | 14 +
R2R/r2r/base/pipeline/__init__.py | 0
R2R/r2r/base/pipeline/base_pipeline.py | 233 ++
R2R/r2r/base/pipes/__init__.py | 0
R2R/r2r/base/pipes/base_pipe.py | 163 +
R2R/r2r/base/providers/__init__.py | 0
R2R/r2r/base/providers/base_provider.py | 48 +
R2R/r2r/base/providers/embedding_provider.py | 83 +
R2R/r2r/base/providers/eval_provider.py | 46 +
R2R/r2r/base/providers/kg_provider.py | 182 +
R2R/r2r/base/providers/llm_provider.py | 66 +
R2R/r2r/base/providers/prompt_provider.py | 65 +
R2R/r2r/base/providers/vector_db_provider.py | 142 +
R2R/r2r/base/utils/__init__.py | 26 +
R2R/r2r/base/utils/base_utils.py | 63 +
R2R/r2r/base/utils/splitter/__init__.py | 3 +
R2R/r2r/base/utils/splitter/text.py | 1979 ++++++++++
R2R/r2r/cli/__init__.py | 0
R2R/r2r/cli/cli.py | 592 +++
R2R/r2r/examples/__init__.py | 0
R2R/r2r/examples/configs/local_neo4j_kg.json | 69 +
R2R/r2r/examples/configs/local_ollama.json | 41 +
R2R/r2r/examples/configs/local_ollama_rerank.json | 46 +
R2R/r2r/examples/configs/neo4j_kg.json | 27 +
R2R/r2r/examples/configs/postgres_logging.json | 7 +
R2R/r2r/examples/hello_r2r.py | 25 +
R2R/r2r/examples/scripts/advanced_kg_cookbook.py | 194 +
R2R/r2r/examples/scripts/basic_kg_cookbook.py | 67 +
R2R/r2r/examples/scripts/run_hyde.py | 33 +
R2R/r2r/examples/scripts/run_web_multi_rag.py | 54 +
R2R/r2r/examples/scripts/run_web_rag.py | 26 +
R2R/r2r/examples/scripts/sample_data_ingestor.py | 81 +
R2R/r2r/examples/scripts/test_e2e.py | 321 ++
R2R/r2r/integrations/__init__.py | 3 +
R2R/r2r/integrations/serper.py | 103 +
R2R/r2r/main/__init__.py | 54 +
R2R/r2r/main/abstractions.py | 58 +
R2R/r2r/main/api/__init__.py | 0
R2R/r2r/main/api/client.py | 377 ++
R2R/r2r/main/api/requests.py | 79 +
R2R/r2r/main/api/routes/__init__.py | 0
R2R/r2r/main/api/routes/base_router.py | 75 +
R2R/r2r/main/api/routes/ingestion.py | 42 +
R2R/r2r/main/api/routes/management.py | 101 +
R2R/r2r/main/api/routes/retrieval.py | 91 +
R2R/r2r/main/app.py | 53 +
R2R/r2r/main/app_entry.py | 84 +
R2R/r2r/main/assembly/__init__.py | 0
R2R/r2r/main/assembly/builder.py | 207 +
R2R/r2r/main/assembly/config.py | 167 +
R2R/r2r/main/assembly/factory.py | 484 +++
R2R/r2r/main/assembly/factory_extensions.py | 69 +
R2R/r2r/main/engine.py | 109 +
R2R/r2r/main/execution.py | 421 +++
R2R/r2r/main/r2r.py | 51 +
R2R/r2r/main/services/__init__.py | 0
R2R/r2r/main/services/base.py | 22 +
R2R/r2r/main/services/ingestion_service.py | 505 +++
R2R/r2r/main/services/management_service.py | 385 ++
R2R/r2r/main/services/retrieval_service.py | 207 +
R2R/r2r/parsers/__init__.py | 27 +
R2R/r2r/parsers/media/__init__.py | 0
R2R/r2r/parsers/media/audio_parser.py | 32 +
R2R/r2r/parsers/media/docx_parser.py | 28 +
R2R/r2r/parsers/media/img_parser.py | 40 +
R2R/r2r/parsers/media/movie_parser.py | 108 +
R2R/r2r/parsers/media/openai_helpers.py | 58 +
R2R/r2r/parsers/media/pdf_parser.py | 34 +
R2R/r2r/parsers/media/ppt_parser.py | 30 +
R2R/r2r/parsers/structured/__init__.py | 0
R2R/r2r/parsers/structured/csv_parser.py | 25 +
R2R/r2r/parsers/structured/json_parser.py | 49 +
R2R/r2r/parsers/structured/xlsx_parser.py | 29 +
R2R/r2r/parsers/text/__init__.py | 0
R2R/r2r/parsers/text/html_parser.py | 15 +
R2R/r2r/parsers/text/md_parser.py | 23 +
R2R/r2r/parsers/text/text_parser.py | 13 +
R2R/r2r/pipelines/__init__.py | 11 +
R2R/r2r/pipelines/eval_pipeline.py | 37 +
R2R/r2r/pipelines/ingestion_pipeline.py | 144 +
R2R/r2r/pipelines/rag_pipeline.py | 115 +
R2R/r2r/pipelines/search_pipeline.py | 140 +
R2R/r2r/pipes/__init__.py | 31 +
R2R/r2r/pipes/abstractions/__init__.py | 0
R2R/r2r/pipes/abstractions/generator_pipe.py | 58 +
R2R/r2r/pipes/abstractions/search_pipe.py | 62 +
R2R/r2r/pipes/ingestion/__init__.py | 0
R2R/r2r/pipes/ingestion/embedding_pipe.py | 218 ++
R2R/r2r/pipes/ingestion/kg_extraction_pipe.py | 226 ++
R2R/r2r/pipes/ingestion/kg_storage_pipe.py | 133 +
R2R/r2r/pipes/ingestion/parsing_pipe.py | 211 ++
R2R/r2r/pipes/ingestion/vector_storage_pipe.py | 128 +
R2R/r2r/pipes/other/eval_pipe.py | 54 +
R2R/r2r/pipes/other/web_search_pipe.py | 105 +
R2R/r2r/pipes/retrieval/kg_agent_search_pipe.py | 103 +
R2R/r2r/pipes/retrieval/multi_search.py | 79 +
R2R/r2r/pipes/retrieval/query_transform_pipe.py | 101 +
R2R/r2r/pipes/retrieval/search_rag_pipe.py | 130 +
R2R/r2r/pipes/retrieval/streaming_rag_pipe.py | 131 +
R2R/r2r/pipes/retrieval/vector_search_pipe.py | 123 +
R2R/r2r/prompts/__init__.py | 3 +
R2R/r2r/prompts/local/__init__.py | 0
R2R/r2r/prompts/local/defaults.jsonl | 12 +
R2R/r2r/prompts/local/r2r_prompt_provider.py | 69 +
R2R/r2r/providers/__init__.py | 0
R2R/r2r/providers/embeddings/__init__.py | 11 +
R2R/r2r/providers/embeddings/ollama/ollama_base.py | 156 +
R2R/r2r/providers/embeddings/openai/openai_base.py | 200 +
.../sentence_transformer_base.py | 160 +
R2R/r2r/providers/eval/__init__.py | 3 +
R2R/r2r/providers/eval/llm/base_llm_eval.py | 84 +
R2R/r2r/providers/kg/__init__.py | 3 +
R2R/r2r/providers/kg/neo4j/base_neo4j.py | 983 +++++
R2R/r2r/providers/llms/__init__.py | 7 +
R2R/r2r/providers/llms/litellm/base_litellm.py | 142 +
R2R/r2r/providers/llms/openai/base_openai.py | 144 +
R2R/r2r/providers/vector_dbs/__init__.py | 5 +
.../providers/vector_dbs/pgvector/pgvector_db.py | 610 +++
R2R/r2r/telemetry/__init__.py | 0
R2R/r2r/telemetry/events.py | 59 +
R2R/r2r/telemetry/posthog.py | 58 +
R2R/r2r/telemetry/telemetry_decorator.py | 56 +
R2R/r2r/vecs/__init__.py | 28 +
R2R/r2r/vecs/adapter/__init__.py | 15 +
R2R/r2r/vecs/adapter/base.py | 111 +
R2R/r2r/vecs/adapter/markdown.py | 88 +
R2R/r2r/vecs/adapter/noop.py | 55 +
R2R/r2r/vecs/adapter/text.py | 151 +
R2R/r2r/vecs/client.py | 313 ++
R2R/r2r/vecs/collection.py | 1132 ++++++
R2R/r2r/vecs/exc.py | 83 +
R2R/rag_out_1.json | 28 +
R2R/rag_out_2.json | 5 +
R2R/tests/test_abstractions.py | 162 +
R2R/tests/test_config.py | 187 +
R2R/tests/test_embedding.py | 162 +
R2R/tests/test_end_to_end.py | 375 ++
R2R/tests/test_ingestion_service.py | 443 +++
R2R/tests/test_llms.py | 59 +
R2R/tests/test_logging.py | 360 ++
R2R/tests/test_parser.py | 159 +
R2R/tests/test_pipeline.py | 291 ++
R2R/tests/test_vector_db.py | 160 +
194 files changed, 26088 insertions(+)
create mode 100755 R2R/.dockerignore
create mode 100755 R2R/.flake8
create mode 100755 R2R/.gitattributes
create mode 100755 R2R/.github/.codecov.yml
create mode 100755 R2R/.github/ISSUE_TEMPLATE/bug_report.md
create mode 100755 R2R/.github/ISSUE_TEMPLATE/custom.md
create mode 100755 R2R/.github/ISSUE_TEMPLATE/feature_request.md
create mode 100755 R2R/.github/workflows/build-main.yml
create mode 100755 R2R/.github/workflows/build-release.yml
create mode 100755 R2R/.github/workflows/ci.yml
create mode 100755 R2R/.github/workflows/links.yml
create mode 100755 R2R/.github/workflows/publish-to-pypi.yml
create mode 100755 R2R/.gitignore
create mode 100755 R2R/.isort.cfg
create mode 100755 R2R/.pre-commit-config.yaml
create mode 100755 R2R/CODE_OF_CONDUCT.md
create mode 100755 R2R/CONTRIBUTING.md
create mode 100755 R2R/Dockerfile
create mode 100755 R2R/LICENSE.md
create mode 100755 R2R/MANIFEST.md
create mode 100755 R2R/README.md
create mode 100755 R2R/SECURITY.md
create mode 100755 R2R/assets/quickstart.gif
create mode 100755 R2R/assets/r2r.png
create mode 100755 R2R/compose.neo4j.yaml
create mode 100755 R2R/compose.og.yaml
create mode 100644 R2R/compose.postgres.yaml
create mode 100755 R2R/compose.yaml
create mode 100755 R2R/config.json
create mode 100644 R2R/ingesting/README.md
create mode 100755 R2R/ingesting/aging_pt1.json
create mode 100755 R2R/ingesting/cmds.txt
create mode 100755 R2R/ingesting/ingest_my_data.py
create mode 100755 R2R/poetry.lock
create mode 100755 R2R/pyproject.toml
create mode 100755 R2R/r2r/__init__.py
create mode 100755 R2R/r2r/base/__init__.py
create mode 100755 R2R/r2r/base/abstractions/__init__.py
create mode 100755 R2R/r2r/base/abstractions/base.py
create mode 100755 R2R/r2r/base/abstractions/document.py
create mode 100755 R2R/r2r/base/abstractions/exception.py
create mode 100755 R2R/r2r/base/abstractions/llama_abstractions.py
create mode 100755 R2R/r2r/base/abstractions/llm.py
create mode 100755 R2R/r2r/base/abstractions/prompt.py
create mode 100755 R2R/r2r/base/abstractions/search.py
create mode 100755 R2R/r2r/base/abstractions/vector.py
create mode 100755 R2R/r2r/base/logging/__init__.py
create mode 100755 R2R/r2r/base/logging/kv_logger.py
create mode 100755 R2R/r2r/base/logging/log_processor.py
create mode 100755 R2R/r2r/base/logging/run_manager.py
create mode 100755 R2R/r2r/base/parsers/__init__.py
create mode 100755 R2R/r2r/base/parsers/base_parser.py
create mode 100755 R2R/r2r/base/pipeline/__init__.py
create mode 100755 R2R/r2r/base/pipeline/base_pipeline.py
create mode 100755 R2R/r2r/base/pipes/__init__.py
create mode 100755 R2R/r2r/base/pipes/base_pipe.py
create mode 100755 R2R/r2r/base/providers/__init__.py
create mode 100755 R2R/r2r/base/providers/base_provider.py
create mode 100755 R2R/r2r/base/providers/embedding_provider.py
create mode 100755 R2R/r2r/base/providers/eval_provider.py
create mode 100755 R2R/r2r/base/providers/kg_provider.py
create mode 100755 R2R/r2r/base/providers/llm_provider.py
create mode 100755 R2R/r2r/base/providers/prompt_provider.py
create mode 100755 R2R/r2r/base/providers/vector_db_provider.py
create mode 100755 R2R/r2r/base/utils/__init__.py
create mode 100755 R2R/r2r/base/utils/base_utils.py
create mode 100755 R2R/r2r/base/utils/splitter/__init__.py
create mode 100755 R2R/r2r/base/utils/splitter/text.py
create mode 100755 R2R/r2r/cli/__init__.py
create mode 100755 R2R/r2r/cli/cli.py
create mode 100755 R2R/r2r/examples/__init__.py
create mode 100755 R2R/r2r/examples/configs/local_neo4j_kg.json
create mode 100755 R2R/r2r/examples/configs/local_ollama.json
create mode 100755 R2R/r2r/examples/configs/local_ollama_rerank.json
create mode 100755 R2R/r2r/examples/configs/neo4j_kg.json
create mode 100755 R2R/r2r/examples/configs/postgres_logging.json
create mode 100755 R2R/r2r/examples/hello_r2r.py
create mode 100755 R2R/r2r/examples/scripts/advanced_kg_cookbook.py
create mode 100755 R2R/r2r/examples/scripts/basic_kg_cookbook.py
create mode 100755 R2R/r2r/examples/scripts/run_hyde.py
create mode 100755 R2R/r2r/examples/scripts/run_web_multi_rag.py
create mode 100755 R2R/r2r/examples/scripts/run_web_rag.py
create mode 100755 R2R/r2r/examples/scripts/sample_data_ingestor.py
create mode 100755 R2R/r2r/examples/scripts/test_e2e.py
create mode 100755 R2R/r2r/integrations/__init__.py
create mode 100755 R2R/r2r/integrations/serper.py
create mode 100755 R2R/r2r/main/__init__.py
create mode 100755 R2R/r2r/main/abstractions.py
create mode 100755 R2R/r2r/main/api/__init__.py
create mode 100755 R2R/r2r/main/api/client.py
create mode 100755 R2R/r2r/main/api/requests.py
create mode 100755 R2R/r2r/main/api/routes/__init__.py
create mode 100755 R2R/r2r/main/api/routes/base_router.py
create mode 100755 R2R/r2r/main/api/routes/ingestion.py
create mode 100755 R2R/r2r/main/api/routes/management.py
create mode 100755 R2R/r2r/main/api/routes/retrieval.py
create mode 100755 R2R/r2r/main/app.py
create mode 100755 R2R/r2r/main/app_entry.py
create mode 100755 R2R/r2r/main/assembly/__init__.py
create mode 100755 R2R/r2r/main/assembly/builder.py
create mode 100755 R2R/r2r/main/assembly/config.py
create mode 100755 R2R/r2r/main/assembly/factory.py
create mode 100755 R2R/r2r/main/assembly/factory_extensions.py
create mode 100755 R2R/r2r/main/engine.py
create mode 100755 R2R/r2r/main/execution.py
create mode 100755 R2R/r2r/main/r2r.py
create mode 100755 R2R/r2r/main/services/__init__.py
create mode 100755 R2R/r2r/main/services/base.py
create mode 100755 R2R/r2r/main/services/ingestion_service.py
create mode 100755 R2R/r2r/main/services/management_service.py
create mode 100755 R2R/r2r/main/services/retrieval_service.py
create mode 100755 R2R/r2r/parsers/__init__.py
create mode 100755 R2R/r2r/parsers/media/__init__.py
create mode 100755 R2R/r2r/parsers/media/audio_parser.py
create mode 100755 R2R/r2r/parsers/media/docx_parser.py
create mode 100755 R2R/r2r/parsers/media/img_parser.py
create mode 100755 R2R/r2r/parsers/media/movie_parser.py
create mode 100755 R2R/r2r/parsers/media/openai_helpers.py
create mode 100755 R2R/r2r/parsers/media/pdf_parser.py
create mode 100755 R2R/r2r/parsers/media/ppt_parser.py
create mode 100755 R2R/r2r/parsers/structured/__init__.py
create mode 100755 R2R/r2r/parsers/structured/csv_parser.py
create mode 100755 R2R/r2r/parsers/structured/json_parser.py
create mode 100755 R2R/r2r/parsers/structured/xlsx_parser.py
create mode 100755 R2R/r2r/parsers/text/__init__.py
create mode 100755 R2R/r2r/parsers/text/html_parser.py
create mode 100755 R2R/r2r/parsers/text/md_parser.py
create mode 100755 R2R/r2r/parsers/text/text_parser.py
create mode 100755 R2R/r2r/pipelines/__init__.py
create mode 100755 R2R/r2r/pipelines/eval_pipeline.py
create mode 100755 R2R/r2r/pipelines/ingestion_pipeline.py
create mode 100755 R2R/r2r/pipelines/rag_pipeline.py
create mode 100755 R2R/r2r/pipelines/search_pipeline.py
create mode 100755 R2R/r2r/pipes/__init__.py
create mode 100755 R2R/r2r/pipes/abstractions/__init__.py
create mode 100755 R2R/r2r/pipes/abstractions/generator_pipe.py
create mode 100755 R2R/r2r/pipes/abstractions/search_pipe.py
create mode 100755 R2R/r2r/pipes/ingestion/__init__.py
create mode 100755 R2R/r2r/pipes/ingestion/embedding_pipe.py
create mode 100755 R2R/r2r/pipes/ingestion/kg_extraction_pipe.py
create mode 100755 R2R/r2r/pipes/ingestion/kg_storage_pipe.py
create mode 100755 R2R/r2r/pipes/ingestion/parsing_pipe.py
create mode 100755 R2R/r2r/pipes/ingestion/vector_storage_pipe.py
create mode 100755 R2R/r2r/pipes/other/eval_pipe.py
create mode 100755 R2R/r2r/pipes/other/web_search_pipe.py
create mode 100755 R2R/r2r/pipes/retrieval/kg_agent_search_pipe.py
create mode 100755 R2R/r2r/pipes/retrieval/multi_search.py
create mode 100755 R2R/r2r/pipes/retrieval/query_transform_pipe.py
create mode 100755 R2R/r2r/pipes/retrieval/search_rag_pipe.py
create mode 100755 R2R/r2r/pipes/retrieval/streaming_rag_pipe.py
create mode 100755 R2R/r2r/pipes/retrieval/vector_search_pipe.py
create mode 100755 R2R/r2r/prompts/__init__.py
create mode 100755 R2R/r2r/prompts/local/__init__.py
create mode 100755 R2R/r2r/prompts/local/defaults.jsonl
create mode 100755 R2R/r2r/prompts/local/r2r_prompt_provider.py
create mode 100755 R2R/r2r/providers/__init__.py
create mode 100755 R2R/r2r/providers/embeddings/__init__.py
create mode 100755 R2R/r2r/providers/embeddings/ollama/ollama_base.py
create mode 100755 R2R/r2r/providers/embeddings/openai/openai_base.py
create mode 100755 R2R/r2r/providers/embeddings/sentence_transformer/sentence_transformer_base.py
create mode 100755 R2R/r2r/providers/eval/__init__.py
create mode 100755 R2R/r2r/providers/eval/llm/base_llm_eval.py
create mode 100755 R2R/r2r/providers/kg/__init__.py
create mode 100755 R2R/r2r/providers/kg/neo4j/base_neo4j.py
create mode 100755 R2R/r2r/providers/llms/__init__.py
create mode 100755 R2R/r2r/providers/llms/litellm/base_litellm.py
create mode 100755 R2R/r2r/providers/llms/openai/base_openai.py
create mode 100755 R2R/r2r/providers/vector_dbs/__init__.py
create mode 100755 R2R/r2r/providers/vector_dbs/pgvector/pgvector_db.py
create mode 100755 R2R/r2r/telemetry/__init__.py
create mode 100755 R2R/r2r/telemetry/events.py
create mode 100755 R2R/r2r/telemetry/posthog.py
create mode 100755 R2R/r2r/telemetry/telemetry_decorator.py
create mode 100755 R2R/r2r/vecs/__init__.py
create mode 100755 R2R/r2r/vecs/adapter/__init__.py
create mode 100755 R2R/r2r/vecs/adapter/base.py
create mode 100755 R2R/r2r/vecs/adapter/markdown.py
create mode 100755 R2R/r2r/vecs/adapter/noop.py
create mode 100755 R2R/r2r/vecs/adapter/text.py
create mode 100755 R2R/r2r/vecs/client.py
create mode 100755 R2R/r2r/vecs/collection.py
create mode 100755 R2R/r2r/vecs/exc.py
create mode 100755 R2R/rag_out_1.json
create mode 100755 R2R/rag_out_2.json
create mode 100755 R2R/tests/test_abstractions.py
create mode 100755 R2R/tests/test_config.py
create mode 100755 R2R/tests/test_embedding.py
create mode 100755 R2R/tests/test_end_to_end.py
create mode 100755 R2R/tests/test_ingestion_service.py
create mode 100755 R2R/tests/test_llms.py
create mode 100755 R2R/tests/test_logging.py
create mode 100755 R2R/tests/test_parser.py
create mode 100755 R2R/tests/test_pipeline.py
create mode 100755 R2R/tests/test_vector_db.py
(limited to 'R2R')
diff --git a/R2R/.dockerignore b/R2R/.dockerignore
new file mode 100755
index 00000000..c4730648
--- /dev/null
+++ b/R2R/.dockerignore
@@ -0,0 +1,20 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis
diff --git a/R2R/.flake8 b/R2R/.flake8
new file mode 100755
index 00000000..5491a9b7
--- /dev/null
+++ b/R2R/.flake8
@@ -0,0 +1,3 @@
+[flake8]
+exclude = playground/*,web/*,chat/*,docs/*,r2r/examples/,r2r/vecs,r2r/vecs/*,tests
+ignore = E501, E722, W503, E203, F541, W293, W291, E266, F601, F403, F405
diff --git a/R2R/.gitattributes b/R2R/.gitattributes
new file mode 100755
index 00000000..c8199463
--- /dev/null
+++ b/R2R/.gitattributes
@@ -0,0 +1,2 @@
+*.html linguist-documentation
+*.ipynb linguist-documentation
diff --git a/R2R/.github/.codecov.yml b/R2R/.github/.codecov.yml
new file mode 100755
index 00000000..8fd648c4
--- /dev/null
+++ b/R2R/.github/.codecov.yml
@@ -0,0 +1,5 @@
+coverage:
+ status:
+ project:
+ default:
+ threshold: 100%
diff --git a/R2R/.github/ISSUE_TEMPLATE/bug_report.md b/R2R/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100755
index 00000000..dd84ea78
--- /dev/null
+++ b/R2R/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/R2R/.github/ISSUE_TEMPLATE/custom.md b/R2R/.github/ISSUE_TEMPLATE/custom.md
new file mode 100755
index 00000000..b894315f
--- /dev/null
+++ b/R2R/.github/ISSUE_TEMPLATE/custom.md
@@ -0,0 +1,8 @@
+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
diff --git a/R2R/.github/ISSUE_TEMPLATE/feature_request.md b/R2R/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100755
index 00000000..bbcbbe7d
--- /dev/null
+++ b/R2R/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/R2R/.github/workflows/build-main.yml b/R2R/.github/workflows/build-main.yml
new file mode 100755
index 00000000..b91de2c3
--- /dev/null
+++ b/R2R/.github/workflows/build-main.yml
@@ -0,0 +1,83 @@
+name: Build and Publish Docker Image
+
+on:
+ workflow_dispatch:
+ inputs:
+ version:
+ description: 'Version to publish (leave empty to use default versioning)'
+ required: false
+ type: string
+
+jobs:
+ build-and-publish:
+ runs-on: ubuntu-latest
+ permissions:
+ packages: write
+ contents: read
+ id-token: write
+ actions: write
+ steps:
+ - name: Checkout Repository
+ uses: actions/checkout@v4
+
+ - name: Google Auth
+ uses: 'google-github-actions/auth@v2'
+ with:
+ credentials_json: '${{ secrets.GCP_SA_KEY }}'
+
+ - name: Set up Cloud SDK
+ uses: 'google-github-actions/setup-gcloud@v2'
+
+ - name: Configure SDK
+ run: 'gcloud auth configure-docker us-east1-docker.pkg.dev'
+
+ - name: Docker Auth
+ uses: docker/login-action@v3
+ with:
+ username: ${{ secrets.DOCKER_USERNAME }}
+ password: ${{ secrets.DOCKER_TOKEN }}
+
+ - name: Set up Docker Buildx
+ uses: docker/setup-buildx-action@v3
+
+ - name: Set up QEMU
+ uses: docker/setup-qemu-action@v3
+
+ - name: Determine version to use
+ id: version
+ run: |
+ if [ -n "${{ github.event.inputs.version }}" ]; then
+ echo "RELEASE_VERSION=${{ github.event.inputs.version }}" >> $GITHUB_OUTPUT
+ else
+ echo "RELEASE_VERSION=main" >> $GITHUB_OUTPUT
+ fi
+
+ - name: Extract metadata (tags, labels) for Docker
+ id: meta
+ uses: docker/metadata-action@v5
+ with:
+ images: |
+ emrgntcmplxty/r2r
+ us-east1-docker.pkg.dev/alert-rush-397022/sciphi-r2r/r2r
+ tags: |
+ type=raw,value=${{ steps.version.outputs.RELEASE_VERSION }}
+ type=raw,value=latest
+
+ - name: Build and Push Docker Image
+ uses: docker/build-push-action@v5
+ with:
+ context: .
+ file: ./Dockerfile
+ push: true
+ tags: ${{ steps.meta.outputs.tags }}
+ labels: ${{ steps.meta.outputs.labels }}
+ platforms: linux/amd64,linux/arm64
+
+ - name: Trigger Dependent Repositories
+ if: success()
+ run: |
+ curl -X POST \
+ -H "Accept: application/vnd.github.v3+json" \
+ -H "Authorization: token ${{ secrets.PERSONAL_ACCESS_TOKEN }}" \
+ https://api.github.com/repos/SciPhi-AI/R2R-basic-rag-template/dispatches \
+ -d '{"event_type": "rebuild", "client_payload": {"r2r_tag": "${{ steps.version.outputs.RELEASE_VERSION }}"}}'
diff --git a/R2R/.github/workflows/build-release.yml b/R2R/.github/workflows/build-release.yml
new file mode 100755
index 00000000..996f7334
--- /dev/null
+++ b/R2R/.github/workflows/build-release.yml
@@ -0,0 +1,43 @@
+name: Links
+
+on:
+ push:
+ branches:
+ - main
+ pull_request:
+ branches:
+ - main
+ schedule:
+ - cron: "00 18 * * *"
+
+jobs:
+ linkChecker:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+
+ - name: Link Checker
+ id: lychee
+ uses: lycheeverse/lychee-action@v1
+ continue-on-error: true
+ with:
+ args: "--output ./docs/lychee/out.md --config ./docs/.lycheerc"
+
+ - name: Get Lychee Exit Code
+ id: get-exit-code
+ run: echo "lychee_exit_code=$?" >> $GITHUB_ENV
+
+ - name: Create Custom Report
+ if: failure()
+ run: |
+ echo "Lychee doesn't play nice with relative imports for .mdx files. Add a link to the .lycheeignore if needed!" > ./docs/lychee/custom_report.md
+ echo "" >> ./docs/lychee/custom_report.md
+ cat ./docs/lychee/out.md >> ./docs/lychee/custom_report.md
+
+ - name: Create Issue From File
+ if: failure()
+ uses: peter-evans/create-issue-from-file@v4
+ with:
+ title: Link Checker Report
+ content-filepath: ./docs/lychee/custom_report.md
+ labels: report, automated issue
diff --git a/R2R/.github/workflows/ci.yml b/R2R/.github/workflows/ci.yml
new file mode 100755
index 00000000..d24bf06e
--- /dev/null
+++ b/R2R/.github/workflows/ci.yml
@@ -0,0 +1,85 @@
+name: CI
+
+on:
+ push:
+ branches:
+ - main
+ - '**/feature/*'
+ pull_request:
+ branches:
+ - main
+
+jobs:
+ pre-commit:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v3
+
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.12.4' # Specify your Python version here
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install poetry
+ poetry install
+
+ - name: Install pre-commit
+ run: poetry run pre-commit install
+
+ - name: Run pre-commit
+ run: poetry run pre-commit run --all-files
+
+ pytest:
+ runs-on: ubuntu-latest
+ timeout-minutes: 15 # Increased timeout to accommodate Ollama setup
+
+ env:
+ OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+ POSTGRES_DBNAME: ${{ secrets.POSTGRES_DBNAME }}
+ POSTGRES_HOST: ${{ secrets.POSTGRES_HOST }}
+ POSTGRES_PORT: ${{ secrets.POSTGRES_PORT }}
+ POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
+ POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }}
+ POSTGRES_VECS_COLLECTION: ${{ secrets.POSTGRES_VECS_COLLECTION }}
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v3
+
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.12.4'
+
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip
+ pip install poetry
+ poetry install
+
+ - name: Install Ollama
+ run: |
+ curl -fsSL https://ollama.com/install.sh | sudo -E sh
+
+ - name: Start Ollama server
+ run: |
+ ollama serve &
+ sleep 5
+ curl -i http://localhost:11434
+
+ - name: Pull Ollama model
+ run: |
+ ollama pull llama2
+
+ - name: Run tests
+ run: poetry run pytest tests/ -k "not redis and not sentence_transformer"
+
+ - name: Upload coverage reports to Codecov
+ uses: codecov/codecov-action@v4.0.1
+ with:
+ token: ${{ secrets.CODECOV_TOKEN }}
diff --git a/R2R/.github/workflows/links.yml b/R2R/.github/workflows/links.yml
new file mode 100755
index 00000000..75ec1758
--- /dev/null
+++ b/R2R/.github/workflows/links.yml
@@ -0,0 +1,39 @@
+name: Links
+
+on:
+ repository_dispatch:
+ workflow_dispatch:
+ schedule:
+ - cron: "00 18 * * *"
+
+jobs:
+ linkChecker:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v2
+
+ - name: Link Checker
+ id: lychee
+ uses: lycheeverse/lychee-action@v1
+ continue-on-error: true
+ with:
+ args: "--output ./docs/lychee/out.md --config ./docs/.lycheerc"
+
+ - name: Get Lychee Exit Code
+ id: get-exit-code
+ run: echo "lychee_exit_code=$?" >> $GITHUB_ENV
+
+ - name: Create Custom Report
+ if: failure()
+ run: |
+ echo "Lychee doesn't play nice with relative imports for .mdx files. Add a link to the .lycheeignore if needed!" > ./docs/lychee/custom_report.md
+ echo "" >> ./docs/lychee/custom_report.md
+ cat ./docs/lychee/out.md >> ./docs/lychee/custom_report.md
+
+ - name: Create Issue From File
+ if: failure()
+ uses: peter-evans/create-issue-from-file@v4
+ with:
+ title: Link Checker Report
+ content-filepath: ./docs/lychee/custom_report.md
+ labels: report, automated issue
diff --git a/R2R/.github/workflows/publish-to-pypi.yml b/R2R/.github/workflows/publish-to-pypi.yml
new file mode 100755
index 00000000..79ac0c83
--- /dev/null
+++ b/R2R/.github/workflows/publish-to-pypi.yml
@@ -0,0 +1,27 @@
+name: Publish to PyPI
+
+on:
+ push:
+ tags:
+ - "*"
+ workflow_dispatch: # This line adds manual trigger support
+
+jobs:
+ publish:
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v2
+
+ - name: Set up Python
+ uses: actions/setup-python@v2
+ with:
+ python-version: 3.9
+
+ - name: Install poetry
+ run: pip install poetry
+
+ - name: Build and publish
+ run: |
+ poetry build
+ poetry publish --username __token__ --password ${{ secrets.PYPI_API_TOKEN }}
diff --git a/R2R/.gitignore b/R2R/.gitignore
new file mode 100755
index 00000000..95c247fa
--- /dev/null
+++ b/R2R/.gitignore
@@ -0,0 +1,20 @@
+.env*
+.DS_Store
+*.gguf
+logs/
+workspace/
+uploads/
+env/
+**/__pycache__
+dump/*
+.next
+node_modules
+
+coverage.xml
+.coverage
+
+**/*.sqlite*
+**/*.sqlite3*
+
+r2r/examples/data/*
+.aider*
diff --git a/R2R/.isort.cfg b/R2R/.isort.cfg
new file mode 100755
index 00000000..927aa0b3
--- /dev/null
+++ b/R2R/.isort.cfg
@@ -0,0 +1,10 @@
+[settings]
+profile = black
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+ensure_newline_before_comments = true
+line_length = 79
+sections = FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER
+skip = .tox,__pycache__,*.pyc,venv*/*,reports,venv,env,node_modules,.env,.venv,dist,my_env
diff --git a/R2R/.pre-commit-config.yaml b/R2R/.pre-commit-config.yaml
new file mode 100755
index 00000000..58fb9158
--- /dev/null
+++ b/R2R/.pre-commit-config.yaml
@@ -0,0 +1,32 @@
+repos:
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.3.0
+ hooks:
+ - id: trailing-whitespace
+ - id: end-of-file-fixer
+ - id: check-added-large-files
+ - id: check-ast
+ - id: check-yaml
+
+ - repo: local
+ hooks:
+ - id: isort
+ name: isort
+ entry: poetry run isort
+ language: system
+ types: [python]
+ args: ["--check", "--diff"]
+
+ - id: black
+ name: black
+ entry: poetry run black
+ language: system
+ types: [python]
+ args: ["--check", "--diff"]
+
+ - id: flake8
+ name: flake8
+ entry: poetry run flake8
+ language: system
+ types: [python]
+ args: ["--config=.flake8"]
diff --git a/R2R/CODE_OF_CONDUCT.md b/R2R/CODE_OF_CONDUCT.md
new file mode 100755
index 00000000..56e0cb92
--- /dev/null
+++ b/R2R/CODE_OF_CONDUCT.md
@@ -0,0 +1,42 @@
+# Contributor Covenant Code of Conduct Summary
+
+TL;DR: Be nice. Be respectful. Be professional. Don't be a jerk.
+
+## Commitment
+
+We strive for a harassment-free, inclusive, and healthy community experience for all, regardless of personal characteristics or background.
+
+## Expected Behaviors
+
+- **Empathy and Kindness**: Show understanding and kindness to others.
+- **Respect**: Value different viewpoints and experiences.
+- **Constructive Feedback**: Offer and accept feedback graciously.
+- **Accountability**: Own up to mistakes and learn from them.
+- **Community Focus**: Prioritize what's best for the whole community.
+
+## Unacceptable Behaviors
+
+- **Sexualized Content**: Avoid sexual language and unwelcome sexual attention.
+- **Disrespect**: No trolling, insults, or derogatory comments.
+- **Harassment**: Public or private harassment is unacceptable.
+- **Privacy Violations**: Do not share private information without consent.
+- **Inappropriate Conduct**: Behavior not suitable for a professional setting is not allowed.
+
+## Enforcement
+
+- **Leaders' Responsibility**: Leaders clarify standards and take corrective actions.
+- **Scope**: Applies to all community spaces and when representing the community.
+- **Reporting**: Incidents can be reported to owen@sciphi.ai.
+
+## Enforcement Guidelines
+
+- **Correction**: Private warning for unprofessional behavior.
+- **Warning**: Consequences for repeated violations.
+- **Temporary Ban**: For serious or sustained inappropriate behavior.
+- **Permanent Ban**: For egregious violations, including harassment.
+
+## Attribution
+
+Adapted from the [Contributor Covenant version 2.1](https://www.contributor-covenant.org/version/2/1/code_of_conduct.html), with Community Impact Guidelines inspired by [Mozilla's code of conduct enforcement ladder](https://www.mozilla.org/en-US/about/governance/policies/participation/).
+
+For more details and FAQs, visit [https://www.contributor-covenant.org/faq](https://www.contributor-covenant.org/faq). Translations are available [here](https://www.contributor-covenant.org/translations).
diff --git a/R2R/CONTRIBUTING.md b/R2R/CONTRIBUTING.md
new file mode 100755
index 00000000..a5469158
--- /dev/null
+++ b/R2R/CONTRIBUTING.md
@@ -0,0 +1,18 @@
+# R2R Contribution Guide
+
+## Quick Start
+
+- **Pre-Discussion**: Feel free to propose your ideas via issues, [Discord](https://discord.gg/p6KqD2kjtB) if you want to get early feedback.
+- **Code of Conduct**: Adhere to our [Code of Conduct](./CODE_OF_CONDUCT.md) in all interactions.
+- **Pull Requests (PRs)**: Follow the PR process for contributions.
+
+## Pull Request Process
+
+1. **Dependencies**: Ensure all dependencies are necessary and documented.
+2. **Documentation**: Update README.md with any changes to interfaces, including new environment variables, exposed ports, and other relevant details.
+3. **Versioning**: Increment version numbers in examples and README.md following [SemVer](http://semver.org/).
+4. **Review**: A PR can be merged after receiving approval from at least two other developers. If you lack merge permissions, request a review for merging.
+
+## Attribution
+
+This Code of Conduct adapts from the [Contributor Covenant, version 1.4](http://contributor-covenant.org/version/1/4/).
diff --git a/R2R/Dockerfile b/R2R/Dockerfile
new file mode 100755
index 00000000..ffce8344
--- /dev/null
+++ b/R2R/Dockerfile
@@ -0,0 +1,37 @@
+FROM python:3.10-slim AS builder
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ gcc g++ musl-dev curl libffi-dev gfortran libopenblas-dev \
+ && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir poetry
+
+# Copy the dependencies files
+COPY pyproject.toml poetry.lock* ./
+
+# Install the dependencies, including gunicorn and uvicorn
+RUN poetry config virtualenvs.create false \
+ && poetry install --no-dev --no-root \
+ && pip install --no-cache-dir gunicorn uvicorn
+
+# Create the final image
+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Copy the installed packages from the builder
+COPY --from=builder /usr/local/lib/python3.10/site-packages /usr/local/lib/python3.10/site-packages
+COPY --from=builder /usr/local/bin /usr/local/bin
+
+# Copy the application and config
+COPY r2r /app/r2r
+COPY config.json /app/config.json
+
+# Expose the port
+EXPOSE 8000
+
+# Run the application
+CMD ["uvicorn", "r2r.main.app_entry:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/R2R/LICENSE.md b/R2R/LICENSE.md
new file mode 100755
index 00000000..ca0f8c51
--- /dev/null
+++ b/R2R/LICENSE.md
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2024 EmergentAGI Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/R2R/MANIFEST.md b/R2R/MANIFEST.md
new file mode 100755
index 00000000..cb752d09
--- /dev/null
+++ b/R2R/MANIFEST.md
@@ -0,0 +1,3 @@
+# The R2R Manifest
+
+We will do our best to build useful AI tools for developers _(before AGI)_.
diff --git a/R2R/README.md b/R2R/README.md
new file mode 100755
index 00000000..903afba8
--- /dev/null
+++ b/R2R/README.md
@@ -0,0 +1,317 @@
+
+ 
+ 
+ 
+ 
+ 
+
+
+
+
+The ultimate open source RAG answer engine
+
+
+# About
+R2R was designed to bridge the gap between local LLM experimentation and scalable, production-ready Retrieval-Augmented Generation (RAG). R2R provides a comprehensive and SOTA RAG system for developers, built around a RESTful API for ease of use.
+
+For a more complete view of R2R, check out the [full documentation](https://r2r-docs.sciphi.ai/).
+
+## Key Features
+- **📁 Multimodal Support**: Ingest files ranging from `.txt`, `.pdf`, `.json` to `.png`, `.mp3`, and more.
+- **🔍 Hybrid Search**: Combine semantic and keyword search with reciprocal rank fusion for enhanced relevancy.
+- **🔗 Graph RAG**: Automatically extract relationships and build knowledge graphs.
+- **🗂️ App Management**: Efficiently manage documents and users with rich observability and analytics.
+- **🌐 Client-Server**: RESTful API support out of the box.
+- **🧩 Configurable**: Provision your application using intuitive configuration files.
+- **🔌 Extensible**: Develop your application further with easy builder + factory pattern.
+- **🖥️ Dashboard**: Use the [R2R Dashboard](https://github.com/SciPhi-AI/R2R-Dashboard), an open-source React+Next.js app for a user-friendly interaction with R2R.
+
+## Table of Contents
+1. [Install](#install)
+2. [R2R Quickstart](#r2r-quickstart)
+3. [R2R Dashboard](#r2r-dashboard)
+4. [Community and Support](#community-and-support)
+5. [Contributing](#contributing)
+
+# Install
+
+> [!NOTE]
+> Windows users are advised to use Docker to run R2R.
+
+
+Installing with Pip 🐍
+
+```bash
+pip install r2r
+
+# setup env, can freely replace `demo_vecs`
+export OPENAI_API_KEY=sk-...
+export POSTGRES_USER=YOUR_POSTGRES_USER
+export POSTGRES_PASSWORD=YOUR_POSTGRES_PASSWORD
+export POSTGRES_HOST=YOUR_POSTGRES_HOST
+export POSTGRES_PORT=YOUR_POSTGRES_PORT
+export POSTGRES_DBNAME=YOUR_POSTGRES_DBNAME
+export POSTGRES_VECS_COLLECTION=demo_vecs
+```
+
+
+
+Installing with Docker 🐳
+Docker allows users to get started with R2R seamlessly—providing R2R, the R2R Dashboard, and a pgvector database all in one place.
+
+First, clone the R2R repository:
+```bash
+git clone https://github.com/SciPhi-AI/R2R.git
+cd R2R
+# for R2R CLI and Python client
+pip install .
+```
+
+Then, run the following command to start all containers:
+
+For hosted LLMs (e.g., OpenAI):
+```bash
+# Be sure to set an OpenAI API key
+export OPENAI_API_KEY=sk-...
+export CONFIG_NAME=default
+docker-compose up -d
+```
+
+For local LLMs (e.g., Ollama):
+```bash
+export OLLAMA_API_BASE=http://host.docker.internal:11434
+export CONFIG_NAME=local_ollama
+docker-compose up -d
+```
+
+Note: Settings relating to Postgres+pgvector can be overriden by setting the appropriate environment variables before calling `docker-compose`.
+```bash
+export POSTGRES_USER=$YOUR_POSTGRES_USER
+export POSTGRES_PASSWORD=$YOUR_POSTGRES_PASSWORD
+export POSTGRES_HOST=$YOUR_POSTGRES_HOST
+export POSTGRES_PORT=$YOUR_POSTGRES_PORT
+export POSTGRES_DBNAME=$YOUR_POSTGRES_DBNAME
+export POSTGRES_VECS_COLLECTION=$MY_VECS_COLLECTION
+docker-compose up -d
+```
+The `POSTGRES_VECS_COLLECTION` defines the collection where all R2R related tables reside. This collection should be changed when selecting a new embedding model.
+
+
+
+# Updates
+Star R2R on GitHub by clicking "Star" in the upper right hand corner of the page to be instantly notified of new releases.
+
+
+# R2R Quickstart
+
+## Demo Video
+
+
+
+## Start the R2R server
+
+Start the R2R server in Docker
+
+Edit r2r_env the environment file that defines the DB names and sets the keys to be used by R2R. Below is an example of the file contents:
+```plaintext
+
+# Environment variables for LLM provider(s)
+export OPENAI_API_KEY=sk-ajdsfioadufaiouweiru923048-910235r8fpal...
+# Environment varialbes for the Postgres database
+export POSTGRES_USER=you
+export POSTGRES_PASSWORD=youpassword
+export POSTGRES_HOST=yourhost
+export POSTGRES_PORT=5432
+export POSTGRES_DBNAME=youcomeupwiththis
+export POSTGRES_VECS_COLLECTION=you_rag_vecs
+export CONFIG_OPTION=default
+
+export STORAGE_DIRECTORY=/home/user/code/dir_a/storit
+export CODE_DIRECTORY=/home/user/code/dir_a/dir_b
+```
+
+Go to the R2R directory and execute the run bash script. The script will load the file r2r.env into the environment and run the docker container.
+
+**WATCH OUT** for the existing volumes in your docker instance. If the postgres details match an existing container, but the postgres db does not contain the proper tables, your docker composition will fail.
+
+```bash
+cd R2R
+sh run.sh
+docker exec -it rag_eval_devenv /bin/bash
+```
+
+
+
+
+Serving the R2R CLI ✈️
+
+```bash
+r2r serve --port=8000
+```
+
+```plaintext Terminal Output
+2024-06-26 16:54:46,998 - INFO - r2r.core.providers.vector_db_provider - Initializing VectorDBProvider with config extra_fields={} provider='pgvector' collection_name='demo_vecs'.
+2024-06-26 16:54:48,054 - INFO - r2r.core.providers.embedding_provider - Initializing EmbeddingProvider with config extra_fields={'text_splitter': {'type': 'recursive_character', 'chunk_size': 512, 'chunk_overlap': 20}} provider='openai' base_model='text-embedding-3-small' base_dimension=512 rerank_model=None rerank_dimension=None rerank_transformer_type=None batch_size=128.
+2024-06-26 16:54:48,639 - INFO - r2r.core.providers.llm_provider - Initializing LLM provider with config: extra_fields={} provider='litellm'
+```
+
+
+
+
+Serving with Docker 🐳
+
+Successfully completing the installation steps above results in an R2R application being served over port `8000`.
+
+
+
+## Ingest a file
+
+```bash
+r2r ingest
+# can be called with additional argument,
+# e.g. `r2r ingest /path/to/your_file_1 /path/to/your_file_2 ...`
+```
+
+```plaintext
+{'results': {'processed_documents': ["File '.../aristotle.txt' processed successfully."], 'skipped_documents': []}}
+```
+
+
+## Perform a search
+
+
+```bash
+r2r search --query="who was aristotle?" --do-hybrid-search
+```
+
+```plaintext
+{'results': {'vector_search_results': [
+ {
+ 'id': '7ed3a01c-88dc-5a58-a68b-6e5d9f292df2',
+ 'score': 0.780314067545999,
+ 'metadata': {
+ 'text': 'Aristotle[A] (Greek: Ἀριστοτέλης Aristotélēs, pronounced [aristotélɛːs]; 384–322 BC) was an Ancient Greek philosopher and polymath. His writings cover a broad range of subjects spanning the natural sciences, philosophy, linguistics, economics, politics, psychology, and the arts. As the founder of the Peripatetic school of philosophy in the Lyceum in Athens, he began the wider Aristotelian tradition that followed, which set the groundwork for the development of modern science.',
+ 'title': 'aristotle.txt',
+ 'version': 'v0',
+ 'chunk_order': 0,
+ 'document_id': 'c9bdbac7-0ea3-5c9e-b590-018bd09b127b',
+ 'extraction_id': '472d6921-b4cd-5514-bf62-90b05c9102cb',
+ ...
+```
+
+## Perform RAG
+
+
+```bash
+r2r rag --query="who was aristotle?" --do-hybrid-search
+```
+
+```plaintext
+
+Search Results:
+{'vector_search_results': [
+ {'id': '7ed3a01c-88dc-5a58-a68b-6e5d9f292df2',
+ 'score': 0.7802911996841491,
+ 'metadata': {'text': 'Aristotle[A] (Greek: Ἀριστοτέλης Aristotélēs, pronounced [aristotélɛːs]; 384–322 BC) was an Ancient Greek philosopher and polymath. His writings cover a broad range of subjects spanning the natural sciences, philosophy, linguistics, economics, politics, psychology, and the arts. As the founder of the Peripatetic schoo
+ ...
+Completion:
+{'results': [
+ {
+ 'id': 'chatcmpl-9eXL6sKWlUkP3f6QBnXvEiKkWKBK4',
+ 'choices': [
+ {
+ 'finish_reason': 'stop',
+ 'index': 0,
+ 'logprobs': None,
+ 'message': {
+ 'content': "Aristotle (384–322 BC) was an Ancient Greek philosopher and polymath whose writings covered a broad range of subjects including the natural sciences,
+ ...
+```
+
+
+
+## Stream a RAG Response
+
+
+```bash
+r2r rag --query="who was aristotle?" --stream --do-hybrid-search
+```
+
+```plaintext
+"{\"id\":\"004ae2e3-c042-50f2-8c03-d4c282651fba\",\"score\":0.7803140675 ...
+Aristotle was an Ancient Greek philosopher and polymath who lived from 384 to 322 BC [1]. He was born in Stagira, Chalcidi....
+```
+
+# Hello r2r
+
+Building with R2R is easy - see the `hello_r2r` example below:
+
+```python
+
+from r2r import Document, GenerationConfig, R2R
+
+app = R2R() # You may pass a custom configuration to `R2R`
+
+app.ingest_documents(
+ [
+ Document(
+ type="txt",
+ data="John is a person that works at Google.",
+ metadata={},
+ )
+ ]
+)
+
+rag_results = app.rag(
+ "Who is john", GenerationConfig(model="gpt-3.5-turbo", temperature=0.0)
+)
+print(f"Search Results:\n{rag_results.search_results}")
+print(f"Completion:\n{rag_results.completion}")
+
+# RAG Results:
+# Search Results:
+# AggregateSearchResult(vector_search_results=[VectorSearchResult(id=2d71e689-0a0e-5491-a50b-4ecb9494c832, score=0.6848798582029441, metadata={'text': 'John is a person that works at Google.', 'version': 'v0', 'chunk_order': 0, 'document_id': 'ed76b6ee-dd80-5172-9263-919d493b439a', 'extraction_id': '1ba494d7-cb2f-5f0e-9f64-76c31da11381', 'associatedQuery': 'Who is john'})], kg_search_results=None)
+# Completion:
+# ChatCompletion(id='chatcmpl-9g0HnjGjyWDLADe7E2EvLWa35cMkB', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='John is a person that works at Google [1].', role='assistant', function_call=None, tool_calls=None))], created=1719797903, model='gpt-3.5-turbo-0125', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=11, prompt_tokens=145, total_tokens=156))
+```
+
+# R2R Dashboard
+
+Interact with R2R using our [open-source React+Next.js dashboard](https://github.com/SciPhi-AI/R2R-Dashboard). Check out the [Dashboard Cookbook](https://r2r-docs.sciphi.ai/cookbooks/dashboard) to get started!
+
+# Community and Support
+
+- [Discord](https://discord.gg/p6KqD2kjtB): Chat live with maintainers and community members
+- [Github Issues](https://github.com/SciPhi-AI/R2R/issues): Report bugs and request features
+
+**Explore our [R2R Docs](https://r2r-docs.sciphi.ai/) for tutorials and cookbooks on various R2R features and integrations, including:**
+
+### RAG Cookbooks
+- [Multiple LLMs](https://r2r-docs.sciphi.ai/cookbooks/multiple-llms): A simple cookbook showing how R2R supports multiple LLMs.
+- [Hybrid Search](https://r2r-docs.sciphi.ai/cookbooks/hybrid-search): A brief introduction to running hybrid search with R2R.
+- [Multimodal RAG](https://r2r-docs.sciphi.ai/cookbooks/multimodal): A cookbook on multimodal RAG with R2R.
+- [Knowledge Graphs](https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph): A walkthrough of automatic knowledge graph generation with R2R.
+- [Local RAG](https://r2r-docs.sciphi.ai/cookbooks/local-rag): A quick cookbook demonstration of how to run R2R with local LLMs.
+- [Reranking](https://r2r-docs.sciphi.ai/cookbooks/rerank-search): A short guide on how to apply reranking to R2R results.
+
+### App Features
+- [Client-Server](https://r2r-docs.sciphi.ai/cookbooks/client-server): An extension of the basic `R2R Quickstart` with client-server interactions.
+- [Document Management](https://r2r-docs.sciphi.ai/cookbooks/document-management): A cookbook showing how to manage your documents with R2R.
+- [Analytics & Observability](https://r2r-docs.sciphi.ai/cookbooks/observablity): A cookbook showing R2Rs end to end logging and analytics.
+- [Dashboard](https://r2r-docs.sciphi.ai/cookbooks/dashboard): A how-to guide on connecting with the R2R Dashboard.
+
+# Contributing
+
+We welcome contributions of all sizes! Here's how you can help:
+
+- Open a PR for new features, improvements, or better documentation.
+- Submit a [feature request](https://github.com/SciPhi-AI/R2R/issues/new?assignees=&labels=&projects=&template=feature_request.md&title=) or [bug report](https://github.com/SciPhi-AI/R2R/issues/new?assignees=&labels=&projects=&template=bug_report.md&title=)
+
+### Our Contributors
+
+ 
+
diff --git a/R2R/SECURITY.md b/R2R/SECURITY.md
new file mode 100755
index 00000000..68a2f2fd
--- /dev/null
+++ b/R2R/SECURITY.md
@@ -0,0 +1,39 @@
+
+# Security Policy
+
+At R2R, we take the security of our project and its users seriously. We appreciate the contributions of security researchers and developers in helping us identify and address potential vulnerabilities.
+
+## Reporting a Vulnerability
+
+If you discover a potential security vulnerability in R2R, please follow these steps to report it:
+
+1. Create a new issue on the GitHub repository using the "Vulnerability Disclosure" issue template.
+2. Set the issue as "confidential" if you are unsure whether the issue is a potential vulnerability or not. It is easier to make a confidential issue public than to remediate an issue that should have been confidential.
+3. Label the issue with the `security` label at a minimum. Additional labels may be applied by the security team and other project maintainers to assist with the triage process.
+4. Provide a detailed description of the vulnerability, including steps to reproduce, potential impact, and any other relevant information.
+5. If the issue contains sensitive information or user-specific data, such as private repository contents, assign the `keep confidential` label to the issue. If possible, avoid including such information directly in the issue and instead provide links to resources that are only accessible to the project maintainers.
+
+## Vulnerability Handling Process
+
+Once a vulnerability is reported, the R2R security team will follow these steps:
+
+1. Acknowledge receipt of the vulnerability report within 48 hours.
+2. Assess the severity and impact of the vulnerability.
+3. Develop a fix or mitigation plan for the vulnerability.
+4. Notify the reporter about the progress and estimated timeline for the fix.
+5. Once the fix is ready, release a new version of R2R that addresses the vulnerability.
+6. Publicly disclose the vulnerability and the fix after a reasonable period to allow users to update their installations.
+
+## Scope
+
+This security policy applies to the R2R codebase and its dependencies. It does not cover vulnerabilities in the underlying operating systems, hardware, or third-party libraries used by R2R.
+
+## Recognition
+
+We greatly appreciate the efforts of security researchers and developers who responsibly disclose vulnerabilities to us. With your permission, we will acknowledge your contribution in the release notes and any public disclosures related to the vulnerability.
+
+## Contact
+
+If you have any questions or concerns regarding the security of R2R, please contact the project maintainers at [security@r2r.com](mailto:security@r2r.com).
+
+Thank you for helping us keep R2R and its users secure!
diff --git a/R2R/assets/quickstart.gif b/R2R/assets/quickstart.gif
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diff --git a/R2R/compose.neo4j.yaml b/R2R/compose.neo4j.yaml
new file mode 100755
index 00000000..f3d709ca
--- /dev/null
+++ b/R2R/compose.neo4j.yaml
@@ -0,0 +1,42 @@
+
+networks:
+ r2r-network:
+ name: r2r-network
+
+services:
+ r2r:
+ depends_on:
+ neo4j:
+ condition: service_healthy
+
+ neo4j:
+ image: neo4j:5.21.0
+ ports:
+ - "7474:7474" # HTTP
+ - "7687:7687" # Bolt
+ environment:
+ - NEO4J_AUTH=${NEO4J_AUTH:-neo4j/ineedastrongerpassword}
+ - NEO4J_dbms_memory_pagecache_size=${NEO4J_PAGECACHE_SIZE:-512M}
+ - NEO4J_dbms_memory_heap_max__size=${NEO4J_HEAP_SIZE:-512M}
+ - NEO4J_apoc_export_file_enabled=true
+ - NEO4J_apoc_import_file_enabled=true
+ - NEO4J_apoc_import_file_use__neo4j__config=true
+ - NEO4JLABS_PLUGINS=["apoc"]
+ - NEO4J_dbms_security_procedures_unrestricted=apoc.*
+ - NEO4J_dbms_security_procedures_allowlist=apoc.*
+ volumes:
+ - neo4j_data:/data
+ - neo4j_logs:/logs
+ - neo4j_plugins:/plugins
+ networks:
+ - r2r-network
+ healthcheck:
+ test: ["CMD", "neo4j", "status"]
+ interval: 10s
+ timeout: 5s
+ retries: 5
+
+volumes:
+ neo4j_data:
+ neo4j_logs:
+ neo4j_plugins:
diff --git a/R2R/compose.og.yaml b/R2R/compose.og.yaml
new file mode 100755
index 00000000..05c04b3f
--- /dev/null
+++ b/R2R/compose.og.yaml
@@ -0,0 +1,129 @@
+x-depends-on:
+ neo4j: &neo4j-dependency
+ condition: service_healthy
+
+networks:
+ r2r-network:
+ name: r2r-network
+ driver: bridge
+ attachable: true
+ ipam:
+ driver: default
+ config:
+ - subnet: 172.28.0.0/16
+ labels:
+ - "com.docker.compose.recreate=always"
+
+
+services:
+ r2r:
+ image: emrgntcmplxty/r2r:main
+ ports:
+ - "8000:8000"
+ environment:
+ - PYTHONUNBUFFERED=1
+ - POSTGRES_USER=${POSTGRES_USER:-postgres}
+ - POSTGRES_PASSWORD=${POSTGRES_PASSWORD:-postgres}
+ - POSTGRES_HOST=postgres
+ - POSTGRES_PORT=5432
+ - POSTGRES_DBNAME=${POSTGRES_DBNAME:-postgres}
+ - POSTGRES_VECS_COLLECTION=${POSTGRES_VECS_COLLECTION:-${CONFIG_NAME:-vecs}}
+ - NEO4J_USER=${NEO4J_USER:-neo4j}
+ - NEO4J_PASSWORD=${NEO4J_PASSWORD:-ineedastrongerpassword}
+ - NEO4J_URL=${NEO4J_URL:-bolt://neo4j:7687}
+ - NEO4J_DATABASE=${NEO4J_DATABASE:-neo4j}
+ - OPENAI_API_KEY=${OPENAI_API_KEY:-}
+ - OLLAMA_API_BASE=${OLLAMA_API_BASE:-http://host.docker.internal:11434}
+ - CONFIG_NAME=${CONFIG_NAME:-}
+ - CONFIG_PATH=${CONFIG_PATH:-}
+ - CLIENT_MODE=${CLIENT_MODE:-false}
+ depends_on:
+ - postgres
+ networks:
+ - r2r-network
+ healthcheck:
+ test: ["CMD", "curl", "-f", "http://localhost:8000/v1/health"]
+ interval: 10s
+ timeout: 5s
+ retries: 5
+ restart: on-failure
+ volumes:
+ - ${CONFIG_PATH:-/}:${CONFIG_PATH:-/app/config}
+ labels:
+ - "traefik.enable=true"
+ - "traefik.http.routers.r2r.rule=PathPrefix(`/api`)"
+ - "traefik.http.services.r2r.loadbalancer.server.port=8000"
+ - "traefik.http.middlewares.r2r-strip-prefix.stripprefix.prefixes=/api"
+ - "traefik.http.middlewares.r2r-add-v1.addprefix.prefix=/v1"
+ - "traefik.http.routers.r2r.middlewares=r2r-strip-prefix,r2r-add-v1,r2r-headers"
+ - "traefik.http.middlewares.r2r-headers.headers.customrequestheaders.Access-Control-Allow-Origin=*"
+ - "traefik.http.middlewares.r2r-headers.headers.customrequestheaders.Access-Control-Allow-Methods=GET,POST,OPTIONS"
+ - "traefik.http.middlewares.r2r-headers.headers.customrequestheaders.Access-Control-Allow-Headers=DNT,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type,Range,Authorization"
+ - "traefik.http.middlewares.r2r-headers.headers.customresponseheaders.Access-Control-Expose-Headers=Content-Length,Content-Range"
+
+ r2r-dashboard:
+ image: emrgntcmplxty/r2r-dashboard:latest
+ environment:
+ - NEXT_PUBLIC_API_URL=http://traefik:80/api
+ depends_on:
+ - r2r
+ networks:
+ - r2r-network
+ labels:
+ - "traefik.enable=true"
+ - "traefik.http.routers.dashboard.rule=PathPrefix(`/`)"
+ - "traefik.http.services.dashboard.loadbalancer.server.port=3000"
+
+ postgres:
+ image: pgvector/pgvector:pg16
+ environment:
+ POSTGRES_USER: ${POSTGRES_USER:-postgres}
+ POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-postgres}
+ POSTGRES_DB: ${POSTGRES_DBNAME:-postgres}
+ volumes:
+ - postgres_data:/var/lib/postgresql/data
+ networks:
+ - r2r-network
+ healthcheck:
+ test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-postgres}"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ restart: on-failure
+
+ traefik:
+ image: traefik:v2.9
+ command:
+ - "--api.insecure=true"
+ - "--providers.docker=true"
+ - "--providers.docker.exposedbydefault=false"
+ - "--entrypoints.web.address=:80"
+ - "--accesslog=true"
+ - "--accesslog.filepath=/var/log/traefik/access.log"
+ ports:
+ - "88:80"
+ - "8080:8080" # Traefik dashboard
+ volumes:
+ - /var/run/docker.sock:/var/run/docker.sock:ro
+ networks:
+ - r2r-network
+
+ rag_eval_devenv:
+ container_name: rag_eval_devenv
+ #image: paper_eval_img:0.4
+ image: r2r_rag:latest
+ tty: true
+ volumes:
+ - ${CODE_DIRECTORY}:/code
+ networks:
+ - r2r-network
+
+volumes:
+ postgres_data:
+ the_code:
+ driver: local
+ driver_opts:
+ o: bind
+ type: none
+ device: ${STORAGE_DIRECTORY}
+ #device: /home/shebes/Coding/gn-ai/gnqa
diff --git a/R2R/compose.postgres.yaml b/R2R/compose.postgres.yaml
new file mode 100644
index 00000000..9b487fa6
--- /dev/null
+++ b/R2R/compose.postgres.yaml
@@ -0,0 +1,62 @@
+x-depends-on:
+ neo4j: &neo4j-dependency
+ condition: service_healthy
+
+networks:
+ r2r-network:
+ name: r2r-network
+ driver: bridge
+ attachable: true
+ ipam:
+ driver: default
+ config:
+ - subnet: 172.28.0.0/16
+ labels:
+ - "com.docker.compose.recreate=always"
+
+services:
+ postgres:
+ image: pgvector/pgvector:pg16
+ container_name: postgres
+ environment:
+ - POSTGRES_USER=gnqa
+ - POSTGRES_PASSWORD=gnqa
+ - POSTGRES_HOST=gnqa
+ volumes:
+ - postgres_pg_data:/var/lib/postgresql/data
+ networks:
+ - r2r-network
+ healthcheck:
+ test: ["CMD-SHELL", "pg_isready -U gnqa"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ restart: on-failure
+
+ traefik:
+ image: traefik:v2.9
+ container_name: traefik
+ command:
+ - "--api.insecure=true"
+ - "--providers.docker=true"
+ - "--providers.docker.exposedbydefault=false"
+ - "--entrypoints.web.address=:80"
+ - "--accesslog=true"
+ - "--accesslog.filepath=/var/log/traefik/access.log"
+ ports:
+ - "88:80"
+ - "8080:8080" # Traefik dashboard
+ volumes:
+ - /var/run/docker.sock:/var/run/docker.sock:ro
+ networks:
+ - r2r-network
+
+volumes:
+ postgres_data:
+ postgres_pg_data:
+ the_code:
+ driver: local
+ driver_opts:
+ o: bind
+ type: none
+ device: /home/shebes/Research/code/gn/_R2R_old
diff --git a/R2R/compose.yaml b/R2R/compose.yaml
new file mode 100755
index 00000000..20539c6e
--- /dev/null
+++ b/R2R/compose.yaml
@@ -0,0 +1,129 @@
+x-depends-on:
+ neo4j: &neo4j-dependency
+ condition: service_healthy
+
+networks:
+ r2r-network:
+ name: r2r-network
+ driver: bridge
+ attachable: true
+ ipam:
+ driver: default
+ config:
+ - subnet: 172.28.0.0/16
+ labels:
+ - "com.docker.compose.recreate=always"
+
+
+services:
+ r2r:
+ image: emrgntcmplxty/r2r:main
+ container_name: r2r
+ ports:
+ - "8000:8000"
+ environment:
+ - PYTHONUNBUFFERED=1
+ - POSTGRES_USER=${POSTGRES_USER}
+ - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
+ - POSTGRES_HOST=postgres
+ - POSTGRES_PORT=5432
+ - POSTGRES_DBNAME=${POSTGRES_DBNAME}
+ - POSTGRES_VECS_COLLECTION=${POSTGRES_VECS_COLLECTION}
+ - NEO4J_USER=${NEO4J_USER:-neo4j}
+ - NEO4J_PASSWORD=${NEO4J_PASSWORD:-ineedastrongerpassword}
+ - NEO4J_URL=${NEO4J_URL:-bolt://neo4j:7687}
+ - NEO4J_DATABASE=${NEO4J_DATABASE:-neo4j}
+ - OPENAI_API_KEY=${OPENAI_API_KEY:-}
+ - OLLAMA_API_BASE=${OLLAMA_API_BASE:-http://host.docker.internal:11434}
+ - CONFIG_NAME=${CONFIG_NAME:-}
+ - CONFIG_PATH=${CONFIG_PATH:-}
+ - CLIENT_MODE=${CLIENT_MODE:-false}
+ depends_on:
+ - postgres
+ networks:
+ - r2r-network
+ healthcheck:
+ test: ["CMD", "curl", "-f", "http://localhost:8000/v1/health"]
+ interval: 10s
+ timeout: 5s
+ retries: 5
+ restart: on-failure
+ volumes:
+ - ${CONFIG_PATH:-/}:${CONFIG_PATH:-/app/config}
+ labels:
+ - "traefik.enable=true"
+ - "traefik.http.routers.r2r.rule=PathPrefix(`/api`)"
+ - "traefik.http.services.r2r.loadbalancer.server.port=8000"
+ - "traefik.http.middlewares.r2r-strip-prefix.stripprefix.prefixes=/api"
+ - "traefik.http.middlewares.r2r-add-v1.addprefix.prefix=/v1"
+ - "traefik.http.routers.r2r.middlewares=r2r-strip-prefix,r2r-add-v1,r2r-headers"
+ - "traefik.http.middlewares.r2r-headers.headers.customrequestheaders.Access-Control-Allow-Origin=*"
+ - "traefik.http.middlewares.r2r-headers.headers.customrequestheaders.Access-Control-Allow-Methods=GET,POST,OPTIONS"
+ - "traefik.http.middlewares.r2r-headers.headers.customrequestheaders.Access-Control-Allow-Headers=DNT,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type,Range,Authorization"
+ - "traefik.http.middlewares.r2r-headers.headers.customresponseheaders.Access-Control-Expose-Headers=Content-Length,Content-Range"
+
+ r2r-dashboard:
+ image: emrgntcmplxty/r2r-dashboard:latest
+ container_name: r2r-dashboard
+ environment:
+ - NEXT_PUBLIC_API_URL=http://traefik:80/api
+ depends_on:
+ - r2r
+ networks:
+ - r2r-network
+ labels:
+ - "traefik.enable=true"
+ - "traefik.http.routers.dashboard.rule=PathPrefix(`/`)"
+ - "traefik.http.services.dashboard.loadbalancer.server.port=3000"
+
+ postgres:
+ image: pgvector/pgvector:pg16
+ container_name: pg16
+ env_file:
+ - ./env/postgres.env
+ volumes:
+ - postgres_data:/var/lib/postgresql/data
+ networks:
+ - r2r-network
+ healthcheck:
+ test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER}"]
+ interval: 5s
+ timeout: 5s
+ retries: 5
+ restart: on-failure
+
+ traefik:
+ image: traefik:v2.9
+ container_name: traefik
+ command:
+ - "--api.insecure=true"
+ - "--providers.docker=true"
+ - "--providers.docker.exposedbydefault=false"
+ - "--entrypoints.web.address=:80"
+ - "--accesslog=true"
+ - "--accesslog.filepath=/var/log/traefik/access.log"
+ ports:
+ - "88:80"
+ - "8080:8080" # Traefik dashboard
+ volumes:
+ - /var/run/docker.sock:/var/run/docker.sock:ro
+ networks:
+ - r2r-network
+
+ rag_eval_devenv:
+ container_name: rag_eval_devenv
+ image: r2r_rag:latest
+ tty: true
+ volumes:
+ - ${CODE_DIRECTORY}:/code
+ networks:
+ - r2r-network
+
+volumes:
+ postgres_data:
+ the_code:
+ driver: local
+ driver_opts:
+ o: bind
+ type: none
+ device: ${STORAGE_DIRECTORY}
diff --git a/R2R/config.json b/R2R/config.json
new file mode 100755
index 00000000..52af89b0
--- /dev/null
+++ b/R2R/config.json
@@ -0,0 +1,59 @@
+{
+ "app": {
+ "max_logs_per_request": 100,
+ "max_file_size_in_mb": 32
+ },
+ "completions": {
+ "provider": "litellm",
+ "generation_config": {
+ "model": "gpt-4o",
+ "temperature": 0.1,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": false,
+ "functions": null,
+ "skip_special_tokens": false,
+ "stop_token": null,
+ "num_beams": 1,
+ "do_sample": true,
+ "generate_with_chat": false,
+ "add_generation_kwargs": {},
+ "api_base": null
+ }
+ },
+ "embedding": {
+ "provider": "openai",
+ "base_model": "text-embedding-3-small",
+ "base_dimension": 512,
+ "batch_size": 128,
+ "text_splitter": {
+ "type": "recursive_character",
+ "chunk_size": 512,
+ "chunk_overlap": 20
+ },
+ "rerank_model": "None"
+ },
+ "kg": {
+ "provider": "None"
+ },
+ "eval": {
+ "provider": "None"
+ },
+ "ingestion":{
+ "excluded_parsers": [
+ "mp4"
+ ]
+ },
+ "logging": {
+ "provider": "local",
+ "log_table": "logs",
+ "log_info_table": "log_info"
+ },
+ "prompt": {
+ "provider": "local"
+ },
+ "vector_database": {
+ "provider": "pgvector"
+ }
+}
diff --git a/R2R/ingesting/README.md b/R2R/ingesting/README.md
new file mode 100644
index 00000000..4a3951f6
--- /dev/null
+++ b/R2R/ingesting/README.md
@@ -0,0 +1,21 @@
+
+
+
+
+
+
+
+
+
+
+
+Ingesting a library
+
+
+# About
+R2R was designed to bridge the gap between local LLM experimentation and scalable, production-ready Retrieval-Augmented Generation (RAG). R2R provides a comprehensive and SOTA RAG system for developers, built around a RESTful API for ease of use.
+
+ingest_my_data.py in the directory [[ingesting]] shows a process you can use to place multiple pdf files into the RAGs context.
+Because there are limits to how much data can be uploaded and processed by different LLM providers, there are wait times coded into the ingest process.
+
+In the code there are many directories, because the upload limit was around 35 megabytes, and references existed across many years.
\ No newline at end of file
diff --git a/R2R/ingesting/aging_pt1.json b/R2R/ingesting/aging_pt1.json
new file mode 100755
index 00000000..27678257
--- /dev/null
+++ b/R2R/ingesting/aging_pt1.json
@@ -0,0 +1 @@
+{'processed_documents': ["Document '2001 - Human Genome and Diseases.pdf' processed successfully.", "Document '2001 - Demography in the age of genomics.pdf' processed successfully.", "Document '2002 - Roles of the Werner syndrome protein in pathways required.pdf' processed successfully.", "Document '2003 - A Method for Detecting Recent Selection in the Human Genome.pdf' processed successfully.", "Document '2004 - Human blood genomics distinct profiles for gender, age.pdf' processed successfully.", "Document '1996 - Livestock Genomics Comes of Age.pdf' processed successfully.", "Document '2003 - The application of functional genomics.pdf' processed successfully.", "Document '2004 - Genomic instability, aging.pdf' processed successfully.", "Document '2002 - Influence of age, sex, and strength training.pdf' processed successfully.", "Document '2001 - Statistical tests of selective neutrality.pdf' processed successfully.", "Document '1997 - Genetic and functional changes in mitochondria associated with aging.pdf' processed successfully.", "Document '2003 - From basepairs to birdsongs phylogenetic data.pdf' processed successfully.", "Document '2004 - Comparative analysis of processed pseudogenes.pdf' processed successfully.", "Document '2002 - Genome Dynamics in Aging Mice.pdf' processed successfully.", "Document '2004 - Cytonuclear coevolution the genomics.pdf' processed successfully.", "Document '1999 - Functional integrity of mitochondrial genomes in human plateletsand autopsied brain tissues from elderly patients withAlzheimer’s disease.pdf' processed successfully.", "Document '2002 - Large genome rearrangements as a primary cause of aging.pdf' processed successfully.", "Document '2002 - Retroelement Distributions in the Human.pdf' processed successfully.", "Document '2001 - Marked differences in unilateral isolated retinoblastomas.pdf' processed successfully.", "Document '2004 - The emergence of epidemiology.pdf' processed successfully.", "Document '2003 - Functional Genomics of Ageing.pdf' processed successfully.", "Document '1999 - Functional Genomics and Rat Models.pdf' processed successfully.", "Document '2004 - A genome scan for diabetic nephropathy in African Americans.pdf' processed successfully.", "Document '2004 - Endosymbiotic gene transfer.pdf' processed successfully.", "Document '2003 - Gene expression profile of aging in human muscle.pdf' processed successfully.", "Document '2002 - Mitochondrial dysfunction leads to telomere attrition.pdf' processed successfully.", "Document '2004 - Proinflammatory phenotype of coronary arteries promotes endothelial.pdf' processed successfully.", "Document '2003 - Age-related changes in the transcriptional profile of mouse RPE choroid.pdf' processed successfully.", "Document '1996 - Isolation and characterization of genomic and cDNA clones encoding.pdf' processed successfully.", "Document '1999 - Molecular Biology of Aging.pdf' processed successfully.", "Document '2004 - RNA-interference-based functional genomics in mammalian cells.pdf' processed successfully.", "Document '1982 - Is Cell Aging Caused by Respiration-Dependent Injury to the Mitochondrial Genome.pdf' processed successfully.", "Document '2004 - Comparing genomic expression patterns across species.pdf' processed successfully.", "Document '1989 - Genetic instability and aging theories, facts, and future perspectives.pdf' processed successfully.", "Document '2004 - Evolutionary history of Oryza sativa LTR retrotransposons.pdf' processed successfully.", "Document '2004 - Genome-Wide Scan for a Healthy Aging.pdf' processed successfully.", "Document '2000 - A Major Gene Affecting Age-Related Hearing Loss Is Common.pdf' processed successfully.", "Document '2001 - Fungal virulence studies come of age.pdf' processed successfully.", "Document '1999 - The caenorhabditis elegans genome.pdf' processed successfully.", "Document '2001 - Plant Systematics in the Age of Genomics.pdf' processed successfully.", "Document '2001 - Vitamins minerals and genomic stability in humans.pdf' processed successfully.", "Document '2000 - Genome-wide study of aging and oxidative stress.pdf' processed successfully.", "Document '2001 - Genome maintenance mechanisms.pdf' processed successfully.", "Document '2002 - Signatures of Domain Shuffling.pdf' processed successfully.", "Document '2004 - Age-Related Macular Degeneration A High-Resolution Genome Scan.pdf' processed successfully.", "Document '1987 - Genomic 5-Methyldeoxycytidine.pdf' processed successfully.", "Document '2002 - Parkinson’s Disease and Apolipoprotein E Possible.pdf' processed successfully.", "Document '2002 - Functional genomics the coming.pdf' processed successfully.", "Document '2003 - Mitochondrial DNA modifies cognition in interaction.pdf' processed successfully.", "Document '2001 - Methylation meets genomics.pdf' processed successfully.", "Document '2003 - Life-long reduction in MnSOD activity results.pdf' processed successfully.", "Document '2004 - A Transcriptional Profile of Aging.pdf' processed successfully.", "Document '1999 - Rothmund–Thomson Syndrome Responsible Gene, RECQL4.pdf' processed successfully.", "Document '2003 - Caloric restriction promotes genomic stability by induction.pdf' processed successfully.", "Document '2003 - Epigenetic regulation of gene expression how the genome integrates intrinsic and environmental signals.pdf' processed successfully.", "Document '2004 - Genome-wide RNA interference screen identifies.pdf' processed successfully.", "Document '2000 - A full genome scan for age-related maculopathy.pdf' processed successfully.", "Document '1997 - Bioinformatics in a post-genomics age.pdf' processed successfully.", "Document '1990 - Extrachromosomal circular DNAs and genomic sequence plasticity.pdf' processed successfully.", "Document '2004 - Nutritional genomics.pdf' processed successfully.", "Document '2004 - Microbial Genomics and the Periodic Table.pdf' processed successfully.", "Document '1997 - Age associated alterations of the mitochondrial genome.pdf' processed successfully.", "Document '2002 - Genome-Wide Transcript Profiles in Aging.pdf' processed successfully.", "Document '2003 - Genetics, genes, genomics and g.pdf' processed successfully.", "Document '1999 - Cell-by-cell scanning of whole mitochondrial genomes.pdf' processed successfully.", "Document '2004 - Prevalence and correlates of orthostatic.pdf' processed successfully.", "Document '2000 - From life to death – the struggle between chemistry and biology during.pdf' processed successfully.", "Document '1998 - The bottleneck mitochondrial imperatives in oogenesis and ovarian.pdf' processed successfully.", "Document '1993 - Genomic Damage and Its Repair.pdf' processed successfully.", "Document '2001 - Mitochondrial genome instability in human cancers.pdf' processed successfully.", "Document '2001 - The genetics of aging.pdf' processed successfully.", "Document '2003 - Lifelong voluntary exercise in the mouse prevents.pdf' processed successfully.", "Document '2004 - Age-associated alteration of gene expression.pdf' processed successfully.", "Document '1999 - Qualitative assessment of Genotoxicity.pdf' processed successfully.", "Document '2001 - Genomic profiling of short- and long-term caloric.pdf' processed successfully.", "Document '2004 - Ageing, repetitive genomes and DNA.pdf' processed successfully.", "Document '2004 - Genomic DNA methylation of juvenile and mature Acacia mangium.pdf' processed successfully.", "Document '2003 - Welcome to the Genomic Era.pdf' processed successfully.", "Document '2003 - Whole-genome screening indicates a possible burst of formation.pdf' processed successfully.", "Document '2003 - Population Screening in the Age.pdf' processed successfully.", "Document '1997 - Tumour-suppressor genes evolving definitions in the genomic age.pdf' processed successfully.", "Document '2003 - Results of a high-resolution genome screen.pdf' processed successfully.", "Document '2003 - A Whole-Genome Screen of a Quantitative Trait of Age-Related.pdf' processed successfully.", "Document '1997 - Multi-organ characterization of mitochondrial.pdf' processed successfully.", "Document '2003 - Risk Factors for Hodgkin’s Lymphoma by EBV.pdf' processed successfully.", "Document '2002 - Human mitochondrial DNA with large deletions.pdf' processed successfully.", "Document '2002 - Genomic DNA methylation–demethylation during aging.pdf' processed successfully.", "Document '2003 - Age-related impairment of the transcriptional.pdf' processed successfully.", "Document '1995 - Rearranged Mitochondrial Genomes Are Present in Human.pdf' processed successfully.", "Document '2002 - Population Genomics Ageing.pdf' processed successfully.", "Document '2003 - The Dawning era of polymer.pdf' processed successfully.", "Document '2001 - A genome-wide scan for linkage to human.pdf' processed successfully.", "Document '2003 - Molecular mechanisms of reduced adrenergic signaling.pdf' processed successfully.", "Document '2002 - Telomere dysfunction provokes regional amplification.pdf' processed successfully.", "Document '2004 - Mitochondrial Genome Single Nucleotide.pdf' processed successfully.", "Document '1991 - Detection of Epstein-Barr virus genomes in Hodgkin's disease relation to age.pdf' processed successfully.", "Document '1998 - Microarrays biotechnology’s discovery.pdf' processed successfully.", "Document '2003 - The age of the Arabidopsis thaliana genome duplication.pdf' processed successfully.", "Document '2004 - Whole Genome Scan for Obstructive Sleep Apnea.pdf' processed successfully."], 'failed_documents': ["Document '2003 - Telomeres in the chicken genome stability and chromosome ends.pdf': Error code: 429 - {'error': {'message': 'Rate limit reached for text-embedding-3-small in organization org-p90FbCDZwTfuyHzyjGoHvDaR on tokens per min (TPM): Limit 1000000, Used 996746, Requested 12291. Please try again in 542ms. Visit https://platform.openai.com/account/rate-limits to learn more.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}", "Document '1983 - Mitochondrial DNA in Mortal and Immortal Human Cell.pdf': Error code: 429 - {'error': {'message': 'Rate limit reached for text-embedding-3-small in organization org-p90FbCDZwTfuyHzyjGoHvDaR on tokens per min (TPM): Limit 1000000, Used 996385, Requested 12801. Please try again in 551ms. Visit https://platform.openai.com/account/rate-limits to learn more.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}", "Document '2004 - Additive regulation of hepatic gene expression by dwarfism.pdf': Request timed out.", "Document '2004 - A New Resource for Cereal Genomics 22K Barley.pdf': Request timed out.", "Document '1998 - Molecular archaeology of the Escherichia coli genome.pdf': Request timed out.", "Document '2003 - Imaging genomics.pdf': Request timed out.", "Document '2001 - Genomics food and nutrition.pdf': Request timed out.", "Document '2003 - Molecular profiling in the age.pdf': Request timed out."], 'skipped_documents': []}
diff --git a/R2R/ingesting/cmds.txt b/R2R/ingesting/cmds.txt
new file mode 100755
index 00000000..9745b929
--- /dev/null
+++ b/R2R/ingesting/cmds.txt
@@ -0,0 +1 @@
+Time taken to ingest files: 541.67 secondsngest-files ~/SolomonVentures/GeneNetwork/docRequesting...N/Gene\ Network.org\ PDFs/*.pdf
\ No newline at end of file
diff --git a/R2R/ingesting/ingest_my_data.py b/R2R/ingesting/ingest_my_data.py
new file mode 100755
index 00000000..cb0fa551
--- /dev/null
+++ b/R2R/ingesting/ingest_my_data.py
@@ -0,0 +1,99 @@
+from os import listdir
+from os.path import isfile, join
+import time
+import datetime
+import configparser
+from r2r import R2RClient
+
+cfg = configparser.ConfigParser()
+cfg.read('_config.cfg')
+
+client = R2RClient("http://localhost:7272")
+
+#print("The status of the client is {0}".format(client.health()))
+
+# should be read from a configuration file
+main_file_dir = 'full path'
+
+data_dir = cfg['PDF_DIR']
+
+file_paths = [
+ data_dir+cfg['genetics']['diabetes'],
+ data_dir+cfg['genetics']['aging']
+]
+
+print("The defined file paths {0}".format(file_paths))
+
+"""
+file_paths = [
+ main_file_dir+'pt02/b/',
+ main_file_dir+'pt03/c/',
+ main_file_dir+'pt03/a/',
+ main_file_dir+'pt03/b/',
+ main_file_dir+'pt03/c/',
+ main_file_dir+'pt04/a/',
+ main_file_dir+'pt04/b/',
+ main_file_dir+'pt04/c/',
+ main_file_dir+'pt05/a/',
+ main_file_dir+'pt05/b/',
+ main_file_dir+'pt05/c/',
+ main_file_dir+'pt06/a/',
+ main_file_dir+'pt06/b/',
+ main_file_dir+'pt06/c/',
+ main_file_dir+'pt07/a/',
+ main_file_dir+'pt07/b/',
+ main_file_dir+'pt07/c/',
+ main_file_dir+'pt08/a/',
+ main_file_dir+'pt08/b/',
+ main_file_dir+'pt08/c/',
+ main_file_dir+'pt09/a/',
+ main_file_dir+'pt09/b/',
+ main_file_dir+'pt09/c/',
+ main_file_dir+'pt10/a/',
+ main_file_dir+'pt10/b/',
+ main_file_dir+'pt10/c/',
+ main_file_dir+'pt11/a/',
+ main_file_dir+'pt11/b/',
+ main_file_dir+'pt11/c/',
+ main_file_dir+'pt12/a/',
+ main_file_dir+'pt12/b/',
+ main_file_dir+'pt12/c/',
+ main_file_dir+'pt13/a/',
+ main_file_dir+'pt13/b/',
+ main_file_dir+'pt13/c/',
+ main_file_dir+'pt14/a/',
+ main_file_dir+'pt14/b/',
+ main_file_dir+'pt14/c/',
+ main_file_dir+'pt14/d/'
+ ]
+
+
+ndx = 0
+for file_list in file_paths:
+ the_pdfs = [ join(file_list, f) for f in listdir(file_list) if isfile(join(file_list, f))]
+ '''
+ # subroutine to test list content
+ for the_pdf in the_pdfs:
+ print('{0} -> {1}'.format(ndx, the_pdf))
+ ndx += 1
+ '''
+ print(datetime.datetime.now())
+ begin_ingesting = datetime.datetime.now()
+ sleeptime = 30
+ try:
+ ingest_response = client.ingest_files(
+ file_paths=the_pdfs
+ )
+ except:
+ ingest_response = "Nothing ingested from {0}".format(file_list)
+ sleeptime = 1
+
+ end_ingesting = datetime.datetime.now()
+
+ # show results of ingesting documents
+ print("Entry [{0}]\t{1} {2}\n\t\t{3}".format(ndx, file_list, (end_ingesting-begin_ingesting), ingest_response))
+
+ # brace against pinging API too quickly
+ time.sleep(sleeptime)
+ ndx += 1
+"""
\ No newline at end of file
diff --git a/R2R/poetry.lock b/R2R/poetry.lock
new file mode 100755
index 00000000..39eb6d04
--- /dev/null
+++ b/R2R/poetry.lock
@@ -0,0 +1,3942 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.9.5"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
+ {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"},
+ {file = "aiohttp-3.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ae79c1bc12c34082d92bf9422764f799aee4746fd7a392db46b7fd357d4a17a"},
+ {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d3ebb9e1316ec74277d19c5f482f98cc65a73ccd5430540d6d11682cd857430"},
+ {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84dabd95154f43a2ea80deffec9cb44d2e301e38a0c9d331cc4aa0166fe28ae3"},
+ {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c8a02fbeca6f63cb1f0475c799679057fc9268b77075ab7cf3f1c600e81dd46b"},
+ {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c26959ca7b75ff768e2776d8055bf9582a6267e24556bb7f7bd29e677932be72"},
+ {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:714d4e5231fed4ba2762ed489b4aec07b2b9953cf4ee31e9871caac895a839c0"},
+ {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e7a6a8354f1b62e15d48e04350f13e726fa08b62c3d7b8401c0a1314f02e3558"},
+ {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:c413016880e03e69d166efb5a1a95d40f83d5a3a648d16486592c49ffb76d0db"},
+ {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ff84aeb864e0fac81f676be9f4685f0527b660f1efdc40dcede3c251ef1e867f"},
+ {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:ad7f2919d7dac062f24d6f5fe95d401597fbb015a25771f85e692d043c9d7832"},
+ {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:702e2c7c187c1a498a4e2b03155d52658fdd6fda882d3d7fbb891a5cf108bb10"},
+ {file = "aiohttp-3.9.5-cp310-cp310-win32.whl", hash = "sha256:67c3119f5ddc7261d47163ed86d760ddf0e625cd6246b4ed852e82159617b5fb"},
+ {file = "aiohttp-3.9.5-cp310-cp310-win_amd64.whl", hash = "sha256:471f0ef53ccedec9995287f02caf0c068732f026455f07db3f01a46e49d76bbb"},
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[[package]]
+name = "zipp"
+version = "3.19.2"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+ {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+]
+
+[package.extras]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[extras]
+all = ["moviepy", "opencv-python", "sentence-transformers", "tiktoken"]
+ingest-movies = ["moviepy", "opencv-python"]
+local-embedding = ["sentence-transformers"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.9,<3.13"
+content-hash = "a11aaf6da08f70ddd083f7b3d50a1a57cda078e91b23afe97077774b3483aaac"
diff --git a/R2R/pyproject.toml b/R2R/pyproject.toml
new file mode 100755
index 00000000..153e2cd4
--- /dev/null
+++ b/R2R/pyproject.toml
@@ -0,0 +1,108 @@
+[build-system]
+requires = ["poetry-core", "setuptools", "wheel"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "r2r"
+version = "0.2.59"
+description = "SciPhi R2R"
+authors = ["Owen Colegrove "]
+license = "MIT"
+readme = "README.md"
+include = ["config.json", "compose.yaml", "compose.neo4j.yaml"]
+
+[tool.poetry.dependencies]
+# Python Versions
+python = ">=3.9,<3.13"
+
+# Required dependencies
+
+# python
+pydantic = "^2.6.3"
+python-multipart = "^0.0.9"
+
+# infrastructure
+fastapi = "^0.109.2"
+fire = "^0.5.0"
+gunicorn = "^21.2.0"
+requests = "^2.31.0"
+types-requests = "^2.31.0"
+uvicorn = "^0.27.0.post1"
+
+# TODO - Make some of these optional
+# async db providers
+aiosqlite = "^0.20.0"
+asyncpg = "^0.29.0"
+redis = "^5.0.4"
+
+# ingestion
+beautifulsoup4 = "^4.12.3"
+openpyxl = "^3.1.2"
+markdown = "^3.6"
+pypdf = "^4.2.0"
+python-pptx = "^0.6.23"
+python-docx = "^1.1.0"
+nest-asyncio = "^1.6.0"
+opencv-python = { version = "^4.10.0.82", optional = true }
+moviepy = { version = "^1.0.3", optional = true }
+
+# embedding providers
+tiktoken = {version = "^0.5.2", optional = true}
+sentence-transformers = {version = "^2.7.0", optional = true}
+
+# vector db providers
+vecs = "^0.4.0"
+
+# llm providers
+litellm = "^1.35.18"
+openai = "^1.11.1"
+
+# integrations
+fsspec = "^2024.6.0"
+posthog = "^3.5.0"
+sqlalchemy = "^2.0.30"
+ollama = "^0.2.1"
+neo4j = "^5.21.0"
+
+[tool.poetry.extras]
+all = ["tiktoken", "sentence-transformers", "moviepy", "opencv-python"]
+local-embedding = ["sentence-transformers"]
+ingest-movies = ["moviepy", "opencv-python"]
+
+[tool.poetry.group.dev.dependencies]
+black = "^24.3.0"
+codecov = "^2.1.13"
+flake8 = "6.1.0"
+isort = "5.12.0"
+mypy = "^1.5.1"
+pre-commit = "^2.9"
+pytest = "^8.2.0"
+pytest-asyncio = "^0.23.6"
+pytest-dependency = "^0.6.0"
+pytest-mock = "^3.14.0"
+pytest-cov = "^5.0.0"
+
+[tool.poetry.scripts]
+r2r = "r2r.cli.cli:main"
+
+[tool.black]
+line-length = 79
+
+[tool.mypy]
+ignore_missing_imports = true
+exclude = 'playground/.*|deprecated/.*|dump/.*|docs/source|vecs/*'
+
+[[tool.mypy.overrides]]
+module = "yaml"
+ignore_missing_imports = true
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+addopts = "--cov=r2r --cov-report=term-missing --cov-report=xml"
+testpaths = [
+ "tests",
+]
+filterwarnings = [
+ "ignore::DeprecationWarning",
+ "ignore::pytest.PytestUnraisableExceptionWarning",
+]
diff --git a/R2R/r2r/__init__.py b/R2R/r2r/__init__.py
new file mode 100755
index 00000000..492cc13a
--- /dev/null
+++ b/R2R/r2r/__init__.py
@@ -0,0 +1,110 @@
+import logging
+
+# Keep '*' imports for enhanced development velocity
+# corresponding flake8 error codes are F403, F405
+from .base import *
+from .integrations import *
+from .main import *
+from .parsers import *
+from .pipelines import *
+from .pipes import *
+from .prompts import *
+
+logger = logging.getLogger("r2r")
+logger.setLevel(logging.INFO)
+
+# Create a console handler and set the level to info
+ch = logging.StreamHandler()
+ch.setLevel(logging.INFO)
+
+# Create a formatter and set it for the handler
+formatter = logging.Formatter(
+ "%(asctime)s - %(levelname)s - %(name)s - %(message)s"
+)
+ch.setFormatter(formatter)
+
+# Add the handler to the logger
+logger.addHandler(ch)
+
+# Optional: Prevent propagation to the root logger
+logger.propagate = False
+
+__all__ = [
+ "R2RException",
+ "LoggingConfig",
+ "LocalKVLoggingProvider",
+ "PostgresLoggingConfig",
+ "PostgresKVLoggingProvider",
+ "RedisLoggingConfig",
+ "RedisKVLoggingProvider",
+ "KVLoggingSingleton",
+ "VectorEntry",
+ "VectorType",
+ "Vector",
+ "VectorSearchRequest",
+ "VectorSearchResult",
+ "AsyncPipe",
+ "PipeType",
+ "AsyncState",
+ "Prompt",
+ "DataType",
+ "DocumentType",
+ "Document",
+ "Extraction",
+ "ExtractionType",
+ "Fragment",
+ "FragmentType",
+ "SearchPipe",
+ # Parsers
+ "AsyncParser",
+ "CSVParser",
+ "DOCXParser",
+ "HTMLParser",
+ "JSONParser",
+ "MDParser",
+ "PDFParser",
+ "PPTParser",
+ "TextParser",
+ "XLSXParser",
+ "AsyncPipeline",
+ # Providers
+ "EmbeddingConfig",
+ "EmbeddingProvider",
+ "EvalConfig",
+ "EvalProvider",
+ "LLMEvalProvider",
+ "PromptConfig",
+ "PromptProvider",
+ "GenerationConfig",
+ "LLMChatCompletion",
+ "LLMChatCompletionChunk",
+ "LLMConfig",
+ "LLMProvider",
+ "VectorDBConfig",
+ "VectorDBProvider",
+ "R2RConfig",
+ "TextSplitter",
+ "RecursiveCharacterTextSplitter",
+ "generate_run_id",
+ "generate_id_from_label",
+ "R2REngine",
+ # Pipes
+ "EmbeddingPipe",
+ "EvalPipe",
+ "ParsingPipe",
+ "QueryTransformPipe",
+ "SearchRAGPipe",
+ "StreamingSearchRAGPipe",
+ "VectorSearchPipe",
+ "VectorStoragePipe",
+ "R2RPromptProvider",
+ "WebSearchPipe",
+ "R2RBuilder",
+ "R2R",
+ "KGAgentSearchPipe",
+ # Prebuilts
+ "MultiSearchPipe",
+ "R2RPipeFactoryWithMultiSearch",
+ # Integrations
+ "SerperClient",
+]
diff --git a/R2R/r2r/base/__init__.py b/R2R/r2r/base/__init__.py
new file mode 100755
index 00000000..a6794a84
--- /dev/null
+++ b/R2R/r2r/base/__init__.py
@@ -0,0 +1,160 @@
+from .abstractions.base import AsyncSyncMeta, UserStats, syncable
+from .abstractions.document import (
+ DataType,
+ Document,
+ DocumentInfo,
+ DocumentType,
+ Entity,
+ Extraction,
+ ExtractionType,
+ Fragment,
+ FragmentType,
+ KGExtraction,
+ Triple,
+ extract_entities,
+ extract_triples,
+)
+from .abstractions.exception import R2RDocumentProcessingError, R2RException
+from .abstractions.llama_abstractions import VectorStoreQuery
+from .abstractions.llm import (
+ GenerationConfig,
+ LLMChatCompletion,
+ LLMChatCompletionChunk,
+ RAGCompletion,
+)
+from .abstractions.prompt import Prompt
+from .abstractions.search import (
+ AggregateSearchResult,
+ KGSearchRequest,
+ KGSearchResult,
+ KGSearchSettings,
+ VectorSearchRequest,
+ VectorSearchResult,
+ VectorSearchSettings,
+)
+from .abstractions.vector import Vector, VectorEntry, VectorType
+from .logging.kv_logger import (
+ KVLoggingSingleton,
+ LocalKVLoggingProvider,
+ LoggingConfig,
+ PostgresKVLoggingProvider,
+ PostgresLoggingConfig,
+ RedisKVLoggingProvider,
+ RedisLoggingConfig,
+)
+from .logging.log_processor import (
+ AnalysisTypes,
+ FilterCriteria,
+ LogAnalytics,
+ LogAnalyticsConfig,
+ LogProcessor,
+)
+from .logging.run_manager import RunManager, manage_run
+from .parsers import AsyncParser
+from .pipeline.base_pipeline import AsyncPipeline
+from .pipes.base_pipe import AsyncPipe, AsyncState, PipeType
+from .providers.embedding_provider import EmbeddingConfig, EmbeddingProvider
+from .providers.eval_provider import EvalConfig, EvalProvider
+from .providers.kg_provider import KGConfig, KGProvider, update_kg_prompt
+from .providers.llm_provider import LLMConfig, LLMProvider
+from .providers.prompt_provider import PromptConfig, PromptProvider
+from .providers.vector_db_provider import VectorDBConfig, VectorDBProvider
+from .utils import (
+ EntityType,
+ RecursiveCharacterTextSplitter,
+ Relation,
+ TextSplitter,
+ format_entity_types,
+ format_relations,
+ generate_id_from_label,
+ generate_run_id,
+ increment_version,
+ run_pipeline,
+ to_async_generator,
+)
+
+__all__ = [
+ # Logging
+ "AsyncParser",
+ "AnalysisTypes",
+ "LogAnalytics",
+ "LogAnalyticsConfig",
+ "LogProcessor",
+ "LoggingConfig",
+ "LocalKVLoggingProvider",
+ "PostgresLoggingConfig",
+ "PostgresKVLoggingProvider",
+ "RedisLoggingConfig",
+ "AsyncSyncMeta",
+ "syncable",
+ "RedisKVLoggingProvider",
+ "KVLoggingSingleton",
+ "RunManager",
+ "manage_run",
+ # Abstractions
+ "VectorEntry",
+ "VectorType",
+ "Vector",
+ "VectorSearchRequest",
+ "VectorSearchResult",
+ "VectorSearchSettings",
+ "KGSearchRequest",
+ "KGSearchResult",
+ "KGSearchSettings",
+ "AggregateSearchResult",
+ "AsyncPipe",
+ "PipeType",
+ "AsyncState",
+ "AsyncPipe",
+ "Prompt",
+ "DataType",
+ "DocumentType",
+ "Document",
+ "DocumentInfo",
+ "Extraction",
+ "ExtractionType",
+ "Fragment",
+ "FragmentType",
+ "extract_entities",
+ "Entity",
+ "extract_triples",
+ "R2RException",
+ "R2RDocumentProcessingError",
+ "Triple",
+ "KGExtraction",
+ "UserStats",
+ # Pipelines
+ "AsyncPipeline",
+ # Providers
+ "EmbeddingConfig",
+ "EmbeddingProvider",
+ "EvalConfig",
+ "EvalProvider",
+ "PromptConfig",
+ "PromptProvider",
+ "GenerationConfig",
+ "RAGCompletion",
+ "VectorStoreQuery",
+ "LLMChatCompletion",
+ "LLMChatCompletionChunk",
+ "LLMConfig",
+ "LLMProvider",
+ "VectorDBConfig",
+ "VectorDBProvider",
+ "KGProvider",
+ "KGConfig",
+ "update_kg_prompt",
+ # Other
+ "FilterCriteria",
+ "TextSplitter",
+ "RecursiveCharacterTextSplitter",
+ "to_async_generator",
+ "EntityType",
+ "Relation",
+ "format_entity_types",
+ "format_relations",
+ "increment_version",
+ "run_pipeline",
+ "generate_run_id",
+ "generate_id_from_label",
+]
diff --git a/R2R/r2r/base/abstractions/__init__.py b/R2R/r2r/base/abstractions/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/base/abstractions/base.py b/R2R/r2r/base/abstractions/base.py
new file mode 100755
index 00000000..7121f6ce
--- /dev/null
+++ b/R2R/r2r/base/abstractions/base.py
@@ -0,0 +1,93 @@
+import asyncio
+import uuid
+from typing import List
+
+from pydantic import BaseModel
+
+
+class UserStats(BaseModel):
+ user_id: uuid.UUID
+ num_files: int
+ total_size_in_bytes: int
+ document_ids: List[uuid.UUID]
+
+
+class AsyncSyncMeta(type):
+ _event_loop = None # Class-level shared event loop
+
+ @classmethod
+ def get_event_loop(cls):
+ if cls._event_loop is None or cls._event_loop.is_closed():
+ cls._event_loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(cls._event_loop)
+ return cls._event_loop
+
+ def __new__(cls, name, bases, dct):
+ new_cls = super().__new__(cls, name, bases, dct)
+ for attr_name, attr_value in dct.items():
+ if asyncio.iscoroutinefunction(attr_value) and getattr(
+ attr_value, "_syncable", False
+ ):
+ sync_method_name = attr_name[
+ 1:
+ ] # Remove leading 'a' for sync method
+ async_method = attr_value
+
+ def make_sync_method(async_method):
+ def sync_wrapper(self, *args, **kwargs):
+ loop = cls.get_event_loop()
+ if not loop.is_running():
+ # Setup to run the loop in a background thread if necessary
+ # to prevent blocking the main thread in a synchronous call environment
+ from threading import Thread
+
+ result = None
+ exception = None
+
+ def run():
+ nonlocal result, exception
+ try:
+ asyncio.set_event_loop(loop)
+ result = loop.run_until_complete(
+ async_method(self, *args, **kwargs)
+ )
+ except Exception as e:
+ exception = e
+ finally:
+ generation_config = kwargs.get(
+ "rag_generation_config", None
+ )
+ if (
+ not generation_config
+ or not generation_config.stream
+ ):
+ loop.run_until_complete(
+ loop.shutdown_asyncgens()
+ )
+ loop.close()
+
+ thread = Thread(target=run)
+ thread.start()
+ thread.join()
+ if exception:
+ raise exception
+ return result
+ else:
+ # If there's already a running loop, schedule and execute the coroutine
+ future = asyncio.run_coroutine_threadsafe(
+ async_method(self, *args, **kwargs), loop
+ )
+ return future.result()
+
+ return sync_wrapper
+
+ setattr(
+ new_cls, sync_method_name, make_sync_method(async_method)
+ )
+ return new_cls
+
+
+def syncable(func):
+ """Decorator to mark methods for synchronous wrapper creation."""
+ func._syncable = True
+ return func
diff --git a/R2R/r2r/base/abstractions/document.py b/R2R/r2r/base/abstractions/document.py
new file mode 100755
index 00000000..117db7b9
--- /dev/null
+++ b/R2R/r2r/base/abstractions/document.py
@@ -0,0 +1,242 @@
+"""Abstractions for documents and their extractions."""
+
+import base64
+import json
+import logging
+import uuid
+from datetime import datetime
+from enum import Enum
+from typing import Optional, Union
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)
+
+DataType = Union[str, bytes]
+
+
+class DocumentType(str, Enum):
+ """Types of documents that can be stored."""
+
+ CSV = "csv"
+ DOCX = "docx"
+ HTML = "html"
+ JSON = "json"
+ MD = "md"
+ PDF = "pdf"
+ PPTX = "pptx"
+ TXT = "txt"
+ XLSX = "xlsx"
+ GIF = "gif"
+ PNG = "png"
+ JPG = "jpg"
+ JPEG = "jpeg"
+ SVG = "svg"
+ MP3 = "mp3"
+ MP4 = "mp4"
+
+
+class Document(BaseModel):
+ id: uuid.UUID = Field(default_factory=uuid.uuid4)
+ type: DocumentType
+ data: Union[str, bytes]
+ metadata: dict
+
+ def __init__(self, *args, **kwargs):
+ data = kwargs.get("data")
+ if data and isinstance(data, str):
+ try:
+ # Try to decode if it's already base64 encoded
+ kwargs["data"] = base64.b64decode(data)
+ except:
+ # If it's not base64, encode it to bytes
+ kwargs["data"] = data.encode("utf-8")
+
+ doc_type = kwargs.get("type")
+ if isinstance(doc_type, str):
+ kwargs["type"] = DocumentType(doc_type)
+
+ # Generate UUID based on the hash of the data
+ if "id" not in kwargs:
+ if isinstance(kwargs["data"], bytes):
+ data_hash = uuid.uuid5(
+ uuid.NAMESPACE_DNS, kwargs["data"].decode("utf-8")
+ )
+ else:
+ data_hash = uuid.uuid5(uuid.NAMESPACE_DNS, kwargs["data"])
+
+ kwargs["id"] = data_hash # Set the id based on the data hash
+
+ super().__init__(*args, **kwargs)
+
+ class Config:
+ arbitrary_types_allowed = True
+ json_encoders = {
+ uuid.UUID: str,
+ bytes: lambda v: base64.b64encode(v).decode("utf-8"),
+ }
+
+
+class DocumentStatus(str, Enum):
+ """Status of document processing."""
+
+ PROCESSING = "processing"
+ # TODO - Extend support for `partial-failure`
+ # PARTIAL_FAILURE = "partial-failure"
+ FAILURE = "failure"
+ SUCCESS = "success"
+
+
+class DocumentInfo(BaseModel):
+ """Base class for document information handling."""
+
+ document_id: uuid.UUID
+ version: str
+ size_in_bytes: int
+ metadata: dict
+ status: DocumentStatus = DocumentStatus.PROCESSING
+
+ user_id: Optional[uuid.UUID] = None
+ title: Optional[str] = None
+ created_at: Optional[datetime] = None
+ updated_at: Optional[datetime] = None
+
+ def convert_to_db_entry(self):
+ """Prepare the document info for database entry, extracting certain fields from metadata."""
+ now = datetime.now()
+ metadata = self.metadata
+ if "user_id" in metadata:
+ metadata["user_id"] = str(metadata["user_id"])
+
+ metadata["title"] = metadata.get("title", "N/A")
+ return {
+ "document_id": str(self.document_id),
+ "title": metadata.get("title", "N/A"),
+ "user_id": metadata.get("user_id", None),
+ "version": self.version,
+ "size_in_bytes": self.size_in_bytes,
+ "metadata": json.dumps(self.metadata),
+ "created_at": self.created_at or now,
+ "updated_at": self.updated_at or now,
+ "status": self.status,
+ }
+
+
+class ExtractionType(Enum):
+ """Types of extractions that can be performed."""
+
+ TXT = "txt"
+ IMG = "img"
+ MOV = "mov"
+
+
+class Extraction(BaseModel):
+ """An extraction from a document."""
+
+ id: uuid.UUID
+ type: ExtractionType = ExtractionType.TXT
+ data: DataType
+ metadata: dict
+ document_id: uuid.UUID
+
+
+class FragmentType(Enum):
+ """A type of fragment that can be extracted from a document."""
+
+ TEXT = "text"
+ IMAGE = "image"
+
+
+class Fragment(BaseModel):
+ """A fragment extracted from a document."""
+
+ id: uuid.UUID
+ type: FragmentType
+ data: DataType
+ metadata: dict
+ document_id: uuid.UUID
+ extraction_id: uuid.UUID
+
+
+class Entity(BaseModel):
+ """An entity extracted from a document."""
+
+ category: str
+ subcategory: Optional[str] = None
+ value: str
+
+ def __str__(self):
+ return (
+ f"{self.category}:{self.subcategory}:{self.value}"
+ if self.subcategory
+ else f"{self.category}:{self.value}"
+ )
+
+
+class Triple(BaseModel):
+ """A triple extracted from a document."""
+
+ subject: str
+ predicate: str
+ object: str
+
+
+def extract_entities(llm_payload: list[str]) -> dict[str, Entity]:
+ entities = {}
+ for entry in llm_payload:
+ try:
+ if "], " in entry: # Check if the entry is an entity
+ entry_val = entry.split("], ")[0] + "]"
+ entry = entry.split("], ")[1]
+ colon_count = entry.count(":")
+
+ if colon_count == 1:
+ category, value = entry.split(":")
+ subcategory = None
+ elif colon_count >= 2:
+ parts = entry.split(":", 2)
+ category, subcategory, value = (
+ parts[0],
+ parts[1],
+ parts[2],
+ )
+ else:
+ raise ValueError("Unexpected entry format")
+
+ entities[entry_val] = Entity(
+ category=category, subcategory=subcategory, value=value
+ )
+ except Exception as e:
+ logger.error(f"Error processing entity {entry}: {e}")
+ continue
+ return entities
+
+
+def extract_triples(
+ llm_payload: list[str], entities: dict[str, Entity]
+) -> list[Triple]:
+ triples = []
+ for entry in llm_payload:
+ try:
+ if "], " not in entry: # Check if the entry is an entity
+ elements = entry.split(" ")
+ subject = elements[0]
+ predicate = elements[1]
+ object = " ".join(elements[2:])
+ subject = entities[subject].value # Use entity.value
+ if "[" in object and "]" in object:
+ object = entities[object].value # Use entity.value
+ triples.append(
+ Triple(subject=subject, predicate=predicate, object=object)
+ )
+ except Exception as e:
+ logger.error(f"Error processing triplet {entry}: {e}")
+ continue
+ return triples
+
+
+class KGExtraction(BaseModel):
+ """An extraction from a document that is part of a knowledge graph."""
+
+ entities: dict[str, Entity]
+ triples: list[Triple]
diff --git a/R2R/r2r/base/abstractions/exception.py b/R2R/r2r/base/abstractions/exception.py
new file mode 100755
index 00000000..c76625a3
--- /dev/null
+++ b/R2R/r2r/base/abstractions/exception.py
@@ -0,0 +1,16 @@
+from typing import Any, Optional
+
+
+class R2RException(Exception):
+ def __init__(
+ self, message: str, status_code: int, detail: Optional[Any] = None
+ ):
+ self.message = message
+ self.status_code = status_code
+ super().__init__(self.message)
+
+
+class R2RDocumentProcessingError(R2RException):
+ def __init__(self, error_message, document_id):
+ self.document_id = document_id
+ super().__init__(error_message, 400, {"document_id": document_id})
diff --git a/R2R/r2r/base/abstractions/llama_abstractions.py b/R2R/r2r/base/abstractions/llama_abstractions.py
new file mode 100755
index 00000000..f6bc36e6
--- /dev/null
+++ b/R2R/r2r/base/abstractions/llama_abstractions.py
@@ -0,0 +1,439 @@
+# abstractions are taken from LlamaIndex
+# https://github.com/run-llama/llama_index
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from pydantic import BaseModel, Field, StrictFloat, StrictInt, StrictStr
+
+
+class LabelledNode(BaseModel):
+ """An entity in a graph."""
+
+ label: str = Field(default="node", description="The label of the node.")
+ embedding: Optional[List[float]] = Field(
+ default=None, description="The embeddings of the node."
+ )
+ properties: Dict[str, Any] = Field(default_factory=dict)
+
+ @abstractmethod
+ def __str__(self) -> str:
+ """Return the string representation of the node."""
+ ...
+
+ @property
+ @abstractmethod
+ def id(self) -> str:
+ """Get the node id."""
+ ...
+
+
+class EntityNode(LabelledNode):
+ """An entity in a graph."""
+
+ name: str = Field(description="The name of the entity.")
+ label: str = Field(default="entity", description="The label of the node.")
+ properties: Dict[str, Any] = Field(default_factory=dict)
+
+ def __str__(self) -> str:
+ """Return the string representation of the node."""
+ return self.name
+
+ @property
+ def id(self) -> str:
+ """Get the node id."""
+ return self.name.replace('"', " ")
+
+
+class ChunkNode(LabelledNode):
+ """A text chunk in a graph."""
+
+ text: str = Field(description="The text content of the chunk.")
+ id_: Optional[str] = Field(
+ default=None,
+ description="The id of the node. Defaults to a hash of the text.",
+ )
+ label: str = Field(
+ default="text_chunk", description="The label of the node."
+ )
+ properties: Dict[str, Any] = Field(default_factory=dict)
+
+ def __str__(self) -> str:
+ """Return the string representation of the node."""
+ return self.text
+
+ @property
+ def id(self) -> str:
+ """Get the node id."""
+ return str(hash(self.text)) if self.id_ is None else self.id_
+
+
+class Relation(BaseModel):
+ """A relation connecting two entities in a graph."""
+
+ label: str
+ source_id: str
+ target_id: str
+ properties: Dict[str, Any] = Field(default_factory=dict)
+
+ def __str__(self) -> str:
+ """Return the string representation of the relation."""
+ return self.label
+
+ @property
+ def id(self) -> str:
+ """Get the relation id."""
+ return self.label
+
+
+Triplet = Tuple[LabelledNode, Relation, LabelledNode]
+
+
+class VectorStoreQueryMode(str, Enum):
+ """Vector store query mode."""
+
+ DEFAULT = "default"
+ SPARSE = "sparse"
+ HYBRID = "hybrid"
+ TEXT_SEARCH = "text_search"
+ SEMANTIC_HYBRID = "semantic_hybrid"
+
+ # fit learners
+ SVM = "svm"
+ LOGISTIC_REGRESSION = "logistic_regression"
+ LINEAR_REGRESSION = "linear_regression"
+
+ # maximum marginal relevance
+ MMR = "mmr"
+
+
+class FilterOperator(str, Enum):
+ """Vector store filter operator."""
+
+ # TODO add more operators
+ EQ = "==" # default operator (string, int, float)
+ GT = ">" # greater than (int, float)
+ LT = "<" # less than (int, float)
+ NE = "!=" # not equal to (string, int, float)
+ GTE = ">=" # greater than or equal to (int, float)
+ LTE = "<=" # less than or equal to (int, float)
+ IN = "in" # In array (string or number)
+ NIN = "nin" # Not in array (string or number)
+ ANY = "any" # Contains any (array of strings)
+ ALL = "all" # Contains all (array of strings)
+ TEXT_MATCH = "text_match" # full text match (allows you to search for a specific substring, token or phrase within the text field)
+ CONTAINS = "contains" # metadata array contains value (string or number)
+
+
+class MetadataFilter(BaseModel):
+ """Comprehensive metadata filter for vector stores to support more operators.
+
+ Value uses Strict* types, as int, float and str are compatible types and were all
+ converted to string before.
+
+ See: https://docs.pydantic.dev/latest/usage/types/#strict-types
+ """
+
+ key: str
+ value: Union[
+ StrictInt,
+ StrictFloat,
+ StrictStr,
+ List[StrictStr],
+ List[StrictFloat],
+ List[StrictInt],
+ ]
+ operator: FilterOperator = FilterOperator.EQ
+
+ @classmethod
+ def from_dict(
+ cls,
+ filter_dict: Dict,
+ ) -> "MetadataFilter":
+ """Create MetadataFilter from dictionary.
+
+ Args:
+ filter_dict: Dict with key, value and operator.
+
+ """
+ return MetadataFilter.parse_obj(filter_dict)
+
+
+# # TODO: Deprecate ExactMatchFilter and use MetadataFilter instead
+# # Keep class for now so that AutoRetriever can still work with old vector stores
+# class ExactMatchFilter(BaseModel):
+# key: str
+# value: Union[StrictInt, StrictFloat, StrictStr]
+
+# set ExactMatchFilter to MetadataFilter
+ExactMatchFilter = MetadataFilter
+
+
+class FilterCondition(str, Enum):
+ """Vector store filter conditions to combine different filters."""
+
+ # TODO add more conditions
+ AND = "and"
+ OR = "or"
+
+
+class MetadataFilters(BaseModel):
+ """Metadata filters for vector stores."""
+
+ # Exact match filters and Advanced filters with operators like >, <, >=, <=, !=, etc.
+ filters: List[Union[MetadataFilter, ExactMatchFilter, "MetadataFilters"]]
+ # and/or such conditions for combining different filters
+ condition: Optional[FilterCondition] = FilterCondition.AND
+
+
+@dataclass
+class VectorStoreQuery:
+ """Vector store query."""
+
+ query_embedding: Optional[List[float]] = None
+ similarity_top_k: int = 1
+ doc_ids: Optional[List[str]] = None
+ node_ids: Optional[List[str]] = None
+ query_str: Optional[str] = None
+ output_fields: Optional[List[str]] = None
+ embedding_field: Optional[str] = None
+
+ mode: VectorStoreQueryMode = VectorStoreQueryMode.DEFAULT
+
+ # NOTE: only for hybrid search (0 for bm25, 1 for vector search)
+ alpha: Optional[float] = None
+
+ # metadata filters
+ filters: Optional[MetadataFilters] = None
+
+ # only for mmr
+ mmr_threshold: Optional[float] = None
+
+ # NOTE: currently only used by postgres hybrid search
+ sparse_top_k: Optional[int] = None
+ # NOTE: return top k results from hybrid search. similarity_top_k is used for dense search top k
+ hybrid_top_k: Optional[int] = None
+
+
+class PropertyGraphStore(ABC):
+ """Abstract labelled graph store protocol.
+
+ This protocol defines the interface for a graph store, which is responsible
+ for storing and retrieving knowledge graph data.
+
+ Attributes:
+ client: Any: The client used to connect to the graph store.
+ get: Callable[[str], List[List[str]]]: Get triplets for a given subject.
+ get_rel_map: Callable[[Optional[List[str]], int], Dict[str, List[List[str]]]]:
+ Get subjects' rel map in max depth.
+ upsert_triplet: Callable[[str, str, str], None]: Upsert a triplet.
+ delete: Callable[[str, str, str], None]: Delete a triplet.
+ persist: Callable[[str, Optional[fsspec.AbstractFileSystem]], None]:
+ Persist the graph store to a file.
+ """
+
+ supports_structured_queries: bool = False
+ supports_vector_queries: bool = False
+
+ @property
+ def client(self) -> Any:
+ """Get client."""
+ ...
+
+ @abstractmethod
+ def get(
+ self,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> List[LabelledNode]:
+ """Get nodes with matching values."""
+ ...
+
+ @abstractmethod
+ def get_triplets(
+ self,
+ entity_names: Optional[List[str]] = None,
+ relation_names: Optional[List[str]] = None,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> List[Triplet]:
+ """Get triplets with matching values."""
+ ...
+
+ @abstractmethod
+ def get_rel_map(
+ self,
+ graph_nodes: List[LabelledNode],
+ depth: int = 2,
+ limit: int = 30,
+ ignore_rels: Optional[List[str]] = None,
+ ) -> List[Triplet]:
+ """Get depth-aware rel map."""
+ ...
+
+ @abstractmethod
+ def upsert_nodes(self, nodes: List[LabelledNode]) -> None:
+ """Upsert nodes."""
+ ...
+
+ @abstractmethod
+ def upsert_relations(self, relations: List[Relation]) -> None:
+ """Upsert relations."""
+ ...
+
+ @abstractmethod
+ def delete(
+ self,
+ entity_names: Optional[List[str]] = None,
+ relation_names: Optional[List[str]] = None,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> None:
+ """Delete matching data."""
+ ...
+
+ @abstractmethod
+ def structured_query(
+ self, query: str, param_map: Optional[Dict[str, Any]] = None
+ ) -> Any:
+ """Query the graph store with statement and parameters."""
+ ...
+
+ @abstractmethod
+ def vector_query(
+ self, query: VectorStoreQuery, **kwargs: Any
+ ) -> Tuple[List[LabelledNode], List[float]]:
+ """Query the graph store with a vector store query."""
+ ...
+
+ # def persist(
+ # self, persist_path: str, fs: Optional[fsspec.AbstractFileSystem] = None
+ # ) -> None:
+ # """Persist the graph store to a file."""
+ # return
+
+ def get_schema(self, refresh: bool = False) -> Any:
+ """Get the schema of the graph store."""
+ return None
+
+ def get_schema_str(self, refresh: bool = False) -> str:
+ """Get the schema of the graph store as a string."""
+ return str(self.get_schema(refresh=refresh))
+
+ ### ----- Async Methods ----- ###
+
+ async def aget(
+ self,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> List[LabelledNode]:
+ """Asynchronously get nodes with matching values."""
+ return self.get(properties, ids)
+
+ async def aget_triplets(
+ self,
+ entity_names: Optional[List[str]] = None,
+ relation_names: Optional[List[str]] = None,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> List[Triplet]:
+ """Asynchronously get triplets with matching values."""
+ return self.get_triplets(entity_names, relation_names, properties, ids)
+
+ async def aget_rel_map(
+ self,
+ graph_nodes: List[LabelledNode],
+ depth: int = 2,
+ limit: int = 30,
+ ignore_rels: Optional[List[str]] = None,
+ ) -> List[Triplet]:
+ """Asynchronously get depth-aware rel map."""
+ return self.get_rel_map(graph_nodes, depth, limit, ignore_rels)
+
+ async def aupsert_nodes(self, nodes: List[LabelledNode]) -> None:
+ """Asynchronously add nodes."""
+ return self.upsert_nodes(nodes)
+
+ async def aupsert_relations(self, relations: List[Relation]) -> None:
+ """Asynchronously add relations."""
+ return self.upsert_relations(relations)
+
+ async def adelete(
+ self,
+ entity_names: Optional[List[str]] = None,
+ relation_names: Optional[List[str]] = None,
+ properties: Optional[dict] = None,
+ ids: Optional[List[str]] = None,
+ ) -> None:
+ """Asynchronously delete matching data."""
+ return self.delete(entity_names, relation_names, properties, ids)
+
+ async def astructured_query(
+ self, query: str, param_map: Optional[Dict[str, Any]] = {}
+ ) -> Any:
+ """Asynchronously query the graph store with statement and parameters."""
+ return self.structured_query(query, param_map)
+
+ async def avector_query(
+ self, query: VectorStoreQuery, **kwargs: Any
+ ) -> Tuple[List[LabelledNode], List[float]]:
+ """Asynchronously query the graph store with a vector store query."""
+ return self.vector_query(query, **kwargs)
+
+ async def aget_schema(self, refresh: bool = False) -> str:
+ """Asynchronously get the schema of the graph store."""
+ return self.get_schema(refresh=refresh)
+
+ async def aget_schema_str(self, refresh: bool = False) -> str:
+ """Asynchronously get the schema of the graph store as a string."""
+ return str(await self.aget_schema(refresh=refresh))
+
+
+LIST_LIMIT = 128
+
+
+def clean_string_values(text: str) -> str:
+ return text.replace("\n", " ").replace("\r", " ")
+
+
+def value_sanitize(d: Any) -> Any:
+ """Sanitize the input dictionary or list.
+
+ Sanitizes the input by removing embedding-like values,
+ lists with more than 128 elements, that are mostly irrelevant for
+ generating answers in a LLM context. These properties, if left in
+ results, can occupy significant context space and detract from
+ the LLM's performance by introducing unnecessary noise and cost.
+ """
+ if isinstance(d, dict):
+ new_dict = {}
+ for key, value in d.items():
+ if isinstance(value, dict):
+ sanitized_value = value_sanitize(value)
+ if (
+ sanitized_value is not None
+ ): # Check if the sanitized value is not None
+ new_dict[key] = sanitized_value
+ elif isinstance(value, list):
+ if len(value) < LIST_LIMIT:
+ sanitized_value = value_sanitize(value)
+ if (
+ sanitized_value is not None
+ ): # Check if the sanitized value is not None
+ new_dict[key] = sanitized_value
+ # Do not include the key if the list is oversized
+ else:
+ new_dict[key] = value
+ return new_dict
+ elif isinstance(d, list):
+ if len(d) < LIST_LIMIT:
+ return [
+ value_sanitize(item)
+ for item in d
+ if value_sanitize(item) is not None
+ ]
+ else:
+ return None
+ else:
+ return d
diff --git a/R2R/r2r/base/abstractions/llm.py b/R2R/r2r/base/abstractions/llm.py
new file mode 100755
index 00000000..3178d8dc
--- /dev/null
+++ b/R2R/r2r/base/abstractions/llm.py
@@ -0,0 +1,112 @@
+"""Abstractions for the LLM model."""
+
+from typing import TYPE_CHECKING, ClassVar, Optional
+
+from openai.types.chat import ChatCompletion, ChatCompletionChunk
+from pydantic import BaseModel, Field
+
+if TYPE_CHECKING:
+ from .search import AggregateSearchResult
+
+LLMChatCompletion = ChatCompletion
+LLMChatCompletionChunk = ChatCompletionChunk
+
+
+class RAGCompletion:
+ completion: LLMChatCompletion
+ search_results: "AggregateSearchResult"
+
+ def __init__(
+ self,
+ completion: LLMChatCompletion,
+ search_results: "AggregateSearchResult",
+ ):
+ self.completion = completion
+ self.search_results = search_results
+
+
+class GenerationConfig(BaseModel):
+ _defaults: ClassVar[dict] = {
+ "model": "gpt-4o",
+ "temperature": 0.1,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": False,
+ "functions": None,
+ "skip_special_tokens": False,
+ "stop_token": None,
+ "num_beams": 1,
+ "do_sample": True,
+ "generate_with_chat": False,
+ "add_generation_kwargs": None,
+ "api_base": None,
+ }
+
+ model: str = Field(
+ default_factory=lambda: GenerationConfig._defaults["model"]
+ )
+ temperature: float = Field(
+ default_factory=lambda: GenerationConfig._defaults["temperature"]
+ )
+ top_p: float = Field(
+ default_factory=lambda: GenerationConfig._defaults["top_p"]
+ )
+ top_k: int = Field(
+ default_factory=lambda: GenerationConfig._defaults["top_k"]
+ )
+ max_tokens_to_sample: int = Field(
+ default_factory=lambda: GenerationConfig._defaults[
+ "max_tokens_to_sample"
+ ]
+ )
+ stream: bool = Field(
+ default_factory=lambda: GenerationConfig._defaults["stream"]
+ )
+ functions: Optional[list[dict]] = Field(
+ default_factory=lambda: GenerationConfig._defaults["functions"]
+ )
+ skip_special_tokens: bool = Field(
+ default_factory=lambda: GenerationConfig._defaults[
+ "skip_special_tokens"
+ ]
+ )
+ stop_token: Optional[str] = Field(
+ default_factory=lambda: GenerationConfig._defaults["stop_token"]
+ )
+ num_beams: int = Field(
+ default_factory=lambda: GenerationConfig._defaults["num_beams"]
+ )
+ do_sample: bool = Field(
+ default_factory=lambda: GenerationConfig._defaults["do_sample"]
+ )
+ generate_with_chat: bool = Field(
+ default_factory=lambda: GenerationConfig._defaults[
+ "generate_with_chat"
+ ]
+ )
+ add_generation_kwargs: Optional[dict] = Field(
+ default_factory=lambda: GenerationConfig._defaults[
+ "add_generation_kwargs"
+ ]
+ )
+ api_base: Optional[str] = Field(
+ default_factory=lambda: GenerationConfig._defaults["api_base"]
+ )
+
+ @classmethod
+ def set_default(cls, **kwargs):
+ for key, value in kwargs.items():
+ if key in cls._defaults:
+ cls._defaults[key] = value
+ else:
+ raise AttributeError(
+ f"No default attribute '{key}' in GenerationConfig"
+ )
+
+ def __init__(self, **data):
+ model = data.pop("model", None)
+ if model is not None:
+ super().__init__(model=model, **data)
+ else:
+ super().__init__(**data)
diff --git a/R2R/r2r/base/abstractions/prompt.py b/R2R/r2r/base/abstractions/prompt.py
new file mode 100755
index 00000000..e37eeb5f
--- /dev/null
+++ b/R2R/r2r/base/abstractions/prompt.py
@@ -0,0 +1,31 @@
+"""Abstraction for a prompt that can be formatted with inputs."""
+
+from typing import Any
+
+from pydantic import BaseModel
+
+
+class Prompt(BaseModel):
+ """A prompt that can be formatted with inputs."""
+
+ name: str
+ template: str
+ input_types: dict[str, str]
+
+ def format_prompt(self, inputs: dict[str, Any]) -> str:
+ self._validate_inputs(inputs)
+ return self.template.format(**inputs)
+
+ def _validate_inputs(self, inputs: dict[str, Any]) -> None:
+ for var, expected_type_name in self.input_types.items():
+ expected_type = self._convert_type(expected_type_name)
+ if var not in inputs:
+ raise ValueError(f"Missing input: {var}")
+ if not isinstance(inputs[var], expected_type):
+ raise TypeError(
+ f"Input '{var}' must be of type {expected_type.__name__}, got {type(inputs[var]).__name__} instead."
+ )
+
+ def _convert_type(self, type_name: str) -> type:
+ type_mapping = {"int": int, "str": str}
+ return type_mapping.get(type_name, str)
diff --git a/R2R/r2r/base/abstractions/search.py b/R2R/r2r/base/abstractions/search.py
new file mode 100755
index 00000000..b13cc5aa
--- /dev/null
+++ b/R2R/r2r/base/abstractions/search.py
@@ -0,0 +1,84 @@
+"""Abstractions for search functionality."""
+
+import uuid
+from typing import Any, Dict, List, Optional, Tuple
+
+from pydantic import BaseModel, Field
+
+from .llm import GenerationConfig
+
+
+class VectorSearchRequest(BaseModel):
+ """Request for a search operation."""
+
+ query: str
+ limit: int
+ filters: Optional[dict[str, Any]] = None
+
+
+class VectorSearchResult(BaseModel):
+ """Result of a search operation."""
+
+ id: uuid.UUID
+ score: float
+ metadata: dict[str, Any]
+
+ def __str__(self) -> str:
+ return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})"
+
+ def __repr__(self) -> str:
+ return f"VectorSearchResult(id={self.id}, score={self.score}, metadata={self.metadata})"
+
+ def dict(self) -> dict:
+ return {
+ "id": self.id,
+ "score": self.score,
+ "metadata": self.metadata,
+ }
+
+
+class KGSearchRequest(BaseModel):
+ """Request for a knowledge graph search operation."""
+
+ query: str
+
+
+# [query, ...]
+KGSearchResult = List[Tuple[str, List[Dict[str, Any]]]]
+
+
+class AggregateSearchResult(BaseModel):
+ """Result of an aggregate search operation."""
+
+ vector_search_results: Optional[List[VectorSearchResult]]
+ kg_search_results: Optional[KGSearchResult] = None
+
+ def __str__(self) -> str:
+ return f"AggregateSearchResult(vector_search_results={self.vector_search_results}, kg_search_results={self.kg_search_results})"
+
+ def __repr__(self) -> str:
+ return f"AggregateSearchResult(vector_search_results={self.vector_search_results}, kg_search_results={self.kg_search_results})"
+
+ def dict(self) -> dict:
+ return {
+ "vector_search_results": (
+ [result.dict() for result in self.vector_search_results]
+ if self.vector_search_results
+ else []
+ ),
+ "kg_search_results": self.kg_search_results or [],
+ }
+
+
+class VectorSearchSettings(BaseModel):
+ use_vector_search: bool = True
+ search_filters: dict[str, Any] = Field(default_factory=dict)
+ search_limit: int = 10
+ do_hybrid_search: bool = False
+
+
+class KGSearchSettings(BaseModel):
+ use_kg_search: bool = False
+ agent_generation_config: Optional[GenerationConfig] = Field(
+ default_factory=GenerationConfig
+ )
diff --git a/R2R/r2r/base/abstractions/vector.py b/R2R/r2r/base/abstractions/vector.py
new file mode 100755
index 00000000..445f3302
--- /dev/null
+++ b/R2R/r2r/base/abstractions/vector.py
@@ -0,0 +1,66 @@
+"""Abstraction for a vector that can be stored in the system."""
+
+from enum import Enum
+from typing import Any
+from uuid import UUID
+
+
+class VectorType(Enum):
+ FIXED = "FIXED"
+
+
+class Vector:
+ """A vector with the option to fix the number of elements."""
+
+ def __init__(
+ self,
+ data: list[float],
+ type: VectorType = VectorType.FIXED,
+ length: int = -1,
+ ):
+ self.data = data
+ self.type = type
+ self.length = length
+
+ if (
+ self.type == VectorType.FIXED
+ and length > 0
+ and len(data) != length
+ ):
+ raise ValueError(f"Vector must be exactly {length} elements long.")
+
+ def __repr__(self) -> str:
+ return (
+ f"Vector(data={self.data}, type={self.type}, length={self.length})"
+ )
+
+
+class VectorEntry:
+ """A vector entry that can be stored directly in supported vector databases."""
+
+ def __init__(self, id: UUID, vector: Vector, metadata: dict[str, Any]):
+ """Create a new VectorEntry object."""
+ self.vector = vector
+ self.id = id
+ self.metadata = metadata
+
+ def to_serializable(self) -> str:
+ """Return a serializable representation of the VectorEntry."""
+ metadata = self.metadata
+
+ for key in metadata:
+ if isinstance(metadata[key], UUID):
+ metadata[key] = str(metadata[key])
+ return {
+ "id": str(self.id),
+ "vector": self.vector.data,
+ "metadata": metadata,
+ }
+
+ def __str__(self) -> str:
+ """Return a string representation of the VectorEntry."""
+ return f"VectorEntry(id={self.id}, vector={self.vector}, metadata={self.metadata})"
+
+ def __repr__(self) -> str:
+ """Return an unambiguous string representation of the VectorEntry."""
+ return f"VectorEntry(id={self.id}, vector={self.vector}, metadata={self.metadata})"
diff --git a/R2R/r2r/base/logging/__init__.py b/R2R/r2r/base/logging/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/base/logging/kv_logger.py b/R2R/r2r/base/logging/kv_logger.py
new file mode 100755
index 00000000..2d444e9f
--- /dev/null
+++ b/R2R/r2r/base/logging/kv_logger.py
@@ -0,0 +1,547 @@
+import json
+import logging
+import os
+import uuid
+from abc import abstractmethod
+from datetime import datetime
+from typing import Optional
+
+import asyncpg
+from pydantic import BaseModel
+
+from ..providers.base_provider import Provider, ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class RunInfo(BaseModel):
+ run_id: uuid.UUID
+ log_type: str
+
+
+class LoggingConfig(ProviderConfig):
+ provider: str = "local"
+ log_table: str = "logs"
+ log_info_table: str = "logs_pipeline_info"
+ logging_path: Optional[str] = None
+
+ def validate(self) -> None:
+ pass
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return ["local", "postgres", "redis"]
+
+
+class KVLoggingProvider(Provider):
+ @abstractmethod
+ async def close(self):
+ pass
+
+ @abstractmethod
+ async def log(self, log_id: uuid.UUID, key: str, value: str):
+ pass
+
+ @abstractmethod
+ async def get_run_info(
+ self,
+ limit: int = 10,
+ log_type_filter: Optional[str] = None,
+ ) -> list[RunInfo]:
+ pass
+
+ @abstractmethod
+ async def get_logs(
+ self, run_ids: list[uuid.UUID], limit_per_run: int
+ ) -> list:
+ pass
+
+
+class LocalKVLoggingProvider(KVLoggingProvider):
+ def __init__(self, config: LoggingConfig):
+ self.log_table = config.log_table
+ self.log_info_table = config.log_info_table
+ self.logging_path = config.logging_path or os.getenv(
+ "LOCAL_DB_PATH", "local.sqlite"
+ )
+ if not self.logging_path:
+ raise ValueError(
+ "Please set the environment variable LOCAL_DB_PATH."
+ )
+ self.conn = None
+ try:
+ import aiosqlite
+
+ self.aiosqlite = aiosqlite
+ except ImportError:
+ raise ImportError(
+ "Please install aiosqlite to use the LocalKVLoggingProvider."
+ )
+
+ async def init(self):
+ self.conn = await self.aiosqlite.connect(self.logging_path)
+ await self.conn.execute(
+ f"""
+ CREATE TABLE IF NOT EXISTS {self.log_table} (
+ timestamp DATETIME,
+ log_id TEXT,
+ key TEXT,
+ value TEXT
+ )
+ """
+ )
+ await self.conn.execute(
+ f"""
+ CREATE TABLE IF NOT EXISTS {self.log_info_table} (
+ timestamp DATETIME,
+ log_id TEXT UNIQUE,
+ log_type TEXT
+ )
+ """
+ )
+ await self.conn.commit()
+
+ async def __aenter__(self):
+ if self.conn is None:
+ await self.init()
+ return self
+
+ async def __aexit__(self, exc_type, exc_val, exc_tb):
+ await self.close()
+
+ async def close(self):
+ if self.conn:
+ await self.conn.close()
+ self.conn = None
+
+ async def log(
+ self,
+ log_id: uuid.UUID,
+ key: str,
+ value: str,
+ is_info_log=False,
+ ):
+ collection = self.log_info_table if is_info_log else self.log_table
+
+ if is_info_log:
+ if "type" not in key:
+ raise ValueError("Info log keys must contain the text 'type'")
+ await self.conn.execute(
+ f"INSERT INTO {collection} (timestamp, log_id, log_type) VALUES (datetime('now'), ?, ?)",
+ (str(log_id), value),
+ )
+ else:
+ await self.conn.execute(
+ f"INSERT INTO {collection} (timestamp, log_id, key, value) VALUES (datetime('now'), ?, ?, ?)",
+ (str(log_id), key, value),
+ )
+ await self.conn.commit()
+
+ async def get_run_info(
+ self, limit: int = 10, log_type_filter: Optional[str] = None
+ ) -> list[RunInfo]:
+ cursor = await self.conn.cursor()
+ query = f'SELECT log_id, log_type FROM "{self.log_info_table}"'
+ conditions = []
+ params = []
+ if log_type_filter:
+ conditions.append("log_type = ?")
+ params.append(log_type_filter)
+ if conditions:
+ query += " WHERE " + " AND ".join(conditions)
+ query += " ORDER BY timestamp DESC LIMIT ?"
+ params.append(limit)
+ await cursor.execute(query, params)
+ rows = await cursor.fetchall()
+ return [
+ RunInfo(run_id=uuid.UUID(row[0]), log_type=row[1]) for row in rows
+ ]
+
+ async def get_logs(
+ self, run_ids: list[uuid.UUID], limit_per_run: int = 10
+ ) -> list:
+ if not run_ids:
+ raise ValueError("No run ids provided.")
+ cursor = await self.conn.cursor()
+ placeholders = ",".join(["?" for _ in run_ids])
+ query = f"""
+ SELECT *
+ FROM (
+ SELECT *, ROW_NUMBER() OVER (PARTITION BY log_id ORDER BY timestamp DESC) as rn
+ FROM {self.log_table}
+ WHERE log_id IN ({placeholders})
+ )
+ WHERE rn <= ?
+ ORDER BY timestamp DESC
+ """
+ params = [str(ele) for ele in run_ids] + [limit_per_run]
+ await cursor.execute(query, params)
+ rows = await cursor.fetchall()
+ new_rows = []
+ for row in rows:
+ new_rows.append(
+ (row[0], uuid.UUID(row[1]), row[2], row[3], row[4])
+ )
+ return [
+ {desc[0]: row[i] for i, desc in enumerate(cursor.description)}
+ for row in new_rows
+ ]
+
+
+class PostgresLoggingConfig(LoggingConfig):
+ provider: str = "postgres"
+ log_table: str = "logs"
+ log_info_table: str = "logs_pipeline_info"
+
+ def validate(self) -> None:
+ required_env_vars = [
+ "POSTGRES_DBNAME",
+ "POSTGRES_USER",
+ "POSTGRES_PASSWORD",
+ "POSTGRES_HOST",
+ "POSTGRES_PORT",
+ ]
+ for var in required_env_vars:
+ if not os.getenv(var):
+ raise ValueError(f"Environment variable {var} is not set.")
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return ["postgres"]
+
+
+class PostgresKVLoggingProvider(KVLoggingProvider):
+ def __init__(self, config: PostgresLoggingConfig):
+ self.log_table = config.log_table
+ self.log_info_table = config.log_info_table
+ self.config = config
+ self.pool = None
+ if not os.getenv("POSTGRES_DBNAME"):
+ raise ValueError(
+ "Please set the environment variable POSTGRES_DBNAME."
+ )
+ if not os.getenv("POSTGRES_USER"):
+ raise ValueError(
+ "Please set the environment variable POSTGRES_USER."
+ )
+ if not os.getenv("POSTGRES_PASSWORD"):
+ raise ValueError(
+ "Please set the environment variable POSTGRES_PASSWORD."
+ )
+ if not os.getenv("POSTGRES_HOST"):
+ raise ValueError(
+ "Please set the environment variable POSTGRES_HOST."
+ )
+ if not os.getenv("POSTGRES_PORT"):
+ raise ValueError(
+ "Please set the environment variable POSTGRES_PORT."
+ )
+
+ async def init(self):
+ self.pool = await asyncpg.create_pool(
+ database=os.getenv("POSTGRES_DBNAME"),
+ user=os.getenv("POSTGRES_USER"),
+ password=os.getenv("POSTGRES_PASSWORD"),
+ host=os.getenv("POSTGRES_HOST"),
+ port=os.getenv("POSTGRES_PORT"),
+ statement_cache_size=0, # Disable statement caching
+ )
+ async with self.pool.acquire() as conn:
+ await conn.execute(
+ f"""
+ CREATE TABLE IF NOT EXISTS "{self.log_table}" (
+ timestamp TIMESTAMPTZ,
+ log_id UUID,
+ key TEXT,
+ value TEXT
+ )
+ """
+ )
+ await conn.execute(
+ f"""
+ CREATE TABLE IF NOT EXISTS "{self.log_info_table}" (
+ timestamp TIMESTAMPTZ,
+ log_id UUID UNIQUE,
+ log_type TEXT
+ )
+ """
+ )
+
+ async def __aenter__(self):
+ if self.pool is None:
+ await self.init()
+ return self
+
+ async def __aexit__(self, exc_type, exc_val, exc_tb):
+ await self.close()
+
+ async def close(self):
+ if self.pool:
+ await self.pool.close()
+ self.pool = None
+
+ async def log(
+ self,
+ log_id: uuid.UUID,
+ key: str,
+ value: str,
+ is_info_log=False,
+ ):
+ collection = self.log_info_table if is_info_log else self.log_table
+
+ if is_info_log:
+ if "type" not in key:
+ raise ValueError(
+ "Info log key must contain the string `type`."
+ )
+ async with self.pool.acquire() as conn:
+ await self.pool.execute(
+ f'INSERT INTO "{collection}" (timestamp, log_id, log_type) VALUES (NOW(), $1, $2)',
+ log_id,
+ value,
+ )
+ else:
+ async with self.pool.acquire() as conn:
+ await conn.execute(
+ f'INSERT INTO "{collection}" (timestamp, log_id, key, value) VALUES (NOW(), $1, $2, $3)',
+ log_id,
+ key,
+ value,
+ )
+
+ async def get_run_info(
+ self, limit: int = 10, log_type_filter: Optional[str] = None
+ ) -> list[RunInfo]:
+ query = f"SELECT log_id, log_type FROM {self.log_info_table}"
+ conditions = []
+ params = []
+ if log_type_filter:
+ conditions.append("log_type = $1")
+ params.append(log_type_filter)
+ if conditions:
+ query += " WHERE " + " AND ".join(conditions)
+ query += " ORDER BY timestamp DESC LIMIT $2"
+ params.append(limit)
+ async with self.pool.acquire() as conn:
+ rows = await conn.fetch(query, *params)
+ return [
+ RunInfo(run_id=row["log_id"], log_type=row["log_type"])
+ for row in rows
+ ]
+
+ async def get_logs(
+ self, run_ids: list[uuid.UUID], limit_per_run: int = 10
+ ) -> list:
+ if not run_ids:
+ raise ValueError("No run ids provided.")
+
+ placeholders = ",".join([f"${i + 1}" for i in range(len(run_ids))])
+ query = f"""
+ SELECT * FROM (
+ SELECT *, ROW_NUMBER() OVER (PARTITION BY log_id ORDER BY timestamp DESC) as rn
+ FROM "{self.log_table}"
+ WHERE log_id::text IN ({placeholders})
+ ) sub
+ WHERE sub.rn <= ${len(run_ids) + 1}
+ ORDER BY sub.timestamp DESC
+ """
+ params = [str(run_id) for run_id in run_ids] + [limit_per_run]
+ async with self.pool.acquire() as conn:
+ rows = await conn.fetch(query, *params)
+ return [{key: row[key] for key in row.keys()} for row in rows]
+
+
+class RedisLoggingConfig(LoggingConfig):
+ provider: str = "redis"
+ log_table: str = "logs"
+ log_info_table: str = "logs_pipeline_info"
+
+ def validate(self) -> None:
+ required_env_vars = ["REDIS_CLUSTER_IP", "REDIS_CLUSTER_PORT"]
+ for var in required_env_vars:
+ if not os.getenv(var):
+ raise ValueError(f"Environment variable {var} is not set.")
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return ["redis"]
+
+
+class RedisKVLoggingProvider(KVLoggingProvider):
+ def __init__(self, config: RedisLoggingConfig):
+ logger.info(
+ f"Initializing RedisKVLoggingProvider with config: {config}"
+ )
+
+ if not all(
+ [
+ os.getenv("REDIS_CLUSTER_IP"),
+ os.getenv("REDIS_CLUSTER_PORT"),
+ ]
+ ):
+ raise ValueError(
+ "Please set the environment variables REDIS_CLUSTER_IP and REDIS_CLUSTER_PORT to run `LoggingDatabaseConnection` with `redis`."
+ )
+ try:
+ from redis.asyncio import Redis
+ except ImportError:
+ raise ValueError(
+ "Error, `redis` is not installed. Please install it using `pip install redis`."
+ )
+
+ cluster_ip = os.getenv("REDIS_CLUSTER_IP")
+ port = os.getenv("REDIS_CLUSTER_PORT")
+ self.redis = Redis(host=cluster_ip, port=port, decode_responses=True)
+ self.log_key = config.log_table
+ self.log_info_key = config.log_info_table
+
+ async def __aenter__(self):
+ return self
+
+ async def __aexit__(self, exc_type, exc, tb):
+ await self.close()
+
+ async def close(self):
+ await self.redis.close()
+
+ async def log(
+ self,
+ log_id: uuid.UUID,
+ key: str,
+ value: str,
+ is_info_log=False,
+ ):
+ timestamp = datetime.now().timestamp()
+ log_entry = {
+ "timestamp": timestamp,
+ "log_id": str(log_id),
+ "key": key,
+ "value": value,
+ }
+ if is_info_log:
+ if "type" not in key:
+ raise ValueError("Metadata keys must contain the text 'type'")
+ log_entry["log_type"] = value
+ await self.redis.hset(
+ self.log_info_key, str(log_id), json.dumps(log_entry)
+ )
+ await self.redis.zadd(
+ f"{self.log_info_key}_sorted", {str(log_id): timestamp}
+ )
+ else:
+ await self.redis.lpush(
+ f"{self.log_key}:{str(log_id)}", json.dumps(log_entry)
+ )
+
+ async def get_run_info(
+ self, limit: int = 10, log_type_filter: Optional[str] = None
+ ) -> list[RunInfo]:
+ run_info_list = []
+ start = 0
+ count_per_batch = 100 # Adjust batch size as needed
+
+ while len(run_info_list) < limit:
+ log_ids = await self.redis.zrevrange(
+ f"{self.log_info_key}_sorted",
+ start,
+ start + count_per_batch - 1,
+ )
+ if not log_ids:
+ break # No more log IDs to process
+
+ start += count_per_batch
+
+ for log_id in log_ids:
+ log_entry = json.loads(
+ await self.redis.hget(self.log_info_key, log_id)
+ )
+ if log_type_filter:
+ if log_entry["log_type"] == log_type_filter:
+ run_info_list.append(
+ RunInfo(
+ run_id=uuid.UUID(log_entry["log_id"]),
+ log_type=log_entry["log_type"],
+ )
+ )
+ else:
+ run_info_list.append(
+ RunInfo(
+ run_id=uuid.UUID(log_entry["log_id"]),
+ log_type=log_entry["log_type"],
+ )
+ )
+
+ if len(run_info_list) >= limit:
+ break
+
+ return run_info_list[:limit]
+
+ async def get_logs(
+ self, run_ids: list[uuid.UUID], limit_per_run: int = 10
+ ) -> list:
+ logs = []
+ for run_id in run_ids:
+ raw_logs = await self.redis.lrange(
+ f"{self.log_key}:{str(run_id)}", 0, limit_per_run - 1
+ )
+ for raw_log in raw_logs:
+ json_log = json.loads(raw_log)
+ json_log["log_id"] = uuid.UUID(json_log["log_id"])
+ logs.append(json_log)
+ return logs
+
+
+class KVLoggingSingleton:
+ _instance = None
+ _is_configured = False
+
+ SUPPORTED_PROVIDERS = {
+ "local": LocalKVLoggingProvider,
+ "postgres": PostgresKVLoggingProvider,
+ "redis": RedisKVLoggingProvider,
+ }
+
+ @classmethod
+ def get_instance(cls):
+ return cls.SUPPORTED_PROVIDERS[cls._config.provider](cls._config)
+
+ @classmethod
+ def configure(
+ cls, logging_config: Optional[LoggingConfig] = LoggingConfig()
+ ):
+ if not cls._is_configured:
+ cls._config = logging_config
+ cls._is_configured = True
+ else:
+ raise Exception("KVLoggingSingleton is already configured.")
+
+ @classmethod
+ async def log(
+ cls,
+ log_id: uuid.UUID,
+ key: str,
+ value: str,
+ is_info_log=False,
+ ):
+ try:
+ async with cls.get_instance() as provider:
+ await provider.log(log_id, key, value, is_info_log=is_info_log)
+
+ except Exception as e:
+ logger.error(f"Error logging data {(log_id, key, value)}: {e}")
+
+ @classmethod
+ async def get_run_info(
+ cls, limit: int = 10, log_type_filter: Optional[str] = None
+ ) -> list[RunInfo]:
+ async with cls.get_instance() as provider:
+ return await provider.get_run_info(
+ limit, log_type_filter=log_type_filter
+ )
+
+ @classmethod
+ async def get_logs(
+ cls, run_ids: list[uuid.UUID], limit_per_run: int = 10
+ ) -> list:
+ async with cls.get_instance() as provider:
+ return await provider.get_logs(run_ids, limit_per_run)
diff --git a/R2R/r2r/base/logging/log_processor.py b/R2R/r2r/base/logging/log_processor.py
new file mode 100755
index 00000000..e85d8de2
--- /dev/null
+++ b/R2R/r2r/base/logging/log_processor.py
@@ -0,0 +1,196 @@
+import contextlib
+import json
+import logging
+import statistics
+from collections import defaultdict
+from typing import Any, Callable, Dict, List, Optional, Sequence
+
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+
+class FilterCriteria(BaseModel):
+ filters: Optional[dict[str, str]] = None
+
+
+class LogProcessor:
+ timestamp_format = "%Y-%m-%d %H:%M:%S"
+
+ def __init__(self, filters: Dict[str, Callable[[Dict[str, Any]], bool]]):
+ self.filters = filters
+ self.populations = {name: [] for name in filters}
+
+ def process_log(self, log: Dict[str, Any]):
+ for name, filter_func in self.filters.items():
+ if filter_func(log):
+ self.populations[name].append(log)
+
+
+class StatisticsCalculator:
+ @staticmethod
+ def calculate_statistics(
+ population: List[Dict[str, Any]],
+ stat_functions: Dict[str, Callable[[List[Dict[str, Any]]], Any]],
+ ) -> Dict[str, Any]:
+ return {
+ name: func(population) for name, func in stat_functions.items()
+ }
+
+
+class DistributionGenerator:
+ @staticmethod
+ def generate_distributions(
+ population: List[Dict[str, Any]],
+ dist_functions: Dict[str, Callable[[List[Dict[str, Any]]], Any]],
+ ) -> Dict[str, Any]:
+ return {
+ name: func(population) for name, func in dist_functions.items()
+ }
+
+
+class VisualizationPreparer:
+ @staticmethod
+ def prepare_visualization_data(
+ data: Dict[str, Any],
+ vis_functions: Dict[str, Callable[[Dict[str, Any]], Any]],
+ ) -> Dict[str, Any]:
+ return {name: func(data) for name, func in vis_functions.items()}
+
+
+class LogAnalyticsConfig:
+ def __init__(self, filters, stat_functions, dist_functions, vis_functions):
+ self.filters = filters
+ self.stat_functions = stat_functions
+ self.dist_functions = dist_functions
+ self.vis_functions = vis_functions
+
+
+class AnalysisTypes(BaseModel):
+ analysis_types: Optional[dict[str, Sequence[str]]] = None
+
+ @staticmethod
+ def generate_bar_chart_data(logs, key):
+ chart_data = {"labels": [], "datasets": []}
+ value_counts = defaultdict(int)
+
+ for log in logs:
+ if "entries" in log:
+ for entry in log["entries"]:
+ if entry["key"] == key:
+ value_counts[entry["value"]] += 1
+ elif "key" in log and log["key"] == key:
+ value_counts[log["value"]] += 1
+
+ for value, count in value_counts.items():
+ chart_data["labels"].append(value)
+ chart_data["datasets"].append({"label": key, "data": [count]})
+
+ return chart_data
+
+ @staticmethod
+ def calculate_basic_statistics(logs, key):
+ values = []
+ for log in logs:
+ if log["key"] == "search_results":
+ results = json.loads(log["value"])
+ scores = [
+ float(json.loads(result)["score"]) for result in results
+ ]
+ values.extend(scores)
+ else:
+ value = log.get("value")
+ if value is not None:
+ with contextlib.suppress(ValueError):
+ values.append(float(value))
+
+ if not values:
+ return {
+ "Mean": None,
+ "Median": None,
+ "Mode": None,
+ "Standard Deviation": None,
+ "Variance": None,
+ }
+
+ if len(values) == 1:
+ single_value = round(values[0], 3)
+ return {
+ "Mean": single_value,
+ "Median": single_value,
+ "Mode": single_value,
+ "Standard Deviation": 0,
+ "Variance": 0,
+ }
+
+ mean = round(sum(values) / len(values), 3)
+ median = round(statistics.median(values), 3)
+ mode = (
+ round(statistics.mode(values), 3)
+ if len(set(values)) != len(values)
+ else None
+ )
+ std_dev = round(statistics.stdev(values) if len(values) > 1 else 0, 3)
+ variance = round(
+ statistics.variance(values) if len(values) > 1 else 0, 3
+ )
+
+ return {
+ "Mean": mean,
+ "Median": median,
+ "Mode": mode,
+ "Standard Deviation": std_dev,
+ "Variance": variance,
+ }
+
+ @staticmethod
+ def calculate_percentile(logs, key, percentile):
+ values = []
+ for log in logs:
+ if log["key"] == key:
+ value = log.get("value")
+ if value is not None:
+ with contextlib.suppress(ValueError):
+ values.append(float(value))
+
+ if not values:
+ return {"percentile": percentile, "value": None}
+
+ values.sort()
+ index = int((percentile / 100) * (len(values) - 1))
+ return {"percentile": percentile, "value": round(values[index], 3)}
+
+
+class LogAnalytics:
+ def __init__(self, logs: List[Dict[str, Any]], config: LogAnalyticsConfig):
+ self.logs = logs
+ self.log_processor = LogProcessor(config.filters)
+ self.statistics_calculator = StatisticsCalculator()
+ self.distribution_generator = DistributionGenerator()
+ self.visualization_preparer = VisualizationPreparer()
+ self.config = config
+
+ def count_logs(self) -> Dict[str, Any]:
+ """Count the logs for each filter."""
+ return {
+ name: len(population)
+ for name, population in self.log_processor.populations.items()
+ }
+
+ def process_logs(self) -> Dict[str, Any]:
+ for log in self.logs:
+ self.log_processor.process_log(log)
+
+ analytics = {}
+ for name, population in self.log_processor.populations.items():
+ stats = self.statistics_calculator.calculate_statistics(
+ population, self.config.stat_functions
+ )
+ dists = self.distribution_generator.generate_distributions(
+ population, self.config.dist_functions
+ )
+ analytics[name] = {"statistics": stats, "distributions": dists}
+
+ return self.visualization_preparer.prepare_visualization_data(
+ analytics, self.config.vis_functions
+ )
diff --git a/R2R/r2r/base/logging/run_manager.py b/R2R/r2r/base/logging/run_manager.py
new file mode 100755
index 00000000..ac192bca
--- /dev/null
+++ b/R2R/r2r/base/logging/run_manager.py
@@ -0,0 +1,56 @@
+import contextvars
+import uuid
+from contextlib import asynccontextmanager
+from typing import Any
+
+from .kv_logger import KVLoggingSingleton
+
+run_id_var = contextvars.ContextVar("run_id", default=None)
+
+
+class RunManager:
+ def __init__(self, logger: KVLoggingSingleton):
+ self.logger = logger
+ self.run_info = {}
+
+ def generate_run_id(self) -> uuid.UUID:
+ return uuid.uuid4()
+
+ async def set_run_info(self, pipeline_type: str):
+ run_id = run_id_var.get()
+ if run_id is None:
+ run_id = self.generate_run_id()
+ token = run_id_var.set(run_id)
+ self.run_info[run_id] = {"pipeline_type": pipeline_type}
+ else:
+ token = run_id_var.set(run_id)
+ return run_id, token
+
+ async def get_run_info(self):
+ run_id = run_id_var.get()
+ return self.run_info.get(run_id, None)
+
+ async def log_run_info(
+ self, key: str, value: Any, is_info_log: bool = False
+ ):
+ run_id = run_id_var.get()
+ if run_id:
+ await self.logger.log(
+ log_id=run_id, key=key, value=value, is_info_log=is_info_log
+ )
+
+ async def clear_run_info(self, token: contextvars.Token):
+ run_id = run_id_var.get()
+ run_id_var.reset(token)
+ if run_id and run_id in self.run_info:
+ del self.run_info[run_id]
+
+
+@asynccontextmanager
+async def manage_run(run_manager: RunManager, pipeline_type: str):
+ run_id, token = await run_manager.set_run_info(pipeline_type)
+ try:
+ yield run_id
+ finally:
+ # Note: Do not clear the run info to ensure the run ID remains the same
+ run_id_var.reset(token)
diff --git a/R2R/r2r/base/parsers/__init__.py b/R2R/r2r/base/parsers/__init__.py
new file mode 100755
index 00000000..d7696202
--- /dev/null
+++ b/R2R/r2r/base/parsers/__init__.py
@@ -0,0 +1,5 @@
+from .base_parser import AsyncParser
+
+__all__ = [
+ "AsyncParser",
+]
diff --git a/R2R/r2r/base/parsers/base_parser.py b/R2R/r2r/base/parsers/base_parser.py
new file mode 100755
index 00000000..f1bb49d7
--- /dev/null
+++ b/R2R/r2r/base/parsers/base_parser.py
@@ -0,0 +1,14 @@
+"""Abstract base class for parsers."""
+
+from abc import ABC, abstractmethod
+from typing import AsyncGenerator, Generic, TypeVar
+
+from ..abstractions.document import DataType
+
+T = TypeVar("T")
+
+
+class AsyncParser(ABC, Generic[T]):
+ @abstractmethod
+ async def ingest(self, data: T) -> AsyncGenerator[DataType, None]:
+ pass
diff --git a/R2R/r2r/base/pipeline/__init__.py b/R2R/r2r/base/pipeline/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/base/pipeline/base_pipeline.py b/R2R/r2r/base/pipeline/base_pipeline.py
new file mode 100755
index 00000000..3c1eff9a
--- /dev/null
+++ b/R2R/r2r/base/pipeline/base_pipeline.py
@@ -0,0 +1,233 @@
+"""Base pipeline class for running a sequence of pipes."""
+
+import asyncio
+import logging
+from enum import Enum
+from typing import Any, AsyncGenerator, Optional
+
+from ..logging.kv_logger import KVLoggingSingleton
+from ..logging.run_manager import RunManager, manage_run
+from ..pipes.base_pipe import AsyncPipe, AsyncState
+
+logger = logging.getLogger(__name__)
+
+
+class PipelineTypes(Enum):
+ EVAL = "eval"
+ INGESTION = "ingestion"
+ SEARCH = "search"
+ RAG = "rag"
+ OTHER = "other"
+
+
+class AsyncPipeline:
+ """Pipeline class for running a sequence of pipes."""
+
+ pipeline_type: str = "other"
+
+ def __init__(
+ self,
+ pipe_logger: Optional[KVLoggingSingleton] = None,
+ run_manager: Optional[RunManager] = None,
+ ):
+ self.pipes: list[AsyncPipe] = []
+ self.upstream_outputs: list[list[dict[str, str]]] = []
+ self.pipe_logger = pipe_logger or KVLoggingSingleton()
+ self.run_manager = run_manager or RunManager(self.pipe_logger)
+ self.futures = {}
+ self.level = 0
+
+ def add_pipe(
+ self,
+ pipe: AsyncPipe,
+ add_upstream_outputs: Optional[list[dict[str, str]]] = None,
+ *args,
+ **kwargs,
+ ) -> None:
+ """Add a pipe to the pipeline."""
+ self.pipes.append(pipe)
+ if not add_upstream_outputs:
+ add_upstream_outputs = []
+ self.upstream_outputs.append(add_upstream_outputs)
+
+ async def run(
+ self,
+ input: Any,
+ state: Optional[AsyncState] = None,
+ stream: bool = False,
+ run_manager: Optional[RunManager] = None,
+ log_run_info: bool = True,
+ *args: Any,
+ **kwargs: Any,
+ ):
+ """Run the pipeline."""
+ run_manager = run_manager or self.run_manager
+
+ try:
+ PipelineTypes(self.pipeline_type)
+ except ValueError:
+ raise ValueError(
+ f"Invalid pipeline type: {self.pipeline_type}, must be one of {PipelineTypes.__members__.keys()}"
+ )
+
+ self.state = state or AsyncState()
+ current_input = input
+ async with manage_run(run_manager, self.pipeline_type):
+ if log_run_info:
+ await run_manager.log_run_info(
+ key="pipeline_type",
+ value=self.pipeline_type,
+ is_info_log=True,
+ )
+ try:
+ for pipe_num in range(len(self.pipes)):
+ config_name = self.pipes[pipe_num].config.name
+ self.futures[config_name] = asyncio.Future()
+
+ current_input = self._run_pipe(
+ pipe_num,
+ current_input,
+ run_manager,
+ *args,
+ **kwargs,
+ )
+ self.futures[config_name].set_result(current_input)
+ if not stream:
+ final_result = await self._consume_all(current_input)
+ return final_result
+ else:
+ return current_input
+ except Exception as error:
+ logger.error(f"Pipeline failed with error: {error}")
+ raise error
+
+ async def _consume_all(self, gen: AsyncGenerator) -> list[Any]:
+ result = []
+ async for item in gen:
+ if hasattr(
+ item, "__aiter__"
+ ): # Check if the item is an async generator
+ sub_result = await self._consume_all(item)
+ result.extend(sub_result)
+ else:
+ result.append(item)
+ return result
+
+ async def _run_pipe(
+ self,
+ pipe_num: int,
+ input: Any,
+ run_manager: RunManager,
+ *args: Any,
+ **kwargs: Any,
+ ):
+ # Collect inputs, waiting for the necessary futures
+ pipe = self.pipes[pipe_num]
+ add_upstream_outputs = self.sort_upstream_outputs(
+ self.upstream_outputs[pipe_num]
+ )
+ input_dict = {"message": input}
+
+ # Group upstream outputs by prev_pipe_name
+ grouped_upstream_outputs = {}
+ for upstream_input in add_upstream_outputs:
+ upstream_pipe_name = upstream_input["prev_pipe_name"]
+ if upstream_pipe_name not in grouped_upstream_outputs:
+ grouped_upstream_outputs[upstream_pipe_name] = []
+ grouped_upstream_outputs[upstream_pipe_name].append(upstream_input)
+
+ for (
+ upstream_pipe_name,
+ upstream_inputs,
+ ) in grouped_upstream_outputs.items():
+
+ async def resolve_future_output(future):
+ result = future.result()
+ # consume the async generator
+ return [item async for item in result]
+
+ async def replay_items_as_async_gen(items):
+ for item in items:
+ yield item
+
+ temp_results = await resolve_future_output(
+ self.futures[upstream_pipe_name]
+ )
+ if upstream_pipe_name == self.pipes[pipe_num - 1].config.name:
+ input_dict["message"] = replay_items_as_async_gen(temp_results)
+
+ for upstream_input in upstream_inputs:
+ outputs = await self.state.get(upstream_pipe_name, "output")
+ prev_output_field = upstream_input.get(
+ "prev_output_field", None
+ )
+ if not prev_output_field:
+ raise ValueError(
+ "`prev_output_field` must be specified in the upstream_input"
+ )
+ input_dict[upstream_input["input_field"]] = outputs[
+ prev_output_field
+ ]
+
+ # Handle the pipe generator
+ async for ele in await pipe.run(
+ pipe.Input(**input_dict),
+ self.state,
+ run_manager,
+ *args,
+ **kwargs,
+ ):
+ yield ele
+
+ def sort_upstream_outputs(
+ self, add_upstream_outputs: list[dict[str, str]]
+ ) -> list[dict[str, str]]:
+ pipe_name_to_index = {
+ pipe.config.name: index for index, pipe in enumerate(self.pipes)
+ }
+
+ def get_pipe_index(upstream_output):
+ return pipe_name_to_index[upstream_output["prev_pipe_name"]]
+
+ sorted_outputs = sorted(
+ add_upstream_outputs, key=get_pipe_index, reverse=True
+ )
+ return sorted_outputs
+
+
+class EvalPipeline(AsyncPipeline):
+ """A pipeline for evaluation."""
+
+ pipeline_type: str = "eval"
+
+ async def run(
+ self,
+ input: Any,
+ state: Optional[AsyncState] = None,
+ stream: bool = False,
+ run_manager: Optional[RunManager] = None,
+ *args: Any,
+ **kwargs: Any,
+ ):
+ return await super().run(
+ input, state, stream, run_manager, *args, **kwargs
+ )
+
+ def add_pipe(
+ self,
+ pipe: AsyncPipe,
+ add_upstream_outputs: Optional[list[dict[str, str]]] = None,
+ *args,
+ **kwargs,
+ ) -> None:
+ logger.debug(f"Adding pipe {pipe.config.name} to the EvalPipeline")
+ return super().add_pipe(pipe, add_upstream_outputs, *args, **kwargs)
+
+
+async def dequeue_requests(queue: asyncio.Queue) -> AsyncGenerator:
+ """Create an async generator to dequeue requests."""
+ while True:
+ request = await queue.get()
+ if request is None:
+ break
+ yield request
diff --git a/R2R/r2r/base/pipes/__init__.py b/R2R/r2r/base/pipes/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/base/pipes/base_pipe.py b/R2R/r2r/base/pipes/base_pipe.py
new file mode 100755
index 00000000..63e3d04e
--- /dev/null
+++ b/R2R/r2r/base/pipes/base_pipe.py
@@ -0,0 +1,163 @@
+import asyncio
+import logging
+import uuid
+from abc import abstractmethod
+from enum import Enum
+from typing import Any, AsyncGenerator, Optional
+
+from pydantic import BaseModel
+
+from r2r.base.logging.kv_logger import KVLoggingSingleton
+from r2r.base.logging.run_manager import RunManager, manage_run
+
+logger = logging.getLogger(__name__)
+
+
+class PipeType(Enum):
+ INGESTOR = "ingestor"
+ EVAL = "eval"
+ GENERATOR = "generator"
+ SEARCH = "search"
+ TRANSFORM = "transform"
+ OTHER = "other"
+
+
+class AsyncState:
+ """A state object for storing data between pipes."""
+
+ def __init__(self):
+ self.data = {}
+ self.lock = asyncio.Lock()
+
+ async def update(self, outer_key: str, values: dict):
+ """Update the state with new values."""
+ async with self.lock:
+ if not isinstance(values, dict):
+ raise ValueError("Values must be contained in a dictionary.")
+ if outer_key not in self.data:
+ self.data[outer_key] = {}
+ for inner_key, inner_value in values.items():
+ self.data[outer_key][inner_key] = inner_value
+
+ async def get(self, outer_key: str, inner_key: str, default=None):
+ """Get a value from the state."""
+ async with self.lock:
+ if outer_key not in self.data:
+ raise ValueError(
+ f"Key {outer_key} does not exist in the state."
+ )
+ if inner_key not in self.data[outer_key]:
+ return default or {}
+ return self.data[outer_key][inner_key]
+
+ async def delete(self, outer_key: str, inner_key: Optional[str] = None):
+ """Delete a value from the state."""
+ async with self.lock:
+ if outer_key in self.data and not inner_key:
+ del self.data[outer_key]
+ else:
+ if inner_key not in self.data[outer_key]:
+ raise ValueError(
+ f"Key {inner_key} does not exist in the state."
+ )
+ del self.data[outer_key][inner_key]
+
+
+class AsyncPipe:
+ """An asynchronous pipe for processing data with logging capabilities."""
+
+ class PipeConfig(BaseModel):
+ """Configuration for a pipe."""
+
+ name: str = "default_pipe"
+ max_log_queue_size: int = 100
+
+ class Config:
+ extra = "forbid"
+ arbitrary_types_allowed = True
+
+ class Input(BaseModel):
+ """Input for a pipe."""
+
+ message: AsyncGenerator[Any, None]
+
+ class Config:
+ extra = "forbid"
+ arbitrary_types_allowed = True
+
+ def __init__(
+ self,
+ type: PipeType = PipeType.OTHER,
+ config: Optional[PipeConfig] = None,
+ pipe_logger: Optional[KVLoggingSingleton] = None,
+ run_manager: Optional[RunManager] = None,
+ ):
+ self._config = config or self.PipeConfig()
+ self._type = type
+ self.pipe_logger = pipe_logger or KVLoggingSingleton()
+ self.log_queue = asyncio.Queue()
+ self.log_worker_task = None
+ self._run_manager = run_manager or RunManager(self.pipe_logger)
+
+ logger.debug(
+ f"Initialized pipe {self.config.name} of type {self.type}"
+ )
+
+ @property
+ def config(self) -> PipeConfig:
+ return self._config
+
+ @property
+ def type(self) -> PipeType:
+ return self._type
+
+ async def log_worker(self):
+ while True:
+ log_data = await self.log_queue.get()
+ run_id, key, value = log_data
+ await self.pipe_logger.log(run_id, key, value)
+ self.log_queue.task_done()
+
+ async def enqueue_log(self, run_id: uuid.UUID, key: str, value: str):
+ if self.log_queue.qsize() < self.config.max_log_queue_size:
+ await self.log_queue.put((run_id, key, value))
+
+ async def run(
+ self,
+ input: Input,
+ state: AsyncState,
+ run_manager: Optional[RunManager] = None,
+ *args: Any,
+ **kwargs: Any,
+ ) -> AsyncGenerator[Any, None]:
+ """Run the pipe with logging capabilities."""
+
+ run_manager = run_manager or self._run_manager
+
+ async def wrapped_run() -> AsyncGenerator[Any, None]:
+ async with manage_run(run_manager, self.config.name) as run_id:
+ self.log_worker_task = asyncio.create_task(
+ self.log_worker(), name=f"log-worker-{self.config.name}"
+ )
+ try:
+ async for result in self._run_logic(
+ input, state, run_id=run_id, *args, **kwargs
+ ):
+ yield result
+ finally:
+ await self.log_queue.join()
+ self.log_worker_task.cancel()
+ self.log_queue = asyncio.Queue()
+
+ return wrapped_run()
+
+ @abstractmethod
+ async def _run_logic(
+ self,
+ input: Input,
+ state: AsyncState,
+ run_id: uuid.UUID,
+ *args: Any,
+ **kwargs: Any,
+ ) -> AsyncGenerator[Any, None]:
+ pass
diff --git a/R2R/r2r/base/providers/__init__.py b/R2R/r2r/base/providers/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/base/providers/base_provider.py b/R2R/r2r/base/providers/base_provider.py
new file mode 100755
index 00000000..8ee8d56a
--- /dev/null
+++ b/R2R/r2r/base/providers/base_provider.py
@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod, abstractproperty
+from typing import Any, Optional, Type
+
+from pydantic import BaseModel
+
+
+class ProviderConfig(BaseModel, ABC):
+ """A base provider configuration class"""
+
+ extra_fields: dict[str, Any] = {}
+ provider: Optional[str] = None
+
+ class Config:
+ arbitrary_types_allowed = True
+ ignore_extra = True
+
+ @abstractmethod
+ def validate(self) -> None:
+ pass
+
+ @classmethod
+ def create(cls: Type["ProviderConfig"], **kwargs: Any) -> "ProviderConfig":
+ base_args = cls.__fields__.keys()
+ filtered_kwargs = {
+ k: v if v != "None" else None
+ for k, v in kwargs.items()
+ if k in base_args
+ }
+ instance = cls(**filtered_kwargs)
+ for k, v in kwargs.items():
+ if k not in base_args:
+ instance.extra_fields[k] = v
+ return instance
+
+ @abstractproperty
+ @property
+ def supported_providers(self) -> list[str]:
+ """Define a list of supported providers."""
+ pass
+
+
+class Provider(ABC):
+ """A base provider class to provide a common interface for all providers."""
+
+ def __init__(self, config: Optional[ProviderConfig] = None):
+ if config:
+ config.validate()
+ self.config = config
diff --git a/R2R/r2r/base/providers/embedding_provider.py b/R2R/r2r/base/providers/embedding_provider.py
new file mode 100755
index 00000000..8f3af56f
--- /dev/null
+++ b/R2R/r2r/base/providers/embedding_provider.py
@@ -0,0 +1,83 @@
+import logging
+from abc import abstractmethod
+from enum import Enum
+from typing import Optional
+
+from ..abstractions.search import VectorSearchResult
+from .base_provider import Provider, ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingConfig(ProviderConfig):
+ """A base embedding configuration class"""
+
+ provider: Optional[str] = None
+ base_model: Optional[str] = None
+ base_dimension: Optional[int] = None
+ rerank_model: Optional[str] = None
+ rerank_dimension: Optional[int] = None
+ rerank_transformer_type: Optional[str] = None
+ batch_size: int = 1
+
+ def validate(self) -> None:
+ if self.provider not in self.supported_providers:
+ raise ValueError(f"Provider '{self.provider}' is not supported.")
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return [None, "openai", "ollama", "sentence-transformers"]
+
+
+class EmbeddingProvider(Provider):
+ """An abstract class to provide a common interface for embedding providers."""
+
+ class PipeStage(Enum):
+ BASE = 1
+ RERANK = 2
+
+ def __init__(self, config: EmbeddingConfig):
+ if not isinstance(config, EmbeddingConfig):
+ raise ValueError(
+ "EmbeddingProvider must be initialized with a `EmbeddingConfig`."
+ )
+ logger.info(f"Initializing EmbeddingProvider with config {config}.")
+
+ super().__init__(config)
+
+ @abstractmethod
+ def get_embedding(self, text: str, stage: PipeStage = PipeStage.BASE):
+ pass
+
+ async def async_get_embedding(
+ self, text: str, stage: PipeStage = PipeStage.BASE
+ ):
+ return self.get_embedding(text, stage)
+
+ @abstractmethod
+ def get_embeddings(
+ self, texts: list[str], stage: PipeStage = PipeStage.BASE
+ ):
+ pass
+
+ async def async_get_embeddings(
+ self, texts: list[str], stage: PipeStage = PipeStage.BASE
+ ):
+ return self.get_embeddings(texts, stage)
+
+ @abstractmethod
+ def rerank(
+ self,
+ query: str,
+ results: list[VectorSearchResult],
+ stage: PipeStage = PipeStage.RERANK,
+ limit: int = 10,
+ ):
+ pass
+
+ @abstractmethod
+ def tokenize_string(
+ self, text: str, model: str, stage: PipeStage
+ ) -> list[int]:
+ """Tokenizes the input string."""
+ pass
diff --git a/R2R/r2r/base/providers/eval_provider.py b/R2R/r2r/base/providers/eval_provider.py
new file mode 100755
index 00000000..76053f87
--- /dev/null
+++ b/R2R/r2r/base/providers/eval_provider.py
@@ -0,0 +1,46 @@
+from typing import Optional, Union
+
+from ..abstractions.llm import GenerationConfig
+from .base_provider import Provider, ProviderConfig
+from .llm_provider import LLMConfig
+
+
+class EvalConfig(ProviderConfig):
+ """A base eval config class"""
+
+ llm: Optional[LLMConfig] = None
+
+ def validate(self) -> None:
+ if self.provider not in self.supported_providers:
+ raise ValueError(f"Provider {self.provider} not supported.")
+ if self.provider and not self.llm:
+ raise ValueError(
+ "EvalConfig must have a `llm` attribute when specifying a provider."
+ )
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return [None, "local"]
+
+
+class EvalProvider(Provider):
+ """An abstract class to provide a common interface for evaluation providers."""
+
+ def __init__(self, config: EvalConfig):
+ if not isinstance(config, EvalConfig):
+ raise ValueError(
+ "EvalProvider must be initialized with a `EvalConfig`."
+ )
+
+ super().__init__(config)
+
+ def evaluate(
+ self,
+ query: str,
+ context: str,
+ completion: str,
+ eval_generation_config: Optional[GenerationConfig] = None,
+ ) -> dict[str, dict[str, Union[str, float]]]:
+ return self._evaluate(
+ query, context, completion, eval_generation_config
+ )
diff --git a/R2R/r2r/base/providers/kg_provider.py b/R2R/r2r/base/providers/kg_provider.py
new file mode 100755
index 00000000..4ae96b11
--- /dev/null
+++ b/R2R/r2r/base/providers/kg_provider.py
@@ -0,0 +1,182 @@
+"""Base classes for knowledge graph providers."""
+
+import json
+import logging
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Optional, Tuple
+
+from .prompt_provider import PromptProvider
+
+if TYPE_CHECKING:
+ from r2r.main import R2RClient
+
+from ...base.utils.base_utils import EntityType, Relation
+from ..abstractions.llama_abstractions import EntityNode, LabelledNode
+from ..abstractions.llama_abstractions import Relation as LlamaRelation
+from ..abstractions.llama_abstractions import VectorStoreQuery
+from ..abstractions.llm import GenerationConfig
+from .base_provider import ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class KGConfig(ProviderConfig):
+ """A base KG config class"""
+
+ provider: Optional[str] = None
+ batch_size: int = 1
+ kg_extraction_prompt: Optional[str] = "few_shot_ner_kg_extraction"
+ kg_agent_prompt: Optional[str] = "kg_agent"
+ kg_extraction_config: Optional[GenerationConfig] = None
+
+ def validate(self) -> None:
+ if self.provider not in self.supported_providers:
+ raise ValueError(f"Provider '{self.provider}' is not supported.")
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return [None, "neo4j"]
+
+
+class KGProvider(ABC):
+ """An abstract class to provide a common interface for Knowledge Graphs."""
+
+ def __init__(self, config: KGConfig) -> None:
+ if not isinstance(config, KGConfig):
+ raise ValueError(
+ "KGProvider must be initialized with a `KGConfig`."
+ )
+ logger.info(f"Initializing KG provider with config: {config}")
+ self.config = config
+ self.validate_config()
+
+ def validate_config(self) -> None:
+ self.config.validate()
+
+ @property
+ @abstractmethod
+ def client(self) -> Any:
+ """Get client."""
+ pass
+
+ @abstractmethod
+ def get(self, subj: str) -> list[list[str]]:
+ """Abstract method to get triplets."""
+ pass
+
+ @abstractmethod
+ def get_rel_map(
+ self,
+ subjs: Optional[list[str]] = None,
+ depth: int = 2,
+ limit: int = 30,
+ ) -> dict[str, list[list[str]]]:
+ """Abstract method to get depth-aware rel map."""
+ pass
+
+ @abstractmethod
+ def upsert_nodes(self, nodes: list[EntityNode]) -> None:
+ """Abstract method to add triplet."""
+ pass
+
+ @abstractmethod
+ def upsert_relations(self, relations: list[LlamaRelation]) -> None:
+ """Abstract method to add triplet."""
+ pass
+
+ @abstractmethod
+ def delete(self, subj: str, rel: str, obj: str) -> None:
+ """Abstract method to delete triplet."""
+ pass
+
+ @abstractmethod
+ def get_schema(self, refresh: bool = False) -> str:
+ """Abstract method to get the schema of the graph store."""
+ pass
+
+ @abstractmethod
+ def structured_query(
+ self, query: str, param_map: Optional[dict[str, Any]] = {}
+ ) -> Any:
+ """Abstract method to query the graph store with statement and parameters."""
+ pass
+
+ @abstractmethod
+ def vector_query(
+ self, query: VectorStoreQuery, **kwargs: Any
+ ) -> Tuple[list[LabelledNode], list[float]]:
+ """Abstract method to query the graph store with a vector store query."""
+
+ # TODO - Type this method.
+ @abstractmethod
+ def update_extraction_prompt(
+ self,
+ prompt_provider: Any,
+ entity_types: list[Any],
+ relations: list[Relation],
+ ):
+ """Abstract method to update the KG extraction prompt."""
+ pass
+
+ # TODO - Type this method.
+ @abstractmethod
+ def update_kg_agent_prompt(
+ self,
+ prompt_provider: Any,
+ entity_types: list[Any],
+ relations: list[Relation],
+ ):
+ """Abstract method to update the KG agent prompt."""
+ pass
+
+
+def escape_braces(s: str) -> str:
+ """
+ Escape braces in a string.
+ This is a placeholder function - implement the actual logic as needed.
+ """
+ # Implement your escape_braces logic here
+ return s.replace("{", "{{").replace("}", "}}")
+
+
+# TODO - Make this more configurable / intelligent
+def update_kg_prompt(
+ client: "R2RClient",
+ r2r_prompts: PromptProvider,
+ prompt_base: str,
+ entity_types: list[EntityType],
+ relations: list[Relation],
+) -> None:
+ # Get the default extraction template
+ template_name: str = f"{prompt_base}_with_spec"
+
+ new_template: str = r2r_prompts.get_prompt(
+ template_name,
+ {
+ "entity_types": json.dumps(
+ {
+ "entity_types": [
+ str(entity.name) for entity in entity_types
+ ]
+ },
+ indent=4,
+ ),
+ "relations": json.dumps(
+ {"predicates": [str(relation.name) for relation in relations]},
+ indent=4,
+ ),
+ "input": """\n{input}""",
+ },
+ )
+
+ # Escape all braces in the template, except for the {input} placeholder, for formatting
+ escaped_template: str = escape_braces(new_template).replace(
+ """{{input}}""", """{input}"""
+ )
+
+ # Update the client's prompt
+ client.update_prompt(
+ prompt_base,
+ template=escaped_template,
+ input_types={"input": "str"},
+ )
diff --git a/R2R/r2r/base/providers/llm_provider.py b/R2R/r2r/base/providers/llm_provider.py
new file mode 100755
index 00000000..9b6499a4
--- /dev/null
+++ b/R2R/r2r/base/providers/llm_provider.py
@@ -0,0 +1,66 @@
+"""Base classes for language model providers."""
+
+import logging
+from abc import abstractmethod
+from typing import Optional
+
+from r2r.base.abstractions.llm import GenerationConfig
+
+from ..abstractions.llm import LLMChatCompletion, LLMChatCompletionChunk
+from .base_provider import Provider, ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class LLMConfig(ProviderConfig):
+ """A base LLM config class"""
+
+ provider: Optional[str] = None
+ generation_config: Optional[GenerationConfig] = None
+
+ def validate(self) -> None:
+ if not self.provider:
+ raise ValueError("Provider must be set.")
+
+ if self.provider and self.provider not in self.supported_providers:
+ raise ValueError(f"Provider '{self.provider}' is not supported.")
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return ["litellm", "openai"]
+
+
+class LLMProvider(Provider):
+ """An abstract class to provide a common interface for LLMs."""
+
+ def __init__(
+ self,
+ config: LLMConfig,
+ ) -> None:
+ if not isinstance(config, LLMConfig):
+ raise ValueError(
+ "LLMProvider must be initialized with a `LLMConfig`."
+ )
+ logger.info(f"Initializing LLM provider with config: {config}")
+
+ super().__init__(config)
+
+ @abstractmethod
+ def get_completion(
+ self,
+ messages: list[dict],
+ generation_config: GenerationConfig,
+ **kwargs,
+ ) -> LLMChatCompletion:
+ """Abstract method to get a chat completion from the provider."""
+ pass
+
+ @abstractmethod
+ def get_completion_stream(
+ self,
+ messages: list[dict],
+ generation_config: GenerationConfig,
+ **kwargs,
+ ) -> LLMChatCompletionChunk:
+ """Abstract method to get a completion stream from the provider."""
+ pass
diff --git a/R2R/r2r/base/providers/prompt_provider.py b/R2R/r2r/base/providers/prompt_provider.py
new file mode 100755
index 00000000..78af9e11
--- /dev/null
+++ b/R2R/r2r/base/providers/prompt_provider.py
@@ -0,0 +1,65 @@
+import logging
+from abc import abstractmethod
+from typing import Any, Optional
+
+from .base_provider import Provider, ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class PromptConfig(ProviderConfig):
+ def validate(self) -> None:
+ pass
+
+ @property
+ def supported_providers(self) -> list[str]:
+ # Return a list of supported prompt providers
+ return ["default_prompt_provider"]
+
+
+class PromptProvider(Provider):
+ def __init__(self, config: Optional[PromptConfig] = None):
+ if config is None:
+ config = PromptConfig()
+ elif not isinstance(config, PromptConfig):
+ raise ValueError(
+ "PromptProvider must be initialized with a `PromptConfig`."
+ )
+ logger.info(f"Initializing PromptProvider with config {config}.")
+ super().__init__(config)
+
+ @abstractmethod
+ def add_prompt(
+ self, name: str, template: str, input_types: dict[str, str]
+ ) -> None:
+ pass
+
+ @abstractmethod
+ def get_prompt(
+ self, prompt_name: str, inputs: Optional[dict[str, Any]] = None
+ ) -> str:
+ pass
+
+ @abstractmethod
+ def get_all_prompts(self) -> dict[str, str]:
+ pass
+
+ @abstractmethod
+ def update_prompt(
+ self,
+ name: str,
+ template: Optional[str] = None,
+ input_types: Optional[dict[str, str]] = None,
+ ) -> None:
+ pass
+
+ def _get_message_payload(
+ self, system_prompt: str, task_prompt: str
+ ) -> dict:
+ return [
+ {
+ "role": "system",
+ "content": system_prompt,
+ },
+ {"role": "user", "content": task_prompt},
+ ]
diff --git a/R2R/r2r/base/providers/vector_db_provider.py b/R2R/r2r/base/providers/vector_db_provider.py
new file mode 100755
index 00000000..a6d5aaa8
--- /dev/null
+++ b/R2R/r2r/base/providers/vector_db_provider.py
@@ -0,0 +1,142 @@
+import logging
+from abc import ABC, abstractmethod
+from typing import Optional, Union
+
+from ..abstractions.document import DocumentInfo
+from ..abstractions.search import VectorSearchResult
+from ..abstractions.vector import VectorEntry
+from .base_provider import Provider, ProviderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class VectorDBConfig(ProviderConfig):
+ provider: str
+
+ def __post_init__(self):
+ self.validate()
+ # Capture additional fields
+ for key, value in self.extra_fields.items():
+ setattr(self, key, value)
+
+ def validate(self) -> None:
+ if self.provider not in self.supported_providers:
+ raise ValueError(f"Provider '{self.provider}' is not supported.")
+
+ @property
+ def supported_providers(self) -> list[str]:
+ return ["local", "pgvector"]
+
+
+class VectorDBProvider(Provider, ABC):
+ def __init__(self, config: VectorDBConfig):
+ if not isinstance(config, VectorDBConfig):
+ raise ValueError(
+ "VectorDBProvider must be initialized with a `VectorDBConfig`."
+ )
+ logger.info(f"Initializing VectorDBProvider with config {config}.")
+ super().__init__(config)
+
+ @abstractmethod
+ def initialize_collection(self, dimension: int) -> None:
+ pass
+
+ @abstractmethod
+ def copy(self, entry: VectorEntry, commit: bool = True) -> None:
+ pass
+
+ @abstractmethod
+ def upsert(self, entry: VectorEntry, commit: bool = True) -> None:
+ pass
+
+ @abstractmethod
+ def search(
+ self,
+ query_vector: list[float],
+ filters: dict[str, Union[bool, int, str]] = {},
+ limit: int = 10,
+ *args,
+ **kwargs,
+ ) -> list[VectorSearchResult]:
+ pass
+
+ @abstractmethod
+ def hybrid_search(
+ self,
+ query_text: str,
+ query_vector: list[float],
+ limit: int = 10,
+ filters: Optional[dict[str, Union[bool, int, str]]] = None,
+ # Hybrid search parameters
+ full_text_weight: float = 1.0,
+ semantic_weight: float = 1.0,
+ rrf_k: int = 20, # typical value is ~2x the number of results you want
+ *args,
+ **kwargs,
+ ) -> list[VectorSearchResult]:
+ pass
+
+ @abstractmethod
+ def create_index(self, index_type, column_name, index_options):
+ pass
+
+ def upsert_entries(
+ self, entries: list[VectorEntry], commit: bool = True
+ ) -> None:
+ for entry in entries:
+ self.upsert(entry, commit=commit)
+
+ def copy_entries(
+ self, entries: list[VectorEntry], commit: bool = True
+ ) -> None:
+ for entry in entries:
+ self.copy(entry, commit=commit)
+
+ @abstractmethod
+ def delete_by_metadata(
+ self,
+ metadata_fields: list[str],
+ metadata_values: list[Union[bool, int, str]],
+ ) -> list[str]:
+ if len(metadata_fields) != len(metadata_values):
+ raise ValueError(
+ "The number of metadata fields and values must be equal."
+ )
+ pass
+
+ @abstractmethod
+ def get_metadatas(
+ self,
+ metadata_fields: list[str],
+ filter_field: Optional[str] = None,
+ filter_value: Optional[str] = None,
+ ) -> list[str]:
+ pass
+
+ @abstractmethod
+ def upsert_documents_overview(
+ self, document_infs: list[DocumentInfo]
+ ) -> None:
+ pass
+
+ @abstractmethod
+ def get_documents_overview(
+ self,
+ filter_document_ids: Optional[list[str]] = None,
+ filter_user_ids: Optional[list[str]] = None,
+ ) -> list[DocumentInfo]:
+ pass
+
+ @abstractmethod
+ def get_document_chunks(self, document_id: str) -> list[dict]:
+ pass
+
+ @abstractmethod
+ def delete_from_documents_overview(
+ self, document_id: str, version: Optional[str] = None
+ ) -> dict:
+ pass
+
+ @abstractmethod
+ def get_users_overview(self, user_ids: Optional[list[str]] = None) -> dict:
+ pass
diff --git a/R2R/r2r/base/utils/__init__.py b/R2R/r2r/base/utils/__init__.py
new file mode 100755
index 00000000..104d50eb
--- /dev/null
+++ b/R2R/r2r/base/utils/__init__.py
@@ -0,0 +1,26 @@
+from .base_utils import (
+ EntityType,
+ Relation,
+ format_entity_types,
+ format_relations,
+ generate_id_from_label,
+ generate_run_id,
+ increment_version,
+ run_pipeline,
+ to_async_generator,
+)
+from .splitter.text import RecursiveCharacterTextSplitter, TextSplitter
+
+__all__ = [
+ "RecursiveCharacterTextSplitter",
+ "TextSplitter",
+ "run_pipeline",
+ "to_async_generator",
+ "generate_run_id",
+ "generate_id_from_label",
+ "increment_version",
+ "EntityType",
+ "Relation",
+ "format_entity_types",
+ "format_relations",
+]
diff --git a/R2R/r2r/base/utils/base_utils.py b/R2R/r2r/base/utils/base_utils.py
new file mode 100755
index 00000000..12652833
--- /dev/null
+++ b/R2R/r2r/base/utils/base_utils.py
@@ -0,0 +1,63 @@
+import asyncio
+import uuid
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Iterable
+
+if TYPE_CHECKING:
+ from ..pipeline.base_pipeline import AsyncPipeline
+
+
+def generate_run_id() -> uuid.UUID:
+ return uuid.uuid4()
+
+
+def generate_id_from_label(label: str) -> uuid.UUID:
+ return uuid.uuid5(uuid.NAMESPACE_DNS, label)
+
+
+async def to_async_generator(
+ iterable: Iterable[Any],
+) -> AsyncGenerator[Any, None]:
+ for item in iterable:
+ yield item
+
+
+def run_pipeline(pipeline: "AsyncPipeline", input: Any, *args, **kwargs):
+ if not isinstance(input, AsyncGenerator) and not isinstance(input, list):
+ input = to_async_generator([input])
+ elif not isinstance(input, AsyncGenerator):
+ input = to_async_generator(input)
+
+ async def _run_pipeline(input, *args, **kwargs):
+ return await pipeline.run(input, *args, **kwargs)
+
+ return asyncio.run(_run_pipeline(input, *args, **kwargs))
+
+
+def increment_version(version: str) -> str:
+ prefix = version[:-1]
+ suffix = int(version[-1])
+ return f"{prefix}{suffix + 1}"
+
+
+class EntityType:
+ def __init__(self, name: str):
+ self.name = name
+
+
+class Relation:
+ def __init__(self, name: str):
+ self.name = name
+
+
+def format_entity_types(entity_types: list[EntityType]) -> str:
+ lines = []
+ for entity in entity_types:
+ lines.append(entity.name)
+ return "\n".join(lines)
+
+
+def format_relations(predicates: list[Relation]) -> str:
+ lines = []
+ for predicate in predicates:
+ lines.append(predicate.name)
+ return "\n".join(lines)
diff --git a/R2R/r2r/base/utils/splitter/__init__.py b/R2R/r2r/base/utils/splitter/__init__.py
new file mode 100755
index 00000000..07a9f554
--- /dev/null
+++ b/R2R/r2r/base/utils/splitter/__init__.py
@@ -0,0 +1,3 @@
+from .text import RecursiveCharacterTextSplitter
+
+__all__ = ["RecursiveCharacterTextSplitter"]
diff --git a/R2R/r2r/base/utils/splitter/text.py b/R2R/r2r/base/utils/splitter/text.py
new file mode 100755
index 00000000..5458310c
--- /dev/null
+++ b/R2R/r2r/base/utils/splitter/text.py
@@ -0,0 +1,1979 @@
+# Source - LangChain
+# URL: https://github.com/langchain-ai/langchain/blob/6a5b084704afa22ca02f78d0464f35aed75d1ff2/libs/langchain/langchain/text_splitter.py#L851
+"""**Text Splitters** are classes for splitting text.
+
+
+**Class hierarchy:**
+
+.. code-block::
+
+ BaseDocumentTransformer --> TextSplitter --> TextSplitter # Example: CharacterTextSplitter
+ RecursiveCharacterTextSplitter --> TextSplitter
+
+Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive from TextSplitter.
+
+
+**Main helpers:**
+
+.. code-block::
+
+ Document, Tokenizer, Language, LineType, HeaderType
+
+""" # noqa: E501
+
+from __future__ import annotations
+
+import copy
+import json
+import logging
+import pathlib
+import re
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from enum import Enum
+from io import BytesIO, StringIO
+from typing import (
+ AbstractSet,
+ Any,
+ Callable,
+ Collection,
+ Dict,
+ Iterable,
+ List,
+ Literal,
+ Optional,
+ Sequence,
+ Tuple,
+ Type,
+ TypedDict,
+ TypeVar,
+ Union,
+ cast,
+)
+
+import requests
+from pydantic import BaseModel, Field, PrivateAttr
+from typing_extensions import NotRequired
+
+logger = logging.getLogger(__name__)
+
+TS = TypeVar("TS", bound="TextSplitter")
+
+
+class BaseSerialized(TypedDict):
+ """Base class for serialized objects."""
+
+ lc: int
+ id: List[str]
+ name: NotRequired[str]
+ graph: NotRequired[Dict[str, Any]]
+
+
+class SerializedConstructor(BaseSerialized):
+ """Serialized constructor."""
+
+ type: Literal["constructor"]
+ kwargs: Dict[str, Any]
+
+
+class SerializedSecret(BaseSerialized):
+ """Serialized secret."""
+
+ type: Literal["secret"]
+
+
+class SerializedNotImplemented(BaseSerialized):
+ """Serialized not implemented."""
+
+ type: Literal["not_implemented"]
+ repr: Optional[str]
+
+
+def try_neq_default(value: Any, key: str, model: BaseModel) -> bool:
+ """Try to determine if a value is different from the default.
+
+ Args:
+ value: The value.
+ key: The key.
+ model: The model.
+
+ Returns:
+ Whether the value is different from the default.
+ """
+ try:
+ return model.__fields__[key].get_default() != value
+ except Exception:
+ return True
+
+
+class Serializable(BaseModel, ABC):
+ """Serializable base class."""
+
+ @classmethod
+ def is_lc_serializable(cls) -> bool:
+ """Is this class serializable?"""
+ return False
+
+ @classmethod
+ def get_lc_namespace(cls) -> List[str]:
+ """Get the namespace of the langchain object.
+
+ For example, if the class is `langchain.llms.openai.OpenAI`, then the
+ namespace is ["langchain", "llms", "openai"]
+ """
+ return cls.__module__.split(".")
+
+ @property
+ def lc_secrets(self) -> Dict[str, str]:
+ """A map of constructor argument names to secret ids.
+
+ For example,
+ {"openai_api_key": "OPENAI_API_KEY"}
+ """
+ return dict()
+
+ @property
+ def lc_attributes(self) -> Dict:
+ """List of attribute names that should be included in the serialized kwargs.
+
+ These attributes must be accepted by the constructor.
+ """
+ return {}
+
+ @classmethod
+ def lc_id(cls) -> List[str]:
+ """A unique identifier for this class for serialization purposes.
+
+ The unique identifier is a list of strings that describes the path
+ to the object.
+ """
+ return [*cls.get_lc_namespace(), cls.__name__]
+
+ class Config:
+ extra = "ignore"
+
+ def __repr_args__(self) -> Any:
+ return [
+ (k, v)
+ for k, v in super().__repr_args__()
+ if (k not in self.__fields__ or try_neq_default(v, k, self))
+ ]
+
+ _lc_kwargs = PrivateAttr(default_factory=dict)
+
+ def __init__(self, **kwargs: Any) -> None:
+ super().__init__(**kwargs)
+ self._lc_kwargs = kwargs
+
+ def to_json(
+ self,
+ ) -> Union[SerializedConstructor, SerializedNotImplemented]:
+ if not self.is_lc_serializable():
+ return self.to_json_not_implemented()
+
+ secrets = dict()
+ # Get latest values for kwargs if there is an attribute with same name
+ lc_kwargs = {
+ k: getattr(self, k, v)
+ for k, v in self._lc_kwargs.items()
+ if not (self.__exclude_fields__ or {}).get(k, False) # type: ignore
+ }
+
+ # Merge the lc_secrets and lc_attributes from every class in the MRO
+ for cls in [None, *self.__class__.mro()]:
+ # Once we get to Serializable, we're done
+ if cls is Serializable:
+ break
+
+ if cls:
+ deprecated_attributes = [
+ "lc_namespace",
+ "lc_serializable",
+ ]
+
+ for attr in deprecated_attributes:
+ if hasattr(cls, attr):
+ raise ValueError(
+ f"Class {self.__class__} has a deprecated "
+ f"attribute {attr}. Please use the corresponding "
+ f"classmethod instead."
+ )
+
+ # Get a reference to self bound to each class in the MRO
+ this = cast(
+ Serializable, self if cls is None else super(cls, self)
+ )
+
+ secrets.update(this.lc_secrets)
+ # Now also add the aliases for the secrets
+ # This ensures known secret aliases are hidden.
+ # Note: this does NOT hide any other extra kwargs
+ # that are not present in the fields.
+ for key in list(secrets):
+ value = secrets[key]
+ if key in this.__fields__:
+ secrets[this.__fields__[key].alias] = value
+ lc_kwargs.update(this.lc_attributes)
+
+ # include all secrets, even if not specified in kwargs
+ # as these secrets may be passed as an environment variable instead
+ for key in secrets.keys():
+ secret_value = getattr(self, key, None) or lc_kwargs.get(key)
+ if secret_value is not None:
+ lc_kwargs.update({key: secret_value})
+
+ return {
+ "lc": 1,
+ "type": "constructor",
+ "id": self.lc_id(),
+ "kwargs": (
+ lc_kwargs
+ if not secrets
+ else _replace_secrets(lc_kwargs, secrets)
+ ),
+ }
+
+ def to_json_not_implemented(self) -> SerializedNotImplemented:
+ return to_json_not_implemented(self)
+
+
+def _replace_secrets(
+ root: Dict[Any, Any], secrets_map: Dict[str, str]
+) -> Dict[Any, Any]:
+ result = root.copy()
+ for path, secret_id in secrets_map.items():
+ [*parts, last] = path.split(".")
+ current = result
+ for part in parts:
+ if part not in current:
+ break
+ current[part] = current[part].copy()
+ current = current[part]
+ if last in current:
+ current[last] = {
+ "lc": 1,
+ "type": "secret",
+ "id": [secret_id],
+ }
+ return result
+
+
+def to_json_not_implemented(obj: object) -> SerializedNotImplemented:
+ """Serialize a "not implemented" object.
+
+ Args:
+ obj: object to serialize
+
+ Returns:
+ SerializedNotImplemented
+ """
+ _id: List[str] = []
+ try:
+ if hasattr(obj, "__name__"):
+ _id = [*obj.__module__.split("."), obj.__name__]
+ elif hasattr(obj, "__class__"):
+ _id = [
+ *obj.__class__.__module__.split("."),
+ obj.__class__.__name__,
+ ]
+ except Exception:
+ pass
+
+ result: SerializedNotImplemented = {
+ "lc": 1,
+ "type": "not_implemented",
+ "id": _id,
+ "repr": None,
+ }
+ try:
+ result["repr"] = repr(obj)
+ except Exception:
+ pass
+ return result
+
+
+class Document(Serializable):
+ """Class for storing a piece of text and associated metadata."""
+
+ page_content: str
+ """String text."""
+ metadata: dict = Field(default_factory=dict)
+ """Arbitrary metadata about the page content (e.g., source, relationships to other
+ documents, etc.).
+ """
+ type: Literal["Document"] = "Document"
+
+ def __init__(self, page_content: str, **kwargs: Any) -> None:
+ """Pass page_content in as positional or named arg."""
+ super().__init__(page_content=page_content, **kwargs)
+
+ @classmethod
+ def is_lc_serializable(cls) -> bool:
+ """Return whether this class is serializable."""
+ return True
+
+ @classmethod
+ def get_lc_namespace(cls) -> List[str]:
+ """Get the namespace of the langchain object."""
+ return ["langchain", "schema", "document"]
+
+
+class BaseDocumentTransformer(ABC):
+ """Abstract base class for document transformation systems.
+
+ A document transformation system takes a sequence of Documents and returns a
+ sequence of transformed Documents.
+
+ Example:
+ .. code-block:: python
+
+ class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel):
+ embeddings: Embeddings
+ similarity_fn: Callable = cosine_similarity
+ similarity_threshold: float = 0.95
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ def transform_documents(
+ self, documents: Sequence[Document], **kwargs: Any
+ ) -> Sequence[Document]:
+ stateful_documents = get_stateful_documents(documents)
+ embedded_documents = _get_embeddings_from_stateful_docs(
+ self.embeddings, stateful_documents
+ )
+ included_idxs = _filter_similar_embeddings(
+ embedded_documents, self.similarity_fn, self.similarity_threshold
+ )
+ return [stateful_documents[i] for i in sorted(included_idxs)]
+
+ async def atransform_documents(
+ self, documents: Sequence[Document], **kwargs: Any
+ ) -> Sequence[Document]:
+ raise NotImplementedError
+
+ """ # noqa: E501
+
+ @abstractmethod
+ def transform_documents(
+ self, documents: Sequence[Document], **kwargs: Any
+ ) -> Sequence[Document]:
+ """Transform a list of documents.
+
+ Args:
+ documents: A sequence of Documents to be transformed.
+
+ Returns:
+ A list of transformed Documents.
+ """
+
+ async def atransform_documents(
+ self, documents: Sequence[Document], **kwargs: Any
+ ) -> Sequence[Document]:
+ """Asynchronously transform a list of documents.
+
+ Args:
+ documents: A sequence of Documents to be transformed.
+
+ Returns:
+ A list of transformed Documents.
+ """
+ raise NotImplementedError("This method is not implemented.")
+ # return await langchain_core.runnables.config.run_in_executor(
+ # None, self.transform_documents, documents, **kwargs
+ # )
+
+
+def _make_spacy_pipe_for_splitting(
+ pipe: str, *, max_length: int = 1_000_000
+) -> Any: # avoid importing spacy
+ try:
+ import spacy
+ except ImportError:
+ raise ImportError(
+ "Spacy is not installed, please install it with `pip install spacy`."
+ )
+ if pipe == "sentencizer":
+ from spacy.lang.en import English
+
+ sentencizer = English()
+ sentencizer.add_pipe("sentencizer")
+ else:
+ sentencizer = spacy.load(pipe, exclude=["ner", "tagger"])
+ sentencizer.max_length = max_length
+ return sentencizer
+
+
+def _split_text_with_regex(
+ text: str, separator: str, keep_separator: bool
+) -> List[str]:
+ # Now that we have the separator, split the text
+ if separator:
+ if keep_separator:
+ # The parentheses in the pattern keep the delimiters in the result.
+ _splits = re.split(f"({separator})", text)
+ splits = [
+ _splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)
+ ]
+ if len(_splits) % 2 == 0:
+ splits += _splits[-1:]
+ splits = [_splits[0]] + splits
+ else:
+ splits = re.split(separator, text)
+ else:
+ splits = list(text)
+ return [s for s in splits if s != ""]
+
+
+class TextSplitter(BaseDocumentTransformer, ABC):
+ """Interface for splitting text into chunks."""
+
+ def __init__(
+ self,
+ chunk_size: int = 4000,
+ chunk_overlap: int = 200,
+ length_function: Callable[[str], int] = len,
+ keep_separator: bool = False,
+ add_start_index: bool = False,
+ strip_whitespace: bool = True,
+ ) -> None:
+ """Create a new TextSplitter.
+
+ Args:
+ chunk_size: Maximum size of chunks to return
+ chunk_overlap: Overlap in characters between chunks
+ length_function: Function that measures the length of given chunks
+ keep_separator: Whether to keep the separator in the chunks
+ add_start_index: If `True`, includes chunk's start index in metadata
+ strip_whitespace: If `True`, strips whitespace from the start and end of
+ every document
+ """
+ if chunk_overlap > chunk_size:
+ raise ValueError(
+ f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
+ f"({chunk_size}), should be smaller."
+ )
+ self._chunk_size = chunk_size
+ self._chunk_overlap = chunk_overlap
+ self._length_function = length_function
+ self._keep_separator = keep_separator
+ self._add_start_index = add_start_index
+ self._strip_whitespace = strip_whitespace
+
+ @abstractmethod
+ def split_text(self, text: str) -> List[str]:
+ """Split text into multiple components."""
+
+ def create_documents(
+ self, texts: List[str], metadatas: Optional[List[dict]] = None
+ ) -> List[Document]:
+ """Create documents from a list of texts."""
+ _metadatas = metadatas or [{}] * len(texts)
+ documents = []
+ for i, text in enumerate(texts):
+ index = 0
+ previous_chunk_len = 0
+ for chunk in self.split_text(text):
+ metadata = copy.deepcopy(_metadatas[i])
+ if self._add_start_index:
+ offset = index + previous_chunk_len - self._chunk_overlap
+ index = text.find(chunk, max(0, offset))
+ metadata["start_index"] = index
+ previous_chunk_len = len(chunk)
+ new_doc = Document(page_content=chunk, metadata=metadata)
+ documents.append(new_doc)
+ return documents
+
+ def split_documents(self, documents: Iterable[Document]) -> List[Document]:
+ """Split documents."""
+ texts, metadatas = [], []
+ for doc in documents:
+ texts.append(doc.page_content)
+ metadatas.append(doc.metadata)
+ return self.create_documents(texts, metadatas=metadatas)
+
+ def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
+ text = separator.join(docs)
+ if self._strip_whitespace:
+ text = text.strip()
+ if text == "":
+ return None
+ else:
+ return text
+
+ def _merge_splits(
+ self, splits: Iterable[str], separator: str
+ ) -> List[str]:
+ # We now want to combine these smaller pieces into medium size
+ # chunks to send to the LLM.
+ separator_len = self._length_function(separator)
+
+ docs = []
+ current_doc: List[str] = []
+ total = 0
+ for d in splits:
+ _len = self._length_function(d)
+ if (
+ total + _len + (separator_len if len(current_doc) > 0 else 0)
+ > self._chunk_size
+ ):
+ if total > self._chunk_size:
+ logger.warning(
+ f"Created a chunk of size {total}, "
+ f"which is longer than the specified {self._chunk_size}"
+ )
+ if len(current_doc) > 0:
+ doc = self._join_docs(current_doc, separator)
+ if doc is not None:
+ docs.append(doc)
+ # Keep on popping if:
+ # - we have a larger chunk than in the chunk overlap
+ # - or if we still have any chunks and the length is long
+ while total > self._chunk_overlap or (
+ total
+ + _len
+ + (separator_len if len(current_doc) > 0 else 0)
+ > self._chunk_size
+ and total > 0
+ ):
+ total -= self._length_function(current_doc[0]) + (
+ separator_len if len(current_doc) > 1 else 0
+ )
+ current_doc = current_doc[1:]
+ current_doc.append(d)
+ total += _len + (separator_len if len(current_doc) > 1 else 0)
+ doc = self._join_docs(current_doc, separator)
+ if doc is not None:
+ docs.append(doc)
+ return docs
+
+ @classmethod
+ def from_huggingface_tokenizer(
+ cls, tokenizer: Any, **kwargs: Any
+ ) -> TextSplitter:
+ """Text splitter that uses HuggingFace tokenizer to count length."""
+ try:
+ from transformers import PreTrainedTokenizerBase
+
+ if not isinstance(tokenizer, PreTrainedTokenizerBase):
+ raise ValueError(
+ "Tokenizer received was not an instance of PreTrainedTokenizerBase"
+ )
+
+ def _huggingface_tokenizer_length(text: str) -> int:
+ return len(tokenizer.encode(text))
+
+ except ImportError:
+ raise ValueError(
+ "Could not import transformers python package. "
+ "Please install it with `pip install transformers`."
+ )
+ return cls(length_function=_huggingface_tokenizer_length, **kwargs)
+
+ @classmethod
+ def from_tiktoken_encoder(
+ cls: Type[TS],
+ encoding_name: str = "gpt2",
+ model: Optional[str] = None,
+ allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
+ disallowed_special: Union[Literal["all"], Collection[str]] = "all",
+ **kwargs: Any,
+ ) -> TS:
+ """Text splitter that uses tiktoken encoder to count length."""
+ try:
+ import tiktoken
+ except ImportError:
+ raise ImportError(
+ "Could not import tiktoken python package. "
+ "This is needed in order to calculate max_tokens_for_prompt. "
+ "Please install it with `pip install tiktoken`."
+ )
+
+ if model is not None:
+ enc = tiktoken.encoding_for_model(model)
+ else:
+ enc = tiktoken.get_encoding(encoding_name)
+
+ def _tiktoken_encoder(text: str) -> int:
+ return len(
+ enc.encode(
+ text,
+ allowed_special=allowed_special,
+ disallowed_special=disallowed_special,
+ )
+ )
+
+ if issubclass(cls, TokenTextSplitter):
+ extra_kwargs = {
+ "encoding_name": encoding_name,
+ "model": model,
+ "allowed_special": allowed_special,
+ "disallowed_special": disallowed_special,
+ }
+ kwargs = {**kwargs, **extra_kwargs}
+
+ return cls(length_function=_tiktoken_encoder, **kwargs)
+
+ def transform_documents(
+ self, documents: Sequence[Document], **kwargs: Any
+ ) -> Sequence[Document]:
+ """Transform sequence of documents by splitting them."""
+ return self.split_documents(list(documents))
+
+
+class CharacterTextSplitter(TextSplitter):
+ """Splitting text that looks at characters."""
+
+ def __init__(
+ self,
+ separator: str = "\n\n",
+ is_separator_regex: bool = False,
+ **kwargs: Any,
+ ) -> None:
+ """Create a new TextSplitter."""
+ super().__init__(**kwargs)
+ self._separator = separator
+ self._is_separator_regex = is_separator_regex
+
+ def split_text(self, text: str) -> List[str]:
+ """Split incoming text and return chunks."""
+ # First we naively split the large input into a bunch of smaller ones.
+ separator = (
+ self._separator
+ if self._is_separator_regex
+ else re.escape(self._separator)
+ )
+ splits = _split_text_with_regex(text, separator, self._keep_separator)
+ _separator = "" if self._keep_separator else self._separator
+ return self._merge_splits(splits, _separator)
+
+
+class LineType(TypedDict):
+ """Line type as typed dict."""
+
+ metadata: Dict[str, str]
+ content: str
+
+
+class HeaderType(TypedDict):
+ """Header type as typed dict."""
+
+ level: int
+ name: str
+ data: str
+
+
+class MarkdownHeaderTextSplitter:
+ """Splitting markdown files based on specified headers."""
+
+ def __init__(
+ self,
+ headers_to_split_on: List[Tuple[str, str]],
+ return_each_line: bool = False,
+ strip_headers: bool = True,
+ ):
+ """Create a new MarkdownHeaderTextSplitter.
+
+ Args:
+ headers_to_split_on: Headers we want to track
+ return_each_line: Return each line w/ associated headers
+ strip_headers: Strip split headers from the content of the chunk
+ """
+ # Output line-by-line or aggregated into chunks w/ common headers
+ self.return_each_line = return_each_line
+ # Given the headers we want to split on,
+ # (e.g., "#, ##, etc") order by length
+ self.headers_to_split_on = sorted(
+ headers_to_split_on, key=lambda split: len(split[0]), reverse=True
+ )
+ # Strip headers split headers from the content of the chunk
+ self.strip_headers = strip_headers
+
+ def aggregate_lines_to_chunks(
+ self, lines: List[LineType]
+ ) -> List[Document]:
+ """Combine lines with common metadata into chunks
+ Args:
+ lines: Line of text / associated header metadata
+ """
+ aggregated_chunks: List[LineType] = []
+
+ for line in lines:
+ if (
+ aggregated_chunks
+ and aggregated_chunks[-1]["metadata"] == line["metadata"]
+ ):
+ # If the last line in the aggregated list
+ # has the same metadata as the current line,
+ # append the current content to the last lines's content
+ aggregated_chunks[-1]["content"] += " \n" + line["content"]
+ elif (
+ aggregated_chunks
+ and aggregated_chunks[-1]["metadata"] != line["metadata"]
+ # may be issues if other metadata is present
+ and len(aggregated_chunks[-1]["metadata"])
+ < len(line["metadata"])
+ and aggregated_chunks[-1]["content"].split("\n")[-1][0] == "#"
+ and not self.strip_headers
+ ):
+ # If the last line in the aggregated list
+ # has different metadata as the current line,
+ # and has shallower header level than the current line,
+ # and the last line is a header,
+ # and we are not stripping headers,
+ # append the current content to the last line's content
+ aggregated_chunks[-1]["content"] += " \n" + line["content"]
+ # and update the last line's metadata
+ aggregated_chunks[-1]["metadata"] = line["metadata"]
+ else:
+ # Otherwise, append the current line to the aggregated list
+ aggregated_chunks.append(line)
+
+ return [
+ Document(page_content=chunk["content"], metadata=chunk["metadata"])
+ for chunk in aggregated_chunks
+ ]
+
+ def split_text(self, text: str) -> List[Document]:
+ """Split markdown file
+ Args:
+ text: Markdown file"""
+
+ # Split the input text by newline character ("\n").
+ lines = text.split("\n")
+ # Final output
+ lines_with_metadata: List[LineType] = []
+ # Content and metadata of the chunk currently being processed
+ current_content: List[str] = []
+ current_metadata: Dict[str, str] = {}
+ # Keep track of the nested header structure
+ # header_stack: List[Dict[str, Union[int, str]]] = []
+ header_stack: List[HeaderType] = []
+ initial_metadata: Dict[str, str] = {}
+
+ in_code_block = False
+ opening_fence = ""
+
+ for line in lines:
+ stripped_line = line.strip()
+
+ if not in_code_block:
+ # Exclude inline code spans
+ if (
+ stripped_line.startswith("```")
+ and stripped_line.count("```") == 1
+ ):
+ in_code_block = True
+ opening_fence = "```"
+ elif stripped_line.startswith("~~~"):
+ in_code_block = True
+ opening_fence = "~~~"
+ else:
+ if stripped_line.startswith(opening_fence):
+ in_code_block = False
+ opening_fence = ""
+
+ if in_code_block:
+ current_content.append(stripped_line)
+ continue
+
+ # Check each line against each of the header types (e.g., #, ##)
+ for sep, name in self.headers_to_split_on:
+ # Check if line starts with a header that we intend to split on
+ if stripped_line.startswith(sep) and (
+ # Header with no text OR header is followed by space
+ # Both are valid conditions that sep is being used a header
+ len(stripped_line) == len(sep)
+ or stripped_line[len(sep)] == " "
+ ):
+ # Ensure we are tracking the header as metadata
+ if name is not None:
+ # Get the current header level
+ current_header_level = sep.count("#")
+
+ # Pop out headers of lower or same level from the stack
+ while (
+ header_stack
+ and header_stack[-1]["level"]
+ >= current_header_level
+ ):
+ # We have encountered a new header
+ # at the same or higher level
+ popped_header = header_stack.pop()
+ # Clear the metadata for the
+ # popped header in initial_metadata
+ if popped_header["name"] in initial_metadata:
+ initial_metadata.pop(popped_header["name"])
+
+ # Push the current header to the stack
+ header: HeaderType = {
+ "level": current_header_level,
+ "name": name,
+ "data": stripped_line[len(sep) :].strip(),
+ }
+ header_stack.append(header)
+ # Update initial_metadata with the current header
+ initial_metadata[name] = header["data"]
+
+ # Add the previous line to the lines_with_metadata
+ # only if current_content is not empty
+ if current_content:
+ lines_with_metadata.append(
+ {
+ "content": "\n".join(current_content),
+ "metadata": current_metadata.copy(),
+ }
+ )
+ current_content.clear()
+
+ if not self.strip_headers:
+ current_content.append(stripped_line)
+
+ break
+ else:
+ if stripped_line:
+ current_content.append(stripped_line)
+ elif current_content:
+ lines_with_metadata.append(
+ {
+ "content": "\n".join(current_content),
+ "metadata": current_metadata.copy(),
+ }
+ )
+ current_content.clear()
+
+ current_metadata = initial_metadata.copy()
+
+ if current_content:
+ lines_with_metadata.append(
+ {
+ "content": "\n".join(current_content),
+ "metadata": current_metadata,
+ }
+ )
+
+ # lines_with_metadata has each line with associated header metadata
+ # aggregate these into chunks based on common metadata
+ if not self.return_each_line:
+ return self.aggregate_lines_to_chunks(lines_with_metadata)
+ else:
+ return [
+ Document(
+ page_content=chunk["content"], metadata=chunk["metadata"]
+ )
+ for chunk in lines_with_metadata
+ ]
+
+
+class ElementType(TypedDict):
+ """Element type as typed dict."""
+
+ url: str
+ xpath: str
+ content: str
+ metadata: Dict[str, str]
+
+
+class HTMLHeaderTextSplitter:
+ """
+ Splitting HTML files based on specified headers.
+ Requires lxml package.
+ """
+
+ def __init__(
+ self,
+ headers_to_split_on: List[Tuple[str, str]],
+ return_each_element: bool = False,
+ ):
+ """Create a new HTMLHeaderTextSplitter.
+
+ Args:
+ headers_to_split_on: list of tuples of headers we want to track mapped to
+ (arbitrary) keys for metadata. Allowed header values: h1, h2, h3, h4,
+ h5, h6 e.g. [("h1", "Header 1"), ("h2", "Header 2)].
+ return_each_element: Return each element w/ associated headers.
+ """
+ # Output element-by-element or aggregated into chunks w/ common headers
+ self.return_each_element = return_each_element
+ self.headers_to_split_on = sorted(headers_to_split_on)
+
+ def aggregate_elements_to_chunks(
+ self, elements: List[ElementType]
+ ) -> List[Document]:
+ """Combine elements with common metadata into chunks
+
+ Args:
+ elements: HTML element content with associated identifying info and metadata
+ """
+ aggregated_chunks: List[ElementType] = []
+
+ for element in elements:
+ if (
+ aggregated_chunks
+ and aggregated_chunks[-1]["metadata"] == element["metadata"]
+ ):
+ # If the last element in the aggregated list
+ # has the same metadata as the current element,
+ # append the current content to the last element's content
+ aggregated_chunks[-1]["content"] += " \n" + element["content"]
+ else:
+ # Otherwise, append the current element to the aggregated list
+ aggregated_chunks.append(element)
+
+ return [
+ Document(page_content=chunk["content"], metadata=chunk["metadata"])
+ for chunk in aggregated_chunks
+ ]
+
+ def split_text_from_url(self, url: str) -> List[Document]:
+ """Split HTML from web URL
+
+ Args:
+ url: web URL
+ """
+ r = requests.get(url)
+ return self.split_text_from_file(BytesIO(r.content))
+
+ def split_text(self, text: str) -> List[Document]:
+ """Split HTML text string
+
+ Args:
+ text: HTML text
+ """
+ return self.split_text_from_file(StringIO(text))
+
+ def split_text_from_file(self, file: Any) -> List[Document]:
+ """Split HTML file
+
+ Args:
+ file: HTML file
+ """
+ try:
+ from lxml import etree
+ except ImportError as e:
+ raise ImportError(
+ "Unable to import lxml, please install with `pip install lxml`."
+ ) from e
+ # use lxml library to parse html document and return xml ElementTree
+ # Explicitly encoding in utf-8 allows non-English
+ # html files to be processed without garbled characters
+ parser = etree.HTMLParser(encoding="utf-8")
+ tree = etree.parse(file, parser)
+
+ # document transformation for "structure-aware" chunking is handled with xsl.
+ # see comments in html_chunks_with_headers.xslt for more detailed information.
+ xslt_path = (
+ pathlib.Path(__file__).parent
+ / "document_transformers/xsl/html_chunks_with_headers.xslt"
+ )
+ xslt_tree = etree.parse(xslt_path)
+ transform = etree.XSLT(xslt_tree)
+ result = transform(tree)
+ result_dom = etree.fromstring(str(result))
+
+ # create filter and mapping for header metadata
+ header_filter = [header[0] for header in self.headers_to_split_on]
+ header_mapping = dict(self.headers_to_split_on)
+
+ # map xhtml namespace prefix
+ ns_map = {"h": "http://www.w3.org/1999/xhtml"}
+
+ # build list of elements from DOM
+ elements = []
+ for element in result_dom.findall("*//*", ns_map):
+ if element.findall("*[@class='headers']") or element.findall(
+ "*[@class='chunk']"
+ ):
+ elements.append(
+ ElementType(
+ url=file,
+ xpath="".join(
+ [
+ node.text
+ for node in element.findall(
+ "*[@class='xpath']", ns_map
+ )
+ ]
+ ),
+ content="".join(
+ [
+ node.text
+ for node in element.findall(
+ "*[@class='chunk']", ns_map
+ )
+ ]
+ ),
+ metadata={
+ # Add text of specified headers to metadata using header
+ # mapping.
+ header_mapping[node.tag]: node.text
+ for node in filter(
+ lambda x: x.tag in header_filter,
+ element.findall(
+ "*[@class='headers']/*", ns_map
+ ),
+ )
+ },
+ )
+ )
+
+ if not self.return_each_element:
+ return self.aggregate_elements_to_chunks(elements)
+ else:
+ return [
+ Document(
+ page_content=chunk["content"], metadata=chunk["metadata"]
+ )
+ for chunk in elements
+ ]
+
+
+# should be in newer Python versions (3.10+)
+# @dataclass(frozen=True, kw_only=True, slots=True)
+@dataclass(frozen=True)
+class Tokenizer:
+ """Tokenizer data class."""
+
+ chunk_overlap: int
+ """Overlap in tokens between chunks"""
+ tokens_per_chunk: int
+ """Maximum number of tokens per chunk"""
+ decode: Callable[[List[int]], str]
+ """ Function to decode a list of token ids to a string"""
+ encode: Callable[[str], List[int]]
+ """ Function to encode a string to a list of token ids"""
+
+
+def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]:
+ """Split incoming text and return chunks using tokenizer."""
+ splits: List[str] = []
+ input_ids = tokenizer.encode(text)
+ start_idx = 0
+ cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
+ chunk_ids = input_ids[start_idx:cur_idx]
+ while start_idx < len(input_ids):
+ splits.append(tokenizer.decode(chunk_ids))
+ if cur_idx == len(input_ids):
+ break
+ start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
+ cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
+ chunk_ids = input_ids[start_idx:cur_idx]
+ return splits
+
+
+class TokenTextSplitter(TextSplitter):
+ """Splitting text to tokens using model tokenizer."""
+
+ def __init__(
+ self,
+ encoding_name: str = "gpt2",
+ model: Optional[str] = None,
+ allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
+ disallowed_special: Union[Literal["all"], Collection[str]] = "all",
+ **kwargs: Any,
+ ) -> None:
+ """Create a new TextSplitter."""
+ super().__init__(**kwargs)
+ try:
+ import tiktoken
+ except ImportError:
+ raise ImportError(
+ "Could not import tiktoken python package. "
+ "This is needed in order to for TokenTextSplitter. "
+ "Please install it with `pip install tiktoken`."
+ )
+
+ if model is not None:
+ enc = tiktoken.encoding_for_model(model)
+ else:
+ enc = tiktoken.get_encoding(encoding_name)
+ self._tokenizer = enc
+ self._allowed_special = allowed_special
+ self._disallowed_special = disallowed_special
+
+ def split_text(self, text: str) -> List[str]:
+ def _encode(_text: str) -> List[int]:
+ return self._tokenizer.encode(
+ _text,
+ allowed_special=self._allowed_special,
+ disallowed_special=self._disallowed_special,
+ )
+
+ tokenizer = Tokenizer(
+ chunk_overlap=self._chunk_overlap,
+ tokens_per_chunk=self._chunk_size,
+ decode=self._tokenizer.decode,
+ encode=_encode,
+ )
+
+ return split_text_on_tokens(text=text, tokenizer=tokenizer)
+
+
+class SentenceTransformersTokenTextSplitter(TextSplitter):
+ """Splitting text to tokens using sentence model tokenizer."""
+
+ def __init__(
+ self,
+ chunk_overlap: int = 50,
+ model: str = "sentence-transformers/all-mpnet-base-v2",
+ tokens_per_chunk: Optional[int] = None,
+ **kwargs: Any,
+ ) -> None:
+ """Create a new TextSplitter."""
+ super().__init__(**kwargs, chunk_overlap=chunk_overlap)
+
+ try:
+ from sentence_transformers import SentenceTransformer
+ except ImportError:
+ raise ImportError(
+ "Could not import sentence_transformer python package. "
+ "This is needed in order to for SentenceTransformersTokenTextSplitter. "
+ "Please install it with `pip install sentence-transformers`."
+ )
+
+ self.model = model
+ self._model = SentenceTransformer(self.model, trust_remote_code=True)
+ self.tokenizer = self._model.tokenizer
+ self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk)
+
+ def _initialize_chunk_configuration(
+ self, *, tokens_per_chunk: Optional[int]
+ ) -> None:
+ self.maximum_tokens_per_chunk = cast(int, self._model.max_seq_length)
+
+ if tokens_per_chunk is None:
+ self.tokens_per_chunk = self.maximum_tokens_per_chunk
+ else:
+ self.tokens_per_chunk = tokens_per_chunk
+
+ if self.tokens_per_chunk > self.maximum_tokens_per_chunk:
+ raise ValueError(
+ f"The token limit of the models '{self.model}'"
+ f" is: {self.maximum_tokens_per_chunk}."
+ f" Argument tokens_per_chunk={self.tokens_per_chunk}"
+ f" > maximum token limit."
+ )
+
+ def split_text(self, text: str) -> List[str]:
+ def encode_strip_start_and_stop_token_ids(text: str) -> List[int]:
+ return self._encode(text)[1:-1]
+
+ tokenizer = Tokenizer(
+ chunk_overlap=self._chunk_overlap,
+ tokens_per_chunk=self.tokens_per_chunk,
+ decode=self.tokenizer.decode,
+ encode=encode_strip_start_and_stop_token_ids,
+ )
+
+ return split_text_on_tokens(text=text, tokenizer=tokenizer)
+
+ def count_tokens(self, *, text: str) -> int:
+ return len(self._encode(text))
+
+ _max_length_equal_32_bit_integer: int = 2**32
+
+ def _encode(self, text: str) -> List[int]:
+ token_ids_with_start_and_end_token_ids = self.tokenizer.encode(
+ text,
+ max_length=self._max_length_equal_32_bit_integer,
+ truncation="do_not_truncate",
+ )
+ return token_ids_with_start_and_end_token_ids
+
+
+class Language(str, Enum):
+ """Enum of the programming languages."""
+
+ CPP = "cpp"
+ GO = "go"
+ JAVA = "java"
+ KOTLIN = "kotlin"
+ JS = "js"
+ TS = "ts"
+ PHP = "php"
+ PROTO = "proto"
+ PYTHON = "python"
+ RST = "rst"
+ RUBY = "ruby"
+ RUST = "rust"
+ SCALA = "scala"
+ SWIFT = "swift"
+ MARKDOWN = "markdown"
+ LATEX = "latex"
+ HTML = "html"
+ SOL = "sol"
+ CSHARP = "csharp"
+ COBOL = "cobol"
+ C = "c"
+ LUA = "lua"
+ PERL = "perl"
+
+
+class RecursiveCharacterTextSplitter(TextSplitter):
+ """Splitting text by recursively look at characters.
+
+ Recursively tries to split by different characters to find one
+ that works.
+ """
+
+ def __init__(
+ self,
+ separators: Optional[List[str]] = None,
+ keep_separator: bool = True,
+ is_separator_regex: bool = False,
+ **kwargs: Any,
+ ) -> None:
+ """Create a new TextSplitter."""
+ super().__init__(keep_separator=keep_separator, **kwargs)
+ self._separators = separators or ["\n\n", "\n", " ", ""]
+ self._is_separator_regex = is_separator_regex
+
+ def _split_text(self, text: str, separators: List[str]) -> List[str]:
+ """Split incoming text and return chunks."""
+ final_chunks = []
+ # Get appropriate separator to use
+ separator = separators[-1]
+ new_separators = []
+ for i, _s in enumerate(separators):
+ _separator = _s if self._is_separator_regex else re.escape(_s)
+ if _s == "":
+ separator = _s
+ break
+ if re.search(_separator, text):
+ separator = _s
+ new_separators = separators[i + 1 :]
+ break
+
+ _separator = (
+ separator if self._is_separator_regex else re.escape(separator)
+ )
+ splits = _split_text_with_regex(text, _separator, self._keep_separator)
+
+ # Now go merging things, recursively splitting longer texts.
+ _good_splits = []
+ _separator = "" if self._keep_separator else separator
+ for s in splits:
+ if self._length_function(s) < self._chunk_size:
+ _good_splits.append(s)
+ else:
+ if _good_splits:
+ merged_text = self._merge_splits(_good_splits, _separator)
+ final_chunks.extend(merged_text)
+ _good_splits = []
+ if not new_separators:
+ final_chunks.append(s)
+ else:
+ other_info = self._split_text(s, new_separators)
+ final_chunks.extend(other_info)
+ if _good_splits:
+ merged_text = self._merge_splits(_good_splits, _separator)
+ final_chunks.extend(merged_text)
+ return final_chunks
+
+ def split_text(self, text: str) -> List[str]:
+ return self._split_text(text, self._separators)
+
+ @classmethod
+ def from_language(
+ cls, language: Language, **kwargs: Any
+ ) -> RecursiveCharacterTextSplitter:
+ separators = cls.get_separators_for_language(language)
+ return cls(separators=separators, is_separator_regex=True, **kwargs)
+
+ @staticmethod
+ def get_separators_for_language(language: Language) -> List[str]:
+ if language == Language.CPP:
+ return [
+ # Split along class definitions
+ "\nclass ",
+ # Split along function definitions
+ "\nvoid ",
+ "\nint ",
+ "\nfloat ",
+ "\ndouble ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\nswitch ",
+ "\ncase ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.GO:
+ return [
+ # Split along function definitions
+ "\nfunc ",
+ "\nvar ",
+ "\nconst ",
+ "\ntype ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nswitch ",
+ "\ncase ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.JAVA:
+ return [
+ # Split along class definitions
+ "\nclass ",
+ # Split along method definitions
+ "\npublic ",
+ "\nprotected ",
+ "\nprivate ",
+ "\nstatic ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\nswitch ",
+ "\ncase ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.KOTLIN:
+ return [
+ # Split along class definitions
+ "\nclass ",
+ # Split along method definitions
+ "\npublic ",
+ "\nprotected ",
+ "\nprivate ",
+ "\ninternal ",
+ "\ncompanion ",
+ "\nfun ",
+ "\nval ",
+ "\nvar ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\nwhen ",
+ "\ncase ",
+ "\nelse ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.JS:
+ return [
+ # Split along function definitions
+ "\nfunction ",
+ "\nconst ",
+ "\nlet ",
+ "\nvar ",
+ "\nclass ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\nswitch ",
+ "\ncase ",
+ "\ndefault ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.TS:
+ return [
+ "\nenum ",
+ "\ninterface ",
+ "\nnamespace ",
+ "\ntype ",
+ # Split along class definitions
+ "\nclass ",
+ # Split along function definitions
+ "\nfunction ",
+ "\nconst ",
+ "\nlet ",
+ "\nvar ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\nswitch ",
+ "\ncase ",
+ "\ndefault ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.PHP:
+ return [
+ # Split along function definitions
+ "\nfunction ",
+ # Split along class definitions
+ "\nclass ",
+ # Split along control flow statements
+ "\nif ",
+ "\nforeach ",
+ "\nwhile ",
+ "\ndo ",
+ "\nswitch ",
+ "\ncase ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.PROTO:
+ return [
+ # Split along message definitions
+ "\nmessage ",
+ # Split along service definitions
+ "\nservice ",
+ # Split along enum definitions
+ "\nenum ",
+ # Split along option definitions
+ "\noption ",
+ # Split along import statements
+ "\nimport ",
+ # Split along syntax declarations
+ "\nsyntax ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.PYTHON:
+ return [
+ # First, try to split along class definitions
+ "\nclass ",
+ "\ndef ",
+ "\n\tdef ",
+ # Now split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.RST:
+ return [
+ # Split along section titles
+ "\n=+\n",
+ "\n-+\n",
+ "\n\\*+\n",
+ # Split along directive markers
+ "\n\n.. *\n\n",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.RUBY:
+ return [
+ # Split along method definitions
+ "\ndef ",
+ "\nclass ",
+ # Split along control flow statements
+ "\nif ",
+ "\nunless ",
+ "\nwhile ",
+ "\nfor ",
+ "\ndo ",
+ "\nbegin ",
+ "\nrescue ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.RUST:
+ return [
+ # Split along function definitions
+ "\nfn ",
+ "\nconst ",
+ "\nlet ",
+ # Split along control flow statements
+ "\nif ",
+ "\nwhile ",
+ "\nfor ",
+ "\nloop ",
+ "\nmatch ",
+ "\nconst ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.SCALA:
+ return [
+ # Split along class definitions
+ "\nclass ",
+ "\nobject ",
+ # Split along method definitions
+ "\ndef ",
+ "\nval ",
+ "\nvar ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\nmatch ",
+ "\ncase ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.SWIFT:
+ return [
+ # Split along function definitions
+ "\nfunc ",
+ # Split along class definitions
+ "\nclass ",
+ "\nstruct ",
+ "\nenum ",
+ # Split along control flow statements
+ "\nif ",
+ "\nfor ",
+ "\nwhile ",
+ "\ndo ",
+ "\nswitch ",
+ "\ncase ",
+ # Split by the normal type of lines
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.MARKDOWN:
+ return [
+ # First, try to split along Markdown headings (starting with level 2)
+ "\n#{1,6} ",
+ # Note the alternative syntax for headings (below) is not handled here
+ # Heading level 2
+ # ---------------
+ # End of code block
+ "```\n",
+ # Horizontal lines
+ "\n\\*\\*\\*+\n",
+ "\n---+\n",
+ "\n___+\n",
+ # Note that this splitter doesn't handle horizontal lines defined
+ # by *three or more* of ***, ---, or ___, but this is not handled
+ "\n\n",
+ "\n",
+ " ",
+ "",
+ ]
+ elif language == Language.LATEX:
+ return [
+ # First, try to split along Latex sections
+ "\n\\\\chapter{",
+ "\n\\\\section{",
+ "\n\\\\subsection{",
+ "\n\\\\subsubsection{",
+ # Now split by environments
+ "\n\\\\begin{enumerate}",
+ "\n\\\\begin{itemize}",
+ "\n\\\\begin{description}",
+ "\n\\\\begin{list}",
+ "\n\\\\begin{quote}",
+ "\n\\\\begin{quotation}",
+ "\n\\\\begin{verse}",
+ "\n\\\\begin{verbatim}",
+ # Now split by math environments
+ "\n\\\begin{align}",
+ "$$",
+ "$",
+ # Now split by the normal type of lines
+ " ",
+ "",
+ ]
+ elif language == Language.HTML:
+ return [
+ # First, try to split along HTML tags
+ " None:
+ """Initialize the NLTK splitter."""
+ super().__init__(**kwargs)
+ try:
+ from nltk.tokenize import sent_tokenize
+
+ self._tokenizer = sent_tokenize
+ except ImportError:
+ raise ImportError(
+ "NLTK is not installed, please install it with `pip install nltk`."
+ )
+ self._separator = separator
+ self._language = language
+
+ def split_text(self, text: str) -> List[str]:
+ """Split incoming text and return chunks."""
+ # First we naively split the large input into a bunch of smaller ones.
+ splits = self._tokenizer(text, language=self._language)
+ return self._merge_splits(splits, self._separator)
+
+
+class SpacyTextSplitter(TextSplitter):
+ """Splitting text using Spacy package.
+
+
+ Per default, Spacy's `en_core_web_sm` model is used and
+ its default max_length is 1000000 (it is the length of maximum character
+ this model takes which can be increased for large files). For a faster, but
+ potentially less accurate splitting, you can use `pipe='sentencizer'`.
+ """
+
+ def __init__(
+ self,
+ separator: str = "\n\n",
+ pipe: str = "en_core_web_sm",
+ max_length: int = 1_000_000,
+ **kwargs: Any,
+ ) -> None:
+ """Initialize the spacy text splitter."""
+ super().__init__(**kwargs)
+ self._tokenizer = _make_spacy_pipe_for_splitting(
+ pipe, max_length=max_length
+ )
+ self._separator = separator
+
+ def split_text(self, text: str) -> List[str]:
+ """Split incoming text and return chunks."""
+ splits = (s.text for s in self._tokenizer(text).sents)
+ return self._merge_splits(splits, self._separator)
+
+
+class KonlpyTextSplitter(TextSplitter):
+ """Splitting text using Konlpy package.
+
+ It is good for splitting Korean text.
+ """
+
+ def __init__(
+ self,
+ separator: str = "\n\n",
+ **kwargs: Any,
+ ) -> None:
+ """Initialize the Konlpy text splitter."""
+ super().__init__(**kwargs)
+ self._separator = separator
+ try:
+ from konlpy.tag import Kkma
+ except ImportError:
+ raise ImportError(
+ """
+ Konlpy is not installed, please install it with
+ `pip install konlpy`
+ """
+ )
+ self.kkma = Kkma()
+
+ def split_text(self, text: str) -> List[str]:
+ """Split incoming text and return chunks."""
+ splits = self.kkma.sentences(text)
+ return self._merge_splits(splits, self._separator)
+
+
+# For backwards compatibility
+class PythonCodeTextSplitter(RecursiveCharacterTextSplitter):
+ """Attempts to split the text along Python syntax."""
+
+ def __init__(self, **kwargs: Any) -> None:
+ """Initialize a PythonCodeTextSplitter."""
+ separators = self.get_separators_for_language(Language.PYTHON)
+ super().__init__(separators=separators, **kwargs)
+
+
+class MarkdownTextSplitter(RecursiveCharacterTextSplitter):
+ """Attempts to split the text along Markdown-formatted headings."""
+
+ def __init__(self, **kwargs: Any) -> None:
+ """Initialize a MarkdownTextSplitter."""
+ separators = self.get_separators_for_language(Language.MARKDOWN)
+ super().__init__(separators=separators, **kwargs)
+
+
+class LatexTextSplitter(RecursiveCharacterTextSplitter):
+ """Attempts to split the text along Latex-formatted layout elements."""
+
+ def __init__(self, **kwargs: Any) -> None:
+ """Initialize a LatexTextSplitter."""
+ separators = self.get_separators_for_language(Language.LATEX)
+ super().__init__(separators=separators, **kwargs)
+
+
+class RecursiveJsonSplitter:
+ def __init__(
+ self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None
+ ):
+ super().__init__()
+ self.max_chunk_size = max_chunk_size
+ self.min_chunk_size = (
+ min_chunk_size
+ if min_chunk_size is not None
+ else max(max_chunk_size - 200, 50)
+ )
+
+ @staticmethod
+ def _json_size(data: Dict) -> int:
+ """Calculate the size of the serialized JSON object."""
+ return len(json.dumps(data))
+
+ @staticmethod
+ def _set_nested_dict(d: Dict, path: List[str], value: Any) -> None:
+ """Set a value in a nested dictionary based on the given path."""
+ for key in path[:-1]:
+ d = d.setdefault(key, {})
+ d[path[-1]] = value
+
+ def _list_to_dict_preprocessing(self, data: Any) -> Any:
+ if isinstance(data, dict):
+ # Process each key-value pair in the dictionary
+ return {
+ k: self._list_to_dict_preprocessing(v) for k, v in data.items()
+ }
+ elif isinstance(data, list):
+ # Convert the list to a dictionary with index-based keys
+ return {
+ str(i): self._list_to_dict_preprocessing(item)
+ for i, item in enumerate(data)
+ }
+ else:
+ # Base case: the item is neither a dict nor a list, so return it unchanged
+ return data
+
+ def _json_split(
+ self,
+ data: Dict[str, Any],
+ current_path: List[str] = [],
+ chunks: List[Dict] = [{}],
+ ) -> List[Dict]:
+ """
+ Split json into maximum size dictionaries while preserving structure.
+ """
+ if isinstance(data, dict):
+ for key, value in data.items():
+ new_path = current_path + [key]
+ chunk_size = self._json_size(chunks[-1])
+ size = self._json_size({key: value})
+ remaining = self.max_chunk_size - chunk_size
+
+ if size < remaining:
+ # Add item to current chunk
+ self._set_nested_dict(chunks[-1], new_path, value)
+ else:
+ if chunk_size >= self.min_chunk_size:
+ # Chunk is big enough, start a new chunk
+ chunks.append({})
+
+ # Iterate
+ self._json_split(value, new_path, chunks)
+ else:
+ # handle single item
+ self._set_nested_dict(chunks[-1], current_path, data)
+ return chunks
+
+ def split_json(
+ self,
+ json_data: Dict[str, Any],
+ convert_lists: bool = False,
+ ) -> List[Dict]:
+ """Splits JSON into a list of JSON chunks"""
+
+ if convert_lists:
+ chunks = self._json_split(
+ self._list_to_dict_preprocessing(json_data)
+ )
+ else:
+ chunks = self._json_split(json_data)
+
+ # Remove the last chunk if it's empty
+ if not chunks[-1]:
+ chunks.pop()
+ return chunks
+
+ def split_text(
+ self, json_data: Dict[str, Any], convert_lists: bool = False
+ ) -> List[str]:
+ """Splits JSON into a list of JSON formatted strings"""
+
+ chunks = self.split_json(
+ json_data=json_data, convert_lists=convert_lists
+ )
+
+ # Convert to string
+ return [json.dumps(chunk) for chunk in chunks]
+
+ def create_documents(
+ self,
+ texts: List[Dict],
+ convert_lists: bool = False,
+ metadatas: Optional[List[dict]] = None,
+ ) -> List[Document]:
+ """Create documents from a list of json objects (Dict)."""
+ _metadatas = metadatas or [{}] * len(texts)
+ documents = []
+ for i, text in enumerate(texts):
+ for chunk in self.split_text(
+ json_data=text, convert_lists=convert_lists
+ ):
+ metadata = copy.deepcopy(_metadatas[i])
+ new_doc = Document(page_content=chunk, metadata=metadata)
+ documents.append(new_doc)
+ return documents
diff --git a/R2R/r2r/cli/__init__.py b/R2R/r2r/cli/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/cli/cli.py b/R2R/r2r/cli/cli.py
new file mode 100755
index 00000000..4ef38b1e
--- /dev/null
+++ b/R2R/r2r/cli/cli.py
@@ -0,0 +1,592 @@
+import json
+import os
+import subprocess
+import time
+import uuid
+
+import click
+from dotenv import load_dotenv
+
+from r2r.main.execution import R2RExecutionWrapper
+
+
+class JsonParamType(click.ParamType):
+ name = "json"
+
+ def convert(self, value, param, ctx):
+ try:
+ return json.loads(value)
+ except json.JSONDecodeError:
+ self.fail(f"'{value}' is not a valid JSON string", param, ctx)
+
+
+JSON = JsonParamType()
+
+
+@click.group()
+@click.option(
+ "--config-path", default=None, help="Path to the configuration file"
+)
+@click.option(
+ "--config-name", default=None, help="Name of the configuration to use"
+)
+@click.option("--client-mode", default=True, help="Run in client mode")
+@click.option(
+ "--base-url",
+ default="http://localhost:8000",
+ help="Base URL for client mode",
+)
+@click.pass_context
+def cli(ctx, config_path, config_name, client_mode, base_url):
+ """R2R CLI for all core operations."""
+ if config_path and config_name:
+ raise click.UsageError(
+ "Cannot specify both config_path and config_name"
+ )
+
+ # Convert relative config path to absolute path
+ if config_path:
+ config_path = os.path.abspath(config_path)
+
+ if ctx.invoked_subcommand != "serve":
+ ctx.obj = R2RExecutionWrapper(
+ config_path,
+ config_name,
+ client_mode if ctx.invoked_subcommand != "serve" else False,
+ base_url,
+ )
+ else:
+ ctx.obj = {
+ "config_path": config_path,
+ "config_name": config_name,
+ "base_url": base_url,
+ }
+
+
+@cli.command()
+@click.option("--host", default="0.0.0.0", help="Host to run the server on")
+@click.option("--port", default=8000, help="Port to run the server on")
+@click.option("--docker", is_flag=True, help="Run using Docker")
+@click.option(
+ "--docker-ext-neo4j",
+ is_flag=True,
+ help="Run using Docker with external Neo4j",
+)
+@click.option("--project-name", default="r2r", help="Project name for Docker")
+@click.pass_obj
+def serve(obj, host, port, docker, docker_ext_neo4j, project_name):
+ """Start the R2R server."""
+ # Load environment variables from .env file if it exists
+ load_dotenv()
+
+ if docker:
+ if x := obj.get("config_path", None):
+ os.environ["CONFIG_PATH"] = x
+ else:
+ os.environ["CONFIG_NAME"] = (
+ obj.get("config_name", None) or "default"
+ )
+
+ os.environ["OLLAMA_API_BASE"] = "http://host.docker.internal:11434"
+ # Check if compose files exist in the package directory
+ package_dir = os.path.join(
+ os.path.dirname(os.path.abspath(__file__)), "..", ".."
+ )
+ compose_yaml = os.path.join(package_dir, "compose.yaml")
+ compose_neo4j_yaml = os.path.join(package_dir, "compose.neo4j.yaml")
+
+ if not os.path.exists(compose_yaml) or not os.path.exists(
+ compose_neo4j_yaml
+ ):
+ click.echo(
+ "Error: Docker Compose files not found in the package directory."
+ )
+ return
+
+ # Build the docker-compose command with the specified host and port
+ docker_command = f"docker-compose -f {compose_yaml}"
+ if docker_ext_neo4j:
+ docker_command += f" -f {compose_neo4j_yaml}"
+ if host != "0.0.0.0" or port != 8000:
+ docker_command += (
+ f" --build-arg HOST={host} --build-arg PORT={port}"
+ )
+
+ docker_command += f" --project-name {project_name}"
+
+ docker_command += " up -d"
+ os.system(docker_command)
+ else:
+ wrapper = R2RExecutionWrapper(**obj, client_mode=False)
+ wrapper.serve(host, port)
+
+
+@cli.command()
+@click.option(
+ "--volumes",
+ is_flag=True,
+ help="Remove named volumes declared in the `volumes` section of the Compose file",
+)
+@click.option(
+ "--remove-orphans",
+ is_flag=True,
+ help="Remove containers for services not defined in the Compose file",
+)
+@click.option("--project-name", default="r2r", help="Project name for Docker")
+@click.pass_context
+def docker_down(ctx, volumes, remove_orphans, project_name):
+ """Bring down the Docker Compose setup and attempt to remove the network if necessary."""
+ package_dir = os.path.join(
+ os.path.dirname(os.path.abspath(__file__)), "..", ".."
+ )
+ compose_yaml = os.path.join(package_dir, "compose.yaml")
+ compose_neo4j_yaml = os.path.join(package_dir, "compose.neo4j.yaml")
+
+ if not os.path.exists(compose_yaml) or not os.path.exists(
+ compose_neo4j_yaml
+ ):
+ click.echo(
+ "Error: Docker Compose files not found in the package directory."
+ )
+ return
+
+ docker_command = (
+ f"docker-compose -f {compose_yaml} -f {compose_neo4j_yaml}"
+ )
+ docker_command += f" --project-name {project_name}"
+
+ if volumes:
+ docker_command += " --volumes"
+
+ if remove_orphans:
+ docker_command += " --remove-orphans"
+
+ docker_command += " down"
+
+ click.echo("Bringing down Docker Compose setup...")
+ result = os.system(docker_command)
+
+ if result != 0:
+ click.echo(
+ "An error occurred while bringing down the Docker Compose setup. Attempting to remove the network..."
+ )
+
+ # Get the list of networks
+ networks = (
+ subprocess.check_output(
+ ["docker", "network", "ls", "--format", "{{.Name}}"]
+ )
+ .decode()
+ .split()
+ )
+
+ # Find the r2r network
+ r2r_network = next(
+ (
+ network
+ for network in networks
+ if network.startswith("r2r_") and "network" in network
+ ),
+ None,
+ )
+
+ if r2r_network:
+ # Try to remove the network
+ for _ in range(1): # Try 1 extra times
+ remove_command = f"docker network rm {r2r_network}"
+ remove_result = os.system(remove_command)
+
+ if remove_result == 0:
+ click.echo(f"Successfully removed network: {r2r_network}")
+ return
+ else:
+ click.echo(
+ f"Failed to remove network: {r2r_network}. Retrying in 5 seconds..."
+ )
+ time.sleep(5)
+
+ click.echo(
+ "Failed to remove the network after multiple attempts. Please try the following steps:"
+ )
+ click.echo(
+ "1. Run 'docker ps' to check for any running containers using this network."
+ )
+ click.echo(
+ "2. Stop any running containers with 'docker stop '."
+ )
+ click.echo(
+ f"3. Try removing the network manually with 'docker network rm {r2r_network}'."
+ )
+ click.echo(
+ "4. If the above steps don't work, you may need to restart the Docker daemon."
+ )
+ else:
+ click.echo("Could not find the r2r network to remove.")
+ else:
+ click.echo("Docker Compose setup has been successfully brought down.")
+
+
+@cli.command()
+@click.argument("file-paths", nargs=-1)
+@click.option(
+ "--document-ids", multiple=True, help="Document IDs for ingestion"
+)
+@click.option("--metadatas", multiple=True, help="Metadatas for ingestion")
+@click.option(
+ "--versions",
+ multiple=True,
+ help="Starting version for ingested files (e.g. `v1`)",
+)
+@click.pass_obj
+def ingest_files(obj, file_paths, document_ids, metadatas, versions):
+ """Ingest files into R2R."""
+
+ t0 = time.time()
+
+ # Default to None if empty tuples are provided
+ document_ids = None if not document_ids else list(document_ids)
+ metadatas = None if not metadatas else list(metadatas)
+ versions = None if not versions else list(versions)
+
+ response = obj.ingest_files(
+ list(file_paths), document_ids, metadatas, versions
+ )
+ t1 = time.time()
+ click.echo(f"Time taken to ingest files: {t1 - t0:.2f} seconds")
+ click.echo(response)
+
+
+@cli.command()
+@click.argument("file-paths", nargs=-1)
+@click.option(
+ "--document-ids", multiple=True, help="Document IDs for ingestion"
+)
+@click.option("--metadatas", multiple=True, help="Metadatas for ingestion")
+@click.pass_obj
+def update_files(obj, file_paths, document_ids, metadatas):
+ """Ingest files into R2R."""
+ t0 = time.time()
+
+ # Default to None if empty tuples are provided
+ metadatas = None if not metadatas else list(metadatas)
+
+ response = obj.update_files(
+ list(file_paths), list(document_ids), metadatas
+ )
+ t1 = time.time()
+ click.echo(f"Time taken to ingest files: {t1 - t0:.2f} seconds")
+ click.echo(response)
+
+
+@cli.command()
+@click.option(
+ "--query", prompt="Enter your search query", help="The search query"
+)
+@click.option(
+ "--use-vector-search", is_flag=True, default=True, help="Use vector search"
+)
+@click.option(
+ "--search-filters", type=JsonParamType(), help="Search filters as JSON"
+)
+@click.option(
+ "--search-limit", default=10, help="Number of search results to return"
+)
+@click.option("--do-hybrid-search", is_flag=True, help="Perform hybrid search")
+@click.option(
+ "--use-kg-search", is_flag=True, help="Use knowledge graph search"
+)
+@click.option("--kg-agent-model", default=None, help="Model for KG agent")
+@click.pass_obj
+def search(
+ obj,
+ query,
+ use_vector_search,
+ search_filters,
+ search_limit,
+ do_hybrid_search,
+ use_kg_search,
+ kg_agent_model,
+):
+ """Perform a search query."""
+ kg_agent_generation_config = {}
+ if kg_agent_model:
+ kg_agent_generation_config["model"] = kg_agent_model
+
+ t0 = time.time()
+
+ results = obj.search(
+ query,
+ use_vector_search,
+ search_filters,
+ search_limit,
+ do_hybrid_search,
+ use_kg_search,
+ kg_agent_generation_config,
+ )
+
+ if isinstance(results, dict) and "results" in results:
+ results = results["results"]
+
+ if "vector_search_results" in results:
+ click.echo("Vector search results:")
+ for result in results["vector_search_results"]:
+ click.echo(result)
+ if "kg_search_results" in results and results["kg_search_results"]:
+ click.echo("KG search results:", results["kg_search_results"])
+
+ t1 = time.time()
+ click.echo(f"Time taken to search: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option("--query", prompt="Enter your query", help="The query for RAG")
+@click.option(
+ "--use-vector-search", is_flag=True, default=True, help="Use vector search"
+)
+@click.option(
+ "--search-filters", type=JsonParamType(), help="Search filters as JSON"
+)
+@click.option(
+ "--search-limit", default=10, help="Number of search results to return"
+)
+@click.option("--do-hybrid-search", is_flag=True, help="Perform hybrid search")
+@click.option(
+ "--use-kg-search", is_flag=True, help="Use knowledge graph search"
+)
+@click.option("--kg-agent-model", default=None, help="Model for KG agent")
+@click.option("--stream", is_flag=True, help="Stream the RAG response")
+@click.option("--rag-model", default=None, help="Model for RAG")
+@click.pass_obj
+def rag(
+ obj,
+ query,
+ use_vector_search,
+ search_filters,
+ search_limit,
+ do_hybrid_search,
+ use_kg_search,
+ kg_agent_model,
+ stream,
+ rag_model,
+):
+ """Perform a RAG query."""
+ kg_agent_generation_config = {}
+ if kg_agent_model:
+ kg_agent_generation_config = {"model": kg_agent_model}
+ rag_generation_config = {"stream": stream}
+ if rag_model:
+ rag_generation_config["model"] = rag_model
+ t0 = time.time()
+
+ response = obj.rag(
+ query,
+ use_vector_search,
+ search_filters,
+ search_limit,
+ do_hybrid_search,
+ use_kg_search,
+ kg_agent_generation_config,
+ stream,
+ rag_generation_config,
+ )
+ if stream:
+ for chunk in response:
+ click.echo(chunk, nl=False)
+ click.echo()
+ else:
+ if obj.client_mode:
+ click.echo(f"Search Results:\n{response['search_results']}")
+ click.echo(f"Completion:\n{response['completion']}")
+ else:
+ click.echo(f"Search Results:\n{response.search_results}")
+ click.echo(f"Completion:\n{response.completion}")
+
+ t1 = time.time()
+ click.echo(f"Time taken for RAG: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option("--keys", multiple=True, help="Keys for deletion")
+@click.option("--values", multiple=True, help="Values for deletion")
+@click.pass_obj
+def delete(obj, keys, values):
+ """Delete documents based on keys and values."""
+ if len(keys) != len(values):
+ raise click.UsageError("Number of keys must match number of values")
+
+ t0 = time.time()
+ response = obj.delete(list(keys), list(values))
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken for deletion: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option("--log-type-filter", help="Filter for log types")
+@click.pass_obj
+def logs(obj, log_type_filter):
+ """Retrieve logs with optional type filter."""
+ t0 = time.time()
+ response = obj.logs(log_type_filter)
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to retrieve logs: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option("--document-ids", multiple=True, help="Document IDs to overview")
+@click.option("--user-ids", multiple=True, help="User IDs to overview")
+@click.pass_obj
+def documents_overview(obj, document_ids, user_ids):
+ """Get an overview of documents."""
+ document_ids = list(document_ids) if document_ids else None
+ user_ids = list(user_ids) if user_ids else None
+
+ t0 = time.time()
+ response = obj.documents_overview(document_ids, user_ids)
+ t1 = time.time()
+
+ for document in response:
+ click.echo(document)
+ click.echo(f"Time taken to get document overview: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.argument("document_id")
+@click.pass_obj
+def document_chunks(obj, document_id):
+ """Get chunks of a specific document."""
+ t0 = time.time()
+ response = obj.document_chunks(document_id)
+ t1 = time.time()
+
+ for chunk in response:
+ click.echo(chunk)
+ click.echo(f"Time taken to get document chunks: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.pass_obj
+def app_settings(obj):
+ """Retrieve application settings."""
+ t0 = time.time()
+ response = obj.app_settings()
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to get app settings: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option("--user-ids", multiple=True, help="User IDs to overview")
+@click.pass_obj
+def users_overview(obj, user_ids):
+ """Get an overview of users."""
+ user_ids = (
+ [uuid.UUID(user_id) for user_id in user_ids] if user_ids else None
+ )
+
+ t0 = time.time()
+ response = obj.users_overview(user_ids)
+ t1 = time.time()
+
+ for user in response:
+ click.echo(user)
+ click.echo(f"Time taken to get users overview: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option(
+ "--filters", type=JsonParamType(), help="Filters for analytics as JSON"
+)
+@click.option(
+ "--analysis-types", type=JsonParamType(), help="Analysis types as JSON"
+)
+@click.pass_obj
+def analytics(obj, filters, analysis_types):
+ """Retrieve analytics data."""
+ t0 = time.time()
+ response = obj.analytics(filters, analysis_types)
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to get analytics: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option(
+ "--limit", default=100, help="Limit the number of relationships returned"
+)
+@click.pass_obj
+def inspect_knowledge_graph(obj, limit):
+ """Print relationships from the knowledge graph."""
+ t0 = time.time()
+ response = obj.inspect_knowledge_graph(limit)
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to print relationships: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option(
+ "--no-media",
+ default=True,
+ help="Exclude media files from ingestion",
+)
+@click.option("--option", default=0, help="Which file to ingest?")
+@click.pass_obj
+def ingest_sample_file(obj, no_media, option):
+ t0 = time.time()
+ response = obj.ingest_sample_file(no_media=no_media, option=option)
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to ingest sample: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.option(
+ "--no-media",
+ default=True,
+ help="Exclude media files from ingestion",
+)
+@click.pass_obj
+def ingest_sample_files(obj, no_media):
+ """Ingest all sample files into R2R."""
+ t0 = time.time()
+ response = obj.ingest_sample_files(no_media=no_media)
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to ingest sample files: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+@click.pass_obj
+def health(obj):
+ """Check the health of the server."""
+ t0 = time.time()
+ response = obj.health()
+ t1 = time.time()
+
+ click.echo(response)
+ click.echo(f"Time taken to ingest sample: {t1 - t0:.2f} seconds")
+
+
+@cli.command()
+def version():
+ """Print the version of R2R."""
+ from importlib.metadata import version
+
+ click.echo(version("r2r"))
+
+
+def main():
+ cli()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/R2R/r2r/examples/__init__.py b/R2R/r2r/examples/__init__.py
new file mode 100755
index 00000000..e69de29b
diff --git a/R2R/r2r/examples/configs/local_neo4j_kg.json b/R2R/r2r/examples/configs/local_neo4j_kg.json
new file mode 100755
index 00000000..0b4254dc
--- /dev/null
+++ b/R2R/r2r/examples/configs/local_neo4j_kg.json
@@ -0,0 +1,69 @@
+{
+ "kg": {
+ "provider": "neo4j",
+ "batch_size": 1,
+ "text_splitter": {
+ "type": "recursive_character",
+ "chunk_size": 512,
+ "chunk_overlap": 0
+ },
+ "max_entities": 10,
+ "max_relations": 20,
+ "kg_extraction_prompt": "zero_shot_ner_kg_extraction",
+ "kg_extraction_config": {
+ "model": "ollama/sciphi/triplex",
+ "temperature": 1.0,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": false,
+ "functions": null,
+ "skip_special_tokens": false,
+ "stop_token": null,
+ "num_beams": 1,
+ "do_sample": true,
+ "generate_with_chat": false,
+ "add_generation_kwargs": {},
+ "api_base": null
+ }
+ },
+ "completions": {
+ "provider": "litellm",
+ "generation_config": {
+ "model": "ollama/llama3",
+ "temperature": 0.1,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": false,
+ "functions": null,
+ "skip_special_tokens": false,
+ "stop_token": null,
+ "num_beams": 1,
+ "do_sample": true,
+ "generate_with_chat": false,
+ "add_generation_kwargs": {},
+ "api_base": null
+ }
+ },
+ "embedding": {
+ "provider": "ollama",
+ "base_model": "mxbai-embed-large",
+ "base_dimension": 1024,
+ "batch_size": 32
+ },
+ "ingestion":{
+ "excluded_parsers": [
+ "gif",
+ "jpeg",
+ "jpg",
+ "png",
+ "svg",
+ "mp3",
+ "mp4"
+ ]
+ },
+ "vector_database": {
+ "provider": "pgvector"
+ }
+}
diff --git a/R2R/r2r/examples/configs/local_ollama.json b/R2R/r2r/examples/configs/local_ollama.json
new file mode 100755
index 00000000..d6fd68a5
--- /dev/null
+++ b/R2R/r2r/examples/configs/local_ollama.json
@@ -0,0 +1,41 @@
+{
+ "completions": {
+ "provider": "litellm",
+ "generation_config": {
+ "model": "ollama/llama3",
+ "temperature": 0.1,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": false,
+ "functions": null,
+ "skip_special_tokens": false,
+ "stop_token": null,
+ "num_beams": 1,
+ "do_sample": true,
+ "generate_with_chat": false,
+ "add_generation_kwargs": {},
+ "api_base": null
+ }
+ },
+ "embedding": {
+ "provider": "ollama",
+ "base_model": "mxbai-embed-large",
+ "base_dimension": 1024,
+ "batch_size": 32
+ },
+ "ingestion":{
+ "excluded_parsers": [
+ "gif",
+ "jpeg",
+ "jpg",
+ "png",
+ "svg",
+ "mp3",
+ "mp4"
+ ]
+ },
+ "vector_database": {
+ "provider": "pgvector"
+ }
+}
diff --git a/R2R/r2r/examples/configs/local_ollama_rerank.json b/R2R/r2r/examples/configs/local_ollama_rerank.json
new file mode 100755
index 00000000..3a9abbe2
--- /dev/null
+++ b/R2R/r2r/examples/configs/local_ollama_rerank.json
@@ -0,0 +1,46 @@
+{
+ "completions": {
+ "provider": "litellm",
+ "generation_config": {
+ "model": "ollama/llama3",
+ "temperature": 0.1,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": false,
+ "functions": null,
+ "skip_special_tokens": false,
+ "stop_token": null,
+ "num_beams": 1,
+ "do_sample": true,
+ "generate_with_chat": false,
+ "add_generation_kwargs": {},
+ "api_base": null
+ }
+ },
+ "embedding": {
+ "provider": "sentence-transformers",
+ "base_model": "all-MiniLM-L6-v2",
+ "base_dimension": 384,
+ "rerank_model": "jinaai/jina-reranker-v1-turbo-en",
+ "rerank_dimension": 384,
+ "rerank_transformer_type": "CrossEncoder",
+ "batch_size": 32,
+ "text_splitter": {
+ "type": "recursive_character",
+ "chunk_size": 512,
+ "chunk_overlap": 20
+ }
+ },
+ "ingestion":{
+ "excluded_parsers": [
+ "gif",
+ "jpeg",
+ "jpg",
+ "png",
+ "svg",
+ "mp3",
+ "mp4"
+ ]
+ }
+}
diff --git a/R2R/r2r/examples/configs/neo4j_kg.json b/R2R/r2r/examples/configs/neo4j_kg.json
new file mode 100755
index 00000000..67fd0682
--- /dev/null
+++ b/R2R/r2r/examples/configs/neo4j_kg.json
@@ -0,0 +1,27 @@
+{
+ "kg": {
+ "provider": "neo4j",
+ "batch_size": 1,
+ "text_splitter": {
+ "type": "recursive_character",
+ "chunk_size": 1024,
+ "chunk_overlap": 0
+ },
+ "kg_extraction_config": {
+ "model": "gpt-4o",
+ "temperature": 0.1,
+ "top_p": 1.0,
+ "top_k": 100,
+ "max_tokens_to_sample": 1024,
+ "stream": false,
+ "functions": null,
+ "skip_special_tokens": false,
+ "stop_token": null,
+ "num_beams": 1,
+ "do_sample": true,
+ "generate_with_chat": false,
+ "add_generation_kwargs": {},
+ "api_base": null
+ }
+ }
+}
diff --git a/R2R/r2r/examples/configs/postgres_logging.json b/R2R/r2r/examples/configs/postgres_logging.json
new file mode 100755
index 00000000..ec659bf4
--- /dev/null
+++ b/R2R/r2r/examples/configs/postgres_logging.json
@@ -0,0 +1,7 @@
+{
+ "logging": {
+ "provider": "postgres",
+ "log_table": "logs",
+ "log_info_table": "log_info"
+ }
+}
diff --git a/R2R/r2r/examples/hello_r2r.py b/R2R/r2r/examples/hello_r2r.py
new file mode 100755
index 00000000..97a49956
--- /dev/null
+++ b/R2R/r2r/examples/hello_r2r.py
@@ -0,0 +1,25 @@
+from r2r import R2R, Document, GenerationConfig
+
+app = R2R() # You may pass a custom configuration to `R2R` with config=...
+
+app.ingest_documents(
+ [
+ Document(
+ type="txt",
+ data="John is a person that works at Google.",
+ metadata={},
+ )
+ ]
+)
+
+rag_results = app.rag(
+ "Who is john", GenerationConfig(model="gpt-3.5-turbo", temperature=0.0)
+)
+print(f"Search Results:\n{rag_results.search_results}")
+print(f"Completion:\n{rag_results.completion}")
+
+# RAG Results:
+# Search Results:
+# AggregateSearchResult(vector_search_results=[VectorSearchResult(id=2d71e689-0a0e-5491-a50b-4ecb9494c832, score=0.6848798582029441, metadata={'text': 'John is a person that works at Google.', 'version': 'v0', 'chunk_order': 0, 'document_id': 'ed76b6ee-dd80-5172-9263-919d493b439a', 'extraction_id': '1ba494d7-cb2f-5f0e-9f64-76c31da11381', 'associatedQuery': 'Who is john'})], kg_search_results=None)
+# Completion:
+# ChatCompletion(id='chatcmpl-9g0HnjGjyWDLADe7E2EvLWa35cMkB', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='John is a person that works at Google [1].', role='assistant', function_call=None, tool_calls=None))], created=1719797903, model='gpt-3.5-turbo-0125', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=11, prompt_tokens=145, total_tokens=156))
diff --git a/R2R/r2r/examples/scripts/advanced_kg_cookbook.py b/R2R/r2r/examples/scripts/advanced_kg_cookbook.py
new file mode 100755
index 00000000..a4d59a79
--- /dev/null
+++ b/R2R/r2r/examples/scripts/advanced_kg_cookbook.py
@@ -0,0 +1,194 @@
+import json
+import os
+
+import fire
+import requests
+from bs4 import BeautifulSoup, Comment
+
+from r2r import (
+ EntityType,
+ R2RClient,
+ R2RPromptProvider,
+ Relation,
+ update_kg_prompt,
+)
+
+
+def escape_braces(text):
+ return text.replace("{", "{{").replace("}", "}}")
+
+
+def get_all_yc_co_directory_urls():
+ this_file_path = os.path.abspath(os.path.dirname(__file__))
+ yc_company_dump_path = os.path.join(
+ this_file_path, "..", "data", "yc_companies.txt"
+ )
+
+ with open(yc_company_dump_path, "r") as f:
+ urls = f.readlines()
+ urls = [url.strip() for url in urls]
+ return {url.split("/")[-1]: url for url in urls}
+
+
+# Function to fetch and clean HTML content
+def fetch_and_clean_yc_co_data(url):
+ # Fetch the HTML content from the URL
+ response = requests.get(url)
+ response.raise_for_status() # Raise an error for bad status codes
+ html_content = response.text
+
+ # Parse the HTML content with BeautifulSoup
+ soup = BeautifulSoup(html_content, "html.parser")
+
+ # Remove all 
+
+