diff options
Diffstat (limited to '.venv/lib/python3.12/site-packages/r2r')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/r2r/__init__.py | 19 | ||||
| -rw-r--r-- | .venv/lib/python3.12/site-packages/r2r/mcp.py | 150 | ||||
| -rw-r--r-- | .venv/lib/python3.12/site-packages/r2r/r2r.toml | 122 | ||||
| -rw-r--r-- | .venv/lib/python3.12/site-packages/r2r/serve.py | 146 |
4 files changed, 437 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/r2r/__init__.py b/.venv/lib/python3.12/site-packages/r2r/__init__.py new file mode 100644 index 00000000..0ef83aa4 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/r2r/__init__.py @@ -0,0 +1,19 @@ +from importlib import metadata + +from sdk.async_client import R2RAsyncClient +from sdk.sync_client import R2RClient +from shared import * +from shared import __all__ as shared_all + +__version__ = metadata.version("r2r") + +__all__ = [ + "R2RAsyncClient", + "R2RClient", + "__version__", + "R2RException", +] + shared_all + + +def get_version(): + return __version__ diff --git a/.venv/lib/python3.12/site-packages/r2r/mcp.py b/.venv/lib/python3.12/site-packages/r2r/mcp.py new file mode 100644 index 00000000..33490ea1 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/r2r/mcp.py @@ -0,0 +1,150 @@ +# Add to your local machine with `mcp install r2r/mcp.py -v R2R_API_URL=http://localhost:7272` or so. +from r2r import R2RClient + + +def id_to_shorthand(id: str) -> str: + return str(id)[:7] + + +def format_search_results_for_llm( + results, +) -> str: + """ + Instead of resetting 'source_counter' to 1, we: + - For each chunk / graph / web / doc in `results`, + - Find the aggregator index from the collector, + - Print 'Source [X]:' with that aggregator index. + """ + lines = [] + + # We'll build a quick helper to locate aggregator indices for each object: + # Or you can rely on the fact that we've added them to the collector + # in the same order. But let's do a "lookup aggregator index" approach: + + # 1) Chunk search + if results.chunk_search_results: + lines.append("Vector Search Results:") + for c in results.chunk_search_results: + lines.append(f"Source ID [{id_to_shorthand(c.id)}]:") + lines.append(c.text or "") # or c.text[:200] to truncate + + # 2) Graph search + if results.graph_search_results: + lines.append("Graph Search Results:") + for g in results.graph_search_results: + lines.append(f"Source ID [{id_to_shorthand(g.id)}]:") + if hasattr(g.content, "summary"): + lines.append(f"Community Name: {g.content.name}") + lines.append(f"ID: {g.content.id}") + lines.append(f"Summary: {g.content.summary}") + # etc. ... + elif hasattr(g.content, "name") and hasattr( + g.content, "description" + ): + lines.append(f"Entity Name: {g.content.name}") + lines.append(f"Description: {g.content.description}") + elif ( + hasattr(g.content, "subject") + and hasattr(g.content, "predicate") + and hasattr(g.content, "object") + ): + lines.append( + f"Relationship: {g.content.subject}-{g.content.predicate}-{g.content.object}" + ) + # Add metadata if needed + + # 3) Web search + if results.web_search_results: + lines.append("Web Search Results:") + for w in results.web_search_results: + lines.append(f"Source ID [{id_to_shorthand(w.id)}]:") + lines.append(f"Title: {w.title}") + lines.append(f"Link: {w.link}") + lines.append(f"Snippet: {w.snippet}") + + # 4) Local context docs + if results.document_search_results: + lines.append("Local Context Documents:") + for doc_result in results.document_search_results: + doc_title = doc_result.title or "Untitled Document" + doc_id = doc_result.id + summary = doc_result.summary + + lines.append(f"Full Document ID: {doc_id}") + lines.append(f"Shortened Document ID: {id_to_shorthand(doc_id)}") + lines.append(f"Document Title: {doc_title}") + if summary: + lines.append(f"Summary: {summary}") + + if doc_result.chunks: + # Then each chunk inside: + for chunk in doc_result.chunks: + lines.append( + f"\nChunk ID {id_to_shorthand(chunk['id'])}:\n{chunk['text']}" + ) + + result = "\n".join(lines) + return result + + +# Create a FastMCP server + +try: + from mcp.server.fastmcp import FastMCP + + mcp = FastMCP("R2R Retrieval System") +except Exception as e: + raise ImportError( + "MCP is not installed. Please run `pip install mcp`" + ) from e + +# Pass lifespan to server +mcp = FastMCP("R2R Retrieval System") + + +# RAG query tool +@mcp.tool() +async def search(query: str) -> str: + """ + Performs a + + Args: + query: The question to answer using the knowledge base + + Returns: + A response generated based on relevant context from the knowledge base + """ + client = R2RClient() + + # Call the RAG endpoint + search_response = client.retrieval.search( + query=query, + ) + return format_search_results_for_llm(search_response.results) + + +# RAG query tool +@mcp.tool() +async def rag(query: str) -> str: + """ + Perform a Retrieval-Augmented Generation query + + Args: + query: The question to answer using the knowledge base + + Returns: + A response generated based on relevant context from the knowledge base + """ + client = R2RClient() + + # Call the RAG endpoint + rag_response = client.retrieval.rag( + query=query, + ) + + return rag_response.results.generated_answer # type: ignore + + +# Run the server if executed directly +if __name__ == "__main__": + mcp.run() diff --git a/.venv/lib/python3.12/site-packages/r2r/r2r.toml b/.venv/lib/python3.12/site-packages/r2r/r2r.toml new file mode 100644 index 00000000..a07893ab --- /dev/null +++ b/.venv/lib/python3.12/site-packages/r2r/r2r.toml @@ -0,0 +1,122 @@ +[app] +# app settings are global available like `r2r_config.agent.app` +# project_name = "r2r_default" # optional, can also set with `R2R_PROJECT_NAME` env var +default_max_documents_per_user = 10_000 +default_max_chunks_per_user = 10_000_000 +default_max_collections_per_user = 5_000 + +# Set the default max upload size to 200 GB for local testing +default_max_upload_size = 214748364800 + +# LLM used for internal operations, like deriving conversation names +fast_llm = "openai/gpt-4o-mini" + +# LLM used for user-facing output, like RAG replies +quality_llm = "openai/gpt-4o" + +# LLM used for ingesting visual inputs +vlm = "openai/gpt-4o" + +# LLM used for transcription +audio_lm = "openai/whisper-1" + +# Reasoning model, used for `research` agent +reasoning_llm = "openai/o3-mini" +# Planning model, used for `research` agent +planning_llm = "anthropic/claude-3-7-sonnet-20250219" + + +[agent] +rag_agent_static_prompt = "static_rag_agent" +rag_agent_dynamic_prompt = "dynamic_rag_agent" +# The following tools are available to the `rag` agent +rag_tools = ["search_file_descriptions", "search_file_knowledge", "get_file_content"] # can add "web_search" | "web_scrape" +# The following tools are available to the `research` agent +research_tools = ["rag", "reasoning", "critique", "python_executor"] + +[auth] +provider = "r2r" +access_token_lifetime_in_minutes = 60000 +refresh_token_lifetime_in_days = 7 +require_authentication = false +require_email_verification = false +default_admin_email = "admin@example.com" +default_admin_password = "change_me_immediately" + +[completion] +provider = "r2r" +concurrent_request_limit = 64 + + [completion.generation_config] + temperature = 0.1 + top_p = 1 + max_tokens_to_sample = 4_096 + stream = false + add_generation_kwargs = { } + +[crypto] +provider = "bcrypt" + +[database] +default_collection_name = "Default" +default_collection_description = "Your default collection." +collection_summary_prompt = "collection_summary" + + [database.graph_creation_settings] + graph_entity_description_prompt = "graph_entity_description" + graph_extraction_prompt = "graph_extraction" + entity_types = [] # if empty, all entities are extracted + relation_types = [] # if empty, all relations are extracted + automatic_deduplication = true # enable automatic deduplication of entities + + [database.graph_enrichment_settings] + graph_communities_prompt = "graph_communities" + +[embedding] +provider = "litellm" +# For basic applications, use `openai/text-embedding-3-small` with `base_dimension = 512` +# For advanced applications, use `openai/text-embedding-3-large` with `base_dimension = 3072` and binary quantization +base_model = "openai/text-embedding-3-small" +base_dimension = 512 +# rerank_model = "huggingface/mixedbread-ai/mxbai-rerank-large-v1" # reranking model +batch_size = 128 +add_title_as_prefix = false +concurrent_request_limit = 256 +initial_backoff = 1.0 +quantization_settings = { quantization_type = "FP32" } + +[completion_embedding] +# Generally this should be the same as the embedding config, but advanced users may want to run with a different provider to reduce latency +provider = "litellm" +base_model = "openai/text-embedding-3-small" +base_dimension = 512 +batch_size = 128 +add_title_as_prefix = false +concurrent_request_limit = 256 + +[ingestion] +provider = "r2r" +chunking_strategy = "recursive" +chunk_size = 1_024 +chunk_overlap = 512 +excluded_parsers = ["mp4"] +automatic_extraction = true # enable automatic extraction of entities and relations + + [ingestion.chunk_enrichment_settings] + chunk_enrichment_prompt = "chunk_enrichment" + enable_chunk_enrichment = false # disabled by default + n_chunks = 2 # the number of chunks (both preceding and succeeding) to use in enrichment + + [ingestion.extra_parsers] + pdf = "zerox" + +[logging] +provider = "r2r" +log_table = "logs" +log_info_table = "log_info" + +[orchestration] +provider = "simple" + +[email] +provider = "console_mock" # `smtp` | `sendgrid` supported diff --git a/.venv/lib/python3.12/site-packages/r2r/serve.py b/.venv/lib/python3.12/site-packages/r2r/serve.py new file mode 100644 index 00000000..7e9226bc --- /dev/null +++ b/.venv/lib/python3.12/site-packages/r2r/serve.py @@ -0,0 +1,146 @@ +import argparse +import asyncio +import logging +import os +import sys +from typing import Optional + +logger = logging.getLogger(__name__) + +try: + from core import R2RApp, R2RBuilder, R2RConfig + from core.utils.logging_config import configure_logging +except ImportError as e: + logger.error( + f"Failed to start server: core dependencies not installed: {e}" + ) + logger.error("To run the server, install the required dependencies:") + logger.error("pip install 'r2r[core]'") + sys.exit(1) + + +async def create_app( + config_name: Optional[str] = None, + config_path: Optional[str] = None, + full: bool = False, +) -> "R2RApp": + """ + Creates and returns an R2R application instance based on the provided + or environment-sourced configuration. + """ + # If arguments not passed, fall back to environment variables + config_name = config_name or os.getenv("R2R_CONFIG_NAME") + config_path = config_path or os.getenv("R2R_CONFIG_PATH") + + if config_path and config_name: + raise ValueError( + f"Cannot specify both config_path and config_name, got {config_path} and {config_name}" + ) + + if not config_path and not config_name: + # If neither is specified nor set in environment, + # default to 'full' if --full is True, else 'default' + config_name = "full" if full else "default" + + try: + r2r_instance = await R2RBuilder( + config=R2RConfig.load(config_name, config_path) + ).build() + + # Start orchestration worker + await r2r_instance.orchestration_provider.start_worker() + return r2r_instance + except ImportError as e: + logger.error(f"Failed to initialize R2R: {e}") + logger.error( + "Please check your configuration and installed dependencies" + ) + sys.exit(1) + + +def run_server( + host: Optional[str] = None, + port: Optional[int] = None, + config_name: Optional[str] = None, + config_path: Optional[str] = None, + full: bool = False, +): + """ + Runs the R2R server with the provided or environment-based settings. + """ + # Overwrite environment variables if arguments are explicitly passed + if host is not None: + os.environ["R2R_HOST"] = host + if port is not None: + os.environ["R2R_PORT"] = str(port) + if config_path is not None: + os.environ["R2R_CONFIG_PATH"] = config_path + if config_name is not None: + os.environ["R2R_CONFIG_NAME"] = config_name + + # Fallback to environment or defaults if necessary + final_host = os.getenv("R2R_HOST", "0.0.0.0") + final_port = int(os.getenv("R2R_PORT", "7272")) + + try: + configure_logging() + except Exception as e: + logger.error(f"Failed to configure logging: {e}") + + try: + + async def start(): + app = await create_app(config_name, config_path, full) + await app.serve(final_host, final_port) + + asyncio.run(start()) + except Exception as e: + logger.error(f"Failed to start R2R server: {e}") + sys.exit(1) + + +def main(): + """ + Parse command-line arguments and then run the server. + """ + parser = argparse.ArgumentParser(description="Run the R2R server.") + parser.add_argument( + "--host", + default=None, + help="Host to bind to. Overrides R2R_HOST env if provided.", + ) + parser.add_argument( + "--port", + default=None, + type=int, + help="Port to bind to. Overrides R2R_PORT env if provided.", + ) + parser.add_argument( + "--config-path", + default=None, + help="Path to the configuration file. Overrides R2R_CONFIG_PATH env if provided.", + ) + parser.add_argument( + "--config-name", + default=None, + help="Name of the configuration. Overrides R2R_CONFIG_NAME env if provided.", + ) + parser.add_argument( + "--full", + action="store_true", + help="Use the 'full' config if neither config-path nor config-name is specified.", + ) + + args = parser.parse_args() + + run_server( + host=args.host, + port=args.port, + config_name=args.config_name, + config_path=args.config_path, + full=args.full, + ) + + +if __name__ == "__main__": + main() |