Perform a basic vector search:

search_response = client.search("What was Uber's profit in 2020?")
​
query
str
required

The search query.

​
vector_search_settings
Optional[Union[VectorSearchSettings, dict]]
default:
"None"

Optional settings for vector search, either a dictionary, a VectorSearchSettings object, or None may be passed. If a dictionary or None is passed, then R2R will use server-side defaults for non-specified fields.

​
kg_search_settings
Optional[Union[KGSearchSettings, dict]]
default:
"None"

Optional settings for knowledge graph search, either a dictionary, a KGSearchSettings object, or None may be passed. If a dictionary or None is passed, then R2R will use server-side defaults for non-specified fields.

​
Search custom settings

Learn more about the search API here. It allows searching with custom settings, such as bespoke document filters and larger search limits:

# returns only chunks from documents with title `document_title`
filtered_search_response = client.search(
    "What was Uber's profit in 2020?",
    vector_search_settings={
        # restrict results to the Uber document
        "search_filters": {"title": {"$eq": "uber_2021.pdf"}},
        "search_limit": 100
    }
)

Learn more about the dedicated knowledge graph capabilities in R2R here. Combine traditional keyword-based search with vector search:

hybrid_search_response = client.search(
    "What was Uber's profit in 2020?",
    vector_search_settings={
        "use_hybrid_search": True,
        "search_limit": 20,
        "hybrid_search_settings": {
            "full_text_weight": 1.0,
            "semantic_weight": 10.0,
            "full_text_limit": 200,
            "rrf_k": 25,
        },
    }
)

Learn more about the dedicated knowledge graph capabilities in R2R here. You can utilize knowledge graph capabilities to enhance search results, as shown below:

kg_search_response = client.search(
    "What is airbnb",
    vector_search_settings={"use_vector_search": False}
    kg_search_settings={
      "use_kg_search": True,
      "kg_search_type": "local",
      "kg_search_level": "0",
      "generation_config": {
          "model": "openai/gpt-4o-mini",
          "temperature": 0.7,
      },
      "local_search_limits": {
          "__Entity__": 20,
          "__Relationship__": 20,
          "__Community__": 20,
      },
      "max_community_description_length": 65536,
      "max_llm_queries_for_global_search": 250
    }
)

​
Retrieval-Augmented Generation (RAG)

​
Basic RAG

Generate a response using RAG:

rag_response = client.rag("What was Uber's profit in 2020?")
​
query
str
required

The query for RAG.

​
vector_search_settings
Optional[Union[VectorSearchSettings, dict]]
default:
"None"

Optional settings for vector search, either a dictionary, a VectorSearchSettings object, or None may be passed. If a dictionary is used, non-specified fields will use the server-side default.

​
kg_search_settings
Optional[Union[KGSearchSettings, dict]]
default:
"None"

Optional settings for knowledge graph search, either a dictionary, a KGSearchSettings object, or None may be passed. If a dictionary or None is passed, then R2R will use server-side defaults for non-specified fields.

​
rag_generation_config
Optional[Union[GenerationConfig, dict]]
default:
"None"

Optional configuration for LLM to use during RAG generation, including model selection and parameters. Will default to values specified in r2r.toml.

​
task_prompt_override
Optional[str]
default:
"None"

Optional custom prompt to override the default task prompt.

​
include_title_if_available
Optional[bool]
default:
"True"

Augment document chunks with their respective document titles?

​
RAG with custom search settings

Learn more about the RAG API here. It allows performing RAG with custom settings, such as hybrid search:

hybrid_rag_response = client.rag(
    "Who is Jon Snow?",
    vector_search_settings={"use_hybrid_search": True}
)

​
RAG with custom completion LLM

R2R supports configuration on server-side and at runtime, which you can read about here. An example below, using Anthropic at runtime:

anthropic_rag_response = client.rag(
    "What is R2R?",
    rag_generation_config={"model":"anthropic/claude-3-opus-20240229"}
)

​
Streaming RAG

R2R supports streaming RAG responses for real-time applications:

stream_response = client.rag(
    "Who was Aristotle?",
    rag_generation_config={"stream": True}
)
for chunk in stream_response:
    print(chunk, end='', flush=True)

​
Advanced RAG Techniques

R2R supports advanced Retrieval-Augmented Generation (RAG) techniques that can be easily configured at runtime. These techniques include Hypothetical Document Embeddings (HyDE) and RAG-Fusion, which can significantly enhance the quality and relevance of retrieved information.

To use an advanced RAG technique, you can specify the search_strategy parameter in your vector search settings:

from r2r import R2RClient

client = R2RClient()

# Using HyDE
hyde_response = client.rag(
    "What are the main themes in Shakespeare's plays?",
    vector_search_settings={
        "search_strategy": "hyde",
        "search_limit": 10
    }
)

# Using RAG-Fusion
rag_fusion_response = client.rag(
    "Explain the theory of relativity",
    vector_search_settings={
        "search_strategy": "rag_fusion",
        "search_limit": 20
    }
)

For a comprehensive guide on implementing and optimizing advanced RAG techniques in R2R, including HyDE and RAG-Fusion, please refer to our Advanced RAG Cookbook.

​
Customizing RAG

Putting everything together for highly customized RAG functionality at runtime:


custom_rag_response = client.rag(
    "Who was Aristotle?",
    vector_search_settings={
        "use_hybrid_search": True,
        "search_limit": 20,
        "hybrid_search_settings": {
            "full_text_weight": 1.0,
            "semantic_weight": 10.0,
            "full_text_limit": 200,
            "rrf_k": 25,
        },
    },
    kg_search_settings={
        "use_kg_search": True,
        "kg_search_type": "local",
    },
    rag_generation_config={
        "model": "anthropic/claude-3-haiku-20240307",
        "temperature": 0.7,
        "stream": True
    },
    task_prompt_override="Only answer the question if the context is SUPER relevant!!\n\nQuery:\n{query}\n\nContext:\n{context}"
)

​
Agents

​
Multi-turn agentic RAG

The R2R application includes agents which come equipped with a search tool, enabling them to perform RAG. Using the R2R Agent for multi-turn conversations:

messages = [
    {"role": "user", "content": "What was Aristotle's main contribution to philosophy?"},
    {"role": "assistant", "content": "Aristotle made numerous significant contributions to philosophy, but one of his main contributions was in the field of logic and reasoning. He developed a system of formal logic, which is considered the first comprehensive system of its kind in Western philosophy. This system, often referred to as Aristotelian logic or term logic, provided a framework for deductive reasoning and laid the groundwork for scientific thinking."},
    {"role": "user", "content": "Can you elaborate on how this influenced later thinkers?"}
]

rag_agent_response = client.agent(
    messages,
    vector_search_settings={"use_hybrid_search":True},
)

Note that any of the customization seen in AI powered search and RAG documentation above can be applied here.

​
messages
list[Messages]
required

The list of messages to pass the RAG agent.

​
vector_search_settings
Optional[Union[VectorSearchSettings, dict]]
default:
"None"

Optional settings for vector search, either a dictionary, a VectorSearchSettings object, or None may be passed. If a dictionary is used, non-specified fields will use the server-side default.

​
kg_search_settings
Optional[Union[KGSearchSettings, dict]]
default:
"None"

Optional settings for knowledge graph search, either a dictionary, a KGSearchSettings object, or None may be passed. If a dictionary or None is passed, then R2R will use server-side defaults for non-specified fields.

​
rag_generation_config
Optional[Union[GenerationConfig, dict]]
default:
"None"

Optional configuration for LLM to use during RAG generation, including model selection and parameters. Will default to values specified in r2r.toml.

​
task_prompt_override
Optional[str]
default:
"None"

Optional custom prompt to override the default task prompt.

​
Multi-turn agentic RAG with streaming

The response from the RAG agent may be streamed directly back

messages = [
    {"role": "user", "content": "What was Aristotle's main contribution to philosophy?"},
    {"role": "assistant", "content": "Aristotle made numerous significant contributions to philosophy, but one of his main contributions was in the field of logic and reasoning. He developed a system of formal logic, which is considered the first comprehensive system of its kind in Western philosophy. This system, often referred to as Aristotelian logic or term logic, provided a framework for deductive reasoning and laid the groundwork for scientific thinking."},
    {"role": "user", "content": "Can you elaborate on how this influenced later thinkers?"}
]

rag_agent_response = client.agent(
    messages,
    vector_search_settings={"use_hybrid_search":True},
    rag_generation_config={"stream":True}
)

Was this page helpful?

IngestionAuthentication