Configure Pydantic AI inference¶

PowerContext uses Pydantic AI at the provider boundary while keeping Memory services framework-neutral. Generation extracts structured Memory candidates. Embedding supplies vectors for indexes that support vector search. Either capability can be configured independently.

Install the integration¶

The inference integration is part of the built-in implementation:

uv add "powercontext[builtin]"

The Server extra includes Builtin:

uv add "powercontext[server]"

Configure the Server¶

The standard Server builds inference adapters from ServerSettings.inference. A generation model enables Source-to-Memory extraction:

export POWERCONTEXT_SERVER_INFERENCE_GENERATION_MODEL="provider:model-name"

Vector search needs the embedding model and its complete deployment profile:

export POWERCONTEXT_SERVER_INFERENCE_EMBEDDING_MODEL="provider:embedding-model"
export POWERCONTEXT_SERVER_INFERENCE_EMBEDDING_PROFILE_ID="project-embedding-v1"
export POWERCONTEXT_SERVER_INFERENCE_EMBEDDING_DIMENSION="1536"
export POWERCONTEXT_SERVER_DATABASE_VEC1_EXTENSION="/opt/sqlite-extensions/vec1"

Provider credentials remain in the environment variables understood by the selected Pydantic AI provider. They are not fields on PowerContext models.

The Server rejects a partial embedding profile. embedding_model, embedding_profile_id, and embedding_dimension must be configured together. Vec1 also requires that embedding configuration because the index dimension and stored vectors must agree.

Compose generation directly¶

Use direct composition when the application already owns provider model lifecycles:

from powercontext.builtin.artifacts.memory import (
    MEMORY_EXTRACTION_INSTRUCTIONS,
    LLMMemoryCandidatePipeline,
    MemoryExtractionInput,
    MemoryExtractionOutput,
)
from powercontext.builtin.inference.pydantic_ai import (
    InferenceLimits,
    PydanticAIStructuredGenerator,
)

generator = PydanticAIStructuredGenerator(
    model=generation_model,
    instructions=MEMORY_EXTRACTION_INSTRUCTIONS,
    input_type=MemoryExtractionInput,
    output_type=MemoryExtractionOutput,
    limits=InferenceLimits(timeout_seconds=30, max_requests=2),
)
candidate_pipeline = LLMMemoryCandidatePipeline(generator)

generation_model must be an initialized Pydantic AI Model, not a provider name string. The application that opens the model also closes it.

The generator serializes Pydantic input models and validates structured output against the declared output type. Memory validation still runs after generation, so a schema-valid candidate is not automatically accepted for persistence.

Compose embeddings directly¶

from pydantic_ai import Embedder

from powercontext.builtin.artifacts.memory import EmbeddingProfile
from powercontext.builtin.inference.pydantic_ai import (
    InferenceLimits,
    PydanticAIEmbeddingModel,
)

embedding_model = PydanticAIEmbeddingModel(
    embedder=Embedder(provider_embedding_model),
    profile=EmbeddingProfile(
        profile_id="project-embedding-v1",
        model="provider:embedding-model",
        dimension=1536,
        normalization="unit",
    ),
    limits=InferenceLimits(timeout_seconds=30),
)

Pass this adapter to open_builtin_contexts() or open_builtin_runtime() with a SQLiteConfig that selects the Vec1 extension. The adapter verifies output count, order, dimension, and finite numeric values, then applies the declared unit normalization before vectors reach persistence.

An EmbeddingProfile is a deployment contract, not descriptive metadata. Stored projections and query embeddings must use the same profile. When the model, dimension, or normalization changes, rebuild Memory projections from the authoritative revisions.

Failure behavior¶

Generation and embedding failures map provider errors into PowerContext inference errors. Timeouts and temporary provider failures do not commit partial Memory revisions.

For mode="auto" search, a temporary query embedding failure can fall back to FTS when the backend supports it. Explicit vector or hybrid search reports the missing capability instead of silently changing modes.