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feat(blocks): Add pinecone and jina blocks (#8401)
* add pinecone and jina blocks * udpate based on comments * backend updates * frontend updates * type hint * more type hints * another type hint * update run signature * shared jina provider * fix linting * lockfile * remove noqa * remove noqa * remove vector db folder * line * update pincone credentials provider * fix imports * formating * update frontend * Test (#8425) * h * Discard changes to autogpt_platform/backend/poetry.lock * fix: broken dep --------- Co-authored-by: Nicholas Tindle <[email protected]>
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from typing import Literal | ||
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from autogpt_libs.supabase_integration_credentials_store.types import APIKeyCredentials | ||
from pydantic import SecretStr | ||
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from backend.data.model import CredentialsField, CredentialsMetaInput | ||
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JinaCredentials = APIKeyCredentials | ||
JinaCredentialsInput = CredentialsMetaInput[ | ||
Literal["jina"], | ||
Literal["api_key"], | ||
] | ||
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def JinaCredentialsField() -> JinaCredentialsInput: | ||
""" | ||
Creates a Jina credentials input on a block. | ||
""" | ||
return CredentialsField( | ||
provider="jina", | ||
supported_credential_types={"api_key"}, | ||
description="The Jina integration can be used with an API Key.", | ||
) | ||
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TEST_CREDENTIALS = APIKeyCredentials( | ||
id="01234567-89ab-cdef-0123-456789abcdef", | ||
provider="jina", | ||
api_key=SecretStr("mock-jina-api-key"), | ||
title="Mock Jina API key", | ||
expires_at=None, | ||
) | ||
TEST_CREDENTIALS_INPUT = { | ||
"provider": TEST_CREDENTIALS.provider, | ||
"id": TEST_CREDENTIALS.id, | ||
"type": TEST_CREDENTIALS.type, | ||
"title": TEST_CREDENTIALS.type, | ||
} |
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import requests | ||
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from backend.blocks.jina._auth import ( | ||
JinaCredentials, | ||
JinaCredentialsField, | ||
JinaCredentialsInput, | ||
) | ||
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema | ||
from backend.data.model import SchemaField | ||
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class JinaChunkingBlock(Block): | ||
class Input(BlockSchema): | ||
texts: list = SchemaField(description="List of texts to chunk") | ||
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credentials: JinaCredentialsInput = JinaCredentialsField() | ||
max_chunk_length: int = SchemaField( | ||
description="Maximum length of each chunk", default=1000 | ||
) | ||
return_tokens: bool = SchemaField( | ||
description="Whether to return token information", default=False | ||
) | ||
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class Output(BlockSchema): | ||
chunks: list = SchemaField(description="List of chunked texts") | ||
tokens: list = SchemaField( | ||
description="List of token information for each chunk", optional=True | ||
) | ||
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def __init__(self): | ||
super().__init__( | ||
id="806fb15e-830f-4796-8692-557d300ff43c", | ||
description="Chunks texts using Jina AI's segmentation service", | ||
categories={BlockCategory.AI, BlockCategory.TEXT}, | ||
input_schema=JinaChunkingBlock.Input, | ||
output_schema=JinaChunkingBlock.Output, | ||
) | ||
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def run( | ||
self, input_data: Input, *, credentials: JinaCredentials, **kwargs | ||
) -> BlockOutput: | ||
url = "https://segment.jina.ai/" | ||
headers = { | ||
"Content-Type": "application/json", | ||
"Authorization": f"Bearer {credentials.api_key.get_secret_value()}", | ||
} | ||
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all_chunks = [] | ||
all_tokens = [] | ||
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for text in input_data.texts: | ||
data = { | ||
"content": text, | ||
"return_tokens": str(input_data.return_tokens).lower(), | ||
"return_chunks": "true", | ||
"max_chunk_length": str(input_data.max_chunk_length), | ||
} | ||
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response = requests.post(url, headers=headers, json=data) | ||
response.raise_for_status() | ||
result = response.json() | ||
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all_chunks.extend(result.get("chunks", [])) | ||
if input_data.return_tokens: | ||
all_tokens.extend(result.get("tokens", [])) | ||
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yield "chunks", all_chunks | ||
if input_data.return_tokens: | ||
yield "tokens", all_tokens |
44 changes: 44 additions & 0 deletions
44
autogpt_platform/backend/backend/blocks/jina/embeddings.py
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import requests | ||
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from backend.blocks.jina._auth import ( | ||
JinaCredentials, | ||
JinaCredentialsField, | ||
JinaCredentialsInput, | ||
) | ||
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema | ||
from backend.data.model import SchemaField | ||
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class JinaEmbeddingBlock(Block): | ||
class Input(BlockSchema): | ||
texts: list = SchemaField(description="List of texts to embed") | ||
credentials: JinaCredentialsInput = JinaCredentialsField() | ||
model: str = SchemaField( | ||
description="Jina embedding model to use", | ||
default="jina-embeddings-v2-base-en", | ||
) | ||
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class Output(BlockSchema): | ||
embeddings: list = SchemaField(description="List of embeddings") | ||
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def __init__(self): | ||
super().__init__( | ||
id="7c56b3ab-62e7-43a2-a2dc-4ec4245660b6", | ||
description="Generates embeddings using Jina AI", | ||
categories={BlockCategory.AI}, | ||
input_schema=JinaEmbeddingBlock.Input, | ||
output_schema=JinaEmbeddingBlock.Output, | ||
) | ||
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def run( | ||
self, input_data: Input, *, credentials: JinaCredentials, **kwargs | ||
) -> BlockOutput: | ||
url = "https://api.jina.ai/v1/embeddings" | ||
headers = { | ||
"Content-Type": "application/json", | ||
"Authorization": f"Bearer {credentials.api_key.get_secret_value()}", | ||
} | ||
data = {"input": input_data.texts, "model": input_data.model} | ||
response = requests.post(url, headers=headers, json=data) | ||
embeddings = [e["embedding"] for e in response.json()["data"]] | ||
yield "embeddings", embeddings |
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from typing import Literal | ||
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from autogpt_libs.supabase_integration_credentials_store import APIKeyCredentials | ||
from pinecone import Pinecone, ServerlessSpec | ||
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from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema | ||
from backend.data.model import CredentialsField, CredentialsMetaInput, SchemaField | ||
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PineconeCredentials = APIKeyCredentials | ||
PineconeCredentialsInput = CredentialsMetaInput[ | ||
Literal["pinecone"], | ||
Literal["api_key"], | ||
] | ||
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def PineconeCredentialsField() -> PineconeCredentialsInput: | ||
""" | ||
Creates a Pinecone credentials input on a block. | ||
""" | ||
return CredentialsField( | ||
provider="pinecone", | ||
supported_credential_types={"api_key"}, | ||
description="The Pinecone integration can be used with an API Key.", | ||
) | ||
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class PineconeInitBlock(Block): | ||
class Input(BlockSchema): | ||
credentials: PineconeCredentialsInput = PineconeCredentialsField() | ||
index_name: str = SchemaField(description="Name of the Pinecone index") | ||
dimension: int = SchemaField( | ||
description="Dimension of the vectors", default=768 | ||
) | ||
metric: str = SchemaField( | ||
description="Distance metric for the index", default="cosine" | ||
) | ||
cloud: str = SchemaField( | ||
description="Cloud provider for serverless", default="aws" | ||
) | ||
region: str = SchemaField( | ||
description="Region for serverless", default="us-east-1" | ||
) | ||
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class Output(BlockSchema): | ||
index: str = SchemaField(description="Name of the initialized Pinecone index") | ||
message: str = SchemaField(description="Status message") | ||
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def __init__(self): | ||
super().__init__( | ||
id="48d8fdab-8f03-41f3-8407-8107ba11ec9b", | ||
description="Initializes a Pinecone index", | ||
categories={BlockCategory.LOGIC}, | ||
input_schema=PineconeInitBlock.Input, | ||
output_schema=PineconeInitBlock.Output, | ||
) | ||
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def run( | ||
self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs | ||
) -> BlockOutput: | ||
pc = Pinecone(api_key=credentials.api_key.get_secret_value()) | ||
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try: | ||
existing_indexes = pc.list_indexes() | ||
if input_data.index_name not in [index.name for index in existing_indexes]: | ||
pc.create_index( | ||
name=input_data.index_name, | ||
dimension=input_data.dimension, | ||
metric=input_data.metric, | ||
spec=ServerlessSpec( | ||
cloud=input_data.cloud, region=input_data.region | ||
), | ||
) | ||
message = f"Created new index: {input_data.index_name}" | ||
else: | ||
message = f"Using existing index: {input_data.index_name}" | ||
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yield "index", input_data.index_name | ||
yield "message", message | ||
except Exception as e: | ||
yield "message", f"Error initializing Pinecone index: {str(e)}" | ||
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class PineconeQueryBlock(Block): | ||
class Input(BlockSchema): | ||
credentials: PineconeCredentialsInput = PineconeCredentialsField() | ||
query_vector: list = SchemaField(description="Query vector") | ||
namespace: str = SchemaField( | ||
description="Namespace to query in Pinecone", default="" | ||
) | ||
top_k: int = SchemaField( | ||
description="Number of top results to return", default=3 | ||
) | ||
include_values: bool = SchemaField( | ||
description="Whether to include vector values in the response", | ||
default=False, | ||
) | ||
include_metadata: bool = SchemaField( | ||
description="Whether to include metadata in the response", default=True | ||
) | ||
host: str = SchemaField(description="Host for pinecone") | ||
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class Output(BlockSchema): | ||
results: dict = SchemaField(description="Query results from Pinecone") | ||
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def __init__(self): | ||
super().__init__( | ||
id="9ad93d0f-91b4-4c9c-8eb1-82e26b4a01c5", | ||
description="Queries a Pinecone index", | ||
categories={BlockCategory.LOGIC}, | ||
input_schema=PineconeQueryBlock.Input, | ||
output_schema=PineconeQueryBlock.Output, | ||
) | ||
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def run( | ||
self, | ||
input_data: Input, | ||
*, | ||
credentials: APIKeyCredentials, | ||
**kwargs, | ||
) -> BlockOutput: | ||
pc = Pinecone(api_key=credentials.api_key.get_secret_value()) | ||
idx = pc.Index(host=input_data.host) | ||
results = idx.query( | ||
namespace=input_data.namespace, | ||
vector=input_data.query_vector, | ||
top_k=input_data.top_k, | ||
include_values=input_data.include_values, | ||
include_metadata=input_data.include_metadata, | ||
) | ||
yield "results", results |
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