SciPhi API Client Documentation#

Introduction#

The SciPhi API Client is a Python library for interacting with the SciPhi API. It provides methods for performing searches, retrieving search RAG responses, generating completions, and managing client sessions.

Use and Examples#

The SciPhi API Client is designed to simplify interaction with the SciPhi API. It abstracts the complexities of HTTP requests and response handling, providing a convenient interface for Python developers.

Call a pre-configured search agent endpoint:

# Requires SCIPHI_API_KEY in the environment
from agent_search import SciPhi

client = SciPhi()

# Search, then summarize result and generate related queries
agent_summary = client.get_search_rag_response(query='latest news', search_provider='bing', llm_model='SciPhi/Sensei-7B-V1')
print(agent_summary)
# {'response': "The latest news encompasses ... and its consequences [2].", 'related_queries': ['Details on the...', ...], 'search_results' : [...]}

Standalone searches and from the AgentSearch search engine are supported:

from agent_search import SciPhi

client = SciPhi()

# Perform a search
search_response = client.search(query='Quantum Field Theory', search_provider='agent-search')

print(search_response)
# [{ 'score': '.89', 'url': 'https://...', 'metadata': {...} }

Code your own custom search agent workflow:

from agent_search import SciPhi
import json

client = SciPhi()

# Specify instructions for the task
instruction = "Your task is to perform retrieval augmented generation (RAG) over the given query and search results. Return your answer in a json format that includes a summary of the search results and a list of related queries."
query = "What is Fermat's Last Theorem?"

# construct search context
search_response = client.search(query=query, search_provider='agent-search')
search_context = "\n\n".join(
    f"{idx + 1}. Title: {item['title']}\nURL: {item['url']}\nText: {item['text']}"
    for idx, item in enumerate(search_response)
).encode('utf-8')

# Prefix to enforce a JSON response
json_response_prefix = '{"summary":'

# Prepare a prompt
formatted_prompt = f"### Instruction:{instruction}\n\nQuery:\n{query}\n\nSearch Results:\n${search_context}\n\nQuery:\n{query}\n### Response:\n{json_response_prefix}",

# Generate a raw string completion with Sensei-7B-V1
completion = json_response_prefix + client.completion(formatted_prompt, llm_model_name="SciPhi/Sensei-7B-V1")

print(json.loads(completion))
# {
#   "summary":  "\nFermat's Last Theorem is a mathematical proposition first prop ... ",
#   "other_queries": ["The role of elliptic curves in the proof of Fermat's Last Theorem", ...]
# }

By encapsulating the details of the API calls, the SciPhi API Client offers a user-friendly way to leverage the advanced search and AI capabilities of the SciPhi platform. Classes and Methods ——————-

class SciPhi#

Client for interacting with the SciPhi API.

Attributes:: api_base (str): Base URL for the SciPhi API. api_key (str): API key for authenticating requests. timeout (int): Timeout for API requests in seconds. client (httpx.Client): HTTP client for making requests.

search(self, query: str, search_provider: str) → List[Dict]#

Performs a search query using the SciPhi API.

Parameters:

query – str: The search query string.
search_provider – str: The search provider to use.

Returns:

List[Dict]: A list of search results w/ fields that correspond with SearchResult, specified below.

get_search_rag_response(self, query: str, search_provider: str, llm_model: str = 'SciPhi/Sensei-7B-V1', temperature: int = 0.2, top_p: int = 0.95)#

Retrieves a search RAG (Retrieval-Augmented Generation) response from the API.

Parameters:

query – str: The search query string.
search_provider – str: The search provider to use.
llm_model – str: The language model to use.
temperature – int: The temperature setting for the query.
top_p – int: The top-p setting for the query.

Returns:

str: A string containing the completed text.

completion(self, prompt: str, llm_model_name: str = 'SciPhi/Sensei-7B-V1', llm_max_tokens_to_sample: int = 1_024, llm_temperature: float = 0.2, llm_top_p: float = 0.90) → str#

Generates a completion string for a given prompt using the SciPhi API.

Parameters:

prompt – str: The prompt for generating completion.
llm_model_name – str: The language model to use.
llm_max_tokens_to_sample – int: Maximum number of tokens for the sample.
llm_temperature – float: The temperature setting for the query.
llm_top_p – float: The top-p setting for the query.

Returns:

Dict: A dictionary containing the generated completion.

Raises:

ImportError – If the sciphi-synthesizer package is not installed.

close(self) → None#: Closes the HTTP client.

Model Classes#

class SearchResult#

Represents a single search result.

score#: The score of the search result.

title#: The title of the search result.

text#: The raw text of the search result.

url#: The URL of the search result.

metadata#: Optional metadata for the search result.

class SearchRAGResponse#

Represents the response from a search or RAG query.

response#: The response text.

related_queries#: A list of related queries.

search_results#: A list of SearchResult objects.

SciPhi API Client Documentation

Contents

SciPhi API Client Documentation#

Introduction#

Use and Examples#

Model Classes#