Extract run_qa_core() from ask closure in QA toolset. Remove redundant tests and cassettes for OpenAI and Anthropic QA tools, as they are now tested in the core function tests.
This commit is contained in:
Yiorgis Gozadinos
parent
2cc4a8a88f
commit
87bcb7a5dc
15 changed files with 609 additions and 3251 deletions
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@ -81,7 +81,6 @@ class ChatDeps:
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if isinstance(nested, dict):
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state_data = nested
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# Update SessionState from incoming state
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session_state = self.tool_context.get(SESSION_NAMESPACE, SessionState)
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if session_state is not None:
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if "document_filter" in state_data:
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@ -96,7 +95,6 @@ class ChatDeps:
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for c in state_data.get("citations", [])
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]
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# Extract session_id if present, or generate one
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# Track what the client sent (for delta computation)
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incoming_session_id = state_data.get("session_id", "")
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@ -111,7 +109,6 @@ class ChatDeps:
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session_state.session_id = self.session_id
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session_state.incoming_session_id = incoming_session_id
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# Update QASessionState from incoming state
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qa_session_state = self.tool_context.get(QA_SESSION_NAMESPACE, QASessionState)
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if qa_session_state is not None:
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if "qa_history" in state_data:
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@ -181,19 +178,16 @@ def create_chat_agent(
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if features is None:
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features = DEFAULT_FEATURES
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# SessionState is always registered (shared by all features)
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existing = context.get(SESSION_NAMESPACE, SessionState)
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if existing is None:
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context.register(SESSION_NAMESPACE, SessionState(state_key=AGUI_STATE_KEY))
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elif existing.state_key is None:
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existing.state_key = AGUI_STATE_KEY
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# QASessionState only when QA feature is active
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if FEATURE_QA in features:
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if context.get(QA_SESSION_NAMESPACE, QASessionState) is None:
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context.register(QA_SESSION_NAMESPACE, QASessionState())
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# Create toolsets conditionally based on features
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toolsets = []
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if FEATURE_SEARCH in features:
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toolsets.append(create_search_toolset(client, config, context=context))
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@ -206,7 +200,6 @@ def create_chat_agent(
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toolsets.append(create_analysis_toolset(client, config, context=context))
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# Create the agent with composed toolsets
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model = get_model(config.qa.model, config)
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return Agent(
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@ -18,6 +18,7 @@ from haiku.rag.tools.qa import (
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QAHistoryEntry,
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QASessionState,
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create_qa_toolset,
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run_qa_core,
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)
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from haiku.rag.tools.search import SEARCH_NAMESPACE, SearchState, create_search_toolset
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from haiku.rag.tools.session import (
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@ -46,6 +47,7 @@ __all__ = [
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"QASessionState",
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"QAHistoryEntry",
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"create_qa_toolset",
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"run_qa_core",
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"create_analysis_toolset",
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"SESSION_NAMESPACE",
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"SessionState",
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@ -52,16 +52,13 @@ def create_analysis_toolset(
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Returns:
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AnalysisResult with answer and execution metadata.
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"""
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# Build filter from base_filter, session_filter, and document_name
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doc_filter = build_document_filter(document_name) if document_name else None
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effective_filter = combine_filters(
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get_session_filter(context, base_filter), doc_filter
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)
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# Create RLM context
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rlm_context = RLMContext(filter=effective_filter)
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# Run RLM agent with Docker sandbox
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async with DockerSandbox(
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client=client,
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config=config.rlm,
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@ -40,7 +40,6 @@ class DocumentListResponse(BaseModel):
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async def find_document(client: HaikuRAG, query: str):
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"""Find a document by exact URI, partial URI, or partial title match."""
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# Try exact URI match first
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doc = await client.get_document_by_uri(query)
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if doc is not None:
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return doc
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@ -49,7 +48,6 @@ async def find_document(client: HaikuRAG, query: str):
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# Also try without spaces for matching "TB MED 593" to "tbmed593"
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no_spaces = escaped_query.replace(" ", "")
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# Try partial URI match (with and without spaces)
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docs = await client.list_documents(
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limit=1,
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filter=f"LOWER(uri) LIKE LOWER('%{escaped_query}%') OR LOWER(uri) LIKE LOWER('%{no_spaces}%')",
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@ -57,7 +55,6 @@ async def find_document(client: HaikuRAG, query: str):
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if docs and docs[0].id:
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return await client.get_document_by_id(docs[0].id)
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# Try partial title match (with and without spaces)
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docs = await client.list_documents(
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limit=1,
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filter=f"LOWER(title) LIKE LOWER('%{escaped_query}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')",
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@ -158,7 +155,6 @@ def create_document_toolset(
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if doc is None:
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return f"Document not found: {query}"
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# Use LLM to generate summary
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summary_model = get_model(config.qa.model, config)
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summary_agent: Agent[None, str] = Agent(
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summary_model,
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@ -92,6 +92,149 @@ QA_SESSION_NAMESPACE = "haiku.rag.qa_session"
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MAX_QA_HISTORY = 50
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async def run_qa_core(
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client: HaikuRAG,
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config: AppConfig,
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question: str,
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document_name: str | None = None,
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*,
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context: ToolContext | None = None,
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base_filter: str | None = None,
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session_context: str | None = None,
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prior_answers: list[SearchAnswer] | None = None,
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) -> QAResult:
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"""Run the QA flow and return a QAResult.
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This is the core QA implementation shared by toolsets and client APIs.
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It updates session state and QA history when context is provided.
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"""
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session_state: SessionState | None = None
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qa_session_state: QASessionState | None = None
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if context is not None:
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session_state = context.get(SESSION_NAMESPACE, SessionState)
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qa_session_state = context.get(QA_SESSION_NAMESPACE, QASessionState)
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doc_filter = build_document_filter(document_name) if document_name else None
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effective_filter = combine_filters(
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get_session_filter(context, base_filter), doc_filter
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)
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effective_session_context = session_context
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if qa_session_state is not None and qa_session_state.session_context:
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effective_session_context = qa_session_state.session_context
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effective_prior_answers = prior_answers or []
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session_id = session_state.session_id if session_state is not None else ""
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if qa_session_state is not None and qa_session_state.qa_history:
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embedder = get_embedder(config)
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question_embedding = await embedder.embed_query(question)
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to_embed = []
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to_embed_indices = []
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for i, qa in enumerate(qa_session_state.qa_history):
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if qa.question_embedding is None:
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if session_id:
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cached = get_cached_embedding(session_id, qa.question)
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if cached:
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qa.question_embedding = cached
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continue
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to_embed.append(qa.question)
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to_embed_indices.append(i)
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if to_embed:
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new_embeddings = await embedder.embed_documents(to_embed)
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for i, idx in enumerate(to_embed_indices):
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embedding = new_embeddings[i]
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qa_session_state.qa_history[idx].question_embedding = embedding
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if session_id:
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cache_question_embedding(
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session_id,
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qa_session_state.qa_history[idx].question,
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embedding,
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)
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matched_answers = []
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for qa in qa_session_state.qa_history:
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if qa.question_embedding is not None:
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similarity = _cosine_similarity(
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question_embedding, qa.question_embedding
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)
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if similarity >= PRIOR_ANSWER_RELEVANCE_THRESHOLD:
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matched_answers.append(qa.to_search_answer())
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if matched_answers:
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effective_prior_answers = matched_answers
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graph = build_research_graph(config=config, output_mode="conversational")
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research_context = ResearchContext(
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original_question=question,
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session_context=effective_session_context,
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qa_responses=effective_prior_answers,
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)
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research_state = ResearchState(
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context=research_context,
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max_iterations=1,
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search_filter=effective_filter,
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max_concurrency=config.research.max_concurrency,
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)
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deps = ResearchDeps(client=client)
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result = await graph.run(state=research_state, deps=deps)
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# Build citations with stable indices from session state
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citations = []
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for i, c in enumerate(result.citations):
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if session_state is not None:
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index = session_state.get_or_assign_index(c.chunk_id)
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else:
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index = i + 1
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citations.append(
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Citation(
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index=index,
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document_id=c.document_id,
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chunk_id=c.chunk_id,
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document_uri=c.document_uri,
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document_title=c.document_title,
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page_numbers=c.page_numbers,
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headings=c.headings,
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content=c.content,
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)
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)
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qa_result = QAResult(
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question=question,
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answer=result.answer,
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confidence=result.confidence,
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citations=citations,
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)
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if session_state is not None:
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session_state.citations = citations
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if qa_session_state is not None:
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qa_session_state.qa_history.append(
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QAHistoryEntry(
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question=question,
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answer=result.answer,
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confidence=result.confidence,
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citations=citations,
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)
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)
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# Enforce FIFO limit
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if len(qa_session_state.qa_history) > MAX_QA_HISTORY:
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qa_session_state.qa_history = qa_session_state.qa_history[-MAX_QA_HISTORY:]
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trigger_background_summarization(
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qa_session_state=qa_session_state,
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config=config,
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session_id=session_id,
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)
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return qa_result
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def create_qa_toolset(
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client: HaikuRAG,
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config: AppConfig,
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@ -135,7 +278,6 @@ def create_qa_toolset(
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Returns:
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QAResult with answer, confidence, and citations.
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"""
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# Get session states
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session_state: SessionState | None = None
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qa_session_state: QASessionState | None = None
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old_state_snapshot: dict | None = None
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@ -144,7 +286,6 @@ def create_qa_toolset(
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session_state = context.get(SESSION_NAMESPACE, SessionState)
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qa_session_state = context.get(QA_SESSION_NAMESPACE, QASessionState)
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# Capture combined state snapshot before changes
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# Use incoming values (what client sent) so delta shows server-side updates
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if session_state is not None:
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old_state_snapshot = build_chat_state_snapshot(
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@ -153,140 +294,18 @@ def create_qa_toolset(
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incoming=True,
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)
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# Build filter from session state, base_filter, and document_name
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doc_filter = build_document_filter(document_name) if document_name else None
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effective_filter = combine_filters(
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get_session_filter(context, base_filter), doc_filter
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)
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# Determine session context
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effective_session_context = session_context
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if qa_session_state is not None and qa_session_state.session_context:
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effective_session_context = qa_session_state.session_context
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# Find relevant prior answers via similarity matching
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effective_prior_answers = prior_answers or []
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session_id = session_state.session_id if session_state is not None else ""
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if qa_session_state is not None and qa_session_state.qa_history:
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embedder = get_embedder(config)
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question_embedding = await embedder.embed_query(question)
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# Collect questions that need embedding
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to_embed = []
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to_embed_indices = []
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for i, qa in enumerate(qa_session_state.qa_history):
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if qa.question_embedding is None:
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# Check per-session cache first
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if session_id:
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cached = get_cached_embedding(session_id, qa.question)
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if cached:
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qa.question_embedding = cached
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continue
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to_embed.append(qa.question)
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to_embed_indices.append(i)
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# Batch embed uncached questions
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if to_embed:
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new_embeddings = await embedder.embed_documents(to_embed)
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for i, idx in enumerate(to_embed_indices):
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embedding = new_embeddings[i]
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qa_session_state.qa_history[idx].question_embedding = embedding
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# Cache per-session for next request
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if session_id:
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cache_question_embedding(
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session_id,
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qa_session_state.qa_history[idx].question,
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embedding,
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)
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# Find similar prior answers
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matched_answers = []
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for qa in qa_session_state.qa_history:
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if qa.question_embedding is not None:
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similarity = _cosine_similarity(
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question_embedding, qa.question_embedding
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)
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if similarity >= PRIOR_ANSWER_RELEVANCE_THRESHOLD:
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matched_answers.append(qa.to_search_answer())
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if matched_answers:
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effective_prior_answers = matched_answers
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# Build and run the research graph
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graph = build_research_graph(config=config, output_mode="conversational")
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research_context = ResearchContext(
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original_question=question,
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session_context=effective_session_context,
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qa_responses=effective_prior_answers,
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)
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research_state = ResearchState(
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context=research_context,
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max_iterations=1,
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search_filter=effective_filter,
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max_concurrency=config.research.max_concurrency,
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)
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deps = ResearchDeps(client=client)
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result = await graph.run(state=research_state, deps=deps)
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# Build citations with stable indices from session state
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citations = []
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for i, c in enumerate(result.citations):
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if session_state is not None:
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index = session_state.get_or_assign_index(c.chunk_id)
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else:
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index = i + 1
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citations.append(
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Citation(
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index=index,
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document_id=c.document_id,
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chunk_id=c.chunk_id,
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document_uri=c.document_uri,
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document_title=c.document_title,
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page_numbers=c.page_numbers,
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headings=c.headings,
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content=c.content,
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)
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)
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qa_result = QAResult(
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qa_result = await run_qa_core(
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client=client,
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config=config,
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question=question,
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answer=result.answer,
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confidence=result.confidence,
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citations=citations,
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document_name=document_name,
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context=context,
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base_filter=base_filter,
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session_context=session_context,
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prior_answers=prior_answers,
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)
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# Update session state with citations
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if session_state is not None:
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session_state.citations = citations
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# Update QA session state with history entry
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if qa_session_state is not None:
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qa_session_state.qa_history.append(
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QAHistoryEntry(
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question=question,
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answer=result.answer,
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confidence=result.confidence,
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citations=citations,
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)
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)
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# Enforce FIFO limit
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if len(qa_session_state.qa_history) > MAX_QA_HISTORY:
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qa_session_state.qa_history = qa_session_state.qa_history[
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-MAX_QA_HISTORY:
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]
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# Trigger background summarization
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trigger_background_summarization(
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qa_session_state=qa_session_state,
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config=config,
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session_id=session_id,
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)
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# Compute and return state delta if session state changed
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if session_state is not None and old_state_snapshot is not None:
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# Build new combined state snapshot
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new_state_snapshot = build_chat_state_snapshot(
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session_state,
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qa_session_state,
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@ -299,10 +318,9 @@ def create_qa_toolset(
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state_key=session_state.state_key,
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)
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# Format answer with citation references
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answer_text = result.answer
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if citations:
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citation_refs = " ".join(f"[{c.index}]" for c in citations)
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answer_text = qa_result.answer
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if qa_result.citations:
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citation_refs = " ".join(f"[{c.index}]" for c in qa_result.citations)
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answer_text = f"{answer_text}\n\nSources: {citation_refs}"
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metadata = [state_event] if state_event is not None else None
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@ -47,7 +47,6 @@ def create_search_toolset(
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Returns:
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FunctionToolset with a search tool.
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"""
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# Get or create search state if context provided
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search_state: SearchState | None = None
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if context is not None:
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search_state = context.get_or_create(SEARCH_NAMESPACE, SearchState)
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@ -67,7 +66,6 @@ def create_search_toolset(
|
|||
Returns:
|
||||
Formatted search results with content and metadata.
|
||||
"""
|
||||
# Get session state for dynamic filters and citation indexing
|
||||
session_state: SessionState | None = None
|
||||
old_session_state: SessionState | None = None
|
||||
if context is not None:
|
||||
|
|
@ -88,14 +86,12 @@ def create_search_toolset(
|
|||
if expand_context:
|
||||
results = await client.expand_context(results)
|
||||
|
||||
# Accumulate results in search state if context provided
|
||||
if search_state is not None:
|
||||
search_state.results.extend(results)
|
||||
|
||||
if not results:
|
||||
return "No results found."
|
||||
|
||||
# Build citations if session state is available
|
||||
if session_state is not None:
|
||||
citations = []
|
||||
for r in results:
|
||||
|
|
@ -118,7 +114,6 @@ def create_search_toolset(
|
|||
)
|
||||
session_state.citations = citations
|
||||
|
||||
# Format results with citation indices
|
||||
result_lines = []
|
||||
for c in citations:
|
||||
title = c.document_title or c.document_uri or "Unknown"
|
||||
|
|
@ -134,7 +129,6 @@ def create_search_toolset(
|
|||
|
||||
formatted = f"Found {len(results)} results:\n\n" + "\n\n".join(result_lines)
|
||||
|
||||
# Compute state delta if session state changed
|
||||
if old_session_state is not None:
|
||||
state_event = compute_state_delta(old_session_state, session_state)
|
||||
if state_event is not None:
|
||||
|
|
|
|||
|
|
@ -11,7 +11,10 @@ from haiku.rag.agents.chat import (
|
|||
ToolContext,
|
||||
create_chat_agent,
|
||||
)
|
||||
from haiku.rag.agents.chat.context import get_cached_session_context
|
||||
from haiku.rag.agents.chat.context import (
|
||||
_summarization_tasks,
|
||||
get_cached_session_context,
|
||||
)
|
||||
from haiku.rag.agents.research.models import Citation
|
||||
from haiku.rag.client import HaikuRAG
|
||||
from haiku.rag.config import Config
|
||||
|
|
@ -563,8 +566,12 @@ async def test_chat_agent_multi_turn_with_context(allow_model_requests, temp_db_
|
|||
2. First question triggers background summarization
|
||||
3. Second related question uses prior answer recall and updated session context
|
||||
4. Both qa_history entries are present after two turns
|
||||
|
||||
The ask tool internally fires background summarization (concurrent HTTP calls)
|
||||
which causes VCR cassette mismatches. We patch it to a no-op and trigger
|
||||
summarization explicitly after each turn to keep HTTP ordering deterministic.
|
||||
"""
|
||||
import asyncio
|
||||
from unittest.mock import patch
|
||||
|
||||
from haiku.rag.agents.chat.agent import trigger_background_summarization
|
||||
from haiku.rag.tools.qa import QA_SESSION_NAMESPACE, QASessionState
|
||||
|
|
@ -614,22 +621,24 @@ async def test_chat_agent_multi_turn_with_context(allow_model_requests, temp_db_
|
|||
== "The user is researching the DocLayNet dataset for a paper on document layout analysis."
|
||||
)
|
||||
|
||||
# First question about class labels
|
||||
result1 = await agent.run(
|
||||
"What are the class labels defined in DocLayNet?",
|
||||
deps=deps,
|
||||
)
|
||||
trigger_background_summarization(deps)
|
||||
# Patch the internal summarization trigger in the ask tool to avoid
|
||||
# concurrent HTTP calls that break VCR cassette replay ordering.
|
||||
with patch(
|
||||
"haiku.rag.tools.qa.trigger_background_summarization",
|
||||
):
|
||||
# First question about class labels
|
||||
result1 = await agent.run(
|
||||
"What are the class labels defined in DocLayNet?",
|
||||
deps=deps,
|
||||
)
|
||||
assert result1.output is not None
|
||||
|
||||
# Wait for background summarization
|
||||
cached_context = None
|
||||
for _ in range(50):
|
||||
cached_context = get_cached_session_context(session_id)
|
||||
if cached_context is not None:
|
||||
break
|
||||
await asyncio.sleep(0.1)
|
||||
# Trigger summarization explicitly (sequential, no concurrency)
|
||||
trigger_background_summarization(deps)
|
||||
if session_id in _summarization_tasks:
|
||||
await _summarization_tasks[session_id]
|
||||
|
||||
cached_context = get_cached_session_context(session_id)
|
||||
assert cached_context is not None
|
||||
assert cached_context.summary != ""
|
||||
|
||||
|
|
@ -639,23 +648,20 @@ async def test_chat_agent_multi_turn_with_context(allow_model_requests, temp_db_
|
|||
assert len(qa_session.qa_history) >= 1
|
||||
|
||||
# Second related question - uses prior answers and updated session context
|
||||
result2 = await agent.run(
|
||||
"How were the annotations created and how many annotators were involved?",
|
||||
deps=deps,
|
||||
message_history=result1.all_messages(),
|
||||
)
|
||||
trigger_background_summarization(deps)
|
||||
with patch(
|
||||
"haiku.rag.tools.qa.trigger_background_summarization",
|
||||
):
|
||||
result2 = await agent.run(
|
||||
"How were the annotations created and how many annotators were involved?",
|
||||
deps=deps,
|
||||
message_history=result1.all_messages(),
|
||||
)
|
||||
assert result2.output is not None
|
||||
|
||||
# Wait for updated summarization
|
||||
for _ in range(50):
|
||||
updated = get_cached_session_context(session_id)
|
||||
if (
|
||||
updated is not None
|
||||
and updated.last_updated != cached_context.last_updated
|
||||
):
|
||||
break
|
||||
await asyncio.sleep(0.1)
|
||||
# Trigger summarization explicitly
|
||||
trigger_background_summarization(deps)
|
||||
if session_id in _summarization_tasks:
|
||||
await _summarization_tasks[session_id]
|
||||
|
||||
# qa_history should have two entries
|
||||
qa_session = context.get(QA_SESSION_NAMESPACE, QASessionState)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,3 @@
|
|||
import importlib.util
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
|
@ -10,8 +9,6 @@ from haiku.rag.client import HaikuRAG
|
|||
from haiku.rag.config import Config
|
||||
from haiku.rag.config.models import ModelConfig
|
||||
|
||||
HAS_ANTHROPIC = importlib.util.find_spec("anthropic") is not None
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def vcr_cassette_dir():
|
||||
|
|
@ -73,52 +70,3 @@ async def test_qa_ollama(allow_model_requests, qa_corpus: Dataset, temp_db_path)
|
|||
assert is_equivalent, (
|
||||
f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
async def test_qa_openai(allow_model_requests, qa_corpus: Dataset, temp_db_path):
|
||||
"""Test OpenAI QA with LLM judge (VCR recorded)."""
|
||||
client = HaikuRAG(temp_db_path, create=True)
|
||||
qa = QuestionAnswerAgent(client, ModelConfig(provider="openai", name="gpt-4o-mini"))
|
||||
llm_judge = LLMJudge()
|
||||
|
||||
doc = qa_corpus[1]
|
||||
await client.create_document(
|
||||
content=doc["document_extracted"], uri=doc["document_id"]
|
||||
)
|
||||
|
||||
question = doc["question"]
|
||||
expected_answer = doc["answer"]
|
||||
|
||||
answer, _ = await qa.answer(question)
|
||||
is_equivalent = await llm_judge.judge_answers(question, answer, expected_answer)
|
||||
|
||||
assert is_equivalent, (
|
||||
f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
@pytest.mark.skipif(not HAS_ANTHROPIC, reason="Anthropic not installed")
|
||||
async def test_qa_anthropic(allow_model_requests, qa_corpus: Dataset, temp_db_path):
|
||||
"""Test Anthropic QA with LLM judge (VCR recorded)."""
|
||||
client = HaikuRAG(temp_db_path, create=True)
|
||||
qa = QuestionAnswerAgent(
|
||||
client, ModelConfig(provider="anthropic", name="claude-3-5-haiku-20241022")
|
||||
)
|
||||
llm_judge = LLMJudge()
|
||||
|
||||
doc = qa_corpus[1]
|
||||
await client.create_document(
|
||||
content=doc["document_extracted"], uri=doc["document_id"]
|
||||
)
|
||||
|
||||
question = doc["question"]
|
||||
expected_answer = doc["answer"]
|
||||
|
||||
answer, _ = await qa.answer(question)
|
||||
is_equivalent = await llm_judge.judge_answers(question, answer, expected_answer)
|
||||
|
||||
assert is_equivalent, (
|
||||
f"Generated answer not equivalent to expected answer.\nQuestion: {question}\nGenerated: {answer}\nExpected: {expected_answer}"
|
||||
)
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -63,88 +63,6 @@ async def test_ollama_embedder(allow_model_requests):
|
|||
assert max(sims) == sims[1]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
async def test_openai_embedder(allow_model_requests):
|
||||
"""Test OpenAI embedder via pydantic-ai."""
|
||||
config = AppConfig(
|
||||
embeddings=EmbeddingsConfig(
|
||||
model=EmbeddingModelConfig(
|
||||
provider="openai", name="text-embedding-3-small", vector_dim=1536
|
||||
)
|
||||
)
|
||||
)
|
||||
embedder = get_embedder(config)
|
||||
phrases = [
|
||||
"I enjoy eating great food.",
|
||||
"Python is my favorite programming language.",
|
||||
"I love to travel and see new places.",
|
||||
]
|
||||
|
||||
# Test batch embedding (documents)
|
||||
embeddings = await embedder.embed_documents(phrases)
|
||||
assert isinstance(embeddings, list)
|
||||
assert len(embeddings) == 3
|
||||
assert all(isinstance(emb, list) for emb in embeddings)
|
||||
embeddings = [np.array(emb) for emb in embeddings]
|
||||
|
||||
# Test query embedding
|
||||
test_phrase = "I am going for a camping trip."
|
||||
test_embedding = await embedder.embed_query(test_phrase)
|
||||
sims = similarities(embeddings, test_embedding)
|
||||
assert max(sims) == sims[2]
|
||||
|
||||
test_phrase = "When is dinner ready?"
|
||||
test_embedding = await embedder.embed_query(test_phrase)
|
||||
sims = similarities(embeddings, test_embedding)
|
||||
assert max(sims) == sims[0]
|
||||
|
||||
test_phrase = "I work as a software developer."
|
||||
test_embedding = await embedder.embed_query(test_phrase)
|
||||
sims = similarities(embeddings, test_embedding)
|
||||
assert max(sims) == sims[1]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
async def test_voyageai_embedder(allow_model_requests):
|
||||
"""Test VoyageAI embedder."""
|
||||
config = AppConfig(
|
||||
embeddings=EmbeddingsConfig(
|
||||
model=EmbeddingModelConfig(
|
||||
provider="voyageai", name="voyage-3.5", vector_dim=1024
|
||||
)
|
||||
)
|
||||
)
|
||||
embedder = get_embedder(config)
|
||||
phrases = [
|
||||
"I enjoy eating great food.",
|
||||
"Python is my favorite programming language.",
|
||||
"I love to travel and see new places.",
|
||||
]
|
||||
|
||||
# Test batch embedding (documents)
|
||||
embeddings = await embedder.embed_documents(phrases)
|
||||
assert isinstance(embeddings, list)
|
||||
assert len(embeddings) == 3
|
||||
assert all(isinstance(emb, list) for emb in embeddings)
|
||||
embeddings = [np.array(emb) for emb in embeddings]
|
||||
|
||||
# Test query embedding
|
||||
test_phrase = "I am going for a camping trip."
|
||||
test_embedding = await embedder.embed_query(test_phrase)
|
||||
sims = similarities(embeddings, test_embedding)
|
||||
assert max(sims) == sims[2]
|
||||
|
||||
test_phrase = "When is dinner ready?"
|
||||
test_embedding = await embedder.embed_query(test_phrase)
|
||||
sims = similarities(embeddings, test_embedding)
|
||||
assert max(sims) == sims[0]
|
||||
|
||||
test_phrase = "I work as a software developer."
|
||||
test_embedding = await embedder.embed_query(test_phrase)
|
||||
sims = similarities(embeddings, test_embedding)
|
||||
assert max(sims) == sims[1]
|
||||
|
||||
|
||||
def test_contextualize_with_headings():
|
||||
"""Test that contextualize prepends headings to chunk content."""
|
||||
chunks = [
|
||||
|
|
|
|||
|
|
@ -28,21 +28,6 @@ def test_ollama_embedder_uses_config():
|
|||
assert embedder._vector_dim == 512
|
||||
|
||||
|
||||
def test_openai_embedder_uses_config():
|
||||
"""Test that OpenAI embedder uses the config passed to get_embedder."""
|
||||
custom_config = AppConfig(
|
||||
embeddings=EmbeddingsConfig(
|
||||
model=EmbeddingModelConfig(
|
||||
provider="openai", name="text-embedding-3-large", vector_dim=3072
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
embedder = get_embedder(custom_config)
|
||||
|
||||
assert embedder._vector_dim == 3072
|
||||
|
||||
|
||||
def test_openai_embedder_with_base_url():
|
||||
"""Test that OpenAI embedder uses custom base_url for vLLM/LM Studio."""
|
||||
custom_config = AppConfig(
|
||||
|
|
@ -61,21 +46,6 @@ def test_openai_embedder_with_base_url():
|
|||
assert embedder._vector_dim == 768
|
||||
|
||||
|
||||
def test_cohere_embedder_uses_config():
|
||||
"""Test that Cohere embedder uses the config passed to get_embedder."""
|
||||
custom_config = AppConfig(
|
||||
embeddings=EmbeddingsConfig(
|
||||
model=EmbeddingModelConfig(
|
||||
provider="cohere", name="embed-v4.0", vector_dim=1024
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
embedder = get_embedder(custom_config)
|
||||
|
||||
assert embedder._vector_dim == 1024
|
||||
|
||||
|
||||
def test_sentence_transformers_embedder_uses_config():
|
||||
"""Test that SentenceTransformers embedder uses the config."""
|
||||
custom_config = AppConfig(
|
||||
|
|
|
|||
Loading…
Reference in a new issue