469 lines
19 KiB
Python
469 lines
19 KiB
Python
"""
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title: Obsidian RAG Pipeline
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author: Daniel Henry
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version: 0.15
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"""
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import asyncio
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import json
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import time
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import urllib.parse
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from typing import AsyncGenerator
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import aiohttp
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from pydantic import BaseModel, Field
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class Pipe:
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class Valves(BaseModel):
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# Endpoints
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ollama_url: str = Field(default="http://ollama.internal.henryhosted.com:11434")
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qdrant_url: str = Field(default="http://app-01.internal.henryhosted.com:6333")
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rerank_url: str = Field(default="http://ollama.internal.henryhosted.com:7997")
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# Qdrant
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collection_name: str = Field(default="obsidian_vault")
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retrieve_count: int = Field(
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default=50, description="Candidates to fetch from Qdrant"
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)
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qdrant_score_threshold: float = Field(
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default=0.3, description="Minimum similarity score"
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)
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# Reranker
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rerank_enabled: bool = Field(
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default=True, description="Set to False to skip reranking"
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)
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rerank_timeout: float = Field(default=60.0)
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min_rerank_score: float = Field(
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default=0.01, description="Minimum rerank score to keep"
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)
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final_top_k: int = Field(
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default=10, description="Chunks to keep after reranking"
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)
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# LLM
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embedding_model: str = Field(default="nomic-embed-text:latest")
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llm_model: str = Field(default="llama3.2:3b")
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llm_context_size: int = Field(default=8192)
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llm_timeout: float = Field(default=300.0)
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query_rewrite_model: str = Field(
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default="",
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description="Model for query rewriting. Leave empty to use llm_model.",
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)
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# Obsidian
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vault_name: str = Field(
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default="Main", description="For generating obsidian:// links"
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)
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# Display
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show_thinking: bool = Field(default=True)
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show_sources: bool = Field(default=True)
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show_stats: bool = Field(default=True)
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token_warning_threshold: int = Field(
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default=6000, description="Warn if context exceeds this"
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)
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def __init__(self):
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self.valves = self.Valves()
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async def pipe(self, body: dict) -> AsyncGenerator[str, None]:
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messages = body.get("messages", [])
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if not messages:
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yield "No messages provided."
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return
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query = messages[-1].get("content", "").strip()
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if not query:
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yield "Empty query."
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return
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async with aiohttp.ClientSession() as session:
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async for chunk in self._execute(session, query, messages):
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yield chunk
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async def _execute(
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self,
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session: aiohttp.ClientSession,
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query: str,
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messages: list[dict],
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) -> AsyncGenerator[str, None]:
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think = self.valves.show_thinking
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# Start thinking block immediately
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if think:
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yield "<think>\n"
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yield f"**Query:** {query}\n\n"
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# ─────────────────────────────────────────────
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# Step 1: Rewrite query with conversation context
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# ─────────────────────────────────────────────
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if think:
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yield "**Step 1: Query Rewriting**\n"
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t0 = time.time()
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rewrite_model = self.valves.query_rewrite_model or self.valves.llm_model
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# Build conversation context for rewriting
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conversation_for_rewrite = []
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for m in messages[:-1]: # All messages except the last one
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role = m.get("role", "")
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content = m.get("content", "")
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if role == "user":
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conversation_for_rewrite.append(f"User: {content}")
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elif role == "assistant":
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# Truncate assistant responses to avoid bloat
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truncated = content[:500] + "..." if len(content) > 500 else content
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conversation_for_rewrite.append(f"Assistant: {truncated}")
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current_question = messages[-1].get("content", "")
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# If there's prior conversation, rewrite the query
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if conversation_for_rewrite:
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rewrite_prompt = f"""Do not interpret or answer the question. Simply add enough context from the conversation so the question makes sense on its own.
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Conversation:
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{chr(10).join(conversation_for_rewrite)}
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Latest question: {current_question}
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Rewrite the question to be standalone (respond with ONLY the rewritten question, nothing else):"""
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try:
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async with session.post(
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f"{self.valves.ollama_url}/api/generate",
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json={
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"model": rewrite_model,
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"prompt": rewrite_prompt,
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"stream": False,
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},
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timeout=aiohttp.ClientTimeout(total=30),
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) as resp:
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if resp.status == 200:
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data = await resp.json()
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rewritten = data.get("response", "").strip()
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# Sanity check - if rewrite is empty or way too long, use original
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if rewritten and len(rewritten) < 1000:
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search_query = rewritten
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else:
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search_query = current_question
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else:
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search_query = current_question
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except Exception as e:
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if think:
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yield f" ⚠ Rewrite failed: {e}, using original query\n"
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search_query = current_question
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else:
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# No prior conversation, use the question as-is
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search_query = current_question
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if think:
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yield f" Model: {rewrite_model}\n"
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yield f" Original: {current_question}\n"
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yield f" Search query: {search_query}\n"
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yield f" ✓ Done ({time.time() - t0:.2f}s)\n\n"
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# ─────────────────────────────────────────────
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# Step 2: Embed
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# ─────────────────────────────────────────────
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if think:
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yield "**Step 2: Embedding**\n"
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t0 = time.time()
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try:
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async with session.post(
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f"{self.valves.ollama_url}/api/embeddings",
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json={
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"model": self.valves.embedding_model,
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"prompt": search_query,
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"options": {"num_ctx": 8192},
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},
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timeout=aiohttp.ClientTimeout(total=15),
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) as resp:
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if resp.status != 200:
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if think:
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yield f" ✗ HTTP {resp.status}\n</think>\n\n"
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yield f"Embedding failed: HTTP {resp.status}"
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return
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embedding = (await resp.json()).get("embedding")
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except Exception as e:
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if think:
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yield f" ✗ {e}\n</think>\n\n"
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yield f"Embedding failed: {e}"
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return
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if think:
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yield f" ✓ Done ({time.time() - t0:.2f}s)\n\n"
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# ─────────────────────────────────────────────
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# Step 3: Search Qdrant
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# ─────────────────────────────────────────────
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if think:
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yield "**Step 3: Qdrant Search**\n"
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t0 = time.time()
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try:
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async with session.post(
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f"{self.valves.qdrant_url}/collections/{self.valves.collection_name}/points/search",
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json={
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"vector": embedding,
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"limit": self.valves.retrieve_count,
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"with_payload": True,
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"score_threshold": self.valves.qdrant_score_threshold,
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},
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timeout=aiohttp.ClientTimeout(total=15),
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) as resp:
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if resp.status != 200:
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if think:
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yield f" ✗ HTTP {resp.status}\n</think>\n\n"
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yield f"Qdrant search failed: HTTP {resp.status}"
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return
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qdrant_results = (await resp.json()).get("result", [])
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except Exception as e:
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if think:
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yield f" ✗ {e}\n</think>\n\n"
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yield f"Qdrant search failed: {e}"
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return
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if think:
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yield f" ✓ Found {len(qdrant_results)} chunks ({time.time() - t0:.2f}s)\n"
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if not qdrant_results:
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if think:
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yield " ✗ No results\n</think>\n\n"
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yield "No relevant notes found for this query."
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return
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# Show top 5
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if think:
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yield " Top 5:\n"
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for i, r in enumerate(qdrant_results[:5]):
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name = r.get("payload", {}).get("fileName", "?")
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score = r.get("score", 0)
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yield f" {i+1}. [{score:.4f}] {name}\n"
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yield "\n"
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# ─────────────────────────────────────────────
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# Step 4: Rerank (optional)
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# ─────────────────────────────────────────────
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if self.valves.rerank_enabled:
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if think:
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yield "**Step 4: Reranking**\n"
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t0 = time.time()
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docs_for_rerank = [
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r.get("payload", {}).get("content", "") for r in qdrant_results
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]
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try:
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async with session.post(
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f"{self.valves.rerank_url}/rerank",
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json={
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"query": search_query,
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"documents": docs_for_rerank,
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"return_documents": False,
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},
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timeout=aiohttp.ClientTimeout(total=self.valves.rerank_timeout),
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) as resp:
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if resp.status != 200:
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if think:
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yield f" ⚠ Reranker failed: HTTP {resp.status}, using Qdrant order\n\n"
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chunks = qdrant_results[: self.valves.final_top_k]
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else:
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rerank_results = (await resp.json()).get("results", [])
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# Apply rerank scores and filter
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scored = []
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for item in rerank_results:
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idx = item["index"]
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score = item["relevance_score"]
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if score >= self.valves.min_rerank_score:
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chunk = qdrant_results[idx].copy()
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chunk["rerank_score"] = score
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scored.append(chunk)
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scored.sort(key=lambda x: x["rerank_score"], reverse=True)
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chunks = scored[: self.valves.final_top_k]
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if think:
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yield f" ✓ Kept {len(chunks)} chunks ({time.time() - t0:.2f}s)\n"
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if chunks:
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yield " Top 5 after rerank:\n"
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for i, c in enumerate(chunks[:5]):
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name = c.get("payload", {}).get("fileName", "?")
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score = c.get("rerank_score", 0)
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yield f" {i+1}. [{score:.4f}] {name}\n"
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yield "\n"
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except Exception as e:
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if think:
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yield f" ⚠ Reranker error: {e}, using Qdrant order\n\n"
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chunks = qdrant_results[: self.valves.final_top_k]
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else:
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if think:
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yield "**Step 4: Reranking** (disabled)\n\n"
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chunks = qdrant_results[: self.valves.final_top_k]
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if not chunks:
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if think:
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yield " ✗ No chunks after filtering\n</think>\n\n"
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yield "No relevant notes passed the relevance threshold."
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return
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# ─────────────────────────────────────────────
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# Step 5: Build context
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# ─────────────────────────────────────────────
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if think:
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yield "**Step 5: Build Context**\n"
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context_parts = []
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for i, chunk in enumerate(chunks, 1):
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payload = chunk.get("payload", {})
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file_name = payload.get("fileName", "Unknown")
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content = payload.get("content", "").strip()
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source = payload.get("source", "")
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part = f"### Note {i}: {file_name}\n"
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if source:
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part += f"Original source: {source}\n"
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part += f"\n{content}"
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context_parts.append(part)
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context = "\n\n---\n\n".join(context_parts)
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context_chars = len(context)
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estimated_tokens = context_chars // 4
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if think:
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yield f" ✓ {len(chunks)} chunks, {context_chars:,} chars (~{estimated_tokens:,} tokens)\n"
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if estimated_tokens > self.valves.token_warning_threshold:
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yield f" ⚠ Warning: approaching context limit ({self.valves.llm_context_size})\n"
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yield "\n"
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# ─────────────────────────────────────────────
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# Step 6: Build prompt and call LLM
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# ─────────────────────────────────────────────
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if think:
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yield "**Step 6: Generate Response**\n"
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yield "</think>\n\n"
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system_prompt = f"""You are a specialized Research Assistant. Your goal is to synthesize information from the provided user notes.
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### INSTRUCTIONS
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1. **Primary Source:** Answer the user's question using strictly the content found within the <notes> section below.
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2. **Citation:** Every claim you make must be immediately followed by a citation in this format: [Note Name].
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3. **Missing Info:** If the <notes> do not contain the answer, explicitly state: "Your notes don't cover this." Do not attempt to guess or hallucinate an answer.
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4. **Exception Handling (Outside Knowledge):**
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- You are generally FORBIDDEN from using outside knowledge.
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- **HOWEVER**, if the user explicitly asks for external context (e.g., "What am I missing?", "Add outside context"), you may provide it.
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- If you trigger this exception, you must prefix that specific part of the response with: "**Outside Context:**".
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### FORMATTING
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- Be concise and direct.
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- Use bullet points for lists.
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### SOURCE DATA
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<notes>
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{context}
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</notes>
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"""
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# Only keep user/assistant messages
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conversation = [m for m in messages if m.get("role") in ("user", "assistant")]
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# UPDATED: Robustly strip previous "Sources" to prevent pattern matching
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conversation = []
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for m in messages:
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if m.get("role") not in ("user", "assistant"):
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continue
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msg = m.copy()
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if msg["role"] == "assistant":
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content = msg.get("content", "")
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# Split on "**Sources:**" which is the visible header.
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# This catches it even if the newlines/separators are slightly different.
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if "**Sources:**" in content:
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msg["content"] = content.split("**Sources:**")[0].strip()
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conversation.append(msg)
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llm_payload = {
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"model": self.valves.llm_model,
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"messages": [
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{"role": "system", "content": system_prompt},
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*conversation,
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],
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"stream": True,
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"options": {"num_ctx": self.valves.llm_context_size},
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}
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# Stream LLM response
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prompt_tokens = 0
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completion_tokens = 0
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try:
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async with session.post(
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f"{self.valves.ollama_url}/api/chat",
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json=llm_payload,
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timeout=aiohttp.ClientTimeout(total=self.valves.llm_timeout),
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) as resp:
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if resp.status != 200:
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yield f"LLM error: HTTP {resp.status}"
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return
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async for line in resp.content:
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if not line:
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continue
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try:
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data = json.loads(line)
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if text := data.get("message", {}).get("content"):
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yield text
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if data.get("done"):
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prompt_tokens = data.get("prompt_eval_count", 0)
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completion_tokens = data.get("eval_count", 0)
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except json.JSONDecodeError:
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continue
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except asyncio.TimeoutError:
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yield "\n\n⚠️ LLM timed out"
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return
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except Exception as e:
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yield f"\n\nLLM error: {e}"
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return
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# ─────────────────────────────────────────────
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# Sources
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# ─────────────────────────────────────────────
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if self.valves.show_sources:
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# Dedupe by file path, count chunks
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source_counts: dict[str, dict] = {}
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for chunk in chunks:
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payload = chunk.get("payload", {})
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path = payload.get("filePath", "")
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name = payload.get("fileName", "Unknown")
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if path in source_counts:
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source_counts[path]["count"] += 1
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else:
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source_counts[path] = {"name": name, "path": path, "count": 1}
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yield "\n\n---\n**Sources:**\n"
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for src in source_counts.values():
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vault = urllib.parse.quote(self.valves.vault_name)
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path = urllib.parse.quote(src["path"])
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uri = f"obsidian://open?vault={vault}&file={path}"
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count_str = f" ({src['count']} chunks)" if src["count"] > 1 else ""
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yield f"- [{src['name']}]({uri}){count_str}\n"
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# ─────────────────────────────────────────────
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# Stats
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# ─────────────────────────────────────────────
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if self.valves.show_stats:
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yield f"\n*{prompt_tokens:,} in / {completion_tokens:,} out*"
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