ObsidianRAGPipe/ObsidianRAGPipe.py

"""
title: Obsidian RAG Pipeline
author: Daniel Henry
version: 0.15
"""

import asyncio
import json
import time
import urllib.parse
from typing import AsyncGenerator

import aiohttp
from pydantic import BaseModel, Field


class Pipe:

    class Valves(BaseModel):
        # Endpoints
        ollama_url: str = Field(default="http://ollama.internal.henryhosted.com:11434")
        qdrant_url: str = Field(default="http://app-01.internal.henryhosted.com:6333")
        rerank_url: str = Field(default="http://ollama.internal.henryhosted.com:7997")

        # Qdrant
        collection_name: str = Field(default="obsidian_vault")
        retrieve_count: int = Field(
            default=50, description="Candidates to fetch from Qdrant"
        )
        qdrant_score_threshold: float = Field(
            default=0.3, description="Minimum similarity score"
        )

        # Reranker
        rerank_enabled: bool = Field(
            default=True, description="Set to False to skip reranking"
        )
        rerank_timeout: float = Field(default=60.0)
        min_rerank_score: float = Field(
            default=0.01, description="Minimum rerank score to keep"
        )
        final_top_k: int = Field(
            default=10, description="Chunks to keep after reranking"
        )

        # LLM
        embedding_model: str = Field(default="nomic-embed-text:latest")
        llm_model: str = Field(default="llama3.2:3b")
        llm_context_size: int = Field(default=8192)
        llm_timeout: float = Field(default=300.0)
        query_rewrite_model: str = Field(
            default="",
            description="Model for query rewriting. Leave empty to use llm_model.",
        )

        # Obsidian
        vault_name: str = Field(
            default="Main", description="For generating obsidian:// links"
        )

        # Display
        show_thinking: bool = Field(default=True)
        show_sources: bool = Field(default=True)
        show_stats: bool = Field(default=True)
        token_warning_threshold: int = Field(
            default=6000, description="Warn if context exceeds this"
        )

    def __init__(self):
        self.valves = self.Valves()

    async def pipe(self, body: dict) -> AsyncGenerator[str, None]:
        messages = body.get("messages", [])
        if not messages:
            yield "No messages provided."
            return

        query = messages[-1].get("content", "").strip()
        if not query:
            yield "Empty query."
            return

        async with aiohttp.ClientSession() as session:
            async for chunk in self._execute(session, query, messages):
                yield chunk

    async def _execute(
        self,
        session: aiohttp.ClientSession,
        query: str,
        messages: list[dict],
    ) -> AsyncGenerator[str, None]:

        think = self.valves.show_thinking

        # Start thinking block immediately
        if think:
            yield "<think>\n"
            yield f"**LLM Model: ** {self.valves.llm_model}\n\n"
            yield f"**Query:** {query}\n\n"

        # ─────────────────────────────────────────────
        # Step 1: Rewrite query with conversation context
        # ─────────────────────────────────────────────
        if think:
            yield "**Step 1: Query Rewriting**\n"

        t0 = time.time()
        rewrite_model = self.valves.query_rewrite_model or self.valves.llm_model

        # Build conversation context for rewriting
        conversation_for_rewrite = []
        for m in messages[:-1]:  # All messages except the last one
            role = m.get("role", "")
            content = m.get("content", "")
            if role == "user":
                conversation_for_rewrite.append(f"User: {content}")
            elif role == "assistant":
                # Truncate assistant responses to avoid bloat
                truncated = content[:500] + "..." if len(content) > 500 else content
                conversation_for_rewrite.append(f"Assistant: {truncated}")

        current_question = messages[-1].get("content", "")

        # If there's prior conversation, rewrite the query
        if conversation_for_rewrite:
            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.

Conversation:
{chr(10).join(conversation_for_rewrite)}

Latest question: {current_question}

Rewrite the question to be standalone (respond with ONLY the rewritten question, nothing else):"""

            try:
                async with session.post(
                    f"{self.valves.ollama_url}/api/generate",
                    json={
                        "model": rewrite_model,
                        "prompt": rewrite_prompt,
                        "stream": False,
                    },
                    timeout=aiohttp.ClientTimeout(total=30),
                ) as resp:
                    if resp.status == 200:
                        data = await resp.json()
                        rewritten = data.get("response", "").strip()
                        # Sanity check - if rewrite is empty or way too long, use original
                        if rewritten and len(rewritten) < 1000:
                            search_query = rewritten
                        else:
                            search_query = current_question
                    else:
                        search_query = current_question
            except Exception as e:
                if think:
                    yield f"  ⚠ Rewrite failed: {e}, using original query\n"
                search_query = current_question
        else:
            # No prior conversation, use the question as-is
            search_query = current_question

        if think:
            yield f"  Model: {rewrite_model}\n"
            yield f"  Original: {current_question}\n"
            yield f"  Search query: {search_query}\n"
            yield f"  ✓ Done ({time.time() - t0:.2f}s)\n\n"

        # ─────────────────────────────────────────────
        # Step 2: Embed
        # ─────────────────────────────────────────────
        if think:
            yield "**Step 2: Embedding**\n"
        t0 = time.time()

        try:
            async with session.post(
                f"{self.valves.ollama_url}/api/embeddings",
                json={
                    "model": self.valves.embedding_model,
                    "prompt": search_query,
                    "options": {"num_ctx": 8192},
                },
                timeout=aiohttp.ClientTimeout(total=15),
            ) as resp:
                if resp.status != 200:
                    if think:
                        yield f"  ✗ HTTP {resp.status}\n</think>\n\n"
                    yield f"Embedding failed: HTTP {resp.status}"
                    return
                embedding = (await resp.json()).get("embedding")
        except Exception as e:
            if think:
                yield f"  ✗ {e}\n</think>\n\n"
            yield f"Embedding failed: {e}"
            return

        if think:
            yield f"  ✓ Done ({time.time() - t0:.2f}s)\n\n"

        # ─────────────────────────────────────────────
        # Step 3: Search Qdrant
        # ─────────────────────────────────────────────
        if think:
            yield "**Step 3: Qdrant Search**\n"
        t0 = time.time()

        try:
            async with session.post(
                f"{self.valves.qdrant_url}/collections/{self.valves.collection_name}/points/search",
                json={
                    "vector": embedding,
                    "limit": self.valves.retrieve_count,
                    "with_payload": True,
                    "score_threshold": self.valves.qdrant_score_threshold,
                },
                timeout=aiohttp.ClientTimeout(total=15),
            ) as resp:
                if resp.status != 200:
                    if think:
                        yield f"  ✗ HTTP {resp.status}\n</think>\n\n"
                    yield f"Qdrant search failed: HTTP {resp.status}"
                    return
                qdrant_results = (await resp.json()).get("result", [])
        except Exception as e:
            if think:
                yield f"  ✗ {e}\n</think>\n\n"
            yield f"Qdrant search failed: {e}"
            return

        if think:
            yield f"  ✓ Found {len(qdrant_results)} chunks ({time.time() - t0:.2f}s)\n"

        if not qdrant_results:
            if think:
                yield "  ✗ No results\n</think>\n\n"
            yield "No relevant notes found for this query."
            return

        # Show top 5
        if think:
            yield "  Top 5:\n"
            for i, r in enumerate(qdrant_results[:5]):
                name = r.get("payload", {}).get("fileName", "?")
                score = r.get("score", 0)
                yield f"    {i+1}. [{score:.4f}] {name}\n"
            yield "\n"

        # ─────────────────────────────────────────────
        # Step 4: Rerank (optional)
        # ─────────────────────────────────────────────
        if self.valves.rerank_enabled:
            if think:
                yield "**Step 4: Reranking**\n"
            t0 = time.time()

            docs_for_rerank = [
                r.get("payload", {}).get("content", "") for r in qdrant_results
            ]

            try:
                async with session.post(
                    f"{self.valves.rerank_url}/rerank",
                    json={
                        "query": search_query,
                        "documents": docs_for_rerank,
                        "return_documents": False,
                    },
                    timeout=aiohttp.ClientTimeout(total=self.valves.rerank_timeout),
                ) as resp:
                    if resp.status != 200:
                        if think:
                            yield f"  ⚠ Reranker failed: HTTP {resp.status}, using Qdrant order\n\n"
                        chunks = qdrant_results[: self.valves.final_top_k]
                    else:
                        rerank_results = (await resp.json()).get("results", [])

                        # Apply rerank scores and filter
                        scored = []
                        for item in rerank_results:
                            idx = item["index"]
                            score = item["relevance_score"]
                            if score >= self.valves.min_rerank_score:
                                chunk = qdrant_results[idx].copy()
                                chunk["rerank_score"] = score
                                scored.append(chunk)

                        scored.sort(key=lambda x: x["rerank_score"], reverse=True)
                        chunks = scored[: self.valves.final_top_k]

                        if think:
                            yield f"  ✓ Kept {len(chunks)} chunks ({time.time() - t0:.2f}s)\n"

                            if chunks:
                                yield "  Top 5 after rerank:\n"
                                for i, c in enumerate(chunks[:5]):
                                    name = c.get("payload", {}).get("fileName", "?")
                                    score = c.get("rerank_score", 0)
                                    yield f"    {i+1}. [{score:.4f}] {name}\n"
                            yield "\n"

            except Exception as e:
                if think:
                    yield f"  ⚠ Reranker error: {e}, using Qdrant order\n\n"
                chunks = qdrant_results[: self.valves.final_top_k]

        else:
            if think:
                yield "**Step 4: Reranking** (disabled)\n\n"
            chunks = qdrant_results[: self.valves.final_top_k]

        if not chunks:
            if think:
                yield "  ✗ No chunks after filtering\n</think>\n\n"
            yield "No relevant notes passed the relevance threshold."
            return

        # ─────────────────────────────────────────────
        # Step 5: Build context
        # ─────────────────────────────────────────────
        if think:
            yield "**Step 5: Build Context**\n"

        context_parts = []
        for i, chunk in enumerate(chunks, 1):
            payload = chunk.get("payload", {})
            file_name = payload.get("fileName", "Unknown")
            content = payload.get("content", "").strip()
            source = payload.get("source", "")

            part = f"### Note Name {i}: {file_name}\n"
            if source:
                part += f"Original source: {source}\n"
            part += f"\n{content}"
            context_parts.append(part)

        context = "\n\n---\n\n".join(context_parts)
        context_chars = len(context)
        estimated_tokens = context_chars // 4

        if think:
            yield f"  ✓ {len(chunks)} chunks, {context_chars:,} chars (~{estimated_tokens:,} tokens)\n"

            if estimated_tokens > self.valves.token_warning_threshold:
                yield f"  ⚠ Warning: approaching context limit ({self.valves.llm_context_size})\n"

            yield "\n"

        # ─────────────────────────────────────────────
        # Step 6: Build prompt and call LLM
        # ─────────────────────────────────────────────
        if think:
            yield "**Step 6: Generate Response**\n"
            yield "</think>\n\n"

        system_prompt = f"""
                        ### ROLE
                        You are the user's "Knowledge Partner." You are warm, enthusiastic, and helpful. You love the user's notes and want to help them connect ideas.
                        
                        ### THE GOLDEN RULE (HARD WALL)
                        Your knowledge is strictly limited to the provided <notes>.
                        - IF the answer is in the notes: Synthesize it warmly and cite it.
                        - IF the answer is NOT in the notes: You must admit it. Say: "I checked your notes, but I couldn't find info on that."
                        - Be honest with the user.  The user does not want blind support.  You are a friendly research assistant not an overly supportive friend.
                        - **EXCEPTION:** ONLY if the user explicitly types the trigger phrase "System: Add Context" are you allowed to use outside knowledge.
                        
                        ### INSTRUCTIONS
                        1. **Search First:** Look through the <notes> to find the answer.
                        2. **Synthesize:** You may combine facts from different notes to build a complete answer.
                        3. **Cite Everything:** Every single statement of fact must end with a citation in this format: `[Note Name]`.
                        4. **Tone:** Be conversational but professional. Avoid robotic phrases like "According to the provided text." Instead, say "Your note on [Topic] mentions..."
                        5. **Additional:** Avoid asking follow up questions at the end of your output. 
                        
                        ### EXAMPLES (Follow this pattern)
                        
                        **User:** "What did I write about the project deadline?"
                        **You:** "I looked through your project logs! It seems you set the final submission date for October 15th [Project_Alpha_Log]. You also noted that the design phase needs to wrap up by the 1st [Design_Team_Meeting]."
                        
                        **User:** "Who is the president of France?" (Note: This is NOT in your notes)
                        **You:** "I checked your notes, but I don't see any mention of the current president of France. Would you like me to use outside knowledge? If so, just say 'System: Add Context'."
                        
                        ### SOURCE NOTES
                        <notes>
                        {context}
                        </notes>
                        """

        # Only keep user/assistant messages
        conversation = [m for m in messages if m.get("role") in ("user", "assistant")]

        # UPDATED: Robustly strip previous "Sources" to prevent pattern matching
        conversation = []
        for m in messages:
            if m.get("role") not in ("user", "assistant"):
                continue

            msg = m.copy()
            if msg["role"] == "assistant":
                content = msg.get("content", "")
                # Split on "**Sources:**" which is the visible header.
                # This catches it even if the newlines/separators are slightly different.
                if "**Sources:**" in content:
                    msg["content"] = content.split("**Sources:**")[0].strip()

            conversation.append(msg)

        llm_payload = {
            "model": self.valves.llm_model,
            "messages": [
                {"role": "system", "content": system_prompt},
                *conversation,
            ],
            "stream": True,
            "options": {"num_ctx": self.valves.llm_context_size},
        }

        # Stream LLM response
        prompt_tokens = 0
        completion_tokens = 0

        try:
            async with session.post(
                f"{self.valves.ollama_url}/api/chat",
                json=llm_payload,
                timeout=aiohttp.ClientTimeout(total=self.valves.llm_timeout),
            ) as resp:
                if resp.status != 200:
                    yield f"LLM error: HTTP {resp.status}"
                    return

                async for line in resp.content:
                    if not line:
                        continue
                    try:
                        data = json.loads(line)
                        if text := data.get("message", {}).get("content"):
                            yield text
                        if data.get("done"):
                            prompt_tokens = data.get("prompt_eval_count", 0)
                            completion_tokens = data.get("eval_count", 0)
                    except json.JSONDecodeError:
                        continue

        except asyncio.TimeoutError:
            yield "\n\n⚠️ LLM timed out"
            return
        except Exception as e:
            yield f"\n\nLLM error: {e}"
            return

        # ─────────────────────────────────────────────
        # Sources
        # ─────────────────────────────────────────────
        if self.valves.show_sources:
            # Dedupe by file path, count chunks
            source_counts: dict[str, dict] = {}
            for chunk in chunks:
                payload = chunk.get("payload", {})
                path = payload.get("filePath", "")
                name = payload.get("fileName", "Unknown")
                if path in source_counts:
                    source_counts[path]["count"] += 1
                else:
                    source_counts[path] = {"name": name, "path": path, "count": 1}

            yield "\n\n---\n**Sources:**\n"
            for src in source_counts.values():
                vault = urllib.parse.quote(self.valves.vault_name)
                path = urllib.parse.quote(src["path"])
                uri = f"obsidian://open?vault={vault}&file={path}"
                count_str = f" ({src['count']} chunks)" if src["count"] > 1 else ""
                yield f"- [{src['name']}]({uri}){count_str}\n"

        # ─────────────────────────────────────────────
        # Stats
        # ─────────────────────────────────────────────
        if self.valves.show_stats:
            yield f"\n*{prompt_tokens:,} in / {completion_tokens:,} out*"