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Refactor for new A.I. stack

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This commit is contained in:
Daniel Henry
2026-01-22 15:26:10 -06:00
parent e4168e673a
commit 121590b1a7
1 changed files with 140 additions and 107 deletions
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ObsidianRAGPipe.py
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@@ -1,12 +1,14 @@
"""
title: Obsidian RAG Pipeline
author: Daniel Henry
version: 0.15
version: 0.17
description: Updated for llama-swap with llama.cpp (OpenAI-compatible API)
"""
import asyncio
import json
import time
import math
import urllib.parse
from typing import AsyncGenerator
@@ -18,9 +20,8 @@ class Pipe:
class Valves(BaseModel):
# Endpoints
ollama_url: str = Field(default="http://ollama.internal.henryhosted.com:11434")
llamacpp_url: str = Field(default="http://ollama.internal.henryhosted.com:9292")
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")
@@ -35,6 +36,16 @@ class Pipe:
rerank_enabled: bool = Field(
default=True, description="Set to False to skip reranking"
)
rerank_logit: bool = Field(
default=False, description="Enable if reranker outputs logits"
)
rerank_debug: bool = Field(
default=False, description="Output all rerank values into think"
)
rerank_model: str = Field(
default="bge-reranker-v2-m3-q8_0",
description="Reranker model name",
)
rerank_timeout: float = Field(default=60.0)
min_rerank_score: float = Field(
default=0.01, description="Minimum rerank score to keep"
@@ -44,9 +55,17 @@ class Pipe:
)
# 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)
embedding_model: str = Field(
default="nomic-embed-text-v1.5.f16",
description="Embedding model name",
)
llm_model: str = Field(
default="qwen2.5-3b-instruct-q4_k_m",
description="LLM model name",
)
llm_max_tokens: int = Field(
default=2048, description="Max tokens for LLM response"
)
llm_timeout: float = Field(default=300.0)
query_rewrite_model: str = Field(
default="",
@@ -69,6 +88,10 @@ class Pipe:
def __init__(self):
self.valves = self.Valves()
def _estimate_tokens(self, text: str) -> int:
"""Rough token estimate: ~4 chars per token for English text."""
return len(text) // 4
async def pipe(self, body: dict) -> AsyncGenerator[str, None]:
messages = body.get("messages", [])
if not messages:
@@ -92,11 +115,12 @@ class Pipe:
) -> AsyncGenerator[str, None]:
think = self.valves.show_thinking
total_prompt_tokens = 0
# Start thinking block immediately
# Start thinking block
if think:
yield "<think>\n"
yield f"**LLM Model:** {self.valves.llm_model}\n\n"
yield f"**LLM Model:** {self.valves.llm_model}\n"
yield f"**Query:** {query}\n\n"
# ─────────────────────────────────────────────
@@ -107,22 +131,19 @@ class Pipe:
t0 = time.time()
rewrite_model = self.valves.query_rewrite_model or self.valves.llm_model
current_question = messages[-1].get("content", "")
# Build conversation context for rewriting
# Build conversation context for rewriting (only if there's prior conversation)
conversation_for_rewrite = []
for m in messages[:-1]: # All messages except the last one
for m in messages[:-1]:
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.
@@ -135,18 +156,23 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
try:
async with session.post(
f"{self.valves.ollama_url}/api/generate",
f"{self.valves.llamacpp_url}/v1/chat/completions",
json={
"model": rewrite_model,
"prompt": rewrite_prompt,
"messages": [{"role": "user", "content": rewrite_prompt}],
"stream": False,
"max_tokens": 256,
},
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
rewritten = (
data.get("choices", [{}])[0]
.get("message", {})
.get("content", "")
.strip()
)
if rewritten and len(rewritten) < 1000:
search_query = rewritten
else:
@@ -158,7 +184,6 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
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:
@@ -176,20 +201,26 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
try:
async with session.post(
f"{self.valves.ollama_url}/api/embeddings",
f"{self.valves.llamacpp_url}/v1/embeddings",
json={
"model": self.valves.embedding_model,
"prompt": search_query,
"options": {"num_ctx": 8192},
"input": search_query,
},
timeout=aiohttp.ClientTimeout(total=15),
timeout=aiohttp.ClientTimeout(total=30),
) as resp:
if resp.status != 200:
error_text = await resp.text()
if think:
yield f" ✗ HTTP {resp.status}\n</think>\n\n"
yield f" ✗ HTTP {resp.status}: {error_text}\n</think>\n\n"
yield f"Embedding failed: HTTP {resp.status}"
return
embedding = (await resp.json()).get("embedding")
data = await resp.json()
embedding = data.get("data", [{}])[0].get("embedding")
if not embedding:
if think:
yield " ✗ No embedding in response\n</think>\n\n"
yield "Embedding failed: No embedding returned"
return
except Exception as e:
if think:
yield f" ✗ {e}\n</think>\n\n"
@@ -238,7 +269,6 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
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]):
@@ -253,6 +283,7 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
if self.valves.rerank_enabled:
if think:
yield "**Step 4: Reranking**\n"
yield f"**Rerank Model:** {self.valves.rerank_model}\n"
t0 = time.time()
docs_for_rerank = [
@@ -261,26 +292,31 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
try:
async with session.post(
f"{self.valves.rerank_url}/rerank",
f"{self.valves.llamacpp_url}/v1/rerank",
json={
"model": self.valves.rerank_model,
"query": search_query,
"documents": docs_for_rerank,
"return_documents": False,
},
timeout=aiohttp.ClientTimeout(total=self.valves.rerank_timeout),
) as resp:
if resp.status != 200:
error_text = await resp.text()
if think:
yield f" ⚠ Reranker failed: HTTP {resp.status}, using Qdrant order\n\n"
yield f" ⚠ Reranker failed: HTTP {resp.status} - {error_text}, using Qdrant order\n\n"
chunks = qdrant_results[: self.valves.final_top_k]
else:
rerank_results = (await resp.json()).get("results", [])
rerank_data = await resp.json()
rerank_results = rerank_data.get("results", [])
# Apply rerank scores and filter
scored = []
for item in rerank_results:
idx = item["index"]
score = item["relevance_score"]
if self.valves.rerank_logit:
score = 1 / (1 + math.exp(-item["relevance_score"]))
if think and self.valves.rerank_debug:
yield f" • Debug: Doc {idx} score: {score}\n"
if score >= self.valves.min_rerank_score:
chunk = qdrant_results[idx].copy()
chunk["rerank_score"] = score
@@ -291,7 +327,6 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
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]):
@@ -304,7 +339,6 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
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"
@@ -329,22 +363,20 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
content = payload.get("content", "").strip()
source = payload.get("source", "")
part = f"### Note Name {i}: {file_name}\n"
# CHANGE: Explicit bracketed ID format
part = f"[{i}] File: {file_name}\n"
if source:
part += f"Original source: {source}\n"
part += f"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
context_tokens = self._estimate_tokens(context)
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 f" ✓ {len(chunks)} chunks, ~{context_tokens:,} tokens\n"
if context_tokens > self.valves.token_warning_threshold:
yield f" ⚠ Warning: large context may affect quality\n"
yield "\n"
# ─────────────────────────────────────────────
@@ -354,91 +386,77 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
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.
system_prompt = f"""You are a helpful assistant. Use the provided notes to answer the user's question.
### 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.
RULES:
1. Use the <notes> as your source of truth.
2. Cite facts using the bracketed ID number [1].
3. SYNTHESIS: You are encouraged to draw connections between different notes to form a complete answer.
4. INFERENCE: If the answer is not explicitly written but can be logically inferred from the notes, you may answer, but please use phrases like "The notes imply..." or "Based on [1], it suggests..."
5. If the answer is completely absent, say "I couldn't find that in your notes."
### 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>
"""
</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
# Build conversation, stripping previous sources from assistant messages
conversation = []
for m in messages:
if m.get("role") not in ("user", "assistant"):
role = m.get("role")
if 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()
msg = {"role": role, "content": m.get("content", "")}
if role == "assistant" and "**Sources:**" in msg["content"]:
msg["content"] = msg["content"].split("**Sources:**")[0].strip()
conversation.append(msg)
llm_messages = [{"role": "system", "content": system_prompt}] + conversation
# Estimate prompt tokens
prompt_text = system_prompt + "".join(m["content"] for m in conversation)
total_prompt_tokens = self._estimate_tokens(prompt_text)
llm_payload = {
"model": self.valves.llm_model,
"messages": [
{"role": "system", "content": system_prompt},
*conversation,
],
"messages": llm_messages,
"stream": True,
"options": {"num_ctx": self.valves.llm_context_size},
"max_tokens": self.valves.llm_max_tokens,
}
# Stream LLM response
prompt_tokens = 0
completion_tokens = 0
completion_text = ""
try:
async with session.post(
f"{self.valves.ollama_url}/api/chat",
f"{self.valves.llamacpp_url}/v1/chat/completions",
json=llm_payload,
timeout=aiohttp.ClientTimeout(total=self.valves.llm_timeout),
) as resp:
if resp.status != 200:
yield f"LLM error: HTTP {resp.status}"
error_text = await resp.text()
yield f"LLM error: HTTP {resp.status} - {error_text}"
return
async for line in resp.content:
if not line:
continue
line_str = line.decode("utf-8").strip()
if not line_str or line_str.startswith(":"):
continue
if line_str.startswith("data: "):
line_str = line_str[6:]
if line_str == "[DONE]":
break
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)
data = json.loads(line_str)
delta = data.get("choices", [{}])[0].get("delta", {})
if content := delta.get("content"):
yield content
completion_text += content
except json.JSONDecodeError:
continue
@@ -449,32 +467,47 @@ Rewrite the question to be standalone (respond with ONLY the rewritten question,
yield f"\n\nLLM error: {e}"
return
# Estimate completion tokens
completion_tokens = self._estimate_tokens(completion_text)
# ─────────────────────────────────────────────
# Sources
# ─────────────────────────────────────────────
if self.valves.show_sources:
# Dedupe by file path, count chunks
# We now track 'indices' list along with the count
source_counts: dict[str, dict] = {}
for chunk in chunks:
# 'chunks' is still available from Step 4/Step 3
for i, chunk in enumerate(chunks, 1):
payload = chunk.get("payload", {})
path = payload.get("filePath", "")
name = payload.get("fileName", "Unknown")
if path in source_counts:
source_counts[path]["count"] += 1
source_counts[path]["indices"].append(i)
else:
source_counts[path] = {"name": name, "path": path, "count": 1}
source_counts[path] = {
"name": name,
"path": path,
"count": 1,
"indices": [i],
}
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"
# Format indices like: [1, 2, 5]
indices_str = ", ".join(map(str, src["indices"]))
yield f"- [{src['name']}]({uri}) (Chunks: {indices_str})\n"
# ─────────────────────────────────────────────
# Stats
# ─────────────────────────────────────────────
if self.valves.show_stats:
yield f"\n*{prompt_tokens:,} in / {completion_tokens:,} out*"
yield f"\n*~{total_prompt_tokens:,} in / ~{completion_tokens:,} out (estimated)*"