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import pickle
import typing
#19bc8e
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import ORJSONResponse, StreamingResponse
import ollama
from fastapi.staticfiles import StaticFiles
# uvicorn api:app --host 0.0.0.0 --port 8080 {text.replace(/<think>[\s\S]*?<\/think>/g, "") }
#https://arxiv.org/pdf/2202.04850v1
app = FastAPI(
description="Knowledge graph.",
title="FactGPT",
version="0.0.1",
)
origins = [
"http://localhost:8080",
]
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],#origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class Knowledge:
"""This class is a wrapper around the pipeline."""
def __init__(self) -> None:
self.pipeline = None
def start(self):
"""Load the pipeline."""
import os
# Step 1: Change to the subfolder
#os.chdir("knowledge") # replace "folder" with your subfolder name
print("Current directory (inside folder):", os.getcwd())
#"database/pipeline.pkl" data/pipeline.pkl
with open("data/pipeline.pkl", "rb") as f:
self.pipeline = pickle.load(f)
# Step 2: Go back to the parent directory
#os.chdir("..")
print("Current directory (after cd ..):", os.getcwd())
return self
def search(
self,
q: str,
tags: str,
) -> typing.Dict:
"""Returns the documents."""
return self.pipeline.search(q=q, tags=tags)
def plot(
self,
q: str,
k_tags: int,
k_yens: int = 1,
k_walk: int = 3,
) -> typing.Dict:
"""Returns the graph."""
nodes, links = self.pipeline.plot(
q=q,
k_tags=k_tags,
k_yens=k_yens,
k_walk=k_walk,
)
return {"nodes": nodes, "links": links}
knowledge = Knowledge()
async def async_chat(query: str, content: str):
"""Re-rank the documents using a local Ollama model."""
full_prompt = f"""
You are a senior patent-search analyst.
<task>
- Restate the claim in one sentence.
- For each paper, list its title, classify as:
• Matched – fully covers all claim elements
• Similar – covers most elements or if any doubt
• Irrelevant – no meaningful overlap
then give a 1–2 sentence rationale.
</task>
<rules>
• Rely only on the provided text.
• If in doubt, classify as Similar.
• Do not invent content.
</rules>
<claim>
{query}
</claim>
<papers>
{content}
</papers>
"""
response = ollama.chat(
model="qwen3:1.7b", # or "llama3", "mistral", etc.
messages=[{"role": "user", "content": full_prompt}],
stream=True, options={'temperature': .5},
)
answer = "\n"
for chunk in response:
token = chunk["message"]["content"]
answer += token
#answer = answer
yield answer.strip()
@app.get("/search/{sort}/{tags}/{k_tags}/{q}")
def search(k_tags: int, tags: str, sort: bool, q: str):
"""Search for documents."""
tags = tags != "null"#tags = tags.lower() != "false" and tags.lower() != "null"#
documents = knowledge.search(q=q, tags=tags)
if bool(sort):
documents = [
document
for _, document in sorted(
[(document["date"], document) for document in documents],
key=lambda document: datetime.datetime.strptime(
document[0], "%Y-%m-%d"
),
reverse=True,
)
]
return {"documents": documents}
@app.get("/plot/{k_tags}/{q}", response_class=ORJSONResponse)
def plot(k_tags: int, q: str):
"""Plot tags."""
return knowledge.plot(q=q, k_tags=k_tags)
@app.on_event("startup")
def start():
"""Intialiaze the pipeline."""
return knowledge.start()
@app.get("/chat/{k_tags}/{q}")
async def chat(k_tags: int, q: str):
"""LLM recommendation."""
documents = knowledge.search(q=q, tags=False)
content = ""
count=0
for document in documents:
count+=1
content += f"TITLE: {count}" + document["title"] + "\n"
content += "summary: " + document["summary"][:30] + "\n"
content += "targs: " + (
", ".join(document["tags"] + document["extra-tags"]) + "\n"
)
#content += "url: " + document["url"] + "\n\n"
content +='\n\n\n'
if count>10:break
#content = "title: ".join(content[:3000].split("title:")[:-1])
return StreamingResponse(
async_chat(query=q, content=content), media_type="text/plain"
)
app.mount("/", StaticFiles(directory="docs", html=True), name="static")
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