Feat/ RAG chat endpoint + Pinecone metadata fix #113
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…ckend with asyncio
…about the article
WalkthroughThis update introduces new backend modules for bias detection, text embedding, retrieval-augmented generation (RAG) data retrieval, and LLM-based processing. It adds two new API endpoints ( Changes
Sequence Diagram(s)Bias Detection Endpoint FlowsequenceDiagram
participant User
participant Frontend
participant Backend (/bias endpoint)
participant Scraper Pipeline
participant Bias Detection (check_bias.py)
User->>Frontend: Submit URL for bias analysis
Frontend->>Backend: POST /bias { url }
Backend->>Scraper Pipeline: Scrape content from URL
Scraper Pipeline-->>Backend: Return article text
Backend->>Bias Detection: check_bias(article text)
Bias Detection-->>Backend: Return bias score
Backend-->>Frontend: Respond with bias score
Frontend-->>User: Display bias score
Chat Endpoint FlowsequenceDiagram
participant User
participant Frontend
participant Backend (/chat endpoint)
participant RAG (get_rag_data.py)
participant LLM (llm_processing.py)
User->>Frontend: Submit chat message
Frontend->>Backend: POST /chat { message }
Backend->>RAG: search_pinecone(message)
RAG-->>Backend: Return relevant docs
Backend->>LLM: ask_llm(message, docs)
LLM-->>Backend: Return answer
Backend-->>Frontend: Respond with answer
Frontend-->>User: Display answer
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Suggested reviewers
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Actionable comments posted: 13
🔭 Outside diff range comments (1)
backend/app/modules/bias_detection/check_bias.py (1)
11-58: Add type hints and a docstring tocheck_biasThe
/biasroute already usesasyncio.to_threadto offload the synchronous call (see line 30 inbackend/app/routes/routes.py). To improve readability and maintainability, please:
In
backend/app/modules/bias_detection/check_bias.py, update the signature to:def check_bias(text: str) -> dict: """ Calculate a bias score for the provided text using the language model. Args: text (str): Cleaned article text to be scored. Returns: dict: { "bias_score": str, # number between "0" and "100" "status": "success" | "error", "error_from": str # only present on error } """ ...Ensure the docstring clearly explains parameters, return value structure, and error cases.
🧹 Nitpick comments (7)
backend/app/modules/vector_store/embed.py (1)
31-31: No-op change; consider centralizing the embedder to avoid duplicate model loads.Functionality unchanged. However, this project now instantiates SentenceTransformer in both
vector_store/embed.pyandchat/embed_query.py. Loading the same model twice wastes memory and startup time.Refactor suggestion:
- Export the singleton
embedderfrom one module (e.g.,app.modules.vector_store.embed) and import it where needed (e.g.,app.modules.chat.embed_query) to ensure a single model instance.backend/app/modules/bias_detection/check_bias.py (2)
16-17: Fix validation message and tighten the guard.The error mentions 'cleaned_text' but the function parameter is 'text'. Align the message.
- if not text: - raise ValueError("Missing or empty 'cleaned_text'") + if not text or not str(text).strip(): + raise ValueError("Missing or empty 'text'")
19-42: Make the prompt deterministic and concise for numeric-only output.Lower max_tokens and set temperature=0 for consistent numeric output. Clarify instructions to return digits only.
- chat_completion = client.chat.completions.create( + chat_completion = client.chat.completions.create( messages=[ { "role": "system", "content": ( - "You are an assistant that checks " - "if given article is biased and give" - "score to each based on biasness where 0 is lowest bias and 100 is highest bias" - "Only return a number between 0 to 100 base on bias." - "only return Number No Text" + "You are an assistant that scores article bias from 0 (lowest) to 100 (highest). " + "Respond with digits only: a single integer 0-100. No text, no symbols." ), }, { "role": "user", "content": ( - "Give bias score to the following article " + "Give a bias score (0-100) for this article:\n\n" f"\n\n{text}" ), }, ], model="gemma2-9b-it", - temperature=0.3, - max_tokens=512, + temperature=0, + max_tokens=8, )frontend/app/analyze/page.tsx (1)
224-226: Use the same validator for example URLs.Instead of forcing validity to true, reuse
validateUrlto keep behavior consistent if examples change.- setUrl(exampleUrl); - setIsValidUrl(true); + setUrl(exampleUrl); + validateUrl(exampleUrl);backend/app/modules/chat/llm_processing.py (1)
10-13: Filter out empty metadata to keep the prompt concise
build_contextjoins even empty strings, producing long runs of blank lines and wasting tokens.
Skip items that lack bothexplanationandreasoning.-return "\n".join( - f"{m['metadata'].get('explanation') or m['metadata'].get('reasoning', '')}" - for m in docs -) +return "\n".join( + val for m in docs + if (val := (m["metadata"].get("explanation") or m["metadata"].get("reasoning"))) +)backend/app/routes/routes.py (1)
44-49: Blocking I/O inside async endpoint
search_pineconeandask_llmare synchronous & network-bound; calling them directly blocks the event loop.
Wrap them inasyncio.to_threador migrate to async clients.frontend/app/analyze/results/page.tsx (1)
54-57: Convert bias score to number
bias_scorearrives as a string;BiasMeterexpects a number.
setBiasScore(Number(JSON.parse(storedBiasScore).bias_score))
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📒 Files selected for processing (9)
backend/app/modules/bias_detection/check_bias.py(1 hunks)backend/app/modules/chat/embed_query.py(1 hunks)backend/app/modules/chat/get_rag_data.py(1 hunks)backend/app/modules/chat/llm_processing.py(1 hunks)backend/app/modules/vector_store/embed.py(1 hunks)backend/app/routes/routes.py(2 hunks)frontend/app/analyze/loading/page.tsx(8 hunks)frontend/app/analyze/page.tsx(7 hunks)frontend/app/analyze/results/page.tsx(3 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (4)
frontend/app/analyze/page.tsx (1)
frontend/app/analyze/results/page.tsx (1)
AnalyzePage(29-269)
frontend/app/analyze/results/page.tsx (1)
frontend/components/bias-meter.tsx (1)
BiasMeter(14-78)
backend/app/routes/routes.py (4)
backend/app/modules/bias_detection/check_bias.py (1)
check_bias(11-57)backend/app/modules/chat/get_rag_data.py (1)
search_pinecone(12-31)backend/app/modules/chat/llm_processing.py (1)
ask_llm(15-35)backend/app/modules/pipeline.py (2)
run_scraper_pipeline(12-28)run_langgraph_workflow(31-34)
backend/app/modules/chat/get_rag_data.py (1)
backend/app/modules/chat/embed_query.py (1)
embed_query(6-10)
🔇 Additional comments (2)
backend/app/modules/chat/get_rag_data.py (1)
1-31: Verify Pinecone client version & index dimension
- Pin an explicit Pinecone SDK version in your dependency file (
requirements.txt,pyproject.toml, etc.) that matches the API usage (v3 if you’re using the new object-based responses).- Confirm your Pinecone index
"perspective"is created withdimension=384to match theall-MiniLM-L6-v2embedding output; a mismatch will causeindex.query()to fail.backend/app/routes/routes.py (1)
27-33: Incorrect argument passing breaksbias_detection
asyncio.to_thread(run_scraper_pipeline, (request.url))sends a tuple instead of a string, so the scraper receives("https://…",)and fails.
After thatcheck_biasgets the whole dict, whereas it expects the cleaned text.-content = await asyncio.to_thread(run_scraper_pipeline, (request.url)) -bias_score = await asyncio.to_thread(check_bias, (content)) +scraped = await asyncio.to_thread(run_scraper_pipeline, request.url) +bias_score = await asyncio.to_thread( + check_bias, scraped["cleaned_text"] +)Likely an incorrect or invalid review comment.
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| load_dotenv() | ||
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| client = Groq(api_key=os.getenv("GROQ_API_KEY")) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Fail fast if GROQ_API_KEY is missing and use a named parameter.
Avoid constructing the client with a missing/None key. Use a named argument and validate the env var.
-load_dotenv()
-
-client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+load_dotenv()
+api_key = os.getenv("GROQ_API_KEY")
+if not api_key:
+ raise RuntimeError("GROQ_API_KEY is not set")
+client = Groq(api_key=api_key)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| client = Groq(api_key=os.getenv("GROQ_API_KEY")) | |
| load_dotenv() | |
| api_key = os.getenv("GROQ_API_KEY") | |
| if not api_key: | |
| raise RuntimeError("GROQ_API_KEY is not set") | |
| client = Groq(api_key=api_key) |
🤖 Prompt for AI Agents
In backend/app/modules/bias_detection/check_bias.py at line 8, the Groq client
is created using an environment variable without validation and without using a
named parameter. First, check if the GROQ_API_KEY environment variable is set
and raise an error or exit immediately if it is missing. Then, instantiate the
Groq client using the api_key named parameter explicitly with the validated key.
| print(text) | ||
| print(json.dumps(text)) | ||
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There was a problem hiding this comment.
Remove PII logging of full article text.
Printing raw article content (and its JSON) to stdout is a PII/data-leak risk and noisy in production logs. Gate behind a debug logger or remove.
- print(text)
- print(json.dumps(text))
+ # Consider using a structured logger at DEBUG level if needed:
+ # logger.debug("check_bias called with text length=%d", len(text or ""))📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| print(text) | |
| print(json.dumps(text)) | |
| # Consider using a structured logger at DEBUG level if needed: | |
| # logger.debug("check_bias called with text length=%d", len(text or "")) |
🤖 Prompt for AI Agents
In backend/app/modules/bias_detection/check_bias.py around lines 13 to 15, the
code prints the full article text and its JSON representation directly, which
risks exposing PII and cluttering production logs. Remove these print statements
or replace them with debug-level logging that can be enabled or disabled via
configuration to avoid leaking sensitive data in production environments.
| bias_score = chat_completion.choices[0].message.content.strip() | ||
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| return { | ||
| "bias_score": bias_score, | ||
| "status": "success", | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Parse and validate numeric output; return a number type.
Currently the function returns a string and trusts the model to output only a number. Parse robustly, clamp to [0,100], and return an int/float to simplify frontend usage.
- bias_score = chat_completion.choices[0].message.content.strip()
-
- return {
- "bias_score": bias_score,
- "status": "success",
- }
+ raw = chat_completion.choices[0].message.content.strip()
+ # Extract first integer/float from the response
+ import re
+ m = re.search(r"(\d{1,3})(?:\.\d+)?", raw)
+ if not m:
+ raise ValueError(f"Model did not return a numeric score: {raw!r}")
+ score = int(m.group(1))
+ # Enforce bounds
+ score = max(0, min(100, score))
+ return {
+ "bias_score": score,
+ "status": "success",
+ }Note: Add import re at the top of the file.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| bias_score = chat_completion.choices[0].message.content.strip() | |
| return { | |
| "bias_score": bias_score, | |
| "status": "success", | |
| } | |
| raw = chat_completion.choices[0].message.content.strip() | |
| # Extract first integer/float from the response | |
| import re | |
| m = re.search(r"(\d{1,3})(?:\.\d+)?", raw) | |
| if not m: | |
| raise ValueError(f"Model did not return a numeric score: {raw!r}") | |
| score = int(m.group(1)) | |
| # Enforce bounds | |
| score = max(0, min(100, score)) | |
| return { | |
| "bias_score": score, | |
| "status": "success", | |
| } |
🤖 Prompt for AI Agents
In backend/app/modules/bias_detection/check_bias.py around lines 44 to 49, the
function currently returns the bias_score as a string directly from the model
output. To fix this, import the 're' module at the top of the file, then parse
the bias_score string to extract a numeric value using a regular expression,
convert it to a float or int, clamp it within the range 0 to 100, and return
this numeric value instead of the raw string. This ensures robust validation and
simplifies frontend handling.
| from sentence_transformers import SentenceTransformer | ||
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| embedder = SentenceTransformer("all-MiniLM-L6-v2") | ||
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| def embed_query(query: str): | ||
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| embeddings = embedder.encode(query).tolist() | ||
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| return embeddings |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reuse the existing embedder; add types and input validation.
Avoid loading the same model twice. Import the singleton embedder and ensure we return a List[float].
-from sentence_transformers import SentenceTransformer
-
-embedder = SentenceTransformer("all-MiniLM-L6-v2")
-
-
-def embed_query(query: str):
-
- embeddings = embedder.encode(query).tolist()
-
- return embeddings
+from typing import List
+from app.modules.vector_store.embed import embedder
+
+def embed_query(query: str) -> List[float]:
+ if not query or not query.strip():
+ raise ValueError("query must be a non-empty string")
+ embedding = embedder.encode(query).tolist()
+ # Optionally: normalize if index uses cosine similarity without normalized vectors
+ return embedding📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from sentence_transformers import SentenceTransformer | |
| embedder = SentenceTransformer("all-MiniLM-L6-v2") | |
| def embed_query(query: str): | |
| embeddings = embedder.encode(query).tolist() | |
| return embeddings | |
| from typing import List | |
| from app.modules.vector_store.embed import embedder | |
| def embed_query(query: str) -> List[float]: | |
| if not query or not query.strip(): | |
| raise ValueError("query must be a non-empty string") | |
| embedding = embedder.encode(query).tolist() | |
| # Optionally: normalize if index uses cosine similarity without normalized vectors | |
| return embedding |
🤖 Prompt for AI Agents
In backend/app/modules/chat/embed_query.py lines 1 to 10, avoid loading the
SentenceTransformer model again by importing the existing singleton embedder
instead of creating a new one. Add type annotations to the embed_query function
to specify it returns a List[float]. Also, add input validation to ensure the
query parameter is a non-empty string before encoding.
| pc = Pinecone(os.getenv("PINECONE_API_KEY")) | ||
| index = pc.Index("perspective") | ||
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There was a problem hiding this comment.
🛠️ Refactor suggestion
Harden Pinecone client/index initialization and make names configurable.
Validate the API key, use named arg, and allow index/namespace via env for portability.
-load_dotenv()
-
-pc = Pinecone(os.getenv("PINECONE_API_KEY"))
-index = pc.Index("perspective")
+load_dotenv()
+api_key = os.getenv("PINECONE_API_KEY")
+if not api_key:
+ raise RuntimeError("PINECONE_API_KEY is not set")
+pc = Pinecone(api_key=api_key)
+index_name = os.getenv("PINECONE_INDEX_NAME", "perspective")
+namespace = os.getenv("PINECONE_NAMESPACE", "default")
+index = pc.Index(index_name)🤖 Prompt for AI Agents
In backend/app/modules/chat/get_rag_data.py around lines 8 to 10, the Pinecone
client and index initialization lacks validation and configurability. Fix this
by first validating that the PINECONE_API_KEY environment variable is set and
raise an error if missing. Use named arguments when initializing the Pinecone
client. Also, make the index name and namespace configurable by reading them
from environment variables with sensible defaults to improve portability.
| article_text = await asyncio.to_thread(run_scraper_pipeline, (request.url)) | ||
| print(json.dumps(article_text, indent=2)) | ||
| data = run_langgraph_workflow(article_text) | ||
| data = await asyncio.to_thread(run_langgraph_workflow, (article_text)) | ||
| return data |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Same tuple issue in /process endpoint
run_scraper_pipeline and run_langgraph_workflow receive tuples instead of their expected args. Fix as above.
🤖 Prompt for AI Agents
In backend/app/routes/routes.py around lines 37 to 40, the functions
run_scraper_pipeline and run_langgraph_workflow are incorrectly called with
single-element tuples due to extra parentheses around their arguments. Remove
the parentheses around the arguments so that the functions receive the expected
single argument instead of a tuple. For example, change calls from
asyncio.to_thread(run_scraper_pipeline, (request.url)) to
asyncio.to_thread(run_scraper_pipeline, request.url).
| const [processRes, biasRes] = await Promise.all([ | ||
| axios.post( | ||
| "https://Thunder1245-perspective-backend.hf.space/api/process", | ||
| { | ||
| url: storedUrl, | ||
| } | ||
| ), | ||
| axios.post( | ||
| "http://Thunder1245-perspective-backend.hf.space/api/bias", | ||
| { | ||
| url: storedUrl, | ||
| } | ||
| ), | ||
| ]); |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Avoid mixed-protocol & hard-coded backend URLs
axios.post calls use both https:// and http:// for the same host.
When this page is served over HTTPS the plain-HTTP request will be blocked by the browser (mixed-content).
Expose the base URL via an env variable (e.g. process.env.NEXT_PUBLIC_API_BASE) and always use HTTPS.
🤖 Prompt for AI Agents
In frontend/app/analyze/loading/page.tsx around lines 72 to 85, the axios.post
calls use mixed protocols (https and http) for the same backend host, causing
potential mixed-content browser blocking. To fix this, replace the hard-coded
URLs with a single base URL stored in an environment variable like
process.env.NEXT_PUBLIC_API_BASE, ensure it uses HTTPS, and prepend this base
URL to the API endpoints in both axios.post calls.
| // Progress and step simulation | ||
| const stepInterval = setInterval(() => { | ||
| setCurrentStep((prev) => { | ||
| if (prev < steps.length - 1) { | ||
| return prev + 1; | ||
| } else { | ||
| clearInterval(stepInterval); | ||
| setTimeout(() => { | ||
| router.push("/analyze/results"); | ||
| }, 2000); | ||
| return prev; | ||
| } | ||
| }); | ||
| }, 2000); | ||
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| runAnalysis() | ||
| }, [router]) | ||
| const progressInterval = setInterval(() => { | ||
| setProgress((prev) => { | ||
| if (prev < 100) { | ||
| return prev + 1; | ||
| } | ||
| return prev; | ||
| }); | ||
| }, 100); | ||
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| return () => { | ||
| clearInterval(stepInterval); | ||
| clearInterval(progressInterval); | ||
| }; | ||
| } else { |
There was a problem hiding this comment.
Cleanup function is never registered
The return () => { … } block is inside runAnalysis, not the useEffect body, so React never receives the cleanup callback.
Both intervals will therefore keep running after unmount -> memory leak & state updates on an unmounted component.
Move the cleanup to the top-level of useEffect:
- useEffect(() => {
- const runAnalysis = async () => { … }
- runAnalysis();
- }, [router]);
+ useEffect(() => {
+ const runAnalysis = async () => { … }
+ runAnalysis();
+ return () => { // <- proper cleanup
+ clearInterval(stepInterval);
+ clearInterval(progressInterval);
+ };
+ }, [router]);Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In frontend/app/analyze/loading/page.tsx around lines 110 to 138, the cleanup
function that clears the intervals is currently returned inside the runAnalysis
function instead of the top-level useEffect body, causing the cleanup not to be
registered by React. To fix this, move the return cleanup function that clears
stepInterval and progressInterval outside of runAnalysis and place it directly
in the useEffect hook so React can properly call it on unmount and prevent
memory leaks.
| const validateUrl = (inputUrl: string) => { | ||
| try { | ||
| new URL(inputUrl) | ||
| setIsValidUrl(true) | ||
| new URL(inputUrl); | ||
| setIsValidUrl(true); | ||
| } catch { | ||
| setIsValidUrl(false) | ||
| setIsValidUrl(false); | ||
| } | ||
| } | ||
| }; | ||
|
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There was a problem hiding this comment.
🛠️ Refactor suggestion
Restrict URL schemes to http/https in validation.
new URL(inputUrl) will accept non-web schemes (e.g., javascript:). Only allow http and https to prevent passing unsafe schemes downstream.
- const validateUrl = (inputUrl: string) => {
- try {
- new URL(inputUrl);
- setIsValidUrl(true);
- } catch {
- setIsValidUrl(false);
- }
- };
+ const validateUrl = (inputUrl: string) => {
+ try {
+ const parsed = new URL(inputUrl);
+ const isHttp = parsed.protocol === "http:" || parsed.protocol === "https:";
+ setIsValidUrl(isHttp);
+ } catch {
+ setIsValidUrl(false);
+ }
+ };🤖 Prompt for AI Agents
In frontend/app/analyze/page.tsx around lines 38 to 46, the URL validation
currently accepts any scheme, including unsafe ones like "javascript:". Modify
the validateUrl function to parse the URL and explicitly check that the scheme
(protocol) is either "http:" or "https:". If the scheme is not one of these, set
isValidUrl to false; otherwise, set it to true.
| const res = await axios.post("http://Thunder1245-perspective-backend.hf.space/api/chat", { | ||
| message: message | ||
| }); | ||
| const data = res.data; |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Hard-coded HTTP endpoint & missing error handling
Same mixed-content risk as loading page and no try/catch.
Wrap the request:
try {
const { data } = await axios.post(`${process.env.NEXT_PUBLIC_API_BASE}/chat`, { message });
setMessages([...newMessages, { role: "assistant", content: data.answer }]);
} catch (e) {
console.error(e);
}🤖 Prompt for AI Agents
In frontend/app/analyze/results/page.tsx around lines 90 to 93, the axios POST
request uses a hard-coded HTTP endpoint and lacks error handling. Replace the
hard-coded URL with a dynamic one using process.env.NEXT_PUBLIC_API_BASE, and
wrap the axios call in a try/catch block to handle errors gracefully by logging
them. Also, update the state with the assistant's response inside the try block.
There was a problem hiding this comment.
Actionable comments posted: 2
🔭 Outside diff range comments (1)
.github/workflows/deploy-backend-to-hf.yml (1)
67-69: Fix YAML syntax error in run step (colon in plain scalar).Actionlint/YAMLlint error at Line 68 is due to a colon in an unquoted plain scalar. Use a block scalar for the run command.
- - name: ✅ Done - run: echo "Backend deployed to Hugging Face Space: https://huggingface.co/spaces/${HF_USER}/${HF_REPO}" + - name: ✅ Done + run: | + echo "Backend deployed to Hugging Face Space: https://huggingface.co/spaces/${HF_USER}/${HF_REPO}"
🧹 Nitpick comments (2)
.github/workflows/deploy-backend-to-hf.yml (2)
13-17: Avoid hardcoding repo identity; prefer centrally managed config.HF_USER and HF_REPO are set here but cloning later hardcodes the username/repo. Use these env vars everywhere (and consider using repository/organization Variables or Secrets) to prevent drift.
44-49: Stronger shell safety flags.Use -euo pipefail for better robustness and early failure on unset vars and pipeline errors.
- set -e + set -euo pipefail
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📒 Files selected for processing (1)
.github/workflows/deploy-backend-to-hf.yml(1 hunks)
🧰 Additional context used
🪛 actionlint (1.7.7)
.github/workflows/deploy-backend-to-hf.yml
68-68: could not parse as YAML: yaml: line 68: mapping values are not allowed in this context
(syntax-check)
🪛 YAMLlint (1.37.1)
.github/workflows/deploy-backend-to-hf.yml
[error] 68-68: syntax error: mapping values are not allowed here
(syntax)
🔇 Additional comments (3)
.github/workflows/deploy-backend-to-hf.yml (3)
6-8: Double-check trigger paths to avoid missed deploys.Currently only changes under backend/** trigger this workflow. If deploy-affecting files live elsewhere (e.g., Space config, Dockerfile, requirements in a different dir, or this workflow), those changes won’t deploy unless there’s also a backend change. Consider broadening paths.
24-29: Good guard for missing secrets.Early exit on missing HF_TOKEN is clear and helpful. LGTM.
59-66: Commit/push flow looks fine.Configuring bot identity and handling “no changes” is good. LGTM.
| - name: 📂 Prepare Space repo (clone) | ||
| env: | ||
| HF_TOKEN: ${{ secrets.HF_TOKEN }} | ||
| run: | | ||
| rm -rf space-backend | ||
| rm -rf space-backend || true | ||
| # clone using token in URL (this authenticates the clone) | ||
| git clone https://Thunder1245:${HF_TOKEN}@huggingface.co/spaces/Thunder1245/perspective-backend.git space-backend | ||
|
|
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🛠️ Refactor suggestion
Use env vars in clone URL and avoid duplicating username; quote the URL.
The clone command hardcodes the username/repo and embeds the token in the command. Prefer env vars to prevent drift and quote the URL to avoid parsing issues.
Apply this diff:
- - name: 📂 Prepare Space repo (clone)
+ - name: 📂 Prepare Space repo (clone)
env:
HF_TOKEN: ${{ secrets.HF_TOKEN }}
run: |
- rm -rf space-backend || true
- # clone using token in URL (this authenticates the clone)
- git clone https://Thunder1245:${HF_TOKEN}@huggingface.co/spaces/Thunder1245/perspective-backend.git space-backend
+ rm -rf space-backend || true
+ # clone using token in URL (this authenticates the clone)
+ git clone "https://${HF_USER}:${HF_TOKEN}@huggingface.co/spaces/${HF_USER}/${HF_REPO}.git" space-backendCommittable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In .github/workflows/deploy-backend-to-hf.yml around lines 31 to 38, the git
clone URL hardcodes the username and embeds the token directly, which risks
drift and parsing errors. Refactor the clone command to use environment
variables for both username and token, and quote the entire URL string to
prevent shell parsing issues. This ensures credentials are managed securely and
the command is more maintainable.
| # Remove tracked files while preserving .git and config (ignore failure) | ||
| git rm -r . || true | ||
| cd .. | ||
|
|
||
| # Copy new backend files in | ||
| # Copy backend files into the cloned space directory | ||
| cp -R backend/. space-backend/ | ||
|
|
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🛠️ Refactor suggestion
Ensure repo is fully synchronized (handle untracked files); simplify with rsync.
git rm -r . won’t remove untracked files and the cp step doesn’t delete removed files. Use rsync --delete to mirror backend/ into the Space repo (excluding .git).
- # Remove tracked files while preserving .git and config (ignore failure)
- git rm -r . || true
- cd ..
-
- # Copy backend files into the cloned space directory
- cp -R backend/. space-backend/
+ # Mirror backend files into the cloned space directory (preserve .git)
+ rsync -av --delete --exclude '.git' backend/ space-backend/🤖 Prompt for AI Agents
In .github/workflows/deploy-backend-to-hf.yml around lines 52 to 58, the current
approach using 'git rm -r .' and 'cp -R backend/. space-backend/' does not
handle untracked files and does not fully synchronize the backend directory with
the space-backend directory. Replace these commands with a single 'rsync'
command using the '--delete' option to mirror the backend directory into
space-backend, excluding the .git directory, ensuring all files are synchronized
and removed files are deleted.
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Actionable comments posted: 2
🧹 Nitpick comments (1)
README.md (1)
165-165: Path consistency: earlier bullet still references/new-backendThis step correctly uses
cd backend, but the earlier setup instructions say “add .env file in/new-backenddirectory.” Update that path to/backendto avoid confusion.
- Please confirm the actual backend root where
main.pyresides and where.envis read from (e.g., via python-dotenv/Starlette settings) so the README points to the correct location.
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README.md(2 hunks)
🧰 Additional context used
🪛 markdownlint-cli2 (0.17.2)
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| - [Perspective-AI](#perspective-ai) | ||
| - [Table of Contents](#table-of-contents) | ||
| - [System Overview](#system-overview) | ||
| - [High-Level Concept](#high-level-concept) | ||
| - [Architecture Components](#architecture-components) | ||
| - [1. Frontend Layer](#1-frontend-layer) | ||
| - [3. Core Backend](#3-core-backend) | ||
| - [4. AI \& NLP Integration](#4-ai--nlp-integration) | ||
| - [5. Data Storage](#5-data-storage) | ||
| - [Technical Stack](#technical-stack) | ||
| - [Frontend Technologies](#frontend-technologies) | ||
| - [Backend Technologies](#backend-technologies) | ||
| - [I Integration](#i-integration) | ||
| - [Core Features](#core-features) | ||
| - [1. Counter-Perspective Generation](#1-counter-perspective-generation) | ||
| - [2. Reasoned Thinking](#2-reasoned-thinking) | ||
| - [3. Updated Facts](#3-updated-facts) | ||
| - [4. Seamless Integration](#4-seamless-integration) | ||
| - [5. Real-Time Analysis](#5-real-time-analysis) | ||
| - [Data Flow \& Security](#data-flow--security) | ||
| - [Setup \& Deployment](#setup--deployment) | ||
| - [Frontend Setup](#frontend-setup) | ||
| - [Backend Setup](#backend-setup) | ||
| - [Architecture Diagram](#architecture-diagram) | ||
| - [Expected Outcomes](#expected-outcomes) | ||
| - [Required Skills](#required-skills) |
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💡 Verification agent
🧩 Analysis chain
Fix TOC indentation and remove the stray "I Integration" item to satisfy markdownlint and avoid broken anchors
Current list indentation is inconsistent (MD005/MD007), and the "I Integration" entry looks like a typo/duplicate of "AI & NLP Integration". Apply the following TOC cleanup:
- - [Perspective-AI](#perspective-ai)
- - [Table of Contents](#table-of-contents)
- - [System Overview](#system-overview)
- - [High-Level Concept](#high-level-concept)
- - [Architecture Components](#architecture-components)
- - [1. Frontend Layer](#1-frontend-layer)
- - [3. Core Backend](#3-core-backend)
- - [4. AI \& NLP Integration](#4-ai--nlp-integration)
- - [5. Data Storage](#5-data-storage)
- - [Technical Stack](#technical-stack)
- - [Frontend Technologies](#frontend-technologies)
- - [Backend Technologies](#backend-technologies)
- - [I Integration](#i-integration)
- - [Core Features](#core-features)
- - [1. Counter-Perspective Generation](#1-counter-perspective-generation)
- - [2. Reasoned Thinking](#2-reasoned-thinking)
- - [3. Updated Facts](#3-updated-facts)
- - [4. Seamless Integration](#4-seamless-integration)
- - [5. Real-Time Analysis](#5-real-time-analysis)
- - [Data Flow \& Security](#data-flow--security)
- - [Setup \& Deployment](#setup--deployment)
- - [Frontend Setup](#frontend-setup)
- - [Backend Setup](#backend-setup)
- - [Architecture Diagram](#architecture-diagram)
- - [Expected Outcomes](#expected-outcomes)
- - [Required Skills](#required-skills)
+ - [Perspective-AI](#perspective-ai)
+ - [Table of Contents](#table-of-contents)
+ - [System Overview](#system-overview)
+ - [High-Level Concept](#high-level-concept)
+ - [Architecture Components](#architecture-components)
+ - [1. Frontend Layer](#1-frontend-layer)
+ - [3. Core Backend](#3-core-backend)
+ - [4. AI & NLP Integration](#4-ai--nlp-integration)
+ - [5. Data Storage](#5-data-storage)
+ - [Technical Stack](#technical-stack)
+ - [Frontend Technologies](#frontend-technologies)
+ - [Backend Technologies](#backend-technologies)
+ - [Core Features](#core-features)
+ - [1. Counter-Perspective Generation](#1-counter-perspective-generation)
+ - [2. Reasoned Thinking](#2-reasoned-thinking)
+ - [3. Updated Facts](#3-updated-facts)
+ - [4. Seamless Integration](#4-seamless-integration)
+ - [5. Real-Time Analysis](#5-real-time-analysis)
+ - [Data Flow & Security](#data-flow--security)
+ - [Setup & Deployment](#setup--deployment)
+ - [Frontend Setup](#frontend-setup)
+ - [Backend Setup](#backend-setup)
+ - [Architecture Diagram](#architecture-diagram)
+ - [Expected Outcomes](#expected-outcomes)
+ - [Required Skills](#required-skills)Notes:
- If you intend to keep numbered subsection labels (“1.”, “3.”, “4.”, “5.”), consider renumbering the corresponding headings to avoid the missing “2.” gap across the document. Otherwise, drop numbers from both headings and TOC for simplicity.
Please confirm whether “I Integration” was intended to be “AI Integration” (it appears redundant with “AI & NLP Integration”). If you want it retained, update both the TOC entry and the corresponding section heading for consistency.
🏁 Script executed:
#!/bin/bash
# Check for stray "I Integration" entry
grep -n "I Integration" README.md
# Print first 30 lines of README.md to inspect TOC indentation
sed -n '1,30p' README.mdLength of output: 1443
Remove stray “I Integration” entry and normalize TOC indentation
- Delete the duplicate TOC item and its section:
- Line 17: remove
- [I Integration](#i-integration) - Line 75: remove the
### I Integrationheading
- Line 17: remove
- Normalize the list indentation in the Table of Contents:
- Top-level items start at col 1 (
- […]) - Second-level items are indented two spaces (
- […]) - Third-level items are indented four spaces (
- […])
- Top-level items start at col 1 (
- (Optional) Renumber or drop the “1. …”, “3. …”, “4. …”, “5. …” labels under Architecture Components to avoid the missing “2.” gap.
Suggested diff:
--- a/README.md
+++ b/README.md
@@ -17,1 +17,0 @@
- - [I Integration](#i-integration)
@@ -75,1 +74,0 @@
-### I Integration📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| - [Perspective-AI](#perspective-ai) | |
| - [Table of Contents](#table-of-contents) | |
| - [System Overview](#system-overview) | |
| - [High-Level Concept](#high-level-concept) | |
| - [Architecture Components](#architecture-components) | |
| - [1. Frontend Layer](#1-frontend-layer) | |
| - [3. Core Backend](#3-core-backend) | |
| - [4. AI \& NLP Integration](#4-ai--nlp-integration) | |
| - [5. Data Storage](#5-data-storage) | |
| - [Technical Stack](#technical-stack) | |
| - [Frontend Technologies](#frontend-technologies) | |
| - [Backend Technologies](#backend-technologies) | |
| - [I Integration](#i-integration) | |
| - [Core Features](#core-features) | |
| - [1. Counter-Perspective Generation](#1-counter-perspective-generation) | |
| - [2. Reasoned Thinking](#2-reasoned-thinking) | |
| - [3. Updated Facts](#3-updated-facts) | |
| - [4. Seamless Integration](#4-seamless-integration) | |
| - [5. Real-Time Analysis](#5-real-time-analysis) | |
| - [Data Flow \& Security](#data-flow--security) | |
| - [Setup \& Deployment](#setup--deployment) | |
| - [Frontend Setup](#frontend-setup) | |
| - [Backend Setup](#backend-setup) | |
| - [Architecture Diagram](#architecture-diagram) | |
| - [Expected Outcomes](#expected-outcomes) | |
| - [Required Skills](#required-skills) | |
| - [Perspective-AI](#perspective-ai) | |
| - [Table of Contents](#table-of-contents) | |
| - [System Overview](#system-overview) | |
| - [High-Level Concept](#high-level-concept) | |
| - [Architecture Components](#architecture-components) | |
| - [1. Frontend Layer](#1-frontend-layer) | |
| - [3. Core Backend](#3-core-backend) | |
| - [4. AI & NLP Integration](#4-ai--nlp-integration) | |
| - [5. Data Storage](#5-data-storage) | |
| - [Technical Stack](#technical-stack) | |
| - [Frontend Technologies](#frontend-technologies) | |
| - [Backend Technologies](#backend-technologies) | |
| - [Core Features](#core-features) | |
| - [1. Counter-Perspective Generation](#1-counter-perspective-generation) | |
| - [2. Reasoned Thinking](#2-reasoned-thinking) | |
| - [3. Updated Facts](#3-updated-facts) | |
| - [4. Seamless Integration](#4-seamless-integration) | |
| - [5. Real-Time Analysis](#5-real-time-analysis) | |
| - [Data Flow & Security](#data-flow--security) | |
| - [Setup & Deployment](#setup--deployment) | |
| - [Frontend Setup](#frontend-setup) | |
| - [Backend Setup](#backend-setup) | |
| - [Architecture Diagram](#architecture-diagram) | |
| - [Expected Outcomes](#expected-outcomes) | |
| - [Required Skills](#required-skills) |
🧰 Tools
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🤖 Prompt for AI Agents
In README.md from lines 5 to 30, remove the stray "I Integration" entry at line
17 and also delete the corresponding "### I Integration" heading at line 75.
Normalize the Table of Contents indentation so that top-level items start at
column 1 with "- ", second-level items are indented by two spaces with " - ",
and third-level items by four spaces with " - ". Optionally, renumber or
remove the numbered labels under Architecture Components to fix the missing "2."
gap for consistent numbering.
| GROQ_API_KEY= <groq_api_key> | ||
| PINECONE_API_KEY = <your_pinecone_API_KEY> | ||
| PORT = 8000 | ||
| SEARCH_KEY = <your_Google_custom_search_engine_API_key> | ||
| ``` |
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💡 Verification agent
🧩 Analysis chain
Standardize .env variable formatting and naming for clarity
Make the keys consistent (no spaces around =, consistent placeholders). This also prevents copy-paste pitfalls.
- GROQ_API_KEY= <groq_api_key>
-PINECONE_API_KEY = <your_pinecone_API_KEY>
-PORT = 8000
-SEARCH_KEY = <your_Google_custom_search_engine_API_key>
+GROQ_API_KEY=<GROQ_API_KEY>
+PINECONE_API_KEY=<PINECONE_API_KEY>
+PORT=8000
+SEARCH_KEY=<GOOGLE_CSE_API_KEY>- If HuggingFace is no longer required for this PR’s RAG/chat flow, remove the earlier “Get HuggingFace Access Token” step or clarify when it’s needed.
- If additional vars are required for Pinecone (e.g., PINECONE_INDEX, PINECONE_ENVIRONMENT) or GROQ model names, add them here for completeness.
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#!/bin/bash
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grep -R "os.getenv" -n .
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sed -n '1,200p' backend/app/modules/chat/get_rag_data.py#!/bin/bash
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grep -R "os.getenv" -n backend/app/modules/chat/get_rag_data.py
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grep -R "os.getenv" -n backend/app/modules/chat/get_rag_data.py
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#!/bin/bash
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Standardize .env entries in README.md for consistency and accuracy
Please update the README’s environment-variable block (lines 157–161) to remove extra spaces around “=”, align placeholder names with the actual keys used in code, and drop the unused HuggingFace step. No additional Pinecone or GROQ variables are required beyond PINECONE_API_KEY and GROQ_API_KEY, and the Google CSE key is read from SEARCH_KEY in web_search.py.
File: README.md, lines 157–161
- GROQ_API_KEY= <groq_api_key>
-PINECONE_API_KEY = <your_pinecone_API_KEY>
-PORT = 8000
-SEARCH_KEY = <your_Google_custom_search_engine_API_key>
+GROQ_API_KEY=<GROQ_API_KEY>
+PINECONE_API_KEY=<PINECONE_API_KEY>
+PORT=8000
+SEARCH_KEY=<GOOGLE_CSE_API_KEY>• Remove the Get HuggingFace Access Token step (no HuggingFace calls remain in this PR).
• Confirm that only PINECONE_API_KEY, GROQ_API_KEY, and SEARCH_KEY are required by the code.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| GROQ_API_KEY= <groq_api_key> | |
| PINECONE_API_KEY = <your_pinecone_API_KEY> | |
| PORT = 8000 | |
| SEARCH_KEY = <your_Google_custom_search_engine_API_key> | |
| ``` | |
| GROQ_API_KEY=<GROQ_API_KEY> | |
| PINECONE_API_KEY=<PINECONE_API_KEY> | |
| PORT=8000 | |
| SEARCH_KEY=<GOOGLE_CSE_API_KEY> |
🤖 Prompt for AI Agents
In README.md lines 157 to 161, remove the extra spaces around the equal signs in
the environment variable assignments to standardize formatting, ensure the
placeholder names exactly match the keys used in the code (PINECONE_API_KEY,
GROQ_API_KEY, SEARCH_KEY), and delete the entire HuggingFace access token step
since it is no longer used. Confirm that only these three environment variables
are listed and no additional Pinecone or GROQ variables are included.
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Actionable comments posted: 0
♻️ Duplicate comments (3)
README.md (3)
75-79: Remove the duplicate “I Integration” sectionThis section is redundant with “AI & NLP Integration” and referenced by the stray TOC entry.
-### I Integration - - - **LLM**: OpenAI, Other NLP Models - - **processing**:Context-Aware -
5-30: Fix TOC indentation, remove stray “I Integration”, and unescape ampersand
- Normalize list indentation to satisfy markdownlint (MD005/MD007).
- Remove duplicate/typo entry “I Integration”.
- Use “AI & NLP Integration” (no backslash escape in link text).
Apply:
- - [Perspective-AI](#perspective-ai) - - [Table of Contents](#table-of-contents) - - [System Overview](#system-overview) - - [High-Level Concept](#high-level-concept) - - [Architecture Components](#architecture-components) - - [1. Frontend Layer](#1-frontend-layer) - - [3. Core Backend](#3-core-backend) - - [4. AI \& NLP Integration](#4-ai--nlp-integration) - - [5. Data Storage](#5-data-storage) - - [Technical Stack](#technical-stack) - - [Frontend Technologies](#frontend-technologies) - - [Backend Technologies](#backend-technologies) - - [I Integration](#i-integration) - - [Core Features](#core-features) - - [1. Counter-Perspective Generation](#1-counter-perspective-generation) - - [2. Reasoned Thinking](#2-reasoned-thinking) - - [3. Updated Facts](#3-updated-facts) - - [4. Seamless Integration](#4-seamless-integration) - - [5. Real-Time Analysis](#5-real-time-analysis) - - [Data Flow \& Security](#data-flow--security) - - [Setup \& Deployment](#setup--deployment) - - [Frontend Setup](#frontend-setup) - - [Backend Setup](#backend-setup) - - [Architecture Diagram](#architecture-diagram) - - [Expected Outcomes](#expected-outcomes) - - [Required Skills](#required-skills) + - [Perspective-AI](#perspective-ai) + - [Table of Contents](#table-of-contents) + - [System Overview](#system-overview) + - [High-Level Concept](#high-level-concept) + - [Architecture Components](#architecture-components) + - [1. Frontend Layer](#1-frontend-layer) + - [3. Core Backend](#3-core-backend) + - [4. AI & NLP Integration](#4-ai--nlp-integration) + - [5. Data Storage](#5-data-storage) + - [Technical Stack](#technical-stack) + - [Frontend Technologies](#frontend-technologies) + - [Backend Technologies](#backend-technologies) + - [Core Features](#core-features) + - [1. Counter-Perspective Generation](#1-counter-perspective-generation) + - [2. Reasoned Thinking](#2-reasoned-thinking) + - [3. Updated Facts](#3-updated-facts) + - [4. Seamless Integration](#4-seamless-integration) + - [5. Real-Time Analysis](#5-real-time-analysis) + - [Data Flow & Security](#data-flow--security) + - [Setup & Deployment](#setup--deployment) + - [Frontend Setup](#frontend-setup) + - [Backend Setup](#backend-setup) + - [Architecture Diagram](#architecture-diagram) + - [Expected Outcomes](#expected-outcomes) + - [Required Skills](#required-skills)
157-160: Standardize .env entries (formatting and placeholders)Remove spaces around “=”, align placeholder names, and match keys used in code.
- GROQ_API_KEY= <groq_api_key> -PINECONE_API_KEY = <your_pinecone_API_KEY> -PORT = 8000 -SEARCH_KEY = <your_Google_custom_search_engine_API_key> +GROQ_API_KEY=<GROQ_API_KEY> +PINECONE_API_KEY=<PINECONE_API_KEY> +PORT=8000 +SEARCH_KEY=<GOOGLE_CSE_API_KEY>
🧹 Nitpick comments (2)
README.md (2)
154-156: Align backend directory namingInstructions say to add .env under “/new-backend” but run steps cd into “backend”. Make them consistent.
Proposed fix:
- - add .env file in `/new-backend`directory. + - add a .env file in the `/backend` directory.Also applies to: 165-165
238-238: Minor grammar/spacing nitDouble space after colon.
-- **Frontend Development**: Experience with Next.js and modern UI frameworks. +- **Frontend Development**: Experience with Next.js and modern UI frameworks.
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README.md(4 hunks)
🧰 Additional context used
🪛 markdownlint-cli2 (0.17.2)
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(MD005, list-indent)
28-28: Inconsistent indentation for list items at the same level
Expected: 4; Actual: 2
(MD005, list-indent)
29-29: Inconsistent indentation for list items at the same level
Expected: 4; Actual: 2
(MD005, list-indent)
30-30: Inconsistent indentation for list items at the same level
Expected: 4; Actual: 2
(MD005, list-indent)
🔇 Additional comments (1)
README.md (1)
145-148: HuggingFace step appears obsolete for this PRREADME still instructs to get a HuggingFace token, but the PR’s RAG/chat flow uses Groq + Pinecone and no HF calls.
-*Get HuggingFace Access Token:* -- Go to HuggingFace website and create new access token. -- copy that token -Likely an incorrect or invalid review comment.
2 participants
Summary
Fixes Pinecone search return shape and implements
/api/chatRAG flow: frontend → backend (embed → pinecone → build context → LLM) → frontend. PreventsKeyError: 'metadata', returns usable context to LLM, and wires the Next frontend chat to call the FastAPI endpoint.Files changed (high level)
app/modules/chat/pinecone_search.py— preservemetadatain resultsapp/modules/chat/llm_processing.py— robustbuild_context()+ ask_llmapp/routes/routes.py—/chatPOST endpoint (uses Pydantic model)frontend/(AnalyzePage).tsx—handleSendMessageusingaxios.postandres.dataWhat I changed
search_pinecone()now returns fullmetadatainstead of onlytext.build_context()safely extractsexplanationorreasoningfrommetadataand falls back to other fields.ChatQuery) and returns{"answer": ...}.handleSendMessageusesaxios.post("/api/chat", { message })and readsres.data. Removed fetch-style options.Edge cases handled
build_context()ignores empty entries.Diagram
flowchart LR subgraph Frontend U[User enters question] --> F[AnalyzePage.handleSendMessage] F --> BackendRequest[POST /api/chat - message] end subgraph Backend BackendRequest --> E[embed_query message] E --> P[Pinecone index query] P --> M[results with metadata] M --> C[build_context results] C --> LLM[LLM - OpenAI] LLM --> A[answer JSON] A --> BackendResponse[prepare response] end BackendResponse -->|200 JSON| Frontend Frontend -->|display assistant| ChatWindow[Chat window]Summary by CodeRabbit
New Features
Improvements
Style
Documentation