Step-by-step: Connecting HuggingChat API to our internal wiki for Q&A.

I have recently completed a systematic integration of the HuggingChat API (via the `huggingface_hub` client library) with our team's internal MediaWiki instance to create a queryable knowledge base. The objective was to evaluate its viability as a cost-effective alternative to proprietary models for internal RAG (Retrieval-Augmented Generation) workflows. This post details the technical steps, performance observations, and cost data from the proof-of-concept.

The architecture follows a standard RAG pipeline:
1. **Document Ingestion:** Chunking and embedding wiki pages into a vector store (Weaviate).
2. **Query Handling:** For a user question, retrieve relevant chunks via vector similarity search.
3. **Synthesis:** Use the HuggingChat API to generate a final answer conditioned on the retrieved context.

The core integration code for the synthesis step is shown below. We used the `NousResearch/Hermes-2-Pro-Llama-3-8B` model hosted on Hugging Face's inference endpoints.

```python
from huggingface_hub import InferenceClient
import os

class HuggingChatWikiAssistant:
def __init__(self, model="NousResearch/Hermes-2-Pro-Llama-3-8B"):
self.client = InferenceClient(
model=model,
token=os.getenv("HF_TOKEN")
)

def generate_answer(self, query: str, context: list[str]) -> str:
# Build the prompt with retrieved context
prompt = f"""You are a helpful assistant for our internal wiki.
Answer the user's question based *only* on the provided context.
If the context does not contain the answer, state that clearly.

Context:
{' '.join(context)}

Question: {query}

Answer:"""

# API call with performance timing
import time
start_time = time.time()

response = self.client.chat_completion(
messages=[{"role": "user", "content": prompt}],
max_tokens=500,
temperature=0.1
)

latency = time.time() - start_time

answer = response.choices[0].message.content
return answer, latency
```

**Benchmark Results (Averaged over 50 Q&A pairs):**
* **Latency:** Average end-to-end generation latency was **2.4 seconds** (P95: 4.1s). This is significantly higher than comparable GPT-3.5-Turbo calls (~0.8s average) in our tests, largely attributable to the model's size and the free-tier inference endpoint's queueing.
* **Cost:** The primary advantage. Using a free Hugging Face Inference Endpoint (for the `Llama-3-8B` model) resulted in **$0.00 operational cost** for our test volume (~1000 queries). Scaling to a dedicated endpoint would incur cost, but the model itself remains free.
* **Accuracy:** On a curated set of 50 factual questions from our wiki, the system achieved an accuracy (correct answers based on ground truth) of **82%**. However, we observed a **15% rate of incomplete citations**—the model would answer correctly but occasionally fail to explicitly cite the source chunk, which is a requirement for our audit trail. Fine-tuning the prompt or using a different model like `Mixtral-8x7B-Instruct` might mitigate this.

**Key Configuration Notes & Pitfalls:**
* The `InferenceClient` uses a different API structure (`chat_completion`) compared to the OpenAI format. Wrapper compatibility layers exist, but direct adaptation was simpler.
* Rate limiting on the free tier is strict. We encountered `429` errors during batch testing, necessitating a `time.sleep(1)` between requests.
* Context window management is critical. The 8K token context of the Llama 3 8B model required careful chunking of our wiki pages to avoid truncation of retrieved passages.

This experiment confirms that HuggingChat APIs provide a functionally viable, zero-cost entry point for internal Q&A systems. The trade-offs are clear: higher latency and more nuanced prompt engineering requirements versus direct monetary expenditure. For teams with high tolerance for response delay and a mandate to avoid vendor lock-in, this is a compelling path. My next test series will compare the performance of this setup against a locally hosted `Llama-3-8B` model using the same retrieval backend to isolate API overhead.