[DOC]: Add HoneyHive integration to docs

Adds a markdown file with HoneyHive integration info and example.
Updates sidenav.js to include honeyhive.md

docs/docs.trychroma.com/pages/integrations/_sidenav.js

@@ -18,6 +18,7 @@ export const items = [
       links: [
         { href: '/integrations/langchain', children: 'Langchain' },
         { href: '/integrations/llamaindex', children: 'LlamaIndex' },
+        { href: '/integrations/honeyhive', children: 'HoneyHive' },
         { href: '/integrations/braintrust', children: 'Braintrust' },
         { href: '/integrations/haystack', children: 'Haystack' },
         { href: '/integrations/openllmetry', children: 'OpenLLMetry' },

docs/docs.trychroma.com/pages/integrations/honeyhive.md

@@ -0,0 +1,138 @@
+---
+title: HoneyHive
+---
+
+[HoneyHive](https://www.honeyhive.ai/) is the end-to-end AI observability and evaluation platform for building reliable AI agents that work.
+
+HoneyHive offers libraries in both Typescript and Python for executing and recording evaluations, and it seamlessly integrates with Chroma.
+
+Follow the [HoneyHive Installation Guide](https://docs.honeyhive.ai/tracing/integrations/integration-prereqs) to get your API key and initialize the tracer. Ensure that you have the OpenAI API key set in your environment variables.
+
+Next, start the Chroma server (`chroma run --path ./getting-started`) and paste the following code into your evaluation script to trace the RAG pipeline.
+
+Example evaluation script in JavaScript: (for the full implementation, see our [integrations page](https://docs.honeyhive.ai/tracing/integrations/chromadb#example))
+
+```jsx
+import { ChromaClient, OpenAIEmbeddingFunction } from "chromadb";
+import OpenAI from "openai";
+
+import { HoneyHiveTracer } from "honeyhive";
+
+const tracer = await HoneyHiveTracer.init({
+    apiKey: "MY_HONEYHIVE_API_KEY",
+    project: "MY_PROJECT_NAME",
+    sessionName: "chroma",
+});
+
+const openai_client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
+const client = new ChromaClient();
+
+const embeddingFunction = new OpenAIEmbeddingFunction({
+    openai_api_key: process.env.OPENAI_API_KEY ?? "",
+});
+
+const collection = client.getOrCreateCollection({
+    name: "scifact",
+    embeddingFunction,
+});
+
+const article = {
+    doc_id: 1,
+    title:
+        "ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome.",
+    abstract: [
+        "Application of stem cell biology to breast cancer research has been limited by the lack of simple methods for identification and isolation of normal and malignant stem cells.",
+        "Utilizing in vitro and in vivo experimental systems, we show that normal and cancer human mammary epithelial cells with increased aldehyde dehydrogenase activity (ALDH) have stem/progenitor properties.",
+        "These cells contain the subpopulation of normal breast epithelium with the broadest lineage differentiation potential and greatest growth capacity in a xenotransplant model.",
+        "In breast carcinomas, high ALDH activity identifies the tumorigenic cell fraction, capable of self-renewal and of generating tumors that recapitulate the heterogeneity of the parental tumor.",
+        "In a series of 577 breast carcinomas, expression of ALDH1 detected by immunostaining correlated with poor prognosis.",
+        "These findings offer an important new tool for the study of normal and malignant breast stem cells and facilitate the clinical application of stem cell concepts.",
+    ],
+    structured: false,
+};
+
+async function processDocument() {
+    (await collection).add({
+        ids: article["doc_id"].toString(),
+        documents: `${article["title"]}. ${article["abstract"].join(" ")}`,
+        metadatas: { structured: article["structured"] },
+    });
+}
+
+await processDocument();
+
+const buildPromptWithContext = (claim, context) => [
+    {
+        role: "system",
+        content:
+            "I will ask you to assess whether a particular scientific claim, based on evidence provided. " +
+            "Output only the text 'True' if the claim is true, 'False' if the claim is false, or 'NEE' if there's " +
+            "not enough evidence.",
+    },
+    {
+        role: "user",
+        content: `
+            The evidence is the following:
+
+            ${context.join(" ")}
+
+            Assess the following claim on the basis of the evidence. Output only the text 'True' if the claim is true,
+            'False' if the claim is false, or 'NEE' if there's not enough evidence. Do not output any other text.
+
+            Claim:
+            ${claim}
+
+            Assessment:
+        `,
+    },
+];
+
+async function assessClaims(claims) {
+    const claimQueryResult = await (
+        await collection
+    ).query({
+        queryTexts: claims,
+        include: ["documents", "distances"],
+        nResults: 3,
+    });
+    const responses = [];
+
+    for (let i = 0; i < claimQueryResult.documents.length; i++) {
+        const claim = claims[i];
+        const context = claimQueryResult.documents[i];
+        if (context.length === 0) {
+            responses.push("NEE");
+            continue;
+        }
+
+        const response = await openai_client.chat.completions.create({
+            model: "gpt-4o-mini",
+            messages: buildPromptWithContext(claim, context),
+            max_tokens: 3,
+        });
+
+        const formattedResponse = response.choices[0].message.content?.replace(
+            "., ",
+            "",
+        );
+        console.log("Claim: ", claim);
+        console.log("Response: ", formattedResponse);
+        responses.push(formattedResponse);
+    }
+
+    return responses;
+}
+
+const tracedAssessClaims = tracer.traceFunction()(assessClaims);
+
+const tracedMain = async () => {
+    await tracedAssessClaims([
+        "ALDH1 expression is associated with better breast cancer outcomes.",
+        "ALDH1 expression is associated with poorer prognosis in breast cancer.",
+    ]);
+};
+await tracedMain();
+```
+
+To learn more about HoneyHive, visit the
+[official documentation](https://docs.honeyhive.ai/introduction/what-is-hhai).